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"\n\n___________________\n*Corresponding author : E-mail: faizah.metali@ubd.edu.bn\n\n\n\nINTRODUCTION\nThe last few decades have seen rapid and continuing development of tree plantations \nin Asia, particularly in Southeast Asia (Sheil et al., 2009; Krisnawati et al., 2011). \nIn Brunei Darussalam, several plantations of local commercial dipterocarp species \n(e.g.,  Dryobalanops beccarii), high quality tropical hardwoods (e.g., Araucaria \nhunsteinii), and fast growing exotic tropical hardwoods (e.g., Acacia mangium) \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 45-58 (2015)                        Malaysian Society of Soil Science\n\n\n\nSelected Soil Physico-chemical Properties in the Acacia \nmangium Plantation and the Adjacent Heath\n\n\n\nForest at Andulau Forest Reserve\n\n\n\nMatali, S and F. Metali*\n\n\n\nEnvironmental and Life Sciences Group, Faculty of Science, Universiti Brunei \nDarussalam, Jalan Tungku Link, BE1410, Brunei Darussalam\n\n\n\nABSTRACT\nInvasions by exotic plants and the establishment of plantations have been \nassociated with the enrichment of nutrients in the soil. The first aim of this \nstudy was to examine soil physico-chemical properties of the Acacia mangium \nplantation and the nearby undisturbed heath forest (HF) at the Andulau Forest \nReserve, Sungai Liang, Brunei Darussalam. The second aim was to determine \nthe most influential soil properties that accounted for the most variation in the \nAcacia plantation and HF plots. A total of six pairs of 20 m x 20 m plots were \nestablished along two parallel transects (260 m each) in the Acacia plantation (6 \nplots) and the HF (6 plots). Each of the twelve plots were subdivided into four \n10 m x 10 m subplots and one soil core (0 \u2013 15 cm depth) was sampled in each \nsubplot. Soil pH, gravimetric water content (GWC), organic matter (OM), organic \nlayer depth, texture and major nutrient concentrations were determined for each \nsoil core. Significantly higher total Ca concentrations and organic layer depth \nwere found in the soils of the HF than in the Acacia plantation. However, the \nAcacia plantation soils had significantly higher total N concentrations than the HF \nsoils. Non-native A. mangium trees have the ability to change the soil physico-\nchemical properties to improve their growth. Total Ca concentration and GWC \nwere the most influential soil properties in the HF, whilst for the Acacia plantation \nplots, pH was most influential. Studying soil properties of both native forests and \nplantations of exotic species provides insights into how non-native plants change \nsoil properties in ways different from native plant species.\n \nKeywords: Soil properties, Acacia mangium plantation, native forest, \n\n\n\nnutrients enrichment\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201546\n\n\n\nMatali, S and F. Metali\n\n\n\nwere established to meet the country\u2019s demand for timber (Anderson and Marsden, \n1984). Whilst many studies show that tree plantations can improve soil friability \nand permeability of degraded land (Prinsely and Swift, 1986; Mishra et al., 2003; \nSharma and Sharma, 2004), the changes in physical and chemical properties of \nsoils depend on the type of vegetation planted on the land (Binkley and Giardina, \n1998; Bonifacio et al., 2008). Significant changes in soil properties such as pH, \norganic matter (OM), and exchangeable bases can be observed when a population \nof plant species is replaced by a different plant species (Sharma and Sharma, \n2004). \n\n\n\nA one km2 A. mangium plantation was established within the heath forest \n(HF) of the Andulau Forest Reserve of Brunei Darussalam in 1998 because of \nits high relative growth rates and potential as a source of timber (A. Cheng, pers. \ncomm.). After 12 years, the A. mangium trees in the plantation were harvested \nleaving juvenile Acacia trees to regrow without any further management. Soils \nat the nearby HF habitats were characterised as being acidic and sandy, and often \nlacking nutrients, particularly N (Forestry Department, 2011). The soils sit mainly \nover sandstone plateaus and ridge formations on dip slopes (Whitmore, 1984) \noverlying a Pleistocene marine terrace (Br\u00fcnig, 1974).\n\n\n\nThe Food and Agriculture Organization of the United Nations (FAO) \nreported that the genus Acacia to be the most widespread invasive species found \nin more than ten countries in Southeast Asia, including Indonesia and Malaysia \n(FAO, 1999; 2010). A majority of Acacia species, including A. mangium, A. \nauriculiformis and A. cincinnata, are native to Australia. Outside its natural \nhabitats, Acacia is renowned for its wide range of impacts that transform \necosystems and alter ecosystem services (Osunkoya et al., 2005; Le Maitre et \nal., 2011). The key traits of Acacia species are their rapid growth rates as well \nas their ability to outcompete native plants, fix N, accumulate high biomass and \ngenerate massive seed banks (Osunkoya et al., 2005; Morris et al., 2011). These \nfeatures enable them to become a dominant species upon habitat disturbance and \ncan cause serious consequences for the biodiversity and regeneration of native \nplant communities (Le Maitre et al., 2011; Morris et al., 2011). Many studies \nreport that, on average, alien invasive plants increase nutrient pools and fluxes \nin novel ecosystems (Xiong et al., 2008; Le Maitre et al., 2011; Osunkoya and \nPerrett, 2011; Jeddi and Chaieb, 2012). However, not very much is known about \nthe impact of planting Acacia species on soil properties in Brunei Darussalam.\n\n\n\nAcacia mangium Willd. of the family Fabaceae is one of the most widely \nused fast-growing tree species in plantation forestry programmes throughout Asia \nand the Pacific due to its rapid growth, good wood quality and tolerance to a wide \nrange of soils and environments (Krisnawati et al., 2011). Large scale plantations \nof A. mangium are estimated to have a net area of about 453,000 ha with about \n99% of A. mangium plantations being located in tropical Asia where they are \nestablished for industrial purposes. Asian countries with major areas of Acacia \nplantation are Indonesia (67%) and Malaysia (14%) (FAO, 2002).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 47\n\n\n\nSoil Physico-chemical Properties in the Acacia mangium Plantation\n\n\n\nThe aims of this study were two-fold. Firstly the study compared selected \nsoil physico-chemical properties of the A. mangium plantation and the adjacent \nheath forest of Andulau Forest Reserve at Sungai Liang. Secondly, it identified the \ninfluential soil properties that accounted for the most variation in plots from the A. \nmangium plantation and HF.\n\n\n\nMATERIALS AND METHODS\nThe soils were sampled from two study sites: the 1 km2  A. mangium plantation \n(Acacia plantation hereafter) (4o35\u201941.79\u201dN, 114\u00b031\u20193.15\u201dE, elevation 32-56 \nm) and the heath (Kerangas) forest (HF hereafter) (4o37\u201960\u201dN, 114\u00b031\u201959\u201dE, \nelevation 21-37 m) at the Andulau Forest Reserve in Sungai Liang of the Belait \ndistrict in Brunei Darussalam, northwest Borneo from September to October 2013. \nIn 2013, Brunei Darussalam had an average temperature of 33.2oC  and a mean \nannual rainfall of 2704 mm with dry periods from February to April and again in \nJuly to September (records from Sungai Liang Agricultural Station; Department \nof Agriculture and Agrifood, Brunei Darussalam, unpublished data). The Acacia \nplantation was located within the Andulau Forest Reserve but separated from \nthe primary HF by either a 5 m wide gravel pathway or fire breaks. The Acacia \nplantation consisted of many juvenile trees, but some areas were bare and were \nonly dominated by bushy ferns and dead logs, which were probably left after the \nfirst harvest in 2010. \n\n\n\nFigure 1: Illustration of the transects and plot establishment in Acacia mangium \nplantation and the nearby heath forest at the Andulau Forest the Reserve of \n\n\n\nBrunei Darussalam\n\n\n\n13 \n \n\n\n\n\n\n\n\nFigure 1: Illustration of the transects and plot establishment in Acacia mangium \nplantation and the nearby heath forest at the Andulau Forest Reserve of Brunei \n\n\n\nDarussalam \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201548\n\n\n\nTwo line parallel transects 60 m apart (running in a north-south direction) \nfrom the HF into the Acacia plantation were established as shown in Figure 1. The \nline transects in the Acacia plantation were located about 340 m away from the \nline transects in the HF. A total of six pairs of 20 m x 20 m plots were set up along \nthe line transects with  Plots 1-6 set up at the HF and Plots 7-12 at the Acacia \nplantation. Each 20 m x 20 m plot was further subdivided into four 10 m x 10 m \nsubplots (48 subplots in total) and one soil core (0-15 cm depth) was sampled from \na randomly selected location within each subplot in the HF and Acacia plantation, \nrespectively. A total of 48 soil samples were collected. Prior to soil coring, the \ndepth of organic layer was measured using a ruler at each sampling point. Soil \npH, with the soil being suspended in distilled water in a 1:2 ratio,was measured in \nthe laboratory using a portable pH meter (Hanna instruments Ltd, UK) on the day \nof sampling. Gravimetric water content (GWC) and organic matter (OM) content \nwere determined following the procedure by Allen et al. (1989). The texture of \nthe soil samples was determined using the pipette method of the Department of \nAgriculture (2006). \n\n\n\nSoil chemical analysis was conducted following the procedure by Allen et \nal. (1989) with slight modifications.The soils were analysed for total P and total \nN, and total Ca, total Mg and total K after digesting each soil sample (1.0 g soil \nfor total P and total N and 0.3 g soil for total Ca, total Mg and total K) using \nconcentrated H2SO4 and concentrated HNO3, respectively. Soil exchangeable Ca \nconcentrations were determined following extraction with 1 M KCl, whilst soil \nexchangeable K was determined following extraction with 2.5 % v/v acetic acid \n(2.0 g soil for Ca and 2.5 g soil for K).\n\n\n\nTotal P and total N concentrations in the acid-digests were measured using \na flow injector analyser (FIAstar 5000, Sweden). Total Ca, total Mg and total \nK concentrations, and exchangeable Ca, and K concentrations were measured \nusing a flame atomic absorption spectrophotometer (Thermo Scientific iCE 3300, \nAustralia) after diluting the acid-digested samples with LaCl3 (LaCl3:HNO3 in the \nratio of 1:100, which is only applicable to Ca and Mg). Available P in soils was \ndetermined following the methods of the Department of Agriculture (2006). Soil \nsamples (2.0 g) were extracted with Bray\u2019s solution (0.03 N NH4F in 0.025 N \nHCl). The absorbance of each solution was read at 880 nm wave length using an \nultraviolet spectrophotometer (UV-1800, Shimadzu, Japan). \n\n\n\nAll statistical analysis were conducted in R version 2.14.2 (R Development \nCore Team, 2012). Differences in soil properties between the Acacia plantation \nand the HF were determined using unpaired t-tests. Data for GWC and OM \ncontent were transformed using arcsine transformation. Other remaining data \nwere explored to confirm the normality of residuals and homogeneity of variances, \nand where necessary, data were log-transformed. The physical and chemical soil \nvariables between the Acacia plantation and the HF of Andulau Forest Reserve \nwere then subjected to principal component analysis (PCA) to determine the \nvariables which accounted for the most variations in the dataset. \n\n\n\nMatali, S and F. Metali\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 49\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Physical Properties \nThe textures of soils in all sampled plots in the Acacia plantation and the HF \nwere sandy with a range of sand content between 32% to 98% and 54% to 93% \nin the plantation and HF, respectively (Table 1). Sandy soils are characterised \nby the predominance of rigid coarse particles that are associated with a small \namount of clay and this soil type spans a range of rainfall regimes from the arid to \nthe humid tropics (Ghazoul and Sheil, 2010).  Fine textured soils or clayey soils \ntend to have more organic matter content than coarse soils (Schimel et al., 1985; \nFAO, 2005). Nutrients are held better in coarse sand, providing good conditions \nfor plant growth; however, the main problem with coarse sand is that nutrients \ncan be leached very easily (FAO, 2005; Wang et al., 2014). Coarse soils are also \nbetter aerated, and the presence of oxygen results in a more rapid decay of organic \nmatter (FAO, 2005). The low clay content of the sandy soils in the dry grassland \nin Sudan had poor minerals, low organic matter content, water holding capacity \nand cation exchange capacity (El Tahir et al., 2009). \n\n\n\nHF soils had a significantly greater organic layer depth than those of the \nAcacia plantation (Table 1). However, GWC and OM content did not differ \nbetween the soils in the Acacia plantation and HF (P> 0.05, Table 2).\n\n\n\nThe presence of herb and understorey layer in the HF provided shading, \nwhich contributes to the modification in temperature regimes and high moisture \ncontent leading to a decline in the decomposition rate (Butterfield, 1999; FAO, \n\n\n\nTABLE 1\nMeans (mean + standard error of means, SEM) of selected soil physico-chemical \n\n\n\nproperties in the Acacia mangium plantation (n=6 plots) and the heath forest\n(n=6 plots) at Andulau Forest Reserve.\n\n\n\n11 \n \n\n\n\nTABLE 1 \nMeans (mean \uf0b1 standard error of means, SEM) of selected soil physico-\n\n\n\nchemicalproperties in the Acacia mangium plantation (n=6 plots) and the heath forest (n=6 \nplots) at Andulau Forest Reserve. \n\n\n\n \nSoil properties Acacia plantation HF t  P \nTotal N (mg g-1 dry mass) 0.72 \u00b1 0.03 0.37 \u00b1 0.06 -2.67 < 0.05 \nTotal P (mg g-1 dry mass) 0.08 \u00b1 0.004 0.07 \u00b1 0.01 -1.23 > 0.05 \nAvailable P (mg g-1 dry mass) 0.01 \u00b1 0.001 0.01 \u00b1 0.001 -1.82 > 0.05 \nTotal Mg (mg g-1 dry mass) 0.08 \u00b1 0.01 0.06 \u00b1 0.01 -1.04 > 0.05 \nTotal Ca (mg g-1 dry mass) 0.02 \u00b1 0.003 0.08 \u00b1 0.01 4.29 < 0.01 \nTotal K (mg g-1 dry mass) 0.02 \u00b1 0.01 0.05 \u00b1 0.01 1.37 > 0.05 \nExchangeable Ca (mg g-1 dry mass)   0.01 \u00b1 0.002 0.02 \u00b1 0.002 1.44 > 0.05 \nExchangeable K (mg g-1 dry mass) 0.02 \u00b1 0.004 0.04 \u00b1 0.01 1.24 > 0.05 \npH 4.03 \u00b1 0.08 3.87 \u00b1 0.01 -0.87 > 0.05 \nGravimetric water content (%) 14.11 \u00b1 1.22 23.80 \u00b1 3.70 1.07 > 0.05 \nOrganic matter content (%) 6.32 \u00b1 4.09 2.97 \u00b1 0.34 0.93 > 0.05 \nOrganic layer depth (cm) 5.29 \u00b1 0.33 7.46 \u00b1 0.36 3.9 < 0.01 \nSoil texture \n\n\n\nSilt (%) \nClay (%) \nSand (%) \n\n\n\n \n6 \u2013 33  \n1 \u2013 30  \n54 \u2013 93 \n\n\n\n \n1 \u2013 66 \n1 \u2013 29  \n32 \u2013 98  \n\n\n\n \nNA \n\n\n\n \nNA \n\n\n\nNA=data not available. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nSoil Physico-chemical Properties in the Acacia mangium Plantation\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201550\n\n\n\n2005; Singwane and Malinga, 2012). The establishment of plantations comprising \ninvasive plants reduces tree and shrub layers, exposes the leaf litter layer in this \nhabitat to high temperatures (Smith et al., 1998). If this happens, the breakdown \nof leaf litters will be accelerated (Smith et al., 1998) and faster decomposition \nrates could contribute to a decrease in OM content (Butterfield, 1999; Singwane \nand Malinga, 2012). Additionally, Acacia leaf litter is known to have a high \ndecomposition rate (Bernhard-Reversat, 1993; Morris et al., 2011) and the high \nfoliar N of the litter may substantially contribute to high amounts of N in the \nsoil, thus increasing the microbial activity for decomposition (Bernhard-Reversat, \n1993; FAO, 2005; Wang and Wang, 2011). However, the effects of the leaf litter \nlayer and its nutrient concentrations cannot be confirmed as these variables were \nnot determined in this study. Despite the possible correlation between OM content \nand organic layer depth, this study found no significant differences between the \nOM contents of the two forest types. Acacia and HF leaf litter are acknowledged \nto be quite thick and fibrous, and their leaf physico-chemical properties may cause \nslow decomposition (O\u2019Connell and Sankaran, 1997).\n\n\n\nThe organic layer and OM content can impact the water absorption capability \nat the soil surface, for example, low OM content can increase water leaching or \nsurface run-off resulting in low soil moisture content (Smith et al., 1998; Khormali \net al., 2009). An increase in OM content could subsequently lead to an increase in \nsoil fauna and greater pore space, thus making water to infiltrate more readily into \nand be held in the soil (FAO, 2005; Khormali et al., 2009; Gajik, 2013). \n\n\n\nTABLE 2\nVariations from principal component analysis (PCA) of selected soil physico-chemical \n\n\n\nvariables across Acacia plantation (n=6 plots) and heath forest plots (n=6 plots) and \npercentage of total variation explained by each principal component axis.\n\n\n\n12 \n \n\n\n\nTABLE 2 \nVariations from principal component analysis (PCA) of selected soil physico-chemical \n\n\n\nvariables across Acacia plantation (n=6 plots) and heath forest plots (n=6 plots) and \npercentage of total variation explained by each principal component axis. \n\n\n\n\n\n\n\nParameters Principal component axes \n 1 2 \n% of total variation explained 39.6  22.0 \nCumulative % variation explained 39.6  61.6 \nLoadings of soil nutrients and physical traits   \npH 0.09 -0.49 \nOrganic matter content 0.10  -0.11 \nGravimetric water content  -0.12   0.42 \nOrganic layer depth -0.37  0.20 \nTotal N 0.39   0.24 \nTotal P 0.38  -0.12 \nTotal Mg 0.26   0.35 \nTotal Ca -0.42  -0.16   \nTotal K -0.26  -0.20 \nAvailable P 0.22   0.34   \nExchangeable Ca -0.34   0.18   \nExchangeable K -0.27   0.35 \n\n\n\n*Loadings and signs of the correlation coefficient (trait loading) of each nutrient and physical trait \nfor the first two principal component axes were presented.  \n**Variables with the highest loadings are indicated in bold. \n \n\n\n\nMatali, S and F. Metali\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 51\n\n\n\nSoil pH and Nutrient Concentrations\nThe pH values were high in the Acacia plantation but were not significantly \ndifferent from the HF (Table 1). Acacia plantation soils had significantly greater \ntotal N concentrations than those of the HF (Table 1). In contrast, concentrations \nof total Ca were significantly higher in the HF soils than those in the Acacia \nplantations, but concentrations of total P, total Mg and total K, exchangeable Ca \nand K, and available P did not differ between soils from the Acacia plantation and \nthe HF (Table 1).\n\n\n\nThe PCA of the soil properties showed that the first two axes accounted for \n61.6 % of the variation (Table 2). The principal component axis 1 (PC1) and the \nprincipal component axis 2 (PC2) explained 39.6 % and 22.0 % of the variation, \nrespectively (Table 2). Concentrations of total N and total P were positively \nintercorrelated and associated with PC1 (Table 2). On the other hand, organic layer \ndepth, concentrations of total and exchangeable Ca were negatively intercorrelated \nand associated with PC1 (Table 2). Concentrations of total Mg, exchangeable K, \navailable P and GWC were positively intercorrelated and strongly associated with \nPC2. PC2 also represented a gradient of decreasing pH values (Table 2).\n\n\n\nA biplot of the PC1 and the PC2 showed that the Acacia plantation and \nHF plots were partitioned in ordinate space and differentiated on the basis of \nsoil nutrient concentrations (Figure 2). The variables with the longest arrow in \nthe PCA biplot were pH, GWC and concentrations of total Ca (Figure 2). This \nindicated that these were the most influential set of variables and associated \nwith plots from the Acacia plantations for pH and, HF plots for GWC and total \nCa concentrations (Figure 2). The second most influential set of variables were \norganic layer depth, and concentrations of total N and total P.  Concentrations of \ntotal N and total P were associated with Acacia plantation plots, whereas organic \nlayer depth variables were associated with the HF plots. The third influential set of \nvariables were concentrations of total Mg and available P, which were associated \nwith plots from the Acacia plantation and, concentrations of exchangeable Ca and \nK, which were associated with plots from the HF (Figure 2). Concentrations of \ntotal K and OM content were the least influential variables (Figure 2).\n\n\n\nThe biplot of the PCA showed soil pH as the most influential variable in \nplots from the Acacia plantation. The pH ranged between 3.8-4.1 across the HF \nand Acacia plantation plots and the mean pH values in HF and plantation plots \nwere more acidic than those plots in mixed dipterocarp forest (MDF) which \nhad a mean pH of 4.42 \u00b1 0.03 in Andulau Forest Reserve (Metali et al., 2014). \nVijayanathan et al., (2011) and Yamashita et al., (2008) also found that the soils \nof Acacia mangium plantations were more acidic compared to the MDF and the \nImperata grassland. The soil acidification in the Acacia plantation and HF plots \nwas probably caused by a decrease in the concentrations of exchangeable cations \nor bases in soils and it was presumed to be due to translocation of base cations \nfrom soil to plant biomass (Yamashita et al., 2008; Gonzales-Munoz et al., 2012; \nPerrett et al., 2012) or leaching of nutrients (Katagiri et al., 1991). This was \nevident in this study as total and exchangeable Ca concentrations were lower in \n\n\n\nSoil Physico-chemical Properties in the Acacia mangium Plantation\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201552\n\n\n\nAcacia plantation and HF plots in Andulau Forest Reserve (Table 1) than in MDF \nplots in Andulau Forest Reserve (total Ca = 0.15 \u00b1 0.26 mg g-1 and exchangeable \nCa = 0.01 \u00b1 0.32 mg g-1) (Metali et al., 2014). \n\n\n\nLi et al., (2001) also suggest that acidic pH in the A. mangium plantation \nwas probably due to high rates of nitrification from the A. mangium litter \ndecomposition and that protons were released in exchange for nitrate uptake \nby the N-fixing legumes, thus causing soil acidification. Xiong et al. (2008) \nexplained that high production of ammonium from plant material decomposition \ncaused soil acid neutralisation. Another factor which could result in acidic soil \npH was the accumulation of humic acids as a result of high soil OM content \nand slow decomposition rates caused by less microbial activity in acidic soils \n(Augusto et al., 2002; FAO, 2005; Yousefi and Darvishi, 2013). Further research \non forest litter decomposition and microbial biomass in the present study sites \nwould improve the understanding of their importance in nutrient cycling in forest \necosystems. \n\n\n\n14 \n \n\n\n\n \n*Numbers denote the 12 plots censused; plot 1 to plot 6 are plots in the HF and plot 7 to plot 12 \nare plots in the Acaciamangium plantation.  \n**OM, GWC, T.N, T.P, T.Mg, T.Ca, T.K, Av.P, Exch.Ca and Exch.K represents organic matter \n(OM) content, gravimetric water content (GWC), total soil N, P, Mg, Ca and K concentrations, and \navailable soil P concentrations, and exchangeable soil Ca and K concentrations, respectively. \n \n \nFigure 2: Biplot of principal component axes 1 and 2 from principal component analysis \n(PCA) of selected soil physicochemical variables across Acacia plantation (n=6 plots) \n\n\n\nand heath forest (HF) plots (n=6 plots). \n \n\n\n\nFigure 2: Biplot of principal component axes 1 and 2 from principal component analysis \n(PCA) of selected soil physicochemical variables across Acacia plantation (n=6 plots) \n\n\n\nand heath forest (HF) plots (n=6 plots).\n\n\n\nMatali, S and F. Metali\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 53\n\n\n\nThe mean concentrations of total N in the soils of HF in Badas and Bukit \nSawat as reported by Metali (2010) was 6-fold and 3-fold higher than in the soils \nof the HF and Acacia plantation, respectively, in this study. Moran et al. (2000) \ninvestigated the nutrient status of the HF and MDF and report that the turnover \nrate of litter fall was significantly low in the HF, which suggests that the HF might \nexperience a more closed N cycling. The present findings on total N concentrations \nin soils also seem to suggest that the HF is experiencing a more closed cycle of N \nthan in the Acacia plantation. Another reason for the low levels of N in the soils \nwas probably due to the water-logged condition in the HF and the subsequent \nreduction in OM decomposition (Moran et al., 2000). Leaves in tropical HFs were \nthicker and tougher than leaves in MDFs (Turner et al., 2000). The rapid uptake of \nN into the plant biomass in the HF is probably because the typical HF plants need \nto invest more of their resources in protecting their relatively long-lived leaves \n(Turner et al., 2000). The rapid uptake into the plant biomass subsequently caused \nthe soil N concentration to be very low.\n\n\n\nAnother important reason for the higher amount of total N concentration in \nthe Acacia plantation was the ability of the plant to fix atmospheric N (Osunkoya \net al., 2005; Morris et al., 2011). A. mangium has N-fixing ability through \na  symbiotic relationship with bacteria in its root nodules, so they can produce \nleaves that are more N-rich than other tropical leguminous trees (Krisnawati et al., \n2011; Morris et al., 2011). This capability of Acacia trees results in a substantial \ninput of N-enriched litter, which can lead to increased soil N concentrations (El \nTahir et al., 2009; Morris et al., 2011; Jeddi and Chaieb, 2012). Vijayanathan et al. \n(2011) had also found a higher concentration of total soil N in the second-rotation \nof a 0\u20136-month-old A. mangium plantation in Peninsular Malaysia compared to a \nMDF. The formation of new roots of N-fixing legumes in the second rotation of \nthe plantation helped to boost the soil N content. The plantation in this study was \nplanted with A. mangium in 1998 and harvested in 2010, so it was not surprising \nthat the total N was higher in Acacia plantation than the HF at the Andulau Forest \nReserve. However, this cannot be validated as the nutrient content of the leaf litter \nwas not analysed.\n\n\n\nThe HF plots also had significantly higher concentrations of total Ca than \nthe Acacia plantation. Moran et al. (2000) found that the tropical HF in Badas \nwas richer in soil Ca concentrations compared to the MDF. A. mangium is a fast \n-growing species, so the low total Ca, concentrations in the soils of the Acacia \nplantation could be due to higher proportions of these elements being retained in \nthe plant biomass and not returned to the soils (Yamashita et al., 2008) or leaching \nof nutrients in HF (Katagiri et al., 1991).  \n\n\n\nTotal and exchangeable K concentrations in the soil of the Acacia plantation \ninvestigated in this study was not significantly different from that in the soil of \nthe HF. Yamashita et al. (2008) also found that there was lack of difference in \nthe exchangeable K in the soil between an A. mangium plantation, a secondary \nforest and an Imperata grassland. This is probably due to high mobility of K in the \n\n\n\nSoil Physico-chemical Properties in the Acacia mangium Plantation\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201554\n\n\n\nsoil-plant system, which can easily be leached to deeper soil layers (Moran et al., \n2000; Xiong et al., 2008). \n\n\n\nThe concentrations of total P and total Mg, available P and exchangeable \nCa in the soil of the Acacia plantation were also not significantly different from \nthat in the soil of the HF. The generally low concentrations of these nutrients in \nthe HF and the Acacia plantation could be due to plant uptake and sequestration \nof P in the tree biomass (Hagar et al., 1991) or nutrient leaching in the sandy \nsoils. Additionally, Fisher and Binkley (2000) state that the low available P in soil \ncorrelates with the acidic pH and as shown in this study, soils from the Acacia \nplantation and the HF were very acidic, so available P in the soil could be very \nlimited. El Tahir et al. (2009) found high total P concentration in the Acacia \nsenegal plantation soils in Sudan and it was suggested that the ultimate source \nof P in soils could be from the decomposition of leaf litter and the weathering \nof parent rock materials. Leaf litter decomposition and nutrient mineralisation \nare known to be the key components of ecosystem nutrient cycling (Smith et \nal., 1998; Owusu-Sekyere et al., 2006; Perrett et al., 2012; Boudiaf et al., 2013; \nVersini et al., 2014). \n\n\n\nCONCLUSION\nThis study has demonstrated that non-native Acacia trees have the ability to change \nsome soil physico-chemical properties when compared to native and tropical \nprimary forests. It was found that the soils in the A. mangium plantation had a \nsignificantly lower organic layer and total Ca concentrations, and significantly \nhigher total N concentrations than the soils of primary HF. The N-fixing ability \nof the Acacia trees produced N-rich leaves, thus resulting in N-enriched soils in \nthe A. mangium plantation. The N-poor HF appeared to experience more closed \nnutrient cycling than the Acacia plantation due to the deeper organic layer in the \nHF than in the Acacia plantation soils. Total Ca concentrations and GWC were the \ninfluential soil properties which accounted for the most variation in the HF plots, \nwhilst pH was the most influential soil variable associated with Acacia plantation \nplots. \n\n\n\nACKNOWLEDGEMENTS\nThe research was funded by Universiti Brunei Darussalam. The authors would \nlike to thank the Department of Forestry, Ministry of Industries and Primary \nResources, Brunei Darussalam for their kind assistance during the course of \nthis study. Gratitude is also expressed to the anonymous reviewers for their \nconstructive comments towards the improvement of this paper.\n\n\n\nREFERENCES\nAllen, S.E., H.M. Grimshaw, J.A. Parkinson and C. 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Soil changes induced by Acacia \nmangium plantation establishment: comparison with secondary forest and \nImperata cylindrica grassland soils in South Sumatra, Indonesia. Forest Ecology \nand Management 254: 362-370.\n\n\n\nYousefi, A. and L. Darvishi. 2013. Soil changes induced by hardwood and \nconiferous tree plantations establishment: comparison with natural forest soil \nat Berenjestanak lowland forest in north of Iran. International Journal of \nAdvanced Biological and Biomedical Research 1(4): 432-449.\n\n\n\nMatali, S and F. Metali\n\n\n\n\n\n"
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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 107-119  (2020)                         Malaysian Society of Soil Science\n\n\n\nIsolation and Identification of Microorganisms From Total \nPetroleum Hydrocarbon-Contaminated Soil Sites\n\n\n\nJiji, J.1*  and Prabakaran, P.2 \n\n\n\n1PG and Research Department of Microbiology, Marudhupandiyar College \n(Affiliated to Bharathidasan University), Vallam, Thanjavure, Tamilnadu, India.\n\n\n\n2PG and Research Department of Botany, M.R .Government Arts College, \nMannargudi, Thiruvarur, Tamilnadu\n\n\n\nABSTRACT\nCrude petroleum oil is a rich source of hazardous materials that can cause soil and \nwater pollution which can be treated by natural biodegradation processes. The \naim of this study was to isolate and identify native oil degrading microorganisms \nfrom a petroleum-contaminated soil. The study was conducted at Changanassery, \nKottayam district in Kerala, India. A total of six bacterial and four fungal strains \nwere isolated from the samples. Isolated bacterial strains were Pseudomonas \naeruginosa, Bacillus subtilis, Pseudomonas spp, Bacillus spp and Staphylococcus \naureus. Fungal isolates were identified as Aspergillus niger, Aspergillus spp, \nAspergillus flavus, Pencillium spp. It was observed that these organisms were able \nto utilize and degrade crude oil constituents.\n \nKey words: crude oil,  refined petroleum, biodegradation.\n\n\n\n___________________\n*Corresponding author : E-mail: jijisanthosh7@gmail.com\n\n\n\nINTRODUCTION\nEnvironmental pollution by petroleum hydrocarbons has become a serious \nproblem all around the world. Petroleum oil pollution results from human activities \nsuch as drilling, manufacturing, storing, transporting, waste management of oil \nand vandalisation of oil pipe lines. The massive and extensive environmental \npollution by petroleum industries constitutes socio-economic and public health \nhazards (Kobayashi and Rittman 1982). Petroleum oil pollution exerts adverse \neffects on plants indirectly by making toxic minerals in the soil more available to \nplants (Adams and Ellis 1960). Thus petroleum oil is a serious threat to ecology.\n Petroleum is a complex mixture of different hydrocarbons including \naliphatic (linear or branched), cycloalkanes, mono and polyaromatics, asphaltenes \nand resins and the majority of these compounds are stable, toxic and carcinogenic. \nHigh concentrations of these pollutants, due to their toxic and carcinogenic \nnature, can affect cell metabolism (Tanti et al. 2009). These hydrocarbons can \nslowly diffuse deep into the soil and cause pollution of bedrock and underground \nwater resources.  On the other hand, light hydrocarbons evaporate and form a \nlayer of oil-contaminated dust leading to air pollution (De-qing et al. 2007). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020108\n\n\n\nBioremediation has become one of the most popular and promising technologies \nwith growing demand to treat petroleum-contaminated soils because pollutants \ncan be completely removed at low cost. Bioremediation uses beneficial microbes \nto degrade hydrocarbons in soil (Xu 2012). At present, various microorganisms \nhave been detected in petroleum\u2013contaminated soil or water. It has been found that \nindividual microorganisms can mineralize only a limited range of hydrocarbon \nsubstrates. Therefore, a mixed microbial population with a broad range of \nenzymatic capacities is required to increase the rate and optimize petroleum \nbiodegradation (Farinazleen et al. 2004).\n Based on published reports, the most important hydrocarbon-degrading \nbacterial genera in soil environments include Achromobacter, Arthrobacter, \nBacillus, Nocardia, Nocardioides, Pseudomonas,  Rhodococcus, Sphingomonas, \nVariovorax and other unculturable bacterial clones (Chikere et al. 2009; Obayori \nand Salam 2010). Among the fungi, Aspergillus, Candida, Cunninghamella, \nFusarium, Mucor, Penicillium, Phanerochaete Rhodotorula, Sporobolomyces \nand Trichoderma are hydrocarbon-degrading genera frequently isolated from \nsoil (Chikere et al. 2011). The impact of these compounds on human health has \nstimulated great interest in the identification of microbial strains with specific \nhydrocarbon degrading activity ( Mancera-L\u00f3pez et al. 2007).The aim of this \nwork is to isolate and identify bacterial and fungal isolates from total petroleum \nhydrocarbon contaminated soil (TPH sites). \n\n\n\nMATERIALS AND METHODS\n\n\n\nCollection of Soil Samples\nPetroleum hydrocarbon-contaminated soil samples were collected from five \ndifferent sites including petrol pumps and workshops at Changanacherry \nMunicipality, Kottayam District, Kerala, India.  Random soil sampling was \npreferred due to very small size of plots present in this region (Charan et al. 2013). \nHomogeneous samples were obtained in a simple random strategy at <10 cm in \ndepth, following the procedures described by US-EPA (1996). Soils were mostly \nsandy and sandy loam to clay loam texture. All the soil samples were obtained \nduring the month of September, 2015 and were dispensed into sterile containers \nand labeled (Osazee et al. 2013).  Collected samples were gently sieved (<2mm \nfraction) and stored in sealed polythene bags at 4\u00b0C for 7 days prior to microbial \nand biochemical analyses (Sahoo et al. 2010).\n\n\n\nPhysiochemical Parameters of the Soil Samples\nPhysiological properties of the soil samples such as pH, organic carbon, calcium, \nmagnesium, sulphur, iron, manganese, zinc, copper, and boron were determined. \nSoil samples were dried, homogenised, sieved with a 2-mm test sieve, and \nconserved at 4\u00b0C until physicochemical analyses were conducted (Zafra et \nal. 2014). The physico-chemical properties of soil samples were determined \nby following standard protocols with slight modifications. Parameters such as \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 109\n\n\n\ncalcium and magnesium were determined titrimetrically or estimated by flame \nphotometer (model CL 26 D, make ELICO). \n\n\n\nHeavy Metal Analysis\nHeavy metal analysis in soil samples was carried out in triplicate following these \nprocedures. One gram of soil was digested in a glass digestion tube of 250 ml \nalong with 15 ml of nitric acid (HNO3) at 140\u00b0C and the content was evaporated \nto dryness. The dried sample was further treated with 3 ml of perchloric acid \n(HClO4) for oxidation from the sample solution for 30 min at 245\u00b0C. The content \nwas cooled down after digestion, filtered and final volume was made up to 50 \nml with distilled water. Heavy metal measurement was performed at Mancombu \nRice Research Station with a Shi-madzu model AA 6300 Atomic Absorption \nSpectrophotometer. The radiation source was Hallow cathode lamps of metal.\n\n\n\nMetals analysis by Atomic Absorption Spectrophotometer (AAS) \nFor the determination of heavy metals, the water samples were digested with 20 \nmL aqua-regia (HCl/HNO3 3:1, volume ratio) in a beaker (open beaker digestion) \non a thermostatically controlled hot plate. Then 5.0 mL hydrogen peroxide was \nadded to the sample to complete the digestion and the resulting mixture was \nheated again to near dryness in a fume cupboard and filtered by Whatman no. \n42 filter paper and the volume was made up to 50 mL by double distilled water. \nMetal contents of the water samples were analysed by AAS (Model: ECILTM \nAAS\u22124141) following the method of APHA.\n\n\n\nIsolation and Screening of Petroleum Degrading Strains \nOne gram soil samples were weighed and dissolved into 99 ml of sterile prepared \npeptone water diluent under aseptic conditions (Harley and Prescott 2002). Serial \nfold dilutions were then made up to 10-6 and aliquots of each dilution were cultured \non plates of Nutrient Agar for mean heterotrophic bacterial count and Sabouraud \nDextrose Agar (SDA) for mean heterotrophic fungal count respectively by pour \nplate method (CFU/ml). \n\n\n\nIdentification of Bacterial Isolates\nA number of physical and biochemical tests were performed for the identification \nof bacterial isolates with the help of the following standard methods (Barrow and \nFeltham 1993): (1) morphology observation by Olympus-CH40 microscope, (2) \ngram test, (3) indole test, (4) methyl red test, (5) Voges-Proskauer test,  (6) citrate \ntest, (7) catalase test, (8) oxidase test, (9) starch hydrolysis test, and  (10) triple \nsugar iron agar test.\n\n\n\nDetermination of Bacterial Hydrocarbon Growth Capability\nGrowth of isolates on hydrocarbon incorporated in nutrient agar media was \ndetermined by culturing of bacteria with 5% of oil contaminated nutrient agar \nmedia (V/V of petrol) and a control nutrient agar media (without oil). Isolates \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020110\n\n\n\nwhere streaked on the above prepared plates and incubated at 37\u00b0C for 24 h. After \nincubation, the control and oil incorporated plates were examined for the growth \ndiameter.\n\n\n\nIdentification of Fungi\nThe fungal isolates were identified based on cultural and morphological \ncharacteristics. The cultural characteristics were determined by the physical \nappearance of the fungal colony isolates on the culture plates, while the \nmorphological characteristics were determined by observing the mycelia of the \nisolates under the microscope in lactophenol cotton blue stain.\n\n\n\nDetermination of Fungi Hydrocarbon Growth Capability\nQualitative determination of fungi growing on hydrocarbon incorporated in SDA \nmedia was done by culturing of fungi. Three concentrations of oil contaminated \nSDA media (5%, 10% and 15% V/V of petrol) and one control SDA media (non \noil contaminated) were prepared in 16 petri dishes and 4 isolated strains of fungi \nwere inoculated in each. All the petri dishes where incubated at room temperature \n(28\u00b0C to 31\u00b0C) for 10 days. After 10 days, the control and radially growing colony \nwere examined for the growth diameter.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPhysiochemical Parameters of the Soil Samples\nThe results of the physiochemical composition of the oil contaminated soil samples \nshowed slightly acidic to neutral pH reading. These are similar to the results \nobtained by Atlas (1981), who reported that neutral pH enables biodegradation \nactivity of bacteria in soil. Bacteria have limited tolerance for acid conditions \nand fungi are more tolerant. Since the pH in this study was low, it could be \nassumed that fungi were more involved in the degradation of oil due to its ability \nto adopt acidic condition and secretion of acidophilic enzymes. The number of \nculturable bacteria decreased gradually under acidic conditions, while the number \nof culturable fungi remained relatively constant over the acidic pH range. The \nratios of culturable bacteria to culturable fungi were greater than one at pH 6; in \ncontrast, the bacteria-to-fungi ratios were less than one at acid pH (Matthies et al. \n1997).\n The soils were found to contain the highest amounts of organic carbon. \nSulphur, iron, manganese, zinc, and copper were higher in value. The lowest \nconcentrations of calcium, was observed in the sulphur, iron, manganese, zinc, \ncopper, boron, calcium, magnesium, pH and organic carbon in all contaminated \nsoil samples (Table 1). Many studies reveal that the increasing total carbon content \nof the soil with increasing concentration of crude oil may be attributed to the high \ncontent of carbon in the oil. In soil, oil can rapidly decay and mineralise leading to \nthe release of cations and trace elements (Nnaji et al. 2005). Oil products not only \nmodify physicochemical and biological properties of the soil, but also contribute \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 111\n\n\n\nto limiting the productive ability of crop (Wyszkowskwi and Wyszkowskwa \n2005). Microorganisms possess mechanisms by which they degrade the crude oil \ncompounds by utilizing them as carbon and energy sources.\n\n\n\nIsolation of Bacteria and Fungi\nA total 5 samples including one from a petrol pump soil and four  from automobile \nworkshop were collected from different sites. In the present study,six bacteria and  \nfour fungi were isolated from oil contaminated soil. Bacterial strains were isolated \nfrom the crude oil contaminated soil sample using nutrient agar medium and \nfungal strains were isolated using Sabouraud dextrose agar medium respectively \n(Tables 2 and 3). The total bacterial count ranged from 1.123\u00d7107 CFU/ml to \n7.27\u00d7107 CFU/ml. Indeed, the presence of such a significant number of bacteria \nin the contaminated soils reflects their adaptive ability to survive even in the \npresence of various petroleum products. Previous studies show that oil pollution \ncan change the composition and diversity of soil bacteria and this change of soil \nproperties will greatly constrain the biodegradation rate of bioremediation of \npetroleum contaminated sites (Liu et al. 2005).  The total fungal count ranged \nfrom 5.43 \u00d7106 CFU/ml to 4.9 \u00d7107 CFU/ml. In a related experiment, Onifade and \nAbubakar (2007) reported higher hydrocarbon utilizing fungi in crude oil polluted \nsoil than in crude oil free soil. An increase in microbial load with depth of soil \nhas been confirmed and attributed to migration of the oil downward. Adaptation \nof these hydrocarbons utilizing fungi increases with exposure (Head et al. 2006; \nHamamura et al. 2006).\n\n\n\nTABLE 1\nPhysiochemical parameters of soil samples\n\n\n\nfor acid conditions and fungi are more tolerant. Since the pH in this study was low, it could be \n\n\n\nassumed that fungi were more involved in the degradation of oil due to its ability to adopt acidic \n\n\n\ncondition and secretion of acidophilic enzymes. The number of culturable bacteria decreased \n\n\n\ngradually under acidic conditions, while the number of culturable fungi remained relatively constant \n\n\n\nover the acidic pH range. The ratios of culturable bacteria to culturable fungi were greater than one \n\n\n\nat pH 6; in contrast, the bacteria-to-fungi ratios were less than one at acid pH (Matthies et al. 1997). \n\n\n\nThe soils were found to contain the highest amounts of organic carbon. Sulphur, iron, manganese, \n\n\n\nzinc, and copper were higher in value. The lowest concentrations of calcium, was observed in the \n\n\n\nsulphur, iron, manganese, zinc, copper, boron, calcium, magnesium, pH and organic carbon in all \n\n\n\ncontaminated soil samples (Table 1). Many studies reveal that the increasing total carbon content of \n\n\n\nthe soil with increasing concentration of crude oil may be attributed to the high content of carbon in \n\n\n\nthe oil. In soil, oil can rapidly decay and mineralise leading to the release of cations and trace \n\n\n\nelements (Nnaji et al. 2005). Oil products not only modify physicochemical and biological \n\n\n\nproperties of the soil, but also contribute to limiting the productive ability of crop (Wyszkowskwi \n\n\n\nand Wyszkowskwa 2005). Microorganisms possess mechanisms by which they degrade the crude \n\n\n\noil compounds by utilizing them as carbon and energy sources. \n\n\n\nTABLE 1 \n\n\n\nPhysiochemical parameters of soil samples \n\n\n\nParameter Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Average \n\n\n\npH 5.60 6.15 5.89 6.24 7.09 6.194 \n\n\n\nOrganic carbon 2.43 1.89 2.26 2.57 1.78 2.186 \n\n\n\nCalcium (p.p.m) 279 317 254 280 321 290.2 \n\n\n\nMagnesium \n(p.p.m) \n\n\n\n85.40 95.20 88.40 73.30 83.20 85.1 \n\n\n\nSulphur (p.p.m) 11.80 10.40 9.63 12.30 16.80 12.186 \n\n\n\nIron (p.p.m) 236.80 289.60 243.40 255.50 274.30 259.92 \n\n\n\nManganese \n(p.p.m) \n\n\n\n37.37 6.35 18.68 29.92 26.14 23.692 \n\n\n\nZinc (p.p.m) 59.11 4.78 49.29 49.22 49.20 42.32 \n\n\n\nCopper  (p.p.m) 36.86 0.40 4.13 3.76 16.67 12.364 \n\n\n\nBoron (p.p.m) 0.16 0.07 0.15 0.13 0.07 0.116 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020112\n\n\n\nIdentification of Bacterial Isolates\nThe six selected bacterial strains were labeled B1, B2, B3, B4, B5 and B6 based on \ntheir cultural characters. These organisms were identified based on their cultural, \nmorphological and biochemical characteristics (Table 4). Based on these results, \ntogether with the morphological, physiological and biochemical characteristics, \nstrain B1 was identified as Bacillus cereus; strain B2, as Pseudomonas sp., strain \nB3, as Bacillus sp., strain B4 as Bacillus subtilis, strain B5, as Pseudomonas \naeruginosa and strain B6, as Staphylococcus aureus. However, the colony \nmorphology and some physiological and biochemical properties of these \nPseudomonas spp. and Bacillus spp. strains were quite similar. Therefore, \nfurther identification to the species level is needed. Pseudomonas was a common \nbacterium capable of degrading hydrocarbons (Kishore Das and Ashis Mukherjee \n2007). Though Pseudomonas aeruginosa was a clinical strain, it can also grow \non hydrocarbon as sole carbon source is a good degrader of oil (Szoboszlay et \nal. 2003). Bacillus sp could effectively biodegrade crude oil petroleum in liquid \ncultures as well as in polluted soil and sand (Salleh et al. 2003).In this study all \nthe isolates were able to grow in mineral medium contaminated with crude oil. \nThis results support the increased role of these bacterial genera in hydrocarbon \nbiodegradation (Leahy and Colwell 1990).\n\n\n\nTABLE 2\nEstimation of total bacterial population using CFU\n\n\n\nIsolation of Bacteria and Fungi \n\n\n\nA total 5 samples including one from a petrol pump soil and four  from automobile workshop were \n\n\n\ncollected from different sites. In the present study,six bacteria and  four fungi were isolated from oil \n\n\n\ncontaminated soil. Bacterial strains were isolated from the crude oil contaminated soil sample using \n\n\n\nnutrient agar medium and fungal strains were isolated using Sabouraud dextrose agar medium \n\n\n\nrespectively (Tables 2 and 3). The total bacterial count ranged from 1.123\u00d7107 CFU/ml to 7.27\u00d7107 \n\n\n\nCFU/ml. Indeed, the presence of such a significant number of bacteria in the contaminated soils \n\n\n\nreflects their adaptive ability to survive even in the presence of various petroleum products. \n\n\n\nPrevious studies show that oil pollution can change the composition and diversity of soil bacteria \n\n\n\nand this change of soil properties will greatly constrain the biodegradation rate of bioremediation of \n\n\n\npetroleum contaminated sites (Liu et al. 2005).  The total fungal count ranged from 5.43 \u00d7106 \n\n\n\nCFU/ml to 4.9 \u00d7107 CFU/ml. In a related experiment, Onifade and Abubakar (2007) reported higher \n\n\n\nhydrocarbon utilizing fungi in crude oil polluted soil than in crude oil free soil. An increase in \n\n\n\nmicrobial load with depth of soil has been confirmed and attributed to migration of the oil \n\n\n\ndownward. Adaptation of these hydrocarbons utilizing fungi increases with exposure (Head et al. \n\n\n\n2006; Hamamura et al. 2006). \n\n\n\nTABLE 2 \n\n\n\nEstimation of total bacterial population using CFU \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSample No. of colonies No of cells/ml \n\n\n\nSample 1 144 5.73 \u00d7 107 CFU/ml \n\n\n\nSample 2 345 7.24\u00d7 107 CFU/ml \n\n\n\nSample 3 24 1.52 \u00d7 10 7 CFU/ml \n\n\n\nSample 4 67 1.123\u00d7 107 CFU/ml \n\n\n\nSample 5 40 1.325 \u00d7 107 CFU/ml \n\n\n\nTABLE 3 \nEstimation of total fungal population using CFU\n\n\n\n\n\n\n\nTABLE 3  \n\n\n\nEstimation of total fungal population using CFU \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIdentification of Bacterial Isolates \n\n\n\nThe six selected bacterial strains were labeled B1, B2, B3, B4, B5 and B6 based on their cultural \n\n\n\ncharacters. These organisms were identified based on their cultural, morphological and biochemical \n\n\n\ncharacteristics (Table 4). Based on these results, together with the morphological, physiological and \n\n\n\nbiochemical characteristics, strain B1 was identified as Bacillus cereus; strain B2, as Pseudomonas \n\n\n\nsp., strain B3, as Bacillus sp., strain B4 as Bacillus subtilis, strain B5, as Pseudomonas aeruginosa \n\n\n\nand strain B6, as Staphylococcus aureus. However, the colony morphology and some physiological \n\n\n\nand biochemical properties of these Pseudomonas spp. and Bacillus spp. strains were quite similar. \n\n\n\nTherefore, further identification to the species level is needed. Pseudomonas was a common \n\n\n\nbacterium capable of degrading hydrocarbons (Kishore Das and Ashis Mukherjee 2007). Though \n\n\n\nPseudomonas aeruginosa was a clinical strain, it can also grow on hydrocarbon as sole carbon \n\n\n\nsource is a good degrader of oil (Szoboszlay et al. 2003). Bacillus sp could effectively biodegrade \n\n\n\ncrude oil petroleum in liquid cultures as well as in polluted soil and sand (Salleh et al. 2003).In this \n\n\n\nstudy all the isolates were able to grow in mineral medium contaminated with crude oil. This results \n\n\n\nsupport the increased role of these bacterial genera in hydrocarbon biodegradation (Leahy and \n\n\n\nColwell 1990). \n\n\n\nDetermination of Bacterial Hydrocarbon Growth Capability \n\n\n\nIn order to carry out the screening of the petroleum hydrocarbon degrading bacteria, each isolate \n\n\n\nwas plated onto NA medium where crude oil served as the sole source of carbon and energy. The \n\n\n\ngrowth rate of each bacteria showed that Pseudomonas aeruginosa had the highest growth diameter \n\n\n\nSamples No. of colonies CFU/ml \n\n\n\nSample 1 13 4.9 \u00d7 107 CFU/ml \n\n\n\nSample 2 4 4.0 \u00d7 107 CFU/ml \n\n\n\nSample 3 4 5.43 \u00d7 106 CFU/ml \n\n\n\nSample 4 10 1.66 \u00d7 107 CFU/ml \n\n\n\nSample 5 5 1.3 \u00d7 107 CFU/ml \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 113\n\n\n\nDetermination of Bacterial Hydrocarbon Growth Capability\nIn order to carry out the screening of the petroleum hydrocarbon degrading bacteria, \neach isolate was plated onto NA medium where crude oil served as the sole source \nof carbon and energy. The growth rate of each bacteria showed that Pseudomonas \naeruginosa had the highest growth diameter in 5% petrol contaminated NA media \nculture and Bacillus cereus and Pseudomonas sp had the second highest growth \ndiameter in 5% petrol while Staphylococcus aureus had the lowest growth rate at \nall the concentrations. Six isolated strains were capable of growing in polluted NA \nmedia and utilized petrol as sole carbon source (Table 5, Figure 1). The growth \ndiameter of bacteria decreased with increasing petrol concentration except for \nPseudomonas aeruginosa which saw an increase in growth diameter of colony \nwith increasing kerosene concentration. The initial intracellular attack of organic \npollutants is an oxidative process and the activation as well as incorporation of \noxygen is the enzymatic key reaction catalysed by oxygenases and peroxidases \nby many bacterial isolates. Bacillus cereus shows gradual decrease in growth \ndiameter as compared to control while other species show slight changes in the \ndiameter of colony growth, indicating higher degradation capacity due to different \npathways that convert organic pollutants step by step into intermediates.\n\n\n\nTABLE 4\nMorphological features, physiological and biochemical characteristics of the strains\n\n\n\nin 5% petrol contaminated NA media culture and Bacillus cereus and Pseudomonas sp had the \n\n\n\nsecond highest growth diameter in 5% petrol while Staphylococcus aureus had the lowest growth \n\n\n\nrate at all the concentrations. Six isolated strains were capable of growing in polluted NA media and \n\n\n\nutilized petrol as sole carbon source (Table 5, Figure 1). The growth diameter of bacteria decreased \n\n\n\nwith increasing petrol concentration except for Pseudomonas aeruginosa which saw an increase in \n\n\n\ngrowth diameter of colony with increasing kerosene concentration. The initial intracellular attack of \n\n\n\norganic pollutants is an oxidative process and the activation as well as incorporation of oxygen is \n\n\n\nthe enzymatic key reaction catalysed by oxygenases and peroxidases by many bacterial isolates. \n\n\n\nBacillus cereus shows gradual decrease in growth diameter as compared to control while other \n\n\n\nspecies show slight changes in the diameter of colony growth, indicating higher degradation \n\n\n\ncapacity due to different pathways that convert organic pollutants step by step into intermediates. \n\n\n\nTABLE 4 \n\n\n\nMorphological features, physiological and biochemical characteristics of the strains \n\n\n\n\n\n\n\nCharacteristics Strains \nB1 B2 B3 B4 B5 B6 \n\n\n\nMorphological features \nColony form irregular circular circular circular circular circular \nColony colour white Cream cream white cream yellow \nCell shape rod Rod rod rod rod cocci \n\n\n\nPhysiological and biochemical characteristics \nGram staining + - + + - + \n\n\n\nCatalase + + + + + + \nOxidase - + + - + - \nIndole - - - - - - \nM.R - - - - - + \nV.P + - + + - - \nCitrate + + - - + - \nStarch hydrolysis + - + + - + \nTSI A/K(H2S) A/A A/A A/A A/A A/K \n\n\n\nNote: \u201c+\u201d = positive; \u201c-\u201d = negative; A/A- acid butt and acid slant; A/K -acid butt and alkaline slant \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 5 \n\n\n\nNote: \u201c+\u201d = positive; \u201c-\u201d = negative; A/A- acid butt and acid slant; A/K -acid butt and \nalkaline slant\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020114\n\n\n\nTABLE 5\nGrowth diameters of bacterial strains in Oil- contaminated SDA media culture\n\n\n\nTABLE 5 \n\n\n\nGrowth diameters of bacterial strains in Oil- contaminated SDA media culture \n\n\n\n\n\n\n\nBacteria \n\n\n\nGrowth diameter/mm \nControl Concentration of petrol \n\n\n\n5% \n \n\n\n\nBacillus cereus \n\n\n\n7 4 \n6 5 \n5 4 \n\n\n\n\n\n\n\nPseudomonas sp. \n\n\n\n4 5 \n3 4 \n3 3 \n\n\n\n\n\n\n\nBacillus sp. \n\n\n\n5 5 \n5 4 \n4 2 \n\n\n\n\n\n\n\nBacillus subtilis \n\n\n\n6 4 \n7 4 \n5 3 \n\n\n\n\n\n\n\nPseudomonas aeruginosa \n\n\n\n7 6 \n5 4 \n1 4 \n\n\n\n\n\n\n\nStaphylococcus aureus \n\n\n\n4 1 \n2 1 \n2 1 \n\n\n\n\n\n\n\n\n\n\n\n Figure 1. Growth diameters of bacterial strains in oil- contaminated SDA media culture\n\n\n\nTABLE 5 \n\n\n\nGrowth diameters of bacterial strains in Oil- contaminated SDA media culture \n\n\n\n\n\n\n\nBacteria \n\n\n\nGrowth diameter/mm \nControl Concentration of petrol \n\n\n\n5% \n \n\n\n\nBacillus cereus \n\n\n\n7 4 \n6 5 \n5 4 \n\n\n\n\n\n\n\nPseudomonas sp. \n\n\n\n4 5 \n3 4 \n3 3 \n\n\n\n\n\n\n\nBacillus sp. \n\n\n\n5 5 \n5 4 \n4 2 \n\n\n\n\n\n\n\nBacillus subtilis \n\n\n\n6 4 \n7 4 \n5 3 \n\n\n\n\n\n\n\nPseudomonas aeruginosa \n\n\n\n7 6 \n5 4 \n1 4 \n\n\n\n\n\n\n\nStaphylococcus aureus \n\n\n\n4 1 \n2 1 \n2 1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIdentification of Fungi\nThe fungi exhibiting degradation of petroleum hydrocarbon were grown on SDA \nto examine morphology, viz size, mycelia and sporulation, and culture, viz color, \ntexture, substrate color and colonial appearances. The identified funguses were \nAspergillus niger, Aspergillus flavus, Aspergillus sp. and Pencillium sp.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 115\n\n\n\nDetermination of Fungi Hydrocarbon Growth Capability \nIn order to carry out the screening of the petroleum hydrocarbon degrading fungi, \neach isolate was plated onto SDA medium where crude oil served as the sole source \nof carbon and energy. The growth rate of each fungus showed that Aspergillus \nniger had the highest growth diameter in all petrol contaminated SDA media \nculture and Aspergillus flavus had the second highest growth diameter in petrol \ncontaminated SDA media culture while Penicillium sp had the lowest growth \nrate at all the concentrations. A similar observation was reported by Adekunle \nand Adebambo (2007) in which the isolated Rhizopus species from the seed of \nDetarium senegalense showed the highest ability to degrade kerosene compared \nto Aspergillus flavus, Aspergillus niger, Mucor and Talaromyces.\n Four isolated strains were capable of growing in polluted SDA media, \nutilizing petrol as the sole carbon source (Table 6 & Figure 2). A study by Wemedo \net al. (2002) also recorded that the genera of fungi such as Penicillium, Aspergillus \nand Rhizopus are associated with kerosene-polluted soil. As can be seen, the \ngrowth diameter of fungus decreased with increasing petrol concentration except \nfor Aspergillus niger and Aspergillus flavus where the growth diameter of colony \nincreased with increasing petrol concentration. Pencillium sp and Aspergillus sp \nshowed a gradual decrease in growth diameter compared to the control while two \nother species showed slight changes in the diameter of colony growth, indicating \na higher degradation capacity. Table 6 shows that at low and high concentrations \nof petrol contamination, Aspergillus niger had the highest bioremediation activity. \n\n\n\nTABLE 6 \nGrowth diameters of fungi strains in Oil-contaminated SDA media culture\n\n\n\nTABLE 6  \n\n\n\nGrowth diameters of fungi strains in Oil- contaminated SDA media culture \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFungus \n\n\n\nGrowth diameter/cm \n\n\n\nControl Concentration of petrol \n\n\n\n5%                10%              15% \n\n\n\nAspergillus niger 4 4 3.8 3.6 \n\n\n\n4.5 3.8 3.5 3.5 \n\n\n\nAspergillus flavus 3 2 1.8 1.2 \n\n\n\n2.5 1.5 1.3 1 \n\n\n\nAspergillus sp. 2.5 2.5 2.2 2 \n\n\n\n2.2 2.2 2 1.5 \n\n\n\nPencillium sp. 1 0.8 0.8 0.6 \n\n\n\n0.9 0.8 0.7 0.5 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020116\n\n\n\nFigure 2. Growth diameters fungi strains in oil-contaminated SDA media culture\n\n\n\nCONCLUSION\nThe microbiological study of petroleum polluted soil shows the presence of six \nbacteria: Pseudomonas sp., Pseudomonas aeruginosa, Bacillus sp., Bacillus \ncereus, Bacillus subtilis, Staphylococcus aureus and four fungi Aspergillus niger, \nAspergillus flavus, Aspergillus sp, Pencillium sp. The presence of heterotrophic \nbacteria is attributed to the tolerance of these microbes to wide variations of soil \nproperties.\n\n\n\nREFERENCES\nAdams, R.S. and R. Ellis. 1960. Some physical and chemical changes in soil brought \n\n\n\nabout by saturation with natural gas. Soil Science Soc. Amer. Pro. 24: 41-44.\n\n\n\nAdekunle, A.A. and O.A. Adebambo. 2007. Petroleum hydrocarbon utilisation by \nfungi isolated from Detarium senegalense (J.F. Gmelin) seeds. J Am Sci, \n3(1):69-76.\n\n\n\nAtlas, R.M. 1981. Microbial degradation of petroleum hydrocarbons: an environmental \nperspective. 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"\n\n___________________\n*Corresponding author : E-mail: enio@upm.edu.my \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 1-13 (2021)                          Malaysian Society of Soil Science\n\n\n\nPhysico-Chemical Variability of Acid Sulfate Soils at \nDifferent Locations along the Kelantan Plains, Peninsular \n\n\n\nMalaysia \n\n\n\nEnio M.S.K.1*, J. Shamshuddin2, C.I. Fauziah2, M.H.A. Husni2 \nand Q.A. Panhwar3\n\n\n\n1Department of Science and Technical Education, Faculty of Educational Studies, \nUniversiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\n3Soil and Environmental Science Division, Nuclear Institute of Agriculture, \nTandojam, Sindh, Pakistan\n\n\n\nABSTRACT\nMineralogy and physico-chemical properties of pyritic soils from three sites along \nthe coastal plains of Kelantan, Peninsular Malaysia were studied. The aim of the \nstudy was to identify the variability and principal components of soil properties \nin this area. Soil sampling was conducted at three sites, stretching from North \n(Bachok) to south (Pasir Puteh) of the plains. Soil samples were taken from \ndifferent depths at each site based on the presence of pyritic evidence. Soil \nphysico-chemical properties were determined using standard laboratory methods \nwhile  X-ray diffraction and SEM-EDX were carried out to determine the shape \nand stages of pyrite disintegration. Soils with sulfidic materials were found to be \nsporadically distributed throughout the plains, exhibiting different properties and \ndistribution with depth. The soils with a sulfidic materials layer close to the soil \nsurface had pH values below 3.5. Based on principal component analysis (PCA), \nthe variables of all sites could be classified into three components, namely, soil \npH, soil nutrients and organic matter content, which accounted for 83% of the \nsite variability. The different dominant controlling factors in soil variables among \nthe studied sites suggest that acid sulfate soils occurring in the area may have \nembraced many distinctive features even if they occur close to each other. This is \na result of the multiple complex reactions after the sea level rose 3-5 m above the \npresent level many thousands of years ago. Generally, the productivity of the soils \nin the area is low due to the occurrence of excessive acidity and the presence of \ntoxic amounts of Al in the soils.\n\n\n\nKeyword:    Acid sulfate soils, principal component analysis, pyrite, acidity, \n       soil productivity.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 20212\n\n\n\nINTRODUCTION\nIn most years, part of the Kelantan Plains (Figure 1) in the east coast state of \nPeninsular Malaysia is submerged during the monsoon season of November to \nJanuary. To reduce the effects of flooding in this area, the government of Malaysia \nestablished the Kemasin-Semerak Integrated Agricultural Development Project \n(IADP) in 1984. The land in the flood-affected area was drained and irrigation \ncanals were constructed; this was to make way for agriculture, especially for rice \ncultivation. \n The land along the coastal plains in Kelantan was inundated by seawater \nsome time during the Holocene (Enio et al. 2011). It was reported that the sea \nlevel in these areas was 3-5 m above the present (Tjia et al. 1977; Pons et al. 1982) \nabout 6,000 years BP (Haile 1970). The occurrence of sandy beach ridges along \nthe coastal plains in Peninsular Malaysia (Roslan et al. 2010) and the presence of \nnotch-like features above the mean high water tide on cliffs at Langkawi Islands, \nMalaysia (Hodgkin 1970), have been used as evidence for the sea level rise during \nthe Holocene. It was during this period of sea level rise that pyrite (FeS2) was \nmineralised in the alluvial sediments of the Kelantan Plains (Enio et al. 2011). It \nwas, therefore, assumed that the shoreline was a few km away from the present. \nAs the sea prograded, a series of sandy ridges was formed (Roslan et al. 2010) and \npeaty materials accumulated on the top of the pyrite-bearing sediments, forming \nthe present peat soils in the plains.  \n Some years after development, the paddy fields in the area were mostly \ndegraded due to the occurrence of extreme acidity resulting from the oxidation of \npyrite present in the soils (Shazana et al. 2013). Oxidation of pyrite had resulted \nin the formation of jarosite [KFe3(SO4)2(OH)6], appearing as yellowish mottles \nin the soil profile (Shamshudin and Auxtero 1991; Shamshuddin et al. 1995; \nShamshuddin et al. 2004a). This pyrite was formed when the area was inundated \nwith seawater during the Holocene (Roslan et al. 2010). The paddy fields are \non acid sulfate soils that are not only low in pH (< 3.5), but also contain toxic \namounts of Al and/or Fe (Shamshuddin 2006). Acid sulfate soils are chemically \ndegraded and therefore unfit for agricultural production unless they are properly \nalleviated with appropriate amendments.\n.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 3\n\n\n\n\n\n\n\nalleviated with appropriate amendments. \n\n\n\n\n\n\n\nFigure 1. (a) Kelantan Plains and (b) Kemasin-Semerak IADP  \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\nStudy Area \n\n\n\nThe area investigated is situated within the Kemasin-Semerak Integrated \n\n\n\nAgricultural Development Project (IADP), Kelantan, Malaysia (Figure 1b). The \n\n\n\ntotal area of this development project is about 200 ha. There are three sites with \n\n\n\npyritic soil layer denoted as A (Bachok), B (Jelawat) and C (Tok Bali, Pasir \n\n\n\nPuteh). These three sites were chosen based on evidence of the presence of pyrite  \n\n\n\nafter a series of preliminary samplings conducted to decide the sites for this \n\n\n\nstudy. Kemasin-Semerak IADP stretches from Bachok, the northern part of the \n\n\n\nKelantan Plains to Pasir Puteh at the south. Most of the area has been drained \n\n\n\nsince 1984 for agricultural development but then an area in the middle of the \n\n\n\n \nFigure 1. (a) Kelantan Plains and (b) Kemasin-Semerak IADP\n\n\n\n MATERIALS AND METHODS\nStudy Area\nThe area investigated is situated within the Kemasin-Semerak Integrated \nAgricultural Development Project (IADP), Kelantan, Malaysia (Figure 1b). \nThe total area of this development project is about 200 ha. There are three sites \nwith pyritic soil layer denoted as A (Bachok), B (Jelawat) and C (Tok Bali, Pasir \nPuteh). These three sites were chosen based on evidence of the presence of pyrite  \nafter a series of preliminary samplings conducted to decide the sites for this study. \nKemasin-Semerak IADP stretches from Bachok, the northern part of the Kelantan \nPlains to Pasir Puteh at the south. Most of the area has been drained since 1984 \nfor agricultural development but then an area in the middle of the IADP was \nleft undisturbed for future development; this area (denoted as site B), situated \nin Jelawat-Rusa Irrigation Scheme, is occupied by acid sulfate soils overlain by \npeaty materials. This area is now covered by local plant species called gelam \n(Melulueca leucadendron) and nipah palm (Nipa frutescens). These plant species \nare known to survive well even under very acidic conditions. Meanwhile site A \n(Bachok), which has sandy topsoils, is under grass. At site C (Tok Bali), pyrite \npresence on the surface was easiest to identify during the preliminary surveys.  \n\n\n\nField Observations and Soil Sampling  \nGenerally, pyrite was found to occur at varying depths (close to the surface, below \n2 m or in between the two). Additionally, the soils with pyrite at the study sites \nshowed different degrees of association with peaty materials (organic matter). \nDuring preliminary sampling, the soil profiles were dug and were described at \neach site. At several locations in Jelawat-Rusa Irrigation Scheme and Bachok, \nthe soil samples were taken only up to 75cm depths due to a high water table. \nBased on the presence of pyritic or jarosite under the soil profile, three sites were \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 20214\n\n\n\nspecially marked for detailed study and designated as A, B and C, based on the \ndepth of the pyritic layer (Figure 1b). \n After finalising these sites and the spots for sampling, an auger was used \nto take the soil samples from each soil horizon. The samples were then placed \nand sealed in plastic bags before transferring to the laboratory. The time for this \nprocess did not exceed 24 h to make sure minimal oxidation occurred in the \nsample. Upon reaching the laboratory, these soil samples were quickly dried using \nthe vacuum freeze dryer as a measure to avoid oxidation of pyrite. After freeze-\ndrying, the samples were then crushed and sieved to mesh size.  \n\n\n\nLaboratory Analyses\nThe pH of the soil was determined in water (1:2.5), while electrical conductivity \n(EC) was determined using saturated paste. Cation exchange capacity (CEC) was \ndetermined using 1 M NH4OAc buffered at pH 7 (Soil Survey Laboratory Staff \n1992). The basic cations (Ca, Mg, K and Na) present in the NH4OAc extract \nwere measured by atomic absorption spectrophotometer (AAS). Exchangeable \nAl was extracted by 1 M KCl (1:5) and the Al in the extract was determined \nby AAS. Total carbon (TC) was analysed by the dry combustion method using \nCNS analyser. Total N was determined by the Kjeldahl method (Bremner and \nMulvaney 1982) and available P was determined by the method of Bray and Kurtz \n(1945). Soil texture was analysed by sedimentation (Soil Survey Laboratory Staff \n1992) and the clay fraction was used for mineralogical analysis. The clay was \ntreated with Mg, Mg-glycol, K and K-heated at 550\u00b0C. The minerals in the clay \nfraction were identified by XRD analysis using Philips PW3440/60 X\u2019Pert Pro. \nThe samples containing sulfidic materials were studied under scanning electron \nmicroscope (SEM).\n\n\n\nStatistical Analyses\nAll statistical analyses were performed using the Microsoft XLStat 2003. \nArithmetic mean values and statistically significant differences between all study \nsites were determined by analysis of variance. For clarification of the study sites, \nprincipal component analyses (PCA) was performed. Correlation between soil \nproperties was assessed using the Spearman rank correlation coefficient. All \nstatistical considerations were based on 0.05 significant levels.  \n\n\n\nRESULTS AND DISCUSSION\nPyritisation of the Kelantan Plains\nThe sediments at location A were found to have a pyritic layer deep down the \nsoil profiles. This is a plausible explanation for the pyritisation process. In the \nnorthern part of the study area (Figure 1), the areas bordering the shoreline were \nprobably at a higher level. It was assumed that the seawater was able to seep \nthrough the porous riverine alluvial materials a few metres below the surface. Iron \nwas present in high amounts in the sediments of the area (Table 1). We believe that \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 5\n\n\n\norganic matter required by the microbes was also present in sufficient amounts. \nUnder such conditions, pyrite was mineralised continuously.\n At location B, swamps were formed after the sea was prograded. The water \nin these swamps was probably brackish due to intrusion of seawater. In this area, \nGelam and nipah palms had provided sufficient organic matter for the reduction \nprocess to proceed. Pyrite was consequently mineralised in the sediments. The \narea was waterlogged and therefore organic matter from the plant species growing \nin the swamps had accumulated, forming an organic layer above the sediments. \nHence, soils in these areas now contain pyrite overlain by peaty materials. \nThe area at location C (in the southern part) was flat and located close to the \n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nSoil chemical properties \n\n\n\nSites/ \n\n\n\nHorizons  \n\n\n\nDepth pH EC Al CEC Ext. \n\n\n\nFe \n\n\n\nAvail. P Total \n\n\n\nN \n\n\n\nTotal \n\n\n\nC \n\n\n\nSand Silt Clay \n\n\n\n                     (cm) (dS/m) (cmolc /kg) (mg/kg) (%) \n\n\n\nSITE A  \n\n\n\nSulfic Endoaquept  \n\n\n\nA  0-15  5.5  0.14  7.9  10.0  2.3  17.6  0.3  3.3  72.3  12.2  15.5  \n\n\n\nB  15-55  3.6  0.12  6.0  9.0  1.5  19.1  0.2  1.4  61.4  36.3  2.3  \n\n\n\nBw  55-150  3.9  0.10  15.7  9.5  11.3  14.2  0.1  1.3  9.5  52.1  38.4  \n\n\n\nCg  >150  4.2  0.08  10.2  12.4  11.9  75.1  0.2  4.2  1.0  57.4  42.1  \n\n\n\n\n\n\n\nSITE B  \n\n\n\nTypic Sulfihemists  \n\n\n\nOe  0-20  3.2  0.12  16.2  14.2  10.8  10.0  0.2  4.4  n.a  n.a  n.a  \n\n\n\nA  20-30  3.1  0.17  15.5  11.7  10.4  9.5  0.1  2.0  1.6  51.2  47.3  \n\n\n\nBwg  30-45  3.1  0.16  13.8  9.3  8.1  10.1  0.8  0.9  4.9  58.7  36.4  \n\n\n\nCg  45-60  3.3  0.18  15.2  17.6  9.1  10.3  0.4  4.6  n.a  n.a  n.a  \n\n\n\n\n\n\n\nSITE C  \n\n\n\nTypic Sulfaquept  \n\n\n\nAp  0-10  4.0  0.34  8.1  20.4  10.2  14.4  0.4  9.3  3.7  20.5  75.8  \n\n\n\nBj  10-35  3.5  0.42  6.2  14.3  10 .1  14.6  0.2  5.3  4.0  21.7  74.3  \n\n\n\nBw  35-45  3.1  0.14  15.7  20.1  10.5  17.4  0.5  13.3  0.7  25.8  73.5  \n\n\n\nCg  45-75  2.8  0.42  32.1  10.7  10.3  17.0  0.3  15.1  n.a  n.a  n.a  \n\n\n\n\n\n\n\nTABLE 1\nSoil chemical properties\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 20216\n\n\n\npresent coastline, which is always flooded during the rainy season. Gelam and \nnipah palms were also found in this area. Seawater must have intruded this area a \nfew thousand years ago, during which pyrite was mineralised. We detected straw-\nyellow jarosite (which is the product of pyrite oxidation) in the topsoil. \n The area at location C (in the southern part) was flat and located close to \nthe present coastline, which is always flooded during the rainy season. Gelam and \nnipah palms were also found in this area. Seawater must have intruded this area a \nfew thousand years ago, during which pyrite was mineralised. We detected straw-\nyellow jarosite (which is the product of pyrite oxidation) in the topsoil. \n\n\n\nMineralogical Properties of Soils\nThe mineralogy of the clay fraction of the soils under study was determined. Based \non the XRD diffractograms obtained (Figure 2), the Mg-treated sample gave XRD \nreflections at 9.90, 7.10, 4.98, 4.84, 3.57 and 3.33 \u00c5, which show the presence \nof mica (9.90, 4.98 and 3.33 \u00c5), kaolinite (7.10 and 3.57 \u00c5) and gibbsite (4.84 \n\u00c5). Smectite was present as shown by the Mg-saturated and glycolated samples \nwhich gave reflection at 15.60 \u00c5. When the K-saturated sample was subjected to \nX-ray, a peak at about 12 \u00c5 appeared, and when this sample was heated, 14-15 \u00c5 \npeak did not appear on the diffractogram. This gives an indication of the absence \nof chlorite in the soils of the Kelantan Plains. We strongly believe that the smectite \noccurring in the soils came from the weathering of mica. Pyrite or jarosite was not \ndetected because during the process of clay separation, these minerals undergo a \ntransformation process.\n The presence of pyrite in the soils was proven by scanning electron \nmicroscopy using undisturbed/unoxidised samples. SEM studies of the samples \nfrom the sulfidic layers at each study site indicated the presence of pyrite in various \nforms, basically as a result of oxidation (Figure 3). Some of the samples showed \n\n\n\n\n\n\n\nMineralogical Properties of Soils \n\n\n\nThe mineralogy of the clay fraction of the soils under study was determined. \n\n\n\nBased on the XRD diffractograms obtained (Figure 2), the Mg-treated sample \n\n\n\ngave XRD reflections at 9.90, 7.10, 4.98, 4.84, 3.57 and 3.33 \u00c5, which show the \n\n\n\npresence of mica (9.90, 4.98 and 3.33 \u00c5), kaolinite (7.10 and 3.57 \u00c5) and gibbsite \n\n\n\n(4.84 \u00c5). Smectite was present as shown by the Mg-saturated and glycolated \n\n\n\nsamples which gave reflection at 15.60 \u00c5. When the K-saturated sample was \n\n\n\nsubjected to X-ray, a peak at about 12 \u00c5 appeared, and when this sample was \n\n\n\nheated, 14-15 \u00c5 peak did not appear on the diffractogram. This gives an \n\n\n\nindication of the absence of chlorite in the soils of the Kelantan Plains. We \n\n\n\nstrongly believe that the smectite occurring in the soils came from the weathering \n\n\n\nof mica. Pyrite or jarosite was not detected because during the process of clay \n\n\n\nseparation, these minerals undergo a transformation process. \n\n\n\n\n\n\n\n\n\n\n\nFigure 2. XRD diffractograms of the treated clay fraction of the soil: (a) Mg-sat; \n\n\n\n(b) Mg-glycolated; (c) K-saturated; (d) K-heated  \n\n\n\nFigure 2. XRD diffractograms of the treated clay fraction of the soil: (a) Mg-sat; \n(b) Mg-glycolated; (c) K-saturated; (d) K-heated\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 7\n\n\n\npyrite undergoing oxidation while some showed its original form beginning to \ndisintegrate. The SEM-EDX images first showed pyrite crystals in its globular \nform which was sampled from site A, but when the soil was slightly exposed to \noxidation, the pyrite globule disintegrated and we could see the individual pyrite \ncrystals in its cubical shape. This image of pyrite disintegration was captured \nfrom site B while in site C, we found that as the pyrite crystals underwent \nextensive oxidation, their cubical shape had begun to form edgeless crystals. This \nis consistent with the fact that this pyritic layer lies on the surface where the \nploughed topsoil exposes the sulfidic materials to the atmosphere. In the soils, \npyrite oxidises to form jarosite and during the process, acidity is produced and \nAl is released into the soil system. This ultimately affects the productivity of the \nsoils. When jarosite is observed in a soil, soil pH is almost always sure to be low \n(<3.5) (Shamshuddin and Auxtero 1991; Shamshuddin et al. 1995; Shamshudin \net al. 2004b). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPhysico-chemical Properties of the Soils \n\n\n\nDetailed chemical properties of the soils were studied at selected points (Table 1) \n\n\n\nand the minerals present in the clay fraction of the soils were identified (Figure \n\n\n\n2). At these locations, pyrite was found to occur at different depths in the soil \n\n\n\nprofiles. In general, the soils were low in pH, ranging from 3.2 to 4.9; however, \n\n\n\nexchangeable Al was high (>5 cmolc/kg). This means that acidity is high in the \n\n\n\nFigure 3. SEM-EDX images of pyrite crystals taken from Site A (Bachok), Site B \n(Jelawat-Rusa Irrigation Scheme) and Site C (Tok Bali), respectively \n\n\n\nA\n\n\n\nB \n\n\n\nC \n\n\n\nFigure 3. SEM-EDX images of pyrite crystals taken from Site A (Bachok), Site B \n(Jelawat-Rusa Irrigation Scheme) and Site C (Tok Bali), respectively\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 20218\n\n\n\nPhysico-chemical Properties of the Soils\nDetailed chemical properties of the soils were studied at selected points (Table 1) \nand the minerals present in the clay fraction of the soils were identified (Figure \n2). At these locations, pyrite was found to occur at different depths in the soil \nprofiles. In general, the soils were low in pH, ranging from 3.2 to 4.9; however, \nexchangeable Al was high (>5 cmolc/kg). This means that acidity is high in the \nsoils, which could affect crop growth. The CEC was low, which is a reflection of \nkaolinitic mineralogy. \n The distribution of the sulfidic materials with depth in the soil profiles \nlocated within the Kemasin-Semerak IADP is shown in Figure 4. Some of the \nlocations where sulfidic materials were present in the sediments are marked \nas bold stars in Figure 1b. The sulfidic materials were found to occur at three \nconspicuous depths, namely below 2 m, between 0 to 50 cm and in the topsoil. \nThe first type was found mostly at location A (the northern part of the study area), \nwhile the second type was found at location B (the peaty area in the middle part). \nThe third type was found at location C (the southern part of the study area) (Table \n1; Figure 1b).\n \n\n\n\nFigure 4. Occurrence of pyrite in soil at Sites A, B and C\n\n\n\n\n\n\n\nsoils, which could affect crop growth. The CEC was low, which is a reflection of \n\n\n\nkaolinitic mineralogy.  \n\n\n\nThe distribution of the sulfidic materials with depth in the soil profiles \n\n\n\nlocated within the Kemasin-Semerak IADP is shown in Figure 4. Some of the \n\n\n\nlocations where sulfidic materials were present in the sediments are marked as \n\n\n\nbold stars in Figure 1b. The sulfidic materials were found to occur at three \n\n\n\nconspicuous depths, namely below 2 m, between 0 to 50 cm and in the topsoil. \n\n\n\nThe first type was found mostly at location A (the northern part of the study \n\n\n\narea), while the second type was found at location B (the peaty area in the middle \n\n\n\npart). The third type was found at location C (the southern part of the study area) \n\n\n\n(Table 1; Figure 1b). \n\n\n\n\n\n\n\ns  \n\n\n\nFigure 4. Occurrence of pyrite in soil at Sites A, B and C \n\n\n\nWe had problems locating the depth of the sulfidic materials in the soils of the \n\n\n\nnorthern part of the study area. In this area, the pyritic layer was too deep to be \n\n\n\ndetermined by a soil auger. However, at the time of the soil survey, the drainage \n\n\n\nA B C \n\n\n\n We had problems locating the depth of the sulfidic materials in the soils \nof the northern part of the study area. In this area, the pyritic layer was too deep \nto be determined by a soil auger. However, at the time of the soil survey, the \ndrainage canals were undergoing maintenance.  This gave us the opportunity to \nstudy the sediments from the subsoil which were removed by heavy equipment. \nThese samples were left to dry to check if they contained pyrite. It was observed \nthat yellowish mottles appeared in the samples within days and the soil pH was \nfound to be about 3. These yellowish mottles are actually jarosite  formed from \nthe oxidation of pyrite as observed by Shamshuddin and Auxtero (1991) and \nShamshuddin et al. (2013). Note that the drainage canals were more than 2 m \ndeep. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 9\n\n\n\n The soils in the middle part of the study area were mostly overlain by peaty \nmaterials. In this area, samples were taken based on depth using a soil auger. \nWithin 50 cm depth, the pH of the soils was mostly below 3.5, which is consistent \nwith the pH of Sulfhemists (acid sulfate soils) as defined by soil taxonomy (Soil \nSurvey Laboratory Staff 2010). The air-dried samples indicated the presence of \nyellowish jarosite, proving yet again that the soils were acid sulfate soils.\n The soils in the southern part of study area were found to be mostly acidic, \nespecially at depth, and yellowish jarosite mottles appeared within the topsoil. \nThe soils can be classified as Sulfaquepts (Soil Survey Laboratory Staff 2010) as \npyrite/jarosite occurred within the top 50 cm of the soils. The paddy fields in this \narea were abandoned because the soils were too acidic for crop growth. Purun \n(Eleocharis dulcis), a plant species which is Al-tolerant was found to be growing \nin the abandoned paddy fields. \n\n\n\nCharacterisation of Sites\nPrincipal component analysis (PCA) was performed using the data from the three \nsampling sites together to determine the major principal components that are \nassociated with soil properties. The results of the PCA for the soil variables in sites \nA, B and C in this study are shown in Table 2. From the results of the initial eigen \nvalues, the first three principal components were considered, which accounted \nfor 86% of the total variance. The first principal component (PC1) accounted for \n46% of the total variance, and correlated highly with soil pH, exchangeable Al, \ntotal N and total C (r > 0.8) (Table 3), characterising  soil acidity and organic \nmatter content for each site. PC2 accounted for 25% of the total variance with a \nstrong correlation with the extractable Fe, Ca and Mg (r > 0.8), characterising the \nnutrient content of the soils. PC3 accounted for 12% of the total variance, and was \nloaded solely by the soil K (r > 0.8), characterising the nutrient content. \n\n\n\nTABLE 2 \nVariance of soil properties explained by various components of the PCA\n\n\n\n\n\n\n\nTABLE 2  \n\n\n\nVariance of soil properties explained by various components of the PCA \n\n\n\n  PC1 PC2 PC3 \n\n\n\nEigenvalue 5.06 2.76 1.34 \n\n\n\nVariability (%) 46.01 25.09 12.14 \n\n\n\nCumulative % 46.01 71.10 83.24 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nCorrelation matrix of the PCA for soil chemical properties \n\n\n\n  PC1 PC2 PC3 \n\n\n\npH -0.93 -0.06 -0.19 \n\n\n\nEC (dS/m) 0.93 0.14 -0.25 \n\n\n\nExc.Al 0.92 -0.02 -0.16 \n\n\n\nExtract Fe  0.35 0.81 -0.06 \n\n\n\nP -0.29 0.48 -0.16 \n\n\n\nCEC 0.81 -0.41 0.33 \n\n\n\nK -0.12 -0.21 0.90 \n\n\n\nCa 0.15 0.86 0.32 \n\n\n\nMg 0.46 0.81 0.14 \n\n\n\nTotal N 0.72 -0.37 -0.30 \n\n\n\nTotal C 0.91 -0.26 0.13 \n\n\n\n\n\n\n\n\n\n\n\nThe numbers in bold indicate definite assignation to respective components. \n\n\n\nFigure 5 shows the correlation circle of soil properties in all sites. From the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202110\n\n\n\n The numbers in bold indicate definite assignation to respective components. \nFigure 5 shows the correlation circle of soil properties in all sites. From the \ncorrelation circle, we know that there is significant negative correlation between \nsoil pH and exchangeable Al (r=-0.85), while total N is positively correlated with \nCEC (r=0.82). Other variables show indirect correlation (low r), but still contribute \nto site variability. The high amount of exchangeable Al greatly influences the low \nsoil pH at all sites. The peat layer which is a source of high N content contributes to \nthe CEC. Soil pH and exchangeable Al are two of the most important contributing \nfactors for all the sites studied.\n \n\n\n\nTABLE 3\nCorrelation matrix of the PCA for soil chemical properties\n\n\n\n\n\n\n\nTABLE 2  \n\n\n\nVariance of soil properties explained by various components of the PCA \n\n\n\n  PC1 PC2 PC3 \n\n\n\nEigenvalue 5.06 2.76 1.34 \n\n\n\nVariability (%) 46.01 25.09 12.14 \n\n\n\nCumulative % 46.01 71.10 83.24 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nCorrelation matrix of the PCA for soil chemical properties \n\n\n\n  PC1 PC2 PC3 \n\n\n\npH -0.93 -0.06 -0.19 \n\n\n\nEC (dS/m) 0.93 0.14 -0.25 \n\n\n\nExc.Al 0.92 -0.02 -0.16 \n\n\n\nExtract Fe  0.35 0.81 -0.06 \n\n\n\nP -0.29 0.48 -0.16 \n\n\n\nCEC 0.81 -0.41 0.33 \n\n\n\nK -0.12 -0.21 0.90 \n\n\n\nCa 0.15 0.86 0.32 \n\n\n\nMg 0.46 0.81 0.14 \n\n\n\nTotal N 0.72 -0.37 -0.30 \n\n\n\nTotal C 0.91 -0.26 0.13 \n\n\n\n\n\n\n\n\n\n\n\nThe numbers in bold indicate definite assignation to respective components. \n\n\n\nFigure 5 shows the correlation circle of soil properties in all sites. From the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 11\n\n\n\nFigure 5.  Correlation circle of soil chemical properties\n\n\n\n\n\n\n\ncorrelation circle, we know that there is significant negative correlation between \n\n\n\nsoil pH and exchangeable Al (r=-0.85), while total N is positively correlated with \n\n\n\nCEC (r=0.82). Other variables show indirect correlation (low r), but still \n\n\n\ncontribute to site variability. The high amount of exchangeable Al greatly \n\n\n\ninfluences the low soil pH at all sites. The peat layer which is a source of high N \n\n\n\ncontent contributes to the CEC. Soil pH and exchangeable Al are two of the most \n\n\n\nimportant contributing factors for all the sites studied. \n\n\n\n\n\n\n\n\n\n\n\nFigure 5.  Correlation circle of soil chemical properties \n\n\n\nFigure 6 shows the dimensional map of sites A, B and C according to depth. \n\n\n\nFrom the dimensional map, we can see that the sites are well grouped. Site A is \n\n\n\nsignificantly different from B and C in terms of all variables at all depth levels, \n\n\n\nwhile sites B and C show a similar trend and tabulation according to depth. \n\n\n\nFigure 6. Dimensional map of sites A, B and C according to depth\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6. Dimensional map of sites A, B and C according to depth \n\n\n\n\n\n\n\nCONCLUSIONS \n\n\n\nAcid sulfate soils occurring along the Kelantan Plains were uniquely formed \n\n\n\nwhen the sea level was higher than the present, some 6,000 BP. Pyritic sediments \n\n\n\nwere found to be sporadically distributed in the plains and indicate the areas \n\n\n\nwhere acid sulfate soils occur. These pyrite-bearing sediments are easily \n\n\n\ndistinguishable from each other based on their properties and depth. The \n\n\n\noxidation of pyrite leads to high acidity and the presence of extremely high \n\n\n\nconcentration of Al and Fe in the soils. The peaty materials in the soils contribute \n\n\n\nto the apparently high total N and C. Based on PCA, the principal components \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202112\n\n\n\n Figure 6 shows the dimensional map of sites A, B and C according to depth. \nFrom the dimensional map, we can see that the sites are well grouped. Site A is \nsignificantly different from B and C in terms of all variables at all depth levels, \nwhile sites B and C show a similar trend and tabulation according to depth.\n\n\n\nCONCLUSIONS\nAcid sulfate soils occurring along the Kelantan Plains were uniquely formed when \nthe sea level was higher than the present, some 6,000 BP. Pyritic sediments were \nfound to be sporadically distributed in the plains and indicate the areas where acid \nsulfate soils occur. These pyrite-bearing sediments are easily distinguishable from \neach other based on their properties and depth. The oxidation of pyrite leads to \nhigh acidity and the presence of extremely high concentration of Al and Fe in the \nsoils. The peaty materials in the soils contribute to the apparently high total N and \nC. Based on PCA, the principal components belong to soil acidity and organic \nmatter content. The other factor is the nutrient content, contributed by Ca, Mg and \nK. Due to severe acidity and the presence of high Al and Fe, the productivity of \nthe soils in the area is low. In future, studies should be conducted to ameliorate the \nsoils in the Kelantan Plains for sustainable rice production.\n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to thank Universiti Putra Malaysia and the Ministry of \nHigher Education Malaysia for financial and technical support during the conduct \nof the research.\n\n\n\nREFERENCES\nBray, R.H. and Kurtz, L.T. 1945. Determination of total organic and available forms \n\n\n\nof phosphorus in soils. Soil Sci. 59:39-45.\n\n\n\nBremner, J.M. and Mulvaney, C.S. 1982. Nitrogen-total. In: Methods of soil analysis. \nPart 2. Chemical and microbiological properties, Page, A.L., Miller, R.H. and \nKeeney, D.R. Eds., American Society of Agronomy, Soil Science Society of \nAmerica, Madison, Wisconsin, 595-624.\n\n\n\nEnio, M.S.K., J. Shamshuddin, C.I. Fauziah and M.H.A. Husni. 2011. Pyritization of \nthe coastal sediments in the Kelantan Plains in the Malay Peninsula during the \nHolocene. Am. J. of Agri. and Biol. Sci. 6 (3): 393-402.\n\n\n\nHaile, N.S. 1970. Radio carbon dates of Holocene emergence and submergence in \nTembelau and Bungural Islands, Sunda Shelf, Indonesia. Bull. of Geol. Soc. \nMalaysia 3: 135-137.\n\n\n\nHodgkin, E.P.1970. Geomorphology and biological erosion of limestone coast in \nMalaysia. Bull. Geol. Soc. Malaysia 3: 27-51.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 13\n\n\n\nPons, L.J., N. Van Breemen and P.M. Driessen. 1982. Physiography of coastal \nsediments and development of potential acidity. In Acid Sulfate Weathering ed. \nJ.A. Kittrick, D.S. Fanning and L.R. Hossner, pp. 1-18. .Madison: Soil Science \nSociety of America.\n\n\n\nRoslan I., J. Shamshuddin, C.I. Fauziah and A.R. Anuar. 2010. Occurrence and \nproperties of soils on sandy beach ridges in the Kelantan-Terengganu Plains, \nPeninsular Malaysia. Catena 83: 55-63. \n\n\n\nShamshuddin J. and E.A. Auxtero. 1991. Soil solution composition and mineralogy of \nsome active acid sulfate soils in Malaysia as affected by laboratory incubation \nwith lime. Soil Sci. 152: 365-376. \n\n\n\nShamshuddin J. 2006. Acid Sulfate Soils in Malaysia. Serdang, Selangor, Malaysia: \nUPM Press.\n\n\n\nShamshuddin J., S. Muhrizal, I. Fauziah and M.H.A. Husni. 2004b. Effects of adding \norganic materials to an acid sulfate soil on the growth of cocoa (Theobroma \ncacao L.) seedlings. Sci. Total Environ. 323: 33-45. \n\n\n\nShamshuddin J., S. Muhrizal, I. Fauziah and E. Van Ranst. 2004a. A laboratory study \nof pyrite oxidation in acid sulfate soils. Commun. in Soil Sci. Plant Anal. 35: \n117-129. \n\n\n\nShamshudin J., I. Jamilah and J.A. Ogunwale. 1995. Formation of hydroxy-sulfates \nfrom pyrite in coastal acid sulfate soil environments in Malaysia. Commun. \nSoil Sci. Plant Anal. 26(17&18): 2769-2782.\n\n\n\nShamshudin J., M.S.K. Enio, C.I. Fauziah and Q.A. Panhwar. 2013. On the pyritization \nof the coastal sediments in the Malay Peninsula during the Holocene and its \neffects on soil. Malay. J. Soil Sc.  17: 1-15.\n\n\n\nShazana M.A.R.S. 2012. Alleviating the infertility of acid sulfate soils for growing \nrice using basalt and/or organic fertilizer. M.S. thesis, Universiti Putra  \nMalaysia, Malaysia.\n\n\n\nSoil Survey Laboratory Staff. 1992. Soil Survey Laboratory Methods Manual, Soil \nSurvey Investigation Report No. 42. Washington DC: United States Department \nof Agriculture.\n\n\n\nSoil Survey Laboratory Staff. 2010. Keys to Soil Taxonomy. Washington DC: United \nStates Department of Agriculture.\n\n\n\nTjia H.D., S. Fujii and K. Kigoshi.1977. Changes of sea level in South China Sea \nduring the Quaternary. In Malaysian and Indonesian Coastal and Offshore \nAreas, pp. 11-36.  CCOP Technical Publication 5. United Nations and ESCAP.\n\n\n\n\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: anuar@upm.edu.my\n\n\n\nINTRODUCTION\nSoil is a complex heterogeneous medium comprised of various organic and \ninorganic components, water and air. Interactions among these components \ncontrol the micronutrients mobility in soil and their availability to plants (Janssen \net al. 2003; Adriano 2001). Factors like soil pH, cation exchange capacity, \norganic matter and metal oxides have an influence on the Cu equilibrium shift \nbetween the solid and liquid phase, and consequently its concentration in soil \nsolution (Brun et al. 1998; Alva et al. 2000). Generally, Cu plays an important \nfunction in photosynthesis and enzymatic reactions that regulate respiration, \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 17: 99-110 (2013)                          Malaysian Society of Soil Science\n\n\n\nResponse of Brassica rapa var. parachinensis Grown on \nCopper Contaminated Oxisol, Inceptisol and Histosol\n\n\n\nWahida, N.H.1, A.R. Anuar1*, C.I. Fauziah1 and  H.A. Osumanu2\n\n\n\n \n1Department of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia, 43400 Serdang, Selangor, Malaysia.\n2Department of Crop Science, Faculty of Agriculture and Food Sciences, Universiti \n\n\n\nPutra Malaysia, Bintulu Campus, Sarawak, 97008 Bintulu, Sarawak, Malaysia.\n\n\n\nABSTRACT\nCopper (Cu) plays a key role in plant physiological and biochemical function \nbut is harmful to plants when in excess. Copper availability is influenced by the \nmineralogical and chemical properties of soils. High Cu concentration is prominent \nin soils where vegetables are grown intensively with routine application of \nfertilizers and pesticides. A factorial pot experiment was carried out to determine \nCu critical concentration and toxicity threshold for Brassica rapa as well as its soil \nphase association in Oxisol, Inceptisol and Histosol. Copper sulphate was applied \nat rates of 0, 5, 10, 15, 20, 30 and 60 mg Cu kg-1 soil. The soil Cu critical level \nin Oxisol, Inceptisol and Histosol was 5.42, 4.67 and 7.79 mg kg-1, respectively; \nand threshold toxicity level was 12.69, 13.00 and 21.33 mg kg-1, respectively. \nHeight of plants decreased by 50% at a rate of 15 mg Cu kg-1 soil for Oxisol and \nInceptisol, and at 30 mg kg-1 for Histosol. The SPAD value of plant leaves also \ndecreased as the Cu application rate increased starting at 15 mg kg-1 in Oxisol \nand 20 mg kg-1 in Inceptisol. The amount of Cu in different soil fractions for both \nOxisol and Inceptisol applied with Cu at a rate of 60 mg kg-1 was in the order of \norganic > residual > Fe/Mn oxides > carbonates > exchangeable > water soluble. \nThis study indicated that at 60 mg Cu kg-1, Oxisol had a higher ability to retain Cu \nin the carbonate, Fe/Mn oxides and organic-bound fractions whereas in Inceptisol, \nthe highest Cu amount was in the residual fraction.\n\n\n\nKeywords: Brassica rapa, copper, critical level, phase association, \ntoxicity level, tropical soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013100\n\n\n\nlignification, and protein synthesis in plants (Awofulu et al. 2005; Cohu and Pilon \n2010). It is required in small amounts and becomes harmful when present in high \nconcentrations. Garg and Kataria (2009) reported that Brassica juncea root and \nshoot dry weight as well as chlorophyll content decreased with increasing Cu \nconcentration in assay mixtures. \n\n\n\nSeveral studies investigating Cu availability to plants in relation to soil \nphysico-chemical characteristics and Cu phase associations have been carried out \n(Rodriguez and Ramirez 2005; Gharbi et al. 2005). It is important to identify the \nsoil components that primarily control Cu distribution in soils and mechanisms \ninvolved in retaining and releasing Cu into the soil solution. Fractionation study \naids in predicting Cu distribution in different soils and evaluating the critical \nand threshold toxic level of Cu in soils. Therefore, this study set out to evaluate \nBrassica rapa growth response and to determine the Cu critical, threshold toxic \nlevel of soil in Cu contaminated Oxisol, Inceptisol and Histosol; and to evaluate \nthe Cu phase associations in the Oxisol and Inceptisol. \n\n\n\nMETHODOLOGY\n\n\n\nSoil Physico-chemical Analysis\nUncultivated Munchong, Selangor Series soils and peat (0-20 cm depth) were \ncollected around Selangor area to represent the Oxisol, Inceptisol and Histosol, \nrespectively. The soils were air-dried, sieved through a 2-mm sieve and analysed \nfor bulk density (McIntyre and Loveday 1974), porosity (Tan 2005), soil texture \n(Gee and Bauder 1986), cation exchange capacity (Thomas 1982), total organic \ncarbon and soil organic matter (Walkley and Black 1934) and Fe, Mn and Al \noxides content (Mehra and Jackson 1960).\n\n\n\nPot Experiment\nA factorial (3 soils x 7 rates of Cu) pot experiment was carried out at Field 10, \nUniversiti Putra Malaysia (02\u02da 99093\u2019N, 101\u02da 71380\u2019E). Munchong series soil \nand peat were limed to pH 5.5 and Cu was applied to the soil in the form of \ncopper sulphate solution at rates of 0, 5, 10, 15, 20, 30 and 60 mg Cu kg-1 soil with \nthree replications. The soils were incubated for two weeks, followed by seeding of \nBrassica rapa and application of compound fertilizer 15:15:15 at 0.75 g kg-1 soil. \nA total of 63 experimental units were arranged in Randomized Complete Block \nDesign (RCBD). Plant height and SPAD reading (SPAD 502Plus) of leaves were \nrecorded before harvest. Plants were harvested after 30 days of sowing and oven-\ndried at 60\u00b0C until constant weight was achieved. \n\n\n\nCopper Fractionation\nSoil samples were air-dried and sieved to 250 \u00b5m. Copper fractionation was carried \nout using sequential extraction procedure of Tessier et al. (1979) and  modified \nby Salas et al. (1998), Yang and Kimura (1995) and Chlopecka et al. (1999) \nto separate soil Cu into the following fractions: water soluble, exchangeable, \n\n\n\nWahida, N.H., A.R. Anuar, C.I. Fauziah and  H.A. Osumanu\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 101\n\n\n\nBrassica rapa Grown on Copper Contaminated Soil\n\n\n\ncarbonate-bound, Fe/Mn oxides-bound, organic and residual fractions (Table 1). \nFive grams of soil were placed into a falcon tube and mixed with reagents in \naccordance to the types of fraction. Samples were centrifuged at 3000 rpm for \n20 min after each step; the supernatant was then decanted and analyzed for Cu \nconcentration using atomic absorption spectrophotometry (Perkin Elmer Analyst \n400). \n\n\n\nData Analysis\nRegression analysis was used to determine the relationship between Cu levels and \nplant yield, height and SPAD reading of leaves. Data of Cu fractions in Oxisol \nand Inceptisol were subjected to analysis of variance and mean separation using \nStudent-Newman-Keul\u2019s method.  \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Physico-chemical Properties\nA comparison showed Oxisol to have more sand while Inceptisol had more silt. \nAll soils were acidic. Oxisol had the lowest CEC values due to the presence of \nkaolinite and gibbsite. In comparison to Oxisol, a higher CEC in Inceptisol could \nbe due the presence of 2:1 clay minerals and silt. The Histosol had the highest \nCEC (43.14 cmol(+) kg-1) due to a large amount of organic matter as the main \nmaterial of peat (Table 2).    \n\n\n\nTABLE 1\nSequential extraction procedure\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \nSequential extraction procedure \n\n\n\n \nFraction  Procedure \nWater soluble  20 mL distilled water, shaken for 2 h using \n\n\n\nreciprocating shaker  \nExchangeable  20 mL of 1M magnesium chloride, shaken for 2 h \nCarbonate-bound  20 mL of sodium acetate (adjusted to pH 5.0 using \n\n\n\nacetic acid), shaken for 2 h \nFe/Mn oxides-bound 20 mL hydroxylamine hydrochloride in 25% acetic \n\n\n\nacid (v/v), occasionally agitated in water bath (65\u00b0C, \n8 h) \n\n\n\nOrganic  2 mL 0.02M nitric acid, 5 mL of 30% hydrogen \nperoxide (adjusted to pH 2 by 0.1M nitric acid), \nagitated occasionally in water bath (65\u00b0C, 5 h), 3 mL \nof  30%  hydrogen  peroxide  (continued heating  for \n7 h), 5 mL of 3.2M ammonium acetate in 20% (v/v) \nnitric acid, made up to 20 mL with distilled water and \nshaken for 30 min \n\n\n\nResidue  Soil residue was mixed with 50 mL of aqua regia \n(HCl: HNO3), heated at 90\u00b0C for 2 h, made up to 100 \nmL with distilled water \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013102\n\n\n\nCritical Level and Toxicity Threshold Limit for Brassica rapa\nThe yield response of the test crop on Oxisols, Inceptisol and Histosol was \nquadratic:  y=0.83+0.30x\u20130.02x2, y=1.73+0.32x\u20130.02x2 and y=1.68+0.20x\u2013\n0.01x2, respectively (Fig. 1). The maximum yield of Brassica rapa grown in \nOxisol, Inceptisol and Histosol was calculated from the equation. The critical \nsoil Cu level required for 90% maximum yield and the toxicity threshold limits \n(10% reduction in yield) (Guo et al. 2010) was obtained from the maximum yield \n(Table 3). \n\n\n\nPlant growth response could be affected by the mobile fraction concentration \nof Cu present at the time of harvest. Water soluble and exchangeable fractions \ntogether make up the mobile fractions. The results of Cu fractionation showed that \nthe Inceptisol contained a higher summation of water soluble and exchangeable \nfraction (0.260\u20130.271 mg kg-1) compared to Oxisol (0.164\u20130.168 mg kg-1) at Cu \nrates of 0 to 5 mg kg-1. In other words, there was a higher Cu concentration in \nthe soil solution of Inceptisol than that of Oxisol allowing for optimal growth \nof Brassica and tolerance at the aforesaid levels. There was no significant \ndifference of water soluble and exchangeable fractions from 10 to 15 mg Cu kg-1 \nin Oxisol and Inceptisol. This explains the toxicity levels of Oxisol and Inceptisol \nwhich were 12.69 and 13.00 mg Cu kg-1, respectively. As a result, the critical \nCu level and toxicity threshold limits in Inceptisol was the lowest, followed by \nthe Oxisol and Histosol. It has conclusively been shown that Brassica chinensis \nyield decreases with increasing exchangeable fraction of Cu in soil amended with \n\n\n\nTABLE 2\nPhysico-chemical and mineralogical properties of Oxisol, Inceptisol and Histosol\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \nPhysico-chemical and mineralogical properties of Oxisol, Inceptisol and Histosol \n\n\n\n \nSoil properties Soil Order \n\n\n\n Oxisol Inceptisol Histosol \n*USDA Taxonomy \n \n \nSeries name \nBulk density (g cm3) \nPorosity (%) \nTexture \n    % Sand \n    % Silt \n    % Clay \npH (H20) \nCEC (cmol+ kg-1) \nTotal organic carbon (%) \nTotal soil organic matter (%) \nFree oxides (mg g-1) \n    Fe \n    Al \n    Mn \n**Main clay mineral type  \n \n\n\n\nHaplic \nHapludox \n\n\n\n \nMunchong \n\n\n\n1.06 \n53.91 \nClay \n26.23 \n7.86 \n\n\n\n65.79 \n4.83 \n7.49 \n2.40    \n4.13 \n\n\n\n \n33.25 \n7.87 \n0.01 \n\n\n\nKaolinite, \ngibbsite \n\n\n\nTypic \nTropaquept \n\n\n\n \nSelangor \n\n\n\n1.03 \n55.22 \nClay \n0.62 \n\n\n\n31.40 \n67.93 \n5.10 \n\n\n\n14.79 \n1.99 \n3.42 \n\n\n\n \n9.03 \n1.14 \n0.26 \n\n\n\nKaolinite, \nillite \n\n\n\n\n\n\n\nTypic \nHaplohemist \n\n\n\n \n- \n\n\n\n0.34 \n \n\n\n\nOrganic  \n- \n- \n- \n\n\n\n3.13 \n43.14 \n55.58 \n95.60 \n\n\n\n \n- \n- \n- \n- \n\n\n\n             *Paramananthan (2000); **Akma et al. (2009) \n \n\n\n\n\n\n\n\nWahida, N.H., A.R. Anuar, C.I. Fauziah and  H.A. Osumanu\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 103\n\n\n\ncomposted sludge (He et al. 2007). These findings support the assumption that the \ngrowth of Brassica rapa in the present study was likely to be affected by available \nsoil Cu concentrations.\n\n\n\nPeverill et al. (1999) briefly explained that the quadratic relationship between \na plant response and applied nutrients depicts a large plant yield response up to \nthe point where a maximum yield is attained, then the plant yield decreases as the \napplied nutrient increases. In a study that explored the optimum Cu fertilizer for \ncorn, Rodriguez and Ramirez (2005) reported that the relationship between the \nextracted Cu and the relative yield was quadratic. Results showed Cu to be both \nnutritional and toxic to Brassica rapa. Yield reduction could be due to poor root \ndevelopment in high Cu concentration. A similar observation has been reported \nfor maize roots (Liu et al. 2001). As the Cu application rate increased, root growth \n\n\n\nFig. 1: Yield response to increasing Cu concentration in Oxisol, Inceptisol and Histosol\n\n\n\nTABLE 3\nThe critical and toxicity threshold level of Cu for Brassica rapa\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nFig. 1: Yield response to increasing Cu concentration in Oxisol, Inceptisol and Histosol \n \n \n \n \n \n \n \n \n \n \n\n\n\nCu (mg kg-1soil)\n\n\n\n0 5 10 15 20 25 30 35\n\n\n\nD\nry\n\n\n\n w\nei\n\n\n\ngh\nt (\n\n\n\ng)\n\n\n\n0.0\n\n\n\n0.5\n\n\n\n1.0\n\n\n\n1.5\n\n\n\n2.0\n\n\n\n2.5\n\n\n\n3.0\n\n\n\n3.5\n\n\n\n4.0\n\n\n\nOxisol : 0.833+0.301x-0.017x2, R2=0.64*\nInceptisol : 1.730+0.324x-0.018x2, R2=0.77*\nHistosol : 1.678+0.198x-0.007x2, R2=0.85*\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\n \nTABLE 3 \n\n\n\nThe critical and toxicity threshold level of Cu for Brassica rapa \n \n\n\n\n  \nSoil  Equation Critical level            Toxicity level \n\n\n\n--------------- mg kg-1 --------------- \n \nOxisol \nInceptisol \nHistosol  \n\n\n\n \ny = 0.83+0.30x-0.02x2 \ny = 1.73+0.32x-0.02x2 \ny = 1.68+0.20x-0.01x2 \n\n\n\n        \n   5.42                          12.69 \n   4.67                          13.00 \n   7.79                          21.33 \n\n\n\n\n\n\n\nBrassica rapa Grown on Copper Contaminated Soil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013104\n\n\n\nof maize decreased progressively. Roots fail to explore the soil for water and \nnutrients to be taken up into the plants due to the suppression effect of excess Cu \non root elongation and branching restrictions (Wong and Bradshaw 1982). \n\n\n\nEffects of Cu on Plant Height\nPlant height relates to plant yield (Fig. 2). The highest plant height was observed \nat 10 mg Cu   kg-1 in both Oxisol and Inceptisol. In Histosol, the highest plant \nheight was recorded at 20 mg Cu kg-1. Plants experienced more than 50% in height \nreduction at 15 mg Cu kg-1 compared to that of 10 mg Cu kg-1, for both mineral \nsoils. In Histosol, a Cu application rate ranging from 20 to 30 mg kg-1 reduced \nplant height by 50%. This concurs with Sonmez et al. (2006) who recorded a \nreduction in tomato plant height with increasing Cu application by 40.9 to 50.4% \ncompared to the control treatment. \n\n\n\nEffects of Cu on SPAD Value of Leaves\nSPAD values correlate with the greenness or relative chlorophyll contents \nin leaves (Kariya et al. 1982). SPAD readings in plants grown on Oxisol and \nInceptisol decreased drastically at 15 and 20 mg kg-1 Cu, respectively (Fig. 3). On \nthe other hand, the SPAD values of plant grown on peat soil remained constant \nfrom 0 to 30 mg kg-1 Cu. No data were obtained from plants grown in Oxisol \nat 20 and 30 mg kg-1 Cu and Inceptisol at 30 mg kg-1 Cu due to small leaf size; \nmoreover plants failed to thrive. Bernal et al. (2006) reported adverse effects \nof excess Cu such as the reduction in photosynthetic activity due to decreased \ncontent of photosynthetic pigments and the structure of chloroplasts. Specifically, \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFig. 2: Plant height response to increasing Cu concentrations in Oxisol, Inceptisol and Histosol \n\n\n\n\n\n\n\nCu (mg kg-1 soil)\n\n\n\n0 20 40 60\n\n\n\nH\nei\n\n\n\ngh\nt (\n\n\n\ncm\n)\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\nOxisol : 5.54+0.27x-0.01x2, R2=0.37*\nInceptisol : 8.68+0.09x-0.01x2, R2=0.85*\nHistosol : 7.48+0.11-0.003x2, R2=0.73*\n\n\n\nFig. 2: Plant height response to increasing Cu concentrations in\nOxisol, Inceptisol and Histosol\n\n\n\nWahida, N.H., A.R. Anuar, C.I. Fauziah and  H.A. Osumanu\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 105\n\n\n\nexcess Cu decreases photosystem II activity and electron transfer rates (Janik et \nal. 2010). This could be explained by the decreasing SPAD value of leaves with \nincreasing Cu application. \n\n\n\nFractionation of Cu in Oxisol and Inceptisol\nAll the six soil Cu fractions increased linearly with increasing Cu application \nrates. The relationships between each fraction with increasing Cu concentrations \nare shown in Fig. 4. Oxisol had a significantly higher amount of organic, Fe/Mn \noxides and carbonate-bound Cu fractions than Inceptisols, at a rate of 60 mg Cu \nkg-1. Both the Oxisol and Inceptisol were found to have the same order of Cu \nfractions; organic > residual > Fe/Mn oxides > carbonates > exchangeable > water \nsoluble.\n\n\n\nThe water soluble and exchangeable fractions are the bioavailable Cu, readily \navailable for plant and soil microbial uptake. These fractions were lower due to \nthe high affinity of Cu to metal oxides, organic matter and carbonates (Sposito \n2008). At the rate of 60 mg Cu kg-1, Oxisol had higher carbonate-bound, Fe/Mn \noxides-bound and organic fractions. This could be attributed to the application of \nground magnesium limestone (GML) during soil preparation. Lime amendments \nincrease soil pH but decrease Cu solubility as Cu becomes co-precipitated with \ncarbonates. The presence of high content of Fe and Al hydroxides such as goethite \n(\u03b1-FeOOH), hematite (\u03b1-Fe2O3) and gibbsite (Kabata-Pendias 2001; Anda et al. \n2008) is associated with Oxisol which had undergone advanced weathering. The \nparticle size of aluminum hydroxides are extremely small and have highly reactive \nsurfaces which increase Cu sorption in the soils. Goethite formation occurs in an \noxic soil with a relatively high organic matter content, therefore, partly presenting \n\n\n\nFig. 3: SPAD values of leaves measured on plants grown in soils under\nincreasing Cu concentrations\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nFig. 3: SPAD values of leaves measured on plants grown in soils under increasing Cu \nconcentrations \n\n\n\n\n\n\n\n\n\n\n\nCu (mg kg-1 soil)\n\n\n\n0 5 10 15 20 25 30 35\n\n\n\nSP\nA\n\n\n\nD\n v\n\n\n\nal\nue\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\nOxisol : 47.83+0.74x-0.09x2, R2=0.85*\nInceptisol : 39.62+1.91x-0.10x2, R2=0.83*\nHistosol :                  - \n\n\n\nBrassica rapa Grown on Copper Contaminated Soil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013106\n\n\n\nas ternary complexes as it forms a coating on clay minerals and humic substances \n(Violante et al. 2005), while simultaneously increasing the Fe oxides-bound, \norganic and residual Cu fraction. The Oxisol contained higher (4.13%) soil \norganic matter content than Inceptisol (3.42%). This observation is similar to that \nof Lu et al. (2005) who reported that higher organic material content resulted in \nhigh Cu organic fraction. The Inceptisol showed a higher residual fraction than \nthe Oxisol at 60 mg Cu kg-1. The higher preference of Cu adsorption to organic \nmatter is possible at higher copper concentrations, when the predominant clay \nmineral is kaolinite (Havlin et al. 1999), resulting in the reduction of residual Cu \nin Oxisol. Similarly, there is more resistant Cu fraction in the Inceptisol.\n\n\n\nFig. 4: Six operationally defined Cu fractions extracted from Oxisol and Inceptisol\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nCu (mg kg-1 soil)\n\n\n\n0 10 20 30 40 50 60\n\n\n\nC\nar\n\n\n\nbo\nna\n\n\n\nte\n-b\n\n\n\nou\nnd\n\n\n\n C\nu \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n)\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\nOxisol : y=0.233x, R2=0.93*\n\n\n\nInceptisol : y=0.138x, R2=0.94*\n\n\n\nCu (mg kg-1 soil)\n\n\n\n0 10 20 30 40 50 60\n\n\n\nFe\n/M\n\n\n\nn \nox\n\n\n\nid\nes\n\n\n\n-b\nou\n\n\n\nnd\n C\n\n\n\nu \n(m\n\n\n\ng \nkg\n\n\n\n-1\n)\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\nOxisol : y=0.299x, R2=0.94*\n\n\n\nInceptisol : y=0.189x+0.086, R2=0.94*\n\n\n\n\n\n\n\n    Cu (mg kg-1 soil)\n\n\n\n0 10 20 30 40 50 60\n\n\n\nO\nrg\n\n\n\nan\nic-\n\n\n\nbo\nun\n\n\n\nd \nC\n\n\n\nu \n(m\n\n\n\ng \nkg\n\n\n\n-1\n)\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\nOxisol : y=0.834x+3.218, R2=0.96*\n\n\n\nInceptisol : y=0.466x+0.907, R2=0.96*\n\n\n\n    Cu (mg kg-1 soil)\n\n\n\n0 10 20 30 40 50 60\n\n\n\nRe\nsid\n\n\n\nua\nl C\n\n\n\nu \n(m\n\n\n\ng \nkg\n\n\n\n-1\n)\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\nOxisol : y=0.214+7.390, R2=0.89*\n\n\n\nInceptisol : 0.372x+1.660, R2=0.95*\n\n\n\n\n\n\n\nFig. 4: Six operationally defined Cu fractions extracted from Oxisol and Inceptisol \n \n\n\n\nCu (mg kg-1 soil)\n\n\n\n0 10 20 30 40 50 60\n\n\n\nW\nat\n\n\n\ner\n so\n\n\n\nlub\nle \n\n\n\nC\nu \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n)\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\nOxisol : y=0.010x, R2=0.94*\n\n\n\nInceptisol : y=0.105+0.006x, R2=0.82*\n\n\n\n Cu (mg kg-1 soil)\n\n\n\n0 10 20 30 40 50 60\n\n\n\nEx\nch\n\n\n\nan\nge\n\n\n\nab\nle \n\n\n\nC\nu \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n)\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\nOxisol : y=0.054x, R2=0.91*\n\n\n\nInceptisol : y=0.110x, R2=0.91*\n\n\n\n\n\n\n\nWahida, N.H., A.R. Anuar, C.I. Fauziah and  H.A. Osumanu\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 107\n\n\n\nCONCLUSION\nThe application of Cu at low rates proved beneficial for Brassica rapa but was \ntoxic at high rates as shown by the results of yield response, height and the SPAD \nvalues of leaves. The findings of this study suggest that the soil critical Cu level \nand toxicity threshold limits are influenced by the concentration of water soluble \nand exchangeable Cu. Copper distribution in Oxisol and Inceptisol is mainly \ncontrolled by the organic matter followed by clays, Fe and Al oxides. Soil organic \nmatter content could be effective in reducing phytotoxicity of Cu at high levels of \nsoil Cu concentration.\n\n\n\nREFERENCES\nAdriano, D.C. 2001. Trace Elements in Terrestrial Environments: Biogeochemistry, \n\n\n\nBioavailability and Risks of Metals. New York: Springer.\n\n\n\nAkma, N.M.H., A.W. Samsuri, H.K. Ainie and A.B. Rosenani. 2009. 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"\n\n___________________\nCorresponding author : mimienferdinal@yahoo.com\n\n\n\nINTRODUCTION\nUltisols are marginal soils in Indonesia that  have a wide distribution of 45.8 \nmillion ha or about 25% of total land area of Indonesia (Subagyo et al. 2004). \nThough Ultisols have good potential to be developed for agriculture, its utilisation \nhas been constrained by several factors  that hinder plant growth, such as low \npH (<5.0) with high Al saturation (>42%), low organic-C (<1.15%), low nutrient \ncontent with the range of N and P content being 0.14% and 5.80 ppm respectively, \nand low base saturation of 29% (Rusli 2016).\n\n\n\nEfforts to solve this problem include adding organic material as it contains \nsubstances which have the ability to bind soluble Al, Fe and Mn in soil to form \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 147-160 (2018)                          Malaysian Society of Soil Science\n\n\n\nActivation of Sub-bituminous Powder with Urea and \nDolomite to Improve Nutrient Content of Ultisols and the \n\n\n\nGrowth of Oil Palm (Elaeis guineensis Jacq) Seedlings\n\n\n\nTeguh Budi Prasetyo, Herviyanti, Juniarti, Mimien Harianti, \nNatasya Permatasari Panjaitan\n\n\n\nDepartment of Soil Science, Faculty of Agriculture, University of Andalas\nCampus of Unand Limau Manih, Padang-25163 Indonesia\n\n\n\nABSTRACT\nThis study aimed to examine the interaction effects of sub-bituminous powder and \nan activator to improve the chemical properties of Ultisols and the growth of oil \npalm (Elaeis guineensis Jacq) seedlings. This research was done in the Laboratory \nof Soil Chemistry and Experimental Garden of the Agricultural Faculty of Andalas \nUniversity from December 2016 to June 2017.  A  2-factor factorial Completely \nRandom Design (CRD) was used with three replications. The first factor  was \nthe activator: (A0) without activator, (A1) 10% urea, and (A2) 10% dolomite. The \nsecond factor  was the dose of sub-bituminous powder: (B1) 10 ton.ha-1, (B2) 20 ton. \nha-1 and (B3) 30 ton.ha-1. The results showed that the : (1)  added sub-bituminous \npowder  interacted with the activator to increase total-N of soil and plant height, \nwith the highest achieved by  the dose of 30 ton.ha-1 sub-bituminous powder with \nurea as activator; (2) The addition of sub-bituminous powder at a dose of 30 ton. \nha-1 was able to increase pH, organic-C, available-P, CEC of the Ultisol and the \nlevel of plant N and P at 0.03% and 0.05% compared with the dose of 10 ton ha-1; \n(3) The addition of urea as an activator increased pH by 0.09 unit, organic-C by \n0.18%, available-P in Ultisol by 0.92 ppm, and CEC of 2.10 cmol (+)/kg,  as well \nas decreased Al-exchange by 0.49 cmol (+)/kg  and increased plant nitrogen by \n0.07%, leaf number by 1.64 leaves, seedling dry weight by  9.19 g compared with \ntreatment without activator.  \n\n\n\nKeywords: Sub-bituminous, Ultisols, urea, dolomite, oil-palm seedlings\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018148\n\n\n\ncomplex compounds or organometallic chelate compounds (Stevenson 1994). \nAn organic material that can be utilised is unproductive coal (sub-bituminous) \nwhich has low energy level and therefore cannot be used as an energy source. \nSub-bituminous coal has high percentage of moisture content and loss of ignition \nwith 18% and 80.49% respectively (Mohamad et al, 2013).  Therefore, sub-\nbituminous  powder can be used as a source of humic material to improve soil \nchemical properties. \n\n\n\nHerviyanti et al. (2012) mentioned that the addition of humic material \noriginating from sub-bituminous powder at a dose of 800 ppm (1.6 ton/ha) was \nable to increase available-P and CEC of soil amounting to 22.16 ppm and 8.42 \ncmol(+)/kg  while  also increasing soil Al-exchange to 0.83 cmol (+)/kg  compared \nto non-humic material. Sub-bituminous powder provides more benefits if it \nis activated using urea, KCl, NaCl, NaOH and dolomite (CaMg(CO3)2) thus \nincreasing CEC. Herviyanti et al. (2014) examined the level of sub-bituminous \npowder activity with urea fertiliser as an activator. Optimal results were obtained \nat a recommended urea concentration of 125%.  At that  concentration, a relatively \nneutral pH value (7.25), quite a high CEC of 60.68 cmol(+)/kg,  powder solubility \nof 12.37%, total-N of 5.78%, K-exchange value of 5.25 cmol(+)/kg, Cl concentration \nof 0.23% and Na-exchange concentration of 4.08 cmol(+)/kg  were obtained. \n\n\n\nUrea is a relatively cheap fertiliser that is  more easily available. It reacts \nto alkaline as in the case of dolomite. Therefore dolomite may be  used as the \nactivator of sub-bituminous powder. Hydrolysed dolomite produces OH- which \nfurther activates sub-bituminous coal powder. Sub-bituminous powder has been \napplied in food crop such as rice and maize. In this study, sub-bituminous powder \nwas applied to plantation crop like oil palm. Oil palm is one of the  plantation crops \nthat has high economic value and continues to be the largest foreign exchange \nearner for Indonesia compared to other plantation crops (Sunarko 2007).\n         A limiting factor in the high production of oil palm is seedling production. \nAs seedling production activity is the basis for the preparation of good planting \nmaterial, seedling production activity should be managed well. Several factors \nto be considered in the seedling production process of oil palm  are watering, \nfertilising (basic fertiliser) and plant disturbing organisms (PDO). The purpose \nof this study was to examine the interaction between activator factors and sub-\nbituminous powder and the main effect of each factor in improving the  chemical \nproperties of the Ultisols towards promoting the growth of oil palm seedlings. \n\n\n\nMATERIALS AND METHODS\nThis  research was conducted in the Laboratory of Soil Chemistry and Fertility of \nthe Soil Department and Experimental Garden of the Agricultural Faculty, Andalas \nUniversity, Padang from December 2016 to June 2017. It was a pot experiment \nusing 3 x 3 Factorial Completely Random Design (CRD) with 3 replications. The \nfirst factor was activator (A) which was added at three dose levels:\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 149\n\n\n\n A0 = Without activator\n A1 = 10 % urea of the weight of sub-bituminous powder \n A2 = 10 % dolomite of the weight of sub-bituminous powder\n\n\n\nThe second factor was the dose of sub-bituminous powder which was also  added \nat three dose levels:\n B1 = 40g/pot equal to 10 ton.ha-1\n\n\n\n B2 = 80g/pot equal to 20 ton.ha-1\n\n\n\n B3 = 120g/pot equal to 30 ton.ha-1\n\n\n\nThe data were analysed statistically with the F-test. However, if the  F-test of \ntreatment was higher than the level of 5%, a further DNMRT analysis was carried \nwith the level of significance set at  5%. .  \n\n\n\nResearch Method\nSub-bituminous coal was collected at 1-2 m depth below the soil surface in Ganggo \nMudiak Nagari, Pasaman Regency of West Sumatera. The sub-bituminous coal \nwas cleaned, crued, and sieved using 100 \u00b5m mesh siever. Composite Ultisol \nsoil materials were taken from Dharmasraya Regency at a depth of 0-20 cm, air \ndried, and further sieved using a 2-mm sieve. The  sub-bituminous powder was \nactivated with urea and dolomite at each dose of treatment, to which was added \nwater until field capacity, and evenly mixed. Later, samples were incubated within \nthe soil (soil weight of 8 kg equals to oven dry weight) for 10 days.  Changes in \nthe  chemical properties of the Ultisol  analysed included pH of H2O using the \nelectrometric method, CEC with leaching method, organic-C with Walkley and \nBlack method, total-N with Kjedhal method, available-P with Bray method, and \nAl-exchange with volumetric method. Later, 4-month old oil palm pre-nursery \nseedlings were planted, maintained, and observed for their growth over a period \nof 4 months (main-nursery). The vegetative stage of the  plant was  harvested for \nthe analysis of nutrient level of N and P.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nChemical Properties of Ultisols in Nagari Gunung Selasih, Sub-district of Pulau \nPunjung, Regency of Dharmasraya\nBased on the pre-analysis results (Table 1), it can be seen that the chemical \nproperties of the Ultisol indicate low fertility. Based on soil pH, it   was classified \nas an acid soil. Cation Exchange Capacity (CEC) and content of  total N, P  and \nbase cations were considered low, while Al-exchange was high. These results are \nin line with those of   Rusli (2016) that Ultisols have constrains of low pH (acid) \nof <5.0 with high Al saturation of >42%, low organic material of <1.15%, low \nnutrient with N at  0.14%, P at  5.80 ppm, and CEC at 12.6 me/100 g.\n\n\n\nIt can be seen in Table 1, that the Al-exchange content of the Ultisol was \na high of 2.08 cmol(+)/kg) . It is easy for Al in soil colloids to be hydrolysed to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018150\n\n\n\nproduce H+ ion in the soil. However, the increasing  H+ ion concentrations result in \nan  acid reaction in soil (Hakim 2006). If  H+ concentration continues to increase, \nsoil pH will decrease. As presented in Table 1, it can be seen that pH of the Ultisol \nwas acidic as the  value was 4.60. The low pH of soil causes the  P element to be \nmore easily bound to soil colloids which are protonised due to H+ dissociation. \nMoreover, the low P content of  6.13 ppm is due to  P  being bound by the oxide of \nAl. The CEC of the Ultisol was also low as it was affected by low organic carbon \ncontent of the soil. According to McCauley et al. (2017), the consistent benefit of \nsoil organic matter is that it buffers soil pH change. Availability of nutrients for \nplant uptake varies depending on soil pH. \n\n\n\nLow organic content decrease the loss of base cations as seen in Table 1 \nwhere the  base cations are low, with the Ca content being 2.25 cmol(+)/kg, Mg-\nexchange at 0.42 cmol(+)/kg , Na-exchange at 0.11 cmol(+)/kg  and K-exchange at \n0.10 cmol(+)/kg. The availability of cation nutrients is often hindered by increased \nsusceptibility to leaching or erosion losses in acidic soils. Organic carbon content \nis  the main factor that determines the level of soil fertility, both physically, \nchemically, and biologically, particularly in terms of metal cation binding that is  \navailable for plant growth. Low organic carbon content influences total nitrogen \nas seen in Table 1 which shows a low total N in the soil. This is because   N \ncontent is easily lost in the form of leached nitrate (NO3\n\n\n\n-)  because soil is rapidly \nlost due to  high rainfall in tropical climates (Rosmarkam and Yuwono 2002).\n\n\n\nTABLE 1\nPre-analysis results of several chemical properties of the Ultisol in Nagari Gunung \n\n\n\nSelasih of Pulau Punjung Sub-district, Dharmasraya Regency\n\n\n\n4 \n\n\n\n \nNo Analysis Value Criteria \n\n\n\n1 pH of H2O (1:1) 4.60 Acid* \n2 Al-exchange (cmol (+)/kg ) 2.08 - \n3 K- exchange (cmol (+)/kg ) 0.10 Low* \n4 Ca- exchange (cmol (+)/kg ) 2.25 Low* \n5 Mg- exchange (cmol (+)/kg ) 0.42 Low* \n6 Na- exchange (cmol (+)/kg ) 0.11 Low* \n7 CEC (cmol (+)/kg ) 11.65 Low* \n8 Total-N (%) 0.12 Low* \n9 Organic-C (%) 1.35 Low* \n10 Available-P (ppm)  6.13 Low* \n11 Al Saturation (%) 41.93 High* \n12 C/N 10.75 Low* \n\n\n\n*Source:  Hardjowigeno (2010) \n \n\n\n\nIt can be seen in Table 1, that the Al-exchange content of the Ultisol was a \nhigh of 2.08 cmol (+)/kg) . It is easy for Al in soil colloids to be hydrolysed to \nproduce H+ ion in the soil. However, the increasing  H+ ion concentrations result \nin an  acid reaction in soil (Hakim 2006). If H+ concentration continues to \nincrease, soil pH will decrease. As presented in Table 1, it can be seen that pH of \nthe Ultisol was acidic as the  value was 4.60. The low pH of soil causes the  P \nelement to be more easily bound to soil colloids which are protonised due to H+ \n\n\n\ndissociation. Moreover, the low P content of  6.13 ppm is due to  P  being bound \nby the oxide of Al. The CEC of the Ultisol was also low as it was affected by low \norganic carbon content of the soil. According to McCauley et al. (2017), the \nconsistent benefit of soil organic matter is that it buffers soil pH change. \nAvailability of nutrients for plant uptake varies depending on soil pH.  \n\n\n\nLow organic content decrease the loss of base cations as seen in Table 1 \nwhere the  base cations are low, with the Ca content being 2.25 cmol (+)/kg , Mg-\nexchange at 0.42 cmol (+)/kg , Na-exchange at 0.11 cmol (+)/kg  and K-exchange \nat 0.10 cmol (+)/kg. The availability of cation nutrients is often hindered by \nincreased susceptibility to leaching or erosion losses in acidic soils. Organic \ncarbon content is  the main factor that determines the level of soil fertility, both \nphysically, chemically, and biologically, particularly in terms of metal cation \nbinding that is  available for plant growth. Low organic carbon content influences \ntotal nitrogen as seen in Table 1 which shows a low total N in the soil. This is \nbecause   N content is easily lost in the form of leached nitrate (NO3\n\n\n\n-)  because \nsoil is rapidly lost due to  high rainfall in tropical climates (Rosmarkam and \nYuwono 2002). \n\n\n\n \n \nEffect of Sub-Bituminous to Improve Nutrient Content of the Ultisol and the \n\n\n\nGrowth of Oil Palm Seedlings  \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 151\n\n\n\nEffect of Sub-Bituminous to Improve Nutrient Content of the Ultisol and the \nGrowth of Oil Palm Seedlings \nTable 2 shows that the addition of sub-bituminous powder resulted in significant \neffects on soil properties of the Ultisol. The higher dose of sub-bituminous powder \nincreased soil pH, organic carbon content, available-P and CEC. In general, the \naddition of sub-bituminous powder increased nutrient content of the Ultisol as \nthe  humic material functions as an active substance which is able to improve the \nability of soil colloids in carrying out nutrient exchange. Several studies found \nthat soil fertility is determined by soil humic acid content as it contains high CEC \nand oxygen and  better water holding capacity. Furthermore, humic acid is able to \nbind insoluble metal ions, oxide, and hydroxide and release it slowly. \n\n\n\nTABLE 2\nEffects of adding sub-bituminous and type of activator to improve nutrient content of \n\n\n\nthe  Ultisol\n\n\n\n5 \n\n\n\nTable 2 shows that the addition of sub-bituminous powder resulted in significant \neffects on soil properties of the Ultisol. The higher dose of sub-bituminous \npowder increased soil pH, organic carbon content, available-P and CEC. In \ngeneral, the addition of sub-bituminous powder increased nutrient content of the \nUltisol as the  humic material functions as an active substance which is able to \nimprove the ability of soil colloids in carrying out nutrient exchange. Several \nstudies found that soil fertility is determined by soil humic acid content as it \ncontains high CEC and oxygen and  better water holding capacity. Furthermore, \nhumic acid is able to bind insoluble metal ions, oxide, and hydroxide and release \nit slowly.  \n\n\n\n \nTABLE 2 \n\n\n\nEffects of adding sub-bituminous and type of activator to improve nutrient content \nof the  Ultisol \n\n\n\n\n\n\n\nParameter \nDose of sub-bituminous powder \n\n\n\n10 ton/ha 20 ton.ha-1 30 ton.ha-1 \npH of H2O (unit) 4.73 C 4.99 B 5.13 A \nAl-exch (cmol (+)/kg ) 1.42 A 1.13 B 1.22 B \nK-exch (cmol (+)/kg ) 0.11 C 0.13 B 0.15 A \nCa-exch (cmol (+)/kg ) 4.68 B 5.59 A 6.01 A \nMg-exch (cmol (+)/kg ) 0.49 B 0.58 A 0.59 A \nCEC (cmol (+)/kg ) 13.61 C 16.61 B 21.02 A \nOrganic-C (%) 1.75 C 1.99 B 2.10 A \nAvailable-P (ppm) 7.43 C 8.45 B 9.52 A \n\n\n\n Type of activator \n Without activator Urea Dolomite \n\n\n\npH of H2O (unit) 4.86 B 4.95 AB 5.03 A \nAl-exch (cmol (+)/kg ) 1.71 C 1.22 B 0.84 A \nK-exch (cmol (+)/kg ) 0.12 B 0.13 B 0.15 A \nCa-exch (cmol (+)/kg ) 4.93 C 5.42 B 5.92 A \nMg-exch (cmol (+)/kg ) 0.51 C 0.56 B 0.60 A \nCEC (cmol (+)/kg ) 16.18 B 18.28 A 16.78 B \nOrganic-C (%) 1.77 C 1.95 B 2.10 A \nAvailable-P (ppm) 7.42 C 8.34 B 9.63 A \nValues in the same row followed by capital letters are not significantly different based on DNMRT \nat a significance level of 5%. \n \n\n\n\npH increased due to the increasing dose of sub-bituminous powder which \nresults in a rise in active carboxylic and phenolic groups. Both these groups \nsuppress the concentration of Al3+ in soil solution.  According to Huang and \nSchnitzer (1997) an increase in  the does of the humic acid will result in an \nincrease in the functional group of humic acid, resulting in a complex substance \nbeing formed through the functional (-COOH) and phenolic (-OH) group. The \nincrease in organic-C is caused by the addition of C from the sub-bituminous \n\n\n\npH increased due to the increasing dose of sub-bituminous powder which \nresults in a rise in active carboxylic and phenolic groups. Both these groups \nsuppress the concentration of Al3+ in soil solution.  According to Huang and \nSchnitzer (1997) an increase in  the does of the humic acid will result in an \nincrease in the functional group of humic acid, resulting in a complex substance \nbeing formed through the functional (-COOH) and phenolic (-OH) group. The \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018152\n\n\n\nincrease in organic-C is caused by the addition of C from the sub-bituminous \npowder which contains high carbon content. Poertadji et al. (2006) state that \ncarbon content of sub-bituminous coal is 46.3 %, while Tan (2010) reports that  \nhumic acid is rich in carbon at a  range of 41-57%. \n\n\n\nTable 2 shows that a  decrease in Al-exchange occurred along with the \naddition of a dose of sub-bituminous powder. It was found that the humic acid \ncontained in the sub-bituminous powder was able to chelate Al. The addition \nof sub-bituminous powder with the activator had a significantly different effect \non Al-exchange, Ca-exchange, Mg-exchange, organic-C and available-P of the \nUltisol. In line with the results reported by and Muktamar et al. (1998), several \norganic acids released from sub-bituminous powder are able to bind Al, forming \na complex compound (organometallic complex) which is hard to dissolve. The \ngroup that plays a role in the complex compound formation is a functional group \nsuch as carboxyl, hydroxyl, and phenol that originate from organic acid. \n\n\n\nThe increase in Al content and pH impacts the  availability of Ca and Mg. \nThe increase in available-P contents along with the decreasing Al concentration \nin the Ultisol (Table 2) is due to the addition of dolomite as an alkaline activator \nwhich donates OH- ion causing P to be bound by Al which will be exchanged with \nOH- resulting in P being released from Al-P compound and available in soil as in \nthe reaction below:\n\n\n\n Al(OH)2H2PO4 + OH-    \u2192   Al(OH)3  + H2PO4\n-\n\n\n\nUrea as an activator was able to increase the level of organic-C of soil as seen \nin Table 2, to about 0.18 % compared with treatment without activator. Ammonium \nfrom urea may be expected to be bound at the active side of soil colloids from sub-\nbituminous carbon. Dolomite was also able to increase organic-C of soil  of 0.36 \n% compared to treatment without activator and dolomite was found to be a better \nactivator than urea. This is because dolomite reaction accelerates organic-C from \nsub-bituminous powder because of its high pH in the soil. Herviyanti et al. (2014) \nstate that powder dissolved in urea will be  rapidly dissociated in the soil as urea \nand H2O in the soil will donate CO2 and NH4OH. \n\n\n\nThe addition of urea could increase pH of the Ultisol by 0.09 unit compared \nto treatment without activator. It is expected that urea with its base solvent \ncharacteristic is hydrolysed and will form ammonium carbonate as seen in the \nreaction below:\n\n\n\n CO(NH2)2 + 2H2O \u2192   (NH4)2CO3     \u2192      NH3 + CO2\n\n\n\n NH3           + H2O  \u2192    NH4OH         \u2192       NH4\n+ + OH-\n\n\n\nAmmonium carbonate is an unstable compound that is  decomposed into \nammonia and carbon dioxide (Tisdale and Nelson 1975). Ammonia compound, \nif it reacts with water, will form ammonium and hydroxide. The existence of OH- \n\n\n\nion produced will decrease the concentration of H+ ion thus increasing soil pH.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 153\n\n\n\nDolomite as an activator was able to increase the  pH value of the Ultisol oby \n0.17 unit compared to treatment without activator, a value which was also higher \nthan the value obtained by urea as activator, by about 0.08 unit. This is  expected \nas the  hydrolysed dolomite will provide more OH- ion that will neutralise Al3+ in \nsoil solution; if OH- residue is high, the pH in soil will increase. This condition is \nin accordance with the hydrolysis reaction as follows:\n\n\n\n CaMg (CO3)2 + H2O    \u2192     Ca2+ + Mg2+ + 2HCO3\n- + 2OH-\n\n\n\n In Table 2, it is seen that the addition of the sub-bituminous powder with \ndolomite as an activator produced a soil CEC that was not significantly different \nfrom treatment without activator. Moreover, urea as an activator produced a higher \nCEC than dolomite, 16.78 to 18.28 cmol (+)/kg. This can be attributed to urea \nhaving the ability to increase negative charge which results in increasing the CEC. \nAn increase in cation exchange of Ultisol with the addition of sub-bituminous \npowder and activator was also noted. This is because there is dissociation of proton \nor H+ ions in aromatic, aliphatic, carboxyl and phenolic groups from the sub-\nbituminous powder and activator (Herviyanti et al. 2014). The extent of the cation \nexchange capacity in humic material depends on pH of solvent which  increases \nwith increasing pH, resulting in H+ dissociation derived from the carboxyl group \nwhen pH is low and the phenolic group when  pH is high (Tan 2010).\n\n\n\nThe addition of sub-bituminous powder with activator was able to increase \nthe chemical properties of the Ultisol such as pH, organic-C, available-P, total-N \nof soil, CEC, Ca, Mg and K exchanges (Table 2) as well as decrease Al-exchange. \nAn improvement in soil chemical properties will lead to an increase in  plant \nheight, number of leaves, seedling dry weight, level of N and P in plant (Table \n2), all of which indicate that the  plant root is well developed and supported by \navailable nutrients.\n\n\n\nEffect of Sub-Bituminous Powder and Activator on the Growth of Oil Palm \nSeedlings\nImprovement in soil chemical properties supports plant growth such as plant \nheight, number of leaves, seedling dry weight, level of N and P in plant. Roots \ndevelop well  to ensure better nutrient absorption which is supported by the \navailability of nutrients. In Table 3, the addition of the activator was found to \nprovide a significantly different effect on the increasing level of N. The level of N \nand plant height with urea as an activator increased to about 0.069 % compared to \ntreatment with dolomite or without activator (0.034%). \n\n\n\nThe addition of sub-bituminous powder at a dose of 20 ton/ha increased N \nvalue by 0.02 % compared with the dose of 10 ton.ha-1 (Table 3). The increase in  \nN level occurred due to increasing pH, organic-C level, available-P, total-N and \ndecreasing Al-exchange content (Table 2). Thus better root growth was achieved. \nRoot development continued to increase and was better able to absorb nutrients, \nparticularly N as an essential nutrient for plant growth.  Costa et al. (2002) reported \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018154\n\n\n\nthat root length and root surface area increase under intermediate N levels and that \nroot growth is reduced under both higher and lower fertilisation levels.\n\n\n\nAddition of sub-bituminous powder with urea as an activator led to the \nhighest value of N content compared to dolomite and without the activator. It is \nbecause urea contains 45 % of N. In Table 2, it can be seen that total-N content \nin soil also increased along with the addition of sub-bituminous powder with \nthe activator. Thahirna (2010) notes that humic material have better ability to \nimprove chemical properties in rhizosphere, leading to better root development \nand nutrient absorption. \n\n\n\nAvailability of N element for plant is affected by the environment as plants \nmay obtain nitrogen from the air, and water and organic materials from the soil. \nHardjowigeno (2010) also states that N in soil originates from organic material in \nthe soil and fixation of microorganism while N in the air, originates from fertiliser \nand rain water.\n\n\n\nIn Table 3, the addition of sub-bituminous powder with the activator did  \nnot significantly affect the level of P in plants. Sub-bituminous powder at doses \nof 10 ton/ha and 20 ton/ha was not significantly different but a dose of 30 ton/\nha resulted in a significant difference with the  highest value of plant-P being \nobtained.. This finding is inversely proportional to P content that is available in \nsoil, as seen in Table 2 where the addition of sub-bituminous powder with the \nactivator increased available P in soil. \n\n\n\nArsyad et al. (2012) stated that P in soil is usually stable, not easily carried \naway by water as P is strongly bound to soil components. The addition of high \namounts of phosphate will change phosphate into a fraction that has high binding \npower on red soils and is hard to dissolve since P forms phosphate-Al and \nphosphate-Fe fractions. Phosphate in soil is similar to nitrogen in organic form, \n\n\n\nTABLE 3\nEffect of sub-bituminous and type of activator towards the growth of oil palm \n\n\n\nseedling in the Ultisol\n\n\n\n7 \n\n\n\n     CaMg (CO3)2 + H2O             Ca2+ + Mg2+ + 2HCO3\n- + 2OH- \n\n\n\n \n In Table 2, it is seen that the addition of the sub-bituminous powder with \ndolomite as an activator produced a soil CEC that was not significantly different \nfrom treatment without activator. Moreover, urea as an activator produced a \nhigher CEC than dolomite, 16.78 to 18.28 cmol (+)/kg. This can be attributed to \nurea having the ability to increase negative charge which results in increasing the \nCEC. An increase in cation exchange of Ultisol with the addition of sub-\nbituminous powder and activator was also noted. This is because there is \ndissociation of proton or H+ ions in aromatic, aliphatic, carboxyl and phenolic \ngroups from the sub-bituminous powder and activator (Herviyanti et al. 2014). \nThe extent of the cation exchange capacity in humic material depends on pH of \nsolvent which  increases with increasing pH, resulting in H+ dissociation derived \nfrom the carboxyl group when pH is low and the phenolic group when  pH is high \n(Tan 2010). \n\n\n\nThe addition of sub-bituminous powder with activator was able to increase \nthe chemical properties of the Ultisol such as pH, organic-C, available-P, total-N \nof soil, CEC, Ca, Mg and K exchanges (Table 2) as well as decrease Al-exchange. \nAn improvement in soil chemical properties will lead to an increase in  plant \nheight, number of leaves, seedling dry weight, level of N and P in plant (Table 2), \nall of which indicate that the  plant root is well developed and supported by \navailable nutrients. \n \nEffect of Sub-Bituminous Powder and Activator on the Growth of Oil Palm \nSeedlings \nImprovement in soil chemical properties supports plant growth such as plant \nheight, number of leaves, seedling dry weight, level of N and P in plant. Roots  \n\n\n\n \nTABLE 3 \n\n\n\nEffect of sub-bituminous and type of activator towards the growth of oil palm \nseedling in the Ultisol \n\n\n\n \nParameter Dose of sub-bituminous powder \n\n\n\n \n10 ton/ha 20 ton/ha 30 ton/ha \n\n\n\nPlant N content  (%) 0.19 B 0.21 A 0.22 A \nPlant P content (%) 0.022 B 0.022 B 0.027 A \nNumber of leaves 10.78 B 11.55 A 12.22 A \nDry weight (g) 50.63 B 52.11 AB 53.79 A \n Type of activator \n Without activator Urea Dolomite \nPlant N content  (%) 0.17 C 0.24 A 0.21 B \nPlant P content (%) 0.023 A 0.023 A 0.025 A \nNumber of leaf (leaves) 10.78 B 12.44 A 11.33 B \nDry weight (g) 47.46 C 56.65 B 52.43 A \nValues in the same row followed by capital letter are not significantly different based on DNMRT \ntest at of 5% level of significance. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 155\n\n\n\nthey are both hard to dissolve and not available for plants as  their availability \nis affected by soil pH. Therefore, in order to ensure that plants obtain phosphate \nfrom soil based on their need, the amount of phosphate applied should exceed its \nfixation capacity. Leiwakabessy et al. (2004) stated that phosphorus is the second \nmost important element for plants after nitrogen.\n\n\n\nThe effect of addition of sub-bituminous powder with urea as the activator \nresulted in a higher number of leaves compared with without the activator and \ndolomite (Table 3).  This is because urea contains 40-45 % N and has a function \nin leaf formation (during the vegetative phase). The effect of the addition of sub-\nbituminous powder at a dose of 20 and 30 ton/ha was almost similar, yet it was \nsignificantly different at a dose of 10 ton.ha-1. It is expected that similar amounts \nof nutrients is absorbed by plants at a dose of 20 and 30 ton/ha. However, in terms \nof value, the higher the dose of sub-bituminous powder, the higher the number of \nplant leaves. It is because sub-bituminous powder contains humic material which \nis able to improve the chemical properties of the soil. Thus plant roots will absorb \nmore available nutrients and support vegetative growth of plant. \n\n\n\nFitter and Hay (1981) mentioned that the  number of leaves and leaf area are \nthe main determinants of the growth rate of leaves based on the  assumption that \na higher number of wide leaves means faster growth. Parman (2007) in a study \non potatoes (Solanum tuberosum L) reported that the potato plant experienced \nan increase  in leaf number from 196 to 344 leaves after the addition of organic \nfertilisers. The addition of organic fertiliser or manure accelerates the synthesis of \namino acid and protein, thus stimulating plant growth.\n\n\n\nInteraction of the Addition of Activated Sub-Bituminous Powder on Total-N \nof Ultisol\nAddition of sub-bituminous powder with urea and dolomite as activator showed \nsignificant interaction with total-N of soil. The addition of the activator resulted \nin a significant effect on increasing the total-N content of Ultisol. At a dose of 10 \nton/ha sub-bituminous powder, the addition of dolomite and urea succeeded in \nincreasing total-N of soil to 0.12 % and 0.12 %, while at a dose of 20 ton/ha, it was \nable to increase to 0.10 and 0.13 % compared with treatment without the activator. \nMoreover, at a dose of 30 ton.ha-1, the urea increased total-N to a level higher \nthan treatments with dolomite and without the activator. Though the addition of \ndolomite and without the activator generated fairly similar results on the three \ndose of sub-bituminous powder, the addition of urea at a dose of 30 ton/ha was \nable to increase total-N by 0.22 % and 0.13 % compared to treatments without \nactivator and dolomite.\n\n\n\nUrea-as an activator and sub-bituminous powder at a dose of 30 ton/ha \nobtained the highest total-N value with N from urea contributing to 45% of total \nN; an increase in the dose of sub-bituminous powder  is also expected to provide \nhigher N contribution. Shelly (2014) notes that sub-bituminous powder activated \nwith urea at 125% (375 kg ha-1) is expected to increase total-N of sub-bituminous \npowder to 5.61%. As presented in Table 4, dolomite as an activator was able \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018156\n\n\n\nto increase total-N of soil compared to without the activator. This is because \ndolomite is able to activate  the humic acid of the sub-bituminous powder.\n\n\n\nInteraction of the Addition of Activated Sub-bituminous Powder with Plant Height\nThe interaction between the added activator and sub-bituminous powder resulted \nin an increase in plant height. The single effect of the activator addition to sub-\nbituminous powder towards plant height was found to be significant as seen in \nTable 5. \n\n\n\nAt a dose 10 ton/ha of sub-bituminous powder, the addition of dolomite and \nurea was able to increase plant height by 3 and 4.83 cm, respectively. At a dose of \n20 ton/ha, plant height increased by 3.2 and 7.9 cm compared to treatment without \nthe activator. But at a dose of 30 ton/ha, urea increased plant height to a level \nhigher than in treatment with dolomite and without the activator. The addition of \ndolomite and without the activator generated almost similar results in the three \ndoses of sub-bituminous powder, but with the  addition of urea at a dose of 30 ton/\n\n\n\nTABLE 4\nEffect of addition of sub-bituminous powder activated with urea and dolomite on \n\n\n\ntotal-N of the Ultisol\n\n\n\n9 \n\n\n\nbituminous powder at a dose of 20 and 30 ton/ha was almost similar, yet it was \nsignificantly different at a dose of 10 ton/ha. It is expected that similar amounts of \nnutrients is absorbed by plants at a dose of 20 and 30 ton/ha. However, in terms of \nvalue, the higher the dose of sub-bituminous powder, the higher the number of \nplant leaves. It is because sub-bituminous powder contains humic material which \nis able to improve the chemical properties of the soil. Thus plant roots will absorb \nmore available nutrients and support vegetative growth of plant.  \n\n\n\nFitter and Hay (1981) mentioned that the  number of leaves and leaf area \nare the main determinants of the growth rate of leaves based on the  assumption \nthat a higher number of wide leaves means faster growth. Parman (2007) in a \nstudy on potatoes (Solanum tuberosum L) reported that the potato plant \nexperienced an increase  in leaf number from 196 to 344 leaves after the addition \nof organic fertilisers. The addition of organic fertiliser or manure accelerates the \nsynthesis of amino acid and protein, thus stimulating plant growth. \n\n\n\n\n\n\n\nInteraction of the Addition of Activated Sub-Bituminous Powder on Total-N \nof Ultisol \n\n\n\n \nAddition of sub-bituminous powder with urea and dolomite as activator showed \nsignificant interaction with total-N of soil. The addition of the activator resulted in \na significant effect on increasing the total-N content of Ultisol. At a dose of 10 \nton/ha sub-bituminous powder, the addition of dolomite and urea succeeded in \nincreasing total-N of soil to 0.12 % and 0.12 %, while at a dose of 20 ton/ha, it \nwas able to increase to 0.10 and 0.13 % compared with treatment without the \nactivator. Moreover, at a dose of 30 ton/ha, the urea increased total-N to a level \nhigher than treatments with dolomite and without the activator. Though the \naddition of dolomite and without the activator generated fairly similar results on \nthe three dose of sub-bituminous powder, the addition of urea at a dose of 30 \nton/ha was able to increase total-N by 0.22 % and 0.13 % compared to treatments \nwithout activator and dolomite. \n\n\n\n \nTABLE 4 \n\n\n\nEffect of addition of sub-bituminous powder activated with urea and dolomite on \ntotal-N of the Ultisol \n\n\n\nActivator  Dose of sub-bituminous powder \n10 ton/ha 20 ton ha-1 30 ton ha-1 \n\n\n\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Total-N (%) ..\u2026\u2026\u2026\u2026\u2026\u2026\u2026 \nWithout Activator  0.14 b \n\n\n\nA \n0.16 b \n\n\n\nA \n0.17 c \n\n\n\nA \nUrea  0.26 a \n\n\n\nB \n0.29 a \n\n\n\nB \n0.39 a \n\n\n\nA \nDolomite 0.26 a \n\n\n\nA \n0.26 a \n\n\n\nA \n0.26 b \n\n\n\nA \nCC                                                               13.47 % \n\n\n\nValues in the same row followed by similar capital letters and in the same column followed by \nsimilar subscripts are not significantly different based on the result of DMRT at 5% significance \nlevel. \n\n\n\nTABLE 5.\nEffect of addition of sub-bituminous powder with urea and dolomite as activator \n\n\n\non oil palm height (cm)\n\n\n\n10 \n\n\n\nUrea-as an activator and sub-bituminous powder at a dose of 30 ton/ha \nobtained the highest total-N value with N from urea contributing to 45% of total \nN; an increase in the dose of sub-bituminous powder  is also expected to provide \nhigher N contribution. Shelly (2014) notes that sub-bituminous powder activated \nwith urea at 125% (375 kg/ha) is expected to increase total-N of sub-bituminous \npowder to 5.61%. As presented in Table 4, dolomite as an activator was able to \nincrease total-N of soil compared to without the activator. This is because \ndolomite is able to activate  the humic acid of the sub-bituminous powder. \n \nInteraction of the Addition of Activated Sub-bituminous Powder with Plant \nHeight \n\n\n\nThe interaction between the added activator and sub-bituminous powder resulted \nin an increase in plant height. The single effect of the activator addition to sub-\nbituminous powder towards plant height was found to be significant as seen in \nTable 5.  \n\n\n\n\n\n\n\nTABLE 5. \nEffect of addition of sub-bituminous powder with urea and dolomite as activator \n\n\n\non oil palm height (cm) \nActivator Dose of sub-bituminous powder \n\n\n\n10 ton ha-1 20 ton ha-1 30 ton ha-1 \n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026plant height (cm) .\u2026\u2026\u2026\u2026\u2026\u2026\u2026 \n\n\n\nWithout Activator 52.07 a \nA \n\n\n\n53.43 b \nA \n\n\n\n52.67 b \nA \n\n\n\nUrea 56.90 a \nB \n\n\n\n61.33 a \nB \n\n\n\n67.07 a \nA \n\n\n\nDolomite 55.07 a \nA \n\n\n\n56.63 ab \nA \n\n\n\n56.73 b \nA \n\n\n\nCC 4.01 % \nValues in the same row followed by similar capital letters and in the same column followed by \nsimilar subscripts are not significantly different based on the results of DMRT test at a level of \n5%. \n \n\n\n\nAt a dose 10 ton/ha of sub-bituminous powder, the addition of dolomite \nand urea was able to increase plant height by 3 and 4.83 cm, respectively. At a \ndose of 20 ton/ha, plant height increased by 3.2 and 7.9 cm compared to treatment \nwithout the activator. But at a dose of 30 ton/ha, urea increased plant height to a \nlevel higher than in treatment with dolomite and without the activator. The \naddition of dolomite and without the activator generated almost similar results in \nthe three doses of sub-bituminous powder, but with the  addition of urea at a dose \nof 30 ton/ha was able to increase plant height by14.40 and 10.34 cm compared to \ntreatment without the  activator and with dolomite. \n\n\n\nThe use of urea as the activator and sub-bituminous powder at a dose of 30 \nton/ha produced the highest plant height. The increase in plant height was an \nexpected result of the  treatment which has the ability to increase soil pH, provide \nnutrients in soil, and contain various cations or other micro elements, thus \nsupporting growth and development of the plant. The addition of dolomite as an \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 157\n\n\n\nha was able to increase plant height by14.40 and 10.34 cm compared to treatment \nwithout the  activator and with dolomite.\n\n\n\nThe use of urea as the activator and sub-bituminous powder at a dose of \n30 ton/ha produced the highest plant height. The increase in plant height was \nan expected result of the  treatment which has the ability to increase soil pH, \nprovide nutrients in soil, and contain various cations or other micro elements, thus \nsupporting growth and development of the plant. The addition of dolomite as an \nactivator was also able to increase plant height because Ca and Mg elements in \ndolomite are utilised by the plant during vegetative growth. \n\n\n\nN element is an essential requirement for plant vegetative growth such \nas  root, stem, and leaves; thus increasing N element will further increase plant \nheight. According to Setyamidjaja (2006), N is important for plants to compose \namino acid, amide, nucleotide, and also essential for cell division and cell growth \n(enlargement),  which has an impact on increasing plant height. Leiwakabessy et \nal. (2004) added that nitrogen is needed by plant growth both in vegetative and \ngenerative phase, and this nutrient is mobile within the plant. Plant height figures \nare presented in Figure 1.\n\n\n\nFigure 1: Height of oil palm seedlings after 18 weeks. \n\n\n\n11 \n\n\n\namino acid, amide, nucleotide, and also essential for cell division and cell growth \n(enlargement),  which has an impact on increasing plant height. Leiwakabessy et \nal. (2004) added that nitrogen is needed by plant growth both in vegetative and \ngenerative phase, and this nutrient is mobile within the plant. Plant height figures \nare presented in Figure 1. \n \n\n\n\n \n          Figure 1: Height of oil palm seedlings after 18 weeks.  \n\n\n\n In Figure 1, it is seen that sub-bituminous powder with the activator was \nable to increase plant height compared to treatment without the activator along \nwith the observation of oil palm seedling growth. Organic material from the sub-\nbituminous powder has a positive impact on increasing plant height due to the \navailability of essential macro nutrients (K-exchange) which greatly affect the \ndevelopment and growth of the plant. As confirmed by Rosmarkam and Yuwono \n(2002), potassium (K) plays a role in increasing enzyme activity during the \nreaction of photosynthesis and respiration. \n\n\n\n\n\n\n\n CONCLUSIONS \nBased on the results  obtained, it could be concluded that: (1) the addition of sub-\nbituminous powder was found to interact with the activator in increasing total N \nof soil and plant height, with the highest total-N and plant height being obtained \nin the  treatment of sub-bituminous powder at a dose of 30 ton.ha-1 with urea as \nthe activator; (2) the addition of sub-bituminous powder at a dose of 30 ton/ha \nincreased pH, organic-C, available-P, and CEC of Ultisol to 0.4 unit, 0.35 %, \n2.09, 7.41 cmol (+)/kg , respectively, as well as increased the level of N and P in \nplant by 0.03% and 0.05% compared with a dose of 10 ton/ha; (3) the addition of \nactivator (urea) increased pH by 0.09 unit, organic-C by 0.18 %, and available-P \nin the Ultisol by 0.92 ppm, decreased Al-exchange by 0.49 cmol (+)/kg  and \nincreased N level in plant by 0.07%, leaf number by 1.64 leaves, seedling dry \nweight by 9.19 g compared to treatment without the activator, and increased CEC \nof the Ultisol by 1.5 cmol (+)/kg  compared to the treatment with dolomite. \n \n\n\n\nACKNOWLEDGEMENT \n\n\n\nIn Figure 1, it is seen that sub-bituminous powder with the activator was \nable to increase plant height compared to treatment without the activator along \nwith the observation of oil palm seedling growth. Organic material from the sub-\nbituminous powder has a positive impact on increasing plant height due to the \navailability of essential macro nutrients (K-exchange) which greatly affect the \ndevelopment and growth of the plant. As confirmed by Rosmarkam and Yuwono \n(2002), potassium (K) plays a role in increasing enzyme activity during the \nreaction of photosynthesis and respiration.\n\n\n\n CONCLUSIONS\nBased on the results  obtained, it could be concluded that: (1) the addition of sub-\nbituminous powder was found to interact with the activator in increasing total N \nof soil and plant height, with the highest total-N and plant height being obtained \nin the  treatment of sub-bituminous powder at a dose of 30 ton.ha-1 with urea as \nthe activator; (2) the addition of sub-bituminous powder at a dose of 30 ton/ha \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018158\n\n\n\nincreased pH, organic-C, available-P, and CEC of Ultisol to 0.4 unit, 0.35 %, 2.09, \n7.41 cmol (+)/kg , respectively, as well as increased the level of N and P in plant by \n0.03% and 0.05% compared with a dose of 10 ton/ha; (3) the addition of activator \n(urea) increased pH by 0.09 unit, organic-C by 0.18 %, and available-P in the \nUltisol by 0.92 ppm, decreased Al-exchange by 0.49 cmol (+)/kg  and increased N \nlevel in plant by 0.07%, leaf number by 1.64 leaves, seedling dry weight by 9.19 \ng compared to treatment without the activator, and increased CEC of the Ultisol \nby 1.5 cmol (+)/kg  compared to the treatment with dolomite.\n\n\n\nACKNOWLEDGEMENT\nWe would like to thank the Rector and Chairman of the Institute for Research \nand Community Service of Andalas University Padang, the Ministry of Research \nTechnology and Higher Education of  Republic of Indonesia, for  support of this \nresearch through  a financial grant (Cluster Professor) for the fiscal year 2017 \nSpecial thanks are extended to the Rector for providing  financial support for \nattendance at an international congress in Vietnam 2017.  \n\n\n\nREFERENCES\nArsyad, A., Junedi, H. and Yulfita, F. 2012. Pemupukan kelapa sawit berdasarkan \n\n\n\npotensi produksi untuk meningkatkan hasil tandan buah segar (tbs) pada lahan \nmarginal kumpeh. Penelitian Universitas Jambi Seri Sains 14 (1): 29-36.\n\n\n\nBadan Pusat Statistik. 2014. Statistik Kelapa Sawit 2013-2015. Direktorat Jendral \nPerkebunan. Jakarta: Kementrian Pertanian. Jakarta, 12 hlm.\n\n\n\nBalai Penelitian Tanah. 2005. Analisis Kimia Tanah. Tanaman. Air dan Pupuk. Badan \nPenelitian dan Pengembangan Pertanian Departemen Pertanian, 143 p.\n\n\n\nCosta C, L.M. Dwyer, X. Zhou, P. Dutilleul, C. Hamel, L.M. Reid et al. 2002.  Root \nmorphology of contrasting maize genotypes. Agronomy Journal. 94(1): 96\u2013101. \n\n\n\nFitter A.H. and R.K.M. Hay. 1981. Environmental Physiology of Plants (terjemahan \nSri Andani dan E.D. Purbayanti. 1991, ed. B. Srigandono. Fisiologi Lingkungan \nTanaman). Gadjah Mada  Press, 421p.\n\n\n\nHakim. N. 2006. Pengelolaan Kesuburan Tanah Masam Dengan Teknologi \nPengapuran Terpadu. Padang: Andalas University Press, 67 p..\n\n\n\nHakim. N., M. Y. Nyakpa, A. M. Lubis, A. M. Pulung, R. Saul, M. A. Diha, G. B. \nHong and H. H. Bailey. 1984. Bahan Praktikum Dasar-Dasar Ilmu Tanah. \nBadan Kerja Sama Ilmu Tanah. BKS-PTN/USAID. University of Kentucky. \nWUAE Project. 151 p.\n\n\n\nHardjowigeno. S. 2010. Ilmu Tanah. Jakarta: Akademika Pressindo. 288 p.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 159\n\n\n\nHerviyanti. F. Ahmad, R. Sofyani, Darmawan. Gusnidar and A. Saidi. 2012. Pengaruh \npemberian bahan humat dari ekstrak batubara muda(sub-bituminous) dan \npupuk p terhadap sifat kimia ultisol serta produksi tanaman jagung ( Zea \nmays.). J Solum (IX(1): 15-24.\n\n\n\nHerviyanti, Yusnaweti and A. Rasyidin. 2014. Kajian Stabilitas Bubuk Batubara \ntidak Produktif dan Bahan Humatnya yang Diekstrak dengan Pupuk Buatan \nuntuk Meningkatkan Efisiensi Pemupukan dan Produktifitas Ultisol dan oxisol. \nLaporan Hasil Penelitian. Badan Penelitan dan Pengembangan Pertanian, 55 p.  \n\n\n\nHuang. P.M. and M. Schnitzer. 1997. Interaction of Soil Minerals with Natural \nOrganics and Microbes. SSSA Special Publication Number 17. Soil Science \nSociety of America. Inc. 920 pp. \n\n\n\nLeiwakabessy F., M. Suwarno, and U.M. Wahyudi. 2004. Diktat Kuliah Pupuk dan \nPemupukan. Jurusan Tanah. Bogor: Fakultas Pertanian, Institut Pertanian. \nBogor. 208 p.\n\n\n\nMohamad, N.F, A.R Hidayu, A.A. Sherif and A.S.A.K. Sharifah. 2013. Characteristics \nof Bituminous Coal, Sub-Bituminous Coal and Bottom Ash from a Coal-\nFired Power Plant. IEEE Busniness Engineering and Industrial Application \nColloquium (BEIAC).\n\n\n\nMuktamar. Z., D. Aneri. and Suprapto. 1998. Penurunan aluminium teradsorpsi pada \ntanah asam dengan asam sitrat dan oksalat. Jurnal Penelitian UNIB, 11:1-4.\n\n\n\nMcCauley A., C. Jones and K.O. Rutz. 2017. Soil pH and Organic Matter. Nutrient \nManagement Module No 8. Montana State University Extension. \n\n\n\nNursyamsi, Dedi and Suprihati. 2005. Sifat-Sifat kimia dan mineralogi tanah serta \nkaitan dengan kebutuhan pupuk untuk padi (Oryza sativa), jagung (Zea mays) \ndan kedelai (Glycine max). Bull.Agron. 33(3):  40 pp.\n\n\n\nParman. 2007. Pengaruh pemberian pupuk organik cair terhadap tertumbuhan dan \nproduksi kentang (Solanum tuberosum L.). Buletin Anatomi dan Fisiologi  \n15(2): page nos??? \n\n\n\nPoertadji, S. Nukman and M. Hikam. 2006. The effect of the agglomerating of water-\noil palm to carbon content and calorific value of semi-anthracite, bituminous \nand sub-bituminous coals. J. Indonesia Materials Sci. 7: 68-74\n\n\n\nPusat Penelitian Kelapa Sawit. 2008. Budidaya Kelapa Sawit. Pusat Penelitian Kelapa \nSawit. Medan. 2hlm\n\n\n\nPusat Penelitian Kelapa Sawit. 2016. Petunjuk Teknis Pembibitan Kelapa Sawit. \nMedan. 4 p.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018160\n\n\n\nRisza. S. 2010. Kelapa Sawit. Upaya Peningkatan Produktivitas. Yogyakarta: \nPenerbit Kanisius (Anggota IKAPI), pp 40-44.\n\n\n\nRezki. D. 2007. Ekstraksi Bahan Humat dari Batubara (Sub-bituminous) dengan \nmenggunakan 10 Jenis Pelarut Skripsi Falkutas Pertanian,  Padang: Universitas \nAndalas, 63 p.\n\n\n\nRosmarkam. A.W, Yuwono. 2002. Ilmu Kesuburan Tanah. Yogyakarta: Kanisius, \n182p.\n\n\n\nRusli, M Alibasyah. 2016. Perubahan beberapa sifat fisika dan kimia ultisol akibat \npemberian pupuk kompos dan kapur dolomit pada lahan berteras.  J. Floratek \n11 (1): 75-87.\n\n\n\nSarief, E. S. 1986. Kesuburan dan Pemupukan Tanah. Bandung: Pustaka Buana. 63 p.\n\n\n\nSetyamidjaja. 2006. Budidaya Kelapa Sawit.  Yogyakarta: Kanisius, pp 35-36.\n\n\n\nShelly. N.W. 2014. Pengujian tingkat keaktifan campuran bubuk batubara sub-\nbituminous dengan urea, KCl. NaOH dan NaCl terhadap beberapa ciri kimia \noxisol.Skripsi Fakultas Pertanian. Padang:Universitas Andalas. 60 p. \n\n\n\nSoegiman. 1982. Ilmu Tanah. Terjemahan  H. O. Buckman dan N. C. Brady. The \nNature Properties of Soil. Jakarta : Bharatara Karya Aksara, 788 p.\n\n\n\nStevenson. F. J. 1994. Humus Chemistry : Genesis. Composition. Reactions. New \nYork: John Wiley & Sons Inc. 496 p.\n\n\n\nSubagyo. H., N. Suharta, and A.B. Siswanto. 2004. Tanah-tanah Pertanian di \nIndonesia. Dalam: (Ed.). Sumberdaya Lahan Indonesia dan Pengelolaannya., \nsunting oleh A. Adimihardja. L.I., F. Amien, D. Agus and Djaenudin. Bogor: \nPusat Penelitian dan Pengembangan Tanah dan Agroklimat. Bogor, pp. 21\u221266.  \n\n\n\nSunarko. 2009. Budi Daya Dan Pengelolaan Kebun Kelapa Sawit dengan System \nKemitraan. Cetakan Pertama.Jakarta: Agromedia Pustaka. 57 hlm.\n\n\n\nTan. K. H . 2010. Principles of Soil Chemistry. New York: CRC Press Taylor and \nFrancis Group. 362 p. \n\n\n\nThahirna. 2010. Pengaruh Pemberian Bahan Humat dari Ekstrak Kompos dan SP-36 \nterhadap Sifat Kimia Ultisol. Serta Produksi Tanaman Jagung (Zea Mays L.). \nSkripsi Fakultas Pertanian Universitas Andalas, Padang. 62p.\n\n\n\nTisdale. S and W. Nelson. 1975. Soil Fertility and Fertilizer (3rd ed.) New York: \nMacmillan Publishing Co. Inc. 694 p.\n\n\n\n\n\n"
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"\n\n___________________\n*Corresponding author : E-mail: \n\n\n\nINTRODUCTION\nSoil acidity results in declining crop production all over the world (Samac and \n\n\n\nin the world (Kochian et al\nhas increased to 4 billion hectares and importantly 178 million hectares were \n\n\n\net al\n\n\n\nAcid Tolerance Mechanisms in Soil Grown Plants \n\n\n\nIqbal, M.T.\n\n\n\nDepartment of Agronomy and Agricultural Extension, \nUniversity of Rajshahi Rajshahi 6205, Bangladesh\n\n\n\nABSTRACT\n\n\n\nand subtropical regions where agriculture is the dominant business. Soil acidity, \n\n\n\nthe critical constraints limiting crop production on these acid soils. Research on \nacid-tolerance mechanisms of soil grown plants will have important implication in \nfacilitating soil and crop management in the tropical and subtropical regions. Soil \n\n\n\nis common due to solubilisation of Al\nsymptom of Al\n\n\n\nhighly acid soils. Low pH stress facilitates H\n\n\n\nP availability in these soils. Different Al tolerance mechanisms such as an increase \n\n\n\napoplast are discussed in this review. This review concludes that P may play an \nimportant role in Al tolerance. Thus, understanding acid tolerance mechanisms \nin soil grown plants has important implications for sustainable agriculture in the \ntropical and subtropical regions. This review suggests that introduction of Al-\ntolerant crops in acid soils will improve crop production in these soils. \n\n\n\nKeywords:  Organic acid exudation, Al and P interaction, apoplast, plant                  \n       tissue tolerance.\n\n\n\n\n\n\n\n\nAl becomes solubilised in low pH soils that results in increased activity of \nAl  ions (Hoekenga et al. \n\n\n\n et al  et al\nLittle work has been done on acid-tolerance mechanisms on soil-grown plants \n\n\n\n(Ishikawa et al.\nculture show largely different results in soils (Ishikawa et al. et al. \n\n\n\net al\nUnderstanding soil chemistry and plant-soil interactions in acid soils is \n\n\n\nuseful for making sound and cost-effective decisions to manage soil acidity. Thus, \n\n\n\nAl-tolerance mechanisms and a possible role of P in Al tolerance mechanisms.\n\n\n\nTHE CHEMISTRY OF SOIL ACIDITY\n\n\n\nagricultural practices (Bolan et al.\nthe release of protons (H\n\n\n\nThus, inputs of H  ions, changes in soil C, N and S can have adverse impacts when \nsoils are unable to buffer against pH decrease (Mason et al.\n\n\n\n ions. This \nH - -\n\n\n\n4 -S by \n4  and \n\n\n\n- with charge-balancing basic cations (Ca , Mg , K  or Na\nH\n\n\n\n4\nof NH4  or N  occur in plants, H\n\n\n\n ions are then separated from the rhizosphere soil and move to bulk \nsoil, due to accumulation of organic N in the rhizosphere soil. This accumulation \n\n\n\nanions. As more organic N accumulates in the rhizosphere soil, acidity increases, \n\n\n\nNH\nthe N input is added in the form of  NH4  by the application of ammonium based \n\n\n\n\n\n\n\n\nAcid Tolerance Mechanisms in Soil Grown Plants\n\n\n\nfor soil acidity. The Al has a high ionic charge and a small crystalline radius, \nwhich confers it a level of reactivity that is unmatched by other soluble metals. \n\n\n\n Al Behaviour in Soil\n\n\n\nscheme of Al forms in soil can be shown as below:\n \n\n\n\n, \n\n\n\nEffect of pH on Al Species\nBelow pH 5, Al is the main source of H  due to dissociation of Al from clay \n\n\n\n6 ], \n.\n\n\n\n Al\n\n\n\nEach reaction releases H  and regenerates the Al  ions enabling further \n\n\n\nconcentration of Al  decreases with increasing pH,  and Al(H 6  undergoes \n\n\n\nsolutions, polynuclear forms of Al, contain more than one Al with the most \n4Al (H , referred to as \n\n\n\n\n\n\n\n\n4\n\n\n\nAl 4\n-, that is, aluminate ions are \n\n\n\nPhosphorous chemistry in acid soils can be affected by some physico-chemical \n\n\n\nand interactions with other ions. These processes/properties are described below:\n\n\n\nPrecipitation\n\n\n\n(Sanchez et al. \n\n\n\nthe adsorption process. The adsorption process is regarded as the accumulation \n\n\n\nreaction of phosphate ions in soil solution at the surface of soil constituents, \n\n\n\nas the principal mechanism by which P is adsorbed to variable-charge surfaces of \n\n\n\nFig. 1\nP becomes less available due to the molecular rearrangement of the adsorbed P. \n\n\n\ncrystalline Al phosphate is considered completely unavailable for plant growth.\n\n\n\n \nFig. 1: Mechanism of P adsorption on Al oxide surface\n\n\n\n\n\n\n\n\n5\n\n\n\nAcid Tolerance Mechanisms in Soil Grown Plants\n\n\n\nEffect of pH on P Availability\n\n\n\net al.\n(Bowden et al.\nconcentration of P in the soil solution that is controlled by precipitation reactions \n\n\n\net al.\navailability of P also through increased P sorption reaction.\n\n\n\net al.\n\n\n\nof P in soils characterised by cracking clays, where Al and Fe dominate. The P \n\n\n\nalkaline soils due to Fe, Al and Ca is shown in Fig. 2.\n\n\n\nInteraction between Soluble Al and P Availability in Acid Soils\nThe addition of soluble phosphates to acid soils reduces the soluble Al (Brown et \nal.\nacid to neutral. This could be due to high amounts of Al available in acid soils \nas compared to neutral soils that help to increase P availability in neutral soils \n\n\n\navailability of soluble Al. The Al phosphate forms surface coatings on particles of \nAl compounds that would have otherwise dissolved in highly acid soil. Thus the \n\n\n\nIn many acid soils, P is concentrated at the surface of the soil while a high \n\n\n\n      (Source : CSIRO 2006)\n\n\n\nFig\n\n\n\n\n\n\n\n\n6\n\n\n\nIMPACT OF SOIL ACIDITY ON PLANT GROWTH\n\n\n\n, Mn  and \nH\nfurther in the following sections.\n\n\n\nAl Toxicity\n\n\n\net al.\n\n\n\nsolution affect root cell division and the ability of the root to elongate. This results \n\n\n\net al.\n(Fig. 3\nsensitive to Al and accumulate Al easily. Greater physiological damage occurs \n\n\n\n(Source: Delhaize and Ryan 1995)\n\n\n\nFig. 3: Healthy root tip (left) compared to a root tip affected by Al toxicity (right). \nPhotograph taken for healthy root tip on Al-tolerant (ET8) and affected root tip on Al-\nsensitive (ES8) wheat genotypes that differ at the Alt1 locus. The seedlings were grown \n\n\n\n3 2 at pH 4.3 \n\n\n\n \n\n\n\n\n\n\n\n\n7\n\n\n\nAcid Tolerance Mechanisms in Soil Grown Plants\n\n\n\nwithin meristematic and root cap cells, as well as a disruption of dictyosomes and \ntheir secretory function (Poschenrieder et al.\n\n\n\ninteractions within the cell wall, the plasma membrane or the root symplasm \n\n\n\nwith the cell wall and be absorbed in the root cell wall by the negative charges \n\n\n\nstunting, small, dark green leaves and late maturity, purpling of stems, leaves and \nleaf veins, yellowing and death of leaf tips (Bouma et al.\nis due to Al dislocation of the plant P metabolism. The occasional observation \n\n\n\nOryza sativa\n(Triticum aestivum Sorghum bicolor \n\n\n\nin leaves, reduced stomata opening, decreased photosynthetic activity, chlorosis \nand foliar necrosis (Vitorello et al.\n\n\n\nMn Toxicity\n\n\n\noccur in acid soils (Delhaize et al.\net al.\n\n\n\nsoils (Sumner et al.\n\n\n\nwith the effect being pronounced in shoots. Shoot growth becomes stunted and \n\n\n\ncrinkling or cupping of shoots and splotches of chlorotic tissue (Alam et al. \n\n\n\nunderstood. Horst et al\n et al.\n\n\n\ntolerance. They also suggested that more detailed studies with emphasis on very \n\n\n\n\n\n\n\n\n8\n\n\n\nLow pH\n\n\n\nacid soils (Kinraide et al.\nroot growth (Yang et al\ntissues that result in poor plant growth (Rangel et al.\nto understand H\nmore tolerant to H  stress in acid soils. At a low pH, the H  ions themselves are \n\n\n\nroot membranes. Low pH activates rapid H\net al.\n\n\n\ndistal elongation zones in terms of H\nwhich might contribute to the differential mechanisms of plant adaptation to acid \nsoils (Bose et al.\n\n\n\nSpecies or Genotypic Variation in Response to Soil Acidity\nTolerance to soil acidity varies across plant species and between genotypes of \na same species. Some plant genotypes or species have evolved mechanisms to \ntolerate Al stress (Fukuda et al. et al.\nwheat varieties collected from acid-soil regions are more likely to be Al tolerant \nthan those collected from regions with natural or basic soils. Thus, the differences \nin Al tolerance among species and between genotypes are important to develop \nvarieties that are suitable for cultivation in acid soils (Yang et al.\n\n\n\nMECHANISMS OF AL TOLERANCE\n\n\n\nchanges in rhizosphere pH and plant tissue tolerance to Al.\n\n\n\nExudation of Organic Acid Anions\n\n\n\ntolerance mechanisms. More than twelve Al-tolerant plant species are known to \n\n\n\ntreatments from Al-tolerant plant species (Ma et al\nmalate are some of the commonly released organic acid anions (Ma et al.\n\n\n\nZea mays et al.\nFogopyrum esculentum et al. \n\n\n\net al., \n\n\n\nAl (Guo et al\net al\n\n\n\net al\n\n\n\n\n\n\n\n\n9\n\n\n\nAcid Tolerance Mechanisms in Soil Grown Plants\n\n\n\nstudy on protoplasts prepared from wheat roots showed that Al  activates an \nanion channel in the plasma membrane which is permeable to malate (Ryan et al. \n\n\n\nthat differed in Al tolerance at a single genetic locus, Al  was found to activate \n\n\n\net al. \n\n\n\ntreatment by the activation of an anion channel on the plasma membrane (Pineros \n\n\n\nIncrease in Rhizosphere pH\net al\n\n\n\net al.\n et al - and H  are transported across the \n\n\n\nplasma membrane in the root tips of wheat to maintain rhizosphere pH, and to alter \n\n\n\net al\nin rhizosphere pH occurs in Al-tolerant wheat cultivar, Atlas 66, compared to Al-\nsensitive wheat cultivar, Brevor. \n\n\n\n and have an \n-\n\n\n\nnot. This difference may relate to their genotypic tolerance to Al.\n\n\n\nTissue Tolerance\nThere is evidence that Al tolerance is associated with plant tissue tolerance to Al. \nSome plant species that can accumulate Al to high levels in the shoot appear to be \n\n\n\net al.\n-1 in leaves when grown on an \n\n\n\net al.\ngenotype, Barbela\nthe Al-sensitive wheat genotype, Anahuac\nreported that Al-tolerant wheat cultivars accumulate more Al in its root than Al-\nsensitive wheat cultivar. \n\n\n\ntissue Al tolerance in wheat is metabolism-dependent. Poschenrieder et al.\nspeculated that the accumulation of Al in shoot occurs in cell wall or in leaf vacuoles. \nLikewise, Haridasan et al.\n\n\n\naccompanied by their disintegration may allow the surrounding tissue to survive \n\n\n\n\n\n\n\n\nRole of Apoplast in Al Tolerance\n\n\n\n(Poschenrieder et al\naccumulates in roots corresponds to Al in this apoplastic space (Heim et al.\n\n\n\n is the \net al et al\n\n\n\nfound that differences in cell wall pectin and its degree of methylation contribute \nto genotypic differences in Al tolerance in maize in addition to the release of \norganic acid anions discussed above. In addition, Horst et al\n\n\n\ncontributes to Al tolerance.\n\n\n\nAs discussed above, P  forms insoluble compounds with soluble Al. Thus, application \n\n\n\nP and Al Reactions in Acid Soils \nThe most common Al and P reaction in acid soils is precipitation (Iyamuremye \n\n\n\nin acid soil (Gallardo et al\n\n\n\net al.\n\n\n\nRole of P on Al Tolerance Mechanisms in Plants \n\n\n\nthe presence of P. These include utilization of P as a nutrient, immobilization of Al \n\n\n\nAl-P interactions in the root apoplast.\n\n\n\n(Akinrinde et al.\n\n\n\nunder Al-stress conditions appear to stimulate P uptake, enabling survival in Al-\n\n\n\nThe P nutrition may affect tolerance to Al through changes in cation-anion \n\n\n\n\n\n\n\n\n11\n\n\n\nAcid Tolerance Mechanisms in Soil Grown Plants\n\n\n\ncation uptake tended to increase with decreasing P supply and at a high Al stress. \nThis causes Al-induced changes in root morphology and rises to higher root proton \n\n\n\nImmobilization of Al by P within the root tissue is one of the Al-tolerance \n et al.\n\n\n\nAl in the roots of Al-tolerant cultivar, Jiangxi, were higher than the Al-sensitive, \nShanxi cultivar. They also found that more Al had localized in the cell walls of \nJiangxi than the Shanxi cultivar. They suggested that immobilization of Al in the \nroot with P may contribute to the genotypic differences in buckwheat. Similarly, \nGaume et al.\n\n\n\nInsoluble Al-P precipitates can accumulate on the root surface and in the cell \n\n\n\net al.\nhigher P uptake in the presence of Al relative to the Al-sensitive genotype, IT89KD-\n\n\n\nclimatic conditions for a period of 8 days. They postulated that Al, in the presence \n\n\n\net al.\n\n\n\n4, might be helpful in retarding the uptake of Al \ninto the cytosol. Thus, P may be involved in Al-tolerance mechanisms through \n\n\n\nprecipitation of Al in the roots, but also through Al-P interactions in the root \napoplast or even at the root cell plasma membrane. An earlier study conducted \n\n\n\n4 reaction occurs at the root \nsurface. They also suggested that adsorption reaction between Al and P occurred \n\n\n\net al\nthat Al and P interaction may occur in the root apoplast of Al-tolerant buckwheat \n\n\n\net al\n\n\n\nCONCLUDING REMARKS AND RESEARCH GAPS\n\n\n\nAt low pH, Al becomes solubilized in soil solution and Al  activity is high. \nThis Al  primarily decreases root proliferation of plants that result in poor root \n\n\n\nintroduction of acid-tolerant crops is necessary for optimal crop production in \nacid soils.\n\n\n\n\n\n\n\n\nPlant genotypic characteristics are involved in plant tolerance to Al (Kochian \net. al.\n\n\n\ntolerance to Al. Phosphorous appears to play a role in improving Al-tolerance \nin plants. However, little is known about the mechanisms by which P alleviates \n\n\n\ninteractions between P and Al in an attempt to improve understanding of Al-\ntolerance in plants.\n\n\n\nACKNOWLEDGEMENT\nThe author is thankful to the Australian government and La Trobe University \n\n\n\nfor student support to attend 19th\n\n\n\nREFERENCES\n\n\n\nJournal of Plant Nutrition\n\n\n\ngenotypes under phosphorus limitation. Journal of Applied Sciences. 6: 854-\n859.\n\n\n\n transport \nby Arabidopsis root hairs at low pH. Australian Journal of Plant Physiology. \n\n\n\nHandbook of Soil Acidity\nDekker, Inc. \n\n\n\nnitrogen cycling with emphasis on legume based pastures. Plant and Soil.\n\n\n\nresponses. Physiologia Plantarum.\n\n\n\n\n\n\n\n\nAcid Tolerance Mechanisms in Soil Grown Plants\n\n\n\nBouma D, E.J. Dowling and D.J. David. 1981. 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"\n\nINTRODUCTION\nTermites are destructive polyphagous insect pests, damaging household and \nfinished products as well as plants such as sugarcane, millet, barley, and rice \n(Upadhyay et al. 2010). Of the 3106 identified termites, 363 are invasive species \n(Krishna et al. 2013). Termite infestation caused losses of between USD22 billion \nto USD40 billion in global property and annual losses of about USD400 million \nin Southeast Asia from 2007 to 2012 (Rust and Su 2012; Lee 2007). Subterranean \ntermites attack accounted for 90% of total economic loss and 70% of building \ndamages annually.  (Kuswanto et al. 2015). Twelve different subterranean \ntermite species are identified in Malaysia and Singapore comprising of genera \nsuch as coptotermes, macrotermes, microtermes, globitermes, odontotermes, \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 33-48  (2020)                         Malaysian Society of Soil Science\n\n\n\nEffect of Different Temperatures on the Degradation Rate \nand Half-Life of Termiticides in Tropical Soils under \n\n\n\nLaboratory Condition\n\n\n\nMohd Fawwaz Mohd Rashid and Abdul Hafiz Ab Majid*\n\n\n\nHousehold and Structural Urban Entomology Laboratory, Vector Control Research \nUnit, School of Biological Sciences, Universiti Sains Malaysia, 11800 Minden, \n\n\n\nPenang, Malaysia\n\n\n\nABSTRACT\nSoil termiticide treatment is a fundamental method to control termite population \nand infestation by creating a continuous barrier surrounding the structures. \nDissipation of termiticides depends on half-life, degradation rate, leaching activity, \nand storage method. The use of termiticides without understanding their fate would \nlead to environmental contamination. This study determined the degradation rate \nand half-life of three commercially available termiticides (bifenthrin, fipronil, and \nimidacloprid) in soils having different textures, a sandy loam and loamy sand, \nunder a laboratory setting. The remaining termiticides in the soils were extracted \nand analysed using an Ultra-Performance Liquid Chromatography (UPLC) \nsystem. It was found that bifenthrin had the highest half-life (166.88 days) and \nthe lowest degradation rate (4.28ppm/day),  compared to fipronil (56.05 days and \n5.43 ppm/day) and imidacloprid (50.02 days and 5.46 ppm/day). On account of the \nhigh half-life, lower degradation rates, and good soil bonding capacities, fipronil \nand bifenthrin are recommended as termite control in this study. These features \nmake fipronil and bifenthrin termiticides suitable for buildings environmental \nprotection. \n\n\n\nKey words: Degradation rate, half-life, imidacloprid, fipronil, bifenthrin\n\n\n\n___________________\n*Corresponding author : E-mail: abdhafiz@usm.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202034\n\n\n\nschedorhinotermes, and microcerotermes. They can be easily found inside and \naround buildings and structures (Lee et al. 2007). \n Chemical termiticides, mainly soil termiticides, make up nearly 90% \nof all the subterranean control products, with bifenthrin and imidacloprid being \nsignificant products (Koehler et al. 2011). Properly applied soil-termiticide \ntreatment should provide adequate protection for at least five years (Richman \net al. 2006). The effectiveness of soil termiticide treatment can vary by locality \n(Ramakrishnan et al. 2000), and termiticide susceptibility can differ by species \n(Dhang 2011). Two critical factors in the determination of the efficacy of the soil \ntreated with termiticides are toxicity and the mode of action.\n Imidacloprid has a half-life of between 2.01 and 229 days (Anhalt et al. \n2008; Fossen 2006; Oi 1999; Rouchaud et al. 1994; Sanyal et al. 2006; Sarkar et \nal. 2001). The half-life of fipronil ranges from 2 to 342 days (Connelly 2001; Lin \net al. 2009a; 2009b; Ngim and Crosby 2001; Shuai et al. 2012; Ying and Kookana \n2006). Fipronil may degrade to its primary metabolites in the form of sulfone \noxidation, sulphide reduction, amide hydrolysis, and dessulfinyl photodegrade \nphotolysis (Bobe et al. 1998; Hainzl and Casida 1996; Ngim and Crosby 2001; \nYing and Kookana 2002). Meanwhile, previous studies report that the half-life \nof bifenthrin ranges from 122 to 390 days (Fecko 1999; Mulrooney et al. 2006), \nwhile  bifenthrin is stable in soil with a high pH, and degrades at a slow rate \n(Kamble and Saran 2005). \n Treatment of soil with termiticide can be performed during pre- and \npost-construction (Lee 2002), with post-construction treatment considered as \ndominant, representing 52% of the total treatment (Lee 2002). Soil treatment \nbefore construction involves the base of the concrete plate, whereas soil treatment \nafter construction involves structural perimeter treatment (Peterson 2007). The \nminimum termiticide residue in the soil is between 2.85 years and 5.4 years, in \nboth pre-and post-construction. \n Termites must be exterminated, and structures must be protected against \ntermite invasion (Tsunoda 2005). Non-repellent termiticides are thus more \nfavourable than repellent termiticides, which allow poisoned termites to transfer \nthe toxic substance to members of the colony (Hu 2005). Soil treatment creates \nan environment barrier with no impact on the population of termites (Forschler \nand Jenkins 2000). Termiticides in soil may degrade overtime, and the rate of \ndegradation depends on environmental factors such as humidity, temperature, and \nsoil pH (Wiltz 2012). Thus, the estimation of degradation on different commercial \ntermiticides under field conditions could provide further useful information on \nsoil termiticide degradation rate.\n Many researchers around the world have studied the fate of bifenthrin, \nfipronil, and imidacloprid but these subject areas have not been carried out in a \ntropical climate. Therefore, the objectives of this study were to determine the \ndegradation rates and half-life of bifenthrin, fipronil, and imidacloprid in different \nsoils (sandy loam and loamy sand) at different temperatures (30\u00b0C and 40\u00b0C).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 35\n\n\n\nMATERIALS AND METHODS\n\n\n\nSoil Sampling\nSoil samples were collected from two sites, i.e. Durian Valley, Universiti Sains \nMalaysia (USM; 5\u00b021.35\u2019N; 100\u00b018.16\u2019E) for sandy loam and Teluk Bahang, \nPenang (5\u00b026.47\u2019N; 100\u00b013.04\u2019E) for loamy sand. The two types of soil chosen \nwere based on the suggestion of Ab Majid and Hassan (2013) that 44% of termites \nin Penang are found in loamy sand and 56% in sandy loam soil. Only two soil \ntypes, namely sandy loam and loamy sand, have been recorded with termites. \nThe soils were taken from the top layer (A-horizon) at approximately 10 cm. The \nremaining debris such as stones, vegetation, and macrofauna were removed. The \nsoils were air-dried at room temperature (20\u201325\u00b0C). The soils were then sieved \nthrough a 2-mm sieve, stored at ambient temperature, and were kept air-dried. The \nsoils were subsequently analysed for particle size, pH, and organic matter content. \nSoil pH was determined using a pH meter (HANNA HI 8424, Romania). The soils \nwere mixed with distilled water at a ratio of 1:2 and left overnight to obtain the pH \nvalue (Ab Majid and Hassan 2013).\n\n\n\nSoil Properties\nThe soil samples were first analysed for particle size, pH, and content of organic \nmatter. A method proposed by Bouyoucus (1962) was applied to the soil texture. \nApproximately 50 g of the soil tested was mixed in a 500 mL beaker with 100 \nmL of 6% hydrogen peroxide (H2O2). The mixture was left overnight at room \ntemperature. The beaker was then placed on a hot plate at 90\u00b0C for 10 min, \nfollowed by the addition of 50 mL of 1M NaOH. The mixture was topped up with \ndistilled water to 400 mL and left for 20 min. Next, the mixture was stirred using \na magnetic stirrer for 10 min. The mixture was subsequently transferred into a 1L \nmeasuring cylinder, followed by the addition of 1L of distilled water. The mixture \nwas allowed to reach thermal equilibrium, at which point the temperature was \nrecorded. The measuring cylinder was gently tilted several times for the contents \nto be thoroughly mixed. The hydrometer was immersed in the mixture, and the \nreading was recorded after 40 sec. This step was repeated after 2 h. Readings from \nthe hydrometer and thermometer were used to obtain the percentage of sand, silt, \nand clay. Lastly, soil texture was determined using the United States Department \nof Agriculture (USDA) textural triangle. Soil pH was also determined using a pH \nmeter by mixing the soil with distilled water at a ratio of 1:2 and left overnight \n(Ab Majid and Hassan 2013). Organic matter content was obtained using a loss \nof ignition technique.\n\n\n\nTermiticides\nThe termiticides used in this study are representatives of three chemical classes: \nchloronicotinyl (imidacloprid, Prothor), phenyl pyrazole (fipronil, Chalcid 5.0), \nand pyrethroid (bifenthrin, Maxxthor). Formulated products, i.e. Prothor 200 SC \n(Ensystex, MAlaysia Sdn. Bhd., Kuala Lumpur), Maxxthor 100 SC (Ensystex, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202036\n\n\n\nMalaysia Sdn. Bhd., Kuala Lumpur), and Chalcid 5.0 SC (Hextar Chemicals Sdn. \nBhd., Selangor, Malaysia) were purchased from a local distributor.\n\n\n\nSoil Treatment\nA stock solution for each termiticide was prepared in 1000 mL deionised distilled \nwater. Three termiticide concentrations were prepared: high (2000 ppm), medium \n(1000 ppm), and low (500 ppm). Then, 100 mL of each termiticide from the stock \nsolution was applied to 1000 g of soil (sandy loam and loamy sand) and were \nfilled into two plastic containers (1 L). Each plastic container was placed in the \noven at temperatures of 30\u00b0C and 40\u00b0C. Each concentration was replicated thrice. \nFor control, 1000 g of the soil samples were treated with 100 mL of deionised \nwater. The soil samples were collected during the first, third, ninth, and twelfth \nmonth for analysis. \n\n\n\nSoil and Residual Analysis\nTechnical grade standards of bifenthrin (98.8%), fipronil (97.9%) (Sigma-Aldrich, \nMalaysia), and imidacloprid (99.5%, Chem Service) were used. Acetonitrile \nHPLC grade (Baker) was used as a solvent to dissolve all termiticides. HPLC \ngrade acetonitrile and ultra-pure water from a Milli-Q (Millipore Asia Limited, \nSelangor, Malaysia) purification system were used as the mobile phase in the \nchromatographic analysis.\n Soil samples (previously stored in a freezer below 0\u00b0C) were air-dried for \n24 h. Ten g of the soil samples for each replicate was weighed in a 200 mL conical \nflask followed by the addition of 40 mL of acetonitrile. The flasks were covered \nwith aluminium foil and placed in a shaker maintained at 20\u00b0C and agitated at 200 \nrpm overnight. The samples were allowed to stand for 1 h to allow soil particles to \nsettle. Then, 1.5 mL of clear supernatant was filtered through a 3-cc glass syringe \nequipped with a 0.2 \u00b5m Acrodisc\u00ae Syringe Filter into a 2.0 mL microcentrifuge \ntube. Aliquots were centrifuged (Eppendorf centrifuge 5424) at 12 000 rpm for \n20 min. One mLof the supernatant was transferred into a 2 mL auto-injector vial, \nsealed with a PTFE-lined screw cap after passing through a 3-cc glass syringe. \nSamples were analysed after the extraction.\n Extracted termiticides were separately analysed using UPLC. Data \ncollection and peak analysis were performed using the Waters Acquity UPLC \ndetected by a photo-diode array (PDA) connected to a computer. A single UPLC \nBEH C18 column (1.7\u00b5m i.d, 2.1mm x 100mm) was used for the separation. \nInjection volume was set at 5.0 \u00b5L, and the flow rate at 1.0mL/min.\n\n\n\nDegradation Rate and Half-life of Termiticides\nThe termiticide residue was measured following the California Department of \nFood and Agriculture (CDFA 2011) standard:\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 37\n\n\n\n (Spl H)(Std C)(Std V)(V)\n     R=  ------------------------------ ,\n (Std H)(Spl V)(Spl W)\n\n\n\nwhere, R is termiticide residue of part per million (ppm), Spl H is the sample \npeak height area, Std C is the standard concentration (ppm), Std V is the standard \nvolume injected (\u00b5l), V is the volume after the injection (ml), Std H is the standard \npeak height area, Spl V is the sample volume injected (\u00b5l), and Spl W is the sample \nweight (g).\n The half-life of a substance was calculated according to Ong et al. (2016) \nand the degradation rate of termiticides was calculated based on the decrease in \nconcentrations between the sampling time (Lin et al.  2008; Mahiudddin et al.  \n2014). Statistical analysis was performed on IBM SPSS Statistics Version 22. \nIn the degradation study, a 2-way ANOVA was performed with 99% confidence \nlimit. A factorial analysis of variance (ANOVA) was performed using termiticide, \nconcentration, temperature, and soil types as independent variables to determine \nthe significant differences between the above independent variables and on \ntermiticide residual (dependent variable).  \n\n\n\nRESULTS\nSandy loam had a higher percentage of clay (16.4%) compared to loamy sand \n(11%),while loamy sand had a higher percentage of sand (84.6%) and silt (4.4%) \ncompared to sandy loam (Table 1). Sandy loam appeared to be slightly acidic \n(pH= 4.4) compared to loamy sand (pH= 4.8,Table 1). \n The degradation rate of termiticides was more significant at a high level \nof concentration (2000 ppm) compared to the medium (1000 ppm) and low \n(500 ppm) concentrations. Meanwhile, the half-life of termiticides tested in the \nlaboratory study showed no clear pattern (Table 2). \n\n\n\n19 \n \n\n\n\nTABLE 1 446 \n\n\n\nSoil characteristics for sandy loam and loamy sand used in this degradation study 447 \n(mean\u00b1standard deviation) 448 \n\n\n\nCharacteristics Sandy loam Loamy sand \n\n\n\npH 4.4\u00b10.05 4.8\u00b10.04 \n\n\n\nOrganic matter content (%) 11.4\u00b10.14 8.5\u00b10.45 \n\n\n\nSand (%) 80.0 84.6 \n\n\n\nClay (%) 16.4 11.0 \n\n\n\nSilt (%) 3.6 4.4 \n\n\n\n 449 \n\n\n\n  450 \n\n\n\nTABLE 1\nSoil characteristics for sandy loam and loamy sand used in this degradation study \n\n\n\n(mean\u00b1standard deviation)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202038\n\n\n\n The termiticide residuals in the laboratory study are depicted in Figure 1. \nOver time, the residual of all termiticides decreased. Bifenthrin in the sandy loam \nat 30\u00b0C (SLBH30) showed the highest residual in the first month (2327.94 ppm), \nwhile almost all imidacloprid indicated a low residual in the first month (average \n32.06 ppm). Bifenthrin remained high compared to fipronil and imidacloprid \nduring the 12 months. Factorial ANOVA analysis showed that the difference \nbetween the month and termiticide degradation was significant (F=77.38; df=3; \nP=0.00). The residues in the first month were higher, decreasing progressively \nuntil the twelfth month. There was also a substantial difference in the termiticides \ntested (F= 176.66; df=2; P=0.00). Bifenthrin residual in the soil remained high 20 \n \n\n\n\nTABLE 2 451 \nDegradation rate and half-life of bifenthrin, fipronil and imidacloprid in the laboratory 452 \n\n\n\ndegradation study 453 \nSoil Termiticide Concentration  \n\n\n\nlevel \nTemperature \n\n\n\n(\u2070C) \nDegradation \n\n\n\nrate (ppm/day) \nHalf-life \n(days) \n\n\n\nSandy \nloam \n\n\n\nBifenthrin High 30 5.34 70.33 \n40 4.28 166.88 \n\n\n\nMedium 30 2.67 71.54 \n40 2.45 114.26 \n\n\n\nLow 30 1.31 82.92 \n40 1.26 100.51 \n\n\n\nFipronil  High 30 5.49 33.19 \n40 5.43 56.05 \n\n\n\nMedium 30 2.75 36.52 \n40 2.71 57.12 \n\n\n\nLow 30 1.37 40.60 \n Imidacloprid  High 30 5.49 33.19 \n\n\n\n40 5.46 50.02 \nMedium 30 2.75 36.52 \n\n\n\n40 2.74 45.25 \nLow 30 1.37 40.60 \n\n\n\n40 1.36 55.51 \nLoamy \nsand \n\n\n\nBifenthrin   High 30 5.35 69.41 \n40 4.28 166.88 \n\n\n\nMedium 30 2.65 75.42 \n40 2.45 114.26 \n\n\n\nLow 30 1.30 88.10 \n40 1.26 100.51 \n\n\n\nFipronil  High 30 5.49 33.19 \n40 5.44 54.10 \n\n\n\nMedium 30 2.75 36.52 \n40 2.72 55.59 \n\n\n\nLow 30 1.37 40.60 \n40 1.36 53.13 \n\n\n\nImidacloprid High 30 5.49 33.19 \n 40 5.39 63.94 \n\n\n\nMedium 30 2.75 36.52 \n40 2.69 65.46 \n\n\n\nLow 30 1.37 40.60 \n40 1.35 62.38 \n\n\n\n 454 \n\n\n\n  455 \n\n\n\nTABLE 2\nDegradation rate and half-life of bifenthrin, fipronil and imidacloprid in the laboratory \n\n\n\ndegradation study\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 39\n\n\n\n22\n \n\n\n\n  \n46\n\n\n\n0 \n\n\n\n \n46\n\n\n\n1 \n\n\n\nFi\ngu\n\n\n\nre\n 1\n\n\n\n. T\nhe\n\n\n\n d\neg\n\n\n\nra\nda\n\n\n\ntio\nn \n\n\n\npa\ntte\n\n\n\nrn\ns \n\n\n\nof\n te\n\n\n\nrm\niti\n\n\n\nci\nde\n\n\n\ns \nte\n\n\n\nst\ned\n\n\n\n in\n d\n\n\n\niff\ner\n\n\n\nen\nt s\n\n\n\noi\nl t\n\n\n\nyp\nes\n\n\n\n (s\nan\n\n\n\ndy\n lo\n\n\n\nam\n a\n\n\n\nnd\n lo\n\n\n\nam\ny \n\n\n\nsa\nnd\n\n\n\n) a\nnd\n\n\n\n te\nm\n\n\n\npe\nra\n\n\n\ntu\nre\n\n\n\n (3\n0\u2070\n\n\n\nC\n a\n\n\n\nnd\n 4\n\n\n\n0\u2070\nC\n\n\n\n) \n46\n\n\n\n2 \nw\n\n\n\nith\nin\n\n\n\n 1\n2 \n\n\n\nm\non\n\n\n\nth\ns f\n\n\n\nor\n la\n\n\n\nbo\nra\n\n\n\nto\nry\n\n\n\n d\neg\n\n\n\nra\nda\n\n\n\ntio\nn \n\n\n\nst\nud\n\n\n\ny \n46\n\n\n\n3 \n\n\n\n*S\nL:\n\n\n\n sa\nnd\n\n\n\ny \nlo\n\n\n\nam\n; L\n\n\n\nS:\n lo\n\n\n\nam\ny \n\n\n\nsa\nnd\n\n\n\n; F\n: f\n\n\n\nip\nro\n\n\n\nni\nl; \n\n\n\nB\n: b\n\n\n\nife\nnt\n\n\n\nhr\nin\n\n\n\n; I\n: I\n\n\n\nm\nid\n\n\n\nac\nlo\n\n\n\npr\nid\n\n\n\n; H\n: h\n\n\n\nig\nh \n\n\n\nco\nnc\n\n\n\nen\ntra\n\n\n\ntio\nn;\n\n\n\n M\n: m\n\n\n\ned\niu\n\n\n\nm\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn;\n\n\n\n L\n: l\n\n\n\now\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\n46\n4 \n\n\n\n-5\n000\n\n\n\n50\n0\n\n\n\n10\n00\n\n\n\n15\n00\n\n\n\n20\n00\n\n\n\n25\n00\n\n\n\nConcentration (ppm) \n\n\n\n1m\n3m\n\n\n\n9m\n12\n\n\n\nm\n\n\n\nFi\ngu\n\n\n\nre\n 1\n\n\n\n. T\nhe\n\n\n\n d\neg\n\n\n\nra\nda\n\n\n\ntio\nn \n\n\n\npa\ntte\n\n\n\nrn\ns o\n\n\n\nf t\ner\n\n\n\nm\niti\n\n\n\nci\nde\n\n\n\ns t\nes\n\n\n\nte\nd \n\n\n\nin\n d\n\n\n\niff\ner\n\n\n\nen\nt s\n\n\n\noi\nl t\n\n\n\nyp\nes\n\n\n\n (s\nan\n\n\n\ndy\n lo\n\n\n\nam\n a\n\n\n\nnd\n lo\n\n\n\nam\ny \n\n\n\nsa\nnd\n\n\n\n) a\nnd\n\n\n\n te\nm\n\n\n\npe\nra\n\n\n\ntu\nre\n\n\n\n (3\n0\u00b0\n\n\n\nC\n a\n\n\n\nnd\n 4\n\n\n\n0\u00b0\nC\n\n\n\n) \nw\n\n\n\nith\nin\n\n\n\n 1\n2 \n\n\n\nm\non\n\n\n\nth\ns f\n\n\n\nor\n la\n\n\n\nbo\nra\n\n\n\nto\nry\n\n\n\n d\neg\n\n\n\nra\nda\n\n\n\ntio\nn \n\n\n\nst\nud\n\n\n\ny \n\n\n\n*S\nL:\n\n\n\n sa\nnd\n\n\n\ny \nlo\n\n\n\nam\n; L\n\n\n\nS:\n lo\n\n\n\nam\ny \n\n\n\nsa\nnd\n\n\n\n; F\n: fi\n\n\n\npr\non\n\n\n\nil;\n B\n\n\n\n: b\nife\n\n\n\nnt\nhr\n\n\n\nin\n; I\n\n\n\n: I\nm\n\n\n\nid\nac\n\n\n\nlo\npr\n\n\n\nid\n; H\n\n\n\n: h\nig\n\n\n\nh \nco\n\n\n\nnc\nen\n\n\n\ntra\ntio\n\n\n\nn;\n M\n\n\n\n: m\ned\n\n\n\niu\nm\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn;\nL:\n\n\n\n lo\nw\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202040\n\n\n\ncompared to imidacloprid and fipronil. Termiticides did affect the termiticide \nresidual during the experiment. Termiticides concentration revealed a significant \ndifference between termiticides (F= 19.06; df= 2; P= 0.00).  The degradation rate \nof termiticides was higher at a high concentration level compared to medium and \nlow concentrations levels.\n The combination of month, termiticide and temperature significantly \naffected termiticide degradation (F= 5.69, df= 6, P= 0.00) although temperature \ndid not affect the degradation individually (Table 3). A lower termiticide \nconcentration at a higher temperature (40\u00b0C) took a more prolonged time (months) \nto degrade. Furthermore, the concentration of the termiticide was also dependent \non the type of termiticide used. The concentration of bifenthrin was higher in the \nlower temperature (30\u00b0C) in the first month compared to fipronil and imidacloprid \n(Figure 1).\n\n\n\n21 \n \n\n\n\n 456 \n\n\n\nTABLE 3 457 \n\n\n\nEffects of month, termiticide and layer on degradation rate in the field degradation study 458 \n\n\n\nSource df Mean square F p value \nMonth 4 2190.81 985.73 0.001 \nTermiticide 2 5983.84 2692.36 0.001 \nLayer 1 0.24 0.11 0.74 \nMonth * Termiticide 8 667.40 300.29 0.001 \nMonth * Layer 4 2.18 0.98 0.43 \nTermiticide * Layer 2 2.38 1.07 0.35 \nMonth * Termiticide * \nLayer 8 2.20 0.99 0.45 \n  459 \n\n\n\nTABLE 3\nEffects of month, termiticide and layer on degradation rate in the field degradation study\n\n\n\n The termiticide degradation pattern in sandy loam soil within 12 months \nis illustrated in Figure 2. In all the months tested, bifenthrin showed the highest \nconcentration compared to imidacloprid and fipronil. The concentration of \nbifenthrin decreased substantially in the third month after treatment. During the \nfirst and last months of the experiment, the level of imidacloprid was the lowest \ncompared tobifenthrin and fipronil. Loamy sand soil showed similar patterns with \nsandy loam in terms of termiticide degradation. During the first three months, \nbifenthrin showed a higher rate of degradation and gradually degraded over \ntime. Bifenthrin also demonstrated an increased level of soil residue. In contrast, \nimidacloprid showed the lowest concentration from the first month to the twelfth \nmonth (Figure. 3).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 41\n\n\n\nFigure 2. Degradation patterns of termiticides (bifenthrin, fipronil and imidacloprid)\nin sandy loam soil in the laboratory degradation study\n\n\n\nFigure 3.  Degradation patterns of termiticides (bifenthrin, fipronil and imidacloprid)\nin loamy sand soil for laboratory degradation study\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202042\n\n\n\nDISCUSSION\nA laboratory degradation study found that bifenthrin had the highest concentration \nunder all the conditions tested over time, compared to fipronil and imidacloprid. \nBifenthrin was the most persistent termiticide as it decreased by less than 50% over \nthe year (Horwood  2007). In previous studies, the half-life for field degradation \nwas reported to be 122 to 345 days (Fecko 1999) and 13 months (Mulrooney et al. \n2006). A study by Manzoor and Pervez (2017) in sandy loam and sandy clay loam \nsoils following three years of application also reported bifenthrin as being stable \nover time. However, Su et al. (1999) and You et al. (2013) obtained contradictory \nresults concerning bifenthrin. Bifenthrin was reported to possess less protection \namongst other pyrethroids (Su et al. 1999), and dissipated at 84.6% for 30 days \nafter treatment (You et al. 2013). Several factors affected the degradation process \nof pyrethroids such as the types of pyrethroids, pH and temperature, soil types, \nclimate, and size of microbial populations (Gupta and Gajbhiye 2008; Tariq et al. \n2017). Soil microbial activity has a vital role in the degradation of termiticides \n(Roy and Singh 2006; Singh et al. 2008). According to Sharma and Singh (2012), \nbifenthrin dissipates more quickly compared to sterile soil. The half-life values \nin the sterile and non-sterile soils were 330 and 147 days, respectively. This vast \ndifference in the half-life values in both soils showed that microbial degradation \nwas substantial in bifenthrin dissipation (Sharma and Singh 2012). Analysis of the \nHPLC showed that bifenthrin had a higher persistence than fipronil in sandy loam \nand sandy clay loam soils (Manzoor and Pervez 2017).\n The half-life of termiticides varied according to various factors. The half-\nlife for fipronil was reported to be within 2 to 342 days (Connelly 2001; Lin et \nal. 2009a, 2009b; Ngim and Crosby 2001; Shuai et al. 2012; Ying and Kookana \n2006). Initial fipronil concentrations influenced the rate of fipronil degradation. \nFipronil rapidly dissipated at a higher concentration and disappeared completely \nat a lower concentration (Manzoor and Pervez 2017). Therefore, the application \nof fipronil at the highest label rate caused low degradation rates (Manzoor and \nPervez 2017; Saran and Kamble 2008). Fipronil tested in the sandy loam and \nsandy clay loam soils had a higher half-life, i.e. 270 to 555 days (Manzoor and \nPervez 2017). Accordingly, Zhu et al. (2004) have also noted a slower degradation \nof fipronil in clay loam soil.\n The fipronil half-life decreases with increasing temperature (Ying and \nKookana 2002; Zhu et al. 2004). An extensive study has revealed that fipronil \ndegradation is faster at 35\u00b0C than 25 \u00b0C (Zhu et al. 2004). Temperature could, \ntherefore, affect fipronil degradation. Another factor in the degradation of fipronil \nin the soil is microbial activities (Ying and Kookana 2002; Zhu et al. 2004). \nSoil microbes consume fipronil as a source of energy, carbon, and/or nitrogen \nduring the microbial degradation process. Inhibition of microbial soil activities \nmay, therefore, prolong fipronil persistence at high application rates (Ying and \nKookana 2006).\n Imidacloprid was less persistent compared to fipronil and bifenthrin. \nHorwood (2007) demonstrated the least persistence in imidacloprid, decreasing \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 43\n\n\n\nby about 95% by  the end of the experiment. The half-life report was between \n2.01 and 229 days (Anhalt et al. 2008; Fossen 2006; Oi 1999; Rouchaud et al. \n1994; Sanyal et al. 2006; Sarkar et al. 2001). Gold et al. (1996) have reported \nthat over time, termiticides have become less effective. Gold et al. (1996) further \ndemonstrated that in 180 days, concentrations of all termiticides (chlorpyriphos, \nimidacloprid, and deltamethrin) in the trials decreased significantly (wt:wt basis \nin soil). \n In this study, bifenthrin was persistent compared to fipronil and \nimidacloprid. The above mentioned two termiticides showed slightly different \nhalf-life values, with bifenthrin being between 122 to 345 days (Fecko 1999), \nand fipronil between 2 to 342 days (Connelly 2001; Lin et al. 2009a, 2009b; \nNgim and Crosby 2001; Shuai et al. 2012; Ying and Kookana 2006). Therefore, \nthe different results achieved between field studies and laboratory studies are \ndetermined by the half-life of the termiticide. Furthermore, the method used for \ntermiticide treatment also influenced the termiticide concentration. For the field \nstudy, termiticides were treated by using the trenching method at 2500 ppm, while \nin the laboratory study, the termiticide was treated in a small amount and placed \nin plastic containers at 2000 ppm, 1000 ppm, and 500 ppm.\n\n\n\nCONCLUSION\nIn the laboratory study, bifenthrin had a higher half-life and a lower rate of \ndegradation than fipronil and imidacloprid. Thus, fipronil and bifenthrin were \npersistent compared to imidacloprid. Soil types and temperature did not have \na significant impact on the degradation and bioavailability of the termiticides \ntested in this study. In this study, the percentage of clay in sandy loam did not \nshow much difference from the loamy sand. Therefore, a study on other types of \nsoils with a higher difference in clay percentage might provide new information \nconcerning termiticide degradation. Besides, a degradation study should also be \ndone on other commercially available termiticides such as chlorantraniliprole and \nchlorpyrifos. In addition, the bioavailability of termiticides should be tested on \nother beneficial insects, for instance, bees, ants and butterflies rather than only on \ntermites.\n\n\n\nACKNOWLEDGEMENT\nThe project was carried out under the (USM) Research University Grant (RU) \n1001/PBIOLOGI/811241\n \n\n\n\nREFERENCES\nAbbott, W. S. 1925. 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"\n\nINTRODUCTION\nSoil compaction induced by agricultural machinery sometimes extends to the plow \nlayer. This may alter the pore system and consequently reduce water infiltration \nthat eventually results in yield reduction. Compaction is one of the major threats \nto soil quality as it reduces pore volume and modifies pore geometry (Tolon-\nBecerra et al. 2012). Soil compaction is not only associated with agriculture but \nalso with forest harvesting, amenity land use, pipeline installation, land restoration \nand wildlife trampling (Batey 2009). Military training exercises using heavy-\ntracked vehicles is an intensive land-use activity that may result in vegetation \ndisturbances and soil compaction (Lindsey et al, 2012). Also agricultural lands \nare recently under pressure of heavy machinery (weighing as much as 30 to 60 \nMg) used for constructional projects and activities (Berli et al. 2004).\n\n\n\nThe compaction of soil affects nearly all properties of the soils: physical, \nchemical and biological. Soil compaction alters its structure by crushing aggregates \nor combining them into larger units, increasing its bulk density and decreasing \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 47- 61 (2017)                         Malaysian Society of Soil Science\n\n\n\nTractor Wheel Compaction Effect on Soil Water Infiltration, \nHydraulic Conductivity  and Bulk Density \n\n\n\nNooshin Ramezani,* G. Abbas Sayyad  and  A. Rahman Barzegar\n\n\n\nDepartment of Soil Science, College of Agriculture, Shahid Chamran \nUniversity of Ahwaz, Ahwaz, Iran.\n\n\n\nABSTRACT\nSoil compaction alters the soil pore system, and may adversely affect the availability \nof water and air to plants and microorganisms. This study was conducted on a \nloamy soil to investigate water flow path using dye patterns. Five treatments \nwere compared: control (no traffic), single, two, four and eight passages in three \nreplications in the field. A dye tracer of Brilliant Blue FCF solution was uniformly \nadded to each treatment for eight hours at a rate of 5 mm/h using a rain simulator. \nFlow paths were photographed with a digital camera. The images were processed \nby digital image analysis in order to analyse the spatial distribution of the stained \narea. Results indicated that induced compaction significantly altered the hydraulic \nproperties of the soil. Highest impact was observed at 0-20 cm soil depth; no \nvisible changes were observed in soil physical properties for subsoil. Results also \nshowed that stained area as index of water infiltration was reduced by 77.5% in \neight times passages treatment compared to control. Dye infiltration was uniform \nin control treatment while in the four and eight times tractor traffic treatments, dye \ninfiltration was low on the surface and preferential flow of dye was observed in \ndeeper parts of the profile. \n\n\n\nKeywords: Dye tracer, image processing, rainfall simulator.\n\n\n\n___________________\n*Corresponding author : E-mail: Ramezani_nooshin@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201748\n\n\n\nthe number of coarser pores leading to reduced permeability of water and air. It \nalso increases surface runoff, erosion, flooding and reduces groundwater recharge \n(Batey 2009 ).\n\n\n\nSoil compaction may be assessed in terms of parameters such as bulk density \nand porosity. Although these parameters may be used to easily evaluate soil \ncompaction, they do not evaluate changes in the pore size and continuity due to \nsoil compaction. It is practically difficult to predict the effect of soil compaction \non water movement only by the above measured indicators. Therefore, techniques \nhave been  developed to directly or indirectly measure the pore volume and the \nchanges in soil structure (Sander and Gerke 2007). Tracing experiment is a method \nto determine the effect of soil compaction on water movement. Mooney and \nNippatsuk (2003) quantified the effects of soil compaction on water flow using \ndye tracers and image analysis and reported that visual techniques of dye tracing \nand image analysis could enable improved understanding of flow pathways of soil \nwater associated with soil compaction.\n\n\n\nDye tracing experiment is a suitable technique has been used by researchers \nin recent years for assessing flow patterns (Flury and Fluhler 1994). Spatial \ndistribution of a dye tracer may be used as an effective means to study compacted \nsoil layer. Kulli et al. (2003) investigated the flow patterns of stained areas for the \nsoils of similar plots before and after compaction, and reported that the traffic of \nheavy machinery significantly modified infiltration patterns. \n\n\n\nImage processing seems to be a suitable technique to show stained paths and \nquantify flow patterns in soils (Markus and Fl\u00fchler 2004). Dye tracing infiltration \nmethods have been used together with image processing to evaluate the role of \nsoil structure as well as preferential flow (Flury and Fl\u00fchler 1994; Ghodrati and \nJury 1990). Various tracers were used in different experiments to show water \nmovement. The more common type is Brilliant Blue FCF (Sander and Gerke \n2007), because it is not toxic, can easily be seen in the soil, and is not adsorbed \nby soil particles (Ohrstrom et al. 2002). Researchers (Germa\u00b4n Heis  and Flury  \n2000; Turpin et al., 2007) tested Brilliant blue FCF for toxicity, adaptation and \nmovement. They reported that this tracer was one of the best tracers to study water \nflow in soils.\n\n\n\nA few studies have quantitatively shown the effect of soil compaction on \nwater flow. Low organic matter of soils and frequent agricultural machinery \ntraffic in different stages of crop production are considered important factors in \nagricultural compaction.  \n\n\n\nThe aim of this study was to quantify the effect of compaction on water \nflow through the soil, especially by examining water preferential flow using a dye \ntracer and to compare it with other indicators such as bulk density and porosity.\n\n\n\nMATERIALS AND METHODS\nThe experiment was conducted in Ahwaz, southwest of Iran (31o20\u2019N; 48o40\u2019E) \nat an elevation of 20 m above sea level. A factorial design experiment was used in \nthe frame of complete randomized blocks with five treatments and six depths and \nthree replications. Some physical properties of soil are shown in Table 1.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 49\n\n\n\nPlots were irrigated prior to implementing the treatments. Therefore, at the time \nof the experiment (tractor passage), soil surface moisture was adjusted to near \nfield capacity. Soil moisture contents at depths of 10, 20, 30, 40, 50 and 60 cm \nwere measured. After irrigation, treatments including 0 (no tractor passage), 1-, \n2-, 4- and 8- time  tractor passages (T0, T1, T2, T4 and T8 respectively) were \nimplemented. A tractor model of MF399 (Table 2) was used.\n\n\n\nOne day after tractor passage, Brilliant Blue FCF solution with concentration \nof 4 g L-1 (Flury and Fl\u00fchler 1994) was added to each treatment for a period \nof 8 hours and intensity of 5 mm hr-1 using a rainfall simulator. The simulation \nspecifications were as follow (Fig. 1): rain intensity of 5 mm/hr, outlet flow of \n330 mLhr-1, and tank volume of 200 L circle with diameter of 160 cm covered by \na rain simulator.\n\n\n\nConsidering the tiny droplets produced by the simulator, the perimeter of \nthe device was covered by a tarp sheet to minimise the effect of wind and other \nexternal factors.\n\n\n\nA profile was dug in each treatment and the flow paths photographed by \na digital camera of high resolution, 24 h after dye application. Pictures were \nanalysed and areas of water flow paths determined. The flow path areas were \ncompared to distinguish the differences of compacted treatments.\n\n\n\nTABLE 1\nSome physical properties of the studied soil\n\n\n\n5 \n\n\n\nA few studies have quantitatively shown the effect of soil compaction on water flow. Low 1 \n\n\n\norganic matter of soils and frequent agricultural machinery traffic in different stages of crop 2 \n\n\n\nproduction are considered important factors in agricultural compaction.   3 \n\n\n\n 4 \n\n\n\nThe aim of this study was to quantify the effect of compaction on water flow through the soil, 5 \n\n\n\nespecially by examining water preferential flow using a dye tracer and to compare it with other 6 \n\n\n\nindicators such as bulk density and porosity. 7 \n\n\n\n 8 \n\n\n\nMATERIALS AND METHODS 9 \n\n\n\nThe experiment was conducted in Ahwaz, southwest of Iran (                ) at an elevation 10 \n\n\n\nof 20 m above sea level. A factorial design experiment was used in the frame of complete 11 \n\n\n\nrandomized blocks with five treatments and six depths and three replications. Some physical 12 \n\n\n\nproperties of soil are shown in Table 1. 13 \n\n\n\nTABLE 1 14 \n\n\n\nSome physical properties of the studied soil 15 \nDepth Sand Silt Clay Soil \n\n\n\ntexture \nBulk \ndensity \n\n\n\nPorosity Organic \nmatter \n\n\n\ncm --------------------%----------------  (Mg m-3) -------------%----------- \n0-10 40.5 30.5 29.0 Loam 1.3 50.1 0.67 \n10-20 47.5 34.0 18.5 Loam 1.3 49.4 0.37 \n20-30 43.5 34.0 22.5 Loam 1.3 49.4 0.30 \n30-40 45.0 28.0 27.0 Loam 1.4 49.0 0.22 \n40-50 51.0 20.0 29.0 Sandy \n\n\n\nclay \nLoam \n\n\n\n1.3 49.4 0.17 \n\n\n\n50-60 65.0 20.0 15.0 Sandy \nloam \n\n\n\n1.4 48.6 0.13 \n\n\n\n 16 \n\n\n\nPlots were irrigated prior to implementing the treatments. Therefore, at the time of the 17 \n\n\n\nexperiment (tractor passage), soil surface moisture was adjusted to near field capacity. Soil 18 \n\n\n\nTABLE 2\nCharacteristics of tractor used for treatments\n\n\n\n6 \n\n\n\nmoisture contents at depths of 10, 20, 30, 40, 50 and 60 cm were measured. After irrigation, 1 \n\n\n\ntreatments including 0 (no tractor passage), 1-, 2-, 4- and 8- time  tractor passages (T0,T1,T2, T4 2 \n\n\n\nand T8        respectively) were implemented. A tractor model of MF399 (Table 2) was used. 3 \n\n\n\nTABLE 2 4 \nCharacteristics of tractor used for treatments 5 \n\n\n\nSpecification Unit \nModel    MF399 \nTractor width 2m \nTractor length 4.3m \nRear tire pressure 1.1 Bar \nFront tire pressure 1.4 Bar \nTractor weight  \n(with full tank) \n\n\n\n3677 kg \n\n\n\nMaximum static load of front axial 49.4 kg \nVelocity 5.7 km h-1 \n\n\n\n 6 \n\n\n\nOne day after tractor passage, Brilliant Blue FCF solution with concentration of 4 g L-1 (Flury 7 \n\n\n\nand Fl\u00fchler 1994) was added to each treatment for a period of 8 hours and intensity of 5 mm/hr 8 \n\n\n\nusing a rainfall simulator. The simulation specifications were as follow (Fig. 1): rain intensity of 9 \n\n\n\n5 mm/hr, outlet flow of 330 mLhr-1, and tank volume of 200 L circle with diameter of 160 cm 10 \n\n\n\ncovered by a rain simulator. 11 \n\n\n\n 12 \n\n\n\nFigure 1. View of the rain simulator in field and laboratory 13 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201750\n\n\n\nMATLAB software was used for image processing. Dye tracer paths were \ntranslated into flow patterns by image processing techniques. Then, the ratio of \nstained surface area to total surface, stained area of each layer to total surface, and \nstained area of each layer to area of that layer were quantitatively measured for \ndifferent depths of soil profile of each treatment. \n\n\n\nOne day after treatments application, bulk density was determined using \nthe sample holders of core sampler and total porosity of the soil was calculated \n(Sultani et al. 2007). Aggregate stability was determined using the wet sieving \nmethods and mean weight diameter (MWD index) calculated (Barzegar et al. \n2004). Soil moisture characteristic curve of each treatment was measured using \npressure plate apparatus (Sultani et al., 2007(2016 in ref list). Measurement of soil \nhydraulic properties is important to improve the understanding of soil physical \nbehaviour (Soracco et al. 2011). Hydraulic properties including number of pores \n(Watson and Luxmoore 1986), hydraulic conductivity and the percent of water \nflowing through the pores were measured using disc infiltrometer at four different \ntensions (h = 0, -3, -5, -15 cm H2O) for each treatment (Ankeny et al. 1991). \nAnalysis of data was performed by MSTATC, version 2.1 developed by Russel \n(1994) and data significance levels were distingushed by LSD tests at p\u22640.05.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPhysical and Hydraulic Properties of the Soil\nSoil moisture before applying treatments was 18.5 \u00b1 1%. Soil textures were \nsimilar in all treatments; loam from 0-40 cm, sandy clay loam from 40-50 cm and \nsandy loam from 50-60 cm. Hydraulic and physical properties of the soil changed \nnotably with increasing the compaction levels. Increasing compaction levels \nresulted in higher soil bulk density and lower total porosity (P\u22640.01 (Table 3). \n\n\n\nFig. 1: View of the rain simulator in field and laboratory\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 51\n\n\n\nWith 8-time tractor passages, average bulk density increased by 5.3% compared \nto control, and the average total pore space decreased by 6%. Major effect of \ncompaction was observed from surface down to a depth of 20 cm (Fig. 2). The \nhighest bulk density and lowest total porosity were at 10-20 cm depth from the \nsurface. Kuht and Reintame, (2004) reported that compaction affects surface and \nnear soil surface characteristics. \n  \n\n\n\n   (a)    (b)\nFig. 2:  (a) Changes of bulk density and (b) totalporosity of different tractor passages; T \n\n\n\nsubscript numbers indicates the number of tractor passes\n\n\n\nControl treatment had the highest MWD (an index of soil structural stability) and \n8-time tractor passage had the lowest. Maximum MWD index was obtained at \ndepths of 20-30 cm. and lowest at depths of 10-20 cm. The surface layer of 0-10 \ncm depth was the least affected due to presence of plant cover and higher organic \nmatter content (Fig. 3). Boizard et al. (2002) reported that compaction changes \nphysical properties of upper soil layers. Barzegar et al. (2004) indicated that \nhigher passages of agricultural machinery may result in the formation of smaller \naggregates especially when the soil is dry.\n\n\n\n\n\n\n\nTABLE 3\nStatistical analysis of compaction effect on bulk density and percent pore space\n\n\n\n8 \n\n\n\n(Ankeny et al. 1991). Analysis of data was performed by MSTATC, version 2.1 developed by 1 \n\n\n\nRussel (1994) and data significance levels were distingushed by LSD tests at p\u22640.05. 2 \n\n\n\n 3 \n\n\n\nRESULTS AND DISCUSSION 4 \n\n\n\nPhysical and Hydraulic Properties of the Soil 5 \n\n\n\nSoil moisture before applying treatments was 18.5 \u00b1 1%. Soil textures were similar in all 6 \n\n\n\ntreatments; loam from 0-40 cm, sandy clay loam from 40-50 cm and sandy loam from 50-60 cm. 7 \n\n\n\nHydraulic and physical properties of the soil changed notably with increasing the compaction 8 \n\n\n\nlevels. Increasing compaction levels resulted in higher soil bulk density and lower total porosity 9 \n\n\n\n(P\u22640.01 (Table 3).  10 \n\n\n\nTABLE 3 11 \nStatistical analysis of compaction effect on bulk density and percent pore space 12 \n\n\n\nSource of variations  \n                    \n\n\n\ndf                                   F-value                     \n\n\n\n   Total porosity (%) Bulk density \nCompaction (c) 4 **79.64 **55.79 \nDepth (d) 5 **76.11 **175.93 \n\n\n\nd *c 20 **30.35 **30.33 \np\u22640.01** 13 \n\n\n\n 14 \n\n\n\nWith 8-time tractor passages, average bulk density increased by 5.3% compared to control, and 15 \n\n\n\nthe average total pore space decreased by 6%. Major effect of compaction was observed from 16 \n\n\n\nsurface down to a depth of 20 cm (Fig. 2). The highest bulk density and lowest total porosity 17 \n\n\n\nwere at 10-20 cm depth from the surface. Kuht and Reintame, (2004) reported that compaction 18 \n\n\n\naffects surface and near soil surface characteristics.  19 \n\n\n\n9 \n\n\n\n 1 \n\n\n\n(a)                                                                (b) 2 \n\n\n\nFigure 2.  (a) Changes of bulk density and (b) totalporosity of different tractor passages; T subscript 3 \nnumbers indicates the number of tractor passes 4 \n\n\n\n 5 \nControl treatment had the highest MWD (an index of soil structural stability) and 8-time tractor 6 \n\n\n\npassage had the lowest. Maximum MWD index was obtained at depths of 20-30 cm. and lowest 7 \n\n\n\nat depths of 10-20 cm. The surface layer of 0-10 cm depth was the least affected due to presence 8 \n\n\n\nof plant cover and higher organic matter content (Fig. 3). Boizard et al. (2002) reported that 9 \n\n\n\ncompaction changes physical properties of upper soil layers. Barzegar et al. (2004) indicated that 10 \n\n\n\nhigher passages of agricultural machinery may result in the formation of smaller aggregates 11 \n\n\n\nespecially when the soil is dry. 12 \n\n\n\n 13 \n\n\n\n-70\n\n\n\n-60\n\n\n\n-50\n\n\n\n-40\n\n\n\n-30\n\n\n\n-20\n\n\n\n-10\n\n\n\n0\n1.3 1.4 1.5 1.6\n\n\n\n(\ncm)\n\n\n\nD\nep\n\n\n\nth\n \n\n\n\n\n\n\n\nBulk density (Mg m-3)  \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT4\n\n\n\nT8\n-70\n\n\n\n-60\n\n\n\n-50\n\n\n\n-40\n\n\n\n-30\n\n\n\n-20\n\n\n\n-10\n\n\n\n0\n0 20 40 60\n\n\n\nD\nep\n\n\n\nth\n (c\n\n\n\nm\n) \n\n\n\nTotal porosity (%) \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT4\n\n\n\nT8\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201752\n\n\n\nThe soil water holding capacity was significantly affected by soil compaction \nlevels (P\u22640.01) (Table 4). Control treatment had the maximum soil water holding \ncapacity. The lowest amount of water stored in the soil was observed in the 8-time \ntractor passage treatment (Fig. 4). \n\n\n\nEffect of water potential on water holding capacity was significant (P\u22640.01). \nAccording to capillary principal, water outflow occurs in pores with different \nradius at variable tensions.\n\n\n\nNumber of pores decrease with increasing compaction levels. The highest \nnumber of pores (large and medium) was measured in control, while the lowest \nnumber occurred in the treatment with 8-time tractor passages (Table 5).\n\n\n\nFig. 3: Changes of MWD with depth of different treatments; T subscript numbers \nindicate the number of tractor passes\n\n\n\n10 \n\n\n\n 1 \n\n\n\nFigure 3. Changes of MWD with depth of different treatments; T subscript numbers indicate the number 2 \nof tractor passes 3 \n\n\n\n 4 \n\n\n\nThe soil water holding capacity was significantly affected by soil compaction levels (P\u22640.01) 5 \n\n\n\n(Table 4). Control treatment had the maximum soil water holding capacity. The lowest amount 6 \n\n\n\nof water stored in the soil was observed in the 8-time tractor passage treatment (Fig. 4).  7 \n\n\n\nTABLE 4 8 \nStatistical analysis of compaction effect on soil water holding capacity at different water 9 \n\n\n\npotentials 10 \nSource of variations               \n               \n\n\n\ndf F-value                               \n\n\n\nCompaction (c) 4 **88.4531 \nwater potential (w) 5 **133022.26 \nc\u00d7w 20 2071.88** \n\n\n\np\u22640.01** 11 \n 12 \n\n\n\n-30\n\n\n\n-25\n\n\n\n-20\n\n\n\n-15\n\n\n\n-10\n\n\n\n-5\n\n\n\n0\n0 0.1 0.2 0.3 0.4\n\n\n\nD\nep\n\n\n\nth\n (c\n\n\n\nm\n) \n\n\n\nT0\n\n\n\n T1\n\n\n\n T2\n\n\n\n T4\n\n\n\nT8\n\n\n\n10 \n\n\n\n 1 \n\n\n\nFigure 3. Changes of MWD with depth of different treatments; T subscript numbers indicate the number 2 \nof tractor passes 3 \n\n\n\n 4 \n\n\n\nThe soil water holding capacity was significantly affected by soil compaction levels (P\u22640.01) 5 \n\n\n\n(Table 4). Control treatment had the maximum soil water holding capacity. The lowest amount 6 \n\n\n\nof water stored in the soil was observed in the 8-time tractor passage treatment (Fig. 4).  7 \n\n\n\nTABLE 4 8 \nStatistical analysis of compaction effect on soil water holding capacity at different water 9 \n\n\n\npotentials 10 \nSource of variations               \n               \n\n\n\ndf F-value                               \n\n\n\nCompaction (c) 4 **88.4531 \nwater potential (w) 5 **133022.26 \nc\u00d7w 20 2071.88** \n\n\n\np\u22640.01** 11 \n 12 \n\n\n\n-30\n\n\n\n-25\n\n\n\n-20\n\n\n\n-15\n\n\n\n-10\n\n\n\n-5\n\n\n\n0\n0 0.1 0.2 0.3 0.4\n\n\n\nD\nep\n\n\n\nth\n (c\n\n\n\nm\n) \n\n\n\nT0\n\n\n\n T1\n\n\n\n T2\n\n\n\n T4\n\n\n\nT8\n\n\n\nTABLE 4\nStatistical analysis of compaction effect on soil water holding capacity at different\n\n\n\nwater potentials\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 53\n\n\n\nIn each row the capital letters show the difference in the number of pores between \nthe two classes of pores in each treatment, and lowercase letters in each column \nindicates differences between treatments in the number of pores (significant level \nwas 0.01).\n\n\n\nBoth macro- and meso-pores were reduced as the compaction levels \nincreased. However, the number of medium size pores was more than the macro-\npores in all treatments. With just one passage of tractor, the number of large and \nmedium pores decreased 16.5 and 13%, respectively, relative to control treatment. \nThese changes for the 8-time tractor passages were 85 and 62.5%. Results clearly \nindicated that the macro-pores were more affected by compaction levels than the \nmeso-pores (Table 5).\n\n\n\nBy increasing the compaction levels, the percentage of effective porosity \ndecreased significantly for both pore size classes (Fig 5). In all of the treatments, \nthe difference in effective porosity between large and medium size pores was \nsignificant. The highest water flow for both pore sizes was in control treatment \nand the lowest was for the 8-time tractor passages. For all of the treatments, \nthe maximum flow occurred in large pores and minimum in the medium pores. \nAlthough large pores consist of a small portion of soil total porosity, they were the \nprincipal path for water movement.\n\n\n\n11 \n\n\n\n 1 \n\n\n\nFigure 4. Soil moisture characteristic curve of different treatments; T subscript numbers indicate the 2 \nnumber of tractor passes 3 \n\n\n\n 4 \n\n\n\nEffect of water potential on water holding capacity was significant (P\u22640.01). According to 5 \n\n\n\ncapillary principal, water outflow occurs in pores with different radius at variable tensions. 6 \n\n\n\nNumber of pores decrease with increasing compaction levels. The highest number of pores (large 7 \n\n\n\nand medium) was measured in control, while the lowest number occurred in the treatment with 8 \n\n\n\n8-time tractor passages (Table 5). 9 \n\n\n\nTABLE 5 10 \nNumber of pores per square meter (N) in different treatments 11 \nTreatments   \n\n\n\n Large pores Medium pores \n no traffic (TNN 12Ff 1905Aa \nsingle passage (T1) 10Ff 1663Bb \ntwo times passage (T2) 7Ff 1350Cc \nfour times passage (T4) 3Ff 1070Dd \neight times passage (T8) 2Ff 716Ee \n 12 \nIn each row the capital letters show the difference in the number of pores between the two classes of pores in each 13 \ntreatment, and lowercase letters in each column indicates differences between treatments in the number of pores 14 \n(significant level was 0.01). 15 \n 16 \n\n\n\nBoth macro- and meso-pores were reduced as the compaction levels increased. However, the 17 \n\n\n\nnumber of medium size pores was more than the macro-pores in all treatments. With just one 18 \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n-1500-1000-5000\n\n\n\n(%\n(\n\n\n\n \u03b8\nv \n\n\n\n\n\n\n\n(kPa( water potential   \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT4\n\n\n\nT8\n\n\n\nFig. 4: Soil moisture characteristic curve of different treatments; T subscript numbers \nindicate the number of tractor passes\n\n\n\nTABLE 5\nNumber of pores per square meter (N) in different treatments\n\n\n\n11 \n\n\n\n 1 \n\n\n\nFigure 4. Soil moisture characteristic curve of different treatments; T subscript numbers indicate the 2 \nnumber of tractor passes 3 \n\n\n\n 4 \n\n\n\nEffect of water potential on water holding capacity was significant (P\u22640.01). According to 5 \n\n\n\ncapillary principal, water outflow occurs in pores with different radius at variable tensions. 6 \n\n\n\nNumber of pores decrease with increasing compaction levels. The highest number of pores (large 7 \n\n\n\nand medium) was measured in control, while the lowest number occurred in the treatment with 8 \n\n\n\n8-time tractor passages (Table 5). 9 \n\n\n\nTABLE 5 10 \nNumber of pores per square meter (N) in different treatments 11 \nTreatments   \n\n\n\n Large pores Medium pores \n no traffic (TNN 12Ff 1905Aa \nsingle passage (T1) 10Ff 1663Bb \ntwo times passage (T2) 7Ff 1350Cc \nfour times passage (T4) 3Ff 1070Dd \neight times passage (T8) 2Ff 716Ee \n 12 \nIn each row the capital letters show the difference in the number of pores between the two classes of pores in each 13 \ntreatment, and lowercase letters in each column indicates differences between treatments in the number of pores 14 \n(significant level was 0.01). 15 \n 16 \n\n\n\nBoth macro- and meso-pores were reduced as the compaction levels increased. However, the 17 \n\n\n\nnumber of medium size pores was more than the macro-pores in all treatments. With just one 18 \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n-1500-1000-5000\n\n\n\n(%\n(\n\n\n\n \u03b8\nv \n\n\n\n\n\n\n\n(kPa( water potential   \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT4\n\n\n\nT8\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201754\n\n\n\n    (b)                                                                (a)\nFig. 5: (a) Flow distribution and  (b) effective porosity percent of each category of the \nsoil pores in different treatments; the capital letters indicate the effect of compaction \non flow distribution and  effective porosity percent  and lower case letters show the \n\n\n\ndifference between the two classes of pores. (P\u22640.01); \nT subscript numbers indicate the number of tractor passes.\n\n\n\nHydraulic conductivity also decreased as the compaction levels increased. The \nmaximum rate of saturated hydraulic conductivity was in control treatment and \nthe lowest rate was observed in the 8-time tractor passes (Fig. 6).\n\n\n\n12 \n\n\n\npassage of tractor, the number of large and medium pores decreased 16.5 and 13%, respectively, 1 \n\n\n\nrelative to control treatment. These changes for the 8-time tractor passages were 85 and 62.5%. 2 \n\n\n\nResults clearly indicated that the macro-pores were more affected by compaction levels than the 3 \n\n\n\nmeso-pores (Table 5). 4 \n\n\n\n 5 \n\n\n\nBy increasing the compaction levels, the percentage of effective porosity decreased significantly 6 \n\n\n\nfor both pore size classes (Fig 5). In all of the treatments, the difference in effective porosity 7 \n\n\n\nbetween large and medium size pores was significant. The highest water flow for both pore sizes 8 \n\n\n\nwas in control treatment and the lowest was for the 8-time tractor passages. For all of the 9 \n\n\n\ntreatments, the maximum flow occurred in large pores and minimum in the medium pores. 10 \n\n\n\nAlthough large pores consist of a small portion of soil total porosity, they were the principal path 11 \n\n\n\nfor water movement. 12 \n\n\n\n 13 \n\n\n\n(b                                                                      )14 \n(a) 15 \n\n\n\nFigure 5. (a) Flow distribution and  (b) effective porosity percent of each category of the soil pores in 16 \ndifferent treatments; the capital letters indicate the effect of compaction on flow distribution and  effective 17 \nporosity percent  and lower case letters show the difference between the two classes of pores. (P\u22640.01); T 18 \n\n\n\nsubscript numbers indicate the number of tractor passes. 19 \n 20 \n\n\n\nJj Ii Hh Gg Ff \n\n\n\nAa \nBb \n\n\n\nCc \n\n\n\nDd \nEe \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\nT0 T1 T2 T4 T8\n\n\n\nFl\now\n\n\n\n d\nis\n\n\n\ntri\nbu\n\n\n\ntio\nn \n\n\n\n(%\n) \n\n\n\nMedium\nPores\n\n\n\nLarge\npores\n\n\n\nAa \n\n\n\nABb \n\n\n\nBCc \n\n\n\nCd \n\n\n\nCDe \n\n\n\nDf Df Df Df Df \n0\n\n\n\n0.001\n\n\n\n0.002\n\n\n\n0.003\n\n\n\n0.004\n\n\n\n0.005\n\n\n\n0.006\n\n\n\n0.007\n\n\n\n0.008\n\n\n\nT0 T1 T2 T4 T8\nEf\n\n\n\nfe\nct\n\n\n\niv\ne \n\n\n\npo\nro\n\n\n\nsi\nty\n\n\n\n  (\n%\n\n\n\n) \n\n\n\nMedium\npores\n\n\n\nLarge\npores\n\n\n\nFig. 6. Hydraulic conductivity of different compaction levels measured at different soil \nwater potential; T subscript numbers indicate the number of tractor passes\n\n\n\n13 \n\n\n\nHydraulic conductivity also decreased as the compaction levels increased. The maximum rate of 1 \n\n\n\nsaturated hydraulic conductivity was in control treatment and the lowest rate was observed in the 2 \n\n\n\n8-time tractor passes (Fig. 6). 3 \n\n\n\n 4 \n 5 \n\n\n\nFigure 6. Hydraulic conductivity of different compaction levels measured at different soil water potential; 6 \nT subscript numbers indicate the number of tractor passes 7 \n\n\n\n 8 \n  9 \n\n\n\nIn the single tractor passing treatment, the saturated hydraulic conductivity decreased 17.3% 10 \n\n\n\ncompared to the control. However, this reached 71% for 8-time tractor passages. The same trend 11 \n\n\n\nwas also observed for unsaturated hydraulic conductivity. The differences between one and eight 12 \n\n\n\ntimes tractor passages with respect to control were 17.5 and 66.5%, respectively. Since 13 \n\n\n\ncompaction has more effect on large pores, it is conceivable that larger pores diminish by 14 \n\n\n\ncompaction and thus the hydraulic conductivity decreases. Zhang et al. (2006) reported that 15 \n\n\n\nhydraulic conductivity is lower for the soil with several tractor passages compared with no 16 \n\n\n\npassage. Mossadeghi-Bj\u00f6rklund et al. (2016) reported that compaction is the process by which 17 \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n-16-14-12-10-8-6-4-20\n\n\n\nH\nyd\n\n\n\nra\nul\n\n\n\nic\n c\n\n\n\non\ndu\n\n\n\nct\niv\n\n\n\nity\n (\n\n\n\ncm\n/d\n\n\n\nay\n) \n\n\n\nwater potential (water cm)  \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT4\n\n\n\nT8\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 55\n\n\n\nIn the single tractor passing treatment, the saturated hydraulic conductivity \ndecreased 17.3% compared to the control. However, this reached 71% for 8-time \ntractor passages. The same trend was also observed for unsaturated hydraulic \nconductivity. The differences between one and eight times tractor passages with \nrespect to control were 17.5 and 66.5%, respectively. Since compaction has more \neffect on large pores, it is conceivable that larger pores diminish by compaction \nand thus the hydraulic conductivity decreases. Zhang et al. (2006) reported that \nhydraulic conductivity is lower for the soil with several tractor passages compared \nwith no passage. Mossadeghi-Bj\u00f6rklund et al. (2016) reported that compaction \nis the process by which soil bulk density increases and porosity decreases. \nCompaction not only reduces total pore volume but also modifies the pore size \ndistribution and reduces the saturated and near-saturated hydraulic conductivity \nof soil.\n\n\n\nFlow pattern\nThe rate of dye movement into the soil decreased when compaction levels \nincreased (p \u2264 0.01). The highest dye area was observed in control treatment \nwhile the lowest area was measured in the 8-time tractor passage. The stained area \nin the treatment with 8-time tractor passages showed a 77.5% decline compared \nto control treatment. Dye infiltration (stained area) decreased with increasing \ndepth, with the difference between 0-10 cm and 50-60 cm being 97%. When dye \ntracer was added to the soil, it infiltrated directly into the soil of blank treatment. \nBut for the compacted treatment (especially 4x and 8x passage), a kind of water \nlogging developed on the surface. There were few flow paths exactly beneath the \nwheel, but for the sections under its right or left side, flow patterns were similar \nto control (Fig. 7). Kulli et al. (2003) also concluded that the difference in flow \npaths beneath the wheel and on either side of compacted treatments was due to \nboth compression and lower permeability. Weiler and Naef (2000) showed that \nthe preferential flow on the soil surface starts in the saturated zone. Yasuda et \nal. (2001) reported that because of water accumulation on the soil surface and \nexpansion of clay particles a significant portion of soil water is bypassing the soil \nmatrix and occurring in the preferential flow. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201756\n\n\n\nThe T0 of Fig. 8 and P0 of Fig. 9 depict the stained area and flow pattern of the \ncontrol treatment. As it is evident in the image, the maximum stained area was \nobserved in the control compared to other treatments. \n\n\n\nFig. 7: Comparison of infiltration rate under wheel and on either side in the treatment \nwith 8x passage. \u05c0 indicates flow path beneath the wheel and  \u05c0\u05c0shows the side of \n\n\n\ncompacted treatment.\n\n\n\nFig. 8. Images of dye tracer penetration of different treatments; T subscript numbers \nindicate the number of tractor passages.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 57\n\n\n\nFig. 9: Flow patterns (P) of different treatments; subscripts represent the number \nof tractor passages\n\n\n\n \nThe distribution of available pores for solution infiltration in control was more \nsuitable??? and uniform than other treatments. The highest stained area was \nobserved in 0-10 cm of soil surface. About 93% of this layer was stained by \ntracer infiltration (Table 6). In this treatment, the stained area decreased with \nincreasing depth. Dye tracer infiltrated down to a depth of 40 cm and from that \npoint downward, no dye was observed. Table 6 shows the percent stained area for \ndifferent treatments.\n\n\n\nIn the treatment of one tractor passage, the uniformity of pores and percent \nstained area was less relative to the control treatment (T1 of Fig. 8 and P1 of Fig. \n9). Stained area with one passage reduced 36.8% relative to the control treatment. \nThe higher bulk density and lower porosity reduced the infiltration rate of dye \ntracer in the treatment with one tractor passage relative to control.\n\n\n\nStrong decline of dye infiltration was observed in the treatment with  eight \ntimes tractor passages (T8 of Fig. 8 and P8 of Fig. 9). The difference in dye tracer \ninfiltration between 8- and 4-time passages was 25%. Because of the lowest pore \nnumbers and soil total porosity, hydraulic conductivity, aggregate stability and \nconsequently the highest bulk density resulted in a decline of the stained area and \npercent stained area.\n\n\n\n17 \n\n\n\n 1 \nP1P0 2 \n\n\n\n 3 \nP8P4P2 4 \n\n\n\n 5 \n 6 \n\n\n\nFigure 9.  Flow patterns (P) of different treatments; subscripts represent the number of tractor passages 7 \n  8 \nThe distribution of available pores for solution infiltration in control was more suitable??? and 9 \n\n\n\nuniform than other treatments. The highest stained area was observed in 0-10 cm of soil surface. 10 \n\n\n\nAbout 93% of this layer was stained by tracer infiltration (Table 6). In this treatment, the stained 11 \n\n\n\narea decreased with increasing depth. Dye tracer infiltrated down to a depth of 40 cm and from 12 \n\n\n\nthat point downward, no dye was observed. Table 6 shows the percent stained area for different 13 \n\n\n\ntreatments. 14 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201758\n\n\n\nComparison of Dye Tracer Infiltration Rate at Different Depths\nThe highest percent of stained area was observed at 0-10 cm depth of control \ntreatment. At this depth, infiltration rate decreased significantly with increasing \ncompaction level. The lowest rate at this depth was in the treatment with 8-time \npassages. In single time passage, dye infiltration rate decreased 14% relative to \ncontrol, while it reached 70% for 8-time tractor passages.\n\n\n\nFor the 10-20 cm depth, the stained area rate also decreased significantly \nwith increasing compaction level. The maximum stained area percentage was \ndetected in control and the minimum was noticed in the treatment with 8-time \npassages. The difference in stained area percentages between once and 8-time \npassages relative to control at this depth were 39.5 and 96.5%, respectively. This \nindicates compaction has more effect at this depth compared to the above layer. As \nmentioned earlier, the main compaction effect on bulk density and total porosity \noccurred at 10-20 cm. At this depth, infiltration rate for the treatment with 4-time \ntractor passes was 32% more than 2-time passages. Compaction produced by \n4-time treatments might be the reason for developing preferential flow and an \nincrease in infiltration for this treatment in comparison to 2-time tractor passages.\n\n\n\nAt a depth of 20-30 cm, a significant decrease in stained area was also evident. \nNo infiltration was observed at this depth with 2-time passage treatment. For 30-40 \ncm depth, the highest stained area was measured in the control treatment followed \nby the 8-time passages due to preferential flow at which macro-pore continuity \nwas still maintained despite reductions in macroporosity (Mossadeghi-Bj\u00f6rklund \net al. 2016). Kulli et al. (2003) showed that the above postulated decrease in \npermeability caused by the vehicle traffic led to local ponding, leading to enhanced \npreferential flow in the compacted plots. No infiltration of dye tracer was observed \nfor the 1 and 2- time passage treatments at 40-50 cm depth; infiltration occurred \n\n\n\nTABLE 6\nPercent stained area at different depths of different treatments\n\n\n\nT subscripts represent the number of tractor passage; A1 is the ratio of stained area\nto the area of each layer (%), and A2 is the ratio of stained area to the total area (%).\n\n\n\n18 \n\n\n\nTABLE 6 1 \nPercent stained area at different depths of different treatments 2 \n\n\n\nPercent \nstained \n\n\n\narea \n\n\n\nDepth \n(cm) \n\n\n\n0-10 10-20 20-30 30-40 40-50 50-60 \n\n\n\n treatment       \nA1 T0 \n\n\n\n\n\n\n\n93.00 78.10 42.50 24.90 0.00 0.00 \nA2 36.59 31.09 17.89 14.11 0.00 0.00 \nA1 T1 80.00 47.10 29.70 0.00 0.00 0.00 \nA2  51.00 29.50 19.30 0.00 0.00 0.00 \nA1 T2 75.24 14.83 0.00 0.00 0.00 0.00 \nA2  82.68 16.30 0.00 0.00 0.00 0.00 \nA1 T4 42.00 21.80 13.90 2.80 0.00 0.00 \nA2  52.00 27.60 17.27 3.49 0.00 0.00 \nA1 T8 30.00 2.40 7.80 16.00 4.60 0.20 \nA2  49.50 3.38 12.79 26.36 3.79 0.37 \n\n\n\nT subscripts represent the number of tractor passage; A1 is the ratio of stained area to the area of each 3 \nlayer (%), and A2 is the ratio of stained area to the total area (%). 4 \n 5 \n 6 \n\n\n\nIn the treatment of one tractor passage, the uniformity of pores and percent stained area was less 7 \n\n\n\nrelative to the control treatment (T1 of Fig. 8 and P1 of Fig. 9). Stained area with one passage 8 \n\n\n\nreduced 36.8% relative to the control treatment. The higher bulk density and lower porosity 9 \n\n\n\nreduced the infiltration rate of dye tracer in the treatment with one tractor passage relative to 10 \n\n\n\ncontrol. 11 \n\n\n\nStrong decline of dye infiltration was observed in the treatment with  eight times tractor passages 12 \n\n\n\n(T8 of Fig. 8 and P8 of Fig. 9). The difference in dye tracer infiltration between 8- and 4-time 13 \n\n\n\npassages was 25%. Because of the lowest pore numbers and soil total porosity, hydraulic 14 \n\n\n\nconductivity, aggregate stability and consequently the highest bulk density resulted in a decline 15 \n\n\n\nof the stained area and percent stained area. 16 \n\n\n\n 17 \n\n\n\nComparison of Dye Tracer Infiltration Rate at Different Depths 18 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 59\n\n\n\nonly in the treatment with 8-time passages. The situation at 50-60 cm depth was \nsimilar to the layer above.\n\n\n\nCONCLUSIONS\nThe compaction changed the continuity and pore size distribution resulting in \nreduced infiltration, hydraulic conductivity, total porosity, aggregate stability, soil \nwater retention and increased bulk density. The results of this study showed that \ninfiltration rate of dye tracer reduced significantly with induced compaction. Our \nresults showed that the compaction effect by tractor is only limited to 0-20 cm soil \ndepth. Reduction of large pores in the soil surface and uniformity of infiltration \npaths were shown by flow patterns. Despite very little surface infiltration in the \ntreatment with 4-time and 8-time tractor passages, dye tracer was observed in \nthe lower depths because of the preferential flow. Reduced infiltration of the dye \ntracer due to machinery traffic caused a local water-logged condition followed \nby an increase in preferential flows in the compacted treatments. Results of this \nstudy indicate that laboratory measurements provide an insight on the relationship \nbetween compaction depth and infiltration of the dye tracer. Flow patterns not \nonly provide information about compaction effect, but indicate how compaction \nmay change the flow of water. Dye-tracing experiments complement the classic \nmethods for evaluation of compaction impacts on water infiltration and transfer. \n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to thank Dr. Yaghoub Mansouri and Engineer Salman \nTaherizadeh for their suggestions and the helpful comments.\n\n\n\nREFERENCES\nAnkeny, M.D., M. Ahmed, T.C. Kasper and R. Horton. 1991. Simple field method for \n\n\n\ndetermining unsaturated hydraulic conductivity. Soil Science Soceity American \nJournal 55: 476-470.\n\n\n\nBarzegar, A.R., A.M. Hashemi, S.J.Herbert and M.A. Asoodar. 2004. 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\uf0bf\uf0b7\uf0ae\uf020 \uf0ac\uf0bb\uf0b3\uf0b0\uf0bb\uf0ae\uf0bf\uf0ac\uf0ab\uf0ae\uf0bb\uf020 \uf0bf\uf0b2\uf0bc\uf020 \uf0ae\uf0bb\uf0b4\uf0bf\uf0ac\uf0b7\uf0aa\uf0bb\uf020\uf0b8\uf0ab\uf0b3\uf0b7\uf0bc\uf0b7\uf0ac\uf0a7\uf020 \uf0bf\uf0ae\uf0bb\uf020 \uf0ac\uf0b8\uf0bb\uf020\uf0b3\uf0bb\uf0bf\uf0b2\uf020 \uf0b1\uf0ba\uf020\uf0bb\uf0bf\uf0bd\uf0b8\uf020\n\uf0ad\uf0bb\uf0bf\uf0ad\uf0b1\uf0b2\uf020 \uf0f8\uf06f\uf0cd\uf0db\uf0e5\uf020 \uf0b2\uf0e3\uf0eb\uf0f7\uf020 \uf0bf\uf0b2\uf0bc\uf020 \uf0ae\uf0bb\uf0ad\uf0ac\uf020 \uf0b1\uf0ba\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020 \uf0bf\uf0ae\uf0bb\uf020 \uf0ac\uf0b8\uf0bb\uf020\uf0b3\uf0bb\uf0bf\uf0b2\uf020 \uf0b1\uf0ba\uf020 \uf0ba\uf0b1\uf0ab\uf0ae\uf020 \uf0ad\uf0bb\uf0bf\uf0ad\uf0b1\uf0b2\uf0ad\uf020 \uf0bf\uf0bd\uf0ae\uf0b1\uf0ad\uf0ad\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0a7\uf0bb\uf0bf\uf0ae\uf020 \uf0f8\uf06f\uf0cd\uf0db\uf0e5\uf020\n\uf0b2\uf0e3\uf0ee\uf0f0\uf0f7\uf0f2\uf020 \uf020\n\n\n\n\n\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf020\nFig. 1: Seasonal variation in  microbial biomass C, N and P ( g g-1) in the undisturbed, \n\n\n\nmoderately disturbed and highly disturbed stands. Vertical lines represent standard error (n=5). 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\uf0bb\uf0bf\uf0ae\uf0b4\uf0a7\uf020 \uf0b7\uf0b2\uf0bc\uf0b7\uf0bd\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020 \uf0bd\uf0b8\uf0bf\uf0b2\uf0b9\uf0bb\uf0ad\uf020 \uf0b7\uf0b2\uf020 \uf0ac\uf0b1\uf0ac\uf0bf\uf0b4\uf020 \uf0ad\uf0b1\uf0b7\uf0b4\uf020 \uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0b3\uf0bf\uf0ac\uf0ac\uf0bb\uf0ae\uf020 \uf0bc\uf0ab\uf0bb\uf020 \uf0ac\uf0b1\uf020\n\n\n\n\uf0ad\uf0ac\uf0ae\uf0bf\uf0a9\uf020\uf0b7\uf0b2\uf0bd\uf0b1\uf0ae\uf0b0\uf0b1\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0f2\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0de\uf0b7\uf0b1\uf0b4\uf0f2\uf020\uf0de\uf0b7\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0f2\uf020\uf0ef\uf0e7\n\n\n\n\uf0b0\uf0b1\uf0b0\uf0ab\uf0b4\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ad\uf0ac\uf0ae\uf0ab\uf0bd\uf0ac\uf0ab\uf0ae\uf0bb\uf020\uf0b7\uf0b2\uf020\uf0bf\uf020\uf0ad\uf0ab\uf0be\uf0ac\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0be\uf0ae\uf0b1\uf0bf\uf0bc\uf0f3\uf0b4\uf0bb\uf0bf\uf0aa\uf0bb\uf0bc\uf020\uf0ba\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf020\uf0bf\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0bf\uf020\uf0bc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bf\uf0b2\uf0bd\uf0bb\uf020\uf0b9\uf0ae\uf0bf\uf0bc\uf0b7\uf0bb\uf0b2\uf0ac\uf0f2\uf020\n\n\n\n\uf0ca\uf0bb\uf0b9\uf0bb\uf0ac\uf0bf\uf0ac\uf0b7\uf0b1\uf0f2\uf020\uf0e8\uf0e8\n\n\n\n\n\n\n\n\n\uf0ec\uf0ed\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0bd\uf0b8\uf0bf\uf0ae\uf0bf\uf0bd\uf0ac\uf0bb\uf0ae\uf0b7\uf0ad\uf0ac\uf0b7\uf0bd\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0bf\uf020\uf0ae\uf0bf\uf0b2\uf0b9\uf0bb\uf020\uf0b1\uf0ba\uf020\uf0d2\uf0bb\uf0a9\uf020\uf0c6\uf0bb\uf0bf\uf0b4\uf0bf\uf0b2\uf0bc\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf020\uf0ab\uf0b2\uf0bc\uf0bb\uf0ae\uf020\uf0bb\uf0ad\uf0ac\uf0bf\uf0be\uf0b4\uf0b7\uf0ad\uf0b8\uf0bb\uf0bc\uf020\uf0b0\uf0bf\uf0ad\uf0ac\uf0ab\uf0ae\uf0bb\uf0ad\uf0f2\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0de\uf0b7\uf0b1\uf0b4\uf0f2\uf020\n\n\n\n\uf0de\uf0b7\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0f2\uf020\uf0ef\uf0ea\uf0e6\uf0ef\uf0e9\uf0e9\uf0f3\uf0ef\uf0e8\uf0ed\uf0f2\n\n\n\n\uf0ac\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0d7\uf0b2\uf0bc\uf0b7\uf0bf\uf0f2\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0de\uf0b7\uf0b1\uf0b4\uf0f2\uf020\uf0de\uf0b7\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0f2\uf020\uf0ee\uf0f0\uf0e6\uf0e9\uf0ec\uf0ed\uf0f3\uf0e9\uf0ec\uf0e9\uf0f2\n\n\n\n\uf0dc\uf0a7\uf0b2\uf0bf\uf0b3\uf0b7\uf0bd\uf0ad\uf020\uf0b1\uf0ba\uf020\n\n\n\n\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0d1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0d3\uf0bf\uf0ac\uf0ac\uf0bb\uf0ae\uf020 \uf0b7\uf0b2\uf020\uf0cc\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0db\uf0bd\uf0b1\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\n\n\n\n\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0de\uf0b7\uf0b1\uf0b4\uf0f2\uf020\uf0de\uf0b7\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0f2\uf020\uf0ef\uf0e7\uf0e6\uf0e9\uf0f0\uf0ed\uf0f3\uf0e9\uf0f0\uf0e9\uf0f2\n\n\n\n\uf0db\uf0bd\uf0b1\uf0b4\uf0f2\uf020\n\n\n\n\uf0e9\uf0eb\uf0e6\uf0ec\uf0ef\uf0e8\uf0f3\uf0ec\uf0ee\uf0e7\uf0f2\n\n\n\n\uf0bb\uf0bd\uf0b1\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\uf0ad\uf0e6\uf020\uf0bf\uf020\uf0bd\uf0b1\uf0b2\uf0bc\uf0b7\uf0ac\uf0b7\uf0b1\uf0b2\uf0bf\uf0b4\uf0f3\uf0ad\uf0bd\uf0bf\uf0b4\uf0bb\uf020\uf0ad\uf0ac\uf0ab\uf0bc\uf0a7\uf0f2\uf020\uf0db\uf0bd\uf0b1\uf0b4\uf0f2\uf020\uf0e9\uf0eb\uf0e6\uf0ee\uf0ed\uf0ed\uf0ed\uf0f3\uf0ee\uf0ed\uf0ec\uf0e9\uf0f2\n\n\n\n\uf0b2\uf0bc\n\n\n\n\uf0d6\uf0bb\uf0ae\uf0ad\uf0bb\uf0a7\uf0f2\n\n\n\n\uf0df\uf0b2\uf0ac\uf0b8\uf0ae\uf0b1\uf0b0\uf0b1\uf0b9\uf0bb\uf0b2\uf0b7\uf0bd\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bf\uf0b2\uf0bd\uf0bb\uf020\uf0b7\uf0b2\uf020\uf0bd\uf0bd\uf020\uf0cc\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0ce\uf0bf\uf0b7\uf0b2\uf0ba\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf020\uf0db\uf0bd\uf0b1\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: mahsa1593@yahoo.com\n\n\n\nINTRODUCTION\nOphiolite-related ultramafic rocks, particularly serpentinites, crop out in fold-\norogenic belts and on stable interior platforms of every continent. Circulation of \nthe fluids that detach from the subducted block through the hot mantle peridotites \nwhich take place during the tectonic displacements and under temperatures of less \nthan 500\u00b0C and fluid pH above 10 in the presence of low partial pressures of carbon \ndioxide (pCO2) leads to serpentinisation. This process progressively changes the \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 35-49 (2014)                          Malaysian Society of Soil Science\n\n\n\nGeochemical Characteristics of Serpentinite Soils from \nMalaysia \n\n\n\nMahsa Tashakor1,  *Wan Zuhairi Wan Yaacob2, Hamzah Mohamad3 and\nAzman A. Ghani1\n\n\n\n1Department of Geology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia\n2Faculty of Science and Technology, Universiti Kebangsaan Malaysia,\n\n\n\n43600 Bangi, Selangor, Malaysia \n3Center for Global Archaeological Research, Universiti Sains Malaysia,\n\n\n\n11800 Penang, Malaysia\n\n\n\nABSTRACT\nThe geochemistry of ophiolite related serpentinite soils has been one of the most \nchallenging concerns among soil scientists and ecologists for several decades. \nDespite increasing global knowledge about the specificity of serpentinites, they \nhave received limited attention in Malaysia. Considering the role of climate in \nthe chemical composition of the derived soils, this study focused on tropical \nserpentinite soils in Malaysia. A total of 27 soil samples was collected from five \nserpentinite outcrops in Peninsular Malaysia and Sabah and analysed elementally. \nBased on their major oxide contents, the soils were divided into two groups of \u2018rich \nin Fe\u2019 and \u2018rich in Mg\u2019 which represent mature and immature soils, respectively. \nHowever, the most striking result that emerged from this study was the anomalous \nconcentration of three trace metals of chromium, nickel and cobalt in the studied \nserpentinite soils in comparison with those of the adjacent sedimentary soils (soils \nof Crocker). The observed elemental ranges were Cr 2,427-27,863, Ni 850-4,753 \nand Co 35-167 (in \u03bcg g-1), while the ranges for these elements for the soils of \nCrocker formation were Cr 67-182, Ni 33-270 and Co 11-23 (in \u03bcg g-1). It is \nobvious that the amounts of Cr, Ni and Co in the studied serpentinites soils were \n105, 15 and 6 times higher, respectively, than those in Crocker soils comparing \nwith the Dutch List standard and Great London Council guidelines, serpentinite \nsoils of Malaysia can be considered  to be heavily contaminated with Cr, Ni and \nCo.\n\n\n\nKeywords:  Chromium, nickel, cobalt, geochemistry, serpentinite soils \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201436\n\n\n\nchemical and mineralogical composition of the initial ultramafic rocks. The sea-\nwater-rock reaction transforms primary silicate minerals of olivine (Mg,Fe)2SiO4, \nand pyroxenes (Mg,Fe)2Si2O6 to serpentine minerals Mg3Si2O5(OH)4 (Alexander \n2004; Coleman 1967; Gough et al. 1989; O\u2019Hanley 1996; Oze et al. 2004a). Ser-\npentinites are scattered in many places throughout the world including Southeast \nAsia. In Peninsular Malaysia, they are found in several small isolated lenses along \nthe Bentong-Raub suture zone (Hutchison 2005). In Sabah, the eastern state of \nMalaysia, serpentinized ultramafic rocks form a widely extended massif of about \n3500 km2 (Repin 1998). \n\n\n\nThe specific geochemistry of serpentinite rocks is reflected in the derived \nsoils. They are generally very poor in silica content (less than 45% SiO2) and \nmajor plant nutrients such as nitrogen, potassium and phosphorus. In contrast, \nthey are characterised by large amounts of iron and magnesium. It has been well \ndocumented that serpentinized-ultramafic rocks produce a high geochemical \nbackground with chromium, nickel and cobalt in the soil horizons (Amir and Pineau \n2003; Becquer et al. 2006; Bonifacio et al. 1997; Caillaud et al. 2009; Graham et \nal. 1990; Kierczak et al. 2007; Lee et al. 2004; Lewis et al. 2006; Quantin et al. \n2002; Quantin et al. 2008; Schwertmann and Latham 1986; Shallari et al. 1998; \nSiebecker 2010; Skordas and Kelepertsis 2005; Tashakor et al. 2012; Turekian and \nWedepohl 1961). Serpentinite soils are known to be naturally metal polluted with \na high potential adverse impact on the environment and human health (Hseu 2006; \nKierczak et al. 2008; Oze et al. 2004a; Oze et al. 2004b).  Many plants cannot \ngrow on serpentinite soils (Brearley 2005; Proctor 2003). There are also some \naccounts of the effects of the chemical properties of serpentinite soils on animals \nand microorganisms (Alexander et al. 2006). Growing awareness of the chemistry \nof serpentinite soils led to additional importance of these soils in tropical areas. \nSerpentinite bodies located in tropical climates have tolerated elevated degrees \nof weathering which eases the liberation of the elemental components of soils. \nSerpentinite formations in Malaysia have been  scarcely studied previously (Baker \nand Brooks 1988; Proctor 2003). This study set out with the aim of expanding the \ngeneral knowledge of the geochemistry of serpentinites in Malaysia. Aside from \nthe five serpentinite outcrops of Bukit Rokan, Petasih, Bukit Malim and Cheroh \nin Peninsular Malaysia and Ranau in Sabah, a clastic sedimentary formation of \nCrocker soils was additionally studied as a control factor, due to its known low \ncontent of the studied elements of Cr, Ni and Co.\n\n\n\nMahsa Tashakor, Wan Zuhairi Wan Yaacob, Hamzah Mohamad and Azman A. Ghani\n\n\n\nMATERIALS AND METHODS\n\n\n\nField and Samples\nThe studied materials were superficial (less than 10 cm thick) weathered soils \ndeveloped on serpentinite bodies in Malaysia.  A number of 15 soil samples was \ncollected from four serpentinite sites in Peninsular Malaysia, namely at Bukit \nRokan (BR) and Petasih (PS) in Negeri Sembilan and Cheroh (CH) and Batu \nMalim (BM) in Pahang state. In Sabah, the sampling of 12 soils was accomplished \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 37\n\n\n\nGeochemical Characteristics of Serpentinite Soils\n\n\n\nfrom the extended serpentinite massif of Ranau located between N 5 \u00b0 57 \u2018- N 6 \n\u00b0 02 \u2018 and E 116 \u00b0 40 \u2018- E 116 \u00b0 45. In addition, four samples from sedimentary \nCrocker soils (formed on shale and sandstone) in the vicinity of serpentinites of \nRanau were taken for comparison purposes. The locations of the surveyed areas \nare shown in Figure 1. The choice of the most suitable sampling sites for both \nserpentinite and Crocker soils were subject to accessibility which was limited \nby dense forest and the rugged nature of the lands. The mode of serpentinite \noccurrence in Ranau was mainly road cut and stream banks. In Bukit Rokan, \nthick layers of serpentinite soils surround the village and housing estate regions. \nSerpentinite soils of Petasih were collected from an under a construction hill \ncut. In Cheroh, the serpentinite body was covered by palm trees plantations. The \nserpentinites in Batu Malim had been exposed through mining.\n\n\n\nSerpentinites of all the studied areas have produced thick layers of \nreddish-brown lateritic soils due to the high degree of weathering under tropical \nenvironment. \n\n\n\nElemental Analysis\nThe bulk chemical composition of serpentinite and Crocker soils comprising 10 \nmajor oxides (SiO2,TiO2, Al2O3, Fe2O3,MnO, MgO, CaO, Na2O, K2O, P2O5 )  and \n20 trace elements (As, Ba, Ce, Co, Cr, Cu, Ga, Hf, La, Nb, Ni, Pb, Rb, Sr, Th, U, \nV, Y, Zn, Zr) was determined by X-ray fluorescence spectrometer (XRF, Bruker \nS8 Tiger). Soil samples were powdered to 30 \u03bcm grain size after air-drying and \npulverisation. Thereafter, the soils were made into 32 mm diameter fused-beads \n\n\n\n15 \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: The location of the studied areas in Peninsular Malaysia and Sabah  Figure 1: The location of the studied areas in Peninsular Malaysia and Sabah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201438\n\n\n\nMahsa Tashakor, Wan Zuhairi Wan Yaacob, Hamzah Mohamad and Azman A. Ghani\n\n\n\nfor determining major oxides. The fused beads were prepared by igniting 0.5g \nof sample with 5.0g of Johnson-Matthey 110 spectroflux, giving a dilution ratio \nof 1:10. In order to determine the trace elements, 32 mm diameter press-powder \npallets were prepared by applying a pressure of 20 tonnes for one minute to 1g of \nsample against 6 g of pure boric acid powder.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nBulk Composition of Serpentinite Soils\nTable 1 shows the values of major oxides in the analysed soils. The ultramafic \nnature of the analysed serpentinite soils is obvious from the SiO2 content which \nis less than 45% in almost all of the samples. Amounts of Al2O3 and Fe2O3 in the \nsoils show similar trends, in contrast to the MgO concentration trend. Fe-rich soils \nin Peninsular Malaysia contain elevated amounts of Al2O3 (11.25 - 26.91 wt %) \nand Fe2O3 (23.80-55.65 wt %), whilst the studied samples were devoid of MgO \n(0.21 - 0.6 wt %). The rest of the Mg-rich samples showed an inverse relationship, \nwith very poor values of Al2O3 (0.2 to 0.51 wt %) and Fe2O3 (0.02 to 0.1 wt %) \nand high concentrations of MgO (49.16 to 65.88 wt %). The concentration ranges \nof Al2O3, Fe2O3 and MgO in the soils of Ranau were 5.87-22.89, 11.35-45.47 and \n0.27-8.71, respectively. \n\n\n\nThe division becomes clearer after plotting 20 soil samples on (SiO2, MgO, \nAl2O3+Fe2O3) ternary diagram (Figure 2). As shown in the triangle, 75 % of the \nsoil samples contained between 60% to >80 % of Al2O3 + Fe2O3. However, they \nhad been depleted of magnesium oxide as indicated by having only 0.2 to about \n10 % MgO. An inverse condition existed for the remaining 25 % of samples which \nwere very poor in Al2O3 and Fe2O3 content (0.2 % to 0.7 %, respectively) but had \nhigh concentrations of MgO (60% to 81%).\n\n\n\nThis rather contradictory result is perhaps due to the degree of weathering. \nThe intensity of weathering diverse in six grades (Arikan and Aydin 2012): \nfresh (I), slightly weathered (II), moderately weathered (III), highly weathered \n(IV), completely weathered (V) and residual soil (VI). During the weathering \nand pedogenesis of the serpentinites, clay minerals destabilise. Therefore, highly \nmobile elements such as Mg and Si are leached entirely from the soil profile at \nearly stages and the newly formed clay minerals become enriched with less mobile \nelements. Consequently, as the weathering process progresses, soils especially in \nwell drained profiles become devoid of Mg and Si, and the stable minerals with \nhigher valency such as Al and Fe accumulate. This phase usually occurs in a \ntropical climate and produces ferralitic mature soils (Dissanayake and Chandrajith \n2009). However, in soils rich in Mg it seems possible that some ferromagnesium \nminerals are still in solid form because of the low weathering grade and they are \nnot integrated and flushed out from the system. They create immature soils which \nhave closer composition to parent rocks. This loss and accumulation pattern is \nvery similar to those pedalogical observations on serpentinite weathering under \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 39\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nTh\ne \n\n\n\nco\nnc\n\n\n\nen\ntra\n\n\n\ntio\nn \n\n\n\nof\n m\n\n\n\naj\nor\n\n\n\n o\nxi\n\n\n\nde\ns (\n\n\n\nin\n w\n\n\n\nei\ngh\n\n\n\nt %\n) a\n\n\n\nnd\n tr\n\n\n\nac\ne \n\n\n\nel\nem\n\n\n\nen\nts\n\n\n\n (i\nn \n\n\n\n\u03bcg\n g\n\n\n\n-1\n) i\n\n\n\nn \nSe\n\n\n\nrp\nen\n\n\n\ntin\nite\n\n\n\n a\nnd\n\n\n\n C\nro\n\n\n\nck\ner\n\n\n\n so\nils\n\n\n\n o\nf d\n\n\n\niff\ner\n\n\n\nen\nt s\n\n\n\ntu\ndy\n\n\n\n a\nre\n\n\n\nas\n in\n\n\n\n \nPe\n\n\n\nni\nns\n\n\n\nul\nar\n\n\n\n M\nal\n\n\n\nay\nsi\n\n\n\na \nan\n\n\n\nd \nSa\n\n\n\nba\nh \n\n\n\n\n\n\n\n10\n \n\n\n\n\n\n\n\n\n\n\n\nM\naj\n\n\n\nor\n e\n\n\n\nle\nm\n\n\n\nen\nts\n\n\n\n (w\nt %\n\n\n\n) \nTr\n\n\n\nac\ne \n\n\n\nm\net\n\n\n\nal\ns (\n\n\n\n\u03bcg\n g\n\n\n\n-1\n) \n\n\n\n\n\n\n\nSi\nO\n\n\n\n2 \nTi\n\n\n\nO\n2 \n\n\n\nA\nl 2O\n\n\n\n3 \nFe\n\n\n\n2O\n3 \n\n\n\nM\nnO\n\n\n\n \nM\n\n\n\ngO\n \n\n\n\nC\naO\n\n\n\n \nN\n\n\n\na 2\nO\n\n\n\n \nK\n\n\n\n2O\n \n\n\n\nP 2\nO\n\n\n\n5 \nL.\n\n\n\nO\n.I \n\n\n\nTo\nta\n\n\n\nl \nC\n\n\n\no \nC\n\n\n\nr \nN\n\n\n\ni \n\n\n\nSe\nrp\n\n\n\nen\ntin\n\n\n\nite\n p\n\n\n\nen\nin\n\n\n\nsu\nla\n\n\n\nr \nB\n\n\n\nM\n.1\n\n\n\n \n20\n\n\n\n.9\n6 \n\n\n\n0.\n4 \n\n\n\n13\n.3\n\n\n\n2 \n55\n\n\n\n.5\n5 \n\n\n\n0.\n38\n\n\n\n \n0.\n\n\n\n38\n \n\n\n\n0.\n17\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n0.\n22\n\n\n\n \n0.\n\n\n\n08\n \n\n\n\n9.\n56\n\n\n\n \n10\n\n\n\n1.\n08\n\n\n\n \n23\n\n\n\n3 \n83\n\n\n\n39\n \n\n\n\n84\n1 \n\n\n\n \nB\n\n\n\nM\n.2\n\n\n\n \n21\n\n\n\n.1\n3 \n\n\n\n0.\n05\n\n\n\n \n0.\n\n\n\n46\n \n\n\n\n0.\n09\n\n\n\n \n14\n\n\n\n.1\n8 \n\n\n\n53\n.3\n\n\n\n5 \n0.\n\n\n\n19\n \n\n\n\n0.\n29\n\n\n\n \n0.\n\n\n\n21\n \n\n\n\n0.\n31\n\n\n\n \n9.\n\n\n\n43\n \n\n\n\n99\n.6\n\n\n\n9 \n34\n\n\n\n0 \n89\n\n\n\n96\n \n\n\n\n10\n70\n\n\n\n\n\n\n\n \nB\n\n\n\nM\n.3\n\n\n\n \n21\n\n\n\n.4\n1 \n\n\n\n0.\n04\n\n\n\n \n0.\n\n\n\n36\n \n\n\n\n0.\n1 \n\n\n\n11\n.4\n\n\n\n5 \n53\n\n\n\n.2\n8 \n\n\n\n0.\n18\n\n\n\n \n0.\n\n\n\n36\n \n\n\n\n0.\n2 \n\n\n\n0.\n44\n\n\n\n \n12\n\n\n\n.5\n4 \n\n\n\n10\n0.\n\n\n\n36\n \n\n\n\n25\n2 \n\n\n\n13\n97\n\n\n\n \n86\n\n\n\n1 \n\n\n\n \nB\n\n\n\nM\n.4\n\n\n\n \n22\n\n\n\n.8\n3 \n\n\n\n0.\n43\n\n\n\n \n13\n\n\n\n.2\n8 \n\n\n\n53\n.6\n\n\n\n4 \n0.\n\n\n\n29\n \n\n\n\n0.\n37\n\n\n\n \n0.\n\n\n\n23\n \n\n\n\n0.\n05\n\n\n\n \n0.\n\n\n\n22\n \n\n\n\n0.\n08\n\n\n\n \n11\n\n\n\n.4\n8 \n\n\n\n10\n2.\n\n\n\n9 \n21\n\n\n\n2 \n81\n\n\n\n10\n \n\n\n\n66\n4 \n\n\n\n \nB\n\n\n\nR\n.1\n\n\n\n \n9.\n\n\n\n44\n \n\n\n\n0.\n02\n\n\n\n \n0.\n\n\n\n2 \n0.\n\n\n\n02\n \n\n\n\n9.\n15\n\n\n\n \n65\n\n\n\n.8\n8 \n\n\n\n0.\n18\n\n\n\n \n0.\n\n\n\n72\n \n\n\n\n0.\n12\n\n\n\n \n0.\n\n\n\n45\n \n\n\n\n10\n.8\n\n\n\n9 \n97\n\n\n\n.0\n7 \n\n\n\n46\n4 \n\n\n\n94\n87\n\n\n\n \n16\n\n\n\n87\n \n\n\n\n \nB\n\n\n\nR\n.2\n\n\n\n \n21\n\n\n\n.1\n5 \n\n\n\n0.\n8 \n\n\n\n12\n.1\n\n\n\n6 \n49\n\n\n\n.7\n \n\n\n\n0.\n31\n\n\n\n \n0.\n\n\n\n33\n \n\n\n\n0.\n19\n\n\n\n \n0.\n\n\n\n5 \n0.\n\n\n\n21\n \n\n\n\n0.\n06\n\n\n\n \n15\n\n\n\n.4\n8 \n\n\n\n10\n0.\n\n\n\n89\n \n\n\n\n15\n4 \n\n\n\n21\n33\n\n\n\n \n43\n\n\n\n5 \n\n\n\n \nB\n\n\n\nR\n.3\n\n\n\n \n32\n\n\n\n.8\n5 \n\n\n\n2.\n12\n\n\n\n \n26\n\n\n\n.9\n1 \n\n\n\n23\n.0\n\n\n\n8 \n0.\n\n\n\n36\n \n\n\n\n0.\n3 \n\n\n\n0.\n18\n\n\n\n \n0.\n\n\n\n03\n \n\n\n\n0.\n18\n\n\n\n \n0.\n\n\n\n05\n \n\n\n\n17\n.5\n\n\n\n3 \n10\n\n\n\n3.\n59\n\n\n\n \n98\n\n\n\n \n12\n\n\n\n48\n \n\n\n\n18\n9 \n\n\n\n \nB\n\n\n\nR\n.4\n\n\n\n \n23\n\n\n\n.8\n4 \n\n\n\n0.\n5 \n\n\n\n0.\n51\n\n\n\n \n0.\n\n\n\n04\n \n\n\n\n10\n.8\n\n\n\n2 \n52\n\n\n\n.1\n1 \n\n\n\n0.\n18\n\n\n\n \n0.\n\n\n\n04\n \n\n\n\n0.\n21\n\n\n\n \n0.\n\n\n\n08\n \n\n\n\n12\n.4\n\n\n\n9 \n10\n\n\n\n0.\n82\n\n\n\n \n23\n\n\n\n6 \n11\n\n\n\n27\n6 \n\n\n\n48\n3 \n\n\n\n \nC\n\n\n\nH\n.1\n\n\n\n \n23\n\n\n\n.7\n1 \n\n\n\n0.\n33\n\n\n\n \n11\n\n\n\n.2\n5 \n\n\n\n51\n.9\n\n\n\n \n0.\n\n\n\n41\n \n\n\n\n0.\n58\n\n\n\n \n0.\n\n\n\n2 \n0.\n\n\n\n05\n \n\n\n\n0.\n21\n\n\n\n \n0.\n\n\n\n09\n \n\n\n\n12\n.9\n\n\n\n1 \n10\n\n\n\n1.\n64\n\n\n\n \n17\n\n\n\n1 \n84\n\n\n\n37\n \n\n\n\n53\n6 \n\n\n\n \nC\n\n\n\nH\n.2\n\n\n\n \n19\n\n\n\n.8\n4 \n\n\n\n0.\n32\n\n\n\n \n11\n\n\n\n.9\n2 \n\n\n\n55\n.6\n\n\n\n5 \n0.\n\n\n\n44\n \n\n\n\n0.\n6 \n\n\n\n0.\n18\n\n\n\n \n0.\n\n\n\n03\n \n\n\n\n0.\n21\n\n\n\n \n0.\n\n\n\n07\n \n\n\n\n10\n.9\n\n\n\n6 \n10\n\n\n\n0.\n22\n\n\n\n \n20\n\n\n\n8 \n11\n\n\n\n98\n0 \n\n\n\n78\n4 \n\n\n\n \nC\n\n\n\nH\n.3\n\n\n\n \n27\n\n\n\n.2\n5 \n\n\n\n0.\n42\n\n\n\n \n11\n\n\n\n.8\n7 \n\n\n\n45\n.5\n\n\n\n8 \n0.\n\n\n\n17\n \n\n\n\n0.\n54\n\n\n\n \n0.\n\n\n\n19\n \n\n\n\n0.\n05\n\n\n\n \n0.\n\n\n\n16\n \n\n\n\n0.\n07\n\n\n\n \n15\n\n\n\n.9\n7 \n\n\n\n10\n2.\n\n\n\n27\n \n\n\n\n28\n6 \n\n\n\n10\n14\n\n\n\n1 \n10\n\n\n\n95\n \n\n\n\n \nPS\n\n\n\n.1\n \n\n\n\n21\n.5\n\n\n\n2 \n0.\n\n\n\n09\n \n\n\n\n0.\n44\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n16\n.2\n\n\n\n7 \n49\n\n\n\n.1\n6 \n\n\n\n0.\n18\n\n\n\n \n0.\n\n\n\n56\n \n\n\n\n0.\n21\n\n\n\n \n0.\n\n\n\n74\n \n\n\n\n12\n.7\n\n\n\n5 \n10\n\n\n\n1.\n98\n\n\n\n \n10\n\n\n\n4 \n41\n\n\n\n64\n \n\n\n\n37\n1 \n\n\n\n \nPS\n\n\n\n.2\n \n\n\n\n23\n.3\n\n\n\n6 \n0.\n\n\n\n8 \n17\n\n\n\n.4\n1 \n\n\n\n44\n.0\n\n\n\n3 \n0.\n\n\n\n3 \n0.\n\n\n\n5 \n0.\n\n\n\n18\n \n\n\n\n0.\n03\n\n\n\n \n0.\n\n\n\n14\n \n\n\n\n0.\n07\n\n\n\n \n14\n\n\n\n.3\n \n\n\n\n10\n1.\n\n\n\n12\n \n\n\n\n14\n9 \n\n\n\n92\n97\n\n\n\n \n58\n\n\n\n7 \n\n\n\n \nPS\n\n\n\n.3\n \n\n\n\n25\n.9\n\n\n\n8 \n0.\n\n\n\n97\n \n\n\n\n20\n.1\n\n\n\n2 \n37\n\n\n\n.6\n5 \n\n\n\n0.\n21\n\n\n\n \n0.\n\n\n\n33\n \n\n\n\n0.\n19\n\n\n\n \n0.\n\n\n\n03\n \n\n\n\n0.\n14\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n9.\n39\n\n\n\n \n95\n\n\n\n.0\n7 \n\n\n\n12\n6 \n\n\n\n81\n28\n\n\n\n \n52\n\n\n\n6 \n\n\n\n \nPS\n\n\n\n.4\n \n\n\n\n30\n.7\n\n\n\n2 \n1.\n\n\n\n32\n \n\n\n\n25\n.1\n\n\n\n5 \n27\n\n\n\n.3\n4 \n\n\n\n0.\n17\n\n\n\n \n0.\n\n\n\n21\n \n\n\n\n0.\n18\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n0.\n13\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n16\n.2\n\n\n\n1 \n10\n\n\n\n1.\n55\n\n\n\n \n95\n\n\n\n \n45\n\n\n\n23\n \n\n\n\n34\n2 \n\n\n\nSe\nrp\n\n\n\nen\ntin\n\n\n\nite\n S\n\n\n\nab\nah\n\n\n\n \nS1\n\n\n\n \n60\n\n\n\n.5\n \n\n\n\n0.\n63\n\n\n\n \n16\n\n\n\n.8\n4 \n\n\n\n11\n.3\n\n\n\n5 \n0.\n\n\n\n08\n \n\n\n\n0.\n76\n\n\n\n \n0.\n\n\n\n36\n \n\n\n\n0.\n09\n\n\n\n \n1.\n\n\n\n95\n \n\n\n\n0.\n12\n\n\n\n \n6.\n\n\n\n8 \n99\n\n\n\n.4\n8 \n\n\n\n13\n6 \n\n\n\n13\n92\n\n\n\n9 \n16\n\n\n\n75\n \n\n\n\n \nS3\n\n\n\n \n26\n\n\n\n.4\n \n\n\n\n0.\n25\n\n\n\n \n9.\n\n\n\n77\n \n\n\n\n41\n.0\n\n\n\n4 \n0.\n\n\n\n73\n \n\n\n\n8.\n47\n\n\n\n \n0.\n\n\n\n1 \n0.\n\n\n\n05\n \n\n\n\n0 \n0.\n\n\n\n05\n \n\n\n\n12\n.2\n\n\n\n5 \n99\n\n\n\n.1\n1 \n\n\n\n12\n2 \n\n\n\n14\n72\n\n\n\n0 \n22\n\n\n\n37\n \n\n\n\n \nS4\n\n\n\n \n14\n\n\n\n.5\n7 \n\n\n\n0.\n47\n\n\n\n \n17\n\n\n\n.7\n5 \n\n\n\n38\n.8\n\n\n\n2 \n0.\n\n\n\n59\n \n\n\n\n2.\n83\n\n\n\n \n0.\n\n\n\n03\n \n\n\n\n0.\n01\n\n\n\n \n0 \n\n\n\n0.\n03\n\n\n\n \n25\n\n\n\n.8\n4 \n\n\n\n10\n0.\n\n\n\n94\n \n\n\n\n14\n6 \n\n\n\n16\n38\n\n\n\n1 \n15\n\n\n\n73\n \n\n\n\n \nS5\n\n\n\n \n5.\n\n\n\n17\n \n\n\n\n0.\n94\n\n\n\n \n22\n\n\n\n.8\n9 \n\n\n\n42\n.6\n\n\n\n9 \n0.\n\n\n\n29\n \n\n\n\n0.\n31\n\n\n\n \n0.\n\n\n\n04\n \n\n\n\n0.\n03\n\n\n\n \n0.\n\n\n\n00\n5 \n\n\n\n0.\n05\n\n\n\n \n26\n\n\n\n.3\n9 \n\n\n\n98\n.8\n\n\n\n \n11\n\n\n\n2 \n14\n\n\n\n02\n9 \n\n\n\n85\n0 \n\n\n\n \nS6\n\n\n\n \n17\n\n\n\n.6\n4 \n\n\n\n0.\n24\n\n\n\n \n12\n\n\n\n.8\n5 \n\n\n\n42\n.9\n\n\n\n \n0.\n\n\n\n75\n \n\n\n\n7.\n97\n\n\n\n \n0.\n\n\n\n17\n \n\n\n\n0.\n07\n\n\n\n \n0.\n\n\n\n00\n3 \n\n\n\n0.\n04\n\n\n\n9 \n17\n\n\n\n.8\n5 \n\n\n\n10\n0.\n\n\n\n49\n \n\n\n\n16\n7 \n\n\n\n19\n02\n\n\n\n5 \n47\n\n\n\n53\n \n\n\n\n \nS7\n\n\n\n \n13\n\n\n\n.6\n7 \n\n\n\n0.\n61\n\n\n\n \n19\n\n\n\n.3\n4 \n\n\n\n45\n.4\n\n\n\n7 \n0.\n\n\n\n81\n \n\n\n\n0.\n86\n\n\n\n \n0.\n\n\n\n02\n \n\n\n\n0.\n02\n\n\n\n \n0.\n\n\n\n00\n5 \n\n\n\n0.\n04\n\n\n\n6 \n19\n\n\n\n.6\n1 \n\n\n\n10\n0.\n\n\n\n46\n \n\n\n\n12\n7 \n\n\n\n17\n23\n\n\n\n3 \n12\n\n\n\n19\n \n\n\n\n \nS8\n\n\n\n \n58\n\n\n\n.0\n5 \n\n\n\n0.\n61\n\n\n\n \n10\n\n\n\n.1\n8 \n\n\n\n11\n.4\n\n\n\n7 \n0.\n\n\n\n17\n \n\n\n\n3.\n7 \n\n\n\n0.\n13\n\n\n\n \n0.\n\n\n\n16\n \n\n\n\n0.\n63\n\n\n\n \n0.\n\n\n\n02\n9 \n\n\n\n16\n.2\n\n\n\n1 \n10\n\n\n\n1.\n33\n\n\n\n \n35\n\n\n\n \n24\n\n\n\n27\n \n\n\n\n86\n5 \n\n\n\n \nS1\n\n\n\n0 \n19\n\n\n\n.1\n2 \n\n\n\n0.\n99\n\n\n\n \n22\n\n\n\n.6\n3 \n\n\n\n42\n.0\n\n\n\n5 \n0.\n\n\n\n4 \n1.\n\n\n\n31\n \n\n\n\n0.\n09\n\n\n\n \n0.\n\n\n\n04\n \n\n\n\n0.\n01\n\n\n\n7 \n0.\n\n\n\n06\n8 \n\n\n\n13\n.2\n\n\n\n \n99\n\n\n\n.9\n1 \n\n\n\n11\n4 \n\n\n\n15\n80\n\n\n\n7 \n13\n\n\n\n11\n \n\n\n\n \nS1\n\n\n\n2 \n25\n\n\n\n.3\n8 \n\n\n\n0.\n29\n\n\n\n \n5.\n\n\n\n87\n \n\n\n\n25\n.6\n\n\n\n2 \n0.\n\n\n\n12\n \n\n\n\n0.\n29\n\n\n\n \n0.\n\n\n\n1 \n0 \n\n\n\n0.\n01\n\n\n\n4 \n0.\n\n\n\n04\n8 \n\n\n\n43\n.1\n\n\n\n8 \n10\n\n\n\n0.\n91\n\n\n\n \n93\n\n\n\n \n11\n\n\n\n27\n7 \n\n\n\n97\n2 \n\n\n\n \nS1\n\n\n\n3 \n54\n\n\n\n.6\n \n\n\n\n0.\n57\n\n\n\n \n11\n\n\n\n.8\n8 \n\n\n\n12\n.7\n\n\n\n8 \n0.\n\n\n\n18\n \n\n\n\n8.\n71\n\n\n\n \n0.\n\n\n\n33\n \n\n\n\n0.\n11\n\n\n\n \n1.\n\n\n\n26\n6 \n\n\n\n0.\n02\n\n\n\n7 \n9.\n\n\n\n46\n \n\n\n\n99\n.9\n\n\n\n1 \n46\n\n\n\n \n28\n\n\n\n28\n \n\n\n\n14\n18\n\n\n\n\n\n\n\n \nS1\n\n\n\n4 \n19\n\n\n\n.3\n7 \n\n\n\n0.\n91\n\n\n\n \n20\n\n\n\n.9\n9 \n\n\n\n41\n.8\n\n\n\n2 \n0.\n\n\n\n8 \n0.\n\n\n\n27\n \n\n\n\n0.\n02\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n03\n\n\n\n7 \n0.\n\n\n\n04\n9 \n\n\n\n14\n.9\n\n\n\n8 \n99\n\n\n\n.2\n6 \n\n\n\n14\n1 \n\n\n\n14\n97\n\n\n\n2 \n12\n\n\n\n03\n \n\n\n\n \nS1\n\n\n\n5 \n17\n\n\n\n.6\n8 \n\n\n\n0.\n9 \n\n\n\n22\n.7\n\n\n\n8 \n40\n\n\n\n.9\n1 \n\n\n\n0.\n19\n\n\n\n \n1.\n\n\n\n68\n \n\n\n\n0.\n02\n\n\n\n \n0.\n\n\n\n05\n \n\n\n\n0.\n00\n\n\n\n2 \n0.\n\n\n\n03\n7 \n\n\n\n15\n.2\n\n\n\n6 \n99\n\n\n\n.5\n1 \n\n\n\n10\n5 \n\n\n\n27\n86\n\n\n\n3 \n16\n\n\n\n93\n \n\n\n\nC\nro\n\n\n\nck\ner\n\n\n\n S\nab\n\n\n\nah\n \n\n\n\nS2\n \n\n\n\n60\n.5\n\n\n\n0 \n0.\n\n\n\n83\n \n\n\n\n19\n.0\n\n\n\n3 \n7.\n\n\n\n93\n \n\n\n\n0.\n17\n\n\n\n \n1.\n\n\n\n09\n \n\n\n\n0.\n03\n\n\n\n \n0.\n\n\n\n19\n \n\n\n\n3.\n63\n\n\n\n \n0.\n\n\n\n09\n \n\n\n\n6.\n52\n\n\n\n \n10\n\n\n\n0.\n01\n\n\n\n \n21\n\n\n\n \n18\n\n\n\n2 \n27\n\n\n\n0 \n\n\n\n \nS9\n\n\n\n \n69\n\n\n\n.8\n1 \n\n\n\n0.\n71\n\n\n\n \n13\n\n\n\n.6\n9 \n\n\n\n6.\n27\n\n\n\n \n0 \n\n\n\n0.\n47\n\n\n\n \n0.\n\n\n\n03\n \n\n\n\n0.\n21\n\n\n\n \n1.\n\n\n\n47\n \n\n\n\n0.\n03\n\n\n\n \n7.\n\n\n\n21\n \n\n\n\n99\n.9\n\n\n\n.0\n \n\n\n\n16\n \n\n\n\n17\n3 \n\n\n\n35\n \n\n\n\n \nS1\n\n\n\n1 \n65\n\n\n\n.9\n5 \n\n\n\n0.\n35\n\n\n\n \n20\n\n\n\n.1\n7 \n\n\n\n3.\n29\n\n\n\n \n0 \n\n\n\n0.\n25\n\n\n\n \n0.\n\n\n\n03\n \n\n\n\n0.\n03\n\n\n\n \n0.\n\n\n\n94\n \n\n\n\n0.\n01\n\n\n\n \n9.\n\n\n\n11\n \n\n\n\n10\n0.\n\n\n\n13\n \n\n\n\n11\n \n\n\n\n67\n \n\n\n\n33\n \n\n\n\n  \nS1\n\n\n\n6 \n63\n\n\n\n.2\n1 \n\n\n\n0.\n84\n\n\n\n \n20\n\n\n\n.9\n7 \n\n\n\n11\n.8\n\n\n\n8 \n0.\n\n\n\n3.\n0 \n\n\n\n3.\n96\n\n\n\n \n0.\n\n\n\n33\n \n\n\n\n0.\n41\n\n\n\n \n4.\n\n\n\n77\n \n\n\n\n0.\n17\n\n\n\n \n4.\n\n\n\n6 \n11\n\n\n\n1.\n44\n\n\n\n \n23\n\n\n\n \n12\n\n\n\n1 \n91\n\n\n\n\n\n\n\n\n\n\n\nGeochemical Characteristics of Serpentinite Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201440\n\n\n\nhumid tropical conditions reported by other researchers (Brooks 1987; Caillaud \net al. 2009).\n\n\n\nThe other striking result that emerges from Table 1 is the contents of CaO, \nNa2O, K2O and P2O5 which are detected in almost negligible amounts in all of the \nsamples (a similar average of 0.1% wt). The obtained values of major oxides from \nserpentinite soils in Malaysia are consistent with the chemical composition of the \nmany ultramafic massifs from throughout the world as listed by Brooks (1987). \n\n\n\nAmong the 20 analysed trace elements in the serpentinites, chromium, nickel \nand cobalt show anomalous elevated values. As seen in Table 1, the concentration \nrange of these metals in serpentinite soils of Peninsular Malaysia is as follows: \nCr 1,248-11,980 \u03bcg g-1, Ni 189-1,687 \u03bcg g-1 and Co 95 \u2013 464 \u03bcg g-1. In Sabah, the \nconcentration ranges of the trace metals were Cr 2,427-27,863; Ni 850-4,753; and \nCo 35-167 (in \u03bcg g-1). On average, the concentration ranges of Cr, Ni and CO of \nthe studied serpentinite soils of Peninsular Malaysia and Sabah were 10,302 \u03bcg \ng-1, 1120 \u03bcg g-1 and 166 \u03bcg g-1, respectively.\n\n\n\nThe findings of this research are in agreement with those of other studies \nwhich suggest the occurrence of the highest geogenic concentration of Cr, Ni and \nCo in Malaysian ultramafic soils (Osama 2007).  According to Kabata-Pendias \nand Mukherjee (2007), the world average content of chromium in soils is 54 \u00b5g/g, \nwhile the Cr content can reach up to 125000 \u00b5g g-1 in serpentinised ultramafic \nsoils (Shanker et al. 2005; Adriano 2001). The common background range of \naverage soil Ni content is less than 100 \u00b5g g-1 but in the presence of ultramafic \nbedrocks such as serpentinites, values of more than 10,000 \u00b5g g-1 have been cited \n\n\n\nFigure 2: The location of 15 Peninsular  Malaysia and 5 Sabah serpentinite soil samples \non the (SiO2 - MgO- Al2O3+Fe2O3) ternary diagram\n\n\n\nMahsa Tashakor, Wan Zuhairi Wan Yaacob, Hamzah Mohamad and Azman A. Ghani\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 41\n\n\n\nGeochemical Characteristics of Serpentinite Soils\n\n\n\n(Hseu 2006). According to Mattigod and Page (1983), high amounts of heavy \nmetals due to parent materials constitute natural cases of metal pollution. \n\n\n\nFor a comparison of the elemental composition of serpentinite soils with \nthat of non-serpentinite soils, clastic sedimentary formations of Crocker were \nadditionally analysed. As shown in Table 1, the following major oxides were \nobserved: SiO2 60.5-69.81, Al2O3 13.69-20.97, Fe2O3 3.29-11.88, MnO 0.0-0.3, \nMgO 0.25-3.96, CaO 0.03-0.33, Na2O 0.03-0.41, K2O 0.94-4.77 and P2O5 0.01-\n0.17 (in wt %). Moving to the trace elements, none of them showed any notable \nconcentration. The values of Cr, Ni and Co varied in range from 67-182, 33-270 \nto 11-23 (in \u03bcg g-1), respectively. The average amounts of Cr, Co and Ni were 136, \n18 and 107 \u03bcg g-1, respectively. \n\n\n\nComparison Between Cr, Ni and Co in Serpentinite and Crocker Soils of Ranau\nComparative values of the minimum, average and maximum amounts of some \nrepresentative major and trace elements from the serpentinite and Crocker soils of \nRanau are presented in Table 2. The data show that Crocker soils contain between \n60.50 to 69.81 wt % SiO2, but this amount is only between 5.17 to 60.50 wt % in \nserpentinite soils. On the other hand, the average Fe2O3 (33.08 \u03bcg g-1) and MgO \n(3.10 \u03bcg g-1) in serpentinite soils is considerably higher than that of the Crocker \nsoils (Fe2O3 7.34 and MgO 1.44 \u03bcg g-1). \n\n\n\nThe significant differences between SiO2, Fe2O3 and MgO contents indicate \nthat parent rocks and mineralogical context of serpentinite and Crocker soils \nare very different. Another important finding is the concentration level of Cr, \nNi and Co. Figure 3 compares these amounts in the serpentinite and Crocker \nsoils. Concentration levels of Cr, Ni and Co in serpentinite soils were 2,427-\n27,863, 850-4,753 and 35-167 (in \u03bcg g-1) respectively. However, soils of Crocker \nformation show these amounts to be 67-182 for Cr and 33-270 for Ni and 11-23 \nfor Co (in \u03bcg g-1). It is obvious that the amounts of Cr, Ni and Co in the studied \nserpentinites soils are respectively 105, 15 and 6 times higher than the amounts \nin Crocker soils. \n\n\n\n12 \n \n\n\n\n\n\n\n\nTABLE 2 \nThe comparison between the concentration of SiO2, Fe2O3, MgO (in wt %) and Cr, Ni and Co (in \u00b5g g-1) in \n\n\n\nserpentinite and Crocker soils of Ranau, Sabah \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nSerpentinite soil Crocker soil \n\n\n\nElement Min. Ave. Max. Min. Ave. Max. \n\n\n\nSiO2 5.17 27.68 60.50 60.50 64.87 69.81 \nFe2O3 11.35 33.08 45.47 3.29 7.34 11.88 \nMgO 0.27 3.10 8.71 0.25 1.44 3.96 \nCo 35.00 112.00 167.00 11.00 18.00 23.00 \nCr 2427.00 14208.00 27863.00 67.00 136.00 182.00 \nNi 850.00 1647.00 4753.00 33.00 107.00 270.00 \n\n\n\nTABLE 2\nThe comparison between the concentration of SiO2, Fe2O3, MgO (in wt %) and Cr, Ni \n\n\n\nand Co (in \u00b5g g-1) in serpentinite and Crocker soils of Ranau, Sabah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201442\n17 \n\n\n\n\n\n\n\n\n\n\n\nChromium\n\n\n\nNickel\n\n\n\nCobalt\n\n\n\nFigure 3: Comparison between maximum, average and minimum amounts of Cr, Ni and \nCo (in \u03bcg g-1) in serpentinite and Crocker soils of Ranau, Sabah\n\n\n\nMahsa Tashakor, Wan Zuhairi Wan Yaacob, Hamzah Mohamad and Azman A. Ghani\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 43\n\n\n\nGeochemical Characteristics of Serpentinite Soils\n\n\n\nThere is a noticeable difference in elemental composition between the two \ngroups of soils. The elevated values of heavy metals are strongly associated \nwith serpentinite soils. The reason is that soil composition always reflects the \ngeochemistry and mineralogy of the underlying parent materials. Ionic substitutions \nof ferromagnesian minerals during alterations enrich chromium, cobalt and, nickel \nin serpentinite rocks (Brooks 1987). These elements (sidrophile) substitute cations \nof Fe, Mn, and Mg in octahedral sheets of the primary serpentinite minerals. \nTherefore, soils developed from serpentinite rocks contain elevated amounts of \nheavy metals. Being naturally metal-loaded, serpentinites might specifically pose \nserious ecological risks. \n\n\n\nContent of Cr, Ni and Co in Peninsular Malaysia and Sabah\nThe chemical composition of serpentinite soils in Peninsular Malaysia was \ncompared to that of Sabah. Figure 4 shows the concentrations of Cr, Ni and \nCo in the soils of the surveyed outcrops in Bukit Rokan, Petasih, Batu Malim \nand Cheroh in Peninsular Malaysia and the serpentinite outcrop of Ranau in \nSabah. The first striking result that appeared in the data was that Cr and Ni had \nhigher concentrations in Ranau soils, but Co was highest in Bukit Rokan soils. \n\n\n\nFigure 4: Bar charts of chromium, nickel and cobalt distribution (in \u03bcg g-1) in the five \nstudied areas of Peninsular Malaysia and Sabah\n\n\n\n19 \n \n\n\n\n\n\n\n\nFigure 4: Bar charts of chromium, nickel and cobalt distribution (in \u03bcg g-1) in the five studied \nareas of Peninsular Malaysia and Sabah \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201444\n\n\n\nMahsa Tashakor, Wan Zuhairi Wan Yaacob, Hamzah Mohamad and Azman A. Ghani\n\n\n\nThe amount of Cr in Ranau ranged between a minimum of 2,427 \u03bcg g-1 and a \nmaximum of 27,863 \u03bcg g-1, with an average of 14233 \u03bcg g-1. However, in Bukit \nRokan, Batu Malim and Cheroh, the average amounts of Cr were 8,627, 7,763, and \n7,625 \u03bcg g-1, respectively. The lowest amount of Cr was observed in Petasih where \nit ranged from 2,512 to 9,297 \u03bcg g-1 (average of 5,452 \u00b5g g-1). Ni content in the \nstudied areas showed almost the same trend as Cr. Ranau had the largest amount \nof Ni (between 850-4,753 \u03bcg g-1) in comparison with other areas. Ni in Ranau \naveraged 1,645 \u03bcg g-1 with Cheroh being a close second with Ni amounts ranging \nfrom 536 to 1,622 \u03bcg g-1with the average being 1074 \u03bcg g-1). Ni concentration \nlevel decreased from 897 \u03bcg g-1 in Bukit Rokan to 821 \u03bcg g-1 in Batu Malim. The \nleast amount of Ni with an overall mean of 551 \u03bcg g-1was found in Petasih. The \nelement of Co was dominant in Bukit Rokan with an average value of 286 \u03bcg g-1 \n(Figure 4). Batu Malim and Cheroh areas had almost a similar overall mean of \nCo (250 and 244 \u03bcg g-1, respectively), while the average amount of Co in Petasih \nwas 145 \u03bcg g-1. Interestingly, Ranau had the lowest concentration of Co at 110 \u03bcg \ng-1 (on average).\n\n\n\nIt is apparent from the data that the elemental concentrations in serpentinite \nsoils of the four areas in Peninsular Malaysia show similar patterns with a relatively \nsmall standard deviation from the concentration level trends in Ranau area. This \ndissimilarity possibly occurred as a result of the difference in the genesis, geo-\nsetting and parent bedrocks of the ultramafic clans in West and East Malaysia. \n\n\n\nEvaluation of the Quality of Serpentinite Soils of Peninsular Malaysia and Sabah\nIn order to assess the quality of serpentinite soils, the average amounts of Cr, \nNi and Co in Peninsular Malaysia and Sabah samples were compared with the \naverage soil composition of the Earth (Siegel, 1975), Dutch List Standard (2001) \nand G.I.C guidelines (2001) (Table 3). A comparison of the metal concentration \nwith the global average soil composition showed that all of the analysed soils \npresented Cr, Ni and Co concentrations that were significantly higher than the\naverage soil composition given by Siegel (1975). The average amounts of Cr, Ni \nand Co in serpentinite soils of Peninsular Malaysia were respectively 72, 17 and \n21 times higher than the average soil composition. Similarly, the values of Cr, Ni \nand Co in Sabah soils were 142, 41 and 11, times, respectively, larger than the \naverage soil composition.\n\n\n\nComparing with the Dutch list standard, the concentration values of Cr \n(Peninsular Malaysia 7,177 and Sabah 14,208 \u03bcg g-1) and Ni (Peninsular 698 and \nSabah 1,647 \u03bcg g-1) in both the studied areas were categorised into group C which \nindicates the presence of crucial pollution and is considered a serious threat to \nthe environment (Table 3). Based on the Co content in the soils of Peninsular \nMalaysia (209 \u03bcg g-1) and Sabah (112 \u03bcg g-1), the studied soils were categorised in \ngroup B of the Dutch list. Group B refers to polluted soils with possible hazardous \nimpact on the environment. Further investigation on the soils of this nature is \nnecessary. Values of Cr, Ni and Co of the studied serpentinite soils exceeded the \nvalues provided by the Dutch List standard, with Cr being 9-18 and Ni being 1-3 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 45\n\n\n\nTABLE 3\nThe average elemental composition of Cr, Ni and Co in Peninsular and Sabah Malaysia \n\n\n\nin comparison with the average soil composition, the Dutch List standard and the \nC.L.C guideline (in \u03bcg g-1)\n\n\n\n13 \n \n\n\n\nTABLE 3 \nThe average elemental composition of Cr, Ni and Co in Peninsular and Sabah Malaysia in comparison with the average soil composition, the \n\n\n\nDutch List standard and the C.L.C guideline (in \u03bcg g-1) \n \n \n \n\n\n\n\n\n\n\nAverage \nSerpentinite soil \n\n\n\nAverage  \nSoil composition1 Dutch List Standard2 G.L.C guidelines3 \n\n\n\nElements Peninsular Sabah \n \n\n\n\nA B C I II III IV V \n\n\n\nCr 7177 14208 100 100 250 800 100 200 500 2500 >2500 \nNi 698 1647 40 50 100 500 20 50 200 1000 >1000 \nCo 209 112 10 20 50 300 - - - - - \n\n\n\n \n1. Average soil composition from (Siegel, 1975) \n2. Dutchlist (2001) standard adopted in Netherland for soil contaminations:  \n\n\n\nA. Reference value below which soils are completely uncontaminated \nB. Value above which for further investigation is required \nC. Value above which a clean-up is needed \n\n\n\n3. G.L.C guidelines(2001); definitions of contaminated soils - suggested range of values: \nI. Typical values for uncontaminated soils \nII. Slight contamination \nIII. Contaminated \nIV. Heavy contamination \nV. Unusually heavy contamination \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGeochemical Characteristics of Serpentinite Soils\n\n\n\ntimes higher than critical values and Co being 2-4 times higher than optimum \nvalues. \n\n\n\nComparing these values to the Great London Council (G.L.C), Peninsular \nMalaysia and Sabah serpentinite soils (by containing more than 2,500 \u03bcg g-1 \n\n\n\nchromium) are registered as unusually heavy contaminated soils (category V). \nThe elevated concentration of Ni (698 \u03bcg g-1) in the serpentinite soils of Peninsular \nMalaysia puts them in the category of a heavy contaminated soil (category IV). \nSerpentinite soils of Sabah are unusually heavily contaminated (category V) as \nthey contain 1647 \u03bcg g-1 Ni. The G.L.C guideline has not furnished a classification \nfor Co values. With regard to the mentioned contamination criteria, the studied \nserpentinite soils of all the areas are polluted by the aforementioned metals. \n\n\n\nHeavy metal contamination of soils is an issue of great concern in \nenvironmental studies. Metal contaminants are serious threats to the environment \nbecause they are very persistent and irreversible even after many years. They can \ncause soil infertility and paucity of vegetation. They are also able to affect the \nhealth of people adversely by penetrating into the water system or entering into \nthe food chain.   \n\n\n\nCONCLUSION\nSerpentinite soils from Peninsular Malaysia and Sabah have been depleted of \nsilica and essential plant nutrients such as calcium, potassium and phosphorus In \ncontrast, they are remarkably rich in Cr, Ni and Co. The significant differences \nbetween the elemental compositions of the serpentinite soils with their adjacent \nsedimentary soils in Ranau Sabah clearly support geogenic hyperaccumulation \nof Cr, Ni and Co. The concentration of the concerned metals within all the \ninvestigated serpentinite soils exceeds the values given by the Dutch list and \n\n\n\n1. Average soil composition from (Siegel, 1975)\n2. Dutchlist (2001) standard adopted in Netherland for soil contaminations: \n\n\n\nA. Reference value below which soils are completely uncontaminated\nB. Value above which for further investigation is required\nC. Value above which a clean-up is needed\n\n\n\n3. G.L.C guidelines(2001); definitions of contaminated soils - suggested range of values:\nI. Typical values for uncontaminated soils\nII. Slight contamination\nIII. Contaminated\nIV. Heavy contamination\nV. Unusually heavy contamination\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201446\n\n\n\nMahsa Tashakor, Wan Zuhairi Wan Yaacob, Hamzah Mohamad and Azman A. Ghani\n\n\n\nGreat London Council standards which corroborate the occurrence of serious \nnatural pollution. Hence, the studied serpentinite derived soils are assumed to be \nnon-conducive environments for fauna and flora.\n\n\n\nREFERENCES\nAdriano, D.C. 2001. Trace Elements in Terrestrial Environments: Biogeochemistry, \n\n\n\nBioavailability, and Risks of Metals  (2 ed.). USA: Springer.\n\n\n\nAlexander, E.B. 2004. Serpentine soil redness, differences among peridotite and \nserpentinite materials, Klamath Mountains, California. International Geology \nReview. 46(8): 754-764.\n\n\n\nAlexander, E.B., R.G. Coleman, T. Keeler-Wolfe and S.P. Harrison. 2006. Serpentine \nGeoecology of Western North America: Geology, Soils, and Vegetation. USA: \nOxford University Press.\n\n\n\nAmir, H. and R. Pineau. 2003. Relationships between extractable Ni, Co, and other \nmetals and some microbiological characteristics of different ultramafic soils \nfrom New Caledonia. Soil Research. 41(2): 215-228.\n\n\n\nArikan, F. and N. Aydin. 2012. Influence of Weathering on the Engineering Properties \nof Dacites in Northeastern Turkey. ISRN Soil Science. 2012:15.\n\n\n\nBaker, A.J.M. and R.R. Brooks. 1988. Botanical exploration for minerals in the humid \ntropics. Journal of Biogeography. 15(1): 221-229.\n\n\n\nBecquer, T., C. Quantin, S. Rotte-Capet, J. Ghanbaja, C.  Mustin and A.J.  Herbillon. \n2006. Sources of trace metals in Ferralsols in New Caledonia. European journal \nof Soil Science. 57(2): 200-213.\n\n\n\nBonifacio, E., E. Zanini, V. Boero and M. Franchini-Angela. 1997. Pedogenesis in a \nsoil catena on serpentinite in north-western Italy. Geoderma. 75(1\u20132): 33-51.\n\n\n\nBrearley, F. 2005. Nutrient limitation in a Malaysian ultramafic soil. 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Intervention values and target values - soil quality standards (online) \nhttp://www.contaminatedland.co.uk/std-guid/dutch-l.htm. The Ministry of \nHousing, Spatial Planning and Environment [Retrieved 7 May 2009].\n\n\n\nGough, L.P., G. Meadows, L. Jackson and S. Dudka. 1989. Biogeochemistry of a \nHighly Serpentinized, Chromite-Rich Ultramafic Area, Tehama County, \nCalifornia. USA: Government Printing Office.\n\n\n\nGraham, R.C., M.M. Diallo and L.J. Lund.  1990. Soils and mineral weathering on \nphyllite colluvium and serpentinite in Northwestern California. Soil Science \nSociety of America Journal. 54(6): 1682-1690.\n\n\n\nGreat London Council. 2001. Guidelines for Contaminated Soils (on-line) http://www.\ncontaminatedland.co.uk/std-guid/kelly-l.htm. Society of the Chemical Industry \n[Retrieved 11 May 2009].\n\n\n\nHseu, Z.-Y. 2006. Concentration and distribution of chromium and nickel fractions \nalong a serpentinitic toposequence. 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Proenza and J. Jim\u00e9nez. 2006. Ophiolite-related \nultramafic rocks (serpentinites) in the Caribbean region: A review of their \noccurrence, composition, origin, emplacement and Ni-laterite soil formation. \nGeologica Acta: An International Earth Science Journal. 4(1): 237-264.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201448\n\n\n\nMattigod, S. and A. Page. 1983. Assessment of metal pollution in soils. Applied \nEnvironmental Geochemistry. London:  Academic Press. 355-394.\n\n\n\nO\u2019Hanley, D.S. 1996. Serpentinites: Records of Tectonic and Petrological History. \nNew York: Oxford University Press \n\n\n\nOsama, A.A.T. 2007. A study on the alternative resources of chromium, cobalt \nand nickel from ultrabasic soils of Sabah, Malaysia. Ph.D thesis, School \nof Environmental and Natural Resource Sciences, Universiti Kebangsaan \nMalaysia.\n\n\n\nOze, C., S. Fendorf, D.K. Bird and R.G. Coleman. 2004a. Chromium geochemistry in \nserpentinized ultramafic rocks and serpentine soils from the Franciscan complex \nof California. American Journal of Science. 304(1): 67-101.\n\n\n\nOze, C., S. Fendorf, D.K. Bird and R.G. Coleman. 2004b. Chromium geochemistry of \nserpentine soils. International Geology Review. 46(2): 97-126.\n\n\n\nProctor, J. 2003. Vegetation and soil and plant chemistry on ultramafic rocks in the \ntropical Far East. Perspectives in Plant Ecology, Evolution and Systematics. \n6(1-2): 105-124.\n\n\n\nQuantin, C., T. Becquer, J. Rouiller and  J. Berthelin. 2002. Redistribution of metals \nin a New Caledonia Ferralsol after microbial weathering. Soil Science Society \nof America Journal. 66(6): 1797-1804.\n\n\n\nQuantin, C., V. Ettler, J. Garnier and O. \u0160ebek. 2008. Sources and extractibility of \nchromium and nickel in soil profiles developed on Czech serpentinites. Comptes \nRendus Geoscience. 340(12): 872-882.\n\n\n\nRepin, R. 1998. Preliminary survey of serpentine vegetation areas in Sabah. Sabah \nParks Nature Journal. 1: 19\u201328.\n\n\n\nSchwertmann, U. and M. Latham. 1986. Properties of iron oxides in some new \ncaledonian oxisols. Geoderma. 39(2): 105-123.\n\n\n\nShallari, S., C. Schwartz, A. Hasko and J.L. Morel. 1998. Heavy metals in soils \nand plants of serpentine and industrial sites of Albania. Science of The Total \nEnvironment. 209(2\u20133): 133-142.\n\n\n\nShanker, A.K., C. Cervantes, H.  Loza-Tavera and S. Avudainayagam. 2005. Chromium \ntoxicity in plants. Environment International. 31(5): 739-753.\n\n\n\nSiebecker, M. 2010. Nickel speciation in serpentine soils using synchrotron radiation \ntechniques. 19th World Congress of Soil Science: Soil Solutions for a Changing \nWorld. Brisbane Australia, August 1-6 \n\n\n\nMahsa Tashakor, Wan Zuhairi Wan Yaacob, Hamzah Mohamad and Azman A. Ghani\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 49\n\n\n\nSiegel, F.R. 1975. Applied Geochemistry. New York: Wiley.\n\n\n\nSkordas, K. and A. Kelepertsis. 2005. Soil contamination by toxic metals in the \ncultivated region of Agia, Thessaly, Greece. Identification of sources of \ncontamination. Environmental Geology. 48(4): 615-624.\n\n\n\nTashakor, M., W.Y. Wan Zuhairi and M. Hamzah. 2012. Does rock and soil \ngeochemistry of ultrabasic land affect the run-off water? A case study at Ranau, \nSabah. pp. 252-256. 3rd International Conference on Environmental Research \nand Technology (ICERT), Penang, Malaysia \n\n\n\nTurekian, K.K.and K.H. Wedepohl. 1961. Distribution of the elements in some major \nunits of the earth\u2019s crust. Geological Society of America Bulletin. 72(2): 175-\n192.\n\n\n\nGeochemical Characteristics of Serpentinite Soils\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 80-87 \n\n\n\n80 \n\n\n\n\n\n\n\n\n\n\n\nInfluence of Potassium Fertilizers on Stable Carbon Isotopic Ratio in Rice \n\n\n\n \nNuraini, S.M.A., Adibah, M.A.* and Sulaiman, M.F. \n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400 \n\n\n\nSerdang, Selangor, Malaysia \n\n\n\n \n*corresponding author email: adibahamin@upm.edu.my \n\n\n\n\n\n\n\nABSTRACT \n\n\n\nIsotope measurements are associated with critical plant resources. Thus, stable rice crop\u2019s carbon \nisotope composition in response to potassium (K) fertilizer was determined,since K is a primary plant \n\n\n\nnutrient that plays a major role in achieving maximum economic yields. Rice plants were grown in \n\n\n\nthe field and rain shelter under five treatments, which are T1 (no-K), T2 (MOP), T3 (SOP), T4 \n(Polyhalite) and T5 (conventional fertilizer). The fertilizers were applied 3 days after planting (DAP), \n\n\n\n15 DAP, 55 DAP and 75 DAP. Leaf photosynthesis and stomata conductance measurements were \n\n\n\ntaken 85 DAP. The samples were then dried and reserved for carbon isotope analyses. Photosynthesis \n\n\n\ndeclined due to K deficiency in the no-K treatment. From this research, we can conclude that stomatal \nconductance is affected by K fertilizer application where it controls the carboxylation efficiency which \n\n\n\nmay affect the rate of photosynthesis. Later, photosynthesis may influence the discrimination of \u03b413C \n\n\n\nisotope value. There is discrimination against this heavier isotope of CO2, this is because CO2 diffuses \nthrough stomata by carbon 12 faster than carbon 13 so that is why there is discrimination against \n\n\n\ncarbon 13 in the stomata.  \n\n\n\n \nKeywords:  Oryza sativa L; Potassium fertilizer; Isotope; Photosynthesis \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\nRice (Oryza sativa L.) is the most widely consumed staple food, consumed by more than half \n\n\n\nof the world\u2019s population. Rice is the third most widely planted crop in Malaysia, after oil palm \n\n\n\nand rubber. In the year 2014, 679,239 ha of Malaysia\u2019s land was planted with rice (DOA Crop \n\n\n\nStatistic 2017). In 2017, the area was 730,145 ha of which 558,203 ha were in Peninsular \n\n\n\nMalaysia, with the remaining in Sabah and Sarawak (DOA Crop Statistic 2017). Increasing \n\n\n\npopulation and preferences has caused Malaysia to be among the largest rice importer in the \n\n\n\nworld. Malaysia requires more than 1000 metric tonnes of rice to fulfil its local demand. \n\n\n\nLike most crops, rice requires Potassium (K) to improve its yield. K increases the plant\u2019s ability \n\n\n\nto resist various biotic and abiotic stress by regulating stomatal conductance to improve \n\n\n\nphotosynthesis, improving osmotic adjustment, regulating enzyme functions and protein \n\n\n\nsynthesis and maintaining ionic homeostasis (Wang et al. 2013). Potassium is a primary plant \n\n\n\nnutrient that plays a major role in achieving maximum economic yield. Furthermore, K is the \n\n\n\nnutrient that most frequently limits plant growth and crop yields. Increased application of K \n\n\n\nhas been shown to enhance photosynthetic rate, plant growth, yield, and drought resistance in \n\n\n\ndifferent crops under water stress conditions (Egilla et al. 2005).  \n\n\n\nPlants utilize K as the positive charged K+ cation and a large amount of K is needed to achieve \n\n\n\nmaximum yield potential. K is relatively immobile in most soils since K+ ions are held in an \n\n\n\n\nmailto:adibahamin@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 80-87 \n\n\n\n81 \n\n\n\n\n\n\n\nexchangeable form by negatively charged clay particles. In addition, Romheld and Kirkby \n\n\n\n(2010) stated that essential roles for K are found in energy transfer and utilization, protein \n\n\n\nsynthesis, carbohydrate metabolism, transport of sugars from leaves to fruits, and production \n\n\n\nand accumulation of oils. \n\n\n\nCarbon isotope is a widely used and powerful method for looking at plant physiology. The \n\n\n\n\u03b413C value has long been used as a standard technique for determining drought tolerance and \n\n\n\nimprovement in C3 genotypes. Igamberdiev et al. (2004), Robinson et al. (2000) and Farquhar \n\n\n\net al. (1989) found that drought reduces leaf \u03b413C abundance, which is linked to stomatal \n\n\n\naperture, photosynthetic impacts by carboxylation and changes in water use efficiency (WUE). \n\n\n\nIt can be used to infer variation in the ratio of photosynthesis to stomatal conductance. \n\n\n\nTherefore, in this study, the response in carbon isotope composition (\u03b413C), stomatal \n\n\n\nconductance and photosynthesis rate were analyzed in rice grown in the field and rain shelter \n\n\n\nunder different treatments of K fertilizers. This will allow us to determine whether K fertlizer \n\n\n\napplication ensures improvement in rice plant\u2019s physiology. \n\n\n\n\n\n\n\nMATERIALS & METHODS \n\n\n\n\n\n\n\nPlanting material \n\n\n\nMR 219 rice variety was used as the planting material in this study. This is due to almost 90% \n\n\n\nof rice cultivated in Malaysia are MR 219 variety, making it the most popular rice variety in \n\n\n\nMalaysia. It was made from a cross between the MR 137 and MR 151 varieties which was \n\n\n\nreleased by the Malaysian Agricultural Research and Development Institute (MARDI) in 2001. \n\n\n\nMR 219 is known to have a short maturation period (105 \u2013 111 days) and is highly resistant to \n\n\n\nblast, bacterial leaf blight and brown plant hopper (Elixon et al. 2012). It has a soft texture due \n\n\n\nto the low amylose content and is classified as a long grain grade. With good water \n\n\n\nmanagement and fertilizer input, MR 219 has the potential to produce up to 10 t/ha yield.  \n\n\n\nStudy area \n\n\n\nThe study was conducted in the two largest granary areas in Malaysia (KADA and MADA): \n\n\n\n1) Alor Bakat, KADA, Kelantan on the east coast of the Peninsula (5.955354 N, 102.332283 \n\n\n\nE) and 2) PPK Sungai Limau, MADA, Kedah in the west coast of the Peninsula (5.887880 N, \n\n\n\n100.439819 E). MADA and KADA are the largest rice granary areas in Malaysia, and the \n\n\n\nperformance of rice in these areas are extremely important as it influences the overall \n\n\n\nperformance of the rice industry.  \n\n\n\n\n\n\n\nMeanwhile, a rain shelter experiment was conducted since some of the parameters \n\n\n\nobserved are not statistically different between treatments due to high spatial variability in the \n\n\n\nfield. Thus, rice was also grown in rain shelters using soils from KADA and MADA paddy \n\n\n\nfields. The topsoil range of 0-15 cm were taken from the paddy fields. The rain shelter study \n\n\n\nsite was at the Faculty of Agriculture UPM, Selangor (2.98334 N, 101.73492 E).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 80-87 \n\n\n\n82 \n\n\n\n\n\n\n\n \nFigure 1: Distribution map of rice production areas in Peninsular Malaysia \n\n\n\nSource: DOA Crop Statistic (2015) \n\n\n\n\n\n\n\nStable Isotopes Determination \n\n\n\nFive treatments were applied which are T1) No K, T2) MOP, T3) SOP, T4) Polyhalite and T5) \n\n\n\nConventional Fertilizer. The area for each plot in the field study is 4 m2 (2 m x 2 m), with five \n\n\n\nreplicates. The experimental layout for the field treatment was done using Randomized \n\n\n\nComplete Block Design (RCBD). The plant samples were analysed for their carbon and \n\n\n\nnitrogen isotopes using an Isotope Ratio Mass Spectrometer (IRMS) in Jabatan Kimia \n\n\n\nMalaysia. Three mg of dry samples were weighed into tin capsules and introduced using an \n\n\n\nautosampler into the elemental analyser. The elements of carbon and nitrogen in samples was \n\n\n\ncombusted into CO2 and N2 gas, and then the CO2 was diluted by a dilutor. After that, carrier \n\n\n\ngas was channeled into an isotope ratio mass spectrometer. The Carbon stable isotope \n\n\n\ncompositions of each sample was determined by the same analysis. \n\n\n\nLeaf Physiology \n\n\n\nMeasurements of net CO2 assimilation (A) and stomatal conductance (gs) were determined \n\n\n\nusing the flag leaves of the representative plants using an open-top portable photosynthesis \n\n\n\nsystem (Li-6400XT, LI-COR, Lincoln, Nebraska \u2013 USA).  Saturating red LED light (1800 \n\n\n\n\u03bcmol m\u22122 s\u22121) with 10% blue light of the system was used during the measurements. A \n\n\n\nCO2 cartridge was also used to supply a constant 400 ppm concentration as the reference line \n\n\n\nsetting in the leaf chamber. During gas exchange measurements, the air temperature and the \n\n\n\nhumidity in the leaf chamber were set to match the current environmental conditions, and the \n\n\n\nvapor pressure deficit (VPD) was set to 1.8 for consistency purposes. All the physiological \n\n\n\nparameters (A, and gs) were taken between 9 am and 12 pm. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 80-87 \n\n\n\n83 \n\n\n\n\n\n\n\nData Analysis \n\n\n\nData obtained from this study were analyzed by one-way ANOVA for analysis of variance and \n\n\n\nTukey test for mean comparison using SAS version 9.2 (SAS Institute, Inc., Cary, N.C., USA). \n\n\n\nSignificant differences were calculated by post hoc tests across all methods for all materials \n\n\n\nusing Tukey's HSD at P < 0.05.  \n\n\n\n\n\n\n\nRESULT AND DISCUSSION \n\n\n\n\n\n\n\n\u03b413C Value in Rice Plant Tissue at Different Fertilizer Applications \n\n\n\nWe evaluated rice plants\u2019 \u03b413C trough rice leaves with different types of fertilizer under 4 \n\n\n\nseparate experiments. As seen in Table 1, there were no significant differences between the \n\n\n\n\u03b413C value in KADA field and KADA rain shelter experiment while the \u03b413C value is \n\n\n\nsignificantly different in MADA field and MADA rain shelter. The result in MADA field \n\n\n\nshowed a more positive \u03b413C value on T1. This is because of low rate of photosynthesis which \n\n\n\nis 3.78 \u00b5mol CO2 m\n-2 s-1 due to no K fertilizer applied. Meanwhile in MADA rain shelter T2 \n\n\n\nshowed a more positive \u03b413C value than in other treatments. \n\n\n\n\n\n\n\nTable 1 \n\n\n\n\u03b413C value of each research plot \n\n\n\nTreatment \nKADA FIELD \n\n\n\n(\u2030) \nMADA FIELD \n\n\n\n(\u2030) \nRain shelter \nKADA (\u2030) \n\n\n\nRain shelterMADA \n(\u2030) \n\n\n\nNo K (T1) -29.78a \u00b1 0.91 -28.56a \u00b1 0.86 -31.27a \u00b1 0.30 -29.83b \u00b1 0.12 \n\n\n\nMOP (T2) -30.02a \u00b1 0.28 -30.48ab \u00b1 0.16 -30.57a \u00b1 0.56 -29.12a \u00b1 0.37 \n\n\n\nSOP (T3) -29.50a \u00b1 0.25 -30.52ab \u00b1 0.05 -30.29a \u00b1 0.07 -29.83b \u00b1 0.07 \n\n\n\nPolyhalite (T4) -29.39a \u00b1 0.11 -29.90ab \u00b1 0.44 -30.77a \u00b1 0.04 -29.96b \u00b1 0.03 \n\n\n\nConventional \n\n\n\nFertilizer (T5) \n-28.20a \u00b1 0.69 -31.03b \u00b1 0.11 -30.67a \u00b1 0.04 -29.92b \u00b1 0.12 \n\n\n\nMeans followed by the same letter within a column were not significantly different (Tukey\u2019s test, at P>0.05) \u00b1 \n\n\n\nstandard error value \n\n\n\nDiscrimination against 13C (\u039413C) occurs during the diffusion of CO2 through stomata \n\n\n\nand assimilation by photosynthesis. Similarly, carbon isotope discrimination \u03b413C increased \n\n\n\nwith increasing ca (carbon in the atmosphere). Changes in ci (internal carbon) can also result \n\n\n\nin a change in the 13C composition of a plant sample relative to the 12C composition. Ci/Ca \n\n\n\nrepresents the balance between the rates of inward CO2 diffusion (controlled by stomatal \n\n\n\nconductance) and CO2 assimilation.  \n\n\n\nThis ratio will differ under conditions of limiting light, poor nutrient status, or other \n\n\n\nconditions in which CO2 uptake is enzyme or diffusion-limited (Farquhar et al., 1989). \n\n\n\nStomatal conductance is small concerning the capacity for CO2 fixation, ci is small and \u03b413C \n\n\n\nbecomes less negative, indicating less discrimination against the heavier isotope. Conversely, \n\n\n\nwhen stomatal conductance is large, ci approaches ca and \u03b413C becomes more negative, \n\n\n\nindicating increased discrimination against 13CO2 (Farquhar et al., 1982). \n\n\n\nIn the 1950s, Craig (1953, 1954) measured \u03b413C values of a variety of natural materials, \n\n\n\nincluding plants. They found that most plants had \u03b413C values in the range of -25 to -35\u2030. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 80-87 \n\n\n\n84 \n\n\n\n\n\n\n\nBased on the result, we did get the range of \u03b413C value same as the theory stated. Apart from \n\n\n\nthe biological impacts initiated by controlling the photosynthetic rate and stomatal openness, \n\n\n\nthe physical parameters, such as aerodynamic conditions (e.g., atmospheric stability) and the \n\n\n\nconditions of the soil\u2013atmosphere diffusion systems may also regulate \u03b413C, although they \n\n\n\nseem to act as distracters, impairing the relationship between environmental factors and \u03b413C. \n\n\n\n\n\n\n\nEffects of Potassium fertilizer (K) on stomatal conductance \n\n\n\nIn principle, increases in stomatal conductance (gs), which regulates gas exchange (CO2 and \n\n\n\nwater), can allow plants under well-watered growth conditions to increase their CO2 uptake \n\n\n\nand subsequently enhance photosynthesis. However, the relationship between stomatal \n\n\n\nconductance, CO2 uptake, and photosynthesis is not so simple. Since a large number of \n\n\n\nenvironmental factors affect stomatal aperture, the contribution of stomatal regulation to \n\n\n\nphotosynthesis also can vary depending on the plant species. \n\n\n\nThe result in Table 2 shows that there are significant differences in MADA field and  \n\n\n\nrain shelter experiments, whereby treatment with no K applied lead to low stomatal \n\n\n\nconductance among the treatment combinations tested while K fertilizer in the form of \n\n\n\npolyhalite shows high stomatal conductance compared to other treatments suggesting \n\n\n\nmicronutrients presence in polyhalite could significantly improve leaf physiological \n\n\n\nperformance.  \n\n\n\nProper stomatal regulation (opening and closing) is necessary for the uninterrupted \n\n\n\nproduction of energy during the photosynthesis process, plant cooling, and water and nutrient \n\n\n\ntransport. In the presence of K+, stomatal guard cells are swollen by absorbing water followed \n\n\n\nby stomatal opening and the allowance of gaseous movement in between plants and the \n\n\n\nenvironment. Findings by Thomas (2009) highlight the vital role of K in plants provided \n\n\n\nconvincing evidence that K plays a significant role in stomatal opening and closing. Stomatal \n\n\n\nnumber and aperture size may also be affected by K nutrition (Pirasteh et al. 2016) which \n\n\n\nwould imply better CO2 diffusion into the leaf and higher rates of photosynthesis. \n\n\n\nTable 2 \n\n\n\nStomatal conductance at each research plot \n\n\n\nTreatment \nKADA  (mmol \n\n\n\nm-2 s-1) \n\n\n\nMADA \n\n\n\n(mmol m-2 s-1) \n\n\n\nRain Shelter \n\n\n\nKADA  \n\n\n\n(mmol m-2 s-1) \n\n\n\nRain shelter MADA  \n\n\n\n(mmol m-2 s-1) \n\n\n\nNo K (T1) 0.75a \u00b1 0.07 0.05e \u00b1 0 0.91e \u00b1 0 1.12e \u00b1 0 \n\n\n\nMOP (T2) 1.02a \u00b1 0.34 0.19b \u00b1 0 1.65b \u00b1 0 2.01b \u00b1 0 \n\n\n\nSOP (T3) 0.58a \u00b1 0.07 0.12d \u00b1 0 1.14c \u00b1 0 1.77c \u00b1 0 \n\n\n\nPolyhalite (T4) 1.01a \u00b1 0.06 0.23a \u00b1 0 2.08a \u00b1 0 2.90a \u00b1 0 \n\n\n\nConventional \nFertilizer (T5) \n\n\n\n0.48a \u00b1 0.11 0.15c \u00b1 0 1.13d \u00b1 0 1.62d \u00b1 0 \n\n\n\nMeans followed by the same letter within a column were not significantly different (Tukey\u2019s test, at P>0.05) \u00b1 \n\n\n\nstandard error value \n\n\n\nK Fertilizer Effect on Photosynthesis Rate \n\n\n\nThe result in Table 3 shows significant differences between treatments in both fields KADA \n\n\n\nand MADA also in the rain shelter experiments of KADA and MADA soil, whereby T1 \n\n\n\nshowed a low photosynthesis rate in paddy leaves respectively compared to other treatments. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 80-87 \n\n\n\n85 \n\n\n\n\n\n\n\nThis is because stomatal closure is the main limiting factor for the photosynthetic rate in \n\n\n\nresponse to alterations in CO2. Potassium deficiency resulting in reduced stomatal conductance \n\n\n\nincreased the mesophyll resistance and lowered the ribulose-1,5-bisphosphate \n\n\n\ncarboxylase/oxygenase (RuBisCO) activity in plants, which eventually decreased the total \n\n\n\nphotosynthesis rate. Other than that, the basis of the approach is that when K supply is \n\n\n\ninadequate, the root:shoot ratio will be low because the consequent low concentration of leaf \n\n\n\nK will impair photosynthate loading into the phloem and translocation to the roots. \n\n\n\nMoreover, the result shows that the application of polyhalite resulted in the highest \n\n\n\nphotosynthesis rate among the treatments. This occurs when higher stomatal conductance \n\n\n\nenhanced CO2 diffusion into chloroplasts, the photosynthetic activity of plants increases \n\n\n\n(Kusumi et al. 2012). Stomatal aperture, as well as conductance, is strongly correlated with \n\n\n\nleaf photosynthesis, the decrease in stomatal conductance and the restriction of photosynthetic \n\n\n\nenzyme activity will eventually lead to an increase in plant \u03b413C value (Jia et al. 2016; Lavergne \n\n\n\net al. 2020). It is generally accepted that in photosynthesizing leaves, stomatal conductance is \n\n\n\ncorrelated with photosynthetic rate and coordinated with the CO2 requirement of the \n\n\n\nmesophyll, such that the Ci/Ca ratio is maintained at a constant value. \n\n\n\nTable 3 \n\n\n\n Photosynthesis rate of rain shelter experiments \n\n\n\nTreatment \n\n\n\nKADA FIELD \n\n\n\n(\u00b5mol CO2 \n\n\n\nm-2 s-1) \n\n\n\nMADA FIELD \n\n\n\n(\u00b5mol CO2 \n\n\n\nm-2 s-1) \n\n\n\nRAIN SHELTER \n\n\n\nKADA (\u00b5mol \n\n\n\nCO2 m-2 s-1) \n\n\n\nRAIN SHELTER \n\n\n\nMADA (\u00b5mol \n\n\n\nCO2 m-2 s-1) \n\n\n\nNo K (T1) 7.22b \u00b1 4.14 3.78d \u00b1 0.01 38.47e \u00b1 0.03 41.28d \u00b1 0.06 \n\n\n\nMOP (T2) 11.63b \u00b1 2.61 11.52c \u00b1 0.01 48.65b \u00b1 0.17 53.39c \u00b1 0.05 \n\n\n\nSOP (T3) 9.92b \u00b1 3.13 13.57b \u00b1 0.002 43.59c \u00b1 0.05 54.17b \u00b1 0.09 \n\n\n\nPolyhalite (T4) 18.64a \u00b1 3.53 14.49a \u00b1 0.02 57.62a \u00b1 0.03 61.49a \u00b1 0.26 \n\n\n\nConventional Fertilizer (T5) 8.85b \u00b1 4.23 11.59c \u00b1 0.01 42.32d \u00b1 0.04 53.50c \u00b1 0.04 \n\n\n\nMeans followed by the same letter within a column were not significantly different (Tukey\u2019s test, at P>0.05) \u00b1 \n\n\n\nstandard error value. \n\n\n\nCONCLUSION \n\n\n\nThis study determined how rice \u03b413C is related to whole-plant photosynthesis and stomatal \n\n\n\nconductance under conditions of different types of K fertilizer. During photosynthesis, plants \n\n\n\ndiscriminate against C because of small differences in chemical and physical properties \n\n\n\nimparted by the difference in mass. This discrimination can be used to assign plants to various \n\n\n\nphotosynthetic groups. The isotope fractionation also reflects limitations on photosynthetic \n\n\n\nefficiency imposed by the various diffusional and chemical components of CO2 uptake. All \n\n\n\n\u03b413C values pointed to relatively open stomata for rice as C3 plants.  Other than that, improved \n\n\n\nphotosynthesis rate under different types of K fertilizer was achieved by the application of \n\n\n\npolyhalite fertilizer minimizing water loss through stomata by evapotranspiration and \n\n\n\nemploying phenotypic flexibility and morphological mechanisms of drought avoidance \n\n\n\nleading to a selective biomass loss. \n\n\n\n\n\n\n\nACKNOWLEDGMENT \n\n\n\nThe authors would like to thank Universiti Putra Malaysia for funding this study under  the \n\n\n\nPutra Muda research grant (GP-IPM/2018/9665800) and Jabatan Kimia Malaysia for research \n\n\n\nsupport and providing services such as the Isotope Ratio Mass Spectrometry (IRMS) analyses. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 80-87 \n\n\n\n86 \n\n\n\n\n\n\n\nMoreover, we would like to also thank the Institute of Tropical Agriculture and Food Security \n\n\n\n(ITAFoS), UPM  for handling the open-top portable photosynthesis system (Li-6400XT, LI-\n\n\n\nCOR, Lincoln, Nebraska \u2013 USA).  \n\n\n\n\n\n\n\nREFERENCES \n\n\n\n\n\n\n\nCraig, H. 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"\n\n___________________\n*Corresponding author : E-mail: halimi@upm.edu.my \n\n\n\nINTRODUCTION\nChromobacterium violaceum is a Gram-negative facultative anaerobic pathogenic \nbacterium to mammals (David et al. 2012; Hammerschmitt et al. 2017; Donny et \nal. 2018) and humankind (Shao et al. 2002; Kothari et al. 2017). It inhabits soil \nand water and is widely found in the tropical and subtropical regions of the world \n(McGowan and Steinberg 1995). It produces an antibiotic, violacein, a purple \npigment, that gives C. violaceum its characteristic violet colour, and which is \nactive against most microbes including protozoa, fungi, bacteria and virus (Forbes \net al. 2002; Duran et al. 2007; Duran et al. 2016). Other antibiotics produced from \nC. violaceum are aerocyanidine which is active against Gram-positive organisms, \naerocavin which is active against Gram-positive and Gram-negative organisms, \n3,6-dihydroxyindoxazene and Factor Y-T0678H (6-hydroxy-3-oxo-1,2-\nbenzisoxazolin), both of which are active against Gram-negative bacteria and also \nseveral types of antibiotics which are active against amoebae and trypanosomes \n(Nelson and Carlos 2001). The production of all the antibiotics is controlled by a \nquorum sensing system (McClean et al. 1997; Lee et al. 2013). \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 59-65 (2021)                          Malaysian Society of Soil Science\n\n\n\nEffect of Chromobacterium violaceum on Plant Growth-\nPromoting Rhizobacteria (PGPR) under In-vitro Conditions\n\n\n\nLoke W.K. and Saud H.M.\n\n\n\nDepartment of Agriculture Technology, \nFaculty of Agriculture Universiti Putra Malaysia 43400 UPM Serdang,\n\n\n\nSelangor Darul Ehsan, Malaysia\n\n\n\nABSTRACT\nChromobacterium violaceum is a pathogenic soil bacterium that produces \nviolacein and several types of antibiotics which are active against amoebae, \ntrypanosomes, and Gram-positive and Gram-negative bacteria. The production of \nantibiotics is controlled by a quorum sensing system with a signal molecule called \nhomoserine lactone (C6-HSL). In both methods (interaction and non-interaction), \nC. violaceum which reached quorum level produced antibiotics and killed all the \nselected PGPR (Azospirillum brasilense Sp7, Rhizobium UPMR1102 and Bacillus \nsphaericus UPMB10) but did not kill the selected PGPR in concentration below \ntheir quorum level. This study indicates that quorum sensing is involved in the \neffect of C. violaceum on selected PGPR and has the potential to threaten the use \nof PGPR in agriculture.\n\n\n\nKeyword: Chromobacterium violaceum, Plant Growth-Promoting \nRhizobacteria (PGPR), quorum sensing.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202160\n\n\n\n Quorum sensing is a cell-to-cell communication system used by bacteria to \ncontrol gene expression by signal molecules where the bacteria are able to produce \nan antibiotic after reaching a certain level of cell-population density or quorum \nlevel (Miller and Bassler 2001; Lowery et al. 2008). In this system, C. violaceum \naccumulates a certain level of signal molecules to form a signal-receptor complex \nbinding to activate transcription for antibiotics production. No production occurs \nif accumulation is below the quorum level (Sun et al. 2004; Stauff and Bassler \n2011). The signal used by C. violaceum to control the production of antibiotics is \nhomoserine lactone (C6-HSL) (McClean et al. 1997; Srivastava and Gera 2006).\n The tropical climate in Malaysia offers a very conductive environment for \nthe growth of C. violaceum and is believed that it is widely distributed locally \nin agriculture and non-agriculture soils. It was reported that Malaysia has the \nhighest human infection of C. violaceum in Southeast Asia (Jitmuang 2008). The \nwidely distributed C. violaceum in soil is directly in contact with other beneficial \nmicrobes like plant growth-promoting rhizobacteria (PGPR). \n Plant growth-promoting rhizobacteria (PGPR) were first described by \nKloepper and Schroth (1987); the use of soil bacteria may be highly advantageous \nto plants by colonising roots and following inoculation of the seeds to enhance \nplant growth. In recent years, many PGPR have been isolated from soil and each \nisolated PGPR was found to contain one or more functions to improve plant growth \n(Zahir et al. 2004; Smith et al. 2015). It gives direct and indirect benefits that \nenhance the plant growth by improving the plant metabolites (Backer et al. 2018). \nHowever, the interaction and effect between C. violaceum and PGPR remains \nunknown. The aim of this work was to determine the effect of C. violaceum on \nselected PGPR. \n\n\n\nMATERIALS AND METHODS\nTwo tests were conducted to investigate the effect of C. violaceum on selected \nPGPR. In test 1, there was an interaction between C. violaceum and PGPR during \nthe culturing process while in test 2, no interaction was found between both \nbacteria until C. violaceum reached the final concentration. These tests are able to \nshow whether the interaction between both bacteria has an effect on the quorum \nsensing mechanism and the production of antibiotics.\n\n\n\nTest 1 - Interaction\nThe selected PGPR used in this experiment were Bacillus sphaericus UPMB10 \n(Gram-positive), Rhizobium UPMR1102 (Gram-negative) and Azospirillum \nbrasilense Sp7 (Gram-negative). The inoculating loop was sterilised and used to \npick up a single colony of C. violaceum and selected PGPR with each combination \ninto 6 tubes (A1, A2, A3 and B1, B2, B3) containing LB broth. In tubes A1, A2 \nand A3, C. violaceum was cultured at 30\u00b0C with shaking for 16 h before it reached \nquorum level; in tubes B1, B2 and B3, C. violaceum was left to incubate for a \nfurther 3 days until it reached quorum level and the solutions had turned purple in \ncolour. Ten-fold serial dilutions were made by transferring 1.0 ml of solution from \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 61\n\n\n\ntubes A and B of each combination to the new tubes to achieve a final dilution of \n1:102 in the final tubes. One ml was transferred from the tubes to petri plates of LB \nagar and spread onto the surface of agar using an alcohol-flamed glass rod. The \nPetri plates were incubated at 30\u00b0C for 3 days.\n\n\n\nTest 2 \u2013 Non Interaction\nA single colony of C. violaceum was transferred into the LB broth media test \ntubes (C1, C2, C3 and D1, D2, D3). C1, C2 and C3 test tubes were cultured at \n30\u00b0C by shaking for 16 h before the concentrations of the bacteria in both tubes \nreached quorum level; in test tubes D1, D2 and D3,  the colony was cultured at \n30\u00b0C by shaking for 3 days until quorum level was achieved and the solution \nturned  purple in colour. Tetracycline antibiotic was added into both the test tubes \nto kill the C. violaceum and the concentration was maintained at non-quorum \nlevel in test tubes C1, C2 and C3 while it was maintained at quorum level in test \ntubes D1, D2 and D3.\n Single colonies of Azospirillum brasilense Sp7, Rhizobium UPMR1102 \nand Rhizobium UPMR1013 containing a cosmid vector pLAFR1 (Vanbleu et al. \n2004) that is resistant to Tetracycline antibiotic (Figure 1), and 2 ml fresh LB broth \nwere transferred into different test tubes (C1, C2, C3 and D1, D2, D3) containing \nC. violaceum at quorum level and non-quorum level concentration and cultured at \n30\u00b0C by shaking for 16 h. A ten-fold serial dilution was made by transferring 1.0 \nml of solution from tubes C1, C2, C3 and D1, D2, D3 to new tubes to achieve a \nfinal dilution of 1:102 in the final tubes. A sterilised inoculating loop was used to \nstreak the 1.0 ml solutions from the test tubes on the selective media agar surface \nof petri plates which contained the Tetracycline antibiotic. The petri plates were \nincubated at 30\u00b0C for 3 days. \n\n\n\nFigure 1. Cosmid pLAFR1\n\n\n\n\n\n\n\nFigure 1. Cosmid pLAFR1 \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\nIn test 1, there was contact between C. violaceum and PGPR before C. violaceum \n\n\n\nreached quorum level. The interaction between both bacteria may affect growth \n\n\n\nrate and signal molecule production. This may have been the cause for some of \n\n\n\nthe test tubes requiring more than 3 days to reach quorum level. From the results, \n\n\n\npetri plates with the content of test tubes B1, B2, B3 showed only the C. \n\n\n\nviolaceum growing on the agar surface while the petri plates with the content of \n\n\n\ntest tubes A1, A2, A3 showed the selected PGPR Bacillus sphaericus UPMB10, \n\n\n\nRhizobium UPMR1102 and Azospirillum brasilense Sp7 growing together with C. \n\n\n\nviolaceum (Figure 2). These results show that C. violaceum in B1, B2 and B3 will \n\n\n\nonly produce antibiotics after reaching quorum level and having killed the \n\n\n\nselected PGPR in the test tubes. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202162\n\n\n\nRESULTS AND DISCUSSION\nIn test 1, there was contact between C. violaceum and PGPR before C. violaceum \nreached quorum level. The interaction between both bacteria may affect growth \nrate and signal molecule production. This may have been the cause for some \nof the test tubes requiring more than 3 days to reach quorum level. From the \nresults, petri plates with the content of test tubes B1, B2, B3 showed only the C. \nviolaceum growing on the agar surface while the petri plates with the content of \ntest tubes A1, A2, A3 showed the selected PGPR Bacillus sphaericus UPMB10, \nRhizobium UPMR1102 and Azospirillum brasilense Sp7 growing together with \nC. violaceum (Figure 2). These results show that C. violaceum in B1, B2 and B3 \nwill only produce antibiotics after reaching quorum level and having killed the \nselected PGPR in the test tubes.\n\n\n\nFigure 2. Effect of C. violaceum on PGPR from tube B (left) and tube A (right)\n\n\n\n\n\n\n\n\n\n\n\nFigure 2. Effect of C. violaceum on PGPR from tube B (left) and tube A (right) \n\n\n\n\n\n\n\n In test 2, there was no contact between C. violaceum with PGPR before C. \n\n\n\nviolaceum reached quorum level and was killed by the antibiotic. The \n\n\n\nconcentration of C. violaceum from both test tubes C and D was controlled by \n\n\n\nTetracycline antibiotic before coming into contact with PGPR. The petri plates \n\n\n\nwith the content of test tubes C1, C2, C3 showed that Azospirillum brasilense \n\n\n\nSp7, Rhizobium UPMR1102 and Rhizobium UPMR1013 contained cosmid \n\n\n\npLAFR1 that is resistant to Tetracycline antibiotic growing on the agar surface \n\n\n\nwhile the petri plates with the content of test tubes D1, D2, D3 showed that no \n\n\n\nbacteria can be cultured. The inhibited C. violaceum in quorum level produced \n\n\n\nantibiotics and kill all the selected PGPR in tubes D1, D2 and D3 though there \n\n\n\nwas no interaction.  \n\n\n\n\n\n\n\n In test 2, there was no contact between C. violaceum with PGPR before \nC. violaceum reached quorum level and was killed by the antibiotic. The \nconcentration of C. violaceum from both test tubes C and D was controlled by \nTetracycline antibiotic before coming into contact with PGPR. The petri plates \nwith the content of test tubes C1, C2, C3 showed that Azospirillum brasilense Sp7, \nRhizobium UPMR1102 and Rhizobium UPMR1013 contained cosmid pLAFR1 \nthat is resistant to Tetracycline antibiotic growing on the agar surface while the \npetri plates with the content of test tubes D1, D2, D3 showed that no bacteria can \nbe cultured. The inhibited C. violaceum in quorum level produced antibiotics and \nkill all the selected PGPR in tubes D1, D2 and D3 though there was no interaction. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 63\n\n\n\nCONCLUSION\nFrom both these tests on the effect of C. violaceum on selected PGPR, it is \nconcluded that C. violaceum will only produce antibiotics or kill the beneficial \nmicrobes after reaching quorum level; moreover, it will not have any impact if \nthe concentration is below their quorum level. Although in natural conditions, it is \nvery difficult to achieve a quorum level, the existence of C. violaceum in our local \nsoils and their effect cannot be underestimated. The tropical climate in Malaysia \noffers a very conductive environment for the growth of C. violaceum and it has \nthe potential to serve as a threat to beneficial bacteria in our agricultural areas.\n\n\n\nREFERENCES\nBacker, R., J.S. Rokem, G. Ilangumaran, J. Lamont, D. Praslickova, E. Ricci, S. \n\n\n\nSubramanian  and D.L Smith. 2018. Plant growth-promoting rhizobacteria: \ncontext, mechanisms of action, and roadmap to commercialization of \nbiostimulants for sustainable agriculture. Frontiers in Plant Science 9(1473): \n1-17.\n\n\n\nDavid, X. L., J.D. Peter and B.P. Gail. 2012. Chromobacterium violaceum infections \nin 13 non-human primates. J Med. Primatol. 41(2): 107\u2013114.\n\n\n\nDuran, N., G.Z. Justo, C.V. Ferreira, P.S. Melo, L. Cordi and D. Martins. 2007. \nViolacein: properties and biological activities. Biotechnol. Appl. Biochem. 48: \n127\u2013133.\n\n\n\nFigure 3. Effect of C. violaceum on PGPR from tubes C and D\n\n\n\n\n\n\n\nFigure 3. Effect of C. violaceum on PGPR from tubes C and D \n\n\n\n \nCONCLUSION \n\n\n\nFrom both these tests on the effect of C. violaceum on selected PGPR, it is \n\n\n\nconcluded that C. violaceum will only produce antibiotics or kill the beneficial \n\n\n\nmicrobes after reaching quorum level; moreover, it will not have any impact if the \n\n\n\nconcentration is below their quorum level. Although in natural conditions, it is \n\n\n\nvery difficult to achieve a quorum level, the existence of C. violaceum in our local \n\n\n\nsoils and their effect cannot be underestimated. The tropical climate in Malaysia \n\n\n\noffers a very conductive environment for the growth of C. violaceum and it has \n\n\n\nthe potential to serve as a threat to beneficial bacteria in our agricultural areas. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202164\n\n\n\nDuran, N., G.Z. Justo, M. Duran, M. Brocchi, L. Cordi, L. Tasic, G.R. Castro and \nG. Nakazato. 2016. Advances in Chromobacterium violaceum and properties \nof violacein - its main secondary metabolite: a review. Biotechnol. Adv. 34: \n1030\u20131045.\n\n\n\nDonny, Y., A.J. Faez Firdaus, A.M. Azman Shah, N.A. Simaa, A.T. Tuba Thabitah, \nR.M Mariani, A.A.R. Firdaus and T. Rahmat. 2018. Chromobacterium \nviolaceum infection in two black-handed gibbons: a veterinary case report. \nMalaysian Journal of Veterinary Research 9(1): 103-109.\n\n\n\nForbes, B. A., F.S. Daniel and S.W. Alice. 2002. Diagnostic Microbiology (11th Ed.). \nMosby. St. Louis, Missouri, pp 423-434.\n\n\n\nJitmuang, A. 2008. Human Chromobacterium violaceum infection in Southeast \nAsia: case reports and literature review. Southeast Asian Journal of Tropical \nMedicine and Public Health 39(3): 452-460.\n\n\n\nKloepper, J. W. and M.N. Schroth. (1978). Plant growth-promoting rhizobacteria on \nradishes. Proceedings of the 4th International Conference on Plant Pathogenic \nBacteria. Angers, France: Station de Pathologie V\u00e9g\u00e9tale et Phytobact\u00e9riologie \n2: 879\u2013882.\n\n\n\nKothari, V., S. Sharma and D. Padia. 2017. Recent research advances on \nChromobacterium violaceum. Asian Pacific Journal of Tropical Medicine, \n10(8): 744\u2013752.\n\n\n\nLee, J., J. Wu, Y.Y. Deng, J. Wang, C. Wang, J.H. Wang, C. Chang, Y. Dong, P. \nWilliams and L.H. Zhang. 2013. A cell-cell communication signal integrates \nquorum sensing and stress response. Nat. Chem. Biol. 9: 339-343.\n\n\n\nLowery, C. A., T.J. Dickerson and K.D. Janda. 2008. Interspecies and interkingdom \ncommunication mediated by bacterial quorum sensing. Chem. Soc. Rev. 37: \n1337\u20131346.\n\n\n\nHammerschmitt, M. E., V.M. Rolim, G.G. Snel, F.M. Siqueira, D. Driemeier and S.P. \nPavarini. 2017. Chromobacterium violaceum infection in a horse. Journal of \nComparative Pathology. 156(4): 334-338.\n\n\n\nMcClean, K. H., M.K. Winson, L. Fish, A. Taylor, S.R. Chhabra, M. Camara, M. \nDaykin, J.H. Lamb, S. Swift, B.W. Bycroft, G.S. Stewart and P. Williams. \n1997. Quorum sensing and Chromobacterium violaceum: exploitation of \nviolacein production and inhibition for the detection of N-acyl homoserine \nlactones. Microbiology 143: 3703\u20133711.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 65\n\n\n\nMcGowan, Jr J. E. and J.P. Steinberg. 1995. Other gram negative bacilli. Principles \nand Practice of Infectious Diseases (4th ed) ed. G.J. Mandell, J.E. Bennett and \nR. Dolin, pp 2106-2115. New York, NY: Churchill Livingstone.\n\n\n\nMiller, M. B. and B.L. Bassler. 2001. Quorum sensing in bacteria. Annual Review of \nMicrobiology 55: 165-199.\n\n\n\nNelson, D. and F.M. Carlos. 2001. Chromobacterium violaceum: A Review of \nPharmacological and Industrial Perspectives. Critical Reviews in Microbiology  \n27(3): 201-222.\n\n\n\nShao, P. L., P.R. Hsueh, Y.C. Hang, C.Y. Lu, P.Y. Lee, C.Y. Lee and L.M. Huang. 2002. \nChromobacterium violaceum infection in children: a case of fatal septicaemia \nwith nasopharyngeal abscess and literature review. Pediatr Infect Dis. J. 21: \n707\u20139.\n\n\n\nSmith, D. L., D. Praslickova and G. Ilangumaran. 2015. Inter-organismal signaling \nand management of the phytomicrobiome. Front. Plant Sci. 6: 722.\n\n\n\nSrivastava, S. and C. Gera. 2006. Quorum-sensing: the phenomenon of microbial \ncommunication. Current Science 90: 10-15.\n\n\n\nStauff, D. L. and B.L. Bassler. 2011. Quorum sensing in Chromobacterium violaceum: \nDNA recognition and gene regulation by the CviR receptor. J. Bacteriol. \n193(15): 3871-3878.\n\n\n\nSun, J., R. Daniel, I. Wagner-Dobler and A.P. Zeng. 2004. Is autoinducer-2 a \nuniversal signal for interspecies communication: A comparative genomic and \nphylogenetic analysis of the synthesis and signal transduction pathways. BMC \nEvol. Biol. 4: 36\u201346.\n\n\n\nVanbleu, E., K. Marchal and J. Vanderleyden. 2004. Genetic and physical map of the \npLAFR1 vector. DNA Sequence 15(3): 225-227.\n\n\n\nZahir, A. Z., M. Arshad, W.T. Frankenberger. 2004. Plant  growth  promotingrhizo \nbacteria: applications and perspectives in agriculture. Adv. Agron. 81: 97\u2013168.\n\n\n\n. \n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nAM Inoculation and P Mobility in P-Fixing Sweetpotato Soils\n\n\n\n45\n\n\n\nISSN: 1394-7990\nMalaysian Society of Soil ScienceMalaysian Journal of Soil Science Vol.11 : 45-56 (2007)\n\n\n\nArbuscular Mycorrhizal Inoculation and Phosphorus\nMobility in Phosphorus-Fixing Sweetpotato Soils\n\n\n\n1V.S. Harikumar*  &  2V. P. Potty\n\n\n\n1Department of Post Graduate Studies & Research in Botany, Sanatana Dharma\nCollege University of Kerala, Alappuzha-688 003, Kerala, India\n\n\n\n2Central Tuber Crops Research Institute, Thiruvananthapuram-695 017\nKerala, India\n\n\n\n*Department of Biology, Eritrean Institute of Technology, Mai -Nefhi\nState of Eritrea, NE Africa\n\n\n\nABSTRACT\nPhosphorus (P) mobility in three P-fixing (laterite, red and sandy)\nsweetpotato soils in relation to inoculation with arbuscular mycorrhizal\n(AM) fungi Glomus microcarpum has been studied through a pot culture\ntrial for two seasons. In all the inoculated soils, irrespective of season, the\nsweetpotato plants had a comparatively high rate of colonisation. How-\never, increased P level in the soil tended to decrease colonisation. Percent-\nage root colonisation increased with days after planting (DAP) while, the\nspore density in the root-zone soil decreased with DAP. Soil P availability\nvaried between inoculated and uninoculated treatments and different soil\ntypes. In general, inoculated treatments showed a low soil P availability\nbut the rate of removal of P from soil to plant tissue was more in mycorrhiza\ninoculated treatments in both the seasons and at different DAP. The rate of\nremoval of P by mycorrhizal plants was maximum in laterite and sandy\nsoils at all DAP. Mycorrhizal inoculation did not give any added benefit on\nsoil P release and fixation in the soil types studied.\n\n\n\nKeywords: AM fungi, G. microcarpum, P-fixing soil, P mobility, sweetpotato\n\n\n\nINTRODUCTION\nPhosphorus (P) is one of the most important nutrients for plant growth, develop-\nment and reproduction. In many regions of the world, soil P is often a major\nfactor limiting crop production. To improve P nutrition of plants, the conven-\ntional approach is to apply large amounts of P fertilisers to soils. It has been\nestimated that nearly 36.78 million tonnes of P-based fertilisers (in terms of\nP2O5) are applied world wide every year (International Fertilizer Industry Asso-\nciation 2006) However, use efficiency of applied P is generally very low ranging\nfrom 10 to 30% in the year applied (McLaughlin et al. 1991).\n\n\n\n* Corresponding author:  Email: vsharikumar@yahoo.co.in\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM45\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200746\n\n\n\nV.S. Harikumar  &  V. P. Potty\n\n\n\nMany soils in the tropics are fragile and prone to degradation. Some charac-\nteristics of tropical soils put severe constraints on plant production. This in-\ncludes soil moisture stress, low nutrient capital, erosion risks, low pH with alu-\nminium toxicity, high P fixation, low levels of soil organic matter and loss of\nsoil biodiversity (Sanchez et al. 2003). Improving plant uptake of P from soils is\nan obvious alternative to the management of such soils and the enhancement of\nuse efficiency of P fertilisers.\n\n\n\nThe association of arbuscular mycorrhizal (AM) fungi with plant roots al-\nters plant-soil interactions and enhances plant growth and nutrition under stress-\nful edaphic conditions (Smith and Read 1997). Increased growth of mycorrhizal\nplants compared with non-mycorrhizal plants commonly observed in low P soils\nin glass house studies and also in the field, has largely been related to increased P\nuptake and more effective P nutrition. Exploration of large soil volume (Tinker\n1978), faster movement of P into AM hyphae (Cress et al. 1979; Bolan et al.\n1987) and solubilisation of relatively immobile sources of P (Hetrick 1989) are\nreported as the mechanism for increased P uptake by AM plants. AM fungal\nhyphae contribute to absorption and translocation of P from sites in soil that are\nnot accessible to plant roots (Sanders and Tinker 1971) and depletion zone around\nnon-mycorrhizal plants of a few millimeters (Li et al. 1991; George et al. 1992).\n\n\n\nSweetpotato (Ipomoea batatas L.) is a foremost tuber crop in respect of\ncalorific value and is grown in almost all soil types (Harikumar 1997). Several\nreports document the incidence (Potty 1978; O\u2019Keefe and Sylvia 1993), geno-\ntype dependent variation in AM colonisation (Harikumar and Potty 2002a) and\nresponse of the crop to P fertilisation (O\u2019Keefe and Sylvia 1992). Despite these\nadvances, we still know little about the role of AM association on P mobility in P-\nfixing soils where the crop is usually grown.  We report in the present study the\nrole of AM inoculation on changes in P mobility in three sweetpotato growing\nsoils.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSoils\nThree different soils (Table 1) were collected one month after harvesting of the\nstanding crop from fields where sweetpotato cultivation was practised for sev-\neral years. They were cleared off weeds and debris prior to filling the pots.\n\n\n\nBiological Materials\nSweetpotato (cv. Kanjangad) was selected for use in the study. A local isolate\n(CTAM 69) of AM fungi Glomus microcarpum Tul. & Tul. served as the mycor-\nrhizal symbiont.\n\n\n\nExperimental Set-up and Design\nThe experiment was carried out over two seasons in earthen pots of 13 cm diam-\neter having a holding of 5 kg soil. The pots were filled with soils sterilised with\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM46\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nAM Inoculation and P Mobility in P-Fixing Sweetpotato Soils\n\n\n\n47\n\n\n\nformalin 15 days prior to planting. Before the onset of the experiment, each pot\nwas fertilised per kg soil with 2.8 ppm nitrogen (N) as urea, 4.2 ppm potassium\n(K) as muriate of potash and 140 ppm phosphorus (P) as Mussorie rock phos-\nphate.  Half of the pots received mycorrhization by way of planting rooted G.\nmicrocarpum infected vine cuttings (Harikumar and Potty 2002b), one per pot.\nAll pots received native microflora (except AM fungi) as sieving.  The experi-\nment consisted of three soil types (laterite, red and sandy) and two levels of\nendomycorrhization (mycorrhizal (M1) and non-mycorrhizal M0) with a com-\nplete 3 \u00d7 2 factorial design with 10 replications per treatment. The set-up was\nmaintained in a glass house for observing the changes under different treat-\nments. Two pots were set aside from each treatment at 15-day interval for moni-\ntoring various parameters. Final sampling of pots from each treatment was done\nat the termination of the experiment at 45 days after planting (DAP).\n\n\n\nAssessment of AM Colonisation\nFor determination of AM colonisation, fresh root samples (approximately 0.2 g)\nwere thoroughly washed in running tap water and cut into 1 cm long segments.\nThe root segments were cleared in 10% (w/v) KOH (30 min, 900C), acidified\nwith lactic acid (10 min) and stained with 0.5% Trypan blue (Phillips and Hayman\n1970).  Fifty root fragments (approximately 1 cm long were mounted on slides\nin a polyvinyl alcohol-lactic acid-glycerol solution (Koske and Tessier 1983) and\nexamined at 100\u00d7 magnification under Nikon Eclipse E400 microscope to obtain\nthe percentage of root length colonised by AM fungi. The percentage of root\nlength colonised by AM fungi was determined using the magnified line-intersect\nmethod of McGonigle et al. (1990).\n\n\n\nRecovery and Counting of AM Fungal Spores\nSpores of AM fungi were extracted from 50 ml of air-dried sub-samples of each\nsoil sample by wet sieving followed by floatation centrifugation in 50 % sucrose\n(Dalp\u00e9 1993). The finest sieve used was 45 m. The spores were collected on a\ngrid patterned (4x4) filter paper, washed three times with distilled water to spread\n\n\n\nTABLE 1\n Characteristics of soils used in the experiment\n\n\n\nSoils\n\n\n\nCharacteristics Laterite Red Sandy\n\n\n\nSoil Group Oxisol Alfisol Entisol\npH 5.5 4.6 5.6\nOrganic Carbon (%) 0.92 0.46 0.87\nAvailable P (ppm) 151.12 154.57 118.54\nAl (ppm) 0.68 12.10 88.16\nFe (ppm) 0.29 1.12 12.07\nZn (ppm) 0.22 0.21 0.29\nSesquioxide (ppm) 0.02 0.24 0.26\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM47\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200748\n\n\n\nV.S. Harikumar  &  V. P. Potty\n\n\n\nthem evenly over the entire grid and counted using stereo microscope (Zeiss\nStemi- DV4) at 30 \u00d7 magnification.\n\n\n\nMeasurement of Soil P Mobility\nSoil available P was estimated by Bray\u2019s No.1 extract method (Jackson 1973).\nSoil P mobility was calculated at different DAP using the following formulae:\n\n\n\nP released (%) = (Soil available P + P absorbed by plants) x 100\nInitial soil P\n\n\n\nP removed (%) = P absorbed by plants x 100\nInitial soil P\n\n\n\nP fixed (%) = Initial soil P- (Soil available P + P absorbed by plants)\n\n\n\nStatistical Analysis\nData were statistically analysed by analysis of variance with IRRISTAT PRO-\nGRAM (IRRI Philippines). Treatment means were separated by Duncan\u2019s Mul-\ntiple Range Test (DMRT) (Little and Hills 1978).\n\n\n\nRESULTS\n\n\n\nMycorrhizal Colonisation\nIn all the inoculated soils, irrespective of seasons, the sweetpotato plants had a\ncomparatively higher rate of colonisation.  However, increased P levels in the\nsoil led to decreased colonisation in all soil types.  Among the inoculated soils,\nduring Season I, maximum percentage root colonisation was observed in plants\ngrown in sandy soil while the lowest was in laterite soil.  In Season II, the trend\nwas, however, reversed with a relatively higher colonisation in plants raised in\nlaterite and red soils.  In inoculated treatments percentage root colonisation in-\ncreased with DAP.  The same trend was observed in season II as well.\nUninoculated plants remained non-mycorrhizal during both the seasons.\n\n\n\nThe spore density in the rhizosphere of inoculated plants also showed sig-\nnificant (p<0.05) differences in most cases.  In general, more spore density was\nrecorded in laterite and sandy soils during both the seasons. In both the seasons\nspore density decreased with DAP (Table 2).\n\n\n\nSoil P Availability and Removal\nThere was significant (P<0.05) difference in the soil P availability between in-\noculated and uninoculated treatments and between different soil types. The dif-\nferences were observed at all DAP.  In general, the soil available P was low in\ninoculated treatments in all soil types during Season I.  However, in Season II a\nnoticeable reduction was observed only at late stages of plant growth, that is, at\n45 DAP.  Soil P availability did not show any definite trend with an increase in\nDAP in Season I. In Season II, the soil availability, however, decreased with an\nincrease in DAP.\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM48\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nAM Inoculation and P Mobility in P-Fixing Sweetpotato Soils\n\n\n\n49\n\n\n\nTA\nB\n\n\n\nLE\n 2\n\n\n\nEf\nfe\n\n\n\nct\n o\n\n\n\nf \nG\n\n\n\n. \nm\n\n\n\nic\nro\n\n\n\nca\nrp\n\n\n\num\n i\n\n\n\nno\ncu\n\n\n\nla\ntio\n\n\n\nn \non\n\n\n\n p\ner\n\n\n\nce\nnt\n\n\n\nag\ne \n\n\n\nco\nlo\n\n\n\nni\nsa\n\n\n\ntio\nn \n\n\n\nan\nd \n\n\n\nrh\niz\n\n\n\nos\nph\n\n\n\ner\ne \n\n\n\nsp\nor\n\n\n\ne \nde\n\n\n\nns\nity\n\n\n\n i\nn\n\n\n\nsw\nee\n\n\n\ntp\not\n\n\n\nat\no \n\n\n\ngr\now\n\n\n\nn \nin\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nt \nso\n\n\n\nil \nty\n\n\n\npe\ns\n\n\n\nSe\nas\n\n\n\non\n I\n\n\n\nSe\nas\n\n\n\non\n I\n\n\n\nI\n\n\n\nA\nM\n\n\n\nF\nSo\n\n\n\nil \nty\n\n\n\npe\n%\n\n\n\n C\nol\n\n\n\non\nis\n\n\n\nat\nio\n\n\n\nn\nSp\n\n\n\nor\ne \n\n\n\nde\nns\n\n\n\nity\n/5\n\n\n\n0m\nl \n\n\n\nso\nil\n\n\n\n%\n C\n\n\n\nol\non\n\n\n\nis\nat\n\n\n\nio\nn\n\n\n\nSp\nor\n\n\n\ne \nde\n\n\n\nns\nity\n\n\n\n/5\n0m\n\n\n\nl \nso\n\n\n\nil\n\n\n\n15\n30\n\n\n\n45\n15\n\n\n\n30\n45\n\n\n\n15\n30\n\n\n\n45\n15\n\n\n\n30\n45\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nM\n0\n\n\n\nLa\nte\n\n\n\nri\nte\n\n\n\n0.\n00\n\n\n\nc\n0.\n\n\n\n00\nc\n\n\n\n0.\n00\n\n\n\nd\n0.\n\n\n\n00\nd\n\n\n\n0.\n00\n\n\n\nc\n0.\n\n\n\n00\nb\n\n\n\n0.\n00\n\n\n\nd\n0.\n\n\n\n00\nc\n\n\n\n0.\n00\n\n\n\nc\n0.\n\n\n\n00\nd\n\n\n\n0.\n00\n\n\n\nb\n0.\n\n\n\n00\nc\n\n\n\nR\ned\n\n\n\n0.\n00\n\n\n\nc\n0.\n\n\n\n00\nc\n\n\n\n0.\n00\n\n\n\nd\n0.\n\n\n\n00\nd\n\n\n\n0.\n00\n\n\n\nc\n0.\n\n\n\n00\nb\n\n\n\n0.\n00\n\n\n\nd\n0.\n\n\n\n00\nc\n\n\n\n0.\n00\n\n\n\nc\n0.\n\n\n\n00\nd\n\n\n\n0.\n00\n\n\n\nb\n0.\n\n\n\n00\nc\n\n\n\nSa\nnd\n\n\n\ny\n0.\n\n\n\n00\nc\n\n\n\n0.\n00\n\n\n\nc\n0.\n\n\n\n00\nd\n\n\n\n0.\n00\n\n\n\nd\n0.\n\n\n\n00\nc\n\n\n\n0.\n00\n\n\n\nb\n0.\n\n\n\n00\nd\n\n\n\n0.\n00\n\n\n\nc\n0.\n\n\n\n00\nc\n\n\n\n0.\n00\n\n\n\nd\n0.\n\n\n\n00\nb\n\n\n\n0.\n00\n\n\n\nc\n\n\n\nM\n1\n\n\n\nLa\nte\n\n\n\nri\nte\n\n\n\n31\n.5\n\n\n\n0b\n32\n\n\n\n.5\n0b\n\n\n\n36\n.4\n\n\n\n5c\n33\n\n\n\n.0\n0b\n\n\n\n23\n.0\n\n\n\n0a\n21\n\n\n\n.0\n0a\n\n\n\n14\n.1\n\n\n\n5b\n16\n\n\n\n.3\n3a\n\n\n\n33\n.1\n\n\n\n7a\n47\n\n\n\n.0\n0a\n\n\n\n16\n.0\n\n\n\n0a\n24\n\n\n\n.0\n0a\n\n\n\nR\ned\n\n\n\n55\n.0\n\n\n\n0a\n77\n\n\n\n.5\n0a\n\n\n\n53\n.5\n\n\n\n0b\n19\n\n\n\n.0\n0c\n\n\n\n21\n.0\n\n\n\n0a\n18\n\n\n\n.0\n0a\n\n\n\n19\n.5\n\n\n\n0a\n8.\n\n\n\n16\nb\n\n\n\n32\n.6\n\n\n\n7a\n23\n\n\n\n.0\n0c\n\n\n\n19\n.0\n\n\n\n0a\n15\n\n\n\n.0\n0b\n\n\n\nSa\nnd\n\n\n\ny\n52\n\n\n\n.0\n0a\n\n\n\n75\n.0\n\n\n\n0a\n72\n\n\n\n.5\n0a\n\n\n\n41\n.0\n\n\n\n0a\n15\n\n\n\n.0\n0b\n\n\n\n17\n.0\n\n\n\n0a\n7.\n\n\n\n42\nc\n\n\n\n16\n.3\n\n\n\n3a\n15\n\n\n\n.6\n3b\n\n\n\n41\n.0\n\n\n\n0b\n16\n\n\n\n.0\n0a\n\n\n\n15\n.0\n\n\n\n0b\n\n\n\nM\nea\n\n\n\nns\n in\n\n\n\n c\nol\n\n\n\num\nn \n\n\n\nw\nith\n\n\n\n th\ne \n\n\n\nsa\nm\n\n\n\ne \nsu\n\n\n\npe\nrs\n\n\n\ncr\nip\n\n\n\nts\n d\n\n\n\no \nno\n\n\n\nt d\niff\n\n\n\ner\n s\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\ntly\n a\n\n\n\nt P\n=0\n\n\n\n.0\n5 \n\n\n\nle\nve\n\n\n\nl b\ny \n\n\n\nD\nM\n\n\n\nR\nT\n\n\n\nIn\niti\n\n\n\nal\n s\n\n\n\noi\nl P\n\n\n\n in\n S\n\n\n\nea\nso\n\n\n\nn \nI: \n\n\n\nLa\nte\n\n\n\nrit\ne \n\n\n\n14\n9 \n\n\n\npp\nm\n\n\n\n; R\ned\n\n\n\n  \n15\n\n\n\n2.\n75\n\n\n\n p\npm\n\n\n\n; S\nan\n\n\n\ndy\n 1\n\n\n\n16\n.5\n\n\n\n p\npm\n\n\n\nSo\nil \n\n\n\nP \nin\n\n\n\n S\nea\n\n\n\nso\nn \n\n\n\nII\n:\n\n\n\nLa\nte\n\n\n\nrit\ne\n\n\n\nM\n0-\n\n\n\n25\n3.\n\n\n\n04\n p\n\n\n\npm\nM\n\n\n\n1-\n26\n\n\n\n1.\n45\n\n\n\n p\npm\n\n\n\nR\ned\n\n\n\nM\n0-\n\n\n\n27\n4.\n\n\n\n31\n p\n\n\n\npm\nM\n\n\n\n1-\n27\n\n\n\n4.\n80\n\n\n\n p\npm\n\n\n\nSa\nnd\n\n\n\ny\nM\n\n\n\n0-\n23\n\n\n\n6.\n81\n\n\n\n p\npm\n\n\n\nM\n1-\n\n\n\n23\n7.\n\n\n\n35\n p\n\n\n\npm\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM49\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200750\n\n\n\nV.S. Harikumar  &  V. P. Potty\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nEf\nfe\n\n\n\nct\n o\n\n\n\nf \nG\n\n\n\n. m\nic\n\n\n\nro\nca\n\n\n\nrp\num\n\n\n\n i\nno\n\n\n\ncu\nla\n\n\n\ntio\nn \n\n\n\non\n s\n\n\n\noi\nl \n\n\n\nP \nav\n\n\n\nai\nla\n\n\n\nbi\nlit\n\n\n\ny \nan\n\n\n\nd \nre\n\n\n\nm\nov\n\n\n\nal\n i\n\n\n\nn \nsw\n\n\n\nee\ntp\n\n\n\not\nat\n\n\n\no \nso\n\n\n\nils\n\n\n\nSe\nas\n\n\n\non\n I\n\n\n\nSe\nas\n\n\n\non\n I\n\n\n\nI\n\n\n\nA\nM\n\n\n\nF\nSo\n\n\n\nil \nty\n\n\n\npe\nSo\n\n\n\nil \nP \n\n\n\n(p\npm\n\n\n\n)\nP \n\n\n\nre\nm\n\n\n\nov\nal\n\n\n\n (\n%\n\n\n\n)\nSo\n\n\n\nil \nP \n\n\n\n(p\npm\n\n\n\n)\nP \n\n\n\nre\nm\n\n\n\nov\nal\n\n\n\n (\n%\n\n\n\n)\n\n\n\n15\n30\n\n\n\n45\n15\n\n\n\n30\n45\n\n\n\n15\n30\n\n\n\n45\n15\n\n\n\n30\n45\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nM\n0\n\n\n\nL\nat\n\n\n\ner\nite\n\n\n\n9.\n53\n\n\n\nc\n17\n\n\n\n.2\n5b\n\n\n\n13\n.2\n\n\n\n5c\n0.\n\n\n\n69\nb\n\n\n\n0.\n26\n\n\n\nc\n0.\n\n\n\n73\na\n\n\n\n76\n.2\n\n\n\n5c\n49\n\n\n\n.5\n0c\n\n\n\n58\n.9\n\n\n\n6c\n1.\n\n\n\n03\ncd\n\n\n\n0.\n86\n\n\n\nd\n1.\n\n\n\n23\nb\n\n\n\nR\ned\n\n\n\n3.\n92\n\n\n\nd\n5.\n\n\n\n42\ne\n\n\n\n10\n.0\n\n\n\n0d\n0.\n\n\n\n40\nc\n\n\n\n0.\n38\n\n\n\nbc\n0.\n\n\n\n40\nb\n\n\n\n12\n.5\n\n\n\n0f\n12\n\n\n\n.2\n5f\n\n\n\n4.\n00\n\n\n\ne\n0.\n\n\n\n54\ne\n\n\n\n0.\n50\n\n\n\ne\n0.\n\n\n\n63\nc\n\n\n\nSa\nnd\n\n\n\ny\n23\n\n\n\n.1\n7a\n\n\n\n20\n.9\n\n\n\n6a\n20\n\n\n\n.9\n8b\n\n\n\n0.\n70\n\n\n\nb\n0.\n\n\n\n52\nab\n\n\n\n0.\n36\n\n\n\nb\n10\n\n\n\n4.\n25\n\n\n\na\n45\n\n\n\n.2\n5d\n\n\n\n90\n.7\n\n\n\n5a\n1.\n\n\n\n26\nb\n\n\n\n1.\n12\n\n\n\nc\n1.\n\n\n\n35\nb\n\n\n\nM\n1\n\n\n\nL\nat\n\n\n\ner\nite\n\n\n\n4.\n08\n\n\n\nd\n13\n\n\n\n.1\n7c\n\n\n\n13\n.1\n\n\n\n7c\n0.\n\n\n\n94\na\n\n\n\n0.\n71\n\n\n\na\n0.\n\n\n\n72\na\n\n\n\n79\n.2\n\n\n\n5b\n71\n\n\n\n.2\n5b\n\n\n\n32\n.2\n\n\n\n5d\n1.\n\n\n\n10\nbc\n\n\n\n1.\n40\n\n\n\nb\n1.\n\n\n\n57\na\n\n\n\nR\ned\n\n\n\n2.\n92\n\n\n\nd\n8.\n\n\n\n16\nd\n\n\n\n4.\n15\n\n\n\ne\n0.\n\n\n\n50\nbc\n\n\n\n0.\n43\n\n\n\nbc\n0.\n\n\n\n46\nb\n\n\n\n25\n.2\n\n\n\n5e\n18\n\n\n\n.7\n5e\n\n\n\n3.\n96\n\n\n\ne\n0.\n\n\n\n88\nd\n\n\n\n0.\n59\n\n\n\ne\n0.\n\n\n\n69\nc\n\n\n\nSa\nnd\n\n\n\ny\n15\n\n\n\n.4\n2b\n\n\n\n18\n.1\n\n\n\n7b\n25\n\n\n\n.8\n3a\n\n\n\n1.\n07\n\n\n\na\n0.\n\n\n\n54\nab\n\n\n\n0.\n46\n\n\n\nb\n60\n\n\n\n.3\n8d\n\n\n\n91\n.2\n\n\n\n5a\n71\n\n\n\n.2\n5b\n\n\n\n1.\n82\n\n\n\na\n1.\n\n\n\n82\na\n\n\n\n1.\n58\n\n\n\na\n\n\n\nM\nea\n\n\n\nns\n in\n\n\n\n c\nol\n\n\n\num\nn \n\n\n\nw\nith\n\n\n\n th\ne \n\n\n\nsa\nm\n\n\n\ne \nsu\n\n\n\npe\nrs\n\n\n\ncr\nip\n\n\n\nts\n d\n\n\n\no \nno\n\n\n\nt d\niff\n\n\n\ner\n s\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\ntly\n a\n\n\n\nt P\n=0\n\n\n\n.0\n5 \n\n\n\nle\nve\n\n\n\nl b\ny \n\n\n\nD\nM\n\n\n\nR\nT\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM50\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nAM Inoculation and P Mobility in P-Fixing Sweetpotato Soils\n\n\n\n51\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nEf\nfe\n\n\n\nct\n o\n\n\n\nf \nG\n\n\n\n. m\nic\n\n\n\nro\nca\n\n\n\nrp\num\n\n\n\n i\nno\n\n\n\ncu\nla\n\n\n\ntio\nn \n\n\n\non\n P\n\n\n\n r\nel\n\n\n\nea\nse\n\n\n\n a\nnd\n\n\n\n f\nix\n\n\n\nat\nio\n\n\n\nn \nin\n\n\n\n s\nw\n\n\n\nee\ntp\n\n\n\not\nat\n\n\n\no \nso\n\n\n\nils\n\n\n\nSe\nas\n\n\n\non\n I\n\n\n\nSe\nas\n\n\n\non\n I\n\n\n\nI\n\n\n\nA\nM\n\n\n\nF\nSo\n\n\n\nil \nty\n\n\n\npe\nP \n\n\n\nre\nle\n\n\n\nas\ne \n\n\n\n(%\n)\n\n\n\nP \nfix\n\n\n\nat\nio\n\n\n\nn \n(%\n\n\n\n)\nP \n\n\n\nre\nle\n\n\n\nas\ne \n\n\n\n(%\n)\n\n\n\nP \nfix\n\n\n\nat\nio\n\n\n\nn \n(%\n\n\n\n)\n\n\n\n5\n30\n\n\n\n45\n15\n\n\n\n30\n45\n\n\n\n15\n30\n\n\n\n45\n15\n\n\n\n30\n45\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nD\nA\n\n\n\nP\nD\n\n\n\nA\nP\n\n\n\nM\n0\n\n\n\nL\nat\n\n\n\ner\nite\n\n\n\n6.\n31\n\n\n\nc\n11\n\n\n\n.2\n9ab\n\n\n\n6.\n62\n\n\n\nc\n93\n\n\n\n.7\n1bc\n\n\n\n88\n.2\n\n\n\n1b\n94\n\n\n\n.5\n4ab\n\n\n\n43\n.3\n\n\n\n5c\n27\n\n\n\n.5\n0c\n\n\n\n45\n.3\n\n\n\n5b\n56\n\n\n\n.1\n5d\n\n\n\n82\n.4\n\n\n\n3c\n66\n\n\n\n.3\n2c\n\n\n\nR\ned\n\n\n\n2.\n53\n\n\n\nd\n3.\n\n\n\n64\nd\n\n\n\n6.\n46\n\n\n\nc\n97\n\n\n\n.4\n6ab\n\n\n\n94\n.8\n\n\n\n8a\n95\n\n\n\n.1\n5ab\n\n\n\n8.\n22\n\n\n\nb\n8.\n\n\n\n11\ne\n\n\n\n2.\n55\n\n\n\ne\n91\n\n\n\n.3\n8a\n\n\n\n93\n.6\n\n\n\n0a\n97\n\n\n\n.4\n4a\n\n\n\nSa\nnd\n\n\n\ny\n13\n\n\n\n.4\n2a\n\n\n\n12\n.4\n\n\n\n2a\n12\n\n\n\n.4\n4b\n\n\n\n91\n.1\n\n\n\n1c\n87\n\n\n\n.5\n7b\n\n\n\n90\n.2\n\n\n\n3c\n64\n\n\n\n.0\n5a\n\n\n\n27\n.3\n\n\n\n7c\n59\n\n\n\n.3\n1a\n\n\n\n35\n.4\n\n\n\n5f\n72\n\n\n\n.1\n3d\n\n\n\n40\n.6\n\n\n\n8d\n\n\n\nM\n1\n\n\n\nL\nat\n\n\n\ner\nite\n\n\n\n2.\n63\n\n\n\nd\n8.\n\n\n\n41\nc\n\n\n\n6.\n33\n\n\n\nc\n94\n\n\n\n.5\n3ab\n\n\n\nc\n94\n\n\n\n.7\n6a\n\n\n\n92\n.5\n\n\n\n6ab\n51\n\n\n\n.5\n0b\n\n\n\n46\n.0\n\n\n\n8b\n6.\n\n\n\n13\nd\n\n\n\n48\n.5\n\n\n\n0e\n53\n\n\n\n.4\n3e\n\n\n\n92\n.5\n\n\n\n7b\n\n\n\nR\ned\n\n\n\n1.\n89\n\n\n\nd\n5.\n\n\n\n27\nd\n\n\n\n2.\n68\n\n\n\nd\n98\n\n\n\n.1\n1a\n\n\n\n94\n.7\n\n\n\n2a\n97\n\n\n\n.3\n2a\n\n\n\n16\n.3\n\n\n\n9e\n13\n\n\n\n.2\n1d\n\n\n\n3.\n28\n\n\n\ne\n83\n\n\n\n.1\n1b\n\n\n\n87\n.4\n\n\n\n1b\n96\n\n\n\n.7\n3a\n\n\n\nSa\nnd\n\n\n\ny\n8.\n\n\n\n60\nb\n\n\n\n10\n.4\n\n\n\n4b\n15\n\n\n\n.3\n2a\n\n\n\n91\n.4\n\n\n\n4c\n85\n\n\n\n.4\n4b\n\n\n\n89\n.7\n\n\n\n5c\n37\n\n\n\n.0\n9d\n\n\n\n55\n.5\n\n\n\n0a\n37\n\n\n\n.1\n6c\n\n\n\n62\n.4\n\n\n\n1c\n44\n\n\n\n.2\n5f\n\n\n\n62\n.8\n\n\n\n3c\n\n\n\nM\nea\n\n\n\nns\n in\n\n\n\n c\nol\n\n\n\num\nn \n\n\n\nw\nith\n\n\n\n th\ne \n\n\n\nsa\nm\n\n\n\ne \nsu\n\n\n\npe\nrs\n\n\n\ncr\nip\n\n\n\nts\n d\n\n\n\no \nno\n\n\n\nt d\niff\n\n\n\ner\n s\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\ntly\n a\n\n\n\nt P\n=0\n\n\n\n.0\n5 \n\n\n\nle\nve\n\n\n\nl b\ny \n\n\n\nD\nM\n\n\n\nR\nT\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM51\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200752\n\n\n\nV.S. Harikumar  &  V. P. Potty\n\n\n\nWhen the rate of P removal by plant was compared, it was more in inoculated\ntreatments in both the seasons at all DAP.  In general, higher values for the\npercentage of removal were noted in laterite and sandy soil.  The rate of removal\nshowed a general decline as DAP advanced (Table 3).\n\n\n\nP Release and Fixation in Soil\nThe P release and fixation in inoculated and uninoculated treatments varied sig-\nnificantly (p<0.05) in all soil types.  However, mycorrhizal inoculation did not\ngive any added benefit to soil P release and fixation irrespective of season.  Among\nthe inoculated treatments, the rate of P release and fixation continued to be good\nin laterite and sandy soils (Table 4).\n\n\n\nDISCUSSION\nA series of edaphic constraints of the tropics limit crop production.  Infertile\nsoils are either acidic or alkaline and represent deficiencies of P and high P-\nfixing capacity. In acid soils, when phosphatic fertilisers are incorporated, the\nmajor share of P is fixed thereby making it unavailable to plants. Most of the\nphosphate fertiliser in P-fixing soils ends up in fixed pools, having a recovery of\nonly approximately 10 - 20% (Janssen 2006).\n\n\n\nThe principle function of AM is to enhance P acquisition of the host plants\nfrom, primarily, liable sources in soil (Smith and Read 1997) although non-labile\nsources (e.g. Al-and Fe-bound P, Ca-phytate) can also be used by some plants\n(Shibata and Yano 2003).  Sweetpotato is widely grown in P-fixing acidic soils\nof Kerala. The situation, however, favoured the growth and establishment of\nAM fungi G. microcarpum in the crop grown in all soil types as evident in the\npresent study.\n\n\n\nMost studies testing the relationship between P availability and mycorrhizal\ncolonisation suggest that increased P availability in soil (Abbot and Robson 1984;\nLiu et al. 2000) as well as plant tissue (Smith and Read 1997) leads to decreased\ncolonisation. This hypothesis might have worked for the present study also where\nincreased P availability in soils and removal to the plant tissue led to a decrease in\npercentage colonisation and spore density in the rhizosphere of plants grown in\nall soils types during the second season. However mycorrhizal inoculation sig-\nnificantly improved spore density in the rhizosphere of sweetpotato, which was\nparticularly apparent in laterite and sandy soils. A similar increase in rhizosphere\nspore density as a consequence to mycorrhizal inoculation has been reported in\nseveral crops such as rice (Secilia and Bagyaraj 1994) and cow pea (Muthukumar\nand Udaiyan 2002).\n\n\n\nAnother important feature observed in our study was the rhizosphere spore\ndensities decreased with increasing percentage root colonisation and DAP in all\nthe soils types. This could be due to the reason that more spore germination\nmight have taken place to offer infection to the newly emerged roots as the days\nadvanced. This is in accordance with the findings of Gaur and Adholeya (2002)\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM52\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nAM Inoculation and P Mobility in P-Fixing Sweetpotato Soils\n\n\n\n53\n\n\n\nwho also observed a significant correlation between infective propagules, spore\ndensity and percentage colonisation.\n\n\n\nIn P-fixing soils, the P deficiency is mainly caused by strong adsorption of\nH2PO4to aluminium (Al) and iron (Fe) (hydro) oxides, which turns large propor-\ntions of total P into forms that are unavailable to plants.  The role of AM sym-\nbiont in enhancing P availability in such soil is by blocking Fe and Al from\ncoming into contact with H2PO4.  This is possibly by retaining the Fe and Al in\nthe hyphae of the fungus within the root cells or the fungus provides the plant\nadditional binding sites for metal adsorption. The finding of Fabig (1982) and\nSch\u00fcpp et al. (1987) that fungal cell walls have a large number of complexing\nsites for metallic cations strengthens the above postulation. Therefore, the roots\nhighly infected by AM fungi provide the plant an increased surface area for\nmetal adsorption. Perhaps this may be the mechanism at work in P-fixing acid\nsoils in enhancing P availability to mycorrhizal plants.  However, in the present\nstudy, we noticed a comparatively low level of P availability in mycorrhizal\ntreatments. This is likely to have resulted from fast removal of P by an increased\ndevelopment of external hyphae around the root system of mycorrhizal plants.\nThe general increment in P removal to the tissues of mycorrhizal plants coin-\ncided with the decrease in soil P availability, further supporting this theory.\nPrevious works (Jacobsen et al. 1992; Johansen et al. 1993; Liu et al. 2003) also\nshow that increased external hyphal growth in soil enhanced depletion of P in\nsoil.  In this context, it is worthwhile to mention the recent finding of Smith et\nal. (2003; 2004) that P uptake at the root surface can be reduced in AM plants\nand that much of the P enters via the AM pathway.\n\n\n\nWe could not disentangle the relationship between mycorrhizal inoculation\nP release and fixation in the soil types studied.  It seems that mycorrhizal inocu-\nlation did not give any added benefit to P release and fixation.  If this is the case\nin P-fixing sweetpotato soils, the introduction of suitable phosphate solubilising\nmicroorganisms (PSM) in conjunction with AM fungi may yield promising re-\nsults. The P-solubilising organisms dissolve unavailable forms of P by exerting\norganic acids and chelating substances (Kucey et al. 1989; Kapoor 1995) which\ncan be readily absorbed by the AM fungus. The potential of dual inoculation\nwith PSM along with rock phosphate has been reported by many workers (Azcon\net al. 1976; Barea et al. 1983; Singh and Singh 1993).  However, extensive\nevaluation particularly in field conditions in the presence of native microbial\ncompetitors is required before arriving at a definite conclusion.\n\n\n\nCONCLUSION\nIn summary,  increased removal of P from P-fixing soils to the plant tissue as a\nresult to the inoculation of G. microcarpum can be considered as an improve-\nment in plant nutrition under the conditions of our experiment.\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM53\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200754\n\n\n\nV.S. Harikumar  &  V. P. Potty\n\n\n\nACKNOWLEDGEMENTS\nWe are thankful to the Director, Central Tuber Crops Research Institute (ICAR),\nThiruvananthapuram for providing research facilities and to the anonymous re-\nviewers for their comments on earlier versions of the manuscript.\n\n\n\nREFERENCES\nAbbot, L.K. and A.D. Robson. 1984. The effect of VA mycorrhizae on plant growth.\n\n\n\nIn VA Mycorrhizae, ed. C.L. Powell and D.J. 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Nature, 233: 278-279.\n\n\n\nSch\u00fcpp, H., B. Dehn and H. Sticher. 1987. Interaction between VA-mycorrhizas and\nheavy metal stress. Angewandte Botanik. 61: 161-176.\n\n\n\nSecilia, J. and D.J. Bagyaraj. 1994. Evaluation and first-year field testing of efficient\nvesicular arbuscular mycorrhizal fungi for inoculation of wetland rice seed-\nlings. World Journal of Microbiology and Biotechnology, 10: 381-384.\n\n\n\nShibata, R. and K. Yano. 2003. Phosphorus acquisition from non-labile sources in\npeanut and pigeonpea with mycorrhizal interaction. Appl. Soil. Ecol. 24: 133-\n141.\n\n\n\nSingh, H.P. and T.A. Singh. 1993. The interaction of rockphosphate, Bradyrhizobium,\nvesicular-arbuscular mycorrhizae and phosphate-solubilizing microbe on soy-\nbean grown in a sub Himalayan mollisol. Mycorrhiza 4: 37-43.\n\n\n\nSmith, S. E. and D.J. Read. 1997. Mycorrhizal Symbiosis. San Diego: Academic\nPress.\n\n\n\nSmith, S.E., F.A., Smith and I. Jakobsen. 2003. Mycorrhizal fungi can dominate\nphosphate supply to plants irrespective of growth responses. Plant Physiol.\n133: 16-20.\n\n\n\nSmith, S. E., F.A. Smith and I. Jakobsen. 2004. Functional diversity in arbuscular\nmycorrhizal (AM) symbioses: the contribution of the mycorrhizal P uptake path-\nway is not correlated with mycorrhizal responses in growth or total P uptake.\nNew Phytol. 162: 511-524.\n\n\n\nTinker, P.B.1 978. Effects of vesicular arbuscular mycorrhizas on plant nutrition and\nplant growth. Phys. V\u00e9g. 16: 743-751.\n\n\n\nMJ of Soil Science 045-056.pmd 08-Apr-08, 10:45 AM56\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 73-79 \n\n\n\n\n\n\n\n73 \n\n\n\n\n\n\n\n\n\n\n\nInfluence of Oil Palm Replanting, Age and Management Zones  \n\n\n\non Soil Carbon \n \n\n\n\nFadzilah, Songkongon1, Adibah, M. Amin1, Sim, C. Cheak2, Sulaiman, Muhammad F.1*\n \n\n\n\n \n1Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400 \n\n\n\nSerdang, Selangor, Malaysia \n\n\n\n 2Sime Darby Plantation Research Sdn. Bhd., Jalan Pulau Carey, 42960 Pulau Carey, Selangor, \n\n\n\nMalaysia  \n\n\n\n \n*Corresponding author: muhdfirdaus@upm.edu.my  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nOil palm (Elaeis guineensis Jacq.) cultivation is said to have caused losses of soil carbon due to \n\n\n\ndeforestation. However, current oil palm cultivation is carried out either on previously rubber and \n\n\n\ncocoa plantation or replanted on first- or second-generation oil palm. The present study was \nconducted to assess whether there is a build-up of soil carbon throughout the growth of oil palm and \n\n\n\nwill those amassed carbon (if any) be lost during replanting. The study was conducted at oil palm ages \n\n\n\n5, 10, 15 years old, and newly replanted oil palm. At each age, soils were sampled at the frond heap \npile (FH), the harvesting path (HP), and the inter-row (IR). Soil carbon content at all plots was not \n\n\n\nsignificantly different between ages with a mean of 2.24%. Between sections, soil carbon content at \n\n\n\nFH (3.10\u00b11.42%) was significantly higher than the other sections. Our results showed that age of oil \n\n\n\npalm did not influence the accumulation of soil carbon. Replanting was also found not to have caused \nlosses of soil carbon. As the different sections of the plantation yielded different results, future \n\n\n\nmeasurements of soil carbon should consider these different sections to properly represent the whole \n\n\n\nplantation. \n\n\n\n\n\n\n\nKey words: oil palm age, replanting, soil carbon, management zones \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nOil palm (Elaeis guineensis Jacq.) is an important oil seed crop in Malaysia and Indonesia, \n\n\n\nthe world\u2019s two largest exporters. Malaysia and Indonesia supply 85% of the world\u2019s palm \n\n\n\noil, and palm oil export earnings contribute immensely to the economy of both countries. As \n\n\n\nimportant as the oil palm industry may be to the two countries, large scale cultivation of oil \n\n\n\npalm has been drawing criticism from the international community, among other in regard to \n\n\n\nthe losses of soil carbon from deforestation and land use change. This has led the European \n\n\n\nUnion (EU) to pass a resolution in the European Parliament in 2018 to prohibit the \n\n\n\nimportation of oil palm for biofuel into the EU by 2020. Adoption of the Roundtable on \n\n\n\nSustainable Palm Oil (RSPO) certification by the majority of oil palm plantations have seen a \n\n\n\nslowing rate of deforestation across Malaysia and Indonesia (Carlson et al. 2018). The \n\n\n\ncurrent practice for oil palm cultivation is either through replanting of palms more than 25 \n\n\n\nyears old or planting on land that was previously planted with other commodity crops such as \n\n\n\nrubber and cocoa (Gaveau et al. 2016). \n\n\n\n\n\n\n\nLike all chlorophyll containing plants, oil palm undergoes photosynthesis, taking up \n\n\n\ncarbon dioxide (CO2) from the atmosphere and transforming it into plant biomass. Carbon \n\n\n\nfrom plant biomass is either returned to soil through the decomposition of pruned fronds \n\n\n\nwhich are commonly left stacked on the soil surface, the decomposition of sloughed roots \n\n\n\n\nmailto:muhdfirdaus@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 73-79 \n\n\n\n\n\n\n\n74 \n\n\n\n\n\n\n\nbelow ground and the decomposition of chipped oil palm trunks during replanting (Kho et al. \n\n\n\n2019). \n\n\n\n\n\n\n\nTherefore, the question that arises is whether the absorbed carbon throughout the 25 \n\n\n\nyears of the oil palm\u2019s growth will be lost to the atmosphere during replanting or will there be \n\n\n\nan incremental increase in soil carbon with each successive replanting. The present study was \n\n\n\ncarried out with the aim of determining the changes in soil carbon of oil palm over time and \n\n\n\nthe effects of replanting on the fate of soil carbon. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nStudy Site \n\n\n\n\n\n\n\nThe study was conducted at oil palm plots grown around the Universiti Putra Malaysia, \n\n\n\nSerdang campus. The soil at the site is mainly of the Munchong-Seremban soil series \n\n\n\nassociation (very-fine, kaolinitic, isohyperthermic, Typic Hapludox to clayey-skeletal, \n\n\n\nkaolinitic, isohyperthermic, Typic Hapludults). At both sites, dynamics of soil carbon was \n\n\n\nmeasured by taking soil samples from the 0-15 cm layer on oil palm ages 5, 10, 15 years old, \n\n\n\nand newly replanted oil palm. For each age, soils were sampled at the three distinct sections \n\n\n\nwithin an oil palm tree, which are the frond heap pile (FH), the harvesting path (HP), and the \n\n\n\ninter-row (IR). Five measurement replications were made at the three sections within each \n\n\n\nage. \n\n\n\n\n\n\n\nSeveral assumptions were made at the onset of this study: \n\n\n\na) The management practices were the same i.e., fertilization was applied accordingly \n\n\n\nbased on crop age and maintenance and upkeep of the pest and diseases was similar \n\n\n\nacross ages. \n\n\n\nb) The history of study plots i.e., the previous crops planted were ignored. \n\n\n\nc) Micro relief effects are ignored. \n\n\n\nd) Differences in the parameters measured were due to differences in oil palm age and \n\n\n\nmanagement zones. \n\n\n\ne) Loss of carbon from deforestation was disregarded. \n\n\n\n\n\n\n\nSoil and Fine Root Sample Collection and Analysis \n\n\n\n\n\n\n\nSoils were sampled from the top 15 cm layer at each of the three sections using an Edelman \n\n\n\nauger (\u00d8 7 cm) (Royal Eijkelkamp B.V., Giesbeek, Netherlands). The samples were air dried, \n\n\n\ncrushed using a mortar and pestle, and sieved to pass through a 2-mm sieve. The soil samples \n\n\n\nwere analyzed for total carbon content using the Dumas method (TruMac CNS Macro \n\n\n\nAnalyzer, LECO Inc., Michigan, USA). Soil bulk density at each soil sampling point was \n\n\n\ndetermined by extracting an undisturbed core sample using a 100 cm3 stainless steel ring. The \n\n\n\nundisturbed core samples were oven-dried at 105\u00b0C for 24 h and bulk density was determined \n\n\n\nas the water-free weight of the core sample over its volume. Using the soil carbon content \n\n\n\n(SC%) and the soil bulk density (BD), the soil carbon stock was determined as: \n\n\n\n\n\n\n\n\ud835\udc46\ud835\udc5c\ud835\udc56\ud835\udc59 \ud835\udc36 \ud835\udc46\ud835\udc61\ud835\udc5c\ud835\udc50\ud835\udc58 = \ud835\udc35\ud835\udc37\n\ud835\udc54\n\n\n\n\ud835\udc50\ud835\udc5a3\n\u00d7 \ud835\udc46\ud835\udc36% \u00d7\n\n\n\n15 \ud835\udc50\ud835\udc5a\n\n\n\n\ud835\udc60\ud835\udc4e\ud835\udc5a\ud835\udc5d\ud835\udc59\ud835\udc56\ud835\udc5b\ud835\udc54 \ud835\udc59\ud835\udc4e\ud835\udc66\ud835\udc52\ud835\udc5f\n\u00d7\n\n\n\n1 \u00d7 10\u22123\ud835\udc58\ud835\udc54\n\n\n\n\ud835\udc54\n\u00d7\n\n\n\n\ud835\udc50\ud835\udc5a2\n\n\n\n1 \u00d7 10\u22128\u210e\ud835\udc4e\n \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 73-79 \n\n\n\n\n\n\n\n75 \n\n\n\n\n\n\n\nSimilar to the soil sample, the fine roots were sampled from the top 15 cm layer of the \n\n\n\nthree sections by using a root auger (\u00d8 8 cm) (Royal Eijkelkamp B.V., Giesbeek, \n\n\n\nNetherlands). The core samples were air dried, and the roots were picked out using tweezers, \n\n\n\ncleaned from attached soil particles and oven-dried at 60\u00b0C until constant weight and the \n\n\n\nfinal weight was recorded. \n\n\n\n\n\n\n\nStatistical Analysis \n\n\n\n\n\n\n\nMeasurements of soil carbon and fine root biomass were analyzed using one-way ANOVA \n\n\n\nwith age and management zones as individual factors using R ver. 4.0.3. Means were \n\n\n\nseparated using the least significant difference (LSD) test at \u03b1 = 0.05. \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nSoil Carbon \n\n\n\n\n\n\n\nThe soil properties (i.e., total carbon content, bulk density, and carbon stock) are listed in \n\n\n\nTable 1. Soil total carbon content was not significantly different between oil palm ages. There \n\n\n\nwere however variations of soil bulk density between oil palm ages with the replanted oil \n\n\n\npalm having the highest soil bulk density while the 15-year old oil palm had the lowest soil \n\n\n\nbulk density. \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nMean \u00b1 standard deviation of soil bulk density and carbon content across oil palm ages. \n\n\n\nAge (years) Bulk Density (g cm-3) Soil carbon content (%) \n\n\n\n5 1.11\u00b10.14ab 2.40\u00b10.87a \n\n\n\n10 1.18\u00b10.18ab 2.26\u00b10.65a \n\n\n\n15 1.06\u00b10.22b 2.18\u00b10.21a \n\n\n\nReplanted 1.22\u00b10.15a 2.11\u00b10.22a \nNote: Same superscript letters indicate means were not significantly different within the same \ncolumns at p<0.05. \n\n\n\n\n\n\n\n\n\n\n\nDue to the differences in soil bulk density despite having the same soil carbon content \n\n\n\nacross ages, there were variations of estimated soil carbon stock between the different oil \n\n\n\npalm ages (Figure 1). Nevertheless, the only contrasting difference was between oil palm \n\n\n\nages 10 and 15, where the estimated soil carbon stock (mean \u00b1 standard deviation) were \n\n\n\n40.95 \u00b1 9.01 t C ha-1 and 32.25 \u00b1 6.59 t C ha-1, respectively. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 73-79 \n\n\n\n\n\n\n\n76 \n\n\n\n\n\n\n\nA\n\n\n\nB\nB\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n3\n\n\n\n3.5\n\n\n\nFH IR HP\n\n\n\nS\no\n\n\n\nil\n C\n\n\n\na\nrb\n\n\n\no\nn\n\n\n\n C\no\n\n\n\nn\nte\n\n\n\nn\nt \n\n\n\n(%\n)\n\n\n\nManagement zone\n\n\n\n\n\n\n\n \nFigure 1: Mean carbon stock across oil palm ages. \n\n\n\nNote: Bars indicate standard error. Same letters indicate means were not significantly \n\n\n\ndifferent at p<0.05. \n \n\n\n\nHowever, between the different management zones (Figure 2), soil carbon content \n\n\n\n(mean \u00b1 standard deviation) was found to be the highest at the frond heap (2.72 \u00b1 0.92%) \n\n\n\nwhile soil carbon content at the inter-row (2.18 \u00b1 0.81%) and harvesting path (1.91 \u00b1 0.60%) \n\n\n\nwere not significantly different from each other. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nFigure 2: Mean soil carbon content of the different management zones. \n\n\n\nNote: Bars indicate standard error. Same letters indicate means were not significantly \n\n\n\ndifferent at p<0.05. \n\n\n\n\n\n\n\nFine Root Biomass \n\n\n\n\n\n\n\nFigure 3 shows the fine root biomass across ages. There were significant differences in fine \n\n\n\nroot biomass across the different oil palm ages. Fifteen-year-old palms showed the highest \n\n\n\nroot biomass with a mean weight per volume of 8.87 mg cm-3 followed by the replanted \n\n\n\npalms (6.35 mg cm-3) while palm aged 5 and 10 years were not significantly different from \n\n\n\neach other with means of 3.12 mg cm-3 and 2.12 mg cm-3, respectively. \n\n\n\nAB A\n\n\n\nB\n\n\n\nAB\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n5 10 15 Repl.S\no\n\n\n\nil\n C\n\n\n\na\nrb\n\n\n\no\nn\n\n\n\n S\nto\n\n\n\nc\nk\n\n\n\n (\nt \n\n\n\nC\n/h\n\n\n\na\n)\n\n\n\nPalm Age (Year)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 73-79 \n\n\n\n\n\n\n\n77 \n\n\n\n\n\n\n\nA\n\n\n\nA\n\n\n\nB\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\nFH IR HP\n\n\n\nF\nin\n\n\n\ne\n R\n\n\n\no\no\n\n\n\nt \nB\n\n\n\nio\nm\n\n\n\na\ns\n\n\n\ns\n \n\n\n\n(m\ng\n\n\n\n/c\nm\n\n\n\n3\n)\n\n\n\nPalm Age (Year)\n\n\n\nC\n\n\n\nC\n\n\n\nA\n\n\n\nB\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n5 10 15 Repl.F\nin\n\n\n\ne\n R\n\n\n\no\no\n\n\n\nt \nB\n\n\n\nio\nm\n\n\n\na\ns\n\n\n\ns\n (\n\n\n\nm\ng\n\n\n\n/c\nm\n\n\n\n3\n)\n\n\n\nPalm Age (Year)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Mean fine root biomass across oil palm ages. \n\n\n\nNote: Bars indicate standard error. Same letters indicate means were not significantly \n\n\n\ndifferent at p<0.05. \n\n\n\n\n\n\n\nOur findings of the fine root biomass contradicted with the findings of soil carbon \n\n\n\nstock across ages. Fine root biomass was highest in 15-year-old oil palms, but soil carbon \n\n\n\nstock was lowest in 15- year-old oil palms. However, between management zones, there were \n\n\n\nno significant differences between the frond heap and inter-row with the harvesting path \n\n\n\n(Figure 4). The harvesting path had the lowest fine root biomass with a mean of 3.17 mg cm-3 \n\n\n\ncompared with mean fine root biomass of 7.44 mg cm-3 and 3.17 mg cm-3 for the frond heap \n\n\n\nand inter-row, respectively. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nFigure 4: Mean fine root biomass of the different management zones. \n\n\n\nNote: Bars indicate standard error. Same letters indicate means were not significantly \n\n\n\ndifferent at p<0.05. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 73-79 \n\n\n\n\n\n\n\n78 \n\n\n\n\n\n\n\nInfluence of Replanting and Palm Age on Soil Carbon \n\n\n\n\n\n\n\nOur findings do not show a clear trend of increasing soil carbon as the palm ages. This was in \n\n\n\nline with the findings of Khasanah et al. (2015) who found that soil carbon stock neither \n\n\n\ndecreased nor increased in current oil palm plantations in Indonesia. They have also noted \n\n\n\nthat historical activities of the site did not influence any carbon losses or build up. \n\n\n\n\n\n\n\nContrary to Khasanah et. al. (2015),  a study by Rahman et al. (2018) on the changes \n\n\n\nin soil carbon from forest to oil palm in Sarawak, Malaysia observed an increase in soil \n\n\n\ncarbon after replanting which was due to the incorporation of oil palm residues. The same \n\n\n\neffect was not observed in the present study perhaps due to the very recent clearing and \n\n\n\nreplanting of the study plot when the research took place. The same effect was also not \n\n\n\nobserved in the 5-year old plots as it was a newly established oil palm site and was not \n\n\n\nreplanted oil palm.  \n\n\n\n\n\n\n\nDespite the differences in estimated soil carbon stock between oil palm ages 10 and \n\n\n\n15 years, the main factor that led to the difference in estimated soil carbon stock was the soil \n\n\n\nbulk density rather than the soil carbon content. Our study found the replanted plot to have \n\n\n\nthe highest bulk density. Soil bulk density decreased with age with the 15-year-old palms \n\n\n\nhaving the lowest bulk density. \n\n\n\n\n\n\n\nUse of heavy machinery during replanting and land preparation may have been a \n\n\n\nfactor for the higher soil bulk density at the replanted site. Zuraidah et al. (2010) in their \n\n\n\nstudy found that heavier machinery compacted soil even more in oil palm plantations which \n\n\n\nresulted in lesser root density. \n\n\n\n\n\n\n\nOur findings of the fine root biomass, however, contradicted with the notion of the \n\n\n\nstagnant or non-evident trend of soil carbon increase. Fine root biomass showed an increasing \n\n\n\ntrend from the fifth and tenth year of planting and peaked upon reaching 15 years, and \n\n\n\ndeclined as the oil palm was replanted. These contradictory findings may suggest that fine \n\n\n\nroot turnover did not have a significant impact on the build-up of carbon in soil.  \n\n\n\n\n\n\n\nInfluence of Management Zones on Soil Carbon \n\n\n\n\n\n\n\nThe different sections of the oil palm plantation i.e., the management zones demonstrated \n\n\n\ndifferences in soil carbon content and fine root biomass. The frond heap had the highest soil \n\n\n\ncarbon content and highest fine root biomass while the harvesting path had the lowest values \n\n\n\nfor both parameters. \n\n\n\n\n\n\n\nHeavy foot and machinery traffic lead to compaction in the harvesting path (Zuraidah \n\n\n\net al. 2010), causing lower root biomass in that zone. Although it has been demonstrated that \n\n\n\nfine root biomass is not a significant contributor to soil carbon stock, the frond heap receives \n\n\n\na continuous input of biomass as fronds will be pruned twice a month to access the fruit \n\n\n\nbunch. The pruned fronds decompose, increasing soil carbon. \n\n\n\n\n\n\n\nDue to the variation between management zones, future soil sampling strategies for \n\n\n\ncarbon accounting in oil palm must take into account the differences of these management \n\n\n\nzones. Underestimation of soil carbon stocks may occur if soils are only sampled in the \n\n\n\nharvesting path. The same recommendation is also echoed by Rahman et al. (2018) who also \n\n\n\nfound variations between management zones in oil palm. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 73-79 \n\n\n\n\n\n\n\n79 \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nSoil carbon content did not increase as the oil palm aged. Soil carbon stock differed with age \n\n\n\nbut did not exhibit an increasing trend with oil palm age. The determining factor of the \n\n\n\nvarying soil carbon stock was soil bulk density rather than soil carbon content. Fine root \n\n\n\nbiomass was not a significant contributor to soil carbon stock as the 15-years-old oil palm \n\n\n\nhad the highest fine root biomass, yet had the lowest soil carbon stock. Our findings indicate \n\n\n\nthat oil palm cultivation and replanting of oil palm did not increase, nor did it decrease soil \n\n\n\ncarbon. Soil carbon may stabilize and become neutral after successive cultivation of oil palm. \n\n\n\nSoil carbon content differed within an oil palm plantation in respect to the different \n\n\n\nmanagement zones. Frond heap had the highest soil carbon content while harvesting path had \n\n\n\nthe lowest. Future work on soil carbon stock accounting in an oil palm ecosystem should \n\n\n\naddress the variation between the management zones to avoid biases. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nThe author would like to express her utmost gratitude to Yayasan Pak Rashid for providing \n\n\n\nthe research grant that allowed for the present study to take place and to Yayasan Sime Darby \n\n\n\nfor providing her a scholarship that allowed her to pursue her postgraduate studies. \n\n\n\n\n\n\n\nREFERENCES \n \n\n\n\nCarlson, K.M., R. Heilmayr, H.K. Gibbs, P. Noojipady, D.N. Burns, D.C. Morton, DN.F. \n\n\n\nWalker, G.D. Paoli and C. Kremen. 2018. Effect of oil palm sustainability \n\n\n\ncertification on deforestation and fire in Indonesia. Proceedings of the National \n\n\n\nAcademy of Sciences,115(1): 121\u2013126. https://doi.org/10.1073/pnas.1704728114 \n\n\n\nGaveau, D. L. A., D. Sheil, Husnayaen, M.A. Salim, S. Arjasakusuma, M. Ancrenaz, P. \n\n\n\nPacheco and E. Meijaard. 2016. Rapid conversions and avoided deforestation: \n\n\n\nExamining four decades of industrial plantation expansion in Borneo. Scientific \n\n\n\nReports 6 (1): 32017. https://doi.org/10.1038/srep32017 \n\n\n\nKhasanah, N., M. van Noordwijk, H. Ningsih and S. Rahayu. 2015. Carbon neutral? No \n\n\n\nchange in mineral soil carbon stock under oil palm plantations derived from forest or \n\n\n\nnon-forest in Indonesia. Agriculture, Ecosystems & Environment. 211: 195\u2013206. \n\n\n\nhttps://doi.org/10.1016/j.agee.2015.06.009 \n\n\n\nKho, L., E. Rumpang, N. Kamarudin and M.H. Harun. 2019. Quantifying total carbon stock \n\n\n\nof mature oil palm. Journal of Oil Palm Research 31(3): 521\u2013527. \n\n\n\nRahman, N., A. Neergaard, J. de Magid, G. W.J. Ven, K.E. van de Giller, and T.B. Bruun. \n\n\n\n2018. Changes in soil organic carbon stocks after conversion from forest to oil palm \n\n\n\nplantations in Malaysian Borneo. Environmental Research Letters 13(10):105001. \n\n\n\nhttps://doi.org/10.1088/1748-9326/aade0f \n\n\n\nZuraidah, Y., H. Aminuddin,T. Jamal,O. Jamarei, H.A. Osumanu and B.J. Mohamadu. 2010. \n\n\n\nOil palm (Elaeis guineensis) roots response to mechanization in Bernam series soil. \n\n\n\nAmerican Journal of Applied Sciences 7(3): 343\u2013348. \n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nINTRODUCTION\nCultivation of oil palm (Elaeis guineensis Jacq.) in the tropics requires a large \ninput of fertilisers to achieve high and sustainable yields. On average, oil palm \nrequires about 120 kg N ha-1 yr-1, 16 kg P ha-1 yr-1 and 286 kg K ha-1 yr-1 to achieve \nyields of 30 t fresh fruit bunch (FFB) yields per ha (Tarmizi and Mohd Tayeb \n2006). Fertilisers account for 60% of field upkeep cost and is the highest cost \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 113-121 (2017)                         Malaysian Society of Soil Science\n\n\n\nNutrient Losses Through Runoff from Several Types of \nFertilisers Under Mature Oil Palm \n\n\n\nVijiandran J.R.1, M.H.A Husni1, C.B.S. Teh1, A. R.  Zaharah1 and \nA. Xaviar2\n\n\n\n1Department of Land Management, Universiti Putra Malaysia, 43400 UPM \nSerdang, Selangor, Malaysia\n\n\n\n2Research Department, United Plantations Berhad, 36009, Teluk Intan, Perak, \nMalaysia\n\n\n\nABSTRACT\nThis study was conducted to understand the effects of fertiliser type (straights, \ncompounds and controlled-release fertilisers) on N, P, K and Mg losses by \nsurface runoff. The study was conducted in a mature oil palm field using three \n20 m by 6 m erosion plots containing two palms per plot with the soil type being \nTypic Kandiudults and slopes ranging from  5.5\u00b0 to 7.5\u00b0. Nutrient losses were \nmeasured in the eroded sediment and runoff water for every rainfall event over a \nperiod of 24 months. Nutrient losses were higher in the runoff water than in the \neroded sediments. Broadcast application of controlled-release fertilisers and its \nslow dissolving nature made it prone to washing down the slope. Hence, higher \nnutrient losses were observed in the controlled-release fertilisers compared to \nother treatments. Compound fertilisers showed lower total losses for N (4.96%), \nK (3.95%) and Mg (0.65%) compared to straight fertilisers. Lower P losses were \nobserved in the straights compared to the compound fertilisers due to higher \npercentage of soluble P in the compound fertilisers. Controlled- release fertilisers \nrecorded high nutrient losses in the sediments caused by the washout Except for \nnitrogen, controlled-release fertilisers recorded higher losses for P (56.56%), \nK (19.83%) and Mg (10.36%) compared to straight fertilisers. Nitrogen losses \nwere 18.15% lower in the controlled-release fertilisers compared to straights. \nCompound fertilisers showed lowest losses for N and K compared to straight \nfertilisers. Based on the data, it is postulated that compound fertilisers can lead \nto better nutrient uptake compared to straight fertilisers. However, this hypothesis \nneeds to be tested through field experiments measuring nutrient uptake and its \neffect on oil palm productivity.\n\n\n\nKeywords: Nutrient loss, surface runoff, oil palm, fertilisers, erosion. \n\n\n\n___________________\n*Corresponding author : E-mail: vijiandran@hotmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017114\n\n\n\nfactor in oil palm production (Goh, Hardter and Fairhust 2003). Hence, most \nagronomic trials have focused on studies related to nutrient uptake, response and \nlosses.\n\n\n\nStudies on soil and nutrient loss through surface runoff and erosion under \nthe oil palm environment have been carried out by several scientists namely \nMaene et al. (1979), Malaysian Palm Oil Board (PORIM 1994) and Chew et. al. \n(1999). These studies provide vital information for agronomists on the magnitude \nof nutrient losses through surface runoff for consideration in the formulation of \nfertiliser recommendations for oil palm. In addition, results of such trials also help \nplanters and agronomists to implement various field management strategies to \nminimise soil and nutrient losses.  However, in the early studies, type of fertiliser \nwas not given due consideration. \n\n\n\nThe current study quantified N, P, K and Mg losses through surface runoff \nfor mature oil palms fertilised with straights, compounds and controlled-release \nfertilisers. \n\n\n\nMETHODOLOGY\nThe study used erosion plots at United Plantation Berhad\u2019s Lima Blas Estate \nlocated at Selangor, Malaysia (3\u00b0 45\u2019 15.762\u201d N, 101\u00b0 20\u2019 23.798\u201d E) for 24 \nmonths from 1 February 2013 to 31 January 2015.\n\n\n\nThree erosion plots measuring 20 by 6 m were established on a rolling terrain \nof a mature oil palm field. Slopes ranged from 5.5\u00b0 to 7.5\u00b0 for all three plots. \nSoil type at the study site was Typic Kandiudult (Serdang Soil Series). Each plot \ncontained two mature oil palms planted in the year 2000.\n\n\n\n Palms in each plot were fertilised with straights, compounds or controlled-\nrelease fertilisers at equivalent amounts (in split applications of three times per \nyear) of nutrients N, P, K and Mg as provided in Table 1.\n\n\n\nPrior to the start of the experiment, the palms in all three plots were not \nfertilised for a period of 12 months to avert effects of residual nutrients from the \nprevious fertiliser application conducted in October 2011. This intervening period \nwas used to measure nutrient losses for an unfertilised plot. Nutrient losses for \nan unfertilised plot was recognised when the nutrient content in both the eroded \nsediment and runoff water had minimal variances between erosion event and this \nwas achieved about 10 months following the previous fertilisation.\n\n\n\nlosses through surface runoff for consideration in the formulation of fertiliser \nrecommendations for oil palm. In addition, results of such trials also help planters and \nagronomists to implement various field management strategies to minimise soil and nutrient \nlosses.  However, in the early studies, type of fertiliser was not given due consideration.  \n\n\n\nThe current study quantified N, P, K and Mg losses through surface runoff for mature \noil palms fertilised with straights, compounds and controlled-release fertilisers.  \n\n\n\n\n\n\n\nMETHODOLOGY \n \nThe study used erosion plots at United Plantation Berhad\u2019s Lima Blas Estate located at \nSelangor, Malaysia (3\u00b0 45\u2019 15.762\u201d N, 101\u00b0 20\u2019 23.798\u201d E) for 24 months from 1 February \n2013 to 31 January 2015. \n\n\n\nThree erosion plots measuring 20 by 6 m were established on a rolling terrain of a \nmature oil palm field. Slopes ranged from 5.5\u00b0 to 7.5\u00b0 for all three plots. Soil type at the \nstudy site was Typic Kandiudult (Serdang Soil Series). Each plot contained two mature oil \npalms planted in the year 2000. \n Palms in each plot were fertilised with straights, compounds or controlled-release \nfertilisers at equivalent amounts (in split applications of three times per year) of nutrients N, \nP, K and Mg as provided in Table 1. \n \n \n\n\n\nTABLE 1 \n Nutrient inputs for erosion plot palms \n\n\n\n \nNutrient Rate (kg palm-1 year-1) \n\n\n\nN 1.10 \n\n\n\nP 0.44 \n\n\n\nK 1.49 \n\n\n\nMg 0.20 \n\n\n\n\n\n\n\nPrior to the start of the experiment, the palms in all three plots were not fertilised for a period \nof 12 months to avert effects of residual nutrients from the previous fertiliser application \nconducted in October 2012. This intervening period was used to measure nutrient losses for \nan unfertilised plot. Nutrient losses for an unfertilised plot was recognised when the nutrient \ncontent in both the eroded sediment and runoff water had minimal variances between erosion \nevent and this was achieved about 10 months following the previous fertilisation. \n\n\n\nA manifold tippling tumbler collection system was adapted to measure soil and runoff \nwater losses from each plot for every rainfall event (Figure 1). \n\n\n\n\n\n\n\nTABLE 1\n Nutrient inputs for erosion plot palms\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 115\n\n\n\nA manifold tippling tumbler collection system was adapted to measure soil \nand runoff water losses from each plot for every rainfall event (Figure 1).\nThe randomised complete block design was adopted for this study using every \nrainfall event that generated soil and runoff losses as a replicate for the study.\n\n\n\nSamples of the collected eroded sediment and runoff water were analysed \nin the laboratory. The Kjeldahl method was adopted for the analysis of nitrogen \n(N), phosphorus (P) was determined using the Bray and Kurtz No 2 method \nwhile potassium (K) and magnesium (Mg) were determined using the Atomic \nAbsorption Spectrophotometer (AAS).  \n\n\n\nFig. 1:  Soil and runoff loss collection mechanism layout\n\n\n\nRESULTS AND DISCUSSION\nRainfall\nTotal annual rainfall recorded for 2013 and 2014 was 2883 and 2441 mm, \nrespectively. In the first year of study (2013), highest rainfall was recorded in \nthe month of November at 546 mm while the highest rainfall in the second year \nwas 434 mm in the month of April. The third trimester in both years had higher \ncumulative rainfall coinciding with the annual monsoon season. Figure 2 below \nshows the rainfall pattern during the study period.  \n\n\n\nSoil and Runoff Losses\nSoil loss and runoff losses were measured at every rainfall episode that yielded \nrunoff or soil loss. In 2013, of 120 erosion events were recorded whilst 116 events \nwere recorded in 2014. Table 2 highlights the mean soil and runoff losses recorded. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017116\n\n\n\nFig. 2: Rainfall distribution\n\n\n\nSoil losses were generally high in all three plots as minimal erosion control \nmeasures and vegetation were in place in the plots in order to amplify the nutrient \nloss effects on application of the various forms of fertilisers. Soil loss recorded \nis noted to be within the soil loss range reported by previous authors (Soon and \nHoong 2002; Chew et al. 1999; PORIM 1994). Mean runoff losses ranged from \n19.23% to 23.20% of the mean rainfall for the two years (1 February 2013 to \n31 January 2015) and were comparable to the range reported by Kee and Chew \n(1996).\n\n\n\n \n \n \n \n \n \n \n \n \n \n                        \n \n \n \n \n  \n \n \n \n   Bars indicate standard deviation  \n\n\n\nFigure. Rainfall distribution \n \n\n\n\n \n \nSoil and Runoff Losses \n \nSoil loss and runoff losses were measured at every rainfall episode that yielded runoff or soil \nloss. In 2013, of 120 erosion events were recorded whilst 116 events were recorded in 2014. \nTable 2 highlights the mean soil and runoff losses recorded.  \n\n\n\n\n\n\n\nTABLE 2 \nMean (\u00b1SE) soil and runoff losses recorded \n\n\n\n\n\n\n\nYear \nSoil Loss (t ha-1 year-1) \n\n\n\nPlot A Plot B Plot C \n\n\n\n2013 9.36 \u00b1 0.18 17.87 \u00b1 0.32 8.19 \u00b1 0.15  \n\n\n\n2014 8.66 \u00b1 0.20 16.25 \u00b1 0.35 7.28 \u00b1 0.16 \n\n\n\nMean 9.01a 17.06b 7.73a \n\n\n\n Runoff Loss (mm) \n\n\n\nYear Plot A Plot B Plot C \n\n\n\n2013 625 \u00b1 9.62 524 \u00b1 7.23 555 \u00b1 8.71 \n\n\n\n2014 566 \u00b1 11.91 500 \u00b1 10.01 529 \u00b1 11.25 \n\n\n\nMean 596a 512a 542a \n\n\n\nPlot A: Straights, Plot B : Compounds, Plot C : CRF \nMeans not sharing a common letter are significantly different by Tukey (p \u2264 0.05).  \n\n\n\n\n\n\n\n0\n\n\n\n100\n\n\n\n200\n\n\n\n300\n\n\n\n400\n\n\n\n500\n\n\n\n600\n\n\n\nFeb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan\n\n\n\n2013 2014\n\n\n\n \n \n \n \n \n \n \n \n \n \n                        \n \n \n \n \n  \n \n \n \n   Bars indicate standard deviation  \n\n\n\nFigure. Rainfall distribution \n \n\n\n\n \n \nSoil and Runoff Losses \n \nSoil loss and runoff losses were measured at every rainfall episode that yielded runoff or soil \nloss. In 2013, of 120 erosion events were recorded whilst 116 events were recorded in 2014. \nTable 2 highlights the mean soil and runoff losses recorded.  \n\n\n\n\n\n\n\nTABLE 2 \nMean (\u00b1SE) soil and runoff losses recorded \n\n\n\n\n\n\n\nYear \nSoil Loss (t ha-1 year-1) \n\n\n\nPlot A Plot B Plot C \n\n\n\n2013 9.36 \u00b1 0.18 17.87 \u00b1 0.32 8.19 \u00b1 0.15  \n\n\n\n2014 8.66 \u00b1 0.20 16.25 \u00b1 0.35 7.28 \u00b1 0.16 \n\n\n\nMean 9.01a 17.06b 7.73a \n\n\n\n Runoff Loss (mm) \n\n\n\nYear Plot A Plot B Plot C \n\n\n\n2013 625 \u00b1 9.62 524 \u00b1 7.23 555 \u00b1 8.71 \n\n\n\n2014 566 \u00b1 11.91 500 \u00b1 10.01 529 \u00b1 11.25 \n\n\n\nMean 596a 512a 542a \n\n\n\nPlot A: Straights, Plot B : Compounds, Plot C : CRF \nMeans not sharing a common letter are significantly different by Tukey (p \u2264 0.05).  \n\n\n\n\n\n\n\n0\n\n\n\n100\n\n\n\n200\n\n\n\n300\n\n\n\n400\n\n\n\n500\n\n\n\n600\n\n\n\nFeb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan\n\n\n\n2013 2014\n\n\n\nTABLE 2\nMean (\u00b1SE) soil and runoff losses recorded\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 117\n\n\n\nDuring the study period, Plot B consistently showed significantly higher soil \nlosses compared to plot A and plot C. This is due to the differences in texture. Plot \nB had a higher percentage of fine sand in the top soil layer compared to plot A and \nplot C (Table 3). \n\n\n\n Soil losses were generally high in all three plots as minimal erosion control measures \nand vegetation were in place in the plots in order to amplify the nutrient loss effects on \napplication of the various forms of fertilisers. Soil loss recorded is noted to be within the soil \nloss range reported by previous authors (Soon and Hoong 2002; Chiew et al. 1999(Chew et \nal. 1999 in ref list??? ; PORIM 1994). Mean runoff losses ranged from 19.23% to 23.20% of \nthe mean rainfall for the two years (1 February 2013 to 31 January 2015) and were \ncomparable to the range reported by Kee and Chew (1996). \n\n\n\nDuring the study period, Plot B consistently showed significantly higher soil losses \ncompared to plot A and plot C. This is due to the differences in texture. Plot B had a higher \npercentage of fine sand in the top soil layer compared to plot A and plot C (Table 3).  \n\n\n\n\n\n\n\nTABLE 3  \nSoil texture analysis \n\n\n\n\n\n\n\nSampling Depth \n0-15 cm 15-30 cm 30-45 cm \n\n\n\nPlot A Plot  \nB \n\n\n\nPlot C Plot A Plot  \nB \n\n\n\nPlot C Plot A Plot  \nB \n\n\n\nPlot  \nC \n\n\n\nParticles Percentage (%) \nClay  \n\n\n\n(<0.002mm) \n36.70 23.50 25.10 38.10 26.10 25.10 33.80 29.80 27.60 \n\n\n\nSilt \n(0.002\u20130.05 mm) \n\n\n\n17.30 11.40 10.40 17.70 14.30 10.40 21.70 12.10 12.60 \n\n\n\nCoarse Sand \n(0.5-2.0 mm)  \n\n\n\n0.40 0.37 23.90 2.70 0.49 15.80 0.36 10.90 4.60 \n\n\n\nFine Sand  \n(0.05 - 0.5 mm) \n\n\n\n45.50 64.80 43.60 41.40 59.10 48.70 44.10 47.30 55.20 \n\n\n\nTotal Sand 45.90 65.17 67.50 44.10 59.59 64.50 44.46 58.20 59.80 \n \n\n\n\n Nutrient loss was measured based on nutrient content in the eroded \nsediments and as dissolved nutrients in the runoff water. Generally, it was noted \nthat nutrient losses were much higher in the runoff water compared to the eroded \nsediments (Table 4), similar to the findings of Kee and Chew, (1996). During \nthe data collection process, granules of the controlled-release fertilisers were \nobserved to be washed down together with the eroded sediments even after a \nweek of fertilisation.\n\n\n\n Controlled-release fertilisers are generally resin-coated to achieve the control \nrelease properties where the resin dissolves at a slow rate in water. The broadcast \napplication of these fertilisers around the palm circle and its slow dissolving rate \nmakes it prone to washing down a slope. Hence, a higher loss of nutrients was \nrecorded in the eroded sediments for the plot fertilised with controlled-release \nfertilisers compared to other treatments. Concomitantly, there were lower nutrient \nlosses in the runoff water.\n\n\n\nBroadcast application of controlled-release fertilisers on slopes under oil \npalm is not a viable option as a large amount of fertiliser is likely to be washed \ndown from the targeted area due to the physical nature of the fertiliser. In contrast, \nstraight and compound fertilisers which are generally hygroscopic, absorb \nmoisture from both the atmosphere and the soil as soon as they are applied causing \nthem to disintegrate from their original physical form and become part of the soil \ncolloids, thus preventing any physical movement of the compound fertilisers. \n\n\n\nThere were no significant differences in nutrient losses recorded between all \nthree treatments for both years of study (Table 4). Compound fertilisers showed \nlower total losses for N (4.96%), K (3.95%) and Mg (0.65%) compared to straight \nfertilisers. The lower losses in compound fertilisers are mainly due to the lower \nnutrient losses in the runoff water rather than the eroded sediments. Nutrient losses \n\n\n\nTABLE 3 \nSoil texture analysis\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017118\n\n\n\n N\nut\n\n\n\nri\nen\n\n\n\nt L\nos\n\n\n\ns \n \n\n\n\n \nTA\n\n\n\nB\nLE\n\n\n\n 4\n \n\n\n\nM\nea\n\n\n\nn \n(\u00b1\n\n\n\nSE\n) N\n\n\n\nPK\n lo\n\n\n\nss\nes\n\n\n\n in\n e\n\n\n\nro\nde\n\n\n\nd \nse\n\n\n\ndi\nm\n\n\n\nen\nts\n\n\n\n a\nnd\n\n\n\n ru\nno\n\n\n\nff\n w\n\n\n\nat\ner\n\n\n\n\n\n\n\nY\nea\n\n\n\nr /\n T\n\n\n\nre\nat\n\n\n\nm\nen\n\n\n\nt \n P\n\n\n\nlo\nt A\n\n\n\n (S\ntra\n\n\n\nig\nht\n\n\n\ns)\n \n\n\n\nPl\not\n\n\n\n B\n (C\n\n\n\nom\npo\n\n\n\nun\nds\n\n\n\n) \nPl\n\n\n\not\n C\n\n\n\n (C\non\n\n\n\ntro\nlle\n\n\n\nd \nR\n\n\n\nel\nea\n\n\n\nse\n) \n\n\n\nSo\nil \n\n\n\nW\nat\n\n\n\ner\n \n\n\n\nTo\nta\n\n\n\nl \nSo\n\n\n\nil \nW\n\n\n\nat\ner\n\n\n\n \nTo\n\n\n\nta\nl \n\n\n\nSo\nil \n\n\n\nW\nat\n\n\n\ner\n \n\n\n\nTo\nta\n\n\n\nl \nkg\n\n\n\n n\nut\n\n\n\nrie\nnt\n\n\n\n h\na-1\n\n\n\n y\nr-1\n\n\n\n\n\n\n\n\n\n\n\nN\n \n\n\n\n20\n13\n\n\n\n \n12\n\n\n\n.3\n0a  \u00b1\n\n\n\n 0\n.2\n\n\n\n7 \n23\n\n\n\n.4\n4 a\n\n\n\n \u00b1\n 0\n\n\n\n.5\n6 \n\n\n\n \n16\n\n\n\n.0\n5 a\n\n\n\n \u00b1\n 0\n\n\n\n.2\n9 \n\n\n\n21\n.1\n\n\n\n2 a\n \u00b1\n\n\n\n 0\n.5\n\n\n\n7 \n \n\n\n\n10\n.4\n\n\n\n9 a\n \u00b1\n\n\n\n 0\n.2\n\n\n\n1 \n20\n\n\n\n.7\n5 a\n\n\n\n \u00b1\n 0\n\n\n\n.5\n4 \n\n\n\n \n20\n\n\n\n14\n \n\n\n\n4.\n96\n\n\n\n a\n \u00b1\n\n\n\n 0\n.1\n\n\n\n6 \n39\n\n\n\n.5\n3 a\n\n\n\n \u00b1\n 1\n\n\n\n.2\n8 \n\n\n\n\n\n\n\n7.\n94\n\n\n\n a\n \u00b1\n\n\n\n 0\n.3\n\n\n\n1 \n31\n\n\n\n.1\n1 a\n\n\n\n \u00b1\n 0\n\n\n\n.7\n8 \n\n\n\n \n14\n\n\n\n.1\n6 a\n\n\n\n \u00b1\n 0\n\n\n\n.4\n7 \n\n\n\n20\n.2\n\n\n\n4 a\n \u00b1\n\n\n\n 0\n.6\n\n\n\n9 \n \n\n\n\nM\nea\n\n\n\nn \n8.\n\n\n\n63\n \n\n\n\n31\n.4\n\n\n\n8 \n40\n\n\n\n.1\n1 \n\n\n\n12\n.0\n\n\n\n0 \n26\n\n\n\n.1\n2 \n\n\n\n38\n.1\n\n\n\n2 \n12\n\n\n\n.3\n3 \n\n\n\n20\n.5\n\n\n\n0 \n32\n\n\n\n.8\n3 \n\n\n\n%\n L\n\n\n\nos\ns v\n\n\n\ns s\ntra\n\n\n\nig\nht\n\n\n\ns \n \n\n\n\n\n\n\n\n\n\n\n\n-4\n.9\n\n\n\n6 \n \n\n\n\n \n-1\n\n\n\n8.\n15\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nP \n20\n\n\n\n13\n \n\n\n\n1.\n09\n\n\n\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n3 \n1.\n\n\n\n92\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n5 \n \n\n\n\n2.\n26\n\n\n\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n6 \n3.\n\n\n\n06\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n5 \n \n\n\n\n1.\n32\n\n\n\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n3 \n1.\n\n\n\n70\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n5 \n \n\n\n\n20\n14\n\n\n\n \n2.\n\n\n\n24\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n9 \n1.\n\n\n\n61\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n7 \n \n\n\n\n2.\n13\n\n\n\n a\n \u00b1\n\n\n\n 0\n.1\n\n\n\n2 \n1.\n\n\n\n29\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n6 \n \n\n\n\n6.\n26\n\n\n\n a\n \u00b1\n\n\n\n 0\n.2\n\n\n\n7 \n1.\n\n\n\n47\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n7 \n \n\n\n\n \nM\n\n\n\nea\nn \n\n\n\n1.\n67\n\n\n\n \n1.\n\n\n\n76\n \n\n\n\n3.\n43\n\n\n\n \n2.\n\n\n\n20\n \n\n\n\n2.\n18\n\n\n\n \n4.\n\n\n\n38\n \n\n\n\n3.\n79\n\n\n\n \n1.\n\n\n\n57\n \n\n\n\n5.\n37\n\n\n\n \n%\n\n\n\n L\nos\n\n\n\ns v\ns s\n\n\n\ntra\nig\n\n\n\nht\ns \n\n\n\n\n\n\n\n\n\n\n\n \n27\n\n\n\n.7\n0 \n\n\n\n\n\n\n\n56\n.5\n\n\n\n6 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nK\n \n\n\n\n20\n13\n\n\n\n \n2.\n\n\n\n37\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n7 \n36\n\n\n\n.4\n0 a\n\n\n\n \u00b1\n 0\n\n\n\n.6\n4 \n\n\n\n \n5.\n\n\n\n85\n a\n \u00b1\n\n\n\n 0\n.1\n\n\n\n9 \n29\n\n\n\n.2\n8 a\n\n\n\n \u00b1\n 0\n\n\n\n.7\n6 \n\n\n\n \n8.\n\n\n\n29\n a\n \u00b1\n\n\n\n 0\n.2\n\n\n\n2 \n30\n\n\n\n.5\n4 a\n\n\n\n \u00b1\n 0\n\n\n\n.5\n4 \n\n\n\n \n20\n\n\n\n14\n \n\n\n\n1.\n95\n\n\n\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n9 \n39\n\n\n\n.7\n7 a\n\n\n\n \u00b1\n 0\n\n\n\n.8\n9 \n\n\n\n \n4.\n\n\n\n65\n a\n \u00b1\n\n\n\n 0\n.3\n\n\n\n2 \n37\n\n\n\n.5\n4 a\n\n\n\n \u00b1\n 0\n\n\n\n.9\n4 \n\n\n\n \n20\n\n\n\n.4\n5 a\n\n\n\n \u00b1\n 0\n\n\n\n.9\n7 \n\n\n\n37\n.1\n\n\n\n7 a\n \u00b1\n\n\n\n 0\n.7\n\n\n\n9 \n \n\n\n\nM\nea\n\n\n\nn \n2.\n\n\n\n16\n \n\n\n\n38\n.0\n\n\n\n9 \n40\n\n\n\n.2\n5 \n\n\n\n5.\n25\n\n\n\n \n33\n\n\n\n.4\n1 \n\n\n\n38\n.6\n\n\n\n6 \n14\n\n\n\n.3\n7 \n\n\n\n33\n.8\n\n\n\n6 \n48\n\n\n\n.2\n3 \n\n\n\n%\n L\n\n\n\nos\ns v\n\n\n\ns s\ntra\n\n\n\nig\nht\n\n\n\ns \n \n\n\n\n\n\n\n\n\n\n\n\n-3\n.9\n\n\n\n5 \n \n\n\n\n \n19\n\n\n\n.8\n3 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nM\n\n\n\ng \n20\n\n\n\n13\n \n\n\n\n0.\n20\n\n\n\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n1 \n1.\n\n\n\n14\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n2 \n \n\n\n\n0.\n55\n\n\n\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n2 \n1.\n\n\n\n10\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n2 \n \n\n\n\n0.\n68\n\n\n\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n2 \n1.\n\n\n\n18\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n2 \n \n\n\n\n20\n14\n\n\n\n \n1.\n\n\n\n51\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n7 \n \n\n\n\n3.\n31\n\n\n\n a\n \u00b1\n\n\n\n 0\n.1\n\n\n\n0 \n \n\n\n\n1.\n92\n\n\n\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n8 \n2.\n\n\n\n56\n a\n \u00b1\n\n\n\n 0\n.0\n\n\n\n8 \n \n\n\n\n2.\n68\n\n\n\n a\n \u00b1\n\n\n\n 0\n.1\n\n\n\n2 \n2.\n\n\n\n37\n a  \u00b1\n\n\n\n 0\n.0\n\n\n\n7 \n \n\n\n\nM\nea\n\n\n\nn \n0.\n\n\n\n86\n \n\n\n\n2.\n23\n\n\n\n \n3.\n\n\n\n09\n \n\n\n\n1.\n24\n\n\n\n \n1.\n\n\n\n83\n \n\n\n\n3.\n07\n\n\n\n \n1.\n\n\n\n68\n \n\n\n\n1.\n73\n\n\n\n \n3.\n\n\n\n41\n \n\n\n\n%\n L\n\n\n\nos\ns v\n\n\n\ns s\ntra\n\n\n\nig\nht\n\n\n\ns \n \n\n\n\n\n\n\n\n\n\n\n\n-0\n.6\n\n\n\n5 \n \n\n\n\n \n10\n\n\n\n.3\n6 \n\n\n\nPl\not\n\n\n\n A\n: S\n\n\n\ntra\nig\n\n\n\nht\ns, \n\n\n\nPl\not\n\n\n\n B\n: C\n\n\n\nom\npo\n\n\n\nun\nds\n\n\n\n, P\nlo\n\n\n\nt C\n: C\n\n\n\nRF\n \n\n\n\nM\nea\n\n\n\nns\n n\n\n\n\not\n sh\n\n\n\nar\nin\n\n\n\ng \na \n\n\n\nco\nm\n\n\n\nm\non\n\n\n\n le\ntte\n\n\n\nr a\nre\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\nly\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nt b\ny \n\n\n\nTu\nke\n\n\n\ny \n(p\n\n\n\n \u2264\n 0\n\n\n\n.0\n5)\n\n\n\n TA\nB\n\n\n\nLE\n 4\n\n\n\nM\nea\n\n\n\nn \n(\u00b1\n\n\n\nSE\n) N\n\n\n\nPK\n lo\n\n\n\nss\nes\n\n\n\n in\n e\n\n\n\nro\nde\n\n\n\nd \nse\n\n\n\ndi\nm\n\n\n\nen\nts\n\n\n\n a\nnd\n\n\n\n ru\nno\n\n\n\nff \nw\n\n\n\nat\ner\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 119\n\n\n\nin the runoff water in the compound fertiliser accounted for about 55% of the total \nnutrient losses. Phosphorus losses were much higher in the compound fertilisers \ncompared to straights as the compound fertiliser used for the study contained \nabout 11% soluble phosphorus in the form of Triple Super Phosphate while in \ntreatment A, phosphate rocks(which dissolve very slowly in water) were used \nas the source of phosphorus. The findings of these study are somewhat different \nfrom the works of Bah et al., 2014 who had reported that CRF tend to have lower \nnutrient losses compared to the conventional mixture. This is attributed to the \nphysical form of the CRF used by Bah et al.(2014)  which was in the form of \nnuggets which are more difficult to be washed down the slope unlike the granular \nCRF used in this study.\n\n\n\n As significant differences were noted in the soil losses between the three \ntreatment plots, nutrient losses were also expressed as per tonne soil loss and per \n100mm runoff to provide an equal comparison between the three plots. Table 5 \ndetails nutrient losses as per Mg (per tonne) sediment and per 100 mm runoff. \n\n\n\n \nTABLE 5  \n\n\n\nMean nutrient loss expressed as per tonne sediment and per 100mm runoff. \n \n\n\n\nNutrient/ \nTreatment \n\n\n\nNutrient loss per tonne sediment Nutrient loss per 100mm runoff \nA B C A B C \n\n\n\n kg nutrient ha-1 \nN 1.06 0.71 1.56 7.41 6.10 4.91 \n\n\n\n% diff from A  -33.02 47.17  -17.68 -33.74 \nP 0.29 0.19 0.53 0.29 0.45 0.25 \n\n\n\n% diff from A  -34.48 82.76  55.17 -13.79 \nK 0.21 0.25 1.51 7.70 7.37 7.19 \n\n\n\n% diff from A  19.05 619.05  -4.29 -6.62 \nMg 0.08 0.07 0.20 0.47 0.47 0.54 \n\n\n\n% diff from A  -12.50 150.00  - 14.89 \n\n\n\n \n  \nCompound fertilisers clearly had much lower losses for nitrogen, phosphorus and magnesium \ncompared to straight fertilisers. Potassium losses were marginally higher for the compounds \ncompared to the straights. As for the controlled-release fertilisers, significantly higher losses \nwere recorded for all nutrients analysed, largely caused by the rolling of the fertiliser granules \ndown the slope.  \n In the runoff water, compound fertilisers showed lower losses of nitrogen and \npotassium compared to the straights.  However, phosphorus losses were higher in the \ncompound fertilisers compared to straights; it is to be noted that the phosphorus in compound \nfertilisers is water soluble. Magnesium losses were comparable to both straight and \ncompound fertilisers.   \n \n \n\n\n\nCONCLUSION \n \nCompound fertilisers generally recorded lowest losses for N and K compared to straight \nfertilisers and are therefore postulated to lead to better nutrient uptake compared to straight \nfertilisers. The lower losses in the compounds also provide the opportunity for the application \nof a lower nutrient rate compared to straights to meet the nutrient requirements of the oil \npalm. However, this hypothesis needs to be verified through field experiments to measure \nnutrient uptake and its effect on oil palm productivity \n As nutrient losses are significantly higher in the runoff water for both straight and \ncompound fertilisers compared to losses in the eroded sediments, efforts should focus on \nminimising runoff water losses as part of erosion control practices for oil palm planting on \nslopes. \n              \n \nREFERENCES  \n \nBah, A., M.H.A. Husni, C.B.S. Teh, M.Y. Rafii, S.R. Syed Omar  and O.H.  Ahmed. (2014). \nReducing runoff loss of applied nutrients in oil palm cultivation using controlled-release \nfertilizers. Advances in Agriculture Vol no?? :1\u20139. https://doi.org/10.1155/2014/285387 \n \n\n\n\nCompound fertilisers clearly had much lower losses for nitrogen, phosphorus \nand magnesium compared to straight fertilisers. Potassium losses were marginally \nhigher for the compounds compared to the straights. As for the controlled-release \nfertilisers, significantly higher losses were recorded for all nutrients analysed, \nlargely caused by the rolling of the fertiliser granules down the slope. \n\n\n\n In the runoff water, compound fertilisers showed lower losses of nitrogen \nand potassium compared to the straights.  However, phosphorus losses were \nhigher in the compound fertilisers compared to straights; it is to be noted that \nthe phosphorus in compound fertilisers is water soluble. Magnesium losses were \ncomparable to both straight and compound fertilisers.  \n\n\n\nCONCLUSION\nCompound fertilisers generally recorded lowest losses for N and K compared to \nstraight fertilisers and are therefore postulated to lead to better nutrient uptake \ncompared to straight fertilisers. The lower losses in the compounds also provide \n\n\n\nTABLE 5 \nMean nutrient loss expressed as per tonne sediment and per 100mm runoff.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017120\n\n\n\nthe opportunity for the application of a lower nutrient rate compared to straights \nto meet the nutrient requirements of the oil palm. However, this hypothesis needs \nto be verified through field experiments to measure nutrient uptake and its effect \non oil palm productivity\n\n\n\n As nutrient losses are significantly higher in the runoff water for both \nstraight and compound fertilisers compared to losses in the eroded sediments, \nefforts should focus on minimising runoff water losses as part of erosion control \npractices for oil palm planting on slopes.\n             \n\n\n\nREFERENCES \nBah, A., M.H.A. Husni, C.B.S. Teh, M.Y. Rafii, S.R. Syed Omar  and O.H.  Ahmed. \n\n\n\n(2014). Reducing runoff loss of applied nutrients in oil palm cultivation using \ncontrolled-release fertilizers. Advances in Agriculture Vol no. 4 :1\u20139. https://\ndoi.org/10.1155/2014/285387\n\n\n\nChew P.S., K.K. Kee and K.J. Goh.1999. Cultural practices and their impacts. In: Oil \nPalm and the Environment \u2013 A Malaysian Perspective, ed.  G.Singh, C.K. Huan, \nT.Leng and D.L. Kow. Malaysian Oil Palm Growers\u2019 Council, Kuala Lumpur.\n\n\n\nGoh K.J., R. H\u00e4rdter and T.Fairhust  2003. Fertilizing for maximum return. In: Oil \nPalm : Management for Large and Sustainable Yields., ed. T. Fairhurst and R. \nHardter.  Potash and Phosphate Institute, Canada\n\n\n\nKee, K.K. and P.S. Chew. 1996. A13: Nutrient losses through surface runoff and \nerosion- implications for improved fertilizer efficiency in mature oil palm. \nApplied Agricultural Research Sdn. Bhd\n\n\n\nMaene L.M., K.C. Thong, T.S Ong and A.M. Mokhtaruddin. 1979. Surface wash \nunder mature oil palm. In:  Proc. Symposium Water in Malaysian Agriculture, \ned. E. Pushparajah. Malaysian Society of Soil Science. Kuala Lumpur. \n\n\n\nPORIM. 1994. Environmental Impacts of Oil Palm Plantations in Malaysia. Palm Oil \nResearch Institute of Malaysia. Malaysia.\n\n\n\nSoon B.B.F and H.W. Hoong. 2002. Agronomic practices to alleviate soil and surface \nrunoff losses in an oil palm estate Malaysian Journal of Soil Science 6(Special \ned):  Page no. 53-64.\n\n\n\nTarmizi AM and D. Mohd. Tayeb. 2006. Nutrient demands of Tenera oil palm planted \non inland soils of Malaysia. Journal of Oil Palm Research 18(June ): page no. \n204-209.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 121\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017122\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 123\n\n\n\nEssentials of Soil Science: Soil formation, functions, use and \nclassification (World Reference Base, WRB).\nWinfried E.H. Blu, Peter Schad, and Stephen Nortcliff. Borntraeger Science \nPublisher, Stuttgart 70176, Germany. 2018. 171 p. \u20ac 27.90. ISBN 978-3-443-\n01090-4.\n\n\n\nThis is a book which need to be read by students at all level, and suitable as a \nquick reference for anyone interested in the field of soil science.  \n\n\n\nSoil science is a field of study that focus on soil as an integrated system. As it \nmay sound complex, this book made it all facile \u00e0 comprendre to understand \nthe definition, concepts and basics of soil. Information is well organized, written \nclearly, and profusely illustrated in total of nine (9) chapters, evermore concise \nand detailed text.\n\n\n\nThis book simplify many important concepts of soil science for reader to easily \ncomprehend the concepts. Soil formation and functions begins with parent \nmaterials, minerals and weathering processes that give rise to secondary minerals \nare well sorted out with emphasis on soil morphology, 1:1, 2:1 clay, ion exchange \n(cations and anions), buffering capacity, pedogenesis, soil-plant interactions, soil \npollution, etc. Alphabetical index system of the book is neat.  \n\n\n\nSoil use and classification were based on World Reference Base for Soil Resources \n(WRB), a system utilized by FAO-UNESO-UNDP in their agriculture related \ndevelopment programs and proved its worth in Africa, Asia, Europe, etc.\n\n\n\nA total of 171 pages of information, with 101 figures in color and 22 tables are \nwell laid out in a user \u2013friendly manner for quick understanding. The figures and \ntables are self-explanatory which make this book suitable as text book of soil \nscience at preuniversity level, college, universities and short-course training on \nsoil science. \n\n\n\nThe bona fide cumulative knowledge of the writers, Emeritus Prof. Dr. Blum, \nProf. Dr. Schad and Emeritus Prof. Dr. Nortcliff are indispensable, with years of \nhighly valuable-experience in soil science. \nA detailed, highly complex data and scientifically in-depth explanations are not the \npurpose of this book. If that is what the readers wants, look elsewhere, otherwise: \nPut this book on your next \u201cmust-read\u201d list. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017124\n\n\n\nROSLAN ISMAIL, Ph.D.\nCHE FAUZIAH ISHAK, Ph.D.\n\n\n\nDept. of Land Management, Faculty of Agriculture, \nUniversiti Putra Malaysia, 43400 SERDANG SELANGOR, \n\n\n\nMALAYSIA. \n(roslanismail@upm.edu.my)\n\n\n\n(cfauziah@upm.edu.my)\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 67-78 (2016)                         Malaysian Society of Soil Science\n\n\n\nRemoval of TPHs from Soil Media using Persulfate Oxidant \nin the Presence of Mineral Siderite\n\n\n\n1Mohammadi, F. *M. Alimohammadi , 1,2A.H. Mahvi, \n1S. Nazmara, 3S. Mazloomi and 1H. Aslani\n\n\n\n   1Department of Environmental Health Engineering, School of Public Health, \nTehran University of Medical Sciences, Tehran, Iran\n\n\n\n2Schools of Public Health and Center for Environmental Research, Tehran \nUniversity of Medical Sciences, Tehran, Iran\n\n\n\n3Department of Environmental Health Engineering, School of Public Health,\nIlam University of Medical Science, Ilam, Iran.\n\n\n\nABSTRACT\nThe objective of the present study was to evaluate the potential of persulfate \n(PS) oxidant in the presence of mineral siderite (SI) for remediation of fuel oil \ncontaminated soils. Clay and sandy soils were selected as representative soils \nwhich were spiked with 5000 mg fuel oil per kilogram of dry soil. The effects \nof controlling factors such as different persulfate concentrations (100-500 mM), \nsiderite concentrations (0.1-0.5 g), pH (3-9), and temperature (20-60\u00b0C) were also \ninvestigated. The results indicated that in clay and sandy soil samples, the highest  \ntotal petroleum hydrocarbon (TPHs) degradation was observed in the following \ncondition: pH= 3, soils temperature =60\u00b0C  and PS/Fe (II) molar ratio = 400 \nmM/0.4 g and 300 mM/0.3 g in clay and sandy soil, respectively. Based on our \nfindings,  using persulfate oxidation in the presence of siderite as an activator is a \npromising technique to remediate soil contaminated by petroleum hydrocarbons.\n\n\n\nKeywords: Contaminated soil, persulfate, remediation, siderite, TPHs\n\n\n\n___________________\n*Corresponding author : E-mail: Mahmood Alimohammadi, m_Alimohammadi@\n             tums.ac.ir\n\n\n\nINTRODUCTION\nMany developing countries around the world are facing problems of soil \ncontamination resulting from human activity (Kaimi et al., 2006). Common causes \nof soil pollution are the  release of petroleum compounds from pipelines and \nstorage tanks (Ferguson et al., 2005). Petroleum hydrocarbon pollution, because \nof its high sorption capacity, lower mobility, low instability, low solubility and \nhigh hydrophobicity, is a major environmental problem (Do et al., 2009; Tsai \nand Kao, 2009). Soil contaminated with petroleum hydrocarbons is a possible \npollution source for groundwater used for drinking and irrigation purposes; \ncontamination of agricultural products is a common consequence (Chien, 2012). \nIn order to prevent groundwater pollution, remediation of petroleum contaminated \nsoil should be a vital course of action worldwide. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201668\n\n\n\nThere are several methods of remediation for petroleum contaminated soils; \nsome examples include on-site remediation using microwave energy (Chien; \n2012); bioremediation (Namkoong et al., 2002; Wang et al., 2008; Wang et al., \n2011); chemical oxidation using hydrogen peroxide (Tsai and Kao, 2009); and \npersulfate (Huling and Pivetz, 2006). Chemical methods using various oxidants \nare being developed for the remediation of contaminated soil and water resources. \nPersulfate is one of the chemical materials used for in situ chemical oxidation \n(ISCO) to remove petroleum compounds (Huling and Pivetz, 2006). Costanza \net al., (2010) studied the oxidation of tetracholoroethylene (TCE) by thermal \nactivation of sodium persulfate in aqueous solution, while Huang et al. (2005) \nstudied the degradation of volatile organic compounds (VOC) with heat-activated \npersulfate. As a strong oxidant, persulfate (S2O8\n\n\n\n2, E0 = 2.01 V) is popular because \nof its stability which allows for its transportation over a longer distance in the \nsubsurface. Furthermore, under certain reactions with Fe2+, persulfate can generate \nsulfate radicals (SO4\n\n\n\n\u2022-, E0 = 2.6 V) (see Eq. (1)), which have higher oxidative \ncapacity than common oxidants and are effective in the removal of petroleum \ncompounds in contaminated soil (Fanaroff et al., 1994; Huling and Pivetz, 2006; \nDo et al.,  2010). Persulfate activated by Fe2+ has been used to degrade various \ncontaminants such as BTEX (Liang et al., 2008)  in the aqueous phase.\n\n\n\nS2O8\n-2 + Fe+2 \u2192 SO4\n\n\n\n+2 + SO4\n-2 + Fe+3   (1)\n\n\n\nThe role of soil minerals on the persulfate activation is proven; however \nthe type of minerals that makes such activation possible is not very clear. Teel \net al. (2011) surveyed the role of 13 common soil minerals and their effects \non persulfate activation; they found that cobaltite, ilmenite, pyrite, and siderite \nincrease persulfate oxidation potential, significantly. Siderite has been used as a \ncatalyst in combination with persulfate and hydrogen peroxide to remove TCE, \nresulting in 100% removal of TCE in 24 h in aqueous phase (Yan et al., 2013). \nThis study aimed to investigate the removal of TPHs   from soil media using \npersulfate oxidant in the presence of mineral siderite as an activator. The TPHs \ndegradation factors of persulfate concentration, siderite dosage, temperature, and \npH were investigated in detail in this study.\n\n\n\nMATERIALS AND METHODS\nMaterials\nThe fuel oil used in this study was purchased from Iran Petroleum Corp. Sodium \npersulfate (Na2S2O8, reagent grade (>99%), Merck Co, Germany) was used as \nan oxidant. Siderite (FeCO3), purchased from Zamin Tavana Tajhiz Co (Tehran, \nIran), was used for ferrous ions supplement to activate persulfate. Contaminated \nsoils were prepared in the laboratory and the soil samples were mechanically \nhomogenised and sieved (10-mesh sieve) to assure uniformity. The soil was \nspiked with fuel oil dispersed in 1 L of a 1:1 (v/v) n-hexane/acetone solution, and \nthen further homogenised. The solvents were allowed to vaporise from the soils \n\n\n\nMohammadi et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 69\n\n\n\nby placing the container of spiked soil in a fume hood, therefore leaving behind \nthe petroleum hydrocarbon in the soil at a theoretical initial total petroleum \nhydrocarbon (TPHs) concentration of approximately 5,000 mg kg-1 of soil. \nProperties of both  the soil samples used in this study are presented in Table 1. \nAll analyses were performed according to ASTM (1998). The soils and siderite \nelements were determined by X-ray fluorescence (XRF) (Table 2)\n\n\n\nBatch Degradation Kinetic Experiments\nSerum bottles (125 mL) were used as reactors for the batch experiments. Each \nreactor was filled with 2.5 g of the contaminated soil, and various concentrations \nof persulfate (100, 200, 300, 400, and 500 mM) and siderite (0.1, 0.2, 0.3, 0.4, and \n0.5 g) were added to the sample. The experiments were conducted in duplicate. \nThe prepared batch reactors were maintained at different temperatures (20-60\u00b0C) \nuntil analysis. In order to simulate static subsurface conditions, the reactors were \nstirred and then placed in the refrigerator. Pseudo-first order reaction model was \napplied to study the kinetics of diesel removal based on the results reported in \nprevious studies (Huang et al., 2002; Xie et al., 2012)\n\n\n\nTABLE 1\nPhysical and chemical characteristics of the soils used in this study\n\n\n\nTABLE 2\nCharacteristics of elemental soil and siderite\n\n\n\n13 \n \n\n\n\nTABLE 1 \n\n\n\nPhysical and chemical characteristics of the soils used in this study \n\n\n\nParameter                                                                        Clay soil            Sandy soil \n\n\n\npH                                                                              7.8                    8.3 \n\n\n\nSoil organic matter (%)                                                 2.5                   0.116 \n\n\n\nElectrical Conductivity (dS m-1)                                    2040                   229 \n\n\n\nTotal organic carbon (%)                                              0.82                   0.39 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nCharacteristics of elemental soil and siderite \n\n\n\n)%(Elements    \n\n\n\nL.O.I \n\n\n\n \nSiO2 Al2O3 Fe2O3 CaO Na2O K2O MgO TiO2 MnO P2O5 S Composition \n\n\n\nSample \n\n\n\n8.76 13.59 0.31 73.16 0.28 0.02 0.03 0.48 0.068 3.021 0.005 0.008 Siderite 1 \n\n\n\n18.26 43.56 9.98 5.86 13.82 0.26 1.87 5.24 0.563 0.124 0.112 0.096 Clay 2 \n\n\n\n22.73 38.52 5.24 2.6 24.91 0.34 0.89 3.86 0.259 0.087 0.071 0.008 Sand 3 \n\n\n\n \n13 \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nPhysical and chemical characteristics of the soils used in this study \n\n\n\nParameter                                                                        Clay soil            Sandy soil \n\n\n\npH                                                                              7.8                    8.3 \n\n\n\nSoil organic matter (%)                                                 2.5                   0.116 \n\n\n\nElectrical Conductivity (dS m-1)                                    2040                   229 \n\n\n\nTotal organic carbon (%)                                              0.82                   0.39 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nCharacteristics of elemental soil and siderite \n\n\n\n)%(Elements    \n\n\n\nL.O.I \n\n\n\n \nSiO2 Al2O3 Fe2O3 CaO Na2O K2O MgO TiO2 MnO P2O5 S Composition \n\n\n\nSample \n\n\n\n8.76 13.59 0.31 73.16 0.28 0.02 0.03 0.48 0.068 3.021 0.005 0.008 Siderite 1 \n\n\n\n18.26 43.56 9.98 5.86 13.82 0.26 1.87 5.24 0.563 0.124 0.112 0.096 Clay 2 \n\n\n\n22.73 38.52 5.24 2.6 24.91 0.34 0.89 3.86 0.259 0.087 0.071 0.008 Sand 3 \n\n\n\n\n\n\n\nRemovals of TPHs from Soil using Persulfate Oxidant\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201670\n\n\n\nAnalytical Methods\nTexas Natural Resource Conservation Commission (TNRCC) Method 1005 was \nused for quantification of the contaminants; this method is one of the more practical \nways of determining hydrocarbons in soil samples using a chromatographic \nprocedure (TNRCC, 2001). Slurry samples in the serum bottle were accurately??? \nshaken in an incubator  (Innova 4048, USA) for about 2 h  at 170 rpm to allow \nthe soil to mix well with the persulfate. Prior to commencing the analyses, TPHs \nwere extracted by 10 mL of N-pentane solution. Following this, 0.6\u00b5L of the \nextracted liquid sample was injected carefully into a gas chromatography  (CP-\n3800, Varian: Holland) equipped with a  flame ionisation detector and capillary \ncolumn (CP- SIL 8 CB column model, 30 m x 0.32 mm, 0.25 \u03bcm film thickness, \nVARIAN, Holland). The injector and detector temperatures were maintained at \n280\u00b0C and 300\u00b0C, respectively. The initial temperature of the column was 35\u00b0C \n(held for 8 min) and increased by 9\u00b0C/minute to 100\u00b0C (held for 8 min).\n\n\n\n \nRESULTS AND DISCUSSION\n\n\n\nEffect of Persulfate Concentration on Fuel Oil Removal\nIn order to determine PS optimum concentration in the range of 100, 200, 300, \n400, and 500 mM for TPHs removal, PS was applied with SI fixed at 0.2 g as an \nactivator. When PS was used solely, TPHs degradation rate was not significant \nat different PS doses (100, 200, 300, 400, and 500 mM), and the maximum \nremoval rate was about 6% (Figure 1). Using SI together with PS led to a higher \ndegradation rate for TPHs which might be due to greater activation of PS because \nof the presence of SI (Teel et al., 2011). Figure 1 shows a clear trend of TPHs \ndegradation rate versus persulfate concentration. It is apparent from Figure 1 that \npersulfate in concentrations of between 100 and 500 mM led to 8% to 18% and \n7% to 20% TPHs removal in sand and clay soil, respectively. As reported by \nAchugasim et al. (2011), diesel degradation rate using 10% and 20% persulfate \nconcentration were 43% and 55%, respectively. Yen et al. (2011) found that  TPHs \n(light crude) removal rate using persulfate activated by Fe(II) for sandy soils was \nmore than 96% under different pH conditions, while Do et al. (2009) have reported \napproximately 35%  efficiency for degradation in diesel-contaminated soils. What \nis interesting in Figure 1 is that the best dosage of persulfate concentration, in \nthe range used, were 400 and 300 mM in clay and sandy soils with efficiency \nrates of 20% and 18%, respectively. Moreover, the fuel oil degradation rate was \nnot positively related to PS concentration in the range of 100\u2013500 mM, because \nfurther increases in PS concentrations, that is, 300 mM for sand and 400 mM for \nclay soil, resulted in a decrease in fuel oil removal rate. The possible explanation \nis PS consumption by sulfate radicals which could be more remarkable than the \nremoval of the target (Liang et al. 2004). Do et al. (2010) suggested that the \nmaximum diesel removal in soil was achieved at PS/Fe (II) ratio of 100:1.\n\n\n\nMohammadi et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 71\n\n\n\nEffect of Siderite Dosage on the Persulfate System\nFuel oil removal rates with initial SI concentration varying from 0.1 to 0.5 g \nSI with a constant PS concentration (400 mM as the best concentration for clay \nand 300 mM for sand) were studied under laboratory conditions (20 \u00b0C), and the \nresults are depicted in Figure 2. The results show  that maximum degradation \nrate for clay soil was achieved at PS/SI ratio of 400 mM/0.4 g, while in the case \nof sandy soil, the best removal rate was achieved at 300 mM/0.3 g which led \nto 27% and 24% removal efficiency, respectively. Satapanajaru et al. (2014) \nstudied reactive black 5 dye (RB5) removal from water using PS and Fe (II), \nand concluded that the optimum conditions for 0.01 mM reactive black 5 dye \n(RB5) treatment were 445 mM PS and 4.91 mM Fe(II). Do et al. (2009) used \nperoxymonosulfate activated with FeCl2 in order to degrade diesel-contaminated \nsoil, and found that the maximum removal rate was 19%. According to Rastogi \net al. (2009), the optimum condition for 2-chloroethylbiphenyl removal from an \naqueous system was achieved when PS/Fe2+ mole ratio was 1:1 and the process \nwas conducted at pH= 3. Moreover, lower fuel oil degradation might be explained \nbecause of sulfate radical consumption by Fe2+ (eq (2)). In these circumstances, \nthe scavenging of the sulfate radicals (SO-\n\n\n\n4\u2022) can be more important than the \ndegradation of the fuel oil. Similar results have been confirmed in other studies \n(Do et al., 2010; Rodriguez et al., 2014; Satapanajaru et al., 2014).\n\n\n\n       Fe2+ + SO4\n.- \u2192 SO4\n\n\n\n2- + Fe3+  (2)\n\n\n\nInterestingly, in our study, it was observed that the removal rates were reduced \nwhen siderite exceeded 0.4 g in clay and 0.3 g in sand soil, respectively. Hence, \nin order to minimise the adverse effects of Fe2+ on sulfate radical production, the \nFe2+ concentrations should be controlled.  It can be inferred from Figure 2 that in \n\n\n\nFigure 1: Fuel oil removal efficiency at different PS concentrations plus 0.2g SI for clay \nand sandy soil at 20\u00b11\u25e6C\n\n\n\n14 \n \n\n\n\n\n\n\n\nFigure 1: Fuel oil removal efficiency at different PS concentrations plus 0.2g SI for clay and \n\n\n\nsandy soil at 20\u00b11\u25e6C \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Fuel oil removal efficiency at different SI concentrations the clay soil received 400 \n\n\n\nmM PS and the sandy soil received 300 mM PS \n\n\n\nRemovals of TPHs from Soil using Persulfate Oxidant\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201672\n\n\n\nthe persulfate system, the best siderite dose is 0.4 g and 0.3 g for clay and sand \nsoil, respectively. Based on the results, removal efficiency for clay was higher \nthan in sandy soil, which is possibly due to the involvement of metal oxides in \nthe clay soil matrix (mineral such as iron and insoluble organic matter ), that have \na synergistic effect on the persulfate oxidation potential and persulfate activation \n(Do et al., 2009; Do et al., 2010).   \n\n\n\nEffect of pH on the Persulfate System\nPersulfate is recognised to be highly reactive under both acidic and basic conditions \n(Block et al., 2004). We evaluated the impact of metal oxides on PS combined \nwith Fe (II) presented in siderite, in order to show fuel oil degradation at pH 3, 6 \nand 9. As expected, the removal rate for PS without siderite was lower than in a \nsystem with siderite. Figure 3 demonstrates TPHs removal in the soil sample at \ndifferent pH levels. It is apparent that in both soils, fuel oil degradation rate was \nhigher with decreasing pH. The finding is similar to other studies (Peyton 1993; \nHuang et al. 2002; Do et al., 2010). However, they suggest that under highly \nacidic conditions (i.e. pH < 2), persulfate might decompose without generating \nsulfate radicals, and can result in reduced reactivity with the target contaminants. \nAs demonstrated in Figure 3, maximum removal rate at pH=3 were 36% and 32% \nin the clay and sandy soil, respectively. Some researchers claim that higher pH \nvalues cause the precipitation of Fe+2 and result in less sulfate radical production \n(Rastogi et al., 200; Satapanajaru et al. 2014). The investigation of Rastogi et \nal. (2009)  showed that degradation of 2-chlorobiphenol in aqueous solutions \nincreased with increasing acidic conditions. Also, Liang et al. (2007) reported that \nmaximum TCE removal by persulfate in aqueous solutions occurred at neutral \npH, and TCE degradation rate was higher at acidic than basic pH. Sulfate radicals \nare the main radicals generated in a persulfate system where the pH  is between 3 \nto 7 (House, 1962).\n\n\n\n14 \n \n\n\n\n\n\n\n\nFigure 1: Fuel oil removal efficiency at different PS concentrations plus 0.2g SI for clay and \n\n\n\nsandy soil at 20\u00b11\u25e6C \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Fuel oil removal efficiency at different SI concentrations the clay soil received 400 \n\n\n\nmM PS and the sandy soil received 300 mM PS \n\n\n\nFigure 2: Fuel oil removal efficiency at different SI concentrations the clay soil received \n400 mM PS and the sandy soil received 300 mM PS\n\n\n\nMohammadi et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 73\n\n\n\n S2O8\n-2 + H+ \u2192 HS2O8\n\n\n\n-    (3)\n\n\n\n HS2O8\n- \u2192 SO4\n\n\n\n-2 + SO4\n.- + H+   (4)\n\n\n\nSulfate radicals can also react with water to produce hydroxyl radicals (eq. 5) \n(Hayon et al., 1972). However this reaction is slow when organic carbon is more \nthan 1 mg L-1 (Peyton,1993).\n\n\n\n SO4\n.- + H2O \u2192 H+ + SO4\n\n\n\n-2 + OH.  (5)\n\n\n\nThe basic condition (pH > 8.5) is the most widely used activator of persulfate \nwhich can produce hydroxyl radicals (OH\u2022) according to eq (6).\n\n\n\n SO4\n.- + H2O \u2192 HSO4\n\n\n\n- + OH.  (6)\n\n\n\nBoth hydroxyl radicals (OH\u2022) and sulfate radicals (SO-\n4\n\u2022) are formed at basic \n\n\n\ncondition (i.e. pH 9). The degradation rate observed in pH=9 might be due to \nformation of both factors i.e. hydroxyl radicals (OH\u2022) and sulfate radicals (SO-\n\n\n\n4\n\u2022); however, at this pH value degradation mainly occurs because of generation \n\n\n\nof sulfate radicals (Dogliotti & Hayon, 1967; Norman et al. 1970; Liang & \nSu, 2009). The lower degradation of contaminants was expected because SO-\n\n\n\n4\n\u2022 \n\n\n\nand OH\u2022 radicals degenerate rapidly because of the reaction with hydroxyl ions \n(Huang et al., 2002).\n\n\n\nFigure 3: Fuel oil removal rate at different pH for sandy and clay soils with different \ntreatments\n\n\n\n15 \n \n\n\n\n\n\n\n\nFigure 3: Fuel oil removal rate at different pH for sandy and clay soils with different treatments  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4: Fuel oil removal rate at different temperature, for sandy and clay soils with different \n\n\n\ntreatments. \n\n\n\nRemovals of TPHs from Soil using Persulfate Oxidant\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201674\n\n\n\nEffect of Temperature on the Persulfate System \nHigh temperature is another important factor which determines the efficiency \nof persulfate activation (Liang & Su 2009). According to Yukselen-Aksoy et \nal. (2010),  persulfate activation was at pH >12 when the PCBs degradation \nwas examined in soil. Although, some activation occurred at temperatures as \nlow as 20oC, most heat activated persulfate in situ chemical oxidation (ISCO) \nsystems utilise temperatures higher than 40oC. Thermal activation changes with \ntemperature  and is one of the ways to control the generation of sulfate free radicals \nas defined in eq. 8 (House 1962; Zhang et al., 2010)\n\n\n\n S2O8\n-2 + heat or UV \u2192 2SO4\n\n\n\n.- (8)\n\n\n\nIn this study, temperature action was studied between 20\u00b0C and 60\u00b0C. Removal rate \nof fuel oil at different temperatures is shown in Figure 4. Temperatures more than \n50\u00b0C were considered because Johnson et al. (2008) reported that the relatively \nshort lifetime of the persulfate at elevated temperatures (e.g., > 50\u00b0C) will limit \ndelivery time in contaminated soils. Nevertheless, as can be seen in Figure 4,  the \ndegradation rate at 60\u00b0C is only slightly higher than at other temperatures (i.e. \n20 and 40\u00b0C), which indicates that the chemical activation rate increases with \nincreasing temperature. Hence, it is apparent from Figure 4, that fuel oil is rapidly \ndegraded with persulfate under experimental conditions. For example, at 60oC \nand pH=3, fuel oil removal efficiency for clay soil was about 41%, and at 20oC, \nremoval was 32%. Our findings indicated that PS=500 without SI at pH=3 and at \n60\u00b0C have nearly 17 and 20 %  removal rates for clay and sandy soil, respectively. \n\n\n\n15 \n \n\n\n\n\n\n\n\nFigure 3: Fuel oil removal rate at different pH for sandy and clay soils with different treatments  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4: Fuel oil removal rate at different temperature, for sandy and clay soils with different \n\n\n\ntreatments. \nFigure 4: Fuel oil removal rate at different temperature, for sandy and clay soils with \n\n\n\ndifferent treatments.\n\n\n\nMohammadi et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 75\n\n\n\nCONCLUSION\nThe highest TPHs degradation in each system containing either clay or sand soil \nwas observed under the following conditions: pH =3 temperature, 60\u00b0C, PS/Fe \n(II) molar ratio = 400 mM/0.4 g, and 300 mM/0.3 g for clay and sandy soil, \nrespectively. The effect of siderite mineral indicated that this metal oxide can \nactivate PS. Moreover, pH effects on clay were more remarkable than in  sandy \nsoil and maximum persulfate concentration needed for clay was higher than \nfor sandy soil. Results of this study will be helpful to determine the optimum \namount of persulfate and siderite needed for the remediation of soil at petroleum \nhydrocarbon contaminated sites. \n\n\n\nACKNOWLEDGEMENTS:\nThis research was supported by the Tehran University of Medical Sciences \n(TUMS) and Health Services Grant (Project Number 93-04-27-25708). \n\n\n\nREFERENCE\nAchugasim, D., L. Osuji and C. Ojinnaka. 2011. Use of activated persulfate in the \n\n\n\nremoval of petroleum hydrocarbons from crude oil polluted soils. Research \nJournal of Chemical Sciences 1: 57-67.\n\n\n\nASTM (American Society for Testing and Materials).1998. Standard Test Method for \nParticle-size Analysis of Soils-Method-D422-63.\n\n\n\nBlock, P. A., R. A. Brown and D. Robinson .2004. 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Bioremediation of petroleum \nhydrocarbons in contaminated soils: comparison of biosolids addition, carbon \nsupplementation, and monitored natural attenuation. Enviromental Pollution \n136(1): 187-195.\n\n\n\nSatapanajaru, T., M. Yoo-iam, P. Bongprom and P. Pengthamkeerati. 2014. \nDecolorisation of Reactive Black 5 by persulfate oxidation activated by ferrous \nion and its optimisation. Desalination & Water Treatment(ahead-of-print): 1-15.\n\n\n\nTeel, A. L., M. Ahmad and R. J. Watts. 2011. Persulfate activation by naturally \noccurring trace minerals. Journal of Hazardous Materials 196: 153-159.\n\n\n\nTNRCC. 2001. Method 1005. Total Petroleum Hydrocarbons, Texas Natural Resource \nConservation Commission.\n\n\n\nTsai, T. and C. Kao. 2009. Treatment of petroleum-hydrocarbon contaminated soils \nusing hydrogen peroxide oxidation catalyzed by waste basic oxygen furnace \nslag Journal of Hazardous Materials 170(1): 466-472.\n\n\n\nWang, J., Z. Zhang, Y. Su, W. He, F. He and H. Song. 2008. Phytoremediation of \npetroleum polluted soil. Petroleum  Science 5(2): 167-171.\n\n\n\nRemovals of TPHs from Soil using Persulfate Oxidant\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201678\n\n\n\nWang, Z., Y. Xu, J. Zhao, F. Li, D. Gao and B. Xing. 2011. Remediation of petroleum \ncontaminated soils through composting and rhizosphere degradation. Journal of \nHazardous Materials 190(1): 677-685.\n\n\n\nXie, X., Y. Zhang, W. Huang and S. Huang. 2012. Degradation kinetics and mechanism \nof aniline by heat-assisted persulfate oxidation. Journal of Environmental \nSciences 24(5): 821-826.\n\n\n\nYan, N., F. Liu and W. Huang. 2013. Interaction of oxidants in siderite catalyzed \nhydrogen peroxide and persulfate system using trichloroethylene as a target \ncontaminant. Chemical  Engineering Journal 219: 149-154.\n\n\n\nYen, C.-H., K.-F. Chen, C.-M. Kao, S.-H. Liang and T.-Y. Chen .2011. Application of \npersulfate to remediate petroleum hydrocarbon-contaminated soil: Feasibility \nand comparison with common oxidants. Journal of Hazardous Materials 186(2): \n2097-2102.\n\n\n\nYukselen-Aksoy, Y., A. P. Khodadoust and K. R. Reddy .2010. Destruction of PCB \n44 in spiked subsurface soils using activated persulfate oxidation. Water, Air & \nSoil  Pollution 209(1-4): 419-427.\n\n\n\nZhang, Y. Q., W. L. Huang and D. E. Fennell. 2010. In situ chemical oxidation of \naniline by persulfate with iron (II) activation at ambient temperature. Chinese \nChemical Letters 21(8): 911-913.\n\n\n\nMohammadi et al.\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nINTRODUCTION\nThe central concept of an Andisol is that of a soil developing in volcanic ejecta  \n(such as volcanic ash, pumice, cinders, lava), and/or in volcaniclastic materials, \nwhose colloidal fraction is dominated by short-range-order materials of Al-humus \ncomplexes. Under special environmental conditions, weathering of primary \nalumino-silicates in parent materials of non-volcanic origin may also lead to the \nformation of short-range-order minerals; some of these soils are also included in \nAndisols (Mizota and van Reewijk 1989).\n\n\n\nMany Andisols have excellent physical properties that make them highly \ndesirable for a wide range of uses. Chemically, they suffer from high phosphate \nretention, and may be deficient in K and some micronutrients. Nevertheless, \nthese soils are amongst the most fertile land in the world and are, therefore, very \nintensively cultivated (Neall 2009). As most of the Andisols in Indonesia are \nvery productive, the soils are intensively cultivated with perennial and annual \ncrops. Because these areas are located more than 700 m above sea level, they \nare mainly used for dry land agriculture (corn, peanut, cassava, and tuber crops), \nhighland vegetables (potato, carrot, cabbage, etc.), and floriculture (tea, coffe, \nclove, vanilla) (Tan 2008; Subagyo et al. 2000; Fiantis et al. 2005). \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 51-60 (2014)                          Malaysian Society of Soil Science\n\n\n\nEffects of Land Use on the Physico-Chemical Properties of \nAndisols in Mt. Sinabung,  North Sumatera, Indonesia\n\n\n\nMukhlis1*, Zulkifli Nasution1 and Budi Mulyanto2\n\n\n\n1Department of Agroecotechnology, Universitas Sumatera Utara, Kampus \nUSU Padang Bulan, 20155 Medan, Indonesia.\n\n\n\n2Department of Soil Science, Institut Pertanian Bogor, Kampus IPB Darmaga, \n16680 Bogor, Indonesia.\n\n\n\nABSTRACT\nLand use  can have an impact on the physical and chemical properties of soils.  \nThe aim of this study was to identify and determine changes in soil physics-\nchemical properties of Andisols under three different land use profiles in the Mt. \nSinabung  area. These were: natural forest, perennial cropland with low-intensity \ncultivation, and annual cropland (horticulture) with high-intensity cultivation. Our \nfindings show that differing land use profiles did not change soil classification, \nsolum thickness, and effective depth of the Andisols of Mt. Sinabung. However, \nintensive cultivation of the Andisols resulted in the top soil having a  more intense \nred colour and increased pH0 and Al-humus complex.\n\n\n\nKeywords: Annual cropland, natural forest, perennial cropland, soil \nclassification, volcanic ash.\n\n\n\n___________________\n*Corresponding author : E-mail: mukhlis_fpusu@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201452\n\n\n\nMt. Sinabung, along with the nearby Mt. Sibayak, are active volcanoes \nin the North Sumatra province and are located on the Karo Highlands, in Karo \nregency. Mt. Sinabung is Pleistocene-Halocene stratovolcano of andisitic and \ndasitic composition, located at about 2460 m above sea level. The coordinates of \nthe peak are 3\u00b010\u201912\u201d N and 98\u00b023\u20193\u201d E. The recorded eruption in the year 1600 \nproduced lava and ash which became parent materials for Andisols in the vicinity \nof the Karo regency and the northern part of Langkat regency (Anon 2013).\n\n\n\nStudies of Andisols in secondary forest, mixed farm, cinnamon and coffee \nfarms at slope 3\u20138 %, 8\u201315 %, 15\u201325 % and > 25 % indicate that organic matter \ncontent, agregate stability and permeability of soils in forest land are higher than \nin other land usage, while soil bulk density and porosity are lower (Endriani and \nZurhalena 2008). The aims of our study were to identify, determine and interpret \nthe properties of Andisols under natural forest, perennial crops and annual crops.\n\n\n\nMATERIALS AND METHODS \nThe study area was located in Kuta Rakyat Village, Naman Teran subregency, \nKaro regency at the  northern slope of  Mt. Sinabung, about 90 km from Medan. In \nthis study, three profiles representing lands under different land use profiles were \ninvestigated formorphology and physico-chemical characteristics of Andisols in \nMt. Sinabung.  These were P1 \u2013natural forest; P2 \u2013perennial cultivated land; and \nP3 \u2013 annual cultivated land. \n\n\n\nThe morphology and physico-chemical characteristics of the three soil \nprofiles were determined based on the field book of Schoeneberger et al. (2012). \nSoil samples were taken from each layer in each profile for soil analysis. \n\n\n\nSoil pH was measured  in water using a 1:2.5 w/v soil solution ratio. The \npH in NaF was measured in a suspension of 1 g soil mixed with a 50 mL 1M NaF \nsolution after stirring for 2 min. The exchangeable bases and cation exchange \ncapacity (CEC) at pH 7 was determined by ammonium acetate method (Van \nReeuwijk 2002).  Exchangeable aluminium was determined by KCl 1 N extraction \nmethod while total soil organic carbon (SOC) was analyzed by Walkley and Black \nmethod (van Reeuwijk 2002). pH0 was determined by the salt titration method of  \nSakurai (1988). Phosphate retention was determined according to the method of \nBlakemore (van Reeuwijk 2002) while Oxalic acid Al-extract (Alo), oxalic acid \nSi-extract (Sio), oxalic acid Fe-extract (Feo), and pyrophosphate Al-extract (Alp) \nwere determined by  the method of van Reeuwijk (2002). Total P was determined \nby HClO4 extraction and available P by Bray II method (van Reeuwijk 2002).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nChanges in the Morphology of the Andisols\nIn this study, the three Andisol profiles represented three landuse profiles: (1) \nProfile 1\u2013natural forest; (2) Profile 2 \u2013perennial crop, consisting of 18-year-old \narabica coffee that was never fertilized; and (3) Profile 3 \u2013under annual crop, \nconsisting of horticultural plants such as potatoes, chilies, or tomatoes which \n\n\n\nMukhlis, Zulkifli Nasution and Budi Mulyanto\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 53\n\n\n\nPhysico-chemical Properties of Andisols from Different Landuse\n\n\n\nhad been continuously cultivated for 30 years. These profiles were located on a \nvolcanic hill in the northern slope of Mt. Sinabung with 3 \u2013 8% inclination. The \nsoils were derived from the same parent materials of  andesitic tuff. There were \n150 \u00b1 5 m of distance between the profiles. The general condition of the three \nprofiles is found in Table 1.\n\n\n\nSoil description and laboratory analyses indicated that the soils in the forest \nland were Typic Haplud and while the cultivated land, both perennial and annual \ncrops, were Typic Fulvudands (Table 2 and Table 3).\n\n\n\nTABLE 1.\nProfile description of Sinabung Andisolsunder different land use profiles\n\n\n\n6 \n \n\n\n\nTABLE 1. \n\n\n\nProfile description of Sinabung Andisolsunder different land use profiles \n\n\n\nLocation : Kuta  Rakyat  Village, Naman Teran Sub-regency, Karo Regency, \nNorth  Sumatera province \n\n\n\nCode  :  Profile 1 Profile 2 Profile 3 \nCcoordinate : N 03\u00ba13\u02b95.0\u201d \n\n\n\nE 98\u00ba23\u02b957.7\u201d \nN 03\u00ba13\u02b97.66\u201d \nE 098\u00ba24\u02b92.4\u201d \n\n\n\nN 03\u00ba13\u02b99.2\u201d \nE 098\u00ba23\u02b959.3\u201d \n\n\n\nClassification : Typic Hapludand Typic Fulvudand Typic Fulvudand \nPhysiography : Volcanic hill Mountainous Mountainous \nSlope \ncharacteristics \n\n\n\n: 3-8 %  3-8% 3-8% \n\n\n\nElevation : 1432 m asl. 1439 m asl . 1438 m asl . \nEffective depth : 180  cm 150  cm 165  cm \nLand use : Forest. Perennial Crops , \n\n\n\nArabica coffe \nAnnual Crops, \nPotatoes \n\n\n\nLand \nmanagement \n\n\n\n: No cultivation Low-intensity \ncultivation \n\n\n\nHigh-intensity \ncultivation \n\n\n\nManuring :  None Chicken manure and \nNPK   \n\n\n\nParent material : Andesitic Tuff \nSinabung \n\n\n\nAndesitic Tuff \nSinabung \n\n\n\nAndesitic Tuff \nSinabung \n\n\n\nDiagnostic \nHorizon \n\n\n\n: Ochric (0\u201310 cm),          \nCambic  (10\u2013 87 cm) \n\n\n\nOchric (0\u201395 cm),          \nCambic (95\u2013170 cm) \n\n\n\nOchric (0 \u2013 40 cm),  \nCambic (60\u2013135 cm) \n\n\n\nDiagnostic  \ncharacter \n\n\n\n:  Andic (10\u2013 134 cm). \n \n\n\n\nAndic (0 \u2013 170 cm). Andic (0\u2013160 cm). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201454\n\n\n\nTA\nB\n\n\n\nLE\n 2\n\n\n\nC\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\ns o\n\n\n\nf A\nnd\n\n\n\nis\nol\n\n\n\n p\nro\n\n\n\nfil\nes\n\n\n\n u\nnd\n\n\n\ner\n n\n\n\n\nat\nur\n\n\n\nal\n fo\n\n\n\nre\nst\n\n\n\n, p\ner\n\n\n\nen\nni\n\n\n\nal\n c\n\n\n\nro\nps\n\n\n\n, a\nnd\n\n\n\n a\nnn\n\n\n\nua\nl c\n\n\n\nro\npl\n\n\n\nan\nd\n\n\n\n7 \n \n\n\n\nH\nor\n\n\n\niz\non\n\n\n\n \nD\n\n\n\nep\nth\n\n\n\n \n (c\n\n\n\nm\n) \n\n\n\nSo\nil \n\n\n\nC\nol\n\n\n\nor\n \n\n\n\nSt\nru\n\n\n\nct\nur\n\n\n\ne \nC\n\n\n\non\nsi\n\n\n\nst\nen\n\n\n\ncy\n \n\n\n\nB\nou\n\n\n\nnd\nar\n\n\n\ny \n\n\n\nPr\nof\n\n\n\nile\n 1\n\n\n\n (F\nor\n\n\n\nes\nt)\n\n\n\n \nO\n\n\n\ne \n0 \n\n\n\n- 1\n0 \n\n\n\n5 \nY\n\n\n\nR\n 2\n\n\n\n/1\n \n\n\n\nB\nro\n\n\n\nw\nni\n\n\n\nsh\n b\n\n\n\nla\nck\n\n\n\n \ncr\n\n\n\num\nb \n\n\n\nlo\nos\n\n\n\ne \nab\n\n\n\nru\npt\n\n\n\n/w\nav\n\n\n\ny \n\n\n\nA\n \n\n\n\n10\n - \n\n\n\n45\n \n\n\n\n10\n Y\n\n\n\nR\n 5\n\n\n\n/6\n \n\n\n\nY\nel\n\n\n\nlo\nw\n\n\n\nis\nh \n\n\n\nbr\now\n\n\n\nn \ncr\n\n\n\num\nb \n\n\n\nlo\nos\n\n\n\ne \ndi\n\n\n\nff\nus\n\n\n\ne/\nsm\n\n\n\noo\nth\n\n\n\n\n\n\n\nA\nB\n\n\n\n \n45\n\n\n\n - \n87\n\n\n\n \n10\n\n\n\n Y\nR\n\n\n\n 5\n/8\n\n\n\n \nY\n\n\n\nel\nlo\n\n\n\nw\nis\n\n\n\nh \nbr\n\n\n\now\nn \n\n\n\nan\ngu\n\n\n\nla\nr b\n\n\n\nlo\nck\n\n\n\ny \nso\n\n\n\nft \ndi\n\n\n\nff\nus\n\n\n\ne/\nsm\n\n\n\noo\nth\n\n\n\n\n\n\n\nB\nw\n\n\n\n1 \n87\n\n\n\n - \n13\n\n\n\n4 \n10\n\n\n\n Y\nR\n\n\n\n 6\n/6\n\n\n\n \nB\n\n\n\nrig\nht\n\n\n\n y\nel\n\n\n\nlo\nw\n\n\n\nis\nh \n\n\n\nbr\now\n\n\n\nn \nan\n\n\n\ngu\nla\n\n\n\nr b\nlo\n\n\n\nck\ny \n\n\n\nso\nft \n\n\n\ndi\nff\n\n\n\nus\ne/\n\n\n\nsm\noo\n\n\n\nth\n \n\n\n\nB\nw\n\n\n\n2 \n13\n\n\n\n4 \n- 1\n\n\n\n85\n \n\n\n\n7.\n5 \n\n\n\nY\nR\n\n\n\n 6\n/8\n\n\n\n \nO\n\n\n\nra\nng\n\n\n\ne \nan\n\n\n\ngu\nla\n\n\n\nr b\nlo\n\n\n\nck\ny \n\n\n\nlo\nos\n\n\n\ne \ncl\n\n\n\nea\nr/s\n\n\n\nm\noo\n\n\n\nth\n \n\n\n\nC\n \n\n\n\n> \n18\n\n\n\n5 \n2.\n\n\n\n5 \nY\n\n\n\nR\n 6\n\n\n\n/6\n \n\n\n\nO\nra\n\n\n\nng\ne \n\n\n\nan\ngu\n\n\n\nla\nr b\n\n\n\nlo\nck\n\n\n\ny \nlo\n\n\n\nos\ne \n\n\n\ncl\nea\n\n\n\nr/s\nm\n\n\n\noo\nth\n\n\n\n\n\n\n\nPr\nof\n\n\n\nile\n 2\n\n\n\n (P\ner\n\n\n\nen\nni\n\n\n\nal\n c\n\n\n\nro\nps\n\n\n\n) \nA\n\n\n\n1 \n0 \n\n\n\n- 7\n0 \n\n\n\n7.\n5 \n\n\n\nY\nR\n\n\n\n 2\n/3\n\n\n\n \nV\n\n\n\ner\ny \n\n\n\nda\nrk\n\n\n\n b\nro\n\n\n\nw\nn \n\n\n\ncr\num\n\n\n\nb \nve\n\n\n\nry\n fr\n\n\n\nia\nbl\n\n\n\ne \ndi\n\n\n\nff\nus\n\n\n\ne/\nw\n\n\n\nav\ny \n\n\n\nA\n2 \n\n\n\n70\n\u2013 \n\n\n\n95\n/1\n\n\n\n10\n \n\n\n\n7.\n5 \n\n\n\nY\nR\n\n\n\n3/\n2 \n\n\n\n B\nro\n\n\n\nw\nni\n\n\n\nsh\nbl\n\n\n\nac\nk \n\n\n\ncr\num\n\n\n\nb \nve\n\n\n\nry\n fr\n\n\n\nia\nbl\n\n\n\ne \ndi\n\n\n\nff\nus\n\n\n\ne/\nsm\n\n\n\noo\nth\n\n\n\n\n\n\n\nB\nw\n\n\n\n1 \n95\n\n\n\n/1\n10\n\n\n\n-1\n25\n\n\n\n \n10\n\n\n\n Y\nR\n\n\n\n 6\n/8\n\n\n\n \nB\n\n\n\nrig\nht\n\n\n\n y\nel\n\n\n\nlo\nw\n\n\n\nis\nh \n\n\n\nbr\now\n\n\n\nn \nan\n\n\n\ngu\nla\n\n\n\nr b\nlo\n\n\n\nck\ny \n\n\n\nfr\nia\n\n\n\nbl\ne \n\n\n\ndi\nff\n\n\n\nus\ne/\n\n\n\nsm\noo\n\n\n\nth\n \n\n\n\nB\nw\n\n\n\n2 \n12\n\n\n\n5 \n\u2013 \n\n\n\n17\n0 \n\n\n\n10\n Y\n\n\n\nR\n 7\n\n\n\n/8\n \n\n\n\nY\nel\n\n\n\nlo\nw\n\n\n\n o\nra\n\n\n\nng\ne \n\n\n\nan\ngu\n\n\n\nla\nr b\n\n\n\nlo\nck\n\n\n\ny \nfr\n\n\n\nia\nbl\n\n\n\ne \nab\n\n\n\nru\npt\n\n\n\n/s\nm\n\n\n\noo\nth\n\n\n\n\n\n\n\nC\n \n\n\n\n> \n17\n\n\n\n0 \n10\n\n\n\n Y\nR\n\n\n\n 5\n/6\n\n\n\n \nY\n\n\n\nel\nlo\n\n\n\nw\nis\n\n\n\nh \nbr\n\n\n\now\nn \n\n\n\nlo\nos\n\n\n\ne \nfir\n\n\n\nm\n \n\n\n\n \nPr\n\n\n\nof\nile\n\n\n\n 3\n (A\n\n\n\nnn\nua\n\n\n\nl c\nro\n\n\n\nps\n) \n\n\n\nA\np \n\n\n\n0 \n- 4\n\n\n\n0 \n5 \n\n\n\nY\nR\n\n\n\n 2\n/2\n\n\n\n \nB\n\n\n\nro\nw\n\n\n\nni\nsh\n\n\n\n b\nla\n\n\n\nck\n \n\n\n\ncr\num\n\n\n\nb \nlo\n\n\n\nos\ne \n\n\n\ncl\nea\n\n\n\nr/w\nav\n\n\n\ny \n\n\n\nA\nB\n\n\n\n \n40\n\n\n\n - \n 6\n\n\n\n0 \n10\n\n\n\n Y\nR\n\n\n\n 4\n/6\n\n\n\n \nB\n\n\n\nro\nw\n\n\n\nn \nan\n\n\n\ngu\nla\n\n\n\nr b\nlo\n\n\n\nck\ny \n\n\n\nve\nry\n\n\n\n fr\nia\n\n\n\nbl\ne \n\n\n\ndi\nff\n\n\n\nus\ne/\n\n\n\nsm\noo\n\n\n\nth\n \n\n\n\nB\nw\n\n\n\n1 \n60\n\n\n\n - \n11\n\n\n\n0 \n10\n\n\n\n Y\nR\n\n\n\n 6\n/8\n\n\n\n \nB\n\n\n\nrig\nht\n\n\n\n y\nel\n\n\n\nlo\nw\n\n\n\nis\nh \n\n\n\nbr\now\n\n\n\nn \nan\n\n\n\ngu\nla\n\n\n\nr b\nlo\n\n\n\nck\ny \n\n\n\nfr\nia\n\n\n\nbl\ne \n\n\n\ndi\nff\n\n\n\nus\ne/\n\n\n\nsm\noo\n\n\n\nth\n \n\n\n\nB\nw\n\n\n\n2 \n11\n\n\n\n0 \n\u2013 \n\n\n\n13\n5 \n\n\n\n10\n Y\n\n\n\nR\n 5\n\n\n\n/8\n \n\n\n\nY\nel\n\n\n\nlo\nw\n\n\n\nis\n b\n\n\n\nro\nw\n\n\n\nn \nan\n\n\n\ngu\nla\n\n\n\nr b\nlo\n\n\n\nck\ny \n\n\n\nfr\nia\n\n\n\nbl\ne \n\n\n\ndi\nff\n\n\n\nus\ne/\n\n\n\nsm\noo\n\n\n\nth\n \n\n\n\nB\nC\n\n\n\n \n13\n\n\n\n5 \n- 1\n\n\n\n75\n \n\n\n\n7.\n5 \n\n\n\nY\nR\n\n\n\n 5\n/8\n\n\n\n \nB\n\n\n\nrig\nht\n\n\n\n b\nro\n\n\n\nw\nn \n\n\n\nan\ngu\n\n\n\nla\nr b\n\n\n\nlo\nck\n\n\n\ny \nfir\n\n\n\nm\n \n\n\n\ndi\nff\n\n\n\nus\ne/\n\n\n\nsm\noo\n\n\n\nth\n \n\n\n\nC\n \n\n\n\n>1\n75\n\n\n\n \n10\n\n\n\n Y\nR\n\n\n\n 5\n/4\n\n\n\n \nD\n\n\n\nul\nl y\n\n\n\nel\nlo\n\n\n\nw\nis\n\n\n\nh \nbr\n\n\n\now\nn \n\n\n\nlo\nos\n\n\n\ne \nve\n\n\n\nry\n fi\n\n\n\nrm\n \n\n\n\n\n\n\n\n\n\n\n\nMukhlis, Zulkifli Nasution and Budi Mulyanto\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 55\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nPh\nys\n\n\n\nic\no-\n\n\n\nch\nem\n\n\n\nic\nal\n\n\n\n c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\ns o\n\n\n\nf A\nnd\n\n\n\nis\nol\n\n\n\ns u\nnd\n\n\n\ner\n n\n\n\n\nat\nur\n\n\n\nal\n fo\n\n\n\nre\nst\n\n\n\n, p\ner\n\n\n\nen\nni\n\n\n\nal\n a\n\n\n\nnd\n a\n\n\n\nnn\nua\n\n\n\nl c\nro\n\n\n\np\n\n\n\nPhysico-chemical Properties of Andisols from Different Landuse\n\n\n\n9 \n \n\n\n\nH\nor\n\n\n\niz\non\n\n\n\n \nD\n\n\n\nep\nth\n\n\n\n \nPa\n\n\n\nrtc\nle\n\n\n\n si\nze\n\n\n\n d\nis\n\n\n\ntri\nbu\n\n\n\ntio\nn \n\n\n\nD\nb \n\n\n\npH\n \n\n\n\npH\n \n\n\n\nN\naF\n\n\n\n \npH\n\n\n\n0 \nC\n\n\n\n- o\nrg\n\n\n\n \nEx\n\n\n\nch\nan\n\n\n\nge\nab\n\n\n\nle\n B\n\n\n\nas\nes\n\n\n\n \nA\n\n\n\nl \nEx\n\n\n\nch\n. \n\n\n\nC\nEC\n\n\n\n \nB\n\n\n\nS \nsa\n\n\n\nnd\n \n\n\n\nsi\nlt \n\n\n\ncl\nay\n\n\n\n \nTe\n\n\n\nxt\nur\n\n\n\ne \nH\n\n\n\n2O\n \n\n\n\nK\nC\n\n\n\nl \n\u2206 \n\n\n\npH\n \n\n\n\nK\n \n\n\n\nC\na \n\n\n\nM\ng \n\n\n\nN\na \n\n\n\n  \n--\n\n\n\n--\n-c\n\n\n\nm\n--\n\n\n\n--\n- \n\n\n\n--\n---\n\n\n\n-%\n --\n\n\n\n---\n-- \n\n\n\n  \n g\n\n\n\ncm\n-3\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n-c\n\n\n\nm\nol\n\n\n\nck\ng-1\n\n\n\n ---\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n- \n\n\n\n--\n%\n\n\n\n- \n\n\n\nPr\nof\n\n\n\nile\n 1\n\n\n\n  (\nFo\n\n\n\nre\nst\n\n\n\n) \nO\n\n\n\ne \n0 \n\n\n\n- 1\n0 \n\n\n\n88\n \n\n\n\n8 \n4 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n35\n \n\n\n\n5.\n65\n\n\n\n \n5.\n\n\n\n20\n \n\n\n\n-0\n.4\n\n\n\n5 \n10\n\n\n\n.6\n9 \n\n\n\n4.\n70\n\n\n\n \n16\n\n\n\n.6\n4 \n\n\n\n0.\n22\n\n\n\n \n2.\n\n\n\n03\n \n\n\n\n5.\n29\n\n\n\n \n0.\n\n\n\n19\n \n\n\n\n1.\n48\n\n\n\n \n54\n\n\n\n.1\n \n\n\n\n14\n.2\n\n\n\n9 \nA\n\n\n\n \n10\n\n\n\n - \n45\n\n\n\n \n96\n\n\n\n \n2 \n\n\n\n2 \nSa\n\n\n\nnd\n \n\n\n\n0.\n34\n\n\n\n \n5.\n\n\n\n37\n \n\n\n\n5.\n14\n\n\n\n \n-0\n\n\n\n.2\n3 \n\n\n\n11\n.3\n\n\n\n2 \n4.\n\n\n\n81\n \n\n\n\n3.\n36\n\n\n\n \n0.\n\n\n\n14\n \n\n\n\n1.\n92\n\n\n\n \n6.\n\n\n\n19\n \n\n\n\n0.\n17\n\n\n\n \n1.\n\n\n\n08\n \n\n\n\n46\n.0\n\n\n\n \n18\n\n\n\n.3\n0 \n\n\n\nA\nB\n\n\n\n \n45\n\n\n\n - \n87\n\n\n\n \n96\n\n\n\n \n2 \n\n\n\n2 \nSa\n\n\n\nnd\n \n\n\n\n0.\n36\n\n\n\n \n5.\n\n\n\n16\n \n\n\n\n5.\n67\n\n\n\n \n0.\n\n\n\n51\n \n\n\n\n11\n.3\n\n\n\n5 \n5.\n\n\n\n69\n \n\n\n\n2.\n98\n\n\n\n \n0.\n\n\n\n19\n \n\n\n\n2.\n32\n\n\n\n \n4.\n\n\n\n66\n \n\n\n\n0.\n25\n\n\n\n \n0.\n\n\n\n80\n \n\n\n\n45\n.2\n\n\n\n \n16\n\n\n\n.4\n1 \n\n\n\nB\nw\n\n\n\n1 \n87\n\n\n\n - \n13\n\n\n\n4 \n92\n\n\n\n \n6 \n\n\n\n2 \nSa\n\n\n\nnd\n \n\n\n\n0.\n39\n\n\n\n \n5.\n\n\n\n36\n \n\n\n\n5.\n84\n\n\n\n \n0.\n\n\n\n48\n \n\n\n\n11\n.1\n\n\n\n0 \n6.\n\n\n\n22\n \n\n\n\n0.\n54\n\n\n\n \n0.\n\n\n\n15\n \n\n\n\n1.\n79\n\n\n\n \n4.\n\n\n\n38\n \n\n\n\n0.\n25\n\n\n\n \n0.\n\n\n\n76\n \n\n\n\n28\n.2\n\n\n\n \n23\n\n\n\n.3\n2 \n\n\n\nB\nw\n\n\n\n2 \n13\n\n\n\n4 \n- 1\n\n\n\n85\n \n\n\n\n94\n \n\n\n\n4 \n2 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n43\n \n\n\n\n5.\n39\n\n\n\n \n5.\n\n\n\n80\n \n\n\n\n0.\n41\n\n\n\n \n11\n\n\n\n.0\n9 \n\n\n\n6.\n02\n\n\n\n \n0.\n\n\n\n22\n \n\n\n\n0.\n26\n\n\n\n \n1.\n\n\n\n74\n \n\n\n\n4.\n38\n\n\n\n \n0.\n\n\n\n26\n \n\n\n\n0.\n88\n\n\n\n \n33\n\n\n\n.0\n \n\n\n\n20\n.1\n\n\n\n5 \nC\n\n\n\n \n> \n\n\n\n18\n5 \n\n\n\n94\n \n\n\n\n4 \n2 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n50\n \n\n\n\n5.\n54\n\n\n\n \n5.\n\n\n\n57\n \n\n\n\n0.\n03\n\n\n\n \n11\n\n\n\n.0\n3 \n\n\n\n5.\n10\n\n\n\n \n0.\n\n\n\n02\n \n\n\n\n0.\n24\n\n\n\n \n2.\n\n\n\n22\n \n\n\n\n4.\n98\n\n\n\n \n0.\n\n\n\n16\n \n\n\n\n0.\n88\n\n\n\n \n26\n\n\n\n.5\n \n\n\n\n28\n.6\n\n\n\n8 \n \n\n\n\nPr\nof\n\n\n\nile\n 2\n\n\n\n (P\ner\n\n\n\nen\nni\n\n\n\nal\n c\n\n\n\nro\nps\n\n\n\n) \nA\n\n\n\n1 \n0 \n\n\n\n- 7\n0 \n\n\n\n90\n \n\n\n\n6 \n4 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n39\n \n\n\n\n4.\n66\n\n\n\n \n4.\n\n\n\n63\n \n\n\n\n-0\n.0\n\n\n\n3 \n11\n\n\n\n.2\n4 \n\n\n\n4.\n29\n\n\n\n \n8.\n\n\n\n53\n \n\n\n\n0.\n15\n\n\n\n \n2.\n\n\n\n21\n \n\n\n\n6.\n52\n\n\n\n \n0.\n\n\n\n22\n \n\n\n\n3.\n04\n\n\n\n \n58\n\n\n\n.3\n \n\n\n\n15\n.6\n\n\n\n2 \nA\n\n\n\n2 \n70\n\n\n\n\u2013 \n95\n\n\n\n/1\n10\n\n\n\n \n94\n\n\n\n \n2 \n\n\n\n4 \nSa\n\n\n\nnd\n \n\n\n\n0.\n50\n\n\n\n \n5.\n\n\n\n13\n \n\n\n\n4.\n86\n\n\n\n \n-0\n\n\n\n.2\n7 \n\n\n\n11\n.1\n\n\n\n7 \n4.\n\n\n\n64\n \n\n\n\n5.\n24\n\n\n\n \n0.\n\n\n\n15\n \n\n\n\n2.\n01\n\n\n\n \n5.\n\n\n\n50\n \n\n\n\n0.\n27\n\n\n\n \n0.\n\n\n\n96\n \n\n\n\n55\n.8\n\n\n\n \n14\n\n\n\n.2\n3 \n\n\n\nB\nw\n\n\n\n1 \n95\n\n\n\n/1\n10\n\n\n\n-1\n25\n\n\n\n \n96\n\n\n\n \n2 \n\n\n\n2 \nSa\n\n\n\nnd\n \n\n\n\n0.\n41\n\n\n\n \n5.\n\n\n\n12\n \n\n\n\n5.\n41\n\n\n\n \n0.\n\n\n\n29\n \n\n\n\n11\n.2\n\n\n\n2 \n5.\n\n\n\n52\n \n\n\n\n1.\n63\n\n\n\n \n0.\n\n\n\n14\n \n\n\n\n1.\n87\n\n\n\n \n6.\n\n\n\n25\n \n\n\n\n0.\n26\n\n\n\n \n0.\n\n\n\n92\n \n\n\n\n40\n.4\n\n\n\n \n21\n\n\n\n.1\n2 \n\n\n\nB\nw\n\n\n\n2 \n12\n\n\n\n5 \n\u2013 \n\n\n\n17\n0 \n\n\n\n96\n \n\n\n\n2 \n2 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n39\n \n\n\n\n5.\n19\n\n\n\n \n5.\n\n\n\n66\n \n\n\n\n0.\n47\n\n\n\n \n11\n\n\n\n.0\n8 \n\n\n\n5.\n98\n\n\n\n \n0.\n\n\n\n63\n \n\n\n\n0.\n17\n\n\n\n \n1.\n\n\n\n68\n \n\n\n\n3.\n44\n\n\n\n \n0.\n\n\n\n29\n \n\n\n\n1.\n04\n\n\n\n \n39\n\n\n\n.7\n \n\n\n\n14\n.0\n\n\n\n7 \nC\n\n\n\n \n> \n\n\n\n17\n0 \n\n\n\n94\n \n\n\n\n2 \n4 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n56\n \n\n\n\n5.\n39\n\n\n\n \n5.\n\n\n\n62\n \n\n\n\n0.\n23\n\n\n\n \n11\n\n\n\n.1\n2 \n\n\n\n5.\n64\n\n\n\n \n0.\n\n\n\n76\n \n\n\n\n0.\n13\n\n\n\n \n1.\n\n\n\n88\n \n\n\n\n7.\n12\n\n\n\n \n0.\n\n\n\n24\n \n\n\n\n1.\n32\n\n\n\n \n26\n\n\n\n.5\n \n\n\n\n35\n.4\n\n\n\n0 \n \n\n\n\nPr\nof\n\n\n\nile\n 3\n\n\n\n (A\nnn\n\n\n\nua\nl c\n\n\n\nro\nps\n\n\n\n) \nA\n\n\n\np \n0 \n\n\n\n- 4\n0 \n\n\n\n90\n \n\n\n\n8 \n2 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n46\n \n\n\n\n5.\n75\n\n\n\n \n5.\n\n\n\n54\n \n\n\n\n-0\n.2\n\n\n\n1 \n11\n\n\n\n.1\n0 \n\n\n\n5.\n46\n\n\n\n \n7.\n\n\n\n90\n \n\n\n\n0.\n17\n\n\n\n \n1.\n\n\n\n58\n \n\n\n\n7.\n86\n\n\n\n \n0.\n\n\n\n22\n \n\n\n\n1.\n24\n\n\n\n \n39\n\n\n\n.4\n \n\n\n\n24\n.9\n\n\n\n4 \nA\n\n\n\nB\n \n\n\n\n40\n - \n\n\n\n 6\n0 \n\n\n\n94\n \n\n\n\n2 \n4 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n41\n \n\n\n\n4.\n82\n\n\n\n \n5.\n\n\n\n19\n \n\n\n\n0.\n37\n\n\n\n \n11\n\n\n\n.3\n7 \n\n\n\n5.\n24\n\n\n\n \n2.\n\n\n\n78\n \n\n\n\n0.\n15\n\n\n\n \n1.\n\n\n\n65\n \n\n\n\n5.\n77\n\n\n\n \n0.\n\n\n\n19\n \n\n\n\n0.\n88\n\n\n\n \n54\n\n\n\n.9\n \n\n\n\n14\n.1\n\n\n\n4 \nB\n\n\n\nw\n1 \n\n\n\n60\n - \n\n\n\n11\n0 \n\n\n\n94\n \n\n\n\n4 \n2 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n39\n \n\n\n\n5.\n41\n\n\n\n \n5.\n\n\n\n81\n \n\n\n\n0.\n40\n\n\n\n \n11\n\n\n\n.2\n1 \n\n\n\n6.\n25\n\n\n\n \n1.\n\n\n\n87\n \n\n\n\n0.\n17\n\n\n\n \n1.\n\n\n\n20\n \n\n\n\n5.\n94\n\n\n\n \n0.\n\n\n\n26\n \n\n\n\n0.\n96\n\n\n\n \n52\n\n\n\n.9\n \n\n\n\n14\n.3\n\n\n\n2 \nB\n\n\n\nw\n2 \n\n\n\n11\n0 \n\n\n\n\u2013 \n13\n\n\n\n5 \n94\n\n\n\n \n4 \n\n\n\n2 \nSa\n\n\n\nnd\n \n\n\n\n0.\n50\n\n\n\n \n5.\n\n\n\n67\n \n\n\n\n6.\n11\n\n\n\n \n0.\n\n\n\n44\n \n\n\n\n11\n.2\n\n\n\n3 \n6.\n\n\n\n84\n \n\n\n\n0.\n99\n\n\n\n \n0.\n\n\n\n14\n \n\n\n\n1.\n68\n\n\n\n \n5.\n\n\n\n49\n \n\n\n\n0.\n27\n\n\n\n \n0.\n\n\n\n72\n \n\n\n\n30\n.3\n\n\n\n \n25\n\n\n\n.0\n3 \n\n\n\nB\nC\n\n\n\n \n13\n\n\n\n5 \n- 1\n\n\n\n75\n \n\n\n\n94\n \n\n\n\n4 \n2 \n\n\n\nSa\nnd\n\n\n\n \n0.\n\n\n\n48\n \n\n\n\n5.\n48\n\n\n\n \n6.\n\n\n\n05\n \n\n\n\n0.\n57\n\n\n\n \n11\n\n\n\n.2\n7 \n\n\n\n6.\n40\n\n\n\n \n0.\n\n\n\n68\n \n\n\n\n0.\n13\n\n\n\n \n1.\n\n\n\n99\n \n\n\n\n3.\n93\n\n\n\n \n0.\n\n\n\n26\n \n\n\n\n1.\n16\n\n\n\n \n34\n\n\n\n.8\n \n\n\n\n18\n.1\n\n\n\n4 \nC\n\n\n\n \n>1\n\n\n\n75\n \n\n\n\n94\n \n\n\n\n4 \n2 \n\n\n\nSa\nnd\n\n\n\n \n--\n\n\n\n- \n5.\n\n\n\n40\n \n\n\n\n5.\n87\n\n\n\n \n0.\n\n\n\n47\n \n\n\n\n11\n.0\n\n\n\n3 \n6.\n\n\n\n45\n \n\n\n\n0.\n54\n\n\n\n \n0.\n\n\n\n19\n \n\n\n\n2.\n18\n\n\n\n \n5.\n\n\n\n16\n \n\n\n\n0.\n25\n\n\n\n \n1.\n\n\n\n20\n \n\n\n\n25\n.8\n\n\n\n \n30\n\n\n\n.1\n8 \n\n\n\nN\not\n\n\n\ne:\n D\n\n\n\nb:\n b\n\n\n\nul\nk \n\n\n\nde\nns\n\n\n\nity\n; C\n\n\n\nEC\n: c\n\n\n\nat\nio\n\n\n\nn-\nex\n\n\n\nch\nan\n\n\n\nge\n c\n\n\n\nap\nac\n\n\n\nity\n;B\n\n\n\nS:\n B\n\n\n\nas\ne \n\n\n\nsa\ntu\n\n\n\nra\ntio\n\n\n\nn \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201456\n\n\n\nField observations showed the soil profile in the forest area to have O horizon \nof litter material of 10 cm thickness, followed by A horizon and Bw horizon. Soils \nunder the perennial crops and annual crops areas did not have O horizon, but \nthe top soil began with A horizon in the soil profile under perennial crops and \nAp horizon in soil profile under annual crops. The three soil profiles had similar \nsolum thickness and an effective depth of more than 100 cm.\n\n\n\nDifferent land use patterns have changed the Andisols colour in the upper \nlayer. In a forest area without cultivation, the A horizon soil had a Hue of 10 \nYR. In the perrenial crops area, soil color of the A horizon was more red with a \nHue of 7.5 YR, and in the annual crops area, which was cultivated intensively, \nthe soil color of the A horizon was even more red with a Hue of 5.0 YR. Land \ncultivation has turned the topsoil colour to red. The colour change in the topsoil \nwas probebly caused by cultivation. Extensive soil tillage and greater exposure to \nthe sun oxidized the iron in the  soil causing the soil to become more red. Oxidized \nsoil iron which is associated withthe mineral shematite, goethite and ferrihydrite \nexhibits a more red colour which increases from 10 YR to 5 YR (Schwertmann \nand Taylor 1989).\n\n\n\nChanges in the Chemical Properties of Andisols\nSoil organic C content in the top layer has been affected significantly by land \ncultivation. Land under more intensive cultivation showed the biggest decrease \nin soil organic C. As land under annual crops is under intensive cultivation, it has \nresulted in a loss of a higher amount of organic C content compared to perennial \ncropland, which is less intensively cultivated. Forest land which was not cultivated \nhad the highest amount of organic C.\n\n\n\npH0 defines the pH where the net variable surface charge is zero (\u03ecv-=\u03ecv+). \nLand  cultivation has affected the pH0 value of the Andisols with soil solum (A, \nB and C horizons) under annual crops having higher pH0 values than soil solum \nunder forest and perennial crops. The higher pH0 value in land under annual crops \nis due to intensive cultivation which causes the soil organic carbon to decrease. \nAs the pH0 value of soils is influenced by soil organic C content, one way of \nlowering pH0 would be to increase the organic matter content of the soil (Uehara \nand Gillman 1981). Removal of organic matter through intensive cultivation has \nled to remarkably high pH0 values (Utami and van Ranst  2002; Shamshuddin and \nAnda 2008). The  relationship between pH0 and organic carbon content in soil \nsolum is shown  in Figure 1.   It is seen that the pH0 decreases linearly as organic \ncarbon increases. \n\n\n\nTable 4 shows that the amount of available P in the three profiles is very \nlow\u2013 less than 8 mg kg-1. Also, it can be seen that total P as P2O5 ranges from low \nto very high, at 0.023 \u2013 0.208 %. Therefore, it can be concluded that P retention \nvalue in these soils is very high, more than 85%, fitting the definition of Andisols. \nThis indicates that land cultivation did not significantly change the natural \ncharacteristics of the Andisols, such as P retention and low availability of P.\n\n\n\nMukhlis, Zulkifli Nasution and Budi Mulyanto\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 57\n\n\n\nPhysico-chemical Properties of Andisols from Different Landuse\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nPh\nys\n\n\n\nic\no-\n\n\n\nch\nem\n\n\n\nic\nal\n\n\n\n c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\n o\n\n\n\nf A\nnd\n\n\n\nis\nol\n\n\n\ns u\nnd\n\n\n\ner\n fo\n\n\n\nre\nst\n\n\n\n, p\ner\n\n\n\nen\nni\n\n\n\nal\n c\n\n\n\nro\nps\n\n\n\n a\nnd\n\n\n\n a\nnn\n\n\n\nua\nl c\n\n\n\nro\np \n\n\n\n(c\non\n\n\n\ntin\nue\n\n\n\nd)\n\n\n\n11\n \n\n\n\n H\nor\n\n\n\niz\non\n\n\n\n \nD\n\n\n\nep\nth\n\n\n\n \nP \n\n\n\nav\nai\n\n\n\nla\nbl\n\n\n\ne \nP \n\n\n\nre\nte\n\n\n\nnt\nio\n\n\n\nn \nP 2\n\n\n\nO\n5 t\n\n\n\not\nal\n\n\n\n \nO\n\n\n\nxa\nla\n\n\n\nte\n e\n\n\n\nxt\nra\n\n\n\nct\n \n\n\n\nPy\nro\n\n\n\nph\nos\n\n\n\nph\nat\n\n\n\ne \nA\n\n\n\nl e\nxt\n\n\n\nra\nct\n\n\n\n \nA\n\n\n\nlo\n +\n\n\n\n\u00bd\nFe\n\n\n\no \nA\n\n\n\nl \nSi\n\n\n\n \nFe\n\n\n\n\n\n\n\n \n--\n\n\n\n--\ncm\n\n\n\n--\n--\n\n\n\n \n--\n\n\n\npp\nm\n\n\n\n--\n \n\n\n\n---\n--\n\n\n\n---\n---\n\n\n\n---\n---\n\n\n\n--\n--\n\n\n\n---\n---\n\n\n\n--\n--\n\n\n\n---\n---\n\n\n\n--\n--\n\n\n\n---\n---\n\n\n\n--\n--\n\n\n\n %\n --\n\n\n\n--\n--\n\n\n\n---\n---\n\n\n\n--\n--\n\n\n\n---\n---\n\n\n\n--\n--\n\n\n\n---\n---\n\n\n\n--\n--\n\n\n\n---\n--\n\n\n\n \nPr\n\n\n\nof\nile\n\n\n\n 1\n (F\n\n\n\nor\nes\n\n\n\nt)\n \n\n\n\nO\ne \n\n\n\n0 \n\u2013 \n\n\n\n10\n \n\n\n\n5.\n92\n\n\n\n \n85\n\n\n\n.8\n1 \n\n\n\n0.\n16\n\n\n\n9 \n2.\n\n\n\n90\n \n\n\n\n2.\n28\n\n\n\n \n0.\n\n\n\n41\n \n\n\n\n0.\n54\n\n\n\n \n3.\n\n\n\n10\n \n\n\n\nA\n \n\n\n\n10\n \u2013\n\n\n\n 4\n5 \n\n\n\n1.\n67\n\n\n\n \n99\n\n\n\n.2\n0 \n\n\n\n0.\n02\n\n\n\n3 \n2.\n\n\n\n90\n \n\n\n\n0.\n45\n\n\n\n \n0.\n\n\n\n38\n \n\n\n\n0.\n60\n\n\n\n \n3.\n\n\n\n09\n \n\n\n\nA\nB\n\n\n\n \n45\n\n\n\n \u2013\n 8\n\n\n\n7 \n1.\n\n\n\n13\n \n\n\n\n99\n.4\n\n\n\n4 \n0.\n\n\n\n03\n4 \n\n\n\n4.\n53\n\n\n\n \n1.\n\n\n\n70\n \n\n\n\n0.\n27\n\n\n\n \n0.\n\n\n\n60\n \n\n\n\n4.\n66\n\n\n\n \nB\n\n\n\nw\n1 \n\n\n\n87\n \u2013\n\n\n\n 1\n34\n\n\n\n \n1.\n\n\n\n67\n \n\n\n\n98\n.1\n\n\n\n9 \n0.\n\n\n\n03\n4 \n\n\n\n4.\n38\n\n\n\n \n1.\n\n\n\n43\n \n\n\n\n0.\n41\n\n\n\n \n0.\n\n\n\n44\n \n\n\n\n4.\n58\n\n\n\n \nB\n\n\n\nw\n2 \n\n\n\n13\n4 \n\u2013 \n\n\n\n18\n5 \n\n\n\n1.\n40\n\n\n\n \n99\n\n\n\n.2\n0 \n\n\n\n0.\n02\n\n\n\n3 \n1.\n\n\n\n30\n \n\n\n\n1.\n33\n\n\n\n \n0.\n\n\n\n45\n \n\n\n\n0.\n42\n\n\n\n \n1.\n\n\n\n52\n \n\n\n\nC\n \n\n\n\n> \n18\n\n\n\n5 \n1.\n\n\n\n13\n \n\n\n\n95\n.0\n\n\n\n4 \n0.\n\n\n\n06\n6 \n\n\n\n1.\n73\n\n\n\n \n0.\n\n\n\n63\n \n\n\n\n0.\n51\n\n\n\n \n0.\n\n\n\n55\n \n\n\n\n1.\n98\n\n\n\n\n\n\n\nPr\nof\n\n\n\nile\n 2\n\n\n\n (P\ner\n\n\n\nen\nni\n\n\n\nal\n c\n\n\n\nro\nps\n\n\n\n) \nA\n\n\n\n1 \n0 \n\u2013 \n\n\n\n70\n \n\n\n\n5.\n92\n\n\n\n \n96\n\n\n\n.6\n4 \n\n\n\n0.\n20\n\n\n\n8 \n2.\n\n\n\n55\n \n\n\n\n0.\n56\n\n\n\n \n0.\n\n\n\n60\n \n\n\n\n0.\n51\n\n\n\n \n2.\n\n\n\n83\n \n\n\n\nA\n2 \n\n\n\n70\n\u2013 \n\n\n\n95\n/1\n\n\n\n10\n \n\n\n\n4.\n18\n\n\n\n \n97\n\n\n\n.4\n2 \n\n\n\n0.\n09\n\n\n\n6 \n2.\n\n\n\n40\n \n\n\n\n0.\n51\n\n\n\n \n2.\n\n\n\n28\n \n\n\n\n0.\n57\n\n\n\n \n2.\n\n\n\n65\n \n\n\n\nB\nw\n\n\n\n1 \n95\n\n\n\n/1\n10\n\n\n\n-\n12\n\n\n\n5 \n1.\n\n\n\n13\n \n\n\n\n98\n.7\n\n\n\n0 \n0.\n\n\n\n02\n3 \n\n\n\n2.\n40\n\n\n\n \n0.\n\n\n\n67\n \n\n\n\n2.\n40\n\n\n\n \n0.\n\n\n\n54\n \n\n\n\n2.\n74\n\n\n\n\n\n\n\nB\nw\n\n\n\n2 \n12\n\n\n\n5 \n\u2013 \n\n\n\n17\n0 \n\n\n\n1.\n13\n\n\n\n \n99\n\n\n\n.2\n0 \n\n\n\n0.\n04\n\n\n\n8 \n1.\n\n\n\n80\n \n\n\n\n0.\n29\n\n\n\n \n2.\n\n\n\n40\n \n\n\n\n0.\n51\n\n\n\n \n1.\n\n\n\n95\n \n\n\n\nC\n \n\n\n\n> \n17\n\n\n\n0 \n1.\n\n\n\n40\n \n\n\n\n98\n.4\n\n\n\n4 \n0.\n\n\n\n11\n7 \n\n\n\n2.\n88\n\n\n\n \n0.\n\n\n\n66\n \n\n\n\n1.\n95\n\n\n\n \n0.\n\n\n\n68\n \n\n\n\n3.\n21\n\n\n\n\n\n\n\nPr\nof\n\n\n\nile\n 3\n\n\n\n (A\nnn\n\n\n\nua\nl c\n\n\n\nro\nps\n\n\n\n) \nA\n\n\n\np \n0 \n\u2013 \n\n\n\n40\n \n\n\n\n2.\n50\n\n\n\n \n94\n\n\n\n.2\n2 \n\n\n\n0.\n18\n\n\n\n1 \n2.\n\n\n\n95\n \n\n\n\n0.\n20\n\n\n\n \n2.\n\n\n\n85\n \n\n\n\n0.\n73\n\n\n\n \n3.\n\n\n\n05\n \n\n\n\nA\nB\n\n\n\n \n40\n\n\n\n - \n 6\n\n\n\n0 \n1.\n\n\n\n40\n \n\n\n\n98\n.9\n\n\n\n5 \n0.\n\n\n\n04\n1 \n\n\n\n3.\n28\n\n\n\n \n0.\n\n\n\n14\n \n\n\n\n1.\n93\n\n\n\n \n0.\n\n\n\n73\n \n\n\n\n3.\n35\n\n\n\n \nB\n\n\n\nw\n1 \n\n\n\n60\n \u2013\n\n\n\n 1\n10\n\n\n\n \n1.\n\n\n\n40\n \n\n\n\n98\n.4\n\n\n\n4 \n0.\n\n\n\n02\n3 \n\n\n\n3.\n73\n\n\n\n \n0.\n\n\n\n22\n \n\n\n\n1.\n38\n\n\n\n \n0.\n\n\n\n54\n \n\n\n\n3.\n84\n\n\n\n \nB\n\n\n\nw\n2 \n\n\n\n11\n0 \n\u2013 \n\n\n\n13\n5 \n\n\n\n1.\n13\n\n\n\n \n98\n\n\n\n.7\n0 \n\n\n\n0.\n14\n\n\n\n2 \n2.\n\n\n\n43\n \n\n\n\n0.\n32\n\n\n\n \n0.\n\n\n\n95\n \n\n\n\n0.\n44\n\n\n\n \n2.\n\n\n\n59\n \n\n\n\nB\nC\n\n\n\n \n13\n\n\n\n5 \n\u2013 \n\n\n\n17\n5 \n\n\n\n1.\n13\n\n\n\n \n98\n\n\n\n.7\n0 \n\n\n\n0.\n09\n\n\n\n6 \n3.\n\n\n\n15\n \n\n\n\n0.\n17\n\n\n\n \n1.\n\n\n\n13\n \n\n\n\n0.\n57\n\n\n\n \n3.\n\n\n\n23\n \n\n\n\nC\n \n\n\n\n>1\n75\n\n\n\n \n1.\n\n\n\n40\n \n\n\n\n97\n.9\n\n\n\n4 \n0.\n\n\n\n08\n9 \n\n\n\n3.\n10\n\n\n\n \n0.\n\n\n\n32\n \n\n\n\n1.\n15\n\n\n\n \n0.\n\n\n\n70\n \n\n\n\n3.\n26\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201458\n\n\n\nMukhlis, Zulkifli Nasution and Budi Mulyanto\n\n\n\nSelective dissolution methods in which Al, Si, and Fe  were extracted by \noxalic acid (Alo, Sio and Feo) are considered to represent the sum of Al, Si, and \nFe in organic complexes, in non crystaline hydrous oxides and in allophane and \nimogolite; and Al extracted by pyrophosphate (Alp) served as  proxy for Al organic \ncomplexes (Soil Survey Staff 1999). The results  of  the selective dissolution \nstudy showed that land cultivation only changed the Alo and Alp contents. Alo \naccumulated in the middle layer of the forest land while in the cultivated land, \nit was distributed in all layers. Intensive cultivation resulted in high Alp content \nwhich accumulated in the top layer, while for land underforest, it  resulted in low \nAlp.\n\n\n\nCONCLUSION\nDiffrent type of landuse, that is natural forest, perennial crops and annual crops, \ndid not change the solum thickness, effective depth, P retention, total P and P \navailable content of the Andisols.  Intensive cultivation of the Andisols caused \nthe colour of the top soil to become more red, increased pH0 value and Al-humic \ncomplex, but decreased organic carbon.\n\n\n\nREFERENCES\nAnonymous. 2011. Welcome to Sinabung.  http://sinabung.com [ Retrieved 15 \n\n\n\nOctober 2011]\n\n\n\nEndriani and Zurhalena. 2008. Kajian beberapa sifat fisika andisol pada beberapa \npenggunaan lahan dan beberapa kelereng andi  kecamatan gunung kerinci. \n\n\n\n13 \n \n\n\n\n\n\n\n\nFigure 1: Relationship between pH0 and organic carbon \n\n\n\ny = -0,178x + 6,091\nR\u00b2 = 0,443\n\n\n\n1.00\n\n\n\n2.00\n\n\n\n3.00\n\n\n\n4.00\n\n\n\n5.00\n\n\n\n6.00\n\n\n\n7.00\n\n\n\n8.00\n\n\n\n0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00\n\n\n\npH0\n\n\n\nOrganic C (%)\n\n\n\nFigure 1: Relationship between pH0 and organic carbon\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 59\n\n\n\nPhysico-chemical Properties of Andisols from Different Landuse\n\n\n\nProsiding Seminar Nasional Sains dan Teknologi  II \u2013 2008. Universitas \nLampung.\n\n\n\nFiantis, D., N. Hakim and E. van Ranst. 2005. Properties and utilization of Andisols \nin Indonesia. JIFS. 2 : 29 \u2013 37.\n\n\n\nMizota, C and L.P. van Reeuwijk. 1989. Clay Mineralogy and Chemistry of Soils \nFormed in Volcanic Material in Diverse Climatic Regions. International Soil \nReference and Information Centre, Wageningen.\n\n\n\nNeall, V.E. 2009. Volcanic soils. In:  Land Use, Land Cover and Soil Science, ed. W.H. \nVerheye. Eolss-Unesco. VII: 23 \u2013 46. \n\n\n\nSakurai, K., Y. Ohdate and K. Kyuma. 1988.  Comparation of salt titration and \npotentiometric titration methods for determination of zero point of charge \n(ZPC). Soil Science and Plant Nutrition. 34 : 171 \u2013 182.\n\n\n\nSchoeneberger, P.J., D.A. Wysocki, E.C. Benham and Soil Survey Staff. 2012. \nField Book for Describing and Sampling Soils. Ver. 3.0. Lincoln, NE: Natural \nResources Conservation Service, National Soil Survey Center,\n\n\n\nSchwertmann, U. and R.M. Taylor. 1989. Iron oxides. In: Minerals in Soil \nEnvironments, ed. J.B. Dixon and S.B. Weeds.  Wisconsin USA: Soil Sci. Soc. \nof America.\n\n\n\nShamshuddin, J. and Anda. M. 2008. Charge Properties of soils in Malaysia dominated \nby Kaolinite, Gibbsite, Goethite and Hematite. Bulletin of the Geological \nSociety of Malaysia. 54 : 27 \u2013 31. \n\n\n\nSoil Survey Staff. 1999. Soil Taxonomy. A Basic System of Soil Classification for \nMaking and Interpreting Soil Surveys. Agriculture Handbook 436(2nd ed.). \nWashington, DC:US Government Printing Office.\n\n\n\nSubagyo, H., N. Suhartadan and A.B. Siswanto. 2000. Tanah-tanah Pertanian Indonesia.  \nIn: Sumber Daya Lahan Indonesia dan Pengelolaannya. Pusat Penelitian Tanah \ndan Agroklimat. Badan Litbang Pertanian. Departemen Pertanian.\n\n\n\nTan, K. H . 2008. Soils in the Humid Tropics and Monsoon Region of Indonesia. Bota \nRaton. CRC Press.\n\n\n\nUehara, G. and G. Gillman. 1981. The Mineralogy, Chemistry, Physics of Tropical \nSoils with Variable Charge Clays. Colorado: Westerview Press.  \n\n\n\nUSDA. 1995. Soil Survey Laboratory Information Manual. Nebraska: USDA-Natural \nResource Conservation Service. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201460\n\n\n\nUtami. S.R. and E. van Ranst. 2002. Surface Charge Characteristics Of Volcanic Ash \nSoils in Java, Indonesia. From http://www.Idd.go.th/WCSS2002/papers/0320.\npdf.\n\n\n\nvan Reeuwijk, L.P. 2002. Procedures for Soil Analysis (6th ed.). International Soil \nReference and Information Centre. \n\n\n\n\n\n\n\nMukhlis, Zulkifli Nasution and Budi Mulyanto\n\n\n\n\n\n"
"\n\nINTRODUCTION\nHevea brasiliensis is a tropical crop which originated from the Amazon forest, \n\n\n\nResponse of  (RRIM 2001) Planted on an \nOxisol to Different Rates of Fertilizer Application \n\n\n\n \nShafar Jefri Mokhatar1, Noordin Wan Daud1* and \n\n\n\nChe Fauziah Ishak2  \n\n\n\n1Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia \n43400 Serdang, Selangor, Malaysia\n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nRubber, Hevea brasiliensis is one of the important commodity crops that has \n\n\n\nin the world. The precise use of fertilizers is very important in a rubber nursery \n\n\n\nof the fertilizing cost. This study will assess the suitability of the current fertilizer \n\n\n\nadvanced planting materials nurseries. The study was conducted using a complete \n\n\n\n-1\n\n\n\ng plant-1 -1 -1\n\n\n\nand dry weight were taken. Tissue analysis was conducted at the end of the study. \n\n\n\ndifferent from other treatments. Compared to established nutrient critical values \n\n\n\nbiomass production and comparison of leaf nutrients with critical nutrient value, \n\n\n\nfor optimum growth, and precise fertilizer application should be considered to \n\n\n\nKeywords: Rubber, Tropeptic Haplorthox, compound fertilizer RISDA 1, \nnursery trials, advanced planting materials.\n\n\n\n___________________\n*Corresponding author : E-mail: \n\n\n\n\n\n\n\n\n58\n\n\n\ncommercially in Malaya (Malaysia was known as Malaya before she gained \n\n\n\nits discovery.   Since then the demand for natural rubber has increased steadily. \n\n\n\nrange of desired characteristics through breeding process have been tested (MRB \n\n\n\nNoordin et al.\n\n\n\nsilty clay loam to silty clay that is yellowish brown to strong brown. The structure \n\n\n\nMalaysia. Trees from these clones are known for their performance, rapid growth \n\n\n\nnutrients for its growth (Shima et al\n\n\n\nAdvanced planting materials are commonly used in planting programmes \n\n\n\nplanted. Usually, plants of the same age with about four to seven whorls are \n\n\n\nplanting materials would be important for replacement. However, fertilizer \nrecommendations for advanced planting materials are currently not available. \nThis study was carried out to assess current fertilizer practice based on nursery \n\n\n\n\n\n\n\n\n59\n\n\n\ntrials and to suggest optimum fertilizer application rates for advanced planting \nmaterials at nursery stage.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSerdang, Selangor, Malaysia. The study called for the planting of high productivity \n\n\n\nper tree (18.75 kg ha-1 -1\n\n\n\nintervals for a period of eight months. The fertilizer application rates were divided \n-1 -1\n\n\n\n-1\n\n\n\nnd\n\n\n\nParticle size distribution was determined by the pipette method (Sheldrick and \n\n\n\nNH4 4  was displaced with \n\n\n\nResponse of Hevea Brasiliensis to Fertilizer Rates\n\n\n\nTABLE 1 \n Nursery fertilization programme of Hevea for each macronutrient \n\n\n\nTotal fertilization rate (kg ha-1\n\n\n\nTABLE 1  \n Nursery fertilization programme of Hevea for each macronutrient (duration of eight \n\n\n\nmonths) \n\n\n\n  Recommended rate  \nAge of plant \n\n\n\n(months)  \ng/tree  N (g)  P (g)  K (g)  Mg (g)  \n\n\n\n2 37.5 0 4.0 1 6.2 3 3.56 0.9 0 \n5 37.5 0 4.0 1 6.2 3 3.56 0.9 0 \n8 37.5 0 4.0 1 6.2 3 3.56 0.9 0 \n\n\n\nTotal  112.5 0 12.03  18.69  10.68  2.70  \nFertilizer  grade: 10.7: 16.6: 9.5: 2.4\n\n\n\nTABLE 2 \nTotal fertilization rate (kg ha-1) of compound fertilizer  \n\n\n\n(three applications for a duration of 8 months) \n \n\n\n\nTreatment  Each application  \n(kg ha-1)  \n\n\n\nTotal  \n(kg ha-1) \n\n\n\nT2 ( 50% recommended rate ) 9.3 8 28.13  \nT3 ( 100% recommended rate ) 18.75  56.25  \nT4 ( 150% recommended rate ) 28.13  84.38  \n*Rate in kg  ha-1  with planting density  of 500 trees  ha-1\n\n\n\n.  \n\n\n\n \n\n\n\n\n\n\n\n\nfour replications and study duration of eight months. Data on growth such as \nplant height and girth were taken and nutritional status was analyzed at the end \n\n\n\nsampling was done according to the foliar sampling method adopted by the \n\n\n\nwhorl were collected as a leaf sample (Rubber Research Institute of Malaysia \n\n\n\nin 5 mL of sulfuric acid (H 4\n\n\n\nand heating was continued for another four minutes. The solution were made up \n\n\n\nRESULTS AND DISCUSSION\nThe Munchong series soil which is derived from sedimentary rock and shale is \nyellowish brown to strong brown in color. As a highly weathered soil, the particle \n\n\n\n \nThe silt content in the soil may indicate the degree of weathering (Anda et al. \n\n\n\nsoils as these minerals will became a part of the clay fraction when it is weathered \n\n\n\nn=2\n\n\n\nnutrient uptake\nnutrient supplied\n\n\n\nTABLE 3  \nSelected physical and chemical properties of Tropeptic Haplorthox soil (n=2) \n\n\n\nParticle size  Soil   pH a CEC b \n(cmol+ kg-1 ) Clay (%)  Silt (%)         Sand (%)  \n\n\n\nMunchong \nSeries  \n\n\n\n4.10     8.40  59 -63  11 -13  26 -28  \n\n\n\na pH measured in H2 O (1:2.5).\nb CEC pH 7 measured in NH4 OAc at pH 7  \n\n\n\n \n\n\n\n\n\n\n\n\n61\n\n\n\nvalue of about 8.4 cmol  kg-1.  This CEC  value indicates the limited ability \n\n\n\nIn terms of growth response, the plant height and girth responded \n0.0705x2 + 3.0327x + 31.468 (Fig. 1) and  y = -0.0023x2 \n\n\n\n+ 0.1184x + 2.6129 (Fig. 2) respectively. From the above regression, it can be \n-1 -1 gave \n\n\n\nResponse of Hevea Brasiliensis to Fertilizer Rates\n\n\n\nFig. 1: Height response of Hevea brasiliensis towards different rates of fertilizer \napplication.\n\n\n\nFig. 2: Relationship between girth of Hevea brasiliensis and rates of fertilizer \napplication.\n\n\n\n\n\n\n\n\nplant growth may be restricted due to the limited growth area. Root penetration \nwas restricted in the polythene bag and this may cause plant growth inhibition \n(Mathers et al\n\n\n\nadverse effects such as scorching of the leaves and will die if the fertilizer is \n\n\n\nThe other growth parameter was biomass production or dry weight of the plant \n\n\n\ny \nTABLE 4\n\n\n\n Biomass production of Hevea brasiliensis in relation to different rates of \nfertilizer application.\n\n\n\nFig. 3: Relationship between total dry weight and different rates of fertilizer application.\n\n\n\nTABLE 4 \n Biomass production of Hevea brasiliensis in relation to different rates of fertilizer \n\n\n\napplication. \n\n\n\nDry weight (g)  Treatment  \nLeaf  Stem  Root  Total  \n\n\n\nT1 ( unfertilized ) 20.49d  42.34d  68.09c  130.91d  \nT2 (9.38 kg ha-1 ) 72.10c  83.04c  70.57c  225.71c  \nT3 (18.75 kg ha-1 ) 77.63b  94.11b  77.71b  249.45b  \nT4 (28.13 kg ha-1 ) 85.53a  108.24a  107.46a  301.23a  \nLSD0.05  3.27  4.24  4.41  10.07  \nMeans (n=4 ) followed by the same letter are not significantly different at 5% significant level by LSD.\n\n\n\n\n\n\n\n\n= -0.1224x2+ 9.1451x + 135.87 (Fig. 3)\nha-1\n\n\n\nof fertilizer may be necessary. Moreover, dry weight and other parameters (height \n\n\n\nTable 5 shows the nutrients content in the tissue of the plant. T4 had \n\n\n\nto higher potassium application as the soil K is low in highly weathered soil \n\n\n\nof the factors to be considered in fertilizer recommendation. Munchong series is \nsoil developed from sedimentary rocks, shale and is highly weathered resulting in \n\n\n\nbecause it is easily leached into the groundwater (Shamshuddin and Noordin \n\n\n\nvery important for growth and yield formation and dry matter production (Pervez \net al.\n\n\n\nThe magnesium content is also higher in T4 resulting from the application \n\n\n\nEach of the nutrients analyzed was compared to the standard range of \n\n\n\nThese critical nutrient values normally have a range which separate the level of \n\n\n\nResponse of Hevea Brasiliensis to Fertilizer Rates\n\n\n\nTABLE 5\nNutrients content in tissue of Hevea brasiliensis due to different fertilizer \n\n\n\napplication rates\n\n\n\nTABLE 5 \nNutrients content in tissue of Hevea brasiliensis due to different fertilizer application \n\n\n\nrates \n\n\n\nNitrogen (N)  Phosphorus (P)  Potassium (K)  Magnesium (Mg)  \nTreatment  \n\n\n\n% \nT1 ( unfertilized)  2.40c 0.14b  0.63c  0.14b \nT2 ( 9.38 kg ha-1) 3.08b 0.15ab  0.72c  0.16b \nT3 ( 18.75 kg ha-1 ) 3.25b 0.19a  0.88b  0.20b \nT4 ( 28.13 kg ha-1)  3.70a 0.19a  1.02a  0.27a \nLSD 0.05 0.28 0.04  0.09 0.06 \nMeans (n=4 ) followed by  the same letter are not significantly different at 5% significant level by LSD.  \n\n\n\n\n\n\n\n\n64\n\n\n\npotassium and magnesium, were low. From visual observation, some plants show \n(Figs. 4, \n\n\n\n5, 6, 7 and 8). It is totally different in T4, where all the nutrients were in the \n\n\n\nTABLE 6 \nTABLE 6  \n\n\n\nLeaf nutrient sufficiency range for immature rubber \n\n\n\nTreatment  Nutrient  Analyzed  Literature classification  \nT1  N 2.4  <2.70 (deficient)  \n  P 0.14  <0.15 (deficient)  \n  K 0.63  <0.85 (deficient)  \n  Mg  0.14  <0.19 (deficient)  \n\n\n\nT2  N 3.08  2.71 - 3.09 (deficient)  \n  P 0.15  <0.15 (deficient)  \n  K 0.72  <0.85 (deficient)  \n  Mg  0.16  <0.19 (deficient)  \n\n\n\nT3  N 3.25  3.10 - 3.60 (sufficient)  \n  P 0.19  0.18 - 0.25 (sufficient)  \n  K 0.88  0.86 - 0 .96 (deficient)  \n  Mg  0.2  0.20 - 0.21 (deficient)  \n\n\n\nT4  N 3.7  3.61 - 3.90 (excess)  \n  P 0.19  0.18 - 0.25 (sufficient)  \n  K 1.02  0.97 - 1.40 (sufficient)  \n  Mg  0.27  0.22 - 0.28 (sufficient)  \n\n\n\n*Nutrient sufficiency level, N -3.10 -3.60, P - 0.18 -0.25, K -0.97 -1.40 , Mg - 0.22 -0.28  \n**Literature classification after Noordin (2011 ); RRIM (1990)   \n \u00a0\n\n\n\n\n\n\n\n\n65\n\n\n\nResponse of Hevea Brasiliensis to Fertilizer Rates\n\n\n\nFrom these results, it can be concluded that the current fertilizer recommendation \nis not at the optimum level, that is, it is low in potassium and magnesium.\n\n\n\n\n\n\n\n\n66\n\n\n\nrates of fertilizer were increased. Nutrient uptake is nutrient content in the plant \n\n\n\n-1 fertilizer applied. However, nutrient \n\n\n\nnutrient to the plant and minimizing nutrient losses from the soil. Nutrient \n\n\n\nand lower above-ground dry matter production (Sariam et al\n\n\n\nCONCLUSION\nFrom the data obtained on plant height, girth and total dry weight production, it \n\n\n\ng plant-1\n\n\n\nmetabolism, photosynthesis in relation to biomass production and partitioning. \nThe management of fertilizer application should be revised in order to optimize \n\n\n\nTABLE 7  \nNutrients use efficiency (recovery efficiency) due to different rates of fertilizer application \n\n\n\nNutrients use efficiency/Recovery efficiency (%) Fertilizer rate \nNitrogen (N) Phosphorus (P) Potassium (K) Magnesium (Mg) \n\n\n\n9.38 kg ha-1 115.50 16.62 73.30 44.58 \n18.75 kg ha-1 67.35 11.64 49.51 30.80 \n28.13 kg ha-1 61.73 9.37 46.19 33.47 \n\n\n\n\n\n\n\nTABLE 7 \n\n\n\napplication\n\n\n\n\n\n\n\n\n67\n\n\n\nResponse of Hevea Brasiliensis to Fertilizer Rates\n\n\n\nfertilizer rate should be revised and increased from the current rate  that is, from \n18.75 kg ha-1 -1\n\n\n\nsuggested that further fertilizer trials be carried out before actual recommendations \nare made.\n\n\n\nREFERENCES\nAbu Bakar. 1985. Teknologi Getah Asli. Kuala Lumpur: Institut Penyelidikan Getah \n\n\n\nMalaysia.\n\n\n\ndifferent parent materials. Geoderma\n\n\n\nHandbook of Plant Nutrition. United States: \nCRC Press.\n\n\n\nHevea In: Proceedings of RRIM \nPlanter\u2019s Conference, 1981, Kuala Lumpur: RRIM.\n\n\n\nanduan Mengenali Siri-Siri Tanah \nDi Semenanjung Malaysia nd\n\n\n\nMalaysia.\n\n\n\nGardner, F.P. 1985. Physiology of Crop Plants. United States of America: The Iowa \nState University Press.\n\n\n\nHaridas, G. 1978. Responses of fertilizers on growth and yield of rubber, In: RRIM \nTraining Manual on Soils, Management of Soils and Nutrition of Hevea. Kuala \nLumpur: RRIM.\n\n\n\nKlon-klon Siri RRIM 2000 untuk Lateks dan Balak. \nKuala Lumpur: Malaysian Rubber Board.\n\n\n\nRubber Plantation and Processing Technologies (1st \n\n\n\nHorticulture \nTechnology.\n\n\n\nPrinciples of Plant Nutrition (5th\n\n\n\nNetherlands: Kluwer Academic Publishers.\n\n\n\n\n\n\n\n\n68\n\n\n\nMalaysia. M.Sc. Thesis, Belgium: I.T.C., State University of Ghent.\n\n\n\nIn: RRIM Training Manual on Soils, Management of Soils and Nutrition of \nHevea,\nMalaysia.\n\n\n\nIsu-isu Semasa Sains \ndan Teknologi,\nEnvironment.\n\n\n\nRubber Plantation: Soil Management & Nutritional Requirements. \nSerdang: UPM, Selangor, Malaysia.\n\n\n\nPersonal Communication.\n\n\n\ndalam penanaman getah pekebun-pekebun kecil In: Persidangan Kebangsaan \nPekebun Kecil\n\n\n\nHevea. In: RRIM Short Course \non Rubber Planting and Nursery Technique. Kuala Lumpur: RRIM. \n\n\n\npotassium nutrition. Malaysian Journal of Soil Science\n\n\n\nIn: Proceedings of RRIM Planter\u2019s Conference\n\n\n\nTeknologi dan Pengurusan Getah. Kuala Lumpur: Misas Advertising \nSdn. Bhd.\n\n\n\nManual for Diagnosing Nutritional \nRequirements for Hevea. Kuala Lumpur: Vinlin Press Sdn Bhd.\n\n\n\nMalaysian Journal of Soil Science. 6: 1-11.\n\n\n\nAcrisol as affected by different source of fertilizer. International Journal of \nApplied Science and Technology\n\n\n\nWeathered Tropical Soils: The Ultisols & \nOxisols. Serdang: UPM Press.\n\n\n\n\n\n\n\n\n69\n\n\n\nResponse of Hevea Brasiliensis to Fertilizer Rates\n\n\n\nweathered soils in Malaysia for production of plantation crops. In: Principles, \nApplication and Assessment in Soil Science, ed. E. Burcu \u00d6zkaraova G\u00fcng\u00f6r, \npp. 75-86. Croatia: InTech.\n\n\n\noil Sampling and \nMethods of Analysis\nPublishers.\n\n\n\nProceedings Soil Peat and Other Soil Factors in Crop Production, 17-19 April, \n\n\n\nShorrocks, V.M. 1964. \nKuala Lumpur: Rubber Research Institute of Malaya.\n\n\n\nShorrocks, V.M. 1965. Magnesium limestone, kieserite and ground serpentine as \nmagnesium fertilizers. Journal of the Rubber Research Institute of Malaya. \n\n\n\nhevea brasiliensis III. Effect of storage, before oven drying, on leaf dry weight \nand leaf nutrient concentrations. Journal of the Rubber Research Institute of \nMalaya.\n\n\n\nTurkish Journal of Agriculture and \nForestry\n\n\n\nMethods of Soil Analysis\nnd \n\n\n\nAmerican Society of Agronomy. \n\n\n\nRRIM Training Manual \non Analytical Chemistry Soil and Foliar Analysis\nRRIM.\n\n\n\nRRIM Training Manual on \nSoils, Management of Soils and Nutrition of Hevea, pp. 1-16. Kuala Lumpur: \nRRIM.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: christeh@yahoo.com\n\n\n\nINTRODUCTION\nChoy sum (Brassica chinensis var. parachinensis) is one of the most widely \nplanted leafy vegetables in Asia, including in Malaysia (Tin et al. 2000). Choy \nsum is planted for its leaves which are rich in glucosinolates that are claimed to \nhave anti-aging, antioxidants, and anti-cancer effects (Halkier and Gershenzon \n2006). Choy sum varies in height between 20 to 30 cm and has a short life cycle \nenabling the plant to be harvested within a month (Edward 2009). Since choy sum \nis a leafy plant and has a succulent stem, choy sum is sensitive to nitrogen (N) and \nwater stress (Nobel 2009).\n\n\n\nAlthough the effects of N and water stress on plant growth have been \nwidely reported for many crops (Clay et al. 2012; Sun et al. 2011; Pandey et \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 1-17 (2014)                          Malaysian Society of Soil Science\n\n\n\nModelling the Growth and Yield of Choy Sum (Brassica \nchinensis Var. Parachinensis) to Include the Effects of \n\n\n\nNitrogen and Water Stress\n\n\n\nKamarudin, N.K.1, Teh, C.B.S.1* and Z.E.J. Hawa2\n\n\n\n1Department of Land Management and 2Department of Crop Science\nFaculty of Agriculture, Universiti Putra Malaysia,\n\n\n\n43400 UPM Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nThe response of choy sum (Brassica chinensis var. parachinensis) to water \nand nitrogen (N) stress is still uncertain and no growth and yield model exists \nspecifically for this crop. Consequently, this study conducted a field experiment \nto determine the growth and yield of choy sum grown under several watering \nfrequencies and N application rates. Measured crop parameters were then used to \naid in the crop model development and in model validation. Choy sum growing \nunder the once-a-day and once-a-week watering treatments did not experience \nwater stress unlike the water-stressed choy sum grown under the once-every-two-\nweeks watering treatment. The optimal volumetric soil water content level and N \napplication rate for maximum yield were determined to be 40% and between 30 to \n40 kg N ha-1, respectively. Model validation showed that the choy sum model had \nan overall mean estimation error of 7.3% for leaves dry weight, 28.9% for stem \ndry weight, 28.9% for roots dry weight, 41.7% for leaf area index, and -0.8% for \nplant height. The model errors could be due to the assumption of an open-field \nenergy balance growing environment and the lack of accuracy on the leaf area \nindex estimation.\n\n\n\nKeywords: Brassica, choy sum, drought, model, nitrogen fertilizer, \nwater stress\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 20142\n\n\n\nal. 2000), much less is known specifically on the response of choy sum to these \ntwo stresses. Different Brassica species can respond differently to water stress. \nFor example, caisin (Brassica rapa subsp. parachinensis) was observed to be \nmore tolerant to waterlogging and water deficiency than Chinese kale (Brassica \noleracea var. alboglabra) (Issarakraisila et al. 2007). Nonetheless, both these two \nBrassica species still experienced reduced total leaf area and leaf weight, delayed \nflowering, and increased tissue N concentration under 14 days of water deficit \nconditions.\n\n\n\nN uptake by plants is influenced by soil-water status (Shangguan et al. 2000). \nPlants experiencing water stress and simultaneously fertilized with the highest N \nrate (140 mg N L-1), for instance, were found to experience high stress levels \n(Scagel et al. 2011). Fertilization under more well-watered conditions can reduce \nplant stress, but fertilization under water-stressed conditions can aggravate plant \nstress (Sun et al. 2011). The decomposition of granular fertilizers to ionic forms \nsuch as urea CH(NH2)2 decomposition to nitrate NO3\n\n\n\n- can occur in the presence \nof soil moisture, urease enzyme, and nitrifying soil bacteria. But fertilizers that \ndissolve under drought condition can instead increase the concentration of ions \naround the root system and at the same time decrease the availability of soil water. \nIn the other words, fertilization during drought condition would have a greater \ndeleterious effect on plant growth than the effect of drought alone. Furthermore, \nshortage of water supply can cause the concentration of N within plant tissues to \nincrease, along with a suppression of yield.\n\n\n\nWater stress experienced by plants fertilized with high N rates can be \nreduced by watering the plants more frequently. However, over-irrigation could \ninstead decrease efficiency of plant N uptake (N uptake per N applied) and alter \nthe biomass allocation in the plants (Scagel et al. 2011). The plants maintain a \ndynamic balance in biomass allocation. Root growth is typically less affected by \ndrought stress than shoot growth (Franco 2011; Alam 1999), and the shoot: roots \nratio is usually used to indicate the effect of drought stress on plant growth and \nbiomass allocation (Franco 2011).\n\n\n\nPlants require a certain amount of glucose for the production of the six \nmajor biochemical groups (carbohydrate, protein, lipid, lignin, organic acid and \nmineral) to synthesise a new material in plant tissues. Consequently, determining \nthe glucose content in the various plant parts is important. According to Vertregt \nand Penning de Vries (1987), glucose content in a plant can be calculated from \ntotal carbon and total nitrogen. This calculation method is inexpensive and more \nrapid and practical compared to measurements of glucose content (Teh 2006). \nGlucose is needed for plant maintenance and growth respiration, with the former \nbeing needed to sustain the survival of existing plant tissues, and the latter to \nsynthesise new structural materials for plant growth. However, maintenance and \ngrowth respiration can vary between plant species, varieties, and even between \nplant parts (Teh 2006).\n\n\n\nNitrogen concentration in a plant varies at each development stage because \nN is mobile within the plant. For instance, N moves from old to young leaves \n\n\n\nKamarudin, N.K., Teh, C.B.S. and Z.E.J. Hawa\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 3\n\n\n\nwhen the plant faces N deficiency. N concentration is usually higher in the early \nstages of plant growth compared to the later stages. Hence, the age of plant tissues \nis also an important factor that determines N content within the various plant \nparts.\n\n\n\nWater loss from the soil is through soil evaporation and plant transpiration. \nThe simultaneous occurrence of these two processes is called evapotranspiration \n(ET) (Teh 2006). The ratio between actual and potential evapotranspiration is \noften used to quantify water stress (Vertregt and Penning de Vries 1987) in crop \nmodels. Some of the crop models calculate the daily crop water stress as 1 \u2013 \nAT/PT where AT is the daily actual water uptake and PT is the daily potential \ntranspiration (Doorenbos and Kassam 1979).\n\n\n\nConsidering choy sum\u2019s response to the simultaneous effects of water and N \nstresses are still uncertain, this study aimed to develop and validate a mathematical \ngrowth and yield model that included the effects of water and N stresses. The aim \nwas also to use this model for estimation of the impact of climate change (such as \nincreases in water deficit levels) on the growth and yield of choy sum in Malaysia. \nThe model could additionally be used to study ways to offset the detrimental \nclimate change effects on the plant  (for example, could higher N rate application \noffset the decrease in choy sum yield due to higher air temperatures?).To help \nachieve these goals, a field experiment was conducted to gauge choy sum\u2019s \nresponse to five rates of N fertilizer and three watering levels. Data collected from \nthe field experiment were used to aid in model development as well as serve as \nmodel parameters to validate the model\u2019s simulation results.\n\n\n\nMATERIALS AND METHODS \n\n\n\nField Experiment\nThe field experiment has been described in great detail by Kamarudin (2012), \nsoonly the necessary details are outlined here. Choy sum was grown in polyethylene \nbags under a rain shelter in March 2011 at the Agronomy Research Farm (2o \n59.47\u2019 N and 101o 42.882\u2019 E), Universiti Putra Malaysia, Serdang, Selangor. Choy \nsum were exposed to three watering frequencies (once-a-day, once-a-week, and \nonce-every-two-weeks), and five N fertilizer application rates (0, 34, 68, 136, and \n272 kg N ha-1). Plant parameters (such as plant height, leaf number, total leaf area, \nplant part dry weights such as leaves including petiole, stem, and roots, and total \nC and N in various plant parts tissues) were collected weekly (0, 7, 14, 21, and 28 \ndays after transplanting) for four weeks. The soil properties used to grow the choy \nsum in the polyethylene bags are shown in Table 1.\n\n\n\nFour meteorological parameters (wind speed, relative humidity, solar \nirradiance, and air temperature) were measured using a portable weather station \n(WatchDog Model 2600) set at 30-minute recording intervals.\n\n\n\nNitrogen and Water-stress Growth Model for Choy Sum\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 20144\n\n\n\nCrop Model Development\nThe choy sum growth model was modified from the generic crop growth model \ndeveloped by Teh (2006). Here we describe the essential key model calculations, in \nparticular what we altered in the generic crop model, to enable model application \nspecifically to choy sum.\n\n\n\nThe study model was written in Microsoft Excel with the modelling add-in \ncalled BuildIt (Teh 2011). The model consisted of four core components: weather, \nphotosynthesis, evapotranspiration, and maintenance and growth respiration. The \nmodel run consisted of nine main steps (Figure 1). The model run started with the \nreading of model parameters; subsequently growth development stage (\u03bes) was \nchecked to determine if the model run should continue or stop. The growth stage \nfor choy sum was set to have three important growth stages (milestones): 0 for \ntransplanting, 1 for maturity, and 2 for harvesting which occurred 28 days after \ntransplanting (DAT).\n\n\n\nModel run would stop if the growth stage reached the harvesting stage (\u03bes=2); \notherwise, the model would run for weather, photosynthesis, evapotranspiration, \nand maintenance and growth respiration components, after which the crop\u2019s \ngrowth stage would be updated by the growth development rate (\u03ber):\n\n\n\n \u03be(s,t+1)=\u03be(s,t)+\u03be(r,t) \u0394t     (1)\n\n\n\nwhere \u03be(s,t) and \u03be(s,t+1) are the growth development stage at time t and subsequent \ntime step t+1, respectively; \u0394t is the time step (taken as 1 day); and \u03be(r,t) (\u22650)is the \ngrowth development rate (days-1) at time t, determined by:\n\n\n\nKamarudin, N.K., Teh, C.B.S. and Z.E.J. Hawa\n\n\n\n \nISSN: 1394-7990 \n\n\n\nMalaysian Journal of Soil Science Vol. 18: 1 \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 \n \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nThe soil properties at study site \n\n\n\nParameters Value \nSoil series Munchong (Typic Hapludox) \npH 6.8 \nEC (dS m-1) 0.62 \nParticle size distribution (%) \n     Clay (2-50\u00b5m) 65.41 \n     Silt (< 2\u00b5m)    7.63 \n     Sand (> 50 \u00b5m) 26.74 \nTexture class (USDA) Clay \nTotal carbon (%) 0.99 \nTotal nitrogen (%) 0.15 \nBulk density (Mg m-3) 1.08 \nVolumetric soil water content, \u0398 (%) \n     Saturation 74.97 \n     Field capacity 44.55 \n     Permanent wilting point 25.32 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 1\nThe soil properties at study site\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 5\n\n\n\nNitrogen and Water-stress Growth Model for Choy Sum\n\n\n\n \u03be(r,t) = \u03b1(Tavg - Tb )      (2)\n \nwhere Tavg is the mean of the minimum and maximum daily air temperature \n(\u00b0C); Tb is the base temperature (\u00b0C), below which crop growth stops; and \u03b1 is a \ncrop-dependent coefficient (\u00b0C-1 day-1). For Brassica species like choy sum, Tb is \napproximately 0\u00b0C and \u03b1 is 0.00102 \u00b0C-1 day-1 (Dixon 2006).\n\n\n\nFigure 1: Main program flow of the choy sum growth and yield model.\n\n\n\n \nISSN: 1394-7990 \n\n\n\nMalaysian Journal of Soil Science Vol. 18: 1 \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nStart \n\n\n\nRead model parameters \nSet \u03bes = 0 \n\n\n\nSet time step = 0 \n\n\n\n\u03bes<2 or max time \nSteps reached? \n\n\n\nNo \n\n\n\n1 \n\n\n\n2 \n\n\n\n3 \n\n\n\nDetermine growth development rate, \u03ber and update \u03bes \n\n\n\nIncrement time step and update simulation date \n\n\n\nDaily weather properties \n\n\n\nPhotosynthesis \n\n\n\nEnergy balance  \n(Evapotranspiration) \n\n\n\n\u03bes is the growth \ndevelopment stage \n\n\n\n(DVS) \n\n\n\n4 \nMaintenance and growth \nrespiration  \n\n\n\nEnd Yes \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 20146\n\n\n\nKamarudin, N.K., Teh, C.B.S. and Z.E.J. Hawa\n\n\n\n Leaf area index (LAI) was calculated as:\n\n\n\n LAI = Wgreenleaves  \n.SLA     (3)\n\n\n\nwhere Wgreenleaves is the weight of green leaves (g dry matter m-2 total ground area); \nand SLA is the specific leaf area (m2 leaf area g-1 dry matter leaf) (Teh et al. 2004).\n\n\n\nMeteorological data collected from the weather station were used to \ndetermine the potential transpiration (ETc; W m-2) based on the energy balance \nby Shuttleworth and Wallace (1985). ETc was scaled down to actual transpiration \n(ETc,\u03b1 ; W m-2) by a reduction factor of \u03b1\n\n\n\n ETc\u03b1 = RD \u00d7ETc      (4)\n\n\n\nwhere RD is the reduction factor (0 to 1) due to water stress, calculated based on \nthe amount of water in the soil as\n\n\n\n RD = ( \u03b8v - \u03b8pwp ) \u2044 (\u03b8cr - \u03b8pwp )    (5)\n\n\n\nwhere \u03b8v is the volumetric soil water content (m3 m-3); \u03b8pwp is the volumetric soil \nwater content at permanent wilting point (m3 m-3); and \u03b8cr is the critical volumetric \nsoil water content (m3 m-3), below which the plant experiences water stress. \u03b8cr is \ncalculated from:\n\n\n\n \u03b8cr = \u03b8pwp + p(\u03b8sat - \u03b8pwp )     (6)\n\n\n\nwhere \u03b8pwp is the volumetric soil water content at saturation point (m3 m-3); and p \nis 0.5 for C3 plants like choy sum (Doorenbos and Kassam 1979).\n\n\n\nThe photosynthesis model component calculated the canopy photosynthetic \nrate (A\u2019canopy; g CH2O m-2 total ground area day-1) following the semi-mechanistic \nphotosynthesis model from Collatz et al. (1991).The assimilates produced from \nphotosynthesis was scaled down due to water and N stress, if any, by:\n\n\n\n Acanopy = A\u2019canopy \u00d7 RD\u00d7ND     (7)\n\n\n\nwhere ND is the reduction factor (0 to 1) due to N stress where ND is determined by\n\n\n\n ND = a+ bNrate      (8)\n\n\n\nwhere coefficients a and b were determined empirically for choy sum by Edward \n(2009) as 0.6 and 0.00118, respectively. Consequently, Acanopy (g CH2O m-2 total \nground area day-1) was the reduced assimilates due to water stress and N stress.\n\n\n\nThese assimilates were then used for maintenance and growth respiration.\nGrowth respiration required a certain amount of glucose to synthesise new \nstructural compounds, and the total glucose requirement (G) was calculated from \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 7\n\n\n\nNitrogen and Water-stress Growth Model for Choy Sum\n\n\n\nGoudriaan and van Laar (1994) as:\n \n       (9)\n\n\n\nwhere Gi is the glucose requirement for plant part i (green leaves, stem, and roots) \nin g CH2O g-1 dry matter; and Fi is the fraction of dry matter of plant part i.The \nglucose requirement for each plant part was calculated separately and expressed \nas a function of the carbon (C) and nitrogen (N) content of the tissues following \nthe calculation by Vertregt and Penning de Vries (1987):\n\n\n\n Gi = 5.4Ci + 6Ni - 1.1     (10)\n\n\n\nwhere the total carbon (Ci; %) and total nitrogen (Ni; %) for plant part i were \nmeasured using the combustion (LECO-CR 412 Carbon Analyser) and wet ashing \nmethods (Jones, 1991; Auto-Analyzer, 2000 Series), respectively.\n Maintenance respiration (RM) rate, corrected for air temperature, was \ncalculated as:\n\n\n\n      (11)\n\n\n\nwhere R\u2019M (g CH2O m-2 total ground area day-1)was calculated as:\n\n\n\n        (12)\n\n\n\nwhere Wi is the weight (g dry matter m-2 ground area) for plant part i; and kM,i is \nthe maintenance respiration coefficient (g CH2O g-1 dry matter day-1), calculated \nfrom Goudriaan and van Laar (1994) as:\n\n\n\n kM,i = 0.04(Ni \u2044 0.16)+0.01     (13)\n\n\n\nwhere Ni is the N concentration (%) in plant part i (green leaves, stem, and roots).\nKnowing the rates of canopy photosynthesis, maintenance respiration, and \n\n\n\ngrowth respiration, the dry weight of plant part i at time t (Wi,t) could then be \nincreased to its new dry weight at the next time step t+1 (Wi,t+1) as follows:\n \n     (14)\n\n\n\nOnce the weight of all plant parts are increased, the model runs the next time step \nby repeating the above calculations until crop growth development stage reaches \nharvesting time (\u03bes=2).\n\n\n\nAmong the collected field parameters needed for crop model parameters \nwere (i) the initial plant part weights of the stem, leaves, and roots and initial \ntotal leaf area (both at time of transplanting);(ii) volumetric soil water content at \npermanent wilting and saturation points;(iii) daily meteorological properties (air \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 20148\n\n\n\ntemperature, solar irradiance, and wind speed); and (iv) the C and N contents in \nthe various plant parts.\n\n\n\nCrop Model Validation\nThe accuracy of the model was tested by comparing model simulations with the \nmeasured crop properties via two methods. The first method was by plotting model \nestimations with measured values in charts to determine, via visual inspection, \nhow closely their corresponding values agree with each other. The second method \nwas by determining the average estimation error, calculated as the mean difference \nbetween the predicted and measured values:\n \n     (15)\n\n\n\nwhere Pi and Oi are the predicted (estimated or simulated) and measured (actual) \nvalues, respectively; and N is the number of observations. A large positive and \nnegative error indicates a strong tendency for the model estimates to be larger \n(model overestimation) and smaller (model underestimation) than measured \nvalues, respectively. A preferable error is a value nearer 0 which indicates little \nmodel bias and a small mean difference between predicted and measured values.\n\n\n\nRESULTS AND DISCUSSION\nResults from the field experiment have been reported in detail by Kamarudin \n(2012), so we will only state the relevant field experiment results here for this part \nof the study. Based on Eq. 6 and values from Table 1, the critical soil water content \nbelow which C4 plants like choy sum would begin to experience water stress was \ndetermined as 0.35 m3 m-3. The average (\u00b1standard error) soil water content under \nonce-a-day and once-a-week watering treatments were 42% (\u00b10.34) and 35% \n(\u00b10.76), respectively, where both values were above the soil critical water level. \nHowever, the least frequent watering treatment (once-every-two-weeks) had an \naverage soil water content of 29% (\u00b11.15), which was below the critical water \nlevel. This meant  that choy sum growing under the once-a-day and once-a-week \nwatering treatments did not experience water stress, but the choy sum under the \nonce-every-two-weeks watering treatment suffered water stress. This was why \nfield results indicated that, at the same N rate applied, there were generally no \ndifferences in the measured growth and yield parameters between the once-a-day \nand once-a-week watering treatments, but the once-every-two-weeks watering \ntreatment gave the lowest growth and yield parameters due to the water stress \neffects.\n\n\n\nMeasured specific leaf area (SLA), which is the ratio between leaf area and \nleaf dry weight, was related to DAT (days after transplanting) and Nrate (N fertiliser \napplied, kg N ha-1) by using multiple linear regression, and the following equation \nwas developed:\n\n\n\nKamarudin, N.K., Teh, C.B.S. and Z.E.J. Hawa\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 9\n\n\n\nNitrogen and Water-stress Growth Model for Choy Sum\n\n\n\n (16)\n\n\n\nwith R2=0.52 and all regression coefficients significant at 5% level. Eq. 16 was \nused to estimate the LAI, as indicated in Eq. 3.\n\n\n\nField experiment results (Kamarudin 2012) also indicated that the optimal \nvolumetric soil water content level and N application rate for maximum yield \nparameters (in terms of highest total leaf area, leaf dry weight, and leaf number) \nwere 40% and between 30 to 40 kg N ha-1, respectively. This is evident in Figures \n2 to 6 which show that maximum growth and yield parameters were generally \nobtained for treatments having once-a-day or once-every-week watering frequency \nand with N application rate of 34 kg N ha-1.\n\n\n\nAlso shown in Figures 2 to 6 are the results of model validation, where \nthe model simulations are compared against field measurements. Errors of model \nestimation (their values are indicated in the charts) were calculated as the mean \ndifference between the predicted and measured values (Eq. 15).\n\n\n\nThere was overall tendency for the model to overestimate the growth and yield \nparameters for once-a-day and once-a-week watering treatments. For once-every-\ntwo-weeks watering treatment, the model, in contrast, tended to underestimate \nthese crop parameters. However, modelling the once-every-two-weeks watering \ntreatment tended to produce smaller estimation errors than the errors for the more \nfrequent watering treatments. The model estimation errors ranged between  33% \nto 163%. For the leaf dry weight, the overall mean estimation error was 7.3%, \nstem dry weight 28.9%, roots dry weight 27.3%, leaf area index (LAI) 41.7%, and \nplant height was -0.8%.\n\n\n\nOne particular source of error in this study was the use of the open-field \nenergy balance equation by Shuttleworth and Wallace (1985). Choy sum in this \nstudy was grown in soil in polyethylene bags under a rain shelter, not in the open \nfield. The energy balance from this kind of partially sheltered environment would \nbe different from that in the open field. Consequently, evapotranspiration rate \nfrom both environments could be different from each other. Another important \nsource of error in this study was the estimation of SLA using DAT and Nrate (Eq. \n16). The multiple linear regression for SLA only accounted about half of the \ntotal variance. A more accurate estimation of SLA would be needed, to enable a \nmore accurate estimation of LAI. An accurate LAI estimation would lead to more \naccurate estimation of the photosynthetic rate of the crop and ultimately the crop \nyield. As stated earlier, the overall mean error of LAI was 41.7%, the highest of all \nthe measured crop parameters. Sensitivity analysis (results not shown) revealed \nthat a  \u00b150% change in SLA resulted in a mean change in choy sum yield by about \n18%.\n\n\n\nNonetheless, this study model is the first developed specifically for choy \nsum, and should serve as a useful tool to estimate the response of choy sum, \na popular leafy vegetable in Malaysia, to various growing conditions such as \ndrought and N stresses and increased air temperatures.\n\n\n\n SLA = 0.02473 + 0.0008724DAT - 0.00004012Nrate\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201410\n\n\n\nKamarudin, N.K., Teh, C.B.S. and Z.E.J. Hawa\n\n\n\n N\not\n\n\n\ne:\n T\n\n\n\nhe\n e\n\n\n\nrr\nor\n\n\n\ns s\nho\n\n\n\nw\nn \n\n\n\nar\ne \n\n\n\nth\ne \n\n\n\nav\ner\n\n\n\nag\ne \n\n\n\nw\nee\n\n\n\nkl\ny \n\n\n\npe\nrc\n\n\n\nen\nta\n\n\n\nge\n o\n\n\n\nf t\nhe\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nce\n b\n\n\n\net\nw\n\n\n\nee\nn \n\n\n\nob\nse\n\n\n\nrv\ned\n\n\n\n a\nnd\n\n\n\n si\nm\n\n\n\nul\nat\n\n\n\ned\n v\n\n\n\nal\nue\n\n\n\ns.\n\n\n\nFi\ngu\n\n\n\nre\n 2\n\n\n\n: O\nbs\n\n\n\ner\nve\n\n\n\nd \n(o\n\n\n\n) a\nnd\n\n\n\n si\nm\n\n\n\nul\nat\n\n\n\ned\n (-\n\n\n\n--\n) l\n\n\n\nea\nve\n\n\n\ns d\nry\n\n\n\n w\nei\n\n\n\ngh\nt o\n\n\n\nf c\nho\n\n\n\ny \nsu\n\n\n\nm\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 11\n\n\n\n N\not\n\n\n\ne:\n T\n\n\n\nhe\n e\n\n\n\nrr\nor\n\n\n\ns s\nho\n\n\n\nw\nn \n\n\n\nar\ne \n\n\n\nth\ne \n\n\n\nav\ner\n\n\n\nag\ne \n\n\n\nw\nee\n\n\n\nkl\ny \n\n\n\npe\nrc\n\n\n\nen\nta\n\n\n\nge\n o\n\n\n\nf t\nhe\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nce\n b\n\n\n\net\nw\n\n\n\nee\nn \n\n\n\nob\nse\n\n\n\nrv\ned\n\n\n\n a\nnd\n\n\n\n si\nm\n\n\n\nul\nat\n\n\n\ned\n v\n\n\n\nal\nue\n\n\n\ns.\n\n\n\nFi\ngu\n\n\n\nre\n3:\n\n\n\n O\nbs\n\n\n\ner\nve\n\n\n\nd \n(o\n\n\n\n) a\nnd\n\n\n\n si\nm\n\n\n\nul\nat\n\n\n\ned\n (-\n\n\n\n--\n) s\n\n\n\nte\nm\n\n\n\n d\nry\n\n\n\n w\nei\n\n\n\ngh\nt o\n\n\n\nf c\nho\n\n\n\ny \nsu\n\n\n\nm\n\n\n\nNitrogen and Water-stress Growth Model for Choy Sum\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201412\n\n\n\nN\not\n\n\n\ne:\n T\n\n\n\nhe\n e\n\n\n\nrr\nor\n\n\n\ns s\nho\n\n\n\nw\nn \n\n\n\nar\ne \n\n\n\nth\ne \n\n\n\nav\ner\n\n\n\nag\ne \n\n\n\nw\nee\n\n\n\nkl\ny \n\n\n\npe\nrc\n\n\n\nen\nta\n\n\n\nge\n o\n\n\n\nf t\nhe\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nce\n b\n\n\n\net\nw\n\n\n\nee\nn \n\n\n\nob\nse\n\n\n\nrv\ned\n\n\n\n a\nnd\n\n\n\n si\nm\n\n\n\nul\nat\n\n\n\ned\n v\n\n\n\nal\nue\n\n\n\ns.\n\n\n\nFi\ngu\n\n\n\nre\n 4\n\n\n\n: O\nbs\n\n\n\ner\nve\n\n\n\nd \n(o\n\n\n\n) a\nnd\n\n\n\n si\nm\n\n\n\nul\nat\n\n\n\ned\n (-\n\n\n\n--\n) r\n\n\n\noo\nts\n\n\n\n d\nry\n\n\n\n w\nei\n\n\n\ngh\nt o\n\n\n\nf c\nho\n\n\n\ny \nsu\n\n\n\nm\n.\n\n\n\n \nIS\n\n\n\nSN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n8:\n\n\n\n 1\n \u2013\n\n\n\nx \n(2\n\n\n\n01\n4)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n S\n\n\n\noc\nie\n\n\n\nty\n o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n8,\n 2\n\n\n\n01\n4 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0 \nkg\n\n\n\n N\n h\n\n\n\na-1\n \n\n\n\n\n\n\n\n34\n k\n\n\n\ng \nN\n\n\n\n h\na-1\n\n\n\n\n\n\n\n\n\n\n\n68\n k\n\n\n\ng \nN\n\n\n\n h\na-1\n\n\n\n\n\n\n\n\n\n\n\n13\n6 \n\n\n\nkg\n N\n\n\n\n h\na-1\n\n\n\n\n\n\n\n\n\n\n\n27\n2 \n\n\n\nkg\n N\n\n\n\n h\na-1\n\n\n\n\n\n\n\n\n\n\n\nEr\nro\n\n\n\nr =\n -2\n\n\n\n0.\n22\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n -2\n\n\n\n9.\n92\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 0\n\n\n\n.2\n8%\n\n\n\n \nEr\n\n\n\nro\nr =\n\n\n\n -6\n.2\n\n\n\n6%\n \n\n\n\nEr\nro\n\n\n\nr =\n -1\n\n\n\n3.\n64\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 2\n\n\n\n0.\n95\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 2\n\n\n\n1.\n14\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 9\n\n\n\n5.\n25\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 5\n\n\n\n9.\n15\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 4\n\n\n\n5.\n11\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 3\n\n\n\n4.\n45\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 2\n\n\n\n3.\n73\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 9\n\n\n\n2.\n60\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 5\n\n\n\n6.\n13\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 3\n\n\n\n0.\n96\n\n\n\n%\n \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nO\nnc\n\n\n\ne\n-a\n\n\n\n-\nda\n\n\n\ny \nof\n\n\n\n \nw\n\n\n\nat\ner\n\n\n\nin\ng \n\n\n\nO\nnc\n\n\n\ne\n-a\n\n\n\n-\nw\n\n\n\nee\nk \n\n\n\nof\n \n\n\n\nw\nat\n\n\n\ner\nin\n\n\n\ng \n\n\n\nO\nnc\n\n\n\ne\n-\n\n\n\nev\ner\n\n\n\ny\n-t\n\n\n\nw\no-\n\n\n\nw\nee\n\n\n\nk\ns o\n\n\n\nf \nw\n\n\n\nat\ner\n\n\n\nin\ng \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nKamarudin, N.K., Teh, C.B.S. and Z.E.J. Hawa\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 13\n\n\n\nN\not\n\n\n\ne:\nTh\n\n\n\ne \ner\n\n\n\nro\nrs\n\n\n\n sh\now\n\n\n\nn \nar\n\n\n\ne \nth\n\n\n\ne \nav\n\n\n\ner\nag\n\n\n\ne \nw\n\n\n\nee\nkl\n\n\n\ny \npe\n\n\n\nrc\nen\n\n\n\nta\nge\n\n\n\n o\nf t\n\n\n\nhe\n d\n\n\n\niff\ner\n\n\n\nen\nce\n\n\n\n b\net\n\n\n\nw\nee\n\n\n\nn \nob\n\n\n\nse\nrv\n\n\n\ned\n a\n\n\n\nnd\n si\n\n\n\nm\nul\n\n\n\nat\ned\n\n\n\n v\nal\n\n\n\nue\ns.\n\n\n\nFi\ngu\n\n\n\nre\n 5\n\n\n\n: O\nbs\n\n\n\ner\nve\n\n\n\nd \n(o\n\n\n\n) a\nnd\n\n\n\n si\nm\n\n\n\nul\nat\n\n\n\ned\n (-\n\n\n\n--\n) l\n\n\n\nea\nf a\n\n\n\nre\na \n\n\n\nin\nde\n\n\n\nx \n(L\n\n\n\nAI\n) o\n\n\n\nf c\nho\n\n\n\ny \nsu\n\n\n\nm\n.\n\n\n\nNitrogen and Water-stress Growth Model for Choy Sum\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201414\n\n\n\nN\not\n\n\n\ne:\nTh\n\n\n\ne \ner\n\n\n\nro\nrs\n\n\n\n sh\now\n\n\n\nn \nar\n\n\n\ne \nth\n\n\n\ne \nav\n\n\n\ner\nag\n\n\n\ne \nw\n\n\n\nee\nkl\n\n\n\ny \npe\n\n\n\nrc\nen\n\n\n\nta\nge\n\n\n\n o\nf t\n\n\n\nhe\n d\n\n\n\niff\ner\n\n\n\nen\nce\n\n\n\n b\net\n\n\n\nw\nee\n\n\n\nn \nob\n\n\n\nse\nrv\n\n\n\ned\n a\n\n\n\nnd\n si\n\n\n\nm\nul\n\n\n\nat\ned\n\n\n\n v\nal\n\n\n\nue\ns\n\n\n\nFi\ngu\n\n\n\nre\n 6\n\n\n\n: O\nbs\n\n\n\ner\nve\n\n\n\nd \n(o\n\n\n\n) a\nnd\n\n\n\n si\nm\n\n\n\nul\nat\n\n\n\ned\n (-\n\n\n\n--\n) p\n\n\n\nla\nnt\n\n\n\n h\nei\n\n\n\ngh\nt o\n\n\n\nf c\nho\n\n\n\ny \nsu\n\n\n\nm\n.\n\n\n\n \nIS\n\n\n\nSN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n8:\n\n\n\n 1\n \u2013\n\n\n\nx \n(2\n\n\n\n01\n4)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n S\n\n\n\noc\nie\n\n\n\nty\n o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n8,\n 2\n\n\n\n01\n4 \n\n\n\n\n\n\n\n\n\n\n\n N\not\n\n\n\ne:\nTh\n\n\n\ne \ner\n\n\n\nro\nrs\n\n\n\n sh\now\n\n\n\nn \nar\n\n\n\ne \nth\n\n\n\ne \nav\n\n\n\ner\nag\n\n\n\ne \nw\n\n\n\nee\nkl\n\n\n\ny \npe\n\n\n\nrc\nen\n\n\n\nta\nge\n\n\n\n o\nf t\n\n\n\nhe\n d\n\n\n\niff\ner\n\n\n\nen\nce\n\n\n\n b\net\n\n\n\nw\nee\n\n\n\nn \nob\n\n\n\nse\nrv\n\n\n\ned\n a\n\n\n\nnd\n si\n\n\n\nm\nul\n\n\n\nat\ned\n\n\n\n v\nal\n\n\n\nue\ns.\n\n\n\n \nFi\n\n\n\ngu\nre\n\n\n\n 6\n: O\n\n\n\nbs\ner\n\n\n\nve\nd \n\n\n\n(o\n) a\n\n\n\nnd\n si\n\n\n\nm\nul\n\n\n\nat\ned\n\n\n\n (\uf0be\n) p\n\n\n\nla\nnt\n\n\n\n h\nei\n\n\n\ngh\nt o\n\n\n\nf c\nho\n\n\n\ny \nsu\n\n\n\nm\n. \n\n\n\n\n\n\n\n0 \nkg\n\n\n\n N\n h\n\n\n\na-1\n \n\n\n\n\n\n\n\n34\n k\n\n\n\ng \nN\n\n\n\n h\na-1\n\n\n\n\n\n\n\n\n\n\n\n68\n k\n\n\n\ng \nN\n\n\n\n h\na-1\n\n\n\n\n\n\n\n\n\n\n\n13\n6 \n\n\n\nkg\n N\n\n\n\n h\na-1\n\n\n\n\n\n\n\n\n\n\n\n27\n2 \n\n\n\nkg\n N\n\n\n\n h\na-1\n\n\n\n\n\n\n\n\n\n\n\nEr\nro\n\n\n\nr =\n 2\n\n\n\n.7\n1%\n\n\n\n \nEr\n\n\n\nro\nr =\n\n\n\n -2\n3.\n\n\n\n46\n%\n\n\n\n \nEr\n\n\n\nro\nr =\n\n\n\n -3\n.5\n\n\n\n3%\n \n\n\n\nEr\nro\n\n\n\nr =\n -2\n\n\n\n2.\n25\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 2\n\n\n\n.4\n5%\n\n\n\n\n\n\n\nEr\nro\n\n\n\nr =\n 5\n\n\n\n.6\n3%\n\n\n\n\n\n\n\nEr\nro\n\n\n\nr =\n -7\n\n\n\n.8\n5%\n\n\n\n\n\n\n\nEr\nro\n\n\n\nr =\n -1\n\n\n\n1.\n77\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n 1\n\n\n\n2.\n68\n\n\n\n%\n \n\n\n\nEr\nro\n\n\n\nr =\n -4\n\n\n\n.0\n0%\n\n\n\n \nEr\n\n\n\nro\nr =\n\n\n\n -1\n.5\n\n\n\n5%\n \n\n\n\nEr\nro\n\n\n\nr =\n 7\n\n\n\n.3\n6%\n\n\n\n \nEr\n\n\n\nro\nr =\n\n\n\n -4\n.8\n\n\n\n1%\n \n\n\n\nEr\nro\n\n\n\nr =\n 7\n\n\n\n.5\n2%\n\n\n\n \nEr\n\n\n\nro\nr =\n\n\n\n -2\n.4\n\n\n\n3%\n \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nO\nnc\n\n\n\ne\n-a\n\n\n\n-\nda\n\n\n\ny \nof\n\n\n\n \nw\n\n\n\nat\ner\n\n\n\nin\ng \n\n\n\nO\nnc\n\n\n\ne\n-a\n\n\n\n-\nw\n\n\n\nee\nk \n\n\n\nof\n \n\n\n\nw\nat\n\n\n\ner\nin\n\n\n\ng \n\n\n\nO\nnc\n\n\n\ne\n-\n\n\n\nev\ner\n\n\n\ny\n-t\n\n\n\nw\no-\n\n\n\nw\nee\n\n\n\nk\ns o\n\n\n\nf \nw\n\n\n\nat\ner\n\n\n\nin\ng \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nDa\nys\n\n\n\n a\nfte\n\n\n\nr t\nra\n\n\n\nns\npl\n\n\n\nan\ntin\n\n\n\ng \n(D\n\n\n\nAT\n) \n\n\n\nKamarudin, N.K., Teh, C.B.S. and Z.E.J. Hawa\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 15\n\n\n\nCONCLUSION\nA choy sum growth and yield model was successfully developed with an overall \nmean estimation error of 7.3% for leaf dry weight, 28.9% for stem dry weight, \n28.9% for roots dry weight, 41.7% for leaf area index, and -0.8% for plant height. \nFuture work is being planned to improve the model\u2019s accuracy because the current \nmodel tended to overestimate crop parameters in non-water stressed conditions \nbut underestimate in water stressed conditions. This could be because the model \nassumed an open-field energy balance growing environment and the lack of \naccurate leaf area index estimation.\n\n\n\nACKNOWLEDGEMENT\nThis research was supported by the Research University Grant Scheme by \nUniversiti Putra Malaysia (Grant no: 01-01-09-0692RU).\n\n\n\nREFFERENCES\nAlam, S.M. 1999. Nutrient uptake by plants under stress conditions. In: Handbook of \n\n\n\nPlant and Crop Stress, ed. M. Pessarakli (2nd edn)(pp. 285-313). New York: \nMarcel Dekker, Inc.\n\n\n\nClay, D.E., T.P. Kharel, C. Reese, D. Beck, C.G. Carlson, S.A. Clay and G. Reicks. \n2012. Winter wheat crop reflectance and nitrogen sufficiency index values are \ninfluenced by nitrogen and water stress. Agronomy Journal. 104:1612-1617.\n\n\n\nCollatz, G.T., J.T. Ball, C. Grivet and J.A. Berry. 1991. Physiological and environmental \n- regulation of stomatal conductance, photosynthesis and transpiration: a model \nthat includes a laminar boundry layer. Agricultural and Forest Meteorology. \n54:107-136.\n\n\n\nDixon, G.R. 2006. Vegetable Brassicas and Related Crucifers. Crop Production \nScience in Horticulture 14. UK: CABI.\n\n\n\nDoorenbos, J. and H. Kassam. 1979. Yield Response to Water. FAO Irrigationand \nDrainage Paper No. 33.Rome: FAO.\n\n\n\nEdward, E. 2009. Modelling the growth of choy sum (brassica chinensis var. \nparachinensis) at different nitrogen fertiliser rates. B. Agr. Sc. report. Universiti \nPutra Malaysia. Serdang: Universiti Putra Malaysia.\n\n\n\nFranco, J.A. 2011. Root development under drought stress. Technology and Knowledge \nTransfer e-Bulletin, Universidad Politecnica de Cartagena. 2:1-3.\n\n\n\nGoudriaan, J. and H.H. van Laar. 1994. Modeling Potential Crop Growth Processes. \nA Textbook with Exercise. Current Issues in Production Ecology. Netherlands: \nKluwer Academic.\n\n\n\nNitrogen and Water-stress Growth Model for Choy Sum\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201416\n\n\n\nHalkier, B.A. and J. Gershenzon. 2006. Biology and biochemistry of glucosinolates. \nAnnual Review of Plant Biololgy. 57:303\u201333.\n\n\n\nIssarakraisila, M., Q. Ma and D.W. Turner. 2007. Photosynthetic and growth \nresponses of juvenile Chinese kale (Brassica oleracea var. alboglabra) and \nCaisin (Brassica rapa subsp. parachinensis) to waterlogging and water deficit. \nScientia Horticulturae. 111:107-113.\n\n\n\nJones, J.B. 2001. Laboratory Guide for Conducting Soil Tests and Plant Analysis. \nBoca Raton, Florida: CRC Press.\n\n\n\nKamarudin, K.N., C.B.S. Teh and Z.E. Hawa Jaafar. 2012. Growth and yield of \nchoy sum (Brassica chinensis var. parachinensis) in response to water stress \nand nitrogen fertilisation levels. In: Proceedings of International Congress:  \nTransforming Agriculture for Future Harvest. Putrajaya, Malaysia\n\n\n\nNobel, P.S. 2009. Physicochemical and Environmental Plant Physiology (4th ed). \nAmsterdam: Elsevier Academic Press.\n\n\n\nPandey, R.K., J.W. Maranville and A. Admou. 2000. Deficit irrigation and nitrogen \neffects on maize in a Sahelian environment: I. Grain yield and yield components. \nAgricultural Water Management. 46:1-13.\n\n\n\nScagel, C.F., G. Bi, L.H. Fuchigami and C.P. Regan. 2011. Effects of irrigation \nfrequency and nitrogen fertilizer rate on water stress, nitrogen uptake, and plant \ngrowth of container-grown rhododendron. Horticultural Science. 46:1598-\n1603.\n\n\n\nShangguan, Z.P., M.A. Shao and J. Dyckmans. 2000. Nitrogen nutrition and water \nstress effects on leaf photosynthetic gas exchange and water use efficiency in \nwinter wheat. Environmental and Experimental Botany. 44: 141-149.\n\n\n\nShuttleworth, W.J. and J.S. Wallace. 1985. Evaporation from sparse crops - an \nenergy combination theory. Quarterly Journal of the Royal Meteorological \nSociety.111:839-855.\n\n\n\nSun, C.X., H. Cao, H.B. Shao, X.T. Lei and Y. Xiao. 2011. Growth and physiological \nresponses to water and nutrient stress in oil palm. African Journal of \nBiotechnology.10:10465-10471.\n\n\n\nTeh, C.B.S. 2006. Introduction to Mathematical Modeling of Crop Growth: How the \nEquations are Derived and Assembled into a Computer Program. Boca Raton, \nFlorida: Brown Press.\n\n\n\nTeh, C.B.S., I.E. Henson, K.J. Goh and M.H.A. Husni. 2004. The effect of leaf shape \non solar radiation interception. In: Agriculture Congress: Innovation towards \n\n\n\nKamarudin, N.K., Teh, C.B.S. and Z.E.J. Hawa\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 17\n\n\n\nNitrogen and Water-stress Growth Model for Choy Sum\n\n\n\nModernized Agriculture ed. Zulkifli H. Shamsuddin et al. (pp. 145-147). \nSerdang, Selangor: Faculty of Agriculture, Universiti Putra Malaysia.\n\n\n\nTeh, C.B.S. 2011. Overcoming Microsoft Excel\u2019s weaknesses for crop model building \nand simulations. Journal of Natural Resources and Life Sciences Education. \n40:122\u2013136.\n\n\n\nTin, K.P., H. Heng and P.N. Avadhani. 2000. A Guide to Common Vegetables. \nSingapore: Science Centre.\n\n\n\nVertregt, N. and F.W. Penning de Vries. 1987. A rapid method for determining the \nefficiency of biosynthesis of plant biomass. Journal of Theoretical Biology. \n128:109-119.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n17 \n \n\n\n\nSpatial Variability of Soil Properties Using R Language \n\n\n\n\n\n\n\nAmany M. Hammam \n\n\n\n \nCentral Laboratory Environment Quality Monitoring (CLEQM) \u2013 National Water Research Center \n\n\n\n(NWRC) \u2013 P.O. Box 13621/6, El-Kanater, Egypt \n\n\n\n \nCorresponding author: Amany2010@hotmail.it  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nAccurate assessment of the spatial variability of soil properties is key component of the agriculture \n\n\n\necosystem and environment modelling. The main objective of the present study is to measure some of \nthe soil properties and their spatial variability. Conventional analytical methods and geostatistical \n\n\n\nmethods were used to analyse the data for spatial variability. During a period of 2018 \u2013 2019, soil \n\n\n\nsamples (n=23) were collected in the field through random sampling in the eastern part of Menoufia \ngovernorate (30\u02da 50\u02b9 N and 31\u02da N). Soil properties of soil Cation Exchange Capacity (CEC), electric \n\n\n\nconductivity (EC), percentage of soil Clay, Silt and Sand were estimated using the geostatistical \n\n\n\napproach methods. An ordinary kriging (OK) interpolation was used for direct visualization of soil \nproperties. The semivariograms of the four soil properties were fit with Gaussian curve, except EC \n\n\n\nwas fit with exponential curve. The results showed the effectiveness of statistical analysis and \n\n\n\ninterpretation in sense of the obtained data. Cross-validation of variogram models through OK \n\n\n\nrepresenting in ME showed that the spatial prediction of the selected soil properties is high. The \npresent study suggests that the OK interpolation could potentially revealed the spatial distribution of \n\n\n\nsoil properties and the sample distance in this study for interpolation. \n\n\n\n\n\n\n\nKeywords: Soil properties, spatial variability, semivariograms, ordinary kriging, mean error \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nSoils are the physical and synthetic results of the interactions among hydrology, geology, \n\n\n\ngeomorphology, climate and biosphere. The interpretation and understanding of the resulting \n\n\n\nsoil patterns and their spatial analysis were a major interest in the reconstruction field of \n\n\n\nenvironmental change and in cases of emphasising the earth surface system. Soil maps and \n\n\n\ntheir specified reports are considered essential information sources in relation to land \n\n\n\nresources.  Thus, spatial distribution of soil properties has intensively been studied by many \n\n\n\nresearchers (Wang et al. 2013; Monda et al. 2017; Xu et al. 2018) \n\n\n\nIn general, traditional geostatistical methods focuses on the prediction and description \n\n\n\nof the quantitative variables, so it is so hard to interpolate qualitative data using these \n\n\n\ntechniques. Various techniques are used for mapping spatial distribution of qualitative \n\n\n\nvariables, but it still requires high technical support and specialized knowledge (Fabiy et al. \n\n\n\n2013; Wang et al. 2017). Nowadays, kriging is widely considered as a technique that can be \n\n\n\nused for predicting continuous soil characteristics at un-sampled locations. Bhunia et al. \n\n\n\n(2016) indicated that unequal to continuous variables, the qualitative variables cannot be \n\n\n\nconsidered in determining as mere linear combinations of neighbouring observations. \n\n\n\nR environment is an open-source used for manipulating data, statistical analysis, and \n\n\n\ndata visualization. R is considered as a modular system where it contains principle packages \n\n\n\nand other standard packages that are loaded after starting R. Besides, to achieve specific \n\n\n\n\nmailto:Amany2010@hotmail.it\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n18 \n \n\n\n\ntasks, there are several thousand packages, such as, Gstat, GoeR, Lattice, etc. (Pebesma \n\n\n\n2004). This paper applies ordinary kriging (OK) as a method using R language and Gstat \n\n\n\npackage to estimate the prediction of soil properties at the observation points. \n\n\n\n\n\n\n\nMATERIALS AND METHODS  \n\n\n\n\n\n\n\nThe current research was carried out in different regions in Menoufia governorate, especially \n\n\n\nthe centre and western parts of the area, in 2018-2019, to study the spatial distribution of \n\n\n\nsome soil properties such as Electric Conductivity (EC); Cation Exchange Capacity (CEC); \n\n\n\nSand; Silt and Clay contents (Figure 1). \n\n\n\nSoil samples were collected randomly from surface soils (0-30 cm depth) from 23 \n\n\n\nlocations across the eastern part of the study area. The exact location of the soil samples was \n\n\n\nprecisely defined in the field using GPS and then, plotted on the map (Figure 1). XY plot \n\n\n\nusing R language clarified samples distribution in the study area (Figure 2). Soil samples \n\n\n\nwere collected using hand auger and prepared for analysis (air-dried, crushed and passed \n\n\n\nthrough a 2 mm sieve). In accordance to Adepetu et al. (2000), sampling units were selected \n\n\n\nrandomly and independently and were irrespective of any judgment regarding spots \n\n\n\npreviously taken. Soil CEC, EC, Clay, Silt and Sand were analysed in accordance to Richards \n\n\n\n(1954) and Van Reeuwijk (2002). \n\n\n\nR language 4.2.1 software was applied and semi-variogram was used in order to \n\n\n\nevaluate the spatial distribution pattern of each soil property. Semi-variogram was calculated \n\n\n\nusing the following Equation (Behera et al. 2018). \n\n\n\n\ud835\udefe(\u210e) =  \n1\n\n\n\n2\ud835\udc41(\u210e)\n\u2211 [\ud835\udc4d(\ud835\udc4b\ud835\udefc + \u210e]2\n\n\n\n\ud835\udc41(\u210e)\n\n\n\n\ud835\udefc=1\n\n\n\n\n\n\n\nWhere \ud835\udefe(\u210e), \ud835\udc41(\u210e), \ud835\udc4d(\ud835\udc4b\ud835\udefc) and \ud835\udc4d(\ud835\udc4b\ud835\udefc + \u210e) represents semi-variance for the lag distance h, \n\n\n\nnumber of sample pairs that separated by lag distance h, the measured value at \u03b1 sample \n\n\n\nlocation and the measured value at point \u03b1 + h sample location, respectively. \n\n\n\n\n\n\n\n\n\n\n\nFigure 1. Samples collected from different regions in Menoufia Governorate \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n19 \n \n\n\n\n\n\n\n\nFigure 2. XY plot using R clarified samples distribution \n\n\n\n\n\n\n\nSpatial dependence Mean error (ME) is considered one of several tools that usually \n\n\n\nused for evaluating semi-variogram models. In general, Gundogdu and Guney (2007) \n\n\n\nproposed that the best fit model was obtained to have mean error \"ME value close to zero\u201d. \n\n\n\nThe equation of criteria is as follows (Johnston et al., 1996). \n\n\n\n\ud835\udc40\ud835\udc38 =  \n1\n\n\n\n\ud835\udc41\n\u2211[\ud835\udc4d(\ud835\udc65\ud835\udc56) \u2212 \ufffd\u0302\ufffd(\ud835\udc65\ud835\udc56)]\n\n\n\n\ud835\udc41\n\n\n\n\ud835\udc56=1\n\n\n\n\n\n\n\nWhere(\ud835\udc65\ud835\udc56): refers to the observed value; \ufffd\u0302\ufffd(\ud835\udc65\ud835\udc56): the predicted value;\ud835\udc41 : the number of values \n\n\n\nfor location \ud835\udc56. \n\n\n\nCambardella et al.  (1994) reported that semi-variogram model is based on the differences \n\n\n\nbetween nugget and sill or nugget to sill ratio, which may be strong (<0.25), moderate (0.25 \u2013 \n\n\n\n0.75) and weak (> 0.75).  \n\n\n\nInterpolation mapping was applied using ordinary kriging technique, which is \n\n\n\nconsidered as a more reliable method than other methods used (Meul and Van Meirvenne \n\n\n\n2003), for determining the soil properties values at unsampled locations. Interpolation \n\n\n\nmapping is an unbiased predictor for the random process like reducing impact of outliers \n\n\n\n(Triantafilis et al. 2001). Kriging technique works on weighting the surrounding measured \n\n\n\nvalues to derive a prediction for unmeasured locations (Meul and Van Meirvenne 2003). \n\n\n\nIn other words, by using the kriging method, the weights are dependent not only on \n\n\n\ndistance between the measured points and the predicted locations, but also on the overall \n\n\n\nspatial arrangement of the measured points. The spatial autocorrelation must be quantified in \n\n\n\ncase of using the spatial arrangement in weights, Thus, in ordinary kriging, the weightage is \n\n\n\nbased on any fitted model to the measured points, the distance to the predicted location, and \n\n\n\nthe spatial relationships among the measured and the predicted location. In this study, \n\n\n\ndifferent semi-variogram models were evaluated depending on the nugget to sill ratio. \n\n\n\nOrdinary kriging validation was also evaluated based on the value of mean error (ME) for the \n\n\n\nselected soil properties in this study. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n20 \n \n\n\n\nRESULTS \n\n\n\nDescriptive analysis \n\n\n\nDescriptive statistics for the soil physical properties in 30 cm soil depth of the cultivated field \n\n\n\nare presented in Table 1.  \n\n\n\nTable 1. Descriptive statistics for the soil properties. \n\n\n\n \nM\n\n\n\nin\nim\n\n\n\nu\nm\n\n\n\n\n\n\n\nM\na\nx\nim\n\n\n\nu\nm\n\n\n\n\n\n\n\n1\nst\n Q\n\n\n\nu\na\nr\nte\n\n\n\nr \n\n\n\nM\ne\nd\n\n\n\nia\nn\n\n\n\n\n\n\n\n3\nrd\n\n\n\n Q\nu\n\n\n\na\nr\nte\n\n\n\nr \n\n\n\nM\ne\na\nn\n\n\n\n\n\n\n\nS\nta\n\n\n\nn\nd\n\n\n\na\nr\nd\n\n\n\n\n\n\n\nD\ne\nv\nia\n\n\n\nti\no\nn\n\n\n\n\n\n\n\nC\no\ne\nff\n\n\n\nic\nie\n\n\n\nn\nt \n\n\n\no\nf \n\n\n\nv\na\nr\nia\n\n\n\nti\no\nn\n\n\n\n\n\n\n\nK\nu\n\n\n\nr\nto\n\n\n\nsi\ns \n\n\n\nS\nk\n\n\n\ne\nw\n\n\n\nn\ne\nss\n\n\n\n\n\n\n\nCEC (cmol.Kg-1) 11.45 38.66 16.15 25.30 27.45 22.94 8.034 0.35 -0.77 0.19 \n\n\n\nEC (dS/m) 0.24 1.15 0.29 0.45 0.707 0.547 0.291 0.53 -0.52 0.83 \n\n\n\nSand % 33.40 84.50 42.35 62.71 80.80 62.71 19.417 0.31 -1.93 -0.17 \n\n\n\nSilt    % 10.90 36.10 13.90 21.70 23.05 19.84 6.975 0.35 -0.35 0.59 \n\n\n\nClay  % 3.30 35.80 5.35 6.90 30.70 17.46 13.227 0.76 -1.99 0.19 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 3. Frequency distribution of some soil properties \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n21 \n \n\n\n\nIn 30 cm soil depth of the cultivated field, soil CEC varied between 11.45 and 38.66 \n\n\n\n(cmolc kg-1), soil EC (0.24 to 1.15 dS/m), sand (33.40 to 84.50%), silt (10.90 to 36.10%) and \n\n\n\nclay (3.30 to 35.80%) values showed variations among the sampling points in the field (Table \n\n\n\n1).As shown in Table 1, the C0 /C0+ C ratio values for CEC, EC, Sand, Silt and Clay were \n\n\n\n31.93,0, 2.31, 41.27, and 1.41, respectively. The nugget/sill ratio was in between 25 and 75 % \n\n\n\nof CEC and Silt, respectively, which indicated a moderate spatial correlation. \n\n\n\nFrequency distributions shown in Figure 3, in a graphical format, clarify the number \n\n\n\nof observations within a given interval for the selected soil properties. Frequency \n\n\n\ndistributions are frequently used for the purpose of summarizing the categorical variables.  \n\n\n\nGeostatistical analysis \n\n\n\nTable 2 clarifies the soil properties where variables characteristic was generated from \n\n\n\nsemivariogram models. C0 is represent the nugget variance; C is the variance structural, and \n\n\n\nC0+C shows degree of the spatial variability. \n\n\n\n\n\n\n\nValues varied from 5.43 for EC and 32.20 for Clay. The lowest value of Nugget \n\n\n\n(0.00) was observed in EC, however, silt recorded the largest nugget with a value of 19.08. \n\n\n\nSill values varied among the selected soil properties in this study, where the largest value was \n\n\n\n688.6 for sand, and the smallest one was 0.08 for soil EC.  \n\n\n\n\n\n\n\nAs shown in Table 1, the C0 /C0+ C ratio values for CEC, EC, sand, silt and clay were \n\n\n\n31.93; 0; 2.31; 41.27; and 1.41, respectively. The nugget/sill ratio was in between 25 and 75 \n\n\n\n% of CEC and silt, respectively, which indicated a moderate spatial correlation. The spatial \n\n\n\ndependence of the soil properties was moderate to strong relation.  \n\n\n\n\n\n\n\nThe semivariogram of the selected soil properties for the soil surface are shown in \n\n\n\nFigure (4). It represents the experimental variogram that fitted to the studied soil properties. \n\n\n\nAll soil characteristics fitted by using Gaussian model with exception for EC, as it fitted with \n\n\n\nExponential model. \n\n\n\n\n\n\n\nTable 2. Calculated semi-variograms properties of soil properties \n\n\n\nSoil \n\n\n\nproperties \nModel Range \n\n\n\nNugget \n\n\n\n(C0) \n\n\n\nSill \n\n\n\n(C0 + C) \n\n\n\n*Nugget/sill \n\n\n\nratio % \n\n\n\nSpatial \n\n\n\ndependence \n\n\n\nCEC Gaussian 11.16 17.75 55.58 31.93 Moderate \n\n\n\nEC Exponential 5.43 0.00 0.08 0 Strong \n\n\n\nSand Gaussian 21.40 15.91 688.6 2.31 Strong \n\n\n\nSilt Gaussian 16.79 19.08 46.23 41.27 Moderate \n\n\n\nClay Gaussian 32.20 7.62 541.1 1.41 Strong \n  Note: Nugget/sill ratio (%) = [C0/(C0 + C)] \u00d7 100 (Cambardella et al, 1994) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nhttps://www.spss-tutorials.com/measurement-levels/#categorical-variable\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n22 \n \n\n\n\n\n\n\n\n\n\n\n\n(a) (b) \n\n\n\n  \n(c) (d) \n\n\n\n  \n(e) \n\n\n\n \nFigure 4. Semivariogram parameters of best-fitted theoretical model, (a) Soil CEC, (b) Soil EC, \n\n\n\n(c) Sand, (d) Silt and (e) Clay \n\n\n\n\n\n\n\nSpatial distribution of soil properties \n\n\n\nFigure 5a\u2013e represents the spatial distribution of soil properties of CEC, EC, Sand, Silt and \n\n\n\nClay using ordinary kriging (OK) as an interpolation method. The spatial correlation map of \n\n\n\nthe soil properties CEC, EC, Sand, Silt and measured Clay was produced, compared and \n\n\n\nanalysed for the results. Spatial variability maps among the soil CEC, EC, Sand, Silt and \n\n\n\npredicted Clay was prepared using R language to highlight the spatial dependence of the \n\n\n\nselected soil properties (Figure 5a-e). \n\n\n\nConcentration of Soil CEC was observed in the eastern part of the study area. The \n\n\n\nspatial map of Soil EC is generated from the measured EC value from the collected samples \n\n\n\nin the study sites. In the central and eastern portion of the study site, higher EC was \n\n\n\nconcentrated. The highest values of sand were in the central and western parts of the study \n\n\n\narea, while the highest values of silt was concentrated in the eastern parts. Clay was spatially \n\n\n\ndistributed in the north-eastern part of the studied area. Future studies are needed in order to \n\n\n\nclarify the spatial variability on larger scale and to better understand the factors controlling \n\n\n\nthe spatial distribution of soil properties. \n\n\n\nValues of ME for CEC, EC and Sand were 0.08, 0.006 and 0.37, respectively. But for \n\n\n\nsilt and clay, ME values were -0.16 and -0.06, respectively. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n23 \n \n\n\n\n\n\n\n\n\n\n\n\n(a) (b) \n\n\n\n\n\n\n\n\n\n\n\n(c) (d) \n\n\n\n\n\n\n\n \n(e) \n\n\n\n \nW-E \n\n\n\nFigure 5. Spatial distribution of some soil properties (a) CEC, (b) EC, (c) Sand, (d) Silt \n\n\n\nand (e) Clay \n\n\n\n\n\n\n\n\n\n\n\nDISCUSSION \n\n\n\n\n\n\n\nIn this study, spatial analysis was applied to selected soil samples from different regions in \n\n\n\nthe Menoufia governorate, especially the centre and western parts. The descriptive analysis \n\n\n\nclarified that, all skewness values of the soil characteristics used in this study ranged from -\n\n\n\n0.17 to 0.83. Gia Pham et al. (2019), indicated that the distributions of all soil variables were \n\n\n\nonly skewed when the skewness value is less than 1.0. \n\n\n\nAs shown from the results, C0+C highlights degree of the spatial variability, \n\n\n\ninfluenced by both structural and stochastic factors. Golaszewski (2002), reported that \n\n\n\ndepending on the range of effectiveness, the sill (C0+C), and the nugget effect (C0) for each \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n24 \n \n\n\n\nparameter, the degree of autocorrelation was in relation to the spatial dependencies \n\n\n\n(Nugget/Sill ratio) among sampling points. \n\n\n\n\n\n\n\nTabi and Ognukunle (2007), mentioned that the range clarifies the distance in a field \n\n\n\nthat determined properties are no longer spatially correlated. The shortest range (5.43 m) was \n\n\n\nobserved for EC and the longest range (32.20 m) was observed for clay content. According to \n\n\n\nthe results, the ranges of spatial influence for the soil physical properties were generally \u2264 \n\n\n\n32.20 m for clay, \u2264 21.40 m for sand, \u2264 16.79 m for Silt, \u2264 11.16 for CEC and \u2264 5.43 m for \n\n\n\nEC. \n\n\n\nAny variable has a strong spatial dependency if the ratio of nugget/sill is given a value \n\n\n\nthat equals to or less than 25%, a moderate ratio of the spatial dependency if the ratio is \n\n\n\nbetween 25 and 75%, and finally, a weak spatial dependency if the ratio recoded a value that \n\n\n\nis greater than 75% (Bo et al., 2003). In regards to the previous study (Denton el al., 2017), \n\n\n\nthe classification of the spatial dependent variables that used, was pointed as a strongly \n\n\n\nspatial dependency if the ratio was <25, moderately spatial dependency if between 25 and \n\n\n\n75%, and weak spatial dependency if it was >75%.  \n\n\n\n\n\n\n\nIn the current study, the nugget/sill ratio was in between 25 and 75 % of CEC and \n\n\n\nSilt, respectively, which indicates a moderate spatial correlation. AbdelRahman et al. (2021) \n\n\n\nreported that variables with a moderate spatial dependence may be because of the soil \n\n\n\nhomogeneity aspects. In this respect, range values are considered as essential measures for \n\n\n\nplanning and experimental evaluations, the range can help in sampling procedure definition. \n\n\n\nIn this sense, the range of the semivariogram was considered to estimate the minimum \n\n\n\nnumber of samples for characterizing the soil spatial variability pattern of soil chemical \n\n\n\nproperties. AbdelRahman et al. (2021), added that low land environments were more \n\n\n\nheterogeneous in case of chemical properties. \n\n\n\nThe C0 /C0+ C ratio were less than 25 % in the three soil properties such as EC, Sand, \n\n\n\nand Clay which indicate a strong spatial correlation. Generally, a strong spatial dependency \n\n\n\nof soil properties is linked to the structural intrinsic factors like texture, parent material and \n\n\n\nmineralogy, but a weak spatial dependency is linked to the random extrinsic factors like \n\n\n\nploughing, fertilization and may other related soil management practices (Zheng et al., 2009). \n\n\n\nAs mentioned above, soil EC characterized by Exponential model was fitted without \n\n\n\nany nugget effect. These results were in agreement with another study that was applied in \n\n\n\nvineyards, the experimental semivariograms were best fitted to the theoretical models without \n\n\n\nany nugget effect (Mir\u00e1s-Avalos et al., 2020). AbdelRahman et al. (2021) suggested that the \n\n\n\nnugget effect is equal to the sill, when the variable under estimation is spatially independent.  \n\n\n\n\n\n\n\nOrdinary kriging method was focused on estimating values of soil properties at un-\n\n\n\nsampled locations taking into consideration the weighted local averaging method. An OK \n\n\n\ntechnique was applied for switching points of soil samples into continuous patterns of the \n\n\n\nselected soil properties. For an accurate prediction model, the absolute values of ME must be \n\n\n\nas small as possible. For the CEC, EC, sand, silt and clay, the absolute values of Mean Error \n\n\n\n(ME) resulted by Ordinary Kriging method are small and close to 0. Values of ME for CEC, \n\n\n\nEC and sand were 0.08, 0.006 and 0.37, respectively. But for silt and clay, ME values were -\n\n\n\n0.16 and -0.06, respectively. This observation is in conformity with Gia Pham et al.(2019), \n\n\n\nwho reported that negative ME values indicate that the actual value recorded may be higher \n\n\n\nthan the predicted value. These statistical values indicate that the prediction accuracy of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. (26): 17-26 \n\n\n\n\n\n\n\n25 \n \n\n\n\nOrdinary Kriging estimate is high, and in agreement with those found by Gia Pham et al. \n\n\n\n(2019). \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nUnderstanding the spatial distribution and mapping accuracy of the soil properties for large \n\n\n\nscale areas are essential for precision farming, environmental monitoring, and modelling. \n\n\n\nThis study used geostatistical models that were fitted using interpolation techniques in R \n\n\n\nlanguage for five selected soil properties; cation exchange capacity (CEC), electrical \n\n\n\nconductivity (EC), Sand, Silt and Clay in different regions in Menoufia Governorate. The \n\n\n\nstudy confirms that this methodology can be used to investigate the spatial variability of soil \n\n\n\nproperties. The results showed that the best fit semivariogram model of EC was Exponential \n\n\n\nmodel, where Gaussian model was the best fit model of Sand, Silt, Clay and CEC. According \n\n\n\nto the spatial distribution map, five zones of spatial variables were identified. The nugget/sill \n\n\n\nratio was between 25 and 75 % for CEC and Silt, respectively, indicating a moderate spatial \n\n\n\ncorrelation, whereas a strong spatial correlation was found with a ratio of less than 25 % in \n\n\n\nthe other soil properties. The results show that the spatial distribution and the spatial \n\n\n\ndependence level of soil properties can be different even within the same area. It also shows \n\n\n\nthat the effectiveness of R environment in sense of data interpretation. Cross-validation of \n\n\n\nvariogram models through OK representing in ME showed that the spatial prediction of the \n\n\n\nselected soil properties is high. However, future studies are needed to a better understand the \n\n\n\nspatial variability on a larger scale and to better understand the factors that control spatial \n\n\n\nvariability of soil properties. \n\n\n\n\n\n\n\nREFERENCES \n \n\n\n\nAdepetu, J.A., H. Nabhan, and A. Osinubi. 2000. Simple soil, water and plant testing \n\n\n\ntechniques for soil resource management. International Institute of Tropical \n\n\n\nAgriculture. Food and Agriculture Organization of The United Nations, Rome.   \n\n\n\nAbdelRahman, M.A.E., Y.M. Zakarya, M.M. Metwaly, and G. Koubouris. 2021. Deciphering \n\n\n\nSoil Spatial Variability through Geostatistics and Interpolation Techniques. \n\n\n\nSustainability, 13, 194. https://doi.org/10.3390/su13010194 \n\n\n\nBehera, S.K., R.K. Mathur, A.K. Shukla, K. Suresh and C. Prakash 2018. 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J., 65: \n\n\n\n869-878. \n\n\n\nVan Reeuwijk, L. 2002. International soil reference and information centre. The Netherlands. \n\n\n\nVariable charges in selected temperate region soils. Geoderma, 32:89\u2013101. \n\n\n\nZheng, H., J. Wu, and S. Zhang. 2009. Study on the spatial variability of farmland soil \n\n\n\nnutrient based on the kriging interpolation. AICI, International conference on \n\n\n\nartificial intelligence and computational intelligence, November 7-8, Vol.4 (p.550 \u2013 \n\n\n\n555). Shanghai, China. \n\n\n\n\n\n\n\n \n\n\n\n\nhttps://doi.org/10.3390/ijgi8030147\n\n\n\n"
"\n\nINTRODUCTION\n\n\n\nmillion hectares of land in almost all Asian countries, most of the countries of \nthe West and North Africa, some countries in Central and East Africa, most of \nthe South and Central American countries, Australia and four states in the United \n\n\n\nthat the application of nitrogen either through organic or green manure or chemical \nfertilizers plays a dominant role in increasing rice yield. The amount of nitrogen \n\n\n\nuse of readily available conventional chemical fertilizers for agricultural land is \nthe main source of ground water contamination (Thomsen et al.\nfertilizers are commonly used in rice cultivation to increase yields. Urea and \n\n\n\nNitrogen Leaching from Paddy Field under Different \nFertilization Rates \n\n\n\n \nM.T. Iqbal \n\n\n\nDepartment of Agronomy and Agricultural Extension,\nUniversity of Rajshahi Rajshahi-6205, Bangladesh\n\n\n\nABSTRACT\n\n\n\ncm soil layers. Results showed that during the paddy growth, NH4\n+ -N was the \n\n\n\nmain form of nitrogen with a high environment risk.  The range of NH4\n+ -N /TN \n\n\n\n4\n+\n\n\n\ndays after three fertilizer application rates;  the highest NH4\n+ -N concentrations \n\n\n\nwere about 38.38 mg L-1 -1 or N4\nwere observed in the two layers between the other four fertilizer application rates. \n\n\n\n-1\n\n\n\n4\n+\n\n\n\nlayer leachate which were less than 3 mg L-1. Nitrogen loss from different nitrogen \n\n\n\nshould be of concern.\n\n\n\nKeywords: Ammonium nitrogen, nitrate nitrogen, nitrate leaching, \n                      ammonium leaching, urea fertilization rate\n\n\n\n___________________\n*Corresponding author : Email: \n\n\n\n\n\n\n\n\nin recent years is the main reason for increased nitrate content in groundwater \n\n\n\nassociated with high application rates of N fertilizer (Strebel et al.\nleaching is caused by an increase in nitrate concentration of surface water in upland \ncultivation systems. Nitrate-nitrogen (NO3\n\n\n\n-\n\n\n\nwith soil water becoming a potential source of ground water pollution. The most \nimportant factors that determine the  amount of NO3\n\n\n\n- -N leaching to ground water \nare soil type, amount of precipitation or irrigation, crop type and the amount of \n\n\n\nThe amount of NO3\n- -N leached to the ground water increases with N fertilizer \n\n\n\napplication rates (Schepers et al\n\n\n\nlayer. Ammonium nitrogen is less subjected to leaching from the soil  compared \nto nitrate because of its adsorption in the cation exchange complex. However, \nlosses of ammonium nitrogen through leaching occur in coarse-textured soil with \n\n\n\ndifferences in soil physical properties and N status of soil. The volume of NO3\n- \n\n\n\nvariability  of the parameters that control N availability (Delcourt et al. 1996; Earl \net al\n\n\n\nurea fertilization rates.\n\n\n\nMATERIALS AND METHODS\n\n\n\nExperimental Site\n\n\n\nin the South-east coastal area of China. This region  has the typical characteristics \n\n\n\n-1. The soil was a \ncoastal saline soil with medium fertility and paddy was cultivated as a crop. The \n\n\n\nC and the average \nmaximum temperature was 34 C.\n\n\n\nExperimental Procedure\nFifteen individual plots separated by ridges were established in a grid. Each plot \n\n\n\n\n\n\n\n\ncm depth at the three edges of the plots to isolate lateral movement of soil water. \nThere were separate hydrants in front of each plot for irrigation with one PVC \npipe lying in the buffer zone for drainage. The surface water was maintained at \nabout 7 cm depth. Excess rainwater automatically drained through a PVC pipe \ninto a tipping bucket sample collection apparatus when the depth of the surface \n\n\n\n(Fig. 1). In the centre of each plot, two soil in situ solution \n\n\n\nupper soil (Fig. 2). Rice seedlings were transplanted on the same day when basal \n\n\n\none season rice (one summer rice crop in rotation with one winter crop species \n\n\n\nFig. 1: View of the experimental plots\n\n\n\nFig. 2: Nitrogen leaching in situ sampler\n\n\n\nNitrogen Leading in Paddy Field\n\n\n\n\n\n\n\n\nTreatment\nTable 1 shows the nitrogen fertilizer application rate in the experimental plots. \n\n\n\n-1\n\n\n\nN1\n-1 -1 \n\n\n\n3\n-1\n\n\n\n4\nha-1\n\n\n\nth \nnd\n\n\n\nSampling and Analysis\nFigure 1 shows the schematics of the in-situ sampler to measure nitrogen leaching. \nThere were three layers at 8 cm depth inside the samplers, with pebble layer in the \n\n\n\nsampler to prevent large-sized soil particles entering the sampler. The samplers \n\n\n\nThe bottom of the sampler had space to store the nitrogen leachate.\nTo avoid disturbing the soil, sampler installation involved digging a trench \n\n\n\noutside the plot area and then digging sideways to insert the samplers into the \n\n\n\ncollected on a daily basis for one week after fertiliser application and at 4-day \n\n\n\nwater samples were analyzed according to standard Chinese methods (National \net al.\n\n\n\nwater volume and concentration by using arithmetic method.\n\n\n\nTABLE 1 \nDifferent nitrogen fertilizer application rates \n\n\n\nNitrogen \ntreatment \n\n\n\nUrea (kg ha-1) Nitrogen (kg ha-1) \n\n\n\nN0 0 0 \nN1 196 90 \nN2 392 180 \nN3 588 270 \nN4 784 360 \n\n\n\n \n\n\n\n\n\n\n\n\nData Analysis\n\n\n\nused in this paper for data analysis, graph preparation and drawing picture. All \ndata were subjected to analysis of variance for a randomized complete block \ndesign with a split plot arrangements of treatments. Test of hypothesis was two \nway covariance i.e. H1 1\n\n\n\nnitrogen treatments on NH4\n+ -N and NO3\n\n\n\n- \n\n\n\nthe legends of tables. \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nNitrogen Concentration Variation at 30-and 60-cm Depths of Soil Layer Leaching \nWater\n\n\n\napplication and the TN concentration at the highest rate peaked within about 1 \nweek of application. Total nitrogen concentration was higher on 8th July, declining \n\n\n\nnd\n\n\n\n3 treatments. \nTN concentration was high in N4 treatment perhaps due to maximum N fertilizer \napplication for that treatment. From 8th July onwards, there was little difference \nin TN content of the four treatments (N 3\nthe N4 treatment. This occurred because rice plants take up more fertilizer from \n\n\n\nFigure 3b shows NH4\n+ -N concentration (mg L-1\n\n\n\ndifferent rates of urea application from 8th July to 8th September. Urea fertilizer \n\n\n\namount of NH4\n+\n\n\n\nth th\n\n\n\n4\n+ -N concentrations were higher on \n\n\n\nimmediate respective application dates. Due to the higher application rate of urea, \nNH4\n\n\n\n+ - N concentration was higher at N4 treatments. It indicated that applications \n\n\n\nwere more pronounced with increasing fertilizer application rates. In the control \ntreatment (N 4\n\n\n\n+ -N concentration remained constant.\n\n\n\nNitrogen Leading in Paddy Field\n\n\n\n\n\n\n\n\nFig. 3: (a) TN, (b) NH4\n+-N and (c) NO3-N concentration in leachate at 30cm soil \n\n\n\ndepth on different days after split N applications (mg L-1)\n\n\n\nTN\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\n(m\ng \n\n\n\nL \n )-1\n\n\n\nN\nH\n\n\n\n   \n-N\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \n(m\n\n\n\ng \nL \n\n\n\n  )-1\n4\n\n\n\nN\nO\n\n\n\n   \n-N\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \n(m\n\n\n\ng \nL \n\n\n\n  )-1\n3\n\n\n\n\n\n\n\n\nFigure 3c shows NO3\n-\n\n\n\n3\n- -N concentrations \n\n\n\nth July, NO3\n- -N concentration increased \n\n\n\n4\nof NO3\n\n\n\n- -N was approximately the same in each treatment except for the N4 \ntreatment. Also, the increase in NO3\n\n\n\n-\n\n\n\nwhich means that NO3\n- -N concentration depends on rate of N application and \n\n\n\nwater or moisture effect.\n\n\n\nintervals is presented in Figure 4a. On 18th July, total nitrogen concentration was \n\n\n\nto 3rd August, TN concentrations at N4 treatment were higher (14 to 45 mg L-1\n\n\n\nand then reached a constant  as in the case of other treatments. It was found that \nTN concentration was actually proportionately related to N application rate but \nthe increased N in the surface water derived from N fertilizer was more readily \n\n\n\nits effects on NH4\n+\n\n\n\nshown in Figure 4b. It shows  NH4\n+\n\n\n\nintervals. On 18th July for each treatment, the NH4\n+ -N concentrations were higher \n\n\n\nbecause sampling commenced earlier, that is, on the second day after fertilizer \napplication and the maximum NH4\n\n\n\n+ -N concentrations following the highest N \napplication occurred very soon after fertilizer application. Gradually, the NH4\n\n\n\n+ \n\n\n\n-N concentrations decreased.  Fluctuations were observed in the control plot, \npossibly due to lateral movement or seepage of NH4\n\n\n\n+ -N concentrations from the \ntreated plot.\n\n\n\nFigure 4c shows the NO3\n-\n\n\n\ntreatments with respect to time. The NO3\n- -N concentrations did not differ among \n\n\n\nand later reached a constant. This could be due to the fact that NO3\n- -N normally \n\n\n\naccumulates in soils because of fertilizer addition and this accumulation is further \nenhanced when crop demand is much less than the rate of NO3\n\n\n\n- -N production. \nOtherwise, nitrate will directly leach into ground water.  \n\n\n\nThe NH4\n+ -N, NO3\n\n\n\n-\n\n\n\n3\n- -N concentrations were \n\n\n\n3\n- -N remained within \n\n\n\n3\n- -N was \n\n\n\nleached into the ground water. Maximum variability was observed in the most \nintensive system, where high values were followed by medium or low values at \nshort intervals of time. The high concentrations may be related to management \n\n\n\nNitrogen Leading in Paddy Field\n\n\n\n\n\n\n\n\n  \nFig. 4: (a) TN (b) NH4\n\n\n\n+-Nand (c) NO3\n\u2013N conc. in leachate at 60cm soil depth on\n\n\n\ndifferent days after split N applications (mgL-1)\n\n\n\nN\nH\n\n\n\n   \n-N\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \n(m\n\n\n\ng \nL \n\n\n\n  )-1\n4\n\n\n\nTN\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\n(m\ng \n\n\n\nL \n  )-1\n\n\n\nN\nO\n\n\n\n   \n-N\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \n(m\n\n\n\ng \nL \n\n\n\n  )-1\n3\n\n\n\n\n\n\n\n\nand in the absence of fertilizer application in the period preceding the rainfall. The \n\n\n\nleaching and pollution of the  environment. Cultural practices played a vital role \nin nitrogen leaching. Due to mechanized cultivation, a hard pan did not exist to \nfacilitate leaching. Other researchers also  have a similar opinion. The presence of \na plough sole, which has a very low hydraulic conductivity, is vital in controlling \nthe leaching rate (Wopereis et al et al. \n\n\n\nsole layer with a low hydraulic conductivity was about ten centimeters thick and \n\n\n\nbe destroyed by agro-behaviours such as tractor plowing and inversely increases \nthe nitrogen leaching potential into shallow groundwater. Chen et al\n\n\n\nby a factor of 3.7.\n\n\n\nEffects of Different Nitrogen Rates on Nitrogen Transformation\n4\n+\n\n\n\n4 \nlevel varied from 4.48 to 38.3 mg L-1 -N3 levels. \nThe NH4\n\n\n\n+\n\n\n\nat N4\n\n\n\n4\n+ -N concentrations were directly affected by N levels. \n\n\n\nThe NH4\n+ -N concentrations between N  and N1\n\n\n\n4\n+ -N concentrations for \n\n\n\nN  and N1\n-1 to 9.75, 11.51 mg L-1, respectively. NH4\n\n\n\n+ -N \n and N1 treatments. However,\n\n\n\nConcentrations of NH4\n+\n\n\n\nafter split N applications (mg L-1\n\n\n\nNitrogen Leading in Paddy Field\n\n\n\n*Within columns, means followed by the same letter are not significantly different by Least Significant \nDifference (LSD) at the 0.05 level \n\n\n\nDays after basal \nFA Days after second FA Days after third FA Nitrogen \n\n\n\ntreatment 3 7 3 7 14 21 3 7 14 30 \nN0 0.91c 0.57c 0.59e 0.76d 0.30c 0.69e 0.65d 0.50c 0.26b 0.39a \n\n\n\nN1 1.43c 2.69c 4.55d 1.53cd 2.33b 4.19d 2.47c 2.09b 1.85a 2.11a \n\n\n\nN2 11.51b 9.76b 6.35c 2.81c 1.58c 5.94c 3.78b 2.22b 2.13a 1.96a \n\n\n\nN3 13.14b 10.46b 7.68b 9.71b 2.23b 9.30b 4.26b 4.78a 1.78a 1.63a \n\n\n\nN4 38.38a* 13.43a 10.03a 28.13a 4.48a 11.98a 6.97a 2.13b 2.51a 2.65a \n\n\n\n\n\n\n\n\nN  and N3 treatments differed after basal N application with the mean concentration  \nbeing similar in both treatments. The NH4\n\n\n\n+ -N concentration varied greatly in \n\n\n\n4\n+ -N concentration in the two soil \n\n\n\n-1\n\n\n\ndifferences between N -N3 levels; the NH4\n+ concentrations for N4 treatment was \n\n\n\n3\n-\n\n\n\n4\n+ -N enhanced \n\n\n\nthe microbial growth and transformation of NH4\n+ -N to NO3\n\n\n\n- -N. The NO3\n- -N  \n\n\n\nconcentration 4 days after fertilizer application was highest for N4 treatment.\n\n\n\nTable 4 shows that due to soil adsorption and bacteria transformation, the NH4\n+ \n\n\n\nfor the N4\n-1 \n\n\n\n-N3 treatments. The NH4\n+\n\n\n\ndepth was not affected by a fertilizer application rate of lower than the N3 rate.\n\n\n\nTABLE 3\nConcentrations of NO3\n\n\n\n-\n\n\n\nsplit N applications (mg L-1\n\n\n\nTABLE 4\nConcentrations of NH4\n\n\n\n+\n\n\n\nsplit N applications (mg L-1\n\n\n\nDays after basal FA Days after second FA Days after third FA Nitrogen \ntreatment 3 7 3 7 14 21 3 7 14 30 \n\n\n\nN0 0.92a* 0.46abc 0.28a 0.34d 0.73bc 0.69a 0.49d 0.41c 0.45a 0.87a \n\n\n\nN1 0.93a 1.91b 1.36a 2.54c 3.42b 1.98a 0.52c 0.27c 0.50a 2.23a \n\n\n\nN2 0.94a 1.19c 1.96a 2.69bc 2.90c 1.86a 0.81c 0.77b 0.80a 1.29a \n\n\n\nN3 1.33a 1.32abc 3.29a 1.93bcd 2.97b 2.57a 1.46b 1.27a 0.89a 0.55a \n\n\n\nN4 1.01a 1.52abc 4.41a 9.21a 7.28a 2.30a 2.00a 0.52b 1.02a 1.38a \n\n\n\n*Within columns, means followed by the same letter are not significantly different by Least Significant \nDifference (LSD) at the 0.05 level \n\n\n\nDays after basal FA Days after second FA Days after third FA Nitrogen \ntreatments 3 7 3 7 14 21 3 7 14 30 \n\n\n\nN0 0.65d* 0.36c 0.40e 0.13b 0.38b 0.59b 0.65d 1.50c 0.56a 0.22a \n\n\n\nN1 5.52c 0.83c 5.77d 0.466b 0.83b 3.54b 3.47c 2.31b 1.93a 2.15a \n\n\n\nN2 7.33b 8.31b 10.79c 1.45b 0.26b 1.47b 5.78b 1.97b 1.55a 1.43a \n\n\n\nN3 6.96bc 12.95a 24.24b 2.30b 1.97b 2.31b 6.26b 3.36b 1.60a 1.55a \n\n\n\nN4 11.96a 15.80a 37.22a 4.22a 12.39a 12.89a 8.97a 4.69a 3.26a 2.03a \n\n\n\n*Within columns, means followed by the same letter are not significantly different by Least Significant \nDifference (LSD) at the 0.05 level \n\n\n\n\n\n\n\n\n111\n\n\n\nEffects of Nitrogen Leaching Concentrations on Water Quality during Paddy \nGrowth\nThe increase in NH4\n\n\n\n+ -N concentration enhanced nitrobacteria growth, leading to \ntransformation of NH4\n\n\n\n+ -N to NO3\n- which inversely led to a lower degree of NH4\n\n\n\n+ \n4\n+\n\n\n\ndepths. In general, the NH4\n+ -N concentration was the main form in the leachates \n\n\n\nat the two soil depths. \n\n\n\n         \nNitrogen Loss \nThe percentages of nitrogen losses for different treatments are shown in Table 7. \nThese results clearly show that the highest TN loss was from N1 treatment (4.57 \nkg ha-1\n\n\n\n4\n+ -N concentration, the highest loss was from N4 treatment \n\n\n\n-1\n\n\n\n4\n+ \n\n\n\n-N, NO3\n- -N and TN losses increased with each treatments. This clearly indicates \n\n\n\nTABLE 5\nConcentrations of NO3\n\n\n\n-\n\n\n\nsplit N applications (mg L-1\n\n\n\nTABLE 6.\nRatios of NH4\n\n\n\n+\n\n\n\n3 urea applications\n\n\n\nNitrogen Leading in Paddy Field\n\n\n\nDays after basal FA Days after second FA Days after third FA Nitrogen \ntreatments 3 7 3 7 14 21 3 7 14 30 \n\n\n\nN0 0.87b* 0.35a 0.67a 0.88a 0.64a 0.22a 0.48c 0.39c 0.38b 0.20a \n\n\n\nN1 0.85b 1.28a 1.05a 2.97a 1.36a 1.87a 0.52c 0.65c 0.27b 0.55a \n\n\n\nN2 0.74b 1.19a 1.30a 3.39a 1.27a 1.85a 0.80b 0.39c 0.63b 0.71a \n\n\n\nN3 0.89b 1.03a 1.42a 2.90a 2.62a 1.75a 1.46b 0.81b 0.43b 0.75a \n\n\n\nN4 1.63a 1.42a 2.59a 1.79a 3.92a 2.34a 2.00a 1.37a 1.24a 0.92a \n*Within columns, means followed by the same letter are not significantly different by Least Significant Difference \n(LSD) at the 0.05 levels \n\n\n\nDays after basal FA Days after second FA* Days after third FA Nitrogen \ntreatment 1 3 7 1 3 7 14 1 3 7 30 \n\n\n\n0.28 0.59 0.53 0.53 0.51 0.37 0.71 0.52 0.51 0.34 0.42 N0 60cm 0.55 0.47 0.61 0.54 0.63 0.48 0.70 0.57 0.57 0.56 0.41 \n30cm 0.43 0.83 0.79 0.70 0.63 0.61 0.53 0.63 0.75 0.81 0.44 N1 60cm 0.79 0.61 0.81 0.76 0.27 0.64 0.50 0.67 0.79 0.71 0.72 \n30cm 0.86 0.87 0.73 0.69 0.58 0.52 0.49 0.66 0.74 0.78 0.55 N2 60cm 0.53 0.73 0.69 0.78 0.76 0.75 0.47 0.75 0.79 0.76 0.39 \n30cm 0.87 0.80 0.38 0.75 0.74 0.59 0.43 0.67 0.68 0.58 0.68 N3 60cm 0.65 0.81 0.76 0.83 0.58 0.57 0.46 0.74 0.74 0.73 0.43 \n30cm 0.89 0.88 0.74 0.82 0.47 0.79 0.69 0.63 0.71 0.73 0.60 N4 60cm 0.77 0.87 0.86 0.85 0.66 0.80 0.74 0.72 0.75 0.85 0.86 \n\n\n\n*FA=fertilizer application \n\n\n\n\n\n\n\n\nindicate that nitrogen leaching occurred in the form of NH4\n+ -N, NO3\n\n\n\n- -N and TN \nthat affected the environment. One of the most important aspects of controlling \nagricultural pollution is to control total nitrogen interconversion which could arise \nrapidly among the N species which comprise of NH4\n\n\n\n+ -N, NO3\n- -N and TN in the \n\n\n\nCONCLUSIONS\n\n\n\nwas higher than the NH4\n+ -N and NO3\n\n\n\n- -N concentration with time. Also, NH4\n+ -N \n\n\n\n3\n-\n\n\n\nof the surface soil. At N4\n-1\n\n\n\n4\n+ and NO3\n\n\n\n- were higher than the \n3\n-\n\n\n\n-1 3\n- -N \n\n\n\n4\n+ -N concentrations were higher, compared \n\n\n\n4\n+ -N, NO3\n\n\n\n- -N and TN \ndeclined. NO3\n\n\n\n-\n\n\n\nO can be \nemitted into the atmosphere from a shallow depth of soil. This study concludes \n\n\n\nthe use of slow release N fertilizers.  \n\n\n\nACKNOWLEDGEMENT\n\n\n\nthis research.\n\n\n\nTABLE 7\n\n\n\nNH4-N NO3-N TN \nNitrogen \n\n\n\ntreatments Loss \n (kg ha-1) \n\n\n\nPercentage \n(%) \n\n\n\nLoss  \n(kg ha-1) \n\n\n\nPercentage  \n(%) \n\n\n\nLoss  \n(kg ha-1) \n\n\n\nPercentage \n(%) \n\n\n\nNO 0.57 - 0.38 - 1.04 - \nN1 2.91 3.23 1.25 1.38 4.57 5.07 \n\n\n\nN2 4.07 2.26 1.14 0.63 5.73 3.18 \n\n\n\nN3 5.21 1.93 1.73 0.64 7.62 2.82 \n\n\n\nN4 11.22 3.11 2.20 0.61 14.75 4.10 \n\n\n\n\n\n\n\n\n113\n\n\n\nREFERENCES\nBouman, B.A.M., M.C.S. Wopereis, M.J. Kropff, H.F.M. Ten Berge and T.P. Tuong. \n\n\n\nlosses. Agricultural Water Management\n\n\n\nC\nJournal of Hydrology\n\n\n\nDelcourt, H., P.L. Darives and J.D. Brademaeker. 1996. The spatial variability of \nsome aspects of topsoil fertility in two Belgian soils. Computers and Electronics \nin Agriculture. 14: 179-196.\n\n\n\nDiercks, R. 1983. Alternativen im landbau. Stuttgart: Eugen Ulmer. pp. 378.\n\n\n\nEarl, R., P.N. Wheeler, B.S. Blackmore and R. J. Godwin. 1996. Precision farming-\nthe management of variability. Journal Institute Agriculture Engineering. \n\n\n\nEis\nEnvironmental Research Letters. 9: 45-53\n\n\n\nFAO. 1997. Food and Agricultural Organization. FAO Production Yearbook. FAO. \nUnited Nations, Rome. pp 78.\n\n\n\nFAI. 1997. Fertilizer Statistics. Fertilizer Association of India, New Delhi. pp. 75.\n\n\n\nM\nunder some management practices for low land rice. Philippine Agricultur\n367-377.\n\n\n\nNational Environmental Bureau. 1996. Methods for the Analysis of Water and Waste \nWater. Beijing: Chinese Environmental Science Press, pp. 53.\n\n\n\nSchepers, J.S., M.G. Moravek, E.E. Alberts and K.D. Frank, 1991. Maize production \nJournal of Environmental Quality. \n\n\n\nStrebel, O., W.H.M.,  Duynisveld and J. Bottcher. 1989. Nitrate pollution of ground \nwater in Western Europe. Agriculture Ecosystem and Environment\n\n\n\nS\nresources. In Utilization and Recycle of Agricultural Wastes and Residues.  New \nYork. USA: CRC Press. pp.1-16.\n\n\n\nThomsen, I.K., J.F. Hansen, V. Kjellerup and B.T. Christensen. 1993. Effects of \ncropping system and rates of nitrogen in animal slurry and mineral fertiliser on \nnitrate leaching from a sand loam. Soil Use and Management\n\n\n\nWo\nresistance in puddle rice soils: measurement and effects on water movement. \nSoil and Tillage Research\n\n\n\nNitrogen Leading in Paddy Field\n\n\n\n\n\n\n\n\n114\n\n\n\nWopereis, M.C.S., J. Bouman, M.J. Kroff, H.F.M ten Berge and A.R. Maligaya. \n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n\n\n\n*Corresponding author : Email: samsudin@agri.upm.edu.my\n\n\n\nINTRODUCTION\n\n\n\nAcid soils (Ultisols and Oxisols) are widespread in Southeast Asia. They are found \n\n\n\nscattered in the low- and upland areas of Malaysia, Thailand and Indonesia. In \n\n\n\nMalaysia, (Paramananthan 2000) said that Ultisols planted with oil palm, rubber, \n\n\n\ncocoa and miscellaneous fruit trees have achieved mixed success. When oil palm \n\n\n\nor rubber is up for replanting, corn or groundnut is usually grown as cash crops \n\n\n\nbetween the rows during their early years of growth. The yields of these cash \n\n\n\ncrops (especially corn) are low, because of the strongly acidic soil reaction, caused \n\n\n\npresumably by high amounts of Al in the soil solution. Furthermore, the soils \n\n\n\nare highly weathered and consequently exhibit low cation exchange capacities \n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 13: 1-12                                      Malaysian Society of Soil Science\n\n\n\nEffect of Dolomitic Limestone and Gypsum Applications on \n\n\n\nSoil Solution Properties and Yield of Corn and \n\n\n\nGroundnut Grown on Ultisols \n\n\n\nJ. Shamshuddin1*, I. Che Fauziah1 & L.C. Bell2\n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\n2Department of Agriculture, University of Queensland, Brisbane, \n\n\n\nQld 4072, Australia\n\n\n\nABSTRACT\nA study was conducted to determine soil solution properties and relative tolerance \n\n\n\nof corn and groundnut plants to soil acidity. Corn followed by groundnut was \n\n\n\nplanted on Ultisols one month after lime or gypsum was incorporated into the \n\n\n\ntopsoil. Soil samples were collected after corn and groundnut harvest. Soil \n\n\n\nsolutions were extracted by the immiscible replacement method of soil water with \n\n\n\nfluorocarbon trichlorofluoroethane. Results showed that total Al, inorganic Al, Ca, \n\n\n\nand Mg concentrations were erratically affected by the treatments. However, total \n\n\n\nAl values were indicated to be high when solution pHs were low, especially at \n\n\n\ntreatments with low amounts of lime or high amounts of gypsum. It appeared \n\n\n\nthat Ca released from the dissolution of gypsum had replaced Al in the exchange \n\n\n\ncomplex, causing the high concentrations of Al in the solution. Solution pH, \n\n\n\ncorresponding to 90 % relative yields of corn and groundnut, were 4.7 and 4.3, \n\n\n\nrespectively. This means that groundnut is more tolerant to soil acidity than corn. \n\n\n\nLiming Ultisols at low rates may be necessary for groundnut cultivation. For corn \n\n\n\ncultivation, the liming rate is 2 t ha-1, which supplies adequate amounts of Ca and \n\n\n\nMg for the growth of corn plants.\n\n\n\nKeywords: Aluminium corn, dolomitic limestone, groundnut, gypsum, \n\n\n\n     peanut, Ultisol\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 20092\n\n\n\n(CEC). The latter results in heavy losses of Ca and Mg due to leaching, given the \n\n\n\nprevailing heavy rainfall common in tropical climates.\n\n\n\n Liming experiments on typical Ultisols (Shamshuddin et al. 1991; Ismail et \n\n\n\nal. 1993) and Oxisols (Shamshuddin et al. 1992) have indicated the need for liming \n\n\n\nfor annual crop production. Applying lime in combination with gypsum would \n\n\n\nbring more Ca and/or Mg further down the soil profile (Shamshuddin and Ismail \n\n\n\n1995), thus alleviating to some extent subsoil acidity. Applications of ground \n\n\n\nmagnesium limestone (GML), usually known as dolomitic limestone, would also \n\n\n\nsupply the necessary Ca and Mg needed for corn and groundnut growth. The \n\n\n\npresence of more Ca in the soils arising from lime and/or gypsum applications is \n\n\n\nalso beneficial because Ca can to a certain extent alleviate Al toxicity (Alva et al. \n\n\n\n1986).\n\n\n\n      One of the methods to study the effects of soil acidity on crop production \n\n\n\nis to determine the chemical properties of the soil solution. Therefore, this \n\n\n\ninvestigation was conducted to study soil solution properties and its nutrient \n\n\n\nelement concentration, their effects on soil acidity and on corn and groundnut \n\n\n\ngrown on Ultisols in Malaysia\n\n\n\nMATERIALS AND METHODS\n\n\n\nLocation and Soils\n\n\n\nField experiments using a random design with three replicates were conducted \n\n\n\nat Puchong and Chembong, located 30 and 80 km, respectively, south of Kuala \n\n\n\nLumpur, Malaysia. The soil at Puchong was classified as the Serdang series, a \n\n\n\nloamy, siliceous, isohyperthermic Typic Paleudult, whereas the soil at Chembong \n\n\n\nwas classified as the Rengam series, a clayey, kaolinitic, isohyperthermic Typic \n\n\n\nPaleudult. Topsoil (0-15 cm depth) was sampled in the experimental plots at both \n\n\n\nlocations after the corn and groundnut were harvested. The physico-chemical \n\n\n\nproperties in the major horizons of both the soils are given in Table 1.\n\n\n\nTABLE 1\n\n\n\nSelected physico-chemical properties of Serdang and Rengam Series\n\n\n\nJ. Shamshuddin, I. Che Fauziah & L.C. Bell\n\n\n\n\n\n\n\nHor.      Depth (cm)     pH                 Exch. cations                 CEC           Org. C        Clay \n\n\n\n                                             Ca       Mg       Na      Ka     Al \n\n\n\n                                             \u2026\u2026\u2026\u2026\u2026..cmolc/kg\u2026\u2026\u2026\u2026\u2026\u2026          \u2026\u2026\u2026%.......... \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSerdang\n\n\n\n\n\n\n\n \nAp           0-27            4.91   1.05    0.03     0.02   0.22   4.02  13.90          1.95            25\n\n\n\n \nB21t       27-75           4.76   0.83    0.18     0.02   0.06   3.98    9.08          0.80            30 \n\n\n\nB22t       75-125         4.92   0.81    0.16     0.01   0.04   3.07    7.15          0.33            28 \n\n\n\nB23t      125-150        5.01   0.77    0.16     0.02   0.04   3.24    6.43          0.26            25 \n\n\n\n \n                                                          Rengam\n\n\n\n\n\n\n\n\n\n\n\nAp           0-20            4.83   1.05    0.17     0.02   0.08   2.68    8.80          2.13            40\n\n\n\n\n\n\n\nB1t         20-60           4.43   0.72    0.14     0.01   0.05   2.83    7.98          1.21            45\n\n\n\n\n\n\n\nB2t         60-98           4.44   0.69    0.14     0.01   0.04   2.30    7.22          0.82            44\n\n\n\n\n\n\n\nBC         98-150          4.44   0.79    0.15     0.02   0.03   2.45    5.75          0.41            35\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 3\n\n\n\nExperimental\n\n\n\nTwo separate experiments using GML or gypsum were carried out at Puchong \n\n\n\n(University Agricultural Park, Universiti Putra Malaysia) and Chembong \n\n\n\n(Department of Agriculture, Peninsular Malaysia) sites. In Experiment 1, GML \n\n\n\nwas incorporated to 15 cm depth, and in Experiment 2 gypsum was incorporated \n\n\n\nto 15 cm depth. The contents of Ca, Mg and other components in the GML and \n\n\n\ngypsum are given in Table 2. The GML used in this study contained 18.5 and 6.7 \n\n\n\n% Ca and Mg, respectively, whereas gypsum contained 25.1 % Ca. Both GML \n\n\n\nand gypsum contained substantial amounts of Fe, amounting to 2819 and 103 \n\n\n\nmg/kg, respectively. The additional Fe could have some effects on the chemical \n\n\n\nproperties of the soil solutions. \n\n\n\nTABLE 2\n\n\n\nElemental composition of ground magnesium limestone and gypsum\n\n\n\n      At each site, the treatments consisted of 0, 0.5, 1.0, 2.0, 4.0 and 8.0 t/ha GML \n\n\n\nor gypsum. The size of each experimental plot was 6.5 m x 4.5 m. Sweet corn \n\n\n\n(Zea mays) was the first crop planted 1 month after GML or gypsum was applied, \n\n\n\nand this crop was immediately followed by groundnut (Arachis hypogaea). At \n\n\n\nharvest, the yield of corn and groundnut were recorded, and relative corn and \n\n\n\ngroundnut yields from the GML treated plots were subsequently calculated. These \n\n\n\nvalues were later plotted against the corresponding soil solution pH to find the pH \n\n\n\nvalues most critical for both the crops in the experiments. \n\n\n\n       Basal fertilisers for the crops were applied on the basis of past experiences \n\n\n\nand leaf analysis of each crop (Table 3). For the corn crop, 120 kg/ha N, 100 kg/\n\n\n\nha P and 150 kg/ha K were applied, whereas no fertiliser was applied for the \n\n\n\ngroundnut crops. \n\n\n\nTABLE 3\n\n\n\nNutrient rates for corn and groundnut cultivation\n\n\n\nDolomitic and Gypsum Applications on Ultisols\n\n\n\n\n\n\n\nElement                                             GML                                                        Gypsum \n\n\n\n\n\n\n\nCa (%)                                               18.5                                                            25.1 \n\n\n\nMg (%)                                                6.7                                                              tr \n\n\n\nP (mg/kg)                                            1.7                                                              tr \n\n\n\nCu (mg/kg)                                        17.6                                                              7.2 \n\n\n\nFe (mg/kg)                                     2819.0                                                          103.0 \n\n\n\nMn (mg/kg)                                       97.0                                                             27.0 \n\n\n\nZn (mg/kg)                                        29.0                                                              8.0 \n\n\n\n\n\n\n\n\n\n\n\nCrop sequence                                 N\n*\n                                   P\n\n\n\n**\n                                   K\n\n\n\n***\n \n\n\n\n\n\n\n\nCorn                                               120                                  100                                   150 \n\n\n\n\n\n\n\nGroundnut                                        0                                      0                                       0 \n\n\n\n\n\n\n\n\n\n\n\n*= as urea; ** = as triple super phosphate; *** = as muriate of potash \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 20094\n\n\n\nExtraction of Soil Solutions\n\n\n\nSelected samples from the Puchong and Chembong sites were incubated with \n\n\n\ndeionised water at a matric suction of 10 kPa for 24 hours. The soil contained \n\n\n\napproximately 20 % moisture by weight. The soil solutions were then extracted \n\n\n\nby immiscible replacement using fluorocarbon trichlorofluoroethane (specific \n\n\n\ngravity >1) and collected by centrifugation (at 34,800 RCF) for 30 minutes. Soil \n\n\n\nsolutions recovered were filtered through 0.22 \u03bcm Millipore filters. The extraction \n\n\n\nof the soil solutions were carried out at the Department of Agriculture, University \n\n\n\nof Queensland, Australia. \n\n\n\nAnalysis of Soil and Soil Solution\n\n\n\nSoil pH was determined in water at a soil to water ratio of 1:1. Cation exchange \n\n\n\ncapacity was determined by 1 M NH\n4\nOAc buffered at pH 7. The Ca, Mg, K and \n\n\n\nNa (exchangeable bases) in the extracts were determined by atomic absorption \n\n\n\nspectrophotometry (AAS). Exchangeable Al was extracted by 1 M KCl and the Al \n\n\n\nin the extract was determined by AAS. Organic carbon was determined by Walkley-\n\n\n\nBlack method. Clay content was determined by successive sedimentation. \n\n\n\n      Soil solution pH was determined immediately after extraction. Total Al (Al\nT\n) in \n\n\n\nthe soil solution was determined by inductively coupled plasma atomic emission \n\n\n\nspectroscopy (ICPAES). Inorganic Al (Al\ninorg\n\n\n\n) was determined by the short-term \n\n\n\npyrocatechol method of Kerven et al. (1989).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nNatural Characteristics of the Soils\n\n\n\nThe Serdang series contains 30 % or less clay and is of loamy texture (Table 1). \n\n\n\nSoil pH in the upper part of the profile is less than 5 and exchangeable Al is very \n\n\n\nhigh, especially in the topsoil, with a value of 4.02 cmol\nc\n/kg soil. In general, \n\n\n\nexchangeable cations are low. Likewise, the Rengam soil is strongly acidic in \n\n\n\nnature, with a pH of less than 5 throughout the profile. Exchangeable bases are \n\n\n\nlow and exchangeable Al is high. In our opinion, both soils require liming at a \n\n\n\nsuitable rate in order to raise soil pH above 5 in order to decrease exchangeable \n\n\n\nAl below 1 cmol\nc\n/kg soil for corn cultivation.\n\n\n\nEffects of Treatments on Solution Characteristics\n\n\n\nSolution pH: Tables 4 and 5 show the chemical characteristics of the soil solutions \n\n\n\nof the Serdang Series as affected by GML and gypsum treatments, whereas \n\n\n\nTable 6 shows the chemical characteristics of the soil solutions of the Rengam \n\n\n\nSeries as affected by GML treatment. Checking through the rates of application \n\n\n\nand duplicates thoroughly, the change in solution pH is observed to be rather \n\n\n\nerratic. This is probably due to soil variability in the experimental plots as well as \n\n\n\nimperfections during soil sampling. Soil erosion occurring during the experiment \n\n\n\ncould have also contributed somewhat to these inconsistent results. Nevertheless, \n\n\n\nin the GML treatments (Tables 4 and 6), pH showed an increasing trend with \n\n\n\nthe rate of application. For the Serdang soil, the lowest pH was 3.71 (the control \n\n\n\nJ. Shamshuddin, I. Che Fauziah & L.C. Bell\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 5\n\n\n\ntreatment), whereas the highest was 6.54 (soil treated with 8 t/ha) (Table 4). On \n\n\n\nthe other hand, the lowest pH observed for Rengam soil was 3.70 and the highest \n\n\n\n6.18 (Table 6).\n\n\n\nTABLE 4\n\n\n\nChemical properties of solutions extracted from Serdang soil as affected by\n\n\n\nground magnesium limestone treatment\n\n\n\nTABLE 5\n\n\n\nChemical properties of solutions extracted from Serdang soil as affected by\n\n\n\ngypsum treatment\n\n\n\n\n\n\n\nRate   Repl.     pH     EC          AlT          Alinorg      Si         Ca        Mg       Fe       Mn       S  \n\n\n\nt/ha                         mS/cm      \u2026\u2026\u2026\u2026\u2026\u2026\u2026.\u03bcM\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. \n\n\n\n\n\n\n\n\n\n\n\n0           1        3.71   0.75       232.2    265.9       25.9    4550.0  1134.0  116.4   34.5     59.3           \n\n\n\n             2        4.10   0.45         37.0      37.8       21.4    5000.0    578.0    25.1   18.2   156.0 \n\n\n\n0.5        1        4.22   0.85         96.0    115.2       24.9    5675.0  1606.0    21.5   32.8   187.0      \n\n\n\n             2        5.58   0.52         25.9      26.5       10.7    3475.0  1071.0      7.2    9.1    156.0 \n\n\n\n1.0        1        5.25   0.60         33.3       nd         24.9    2675.0  1401.0    10.7   12.7   125.0 \n\n\n\n             2        4.26   0.68         37.0      35.7       24.9    4075.0  1277.0    12.5   18.2   156.0 \n\n\n\n2.0        1        4.62   0.57         14.8      17.1       17.8    3150.0  1442.0      5.4     5.5   343.0 \n\n\n\n             2        5.23   0.67         18.5      19.4       17.8    3675.0  1772.0      9.0     7.3   218.0 \n\n\n\n4.0        1        4.93   0.54         11.1       nd         17.8    3500.0  1813.0      5.4     5.5   343.0 \n\n\n\n             2        5.80   0.46           3.7       1.3        10.7    2300.0  1524.0      3.8     1.8   343.0 \n\n\n\n8.0        1        6.54   1.10           3.7       0.7         7.1     5550.0  3790.0      9.0     1.8 2434.0 \n\n\n\n             2        5.68   0.67           3.7       1.2        14.2    3575.0  2183.0      3.6     1.8   873.0  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRate   Repl.     pH     EC          AlT         Alinorg     Si         Ca        Mg       Fe      Mn        S  \n\n\n\nt/ha                         mS/cm      \u2026\u2026\u2026\u2026\u2026\u2026\u2026.\u03bcM\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. \n\n\n\n\n\n\n\n\n\n\n\n0           1       4.83    0.35        37.0      36.3      21.4    2050.0   453.0   19.7   12.7    125.0 \n\n\n\n             2       4.30    0.59        92.6    178.3      24.9    3725.0   494.0   19.7   25.5    562.0 \n\n\n\n0.5        1       4.28    0.65      107.4    117.1      28.5    3300.0   659.0   43.0   25.5    156.0 \n\n\n\n             2       4.22    2.54      381.5    455.1      21.4  12625.0   453.0   68.0   60.1 16380.0 \n\n\n\n1.0        1       4.25    0.37        50.8      54.0      18.7    4700.0   263.0   26.9   12.7     449.0 \n\n\n\n             2       5.38    0.25        16.3       7.3       20.2    3050.0   267.0     0.4   10.9       81.0 \n\n\n\n2.0        1       4.66    2.54       192.9   211.7      19.4  12725.0   871.0     9.0   65.5 17784.0 \n\n\n\n             2       4.67    0.60         24.9     23.7        4.3    6735.0   547.0     2.5   16.9     237.0 \n\n\n\n4.0        1       4.47    0.42         29.7     30.0         tr      4325.0   575.0     1.8   13.3    100.0 \n\n\n\n             2       4.26    0.96       244.9   280.5        1.4    9250.0   477.0   57.3   36.4   2930.0 \n\n\n\n8.0        1       4.55    0.80       129.5   127.0         tr      8500.0   645.0   28.6   32.8   2917.0 \n\n\n\n             2       4.52    0.41         49.0     48.0       23.8   4825.0   366.0   23.3   10.9     496.0 \n\n\n\n\n\n\n\n\n\n\n\nDolomitic and Gypsum Applications on Ultisols\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 20096\n\n\n\nTABLE 6\n\n\n\nChemical properties of solutions extracted from Rengam soil as affected by\n\n\n\nground magnesium limestone treatment\n\n\n\n    The increase in solution pH resulting from GML application is due to production \n\n\n\nof hydroxyl ions when GML is dissolved and subsequently hydrolysed:\n\n\n\n\n\n\n\n           Ca,Mg (CO\n3\n)\n\n\n\n2\n  +  H\n\n\n\n2\nO  \u2192  Ca2+ + Mg2+ + 2CO\n\n\n\n3\n\n\n\n2-\n\n\n\n                           CO\n3\n\n\n\n2- + H\n2\nO  \u2192  HCO\n\n\n\n3\n\n\n\n- + OH-\n\n\n\nThe hydroxyl ions then reacts with Al in the solution to precipitate as aluminum \n\n\n\nhydroxide, which over time may crystallize into gibbsite [Al (OH)\n3\n]:\n\n\n\n                           Al3+ + 3OH-  \u2192  Al (OH)\n3\n\n\n\nThe increase in solution pH would certainly affect the availability of other \n\n\n\nmetals, such as Fe and Mn (Tables 4 and 6).\n\n\n\n    The clay fraction of Ultisols in Malaysia is dominated by kaolinite and \n\n\n\nsesquioxides (Tessens and Shamshuddin 1983; Paramananthan 2000). Serdang \n\n\n\nand Rengam soils (Ultisols) used in this study are soils with variable charge \n\n\n\nminerals. It means that when soil pH increases as a result of liming, the CEC of \n\n\n\nthe soils increases in tandem with the pH increase. As such, losses of cations like \n\n\n\nCa, Mg and K will be prevented because of leaching under a tropical environment \n\n\n\n(Shamshuddin and Ismail 1995).\n\n\n\n        The dissolution of gypsum (CaSO\n4\n.2H\n\n\n\n2\nO) is not expected to have a significant \n\n\n\neffect on solution pH. Results presented in Table 5 prove this assumption beyond \n\n\n\ndoubt. However, gypsum application may affect the availability of Al in Ultisols \n\n\n\ndominated by exchangeable Al in the exchange complex (Shamshuddin and Ismail \n\n\n\n1995). This happens when Ca released from the dissolving gypsum replaces Al:\n\n\n\nJ. Shamshuddin, I. Che Fauziah & L.C. Bell\n\n\n\n\n\n\n\nRate   Repl.    pH     EC       AlT           Alinorg     Si        Ca          Mg        Fe        Mn        S  \n\n\n\nt/ha                         mS/cm   .....\u2026........\u2026\u2026\u2026\u2026\u2026\u2026\u2026.\u03bcM\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. \n\n\n\n\n\n\n\n\n\n\n\n0          1       3.70    1.43     687.5     781.9     9.7     8150.0   1615.0   159.3   136.5   144.0 \n\n\n\n            2       4.01    1.15     199.2     243.9     0.4     8675.0     986.0     53.7   118.3   153.0 \n\n\n\n0.5       1       4.97    0.95      24.1       25.1       tr       7625.0   1804.0    19.7     36.4    140.0 \n\n\n\n            2       4.22    1.02     101.2     121.6    11.1    7425.0    2396.0   77.0     49.1    150.0 \n\n\n\n1.0       1       4.17    1.08     171.4     191.5      8.3    7525.0    2482.0   93.1     56.4    112.0 \n\n\n\n            2       4.49    1.25      53.1      56.6        0.7    8425.0    2487.0   26.9     58.2    140.0 \n\n\n\n2.0       1       4.37    0.98      92.4      104.2      8.3    6900.0    2708.0   53.3     32.8    122.0 \n\n\n\n            2       4.65    1.05      59.0        64.2      0.4    7250.0    3078.0   28.6     30.4    190.0 \n\n\n\n4.0       1       6.00    1.32        8.2         4.1     11.2    8625.0    3806.0     9.7     12.4    509.0 \n\n\n\n            2       5.80    1.10        8.9         9.0       5.8    9550.0      440.0     0.9     14.6    365.0 \n\n\n\n8.0       1       6.18    2.31        3.3         1.0        tr    10800.0    4180.0     9.7       2.4  1045.0 \n\n\n\n            2       6.13    2.31        4.5         3.2        tr    11400.0      801.0     0.7     14.9    711.0 \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 7\n\n\n\n                     clay-Al3+  + Ca2+  \u2192 clay-Ca2+  + Al3+\n\n\n\nThe hydrolysis of this newly available Al lowers solution pH slightly. The \n\n\n\nmagnitude of pH change would depend on the extent of the exchange taking place \n\n\n\nbetween Al and Ca. We would expect this to happen at a high rate of gypsum \n\n\n\napplication. However, for the soils under study, this phenomenon did not take \n\n\n\nplace significantly (Table 5).\n\n\n\nAl in the solution: By and large, the solutions were colourless indicating that \n\n\n\nfulvic or humic acid is low. Some Al in the solution may exist as a complex of \n\n\n\nfulvic acid, a low molecular weight organic acid. Al determined by ICPAES is \n\n\n\nconsidered as total Al (Al\nT\n) and those determined by pyrocatechol is considered as \n\n\n\nAl inorganic (Al\ninorg\n\n\n\n).  Total and inorganic Al in the soils of Serdang and Rengam \n\n\n\nSeries are listed in Table 4 and 6, respectively. If Al\nT\n exceeds Al\n\n\n\ninorg\n value, there is \n\n\n\na possibility of some Al existing in the organic form. However, from data obtained \n\n\n\nfrom this study, it is difficult to make a conclusive statement as the values were \n\n\n\nerratic. As mentioned earlier, failure to obtain consistent values is probably due to \n\n\n\nimperfections during their determination. \n\n\n\n  A very high Al\nT\n value of 232.3 \u03bcM  was obtained in the control treatment of \n\n\n\nSerdang soil (Table 4). The corresponding value for the Rengam soil was 687.5 \n\n\n\n\u03bcM. These values were consistent with the low pH of 3.71 and 3.70, respectively. \n\n\n\nIt is well known that Al decreases exponentially with an increase in solution pH \n\n\n\n(Shamshuddin and Auxtero 1991; Shamshuddin et al. 1991). In the Serdang soil, \n\n\n\nAl\nT\n was less than 20 \u03bcM for samples treated with 2 t GML/ha. The corresponding \n\n\n\nvalue for Rengam soil was more that 50 \u03bcM. These have important implications \n\n\n\non corn and groundnut cultivations on these soils, in terms of their yield and \n\n\n\neconomic viability.\n\n\n\nMn in the soil solution: In the Serdang soil, Mn concentration in the control \n\n\n\ntreatment was moderately high (Tables 4 and 5). However, the corresponding Mn \n\n\n\nconcentration in the Rengam soil was high with a value exceeding 100 \u03bcM (Table \n\n\n\n6). Manganase concentration in the soil solutions decreased exponentially with \n\n\n\nincreasing pH (Tables 4 and 6). This finding is similar to that of Shamshuddin and \n\n\n\nAuxtero (1991).\n\n\n\nFe in the soil solution: Fe concentration in the soils under study was not expected \n\n\n\nto cause any problem to the growing corn or groundnut. Its concentration was \n\n\n\nabove 100 \u03bcM in replicate 1 of the control treatment of Serdang soil (Table 4) \n\n\n\nand Rengam soil (Table 6). Like Mn concentration, the values decreased with an \n\n\n\nincrease in the liming rate.\n\n\n\nCa and Mg in the soil solution: The Ca and Mg concentrations were at best \n\n\n\nerratic, like those of pH and Al\nT\n. From field observations, at a high rate of GML \n\n\n\napplication, corn or groundnut grew extremely well. Hence, the uptake of these \n\n\n\nDolomitic and Gypsum Applications on Ultisols\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 20098\n\n\n\nmacronutrients was expected to be higher compared to those at a low rate (or \n\n\n\ncontrol treatment for that matter). As such, the absolute amounts of Ca and Mg in \n\n\n\nthe solutions would not increase significantly although the dissolving dolomitic \n\n\n\nlimestone resulted in additional Ca and Mg. However, for the gypsum-treated \n\n\n\nsoils, the Ca story is different. At a high rate of application (4 t/ha or higher), \n\n\n\nCa concentration was very high with values exceeding 10,000 \u03bcM.  Like GML \n\n\n\ntreated soils, Mg concentration in the gypsum-treated soils remained unchanged. \n\n\n\nIn fact the values were lower compared to those at a low rate of application. The \n\n\n\nreason being corn or groundnut grew better on soils treated with 4 t gypsum/ha or \n\n\n\nhigher and thus used more Mg for their growth.\n\n\n\n      Gypsum contains 25.1 % Ca (Table 2) and when this gypsum is dissolved after \n\n\n\nits application onto Ultisols, this Ca is adsorbed to the soil colloids, joining the \n\n\n\nexisting Ca pool. It is possible that some of the Ca replaces the Al in the exchange \n\n\n\ncomplex resulting in a concomitant decrease in pH due to the hydrolysis of the \n\n\n\nnewly available Al. Ca has an ability to ameliorate soil acidity (Alva et al. 1986). \n\n\n\nDue to gypsum application, Ca/Al ratio in the soil solution is expected to increase \n\n\n\nsubstantially. According to Shamshuddin et al. (1991), for good growth of corn, \n\n\n\nCa/Al ratio should be > 79. It appears that at a high rate of gypsum application \n\n\n\n(> 4 t/ha), the ratio approaches this value. Hence, there is justification to apply \n\n\n\ngypsum to ameliorate acid soils, such as Ultisols. The best practice is to apply \n\n\n\nlime in combination with gypsum. According to Shamshuddin and Ismail (1995), \n\n\n\nby doing this some Ca moves down the soil profile, resulting in amelioration of \n\n\n\nsubsoil acidity.\n\n\n\nSi and S in soil solution: The concentration values of Si and S in the GML treated \n\n\n\nsoils were also erratic. Si is not a plant nutrient, but S is. S concentration in some \n\n\n\nsamples of the gypsum treated soils was very high (Table 5). This is expected \n\n\n\nsince gypsum contains about 18% S. This uneven distribution of S was probably \n\n\n\ndue to soil variability, soil erosion or uneven distribution amendments during their \n\n\n\napplication.\n\n\n\nRelative Tolerance of Corn and Groundnut to Soil Acidity\n\n\n\nIt was observed that the relative corn or groundnut yield increased exponentially \n\n\n\nwith increasing pH (Fig. 1). This figure was drawn using yield and pH data from \n\n\n\nthe experimental plots treated with GML in the Serdang and Rengam soils. The pH \n\n\n\nvalue corresponding to 90 % relative corn yield was 4.7, while that of groundnut \n\n\n\nwas 4.3. These values can be regarded as the critical pH for the growth of corn \n\n\n\nand groundnut grown on acid Ultisols in Malaysia, respectively. This means that \n\n\n\ncorn and groundnut will not produce satisfactory yields unless the pH is raised to \n\n\n\nvalues above these levels. \n\n\n\n The critical pH values obtained in this study indicate clearly that corn is \n\n\n\nless tolerant to soil acidity than groundnut. It is known that the natural pH of soils \n\n\n\nunder field conditions is mostly below 4.7 for Malaysian Ultisols (Tessens and \n\n\n\nJ. Shamshuddin, I. Che Fauziah & L.C. Bell\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 9\n\n\n\nShamshuddin 1983; Paramananthan 2000). As such, liming at an appropriate rate \n\n\n\nis necessary to make soil conditions suitable for corn cultivation. \n\n\n\nFig. 1: Relationship between relative corn and groundnut yields with\n\n\n\nsoil solution pH\n\n\n\nUnlike corn, groundnut is moderately tolerant to soil acidity. Under normal \n\n\n\ncircumstances, Ultisols in Malaysia do not need to be limed for growing groundnut \n\n\n\nas the pH is about 4.3. However, this study found groundnut yield to be slightly \n\n\n\nbetter for experimental plots treated with GML (data not shown). Therefore, it \n\n\n\nwould be a viable practice to apply a small dosage of GML on Ultisols cultivated \n\n\n\nwith groundnut. Beside a small increase in soil pH, this practice would add \n\n\n\nsufficient amounts of Ca and Mg to the soils, a requirement  for the healthy growth \n\n\n\nof the crop. This is because Ultisols in Malaysia are known to be deficient in Ca \n\n\n\nand Mg (Tessens and Shamshuddin 1983; Paramananthan 2000).\n\n\n\nLooking at Fig. 2, we can now approximate the critical value of Al\nT\n for corn \n\n\n\nand groundnut. The Al\nT\n corresponding to solution pH of 4.7 was 30 \u03bcM. This \n\n\n\nvalue is, however, higher than that obtained earlier by Shamshuddin et al. (1991), \n\n\n\nwhich was 22 \u03bcM. This finding indicates that corn grown on Ultisols in Malaysia \n\n\n\nwill not experience healthy growth if the Al in the soil solution is > 30 \u03bcM. For \n\n\n\nthe more acid tolerant groundnut, the critical Al\nT\n was 70 \u03bcM. To bring the Al \n\n\n\nconcentration to below the critical level of corn, the soils need to be limed at an \n\n\n\nappropriate rate. Likewise, the critical Mn concentration can also be approximated \n\n\n\n(Fig. 3). The value obtained for corn was 20 \u03bcM and that for groundnut was 34 \n\n\n\n\u03bcM. Mn toxicity is a common phenomenon for acid tropical soils, especially the \n\n\n\nUltisols and Oxisols. Fortunately, when the soils are limed to increase soil pH \n\n\n\nwith concomitant addition of Ca and Mg, Mn in the soils will also be reduced. \n\n\n\nThis is shown clearly by data given in Tables 4 and 6.\n\n\n\nDolomitic and Gypsum Applications on Ultisols\n\n\n\nR\ne\n\n\n\nl.\n Y\n\n\n\nie\nld\n\n\n\n (\n%\n\n\n\n)\n\n\n\npH Soil Solutions\n\n\n\nGroundnut , Bungor\nGroundnut, Rengam\nCorn, Bungor\nCorn, Rengam\n\n\n\n2\n\n\n\nY = 99 . 55  -  5.88  X 10   e -1 . 98 X 5\n\n\n\n( Groundnut )\n\n\n\nR  = 0 . 933\n\n\n\n( Corn )\n\n\n\nY  =  100 - 13  -  6.12    X   10       e \n5 1 . 87 X\n\n\n\nR  =  0 . 920\n2\n\n\n\n76543\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\npH (Soil Solutions)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200910\n\n\n\n The next phase is to approximate liming rate to make the soils suitable for \n\n\n\ncorn and groundnut cultivation on Ultisols of Malaysia. The rate to be proposed \n\n\n\nshould satisfy the following criteria: (i) it should bring the pH  up and bring the Al \n\n\n\nand Mn concentrations down to a level below the critical value; and (ii) the rate \n\n\n\nof liming should be affordable to the local farming communities. Based on pH, \n\n\n\nAl\nT\n and Mn concentration data presented in Tables 4 and 6, it is proposed that a \n\n\n\nviable and appropriate liming rate for corn cultivation on Ultisols in Malaysia is 2 \n\n\n\nt GML/ha. This rate (lime requirement) is similar to the finding of Shamshuddin \n\n\n\net al. (1998). \n\n\n\n For the cultivation of corn, followed by groundnut, this liming rate would \n\n\n\nbe able to increase soil pH to the level that Al and Mn concentrations are reduced \n\n\n\nto a level below the critical value, and this would simultaneously alleviate Ca and \n\n\n\nMg deficiencies. At this rate of application of lime, Shamshuddin et al. (1998) \n\n\n\nfound that the ameliorative effects would last more than 4 years. If we were to \n\n\n\ngrow groundnut alone, we suggest that 1 t GML is sufficient to supply enough Ca \n\n\n\nand Mg to the growing crop.\n\n\n\nFig. 2:Relationship between soil solution Al and pH\n\n\n\nJ. Shamshuddin, I. Che Fauziah & L.C. Bell\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 11\n\n\n\nFig. 3:  Relationship between soil solution Mn and pH\n\n\n\n\n\n\n\nCONCLUSION\n\n\n\nThis study found that the critical pH values for growing corn and groundnut \n\n\n\non Ultisols in Malaysia were 4.7 and 4.3, respectively, indicating that corn is \n\n\n\nslightly less tolerant to soil acidity than groundnut. The respective critical Al \n\n\n\nconcentrations were estimated to be about 30 and 70 \u03bcM, while those for Mn \n\n\n\nwere 20 and 34 \u03bcM. Lime application required to ameliorate these soils for corn \n\n\n\ncultivation on these soils is 2 t GML/ha. \n\n\n\n It appears that growing groundnut on Ultisols in Malaysia does not require \n\n\n\nlime application. However, lime application has an additional advantage. Liming \n\n\n\nat a low rate of 1 t GML/ha would add the extra Ca and Mg needed for the growing \n\n\n\ngroundnut. \n\n\n\n Gypsum application did not significantly change solution pH, Al, Fe and \n\n\n\nMn. Calcium and S concentrations in the soil solutions did not show any trend \n\n\n\nwith respect to the rate of gypsum application. However, we could assume the \n\n\n\nadvantage of having additional Ca and S from the dissolving gypsum, which can \n\n\n\nbe taken up by corn or groundnut for healthy growth for we know that Ca is able \n\n\n\nreduce the effects of Al toxicity. We cannot completely discount the possibility of \n\n\n\nincreasing solution Al when a high rate of gypsum is applied on Ultisols.\n\n\n\nACKNOWLEDGEMENTS\n\n\n\nThe authors would like to thank Universiti Putra Malaysia, University of \n\n\n\nQueensland and the Australian Center for International Agricultural Research for \n\n\n\nfinancial and technical support during the conduct of the research and preparation \n\n\n\nof this paper.\n\n\n\nDolomitic and Gypsum Applications on Ultisols\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200912\n\n\n\nREFERENCES\nAlva, A.K, C.J. Asher and D.G. Edwards.1986. The role of calcium in alleviating\n\n\n\n aluminum toxicity. Aust. J. Agric. Res. 37:375-383.\n\n\n\nIsmail, H., J. Shamshuddin  and S.R. Syed Omar.1993. Alleviation of soil acidity in\n\n\n\na Malaysian Ultisol and Oxisol for corn growth. Plant and Soil. 151: 55-65.\n\n\n\nKerven, G.L., D.G. Edwards, C.J. Asher, P.S. Hallman and S. Kokot. 1989.\n\n\n\n Aluminum determination in soil solution. II: Short-term colorimetric\n\n\n\n procedures for the measurement of inorganic monomeric aluminum in the\n\n\n\n presence of organic acid ligands. Aust. J. Soil Res. 27:917-102.\n\n\n\nParamananthan, S. 2000. Soils of Malaysia: Their Characteristics and \n\n\n\n Identification.  Academy of Sciences Malaysia. Kuala Lumpur.\n\n\n\nShamshudin, J. and E.A. Auxtero. 1991. Soil solution composition and mineralogy\n\n\n\n of some active acid acid sulfate soils in Malaysia as affected by laboratory\n\n\n\n incubation with lime. Soil Sci. 152: 365-376.\n\n\n\nShamshuddin, J., I. Che Fauziah and H.A.H. Sharifuddin.1991. Effects of \n\n\n\n limestone and gypsum applications to a Malaysian Ultisol on soil solution \n\n\n\n composition and yields of maize and groundnut. Plant and Soil. 134: 45-52.\n\n\n\nShamshuddin, J., I. Jamilah, H.A.H. Sharifuddin and L.C. Bell.1992. Changes in \n\n\n\n solid phase properties of acid soils as affected by limestone, gypsum, \n\n\n\n palm oil mill effluent and rock phosphate. Pertanika 15:105-114.\n\n\n\nShamshuddin, J., H.A.H. Sharifuddin and L.C. Bell.1998. Longevity of magnesium\n\n\n\n limestone applied to an Ultisol. Commun. Soil Sci & Plant Anal. 29:1299-\n\n\n\n 1313.\n\n\n\nShamshuddin, J. and H. Ismail.1995. Reactions of ground magnesium limestone \n\n\n\n and gypsum in soil soils with variable-charge minerals. Soil Sci. Am. J \n\n\n\n 59:106-112.\n\n\n\nTessens, E. and J. Shamshuddin.1983. Quantitative Relationship between \n\n\n\n Mineralogy and Properties of Tropical Soils. UPM Press, Serdang.\n\n\n\nJ. Shamshuddin, I. Che Fauziah & L.C. Bell\n\n\n\n\n\n"
"\n\nINTRODUCTION\nPlant growth potential is largely controlled by the environment that the media \nprovides for root growth. Roots need water, air, nutrients and enough space \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 19-36 (2016)                         Malaysian Society of Soil Science\n\n\n\nComparison of Three Irrigation Systems for the BX-1 system \nfor Nursery Seedlings\n\n\n\n1Nabayi, A., 2C.B.S. Teh, 2M.H.A. Husni, 2A.H. Jaafar and\nM.S. Isnar2\n\n\n\n1Department of Soil Science Faculty of Agriculture, Federal University Dutse \n(FUD), Nigeria\n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia (UPM)\n\n\n\nABSTRACT\nThe BX-1 system (consisting of the BX-1 media and RB 900 tube) is a new planting \nsystem introduced by a private company to replace the growth of plants in soil-\nfilled polybags. Different irrigation systems influence plant growth differently. \nThe main objective of this study was to compare and determine the best of three \nwater irrigation systems: overhead sprinkler (SPR), drip (DRP) irrigation, and \nthe capillary wick (WCK) system, for the BX-1 system. The test crop was water \nspinach (Ipomoea reptans), and the performance of these three irrigation systems \nwas compared with one another in terms of their effects on plant growth, amount of \nwater and nutrient losses via leaching, water productivity and water use efficiency. \nThe field experiment was carried out under a rain shelter at Field No. 15, Agrobio \nComplex, Universiti Putra Malaysia (2o 59\u2019 4.96\u201d N, 101o 44\u2019 0.70\u201dE), for 5 weeks \nfrom July to August, 2014. The experimental layout was the RCBD with the \ntreatments being the three irrigation systems with three replications per treatment. \nEach experimental unit was planted with 20 water spinach plants. Results from the \nstudy showed that the capillary wick system produced the least leachate volume \nand nutrients loss. In terms of growth, the WCK system gave the highest growth \nfor roots dry weight and leaf area. This was because the WCK treatment had the \nlowest amount of leachate and nutrient losses, so it had the highest nutrient content \nin the plant for N, P, and K, but there was no difference (p>0.05) in the Ca and \nMg content. WCK also had the highest water use efficiency, but there was no \ndifference in water productivity between the three treatments. WCK consumed \nthe highest amount of water (but had least water wastage) to produce the highest \namount of roots biomass and leaf area compared to DRP and SPR treatments. \nThus, it is suggested that the WCK system be used with the  BX-1 system as it was \nfound to be the most effective irrigation system.\n\n\n\nKeywords: BX-1 media, RB 900 tube, overhead sprinkler, drip irrigation, \ncapillary wick irrigation.\n\n\n\n___________________\n*Corresponding author : abbajv2003@gmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201620\n\n\n\nNabayi et al.\n\n\n\nto develop (Nageswara and Jessy, 2007). Choosing the most suitable growing \nmedia for the achievement of successful plant production is very important in \ntube growth. Enough water application to plants generally eliminates water stress, \nwhich affects all plant functions including water and nutrient uptake. The important \ncriteria for successful rooting is a reliable rooting medium. Growing media are the \nmaterials similar to soil that physically support plants growth. A balance between \navailable water and aeration in the growth medium is essential for production \nof quality plants in containers (Ekpo and Sita, 2010). The use of alternative \nsoilless media for the production of potted plants needs an understanding of their \nphysical and chemical behaviour to select the appropriate conditions for plant \ngrowth. Ornamental plants require growing media with adequate water retention \nand aeration (Erstad and Gislerod, 1994) and routine fertilisation that guarantees \ncontinuous nutrient supply (Macz et al., 2001).\n\n\n\nBX-1 media, made from Latvia, and imported into Malaysia by a private \ncompany, consists of 100% neutralised white peat, treated with an unstated slow \nreleasing fertiliser and lime. Wong et al., (2013) reported that oil palm seedlings \nraised under a semi-float condition in the  BX-1 system had significantly greater \nplant height, more leaves and longer leaves compared with those obtained in the \npolybag system.\n\n\n\nRB 900 tube (Figure 1) is another form of root trainer which is a rigid \ncontainer with internal vertical ribs, which direct roots straight down to prevent \nspiral growth. The containers are set on frames or beds above the ground to allow \nair-pruning of roots as they emerge from the containers.\n\n\n\nFigure 1:  RB 900 tube courtesy of Humibox (M) Sdn. Bhd \n\n\n\nDue to abundant rain, most soil nutrients are leached from the soil and as a result, \nthe plant could face nutrient deficiency. So selecting the best irrigation system \nwith proper time scheduling is one solution.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 21\n\n\n\nIrrigation Systems for Rubber Nursery\n\n\n\nWater is an essential element for plant growth. Each crop needs an optimum \namount of water for growth and development. In arid regions, irrigation is the \nmain source of water for agricultural production while in semi-arid regions, \nmuch of  agriculture depends on unreliable rainfall. Depending on rainfall alone \nmay not be sufficient to provide the much needed water to crops. Because of the \nuncertainty of rainfall, irrigation is required. In fact, irrigation may be the only \nway to maintain high and sustainable agricultural productivity. Irrigation water \nhas always been in short supply, and it is becoming a scarce commodity in many \nregions  and even where it is available, the cost of pumping and/or transportation \nmay be high in many locations; moreover water loss varies across irrigation \nsystems. The growth of rubber seedlings is greatly influenced by their  production \nconditions  and these range from irrigation, soil or substrate quality,  to drainage \nand fertilisation. BX-1 system (RB 900 tubr and BX-1 growing media) is a new \nnursery planting system, that aims to replace   the traditional (polybag with soil) \nway of raising seedlings. Utilisation of container nurseries are being developed \nrapidly in Malaysia and the world at large, because it gives better productivity and \nbetter production of nursey seedlings. Different water application methods affect \nthe growth of nursery seedlings regardless of source and rate of nutrient solution \n(Argo and Biernbaum, 1994). The  benefits of growing seedling in containers \nare several: easy handling and transportation, less space, rapid product rotation \nand easy marketing. Hence this system is being adopted over field production.\nThe introduction of BX-1 system brings several benefits such as light weight \nand compact design, eco-friendly as the container can be reused, and a reduction \nin labour work due to efficient design and easy handling. In contrast, the old \nconventional polybags cause negative impact on the environment because they \ncannot be reused and take longer time to disintegrate. Others limitations of soil-\nfilled polybag system includes occupation of more space and heaviness.\n\n\n\nWater spinach was used as a test crop because of its ability to grow very fast \nand its high demand for water and nutrients. The main objective of the study was \nto compare three irrigation systems (overhead sprinkler, drip irrigation, capillary \nwick system)  for use in BX-1 system with water spinach as a test crop. The \nirrigation systems were compared with one another in terms of their effects on the \ngrowth, nutrient content, water use efficiency and productivity of water spinach \ngrown on BX-1 system.\n\n\n\nMATERIALS AND METHODS\nThe experiment was carried out in Field No. 15 Agrobio Complex, under the \nrain-sheltere (2\u00b059\u20194.96\u201dN and 101\u00b044\u20190.70\u201dE) in the Faculty of Agriculture, \nUniversiti Putra Malaysia, Serdang, Selangor. Water spinach (Ipomoea reptans) \nwas planted in 710 cm3 RB 900 tube filled with 180 g of BX-1 media (white peat) \nas a test crop. The exact composition of the media is a trade secret; information on \nthe media was obtained from the bag label. Each tube was planted with four seeds, \nafter which two were removed (leaving two plants) one week after germination. \nThe field experiment was carried out for five weeks from July to August 2014. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201622\n\n\n\nThe study was conducted in a Randomized Complete Block Design (RCBD) with \nthree replications and three treatments: T1= Overhead Sprinkler Irrigation (SPR); \nT2= Drip Irrigation (DPR); and T3= Capillary Wick Irrigation (WCK). Each \nexperiment plot consisted of single tray or tube stand that accommodated 10 RB \n900 tubes with 2 water spinach (Ipomoea reptans) seedlings. The total number of \nwater spinach seedlings used were 20 plants per plot x 9 plots or 180 plants. \n\n\n\nEach tray stand measured 1.5 m long and 0.5 m wide. Within the experimental \nblock, each treatment was  separated from the other by a width of 1.6 m, and by  \na length of 3.1m between the blocks.  The total area of the experiments was about \n50.29 m2 (borders excluded). Water flow from the overhead sprinklers was adjusted \nin such a way as  to prevent the water falling onto the neighbouring plots. A plastic \nsheet was attached around the rain shelter to stop crosswind that might disturb \nthe distribution of water during irrigation by the overhead sprinklers. Irrigation \nwas carried out daily in the mornings for all treatments except the capillary wick \nsystem.  A daily total of 45 mL of water was supplied to each seedling under SPR \nand DRP while for WCK, a known amount of water was applied every day in \nthe PVC, to allow the wick to supply the water to the plants because it is a self \nwatering system. The PVC was emptied every day in order to determine how \nmuch was taken up by the plants; the water uptake  averaged 53 mL per day per \ntube. Water uptake in the capillary wick system could  not be controlled as it is \na self watering system where there is continuous water uptake  through the wick \nvia capillary action. \n\n\n\nPhysical Properties\nThe water content in growing media was measured using a moisture meter \n(FieldScout TDR 100-6440FS, Spectrum Tecnology, Inc., USA) every week to \nmonitor the moisture status of the media. The BX-1 media was analysed for its \nphysical and chemical properties.\n\n\n\nIn terms of  physical analysis, bulk density, moisture content, total porosity \nand water retention of the media were determined. Bulk density was determined \nusing the core method (Blake and Hartge, 1986). Water retention was determined \nusing the pressure plate and pressure membrane described by Richards and \nFireman (1943) and Richards (1947). \n\n\n\nChemical Properties\nTotal C, N and S was measured using CNS analyser (Nelson and Sommers, 1982). \nTotal P, K, Ca and Mg were analysed using dry ashing method. Autoanalyser \n(AA) was used to measure total phosphorus (P) while the content of potassium \n(K), calcium (Ca) and magnesium (Mg) content was  analysed using the atomic \nabsorption spectrophotometer (AAS). Cation exchange capacity (CEC) and \nexchangeable bases were determined by leaching method using 1 M NH4OAc \n(pH 7) method (Thomas, 1982).  The displaced  exchangeable bases and NH4\n\n\n\n+ \nwere determined using  the AA  and AAS.  The pH and EC were also determined \nin water using 1: 5 media: solution ratio.\n\n\n\nNabayi et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 23\n\n\n\n For each week for the five weeks of the experiment, leachate was collected \nfrom each tube, pooled, and then analysed for volume of leachate and N, P, K, Ca, \nand Mg nutrient content after filtering. The nutrients N and P were analysed using \nautoanalyser and nutrients K, Ca and Mg using AAS. The same leachate was  also \nmeasured for  pH and EC.\n\n\n\nEach week, plant samples from  two tubes in each experimental unit were \ndestructively measured for their fresh and dry weights (leaves, stem, and roots), \nleaf area, plant height, and plant nutrient content (N, P, K, Ca, and Mg).\n\n\n\nNutrients in plant tissue were determined using wet digestion method (Jones, \n2001). The N, P, K and Ca, Mg contents were determined by AA and AAS \nrespectively.\n\n\n\nAll data collected were tested using  the statistical analyses system (SAS 9.4 \nSAS system for windows by SAS Institute Inc., Cary, NC, USA). Analysis of \nVariance(Anova) was used to determine the significant treatment effect on various \nmeasured properties with the significance set at  p<0.05. Student \u2013Newman-Keuls \n(SNK) test for means separation was used to detect significant difference between \nmeans.\n\n\n\nRESULTS AND DISCUSSION\nThe result of the BX-1 physical properties (Table 1) showed the media to be very \nlight (0.135 Mg m-3), with about 60% water. It has 0.04 m3 m-3 of available water. \nPorosity is one of the most important physical properties in a growing media \nbecause it determines the space available in a container for air (aeration), water, \nand root growth (Bunt, 1988). Aeration is important because the root system \n\u201cbreathes\u201d (exchanges oxygen and carbon dioxide) in the large, air-filled pores \n(macropores). Poor aeration will adversely affect root form (morphology) and \nstructure (physiology) and will lead to decreased seedling vigour (Scagel and \nDavis, 1988).\n\n\n\nTABLE 1\nPhysical and chemical properties of BX-1 media\n\n\n\nIrrigation Systems for Rubber Nursery\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201624\n\n\n\nThe result of the chemical properties (Table 1) showed that the pH of the \nmedia is within the desired range with  most of the nutrients being available for \nthe plants. An increase in EC of the bulk solution in the growing medium suggests \nthat the fertiliser applied is more than what the plant can take up, while a decrease \nin EC indicates non-availability of nutrients for plant growth (Van, 1999). EC in \nan important parameter in managing the fertility status of growing media. The \npH of growing media is also vital because it can affect the availability of micro \nnutrients to plants (Bailey and Bilderback, 1997). The chemical properties which \ndetermine suitability of a growing media are primarily pH, cation-exchange-\ncapacity (CEC), and fertility (Miller and Jones, 1995). The CEC is also a measure \nof a soil or potting media\u2019s ability to hold nutrients(Miller and Jones, 1995). The \nproportion of the macro nutrients in the media (Table 1) shows that the levels of P, \nK, Ca and Mg are high. This was due to blending of the BX-1 with the fertiliser to \nsustain the growth of seedlings for  a longer time. The percentages of carbon (C) \nand nitrogen (N) in peat containing growth media were in the range of 40 to 60 \n% and 0 to 5 %, respectively (Bujang et al., 2011: Huat, 2004), but the C: N ratio \nwas rather high indicating the possibility of immobilisation of N. \n\n\n\nThe ANOVA result for volumetric water content (VWC) showed that there \nwas no significant difference (p<0.05) between the treatments for each week but \nthe weeks differed significantly from one another (Figure 2). The chart shows a \ndecreasing trend of water content in media BX-1 throughout the week. As the \nplant grew, it used more water making the media contain less water. Plant growth \nwas directly related to soil moisture content (Hagan, 1955). As the rate of growth \nincreased  the water became less in the growing medium. This result showed \nthat the amount of water given every day (45 mL) was enough but ought to be \nincreased if the growing period of the crop is longer than 4 weeks.\n\n\n\nFigure 2: Volumetric water content vs weeks of treatments\n\n\n\n2 \n \n\n\n\n  \nFigure 2: Volumetric water content of the treatment weeks \n\n\n\n\n\n\n\nTable 2. Weeks and treatments interaction for plant growth parameters \n\n\n\nWeek Treatments \n\n\n\nTotal \nfresh \n\n\n\nweight \n\n\n\nTotal \ndry \n\n\n\nweight \n\n\n\nRoot \ndry \n\n\n\nweight  \n\n\n\nTotal \nleaf \narea \n\n\n\n                      g plant-1                     cm2 plant-1 \n\n\n\n \nSPR 1.44a 0.31a 0.04a 20.47a \n\n\n\n1 DRP 1.44a 0.31a 0.04a 20.47a \n  WCK 1.44a 0.31a 0.04a 20.47a \n\n\n\n \nSPR 10.70a 0.97a 0.22a 129.04a \n\n\n\n2 DRP 8.61a 0.79a 0.16a 101.1a \n  WCK 8.71a 0.70a 0.16a 96.95a \n\n\n\n \nSPR 30.78a 2.45a 0.72a 292.83a \n\n\n\n3 DRP 28.37a 2.29a 0.63a 298.63a \n  WCK 33.87a 2.68a 0.76a 311.15a \n\n\n\n \nSPR 43.70b 4.04b 1.38b 349.81b \n\n\n\n4 DRP 49.94b 4.76b 1.49b 430.64a \n  WCK 60.85a 7.07a 3.05a 440.25a \n\n\n\nSPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system. \n\n\n\nFor the same week and plant parameter, means (n=3) followed by same letter are not \nsignificantly different at 5% significant level by SNK. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0 \n\n\n\n10 \n\n\n\n20 \n\n\n\n30 \n\n\n\n40 \n\n\n\n0 1 2 3 4 5 \n\n\n\nVo\nlu\n\n\n\nm\net\n\n\n\nric\n w\n\n\n\nat\ner\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n (%\n) \n\n\n\nWeek number \n\n\n\na \n\n\n\nb \n\n\n\nc \nd \n\n\n\nNabayi et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 25\n\n\n\nTable 2 shows that the significant difference (p<0.05) between the treatments \nin terms of total fresh weight, total dry weight, root dry weight and total leaf area \noccurred in week 4. For the total fresh weight, Table 2 shows that WCK differed \nsignificantly from SRP and DRP irrigation systems in recording the highest total \nfresh weight of 60.85g as compared with SPR (43.70g) and DRP (49.94 g) on \naverage. SPR recorded the lowest total  fresh weight  probably due to lower water \nuse efficiency of the system and high water loss. The better growth in the WCK \ntreatment was due to the  highest  uptake of plant nutrients N, P, and K in this \ntreatment. Capillary wick irrigation compared with overhead irrigation reduced \ncumulative irrigation volume by 86% without sacrificing plant growth (Bryant \nand Yeager, 2002). Overhead sprinklers for small containers are extremely non-\nuniform in watering (Beeson and Yeager, 2003). \n\n\n\nTABLE 2\nTreatments interaction for plant growth parameters (weeks)\n\n\n\nWCK gave the highest total dry weight (7.07 g) and root dry weight (3.05 \ng) (Figure 3a and 3b), which differed significantly (p<0.05) from SPR and DRP. \nThere were no significant differences between SPR and DRP in terms of total \ndry weight and root dry weight. Water plays an important role in dry matter \naccumulation, because the nutrients have to be in solution before they can be \ntaken up by the roots; this could be the reason why WCK had the highest total \ndry weight.\n\n\n\nIrrigation Systems for Rubber Nursery\n\n\n\n2 \n \n\n\n\n  \nFigure 2: Volumetric water content of the treatment weeks \n\n\n\n\n\n\n\nTable 2. Weeks and treatments interaction for plant growth parameters \n\n\n\nWeek Treatments \n\n\n\nTotal \nfresh \n\n\n\nweight \n\n\n\nTotal \ndry \n\n\n\nweight \n\n\n\nRoot \ndry \n\n\n\nweight  \n\n\n\nTotal \nleaf \narea \n\n\n\n                      g plant-1                     cm2 plant-1 \n\n\n\n \nSPR 1.44a 0.31a 0.04a 20.47a \n\n\n\n1 DRP 1.44a 0.31a 0.04a 20.47a \n  WCK 1.44a 0.31a 0.04a 20.47a \n\n\n\n \nSPR 10.70a 0.97a 0.22a 129.04a \n\n\n\n2 DRP 8.61a 0.79a 0.16a 101.1a \n  WCK 8.71a 0.70a 0.16a 96.95a \n\n\n\n \nSPR 30.78a 2.45a 0.72a 292.83a \n\n\n\n3 DRP 28.37a 2.29a 0.63a 298.63a \n  WCK 33.87a 2.68a 0.76a 311.15a \n\n\n\n \nSPR 43.70b 4.04b 1.38b 349.81b \n\n\n\n4 DRP 49.94b 4.76b 1.49b 430.64a \n  WCK 60.85a 7.07a 3.05a 440.25a \n\n\n\nSPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system. \n\n\n\nFor the same week and plant parameter, means (n=3) followed by same letter are not \nsignificantly different at 5% significant level by SNK. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0 \n\n\n\n10 \n\n\n\n20 \n\n\n\n30 \n\n\n\n40 \n\n\n\n0 1 2 3 4 5 \n\n\n\nVo\nlu\n\n\n\nm\net\n\n\n\nric\n w\n\n\n\nat\ner\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n (%\n) \n\n\n\nWeek number \n\n\n\na \n\n\n\nb \n\n\n\nc \nd \n\n\n\nSPR = Overhead sprinkler, DRP=Drip, WCK=Capillary wick irrigation system.\n\n\n\nFor the same week and plant parameters, means (n=3) followed by the same letter\nare not significantly different at 5% significant level.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201626\n\n\n\nThe mineral nutrients of P and N exerted pronounced influence on \nphotosynthate and dry matter partitioning between root and shoots (Costa et al., \n2002). Increased root growth contributes to root biomass and root dry weight \nunder higher atmospheric CO2 regardless of species or study conditions (Rogers \net al., 1994; 1996).\n\n\n\nLeaf area is also a function of water content of plants. From Figure 3c, it can \nbe seen that the treatments differed significantly  in  total leaf  area and this was \nseen in week 4. There was no significant (p>0.05) difference between WCK and \nDRP treatments but they differed from SPR treatment significantly (p\u22640.05). This \nwas because WCK and DRP irrigation systems used more water compared to the \n\n\n\nNabayi et al.\n\n\n\n3 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Treatments according to weeks interaction for (a). Total Dry Weight (b) Root Dry \nWeight (c) Total Leaf Area. SPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick \nirrigation system \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0 \n\n\n\n2 \n\n\n\n4 \n\n\n\n6 \n\n\n\n8 \n\n\n\n10 \n\n\n\n0 1 2 3 4 5 \n\n\n\nTo\nta\n\n\n\nl (\ng \n\n\n\ndr\ny \n\n\n\npl\nan\n\n\n\nt-1\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\na \n\n\n\nb \n\n\n\nb \n\n\n\n0 \n\n\n\n100 \n\n\n\n200 \n\n\n\n300 \n\n\n\n400 \n\n\n\n500 \n\n\n\n0 1 2 3 4 5 \n\n\n\nLe\naf\n\n\n\n a\nre\n\n\n\na \n(c\n\n\n\nm\n2  p\n\n\n\nla\nnt\n\n\n\n-1\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\nb \n\n\n\na \na \n\n\n\nc \n\n\n\n0 \n\n\n\n1 \n\n\n\n2 \n\n\n\n3 \n\n\n\n4 \n\n\n\n0 1 2 3 4 5 \n\n\n\nRo\not\n\n\n\ns (\ng \n\n\n\ndr\ny \n\n\n\npl\nan\n\n\n\nt-1\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\na \n\n\n\nb \nb \n\n\n\nb \n\n\n\na \n\n\n\nFigure 3: Treatments according to weeks interaction for (a). Total Dry Weight (b) \nRoot Dry Weight (c) Total Leaf Area. SPR= Overhead sprinkler, DRP= Drip, WCK= \n\n\n\nCapillary wick irrigation system\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 27\n\n\n\nSPR irrigation system, which is the worst of the three systems studied. SPR had \nabout 90% water loss (900 mL out of 1000 mL) during application due to wind \nand canopy interception which led to  the system appying more water outside \nthe container compared to the two water saving systems (DRP and WCK) where \nwater loss occurred only by leaching and evaporation.\n\n\n\nTable 3 shows the tissue analysis of water spinach under three irrigation \nsystems. ANOVA found significant interaction between weeks and treatment in \nterms of N, P, and K contents in the plant tissue  but no significant difference in \nMg and Ca content between the treatments at 5% level.\n\n\n\nTABLE 3\nInteraction between treatment and weeks for tissue analysis of N, P and K.\n\n\n\n   SPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation \nsystem.\n\n\n\n For the same week and nutrient content, means (n=3) followed by the same \nletter are not significantly different at 5% level.\n\n\n\nDespite not been significant between treatments (for Ca and Mg), the Ca \ncontent of the plant tissue increased from week 1 to week 4 (Figure 4a) while Mg \ncontent was highest in the first week and lowest in the fourth week (Figure 4b). \nThis could be due to the availability of the nutrients in the growing media (BX-1). \nThere were no sign of deficiency for all the nutrients thoughout the experiment.\n\n\n\nFor the N, P and K nutrient contents, ANOVA showed significant (p<0.05) \ndifference in interaction between  weeks and treatments (Table 3). From Figure 5, \nit can be seen that higher N, P and K nutrient contents were recorded in treatment \n3 (WCK). In the WCK irrigation system, the media was kept saturated throughout \nthe experiment and the water was applied slowly and steadily to the plant, making \n\n\n\nIrrigation Systems for Rubber Nursery\n\n\n\nTable 3. Weeks and treatments interaction for tissue analysis of N, P and K. \n\n\n\nWeek Treatments \nN P K \n\n\n\ng plant-1 \n\n\n\n \nSPR 3.29a 0.50a 2.44a \n\n\n\n1 DRP 3.86a 0.47a 2.7a \n\n\n\n \nWCK 4.2a 0.52a 3.14a \n\n\n\n \nSPR 3.95a 0.57a 2.83a \n\n\n\n2 DRP 4.00a 0.57a 2.69a \n\n\n\n \nWCK 3.51a 0.45a 2.03a \n\n\n\n \nSPR 3.03a 0.44a 1.74b \n\n\n\n3 DRP 3.46a 0.44a 2.29a \n\n\n\n \nWCK 3.48a 0.49a 2.3a \n\n\n\n \nSPR 2.9b 0.42a 1.9a \n\n\n\n4 DRP 2.6b 0.35b 1.53b \n\n\n\n \nWCK 3.55a 0.48a 2.25a \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201628\n\n\n\navailable the three elements for plant uptake. When plants are N-deficient, \nrelatively more photosynthate is used by roots as they develop greater length to \naid the plant in obtaining more N (Barber, 1995).\n\n\n\nIrrigation methods influence the water-absorption pattern and several other \nfactors which have an  effect on plant growth (Argo and Biernbaum, 1994; Ku and \nHershey, 1991; Molitor, 1990). For maximum water conservation, the capillary \nwick system should be used (Bainbridge, 2001). The lower nutrient content \nobtained in SPR and DRP was due to the higher amount of leachate observed \nin the systems. Westervet (2003) noted  that lack of uniformity in SPR irrigation \nmeans that more water is needed to irrigate all the plants which leads to over- or \nunder-watering of some plants. It is difficult to uniformly irrigate a crop without \nover- or under-watering some plants (Lienth, 1996).\n\n\n\nTable 4 shows a significant difference in the amount of leachate collected at \nthe end of the experiment between the treatments. The highest amount of leachate \nwas recorded in DRP which does not differ significantly(p>0.05) with SPR but \ndoes differ significantly (p<0.05) with the WCK irrigation system (Figure 6). \nIn the WCK irrigation system, there was no leachate in weeks 3 and 4 as the \nplant used up  all the water. Despite the absence of leachate in the WCK system, \nthe plants did not experience water stress and in fact produced  more biomass \ncompared to other treatments.\n\n\n\nTable 4  also shows that WCK had the lowest cumulative N, P, K, Ca, and Mg \nnutrients leachate contents, differing significantly with SRP and WCK and DRP \n(Figure 7a to 7e).\n\n\n\nSPR and DRP did not differ statistically in recording the highest amount of \ncumulative N leachate while the WCK gave the lowest amount of cumulative N \nleachate at 201.79 mg L-1 (Figure 7a). Leaching occurs when inorganic forms of \nN, particularly nitrite (NO2\n\n\n\n-) and nitrate (NO3\n-) are solubilised and carried with \n\n\n\n4 \n \n\n\n\nTable 3. Weeks and treatments interaction for tissue analysis of N, P and K. \n\n\n\nWeek Treatments \nN P K \n\n\n\ng plant-1 \n\n\n\n \nSPR 3.29a 0.50a 2.44a \n\n\n\n1 DRP 3.86a 0.47a 2.7a \n\n\n\n \nWCK 4.2a 0.52a 3.14a \n\n\n\n \nSPR 3.95a 0.57a 2.83a \n\n\n\n2 DRP 4.00a 0.57a 2.69a \n\n\n\n \nWCK 3.51a 0.45a 2.03a \n\n\n\n \nSPR 3.03a 0.44a 1.74b \n\n\n\n3 DRP 3.46a 0.44a 2.29a \n\n\n\n \nWCK 3.48a 0.49a 2.3a \n\n\n\n \nSPR 2.9b 0.42a 1.9a \n\n\n\n4 DRP 2.6b 0.35b 1.53b \n\n\n\n \nWCK 3.55a 0.48a 2.25a \n\n\n\nSPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system \n\n\n\nFor the same week and nutrient content, means (n=3) followed by same letter are not \nsignificantly different at 5% significant level by SNK. \n\n\n\n\n\n\n\n  \nFigure 4: Trend of (a) Ca content of water spinach in weeks (b) Mg content of water spinach \nin weeks \n\n\n\n\n\n\n\n\n\n\n\n0 \n\n\n\n0.1 \n\n\n\n0.2 \n\n\n\n0.3 \n\n\n\n0.4 \n\n\n\n0 1 2 3 4 5 \n\n\n\nPl\nan\n\n\n\nt C\na \n\n\n\n(%\n) \n\n\n\nWeek number \n\n\n\na \n\n\n\nb b \n\n\n\nc \n\n\n\na \n\n\n\n0 \n\n\n\n0.1 \n\n\n\n0.2 \n\n\n\n0.3 \n\n\n\n0.4 \n\n\n\n0 1 2 3 4 5 \n\n\n\nPl\nan\n\n\n\nt M\ng \n\n\n\n(%\n) \n\n\n\nWeek number \n\n\n\na \nb \n\n\n\nc c \n\n\n\nb \n\n\n\nNabayi et al.\n\n\n\nFigure 4: Trend (in weeks) of (a) Ca content of water spinach and  (b) Mg content of \nwater spinach.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 29\n\n\n\nwater through the soil profile or with surface waters (Hodges, 2010). Nitrogen \nconcentration in each treatment was related to the volume of water leached out by  \nRB 900 tube. Zotarelli et al., (2009) also noted the same trend of nitrate leaching \nas water percolated, resulting in the  amount of nitrogen loss decreasing over time. \n\n\n\nIrrigation Systems for Rubber Nursery\n\n\n\nFigure 5: Interaction between treatments and weeks for (a) N tissue content in \nwater spinach (b) P tissue content in water spinach (c) K tissue content in water spinach. \n\n\n\nSPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system\n\n\n\n5 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Interaction between treatments and weeks for (a) N tissue content in water spinach \n(b) P tissue content in water spinach (c) K tissue content in water spinach. SPR= Overhead \nsprinkler, DRP= Drip, WCK= Capillary wick irrigation system \n\n\n\n\n\n\n\n0 \n\n\n\n1 \n\n\n\n2 \n\n\n\n3 \n\n\n\n4 \n\n\n\n5 \n\n\n\n0 1 2 3 4 5 \n\n\n\nPl\nan\n\n\n\nt N\n (%\n\n\n\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\na \n\n\n\nb \nb \n\n\n\n\n\n\n\n0 \n\n\n\n1 \n\n\n\n2 \n\n\n\n3 \n\n\n\n4 \n\n\n\n0 1 2 3\n\n\n\nPl\nan\n\n\n\nt K\n (%\n\n\n\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\na \nab \n\n\n\nb \n\n\n\n\n\n\n\n0 \n\n\n\n0.2 \n\n\n\n0.4 \n\n\n\n0.6 \n\n\n\n0.8 \n\n\n\n0\n\n\n\nPl\nan\n\n\n\nt P\n (%\n\n\n\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\na \na \nb \n\n\n\n(a)\n\n\n\n(b)\n\n\n\n(c)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201630\n\n\n\nThe WCK cumulative leachate for P differed significantly (p\u22640.05) from \nSPR and DPR (Figure 7b) but there was no difference (p>0.05) between SPR and \nDRP. From Figure 7c, it is noted that the highest cumulative means for K leachate 6 \n\n\n\n\n\n\n\n Table 4. Overall cumulative means for nutrients content and volume leachate. \n\n\n\n          Parameters   \n\n\n\nWeek Treatment \nN P K Ca Mg Volume \n    mg L-1     ml \n\n\n\n \nSPR 143.23a 90.08a 251.45a 13.48a 22.05a 183.93a \n\n\n\n1 DRP 133.083a 84.08a 123.03a 13.31a 20.48a 182.83a \n\n\n\n \nWCK 144.66a 90.58a 216.85a 13.71a 22.35a 182.97a \n\n\n\n  SPR 204.35a 153.23a 563.33a 28.86a 45.9a 304.75a \n2 DRP 205.76a 149.36a 166.21b 16.17a 22.81b 290.28a \n  WCK 201.79a 152.06a 264.00b 17.70a 25.87b 292.60a \n\n\n\n \nSPR 232.53a 191.91a 710.64a 40.72a 63.95a 379.58a \n\n\n\n3 DRP 234.58a 184.11a 333.56b 28.44b 42.69b 393.50a \n\n\n\n \nWCK 201.79b 152.06b 264.00b 17.70c 25.87c 292.60b \n\n\n\n  SPR 246.34a 201.56a 779.45a 43.18a 67.57a 474.03a \n4 DRP 245.09a 208.00a 357.54b 29.48b 44.72b 549.06a \n  WCK 201.79b 152.06b 264.00b 17.70c 25.87c 292.60b \n\n\n\nSPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system \n\n\n\nColumn Means (n=3) for the same week and same parameter followed by same letter are not \nsignificantly different at 5% significant level by SNK \n\n\n\n\n\n\n\n  \nFigure 6: Overall cumulative means of leachate for the treatments in 4 weeks. SPR= \nOverhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system \n\n\n\n\n\n\n\n0 \n\n\n\n100 \n\n\n\n200 \n\n\n\n300 \n\n\n\n400 \n\n\n\n500 \n\n\n\n600 \n\n\n\n0 1 2 3 4 5 \n\n\n\nCu\nm\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nle\nac\n\n\n\nha\nte\n\n\n\n (m\nL)\n\n\n\n\n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\nb \n\n\n\na \n\n\n\na \n\n\n\n6 \n \n\n\n\n Table 4. Overall cumulative means for nutrients content and volume leachate. \n\n\n\n          Parameters   \n\n\n\nWeek Treatment \nN P K Ca Mg Volume \n    mg L-1     ml \n\n\n\n \nSPR 143.23a 90.08a 251.45a 13.48a 22.05a 183.93a \n\n\n\n1 DRP 133.083a 84.08a 123.03a 13.31a 20.48a 182.83a \n\n\n\n \nWCK 144.66a 90.58a 216.85a 13.71a 22.35a 182.97a \n\n\n\n  SPR 204.35a 153.23a 563.33a 28.86a 45.9a 304.75a \n2 DRP 205.76a 149.36a 166.21b 16.17a 22.81b 290.28a \n  WCK 201.79a 152.06a 264.00b 17.70a 25.87b 292.60a \n\n\n\n \nSPR 232.53a 191.91a 710.64a 40.72a 63.95a 379.58a \n\n\n\n3 DRP 234.58a 184.11a 333.56b 28.44b 42.69b 393.50a \n\n\n\n \nWCK 201.79b 152.06b 264.00b 17.70c 25.87c 292.60b \n\n\n\n  SPR 246.34a 201.56a 779.45a 43.18a 67.57a 474.03a \n4 DRP 245.09a 208.00a 357.54b 29.48b 44.72b 549.06a \n  WCK 201.79b 152.06b 264.00b 17.70c 25.87c 292.60b \n\n\n\nSPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system \n\n\n\nColumn Means (n=3) for the same week and same parameter followed by same letter are not \nsignificantly different at 5% significant level by SNK \n\n\n\n\n\n\n\n  \nFigure 6: Overall cumulative means of leachate for the treatments in 4 weeks. SPR= \nOverhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system \n\n\n\n\n\n\n\n0 \n\n\n\n100 \n\n\n\n200 \n\n\n\n300 \n\n\n\n400 \n\n\n\n500 \n\n\n\n600 \n\n\n\n0 1 2 3 4 5 \n\n\n\nCu\nm\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nle\nac\n\n\n\nha\nte\n\n\n\n (m\nL)\n\n\n\n\n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\nb \n\n\n\na \n\n\n\na \n\n\n\nNabayi et al.\n\n\n\nTABLE 4\nOverall cumulative means for nutrients content and volume of leachate.\n\n\n\nNotes: SPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system\n Column Means (n=3) for the same week and same parameter followed by the \n\n\n\nsame letter are not significantly different at 5% level. \n\n\n\nFigure 6: Overall cumulative means of leachate for the treatments in 4 weeks.\nSPR= Overhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 31\n\n\n\nwas obtained in SPR, which differs (p<0.05) from WCK and DRP irrigation \nsystems. Among the treatments, WCK showed the lowest in cumulative means \nfor leachate of P, recording only 33.8% of the SPR potassium leachate. There \nwas no increase in N, P, K, Ca and Mg leachate in WCK after week 2 as there \nwas no leachate in the treatment. The amount of P removed in the BX-1 media by \ndifferent irrigation system was very low compared with other nutrients measured. \nAs P is an immobile element, its amount in the leachate is expected to be less.  \nPhosphorous content in runoff increased after fertilisation (Renou et al., 2000). \nAs the WCK system produced  a lesser amount of leachate,  naturally the amount \nof P was also less. Substantial amounts of the total available K in peat soil is \nalways present in soil solution. Hence, K is strongly mobile and prone to leaching. \nIn addition, K fixation is almost absent in peat soil sand despite its high cation \nexchange capacity, peat soils do not readily adsorb exchangeable K (Andrisse, \n1988). As shown in this experiment, a higher amount of K leachate was recorded \ncompared to the other nutrient elements. \n\n\n\nFrom Figure 7d and 7e, it can be seen that the highest cumulative means for \nCa and Mg were obtained from the leachate  in the SPR irrigation system (43.17 \nand 67.5 mg L-1), which differs significantly (p<0.05) from DRP (29.47 and 44.72 \nmg L-1) and WCK (17.69 and 25.82 mg L-1). The lowest was obtained in the WCK \nsystem because there was no leachate after week 2. Calcium and Mg are mostly \ncations that can be leached from most soils. Considerable amounts of Mg can be \nleached from sandy soils especially after application of fertilisers (Havlin et al., \n1999).\n Figure 8 shows cumulative water use and water use efficiency (WUE) of \nthe three irrigation systems.. The WCK system gave the highest cumulative water \nuse which differed significantly (p>0.05) from SPR and DRP irrigation systems, \nbut between SPR and DRP there was no difference (p<0.05). WCK consumed the \nmost amount of water which explains why it produced the highest plant growth. \nNonetheless, there was no difference in water productivity (amount of biomass \nproduced per unit water consumed) between the three treatments (data not shown). \nThe WCK system had the highest water use because water was non-limited, and \naccumulation of dry matter is dependent not only on nutrients and solar radiation \navailability but also on water availability. \n\n\n\nHowever, WUE (the amount of water used per unit water applied) for WCK \nwas highest, differing significantly (p<0.05) from other treatments, while  the \nlowest was obtained in the SPR system (Figure 8b). There was a huge amount of \nwater loss in the SPR treatment, where water wastage was more than 90%. The \nWCK  system recorded the highest water use efficiency recorded because of the \nway water was applied and taken up by the plants. The system supplied what the \nplants needed exactly, with little wastage. WUE is among the most important \nindices for determining optimal water management practices (Kharrou et al., \n2011). Increasing the efficiency of water use by crops is of vast concern because \nof the increasing demand for water, yet the desired effects are barely achieved \n(Hatfield et al., 2001).\n\n\n\nIrrigation Systems for Rubber Nursery\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201632\n\n\n\n7 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 7: (a) Cumulative N leached by different water treatments (b) P leachate content by \ntreatments in weeks (c) K leachate content by treatments in weeks (d) Ca leachate content by \n\n\n\n0 \n\n\n\n100 \n\n\n\n200 \n\n\n\n300 \n\n\n\n0 1 2 3 4 5 \n\n\n\nCu\nm\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nN\n le\n\n\n\nac\nhe\n\n\n\nd \n(m\n\n\n\ng \nL-\n\n\n\n1 ) \n\n\n\nWeek number \nSPR DRP WCK \n\n\n\nb \n\n\n\na \na \n\n\n\na \n\n\n\n0 \n\n\n\n20 \n\n\n\n40 \n\n\n\n60 \n\n\n\n80 \n\n\n\n0 1 2 3 4 5 \n\n\n\nCu\nm\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nCa\n le\n\n\n\nac\nhe\n\n\n\nd \n(m\n\n\n\ng \nL-1\n\n\n\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\nb \nc \n\n\n\na \n\n\n\nd \n\n\n\n0 \n\n\n\n100 \n\n\n\n200 \n\n\n\n300 \n\n\n\n0 1 2 3 4 5 \n\n\n\nCu\nm\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nP \nle\n\n\n\nac\nhe\n\n\n\nd \n(m\n\n\n\ng \nL-1\n\n\n\n) \n\n\n\nWeek number \nSPR DRP WCK \n\n\n\nb \n\n\n\na \na \n\n\n\nb \n\n\n\n0 \n\n\n\n20 \n\n\n\n40 \n\n\n\n60 \n\n\n\n80 \n\n\n\n0 1 2 3 4 5 \n\n\n\nCu\nm\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nM\ng \n\n\n\nle\nac\n\n\n\nhe\nd \n\n\n\n(m\ng \n\n\n\nL-1\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\nb \n\n\n\nc \n\n\n\na \ne \n\n\n\n0 \n\n\n\n200 \n\n\n\n400 \n\n\n\n600 \n\n\n\n800 \n\n\n\n1000 \n\n\n\n0 1 2 3 4 5 Cu\nm\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nK \nle\n\n\n\nac\nhe\n\n\n\nd \n(m\n\n\n\ng \nL-1\n\n\n\n) \n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\na \n\n\n\nb \nb \n\n\n\nc \n\n\n\nNabayi et al.\n\n\n\nFigure 7: (a) Cumulative N leached by different water treatments (b) P leachate content \nby treatments in weeks (c) K leachate content by treatments in weeks (d) Ca leachate \ncontent by treatments in weeks (e) Mg leachate content by treatments in weeks. SPR= \n\n\n\nOverhead sprinkler, DRP= Drip, WCK= Capillary wick irrigation system.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 33\n\n\n\nCONCLUSIONS\nPhysical and chemical analysis carried out on the BX-1 media showed that it \ncan store large amounts water but available water content to the plant might \nbe low depending on the growing plant. The pH was 5.54 which is within the \ndesired range of most growing media and suitable for many plant species. BX-1 \nis considered as non-saline with an EC of 0.9 dS m-1. Total carbon and total \nnitrogen  were 41.48 % and 1.41%, respectively. The nutrients of P, K, Ca and Mg \nin the BX-1 media were 0.32 %, 0.66 %, 1.02 % and 0.35%, respectively. This  \nshows a high nutrient content and this was  due to the addition of slow releasing \nfertilisers and lime to make the media more suitable for seedling production. It \ncan be concluded that the best irrigation system for the use in RB-900 tube was \nthe WCK irrigation system. The WCK system gave the highest growth for roots \nby dry weight and leaf area. This was because the WCK treatment had the lowest \namount of leachate and nutrient losses, so it had the highest nutrient content in the \nplant for N, P, and K. WCK also had the highest water use efficiency, but there \nwas no difference in water productivity between the three irigation system. WCK \nconsumed the highest amount of water (but had least water wastage) to produce \nthe highest amount of roots biomass and leaf area compared to drip and overhead \nsprinkler treatments.\n\n\n\nACKNOWLEDGEMENT\nWe like to thank Humibox Sdn. Bhd. for providing the financial support for this \nresearch.\n\n\n\n8 \n \n\n\n\ntreatments in weeks (e) Mg leachate content by treatments in weeks. SPR= Overhead \nsprinkler, DRP= Drip, WCK= Capillary wick irrigation system \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 8:  (a) Cumulative water use of water spinach under three irrigation system (b) Water \nuse efficiency of water spinach under three irrigation system. SPR= Overhead sprinkler, \nDRP= Drip, WCK= Capillary wick irrigation system \n \n\n\n\nACKNOWLEDGEMENT \n\n\n\nWe like to thank Humibox Sdn. Bhd. for providing the financial support for this research. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\nAndriesse, J. P. 1988. Nature and Management of Tropical Peat Soils. FAO Soils \n\n\n\n Bulletin 59, FAO, Rome. \n\n\n\n0 \n\n\n\n200 \n\n\n\n400 \n\n\n\n600 \n\n\n\n800 \n\n\n\n1000 \n\n\n\n1200 \n\n\n\n1400 \n\n\n\n0 1 2 3 4 5 \n\n\n\nCu\nm\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nw\nat\n\n\n\ner\n u\n\n\n\nse\nd \n\n\n\n(m\nL)\n\n\n\n\n\n\n\nWeek number \n\n\n\nSPR DRP WCK \n\n\n\na \n\n\n\nb \nb \n\n\n\na \n\n\n\n0 \n\n\n\n0.01 \n\n\n\n0.02 \n\n\n\n0.03 \n\n\n\n0.04 \n\n\n\n0 \n\n\n\n0.2 \n\n\n\n0.4 \n\n\n\n0.6 \n\n\n\n0.8 \n\n\n\n1 \n\n\n\n0 1 2 3 4 5 \n\n\n\nW\nU\n\n\n\nE \n(L\n\n\n\n L\n-1\n\n\n\n) f\nor\n\n\n\n S\nPR\n\n\n\n\n\n\n\nW\nU\n\n\n\nE \n(L\n\n\n\n L\n-1\n\n\n\n) f\nor\n\n\n\n D\nRP\n\n\n\n a\nnd\n\n\n\n W\nCK\n\n\n\n\n\n\n\nWeek number \n\n\n\nDRP WCK SPR \n\n\n\na \n\n\n\nb \nc \n\n\n\nb \n\n\n\nIrrigation Systems for Rubber Nursery\n\n\n\nFigure 8:  (a) Cumulative water use of water spinach under three irrigation system (b) \nWater use efficiency of water spinach under three irrigation system. 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Semifloat system for cost effective \nproduction of oil palm seedlings in the pre-nursery. Planter. 89: 649-656.\n\n\n\nZotarelli, L., Scholberg, J. M., Dukes, M. D., Mu\u00f1oz-Carpena, R. and J. Icerman. \n2009. Tomato yield, biomass accumulation, root distribution and irrigation \nwater use efficiency on a sandy soil, as affected by nitrogen rate and irrigation \nscheduling. Agricultural Water Management. 96(1): 23-34. \n\n\n\nNabayi et al.\n\n\n\n\n\n"
"\n\nINTRODUCTION\n\n\n\npaper industries generated from different stages of paper making. At present, \nMalaysia generates about 1 million metric tonnes of RPMS annually from 19 \n\n\n\n Assessment on the Quality of Recycled Paper Mill Sludge \nMixed with Oil Palm Empty Fruit Bunch Compost\n\n\n\nA. Rosazlin1,2*, C.I. Fauziah2, K. Wan Rasidah3, A.B. Rosenani2 \nand D.R. Kala2\n\n\n\n1Institute of Biological Sciences, Faculty of Science, University of Malaya,\n50603 Kuala Lumpur, Malaysia\n\n\n\n \n2Department of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia, 43400 UPM Serdang, Selangor, Malaysia\n\n\n\n3\n\n\n\nABSTRACT\n\n\n\nthe costs are becoming expensive. Therefore, an alternative disposal through land \n\n\n\ncharacteristics of composted RPMS and EFB mixtures, their phytotoxicity and \nthe effect of the composts on plant growth performance. Composting experiment \n\n\n\naccelerate decomposition. During composting, the compost was turned every three \ndays to ensure that the material on the outside of the pile was turned from the center \noutwards to dissipate heat.  The RPMS and EFB compost mixtures were evaluated \nfor physical, chemical, phytotoxicity and short term plant growth effects. These \n\n\n\nThe concentrations of heavy metals were also within the recommended level of the \n\n\n\nKey words: Compost quality, nutrients, heavy metals, phytotoxicity, compost   \n\n\n\n___________________\n*Corresponding author : E-mail: rosazlin@yahoo.com\n\n\n\n\n\n\n\n\nA. Rosazlin, C.I. Fauziah, K. Wan Rasidah, A.B. Rosenani and D.R. Kala\n\n\n\nevery year which leads to environmental problems in the country. Current practice \n\n\n\nscarce and land cost is becoming more expensive. Some of the wastes from the \npaper manufacturing mill are categorized under hazardous toxic waste by the \n\n\n\nas scheduled waste and unutilized in Malaysia. However, studies need to be \ncarried out to investigate the potential use of RPMS as a value added product, an \napproach that may partly solve the disposal problem. \n\n\n\nAccording to Das et al.\ntechnology in reducing waste problems by converting complex materials into useful \n\n\n\n et al\nmodify the microbial community composition and decrease plant pathogens activity \nby enhancing competition and antagonism among microbes and reducing mass and \nvolume, which makes compost suitable for agricultural applications. Several studies \non the feasibility and the advantage of composting pulp and paper mill sludge have \nbeen carried out mainly in the temperate countries, especially on the composting \nprocesses, physical, chemical and microbial characteristics of the compost and \nthe suitability of compost for plant growth (Atikinson et al., 1997; Graydon et al. \n1999 and Marche et al. et al.\npaper  mill sludge with hardwood  sawdust could be successfully achieved \nwith parameters such as aeration, moisture and close monitoring of C/N ratio.\n\n\n\net al.\n\n\n\nwaste disposal in the country. \nThis study was conducted to determine the physico-chemical characteristics \n\n\n\nof composted RPMS and EFB mixtures, their phytotoxicity, and the effect of the \ncomposts on plant growth performance.\n \n\n\n\nMATERIALS AND METHODS\n\n\n\nRecycled Paper Mill Sludge and Shredded Empty Fruit Bunches\nRaw recycled paper mill sludge from the biological treatment pond was obtained \nfrom United Paper Board, in Ijok, Selangor. The EFBs were obtained from Jugra \nPalm Mill Sdn. Bhd. at Banting, Selangor, and shredded into small pieces.\n\n\n\nExperimental Design and Treatment\nThe composting experiment was carried out on a small scale by using a polystyrene \n\n\n\nconducted in the glasshouse unit of the Faculty of Agriculture, Universiti Putra \nMalaysia. The experiment was arranged in a completely randomized design \n\n\n\n\n\n\n\n\n51\n\n\n\nRecycled Paper Mill Sludge Mixed with Oil Palm Empty Fruit Bunch Compost Quality\n\n\n\ncontent to accelerate decomposition. During composting, the compost was turned \nevery three days to ensure that the material on the outside of the pile was turned \nfrom the center outwards to dissipate the heat.  The temperature in the core of the \nmaterial was recorded. \n\n\n\nCompost Analysis\nCompost was sampled from each box during composting for the analysis of \nNH4\n\n\n\n+-N, NO3\n- -N, Total N and Total C. The composts were analyzed for their \n\n\n\nof volume reduction was calculated by taking the difference between the volume \nof the composting material at the beginning and volume of the compost after the \n\n\n\ndetermined from the supernatant of the mixed sample and distilled water ratio \n\n\n\ncollected from the saturated paste using the EC meter. Total carbon was measured \n\n\n\nMineral N (NH4\n+-N and NO3\n\n\n\n-\n\n\n\n+, Ca , and Mg\ndetermined by the NH4\n\n\n\nAdsorbed NH4\n+ was displaced with potassium sulfate solution and determined \n\n\n\nTotal PAHs were determined to the method described by Zakaria et al.,\n3\n\n\n\n(Zarcinas et al.\n\n\n\nTest for phytotoxicity of composts\n\n\n\na petri dish with 15 seeds of green bean and sweet corn. Plates were incubated at \n\n\n\nnumber of germinated seeds and average root length per dish were expressed in \npercentage based on control. Phytotoxicity results were expressed as germination \n\n\n\n\n\n\n\n\nindex obtained from the percent germination and percent root length divided by \n et al.\n\n\n\nPotting Media. The  effect of  the compost  on  plant growth was \n\n\n\nRPMS: EFB, respectively.  Seeds of chillies, corn and tomato were used as \ntest plants to determine plant growth performance at 14 days after sowing.\n\n\n\nStatistical Analysis\nAll experimental data were analyzed statistically using analysis of variance \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nCharacteristics of Raw Recycled Paper Mill Sludge and Shredded Empty Fruit \nBunches. \n\n\n\nin Table 1. The raw RPMS were wet, sticky and formed cakes and emitted an \n\n\n\nthis condition allows air to pass freely through the compost pile (Suhaimi and \n\n\n\nAccording to Kala et al\nmixtures with sewage sludge resulted in a more rapid rise in temperature.  \n\n\n\nliming agent for amelioration of acid soils. The concentration of N in RPMS was \net al\n\n\n\nlarge amount of ammonia or urea to the aeration tanks to allow for microbial growth.  \n\n\n\n-1.  Meanwhile, the \nconcentration of dioxin in RPMS was 4.87 pg g-1 which is below the recomended \nvalue by USEPA. Therefore, RPMS does not pose any toxicity problem to the \nenvironment. \n\n\n\nIn general, the concentration of heavy metals in RPMS and EFB did not \n\n\n\ndocument for metal contamination for land application of sludge (Commission of \n\n\n\nexceed the limit. Therefore, it is important to closely monitor the effect of RPMS \nutilization as materials for composting as they may pose metal toxicity hazard.\n\n\n\nA. Rosazlin, C.I. Fauziah, K. Wan Rasidah, A.B. Rosenani and D.R. Kala\n\n\n\n\n\n\n\n\n53\n\n\n\nCompost Characteristics during Composting\nThe temperatures of composts are shown in Figure 1. The ratio 1:1 of RPMS: \n\n\n\nstudy, it was observed that the compost did not reach thermophilic stage (> \net \n\n\n\nal\nmixture therefore smaller volume was used. The factors controlling temperature \nrise in a compost pile are type of microorganisms present, materials used for \ncomposting, amount and height of the compost and moisture content. Generally, \n\n\n\ntemperature at the center of the compost heap had cooled down to the ambient \n\n\n\noxidation phase of composting was considered completed (Hachicha et al.\n\n\n\nRecycled Paper Mill Sludge Mixed with Oil Palm Empty Fruit Bunch Compost Quality\n\n\n\nTABLE 1\n\n\n\nParameter EFB RPMS *MPC \nOdour nd strong unpleasant odour  \nColour 10YR 5/4 10YR 3/1  \npH nd 7.18 \nEC, mS-1 nd 3.08 \nOrganic matter, % 55.37 38.67 \nNitrogen, %  1.05 4.05 \nCarbon, % 44.19 33.67 \nC/N ratio 42.08 8.33 \nCEC, cmolc kg-1 nd 28.07 \nP, % 0.05 0.78 \nK, % 1.91 0.42 \nCa, % 1.54 0.53 \nMg, % 0.07 0.45 \nAl, % 0.45 2.76 \nFe, % 0.04 0.17 \nAs, mg kg-1 108 186  \nCd, mg kg-1 1.84 4.09 20-40 \nCr, mg kg-1 29.45 37.01  \nCu, mg kg-1 19.20 88 1000-1750 \nMn, mg kg-1 50.77 325  \nPb, mg kg-1 11.95 177 750-1200 \nZn, mg kg-1 29.88 251 2500-4000 \nNi, mg kg-1 18.61 25.21  \nPAHs, ng g-1\n\n\n\nDioxin, pg g 1\n  nd\n\n\n\nnd\n 218\n\n\n\n4.87 50\n  \n\n\n\n    nd \u2013 not detected \n   * maximum permitted concentration (Commission of European Communities, 1986) \n\n\n\na\n\n\n\na USEPA\n\n\n\n\n\n\n\n\n54\n\n\n\nThe C/N ratio of RPMS and EFB mixture were within the range of 37 to \n\n\n\n(Fig. 2). As the composting process progressed, the C:N ratio decreased because \na large part of the C was continuously released as CO  while the majority of the N \n\n\n\nThe NH4\n+-N and NO3\n\n\n\n--N are two important components of mineral N presents in \nmatured composts. In the early stage of composting, mineralization of organic N\nand other nitrogenous compounds such as protein mineralizes N to ammonium \noccurs. Upon oxidation of NH4\n\n\n\n+-N by oxidizing bacteria, NO3\n- -N is accumulated. \n\n\n\nA. Rosazlin, C.I. Fauziah, K. Wan Rasidah, A.B. Rosenani and D.R. Kala\n\n\n\nFig. 2: Changes in C/N ratio of the composting of RPMS and EFB at different\n\n\n\n15\n\n\n\n18\n\n\n\n21\n\n\n\n24\n\n\n\n27\n\n\n\n30\n\n\n\n33\n\n\n\n36\n\n\n\n39\n\n\n\n42\n\n\n\n45\n\n\n\n0 2 4 6 8 10 12\n\n\n\nweeks\n\n\n\nC\n/N\n\n\n\n ra\ntio\n\n\n\n1 RPMS: 1 EFB\n1 RPMS : 2 EFB\n1 RPMS : 3 EFB\n\n\n\nFig. 1: Changes in temperature of the composting of RPMS and EFB at different \n\n\n\n25\n\n\n\n28\n\n\n\n31\n\n\n\n34\n\n\n\n37\n\n\n\n40\n\n\n\n43\n\n\n\n46\n\n\n\n0 10 20 30 40 50 60 70 80 90 100\n\n\n\ndays of composting\n\n\n\nte\nm\n\n\n\npe\nra\n\n\n\ntu\nre\n\n\n\n ( \n0 C\n\n\n\n)\n\n\n\n1 RPMS : 1 EFB\n1 RPMS : 2 EFB\n1 RPMS : 3 EFB\n\n\n\n\n\n\n\n\n55\n\n\n\nThus, in matured compost, NO3\n- -N should be higher than NH4\n\n\n\n+-N. This study \nproves that the composts produce higher NO3\n\n\n\n- -N compared to NH4\n+-N (Figs. 3 \n\n\n\nand 4\n\n\n\ncomposting. The stability of the compost occurs when it resembles humus, has \n\n\n\nRecycled Paper Mill Sludge Mixed with Oil Palm Empty Fruit Bunch Compost Quality\n\n\n\nFig. 3: Changes in NH4\n+ -N ratio of the composting of RPMS and EFB at different\n\n\n\nFig. 4: Changes in NO3\n- -N ratio of the composting of RPMS and EFB at different\n\n\n\n\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n0 2 4 6 8 10 12\n\n\n\nweeks\n\n\n\nN\nH\n\n\n\n4 +\n - \n\n\n\nN\n (m\n\n\n\ng \nkg\n\n\n\n   \n)  \n\n\n\n-1\n\n\n\n1 RPMS : 1 EFB\n1 RPMS : 2 EFB\n1 RPMS : 3 EFB\n\n\n\n\n\n\n\n0\n\n\n\n3\n\n\n\n6\n\n\n\n9\n\n\n\n12\n\n\n\n15\n\n\n\n18\n\n\n\n21\n\n\n\n24\n\n\n\n0 2 4 6 8 10 12\n\n\n\nweeks\n\n\n\nN\nO\n\n\n\n3-\n- N\n\n\n\n (m\ng \n\n\n\nkg\n   \n\n\n\n)\n-1\n\n\n\n1 RPMS : 1 EFB\n1 RPMS : 2 EFB\n1 RPMS : 3 EFB\n\n\n\n\n\n\n\n\n56\n\n\n\nrespectively. The percentage of volume reduction of three compost mixtures after \n\n\n\nRPMS: EFB. The lower ratio of EFB in RPMS and EFB compost mixture may be \ncompacted with RPMS compared to the higher ratio of EFB.\n\n\n\nbased on their pH and organic matter which are in the range of the regulation values \n\n\n\nwas highest in the 1:3 ratio compost mixture and this may have come from the EFB. \n\n\n\ndays of composting are shown in Table 3. The heavy metals content of RPMS \nneed to be monitored as they may pose a hazard of metal toxicity to crops. Due \nto the reduction in volume during composting, an increase in the concentration \n\n\n\nconcentrations of heavy metals were within the recommended level of the \n\n\n\ncompost mixtures. Therefore these RPMS: EFB compost mixtures could be \nrecommended for land application in Malaysia. Except for Cu and Ni, there \n\n\n\nZn and Fe in these compost mixtures. However studies need to be conducted to \ndetermine the long term effect of RPMS: EFB compost mixtures on soil.\n \nPhytotoxicity Test\n\n\n\nA. Rosazlin, C.I. Fauziah, K. Wan Rasidah, A.B. Rosenani and D.R. Kala\n\n\n\n\n\n\n\n1 RPMS : 1 EFB 1 RPMS : 2 EFB 1 RPMS : 3 EFB \n\n\n\n\n\n\n\n\n57\n\n\n\nextracts and the results showed that the three RPMS: EFB compost mixtures had \n\n\n\ngermination. The results show that RPMS:EFB compost mixtures with ratio 1:1 and \n\n\n\nindex in sweet corn and green beans seeds compared to the 1:3 ratio. \n\n\n\nRecycled Paper Mill Sludge Mixed with Oil Palm Empty Fruit Bunch Compost Quality\n\n\n\nPhysical and chemical characteristics of the RPMS and shredded EFB at different ratios \n\n\n\nParameter Compost \n1:1 \n\n\n\n(RPMS:EFB) \n\n\n\nCompost \n1:2 \n\n\n\n(RPMS:EFB) \n\n\n\nCompost \n1:3 \n\n\n\n(RPMS:EFB) \n\n\n\n* Regulation \nvalue \n\n\n\nOdour Produced \nhumus-like \n\n\n\nodour \n\n\n\nProduced \nhumus-like \n\n\n\nodour \n\n\n\nProduced \nhumus-like \n\n\n\nodour \nColour 10YR 2/1 10 YR 2/2 10 YR 2/2 \nVolume reduction, \n% \n\n\n\n60.96 c 70.45 b 72.12 a  \n\n\n\nParticle size (mm) <10 <16 <18 <24 \npH 7.18 a 7.41 a 7.52 a 5.5-8.0 \nEC, mS-1 4.84 a 5.56 a 5.32 a  \nOrganic matter, % 61.62 b 67.67 a 69.19 a >30 \nNitrogen, %  2.68 a 2.38  ab 2.03 b 0.6a \nCarbon, % 52.52 a 44.03 ab 34.90 b  \nC/N ratio 19.60 a 18.50  ab 17.20 b <22 \nCEC, cmolc kg-1 28.61 a 29.67 a 31.93 a \nP, % 0.71 a 0.73 a 0.63 a \nK, % 0.69 b 0.91 ab 1.37 a \nCa, % 0.47 a 0.48 a 0.43 a \nMg, % 0.29 a 0.29 a 0.30 a \nAl, % 2.84 a 0.84 b 1.45 b  \n\n\n\nSame letters in the row indicate that the values are not significantly different at p>0.05, according to \nTukey test. \n* recommended by Council of European Communities (CEC) for compost, source: Zucconi and \nBertoldi 1987) \na minimum mineral content (% dry wt.) \n\n\n\nTABLE 3\n\n\n\nof composting compared to the regulation value.\n\n\n\nHeavy Metal Compost \n1:1 \n\n\n\n(RPMS:EFB) \n\n\n\nCompost \n1:2 \n\n\n\n(RPMS:EFB) \n\n\n\nCompost \n1:3 \n\n\n\n(RPMS:EFB) \n\n\n\n* Regulation \nValue  \n\n\n\nAs, mg kg-1 150 a 100 a 108 a \nCd, mg kg-1 2.48 a 2.48 a 2.71 a 5 \nCr, mg kg-1 15.33 a 26.85 a 27.86 a 200 \nCu, mg kg-1 87.71 a 86.09 ab 79.99 b 150 \nMn, mg kg-1 342 a 449 a 378 a  \nPb, mg kg-1 26.94 a 26.97 a 21.45 a 750 \nZn, mg kg-1 183 a 228 a 199 a 1000 \nNi, mg kg-1 20.29 b 24.71 a 21.47 ab 50 \nFe, % 0.17 a 0.20 a 0.17 a  \n\n\n\nSame letters in the row indicate that the values are not significantly different at p>0.05, according to \nTukey test. \n* recommended by Council of European Communities (CEC) for compost, source: Zucconi and \nBertoldi 1987) \n\n\n\n\n\n\n\n\n58\n\n\n\nA. Rosazlin, C.I. Fauziah, K. Wan Rasidah, A.B. Rosenani and D.R. Kala\n\n\n\nFigure 6 shows the effects of raw RPMS, RPMS and EFB compost mixtures \ncompared to commercial compost as a potting media on the dry matter weight \nof sweet corn, tomato and chilies after 14 days of planting.  Generally, the dry\nmatter weight of sweet corn, tomato and chillies showed the same trend. The \ndry matter weight of these plants decreased with increasing content of EFB in \nthe RPMS: EFB compost mixtures. This could be due to the lower N content \nand C/N ratio in these potting media. Potting media containing composts of 1:1 \nratio RPMS: EFB exhibited the highest dry matter weight in sweet corn, tomato \nand chillies compared to the other treatments. However, the plants could not \nsurvive in raw RPMS alone as potting media, as shown in Figure 7.  \n\n\n\nTABLE 4\nThe effect of RPMS and EFB compost mixtures on the phytotoxicity germination test \n\n\n\nof sweet corn and green bean \n\n\n\nFig. 6: The effect of raw RPM, RPMS and EFB compost mixtures compared to commercial \ncompost on the dry matter weight of sweet corn, tomato and chillies after 14 days planting. \nThe treatments are: a) C, control (mineral soil), b) raw, raw RPMS alone, c) CP, commercial \n\n\n\ncompost and d) 1:1; 1:2; 1:3 (RPMS:EFB) compost ratios.\n\n\n\n\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\nco\nnt\n\n\n\nro\nl\n\n\n\nra\nw\n\n\n\nC\nP\n\n\n\n1:\n1\n\n\n\n1:\n2\n\n\n\n1:\n3\n\n\n\nco\nnt\n\n\n\nro\nl\n\n\n\nra\nw\n\n\n\nC\nP\n\n\n\n1:\n1\n\n\n\n1:\n2\n\n\n\n1:\n3\n\n\n\nco\nnt\n\n\n\nro\nl\n\n\n\nra\nw\n\n\n\nC\nP\n\n\n\n1:\n1\n\n\n\n1:\n2\n\n\n\n1:\n3\n\n\n\nsweet corn tomato chillies\n\n\n\ng/\npl\n\n\n\nan\nt\n\n\n\nd\n\n\n\ne\n\n\n\nd\n\n\n\na\n\n\n\nb\n\n\n\nc\n\n\n\ne\nf\n\n\n\nd\n\n\n\na\n\n\n\nb\n\n\n\nc\n\n\n\ne\nf\n\n\n\nd\n\n\n\na\n\n\n\nb\n\n\n\nc\n\n\n\nSame letters above bars indicate that the values represented by the bars within crop \nare not significantly different at p>0.05, according to Tukey test.  \n\n\n\n10% compost \nextract \n\n\n\nPercent \ngermination \n\n\n\nPercent \nroot \n\n\n\nlength \n\n\n\nGermination \nIndex \n\n\n\nPercent \ngermination \n\n\n\nPercent \nroot \n\n\n\nlength \n\n\n\nGermination \nIndex \n\n\n\n Green Bean Sweet corn \nControl (Distilled \nWater) \n\n\n\n100 100a 2.00a 100 100a 2.00a \n\n\n\nRPMS 1: EFB 1 100 95.38a 1.95a 100 98.53a 1.98a \nRPMS 1: EFB 2 100 87.72ab 1.88ab 100 79.15ab 1.79ab \nRPMS 1: EFB 3 100 63.51b 1.63b 100 69.46b 1.69b \nSame letters in the column indicate that the values are not significantly different at p>0.05, according to \nTukey test. \n\n\n\n\n\n\n\n\n59\n\n\n\nRecycled Paper Mill Sludge Mixed with Oil Palm Empty Fruit Bunch Compost Quality\n\n\n\nCONCLUSION\nThis study suggests that RPMS and EFB has potential for re-utilization as \ncompost for land application. These composts mixtures had no toxicity effects \n\n\n\nIn conclusion, compost with 1:1 volume ratio could be used for land application \n\n\n\nFig.7: The effect of raw RPM, RPMS and EFB compost mixtures compared to commercial \ncompost on the dry matter weight of a) sweet corn, b) tomato and c) chillies after 14 days \nplanting. The treatments are a) control (mineral soil), b) raw RPMS, c) CF, commercial \n\n\n\ncompost and d) 1:1; 1:2; 1:3 (RPMS:EFB) composts ratio. \n\n\n\n\n\n\n\ncontrol Raw \nRPMS \n\n\n\n1: 3  1: 1  1: 2 \nRPMS: EFB \n\n\n\nCF \n\n\n\ncontrol Raw \nRPMS \n\n\n\n1: 3  CF 1: 1  1: 2 \nRPMS: EFB \n\n\n\ncontrol 1: 1  1: 3  1: 2 \nRPMS: EFB \n\n\n\nCF Raw \nRPMS \n\n\n\nc \n\n\n\nb \n\n\n\na \n\n\n\n\n\n\n\n\nA. Rosazlin, C.I. Fauziah, K. Wan Rasidah, A.B. Rosenani and D.R. Kala\n\n\n\nratio will help the paper mill industry to recycle its waste as more RPMS will \nbe used compared to other compost with other ratios which utilize more EFB. \nStudies on utilization of sludge from paper industry are still at an infancy stage \nin Malaysia. Long term studies should be carried out to evaluate the effect of raw \n\n\n\nACKNOWLEDGEMENT\nWe would like to thank Universiti Putra Malaysia for giving us the permission to \n\n\n\nREFERENCES\nAtkinson C.F., D.D. Jones and J.J. Gauthier. 1997. Microbial activities during \n\n\n\ncomposting of pulp and paper-mill primary solids. World Journal of Microbilogy \nand Biotechnology\n\n\n\nBre\n\n\n\nBrem Methods of Soil Analysis \n(Part 2). Chemical and Microbiological Properties, eds.. A.L. Page, R.H. Miller, \n\n\n\nAgronomy, Madison, WI.\n\n\n\nChan, K.W., I. Watson and K.C. Lim. 1981. Use of oil palm waste material for \n\n\n\nMalaysian Soil Science \nSociety.\n\n\n\nCo\n\n\n\nsewage sludge is used in agriculture. Off. J. European Community L181 (Annex \n\n\n\nDas, \nby-products: scale-up and scasonal effects. Compost Science and Utilization. \n\n\n\nApplied \nand Environmental Microbiology\n\n\n\nGraydon A.R. Hackett, Charles A. Easton and Sheldon J.B Duff. 1999. Composting \nBioresource \n\n\n\nTechnology. \n\n\n\n\n\n\n\n\n61\n\n\n\nRecycled Paper Mill Sludge Mixed with Oil Palm Empty Fruit Bunch Compost Quality\n\n\n\nHa\nassessment of composts prepared with olive mill wastewater and agricultural \nwastes. Waste Management\n\n\n\nKal\npalm wastes and sewage sludge for use in potting media of ornamental plants. \nMalaysian Journal of Soil Science. 13:77-91.\n\n\n\nMarche, T., M. Schnitzer, H. Dinel, T.Pare, P. Champagne, H-R. Schulten and G. \n\n\n\nhardwood sawdust mixture. Geoderma. 116:345-356.\nMerry, R.H. and L.S. Spouncer. 1988. The measurement of carbon in soils using a \n\n\n\nmicroprocessor-controlled resistance furnace. Communication in Soil Science \nand Plant Analysis.\n\n\n\nMP http:\\\\:www.mpob.\ngov.my\n\n\n\nResponse of soil microbial communities to compost amendments. Soil Biology \nBiochemistry\n\n\n\nS\nN.C.: SAS Institute.\n\n\n\nRobert, W. and M. Zeeman. Dioxin / Furans: USEPA ecologist risk assessment for \nland application and disposal methods for paper pulp sludge. Chemosphere. \n\n\n\nSuh\nwww. \n\n\n\nagnet. Org / library / eb /505 \n\n\n\nTho Methods of Soil Analysis. Part 2. \nChemical and Microbiological Properties-Agronomy, eds. A.L. Page, R.H. \n\n\n\nSociety of Agronomy, Madison, WI.\n\n\n\nUn\n\n\n\nZakaria, M. P., H. Takada, S. Tsutsumi, K. Ohno, J. Yamada, E. Kouno, and H. Kumata. \n\n\n\nestuaries in Malaysia: A Widespread Input of Petrogenic PAHs. Environmental \nScience and Technology\n\n\n\n\n\n\n\n\nZarcinas, B.A., B. Cartwright and L.R. Spouncer. 1987. Nitric acid digestion and multi-\nelement analysis of plant material by inductively coupled plasma spectrometry. \nCommunication in Soil Scence and Plant Analysis.\n\n\n\nZ\nBiocycle. \n\n\n\nZucconi, F., A. Monaco and M. Forte. 1985. Phytotoxins during the stabilization of \n\n\n\nother wastes. Elsevier Applied Science Publisher, London.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: mildredshine@yahoo.com\n\n\n\nINTRODUCTION\nDifferent parent materials affect the mineralogy, chemistry and morphology of \nsoils under the same conditions such as topography and vegetation, especially \nin the tropics. Differences in physical, chemical and mineralogical properties of \nsoils are related primarily to parent material (Irmak et al., 2007). According to \nGray and Murphy (2002), parent material is a major source of most nutrients \nnecessary for plant growth, with the notable exceptions being oxygen, hydrogen, \nnitrogen and carbon which are primarily derived from the atmosphere and organic \nmaterial. The productivity of soil and its ability to retain nutrients as indicated by \nits cation exchange capacity (CEC) are influenced by parent material. Despite these \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 73-82 (2015)                          Malaysian Society of Soil Science\n\n\n\nSuitability Evaluation of Soils Derived from Dissimilar \nLithological Materials for Maize and Groundnut Production \n\n\n\nin Owerri Agricultural Zone, Southeastern Nigeria\n\n\n\nAhukaemere, C.M*., I.I. Ekpe and I.C. Unachukwu\n\n\n\nDepartment of Soil Science and Technology, Federal University of Technology\nP.M.B. 1526, Owerri, Imo State, Nigeria  \n\n\n\nABSTRACT\nSuitability evaluation of soils derived from three different parent materials in the \nOwerri agricultural zone for  maize and groundnut cultivation was done using the \nFood and Agricultural Organization of the United Nations\u2019 (FAO) conventional \nmethod. Data were obtained from six pedons, two from each parent material. \nThe results showed that despite climatic factors, soil depth, topography, and base \nsaturation, there was no one highly suitable (S1) land for maize and groundnut \ncultivation. The organic carbon contents of the soils derived from Imo clay shale \nwere highly suitable (S1) for groundnut cultivation. Soils derived from alluvium \nand coastal plain sands were only moderately suitable (S2) for groundnut \ncultivation. Generally, the soils derived from the three different parent materials \nwere moderately suitable (S2) for maize production when their carbon contents \nmatched the organic carbon requirement of the crop. In view of the aggregate \nsuitability ratings, the major constraint  for both groundnut and maize cultivation \nin the soils was soil fertility (f). Though not currently suitable (N) for groundnut \nand maize cultivation,  the soils can still produce increased and sustainable crop \nyield if the appropriate husbandry practices are adopted, with particular reference \nto phosphate fertilizer application. \n\n\n\nKeywords: Soil parent materials, maize, groundnut, land suitable \nevaluation, FAO conventional method\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201574\n\n\n\nAhukaemere, C.M., I.IEkpe and I.C. Unachukwu\n\n\n\nobservations, the suitability of soils, under different land use and management \npractices for crop production in Nigeria, has been over emphasized over the years. \nHowever, a good understanding of the suitability of soils derived from varying \nparent materials for the cultivation and sustainability of major staple and cash \ncrops (especially maize and groundnut) in Nigeria is limited.   \n\n\n\nLand evaluation is the process of estimating the potential of a piece of land \nfor alternative uses (FAO, 1983). Land evaluation tells the farmer the suitability \nor limitations of his/her land for specific uses. This is achieved by matching land \nqualities/characteristics with the requirements of the envisaged land use (Udoh et \nal., 2011). The result of land evaluation should reflect not only possible yield, but \nmore importantly, the ease or difficulty of ensuring the sustained use of the parcel \nof landfor a particular purpose (Baja, 2009). \n\n\n\nGlobally, maize (Zea mays) and groundnut (Arachishypogea) are crops \nof economic importance. Groundnut is a major internationally traded cash crop \nand supports the economy of the producing countries,besides providing gainful \nemployment for  many people. Maize, on the other hand, is a stable food of about \n50% of the world\u2019s population (IITA, 2013). In order to achieve success in large-\nscale production of these crops by governments and individuals, more lands must \nbe cultivated. However, there is paucity of information on the extent to which the \nland qualities of soils derived from coastal plain sands, alluvium and Imo clay \nshale can satisfy the agronomic requirements of maize and groundnut. Therefore, \nthis study aimed to evaluate the suitability and limitations of these soils for \noptimum and sustainable productivity of groundnuts and maize. \n\n\n\nMATERIALS AND METHODS\n\n\n\nDescription of the Study Areas\nThe study was conducted at Umuna in Okigwe (latitude 5o46N and longitude 7o15 \nE), Oguta in Oguta LGA (latitude 5o39N and longitude 6o45E) and Ihiagwa in \nOwerri (latitude 5o2 N and 7o04 E), all in Imo State. Umuna, Oguta and Ihiagwa  \nsoils are derived from Imo clay shale (Imo shale group), alluvium deposits and \ncoastal plain sands, respectively (Figure 1). The climate is humid and tropical, \nwith an average annual rainfall of 2500 mm, a mean annual temperature varying \nbetween 27 and 28oC, and relative humidity of between 75 and 80% (NIMET, \n2008). \n\n\n\nField Work \nThree different parent materials (alluvium, Imo clay shale and coastal plain \nsands) were randomly selected for the study. A total of six soil profile pits were \ndug, two for each parent material. These soil profile pits were dug and described \naccording to FAO\u2019s (2006) guidelines, and samples were collected according to \ngenetic horizons. Five representative soil samples were collected from each of \nthe various identified genetic horizons of the soil profiles for laboratory analyses. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 75\n\n\n\nSoil Suitability for Maize and Groundnut Production\n\n\n\nSoil Analyses \nBulked soil samples collected were air-dried, gently crushed and passed through \na 2-mm sieve to obtain fine earth separates. The processed soil samples were \nanalyzed for some physico-chemical properties following procedures given by \nVan Reeuwijk (2002). Particle size analysis was done using the hydrometer \nmethod.Soil pH in 1:2.5 water suspension was measured with a pH meter. Organic \ncarbon was measured by the Walkley and Black method. Available phosphorous \n(P) was determined according to Bray No. 2 method, and total nitrogen (N) was \ndetermined by the microKjeldahl digestion method. Bulk density was determined \nby the core method, CEC was determined by using the neutral ammonium acetate \nmethod, and base saturation was calculated.\n\n\n\nFigure 1: Geological map of the study area\n\n\n\n\n\n\n\n12 \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Geologicalmap of the study area \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201576\n\n\n\nAhukaemere, C.M., I.IEkpe and I.C. Unachukwu\n\n\n\nLand Evaluation and Data Analysis\nMeans of the data generated from soil laboratory analyses were determined. Land \nsuitability evaluation was carried out using the FAO\u2019s (1976; 1983) guidelines \nfor land evaluation. Key environmental factors considered in the evaluation \nwere climate (annual rainfall and temperature), topography (slope) and soils. \nThe criteria employed for the evaluation of soils were soil depth, soil texture, \ndrainage, pH, available P, organic carbon, total N, effective CEC, and base \nsaturation. The identified soil units were placed in suitability classes by matching \ntheir characteristics with the requirements of the test crops. The most limiting \ncharacteristics dictated overall suitability for each soil. The suitability of each \nfactor for each soil unit was classified as highly suitable (S1), moderately suitable \n(S2), marginally suitable (S3) and not suitable (N). \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nLand Qualities/Characteristics of the  Soils\nThe results of the land characteristics are presented in Table 1. The texture of \nthe soils ranged from clay loam in Umuna soils (Imo clay shale) to sandy loam \nin Ihiagwa soils (coastal plain sands). It suggests that coastal plain sands and \nalluvium are sandier than soils derived from the Imo clay shale.  High sandiness \nof soils developed on the coastal plain sands and alluvium could be attributed to \nthe  nature of their parent rocks and is a reflection of the characteristics of the acid \n\n\n\nTABLE 1\nLand qualities and characteristics of the soils\n\n\n\n\n\n\n\n7 \n \n\n\n\n \nTABLE 1 \n\n\n\nLand qualities and characteristics of the soils \n \n\n\n\nParameters Umuna \n(Imo clay shale ) \n\n\n\nOguta \n( alluvium) \n\n\n\nIhiagwa (Owerri)                  \n(coastal plain sand) \n\n\n\nClimate (C)    \nMean annual rainfall (mm) 2000-2500 2000-2500 2000-2500 \nTemperature (oC) 27-30 27-30 27-30 \nRelative humidity (%) 80 80 80 \n \nSoil physical characteristics (S) \nSlope (%) 1.5 1.5 <1 \nDrainage Moderately drained Imperfectly drained Excessively drained \nSoil depth (cm) 200  157 200 \nSand  (g kg-1) 546 838 812 \nSilt (g kg-1) 120 33 20 \nClay (g kg-1) 334 130 168 \nSoil texture CL LS SL \n \nSoil fertility (F) \n\n\n\n\n\n\n\npH  6.12 6.49 5.13 \nTotal nitrogen (g kg-1) 1.01 0.89 0.84 \nAvailable P (mg kg-1) 1.07 2.02 0.76 \nOrganic C (g kg-1) 12.18 11.05 10.32 \nCEC (cmol+ kg-1) 7.21 5.40 8.89 \nBS (%) 82.34 81.57 82.79 \nCL= clay loam, LS=Loamy sand, SL=Sandy loam, BS = Base saturation \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 77\n\n\n\nSoil Suitability for Maize and Groundnut Production\n\n\n\nsands of Southeastern Nigeria (Enwezor et al,. 1989). The coastal plain sands, \nwhich underlie the major part of Southeastern Nigeria, consist of unconsolidated \nyellow and white sand materials which are sometimes cross-bedded with clays, \nsand clays and sometimes pebbles (FDALR 1990; Edet et al., 1994).  On the \nother hand, soils derived from the Imo clay shale are generally deep, moderately \nto imperfectly drained with dark gray sandy clay loam to clay loam surfaces \nunderlain by dark brown to brown, sometimes mottled clay sub-soils (FDARL, \n1990). \n\n\n\nThe pH values of the soils ranged from moderately acidic (5.13) to slightly \nacidic (6.49). The proportions of organic carbon in the soils investigated were low \nwhen compared with the critical level of 2% (20 g kg-1) reported by Chude et al. \n(2011) in Nigerian soils. This may be due to the prevalence of tropical conditions \nwhere the degradation of organic matter occurs at faster rates, thereby leaving less \norganic carbon in the soils (Nayak et al., 2002). Also, the available P contents of the \nsoils of the different parent materials were given very low ratings as their P values \nwere lower than critical levels of 10 \u2013 16 mg kg-1 (Adeoye and Agboola, 1985) \nand 15 mg kg-1 (FPDD, 1990) for soils of southeastern Nigeria.  Low  available P \nobtained across the soils could be a reflection of the low organic carbon contents \nof these soils. Organic compounds in soils increase P availability by forming \norganophosphate complexes that are more easily assimilated by plants, replace  \nH2PO4\n\n\n\n-\n with anions at adsorption sites, coat Fe/Al oxides with humus to form a \n\n\n\nprotective cover, and reduce P fixation (Selassie and Ayanna, 2013). The influence \nof organic carbon in the soil on P availability has been reported by Idigbor etal. \n(2008).  The nitrogen contents of the soils ranged from 0.84 g kg-1 in soils derived \nfrom the coastal plain sand to 1.01 g kg-1 in those derived from Imo clay shale. \nThe low N contents of the soils may be as a result of the high N losses sustained \nin the soils through the leaching of nitrates, as well as the rapid mineralization of \norganic matter resulting from the exposure of soils to high temperatures (Senjobi \nand Ogunkunle, 2011; Mustafa et al., 2011; Uzoho et al., 2014).\n\n\n\nLand Suitability Evaluation\nWhen climatic requirements for groundnut and maize (FAO, 1976; FAO,1983) \n(Tables 2 and 3) were matched with land quality (rainfall and temperature) of \nthe study area (Table 1), all the soils were highly suitable (S1) for groundnut and \nmaize cultivation. These results indicated that the study area is currently ideal in \nterms of climate for the cultivation of both crops. \n\n\n\nSoil physical characteristics considered for the cultivation of maize and \ngroundnut were soil depth, texture, drainage and topography. Soil depth and \ntopography were highly suitable for maize and groundnut cultivation at all the \nsites. Generally, the slope of <2% found by this study may favour mechanical \noperations (Lawal et al., 2012). However, soil texture was highly suitable for \ngroundnut production in Ihiagwa (coastal plain sands) and Oguta (alluvium) \nbut only moderately suitable (S2) for the cultivation of maize. The texture of \nUmuna soils (clay loam) made the soils highly suitable for maize production (Sys, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201578\n\n\n\nAhukaemere, C.M., I.IEkpe and I.C. Unachukwu\n\n\n\n\n\n\n\n8 \n \n\n\n\n\n\n\n\nTABLE 2 \nLand requirements for Maize \n\n\n\n \n Factor suitability rating \nLand \nQualities/characteristics \n\n\n\nHighly \nsuitable (S1) \n\n\n\nModerately \nsuitable (S2) \n\n\n\nMarginally \nsuitable (S3) \n\n\n\nNot suitable \n(N) \n\n\n\nClimate (c)     \nRainfall (mm) >800 700-800 600-700 <600 \nTemperature (oC) 24-30 20-24 15-20 <15 \n \nSoil physical characteristics (s) \nSoil depth (cm) >120 75-120 30-75 < 30 \nSoil texture  CL,L SL, LS LCS         CS \nTopography (t)     \nSlope (%) 0-2 4-8 8-16 >16 \nDrainage Well drained Moderately \n\n\n\ndrained \nImperfectly \ndrained \n\n\n\n     Poor \n\n\n\nSoil fertility status (f)     \npH 6.0-6.5 5.5-6.0 5.0-5.5 < 5.0 \nTotal N (g kg-1) >1.5 1.0-1.5 0.5-1.0 < 0.5 \nAvailable P (mg kg-1) >40 10-40 3-10 < 3 \nCEC (cmo1(+)kg-1) >25 13-25 6-13 < 6 \nOrganic C (g kg-1) >20 10-20 5-10 <5 \nBase saturation (%) >80 40-80 20-40 < 20 \nSource. (FAO, 1976). \nCL = Clay loam, L = Loam, SL = sandy loam, LS = Loamy sand, LCS = Loamy coarse sand, CS = \nCoarse sand. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nTABLE 2\nLand requirements for Maize\n\n\n\nTABLE 3\nLand requirements for groundnut\n\n\n\n\n\n\n\n9 \n \n\n\n\n\n\n\n\nTABLE 3 \nLand requirements for groundnut \n\n\n\n \n Factor suitability rating \nLand \nqualities/characteristics \n\n\n\nHighly \nsuitable (S1) \n\n\n\nModerately \nsuitable (S2) \n\n\n\nMarginally \nsuitable (S3) \n\n\n\nNot suitable \n(N) \n\n\n\nClimate (C)     \nRainfall (mm) >700 600-700 500-600 <500 \nTemperature  (oC) 22-28 18-22 15-18 <15 \n \nSoil physical characteristics (S)   \nSoil depth (cm) > 120  75-120 30-75 <30 \nSoil texture  SL, SiL, LS CL,SiCL S, SC, SiC   C \nTopography (t)     \nSlope (%) 0-2 2-5 5-8 >8 \nDrainage  Well drained  Moderately-\n\n\n\nwell drained  \nImperfectly \ndrained  \n\n\n\nPoorly \ndrained \n\n\n\nSoil fertility status (f)  \npH \n\n\n\n \n5.8-6.2  \n\n\n\n \n5.5-5.7 6.3-6.5 \n\n\n\n \n5.0-5.4, 6.6-7.0 \n\n\n\n \n<5, > 7 \n\n\n\nTotal N (g kg-1) >0.5 0.2-0.5 0.2 <0.2 \nAvailable (mg kg-1) >20 10-20 5-10 <0.5 \nCEC (cmol(+) kg-1) >12 6-12 4-6 <64 \nBase saturation (%) \nOrganic C (g kg-1)  \n\n\n\n>80 \n>12 \n\n\n\n50-80 \n8-12 \n\n\n\n40-50 \n5-8 \n\n\n\n<40 \n<5 \n\n\n\nSource: FAO (1983). \nLS =  Loamy Sand, SL = Sandy Loam, CL = Clay loam, SiL = Silt Loam, SiCL = Silt Clay Loam,  \nC-Clay , S = Sand, SC = Sandy Clay. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 79\n\n\n\nSoil Suitability for Maize and Groundnut Production\n\n\n\n1983; FAO, 1983). For soil wetness (drainage), the results of matching the crop \nrequirements with land characteristics showed that soils derived from coastal \nplain sands were highly suitable, whilst those of the Imo clay shale were only \nmoderately suitable for the cultivation of maize and groundnut. Soils derived \nfrom alluvium were marginally suitable (S3) for groundnut and maize production.\nFor soil fertility characteristics, available P was a serious constraint to both \nmaize and groundnut cultivation in the study areas as the values were low when \ncompared with the requirements of both crops (FAO, 1976; FAO, 1983 ). Total \nN was highly suitable for the cultivation of groundnut in the soils derived from \nthe three different lithological materials. In soils derived from Imo clay shale, \ntotal N was moderately suitable (S2) whilst in those derived from alluvium and \ncoastal plain sands, it was marginally suitable (S3) for maize cultivation. The \norganic carbon contents of the soils made the soils highly suitable (S1) in soils \nderived from the Imo clay shale, but moderately suitable (S2) in soils derived \nfrom alluvium and coastal plain sands for groundnut cultivation. Generally, soils \nderived from the three different parent materials were moderately suitable (S2) \nfor maize production when the organic carbon requirement of the crop (Table \n2) matched the organic carbon contents of the soils (Table 1). Base saturation \nwas optimum for both maize and groundnut cultivation in all the sites (Tables \n4 and 5). For soils from Umuna and Oguta, pH was optimum (S1) whilst soil \nfrom Ihiagwa was marginally suitable for maize cultivation. Also, soils from \nUmuna (Imo clay shale), Oguta (alluvium) and  Ihiagwa (coastal plain sands) \nwere optimum, moderately suitable and marginally suitable, respectively, for \ngroundnut production when the pH requirement of the crop matched those of the \n\n\n\nTABLE 4\nSuitability assessment of the soils for  maize production\n\n\n\n\n\n\n\n10 \n \n\n\n\nTABLE 4 \nSuitability assessment of the soils for  maize production \n\n\n\n \nLand Parameters Umuna \n\n\n\n(Imo clay shale) \nOguta \n(Alluvium) \n\n\n\nIhiagwa \n(Coastal Plain sand) \n\n\n\nClimate (c)    \nMean annual rainfall (mm) S1 S1 S1 \nTemperature (oC) S1 S1 S1 \n \nSoil characteristics physical (s) \n\n\n\n\n\n\n\nSoil depth S1 S1 S1 \nSoil texture S1 S2 S2 \nTopography (t)    \nSlope S1 S1 S1 \nDrainage S2 S3 S1 \n \nSoil Fertility  (f) \n\n\n\n\n\n\n\npH S1 S1 S3 \nTotal N S2 S3 S3 \nAvailable P N N N \nOrganic Carbon S2 S2 S2 \nBS S1 S1 S1 \nCEC S2 S3 S2 \nOverall Suitability N(f) N(f) N(f) \nS1 = highly suitable, S2 = moderately suitable, S3 = marginally suitable, N = Not suitable, Limitations \n(restrictive features): S = soil characteristics, f = fertility limitation, T = topography, W = \nwetness/drainage \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201580\n\n\n\nsites.  \nFrom the results of the aggregate suitability, all the soils were currently \n\n\n\nnot suitable for the cultivation of both maize and groundnut. The major land \ncharacteristic limiting the cultivation of these crops was soil fertility, constrained \n\n\n\nby P availability.\n\n\n\nCONCLUSION\nThe investigation of land qualities such as mean annual rainfall, temperature, \nsoil depth, slope and base saturation of the soils investigated showed that \nthey were currently highly suitable for both maize and groundnut production. \nHowever, P availability rendered all the soils unsuitable for maize and groundnut \nproduction, irrespective of their parent materials.  Therefore, in order to enhance \nthe productivity level of the land to the optimum level for maize and groundnut \nproduction, proper agronomic practices should be carried out to improve the \nfertility of the soils. As P deficiency was the most important limiting factor, it \nneeds to be alleviated so as to sustain maize and groundnut production in thesoils \ntested.\n\n\n\n TABLE 5\nSuitability assessment of the soils for groundnut production\n\n\n\n\n\n\n\n11 \n \n\n\n\nTABLE 5 \nSuitability assessment of the soils for groundnut production \n\n\n\n \nLand parameters   Umuna \n\n\n\n(Imo clay shale)  \nOguta  \n(alluvium) \n\n\n\nIhiagwa \n(coastal plain sand) \n\n\n\nClimatic (C)     \nMean annual rainfall (mm) S1 S1 S1 \nTemperature  S1 S1 S1 \n \nSoil physical characteristics (S) \n\n\n\n\n\n\n\nSoil depth  S1 S1 S1 \nSoil texture  S2 S1 S1 \nTopography (t)    \nSlope  S1 S1 S1 \nWetness (W)    \nDrainage  S1 S2 S1 \n \nSoil fertility (f) \n\n\n\n\n\n\n\npH S1 S2 S3 \nTotal N  S1 S1 S1 \nAvailable P N N N \nOrganic carbon S1 S2 S2 \nBS S1 S1 S1 \nCEC S2 S3 S2 \nOverall suitability  N(f) N(f) N(f) \n\n\n\nS1 = highly suitable, S2 = moderately suitable, S3 = marginally suitable, N = Not suitable, Limitations \n(restrictive features): S = soil characteristics, f = fertility limitation, T = topography, W = \nwetness/drainage. \n \n \n \n \n \n \n\n\n\nAhukaemere, C.M., I.IEkpe and I.C. Unachukwu\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 81\n\n\n\nREFERENCES\nAdeoye, G.O., and Agboola, A.A. 1985. Critical Levels for Soil pH, Available P, K, \n\n\n\nZn, Mn and Maize Ear \u2013 Leaf Content of P, Cu and Mn in Sedimentary Soils of \nSouthwestern Nigeria. Fertilizer Research 6: 65 \u2013 75.\n\n\n\nBaja, S. 2009. Land choice and land resources assessment in agriculture. A review. \nCAB Service: Perspectives in Agriculture, Veterinary Science, Nutrition and \nNatural Resources 4(15): 1-15. \n\n\n\nChude, V.O., W.B. Malgwi, I.Y. Ampu and A.O. Ano. 2011. Manual on Soil Fertility \nAssessment. Abuja, Nigeria: Federal Fertilizer Department in collaboration with \nNational Programme for Food Security. Pp 102.\n\n\n\nEdeth, A.E., Oden, M.I. and Efretuei, O.E. 1994. Correlation between cone penetration \nresistance and standard penetration in parts of the Coastal plain sand, South-\neastern Nigeria. 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"\n\n___________________\n*Corresponding author : E-mail: mohamad.mahdavi64@gmail.com\n\n\n\nINTRODUCTION\nMoisture transport in porous media is a fundamental subject in soil physics and \ncivil engineering. It has a key role to control the soil mass and energy budget \nin soil water near the surface (Novak 2010, Cahill and Parlange 1998, Smits et al. \n2011). Originally, moisture diffusivity was calculated by using pressure plate \noutflow data (Gardner, 1956). The moisture diffusivity D(\u0275) (usually in cm2 \ns-1) represents a basic parameter to evaluate amount of moisture transported in \nporous media that changes with both moisture and temperature (Philip and De \nVries 1957). In isothermal condition, this value may be calculated with moisture \nchange measurement at soil profile by well-known \u0263-ray attenuation technique \nor nuclear magnetic resonance (NMR) by using Boltzmann transfer function \n(Crausse 1983, Perrin 1985, Bruce and Klute 1956). \u0160imunek et al. (2000) \nproposed an approach to estimate moisture diffusivity by applying horizontally \ninfiltration data. Wang et al. (2004) determined moisture diffusivity by using data \nobtained from wetting front zone, infiltration rate and cumulative infiltration, \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 33-44 (2015)                        Malaysian Society of Soil Science\n\n\n\nAssessment of the Gravimetric Method to Determine \nIsothermal Soil Water Diffusivity\n\n\n\nMohamad Mahdavi* and Mohamad Reza Neyshabouri\n\n\n\nDepartment of Soil Science, University of Tabriz, 5166616471, Iran\n\n\n\nABSTRACT\nMoisture transport in porous media is a fundamental subject in soil physics and \ncivil engineering and therefore it is necessary to understand its mechanisms and \nprocesses involved. In this paper, the isothermal moisture diffusion, is estimated \nwith gravimetric method for loamy sand, silty loam and clay loam soils. The \nbasis of the method is to measure the amount of water absorbed by the small soil \ncolumns with different heights as a function of time. To evaluate the accuracy of \nthe obtained diffusivity data they were compared with those obtained from Bruce-\nKlute method. Considering calculated logarithmic RMSE between two mentioned \nmethods, it was confirmed the gravimetric technique may be more reliable \nto estimate the isothermal moisture diffusivity especially in finer texture soils. \nMoreover, this technique is quite simple and may be carried out with minimum \nlaboratory facilities. Moreover, the results showed that the gravimetric method \nsomewhat underestimated moisture diffusion D(\u0275) as compared to Bruce-Klute \nmethod, and the difference between the two methods increased with increasing at \nmoisture content which will be discussed in the current paper. \n\n\n\nKeywords: Bruce-Klute method, diffusivity, porous media, soil texture, \nsoil columns.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201534\n\n\n\nMohamad Mahdavi and Mohamad Reza Neyshabouri\n\n\n\nbut their assumptions is appropriate for near saturated soils. Ma et al. (2009) \ndeveloped a method to determine moisture diffusivity by estimation of the Brooks \nand Corey model parameters using analytical solution via HYDRUS-1D software. \nEvangelides et al. (2010) could generate soil moisture profile to estimate moisture \ndiffusivity by using data such as sorptivity, initial and final soil water content and \nwetting front zone distance. Apart from their accuracy, these techniques often \nrequire expensive equipment that may not exist in many laboratories especially \nin developing countries. To eliminate this obstacle, Goual et al. (2000) proposed \na gravimetric method that estimates isothermal moisture diffusion coefficient \nin concrete material. In the current work we applied their method to estimate \nsoil moisture diffusivity in three soils with various textures and investigated its \naccuracy with comparing to the Bruce-Klute method (1956).\n\n\n\nTHEORETICAL CONSIDERATION\nThe one-dimensional flow of water in saturated homogeneous porous material in \nvertical direction under the action of hydraulic gradient (pressure and gravitational \ngradients) is described by well-known Darcy\u2019s equation:\n\n\n\nq = - Ks (                     )       (1)\n\n\n\nwhere q is the soil water flux (cm s-1), Ks is the saturated hydraulic conductivity of \nsoil (cm s-1), P is the potential pressure of soil (cm) and Z is the vertical distance \n(cm). \n\n\n\nIn the cases of unsaturated flow in horizontal direction, the extended Darcy \nequation is substituted (Kaufmann, 1997) by:\n\n\n\nq = - K (\u0275)  \u2202\u03c8         (2)\n                   \u2202x       \n                                                                                               \nwhere \u03c8 is the capillary potential (cm), x is the distance (cm) and K(\u0275) is the \nunsaturated hydraulic conductivity (cm s-1).\n\n\n\nSince K(\u0275) is largely sensitive to soil water content, combining equation (2) \nwith the continuity equation and then writing the resultant equation in terms of \n\u0275 by substitution D(\u0275) = K(d\u03c8/d\u0275) for the \u201cisothermal moisture diffusivity\u2019\u2019,  the \nRichards equation is obtained:\n\n\n\n\u2202\u0275    =  \u2202  [D(\u0275) \u2202\u0275 ]         (3)\n\u2202t        \u2202x           \u2202x\n\n\n\n\u2202P + \u2202Z\n\u2202Z    \u2202Z\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 35\n\n\n\nDetermination of Isothermal Soil Water Diffusivity\n\n\n\nMATERIALS AND METHODS\nThe locations for soil samples used in this study were selected from different \nfarm lands around the Khalatpooshan research station (latitude 38o7\u2019N, longitude \n46o20\u2019E), University of Tabriz, Iran, with three different textures of loamy sand, \nsilty loam and clay loam (Table 1). The samples were collected from 0 to 15 \ncm depth which generally corresponds to the Ap horizon in the area (USDA and \nSoil Survey Staff, 2010). Loamy sand soil was single grained. Silty loam and \nclay loam soils were weakly and moderately developed in structure, respectively. \nDisturbed soil samples after air drying were cleaned from plants remains, and \nthen loamy sand soil passed through a 2-mm sieve, the next two were passed \nthrough 4-mm sieve because to preserve soil structure and stored in plastic bottles \nuntil the use. Initial moister content of the samples were measured by oven drying \nat 105oC for 24 hours. The soil textures was determined by the pipette method \n(Gee and Bauder, 1986)). Total organic carbon (TOC) by Walkley and Black wet \n\n\n\nTABLE 1\nBasic physical and chemical properties of soils studied\n\n\n\n9 \n \n\n\n\n \nTABLE 1 \n\n\n\nBasic physical and chemical properties of soils studied \n \n\n\n\nCEC \n(cmol kg-1)  \n\n\n\nActive \nCaCO3 \n(Wt%) \n\n\n\nTOC \n(Wt%)\n\n\n\nb \n\n\n\nf(cm3 cm-3) \n\n\n\na \nParticle \ndensity  \n(g cm-3) \n\n\n\n\n\n\n\nBulk \ndensity \n\n\n\n  (g cm-3) \n \n\n\n\n%Cl %Sil \n \n\n\n\n%S \n \n\n\n\nSoil texture \n\n\n\n14.33 4.8 0.9 0.42 2.59 1.5 8.3 7.8 83.9 Loamy sand \n21.18 6.3 1.3 0.33 2.24 1.5 15.3 62.3 22.4 Silty loam \n39.82 5.1 2.1 0.31 2.2 1.5 36.2 31 32.8 Clay loam \n\n\n\n                            a f= (1- Db/Ds), b percentage by weight \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n3 \n \n\n\n\nwhere \u0275 is the volumetric water content (cm3 cm-3), t (s) the time and D(\u0275) \nis the isothermal soil moisture  diffusivity (cm2 s-1). \n \nUsing Boltzmann transformation, b=xt-0.5, (Equation 3) may be reduced to \nan ordinary differential equation:  \n \n- \ud835\udc4f\ud835\udc4f2 \ud835\udc51\ud835\udc51\u0275\n\n\n\n\ud835\udc51\ud835\udc51\ud835\udc4f\ud835\udc4f = \ud835\udc51\ud835\udc51\n\ud835\udc51\ud835\udc51\ud835\udc4f\ud835\udc4f [D(\u0275) \ud835\udc51\ud835\udc51\u0275\n\n\n\n\ud835\udc51\ud835\udc51\ud835\udc4f\ud835\udc4f]       (4) \n \nThe isothermal soil moisture diffusion would be yielded from the \nintegration of equation (4): \n \nD (\u0275) = - 12 (\ud835\udc51\ud835\udc51\ud835\udc4f\ud835\udc4f\n\n\n\n\ud835\udc51\ud835\udc51\u0275) \u222b \ud835\udc4f\ud835\udc4f(\u0275)\ud835\udc51\ud835\udc51\u0275\u0275\n\u0275\ud835\udc56\ud835\udc56\n\n\n\n        (5)  \n \nwhere \ud835\udc51\ud835\udc51\ud835\udc4f\ud835\udc4f\n\n\n\n\ud835\udc51\ud835\udc51\u0275  is the inverse slope of the average curve \u0275L(b) (Figure 1) and \n\u222b \ud835\udc4f\ud835\udc4f(\u0275)\ud835\udc51\ud835\udc51\u0275\u0275\n\n\n\n\u0275\ud835\udc56\ud835\udc56\n is the area delimited by the average curve \u0275L(b), the vertical axis \n\n\n\nand the horizontal line \u0275 = 0 and a particular soil moisture content(\u0275) in this \nwork (Figure 1) at which the D(\u0275) is to be determined; \u0275L is the local \nvolumetric water content (see Equation 6).  \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n \n\n\n\nThe locations for soil samples used in this study were selected from different farm \nlands around the Khalatpooshan research station (latitude 38o7 'N, longitude 46o20 \n'E), University of Tabriz, Iran, with three different textures of loamy sand, silty \nloam and clay loam (Table 1). The samples were collected from 0 to 15 cm depth \nwhich generally corresponds to the Ap horizon in the area (USDA and Soil \nSurvey Staff, 2010). Loamy sand soil was single grained. Silty loam and clay \nloam soils were weakly and moderately developed in structure, respectively. \nDisturbed soil samples after air drying were cleaned from plants remains, and then \nloamy sand soil passed through a 2-mm sieve, the next two were passed through \n4-mm sieve because to preserve soil structure and stored in plastic bottles until the \nuse. Initial moister content of the samples were measured by oven drying at 1050C \nfor 24 hours. The soil textures was determined by the pipette method (Gee and \nBauder 1986)). Total organic carbon (TOC) by Walkley and Black wet \ndichromate oxidation method (Nelson and Sommers, 1986). Carbonate content \nwas calculated from the amount of CO2  released by reaction with HCl (Loppert \nand Suarez, 1996). Cation exchange capacity (CEC) of each soil sample was \nobtained by exchanging the samples with NH4OAc at pH 7 (Sumner et al., 1996). \n\n\n\nBasic physical and chemical properties of the three examined soils are shown \nin Table 1. The experimental procedure for gravimetric technique expressed as \nfollowing. The basis of the gravimetric method for determining D(\u0275) is to specify \namount of water absorbed by the vertical soil columns with different heights as a \n\n\n\nDetermination of Isothermal Soil Water Diffusivity \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201536\n\n\n\ndichromate oxidation method (Nelson and Sommers, 1986). Carbonate content \nwas calculated from the amount of CO2  released by reaction with HCl (Loppert \nand Suarez, 1996). Cation exchange capacity (CEC) of each soil sample was \nobtained by exchanging the samples with NH4OAc at pH 7 (Sumner et al., 1996).\n\n\n\nBasic physical and chemical properties of the three examined soils are shown \nin Table 1. The experimental procedure for gravimetric technique expressed as \nfollowing. The basis of the gravimetric method for determining D(\u0275) is to specify \namount of water absorbed by the vertical soil columns with different heights as a \nfunction of time. In this work the height of local volume considered in specifying \nthe evolution in moisture content was set equal to 2 cm. The fundamental \nassumptions for this method are that the porous material is homogenous and \nwetting front moves horizontally and there is a sharp boundary between wet and \ndry zone along the soil column (Goual et al., 2000). Moreover, due to small size \nof the soil columns and soil dryness, it is assumed that gravity potential effect on \nwater entry in to the columns is negligible in comparison to the matric potential. It \nmay be a correct assumption in the early stages of water absorption (Philip, 1957). \n\n\n\nFor this technique the various steps were as follows:-\n1. Providing five PVC tubes with 4.7 cm diameter and 2, 4, 6, 8 and 10 cm \n\n\n\nheights. The prepared soils were carefully packed into the tubes to achieve \na uniform bulk density. For this purpose known weights of each soil was \npoured into the PVC tubes in 2 cm interval depths with tapping on the column \nwall (Smits et al. 2011). All of soil columns packed to the bulk density of 1.5 \n(g cm-3). In order to eliminate moisture evaporation from soil surface, top of \nthe column were sealed with a plastic layer.\n\n\n\n2. The second step was determining amount of water absorbed by capillarity \nas a function of elapsed time for each column.  For this purpose the soil \ncolumns were hanged from the hanging scale with \u00b1 0.01 g accuracy and \nwere kept just in contact with the free water surface in a small container \nas presented in Figure 2. Weighing carried out for all columns at the same \ntime intervals (every 10 seconds) until ceasing of the absorption process. \nThe surface of water was always tagged to the end of the columns during \nexperiment by continuous adding of enough water to the container. It has \nbeen assumed that water density was fixed at 1 g cm-3. \n\n\n\n3. In this step, the volume of water absorbed by each soil column was plotted \nas a function of time by  considering the local volume and then applying the \nfollowing equation:\n\n\n\n        \u0275L (x,t) = [VW(x+2cm,t) - VW (x,t)] / VL   (6)\n\n\n\n where \u0275L(x,t) is the local volumetric water content that depend on height of \nthe column and the elapsed time, VW(x,t) is the volume of water absorbed \nby the column of height x, which were 2, 4, 6 and 8 cm, t is the elapsed time \n\n\n\nMohamad Mahdavi and Mohamad Reza Neyshabouri\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 37\n\n\n\nsince the water entry was commenced. VL is the local volume that was set \nequal to volume of smallest column (2 cm height).\n\n\n\n4. The individual water absorbed profiles established in previous step, were \ncombined into an average profile using the Boltzmann transform to find \u0275    \n(b = xt-0.5).\n\n\n\n5. The isothermal moisture diffusivity as a function of water content was \ncomputed considering \u0275 (b) by using (Equation 5).\n\n\n\nBruce-Klute method (Bruce and Klute, 1956) was also applied for these three soil \nsamples with following boundary conditions:\n\u0275 = \u0275i               for b=\u221e\n\u0275 = \u02750              for b=0\n\n\n\nFigure 1. Schematic curve of the local volumetric water content as a function of the \nBoltzmann transform (b).\n\n\n\nFigure 2.Schematic diagram of water absorption by the soil column.\n\n\n\n10 \n \n\n\n\n \n Figure 1. Schematic curve of the local volumetric water content as a function \n\n\n\nof the Boltzmann transform (b). \n \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 2.Schematic diagram of water absorption by the soil column. \n\n\n\n10 \n \n\n\n\n \n Figure 1. Schematic curve of the local volumetric water content as a function \n\n\n\nof the Boltzmann transform (b). \n \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 2.Schematic diagram of water absorption by the soil column. \n\n\n\nDetermination of Isothermal Soil Water Diffusivity\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201538\n\n\n\nwhere \u0275i and \u02750  are the initial  and the inlet water content, respectively.\nFor this purpose, soil columns with 10 cm diameter and 60 cm length were prepared \nfrom the same soils, placed horizontally and water source was allowed to contact \nwith one end of the column with approximately zero head, and then moisture \ncontents were measured with time along the column by using \u0263-ray attenuation \ntechnique. For more information see Bruce and Klute (Bruce and Klute, 1956).\nIn order to compare D(\u0275) obtained from the gravimetric technique with those from \nBruce-Klute method, logarithmic RMSE and MAE were used:\n\n\n\nD(\u0275)(G)i = isothermal moisture diffusion obtained from gravimetric technique\nD(\u0275)(BK)i = isothermal moisture diffusion obtained from Bruce-Klute method\nN= number of coupled data that has been compared Excel 2010, Sigma Plot 12.5 \nand Autodesk 3ds Max 2013 softwares were used to draw the plots and curves.\n\n\n\nRESULTS AND DISCUSSION\nThe amount of water absorbed by the soil columns with various heights (2, 4, \n6, 8 and 10 cm) as a function of time were plotted in (Figure 3). As is shown, \nthe volume of water absorbed by the various soil columns (height and texture) \nincreased with decreasing rate (Figure 3). \n\n\n\nFigure 4 presents evolution in local volumetric water content as a function of \nBoltzmann transform at levels x= 2, 4, 6 and 8 cm, as evaluated from the results of \nFigure 3 and using the equation 6. The series of hydraulic curves for soil columns \nwith various heights were combined into a unique curve (Figure 4). By definition \nof b(\u0275L) and applying Equation 5, the isothermal moisture diffusivity as a function \nof water content could be determined. As illustrated in Figure 4, an exponential \ndecay model was fitted for all three soil samples, and also the fitting power of \ncurves increased in finer textures. Goual et al. (2000) observed the gravimetric \nmethod for determined moisture diffusion coefficient of concrete material that has \nfine pores and stronger capillary absorption fit the exponential decay model well.\n\n\n\nThe comparison of moisture diffusivity curves derived using gravimetric \ntechnique to those obtained by \u0263-ray attenuation method (Bruce-Klute method) \nshowed a distinct similarity in the evolution of D(\u0275) curves between these \ntwo methods (Figure 5). This finding confirms the relative accuracy of data \nobtained using gravimetric technique. As could be seen, the isothermal moisture \ndiffusivity increases with increase moisture content. This trend was reported by \nother researchers (Wang et al., 2004; Evangelides et al., 2010). Also D(\u0275) by \nthe gravimetric method for the three soils is somewhat less than that predicted \nfrom Bruce-Klute method at the wet range (about more than 0.2 cm3/cm3 water \ncontent) in particular for the loamy sand texture. In other words the gravimetric \n\n\n\nMohamad Mahdavi and Mohamad Reza Neyshabouri\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 39\n\n\n\nmethod underestimated D(\u0275) compared to Bruce-Klute method. Difference \nbetween the calculated values of D(\u0275) from the two methods increased with \nincrease in moisture content.This observation may be due to our assumption \nto disregard gravitational gradient in this technique. Since, in the earlier stages \nof water absorption where \u03c8 is dominant, these two methods were similar and \nleads to the same results approximately. With increase in the water absorption \nby soil and so decrease in the matric gradient, the gravitational gradient becomes \ninfluential against matric gradient. And, therefore, our assumption somewhat \nfailed. In this wet range, due to the prevalence of gravitational gradient that acts \ninversely against matric gradient in vertical direction, leading to underestimation \n\n\n\nFigure 3. Volume of the absorbed water by various soil columns height and textureof  a) \nloamy sand, b) silty loam and c) clay loam.\n\n\n\n11 \n \n\n\n\n\n\n\n\n\n\n\n\n0\n\n\n\n8\n\n\n\n16\n\n\n\n24\n\n\n\n32\n\n\n\n40\n\n\n\n0 100 200 300 400 500 600 700 800 900 1000\n\n\n\nA\nbs\n\n\n\nor\nbe\n\n\n\nd \nw\n\n\n\nat\ner\n\n\n\n v\nol\n\n\n\num\ne \n\n\n\n(m\nL\n\n\n\n) \n\n\n\nTime (s)\n\n\n\nSilty Loam\n\n\n\n10 cm\n\n\n\n8 cm\n\n\n\n6 cm\n\n\n\n4 cm\n\n\n\n2 cm\n\n\n\nb)\n\n\n\n0\n\n\n\n8\n\n\n\n16\n\n\n\n24\n\n\n\n32\n\n\n\n40\n\n\n\n0 100 200 300 400 500 600 700 800 900 1000\n\n\n\nA\nbs\n\n\n\nor\nbe\n\n\n\nd \nw\n\n\n\nat\ner\n\n\n\n v\nol\n\n\n\num\ne \n\n\n\n(m\nL\n\n\n\n) \n\n\n\nTime (s) \n\n\n\nLoamy Sand\n\n\n\n10 cm\n\n\n\n8 cm\n\n\n\n6 cm\n\n\n\n4 cm\n\n\n\n2 cm\n\n\n\na)\n\n\n\n12 \n \n\n\n\n \nFigure 3. Volume of the absorbed water by various soil columns height and \n\n\n\ntextureof  a) loamy sand, b) silty loam and c) clay loam. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n0\n\n\n\n8\n\n\n\n16\n\n\n\n24\n\n\n\n32\n\n\n\n40\n\n\n\n0 100 200 300 400 500 600 700 800 900 1000 1100 1200\n\n\n\nA\nbs\n\n\n\nor\nbe\n\n\n\nd \nw\n\n\n\nat\ner\n\n\n\n v\nol\n\n\n\num\ne \n\n\n\n(m\nL\n\n\n\n) \n\n\n\nTime (s) \n\n\n\nClay Loam\n\n\n\n10 cm\n\n\n\n8 cm\n\n\n\n6 cm\n\n\n\n4 cm\n\n\n\n2 cm\n\n\n\nc)\n\n\n\nDetermination of Isothermal Soil Water Diffusivity\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201540\n\n\n\nof D(\u0275) as compared to Bruce-Klute method eventually. This phenomenon may be \nsimilar to infiltration rate which at the early stages is controlled primarily by soil \nmatric suction (Kostiakov, 1932 ; Philip, 1957) . Hence behavior of the soil water \ndiffusivity at low moisture ranges is more or less similar in both methods. With \nincreasing moisture content the intrinsic differences between the two methods \naffect the estimated D(\u0275) values. Logarithmic RMSE for loamy sand, silty loam \nand clay loam were 0.269, 0.233 and 0.193, respectively. Also measured MAE \nfor loamy sand, silty loam and clay loam were 1.54 x 10-7, 1.99 x 10-2 and 4.39 \nx 10-3 cm2 s-1, respectively. It has been inferred that the gravimetric method may \nbe a reliable method to estimate isothermal moisture diffusion in many soils and \n\n\n\nFigure 4. Volumetric water content curves as a function of the Boltzman transform for \nthe three examined soilsof  a) loamy sand, b) silty loam and c) clay loam.\n\n\n\n13 \n \n\n\n\nLoamy Sand   R2=0.61\n \n\n\n\nb=xt-0.5  (cm s-0.5 )\n\n\n\n0.0 0.5 1.0 1.5 2.0 2.5 3.0\n\n\n\nLo\nca\n\n\n\nl v\nol\n\n\n\num\net\n\n\n\nric\n w\n\n\n\nat\ner\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n\n\n\n\n0.00\n\n\n\n0.05\n\n\n\n0.10\n\n\n\n0.15\n\n\n\n0.20\n\n\n\n0.25\n\n\n\n0.30\n\n\n\n0.35\n\n\n\n0.40\n\n\n\n\n\n\n\nSilty Loam   R2=0.66\n\n\n\nb=xt-0.5 (cm s-0.5)\n\n\n\n0.0 0.5 1.0 1.5 2.0 2.5 3.0\n\n\n\nLo\nca\n\n\n\nl V\nol\n\n\n\num\net\n\n\n\nric\n w\n\n\n\nat\ner\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n0.00\n\n\n\n0.05\n\n\n\n0.10\n\n\n\n0.15\n\n\n\n0.20\n\n\n\n0.25\n\n\n\n0.30\n\n\n\n0.35\n\n\n\nb)\n\n\n\n\n\n\n\n14 \n \n\n\n\n0.0 0.5 1.0 1.5 2.0 2.5 3.0\n0.00\n\n\n\n0.05\n\n\n\n0.10\n\n\n\n0.15\n\n\n\n0.20\n\n\n\n0.25\n\n\n\n0.30\n\n\n\n0.35\n\n\n\nClay Loam   R2=0.67\n\n\n\nLo\nca\n\n\n\nl v\nol\n\n\n\num\net\n\n\n\nric\n w\n\n\n\nat\ner\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n\n\n\n\nb=xt-0.5  (cm s-0.5 )\n\n\n\nC)\n\n\n\n \nFigure 4. Volumetric water content curves as a function of the Boltzman \n\n\n\ntransform for the three examined soilsof  a) loamy sand, b) silty loam and c) \nclay loam. \n\n\n\n\n\n\n\nMohamad Mahdavi and Mohamad Reza Neyshabouri\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 41\n\n\n\nmoreover it is easier than the Bruce-Klute method to conduct in most soil physics \nlaboratories. Our results showed that this method for the three samples of soils \nwas satisfactory and that the accuracy increased with finer textures. As shown by \nFigure 5, the values of isothermal moisture diffusivity for clay loam in nearly all \nmoisture ranges are less than silty loam, while the clay fraction in the clay loam \nsoil is more than twice in the silty loam. This result may be due to aggregation \nand the formation of inter-aggregate pores in the clay loam soil. The soil structure \ndevelopment certainly affects pore size distribution that may increase macropores \nand alter textural behavior of soils (Dorner et al., 2010; Durner, 1994).\n\n\n\nFigure 5. Comparison between the gravimetric and Bruce-Klute methods for the three \nsoil samples a) loamy sand, b) silty loam and c) clay loam.\n\n\n\n15 \n \n\n\n\nLoamy Sand\n\n\n\nVolumetric water content (cm3 cm-3)\n\n\n\n0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40\n\n\n\nD\niff\n\n\n\nus\niv\n\n\n\nity\n (c\n\n\n\nm\n2  s\n\n\n\n-1\n)\n\n\n\n10-12\n\n\n\n10-11\n\n\n\n10-10\n\n\n\n10-9\n\n\n\n10-8\n\n\n\n10-7\n\n\n\n10-6\n\n\n\n10-5\n\n\n\nGravimetric method\nBruce-Klute method\n\n\n\n\n\n\n\n\n\n\n\nSilty Loam\n\n\n\nVolumetric water content (cm3 cm-3)\n\n\n\n0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35\n\n\n\nD\niff\n\n\n\nus\niv\n\n\n\nity\n (c\n\n\n\nm\n2  s\n\n\n\n-1\n)\n\n\n\n10-7\n\n\n\n10-6\n\n\n\n10-5\n\n\n\n10-4\n\n\n\n10-3\n\n\n\n10-2\n\n\n\n10-1\n\n\n\n100\n\n\n\nGravimetric method\nBruce-Klute method\n\n\n\n16 \n \n\n\n\nClay Loam\n\n\n\nVolumetric water content (cm3 cm-3)\n\n\n\n0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35\n\n\n\nD\niff\n\n\n\nus\niv\n\n\n\nity\n (c\n\n\n\nm\n2  s\n\n\n\n-1\n)\n\n\n\n10-10\n\n\n\n10-9\n\n\n\n10-8\n\n\n\n10-7\n\n\n\n10-6\n\n\n\n10-5\n\n\n\n10-4\n\n\n\n10-3\n\n\n\n10-2\n\n\n\n10-1\n\n\n\n100\n\n\n\nGravimetric method\nBruce-Klute method\n\n\n\n \n Figure 5. Comparison between the gravimetric and Bruce-Klute methods \n\n\n\nfor the three soil samples a) loamy sand, b) silty loam and c) clay loam. \n \n \n \n \n\n\n\nDetermination of Isothermal Soil Water Diffusivity\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201542\n\n\n\nFigure 6, demonstrates the error bars between the data obtained using \ngravimetric technique and those obtained from the Bruce-Klute method for three \nsoil samples. As could be seen, the differences between two mentioned methods \nincrease with increasing diffusivity and moisture content accordingly. Thus, the \ngravimetric technique is more realible in low moisture levels as compared to \nhigher moisture contents.  \n\n\n\nFigure 6. The error bars curves between the gravimetric and Bruce-Klute methods for \nthe three soil samplesa) loamy sand, b) silty loam and c) clay loam.\n\n\n\n17 \n \n\n\n\nLoamy Sand\n\n\n\nDiffusivity (cm2 s-1), Bruce-Klute method\n\n\n\n10-11 10-10 10-9 10-8 10-7 10-6 10-5\n\n\n\nD\niffu\n\n\n\nsi\nvit\n\n\n\ny \n(c\n\n\n\nm\n2  s\n\n\n\n-1\n), \n\n\n\nG\nra\n\n\n\nvim\net\n\n\n\nric\n m\n\n\n\net\nho\n\n\n\nd\n\n\n\n10-11\n\n\n\n10-10\n\n\n\n10-9\n\n\n\n10-8\n\n\n\n10-7\n\n\n\n10-6\n\n\n\n10-5\n\n\n\n\n\n\n\nSilty Loam\n\n\n\nDiffusivity (cm2 s-1), Bruce-Klute method\n\n\n\n1e-7 1e-6 1e-5 1e-4 1e-3 1e-2 1e-1 1e+0\n\n\n\nD\niff\n\n\n\nus\nivi\n\n\n\nty\n (c\n\n\n\nm\n2  s\n\n\n\n-1\n), \n\n\n\nG\nra\n\n\n\nvim\net\n\n\n\nric\n m\n\n\n\net\nho\n\n\n\nd\n\n\n\n1e-7\n\n\n\n1e-6\n\n\n\n1e-5\n\n\n\n1e-4\n\n\n\n1e-3\n\n\n\n1e-2\n\n\n\n1e-1\n\n\n\n1e+0\n\n\n\n\n\n\n\n18 \n \n\n\n\n\n\n\n\nClay Loam\n\n\n\nDiffusivity (cm2 s-1), Bruce-Klute method\n\n\n\n10-10 10-9 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1 100\n\n\n\nD\niff\n\n\n\nus\nivi\n\n\n\nty\n (c\n\n\n\nm\n2  s\n\n\n\n-1\n), \n\n\n\nG\nra\n\n\n\nvim\net\n\n\n\nric\n m\n\n\n\net\nho\n\n\n\nd\n\n\n\n10-10\n\n\n\n10-9\n\n\n\n10-8\n\n\n\n10-7\n\n\n\n10-6\n\n\n\n10-5\n\n\n\n10-4\n\n\n\n10-3\n\n\n\n10-2\n\n\n\n10-1\n\n\n\n100\n\n\n\n \n Figure 6.The error bars curves between the gravimetric and Bruce-Klute \n\n\n\nmethods for the three soil samplesa) loamy sand, b) silty loam and c) clay \nloam. \n\n\n\n\n\n\n\nMohamad Mahdavi and Mohamad Reza Neyshabouri\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 43\n\n\n\nCONCLUSION\nThe gravimetric method potential for the prediction of isothermal moisture \ndiffusivity was examined for sandy loam, silty loam and clay loam soils. The \nresults showed that the gravimetric technique is a simple and easier method to \napply and has confirmed the reliability of this method especially in fine texture \nsoils. Its accuracy may increase when the soil has more clay and shows stronger \ncapillary absorption behavior that may reduce the prediction errors. There is a \nfurther need to continue to examine the various aspects of the presented technique \nfor more diverse soils particularly in undisturbed samples.\n\n\n\nREFERENCES\nBruce, R.R. and A. Klute. 1956. The Measurement of Soil Moisture Diffusivity. Soil \n\n\n\nSci. Soc. Am. Proc. 20: 458-462.\n\n\n\nCahill, A.T. and M. B. Parlange. 1998. On water vapor transport in field soils. Water \nResour Res. 34: 731-739.\n\n\n\nCrausse, P. 1983. Etude fondamentale des transferts couple\u00c2s de chaleur et \nd\u2019humidite\u00c2 en milieux poreux non sature\u00c2.  PhD thesis, National Polytechnic \nInstitute of Toulouse,  The France. 209 p.\n\n\n\nDorner, J., P. 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Soil Science 165:708-717.\n\n\n\nSumner, M. E., Miller, W. P., Page., A. L., Sparks, D. L., Helmek, P. A., Loeppert, \nR. H., Soltanpour, P. N., Tabatabai, M. A. 1996. Cation exchange capacity and \nexchange coefficients. Method of Soil Analysis. Part 3-Chemical Methods. \n1201-1229.\n\n\n\nWang, Q., Shao, M. and Horton, R. 2004. A simple method for estimating water \ndiffusivity of unsaturated soils. Soil Science Society of America Journal 68:713-\n718.\n\n\n\nMohamad Mahdavi and Mohamad Reza Neyshabouri\n\n\n\n\n\n"
"\n\nINTRODUCTION\nPesticides are used in agricultural fields to control all kinds of pests in order to \nincrease crop yield and to control weeds. Pesticides contain different products \nwith several different functions but the designation is formed by combining the \nnames of pest and the suffix. Though they help farmers and all agriculturists \non a large scale, they are considered hazardous as they can harm humans and \nanimals and are toxic to the environment.There are various types of pesticides, \ne.g., organochloride pesticides, organophosphorous pesticides, carbamates, \nneonicotinoids and pyrethroids. Organochloride pesticides are cumulative in \nthe organisms and pose chronic health effects such as cancer and neurological \nand teratogenic effects (Alewu and Nosiri 2011). The most widely known \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 173-182 (2019)                         Malaysian Society of Soil Science\n\n\n\nIsolation of Pesticide Degrading Bacteria From Paddy Fields \nand Evaluation of Its Bioremediation Potential Efficiency \n\n\n\nG. Manigandan1* A.Raja2 Vajiha Banu H3 and Gajalakshmi P4\n\n\n\n1*PG and Research Department of Biotechnology, J.J College of Arts and Science \n(Autonomous), Pudukkottai, Tamil Nadu\n\n\n\n2,3Department of Microbiology, Jamal Mohamed College (Automonous), \nTiruchirappalli\n\n\n\n4Department of Microbiology, Dhanalakshmi Srinivasan College of Arts and Science \nfor Women (Autonomous), Perambalur, Tiruchirappalli\n\n\n\nABSTRACT\nPesticides are chemicals that are widely used in the agricultural sector to control \npests in the environment. Lambda cyhalothrin is an insecticide that belongs to a \ngroup of pyrethroids. As lambda cyhalothrin is persistent in the soil, there is an \nurgent need to take remedial measures to control environmental pollution. In this \nstudy, the ability of bacteria to degrade lambda cyhalothrin from a paddy field was \nevaluated. Tolerance of bacterial isolates was tested at different concentrations. \nAmong the ten different genera, isolate Pseudomonas sp designated as \nGMMC1was found to tolerate pesticides up to 500 ppm and was selected for \nfurther degradation studies. GMMC1, lambda-cyhalothrin degrading bacterium \nidentified by Sequence BLAST analysis,was isolated from the paddy crop soil \nand found to be the dominant bacteria which tolerates pesticide. Results of a \nphylogenetic analysis of GMMC1 found it to be closely related to Pseudomonas \nfluorescens. Pseudomonas fluorescens (GMMC1) isolate as appears to be the best \nshort term choice for bioremediation of pesticide-contaminated agricultural fields.\n\n\n\nKeywords: Pesticides, lambda cyhalothrin, pyrethroids, bioremediation, \nenvironmental pollution.\n\n\n\n___________________\n*Corresponding author : rgtheertha@gmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019174\n\n\n\norganochlorine pesticide is dichlorodiphenyltrichloroethane, i.e., the insecticide \nDDT (Turusov et al. 2002; Van den Berg 2009). \n Organophosphates, which are promoted as a more ecological alternative to \norganochlorines, include a great variety of pesticides, the most common of which \nis glyphosate. They affect the nervous system of insects and humans, in addition \nto influencing the reproductive system (Colosio et al.2009). The carbamates are \ntransformed into various products as a consequence of several processes such \nas hydrolysis, biodegradation, oxidation, photolysis, biotransformation and \nmetabolic reactions in living organisms (Soriano et al.2001). Some synthetic \npyrethroids such as fenvalerate, sumithrin and permethrin are considered to \nbe safer. There is evidence of pyrethroids affecting the reproductive behaviour \nof animals and humans too. A recent study related more than one pyrethroid \nmetabolite to DNA damages in human sperm, raising concerns about possible \nnegative effects on human reproductive health (Jurewicz et al.2015).The rapid \nincrease in population has resulted in the accumulation of a variety of chemicals \nin the environment. Over 98% of sprayed insecticides and 95% of herbicides \nreach a destination other than their target species, because they are sprayed or \nspread across entire agricultural fields. Earlier techniques which were used to \neliminate them from the environment were landfills, recycling, pyrolysis etc., \nbut these also have adverse effects on the environment and lead to formation \nof toxic intermediates (Debarati et al. 2005). Degradation by microbes depends \nnot only on the presence of degradative enzymes, but also on a wide range of \nenvironmental parameters such as ambient temperature, nutrients status and pH. \nAlso, pesticide concentration is a limiting factor (Singh, 2008).\n    \n\n\n\nMATERIALS AND METHODS\n\n\n\nPesticide Used\nLambda cyhalothrin was purchased from a shop dealing with agricultural products \nin the local market of Pudukkottai, Tamil Nadu.\n\n\n\nCollection of Soil Sample\nSoil samples were collected from a paddy field located in Keelasivalpatti, \nSivaganagai District, Tamil Nadu, India. These fields had been spread with \nLambda cyhalothrin for the past few years. The soil samples were collected in \nsterile polythene bags for further study.\n\n\n\nIsolation of Pesticide Degrading Bacteria\nThe enrichment method was used to isolate pesticide degrading organisms. \nEnrichment of pesticide degraders was carried out by using 150ml soyabean \ncasein digest medium. The medium was sterilized by autoclaving at 121\u00b0C for 15 \nmin. Ten ppm of lambda cyhalothrin was added after autoclaving as a sole carbon \nsource. The soil sample (1g) collected from paddy field was serially diluted with \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 175\n\n\n\nnormal saline and about 1ml of 107 was used to isolate bacterial colonies by pour \nplate method. \n\n\n\nEnrichment of Bacterial Culture and pesticide tolerance\nThe method described by Chen et al. (2011) was used. The tryptic soy brothwas \nautoclaved at 121\u00b0C for 20 min, after which it was aseptically spiked with lambda \ncyhalothrin at different concentrations (100, 250 and 500ppm) andisolated strains \nwere inoculated at OD 1 at 600 nm. Plates were incubated under 35\u00b0C for 48 \nh.The isolate that tolerated 500 ppm was selected for degradation analysis.\n\n\n\nBiodegradation of Lambda cyhalothrin\nThe degradation studies were performed in 1000-ml Erlenmeyer flasks containing \n500 ml of sterile MSM supplemented with 100 ppm of Lambda cyhalothrin. Next, \nthe medium was inoculatedwith 50 ml of 24 h bacterial suspension and medium \nwithout bacterial culture as control. All samples were incubated on an arbitrary \nshaker (120 rpm) in a darkened thermostatic chambermaintained at 30\u00b11\u00b0C. \nSamples of MSM were removed aseptically after 48h and cell free culture filtrate \nwas extracted with ethyl acetate and subjected to Thin Layer Chromatography \n(TLC) and High Pressure Liquid Chromatography (HPLC).\n\n\n\nThin Layer Chromatography (TLC)\nPre-coated silica gel plates (silica gel 60 F254 0.25mm thicknesses, 20\u00d720 cm, \nMerck Ltd.) were used for TLC of bifenthrin. The TLC plates were spotted with \n5\u03bcl sample volume at 1cm apart with micropipette with the same volume of \nstandard bifenthrin in lane 1 for comparison of Rf values. The plates were dried \nand the chromatogram was developed in a pre-saturated tank with Benzene: Ethyl \nacetate (6:1 by volume) as the solvent system. After developing the plates, the \nsolvent front was immediately marked and extra solvent was evaporated in a fume \nhood. The plates were kept under UV at 245 nm for 20 min.  The spots were \nmarked and Rf values were calculated.\n\n\n\nHigh Pressure Liquid Chromatography (HPLC)\nSamples from the treatment were removed on day 2 for pesticide residual analysis. \nAn amount of 0.5 mL of both treated and control was mixed with 0.5 mL of \nacetonitrile in 2 mL Eppendorf tubes followed by centrifuging at 12,000 rpm for 5 \nmin. The supernatant was transferred to amber HPLC vials using Pasteur pipettes \nand kept in a refrigerator. Twenty-five microlitre of each sample was injected \ninto the HPLC. Concentrations of bacteria in suspension were estimated by light \nabsorbance value at300nm.\n\n\n\nIdentification of Active Isolate\nOrganisms showing growth in the presence of 500 ppm lambda cyhalothrin on \nminimal medium were morphologically characterized by colony morphology, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019176\n\n\n\nGram staining, catalase, oxidase and motility. Biochemical methods were used to \nidentify one isolate showing maximum growth.\n\n\n\nKOH Solubility Test\nThe principle behind this test is that lipopolysaccharides present in the bacterial \ncell wall will dissolve in 3% KOH, forming a mucoid thread. A loopful of bacteria \nfrom a well grown colony was mixed in a drop of 3% aqueous KOH solution \nfor not more than 10 s with the help of a toothpick. The tooth pick was raised a \nfew centimeters from the microslide and observed for the formation of a mucoid \nthread. The Gram-positive bacteria did not produce strands even with repeated \nstrokes of the toothpick.\n\n\n\nMolecular Characterization of bacterial isolate\nBased on the method of De Medici et al. (2003), bacterial isolates which were \ncapable of utilizing cyhalothrinon were identified using 16S ribosomal DNA. \nDNA was extracted by applying the boiling method with some amendments. The \n16S rRNA gene was amplified by using the following primers. Forward: 5\u2032-AGA \nGTT TGA TCC TGG CTC AG-3\u2032 Reverse: 5\u2032-GGT TAC CTT GTT ACG ACT \nT-3\u2032.The PCR reaction was performed in a final volume of 50 \u03bcL containing 25 \n\u03bcLTaq Master Mix (Vivantis, Malaysia), 1 \u03bcL of each primer and 2 \u03bcL of bacterial \nDNA template. The final volume was adjusted to 50 \u03bcL using nuclease-free \nwater. DNA amplification was performed in a thermo cycler (BioRad) with the \nfollowing thermal profile: an initial denaturation step of 94\u00b0C for 3 min (1 cycle), \nfollowed by 35 cycles of 94\u00b0C for 1 min, 41\u00b0C for 1 min, and 72\u00b0C for 2 min with \nthe final extension step of 75\u00b0C for 5 min. The amplified DNA was analyzed by \nelectrophoresis on 1.5% agarose (5 \u03bcL aliquot of each PCR product) and stained \nwith ethidium bromide. PCR products were sent to SciGenome, Hyderabad for \npurification and DNA sequencing. Phylogeny was analysed with MEGA version \n6.06 software and distances by neighbor joining method.\n\n\n\nRESULTS AND DISCUSSION\nBacterial Diversity \nOur study analysed the population of bacterial isolates per gram of soil treated \nwith lambdacyhalothrin while the plate count technique was used for colony \nforming units(CFUs). Lambdacyhalothrin is one of the most potent pyrethroid \ninsecticides widely used in pest management (Renata Colombo et al. 2013; \nAmweg and Weston 2005).Incubation for 24 h at room temperature resulted in \nhigh viable counts per gram of soil plated with and without Lambda cyhalothrin. \nThe plate without Lambda cyhalothrin showed 68 \u00d7 107 CFU/g of soil while the \nsoil samples plated with 10 ppm lambdacyhalothrin, the CFU was 18\u00d7 107.Studies \non colony morphology such as colour, size, margin, elevation, etc. were recorded \nand are given in Table 1. Based on colony morphology, ten different colonies were \nselected and designated as GMMC1 to GMMC10.The majority of colonies in the \nten isolates were translucent while a few were opaque. Cell wall nature of isolates \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 177\n\n\n\nrevealed that 50% of isolates were Gram-positive while the rest were Gram-\nnegative. Among the isolates, six were catalasepositive and seven were oxidase-\nnegative. Isolation of Gram negative isolates showed high metabolic adaptability \nto several toxic pollutants (Pirahuata et al. 2006).Predominance of Gram-negative \nisolates from pesticide impacted soils has been reported by Agarry et al.(2013).\n\n\n\nGMMC1 to GMMC10.The majority of colonies in the ten isolates were translucent while a few \n\n\n\nwere opaque. Cell wall nature of isolates revealed that 50% of isolates were Gram-positive while \n\n\n\nthe rest were Gram-negative. Among the isolates, six were catalasepositive and seven were \n\n\n\noxidase-negative. Isolation of Gram negative isolates showed high metabolic adaptability to \n\n\n\nseveral toxic pollutants (Pirahuata et al.2006).Predominance of Gram-negative isolates from \n\n\n\npesticide impacted soils has been reported by Agarry et al.(2013). \n\n\n\nTABLE 1 \n\n\n\nColony morphology and Gram reaction of isolates \n\n\n\nIsolate Opacity Margin Colour Elevation Gram \n\n\n\nstain \n\n\n\nCatalase Oxidase \n\n\n\nGMMC1 Opaque Entire  White Flat  Negative \n\n\n\nrod \n\n\n\nPositive Negative \n\n\n\nGMMC2 Translucent Circular Creamy \n\n\n\nwhite \n\n\n\nconvex Negative \n\n\n\nrod \n\n\n\nPositive Positive \n\n\n\nGMMC3 Translucent Irregular White Flat Positive \n\n\n\nrod \n\n\n\nPositive Negative \n\n\n\nGMMC4 Opaque Round White Undulate Positive \n\n\n\nrod \n\n\n\nNegative Negative \n\n\n\nGMMC5 Translucent Punctiform White Undulate Positive \n\n\n\nrod \n\n\n\nPositive Positive \n\n\n\nGMMC6 Translucent Spindle shape Creamy \n\n\n\nwhite \n\n\n\nRaised Negative \n\n\n\ncocci \n\n\n\nnegative Negative \n\n\n\nGMMC7 Opaque Rhizoid White Raised Positive \n\n\n\nrod \n\n\n\nPositive Negative \n\n\n\nGMMC8 Translucent Round White Raised Negative \n\n\n\nrod \n\n\n\nPositive Negative \n\n\n\nGMMC9 Opaque Filamentous White Flat Positive \n\n\n\ncocci \n\n\n\nNegative Positive \n\n\n\nGMMC10 Translucent Round White Undulate Negative \n\n\n\nrod \n\n\n\nnegative Negative \n\n\n\n\n\n\n\nPesticide Tolerance  \n\n\n\nInvestigation of microbial degradation is useful for developing ecofriendly bioremediation \n\n\n\nmethods for pesticide toxicity. Bacteria with the ability to degrade pesticides have been widely \n\n\n\nstudied but not in practice (Hong et al. 2005).The degradation of lambda cyhalothrin was \n\n\n\nperformed under aerobic conditions by each strain for a period of 72 h in mineral salt medium \n\n\n\nfollowed by evaluation of its tolerance capacity. Cell growth recorded at different concentrations \n\n\n\nis given in Figure 1. The tolerance efficiency of isolates was tested by turbidometry assay of up \n\n\n\nto 500 ppm and GMMC1 was found to be a potent isolate. The growth rate for control was found \n\n\n\nPesticide Tolerance \nInvestigation of microbial degradation is useful for developing ecofriendly \nbioremediation methods for pesticide toxicity. Bacteria with the ability to degrade \npesticides have been widely studied but not in practice (Hong et al. 2005).The \ndegradation of lambda cyhalothrin was performed under aerobic conditions by \neach strain for a period of 72 h in mineral salt medium followed by evaluation of \nits tolerance capacity. Cell growth recorded at different concentrations is given \nin Figure 1. The tolerance efficiency of isolates was tested by turbidometry assay \nof up to 500 ppm and GMMC1 was found to be a potent isolate. The growth rate \nfor control was found to be comparatively slower on minimal medium without \nLambda cyhalothrin. Maximum OD 1.72 was recorded in GMMC 1 followed by \nBacillus sp. 1.54 and 1.52 \u00b1 0.03. It was also observed that the rest of the isolates \nfailed to tolerate lambda-cyhalothrin even at a minimum of 250 ppm level. \nModerate growth was observed up to 100 ppm for these isolates. The decrease in \nCFU at increased concentrations of pesticide is related to the toxic effect of the \npesticide. Plate assays revealed that one Gram-negative, Pseudomonas sp., and \ntwo Gram-positive Bacillus sp. and Micrococcus sp., had a higher tolerance to \nLambda cyhalothrin (250 ppm). \n The growth of lambda-cyhalothrin resistant isolates of Pseudomonas \nsp., GMCM1, and Bacillus sp. (GMMC3 and 5), were observed in the minimal \nbroth amending lambda-cyhalothrin at 500 ppm. Biochemical test results showed \n\n\n\nTABLE 1\nColony morphology and Gram reaction of isolates\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019178\n\n\n\nthe isolate GMMC1 is positive on KOH solubility test, catalase test, indole \ntest, oxidase test, and Citrate utilisation test and negative for starch and gelatin \nhydrolysis. Studies on Pseudomonas putida degrading various pesticides were \nmonitored by genetic recombination and a recombinant strain able to completely \ndegrade 50 mg/L of various pesticide compounds was developed by Ting Gong \net al. (2018). The endosulfan-degrading bacterial strain Pseudomonas fluorescens \nwas isolated (Jesitha et al. 2015) and degradation of endosulfan by freely \nsuspended and calcium-alginate entrapped bacterial cells was investigated in \nbatch as well as in packed bed column studies. Freely suspended Pseudomonas \nfluorescens cells with biomass maximum OD/OD0 value of 1.68 was found to \ndegrade a number of pesticides. Previous studies had observed that Pseudomonas \nsp. has the ability to degrade pyrethroids (Mariutsz Cycori and Zofia Piotrowska-\nSeget 2006). Tang et al. (2018) reported that pyrethroids are synthetic organic \ninsecticides with mammalian toxicity and are widely used in both rural and urban \nareas worldwide.\n\n\n\nHPLC and TLC Analysis\nThe composition of the liquid media at the end of degradation was analysed using \nhigh performance liquid chromatography (HPLC). TLC analysis found that the \ncyhalothrin sample treated with Pseudomonas fluorescens resulted in the formation \nof 7 different fractions with Rf values of 0.04, 0.06, 008, 0.14, 0.18 0.22 and 0.28. \nNo fractionation was observed in control. The HPLC chromatogram illustrates \n(Figure 2a) the separation of soluble metabolites and shows the elution 11 order \nwhich depicts the formed intermediate compounds along with the reduction of \nlambdacyhalothrin with retention times of 3.658, 4.308, 5.213, 6.178, 7.928, \n9.214, 10.598, 11.415, 12.336, 12.998, 17.534. Retention times of 6.178 and \n7.928 corresponded to the presence of cyhalothrin as reported by Chaaieri Oudou \nand Bruun Hansen (2002). HPLC analysis of cyhalothrin degradation by strain \nGMMC1 over time showed 12 different peaks with disappearance of 6.178 and \n7.928 retention times (Figure 2b). The retention time peak value suggests that the \ninsecticide residues of lambda-cyhalothrin for Pseudomonas sp is  2.9834 along \nwith other peak values of 3.979, 4.700, 4.825, 5.276, 9.409, 10.817,10.975,11.658, \n13.641,16.500 and 18.008.\n\n\n\nFig. 1: Effect of Lambda cyhalothrin on growth of bacterial isolates\n\n\n\nto be comparatively slower on minimal medium without Lambda cyhalothrin. Maximum OD \n\n\n\n1.72 was recorded in GMMC 1 followed by Bacillus sp. 1.54 and 1.52 \u00b1 0.03. It was also \n\n\n\nobserved that the rest of the isolates failed to tolerate lambda-cyhalothrin even at a minimum of \n\n\n\n250 ppm level. Moderate growth was observed up to 100 ppm for these isolates. The decrease in \n\n\n\nCFU at increased concentrations of pesticide is related to the toxic effect of the pesticide. Plate \n\n\n\nassays revealed that one Gram-negative, Pseudomonas sp., and two Gram-positive Bacillus sp. \n\n\n\nand Micrococcus sp., had a higher tolerance to Lambda cyhalothrin (250 ppm).  \n\n\n\n\n\n\n\nFigure 1. Effect of Lambda cyhalothrin on growth of bacterial isolates \n\n\n\n The growth of lambda-cyhalothrin resistant isolates of Pseudomonas sp.,GMCM1,and \n\n\n\nBacillus sp. (GMMC3 and 5), were observed in the minimal broth amending lambda-cyhalothrin \n\n\n\nat 500 ppm. Biochemical test results showed the isolate GMMC1 is positive on KOH solubility \n\n\n\ntest, catalase test, indole test, oxidase test, and Citrate utilisation test and negative for starch and \n\n\n\ngelatin hydrolysis. Studies on Pseudomonas putida degrading various pesticides were monitored \n\n\n\nby genetic recombination and a recombinant strain able to completely degrade 50 mg/L of \n\n\n\nvarious pesticide compounds was developed by Ting Gong et al.(2018). The endosulfan-\n\n\n\ndegrading bacterial strain Pseudomonas fluorescens was isolated (Jesitha et al.2015) and \n\n\n\ndegradation of endosulfan by freely suspended and calcium-alginate entrapped bacterial cells \n\n\n\nwas investigated in batch as well as in packed bed column studies. Freely \n\n\n\nsuspended Pseudomonas fluorescens cells with biomass maximum OD/OD0 value of 1.68 was \n\n\n\nfound to degrade a number of pesticides. Previous studies had observed that Pseudomonas sp. \n\n\n\nhas the ability to degrade pyrethroids (Mariutsz Cycori and Zofia Piotrowska-Seget 2006). Tang \n\n\n\net al. (2018) reported that pyrethroids are synthetic organic insecticides with \n\n\n\nmammalian toxicity and are widely used in both rural and urban areas worldwide. \n\n\n\n1.72 \n\n\n\n0 \n\n\n\n1.54 \n\n\n\n0.02 \n\n\n\n1.42 \n\n\n\n0.001 0.004 0 0 0 \n0\n\n\n\n0.5\n1\n\n\n\n1.5\n2\n\n\n\nO\nD\n\n\n\n a\nt 6\n\n\n\n00\nnm\n\n\n\n\n\n\n\nStrain code \n\n\n\n100PPM\n\n\n\n250PPM\n\n\n\n500PPM\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 179\n\n\n\nPhylogenetic Analysis \nBased on the morphological and biochemical results, the selected isolates were \npreliminarily identified as Pseudomonas fluorescens, and confirmed by their 16S \nrRNA genes which were amplified from their genomic DNA, and found to be 1500 \nbp long (Figure 3). Sequence blast showed that the strain GMMC1 had a high \nsimilarity (98%) with Pseudomonas fluorescens (Accession number KF420847.1) \nand identified with Pseudomonas sp. (Accession number KT377040) at 96%.\n\n\n\nFig. 2: HPLC spectrum of lambda cyhalothrin. a)control b)degraded  \n\n\n\nFigure 2. HPLC spectrum of lambda cyhalothrin. a)control b)degraded \n\n\n\nPhylogenetic Analysis  \n\n\n\nBased on the morphological and biochemical results, the selected isolates were preliminarily \n\n\n\nidentified as Pseudomonas fluorescens, and confirmed by their 16S rRNA genes which were \n\n\n\namplified from their genomic DNA, and found to be 1500 bp long (Figure 3). Sequence blast \n\n\n\nshowed that the strain GMMC1 had a high similarity (98%) with Pseudomonas fluorescens \n\n\n\n(Accession number KF420847.1) and identified with Pseudomonas sp. (Accession number \n\n\n\nKT377040) at 96%. \n\n\n\n\n\n\n\namplified from their genomic DNA, and found to be 1500 bp long (Figure 3). Sequence blast \n\n\n\nshowed that the strain GMMC1 had a high similarity (98%) with Pseudomonas fluorescens \n\n\n\n(Accession number KF420847.1) and identified with Pseudomonas sp. (Accession number \n\n\n\nKT377040) at 96%. \n\n\n\n\n\n\n\nFigure 3. Phylogenetic relatedness of isolated Pseudomonas sp GMMC1. \n\n\n\n\n\n\n\n\n\n\n\nCONCLUSION \n\n\n\nLambda-cyhalothrin is broken down in soil through photolysis, chemical hydrolysis and \n\n\n\nmicrobial degradation. In laboratory studies, the dissipation of lambda-cyhalothrin in soil was \n\n\n\nstudied by isolation of bacteria. Diverse groups of bacteria were isolated from soil and bacterial \n\n\n\nstrains able to biodegrade cyhalothrin were screened by enrichment of culture with pesticide. \n\n\n\nGMMC1 was found to tolerate and efficiently degrade cyhalothrin at 500 ppm concentration \n\n\n\nlevel. This is the first report of degradation of cyhalothrin with Pseudomonas fluorescens. These \n\n\n\nfindings reveal that increased concentrations of pesticide have a marked effective biodegradation \n\n\n\nperformance of strain Pseudomonas fluorescens but do not lead to complete inhibition of \n\n\n\ncyhalothrin biodegradation. \n\n\n\n\n\n\n\nACKNOWLEDGEMENT \n\n\n\nCONCLUSION\nLambda-cyhalothrin is broken down in soil through photolysis, chemical \nhydrolysis and microbial degradation. In laboratory studies, the dissipation of \nlambda-cyhalothrin in soil was studied by isolation of bacteria. Diverse groups of \nbacteria were isolated from soil and bacterial strains able to biodegrade cyhalothrin \nwere screened by enrichment of culture with pesticide. GMMC1 was found to \n\n\n\nFig. 3: Phylogenetic relatedness of isolated Pseudomonas sp GMMC1.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019180\n\n\n\ntolerate and efficiently degrade cyhalothrin at 500 ppm concentration level. This \nis the first report of degradation of cyhalothrin with Pseudomonas fluorescens. \nThese findings reveal that increased concentrations of pesticide have a marked \neffective biodegradation performance of strain Pseudomonas fluorescens but do \nnot lead to complete inhibition of cyhalothrin biodegradation.\n\n\n\nACKNOWLEDGEMENT\nThe Department of Biotechnology, JJ College(Autonomous), Pudukottai and \nDepartment of Microbiology, Jamal Mohamed College(Autonomous),Tiruchira\nppalli in allowing some of the experiments to be conducted in the Departmental \nlaboratory.\n\n\n\nREFERENCES\nAgarry, S.E., O.A.Olu-Arotiowa, M.O.Aremu and L.A.Jimoa. 2013.  Biodegradation \n\n\n\nof dichlorovos (Organophosphate Pesticide) in soil by bacterial isolates, Journal \nof Natural Sciences Research 3(8): 12-16.\n\n\n\nAlewu, B and Nosiri C. 2011. Pesticides and human health. In: Stoytcheva M, editor. \nPesticides in the Modern World \u2013  Effects of Pesticides Exposure. 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Dichlorodiphenyltrichloroethane \n(DDT): ubiquity, persistence, and risks. Environ. Health Perspect. 110:125\u20138.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019182\n\n\n\nVan den Berg, H. 2009. Global status of DDT and its alternatives for use in vector \ncontrol to prevent disease. Environ. Health Perspect. 117:1656\u201363.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: shamshud@upm.edu.my\n\n\n\nINTRODUCTION\nThe Malay Peninsula is bordered by Thailand in the North, Singapore in the \nSouth, the Straits of Malacca in the West and South China Sea in the East (Fig. \n1). Studies conducted in the peninsula found that the sea level rose to 3-5 m \nabove the present high tide position, sometime during the Holocene (Tjia et al. \n1977). The progradation of the sea many years later led to the formation of sandy \nbeach ridges running parallel to the present shoreline (Roslan et al. 2010). The \noccurrence of these sandy beach ridges along the coastal plains provides evidence \nof the sea level rise during the Holocene (Tjia 1970). Carbon dating of the oldest \nsediments in the ridges occurring in the Sunda Shelf (the area between the Malay \nPeninsula and Borneo Island) indicates their age to be about 6000 years (Haile \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 17: 1-15 (2013)                          Malaysian Society of Soil Science\n\n\n\nOn the Pyritization of the Coastal Sediments in the Malay \nPeninsula during the Holocene and its Effects on Soil \n\n\n\nShamshuddin, J.*, M.S.K. Enio Kang, C.I. Fauziah and Q.A. Panhwar\n\n\n\nDepartment of Land Management, Faculty of Agriculture,\nUniversiti Putra Malaysia,43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nMuch of the present coastal plains in the Malay Peninsula were inundated by \nseawater some 6,000 years ago. That was the time when pyrite is believed to have \nbeen mineralized in the sediments of the seawater. This paper attempts to explain \nthe process of pyrite mineralization in the coastal sediments during the Holocene \nas well as to show how pyrite oxidation affects plants and aquatic life in their \nvicinity. At that point in time, the sea level was 3-5 meters above the present level. \nUnder reduced conditions, Fe3+ ions existing in the sediments were reduced to Fe2+ \nions, while SO4\n\n\n\n2- anions from seawater were reduced to S2- ions. These reactions \nwere promoted by microorganisms feeding on the organic matter provided by \nnative vegetation. Finally, the ferrous and polysulfide ions reacted to form pyrite \n(FeS2). Over the years, this pyrite accumulated in the sediments, occurring at \nvarying depths. In some sediment of the coastal plains of the Malay Peninsula, \nthere are considerable amounts of pyrite; however, they are environment-friendly. \nWhen the areas are developed for agriculture or otherwise, this pyrite is exposed \nto atmospheric conditions, resulting in its oxidation which in turn leads to acidity \nand the formation of yellowish jarosite [KFe3(SO4)2(OH)6]. Toxic amounts of Al \nand Fe are usually present in the soils and water in the area, affecting crop growth \nand aquatic life.\n\n\n\nKeywords: Acid sulfate soil, aluminum, Holocene, jarosite, Malay \nPeninsula, pyritization \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 20132\n\n\n\n1970). This means that much of the present coastal plains in the Malay Peninsula \nwere once under sea level. This was the time when pyrite was mineralized in the \ncoastal sediments observed today. The soils that developed from pyrite-bearing \nsediments are termed as acid sulfate soils (Shamshuddin 2006).\n\n\n\nAcid sulfate soils occur along the coastal plains of the Malay Peninsula \n(Shamshuddin 2006), Kalimantan, Indonesia (Anda et al. 2009), Bangkok Plains, \nThailand (van Breemen and Harmsen 1975) and Mekong Delta, Vietnam (Husson \net al. 2000). These soils are characterized by the presence of pedogenic pyrite \n(FeS2), which is readily oxides on exposure to atmospheric conditions, releasing \nsulfuric acid as well as Al and Fe into the environment. Finally, a new stable \nmineral called jarosite [KFe3(SO)2(OH)6] is or can be formed (Shamshuddin and \nAuxtero 1991; Shamshuddin et al. 1995; Shamshuddin et al. 2004a). When this \nstraw yellow mineral appears in the soil profile, the pH would be, almost certainly, \nbelow 3.5. In Soil Taxonomy, these soils are mostly classified as Sulfaquepts (Soil \nSurvey Staff 2010). \n\n\n\nAcid sulfate soils have been found to occur sporadically in the Kelantan \nPlains (Soo 1975). Using the soil map he produced, the government of Malaysia \nestablished the Kemasin-Semerak Integrated Agricultural Development \nProject (IADP) in 1982 so as to alleviate hard core poverty among the farming \ncommunities as well as mitigate flood by constructing drainage canals. Many \nyears later some soils in the paddy fields were degraded due to excessive acidity \nrelated to oxidation of pyrite present in the soils. Later, a soil survey carried out \n\n\n\nShamshuddin, J., M.S.K. Enio Kang, C.I. Fauziah and Q.A. Panhwar\n\n\n\nFig. 1: Map of South-east Asia \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 3\n\n\n\nPyritization of the Coastal Sediments\n\n\n\nby Shamshuddin et al. (2002) re-delineated the area covered by these acid sulfate \nsoils. \n\n\n\nOther areas in the peninsula where acid sulfate soils occur include the Kedah-\nPerlis Plains (Azmi 1982), Perak (Wan Noordin 1980), Pulau Indah, Selangor \n(Shamshuddin and Auxtero 1991) and Kuala Linggi, Melaka (Shamshuddin et al. \n2004a). Some of these soils are utilized with mixed success for the cultivation of \nrice (Ting et al. 1993; Suswanto et al. 2007), cocoa (Shamshuddin et al. 2004b) \nand oil palm (Auxtero and Shamshuddin 1991). This paper attempts to explain \nthe process of pyrite mineralization in the coastal sediments during the Holocene \nas well as attempts to show how pyrite oxidation affects plants and aquatic life in \nthe vicinity.\n\n\n\nEvidence Relating Pyrite to Seawater\nWan Noordin (1980) studied in great detail the occurrence of pyrite in some acid \nsulfate soils found in the Malay Peninsula. His study found remnants of diatoms \nin the sediments in close association with pyrite (Fig. 2 modified).  Diatoms \nare living creatures of the ocean. The diatoms could have survived only if the \nareas where the pyrite in the acid sulfate soils occurred were once inundated by \nseawater. As such, we conclude that the formation of pyrite found in the soils is \nclosely related to the sea.\n\n\n\nFig. 2: Diatoms found in pyritic-bearing sediments\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 20134\n\n\n\nShamshuddin, J., M.S.K. Enio Kang, C.I. Fauziah and Q.A. Panhwar\n\n\n\nPyritization of the Malay Peninsula\n\n\n\nThe Kedah-Perlis Plains\nAzmi (1982) studied pyrite which is sporadically distributed in the paddy fields \nof the Kedah-Perlis coastal plains (Fig. 3). The plains are situated in the north-\nwestern part of the peninsula, bordering Thailand (Fig. 1). Drainage has oxidized \nthe pyrite, decreasing pH and releasing a soluble form of Al into the soils in the \nvicinity leading to a reduction in rice yield. Some of the pyrite-bearing soils have \nbeen alleviated using lime, resulting in a small increase in rice yield. Some pyrite-\nbearing sediment was found a few kilometres away from the shoreline, indicating \nthat the areas were once inundated with seawater. This could have occurred when \nthe sea level was at its highest during the Holocene. The occurrence of pyrite in \nthe soils can be used as an evidence of a sea level rise in the plains during the \nHolocene. \n\n\n\nFig. 3:  The Kedah-Perlis coastal plains\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 5\n\n\n\nPyritization of the Coastal Sediments\n\n\n\nPulau Indah, Selangor\nPulau Indah is an island off the coast of Selangor, a few kilometres from Port Klang. \nIt is only a small island with parts of it being occupied by farming communities \n(Fig. 4). The areas utilized for crop production contain true acid sulfate soils with \nlow pH and high concentration of Al in the soil solution, and which have an effect \non crop growth. The rest of the area, intermittently inundated with seawater, is \nstill occupied by native plant species. The soils, containing pyrite, are classified as \npotential acid sulfate soils (Auxtero et al. 1991). The whole island was completely \nsubmerged under seawater 1-2 metres above sea level when the sea level rose \nsome 6,000 years ago. The pyrite in the areas occupied by the farmers was \nprobably mineralized during that time.\n\n\n\nKuala Linggi, Melaka\nShamshuddin et al. (2004a) studied the acid sulfate soils found at Kuala Linggi, \nMalacca. Samples for this study were taken from the Cg horizon of the Linau \nSeries. Fig. 5 shows the beautiful crystals of pyrite found in the samples. \nHowever, jarosite was found in the horizon immediately above the Cg horizon, \ninstead of pyrite.  The occurrence of jarosite in this horizon is a result of pyrite \noxidation when the area was drained. The pH of the topsoil is very low with \nexchangeable Al being very high. No crop yield will be obtained without the soil \n\n\n\nFig. 4: A soil map of Pulau Indah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 20136\n\n\n\nShamshuddin, J., M.S.K. Enio Kang, C.I. Fauziah and Q.A. Panhwar\n\n\n\nbeing alleviated using lime or basalt. The pyrite found in the soil of Linau Series \nwas mineralized at the same time as the soils of Pulau Indah, Selangor when the \narea was inundated with seawater.\n\n\n\nThe Kelantan Plains\nTable 1 show the soil properties of the soils at selected points from three locations \nin the Kelantan Plains where detailed investigations were carried out were studied. \nBy and large, the first type (pyrite occurred 2 m below the soil surface) was found \nmostly at Location a (the northern part), the second type (peat overlying the \npyritic layer) was found at Location b (the partially drained peaty area - the middle \npart) and the third type (pyrite occurring in the topsoil) was found at Location c \n(southern part) (Enio et al. 2011) (Fig. 6).  At these locations, the pyrite containing \nlayer was found to occur at different depths. Soils had low pH, ranging from 3.24 \nto 4.89, and contained remarkably high amounts of exchangeable Al (>5 cmolc/\nkg). Soil pH values less than 3.5 are indicative of high acidity associated with the \noccurrence of pyrite/jarosite. \n\n\n\nPyrite-bearing sediments were found at many places in the plains (Enio et \nal. 2011). They were concentrated in the low lying areas (swales) adjacent to the \nsandy beach ridges (Fig. 7). An imaginary line can be drawn to separate soils \ncontaining pyrite from those without (riverine alluvium). Pyrite occurred in the \nsoils to the East of the line and this so-called pyrite border is consistent with the \nareas demarcated for acid sulfate soils found earlier by Shamshuddin et al. (2002). \nWe can, thus, say that this line was more or less the former shoreline about 6,000 \n\n\n\nFig. 5: SEM micrograph of fresh pyrite\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 7\n\n\n\nW\nhe\n\n\n\nre\n, l\n\n\n\noc\nat\n\n\n\nio\nn \n\n\n\na \n(th\n\n\n\ne \nno\n\n\n\nrth\ner\n\n\n\nn \npa\n\n\n\nrt)\n, t\n\n\n\nhe\n se\n\n\n\nco\nnd\n\n\n\n ty\npe\n\n\n\n w\nas\n\n\n\n fo\nun\n\n\n\nd \nat\n\n\n\n lo\nca\n\n\n\ntio\nn \n\n\n\nb \n(th\n\n\n\ne \npa\n\n\n\nrti\nal\n\n\n\nly\n d\n\n\n\nra\nin\n\n\n\ned\n p\n\n\n\nea\nty\n\n\n\n a\nre\n\n\n\na-\nth\n\n\n\ne \nm\n\n\n\nid\ndl\n\n\n\ne \npa\n\n\n\nrt)\n a\n\n\n\nnd\n th\n\n\n\ne \nth\n\n\n\nird\n ty\n\n\n\npe\n w\n\n\n\nas\n fo\n\n\n\nun\nd \n\n\n\nat\n \n\n\n\nlo\nca\n\n\n\ntio\nn \n\n\n\nc \n(s\n\n\n\nou\nth\n\n\n\ner\nn \n\n\n\npa\nrt)\n\n\n\n.  \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n(S\n\n\n\nou\nrc\n\n\n\ne:\n M\n\n\n\nod\nifi\n\n\n\ned\n fr\n\n\n\nom\n E\n\n\n\nni\no \n\n\n\net\n a\n\n\n\nl. \n20\n\n\n\n11\n)   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n M\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEx\nch\n\n\n\nan\nge\n\n\n\nab\nle\n\n\n\n c\nat\n\n\n\nio\nns\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSa\nm\n\n\n\npl\nes\n\n\n\n \nD\n\n\n\nep\nth\n\n\n\n \npH\n\n\n\n \nEC\n\n\n\n  \nK\n\n\n\n \nC\n\n\n\na \nM\n\n\n\ng \nA\n\n\n\nl \nC\n\n\n\nEC\n \n\n\n\nEx\nt. \n\n\n\nFe\n \n\n\n\nA\nva\n\n\n\nil.\n P\n\n\n\n \nTo\n\n\n\nta\nl N\n\n\n\n \nTo\n\n\n\nta\nl C\n\n\n\n\n\n\n\n  \n (c\n\n\n\nm\n) \n\n\n\n(H\n2O\n\n\n\n)  \n (d\n\n\n\nS \n m\n\n\n\n-1\n) \n\n\n\n(c\nm\n\n\n\nol\nc \nkg\n\n\n\n-1\n) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n(m\n\n\n\ng \nkg\n\n\n\n-1\n) \n\n\n\n(%\n)  \n\n\n\n \n1(\n\n\n\na)\n \n\n\n\n \n0-\n\n\n\n15\n \n\n\n\n \n4.\n\n\n\n44\n \n\n\n\n \n0.\n\n\n\n14\n \n\n\n\n \n1.\n\n\n\n07\n \n\n\n\n \n0.\n\n\n\n57\n \n\n\n\n \n0.\n\n\n\n26\n \n\n\n\n5.\n10\n\n\n\n\n\n\n\n11\n.6\n\n\n\n4 \n1.\n\n\n\n96\n \n\n\n\n \n18\n\n\n\n.5\n5 \n\n\n\n \n0.\n\n\n\n37\n \n\n\n\n \n4.\n\n\n\n64\n \n\n\n\n  \n15\n\n\n\n-3\n0 \n\n\n\n4.\n16\n\n\n\n \n0.\n\n\n\n12\n \n\n\n\n0.\n54\n\n\n\n \n0.\n\n\n\n56\n \n\n\n\n0.\n16\n\n\n\n \n6.\n\n\n\n71\n \n\n\n\n10\n.3\n\n\n\n6 \n1.\n\n\n\n11\n \n\n\n\n18\n.4\n\n\n\n8 \n0.\n\n\n\n18\n \n\n\n\n1.\n68\n\n\n\n\n\n\n\n30\n-4\n\n\n\n5 \n4.\n\n\n\n03\n \n\n\n\n0.\n10\n\n\n\n \n0.\n\n\n\n50\n \n\n\n\n0.\n73\n\n\n\n \n0.\n\n\n\n20\n \n\n\n\n6.\n78\n\n\n\n \n9.\n\n\n\n64\n \n\n\n\n1.\n06\n\n\n\n \n16\n\n\n\n.8\n0 \n\n\n\n0.\n10\n\n\n\n \n1.\n\n\n\n11\n \n\n\n\n  \n45\n\n\n\n-6\n0 \n\n\n\n4.\n01\n\n\n\n \n0.\n\n\n\n08\n \n\n\n\n0.\n63\n\n\n\n \n0.\n\n\n\n83\n \n\n\n\n0.\n36\n\n\n\n \n7.\n\n\n\n87\n \n\n\n\n10\n.7\n\n\n\n9 \n0.\n\n\n\n43\n \n\n\n\n13\n4.\n\n\n\n40\n \n\n\n\n0.\n21\n\n\n\n \n6.\n\n\n\n29\n \n\n\n\n2(\na)\n\n\n\n \n0-\n\n\n\n15\n \n\n\n\n4.\n24\n\n\n\n \n0.\n\n\n\n10\n \n\n\n\n1.\n23\n\n\n\n \n2.\n\n\n\n97\n \n\n\n\n1.\n67\n\n\n\n \n2.\n\n\n\n66\n \n\n\n\n8.\n36\n\n\n\n \n2.\n\n\n\n65\n \n\n\n\n16\n.6\n\n\n\n6 \n0.\n\n\n\n17\n \n\n\n\n1.\n96\n\n\n\n\n\n\n\n15\n-3\n\n\n\n0 \n4.\n\n\n\n50\n \n\n\n\n0.\n08\n\n\n\n \n0.\n\n\n\n83\n \n\n\n\n4.\n12\n\n\n\n \n1.\n\n\n\n97\n \n\n\n\n1.\n36\n\n\n\n \n7.\n\n\n\n64\n \n\n\n\n1.\n90\n\n\n\n \n19\n\n\n\n.8\n1 \n\n\n\n0.\n12\n\n\n\n \n1.\n\n\n\n06\n \n\n\n\n  \n30\n\n\n\n-4\n5 \n\n\n\n4.\n89\n\n\n\n \n0.\n\n\n\n10\n \n\n\n\n0.\n88\n\n\n\n \n7.\n\n\n\n09\n \n\n\n\n2.\n15\n\n\n\n \n0.\n\n\n\n63\n \n\n\n\n9.\n43\n\n\n\n \n1.\n\n\n\n51\n \n\n\n\n11\n.6\n\n\n\n2 \n0.\n\n\n\n11\n \n\n\n\n1.\n40\n\n\n\n\n\n\n\n45\n-6\n\n\n\n0 \n4.\n\n\n\n47\n \n\n\n\n0.\n09\n\n\n\n \n0.\n\n\n\n96\n \n\n\n\n5.\n53\n\n\n\n \n2.\n\n\n\n11\n \n\n\n\n0.\n56\n\n\n\n \n13\n\n\n\n.9\n3 \n\n\n\n3.\n40\n\n\n\n \n15\n\n\n\n.8\n9 \n\n\n\n0.\n12\n\n\n\n \n2.\n\n\n\n13\n \n\n\n\n  \n60\n\n\n\n-7\n5 \n\n\n\n4.\n17\n\n\n\n \n0.\n\n\n\n15\n \n\n\n\n1.\n29\n\n\n\n \n8.\n\n\n\n17\n \n\n\n\n2.\n19\n\n\n\n \n1.\n\n\n\n12\n \n\n\n\n12\n.1\n\n\n\n4 \n2.\n\n\n\n61\n \n\n\n\n19\n.8\n\n\n\n1 \n0.\n\n\n\n19\n \n\n\n\n4.\n32\n\n\n\n \n3(\n\n\n\nb)\n \n\n\n\n0-\n15\n\n\n\n \n3.\n\n\n\n50\n \n\n\n\n0.\n12\n\n\n\n \n0.\n\n\n\n76\n \n\n\n\n0.\n68\n\n\n\n \n0.\n\n\n\n42\n \n\n\n\n6.\n54\n\n\n\n \n15\n\n\n\n.8\n6 \n\n\n\n1.\n28\n\n\n\n \n8.\n\n\n\n12\n \n\n\n\n0.\n27\n\n\n\n \n6.\n\n\n\n42\n \n\n\n\n  \n15\n\n\n\n-3\n0 \n\n\n\n3.\n52\n\n\n\n \n0.\n\n\n\n17\n \n\n\n\n0.\n52\n\n\n\n \n0.\n\n\n\n45\n \n\n\n\n0.\n42\n\n\n\n \n6.\n\n\n\n45\n \n\n\n\n13\n.6\n\n\n\n4 \n0.\n\n\n\n49\n \n\n\n\n9.\n31\n\n\n\n \n0.\n\n\n\n14\n \n\n\n\n2.\n68\n\n\n\n\n\n\n\n30\n-4\n\n\n\n5 \n3.\n\n\n\n51\n \n\n\n\n0.\n16\n\n\n\n \n0.\n\n\n\n56\n \n\n\n\n0.\n41\n\n\n\n \n0.\n\n\n\n43\n \n\n\n\n4.\n40\n\n\n\n \n10\n\n\n\n.2\n9 \n\n\n\n0.\n02\n\n\n\n \n10\n\n\n\n.7\n8 \n\n\n\n0.\n92\n\n\n\n \n1.\n\n\n\n39\n \n\n\n\n  \n45\n\n\n\n-6\n0 \n\n\n\n3.\n23\n\n\n\n \n0.\n\n\n\n18\n \n\n\n\n0.\n48\n\n\n\n \n0.\n\n\n\n50\n \n\n\n\n0.\n52\n\n\n\n \n7.\n\n\n\n61\n \n\n\n\n27\n.4\n\n\n\n3 \n0.\n\n\n\n13\n \n\n\n\n10\n.6\n\n\n\n4 \n0.\n\n\n\n20\n \n\n\n\n8.\n76\n\n\n\n \n4(\n\n\n\nb)\n \n\n\n\n0-\n15\n\n\n\n \n3.\n\n\n\n42\n \n\n\n\n0.\n04\n\n\n\n \n0.\n\n\n\n47\n \n\n\n\n0.\n76\n\n\n\n \n0.\n\n\n\n22\n \n\n\n\n5.\n81\n\n\n\n \n12\n\n\n\n.5\n0 \n\n\n\n0.\n25\n\n\n\n \n11\n\n\n\n.9\n7 \n\n\n\n0.\n18\n\n\n\n \n2.\n\n\n\n34\n \n\n\n\n  \n15\n\n\n\n-3\n0 \n\n\n\n3.\n21\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n0.\n52\n\n\n\n \n0.\n\n\n\n47\n \n\n\n\n0.\n18\n\n\n\n \n4.\n\n\n\n47\n \n\n\n\n9.\n79\n\n\n\n \n0.\n\n\n\n24\n \n\n\n\n9.\n73\n\n\n\n \n0.\n\n\n\n11\n \n\n\n\n1.\n25\n\n\n\n\n\n\n\n30\n-4\n\n\n\n5 \n3.\n\n\n\n85\n \n\n\n\n0.\n06\n\n\n\n \n0.\n\n\n\n42\n \n\n\n\n0.\n52\n\n\n\n \n0.\n\n\n\n11\n \n\n\n\n3.\n19\n\n\n\n \n8.\n\n\n\n29\n \n\n\n\n0.\n26\n\n\n\n \n9.\n\n\n\n38\n \n\n\n\n0.\n72\n\n\n\n \n0.\n\n\n\n42\n \n\n\n\n  \n45\n\n\n\n-6\n0 \n\n\n\n3.\n72\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n0.\n40\n\n\n\n \n0.\n\n\n\n35\n \n\n\n\n0.\n09\n\n\n\n \n2.\n\n\n\n87\n \n\n\n\n7.\n71\n\n\n\n \n0.\n\n\n\n15\n \n\n\n\n10\n.0\n\n\n\n1 \n0.\n\n\n\n66\n \n\n\n\n0.\n37\n\n\n\n \n5(\n\n\n\nc)\n \n\n\n\n0-\n15\n\n\n\n \n4.\n\n\n\n01\n \n\n\n\n0.\n06\n\n\n\n \n0.\n\n\n\n76\n \n\n\n\n0.\n49\n\n\n\n \n0.\n\n\n\n23\n \n\n\n\n2.\n58\n\n\n\n \n19\n\n\n\n.6\n4 \n\n\n\n0.\n27\n\n\n\n \n18\n\n\n\n.6\n2 \n\n\n\n0.\n36\n\n\n\n \n6.\n\n\n\n78\n \n\n\n\n  \n15\n\n\n\n-3\n0 \n\n\n\n3.\n80\n\n\n\n \n0.\n\n\n\n08\n \n\n\n\n0.\n43\n\n\n\n \n0.\n\n\n\n44\n \n\n\n\n0.\n14\n\n\n\n \n6.\n\n\n\n04\n \n\n\n\n13\n.6\n\n\n\n4 \n0.\n\n\n\n10\n \n\n\n\n10\n.9\n\n\n\n2 \n0.\n\n\n\n14\n \n\n\n\n2.\n53\n\n\n\n\n\n\n\n30\n-4\n\n\n\n5 \n3.\n\n\n\n36\n \n\n\n\n0.\n14\n\n\n\n \n0.\n\n\n\n54\n \n\n\n\n0.\n40\n\n\n\n \n0.\n\n\n\n20\n \n\n\n\n9.\n12\n\n\n\n \n20\n\n\n\n.9\n3 \n\n\n\n0.\n33\n\n\n\n \n14\n\n\n\n.2\n8 \n\n\n\n0.\n24\n\n\n\n \n8.\n\n\n\n71\n \n\n\n\n  \n45\n\n\n\n-6\n0 \n\n\n\n3.\n24\n\n\n\n \n0.\n\n\n\n19\n \n\n\n\n0.\n36\n\n\n\n \n0.\n\n\n\n35\n \n\n\n\n0.\n23\n\n\n\n \n3.\n\n\n\n36\n \n\n\n\n20\n.3\n\n\n\n6 \n0.\n\n\n\n12\n \n\n\n\n11\n.6\n\n\n\n2 \n0.\n\n\n\n24\n \n\n\n\n7.\n91\n\n\n\n \n6(\n\n\n\nc)\n \n\n\n\n0-\n15\n\n\n\n \n3.\n\n\n\n84\n \n\n\n\n0.\n04\n\n\n\n \n0.\n\n\n\n82\n \n\n\n\n0.\n34\n\n\n\n \n0.\n\n\n\n28\n \n\n\n\n1.\n96\n\n\n\n \n16\n\n\n\n.0\n7 \n\n\n\n0.\n12\n\n\n\n \n14\n\n\n\n.2\n8 \n\n\n\n0.\n25\n\n\n\n \n5.\n\n\n\n26\n \n\n\n\n  \n15\n\n\n\n-3\n0 \n\n\n\n3.\n61\n\n\n\n \n0.\n\n\n\n04\n \n\n\n\n0.\n45\n\n\n\n \n0.\n\n\n\n41\n \n\n\n\n0.\n12\n\n\n\n \n1.\n\n\n\n79\n \n\n\n\n11\n.9\n\n\n\n3 \n0.\n\n\n\n23\n \n\n\n\n9.\n87\n\n\n\n \n0.\n\n\n\n12\n \n\n\n\n2.\n07\n\n\n\n\n\n\n\n30\n-4\n\n\n\n5 \n3.\n\n\n\n60\n \n\n\n\n0.\n04\n\n\n\n \n0.\n\n\n\n39\n \n\n\n\n0.\n35\n\n\n\n \n0.\n\n\n\n09\n \n\n\n\n1.\n28\n\n\n\n \n8.\n\n\n\n57\n \n\n\n\n0.\n53\n\n\n\n \n10\n\n\n\n.3\n6 \n\n\n\n0.\n87\n\n\n\n \n0.\n\n\n\n94\n \n\n\n\n  \n45\n\n\n\n-6\n0 \n\n\n\n3.\n20\n\n\n\n \n0.\n\n\n\n02\n \n\n\n\n0.\n16\n\n\n\n \n0.\n\n\n\n32\n \n\n\n\n0.\n06\n\n\n\n \n1.\n\n\n\n04\n \n\n\n\n3.\n67\n\n\n\n \n0.\n\n\n\n54\n \n\n\n\n15\n.7\n\n\n\n5 \n0.\n\n\n\n05\n \n\n\n\n0.\n89\n\n\n\n \n7(\n\n\n\nc)\n \n\n\n\n0-\n15\n\n\n\n \n4.\n\n\n\n02\n \n\n\n\n0.\n34\n\n\n\n \n1.\n\n\n\n35\n \n\n\n\n0.\n71\n\n\n\n \n0.\n\n\n\n54\n \n\n\n\n7.\n86\n\n\n\n \n25\n\n\n\n.4\n3 \n\n\n\n0.\n11\n\n\n\n \n10\n\n\n\n.2\n2 \n\n\n\n0.\n51\n\n\n\n \n16\n\n\n\n.0\n0 \n\n\n\n  \n15\n\n\n\n-3\n0 \n\n\n\n4.\n09\n\n\n\n \n0.\n\n\n\n42\n \n\n\n\n1.\n37\n\n\n\n \n0.\n\n\n\n68\n \n\n\n\n0.\n70\n\n\n\n \n10\n\n\n\n.8\n1 \n\n\n\n17\n.2\n\n\n\n1 \n0.\n\n\n\n10\n \n\n\n\n23\n.1\n\n\n\n0 \n0.\n\n\n\n29\n \n\n\n\n11\n.4\n\n\n\n0 \n  \n\n\n\n30\n-4\n\n\n\n5 \n3.\n\n\n\n86\n \n\n\n\n0.\n14\n\n\n\n \n1.\n\n\n\n06\n \n\n\n\n1.\n37\n\n\n\n \n1.\n\n\n\n66\n \n\n\n\n36\n.8\n\n\n\n0 \n30\n\n\n\n.9\n3 \n\n\n\n0.\n62\n\n\n\n \n27\n\n\n\n.5\n8 \n\n\n\n0.\n46\n\n\n\n \n30\n\n\n\n.3\n2 \n\n\n\n  \n45\n\n\n\n-6\n0 \n\n\n\n3.\n24\n\n\n\n \n0.\n\n\n\n42\n \n\n\n\n0.\n83\n\n\n\n \n2.\n\n\n\n88\n \n\n\n\n2.\n05\n\n\n\n \n92\n\n\n\n.0\n0 \n\n\n\n8.\n16\n\n\n\n \n0.\n\n\n\n15\n \n\n\n\n23\n.5\n\n\n\n9 \n0.\n\n\n\n47\n \n\n\n\n36\n.4\n\n\n\n3 \n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nC\nhe\n\n\n\nm\nic\n\n\n\nal\n p\n\n\n\nro\npe\n\n\n\nrti\nes\n\n\n\n o\nf t\n\n\n\nhe\n so\n\n\n\nils\n\n\n\nPyritization of the Coastal Sediments\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 20138\n\n\n\nyears ago.  Therefore, the plains starting from this line eastwards were under sea \nlevel during that particular period of history.\n\n\n\nFig. 6: The depth in soil profile below which pyrite occurs (The first type in which pyrite \noccurred 2 m below the soil surface  was found mostly at Location a (the northern part), \n\n\n\nthe second type where peat overlying the pyritic layer was found at location b (the \npartially drained peaty area-the middle part) and the third type in which pyrite occurring \n\n\n\nin the topsoil was found at Location c (southern part)\n\n\n\nFig. 7: The predicted shoreline about 6000 years ago\n\n\n\n(Source: Modified from Enio et al. 2011)   \n\n\n\nShamshuddin, J., M.S.K. Enio Kang, C.I. Fauziah and Q.A. Panhwar\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\n(Source: Modified from Enio et al. 2011) \n \n\n\n\nFig. 6: The depth in soil profile below which pyrite occurs (The first type in which pyrite \noccurred 2 m below the soil surface  was found mostly at Location a (the northern part), the \n\n\n\nsecond type where peat overlying the pyritic layer was found at location b (the partially drained \npeaty area-the middle part) and the third type in which pyrite occurring in the topsoil was found \n\n\n\nat Location c (southern part) \n \n                                                         \n\n\n\na b c \n\n\n\n0 \n\n\n\n1 \n\n\n\n2 \n\n\n\nDepth (m) \n\n\n\nPyritic layer \n\n\n\nPeat layer \n\n\n\nLegend \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                  Malaysian Society of Soil Science \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\nSemerak,\nPasir Puteh\n\n\n\nN\n\n\n\nJelawat\n\n\n\nSouth China \nSea\n\n\n\nLegend\nPredicted shoreline\n\n\n\nPyrite sites\n\n\n\nKemasin-Semerak\nIADP\n\n\n\n. . . . . . . . . . \n\n\n\nMachok\n\n\n\n1, 100000\n\n\n\nLocation\n\n\n\nPeninsular Malaysia\n \n\n\n\n                                                                                  ( Source: Modified from Enio et al. 2011) \n \n                              Fig. 7: The predicted shoreline about 6000 years ago \n\n\n\n(Source: Modified from Enio et al. 2011)   \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 9\n\n\n\nyears ago.  Therefore, the plains starting from this line eastwards were under sea \nlevel during that particular period of history.\n\n\n\nThe distribution of pyritic layer in the soils of the Kelantan Plains with depth \nwas studied. Some of the locations where pyrite was present in the sediments \nare marked as bold stars. It was found that the pyritic layer occurred at three \nconspicuous depths, namely below 2 m, between 0 to 50 cm and in the topsoil. \n\n\n\nThe pyritic layer in the soils of the northern part of the study area (near \ndowntown Jelawat) was too deep to be identified by soil auger. For this location, \nsamples were taken in the vicinity of the drainage canals as at the time of the \nsurvey, the local authority was cleaning, and widening the canals for maintenance \npurposes. The materials from the canal bed were dug up and placed on the \nshoulders of the canals. Samples were left to dry for a few days to determine if \nthey contained pyrite. Clear yellowish mottles appeared within the samples while \nthe pH was about 3. These yellowish mottles are actually jarosite formed from the \noxidation of pyrite (Shamshuddin and Auxtero 1991; Shamshuddin 2006).  As the \ndrainage canals were more than 2 m deep, some samples were also taken from the \narea using the auger for physico-chemical analyses. The study results found that \nthe soils from the top 75 cm had pH values above 3.5, giving the impression that \nthe soils might not be acid sulfate soils (Enio et al. 2011).\n\n\n\nThe soils within the partially drained area of the Kelantan Plains are overlain \nby peaty materials of the Holocene age (Enio et al. 2011). During field work, \nsamples were taken at intervals of 15 cm depth and subsequently analyzed to \ndetermine their physico-chemical properties. Within a depth of 50 cm, the pH of \nthe soils was found to be generally below 3.5, consistent with the pH value for \nSulfaquepts (acid sulfate soils) as defined by Soil Taxonomy (Soil Survey Staff \n2010). The air-dried samples indicated the presence of yellowish jarosite mottles.\n\n\n\nSoils in the southern part of study area were mostly acidic throughout the \nprofiles. At some locations, yellowish jarosite mottles appeared on the surface of \nthe soils. Clearly, in these soils pyrite occurred within the topsoil. Much of the \narea had been cropped to rice, but was abandoned when the soils became very \nacid and Al was found to be present at toxic level, rendering the soils unsuitable \nfor rice production. Currently, the plant species growing in abandoned paddy \nfields is purun (Eleocharis dulcis), which is Al-tolerant.\n\n\n\nProcess of Pyritization in the Coastal Plains of the Malay Peninsula\n\n\n\nThe pyritization process that began in the past is still ongoing in the peninsula. \nPyrite forms when sulfate (SO4\n\n\n\n2-) from seawater and ferric ions (Fe3+) from marine \nsediments are reduced to sulfide (S2-) and Fe2+ ions, respectively. These reactions \noccur under anaerobic conditions (extremely reducing) where microorganisms \nfeeding on organic matter present in the sediments play an important role in the \nreduction process. \n\n\n\nUnder flooded conditions and in the presence of organic matter, Fe3+ ions in \nthe solution are readily reduced to Fe2+ ions with the help of microbes (Ivarson \n\n\n\nPyritization of the Coastal Sediments\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201310\n\n\n\net al. 1982; Konsten et al. 1994). The organic matter needed for the reduction \nprocess will be provided by native vegetation. The microbes responsible for this \nreaction are most probably Desulfovibro desulfuricans (Bloomfield and Coulter \n1973; Ivarson et al. 1982). The conditions in the area must have been strongly \nreducing, where Eh was less than -220 mV (De Coninck 1978).\n\n\n\nThe sea level at and around the Kelantan Plains about 6000 years BP was 3 \nto 5 m higher than at present (Tjia et al. 1977). The sea level here might have been \nsimilar to what had been proposed for the Bangkok Plains during that period of \nhistory by Pons et al. (1982). Carbon dating of the oldest sediments in the Sunda \nShelf found their age to be about 6,000 years ago (Haile 1970). This means that \nmuch of the present coastal plains in Kelantan were submerged by the sea at that \ntime. \n\n\n\nPyrite found in the study area was probably formed when the sea level was \nat its highest. The process of pyritization can be summarized as follows (Enio et \nal. 2011): \n        \n\n\n\nFe3+ (present in the marine sediments) \u2192   Fe2+\n\n\n\nSO4\n2- (from seawater) \u2192 S2-\n\n\n\nSubsequently, the ferrous and sulfide ions had reacted to form pyrite (FeS2). \nAfter a long period of time (probably a few thousand years), the amount of pyrite \nformed could be substantial in quantity. The overall reaction for the formation \nof pyrite in the soils of the peninsula can be described by the following equation \n(Pons et al. 1982):\n         \nFe2O3(s)  +  4SO4\n\n\n\n2-\n(aq)  +  8CH2O  +  1/2O2 (aq)  \u2192  2FeS2(s)  +  8HCO3(aq)\n\n\n\n-  +  4H2O\n\n\n\nThe above equation shows that it needs sufficient amounts of organic matter for \nthe reduction process to proceed without interruption. Furthermore, there must be \nsome oxygen available in the sediments for the microbes that help convert sulfate \nto sulfide.\n\n\n\nThis reaction is presumed to have occurred while the sea level was higher \nthan that at present. As the sea prograded due to geological reasons, sandy beach \nridges interspersed with swales were created (Roslan et al. 2010). Some of the \nsoils in the swales between the ridges contain pyrite (Shamshuddin et al. 2002), \nconfirmed by the authors during field work at another time.\n\n\n\nSoils at Location a (the northern part of Kelantan, Malaysia) where the first \ntype was found, have a pyritic layer deep down the soil profiles. How did this come \nabout? The plausible explanation for the pyritization process at that location could \nbe as follows. In the northern part of the study area, in the vicinity of Jelawat, the \narea bordering the shoreline was probably at a higher level. Seawater was able \nto seep through the porous riverine alluvial materials containing oxides of Fe at \na few meters below the surface as evidenced by the presence of high amounts \nof extractable Fe in the soils of the area. Organic matter required by microbes \n\n\n\nShamshuddin, J., M.S.K. Enio Kang, C.I. Fauziah and Q.A. Panhwar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 11\n\n\n\nPyritization of the Coastal Sediments\n\n\n\nis also present in sufficient amounts. Given time, pyrite was slowly but surely \nmineralized in the hydromorphic sediments in the presence of Fe and seawater.\n\n\n\nAt Location b, (the partially drained peaty area - the middle part) the \nlandscape was totally different from that of Location a (the northern part of \nthe Kelantan, Malaysia). Here, due to reasons unknown to us, swamps were \nformed when the sea was prograding. The water in these swamps was probably \nbrackish due to intrusion of seawater from time to time, most probably during the \nmonsoon months of November to January. Native plant species have probably \nbeen growing since time immemorial, providing enough organic matter for the \nmicrobes to survive while converting sulfate to sulfide. If sufficient amounts of \nFe3+ are available, pyrite is consequently mineralized in the sediments. Given the \nwaterlogged conditions, the organic matter from the plant species growing in \nthe swamps had accumulated forming an organic layer above the mineral soils. \nHence, the areas now have soils containing pyrite overlain by peaty materials. A \nbig portion of this area has been gazetted as forest reserve, although it has been \npartly drained.\n\n\n\nLocation c (southern part of the area) is rather flat and located close to the \npresent shoreline. The area is not swampy, but floods during the rainy season. \nNative local plant species also flourish here. Seawater must have reached this area \na few thousands years ago, during which time pyrite was mineralized according \nto the mechanism mentioned above. Jarosite appeared in the topsoil in some \nlocalities; jarosite is or can be the product of pyrite oxidation due to drainage.\n\n\n\nBy identifying  the locations where pyrite-bearing sediments are found, \nwe can delineate areas containing pyrite (acid sulfate soils). We can then draw \nan imaginary line separating acid sulfate soils and the original riverine alluvial \nmaterials. This line can be assumed to be the position of the shoreline about \n6,000 years ago. It is known that pyritization of the sediments requires sufficient \namounts of seawater to supply the sulfate. This means that for the pyrite to be \nformed in the area, the sea level must have risen a few metres above the present \nlevel. As such, we can use the presence of pyrite as yet another piece of evidence \nfor the rise in sea level in the Kelantan Plains during the Holocene. This argument \non sea level rise during the Holocene is consistent with the evidence put forward \nby others, which is based on geological records of the area (Tjia 1970; Tjia 1973; \nTjia et al. 1977).\n\n\n\nImplications of Pyrite-Bearing Sediments for Agriculture\n\n\n\nSediments containing pyrite could be a major problem to the livelihood of \nthe people living in the surrounding areas. Pyrite itself is stable under natural \nconditions (submerged under water). When the soils containing pyrite are drained \nfor development (for agriculture or otherwise), the pyrite is exposed to atmospheric \nconditions and subsequently oxidized, releasing high amounts of acidity. The low \npH that follows could accelerate the dissolution of silicates in the soils that result \nin the release of toxic metals such as Al and Fe into the soils and waterways. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201312\n\n\n\nThis condition naturally affects both rice production (Elisa Azura et al. 2011) and \naquatic life alike. The water in the paddy fields contains high amounts of Fe as \nshown by its reddish colour at the onset of the rice growing season.\n\n\n\nThe above process had taken place in the Malay Peninsula. Some paddy \nfields in the peninsula are degraded by soil acidity and marked by extreme levels \nof Al concentration. Growing rice on such unproductive land is uneconomical \nby any means as the yield is far below the national average of 3.8 t ha-1 (Eliza \nAzura 2012). In order to get a good crop of rice from paddy fields in such areas, \ndolomitic limestone has to be applied at high rates (Suswanto 2007; Elisa Azura \n2012). Also, the soils can ameliorated with the application of ground basalt at \nappropriate rates (Shazana et al. 2013).\n\n\n\nCONCLUSION\nPyrite-bearing sediments occur sporadically in the Malay Peninsula. This pyrite \noccurs at varying depths in the sediments; in the Kelantan Plains, it is below 2 m in \nthe soils of the northern part and in the surface horizon in the soils of the southern \npart. Oxidation of this pyrite has caused untold damage to the productivity of \npaddy soils in the area. The pyrite occurring in the coastal sediments of the Malay \nPeninsula is assumed to have been mineralized about 6,000 years ago when the \nsea level rose 3 to 5 m above the present. The occurrence of pyrite in the soils is \nevidence of a sea level rise in the plains during the Holocene age. \n\n\n\nACKNOWLEDGEMENTS\nThe authors acknowledge the financial and technical support by Universiti Putra \nMalaysia and the Ministry of Higher Education Malaysia for Long Term Research \nGrant Scheme (LRGS) fund for food security. \n\n\n\nREFERENCES\nAnda, M., A.B. Siswanto and R.E. Subandiono. 2009. Properties or organic and acid \n\n\n\nsulfate soils and water of a \u2018reclaimed\u2019 tidal backswamp in Central Kalimantan, \nIndonesia. Geoderma. 149: 54-65.\n\n\n\nAuxtero, E. A. and J. Shamshuddin. 1991. Growth of oil palm (Elaies guineensis) \nseedlings on acid sulfate soils as affected by water regime and Al. Plant Soil. \n137: 243-257. \n\n\n\nAuxtero, E.A, J. Shamshuddin and S. Paramananthan. 1991. Mineralogy, morphology \nand classification of acid sulfate soils in Pulau Lumut, Selangor. Pertanika. \n14(1): 43-51.\n\n\n\nAzmi, M.A. 1982. 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The development of \nan acid sulfate area in former mangroves in Merbok, Kedah, Malaysia. In: Acid \nSulfate Soils, eds. Dent D.L. and M.E.F. van Mensvoort, pp. 95-101. Publication \n53. Wageningen, The Netherlands: ILRI.\n\n\n\nTjia, H.D. 1970. Monsoon-control of the eastern shorelines of Malaya. Bulletin of the \nGeological Society of Malaysia. 3: 9-15. \n\n\n\nTjia, H.D. 1973. Geomorphology. In: Geology of the Malay Peninsula, eds. D.J. \nGobbett and C.S. Hutchison, pp. 13-24. New York: John Wiley-Interscince.\n\n\n\nShamshuddin, J., M.S.K. Enio Kang, C.I. Fauziah and Q.A. Panhwar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 15\n\n\n\nPyritization of the Coastal Sediments\n\n\n\nTjia, H.D., S. Fujii and K. Kigoshi. 1977. Changes of sea level in South China Sea \nduring the Quaternary. In: Malaysian and Indonesian Coastal and Offshore \nAreas, pp. 11-36. United Nations ESCAP, CCOP Technical Publication 5, USA. \n\n\n\nvan Breemen, N. and K. Harmsen. 1975. Translocation of iron in acid sulfate soils. \nSoil morphology, and the chemistry and mineralogy of iron in a chronosequence \nof acid sulfate soil. Soil Science Society of America Journal. 39: 1140-148.\n\n\n\nWan Noordin, W.D. 1980. Soil Genesis on Coastal Plains, Perak, Peninsular Malaysia. \nPhD thesis, ITC, University of Ghent, Belgium.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: mgolabi@triton.uog.edu \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 139-157 (2021)                          Malaysian Society of Soil Science\n\n\n\nAgronomic Value of Composted Organic Waste Application \non Porous Soils of Northern Guam \n\n\n\nGolabi, M.H.1*, Galsim, F.P1, Endale, D.2, Tareyama, S.A.1 and \nIyekar, C.1\n\n\n\n1College of Natural & Applied Sciences, Western Pacific Tropical Research Center, \nUniversity of Guam, GU 96923, USA\n\n\n\n2USDA-NRCS, Watkinsville, Georgia, USA\n\n\n\nABSTRACT\nAs an alternative to the application of commercial synthetic fertilizers on land, \ncomposted organic wastes can be applied as organic fertilizer for crop production. \nThis is a more viable waste-management system based on \u2018resource recovery\u2019 \nstrategy. We compared applications of 0, 30, 60 and 90 tons (dry weight) per acre \nof composted organic waste with application of commercial inorganic fertilizers \ncontaining equivalent amounts of nitrogen over three growing seasons in northern \nGuam soils. In season 1 (dry season), the yield from plots receiving compost \n(compost plots) was not significantly higher (p = 0.05) than the plots receiving \nsynthetic fertilizer (fertilizer plots). In season 2, when no compost was applied (for \nresidual effect) but inorganic fertilizer application was continued, the 60- and 90-\nton per acre compost applied plots showed a significantly higher yield than control \n(0-ton compost) plots. However, fertilizer plots performed better than compost \nplots overall. During season 3 (rainy season), on the other hand, compost was \nreapplied, as was the inorganic fertilizer. The 90-ton compost plots showed higher \nyields than equivalent fertilizer plots. Soil organic matter contents of all compost \nplots were also statistically higher than those of fertilizer plots throughout the \nstudy.\n\n\n\nKeyword: Calcareous soils, maize, compost, soil organic matter, soils of \nGuam.\n\n\n\nINTRODUCTION\nThe presence of agronomic development of Guam and its neighboring islands \nmay be constrained by the limited availability of composted organic material \nto farmers. Farmers generally rely on commercial synthetic fertilizers for crop \nproduction, but the resulting long-term benefits to soil fertility are questionable.  \nGuam\u2019s increasing population and its tourism industry, together with limited \nsubsistence farmlands, can put pressure on food availability and its restricted \nIntegrated Solid Waste Management Program (landfill).\n One of the major concerns of agricultural production in Guam and other \ntropical islands of Micronesia is the low soil organic matter content, specifically of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021140\n\n\n\nthe calcareous farms on northern Guam (Golabi et al. 2004).  According to Jackson \net al. (2003), application of composted organic waste increases soil microbial \nbiomass, total soil carbon and nitrogen, reduces soil bulk density, and decreases \nthe potential for groundwater pollution that would otherwise result from nitrate \nleaching below the root zone. The bulk of the land on Guam is unsuitable for \nsubsistence farming because of poor chemical and physical characteristics of the \nsoils. On the other hand, up to 77% of wastes generated from Guam\u2019s households \nare organic and can be used as composting material (Golabi et al. 2014). The \napplication and continued addition of composted organic matter can create a \nsoft, tillable, and healthy soil for plant growth as well as reduce the amount of \nbiodegradable material going into the landfill. The addition of composted organic \nwaste to the soil significantly changed the soil-quality by improving its bulk \ndensity, soil organic-matter content, as well as its nutrient content (Karolle 1991).\n Golabi et al. (2007) has reported that composted organic-waste application \nin a southern Guam soil resulted in higher crop yield (of maize) and improved \nsoil fertility and health. It is worth mentioning that the southern Guam soil used \nin that project were eroded Akina soil series which are formed from volcanic rock \nhaving a pH range of 5.2 to 6.4, an indication of an acidic soil covering southern \nGuam.  On the other hand, the pH range of the northern Guam soils used in the \npresent study have a pH range of 7.1 to 7.9 indicating that the soils of northern \nGuam are much more alkaline in nature due to the calcareous properties of their \nparent material. The present study therefore evaluates the agronomic value of \norganic waste application and its effect on crop productivity and agricultural \nsustainability on calcareous soils of northern Guam.\n\n\n\nMATERIALS AND METHODS\nExperimental Site \nOur study was conducted from August 2013 to December 2016 at the University \nof Guam\u2019s Yigo Research Station in northern Guam.  Guam has a tropical climate \nwith an annual rainfall of  2540 mm and a distinct dry season between January and \nJune, during which the rainfall averages approximately 800 mm (Lander 1994). \nThe mean annual temperature is 26\u00b0C, and the monthly temperature range varies \napproximately \u00b12\u00b0C from the mean (Karolle 1991). \n The soil type underlying the study site and also the dominant soil in northern \nGuam is of \u2018Guam soil series\u2019(clayey, gibbsitic, non-acid, isohypothermic lithic \nUstorthents) from Entisols order, formed in sediment over porous coralline \nlimestone (Young 1988). Entisols exhibit thin or no soil profile or horizon \ndevelopment and are often found in places where deposition is faster than the \nrate of soil development (USDA-SCS 1988). The bedrocks underneath these soils \nare highly porous, so soil water can easily percolate into the groundwater aquifer \nthat supplies 80% of the island\u2019s drinking-water supply (WERI 2017). The soil\u2019s \npermeability is moderately rapid, and water availability is also very low.  Effective \nrooting depth ranges from 5 to 41 cm (USDA-SCS 1988).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 141\n\n\n\nPlot Description and Design\nEach of the 28 study plots had an area of 42.7 m2 (7 m by 6.1 m) with eight 6.1 \nm rows of maize plants; spacing between rows was 0.76 m. Seeds were sown \ndirectly, 0.3 m apart.  A randomized complete block design with four replications \n(blocks) was used to control for variation in soil fertility or structure (Washington \nState University 2018). Plots within blocks were 1.5 m apart, whereas replicated \nblocks were 3 m apart.\n Composted organic waste was applied to half the plots. Treatments levels \nwere 0 (control), 30, 60, and 90 tons (dry weight) per acre. The other half of \nthe 28 plots received levels of synthetic fertilizer (16-16-16) chosen to provide \nequivalent amounts of nitrogen (N) as the compost treatments plots (Table 1).\n\n\n\nTABLE 1 \n\n\n\nRates of application of compost and inorganic fertilizer to experimental plots \n\n\n\nCompost \n\n\n\n(tons/acre, dry weight) \n\n\n\nCompost \n\n\n\n(kg/plot, 40% moisture) \n\n\n\nInorganic fertilizer \n\n\n\n[(16-16-16), kg/plot] \n\n\n\n0 0 0 \n\n\n\n30 287.40 6.35 \n\n\n\n60 574.8 12.70 \n\n\n\n90 862.19 19.30 \n\n\n\nNote: Amount of fertilizer applied was equivalent to the amount of nitrogen \n\n\n\nsupplied by the compost application at each different rate. \n\n\n\nTABLE 1\nRates of application of compost and inorganic fertilizer to experimental plots\n\n\n\nComposting and Compost Application\nThe raw materials used in compost making consisted of, leftover restaurant food \nand paper products, woodchips from Anderson Air Force Base in Guam, coconut \nleaves from the rhinoceros-beetle eradication project, and hog and chicken \nmanures from local farms. In order to obtain enough compost for the study, a \nlarge-scale mechanical composting was used prior to the land application of the \norganic material. For this purpose, a large active aerated windrow was mixed \nregularly by a mechanical compost turner pulled behind a tractor. The entire \namount of compost designated for each plot was applied once, just a week before \nplanting and it was spread as a uniform layer over the entire plot.\n\n\n\nInorganic Fertilizer Application\nThe inorganic fertilizer was applied in two halves (application event); the first \napplication event was just two weeks after planting and the second was six weeks \nafter planting. In both cases, it was applied as a narrow band to the surface of the \nsoil, adjacent to the row of maize seedlings. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021142\n\n\n\nIrrigation\nWater drip lines (20 emitters per row) were used with automatic timers to irrigate \nthe crops for two hours, twice daily. As the maize ears neared maturity, irrigation \ntime was reduced to one hour once a day. Adjustments were made during lengthy \nrains, storms, and dry or wet seasons to minimize erosion and to control soil \nmoisture. Because instrumentation was not available, irrigation was based on \nperceived plant requirements.\n\n\n\nSoil and Compost Testing and Analyses\n\n\n\nTotal Carbon and Nitrogen Content\nThe percentages of carbon (C) and nitrogen (N) in the soil and in the compost \nwere determined with a FlashEA 1112 instrument made by Thermo Electronic \nCorporation. The percentages of C and N obtained were used to determine the \nC-to-N ratio (C: N). A proper C: N ratio is important for successful aerobic \ncomposting and for production of high-quality compost. Both compost and soil \nused in this project were analyzed for C: N before and after planting.\n\n\n\nPhosphorus\nPhosphorus (P) is an essential nutrient (required for plant DNA, RNA, and energy \ntransfer for growth and development) and is often the limiting nutrient after N \n(Conley et al. 2009). Although excess P is not considered toxic to humans, a high \nconcentration of P in fresh water can lead to nutrient pollution or eutrophication \ndue to the rapid growth of algae. Runoff of excess P from farmlands can reach \nnearby streams, rivers, lakes, and surrounding beaches and adversely affect the \nlocal tourism industry, a major contributor to Guam\u2019s economy.\n Phosphorus used in farming is in the form of phosphate. Most phosphatic \nfertilizers are made from highly pure monocalcium (CaHPO4) and/or dicalcium \n(Ca(H2PO4)2) orthophosphate (Van Wazer 2014). Although P is found in many \ntypes of soils, its availability is limited by phosphate aging (formation of calcium \nphosphate, called phosphate fixation) when the soil pH is above 7.5 (Richardson \net al. 2011), as in the calcareous soils of northern Guam. We therefore tested the \nsoil from each plot and the compost used for P, using the Olsen-P determination \nmethod by means of a spectrophotometer. \n\n\n\nExchangeable Potassium, Magnesium, and Calcium:\nEssential macronutrients such as potassium (K) and calcium (Ca) as well as \nmicronutrients such as magnesium (Mg) from the prepared compost and soil \nsamples were measured by atomic absorption spectrometry by means of direct \naspiration into an air-acetylene flame (Fishman and Downs 1966). Samples from \nthe composted organic waste were taken before application to the study plots, and \nsoil samples were taken before planting.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 143\n\n\n\nSoil pH Analysis\nSoil pH is the measure of acidity and alkalinity and is important in many chemical \nprocesses such as plant nutrient availability and overall soil health. As the soils of \nnorthern Guam are calcareous and also because of the effects of crop residues on \nthe soil\u2019s chemistry, pH testing was performed to determine overall soil quality \n(Butterly et al. 2013) during the study period.\n We determined soil pH by using an Oakton glass eletrode pH meter, \nadjusted to 1:2 ratio of soil and water considering the texture of both soil and the \ncompost (Sparks et al. 1996).\n\n\n\nSoil Organic Matter\nSoil organic matter (SOM) is formed from decomposed plants or animals in \nvarious stages of breakdown, including the stable component known as humus \n(Cornell University Fact Sheet 2018). SOM serves as an effective reservoir of \nnutrients for crops, and improves soil aggregation, increases nutrient exchange \ncapacity, retains moisture, reduces compaction and surface crusting, and also \nincreases water infiltration rate into the soil matrix (SSSA 2008).\n The Walkley-Black acid digestion method and a nitrogen carbon analyser \nwere used to test for SOM, and C and N content of both the soil and the compost \nused for this project. The Walkley-Black method is known to be more accurate on \nsoils with less than 2% organic matter (Agvise Laboratories 2018).\n\n\n\nBulk Density\nThe critical value of bulk density for restricting root growth varies with soil type, \nbut in general, bulk densities greater than 1.6 g/cm3 tend to restrict root growth \n(Hunt and Gilkes 1992; McKenzie et al. 2004). In the present study, the soil plots \nwere tilled before application of either compost or fertilizer. Also, most soil plots \ncontained a high number of sodium carbonate rocks which increased the bulk \ndensity of the samples.\n\n\n\nElectrical Conductivity\nElectrical conductivity (EC) is a measurement of soil salinity, which is often \nassociated with irrigated farmlands or with shallow water tables in arid-zone \nregions (Corwin and Lesch 2005). Although plants absorb nutrients in the form \nof soluble salts, excessive salinity can adversely affect plant growth (Shrivastava \nand Kumar 2015). As the soil of northern Guam is highly porous and regularly \nreceives high amounts of rain, any increase in salinity can be attributed to excess \napplication of composted organic wastes.\n\n\n\nStatistical Analysis\nA non-parametric Friedman test was used to determine significant differences \nin crop yield (p < 0.05) with the randomized complete block design in mind.  \nMinitab version 17 Software was used for the analyses.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021144\n\n\n\nRESULTS\nSoil pH Levels\nSoil pH of the study area in Northern Guam is inherently high and remains in the \nalkaline level under natural conditions. However, cultivation of agronomic crops \nwith the compost applied treatments tends to stabilize the pH towards neutral \nconditions. As shown in Table 2, pH determination throughout the study period \nindicated that the pH level remained close to 7 in the control plots during the \nperiod of the experiment but that of the compost plots were stabilized at around \n6.8, indicating that compost had the ability to maintain a balanced pH throughout \nthe study period. On the other hand, fertilizer application tended to increase the pH \nlevel (although not significantly) or remain the same as the control. As reported by \nGolabi et al. (2007), application of compost as a soil amendment increases the pH \nof acidic soils and decreases that of alkaline soils thus reaching a more balanced \nsoil pH in each region. Our study results agree with this conclusion.\n\n\n\nTABLE 2\nAverage pH values of soils from each study plot as they were determined prior to the \n\n\n\ntreatments and just before each planting seasons during the experimental period\n\n\n\nTABLE 2 \n\n\n\nAverage pH values of soils from each study plot as they were determined prior to \n\n\n\nthe treatments and just before each planting seasons during the experimental period \n\n\n\nTreatment Before \n\n\n\ntreatment or \n\n\n\nplanting \n\n\n\nPlanting \n\n\n\nseason 1 \n\n\n\nPlanting \n\n\n\nseason 2 \n\n\n\nPlanting \n\n\n\nseason 3 \n\n\n\nControl \n\n\n\nCompost 30  \n\n\n\n7.34 \n\n\n\n7.20 \n\n\n\n7.02 \n\n\n\n6.92 \n\n\n\n7.07 \n\n\n\n6.93 \n\n\n\n6.98 \n\n\n\n6.90 \n\n\n\nCompost 60 7.17 6.88 6.91 6.83 \n\n\n\nCompost 90 7.06 6.76 6.74 6.67 \n\n\n\nFertilizer 30 7.51 7.06 7.01 6.92 \n\n\n\nFertilizer 60 7.08 7.05 7.03 6.98 \n\n\n\nFertilizer 90 7.01 6.93 6.98 6.91 \n\n\n\n\n\n\n\nSoil Nutrient Analysis\nTables 3 through table 5, list the results of the nutrient analyses.  Note that compost \nwas not applied during the second planting season. This procedure was conducted \nto observe the carry-over effect of compost application from the first planting \nseason. To this effect, Table 4 shows that there was the carry-over effect on nutrient \ncontent from the first season\u2019s compost application. Compost was again applied in \nseason 3, as shown in Table 5. Tables 3 through 5 make it clear that both compost \nand inorganic fertilizer increased the percentages of soil carbon and soil nitrogen. \nIn addition, C:N decreased especially at the higher application rates. Content of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 145\n\n\n\nother nutrients (phosphorus, potassium, calcium, and magnesium) also increased \nas the compost application rates were increased. In season 2, the high nutrient \ncontent persisted in the compost plots, even though compost was not applied. This \nclearly shows that, residual benefits of compost application on treatment plots last \nlonger than those of synthetic fertilizers.\n\n\n\nTABLE 3\nAverage nutrient content of soil during first week of planting in season 1 (August 2013 to \n\n\n\nFebruary 2014, during which both compost as well as fertilizers were reapplied)\n\n\n\nTABLE 4\nAverage nutrient content of soil during first week of planting in season 2 (August 2014 to \n\n\n\nFebruary 2015, during which compost was not reapplied)\n\n\n\nTABLE 3 \n\n\n\nAverage nutrient content of soil during first week of planting in season 1 (August \n\n\n\n2013 to February 2014, during which both compost as well as fertilizers were \n\n\n\nreapplied) \n\n\n\nTreatment %C %N C:N P \n\n\n\n(ppm) \n\n\n\nK \n\n\n\n(ppm) \n\n\n\nCa \n\n\n\n(ppm) \n\n\n\nMg \n\n\n\n(ppm) \n\n\n\nControl 8.56 0.21 42:1 6.1 46.5 9,732.8 78.1 \n\n\n\nCompost 30 6.60 0.26 26:1 7.0 84.9 9,201.0 122.1 \n\n\n\nCompost 60 11.59 0.44 26:1 18.6 104.1 11,887.3 224.5 \n\n\n\nCompost 90 8.02 0.33 25:1 13.6 82.7 10,920.5 207.3 \n\n\n\nFertilizer 30 10.18 0.26 39:1 8.2 71.0 11,340.3 76.0 \n\n\n\nFertilizer 60 10.16 0.23 45:1 12.8 56.3 9,527.3 74.0 \n\n\n\nFertilizer 90 11.46 0.28 42:1 17.3 85.4 11,344.5 111.3 \n\n\n\n \nTABLE 4 \n\n\n\nAverage nutrient content of soil during first week of planting in season 2 (August \n\n\n\n2014 to February 2015, during which compost was not reapplied) \n\n\n\nTreatment %C %N C:N P \n\n\n\n(ppm) \n\n\n\nK \n\n\n\n(ppm) \n\n\n\nCa \n\n\n\n(ppm) \n\n\n\nMg \n\n\n\n(ppm) \n\n\n\nControl 8.76 0.24 37:1 9.7 53.0 10,037.0 90.8 \n\n\n\nCompost 30 8.70 0.38 23:1 22.2 99.0 9,699.5 195.3 \n\n\n\nCompost 60 11.91 0.51 23:1 35.9 229.3 12,125.5 324.8 \n\n\n\nCompost 90 11.50 0.50 23:1 43.1 313.5 11,266.3 405.3 \n\n\n\nFertilizer 30 10.18 0.25 41:1 10.9 55.3 9,567.8 122.8 \n\n\n\nFertilizer 60 10.01 0.28 36:1 15.3 69.0 8,349.0 117.5 \n\n\n\nFertilizer 90 11.31 0.29 39:1 26.6 84.5 10,778.5 147/8 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021146\n\n\n\nTABLE 5\nAverage nutrient content of soil during first week of planting in season 3 (September \n\n\n\n2016 to December 2016, during which both compost as well as fertilizers were reapplied)\n\n\n\nTABLE 5 \n\n\n\nAverage nutrient content of soil during first week of planting in season 3 \n\n\n\n(September 2016 to December 2016, during which both compost as well as \n\n\n\nfertilizers were reapplied) \n\n\n\nTreatment % C % N C:N P \n\n\n\n(ppm) \n\n\n\nK \n\n\n\n(ppm) \n\n\n\nCa \n\n\n\n(ppm) \n\n\n\nMg \n\n\n\n(ppm) \n\n\n\nControl 9.6 0.33 29:1 10.9 48.5 10,203.8 82.3 \n\n\n\nCompost 30 9.2 0.44 21:1 19.9 76.0 10,967.3 133.5 \n\n\n\nCompost 60 11.8 0.53 22:1 32.6 88.0 13,416.0 194.0 \n\n\n\nCompost 90 10.7 0.53 20:1 19.9 83.0 12,882.3 213/0 \n\n\n\nFertilizer 30 11.4 0.38 30:1 16.2 104.3 10,319.5 85.3 \n\n\n\nFertilizer 60 12.1 0.39 31:1 20.7 131.3 10,304.5 83.8 \n\n\n\nFertilizer 90 12.1 0.40 30:1 33.9 236.3 9,317.5 87.3 \n\n\n\n\n\n\n\nSoil Organic Matter\nAs shown in Figures 1 through Figure 3, the organic matter content of the soil \nincreased gradually in all compost applied plots throughout the experimental \nperiod. The organic matter content of the soil continued to increase (Figure 2) in \nthe second season, even though additional compost was not applied. Except during \nthe first season of the experiment, the organic matter content of the soil gradually \nincreased with increasing application rates of the compost (Figures 1\u20133).\n\n\n\nFigure 1.  Average soil organic matter (SOM) content during planting season 1. C30, \nC60, and C90 represent plots receiving 30, 60, and 90 tons (dry weight) of compost per \nacre.  F30, F60, and F90 represent plots receiving inorganic fertiliser at rates equal to \namount of nitrogen provided by compost treatments.  Control plots received no compost \n\n\n\nor fertiliser.\n\n\n\n\n\n\n\n \nFigure 1.  Average soil organic matter (SOM) content during planting season 1. \nC30, C60, and C90 represent plots receiving 30, 60, and 90 tons (dry weight) of \ncompost per acre.  F30, F60, and F90 represent plots receiving inorganic fertiliser \nat rates equal to amount of nitrogen provided by compost treatments.  Control \nplots received no compost or fertiliser. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 147\n\n\n\nFigure 2.  Average soil organic matter (SOM) during planting season 2. F30, F60, \nand F90 represent plots receiving inorganic fertiliser at rates equal to amount of \n\n\n\nnitrogen provided by compost treatments.  Plots designated for compost (C) received no \ntreatment. Control plots received no compost or fertilizer. Effects on SOM of compost \n\n\n\napplication during season 1 carried over into season 2.\n\n\n\n\n\n\n\n\n\n\n\nFigure 2.  Average soil organic matter (SOM) during planting season 2. F30, F60, \nand F90 represent plots receiving inorganic fertiliser at rates equal to amount \nof nitrogen provided by compost treatments.  Plots designated for compost \n(C) received no treatment. Control plots received no compost or fertilizer. \nEffects on SOM of compost application during season 1 carried over into \nseason 2. \n\n\n\n Figure 3. Average soil organic matter (SOM) during planting season 3. C30, C60, and \nC90 represent plots receiving 30, 60, and 90 tons (dry weight) of compost per acre.  F30, \n\n\n\nF60, and F90 represent plots receiving inorganic fertiliser at rates equal to amount of \nnitrogen provided by compost treatments. Control plots received no compost or fertilizer. \n\n\n\n Figure 3. Average soil organic matter (SOM) during planting season 3. C30, C60, \nand C90 represent plots receiving 30, 60, and 90 tons (dry weight) of compost per \nacre.  F30, F60, and F90 represent plots receiving inorganic fertiliser at rates equal \nto amount of nitrogen provided by compost treatments. Control plots received no \ncompost or fertilizer.   \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021148\n\n\n\nBulk Density\nThe bulk density of the soils under study remained identical to the control (steadily \nhigh) on the fertilizer applied plots. On the other hand, as shown in Figure 4, the \naverage bulk density of the soil continued to decrease as the amount of compost \napplied to the study plots increased. This finding agreed with the conclusion \nmade by other researchers (Jackson et al., 2003) regarding the beneficial effect of \ncompost on the physical properties of the soil. \n\n\n\nFigure 4.  Showing Bulk density (g/cm3) of soils for all plots after three seasons of \nplanting and harvesting.  C30, C60, and C90 represent plots receiving 30, 60, and 90 \ntons (dry weight) of compost per acre.  F30, F60, and F90 represent plots receiving \n\n\n\ninorganic fertiliser at rates equal to amount of nitrogen provided by compost treatments. \nControl plots received no compost or fertilizer. \n\n\n\n\n\n\n\nFigure 4.  Showing Bulk density (g/cm3) of soils for all plots after three seasons of \nplanting and harvesting.  C30, C60, and C90 represent plots receiving 30, 60, and \n90 tons (dry weight) of compost per acre.  F30, F60, and F90 represent plots \nreceiving inorganic fertiliser at rates equal to amount of nitrogen provided by \ncompost treatments. Control plots received no compost or fertilizer.   \n\n\n\nElectrical Conductivity\nAlthough application of compost can improve soil fertility, some of its components \nmay contain salt, and research results (Reddy and Crohn 2012) have indicated that \ncompost of high salt content can negatively affect plant growth. In our study, \nhowever, the effects of composted organic wastes on soil salinity were minimal \n(Table 6). Soils were tested for salinity before and after harvest in season 3, and \nresults showed (Table 6) that the electrical conductivity of the compost applied \nplots had decreased following the harvest. \n\n\n\nCrop Yield\nAs shown in Figures 5 through 7, yield varied during the experimental period.  \nIn season one, the yield was higher on the compost applied plots than on the \ncorresponding fertiliser plots, regardless of application rates (Figure 5). In season \ntwo, when fertilizer applied plots continued receiveing same rate of fertiliser \napplication as they received in season one, the yield was considerably higher as \ncompared with the corresponding compost applied plots. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 149\n\n\n\nTABLE 6\nElectrical conductivity (a measure of soil salinity) on each study plots were measured \njust before planting and immediately after the harvest during the 3rd planting season \n\n\n\nTABLE 6 \n\n\n\nElectrical conductivity (a measure of soil salinity) on each study plots were \n\n\n\nmeasured just before planting and immediately after the harvest during the 3rd \n\n\n\nplanting season  \n\n\n\nTreatment Average dS/m \n\n\n\nbefore planting \n\n\n\nSuitability for \n\n\n\nagriculture \n\n\n\nAverage dS/m \n\n\n\nafter harvest \n\n\n\nSuitability for \n\n\n\nagriculture \n\n\n\nControl  0.20 Excellent 0.14 Excellent \n\n\n\nCompost 30 0.26 Good 0.20 Excellent \n\n\n\nCompost 60 0.24 Good 0.20 Excellent \n\n\n\nCompost 90 0.27 Good 0.21 Excellent \n\n\n\nFertilizer 30 0.22 Excellent 0.16 Excellent \n\n\n\nFertilizer 60 0.24 Excellent 0.16 Excellent \n\n\n\nFertilizer 90 0.24 Excellent 0.17 Excellent \n\n\n\n\n\n\n\n Although, yields on compost plots were lower than those of the \ncorresponding fertiliser plots, they continued to be higher than the control plots. \nIn season three hoewver, the 90 tons per acre of compost applied plots showed \nconsiderably higher yield than those of the cooresponding fertilizer applied plots \nwith equivalent application rate.  \n\n\n\nFigure 5. Yield of maize from planting season 1. C30, C60, and C90 represent plots \nreceiving 30, 60, and 90 tons (dry weight) of compost per acre.  F30, F60, and F90 \nrepresent plots receiving inorganic fertiliser at rates equal to amount of nitrogen \nprovided by compost treatments. Control plots received no compost or fertilizer. \n\n\n\n\n\n\n\nFigure 5. Yield of maize from planting season 1. C30, C60, and C90 represent plots \nreceiving 30, 60, and 90 tons (dry weight) of compost per acre.  F30, F60, and F90 \nrepresent plots receiving inorganic fertiliser at rates equal to amount of nitrogen \nprovided by compost treatments. Control plots received no compost or fertilizer.   \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021150\n\n\n\nFigure 6. Yield from planting season 2 during which plots designated for compost \nreceived no treatment.  C30, C60, and C90 represent plots receiving 30, 60, and 90 tons \n(dry weight) of compost per acre.  F30, F60, and F90 represent plots receiving inorganic \nfertiliser at rates equal to amount of nitrogen provided by compost treatments. Control \n\n\n\nplots received no compost or fertilizer.  \n\n\n\n\n\n\n\n \nFigure 6. Yield from planting season 2 during which plots designated for compost \nreceived no treatment.  C30, C60, and C90 represent plots receiving 30, 60, and \n90 tons (dry weight) of compost per acre.  F30, F60, and F90 represent plots \nreceiving inorganic fertiliser at rates equal to amount of nitrogen provided by \ncompost treatments. Control plots received no compost or fertilizer.   \n\n\n\nFigure 7. Yield from planting season 3, which had more rain than first two seasons. C30, \nC60, and C90 represent plots receiving 30, 60, and 90 tons (dry weight) of compost per \nacre.  F30, F60, and F90 represent plots receiving inorganic fertiliser at rates equal to \namount of nitrogen provided by compost treatments. Control plots received no compost \n\n\n\nor fertilizer. \n\n\n\n\n\n\n\n \n \nFigure 7. Yield from planting season 3, which had more rain than first two seasons. \nC30, C60, and C90 represent plots receiving 30, 60, and 90 tons (dry weight) of \ncompost per acre.  F30, F60, and F90 represent plots receiving inorganic fertiliser \nat rates equal to amount of nitrogen provided by compost treatments. Control plots \nreceived no compost or fertilizer.   \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 151\n\n\n\nTABLE 7\nResults for season 1 of Friedman test (n = 4) for crop yield versus treatments as blocks  \n\n\n\nTABLE 7 \n\n\n\nResults for season 1 of Friedman test (n = 4) for crop yield versus treatments as \n\n\n\nblocks   \n\n\n\nTreatments/blocks \u03c72 p Sums of ranks Results \n\n\n\nC30 vs. control 4.00 0.0455 C = 8, control = 4 C30 > control \n\n\n\nF30 vs. control 4.00 0.0455 F = 8, control = 4 F30 > control \n\n\n\nF30 vs. C30 1.00 0.3173 F = 5, C = 7 F30 = C30 \n\n\n\nC60 vs. control 4.00 0.0455 C = 8, control = 4 C60 > control \n\n\n\nF60 vs. control 4.00 0.0455 F = 8, control = 4 F60 > control \n\n\n\nF60 vs. C60 4.00 0.3173 F = 5, C = 7 F60 = C60 \n\n\n\nC90 vs. control 4.00 0.0455 C = 8, control = 4 C90 > control \n\n\n\nF90 vs. control 4.00 0.0455 F = 8, control = 4 F90 > control \n\n\n\nF90 vs. C90 1.00 0.3173 F = 5, C = 7 F90 = C90 \n\n\n\nNote: F = inorganic fertilizer; C = compost. \n\n\n\nTABLE 8\nResults for season 2 of Friedman test (n = 4) for crop yield versus treatments as blocks.  \n\n\n\nTABLE 8 \n\n\n\nResults for season 2 of Friedman test (n = 4) for crop yield versus treatments as \n\n\n\nblocks.   \n\n\n\nTreatments \u03c72 p Sums of ranks Results \n\n\n\nC30 vs. control 1.00 0.3173 C = 7, control = 5 C30 = control \n\n\n\nF30 vs. control 4.00 0.0455 F = 8, control = 4 F30 > control \n\n\n\nF30 vs. C30 4.00 0.0455 F = 4, C = 8 F30 > C30 \n\n\n\nC60 vs. control 4.00 0.0455 C = 8, control = 4 C60 > control \n\n\n\nF60 vs. control 4.00 0.0455 F = 8, control = 4 F60 > control \n\n\n\nF60 vs. C60 1.00 0.3173 F = 5, C = 7 F60 = C60 \n\n\n\nC90 vs. control 4.00 0.0455 C = 8, control = 4 C90 > control \n\n\n\nF90 vs. control 4.00 0.0455 F = 8, control = 4 F90 > control \n\n\n\nF90 vs. C90 1.00 0.3173 F = 5, C = 7 F90 = C90 \n\n\n\nNote: F = inorganic fertilizer; C = compost. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021152\n\n\n\nDuring season three, when planting coincided with the rainy season (September \n2016 to December 2016), even though both compost and fertilizer were reapplied \nat the same rate as the year one, all plots performed poorly because intensive rain \nevents washed the nutrients from both compost and fertilizer plots down through \nthe soil matrix and out of the root uptake zone.  However, fertilizer plots performed \nslightly better than compost plots under these conditions possibly due to the \nreadily available nutrients within the soil solution at the time of uptake. During \nthis 3-month period, Guam received 694 mm of rainfall (Weather Underground, \n2016) compared to 523.2 mm in season one of the experimental periods.  \n It is worth noting that, the low yield from compost plots might have been \ncaused by the high C:N ratio, resulting from depletion of soil nitrogen due to \nincreased microbial activities which was enhanced by high moisture content of \nthe soil during the intense rain events. Weed growth was also rapid on the compost \nplots during these intense rain events and might have aggravated the nitrogen \ndepletion, thus contributed to the lower yield therefore aggressive weed growth. \nFurthermore, since the maize variety used in season three was short height variety \n(< 1.5 m), thus it was more vulnerable to competition from weeds. Effective weed \nmanagement such as mulching therefore is the key factor (Knight et al. 2017) that \nshould be considered during future research.\n\n\n\nTABLE 9\nResults for season 3 of Friedman test (n = 4) for crop yield versus treatments as blocks \n\n\n\nTABLE 9 \n\n\n\nResults for season 3 of Friedman test (n = 4) for crop yield versus treatments as \n\n\n\nblocks   \n\n\n\nTreatments \u03c72 p Sums of ranks Results \n\n\n\nC30 vs. control 4.00 0.0455 C = 8, control = 4 C30 > control \n\n\n\nF30 vs. control 4.00 0.0455 F = 8, control = 4 F30 > control \n\n\n\nF30 vs. C30 4.00 0.0455 F = 4, C = 8 F30 > C30 \n\n\n\nC60 vs. control 4.00 0.0455 C = 8, control = 4 C60 > control \n\n\n\nF60 vs. control 4.00 0.0455 F = 8, control = 4 F60 > control \n\n\n\nF60 vs. C60 1.00 0.3173 F = 7, C = 5 F60 = C60 \n\n\n\nC90 vs. control 4.00 0.0455 C = 8, control = 4 C90 > control \n\n\n\nF90 vs. control 4.00 0.0455 F = 8, control = 4 F90 > control \n\n\n\nF90 vs. C90 1.00 0.3173 F = 5, C = 7 F90 = C90 \n\n\n\nNote: F = inorganic fertilizer; C = compost \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 153\n\n\n\nDISCUSSION\nCrop Yield\nDuring season one of this study (October 2013 to January 2014), compost-treated \nplots, produced slightly higher (although not statistically significant) maize than \nfertilizer-treated plots (Figure 5). Both compost and fertilizer plots produced \nstatistically significantly higher yields than control plots.\n During season two (June 2014 to February 2015), compost plots received \nno additional compost application, as a test of long-term carry-over from the \ntreatment effect, however, fertilizer plots continued to receive same amount \nof inorganic fertilizer treatments as in season one. As expected, fertilizer plots \nproduced significantly higher yield than untreated compost and control plots \n(Figure 6), but compost plots continued to produce higher yield (1.9 to 2.8 times) \nthan the untreated control plots (Figure 6) throughout the experimental period.\n During season three, which coincided with a rainy season (September \n2016 to December 2016), both compost and fertilizer plots received same rate of \ntreatment application as they did in season one. However, the low yield harvest \nfrom composted applied plots were believed to be resulted from the high C:N \nratio of the compost at the time of application. Additionally, aggressive weed \ngrowth due to high moisture content resulted from intensive rain events might \nhave negatively impacted the yield of the composted applied plots during the \nstudy period. Because the same maize variety was not available for the season \nthree planting, the variety used for that season was therefore more vulnerable \nto competition from weeds resulting in low yield production. Effective weed \nmanagement such as mulching is therefore a key factor (Knight et al. 2017), \naffecting yield that could be considered for future cases, following compost \napplication in similar field studies.\n\n\n\nOrganic Matter\nCompost plots maintained higher soil organic matter (SOM) content than did \nfertilizer plots throughout the study period, notably during the re-application of \ncompost in season three. The higher SOM led to the carry-over effect with the yield \nbeing higher than that of control plots even during season two, when the compost \nwas not re-applied. Compost also improved the physical properties of the porous \nsoil of northern Guam by lowering soil bulk density (Figure 4) and increasing \nSOM. Higher SOM is known to increase soil-water and nutrient-holding capacity \n(cation exchange capacity) thereby, limiting leaching of nutrients (N, P) into the \nground water.\n Low SOM leads to high soil bulk density, negatively affecting plant \ngrowth and development and consequently having a negative impact on the crop \nyield. SOM contributes to soil structure that promotes better root penetration \nand proliferation. Application of composted organic waste to the porous soil \nof northern Guam decreased soil bulk density and increased SOM, benefiting \nplant root development, and contributing to plant growth and performance thus \nincreasing the crop yield.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021154\n\n\n\nBulk Density\nFertilizer and control plots showed higher bulk density (mean of 1.36 g/cm3) \nthan in compost treated plots (mean 1.16 g/cm3), indicating that the application \nof composted organic waste can lower the bulk density thus improving physical \nproperties of the soil under these treatments.\n\n\n\nCONCLUSION\nThe idea that composted organic waste has agronomic value and could be used \nas a \u2018resource recovery\u2019 management strategy, sounds appealing and, in fact, has \nbeen shown to be of great benefit to soil quality and crop productivity on the \nisland of Guam (Golabi et al. 2003). Guam also has limited landfill space and \ncan benefit from reduced organic matter disposal via compost and composting. \nOn the other hand, the SOM deficiency in most Guam soils can be alleviated \nby land application of compost for soil quality improvement and agricultural \nsustainability. At least initially, the added SOM can slow down leaching (Galsim \net. al., 2020), retain nutrients in the soil-water within the root zone that would \notherwise drain down beyond the depth available to plant roots (Golabi et al. \n2007).\n Application of composted organic wastes on land also recycles nutrients \nand effectively recovers valuable resources that would otherwise be disposed of \nin the landfill as waste. Additionally, land application of composted organic waste \nin fact represents an important economic benefit in terms of \u2018resource recovery \nmanagement\u2019 as well as soil improvement for agricultural sustainability in Guam \nand the other islands of Micronesia.\n \n\n\n\nACKNOWLEDGEMENTS\nWe thank the Tropical-Subtropical Agriculture Research (T-STAR) Program \nsponsored by the Cooperative State Research, Education, and Extension Service \n(CSREES) of the United States Department of Agriculture (USDA) for funding \nthis research.\n \n\n\n\nREFERENCES\nAgvise Laboratories.  2018.  Accessed 30 April 2018  from https://www.agvise.com/ \n\n\n\neducational-articles/soil-organic-matter-a-choice-of-methods/.  \n\n\n\nButterly, C. R., J. A. Baldock and C. Tang.  2013.  The contribution of crop residues \nto changes in soil pH under field conditions.  Plant and Soil  366:  185\u2013198.  \ndoi:10.1007/s11104-012-1422-1.\n\n\n\nConley, D. J., H. W. Paerl, R. W. Howarth, D. F. 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"\n\nINTRODUCTION\nLow soil fertility due to monoculture cereal production systems, inadequate \nfertiliszer application, biomass removal, soil erosion, nutrient losses through \nrunoff and leaching are recognised as some of the major causes for declining \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 13-28 (2017)                         Malaysian Society of Soil Science\n\n\n\nIntegrated Application of Poultry Manure and Chemical \nFertiliser on Soil Chemical Properties and Nutrient Uptake \n\n\n\nof Maize and Soybean \n\n\n\nM.G. Almaz, R.A. Halim, M.Y. Martini and A.W. Samsuri\n\n\n\nFaculty of Agriculture, Universiti Putra Malaysia, 43400 Serdang, \nSelangor, Malaysia\n\n\n\nABSTRACT\nLow soil fertility due to monoculture cereal production systems and inadequate \nfertiliser application are some of the major causes for declining crop production \nin developing countries. Integrated use of organic and inorganic fertilisers is \nan option to alleviate soil fertility problem as it utilises available organic and \ninorganic nutrients for sustainable agricultural production and productivity. A \nfield experiment was conducted in 2014 at Universiti Putra Malaysia to evaluate \nthe effect of the integrated application of poultry manure and inorganic fertiliser \non soil chemical properties and nutrient uptake of maize and soybean in maize-\nsoybean intercropping. Treatments comprised combinations of three cropping \nsystems (sole maize, sole soybean, and maize + soybean) and four fertilisation \nregimes (control, 100% NPK, 100% poultry manure (PM) and 50% NPK + 50% \nPM). The experiment was laid out in a randomised complete block design (RCBD) \nwith three replications. Results showed that either growing soybean alone or as \nan intercrop with maize resulted in increased soil organic matter (OM) (P<0.05), \ntotal N (P<0.0001), soil available P (P<0.0001) and soil cation exchange capacity \n(CEC)(P<0.05).  Intercropping maize with soybean significantly reduced N, P \nand K uptake of soybean (P<0.0001), but uptake of N, P and K by maize was \nnot significantly (P>0.05) affected by intercropping. Application of 100% PM \nand integrated application of 50% NPK+50% PM gave significantly higher soil \npH (P<0.001), soil OM (P<0.0001), soil total N(P<0.0001), soil available P \n(P<0.0001), soil exchangeable K (P<0.001) and soil CEC(P<0.0001) compared \nto control and 100% NPK.  For both maize and soybeans, the highest uptake of \nN, P and K was observed from the integrated application of  50% NPK+50% \nPM (P<0.0001). It can be concluded that integrated application of organic and \ninorganic fertiliser is the best option to improve soil chemical properties and \nnutrient uptake of maize and soybean.\n\n\n\nKeywords: Intercropping, chemical fertiliser, NPK, poultry manure, soil \nchemical properties.\n\n\n\n___________________\n*Corresponding author : ridzwan@upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201714\n\n\n\ncrop production in developing countries (Negassa et al. 2007). Application of \ninorganic fertilisers is considered the most efficient way to reverse soil nutrient \ndepletion and improve crop production (Bationo et al., 2007). However, the use \nof inorganic fertilisers in developing countries is insignificant as most of the \nsmallholder farmers cannot afford even a single bag to apply to their crops (Tesfa \net al., 2001). Continuous use of chemical fertilisers in intensive cropping systems \nleads to increased soil acidity and nutrient imbalance which adversely affects soil \nhealth due to their susceptibility to losses through gaseous form and by leaching \n(Amoah et al., 2012). These effects can be alleviated through the use of organic \nfertilisers which can improve soil physical and chemical properties. Palm et al. \n(1997) reported that organic manure improved organic matter and soil nutrient \navailability (N, P and K) through the total nutrients added by controlling net \nmineralisation-immobilisation patterns. Poultry manure is considered as one of \nthe best organic manures as it contains both macro-and micronutrients (Dekisissa \net al., 2008). However, application of organic manure alone to sustain soil and \ncrop productivity is inadequate due to their relatively low nutrient content and \nslow release of nutrients (Negassa et al., 2007).  \n\n\n\nHence, neither the chemical fertilisers nor the organic sources can \nexclusively achieve the sustainable productivity of soils as well as crops under \nhighly intensive cropping systems. To achieve sustainability of soil fertility and \ncrop productivity, integrated use of organic manures and inorganic fertilisers \ntogether with other fertilisation practices like intercropping with legumes are very \nimportant. Integrated use of poultry manure and inorganic fertiliszer was has been \nshown to improve soil pH, organic matter content, cation exchange capacity and \nsoil N, P, and K status (Amusan et al., 2011). \n\n\n\nMaize (Zea mays L) and soybean (Glycine max L. Merril) are important \ncereal and legume crops in the world respectively. Maize has high yield potential \ncompared to any other cereal crops and therefore has a high requirement for \nnutrients, especially nitrogen (N), phosphorus (P) and potassium (K).  Soybean \n(Glycine max L. Merril) has a capability of supplying nitrogen for its growth \nand component cereals through symbiotic nitrogen fixation, thus reducing the \nrequirement for costly and environmentally polluting nitrogen fertilisers (Zerihun \net al., 2013). Maize takes up high amounts of N, P and K from the soil, while \nsoybean derives a significant part of  its N uptake from biological nitrogen fixation \nand P and K uptake from the soil (Roy et al., 2006).  Therefore, it is important \nto ensure that the overall supply of soil N, P and K is sufficient for high yields \n(Roy et al., 2006). An integrated application of organic and inorganic fertilisers \nappears to be an ideal method to meet nutrient requirements of crops rather than \na sole application of either source. The synergistic effect of organic and inorganic \nsources on mineralisation and continued supply of essential nutrients improved \nsoil chemical properties and uptake of nutrients, thus enhancing crop yield \n(Adeniyan and Ojeniyi, 2005). Therefore, this study was aimed to evaluate the \neffects of integrated application of poultry manure and inorganic fertiliszer on soil \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 15\n\n\n\nchemical properties and nutrient uptake of maize and soybean in maize-soybean \nintercropping. \n\n\n\nMATERIALS AND METHODS\n\n\n\nSite Description\nAn experiment was conducted at Field 2, Universiti Putra Malaysia (UPM) \nSerdang, Selangor, Malaysia (latitude 3: 02\u2019 N, longitude of 101: 42\u2019 E and altitude \n31 m above sea level. Total annual rainfall in the year 2014 was approximately \n1623.5 mm. Mean annual minimum and maximum temperatures were 24.5\u00b0C and \n32.2\u00b0C, respectively, while the mean relative humidity was 78.9%. \n\n\n\nThe experimental soil was classified as Bungor series (Typic Paleudult) \naccording to USDA soil taxonomy. Data on initial physico-chemical properties of \nsoil at the experimental site are presented in Table 1. The data indicated that the \nsoils were sandy loam, slightly acidic, low in organic matter (OM), total nitrogen \n(N), available phosphorus (P), cation exchange capacity (CEC) and exchangeable \npotassium (K).\n\n\n\nTABLE 1\nInitial chemical properties of the soil used in the experiment\n\n\n\n  23 \n \n\n\n\nZerihun, A., J. Sharma, D. Nigussie, and  K. Fred. 2013. The effect of integrated organic and \n\n\n\ninorganic fertilizer rates on performances of soybean and maize component crops of a \n\n\n\nsoybean/maize mixture at Bako, Western Ethiopia. African Journal of Agricultural \n\n\n\nResearch . 8(29): 3921-3929 \n\n\n\n\n\n\n\nZhao, Y., P. Wang, J. Li, Y. Chen, X. Ying, and S. Liu. 2009.  The effect of two organic manures \n\n\n\non soil properties and crop yields on a temperate calcareous soil under a wheat-maize \n\n\n\ncropping system. Europian Journal of Agronomy, 31: 36-42.  \n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \n \nInitial chemical properties of the soil used in the experiment (n=3)  \nSoil Properties Value (+ S.E) \n\n\n\npH 5.62 + 0.19 \n\n\n\nTotal N(%) 0.08 + 0.01 \n\n\n\nAvailable P (mg kg-1) 16.0  + 1.95 \n\n\n\nK(cmolckg-1) 0.33 + 0.12 \n\n\n\nCEC(cmolckg-1) 14.5 + 0.37 \n\n\n\nOM(%) 2.2  + 0.32 \n\n\n\nTexture Sandy loam \n\n\n\n Clay (%) 18.98 \n\n\n\nSand (%) 65.73 \n\n\n\nSilt (%) 15.29 \n\n\n\nCEC = Cation Exchange Capacity, OM= organic matter, S.E= standard error \n \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201716\n\n\n\nExperimental Design and Treatments\nThe experimental design was a three by four factorial combinations of cropping \nsystems (sole maize, sole soybean, and combination of maize + soybean), and \nfertilisation (control, 100% NPK, 50% NPK + 50% poultry manure (PM) and \n100% PM), laid out in Randomiszed Complete Block Design (RCBD) with three \nreplications. A plot size of 3.6 m x 3 m was used for all treatments. The sole \nmaize and soybean were seeded in six rows spaced 60 cm between rows in the \nmonoculture plots. Maize and soybean were intercropped in 1:1 alternate row, \ni.e. 60 cm row spaced between maize crop and soybean was planted between two \nrows of maize crops. The spacing between plants for maize was 25 cm and for \nsoybean was 15 cm. \n\n\n\nThe amount of PM was based on N equivalent and applied on a dry \nweight basis two weeks before planting.The amounts of PM in sole maize and \nintercropping plot were 3 t ha-1 and in sole soybean plot, it  was 0.4 t ha-1. The \n50% PM treatment received half the rate of the PM treatment. The chemical \ncomposition of PM is presented in Table 2. The rates of N:P2O5:K2O for 100% NPK \ntreatment were, 120:60:40 kg ha-1 (N:P2O5 : K2O) and 20:60:40 kg ha-1 (N: P2O5 \n: K2O) for maize and soybean, respectively. The intercropped plots receivedthe \nrecommended fertiliszer rate of maize (N : P2O5 : K2O  at 120:60:40 kg ha-1). The \nfull dose of P and K and one-third of N fertiliszer were applied at planting time. \nThe remaining two-thirds of N fertiliszer was applied at the 8-leaf stage of maize, \nwhile for soybean plots, the entire dose of NPK was applied at planting.  All other \nagronomic practices were kept uniform for all treatments. \n\n\n\nSoil Sampling and Chemical Analysis\nComposite initial soil samples at a depth of 0-30 cm were taken from ten random \nspots within the experimental site prior to treatment application and after harvest \nfrom each plot. The composite samples were air-dried, sieved to pass through \na 2-mm mesh and analysed for selected physico-chemical properties, including \ntexture (percentage of sand, silt, and clay), pH, total nitrogen, organic matter \ncontent, available phosphorus, exchangeable K and cation exchange capacity \n(CEC). \n\n\n\n  23 \n \n\n\n\nTABLE 2 \n \n\n\n\nChemical composition of the poultry manure (PM) used \nNutrient Element Percent content \n\n\n\npH 7.10 \n\n\n\nN  4.50 \n\n\n\nP  1.08 \n\n\n\nK 1.66 \n\n\n\nCa 1.43 \n\n\n\nMg  0.60 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \n \n\n\n\nEffect of cropping system and fertiliszation on soil chemical properties, including soil pH, organic matter \n(OM), total N, available P, exchangeable K, Ca, Mg and cation exchange capacity (CEC) \nCropping system (C)         \n\n\n\nTreatment pH OM \n(%) \n\n\n\nTN \n(%) \n\n\n\nAv. P \n(mg kg-1) \n\n\n\nEx. \nK(cmolckg-\n\n\n\n1) \n\n\n\nCa  \n(mg kg-1) \n\n\n\nMg  \n(mgkg-1) \n\n\n\nCEC \n(cmolckg-1) \n\n\n\n\n\n\n\nSole Maize 6.31a 2.13b 0.13b 21.2b 0.31a 2.76a 0.86a 15.9b \n\n\n\nSole Soybean 6.32a 2.56a 0.22a 22.4b 0.32a 3.12a 0.77ab 17.9a \n\n\n\nIntercropping 6.21a 2.57a 0.17ab 26.3a 0.35a 2.80a 0.74b 17.7a \n\n\n\nLSD0.05 0.39ns 0.19 0.05 4.05 0.07ns 0.12 ns 0.12 1.29 \n\n\n\nP-value 0.91 0.03 <0.0001 <0.0001 0.24 0.47 0.04 0.007 \n\n\n\nFertiliszation (F)         \n\n\n\nControl 5.40b 2.13b 0.11c 15.6c 0.26b 2.20c 0.65c 12.9d \n\n\n\nFormatted: Centered\n\n\n\nFormatted: Right:  -0.69\"\n\n\n\nFormatted: Centered\n\n\n\nTABLE 2: \nChemical composition of the poultry manure (PM) used\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 17\n\n\n\nThe soil organic carbon content, total nitrogen and sulfur were determined \nby dry combustion with CHNS LECO analyszer (Jimenez and Ladho, 1993). Soil \npH was determined using the glass electrode pH meter in a 1:2.5 soil to water ratio \n(Van Reeuwijk, 1992), and CEC was measured by ammonium acetate method \n(NH4OAC) by saturating the soil with 1N NH4OAC  and displacing it with 1N \nK2SO4 (Chapman, 1965).  Exchangeable K was extracted with 1N NH4OAc at pH \n7 and the extract was read using an atomic absorption Spectrophotometer (Perkin-\nElmer, Massachusetts, USA). Available phosphorus was determined by the Bray-\nII method (Bray and Kurtz, 1945) and determined by an auto-analyzer (Lachat \ninstrument, WI, USA). Soil texture was determined by the pipette method (Day, \n1965). The texture class was determined using the United States Department of \nAgriculture (USDA) soil textural triangle. \n\n\n\nPlant Sampling and Chemical Analysis\nAt harvest, maize and soybean plants were sampled for nutrient analysis. Five \nplants were sampled from the middle rows of each plot. The plant samples were \noven-dried at 70 0C for 72 hours and dry weight was recorded. The grain and \nstover samples were grinded separately to pass through a 1mm sieve in a Thomas-\nWiley laboratory Mill (Thomas Scientific, Swedesboro, NJ). The ground materials \nwere digested in H2SO4 and H2O2 using a Seal Digestion Block (Foss Tecator, \nHilleroed, Denmark) at a temperature of 285 0C in accordance with the method \ndescribed by AOAC (1995). The N and P contents in the digested samples were \ndetermined using an autoanalyser (Lachat instrument, WI, USA) and K content \nwas determined using an atomic absorption spectrophotometer (Perkin Elmer, \nMassachusetts, USA).\n\n\n\nAfter the determination of leaf, stem and grain N, P and K contents, uptake  \nof  N, P, and K amounts were calculated (Sharma et al., 2012). \n\n\n\nN,P,or K uptake (kg ha-1) =  N or P or K content (% ) x dry matter (kg/ha)\n      100 \n\n\n\nTotal N, P or K uptake (kg ha-1) = Leaf N, P or K uptake + Stem N, P or K uptake \n+ Grain N, P or K uptake.\n\n\n\nStatistical Analysis\nData were analysed using the analysis of variance (ANOVA) procedure using \nSAS version 9.3. Least Significance Difference (LSD) at 95% of significance \nlevel was used for mean separation determination. Pearson correlation was used to \nassess relationships between nutrient uptake of maize and soybean and chemical \nproperties of the soil.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201718\n\n\n\nNurjanto et al.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Chemical Properties \nSoil pH, soil organic matter, soil total N, soil available P, soil exchangeable K \nand soil CEC were significantly affected (P>0.0001) by fertilisation after harvest. \nSoil OM (P<0.05), soil total N (P<0.001), soil available P (P<0.001), and soil \nCEC (P<0.05) were significantly affected by cropping system. However, soil pH \nand soil exchangeable K were not significantly(P>0.05) affected by cropping \nsystem (Table 3). An  interaction effect was observed between cropping system \nand fertilisation of the soil CEC(P<0.0001) (Table 4). \n\n\n\nApplication of 100% PM and 50 % NPK + 50% PM resulted in soil pH \nincreasing from 5.40 to 6.82 and 6.63 in 100% PM  and 50% NPK + 50% PM \ntreatment, respectively. Inorganic treatment (100% NPK) showed similar pH with \ncontrol (Table 3). The higher Ca content of the PM may be responsible for the \nincrease in soil pH (Table 2). In addition, decomposition of organic manure added \n\n\n\n  24 \n \n\n\n\nTABLE 3 \n \n\n\n\nEffect of cropping system and fertiliszation on soil chemical properties, including soil pH, organic matter \n(OM), total N, available P, exchangeable K, Ca, Mg and cation exchange capacity (CEC) \nCropping system (C)         \n\n\n\nTreatment pH OM \n(%) \n\n\n\nTN \n(%) \n\n\n\nAv. P \n(mg kg-1) \n\n\n\nEx. \nK(cmolckg-\n\n\n\n1) \n\n\n\nCa  \n(mg kg-1) \n\n\n\nMg  \n(mgkg-1) \n\n\n\nCEC \n(cmolckg-1) \n\n\n\n\n\n\n\nSole Maize 6.31a 2.13b 0.13b 21.2b 0.31a 2.76a 0.86a 15.9b \n\n\n\nSole Soybean 6.32a 2.56a 0.22a 22.4b 0.32a 3.12a 0.77ab 17.9a \n\n\n\nIntercropping 6.21a 2.57a 0.17ab 26.3a 0.35a 2.80a 0.74b 17.7a \n\n\n\nLSD0.05 0.39ns 0.19 0.05 4.05 0.07ns 0.12 ns 0.12 1.29 \n\n\n\nP-value 0.91 0.03 <0.0001 <0.0001 0.24 0.47 0.04 0.007 \n\n\n\nFertiliszation (F)         \n\n\n\nControl 5.40b 2.13b 0.11c 15.6c 0.26b 2.20c 0.65c 12.9d \n\n\n\n100% NPK 5.81b 2.19b 0.14c 18.5c 0.30b 2.02c 0.72c 14.7c \n\n\n\n100% PM 6.82a 2.96a 0.24a 36.0a 0.36a 5.12a 1.02a 22.7a \n\n\n\n50% NPK+ 50% \n\n\n\nPM \n\n\n\n6.63a 2.74a 0.17b 25.7b 0.39a 3.15b 0.86b 18.4b \n\n\n\nLSD0.05 0.41 0.22 0.04 4.49 0.50 0.76 0.15 1.5 \n\n\n\np-value 0.0002 <0.0001 <0.0001 <0.0001 <0.0001 0.007 0.01 <0.0001 \n\n\n\nC*F  0.83ns 0.07ns 0.95ns 0.62ns 0.06ns 0.97ns 0.08ns <0.001 \n\n\n\nCV (%) 7.1 18.8 21.3 9.00 21.2 26 18 8.9 \n\n\n\nMeans followed by the same letter in the same column are not significantly different (LSD 0.05), ns  = non-\nsignificant at \u03b1=0.05, CV (%) = coefficient of variation, Av. P= available P, EX. K= exchangeable K \n \n\n\n\n\n\n\n\n\n\n\n\nFormatted: CenteredTABLE 3\nEffect of cropping system and fertilisation on soil chemical properties, including soil pH, \n\n\n\norganic matter (OM), total N, available P, exchangeable K, Ca, Mg and cation\nexchange capacity (CEC)\n\n\n\n\n\n\n\n\nbasic plant nutrients to the soil that contributed to the increase of in soil pH. This \nresult is in agreement with Islam et al. (2013) who reported that the application of \nPM raised the pH of the soil.\n\n\n\nSignificantly higher (P<0.05) OM was observed in intercropping and sole \nsoybean treatment than in the sole maize treatment (Table 3). Ali et al. (2015) found \nhigher soil OM in sole soybean treatment than in sole maize. In addition, Matusso \net al. (2014) reported that soil OM in   intercropping treatment was higher than in \nsole maize treatment. Among fertilisation treatments, soil OM was significantly \nhigher (P<0.0001) in the 100% PM and 50% NPK + 50% PM treatments than in \nsole 100% NPK and control treatment. An increase in soil OM in the PM treatment \nmay be due to the effects of manure, which act as the storehouse of different plant \nnutrients. Zhao et al. (2009) reported that the application of organic manure singly \nor in combination with inorganic fertiliszer resulted in higher OM content than the \nexclusive application of chemical fertilisers (NPK).\n\n\n\nSignificantly higher soil N was observed in sole soybean treatment than \nin the sole maize treatment (Table 3). In which ever plot  soybean crop was \ngrown, total N was significantly higher (P<0.001) compared to maize crop. The \nincrease in the total nitrogen was probably due to the ability of the soybean to \nfix atmospheric N in the soil through symbiotic N fixation. Matusso et al. (2014) \nreported that mineral N was significantly affected by the intercropping treatment \nand the highest N was observed from a sole soybean treatment.\n\n\n\nRegarding fertilisation treatments, total N was significantly higher in the \n100% PM and 50% NPK + 50% PM treatment than in 100% NPK and control \ntreatment (P<0.001).  Application of 100% NPK and control treatment were \nnot significantly different between each other in soil total N. This may be due \nto the N from inorganic fertiliszer being rapidly used up by the plant or could \nhave  leached (Masaka et al., 2015).  This finding is in agreement with Datt et \nal. (2013) who observed low N in the inorganic fertiliszer treatment. The high \ntotal N in PM treatment was due to the slow release of N during mineralisation of \nPM. Similarly, higher total N in integrated treatments may be due to the fact that \nintegration of organic and chemical fertilisers increased the mineralisation owing \nto the  narrow C/N ratio. This  result is in agreement with Ayeni and Adetunji \n(2010) who reported PM and their combinations with NPK fertiliszer increased \nsoil OM, N, P and K in soil.\n\n\n\nAvailable P increased from 21.2 and 22.4mg/kg in mono crop of maize and \nsoybean respectively, to 26.3mg/kg in intercropping which may be due to more \nefficient utilisation of plant nutrients in intercropping than in mono cropping \n(Miyazawa et al., 2010). As there is a synergistic relation of N with P, K, and S, \nthis might have helped in increasing the soil available P in sole soybean treatment \nthan in sole maize treatment (Vidyavathi et al., 2012b). The result is supported \nby Khan et al. (2014) who found that intercropping can increase soil available \nnutrients and improve soil fertility compared to sole cropping. Application of \n100% PM resulted in available P that was significantly higher than in control \nand 50% PM + 50% NPK treatments (P<0.0001). The combined application of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201720\n\n\n\ninorganic fertilizser and PM gave higher soil P concentration compared to the \ncontrol and 100% NPK fertiliszer treatments. An increase in soil available P in \n100% PM treatments may be due to mineralisation of organic P and production \nof organic acids that makes soil P more available and reduces P fixation. This \nresult corroborates the findings of Islam et al. (2013), who explained that soils \ntreated with either organic fertilisers or, inorganic fertilisers or combination of \nthese fertilisers, higher values of available P will be obtained compared to the \ncontrol treatments. Boateng et al. (2006) also reported that the application of PM \nincreased soil available P by 31% compared to the control treatment.\n\n\n\nExchangeable K was significantly higher in  100% PM and 50% NPK+50% \nPM treatments than control treatment (P<0.0001). The increase in soil \nexchangeable K in a combined application of inorganic and organic fertiliszer \ntreatments may be due to the direct of potassium addition in the soil K pool. This \nresult is supported by Islam et al. (2013) who reported that soil exchangeable K \nincreased when organic or both organic and chemical fertilisers were applied. \nAhmad et al. (2013) also reported that the highest soil OM, total N, available P \nand exchangeable K, after maize was harvested, was from treatments receiving \norganic sources with 50% of recommended NPK fertilisers. \n\n\n\nIn the sole maize cropping system, soil CEC under application of 100% \nPM was not significantly different (P>0.05) from combined application of 50% \nNPK + 50% PM (Table 4). However, in sole soybean and maize + soybean \nintercropping system, soil CEC under application of 100% PM was significantly \nhigher (P<0.0001) than in combined application of 50% NPK + 50% PM. The \nincrease in soil CEC with PM application was due to the addition of basic cations \nin the soil such as K, Mg and Ca from the decomposition of poultry manure (Table \n3). Adekayode and Ogunkoyaka (2011) reported higher soil total N, available \nP, exchangeable K and CEC in organic compost treatments compared to NPK \nfertiliszer treatments.\n\n\n\nTABLE 4\nInteraction effects of cropping systems and fertilisation on  soil cation exchange \n\n\n\ncapacity (CEC)\n\n\n\n  25 \n \n\n\n\nTABLE 4 \n \n\n\n\nInteraction effects of cropping systems and fertiliszation on  soil cation exchange capacity (CEC) \n\n\n\n Fertiliszation \n \n\n\n\nCEC (cmolckg-1) \n \n\n\n\nCropping system \n\n\n\nSole Maize  Sole Soybean Maize + Soybean \n\n\n\nControl 12.8b 12.8c 13.1c \n\n\n\n100% NPK 13 8b 13.8c 13.6c \n\n\n\n100% PM 18.2a 26.3a 23.7a \n\n\n\n50% NPK+50% PM 16.6a 18.8b 20.3b \nLSD0.05 \n\n\n\n2.67 2.95 2.06 \np-value \n\n\n\n<0.0001 <0.0001 <0.0001 \nMeans followed by the same letter in the same column are not significantly different(LSD 0.05) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFormatted: Centered\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 21\n\n\n\nFurther, the increased soil CEC in sole soybean treatment compared to sole \nmaize treatment was associated with a rise in soil organic matter content in sole \nsoybean treatments (Table 4). Bronick and Lal (2005) reported the positive effect \nof legume intercropping on soil CEC. \n\n\n\nNutrient Uptake of Maize and Soybean\nUptake of N, P and K of soybean was not significantly affected by cropping \nsystem (P>0.05) (Table 6). However, N, P and K uptake by soybean wasreduced \n(P<0.0001) when intercropped with maize compared to monocropped soybean \n(Table 5) (Author: Table 5 should be cited before Table 6) . The reduction of \nN uptake of soybean in an intercropping treatment may be due to the shading \neffect of the tall maize that limited photosynthetic assimilation of soybean which \nthereby indirectly influenced the process of nodulation or N fixation and (Table \n6)  N uptake. P and K uptake of soybean was reduced due to the more competitive \nability of maize with respect to the uptake of nutrients from the rhizosphere, as \nmaize had a greater root length than soybean. The result is in line with Matusso \net al. (2013) who reported that N, P and K uptake by soybean was significantly \nhigher in monocropping than intercropping. Similarly, Prajapat et al. (2015) \nreported that the cropping system adopted  had a significant effect on N, P, and K, \nuptake of soybean. \n\n\n\nAmong fertilisation treatments, the highest uptake of N, P and K by maize and \nsoybean werewas recorded in the application of 50% NPK + 50%PM (P<0.0001) \nfollowed by sole application of 100% PM and 100% NPK (Table 5). In contrast, \nthe control treatments had the lowest N, P, and K uptake. An increase in nutrient \nuptake in a combination of NPK and PM  may be  due to the increase in supply of \nNPK directly through the organic and inorganic source to the crop, the reduction in \nlosses of nutrient from the soil solution and the increase in nutrient use efficiency \n(Dubey et al., 2012). Therefore, the high nutrient uptake in combined application \nof 50% NPK + 50% PM treatment is attributed to the relative availability of N, \nP and K throughout the cropping seasons as the nutrients from inorganic sources \nwere available to the crop in the early stages; in the later stages of crop growth, \nthe slow and continuous release of nutrients from the organic source became \navailable. Moreover, the highest N uptake of soybean  in combined application \nof organic and inorganic fertiliser might be due to increased nodulation (Table 6).  \nThe result is in line with Tagoe et al. (2008) and Verde et al. (2013) who observed \nhigher N, P, and K uptake by soybean under application of organic manure and \nin combination with inorganic fertiliser. Similarly, Shashidhar et al. (2009) and \nVidyavathi et al. (2012a) reported that integrated fertilisation practices give \nsignificantly higher uptake of N, P and K by maize when compared to the sole \napplication of inorganic and organic fertilisers.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201722\n\n\n\nTABLE 5: \nEffect of cropping system and fertilisation on nutrient uptake of maize and soybean\n\n\n\n  27 \n \n\n\n\nTABLE 5 \n \n\n\n\n Effect of cropping system and fertiliszation on nutrient uptake of maize and soybean \n Cropping system (C) Maize Soybean \n\n\n\nTreatment N uptake \n(kgha-1) \n\n\n\nP uptake \n(kg ha-1) \n\n\n\nK uptake \n(kg ha-1) \n\n\n\nN uptake \n(kg ha-1) \n\n\n\nP uptake \n(kg ha-1) \n\n\n\nK uptake \n(kg ha-1) \n\n\n\nSole maize 51.0a 10.5a 23.6a _   _  _ \n\n\n\nSole soybean _ _ _ 52.5a 38.1a 46.9a \n\n\n\nMaize + soybean 47.8a 10.4a 21.1a \n38.5b 23.9b 33.4b \n\n\n\nLSD0.05 5.4ns 1.78ns 3.2ns 3.8 6.6 3.7 \np-value 0.78 0.31 0.76 <0.0001 <0.0001 <0.0001 \n\n\n\nFertiliszation (F)    \n   \n\n\n\nControl 27.2c 3.61c 12.2c 21.5c 8.6c 17.9c \n\n\n\n100% NPK 49.1b 10.5b 24.8b 49.8b 33.3b 43.1b \n\n\n\n100% PM 51.4b 11.2b  22.3b 49.9b 37.4b 45.3b \n\n\n\n50 % NPK+ 50% PM 70.0a 16.5a 30.1a 60.7a 49.4a 54.4a \n\n\n\nLSD0.05 7.64 2.52 4.5 5.4 8.6 5.3 \n\n\n\np-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 \n\n\n\nC*F 0.35ns 0.38ns 0.20ns 0.57ns 014ns 0.12ns \n\n\n\nCV (%) 12.49 19.41 16.4 9.5 20.1 10.6 \n\n\n\nMeans in the same column followed by the same letters are not significantly different (LSD 0.05), ns= non-significant, \nns  = non-significant at \u03b1=0.05,CV (%) = coefficient of variation \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFormatted: Right:  0.31\"\n\n\n\nFormatted: Right:  -0.81\"\n\n\n\nTABLE 6\nEffect of cropping system and nutrient management on number of nodules and \n\n\n\nphotosynthesis rate of soybean\n\n\n\n  27 \n \n\n\n\nTABLE 6 \n\n\n\nEffect of cropping system and nutrient management on number of nodule and photosynthesis of \nsoybean \nTreatment Photosynthetic rate  \n\n\n\n(\u00b5molm-2 s-1) \n\n\n\nNo. Nodules/plant \n\n\n\nCropping system (C)   \n\n\n\nSole soybean 22.2a 48.6a \n\n\n\nMaize +soybean 18.9b 34.7b \n\n\n\nLSD (p<0.05) 1.53 4.7 \n\n\n\np-value <0.0001 <0.0001 \n\n\n\nNutrient Management (N)   \n\n\n\nControl 15.3c 28.7d \n\n\n\n100% NPK 21.8ab 37.5c \n\n\n\n100% PM 20.2b 44.4b \n\n\n\n50 % NPK+ 50% PM 23.2a 56.0a \n\n\n\nLSD (p<0.05) 1.81 6.68 \n\n\n\np-value <0.0001 <0.0001 \n\n\n\nC*N 0.32ns 0.51ns \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFormatted: Centered\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 23\n\n\n\nApplication of sole NPK and PM increased the nutrient uptake over control, which \nimplies that the application of NPK and PM either separately or in combination \nimproves the availability of N, P and K to the maize and soybean. This may be \ndue to the addition of organic manure which improved the soil properties, hence, \nenhancing N, P and K uptake. The chemical properties of soil were positively \ncorrelated with N, P and K uptake of maize (Table 7) and soybean (Table 8). \nThis result is in agreement with Vidyavathi et al. (2012b) who reported increased \nnutrient uptake as soil properties improved. Moreover, the organic fertiliszer \nmight have supplied other nutrients which enhanced N, P and K uptake (Verma \net al., 2006).\n\n\n\nTABLE  7\nPearson linear correlation coefficients between N, P and K uptake of maize and \n\n\n\nsoil chemical properties\n\n\n\n  28 \n \n\n\n\nTABLE  7 \n\n\n\nPearson linear correlation coefficients between N, P and K uptake of maize and soil chemical \nproperties (n=3) \n\n\n\n  N \nuptake \n(kg ha-1) \n\n\n\nP uptake \n(kg ha-1) \n\n\n\nK uptake \n(kg ha-1) \n\n\n\npH OM \n(%) \n\n\n\nTN \n(%) \n\n\n\nAv. P \n(mg kg-1) \n\n\n\nEx \nK(cmolc \nkg-1) \n\n\n\nCEC \n(cmolc \nkg-1) \n\n\n\nN uptake \n(kg ha-1) \n\n\n\n1                 \n\n\n\nP uptake \n(kg ha-1) \n\n\n\n0.99** \n(0.001) \n\n\n\n1               \n\n\n\nK uptake \n(kg ha-1) \n\n\n\n0.97* \n(0.002) \n\n\n\n0.97* \n(0.02) \n\n\n\n1             \n\n\n\npH 0.79 \n(0.20) \n\n\n\n0.79 \n(0.21) \n\n\n\n0.69 \n(0.30) \n\n\n\n1           \n\n\n\nOM \n(%) \n\n\n\n0.66 \n(0.30) \n\n\n\n0.66 \n(0.33) \n\n\n\n0.52 \n(0.47) \n\n\n\n0.98* \n(0.02) \n\n\n\n1         \n\n\n\nTN \n(%) \n\n\n\n0.51 \n(0.49) \n\n\n\n0.52 \n(0.48) \n\n\n\n0.42 \n(0.57) \n\n\n\n0.91 \n(0.08) \n\n\n\n0.93 \n(0.06) \n\n\n\n1       \n\n\n\nAV. P \n(mg kg-1) \n\n\n\n0.52 \n(0.48) \n\n\n\n0.53 \n(0.46) \n\n\n\n0.41 \n(0.58) \n\n\n\n0.93* \n(0.05) \n\n\n\n0.96* \n(0.03) \n\n\n\n0.99* \n(0.006) \n\n\n\n1     \n\n\n\nEX. K \n(cmolckg-1) \n\n\n\n0.93 \n(0.07) \n\n\n\n0.93 \n(0.07) \n\n\n\n0.83 \n(0.16) \n\n\n\n0.95* \n(0.26) \n\n\n\n0.88 \n(0.11) \n\n\n\n0.73 \n(0.29) \n\n\n\n0.77 \n(0.24) \n\n\n\n1   \n\n\n\nCEC \n(cmolckg-1) \n\n\n\n0.58 \n(0.42) \n\n\n\n0.59 \n(0.40) \n\n\n\n0.48 \n(0.52) \n\n\n\n0.95* \n(0.04) \n\n\n\n0.97* \n(0.02) \n\n\n\n0.99* \n(0.009) \n\n\n\n0.99* \n(0.002) \n\n\n\n0.80* \n(0.01) \n\n\n\n1 \n\n\n\nValue within brackets is p-value, *,** significant level of p< 0.05 and p<0.01, TN= Total Nitrogen, AV. \nP= available phosporous, Ex. K  = exchangeable potassium \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201724\n\n\n\nCONCLUSIONS\n\n\n\nApplication of 50% NPK + 50% PM and 100% PM improved soil chemical \nproperties and nutrient uptake of maize and soybean. Soil OM, total N, available \nP and CEC were high in sole soybean and intercropping with maize. Intercropping \nreduced nutrient uptake of soybean but had no significant effect on nutrient uptake \nof maize. Therefore an integrated soil fertility management, specifically, the \ncombined application of PM with inorganic fertiliszer and intercropping soybean \nwith maize can increase pH, OM, and soil nutrient availability in comparison with \nmonocropped maize with 100% inorganic fertiliser. In addition this practice can \nresult in a reduced cost of production.\n\n\n\nACKNOWLEDGMENTS\nThe author is grateful to Organization for Women in Science for the Developing \nWorld (OWSD), Swedish International Development Cooperation Agency \n(SIDA) and Universiti Putra Malaysia (UPM), for sponsoring the study.\n\n\n\nREFERENCES\nAdekayode, F. O. ,and M.O. Ogunkoya. 2011. Comparative effects of organic \n\n\n\ncompost and NPK fertilizer on soil fertility, yield and quality of amaranth in \nSouthwest Nigeria. International Journal of Biological and Chemical Sciences. \n5(2): 490-499.\n\n\n\nTABLE  8\nPearson linear correlation coefficients between N, P and K uptake of soybean and \n\n\n\nsoil chemical properties\n\n\n\n  30 \n \n\n\n\nTABLE  8 \n\n\n\nPearson linear correlation coefficients between N, P and K uptake of soybean and soil chemical properties \n(n=3) \n\n\n\n  N \nuptake \n(kg ha-\n\n\n\n1) \n\n\n\nP uptake \n(kg ha-1) \n\n\n\nK uptake \n(kg ha-1) \n\n\n\npH OM \n(%) \n\n\n\nTN \n(%) \n\n\n\nAv. P \n(mg kg-\n\n\n\n1) \n\n\n\nEx \nK(cmolck\ng-1) \n\n\n\nCEC \n(cmolc\nkg-1) \n\n\n\nN uptake \n(kg ha-1) \n\n\n\n1                 \n\n\n\nP uptake \n(kg ha-1) \n\n\n\n0.99** \n(0.0005) \n\n\n\n1               \n\n\n\nK uptake \n(kg ha-1) \n\n\n\n0.99* \n(0.02) \n\n\n\n0.99* \n(0.02) \n\n\n\n1             \n\n\n\npH 0.79 \n(0.20) \n\n\n\n0.84 \n(0.20) \n\n\n\n0.82 \n(0.31) \n\n\n\n1           \n\n\n\nOM \n(%) \n\n\n\n0.64 \n(0.34) \n\n\n\n0.71 \n(0.33) \n\n\n\n0.68 \n(0.48) \n\n\n\n0.98* \n(0.02) \n\n\n\n1         \n\n\n\nTN \n(%) \n\n\n\n0.58 \n(0.49) \n\n\n\n0.61 \n(0.46) \n\n\n\n0.62 \n(0.58) \n\n\n\n0.91 \n(0.08) \n\n\n\n0.93 \n(0.06) \n\n\n\n1       \n\n\n\nAV. P \n(mg kg-1) \n\n\n\n0.56 \n(0.48) \n\n\n\n0.61 \n(0.47) \n\n\n\n0.60 \n(0.59) \n\n\n\n0.93 \n(0.07) \n\n\n\n0.96* \n(0.03) \n\n\n\n0.99* \n(0.006) \n\n\n\n1     \n\n\n\nEX. K \n(cmolckg-1) \n\n\n\n0.88 \n(0.07) \n\n\n\n0.93 \n(0.07) \n\n\n\n0.90 \n(0.16) \n\n\n\n0.95* \n(0.05) \n\n\n\n0.88 \n(0.11) \n\n\n\n0.73 \n(0.27) \n\n\n\n0.76 \n(0.26) \n\n\n\n1   \n\n\n\nCEC \n(cmolckg-1) \n\n\n\n0.62 \n(0.42) \n\n\n\n0.66 \n(0.40) \n\n\n\n0.66 \n(0.52) \n\n\n\n0.95* \n(0.04) \n\n\n\n0.97* \n(0.002) \n\n\n\n0.99* \n(0.009)) \n\n\n\n0.99* \n(0.002) \n\n\n\n0.80* \n0.05 \n\n\n\n1 \n\n\n\nValue in the brackets is p\u2013value, *,** significant level of p< 0.05 and p<0.01, TN= Total Nitrogen, AV. P= available \nphosporous, Ex.K  = exchangeable potassium \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 25\n\n\n\nAdeniyan, O., and S. Ojeniyi. 2005. Comparative effectiveness of different levels of \npoultry manure with NPK fertilizer on residual soil fertility, nutrient uptake and \nyield of maize. Moor Journal of Agricultural Research. 4(2): 191-197. \n\n\n\nAhmad, W., Z. Shah, F. Khan, S. Ali, and W. Malik. 2013. 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Combined use of organic and \ninorganic nutrient sources for soil fertility maintenance and replenishment. \nReplenishing soil Fertility in Africa, 193-217.\n\n\n\nPrajapat, K.,  A. K. Vyas, and  S. Dhar. 2015. Effect of cropping systems and \nfertilizationpractices on growth, productivity, economics and nutrient uptake \nof soybean (Glycine max). The Indian Journal of Agricultural Sciences,. 85(9):  \nPage nos????.\n\n\n\nRoy, R.N., A.Finck, G.J. Blair, and H.L.S.Tandon. 2006. Plant nutrition for food \nsecurity, a guide for  integrated nutrient management. FAO Fertilizer and Plant \nNutrition Bulletin 16. FAO, Rome. \n\n\n\nShashidhar, C.U., H.K. Veeranna, Y.M.  Ramesh, P.R. Somashekarappa, and V. \nMahantesh. 2009. Effect of different fertilisation practices on yield, economics \nand nutrient uptake in maize (Zea mays L.). Research on Ccrops. 10 (2): 27- \n230.\n\n\n\nSharma, N.K.  R.J. Singh, and K. Kumar., (2012). Dry matter accumulation and \nnutrient uptake by wheat (Triticuma estivum L.) under poplar (Populus \ndeltoides) Based Agroforestry System. ISRN Agronomy. 1212: 1-7.\n\n\n\nTagoe, S.O.,  T. Horiuchi, and T. Matsui. 2008. Effects of carbonized and dried \nchicken manures on the growth, yield, and N content of soybean. Plant and \nSoil. 306 (1-2): 211-220.\n\n\n\nTesfa, B.,  D. Tolessa,G. Setegn, T. Tamado, G. Negash, and W. Tenaw. 2001. \nDevelopment of Appropriate Cropping Systems for Various Maize Producing \nRegions of Ethiopia. Second National Maize Workshop of Ethiopia. 12-16 \nNovember, 2001,. Addis Ababa.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201728\n\n\n\nVan Reeuwijk, L.P. 1992. Procedures for Ssoil Aanalysis (3rd Ed.). International Soil \nReference Center (ISRIC), Wageningen, The Netherlands.\n\n\n\nVerde, B.S.,  B.O. Danga, and J.N. Mugwe. 2013. Effects of manure, lime and mineral \nP fertilizer on soybean yields and soil fertility in a humic Nitisols in the central \nhighlands of Kenya. International Journal of Agricultural Science Research. 2: \n283-291.\n\n\n\nVerma, A., V. Nepalia, and P. C. Kanthaliya. 2006., Effect of integrated nutrient \nsupply on growth, yield and nutrient uptake by maize-wheat cropping system,, \nIndian Journal of  Agronomy. 51 (1): 3-6.\n\n\n\nVidyavathi G, S. Dasog, H.B. Babalad, N.S. Hebsur, S.K. Gali, S.G. Patil, and A.R. \nAlagawadi.  2012a. Influence of fertilization practices on crop response and \neconomics in different cropping systems in a Vertisol. Karnataka Journal of \nAgricultural Sciences . 24(4):455-460. \n\n\n\nVidyavathi, V., G. Dasog,  H. Babalad, N.S.Hebsur, S.K. Gali, S. G. Patil, and A.R. \nAlagawadi., 2012b. Nutrient status of soil under different nutrient and crop \nmanagement practices. Karnataka Journal of Agricultural Sciences. 25 (2): \npage nos????.\n\n\n\nZerihun, A., J. Sharma, D. Nigussie, and  K. Fred. 2013. The effect of integrated \norganic and inorganic fertilizer rates on performances of soybean and maize \ncomponent crops of a soybean/maize mixture at Bako, Western Ethiopia. \nAfrican Journal of Agricultural Research. 8(29): 3921-3929\n\n\n\nZhao, Y., P. Wang, J. Li, Y. Chen, X. Ying, and S. Liu. 2009.  The effect of two organic \nmanures on soil properties and crop yields on a temperate calcareous soil under \na wheat-maize cropping system. Europian Journal of Agronomy, 31: 36-42. \n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : patrick.michael@pnguot.ac.pg\n\n\n\nINTRODUCTION\nAcid sulphate soils (ASS) are naturally occurring soils, sediments or substrates \nformed under waterlogged (reduced) conditions (Fitzpatrick et al. 2008; Fanning \n2012; Michael 2013). Pons (1973) described ASS as the \u201cnastiest soils\u201d on earth \nbecause of a series of serious ecological and environmental impacts (Baldwin \nand Fraser 2009; Buschmann et al. 2008). Some of these negative impacts have \nbeen reviewed and documented by Michael (2013).  The negative impacts are \nof global significance because ASS distribution is estimated to be 17-24 million \nha (Poch et al. 2009) of which 6.5 million ha occur in Asia, 4.5 million ha in \nAfrica, 3 million ha in Australia, 3 million ha in Latin America, 200 000 ha in \nFinland and 100 000 ha  in North America, respectively (Simpson and Pedini \n1985). ASS containing sulphuric acid has been classified as sulphuric material \n(pH<4) and those containing iron sulphide (pyrite, FeS2) as sulphidic material (pH \n>4), respectively (Isbell 2002). \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 1-18 (2018)                          Malaysian Society of Soil Science\n\n\n\nEffects of Live and Dead Plant Matter on the Stability of \npH, Redox Potential and Sulphate Content of Sulphuric Soil \nMaterial Neutralised by Addition of Alkaline Sandy Loam\n\n\n\nPatrick S. Michaela,b\n\n\n\na School of Biological Sciences, The University of Adelaide, SA 5005, Australia\nb Department of Agriculture, PNG University of Technology, Lae, MP 411,\n\n\n\nPapua New Guinea\n\n\n\nABSTRACT\nSulphuric soil material of acid sulfate soils is mainly managed by the addition of \nmineral lime but lime availability is limited to a few regions and pure mineral lime \nis expensive. This study assessed the stability of sulphuric soil material neutralised \nby the addition of alkaline sandy loam, with organic matter amendment or \nestablishment of plants. Incorporation of organic matter stabilised the pH but did \nnot prevent oxidation under aerobic conditions. Under flooded conditions, the pH \nwas more stable and increased when organic matter was incorporated. Application \nof organic matter to the surface was only effective under flooded conditions. In \ncontrast to the effects of plants on sulphidic soil material and sulphuric soil material \nwhere the tendency was to increase growth of plants on neutralised sulphuric soil \nmaterial had little influence on pH. In addition, the changes induced by smaller \nplants were comparatively lower, and dependent on the organic matter turnover \nfrom above and below ground biomass. \n\n\n\nKeywords: Sulphuric soil material, alkaline sandy loam, neutralised \nsoil, organic matter, live plants, pH, Eh\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 20182\n\n\n\nUnder inundated (reduced) conditions, the negative impacts of ASS are considered \nminimal but if  these soils are exposed (disturbed), the sulphides are oxidised and \nsulphuric acid  is produced and released (Simpson et al. 2010). The principle \noption to manage sulphuric material is to neutralise the \u201csulphuric acidity\u201d with \na neutralising agent (Fitzpatrick et al. 2010), e.g. agricultural lime. The principle \nmanagement option of sulphidic material is to minimise disturbance and prevent \nexposure to atmospheric oxygen. Sulphuric acidity is neutralised by application \nof agricultural lime but its availability is limited to a few areas and is relatively \nexpensive in other regions (Shamshuddin et al. 2004). Oxidation of sulphidic \nmaterial by exposure is mainly prevented by submergence through flooding \nbut under conventional soil use and management conditions, land users and \nmanagers consider flooding as not desirable (Hanhart et al. 1997). In addition, \nthe ever increasing need for food and socio-economic development is exerting \ncontinuous pressure on wetlands containing sulphidic material to be converted \ninto agricultural farms and as space for infrastructure development consequently \nexpos sulphidic material. The negative impacts of ASS and the convention of \nleaving material untouched under inundation to prevent oxidation provides for a \nvulnerable situation. Research is certainly needed to develop effective and user-\nfriendly management strategies.\n\n\n\nVarious studies have established that incorporation of organic matter \nneutralises sulphuric soil material acidity (i.e. increases the pH) and prevents \nsulphidic soil oxidation (i.e. stabilises the pH) when exposed and is an alternative \nmanagement option for ASS (Michael et al. 2015; Michael 2015; Michael et \nal. 2016). Similar results were reported by Jayalath et al. (2016) and Reid and \nButcher (2011).  The advantages of using organic matter are that it is relatively \ncheap and readily available compared to mineral lime. We have shown too that \nthe presence of plants on either sulphuric soil or sulphidic soil material increases \nacidification when vegetated even if the  turnover of plant materials offsets the \nnegative impacts (Michael et al. 2017).\n\n\n\nIn many ASS, a heavy texture does not allow penetration of neutralising \nagents, such as lime, and are not conducive to plant growth. Improvement to poor \ntexture can be achieved by various methods to allow better penetration of water \nto facilitate leaching of excess salts, and better penetration of oxygen for plant \ngrowth. In a short incubation study lasting 2 weeks, we have shown that addition \nof alkaline sandy loam neutralises sulphuric soil material acidity and prevents \noxidation of sulphidic soil material o (Michael et al. 2012). This short-term study \nwas extended to assess long-term (6 months) stability when alkaline sandy loam \nwas added to sulphuric soil material and the pH raised to near neutral of 6.7. The \nresults obtained indicated that the neutrality obtained after mixing the sulphuric \nsoil material with alkaline sandy loam was stable over a period of 6 months \n(Michael, 2014). The stability of the neutralised sulphuric soil material, under \nnormal soil use and management conditions, such as in revegetation of an acid \nscalded environment, however was not clearly established. Therefore, this study \nwas conducted to assess the effects of live and dead plant matter on the stability \n\n\n\nPatrick\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 3\n\n\n\nof pH and the redox potential of sulphuric soil material of ASS neutralised by \naddition of alkaline sandy soil.\n\n\n\nMATERIALS AND METHODS\n\n\n\nAcid Sulfate Soils \nSulphuric soil material (Soil Survey Staff 2014) was collected from the surface soil \n(within a profile of 0.5-1.5 m) in Gillman (34\u00b082\u203292.3\u2033S, 138\u00b054\u203205.0\u2033E), Finnis \nRiver, South Australia. The pH of the freshly collected sulphuric soil material \nmeasured in water (pHw) 1:5 (soil: water) was 2 and the pH following peroxide \ntreatment (pHox) (Sullivan et al. 2009; Sullivan et al. 1999) was 0.9, with the water \nholding capacity estimated to be 79%. pHw, pHox and water holding capacity of the \nsulphidic soil material collected from the same site at a depth of 1.6-3.5 m ranged \nfrom 5.6-6.7, 1.9-2.3, and 48%, respectively. The residual organic matter content, \nestimated using the weight loss-on-ignition method (Schulte and Hopkins 1996).\n\n\n\nAlkaline Sandy Loam and Neutralisation of Sulphuric Soil Material\nIn order to neutralise the sulphuric soil material by mixing, bags of alkaline sandy \nloam (hereafter ASL) were purchased from a local supplier in Adelaide, South \nAustralia. The pHw was 9.4 and pHox was 7.2, respectively. To neutralise the \nsulphuric soil material, ASL was initially added at a ratio of 3:1 (ASL: Sulphuric \nsoil material, w/w) and mixed using a cement mixer. The process was repeated \nuntil an adequate amount of the neutralised soil was obtained. The final pHw, pHox \nwater holding capacity and sulfate content of the neutralised soil prior to use was \n6.7, 2.8, 28% and 23 \u03bcmol g-1 soil, respectively. The \u201cneutralised sulphuric soil \nmaterial\u201d is hereafter referred to as \u201cneutralised soil\u201d.\n\n\n\nOrganic Matter \nDried leaves of Phragmites australis (3.7% nitrogen analysed using LECO), \ncollected from the second visible dewlap (the last 3 leaves of first 6 leaves) were \nground using an electric blender to pass through a 0.5 mm sieve and used as \norganic matter (Michael et al. 2015). Drying was done by leaving the leaves in an \noven at 600C for 3 days until the leaves were sufficiently brittle to be ground by \nplacing them in the blender.\n\n\n\nPlants\nTo investigate the stability of the neutralised soil in the presence of small plants, \nwheat and lucerne were established by direct sowing of seeds obtained from \ncrops grown for this study. To investigate the effects of big plants, Allocasurina, \nEucalyptus calycogona and Melaleuca amillaris were established by transplanting \n6-8 weeks old seedlings. These seedlings were raised in a medium (compost: sand \nloan 2:1) from seeds purchased from a local supplier in Adelaide, South Australia.\n\n\n\nEffects of Live and Dead Plant Matter\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 20184\n\n\n\nExperimental Treatments\nExperiment 1\nUnder general soil use and management conditions, dead plant matter is added \neither on the surface (e.g. leaf litter) or incorporated into the soil as organic matter \n(e.g. in the form of death roots). Naturally, addition of organic matter conserves \nsoil moisture and controls weeds, whereas incorporation of organic matter \nsupplements the soil with nutrients and improves soil texture by the addition of \nfibre. The scenarios of dead plant matter being added either on the soil surface or \nincorporated throughout the soil profile were investigated in experiment 1. \n\n\n\nUsing small pots, two sets of experiments were set. In both sets, 1 g of \nground Phragmites australis (common reed) leaf as \u201corganic matter\u201d was \neither \u201coverlaid\u201d on the surface or \u201cincorporated\u201d by weighing using 90g of the \nneutralised soil or sulphidic soil material. Where organic matter was incorporated, \ncareful mixing was done using a soil scoop to ensure homogeneity. In the first \nset, organic matter was overlaid on the surface and kept under either aerobic or \nanaerobic condition. In the second set, organic matter was overlaid on the surface \nbut maintained under flooded (\u201canaerobic\u201d)soil condition only. This study was set \nusing naturally occurring sulphidic soil material collected from Gilman. Mixing \nof this soil with ASL was done as described above. \n\n\n\nExperiment 2\nThis experiment was set using 50 cm long stormwater tubes (pipes) with one end \ntightly capped and sealed with waterproof sealants. The bottom 22 cm capped end \nof each tube was filled with acid washed, carbonate free sand and the top 22 cm \nfilled with 1300 kg of the neutralised soil. As in the first experiment, weighing \nwas done to ensure equal amounts of sand and neutralised soil were added.\n\n\n\nIn the neutralised soil, wheat as crop, lucerne as forage and Allocasurina, \nMelaleuca and Eucalyptus as trees were established. The crop and forage plants \nwere sown directly as seeds and the trees established by transplanting 6-8 weeks \nold seedlings. Throughout the study, all the plants that grew reached maturity. \nIn all the experiments, control treatments were set up in a similar manner as the \ntreatments except that no organic matter was either overlaid or incorporated in \nExperiment 1, and no plant was established in Experiment 2. The treatments were \nreplicated four times and set up in a randomised complete design (RCD) under \nglasshouse conditions. Only three of the replicates were used for data collection.\n\n\n\nWatering and Management \nWatering was done as per Michael et al. (2017). All the treatments under aerobic \ncondition received water on three days (Monday, Wednesday and Saturday). The \ntreatments under anaerobic conditions were watered twice daily to ensure that \norganic matter and the neutralised soil were covered in water, with a sufficient \namount of water ponding on the surface to maintain anaerobic soil condition.  All \nthe organic matter and soil moisture experiments were maintained for 6 months \nto ensure complete decomposition of the dead plant matter (Michael et al. 2015).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 5\n\n\n\nAs would be in a farm, the crop and forage plants were fertilised with pre-\nprepared 100ml of a complete Hoagland solution on a monthly basis until \nharvest. An additional treatment with a similar number of replicates without \nplants was included and fertilised to assess whether fertilising had an effect on \nthe measurements made. The crop and forage plants as well as the trees were \nharvested (measurements) after 12 months of growth. As would be under natural \nconditions, senesced leaves that had fallen were not removed. The fallen leaves \non the surface and dead roots throughout the profiles, upon decomposition, would \ninduce change in the soil chemical characteristics measured.\n\n\n\nBiomass \nRoot biomass was quantified as described by Michael et al. (2017). To quantify \nthe root biomass followed by soil sampling for pH measurement, the storm water \ntubes were marked out at 0-20, 20-50, 50-100, 100-150, 150-200 and 200-300 mm \n(the profiles from which the changes in soil redox potential (Eh) in the presence of \nplants were measured) and cut into small sections using a handheld electric saw. \nSoil from these sections were placed in a sieve (0.05 mm) and held under gentle \nrunning tap water and the soil carefully broken up to free the roots using the aid \nof forceps. \n\n\n\nThe loose soil particles were allowed to drain through but roots that were \ntrapped by the sieve and started floating during washing were collected. These \nroots were taken, gently washed again to remove soil material, placed in weighing \nboats and oven dried for 48 h. Dry weight was taken by weighing and the weights \nof the replicates were pooled, averaged and served as final data. Only data from \nthe surface (20 mm), middle (100 and 200 mm) and deep (300 mm) are presented.\n\n\n\nMeasurements\nRedox was measured using a single Ag/AgCl reference and platinum (Pt) electrode \ncombination using an automated data logger (Dowley et al.1998). To measure the \nEh, the frame of the redox probe was marked as per the targeted profile from \nthe tip (e.g. 0-10 mm etc. to 300 mm). To do this, the storm water tubes used in \nExperiment 2 were marked out as per the intended depths, and an electric drill \nwith a bit head similar to the size of the Pt tip was used to make holes. The holes \nwere sealed immediately with packing tape to prevent oxygen entry prior to the \nEh measurement.\n\n\n\nDuring Eh measurement, the reference electrode was inserted into the \nsurface and Pt electrode either in the surface (Experiment 1) or from the side \nusing the holes created as described above (Experiment 2). In all the experiments, \nthe reference electrode remained inserted while the Pt electrode was driven \ninvariably one at a time into the soil from the surface (Experiment 1) or from \nthe side (Experiment 2). Depending on the profile depth, the Eh was intended \nto be logged (Michael et al. 2015; 2016). This was allowed to equilibrate for \n10 min and then Eh logged at 1 min intervals for the next 10 min and averaged \n(Rabenhorst et al. 2009). These values were corrected for the reference offset to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 20186\n\n\n\nbe relative to the potential of a standard hydrogen electrode by adding 200 mV \n(Fiedler et al. 2007). The stability and accuracy of the electrodes were maintained \nas per Fiedler et al. (2007).\n\n\n\nTo obtain soil samples for pH measurement of Experiment 1, a core sampler \n(25 mm in diameter) was driven into the soil, and a core carefully taken out. The \nsoil cores were placed along a 30 cm ruler and cut into pieces using the graduation \non the ruler as a guide, with a piece of core containing soil from which Eh was \nmeasured. For the studies described under Experiment 2, a cut section containing \nthe soil from which Eh was measured was used. pH was measured using 2 g \nsoil (1:5 water) sample (replicate) either from the cut cores or stormwater tube \nsections with a pre-calibrated Orion pH meter (720SA model) (Michael et al. \n2015).\n\n\n\nIn addition to the effects on pH and Eh, dead plant matter in the form of dead \nleaves (that fall on the soil surface) and root (in different profiles of the soil) have \nan effect on soil sulphate content (Michael 2015).  This scenario was tested by \nqualifying the sulphate content using soil samples of Experiment 1 where organic \nmatter was added. The sulphate content of the neutralised soil in Experiment 2 \nwas not done as the data from Experiment 1 with organic matter overlaid on the \nsurface or incorporated in the soil would serve the same purpose, under varying \nsoil moisture conditions.\n\n\n\nSulphate was extracted according to the method of Hoeft et al. (1973) \nfor soluble soil sulphate. Replicate samples (0.5 g each) from the cut soil core \nof Experiment 1 or soil inside the cut stormwater tubes of Experiment 2 were \nplaced in tubes with 1.5 mls of an extraction solution (0.2 g CaH2PO4, 12 g glacial \nacidic acid, and 88.5 g deionised water). After 30 min, the soil was sedimented \nby centrifugation for 5 min and duplicate aliquots from the three replicates were \ntransferred into 4 ml cuvettes and diluted with 1.5 ml of the extraction solution. \nThe samples were mixed with 0.7 ml of 0.5 M HCl and then 0.7 ml of 0.1 M \nbarium chloride-polyethylene glycol reagent was added and mixed again. After \n10 min, the samples were mixed again and the absorbance read at 600 nm using a \nspectrophotometer. The readings were compared to a standard solution of 0-2 mM \nNa2SO4. The initial sulphate content of the sulphuric and sulphidic soils ranged \nfrom  21-32 to 12-16 \u00b5mol g-1 soil.. The detection limit based on an absorbance \nreading of 0.1 of the Hoeft et al. (1973) method was 0.6 \u00b5mol g-1 soil. \n\n\n\nIn Experiment 1, measurements made from the 0-10, 10-20, 20-40 and \n40-80 mm profiles are presented. For the first set of experiment with organic \nmatter overlaid and maintained under aerobic conditions, only the surface (10 \nmm), middle (40 mm) and deep (80 mm) profiles data are presented. For the \nsame experiment with both the organic matter and neutralised soil maintained \nunder anaerobic conditions, data from the surface (10 mm) and deep (80 mm) \nprofiles only are presented. The reason for only  surface and deep data being used \nwas because the surface soil was under frequent fluctuating water level whereas \nthe deep soil was consistently submerged in water, as would be under flooded \n(reduced) soil conditions. In the second set of experiments where organic matter \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 7\n\n\n\nwas incorporated and maintained under aerobic conditions, data from the surface \n(10 mm), middle (40 mm) and deep (80 mm) are presented. The component of this \nexperiment maintained under flooded soil conditions, data on the surface (10 mm) \nand deep (80 mm) profiles are presented..\n\n\n\nIn Experiment 2 (set up in 45 cm stormwater tubes), measurements were \nmade from the 0-20, 20-50, 50-100, 100-150, 150-200 and 250-300 mm profiles. \nFor both sets of experiments (crop and forage plants receiving nutrient)and tree \nplants, data collect from the surface (20 mm), middle (100 - 200 mm) and deep \n(300 mm) are presented. \n\n\n\nIn all the experiments, the profiles from which the data were collected from \nthe two experiments were dictated by the size of the small pots and the stormwater \ntubes used.\n\n\n\nStatistical Analysis\nStatistical analysis was done as reported in various studies (Michael et al. 2015; \n2016; 2017). The Eh values obtained over a 10-min period were averaged and a \ntreatment average obtained by taking the mean of the three replicates. Similarly, \ntreatment average pH was obtained by taking the mean of the three replicates. To \ncompare the treatment means, significant differences (p<0.05) between treatment \nmeans of each profile was determined by two-way ANOVA using statistical \nsoftware JMPIN, AS Institute Inc., SAS Campus Drive, Cary, NC, USA 27513. \nIf an interaction between the treatments and profile depths was found, one-way \nANOVA with all combinations was performed using Tukey\u2019s HSD (honest \nsignificant difference) and pairwise comparisons.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nEffects of Organic Matter on Neutralised Soil Eh, pH and Sulphate Content\nThe changes in neutralised soil pH, redox and sulfate content measured following \nincorporation of organic matter and maintained under aerobic conditions are \nshown in Figure 1. During the  months of incubation, pH of the unamended \ncontrol soil was stable at the surface but decreased sharply to 4.5 at depth (Figure. \n1a). Incorporation of organic matter sustained the pH between 5 and 6 across the \nprofiles, but became more acidic than the initial pH by 1 unit. \n\n\n\nThe redox changes were hard to interpret (Figure 1b). In all cases, the \nsoils remained moderately to highly oxidised with similar values recorded for \nthe control soil at pH 6.6 (surface) and pH 4.4 (80 mm depth) (Figure 1b). The \nsulphate content was fairly consistent across the profile in the control soil (Figure \n1c). In the organic matter amended treatment, sulphate content  decreased sharply \nfrom the surface to the deep profiles. \n\n\n\n Figure 2 shows the changes in the neutralised soil pH, redox and sulphate \ncontent measured following incorporation of organic matter and maintained \nunder flooded conditions. Compared to the initial pH, the unamended control soil \nacidified to near 5 at the surface but increased slightly to 7 at depth (Figure 2a). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 20188\n\n\n\n10 \n \n\n\n\n 286 \n\n\n\nFigure 1: Effects of incorporated organic matter on (a) pH, (b) redox and (c) sulphate 287 \ncontent of neutralised soil maintained under aerobic conditions for 6 months. The red 288 \ndotted line is the initial pH. The initial sulphate content of the neutralised soil is 23 \u00b5mol g-289 \n1soil. The values are means \u00b1 s.e. of three measurements (n=3). Asterisks indicate 290 \nsignificant differences (p<0.05) between treatments and control at the same depth. 291 \n 292 \n\n\n\nThe redox changes were hard to interpret (Figure 1b). In all cases, the soils remained 293 \n\n\n\nmoderately to highly oxidised with similar values recorded for the control soil at pH 6.6 294 \n\n\n\n(surface) and pH 4.4 (80 mm depth) (Figure 1b). The sulphate content was fairly 295 \n\n\n\nconsistent across the profile in the control soil (Figure 1c). In the organic matter 296 \n\n\n\namended treatment, sulphate content  decreased sharply from the surface to the deep 297 \n\n\n\nFigure 1: Effects of incorporated organic matter on (a) pH, (b) redox and (c) sulphate \ncontent of neutralised soil maintained under aerobic conditions for 6 months. The red \ndotted line is the initial pH. The initial sulphate content of the neutralised soil is 23 \u00b5mol \ng-1 soil. The values are means \u00b1 s.e. of three measurements (n=3). Asterisks indicate \nsignificant differences (p<0.05) between treatments and control at the same depth.\n\n\n\nOrganic matter caused the pH to increase by 0.6 \u2013 1 unit across the profile. The \npH changes broadly corresponded to the reciprocal changes in Eh (Figure 2b) and \nsulphate content (Figure 2c).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 9\n\n\n\nWhen organic matter was overlaid and maintained under aerobic conditions, \nthe pH of the control treatment remained stable around 6.8 at the surface, but \ndecreased strongly to around 4.3 at depth (Figure 3a). Addition of organic matter \nto the surface caused moderate acidification, which increased slightly with depth. \n\n\n\n11 \n \n\n\n\nprofiles.  298 \n\n\n\n 299 \n\n\n\nFigure 2: Effects of incorporated organic matter on (a) pH, (b) redox and (c) sulphate 300 \ncontent of neutralised soil maintained under anaerobic conditions for 6 months. The red 301 \ndotted line is the initial pH. The initial sulphate content of the neutralised soil is 23 \u00b5mol g-302 \n1soil. Values are means \u00b1 s.e. of three measurements (n=3). Asterisks indicate significant 303 \ndifferences (p<0.05) between treatments and control at the same depth. 304 \n 305 \nFigure 2 shows the changes in the neutralised soil pH, redox and sulphate content 306 \n\n\n\nmeasured following incorporation of organic matter and maintained under flooded 307 \n\n\n\nconditions. Compared to the initial pH, the unamended control soil acidified to near 5 at 308 \n\n\n\nthe surface but increased slightly to 7 at depth (Figure 2a). Organic matter caused the 309 \n\n\n\nFigure 2: Effects of incorporated organic matter on (a) pH, (b) redox and (c) sulphate \ncontent of neutralised soil maintained under anaerobic conditions for 6 months. The red \ndotted line is the initial pH. The initial sulphate content of the neutralised soil is 23 \u00b5mol \ng-1soil. Values are means \u00b1 s.e. of three measurements (n=3). Asterisks indicate significant \ndifferences (p<0.05) between treatments and control at the same depth.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201810\n\n\n\nDespite the changes in pH, the Eh (Figure 3b) and sulphate content (Figure 3c) of \nall treatments varied very little. However, the sulphate content after 6 months was \nmuch lower than the measured initial content, indicating the disappearance (most \nlikely by reduction) of a significant amount of sulphate.\n\n\n\nUnder flooded conditions, the pH of the control sulphidic soil decreased \nslightly to near 6 at the surface and to 5.3 at depth, whereas in the organic matter \ntreatment, the pH rose sharply at the surface to more than 8 but was similar to the \n\n\n\n12 \n \n\n\n\npH to increase by 0.6 \u2013 1 unit across the profile. The pH changes broadly corresponded 310 \n\n\n\nto the reciprocal changes in Eh (Figure 2b) and sulphate content (Figure 2c). 311 \n\n\n\n 312 \n\n\n\nFigure 3: Effects of overlaid organic matter on (a) pH, (b) redox and (c) sulphate content of 313 \nneutralised soil maintained under aerobic conditions for 6 months. The red dotted line is 314 \nthe initial pH. The initial sulphate content of the neutralised soil is 23 \u00b5mol g-1soil. The 315 \nvalues are means \u00b1 s.e. of three measurements (n=3). Asterisks indicate significant 316 \ndifferences (p<0.05) between treatments and control at the same depth. 317 \n 318 \nWhen organic matter was overlaid and maintained under aerobic conditions, the pH of 319 \n\n\n\nthe control treatment remained stable around 6.8 at the surface, but decreased strongly 320 \n\n\n\nto around 4.3 at depth (Figure 3a). Addition of organic matter to the surface caused 321 \n\n\n\nmoderate acidification, which increased slightly with depth. Despite the changes in pH, 322 \n\n\n\nFigure 3: Effects of overlaid organic matter on (a) pH, (b) redox and (c) sulphate content \nof neutralised soil maintained under aerobic conditions for 6 months. The red dotted line \nis the initial pH. The initial sulphate content of the neutralised soil is 23 \u00b5mol g-1 soil. \nThe values are means \u00b1 s.e. of three measurements (n=3). Asterisks indicate significant \ndifferences (p<0.05) between treatments and control at the same depth.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 11\n\n\n\ncontrol soil at depth (around 5.7) (Figure 4a). Despite the flooded soil condition, \nthe presence of sandy loam soil facilitated movement of oxygen into the soil, \nallowing the control soil to remain fairly oxidised, compared to the organic matter \ntreatment which was quite reduced (Figure 4b). There was only a very weak \ncorrelation between pH changes (Figure 4a) and the sulphate content (Figure 4c); \nthe soils with higher pH values tended to have a low sulphate content.\n\n\n\n13 \n \n\n\n\nthe Eh (Figure 3b) and sulphate content (Figure 3c) of all treatments varied very little. 323 \n\n\n\nHowever, the sulphate content after 6 months was much lower than the measured 324 \n\n\n\ninitial content, indicating the disappearance (most likely by reduction) of a significant 325 \n\n\n\namount of sulphate. 326 \n\n\n\n 327 \nFigure 4: Effects of overlaid organic matter on (a) pH, (b) redox and (c) sulhate content of 328 \nsulphidic soil maintained under anaerobic conditions for 6 months. The red dotted line is the 329 \ninitial pH. The initial sulphate content of the unamended sulpidic soil is 16 \u00b5mol g-1soil. Values 330 \nare means \u00b1 s.e. of three measurements (n=3). Asterisks indicate significant differences (p<0.05) 331 \nbetween treatments and control at the same depth. 332 \n 333 \nUnder flooded conditions, the pH of the control sulphidic soil decreased slightly to near 334 \n\n\n\n6 at the surface and to 5.3 at depth, whereas in the organic matter treatment, the pH 335 \n\n\n\nFigure 4: Effects of overlaid organic matter on (a) pH, (b) redox and (c) sulhate content \nof sulphidic soil maintained under anaerobic conditions for 6 months. The red dotted line \nis the initial pH. The initial sulphate content of the unamended sulpidic soil is 16 \u00b5mol \ng-1soil. Values are means \u00b1 s.e. of three measurements (n=3). Asterisks indicate significant \ndifferences (p<0.05) between treatments and control at the same depth.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201812\n\n\n\nThe data presented in Figure 2b show that under similar anaerobic conditions, \nincorporation of organic matter highly reduced the Eh of the neutralised soil and \nkept that of the control treatment fairly reduced (Figure 2b). These contradicting \nresults may mean that addition of ASL into naturally occurring sulphidic soil \nmaterial is not the  best option but is a better option to manage sulphuric soil \nmaterial.  These results could mean too that overlaying of organic matter is not an \nideal strategy compared to the effectiveness of incorporation of organic matter to \nhave an effect on soil properties (Michael et al. 2015). \n\n\n\nImpacts of Vegetation on Neutralised Sulfuric Soil pH and Redox\nMixing of ASL with sulphuric soil material has the combined advantages of \nraising the pH into the range where most plants grow optimally, and creating a \nmore open texture for root growth. This is an important approach to manage areas \nlike an acid scalded farm to establish plants. In this study, a range of plants was \ngrown with or without supplemental fertiliser in the neutralised soil to examine \ntheir impact. \n\n\n\nThe root biomass obtained from the profiles 20 (0-20 mm), 100 (50-100 \nmm), 200 (150-200 mm) and 300 mm (250-300 mm) of the smaller and larger \nplants are respectively shown in Figure 5a. Fertilising had a small to no effect on \nthe neutralised soil pH and Eh remained within the oxidised ranged as in the other \ntreatments (data not shown). Fertilisation however greatly increased the growth \nof both lucerne and wheat as expected but the root biomass of the lucerne within \nthe surface was comparably higher (2 g), compared to the wheat treatment (Figure \n5a). This is not surprising as root biomass of legumes accumulates greatly near \nthe surface, particularly when fertilised, e.g. under crop plantations. Compared to \nthe control, lucerne acidified the neutralised soil pH, consistent with the results of \nYan et al. (1996). \n\n\n\nNear the surface, the pH remained relatively stable for all treatments except \nfor the fertilised treatment with wheat plants (near 8 units), compared to the \nother treatments which remained unchanged (Figure 5b). Lower in the profile, \nthe control soil acidified to around pH 5, with similar values recorded for most \nof the treatments. There was no clear pattern of change connecting pH to Eh, \nthe only noticeable feature being that the Eh of the unfertilised wheat treatment \ntended to be lower than the other treatments (Figure 5c). The apparent lack of \neffect of the planted treatments may be due to the relatively small amount of \nbiomass turnover contributed by the roots. Presence of sandy loam, plant roots \nand the aerobic condition facilitated oxygen entry, allowing the neutralised soil \nEh to remain highly oxidised (Eh ranging from 450-600 mV) (Figure 5c). Under \nhighly oxidised soil conditions, soil pH is expected to be lower (pH\u22644).  This was \nobserved, at least in the lower profiles (Figure 5b), a clear indication that changes \nin soil properties measured were induced by natural processes, as would be, in \nany soil type.   \n\n\n\nFigure 6 shows the effects of the larger plants, Allocasuarina, Eucalyptus \nand Melaleuca. The results were highly variable and no clear trend was discerned, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 13\n\n\n\nalso no predictable relationship between root biomass, pH and Eh was found. \nExcept in the 100 mm profile of the Melaleuca treatment, root biomass was \nequally produced by all the plants throughout the profiles (Figure 6a). However, \nin all cases, the final pH of all the treatments was higher than the initial pH (Figure \n6a), which is more likely due to the stabilising effect of alkaline components of \nthe sandy loam as well as to the fact that the neutralised soil may contain fewer \noxidisable sulphides.\n\n\n\nFigure 5: Effects of (a) roots of wheat and lucerne plants on (b) pH and (c) redox of \nneutralised sulphuric soil maintained under aerobic conditions for 12 months. The red \ndotted line is the initial pH. Values are means \u00b1 s.e. of three measurements (n=3). No \nsignificant differences (p<0.05) between treatments and control soil properties were \nobserved at the same depth.\n\n\n\n15 \n \n\n\n\n 369 \n\n\n\nFigure 5: Effects of (a) roots of wheat and lucerne plants on (b) pH and (c) redox of 370 \nneutralised sulphuric soil maintained under aerobic conditions for 12 months. The red 371 \ndotted line is the initial pH. Values are means \u00b1 s.e. of three measurements (n=3). No 372 \nsignificant differences (p<0.05) between treatments and control soil properties were 373 \nobserved at the same depth. 374 \n 375 \nNear the surface, the pH remained relatively stable for all treatments except for the 376 \n\n\n\nfertilised treatment with wheat plants (near 8 units), compared to the other treatments 377 \n\n\n\nwhich remained unchanged (Figure 5b). Lower in the profile, the control soil acidified to 378 \n\n\n\naround pH 5, with similar values recorded for most of the treatments. There was no 379 \n\n\n\nclear pattern of change connecting pH to Eh, the only noticeable feature being that the 380 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201814\n\n\n\nThe data presented in Figures 5 and 6 show no clear relationship between \nthe plant types and the changes in soil chemistry, measured on the neutralised \nsoil. The changes in pH and Eh in the planted treatments were similar to those \n\n\n\n17 \n \n\n\n\n 410 \nFigure 6: Effects of (a) roots of trees on (b) pH and (c) redox of neutralised sulphuric soil maintained 411 \nunder aerobic conditions for 12 months. The values are means \u00b1 s.e. of three measurements 412 \n(n=3).The blue dotted line is the initial pH. No significant differences (p<0.05) between treatments 413 \nand control soil properties were observed at the same depth. 414 \n 415 \n 416 \n 417 \n 418 \n 419 \n\n\n\nFigure 6: Effects of (a) roots of trees on (b) pH and (c) redox of neutralised sulphuric \nsoil maintained under aerobic conditions for 12 months. The values are means \u00b1 s.e. of \nthree measurements (n=3).The blue dotted line is the initial pH. No significant differences \n(p<0.05) between treatments and control soil properties were observed at the same depth.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 15\n\n\n\nof the control, a strong indication that the chemistry of the neutralised soil was \nstable, regardless of whether small plants or bigger ones were established. The \ndata showed too that in addition to the changes induced by the alkaline nature of \nthe sandy loam, the noticeable changes in soil properties, especially in the surface \nprofiles, were plant dependent. A smaller amount of organic matter turnover \noccurred in the planted treatments, compared to the bigger plants. This is the \nreason the changes in surface soil pH measured in the presence of small plants \nwas small (Figure 5b) compared to the surface soil pH of the treatments with \nbigger plants (Figure 6b).      \n\n\n\nCONCLUSION\nNeutralised soil is expected to contain fewer sulphides compared to the equivalent \nnaturally occurring sulphidic soil material before oxidation. Nevertheless, these \nsoils still acidified at depth, but less than the sulphidic soil material. Incorporation \nof organic matter stabilised the pH but did not prevent oxidation under aerobic \nconditions. Under flooded conditions, the pH was more stable and increased when \norganic matter was incorporated. Application of organic matter to the surface was \nonly effective under flooded conditions. In contrast to the effects of plants on \nsulphidic soil and sulphuric soil materials where the tendency was for plants to \nincrease pH level, growth of plants on neutralised sulphuric soil material had little \nor no influence on pH. The change in pH measured on the neutralised soil induced \nby organic matter turnover of live plants was dependent on plant type. The pH of \nthe neutralised soil with smaller plants (wheat and lucerne) with smaller organic \nmatter turnover acidified, compared to the treatments with trees (Allocasurina, \nEucalyptus and Melaleuca) with bigger organic matter turnover.  \n\n\n\nACKNOWLEDGEMENT\nThis study was funded by the Commonwealth of Australia through an ADS \nscholarship provided to Patrick S. Michael. The author thanks Prof. Robert Reid \nand Prof. Robert W. Fitzpatrick, for generous assistance provided to this study.\n\n\n\nREFERENCES\nBaldwin, D. S. and M. Fraser. 2009. 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Soil Science Society of American Journal 37: 401-404\n\n\n\nIsbell, R.F. 2002. The Australian Soil Classification. CSIRO Publishing, CSIRO: \nCollingwood, Victoria, Australia\n\n\n\nJayalath,J., L. M. Mosely,. and R. W. Fitzpatrick. 2016. Addition of organic matter \ninfluences pH changes in reduced and oxidised acid sulfate soils. Geoderma \n262: 125-132\n\n\n\nLjung, K., F. Maley, A. Cook, and P. Weinstein. 2009. Acid sulphate soils and human \nhealth-A millenium ecosystem assessment. Environment International 25: \n1234-1242\n\n\n\nMichael,P. S., R. Reid and R. W. Fitzpatrick. 2012. Amelioration of slowly \npermeable hypersaline peaty-clayey sulfuric and sulfidic materials in acid \nsulfate soils by mixing with friable sandy loam soil. In: Proceedings of the 5th \nJoint Australian and New Zealand Soil Science Conference: Soil Solutions for \nDiverse Landscapes, ed. L. L. Burkitt and L. A. Sparrow, pp: 146-149.  Hobart, \nTasmania, Australia. \n\n\n\nhttp://soilscienceaustralia.com.au/images/sampledata/publications_tab/\nconfproceedings433/WCSS/2012NatConf_Hobart/Soil_Science_Proceedings_\nFinal.pdf\n\n\n\nMichael, P. S. 2013. Ecological impacts and management of acid sulphate soil: A \nreview. Asian Journal of Water, Environment and Pollution10:13-24\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 17\n\n\n\nMichael,P. S., R. Reid and R. W. Fitzpatrick. 2014. Effects of organic matter \namendment on acid sulfate soil chemistry. In: Proceedings of the 4th National \nAcid Sulfate Soil Conference, ed. C. Clay., pp: 80-81. Rendezvous Hotel, Perth, \nAustralia.\n\n\n\nhttp://soilscienceaustralia.com.au/soil2014/proceedings/Michael.pdf \n\n\n\nMichael,P. S., R. W. Fitzpatrick and R. Reid. 2015. The role of organic matter in \nameliorating acid sulfate soils with sulfuric horizons. Geoderma 225:42-49\n\n\n\nMichael, P. S. 2015. Effects of alkaline sandy loam on sulfuric soil acidity and sulfidic \nsoil oxidation. International Journal of Environment 4: 42-54\n\n\n\nMichael, P. S., R. W. Fitzpatrick and R. Reid. 2016. The importance of carbon and \nnitrogen for amelioration of acid sulphate soils. Soil Use and Management 32: \n97-105.\n\n\n\nMichael, P. S., R. W. Fitzpatrick and R. Reid. 2017. Effects of live wetland plant \nmacrophytes on acidification, redox potential and sulphate content in acid \nsulphate soils. Soil Use and Management 33: 471-481\n\n\n\nPoch, R.M., Thomas, B.P., Fitzpatrick, R.W., Merry, R.H., 2009. Micromorphological \nevidence for mineral weathering pathways in a coastal acid sulphate soil \nsequence with Mediteranean-type climate, South Australia. Australia Journal \nof Soil Research 47: 403-422\n\n\n\nPons, L. J.1973. Outline of the genesis, characteristics, classifications and \nimprovement of acid sulphate soils. In: International Symposium on Acid \nSulphate Soils. Introductory Papers and Bibliography, ed. H. Dost, pp. 3-27. \nInternational Institute for Land Reclaimation and Improvement, Wageningen: \nThe Netherlands. \n\n\n\nRabenhorst, M. C., W. D. Hively. and B. R. James.2009. Measurements of soil redox \npotential. Soil Science Society of America Journal 73: 668-674\n\n\n\nReid, R. J. and C. S. Butcher.2011. Positive and negative impacts of plants on acid \nproduction in exposed acid sulphate soils. Plant and Soil 349:183-190\n\n\n\nSchulte, E. E. and B. G. Hopkins.1996. Estimation of soil organic matter by weight \nloss-on-ignition. In: Soil Organic Matter: Analysis and Interpretation, ed.  F. R. \nMagdoff, M. A. Tabatabai and E. A. Hanlon, pp.21-31. Soil Science Society of \nAmerica. \n\n\n\nShamshuddin, J., S. Muhrizal,I. Fauziah and M. H. A. Husni. 2004. Effects of adding \norganic materials to an acid sulfate soil on the growth of cocoa (Theobroma \ncacao L.) seedlings. Science of the Total Environment 323:33-45\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201818\n\n\n\nSimpson, H. and P. Pedini. 1985. Brackish water aquaculture in the tropics: the \nproblem of acid sulfate soil environment. Applied Geochemistry19:1837-1853\n\n\n\nSimpson, S. L., R. W. Fitzpatrick, P. Shand, B. M. Angel, D. A. Spadaro and L. \nMosley. 2010. Climate-driven mobilisation of acid and metals from acid sulfate \nsoils. Marine and Freshwater Research 61:129\u2013138\n\n\n\nSoil Survey Staff, 2014. Keys to Soil Taxonomy. United States Department of \nAgriculture Natural Resources Conservation Service, Washington, D.C.\n\n\n\nSullivan, L. A., N. J. Ward, R. T. Bush and E. D. Burton. 2009. Improved identification \nof sulfuric soil materials by a modified method. Geoderma 149:33-38\n\n\n\nSullivan,L. A., R. T. Bush, D. McConchie, G. Lancaster, P. G. Haskins and M. \nW. Clark.1999. Comparison of peroxide-oxidisable sulfur and chromium- \nreducible sulfur methods for determination of reduced inorganic sulfur in soil. \nSoil Research 37:255-266\n\n\n\nYan,F., S. Schubert and K. Mengel. 1996. Soil pH changes during legume growth and \napplication of plant materials. Biology and Fertility of Soils 23:236-242\n\n\n\n\n\n"
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"\n\nINTRODUCTION\nTropical peat soils cover an area of 2.7 million hectares (Mha) in Malaysia, \naccounting for about 8% of the country\u2019s total land area (Abat et al., 2012; Mutalib \net al., 1991). In Sarawak alone, the peat area occupies  1.7 Mha  equivalent to 13% \nof the state\u2019s land area (Abat et al., 2012; Tie and Kueh, 1979). Increasing demand \nfor global oil palm products has led to an expansion in the area under oil palm  \ncultivation. Reclamation of peat soil for agricultural purposes requires drainage \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 13-30 (2019)                         Malaysian Society of Soil Science\n\n\n\nInfluence of Water Table Depths, Nutrients Leaching Losses, \nSubsidence of Tropical Peat Soil and Oil Palm (Elaeis \n\n\n\nguineensis Jacq.) Seedling Growth\n\n\n\nHashim S. A1, Teh C.B.S. 2 and  Ahmed O.H. 3, 4\n\n\n\n1Department of Agronomy, Faculty of Agriculture, Taraba State University Jalingo, \nTaraba State, Nigeria \n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 UPM Serdang, Selangor, Malaysia \n\n\n\n3Department of Crop Science, Faculty of Agriculture and Food Sciences, Universiti \nPutra Malaysia, Bintulu Sarawak Campus, Sarawak, Malaysia\n\n\n\n4Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, \n43400UPM Serdang, Selangor Malaysia\n\n\n\nABSTRACT\nInadequate availability of nutrients and leaching losses due to water table \nfluctuations is a serious concern in oil palm cultivation on tropical peat land. The \nobjectives of the study were to determine peat subsidence and leaching losses \nof N, P, K, Mg, Ca, Cu, and Zn from tropical peat soil under cultivation of oil \npalm seedlings at different water table depths. The study was conducted using \ncylindrical lysimeters with five water table depths namely, 25, 40, 55, 70, and 85 \ncm from the soil surface. The experimental layout was a Randomised Completely \nBlock design. Leachate from each lysimeter was collected after a rainfall event \nto determine the leaching loss of nutrients. The highest water table depth (25 cm) \nfrom the soil surface showed the highest nutrient leaching losses, and the lowest \nwater table depth (85 cm), showed the highest subsidence and lowest nutrients \nleaching losses. Plant growth was highest under the 55 cm water table depth, and \nthe lowest under the highest and lowest water table depths of 25 and 85 cm. The \n55 cm water table depth was the best for oil palm growth because the active root \nzone of oil palm is within the 60 cm soil depth.\n\n\n\nKeywords: Peat soils, oil palm growth, nutrients leaching, subsidence, water \ntable depth\n\n\n\n___________________\n*Corresponding author : E-mail: safiyanuabubakarhashim@gmail.com \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201914\n\n\n\nthat involves lowering of the water table and soil compaction for good aeration of \nthe crop root zone (Luta et al., 2017). Large areas of tropical peat soil have been \ncleared (especially in South-east Asia particularly Malaysia and Indonesia) for \noil palm cultivation because of the expected economic returns from the product.\nTropical peat soils have been considered as a problematic soil in their natural \nstate because of unsuitability to crop cultivation as they are characterised by low \npH (3-5) (Funakawa et al., 1996),  deficient in plant-available nutrient contents, \nespecially Cu and Zn (Miyamoto et al., 2009). They also have high water tables \nthat pose problems in crop production. Moreover, water tables in peat soils within \na certain range depend on weather conditions, and this has specific effects on the \ndecomposition process (Laiho, 2006).\n Peat soils have a serious problem of subsidence as they subside at a \nconstant rate. Subsidence is termed as the permanent lowering of soil surface \nelevation. The height of water table, oxidation of soil organic matter, and soil \nshrinkage are among the factors responsible for peat subsidence. The rate of  \nsubsidence depends on the extent of control over the water table, organic matter \ncontent and cultivation practices. Tropical peat subsidence also occurs as a result \nof the removal of excess water that normally appears as flood waters due to the \nhigh water table, and compaction of soil from agricultural activities. Therefore, \nthe level of water should be maintained to reduce subsidence as well as increase \noil palm yield. Lowering the water table, allows  peat lands to function more \neffectively (Macrae et al., 2013), because a lower water table causes the  peat \nsurface to dry (Waddington et al., 2010) resulting in reduced moisture storage \n(Holden et al., 2004). Many studies have been conducted on peat subsidence \nbased on different water tables and the general conclusion is that the lower the \nwater table, the higher the peat subsidence (Shih et al., 1979).  Subsidence has \nbeen recorded in many countries over long periods of time such as 3.4 cm yr-1 s in \nNew Zealand (Schipper and McLeod, 2002) and 6 cm year-1 in Sarawak at 75 \u2013 \n100 cm water table depth (Tie and Kueh, 1979).\n Soil fertility is altered by many processes, one of which is leaching. Nutrient \nleaching from agricultural land is a major environmental problem because of its \neffects on surface and groundwater pollution (for example, eutrophication due to \nN and P leaching). Plant cultivation is among the fundamentally most important \nsources of nutrient leaching (Nachmansohn, 2016). The rate at which the nutrients \nare removed from the soil solution, taken up by the plant roots or immobilised \nby microorganisms is influenced by water movement and leaching(Oliveira et \nal. 2002). Leaching of water soluble plant nutrients from the soil occurs mostly \nas a result of rainfall and irrigation water which wash the nutrients down beyond \nthe root-zone, thus depriving them of the nutrients. Saffigna and Philips (2006) \nconsidered leaching as the downward movement (with the drainage water) of \nmineral nutrients or soil waste materials. As nutrients leach downward beyond \nthe roots, they become unavailable for uptake by plant and are thereby lost from \nthe soil-plant system (Ah et al., 2009). Leaching losses especially, N and K, in \ntheir soluble forms is a problem in an area with a high amount of rainfall for crop \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 15\n\n\n\nproduction (Henson, 1999). Nutrient leaching losses have become a very serious \nissue in the plantation due to the rapid increase in the cost of fertilisers. Nutrients \nare important for plants growth and development, but if applied in excess, Cu and \nZn especially can cause surface and groundwater pollution (Pedrosa et al., 2017). \nThe movement of Cu and Zn in the soil profile depends largely on the physical \nand chemical properties of the soil and the physic-chemical properties of the metal \nions (Campos, 2010). The mobility of Cu and Zn in soil profile is low and leads to \ntheir accumulation on the soil surface, decreasing its leaching power (Pedrosa et \nal., 2017).  Campos (2010) also stressed that variation in pH, biological processes, \nand chemical toxicity of the Cu and Zn and environment also play an important \nrole in their availability and mobility within the soil.\n In view of the above facts, this study was carried out to determine the \neffects of different water table depths on nutrients (N, P, K, Mg, Ca, Cu, and Zn) \nleaching losses, subsidence and oil palm seedling growth cultivated on a tropical \npeat soil.\n\n\n\nMATERIALS AND METHODS\nExperimental Site \nThe experiment was conducted using lysimeters at Universiti Putra Malaysia \nCampus Bintulu Sarawak (3\u030a 12\u2032 13.58\u2033N, 113\u030a 4\u2032 16.96\u2033E).  The study area has a \nhumid tropic weather with yearly average of low and high temperatures of 23\u00b0C \nand 34\u00b0C, respectively. The annual precipitation of this area is 2200 mm (Sarawak \nMeteorological Department2014). Tropical peat soil was sampled from Taan oil \npalm plantation located at 3\u00b004\u2032 00.143\u2033N, 112\u00b0 54\u2032 21.515\u2033E. Based on the von \npost scale of H7 to H9, the peat soil was classified as well decomposed dark \nbrown to dark coloured sapric peat soil with a thickness of 0.5 to 3.0 m.\n\n\n\nDescription of Lysimeters and Set Up\nFifteen cylindrical field lysimeters constructed from  high-density polyethylene \n(HDPE) measuring 0.50 m in diameter and 1 m in height were set up to mimic \nthe natural condition of drained tropical peats. The size and shape of the lysimeter \nwas designed in such a way that it ensured satisfactory growth and development \nof the oil palm plants. The lysimeters were equipped with a water spillage opening \nthat was attached to clear tubes mounted on the outside of the vessel to collect the \nleachate, and to regulate and monitor the water table. Each of the lysimeters was \nthen filled with fresh peat soil to 1 m depth using a hydraulic excavator machine. \nThe lysimeters with the peat soil were left in the field for one week to make sure \nthe peat had settled before initiating the study. \n\n\n\nExperimental Design\nThe experiment was conducted in a randomised completely block design (RCBD) \nconsisting of five different water table depths as treatments, that is, 25, 40, 55, 70, \nand 85 cm from the peat surface with three replication each. The water table depth \nlevel in the experiment was controlled based on the oil palm root zone depths in \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201916\n\n\n\naccordance with the water table management system in place for oil palm grown \nin tropical peat soils. Water table depths were adjusted after rainfall events based \non the actual water table depths measured by using a measuring tape.\n Four-month old oil palm seedlings (Elaeis guineensis Jacq.) were planted \nin each lysimeter following estate procedures. N, P, K, and Mg were applied as \nurea (46%), rock phosphate (28%), muriate of potash (60%), and magnesium oxide \n(60.3%). Micronutrients were applied as Zincobor containing 3%, 6%, and 6% for \nZn and Cu respectively. Fertilisation schedules and rates were in accordance with \nestate practices. \n\n\n\nSoil Sample Preparation and Analysis \nThe soil samples collected during sampling were air-dried, ground and sieved \nusing a 2-mm sieve before use for analysis of selected physical and chemical \nproperties. Soil bulk density was determined using a core sampling method (Tan \n2005). Soil pH was determined using the potentiometric method at a  ratio of \n1:10 soil to distilled water (Peech 1965). Cation exchange capacity of the soil and \nexchangeable cations (K, Mg, Ca, Cu and Zn) were determined using ammonium \nacetate (leaching method). Total N from the soil was determined using the Kjeldahl \nmethod (Bremner, 1965). The dry combustion method (loss-on-ignition method) \nwas used to determine organic matter and total carbon from the soil sample. \nInorganic N (exchangeable NH4\n\n\n\n+ and NO3\n-) was determined using the method of \n\n\n\n2M KCl solution (Keeney and Nelson 1982). Single dry ashing method (Cottenie \n1980) was used to determine total P, K, Mg, Ca, Cu and Zn of soil. Soil available \nP was determined using a double acid method (Mehlich, 1953).\n\n\n\nDetermination of Nutrients Losses \nLeachate samples from the lysimeter were collected after rainfall in polyethylene \nbottles, which were washed once with leachate prior to final collection after every \nrainfall event, provided there was leaching subsequent to rainfall events. The pH \nof the leachate was immediately determined using the potentiometric method \n(Peech 1965), after which the samples were stored in the refrigerator prior to \nthe analysis. Micro\u2013Kjeldahl distillation method (Bremner, 1965) was used to \ndetermine total N concentration from the leachate collected. Phosphorous from \nthe leachate was filtered using Whatman No. 2 filter paper and determined using \nthe  blue colour method of Murphy and Riley (1962). Cations (K, Mg, Ca, Cu, \nand Zn) were determined using atomic absorption spectrophotometer (AAnalyst \n800, Perkin Elmer Instrument, Norwalk, CT). Growth of oil palm seedlings was \ndetermined by taking the dry weight of the seedling after the study. Tropical peat \nsubsidence was determined using the Principal and Criteria for the Production of \nSustainable Palm Oil (2013) method of inserting a long meter rule in each of the \nlysimeters and taking  the reading at the end of the study period.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 17\n\n\n\nTABLE 1 \nSelected chemical properties of sapric peat soil used in this study compared with\n\n\n\nthose from other studies\n\n\n\n2.5 Statistical Analysis\nStatistical analysis was employed following standard procedures for a randomised \ncomplete block design. Treatments effects were tested using analysis of variance \n(ANOVA) and the means of the treatments were compared using Tukey\u2019s test at p \n\u2264 0.05 using a Statistical Analysis System version 9.4 SAS (2008).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPhysio-chemical Characteristics of the Tropical Peat Soil in the Sampling Site\nThe chemical characteristics of the peat soil are presented in Table 1. Soil pH was \nless than 4, denoting the acidic conditions of the peat. Bulk density was within the \nrange of 0.05 to less than 0.5 g cm-3 in fabric and sapric peat as reported by Tie \nand Kueh (1979). The CEC of the peat soil could be high because of the formation \nof lignin-derivates during decomposition of organic materials as reported by \nAndriesse (1988). The peat soil had a very high organic matter content of nearly \n\n\n\n10 \n \n\n\n\nTABLE 1 \n\n\n\nSelected chemical properties of sapric peat soil used in this study compared with \nthose from other studies \n\n\n\nProperty This study Other studies \n\n\n\nBulk density (g cm-3) 0.35 0.05-0.5d \npHwater 3.3 3-4.5a \nOrganic matter (%) 93.8 99.1c \nTotal carbon (%) 54.6 12-60a \nTotal N (%) 0.8 0.3-4b \nC/N ratio  68.25 23.4c \nCEC (cmolc kg-1) \n 161.1 200a \n\n\n\n( mgkg-1)   \n            Total P 232.2 100-5000 b \n            Total K 206.9 10-8000 b \n            Total Mg 205.6 100-15000b \n            Total Ca 596.8 100-60000b \n            Total Cu 8.7 3-100b \n            Total Zn 28.0 10-4000b \n            Avail. P                    15.7 n.a \n            Exch. NH4\n\n\n\n+ 202.5 58.2c \n            Avail. NO3\n\n\n\n- 80 198.4c \n            Exch. K 135.0 n.a \n            Exch. Mg 166.5 n.a \n            Exch. Ca 421.7 n.a \n            Exch. Cu  1.6 n.a \n            Exch. Zn 9.1 n.a \n\n\n\nNotes: aAndriesse (1988); bLucas (1982); cMelling et al., (2007); dTie and Kueh \n(1979); n.a : not available. \n\n\n\n\n\n\n\nNutrients Leaching Losses \n\n\n\nThe highest loss of all nutrients was from the treatment with the highest water \n\n\n\ntable (25 cm) (Figure 1). The percentage decrease in leaching losses from the \n\n\n\nhighest (25 cm) to lowest (85 cm) water table depth were 22 % N, 67% P, 31% K, \n\n\n\n41% Mg, 27% Ca, 45% Cu, and 65% Zn respectively. For each water table, \n\n\n\nnutrients leaching losses decreased under low water table depth Peat soil was \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201918\n\n\n\n94 %. Total carbon content of the peat was 54.6 % while the low N content of 0.8 \n% resulted in a high C: N ratio of 68.25. Total C and N were all within the range \nas reported by Andriesse (1988). Total P, K, Mg, Ca, Cu and Zn was low and \nwithin the range as reported by Lucas (1982). The very low total concentration of \nexchangeable cations (Ca, Mg, K, Cu, and Zn) indicate that the exchangeable sites \nin peat are dominated by acidic cations (H+, Al3+, and Fe2+) and liming is necessary \nto reduce their acidic effect. A reported range for NH4\n\n\n\n+ and NO3\n- is not available. \n\n\n\nSoil available P, exchangeable K+, Mg2+, Ca2+, Cu2+ and Zn2+ were all low which \ncould be due to rapid uptake by plant at the site.\n\n\n\nNutrients Leaching Losses\nThe highest loss of all nutrients was from the treatment with the highest water \ntable (25 cm) (Figure 1). The percentage decrease in leaching losses from the \nhighest (25 cm) to lowest (85 cm) water table depth were 22 % N, 67% P, 31% \nK, 41% Mg, 27% Ca, 45% Cu, and 65% Zn respectively. For each water table, \nnutrients leaching losses decreased under low water table depth Peat soil was \nfound to have high N content, low C/N ratio value, which increased the rate of \nmineralisation, but had a low content  of  other nutrients such as P, K, Mg, Cu, and \nZn (Tayeb, 2005). As such, the fertility of tropical peat soil has to be improved \nthrough good water table depth management to reduce the loss of nutrients through \nleaching. For N to be available for plant uptake, organic N has to mineralised, \nwhich occurs under good aerated conditions enabling NH4\n\n\n\n+ to quickly oxidise into \nNO3\n\n\n\n- (Kurnain, 2005). The amount of N in the soil that is not adsorbed to the soil \nparticles is expected to move to high depths mostly as NH4\n\n\n\n+ and NO3\n-which can be \n\n\n\nleached out easily. Also, the seasonal changes in rainfall patterns and distribution \ncould also influence N leaching losses into the high water table depths (Rekha et \nal., 2011).\n Nitrogen leaching losses are mostly in the forms of NH4\n\n\n\n+ and NO3\n- and \n\n\n\nthe latter is a negative charge ion that cannot be bound to the functional groups \nin peat soil, thus rendering it susceptible to leaching as reported by Ruckauf et \nal., (2004). Owens et al. (2000) and Zhao et al., (2001) reported that N can also \nleach out as surface or subsurface flow. The decrease in concentration under low \nwater table depths could be a result of subsurface flow and sometimes due to the \nreduction of N through denitrification as previously reported by Spalding and \nParrot (1994) and Mohammed et al. (2003). Most of the N in peat soil surface \nis quickly leached, thus affecting the quality of surface water (Droogers et al., \n2007).\n P and K easily leach out from the peat soil due to their low adsorption \ncapacity (Maas, 1997). The ability of peat soil to retain P is very low making it \nnecessary for cations to bond with the functional group of peat with P. This results \nin P remaining on the exchange complex and preventing it from leaching. The \ndecrease in P losses under a low water table depth could be due to the function \nof base cations which serve as a bridge between ions and organic groups, thus \npreventing P from being leached (Maftuah et al., 2014). Moilanen et al. (2005) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 19\n\n\n\nalso reported that the application of base cations such as Ca and Mg on peat soils \ncan reduce the loss of P from fertilisers, because P is adsorbed by Ca and Mg in \nthe soil. High water table depths increase the immobilisation of P which in turn \nincreases the amount of P leached due to the increase in sorbed P. This is due to \nreduction and dissolution of Fe3+ oxides and hydrolysis of Al3+ phosphates within \nthe soil profile (Obour et al., 2011). Higher P leaching from the high water table \ncoincided with high amounts and intensity of rainfall encountered in this study. \nThis result is consistent with the finding of Obour et al. (2011) who also found \nthat P losses were higher in the higher soil depths (<30 cm). Terry et al. (1980) \nreported losses of P at 30 cm water table depth which was 20 times higher than \nP losses when the water table was at 90 cm depth. This finding is similar to that \nof Miyamoto et al. (2013) who reported high P leaching losses from peat soil \nunder flooded compared to no flood conditions. Martin et al. (1997) also found P \nleaching from Histosols to increase with increasing high water table depths\n As potassium is mobile in soils, it suffers from high amounts of leaching \nlosses. Application of fertiliser and the  extent of drainage water contribute \nsignificantly to K leaching losses (Alfaro et al., 2004). Potassium can easily \ndissolve and leach out from within the root zone because it exists in the ionic form \nof K+ (Rosenani et al., 2016). High K solubility means K is mobile and moves \nfreely with the draining water. Peat soil holds most strongly to positively charged \nnutrients except for K because of the weak attraction that exists between the peat \nsoil and K+. Consequently, K leaches easily from peat soil and becomes relatively \nlow in available form for plant uptake. As reported by Miyamoto et al. (2009), \nthe application of micro-nutrient fertiliser increased the leaching losses of K, Mg \nand Ca from a tropical peat soil which could be attributed to the replacement of \nexchangeable bases with the micronutrients. The finding of this study is similar \nto that of Damman (1978) who also found that K losses decrease under low water \ntable depths.\n The lysimeter results from this study suggest that the amount of Ca and Mg \nleaching losses from the soil does not depend on the initial Ca and Mg content \nin the soil, but on the amount of water moving through the soil. Therefore, high \namounts of these elements would be leached from soils with high water table \ndepths. Application of fertiliser increased the movement of Mg as well, but the \nconcentration of Mg during the leaching study in the lysimeter was lower as the \nwater table depth was low, and this could be the result of strong retention of Mg \nby soil particles (Vigovskis et al., 2015). The high volume of water and moisture \ncontent associated with a high water table led to increased dissolution of nutrients, \npeat and the applied fertiliser which  resulted in high Mg leaching losses compared \nto soils with a  low water table depth because the former soils would have lesser \nmoisture content. \n Application of fertiliser considerably increased the Ca content in the \nleachate and this could result in high Ca-release during the process of higher soil \norganic matter mineralisation (Vigovskis et al., 2015). A high water table depth \nresulted in a large volume of water which accelerated the dissolution of nutrients \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201920\n\n\n\nthat subsequently leached out easily from the soil. Low Ca leaching losses from \npeat with low water table depths could also be attributed to high CEC and organic \nmatter in the tropical peat soil (Table 1) which leads to Ca being adsorbed to the \nsurface forming solid complexes and preventing it from moving freely in soil \nsolution; it is therefore not leached out easily through the draining water. \n\n\n\nFig. 1: Mean cumulative losses of nutrients for different water table depths at 46\ndays of rainfall events. Means with different letters are significantly\n\n\n\ndifferent by Tukey\u2019s test at p \u2264 0.05).\n\n\n\n15 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1. Mean cumulative losses of nutrients for different water table depths at \n46 days of rainfall events. Means with different letters are significantly different \nby Tukey\u2019s test at p \u2264 0.05). \n\n\n\n\n\n\n\na ab bc c \nd \n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n25 40 55 70 85\n\n\n\nN\n (m\n\n\n\ng \nL-1\n\n\n\n) \n\n\n\nWater table depths (cm) \n\n\n\na a \nb \n\n\n\nc \n\n\n\nd \n\n\n\n0\n10000\n20000\n30000\n40000\n50000\n\n\n\n25 40 55 70 85\nP \n\n\n\n(m\ng \n\n\n\nL-1\n) \n\n\n\nWater table depths (cm) \n\n\n\na \nb b c \n\n\n\nd \n\n\n\n0\n\n\n\n10000\n\n\n\n20000\n\n\n\n30000\n\n\n\n40000\n\n\n\n25 40 55 70 85\n\n\n\nK\n (m\n\n\n\ng \nL-1\n\n\n\n) \n\n\n\nWater table depths (cm) \n\n\n\na \nb b \n\n\n\nc d \n\n\n\n0\n1000\n2000\n3000\n4000\n5000\n6000\n\n\n\n25 40 55 70 85\n\n\n\nM\ng \n\n\n\n(m\ng \n\n\n\nL-1\n) \n\n\n\nWater table depths (cm) \n\n\n\na \nb bc cd d \n\n\n\n0\n1000\n2000\n3000\n4000\n5000\n\n\n\n25 40 55 70 85\n\n\n\nC\na \n\n\n\n(m\ng \n\n\n\nL-1\n) \n\n\n\nWater table depths (cm) \n\n\n\na \n\n\n\nb b \nc c \n\n\n\n0\n20\n40\n60\n80\n\n\n\n100\n\n\n\n25 40 55 70 85\n\n\n\nC\nu \n\n\n\n(m\ng \n\n\n\nL-1\n) \n\n\n\nWater table depths (cm) \n\n\n\na \nb b b \n\n\n\nc \n\n\n\n0\n\n\n\n100\n\n\n\n200\n\n\n\n300\n\n\n\n400\n\n\n\n25 40 55 70 85\n\n\n\nZn\n (m\n\n\n\ng \nL-1\n\n\n\n) \n\n\n\nWater table depths (cm) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 21\n\n\n\n Copper and Zn are mostly retained in the soil and their mobility increases \nas the soil pH decreases (Linn and Elliot, 1988). A little of these nutrients can be \nleached out from the soil as a result of rainfall or irrigation. Movements of organic \nand inorganic constituents often follow Cu and Zn movement within the soil. The \nmovement of dissolved micro-nutrients like Cu and Zn through the soil profile as \na result of water application is solely related to either the structure or texture of \nthe soil as reported by Alvarez et al. (2001). Therefore, to explain the mobility and \nleaching of  Cu and Zn, it is necessary to look at the relative stability of  Cu and \nZn complexes in the soil (Gonzalez et al., 2015).\n Copper and Zn leaching losses in the soil have been reported to mainly \ndepend on the quantity of Cu and Zn applied, the type of clay mineral, and the \namount of organic matter content in the soil (Alvarez et al., 2001). The leaching \nof Cu and Zn in a cultivated soil under different water tables was observed to \nincrease after Cu and Zn fertilisers were added to the soil. Under high water \ntable depths, Cu and Zn can be easily reduced to soluble Cu and Zn sulphide \nwhich makes them mobile, facilitating leaching from the soil (Damman, 1978). \nAdditionally, a large amount of organic matter in the soil increases the amount of \ndissolved organic C and results in higher Cu and Zn losses within the soil solution \nas reported by Stephan et al. (2008). The high organic matter content in peat soil \nmay provide greater opportunities for Cu and Zn leaching losses. The presence \nof organic matter provides negative charges to the ground, and this in turn serves \nto maintain the adsorbment of positively charged Cu and Zn, which consequently \ndecreases their mobility in the soil profile (Pedrosa et al., 2017).\n\n\n\nNutrients Content in the Peat Soil\nExcept for total N, there was a significant difference between the different water \ntable depths for total peat nutrients content. Low water table depths showed high \nnutrient content compared with high water table depths (Figure 2). A higher \nconcentration of nutrients retained in the soil was observed under the low water \ntable depth, indicating low mobility of these elements in tropical peat soil.\n Low water table depth results in a rapid expansion of microbial activity \nbut raising of water table depth near to the peat surface, on the other hand, results \nin limited N utilisation by micro-organisms (Williams 1974). Decomposition of \norganic matter generally depends on  factors such as environmental conditions, \nsubstrate quality, presence of micro-organisms and availability of nutrients \n(Laiho, 2006). Regardless of the different water table depths, the concentration \nof total nutrients with the exception of N increased under low water table depths \nand this could be possible because a low water table depth in the peat surface \nwill decrease the water content, and consequently increase the oxygen content \nby air-filled porosity (Boggie, 1977). Conditions for the aerobic decomposition \nof organic matter, which is faster than anaerobic decomposition, improved due \nto the changes in low water table depth (Laiho, 2006). Leaching studies on the \neffects of water table depth on nutrients have revealed significant changes in \nthe concentration of nutrient content of tropical peat soil. The concentration of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201922\n\n\n\nFig. 2: Mean (\u00b1 s.e) of different water table depths (25, 40, 55, 70 and 85 cm) on total \n(a) N, (b) P, (c) K, (d) Mg, (e) Ca, (f) Cu, and (g) Zn retained in the soil after the study \n\n\n\nperiod.  Means with different letters are significantly different by Tukey\u2019s test at p \u2264 0.05\n\n\n\n17 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2. Mean (\u00b1 s.e) of different water table depths (25, 40, 55, 70 and 85 cm) \non total (a) N, (b) P, (c) K, (d) Mg, (e) Ca, (f) Cu, and (g) Zn retained in the soil \n\n\n\na a a a \n\n\n\na \n\n\n\n0\n0.2\n0.4\n0.6\n0.8\n\n\n\n1\n\n\n\n25 40 55 70 85\n\n\n\nN\n (%\n\n\n\n) \n\n\n\nWater table depths (cm) \n\n\n\na) \n\n\n\nc c \nb a a \n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n25 40 55 70 85\nTo\n\n\n\nta\nl P\n\n\n\n (%\n) \n\n\n\nWater table depths (cm) \n\n\n\nb) \n\n\n\nb b ab b \na \n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n25 40 55 70 85\n\n\n\nTo\nta\n\n\n\nl K\n (%\n\n\n\n) \n\n\n\nWater table depths (cm) \n\n\n\nc) \n\n\n\nd cd \nb \n\n\n\nc \n\n\n\na \n\n\n\n0\n0.2\n0.4\n0.6\n0.8\n\n\n\n1\n1.2\n\n\n\n25 40 55 70 85\n\n\n\nTo\nta\n\n\n\nl M\ng \n\n\n\n(%\n) \n\n\n\nWater table depths (cm) \n\n\n\nd) \n\n\n\nb \nab \n\n\n\nab \nab \n\n\n\na \n\n\n\n0\n0.4\n0.8\n1.2\n1.6\n\n\n\n2\n\n\n\n25 40 55 70 85\n\n\n\nTo\nta\n\n\n\nl C\na \n\n\n\n(%\n) \n\n\n\nWater table depths (cm) \n\n\n\ne) \n\n\n\nc c c \nb \n\n\n\na \n\n\n\n0\n0.3\n0.6\n0.9\n1.2\n1.5\n\n\n\n25 40 55 70 85\n\n\n\nTo\nta\n\n\n\nl C\nu \n\n\n\n(%\n) \n\n\n\nWater table depths (cm) \n\n\n\nf) \n\n\n\nc c \n\n\n\nb b \na \n\n\n\n0\n0.1\n0.2\n0.3\n0.4\n0.5\n\n\n\n25 40 55 70 85\n\n\n\nTo\nta\n\n\n\nl Z\nn \n\n\n\n(%\n) \n\n\n\nWater table depths (cm) \n\n\n\ng) \n\n\n\nnutrients is known to decrease after leaching but the extent of leaching depends \non water table depths. The initial increase in nutrients from low water table depths \nis consistent with the increase in leaching of nutrients observed under high water \ntable depth.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 23\n\n\n\n Oil palm Seedling Growth\nThere was a significant difference between the treatments with  the 55 cm water \ntable depth showing highest plant growth compared with 25, 40, 70 and 85 cm \n(Figure 3). No significant difference was recorded between 40 and 85 cm water \ntable depths.  Lowest plant growth was recorded at 25 cm water table depth. \n\n\n\nFig. 3: Mean (\u00b1 s.e) total dry plant biomass at harvesting time from the treatments of \n25, 40, 55, 70 and 85 cm. Biomass means with different letters are significantly \n\n\n\ndifferent by Tukey\u2019s at p \u2264 0.05\n\n\n\n19 \n \n\n\n\npreserve water for as long as possible during the dry season. The results of our \n\n\n\nstudy are similar  to that of Sharma (2013) who also found that FFB yield was \n\n\n\nhigh when the water table depth was at 50-75 cm compared with 0-25, 25-50, 50-\n\n\n\n75, 75-100, and >100 cm water table depths. Other studies on Myriga gale L. \n\n\n\nplants reported similar highest growth response when the water table was at 29 cm  \n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Mean (\u00b1 s.e) total dry plant biomass at harvesting time from the \ntreatments of 25, 40, 55, 70 and 85 cm. Biomass means with different letters are \nsignificantly different by Tukey\u2019s at p \u2264 0.05 \n\n\n\n\n\n\n\nPeat Subsidence  \n\n\n\nPeat subsidence increased with decreasing water table depth, as seen in Figure 4, \n\n\n\nwhere the highest peat subsidence is seen in the 55 to 85 cm depths while the \n\n\n\nlowest subsidence is seen in the 25 and 40 cm water table depths. The subsidence \n\n\n\nrecorded in the low water table depths could be attributed to high organic matter \n\n\n\nc \nbc \n\n\n\na \n\n\n\nb \nbc \n\n\n\n0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n\n\n\n25  40 55  70 85  \n\n\n\nBi\nom\n\n\n\nas\ns w\n\n\n\nei\ngh\n\n\n\nt (\ng \n\n\n\npe\nr p\n\n\n\nla\nnt\n\n\n\n) \n\n\n\nWater table depths (cm)  \n\n\n\nTwo patterns of plant growth were noticed. First, there was limited plant growth \nat 25 and 40 cm water table depths and this could be as the result of the roots \nbeing submerged in water, thus reducing soil aeration. The second pattern of plant \ngrowth was at 70 and 85 cm which was also limited and this could be due to \ndrier soil that made lesser water available for nutrients uptake by the plants. The \nhighest seedlings growth was found at 55 cm being found at adepth of 0-50 cm \n(Melling et al., 2007).  Gurmit et al., (1987) and Tayeb (2005) recommend that \nthe water table be controlled and maintained within a  depth of 50 to 70 cm from \nthe peat surface. A good water table management practice for oil palm cultivation \non peat soil which is one that effectively maintains the water table depth of 40-\n60 cm (Lim et al. 2012). The physiology and growth of oil palm seedlings is \nenhanced with the application of fertilisers. Oil palm active roots should not be \nwaterlogged, as very low or high water content within the palm rooting zone will \nseriously affect nutrient uptake and FFB production (Lim et al. 2012). Therefore, \nthe water table should be constantly monitored to be able to control the access \nsurface and subsurface water rapidly during the raining season and be able to \npreserve water for as long as possible during the dry season. The results of our \nstudy are similar  to that of Sharma (2013) who also found that FFB yield was \nhigh when the water table depth was at 50-75 cm compared with 0-25, 25-50, \n50-75, 75-100, and >100 cm water table depths. Other studies on Myriga gale L. \nplants reported similar highest growth response when the water table was at 29 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201924\n\n\n\ncm compared with 3, 15, 29, 42, 52, 69, and 79 cm water table depths (Schwintzer \nand Lancelle 1983). Our study also confirmed the results presented by Rivard et \nal. (1990) on marsh reed grass (Calamagrostis canadensis) where total biomass \ngrowth was greatest at the 20 cm water table depth compared to water table depths \nfrom 10 and 40 cm. Another study with similar findings was that of  Zhu et al. \n(2013) on the growth of soybeans who found highest growth at 2 m water table \ndepth compared with 0.2, 0.4, 0.6, 0.8, 1, 3, 4, and 5 m water table depths. \n\n\n\nPeat Subsidence \nPeat subsidence increased with decreasing water table depth, as seen in Figure \n4, where the highest peat subsidence is seen in the 55 to 85 cm depths while the \nlowest subsidence is seen in the 25 and 40 cm water table depths. The subsidence \nrecorded in the low water table depths could be attributed to high organic matter \ndecomposition due to the availability of oxygen which speeds up the activities of \nmicro-organisms (Laiho and Pearson, 2016). High water table depths could also \ndecrease the mineralisation rate of organic matter from peat soil and therefore \nreduce the subsidence (Tan and Ambak, 1989; Best and Jacobs, 1997; Wosten et \nal., 1997; Potvin et al., 2015). Our study results  are also similar to the findings of \nMillette and Broughton (1984) who found high organic soil subsidence when the \nwater table depth was low, from 0.6 to 0.9 m from the surface.\n \n\n\n\nFig. 4: Mean (\u00b1 s.e) effects of treatments (25, 40, 55, 70 and 85 cm) after the study, \nand subsidence mean with different letters being significantly different  by T\n\n\n\nukey\u2019s test at p \u2264 0.05\n\n\n\n20 \n \n\n\n\ndecomposition due to the availability of oxygen which speeds up the activities of \n\n\n\nmicro-organisms (Laiho and Pearson, 2016). High water table depths could also \n\n\n\ndecrease the mineralisation rate of organic matter from peat soil and therefore \n\n\n\nreduce the subsidence (Tan and Ambak, 1989; Best and Jacobs, 1997; Wosten et \n\n\n\nal., 1997; Potvin et al., 2015). Our study results  are also similar to the findings of \n\n\n\nMillette and Broughton (1984) who found high organic soil subsidence when the \n\n\n\nwater table depth was low, from 0.6 to 0.9 m from the surface. \n\n\n\n\n\n\n\nFigure 4. Mean (\u00b1 s.e) effects of treatments (25, 40, 55, 70 and 85 cm) after the \nstudy, and subsidence mean with different letters being significantly different  by \nTukey\u2019s test at p \u2264 0.05 \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\nDifferent soil water table depths significantly affected the amount of nutrient \n\n\n\nlosses in oil palm cultivation on tropical peat soil. Leaching losses of nutrients \n\n\n\nb b \n\n\n\na a \n\n\n\na \n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\n25 40 55 70 85\n\n\n\nSu\nbs\n\n\n\nid\nen\n\n\n\nce\n (c\n\n\n\nm\n) \n\n\n\n Water table depth (cm) \n\n\n\nCONCLUSION\nDifferent soil water table depths significantly affected the amount of nutrient \nlosses in oil palm cultivation on tropical peat soil. Leaching losses of nutrients \nincreased with higher water table depths, but the water table depth of 55 cm \ngave the highest oil palm growth because the active roots of oil palm were not \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 25\n\n\n\nREFERENCES \nAbat, M., M.J. McLaughlin, J.K.Kirby and S.P.Stace. 2012. Adsorption and desorption \n\n\n\nof copper and zinc in tropical peat soils of Sarawak, Malaysia. Geoderma 175: \n58-63.\n\n\n\nAh, T., K.Y. Mohd, M.M Nik, G. Joo and G.Huang. 2009. 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"\n\nINTRODUCTION\nSoils, the most endangered component of the terrestrial ecosystem, is habitat for \n\n\n\narising from human activities. In this respect, heavy metals are among the \nserious pollutants in soil due to their toxicity, persistence, and bio-accumulation \n(Morton-Bermea et al. \n\n\n\nSpatial Distribution of Lead in Calcareous Soils and Rice \nSeeds of Khuzestan, Iran\n\n\n\n \nA. Chamannejadian1*, A. A. Moezzi1, G.A. Sayyad1, A. Jahangiri 2 \n\n\n\nand A. Jafarnejadi 3 \n\n\n\n1Department of Soil Science, College of Agriculture, Shahid Chamran University, \nAhvaz, Iran\n\n\n\n2Nanotechnology Research Center and Department of Medicinal Chemistry, School \nof Pharmacy, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran\n\n\n\n3Soil and Water Department, Khuzestan Agricultural and Natural\nResearch Resource Center\n\n\n\nABSTRACT\nIn this study, soil samples of different places of Khuzestan province were \nsampled. The sampling positions were registered and determined through GPS. \n\n\n\napplied, and lognormal kriging were used to map the spatial patterns of Pb. Both \n\n\n\nrespectively. The mean content of extractable soil lead and plant lead in all the \n-1, respectively. Both Pb-DTPA \n\n\n\nand Pb in rice seeds had moderate spatial dependence due to the effects of natural \nfactors including parent material, topography and soil type. The statistical survey \nto determine the possible correlation between some soils characteristics with lead \ndistribution in rice seeds was done through SPSS statistical software. The results \nshowed that close relationships existed between Pb-DTPA with organic matter \n\n\n\nKey Words: Geostatistics, lead, calcareous soil, anaerobic rice, spatial\n                         variability\n\n\n\n___________________\n*Corresponding author : Email: chamannejadian@gmail.com\n\n\n\n\n\n\n\n\n116\n\n\n\ncountries have been affected by water and soil pollution crisis due to heavy \nmetals contamination (Smith et al.\nof these types of pollution through different actions, for example, application of \nmetal-contaminated sewage sludge, fertilizers, and animal manure on plants. All \nthese actions can result in high concentrations of heavy metals in agricultural \nsoils (Wu et al.\nespecially in agricultural lands has led to soil degradation and environmental \n\n\n\n-1, with a \nmean of 15 mg kg-1\n\n\n\ncaused by low-level exposure to Pb have been extensively documented. Such \n\n\n\nthe central nervous system (Needleman 1983; Needleman et al.\nthe Pb and Cd contents in rice samples of North Iran were found to be higher than \n\n\n\nplant and its entry into the food chain is mainly dependent on the absorption of \n\n\n\nby plants is the solution the plant roots are in contact with. In general, the more \nsoil heavy metal concentration, the more it is accessible to the plant (McBride \n\n\n\ncharacteristics such as soil pH, organic matter, clay content, soil salt concentration, \nchloride concentration, carbonate and lime as well as the plant species (Chaney \n\n\n\net al. et al.\nand Ghafoor et al\n\n\n\nin soil variables and has become a useful tool for the study of spatial uncertainty \nand hazard assessment (McGrath et al\nThe geostatistical approach is a popular application to analyze spatial structure \nand spatial distribution of soil heavy metals (Imperato et al. et al.\nAhsan et al et al. et al\nestimator for spatial data analysis as it is unbiased and minimizes total uncertainty \n\n\n\nagricultural and industrial areas of Iran, the spatial distribution of soil heavy \nmetal concentrations is still largely unknown. The aim of the present work was to \nelucidate the spatial distribution of heavy metals in soils of Khuzestan province. \n\n\n\nand rice seeds.\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area\nThis study focuses on the cultivated land of Khuzestan Province which is located \n\n\n\nA. Chamannejadian, A. A. Moezzi, G.A. Sayyad, A. Jahangiri and A. Jafarnejadi\n\n\n\n\n\n\n\n\n117\n\n\n\nShoushtar, Ramhurmoz and Baghmalek (Fig. 1)\nselected in the study region and soil and anaerobic rice seed samples were taken. \n\n\n\nSoil Sampling and Analysis\n\n\n\nwas collected from each soil type (Fig. 1). When sampling, soils in top layer \nfrom 6 to 8 points in each site of an area were collected and then fully mixed, and \n\n\n\nplastic spade to avoid any heavy metal contamination. Gravels and coarse organic \nmatter or plant root residues were removed, soil samples were air dried and passed \n\n\n\nFig. 1:  Distribution of sampling locations\n\n\n\nLead in Rice of Calcareous Soil\n\n\n\n\n\n\n\n\n118\n\n\n\ntitration method (Oustan et al\n3:HClO4\n\n\n\nprocedure (Simmons et al.\nLead concentrations in plant and soil samples were analyzed by Inductively \n\n\n\nprocedure. Lead concentrations in rice seeds were compared with the guide values \n-1 et al. \n\n\n\nGeostatistical methods\n\n\n\ncorrelation such that samples close together in space are more alike than those that \n\n\n\nto measure the spatial variability of a regionalized variable, and provides the input \nparameters for the spatial interpolation of kriging (Jiachun et al.\n\n\n\nwere skewed, the experimental semivariograms were developed using transformed \n\n\n\nused in the study. The semi-variogram function is expressed as:\n\n\n\n\n\n\n\nnumber of pairs of sample points separated by the lag distance h (Jiachun et al. \n\n\n\nlinear, spherical, Gaussian and linear to sill models, using statistical indices (i.e. \n\n\n\nthe highest r  and the lowest RSS values was selected. Before kriging, the spatial \ndependency of Pb in soil and rice seed was evaluated using Nugget/Sill ratio. In \ntheory, the Nugget/Sill ratio in the geostatistics can be regarded as a criterion to \n\n\n\ntwo thresholds for the relative strength index of spatial correlations. The variable \n\n\n\nA. Chamannejadian, A. A. Moezzi, G.A. Sayyad, A. Jahangiri and A. Jafarnejadi\n\n\n\ny(h 1\n2N(h\n\n\n\nN(h\n\n\n\ni =1\n\n\n\nZ (xi - Z(xi+h\n\n\n\n\n\n\n\n\n119\n\n\n\net al.\nThereafter, Pb concentrations in soil and rice seeds were estimated for unsampled \nlocations using lognormal ordinary kriging method.\n\n\n\nThe most important trait of kriging that separates it from other estimators \n\n\n\n\n\n\n\nvariable amounts in measured points. \nThe kriging plots and the error estimation plots are measured and calculated \n\n\n\nusing calculating system of GS+ software. \n\n\n\nData analysis\nData sets were analyzed with different software packages. The descriptive statistical \nparameters were calculated with statistical software. Maps were produced with \narcGIS software and its extension of Spatial Analyst.  The geostatistical analyses \nand the probability calculation were carried out with GS+ software package.\n\n\n\nRESULTS \n\n\n\nDescriptive Parameters and Probability Distribution of the Raw Data Set\nThe descriptive statistics of studied variable are given in Table 1. In this study, \nthe direct relationship between extractable lead by DTPA and the absorbed lead \n\n\n\nmethod with the lowest RSS and highest r (Table 3 , Fig. 2a and 2b). At the end \nof the kriging process, the resulting grid values were back-transformed to create \ninterpolated lead distribution maps of soils and rice seeds in Khuzestan province \n(Fig. 2c and 2d). \n\n\n\n \nGeostatistical analysis\n\n\n\nto predict attribute values at unsampled points of cultivated land. The experimental \n\n\n\npresented in Figure 3. The results showed that Pb-DTPA and Pb in rice seeds \n\n\n\nDTPA and Pb in rice seeds are summarized in Table 3. The Nugget/Sill ratios for \n\n\n\nLead in Rice of Calcareous Soil\n\n\n\n\n\n\n\n\nSpatial Distributions and Risk Assessment\nMapping metal contents is often a preliminary step towards decision making, \n\n\n\nfor crop growth. For soil pollution, a straightforward approach is to delineate all \ncontaminated locations where the estimated pollutant content exceeds the guide \n\n\n\n-1\n\n\n\nand Pb in rice seeds in paddy soils of Khuzestan province generated from their \nsemi-variograms.\n\n\n\nTABLE 1\nStatistical summary of heavy metal contents in the topsoil collected from the study area\n\n\n\nTABLE 3\n\n\n\nA. Chamannejadian, A. A. Moezzi, G.A. Sayyad, A. Jahangiri and A. Jafarnejadi\n\n\n\n Soil attributes N Min Max Mean SD  CV (%) Skewness Kurtosis \npH 70 6.8 7.7 7.22 0.22 3.03 0.43 -0.64 \nECe (dS m-1) 70 1.2 40.5 7.63 6.76 8.86 3.47  1. 45 \nClay (%) 70 16 52 33.41 9.04 2. 70 -0.02 -0.89 \nCCE 70 22.4 49.91 48.45 3.55 7.33 -6.03   4.31 \nOM (%) 70 0.28 1.69 0.81 0.25 3.08 1.08   2.21 \nPb-seed (\u00b5g kg-1) 70 100 219 122 15.18 12.46 3.89    2. 38 \n\n\n\nPb-DTPA (\u00b5g kg-1) 70 199 1450 703 250 3.55  0.71   -0.60 \nMin - minimum, Max- maximum, SD - standard deviation, CV - coef cient of variation \n\n\n\n  ECe Clay pH CCE OM Pb-Seed Pb-DTPA \nECe  1    \nClay  0.127 1     \npH -0.183 -0.056 1   \nCCE 0.090   0.013    -0.137 1 \nOM  -0.139 0.375** -0.084 0.012 1   \n\n\n\nPb-Seed - -0.035 -0.048 0.263*   0.716**    -0.061 1 \n\n\n\nPb-DTPA 0.032  0.046 0.149  0.084   0.376** 0. 68** 1 \n\n\n\n*p<0.05, **p< 0.01, OM -  organic matter, CCE - Calcium carbonate equivalent ,  \nPb-DTPA extractable soil lead, Pb-Seed lead in rice seed. \n\n\n\n \u00a0\n\n\n\nSoil attributes Model C0 C + C0 C0/C + C0 \nEffective \n\n\n\nranges \n(km) \n\n\n\nR2 RSS \n\n\n\nPb-DTPA  Gaussian 0.0407 0.07 0.419 85 0.505 0.001076 \n\n\n\nPb-Seed  Gaussian  0.0419 0.081 0.483 89.5 0.513 0.00166 \nC0  nugget variance, C structural variance, C + C0  sill variance  \n\n\n\n\n\n\n\n\nand related kriging maps (c and d) (\u00b5g kg-1).\n\n\n\nLead in Rice of Calcareous Soil\n\n\n\n\n\n\n\n\nDISCUSSION\nThe spatial variability of soil attributes can be affected by both soil pedogenic factors \n\n\n\nrevealing that the anthropogenic factors such as industrial production, fertilization \nand other soil management practices can change their spatial correlation after a \n\n\n\nnatural and anthropogenic contributions, the correlations between Pb-DTPA and \n\n\n\nproperties on soil and rice seed Pb. Hence, different anthropogenic activities can \nhave an affect on soil and seed variability by affecting other soil properties.\n\n\n\nResearchers reported that the heavy metals absorbable concentrations \nespecially lead, is related to both amount and type of organic matter which exists \nin soil. Therefore, the analysis of organic heavy metals type, which results in \nthe release of these elements in the bioavailable form, which might be toxic for \nagricultural crops, needs to be carried out (Jing at el.\nDel Castilho et al. \n\n\n\nionic strength increase and soluble organic matters. In a research conducted on the \neffect of liming on lead uptake by wheat, it was concluded that the high amount of \nliming can decrease metal uptake by wheat. The absorbed lead accumulated more \nin the stem and seed (Tlusto\u0161 et al.\nin the soil increases as the amount of liming increases due to pH increase, and \nalso, because of competition between calcium with lead in calcareous soils, the \nlead transported to the plant is lower. The same phenomenon could have occurred \nin the calcareous soils in this study. This emphasizes the indirect relationship \nbetween plant lead and the liming amount within soil, and, the direct relationship \nbetween extractable lead rate and soil liming amount. Thus, as the heavy metals \nconcentration of soil increases, their accessibility to plant will increase (De \n\n\n\nThe mean and maximum contents of Pb in rice seed are shown in Table 1. Lead \n\n\n\n-1 Pb suggested by the Iranian Ministry of Health (Janati et al.\nplaces, there is no Pb health problem resulting from Khuzestan rice consumption. \nThus, Pb in this paddy ecosystem is unlikely to exhibit a risk of environmental \npollution or threat to human health. However, there were sixteen samples that had \nPb contents which exceeded the guide value. Most of these sites are located in \ncentral and western parts of Khuzestan province (Fig. 2c), suggesting that some \ncultivated lands in Khuzestan province need to be monitored.\n\n\n\nA. Chamannejadian, A. A. Moezzi, G.A. Sayyad, A. Jahangiri and A. Jafarnejadi\n\n\n\n\n\n\n\n\nCONCLUSIONS\nDetailed geostatistical analysis on lead distributions will help to gather more \ninformation on the variability of Pb concentrations in soil and rice seeds in this \nregion. Sixteen rice seed samples in the area have Pb concentrations higher than \n\n\n\n-1. Lead concentrations in soil and rice seed samples show moderate \n\n\n\nkriging methods provide the best results of Pb estimation with regard to the lowest \nstatistical measures. Over the long history of land utilization, the spatial variability \n\n\n\nand Pb extracted by DTPA indicated low risks for environmental pollution and \nhuman health. Both natural and anthropogenic factors have contributed to the \ngenesis of the pollution process. The association of the higher soil heavy metal \nconcentrations with soil texture and soil organic matter is largely attributed to to \nthe higher metal-holding capacity of clay and SOM when compared with sand. \nThe probability map produced based on kriging provides useful information for \nhazard assessment in the decision-making process.\n\n\n\nACKNOWLEDGMENT\nWe gratefully acknowledge support of this work by the Shahid Chamran University \nof Ahvaz.\n\n\n\nREFERENCES\n\n\n\nBulletin of Environmental \nContamination and Toxicology\n\n\n\nAll\nprofessional, Glasgow, UK.\n\n\n\nA\nand lead sorption in three tropical soils. Journal of Environmental  Quality. 31: \n581-589.\n\n\n\nChaney, R. L. and S. B. Hornick. 1978. Accumulation and effects of cadmium on \ncrops Cadmium 77. Proc 1st Int Cadmium Conf, San Francisco. London: Metal \n\n\n\nDe Temmerman, L. O., H. Hoeing and P. O. Scokart. 1984. 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Haiping \n\n\n\nstudy of Changxing, China. Environmental Geology\n\n\n\nJi\nchemical extractability and plant uptake. Journal of Environmental  Quality. \n\n\n\nK\nInternational Journal of Agriculture and Biology. \n\n\n\nL\n\n\n\nEnvironmental Pollution\n\n\n\nMatheron, G. 1963. Principles of geostatistics. Economic  Geology.\n\n\n\nMcBride, M.  B. 1995.  Toxic  metal  accumulation  from  agricultural  use  of  sludge:  \nAre  USEPA  regulations Protective. Journal of  Environmental Quality. \n\n\n\nM\nassessment on soil lead in Silvermines Area, Ireland. Environmental Pollution. \n\n\n\nM\nconcentrations in surface soils from Mexico City. Bulletin of Environmental \nContamination and Toxicology.\n\n\n\nN\nterm effects of exposure to low doses of lead in childhood: an 11-year follow-up \nreport. The New England Journal of Medicine\n\n\n\nA. Chamannejadian, A. A. Moezzi, G.A. Sayyad, A. Jahangiri and A. Jafarnejadi\n\n\n\n\n\n\n\n\nNeedleman, H.L. 1983. Low level lead exposure and neuropsychological performance. \nIn: Rutter M, Russell Jones R, editors. Lead versus health sources and effects of \n\n\n\nOu\nRemoval of heavy metals from a contaminated calcareous soil using oxalic and \nacetic acids as chelating agents. IPCBEE., vol.8\n\n\n\nR\nComputers and Electronics \n\n\n\nin Agriculture\n\n\n\nSi\nlevels of cadmium and zinc in paddy soils and elevated levels of cadmium in \nrice grain downstream of a zinc mineralized area in Thailand: Implications for \npublic health. Environmental Geochemistry and Health\n\n\n\nSmith, E., R. Naidu and A. M. Alston. 1998. Arsenic in the soil environment: A \nReview. Advance Agronomy. 64:149-195\n\n\n\nT\neffect of liming on cadmium, lead and zinc uptake reduction by spring wheat \ngrown in contaminated soil. Plant, Soil and Environment\n\n\n\nWe\nAcids and Nitrate in the Long-Distance Transport of Cobalt in Xylem Saps \nof Alyssum murale and Trifolium subterraneum.  Biological Trace Element \nResearch. 131: 165-176\n\n\n\nW Geostatistics for environmental scientists. Wiley, \n\n\n\nWu,\nthe three gorges area: Multivariate and geostatistical analyses. Environmental \nMonitoring and Assessment\n\n\n\nYu\nby Paddy Rice and Soil Available Cd under Water Flooding as Affected by \nNitrogen Fertilizer. ICBBE, 4th International Conference on Bioinformatics and \nBiomedical Engineering.\n\n\n\nZh\nsoil heavy metal sources from anthropogenic activities and pollution assessment \nof Fuyang County, China. Environmental Monitoring and Assessment. 154: \n\n\n\nZimdahl, R.L. and R.K. Skogerboe. 1997. Behavior of lead in soil. Environmental \nScience and Technology\n\n\n\nLead in Rice of Calcareous Soil\n\n\n\n\n\n"
"\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffect of Organic-based & Foliar Fertilisers on Cocoa Grown on an Oxisol in Malaysia\n\n\n\n29\n\n\n\nISSN: 1394-7990\nMalaysian Society of Soil ScienceMalaysian Journal of Soil Science Vol.11 : 29-43 (2007)\n\n\n\nEffect of Organic-based and Foliar Fertilisers on\nCocoa (Theobroma cacao L.) Grown on an Oxisol in Malaysia\n\n\n\n N. Noordiana1,  S. R. Syed Omar1*,  J. Shamshuddin1\n\n\n\n&  N. M. Nik Aziz2\n\n\n\n1Department of Land Management, Faculty of Agriculture\n Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia\n\n\n\n2Cocoa Research and Development Centre, Malaysian Cocoa Board\nP.O. Box 34, 28000 Temerloh, Pahang, Malaysia\n\n\n\nABSTRACT\nThe Malaysian cocoa industry is facing many problems due to cocoa being\ngrown on marginal soils, such as Ultisols and Oxisols.  These soils are\ngenerally acidic, low in basic cations and also low in soil cation exchange\ncapacity.  A field study was undertaken to investigate the effect of organic-\nbased and foliar fertilisers on soil fertility improvement, the growth of\nmatured trees, yield and quality of cocoa grown on an Oxisol in Malaysia.\nThe treatments (with four replications) consisted of T1: NPK (fertiliser)\n(control), T2: organic-based fertiliser + NPK, T3: foliar + NPK, T4: foliar +\nCa-foliar + NPK and T5: organic-based fertiliser + foliar + Ca-foliar + NPK\napplied on approximately 5-year-old cocoa plants located at the Malay-\nsian Cocoa Board Experimental Station, Jengka, Pahang.  The results showed\nthat the combination of these fertilisers gave negative response on the\ngrowth, yield and quality of cocoa.  For clone PBC 130, T2 (organic-based\nfertiliser + NPK) gave greater pod weight compared to other treatments.\nManganese toxicity is possibly the most limiting factor observed in this\nstudy.\n\n\n\nKeywords: Organic-based fertiliser, foliar fertiliser, cocoa yield, bean\nquality, Mn toxicity\n\n\n\nINTRODUCTION\nThe average yield of cocoa in Malaysia has shown very little increase and in\nsome  areas,  it has actually decreased.  One of the problems related to the\ndecrease in cocoa bean production over the years is low soil productivity, achieving\nonly 0.98 tonnes per hectare in 2003.  This is far behind the targeted yield of 1.5\ntonnes per hectare (Malaysian Cocoa Board 2003).  Some cocoa in the country\nis grown on highly weathered soils known as Ultisols and Oxisols.  The low\nproductivity of the soils is due to acid reaction, low cation exchange capacity\nand high Al.  The area under cocoa has decreased drastically.  The decrease is\n\n\n\n* Corresponding author: Email:syedomar@agri.upm.edu.my\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM29\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200730\n\n\n\n N. Noordiana, S. R. Syed Omar, J. Shamshuddin & N. M. Nik Aziz\n\n\n\nattributed principally to low soil productivity, diseases and poor prices of cocoa\nbeans.  Areas which are suitable for cocoa are replanted with other crops such as\noil palm and rubber while marginal soils are used for new cocoa production\n(Malaysian Agricultural Directory and Index 2003).  Another problem related to\npoor yield of cocoa beans is cherelle wilt.  Cocoa production depends on the\namount of flowers produced and the percentage of flowers that turns into cherelles\nand successfully become pods.  Normally, successful cherelles that form pods\nare only a small quantity (1 to 5 %) from the whole amount of flowers produced.\nAbout 60 - 93% of potential pods will vanish because of cherelle wilt.  Cherelle\nwilt is a fruit thinning mechanism (Kasran and Amirudin 1993).  Competition\nbetween cherelles and new shoots is one of the factors which causes cherelle\nwilt.  This may be due to competition to get nutrients and water not only among\nnew cherelles but also among cherelles and new shoots (Omran 1988).  This\nphenomenon  may be also related to nutrient deficiencies or an imbalance in\nnutrient supply.\n\n\n\nBoron deficiency can cause incomplete formation of cocoa pod, but this\nproblem cannot be solved solely by supplying boron since other nutrients may\nalso be important.  There might be some antagonistic effects between the nutri-\nents which may disturb nutrient functions such as nutrient absorption and trans-\nlocation.  Kasran (1989) has found that the wilted cherelles have lower nitrogen,\ncalcium, magnesium, copper, manganese, zinc and boron concentrations com-\npared to non-wilted cherelles.  Calcium deficiencies in cocoa may lead to\nirresistance to fungus attack, and cherelle wilt will continuously occur even though\nthe formation of pods has completed.  Therefore, one of the alternatives that can\nbe taken to reduce cherelle wilt and increase cocoa yield is to apply fertilisers\ncontaining all the nutrients needed for cocoa in sufficient amounts.  Application\nof organic fertilisers produces a variety of organic acids during its decomposi-\ntion which form stable complexes with aluminium and iron, thereby, blocking\nphosphorus retention sites and resulting in higher phosphorus use efficiency in\ncrops (Sharma et al. 1990).  Besides the organic-based fertilisers, foliar fertilisers\nmay aid in supplying all the nutrients needed, especially during the pollination\nstage and development of pods. Besides macronutrients (nitrogen, phosphorus,\npotassium,  calcium  and  magnesium), cocoa needs micronutrients such as cop-\nper, manganese, zinc, iron and molybdenum for growth (National Agricultural\nResearch Centre 1986).  Cocoa yield can be increased with the application of\nfoliar fertilisers because many soils and fertilisers cannot supply  enough  nutri-\nents  during  the  productive stage.  Therefore, a field experiment was conducted\nto determine the effects of fertiliser treatments on soil fertility (Oxisol), the\ngrowth of matured cocoa, yield and cocoa bean quality.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSite and Soil\nThis experiment was conducted at the Malaysian Cocoa Board Experimental\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM30\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffect of Organic-based & Foliar Fertilisers on Cocoa Grown on an Oxisol in Malaysia\n\n\n\n31\n\n\n\nStation, Jengka, Pahang.  The soil was Segamat Series (Jabatan Pertanian\nSemenanjung Malaysia 1993), which belongs to the clayey, oxidic,\nisohyperthermic family of Typic Hapludox (according to soil taxonomy).\n\n\n\nExperimental Treatments and Design\nOrganic-based, foliar and calcium-foliar fertilisers were used in this study.  The\nfive  treatments  with four replications consisted of T1: NPK (control), T2:\norganic-based fertiliser + NPK, T3: foliar + NPK, T4: foliar + Ca-foliar + NPK\nand T5: organic-based fertiliser + foliar + Ca-foliar + NPK. Treatments T3, T4\nand  T5  (which consisted of foliar application) were applied monthly.  The\napplication of foliar treatments was done in May 2004 to August 2004 (for the\nfirst season) and in December 2004 to June 2005 (for the second season), using\nrecommended rates.  Leaves were sprayed using motorised sprayer before 10\no\u2019clock in the morning, since stomata was known to be opened during this pe-\nriod.  The application of foliar fertiliser was carried out on the whole cocoa\ncrop, including the leaves, trunks, flowers and pods.  Organic-based fertiliser\n(derived from composted sugar-cane waste with pH of about 8.0) was applied\nagain after 6 months (November 2004) from the first application which was in\nMay 2004.  NPK compound fertiliser (fertiliser grade, 12:12:17:2) was applied\n3 times per year (starting in May 2004, followed by September 2004 and finally\nin January 2005), following MARDI\u2019s (1990) recommendation rate of 400 g per\nplant.  Organic-based and NPK compound fertilisers were incorporated into the\nsoil.  The PBC 130 and KKM 22 cocoa clones were approximately 5 years old at\nthe time of the trial.  Randomised complete block design (RCBD) was used in\nthis experiment with the block perpendicular to the soil gradient.  Each plot\ncontained 12 plants and was surrounded by guard rows with planting distance of\n3 m x 3 m.\n\n\n\nPlant Sampling, Soil Sampling, Preparation and Analyses\nIn order to determine the nutrient status of cocoa, soil and leaves of clone KKM\n22 and PBC 130 at the experimental plots were sampled in April 2004 before the\ntrial.  A random sampling of the soil was done with a distance of 3 m x 3 m,\nusing a stainless steel auger. Samples were taken from two depths, 0 \u2013 20 cm and\n20 \u2013 40 cm.  The soil samples were air-dried in a plastic bag.  After air-drying,\nsoil samples were crushed in a pestle and mortar, then sieved through a 2.0 mm\nsieve for the analyses of soil pH and other nutrients.  The samples were stored in\na plastic container.  The analysis of soil and leaf samples was done in replicates\nwith 4 replications.  The soil chemical and nutrient analyses included pH:water\nratio 1:2.5  (McLean 1973),  total N using Kjeldahl method (Bremner 1965),\navailable P using Bray and Kurtz no. 2 method (Olsen and Sommers 1982),\nexchangeable K, Ca and Mg using ammonium acetate leaching method (Thomas\n1982), available Cu, Fe, Mn and Zn using double acid method \u2013 Mehlich no. 1\n(Soil and Plant Analysis Council 2000) and B using hot water soluble extractable\nmethod (Bingham 1982).\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM31\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200732\n\n\n\n N. Noordiana, S. R. Syed Omar, J. Shamshuddin & N. M. Nik Aziz\n\n\n\nFor plant tissue, the fourth leaf from the apex of matured branch was taken\nas indicator leaf (15 leaves per tree) as this is the most active part of the cocoa\nplant that absorbs nutrient from the soil (Denamany and Rosinah 1994).  The\npieces  of  leaflet were washed carefully with distilled water to remove any\ncontamination from the leaf surface prior to extraction.  Minimal washing was\ndone to avoid rapid leaching of plant nutrients during washing.  The plant samples\nwere oven-dried at 65\u00b0C for about 7 days.  Leaf samples were then ground and\npassed through a 1.0 mm sieve and stored in a plastic container.  These samples\nwere then used for the determination of macro- and micro-nutrients in the leaf.\nNutrient content, P, K, Ca, Mg, Cu, Fe, Mn, Zn an B, in plant tissue was deter-\nmined using the dry ashing method (Gupta 2004) and the Kjeldahl method (Bremner\nand Mulvaney 1982) was used to determine total N.\n\n\n\nDetermination of Pods Production, Actual Yield of Harvested Mature Pods and\nPotential Calculated Yield\nThe number of pods/tree was taken to determine the production of pods.  Success-\nful cherelles (pods) having a circumference of 20 \u2013 25 cm were counted and\nmarked with blue paint.  The number of pods was recorded every month, start-\ning from 2 months after all treatments were started (T1 to T5 were applied\nbeginning May 2004).  Mature pods were harvested monthly, starting from Oc-\ntober 2004 until June 2005.  Total number of harvested mature pods was re-\ncorded to determine the actual yield of harvested mature pods.  A minimum of 30\npods/plot was sampled to determine potential calculated yield.  Then, the cocoa\nbeans were taken out from the pods and  total number of beans was counted.\nThe counted beans were then fermented for 7 days.  One hundred beans were\nsampled randomly from the fully fermented beans.  These 100 beans were weighed\nto determine the wet weight of fermented beans and  were then oven-dried at 80\n\u00baC for 24 hours.  They were weighed to determine the dry weight of fermented\nbeans.  The potential yield was estimated by the following formula given by\nOsman et al. (1994):\n\n\n\nDry bean yield (g / pod per year), N = (M / L x K)\nJ\n\n\n\nJ = total number of pods sampled\nK = wet weight of fermented beans from J\nL = wet weight of 100 fermented beans sampled from K\nM = dry weight of 100 fermented cocoa beans\nTherefore, yield / plot per year = N x total number of pods/plot\n\n\n\n= (M / L x K) x total number of pods/plot\nJ\n\n\n\nPod and Bean Quality Determination\nPod analysis was conducted by the method of Sapiyah (1994) where 5 pods\nwere sampled out from each clone.  This was done for each pod yield of each\ncocoa clone that was harvested during peak harvest, which was in December\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM32\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffect of Organic-based & Foliar Fertilisers on Cocoa Grown on an Oxisol in Malaysia\n\n\n\n33\n\n\n\n2004. Parameters determined were length and diameter of pods, weight of har-\nvested pods, average number of beans/pod, single dry bean weight, pod index\n(number of pods to produce 1 kg dry beans) and dry weight of shell (derived\nfrom bean samples).\n\n\n\nStatistical Analysis\nThe analysis of variance (ANOVA) was done using PROC ANOVA of the Statis-\ntical Analysis System (SAS 2001) and the Tukey Test was used for mean value\ncomparison if the treatments were significantly different.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Properties at Experimental Site\nThe chemical properties of the untreated soil are given in Table 1.  Total organic\ncarbon content was higher in the subsoil (0.53 %) compared to the topsoil (0.44\n%) which was rather unusual.  This may be due to soil erosion.  It has been\nsuggested that a minimum requirement of about 2 % organic carbon in the top 15\ncm of soil is good for cocoa growth (Ahenkorah 1979).  However, this soil\ncontained organic carbon below the recommended levels.\n\n\n\nAvailable P was 33 mg/kg in the topsoil, while the value in the subsoil was 26\nmg/kg.  Soil exchangeable calcium, magnesium and potassium were low.  Soil\npH was below 5.  As such, much Al was expected to be present in the soil\nsolution.  Extractable Mn was very high, with a value of 113 mg/kg in the\ntopsoil.  It was even higher in the subsoil (> 149 mg/kg).  These values of Mn\nare in the toxic range for plant growth (Graham et al. 1988).\n\n\n\nTABLE 1\nThe initial chemical characteristics of the Segamat soil\n\n\n\nVariables Topsoil (0 \u2013 20 cm) Subsoil (20 \u2013 40cm)\n\n\n\npH (H2O) 4.80 \u00b1 0.07 4.53 \u00b1 0.04\nN % 0.14 \u00b1 0.01 0.13 \u00b1 0.01\n\n\n\nAvailable P (mg/kg) 33.14 \u00b1 2.44 26.18 \u00b1 1.58\nExch. Ca (cmolc/kg soil) 2.31 \u00b1 0.18 1.63 \u00b1 0.16\nExch. Mg (cmolc/kg soil) 2.04 \u00b1 0.14 1.40 \u00b1 0.10\nExch. K (cmolc/kg soil) 0.16 \u00b1 0.01 0.12 \u00b1 0.01\nExch. Al (cmolc/kg soil) 0.15 \u00b1 0.03 0.32 \u00b1 0.03\n\n\n\nOrganic C % 0.44 \u00b1 0.06 0.53 \u00b1 0.10\nCu (mg/kg) 4.59 \u00b1 0.68 4.97 \u00b1 0.89\nFe (mg/kg) 55.25 \u00b1 3.37 69.53 \u00b1 3.46\nMn (mg/kg) 113.23 \u00b1 7.84 149.60 \u00b118.79\nZn (mg/kg) 2.51 \u00b1 0.08 2.98 \u00b1 0.26\nB (mg/kg) 0.18 \u00b1 0.01 0.20 \u00b1 0.09\n\n\n\n* Mean \u00b1 standard error\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM33\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200734\n\n\n\n N. Noordiana, S. R. Syed Omar, J. Shamshuddin & N. M. Nik Aziz\n\n\n\nPlant Tissue Nutrient Status\nData in Table 2 show the concentrations of macro- and micro-nutrients in the\ncocoa leaf.  The concentrations of nitrogen, phosphorus, potassium, calcium\nand magnesium for tissue samples of cocoa clone KKM 22 and PBC 130 were\nmore or less the same.\n\n\n\nHowever, a slight difference can be seen for micronutrient concentrations.\nThe concentrations of copper, iron and manganese in the tissue samples of clone\nPBC 130 were a bit higher compared to clone KKM 22.  Zinc and boron concen-\ntrations in the tissue samples of clone KKM 22 showed higher values compared\nto clone PBC 130.  The N concentration was low, but the concentration of Mn\nwas high, with the value at a toxic level.\n\n\n\nEffects of Fertiliser Treatment on Pod Production\nThe development of the pods to maturity took 5 \u2013 6 months from flowering to\nfull ripeness.  Data in Fig. 1 show that pod harvesting continued at all times of\nthe year with two peak harvest periods observed per year, which were in Octo-\nber and December.  Both clones, the KKM 22 and PBC 130, showed a similar\ntrend of pod production throughout the year.\n\n\n\nData in Fig. 2 which refers to pod production on a yearly basis (July 2004 to\nJune 2005) show that all treatments gave no significant difference to pod pro-\nduction for clone PBC 130.  However, for clone KKM 22, treatment T5 (or-\nganic-based fertiliser + foliar + Ca-foliar + NPK) showed a slight increase in the\nnumber of pods per plot compared to other treatments.\n\n\n\nEffects of Fertiliser Treatment on Yield\nDuring the 8 months of the harvesting period (October 2004 to June 2005), the\ndry weight of beans per plot over the year were not significantly different in all\ntreatments  for both clones, KKM 22 and PBC 130 (Fig. 3).  Since pod  produc-\ntion was not affected by the treatments, it is reasonable to assume that the yield\n\n\n\nTABLE 2\nThe initial nutrient status of the cocoa leaves\n\n\n\nVariables Clone KKM 22 Clone PBC 130\n\n\n\nN % 1.78\u00b1 0.02 1.73\u00b1 0.03\nP % 0.12\u00b1 0.01 0.14\u00b1 0.01\nK % 0.34\u00b1   0.01 0.38\u00b1 0.01\nCa % 0.71\u00b1   0.02 0.72\u00b1 0.03\nMg % 0.52\u00b1   0.01 0.51\u00b1 0.01\nCu (ppm) 11.49\u00b1 0.27 12.79\u00b1 0.80\nFe (ppm) 68.42\u00b1 2.72 70.91\u00b1 0.86\nMn (ppm) 1058.91\u00b1 60.23  1119.27\u00b1 29.02\nZn (ppm) 55.98\u00b1 2.62 51.79\u00b1 1.68\nB (ppm) 16.9 \u00b1 1.39 15.93\u00b1 0.86\n\n\n\n* Mean \u00b1 standard error\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM34\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffect of Organic-based & Foliar Fertilisers on Cocoa Grown on an Oxisol in Malaysia\n\n\n\n35\n\n\n\nFig. 1: Pods production at different months (means followed by a common letter within\ncocoa clone are not significantly different at 5 % level by Tukey Test)\n\n\n\nFig. 3: Dry bean yield according to treatments (means followed by a common letter are not\nsignificantly  different at 5 % level by Tukey Test)\n\n\n\nFig. 2: Pods production according to treatments (means followed by a common letter within\ncocoa clone are not significantly different at 5 % level by Tukey Test)\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM35\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200736\n\n\n\n N. Noordiana, S. R. Syed Omar, J. Shamshuddin & N. M. Nik Aziz\n\n\n\nper plot gave the same results.  It has been described in the formula that yield\nestimation depends on pod production.  Hence, it can be assumed that the yield\nwas limited by yield components such as pod number per tree, bean number per\npod and bean size which contributed to the weight of cocoa beans.\n\n\n\nEffects of Fertiliser Treatment on Pod and Bean Quality\nThe results of pod and bean qualities suggest that there are no significant differ-\nences between the treatments (Table 3).  However, data in Table 4 show that for\nclone PBC 130, treatment T2 (organic-based fertiliser + NPK blue) gave rela-\ntively higher mean value than the others, with 690.96 g for pod weight.  Single\ndry bean weight for both clones was between 1.2 g to 1.6 g, which was beyond\nthe expected average bean weight of 1 g to 1.2 g.  This shows that bigger bean\nsize has been produced and this gives a rough indication of a good yield.  Both\nclones gave a relatively lower pod index ranging from 16 to 22 which was within\n\n\n\nTABLE 3\nEffects of treatments on the variables measured (clone KKM 22)\n\n\n\nKKM 22\n\n\n\nVariables T1 T2 T3 T4 T5\n\n\n\nPod length (cm) 20.89a 19.84a 20.91a 20.36a 19.64a\n\n\n\nPod diametre (cm) 9.81a 9.65a 9.73a 9.58a 9.73a\n\n\n\nWeight of pod (g) 626.90a 607.90a 593.45a 607.80a 629.40a\n\n\n\nDry weight of shell (g) 10.15a 9.50a 7.05a 7.88a 11.33a\n\n\n\nNumber of beans/pod 36.35a 34.65a 37.85a 37.65a 36.80a\n\n\n\nSingle dry bean weight (g) 1.31a 1.31a 1.30a 1.20a 1.34a\n\n\n\nPod index 21.06a 22.13a 20.53a 22.40a 20.73a\n\n\n\n(Means followed by a common letter within the row are not significantly different at 5 % level\nby Tukey Test)\n\n\n\nTABLE 4\nEffects of treatments on the variables measured (clone PBC 130)\n\n\n\nPBC 130\n\n\n\nVariables T1 T2 T3 T4 T5\n\n\n\nPod length (cm) 22.26a 23.51a 21.73a 22.95a 23.00a\n\n\n\nPod diametre (cm) 9.25b 9.62ab 9.79ab 9.46ab 9.18b\n\n\n\nWeight of pod (g) 596.49ab 690.96a 643.30ab 622.52ab 567.70b\n\n\n\nDry weight of shell (g) 10.30a 8.75a 9.28a 11.55a 7.85a\n\n\n\nNumber of beans/pod 39.80a 38.55a 40.35a 36.75a 38.30a\n\n\n\nSingle dry bean weight (g) 1.57a 1.66a 1.64a 1.61a 1.51a\n\n\n\nPod index 16.56a 15.84a 15.72a 17.19a 17.47a\n\n\n\n(Means followed by a common letter within the row are not significantly different at 5 % level\nby Tukey Test)\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM36\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffect of Organic-based & Foliar Fertilisers on Cocoa Grown on an Oxisol in Malaysia\n\n\n\n37\n\n\n\nthe range of an acceptable pod index of 25 and below.  This result indicates the\namount of pods needed to produce 1 kg dry beans.  Pod index of clone PBC 130\nin treatment T3 (foliar + NPK) gave the lowest value of 15.72 which was related\nto the highest number of beans per pod of 40.35 for the same treatment.  The\ngreatest number of beans per pod was found to give the lowest pod index.\n\n\n\nEffects of Fertiliser Treatments on Soil Fertility\nSince all the treatments did not give significant results on either yield or quality of\ncocoa, further soil and leaf analysis was carried out to identify the problem of\nyield limitation by determining the concentration of macro- and micro-nutrients.\nFrom Table 5, it is observed that the soil pH is between 4.4 and 5.0, which is\nlower than the recommended adequate range.  The concentrations of phospho-\nrus, potassium, calcium, copper, iron, zinc and boron in soil were sufficient for\ncrop growth in all treatments.  In contrast, the concentrations of nitrogen and\nmagnesium in soil were below the adequate range.  Soil manganese concentra-\ntion was much higher than the adequate range.  It is known that the optimum pH\nfor cocoa is 6.5 and the soils within the range of 5.5 \u2013 7.0 should be selected\nwhere major nutrients and trace elements will be available.  With increasing\nacidity, the major nutrients, phosphorus in particular, become less available\nand others like iron, manganese, copper and zinc become more available, possi-\nbly creating toxicity.  With soil pH below 6, manganese reserves in soils may\ndissolve, leading to toxicity.  Crop removal helps make soils more acidic by\ndepleting the reserves of calcium, magnesium and potassium.  Although manga-\nnese can be concentrated in various soil horizons, higher manganese levels are\noften reported for soils rich in iron and/or organic matter, which accumulate in\nthe topsoil as a result of its fixation by organic matter (Demirevska-Kepova et\nal. 2004).\n\n\n\nEffects of Fertiliser Treatments on Plant Nutrient Status\nData in Table 6 indicate that concentrations of nitrogen and potassium in plant\nwere relatively low.  The concentrations of phosphorus, iron, boron and zinc in\nplant were sufficient.  An excess of calcium, magnesium and copper concentra-\ntions in plant can be seen.  Furthermore, manganese concentration was about 2-\nto 3-fold higher than the sufficient range, and this trend was also observed in the\nsoil manganese content.  According to Tan (1996), a manganese concentration\nof more than 500 mg/kg in plant tissue was excessive and may cause toxicity to\nplants.\n\n\n\nIn the natural environment, plant growth is often adversely affected by a\nnumber of factors.  One of the occasional factors limiting crop production on\nacid soil is manganese toxicity which comprises about 30 % of the world\u2019s total\nland area especially in mineral soils at low pH or in waterlogged soils (Mengel et\nal. 2001).  Generally, the proportion of exchangeable manganese steeply increases\nas soil pH decreases with the proportion of manganese oxides and manganese\nbound to manganese and iron oxides decreasing (Tanaka and Navasero 1966).\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM37\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200738\n\n\n\n N. Noordiana, S. R. Syed Omar, J. Shamshuddin & N. M. Nik Aziz\n\n\n\nTA\nB\n\n\n\nLE\n 5\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\n s\nta\n\n\n\ntu\ns \n\n\n\nin\n s\n\n\n\noi\nl \n\n\n\naf\nte\n\n\n\nr \ntre\n\n\n\nat\nm\n\n\n\nen\nts\n\n\n\nSo\nil \n\n\n\nre\nqu\n\n\n\nir\nem\n\n\n\nen\nt\n\n\n\nA\nde\n\n\n\nqu\nat\n\n\n\ne \nra\n\n\n\nng\ne\n\n\n\nT\n1\n\n\n\nT\n2\n\n\n\nT\n3\n\n\n\nT\n4\n\n\n\nT\n5\n\n\n\nD\n0-\n\n\n\n20\nD\n\n\n\n20\n-4\n\n\n\n0\nD\n\n\n\n0-\n20\n\n\n\nD\n20\n\n\n\n-4\n0\n\n\n\nD\n0-\n\n\n\n20\nD\n\n\n\n20\n-4\n\n\n\n0\nD\n\n\n\n0-\n20\n\n\n\nD\n20\n\n\n\n-4\n0\n\n\n\nD\n0-\n\n\n\n20\nD\n\n\n\n20\n-4\n\n\n\n0\n\n\n\n*p\nH\n\n\n\nw\n5.\n\n\n\n5 \n- \n\n\n\n7.\n0\n\n\n\n4.\n7a\n\n\n\n4.\n4a\n\n\n\n5.\n0a\n\n\n\n4.\n5a\n\n\n\n4.\n7a\n\n\n\n4.\n5a\n\n\n\n4.\n9a\n\n\n\n4.\n6a\n\n\n\n5.\n0a\n\n\n\n4.\n6a\n\n\n\n*%\n N\n\n\n\n0.\n2 \n\n\n\n- \n0.\n\n\n\n5\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n0.\n\n\n\n1a\n\n\n\n*P\n (\n\n\n\nm\ng/\n\n\n\nkg\n)\n\n\n\n12\n -\n\n\n\n 2\n0\n\n\n\n12\na\n\n\n\n13\na\n\n\n\n31\na\n\n\n\n15\na\n\n\n\n34\na\n\n\n\n32\na\n\n\n\n13\na\n\n\n\n10\na\n\n\n\n38\na\n\n\n\n36\na\n\n\n\n*K\n (\n\n\n\nm\ng/\n\n\n\nkg\n)\n\n\n\n59\n -\n\n\n\n 1\n76\n\n\n\n47\na\n\n\n\n78\na\n\n\n\n92\na\n\n\n\n53\nab\n\n\n\n73\na\n\n\n\n53\nab\n\n\n\n85\na\n\n\n\n48\nab\n\n\n\n92\na\n\n\n\n30\nb\n\n\n\n*C\na \n\n\n\n(m\ng/\n\n\n\nkg\n)\n\n\n\n34\n -\n\n\n\n 6\n8\n\n\n\n33\na\n\n\n\n51\na\n\n\n\n58\na\n\n\n\n55\na\n\n\n\n45\na\n\n\n\n51\na\n\n\n\n61\na\n\n\n\n42\na\n\n\n\n65\na\n\n\n\n42\na\n\n\n\n*M\ng \n\n\n\n(m\ng/\n\n\n\nkg\n)\n\n\n\n30\n -\n\n\n\n 6\n0\n\n\n\n6a\n10\n\n\n\na\n8a\n\n\n\n9a\n8a\n\n\n\n9a\n12\n\n\n\na\n10\n\n\n\na\n10\n\n\n\na\n12\n\n\n\na\n\n\n\n**\nC\n\n\n\nu \n(m\n\n\n\ng/\nkg\n\n\n\n)\n2 \n\n\n\n- 6\n4a\n\n\n\n4a\n4a\n\n\n\n4a\n2a\n\n\n\n4a\n5a\n\n\n\n5a\n5a\n\n\n\n4a\n\n\n\n**\n*F\n\n\n\ne \n(m\n\n\n\ng/\nkg\n\n\n\n)\n30\n\n\n\n -\n 5\n\n\n\n0\n41\n\n\n\na\n35\n\n\n\na\n38\n\n\n\na\n29\n\n\n\na\n25\n\n\n\na\n30\n\n\n\na\n41\n\n\n\na\n33\n\n\n\na\n39\n\n\n\na\n28\n\n\n\na\n\n\n\n**\n**\n\n\n\nM\nn \n\n\n\n(m\ng/\n\n\n\nkg\n)\n\n\n\n20\n -\n\n\n\n 4\n0\n\n\n\n14\n8a\n\n\n\n92\na\n\n\n\n14\n2a\n\n\n\n54\na\n\n\n\n50\na\n\n\n\n74\na\n\n\n\n10\n0a\n\n\n\n87\na\n\n\n\n37\na\n\n\n\n83\na\n\n\n\n**\nZn\n\n\n\n (\nm\n\n\n\ng/\nkg\n\n\n\n)\n1.\n\n\n\n5 \n\u2013 \n\n\n\n5.\n0\n\n\n\n2a\n2a\n\n\n\n4a\n2a\n\n\n\n2a\n2a\n\n\n\n3a\n3a\n\n\n\n2a\n4a\n\n\n\n*B\n (\n\n\n\nm\ng/\n\n\n\nkg\n)\n\n\n\n1 \n- 3\n\n\n\n1a\n1a\n\n\n\n1a\n1a\n\n\n\n1a\n1a\n\n\n\n1a\n1a\n\n\n\n1a\n1a\n\n\n\nM\nea\n\n\n\nns\n f\n\n\n\nol\nlo\n\n\n\nw\ned\n\n\n\n b\ny \n\n\n\na \nco\n\n\n\nm\nm\n\n\n\non\n le\n\n\n\ntte\nr \n\n\n\nw\nith\n\n\n\nin\n th\n\n\n\ne \nro\n\n\n\nw\n a\n\n\n\ncc\nor\n\n\n\ndi\nng\n\n\n\n to\n s\n\n\n\noi\nl d\n\n\n\nep\nth\n\n\n\n a\nre\n\n\n\n n\not\n\n\n\n s\nig\n\n\n\nni\nfic\n\n\n\nan\ntly\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nt a\nt 5\n\n\n\n %\n le\n\n\n\nve\nl b\n\n\n\ny \nTu\n\n\n\nke\ny \n\n\n\nTe\nst\n\n\n\n* \nB\n\n\n\noo\nke\n\n\n\nr \nTr\n\n\n\nop\nic\n\n\n\nal\n S\n\n\n\noi\nl \n\n\n\nM\nan\n\n\n\nua\nl (\n\n\n\nLa\nnd\n\n\n\non\n 1\n\n\n\n98\n4)\n\n\n\n**\n M\n\n\n\nic\nro\n\n\n\nnu\ntri\n\n\n\nen\nt a\n\n\n\nss\nes\n\n\n\nsm\nen\n\n\n\nt a\nt t\n\n\n\nhe\n c\n\n\n\nou\nnt\n\n\n\nry\n le\n\n\n\nve\nl: \n\n\n\nan\n in\n\n\n\nte\nrn\n\n\n\nat\nio\n\n\n\nna\nl s\n\n\n\ntu\ndy\n\n\n\n (\nSi\n\n\n\nlla\nnp\n\n\n\n\u00e4\u00e4\n 1\n\n\n\n99\n0)\n\n\n\n**\n* \n\n\n\nTr\nac\n\n\n\ne \nEl\n\n\n\nem\nen\n\n\n\nts\n in\n\n\n\n S\noi\n\n\n\nls\n a\n\n\n\nnd\n P\n\n\n\nla\nnt\n\n\n\ns \n(K\n\n\n\nab\nat\n\n\n\na-\nPe\n\n\n\nnd\nia\n\n\n\ns \n20\n\n\n\n01\n)\n\n\n\n**\n**\n\n\n\n M\nan\n\n\n\nga\nne\n\n\n\nse\n in\n\n\n\n S\noi\n\n\n\nls\n a\n\n\n\nnd\n P\n\n\n\nla\nnt\n\n\n\ns \n(G\n\n\n\nra\nha\n\n\n\nm\n e\n\n\n\nt a\nl. \n\n\n\n19\n88\n\n\n\n)\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM38\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffect of Organic-based & Foliar Fertilisers on Cocoa Grown on an Oxisol in Malaysia\n\n\n\n39\n\n\n\nTA\nB\n\n\n\nLE\n 6\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\n s\nta\n\n\n\ntu\ns \n\n\n\nin\n l\n\n\n\nea\nf \n\n\n\naf\nte\n\n\n\nr \ntre\n\n\n\nat\nm\n\n\n\nen\nts\n\n\n\nN\nut\n\n\n\nri\nen\n\n\n\nts\n*S\n\n\n\nuf\nfi\n\n\n\nci\nen\n\n\n\nt \nra\n\n\n\nng\ne\n\n\n\nT\n1\n\n\n\nT\n2\n\n\n\nT\n3\n\n\n\nT\n4\n\n\n\nT\n5\n\n\n\nK\nK\n\n\n\nM\nPB\n\n\n\nC\nK\n\n\n\nK\nM\n\n\n\nPB\nC\n\n\n\nK\nK\n\n\n\nM\nPB\n\n\n\nC\nK\n\n\n\nK\nM\n\n\n\nPB\nC\n\n\n\nK\nK\n\n\n\nM\nPB\n\n\n\nC\n22\n\n\n\n13\n0\n\n\n\n22\n13\n\n\n\n0\n22\n\n\n\n13\n0\n\n\n\n22\n13\n\n\n\n0\n22\n\n\n\n13\n0\n\n\n\n%\n N\n\n\n\n2.\n0 \n\n\n\n- \n2.\n\n\n\n5\n1.\n\n\n\n8a\n2.\n\n\n\n0a\n1.\n\n\n\n8a\n1.\n\n\n\n9a\n1.\n\n\n\n9a\n1.\n\n\n\n8a\n1.\n\n\n\n9a\n1.\n\n\n\n9a\n1.\n\n\n\n8a\n2.\n\n\n\n1a\n\n\n\n%\n P\n\n\n\n0.\n1 \n\n\n\n- \n0.\n\n\n\n3\n0.\n\n\n\n1a\n0.\n\n\n\n2a\n0.\n\n\n\n1a\n0.\n\n\n\n2a\n0.\n\n\n\n1a\n0.\n\n\n\n2a\n0.\n\n\n\n2a\n0.\n\n\n\n2a\n0.\n\n\n\n1a\n0.\n\n\n\n2a\n\n\n\n%\n K\n\n\n\n1.\n3 \n\n\n\n-2\n.2\n\n\n\n0.\n4a\n\n\n\n0.\n5a\n\n\n\n0.\n4a\n\n\n\n0.\n5a\n\n\n\n0.\n5a\n\n\n\n0.\n5a\n\n\n\n0.\n4a\n\n\n\n0.\n5a\n\n\n\n0.\n4a\n\n\n\n0.\n5a\n\n\n\n%\n C\n\n\n\na\n0.\n\n\n\n3 \n- \n\n\n\n0.\n6\n\n\n\n0.\n7a\n\n\n\n0.\n6a\n\n\n\n0.\n7a\n\n\n\n0.\n6a\n\n\n\n0.\n7a\n\n\n\n0.\n7a\n\n\n\n0.\n7a\n\n\n\n0.\n6a\n\n\n\n0.\n9a\n\n\n\n0.\n9a\n\n\n\n%\n M\n\n\n\ng\n0.\n\n\n\n2 \n- \n\n\n\n0.\n5\n\n\n\n0.\n6a\n\n\n\n0.\n6a\n\n\n\n0.\n6a\n\n\n\n0.\n6a\n\n\n\n0.\n6a\n\n\n\n0.\n6a\n\n\n\n0.\n7a\n\n\n\n0.\n6a\n\n\n\n0.\n7a\n\n\n\n0.\n7a\n\n\n\nFe\n (\n\n\n\npp\nm\n\n\n\n)\n60\n\n\n\n -\n 2\n\n\n\n00\n52\n\n\n\na\n65\n\n\n\na\n56\n\n\n\na\n61\n\n\n\na\n69\n\n\n\na\n62\n\n\n\na\n81\n\n\n\na\n65\n\n\n\na\n75\n\n\n\na\n71\n\n\n\na\n\n\n\nM\nn \n\n\n\n(p\npm\n\n\n\n)\n50\n\n\n\n -\n 3\n\n\n\n00\n84\n\n\n\n9a\n81\n\n\n\n4a\n88\n\n\n\n9a\n65\n\n\n\n8a\n12\n\n\n\n20\na\n\n\n\n95\n9a\n\n\n\n97\n4a\n\n\n\n86\n0a\n\n\n\n93\n4a\n\n\n\n85\n9a\n\n\n\nB\n (\n\n\n\npp\nm\n\n\n\n)\n25\n\n\n\n -\n 7\n\n\n\n0\n36\n\n\n\na\n60\n\n\n\na\n44\n\n\n\na\n55\n\n\n\na\n51\n\n\n\na\n61\n\n\n\na\n61\n\n\n\na\n61\n\n\n\na\n59\n\n\n\na\n64\n\n\n\na\n\n\n\nC\nu \n\n\n\n(p\npm\n\n\n\n)\n8 \n\n\n\n- 1\n2\n\n\n\n20\na\n\n\n\n24\na\n\n\n\n18\na\n\n\n\n22\na\n\n\n\n22\na\n\n\n\n21\na\n\n\n\n24\na\n\n\n\n24\na\n\n\n\n19\na\n\n\n\n19\na\n\n\n\nZn\n (\n\n\n\npp\nm\n\n\n\n)\n20\n\n\n\n -\n 1\n\n\n\n00\n29\n\n\n\na\n40\n\n\n\nab\n47\n\n\n\na\n41\n\n\n\nab\n35\n\n\n\na\n33\n\n\n\nb\n48\n\n\n\na\n44\n\n\n\nab\n48\n\n\n\na\n52\n\n\n\na\n\n\n\nM\nea\n\n\n\nns\n f\n\n\n\nol\nlo\n\n\n\nw\ned\n\n\n\n b\ny \n\n\n\na \nco\n\n\n\nm\nm\n\n\n\non\n le\n\n\n\ntte\nr \n\n\n\nw\nith\n\n\n\nin\n th\n\n\n\ne \nro\n\n\n\nw\n a\n\n\n\ncc\nor\n\n\n\ndi\nng\n\n\n\n to\n c\n\n\n\noc\noa\n\n\n\n c\nlo\n\n\n\nne\n a\n\n\n\nre\n n\n\n\n\not\n s\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\ntly\n d\n\n\n\niff\ner\n\n\n\nen\nt a\n\n\n\nt 5\n %\n\n\n\n le\nve\n\n\n\nl b\ny \n\n\n\nTu\nke\n\n\n\ny \nTe\n\n\n\nst\n\n\n\n* \nPl\n\n\n\nan\nt a\n\n\n\nna\nly\n\n\n\nsi\ns \n\n\n\nha\nnd\n\n\n\nbo\nok\n\n\n\n I\nI \n\n\n\n(M\nill\n\n\n\ns \nan\n\n\n\nd \nJo\n\n\n\nne\ns \n\n\n\n19\n91\n\n\n\n)\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM39\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200740\n\n\n\n N. Noordiana, S. R. Syed Omar, J. Shamshuddin & N. M. Nik Aziz\n\n\n\nIt has been found in field experiments that manganese toxicity limits the\nyield potential.  The presence of highly toxic manganese concentration both in\nsoil and leaf samples indicate that the manganese concentration in soil and plant\nshow a fair degree of closeness with plant performance.  The results from soil\nanalysis in field experiments show that the manganese concentration in the leaf\nis generally associated with the high manganese found in the soils.  Although\nmanganese toxicity has been rarely reported in the acidic soils of Malaysia, the\nmanganese toxicity symptoms in rubber trees, planted in the field on soils with\nhigh total manganese has been observed by Bachik et al. (1984).  Manganese is\nan essential plant nutrient but its presence in excess can readily cause toxicity to\nplants.  Solubility and availability of soil manganese increase steeply with de-\ncreasing pH, especially when this falls below 5.6.  The Segamat Series used in\nthe trials developed over andesite.  This soil is known to contain high amounts of\ntotal manganese with a value of 935 mg/kg in A horizon and 420 \u2013 650 mg/kg in\nB horizon (Paramananthan 2000).\n\n\n\nVisual symptoms of Mn toxicity were not observed during the experiment so\nthe factor suppressing the yield and quality of cocoa (manganese toxicity) was\nonly realised after the experiment was completed.  Most crops with high calcium\ndemand are also sensitive to relatively high concentrations of aluminium and\nmanganese ions present in acid soils.  Therefore, these soils must have higher\nsoil pH, raised by adding lime.  Available manganese concentration of acid soil is\nreduced by liming.  For the soil under study, addition of lime was recommended\nto raise the soil pH and eliminate manganese toxicity.  The raised pH reduces\nexcess soluble manganese (as possible toxins) by causing it to form insoluble\nhydroxides (Khanna and Mishra 1978).  It has been suggested in a previous\nstudy that the growth inhibition of plants grown in manganese-toxic conditions\nmay be overcome by a higher calcium supply (Gupta 1972).\n\n\n\nCONCLUSIONS AND RECOMMENDATIONS\nThere were no significant differences among treatments on cocoa yield and qual-\nity. For clone PBC 130, T2 (organic-based fertiliser + NPK) gave greater pod\nweight compared to other treatments.  No significant influence was also seen on\nthe effects of treatments on successful cherelle development (or potential pods to\nform cocoa pods).  Based on soil nutrient status, cocoa is not grown on good\nsoil.  Manganese toxicity has been found to be the possible reason for low cocoa\nyield and quality limitations.  The parent rock of Segamat Series contains mineral\nsuch as pyroxene which contributes to Mn.  The nitial status of Segamat soil has\nindicated that Mn concentration was 113 \u2013 150 mg/kg, beyond the adequate\nrange of 20 \u2013 40 mg/kg.  Addition of Mn through fertiliser application made the\nsituation worse.  To obtain better yields and quality of cocoa grown on Segamat\nsoil, it is recommended that enough lime be applied; the present liming programme\n(500 g/plant or 0.5 \u2013 1 t/ha/year, broadcast once a year) appears to be insuffi-\ncient to raise the soil pH to the adequate range of 5.5 \u2013 7.0.\n\n\n\nMJ of Soil Science 029-043.pmd 08-Apr-08, 10:44 AM40\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffect of Organic-based & Foliar Fertilisers on Cocoa Grown on an Oxisol in Malaysia\n\n\n\n41\n\n\n\nACKNOWLEDGEMENTS\nThe authors gratefully acknowledge the financial assistance provided by the Min-\nistry of Science, Technology and Innovation, Malaysia (MOSTI) under the IRPA\nproject, number 01-02-04-0503-EA001, and the South East Asia Research Cen-\ntre for Agriculture (SEAMEO SEARCA) scholarship.  A special thanks is due to\nthe Malaysian Cocoa Board Experimental Station, Jengka, Pahang in providing\nthe equipment and labour for the field experiment.  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"\n\nINTRODUCTION\nGlobal warming has been described as an increase in surface temperature of the \nearth. Global warming can lead to environmental imbalance. Agriculture is a \nmajor contributor of GHG to the environment and agricultural emissions (60 % \nof N2O and 50 % of CH4) represented 10\u201312 % of the total global anthropogenic \nemissions in 2005 (Smith et al., 2007). In semi-arid regions characterised by \nlow rainfall that is restricted to several months in winter and high temperatures \nin the summer, soils are typically poor in organic matter. Normally, these soils \ncontribute little to global GHG emissions (McLain  and Martens, 2006). However, \nwith irrigation and the addition of fertilisers and organic waste, these soils can \ncontribute to net CO2 losses to the atmosphere (Schlesinger, 1999). \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 79-94 (2016)                         Malaysian Society of Soil Science\n\n\n\nEmission of Greenhouse Gases from Soil in a Semi-arid Area \nApplied with Organic Matter  in South-western Iran\n\n\n\nI.Dalileh Dezfuli, F. Farjaiea and A.A Moezzib\n\n\n\na Collage of Applied Science and Agriculture and Qatreh Sazan Khouzestan Co, \nDezful, Iran.\n\n\n\nb Department of Soil Science, Faculty of Agriculture, Shahid Chamran University, \nAhvaz, Iran.\n\n\n\nCorresponding Author: Iman Dalileh Dezfuli, Collage of Applied Science and \nAgriculture and Qatreh Sazan Khouzestan Co, Dezful, Iran.\n\n\n\nABSTRACT\nOne of the problems of the modern world is global warming. Agriculture, after \nindustry, is a source of production of greenhouse gases (GHGs). However, suitable \nagricultural management practices will enable the reduction in emission of GHGs \nfrom fields. The objective of this research was to determine the effect of organic \nmatter such as filter mud, bagasse, manure, poultry and biochar on the production \nof GHGs in a wheat-corn-wheat rotation semi-arid area of south-western Iran \nfrom December 2011 to May 2013. The results from this study show there is a \nfluctuation in production of CH4, CO2 and N2O with the change in seasons, with \nthis behaviour being related to temperature and moisture changes of the season. \nThere were significant differences between treatments and control with more \nemission from soil after adding organic matter. A comparison of the treated soils \nshowed that biochar had the lowest emissions of CO2 and N2O besides experiencing \nincreased immobilisation of CH4 by the soil for eighteen months.\n\n\n\nKeywords: Wheat-corn-wheat rotation, filter mud, bagasse, manure, \nbiochar \n\n\n\n___________________\n*Corresponding author : E-mail: imandalileh@gmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201680\n\n\n\nDezfuli et al.\n\n\n\nSoil temperature, soil moisture, soil type, vegetation type, organic substrate \ntype and quantity, and the addition of organic waste can affect the production \nof GHG (Buyanovsky and Wagner, 1983; Johnson et al., 2007). The cumulative \neffects of a rapid accumulation of GHG in the atmosphere have led to changes in \nthe earth\u2019s energy balance and the concern is that \u2018\u2018most of the warming observed \nover the last 50 years is attributable to human activities\u2019\u2019 (Houghton et al., 2001). \nMethane and nitrous oxide are both produced as a result of microbial processes \nin the soil (Conrad, 1996). In soils, methane can be formed under anaerobic \nconditions by methanogens. Under aerobic conditions, both methane that has \nbeen produced in anaerobic parts of the soil and atmospheric methane diffusing \ninto the topsoil can be oxidised by methanotrophs (Le Mer and Roger, 2001). \nNitrous oxide is naturally produced in soils by microbial processes of nitrification \nand denitrification (Bleakley and Tiedje, 1982; Bowden, 1986). Impaired aeration \nin moist soils reduces oxygen availability and promotes the development of \npartial anaerobic conditions, which, coupled with low redox potential, provide \nideal conditions for N2O production (Ciarlo et al., 2007). Therefore, interactions \nbetween soil water content and organic soil amendments will have a profound \ninfluence on N2O emissions. Manure from poultry and livestock contain proteins, \namino acids, and carbohydrates and provide a source of energy for bacteria, \nwhich, upon decomposition, can release GHGs such as CO2 and CH4 (Nyakatawa \net al., 2011). While agriculture releases significant amounts of CH4 and N2O to \nthe atmosphere, the net GHGs emission in CO2 equivalent from farming activities \ncan potentially be decreased by increasing soil organic carbon storage and/or \ndecreasing CH4 and N2O emissions through improving crop management (Mosier \net al., 2006; Smith et al., 2008). Increased soil porosity, associated with soil \nbiochar amendment results in improved soil aeration, which ultimately suppresses \nN2O emissions (Richardson et al., 2009; Clough et al., 2013). Therefore, the extent \nof N2O emissions from biochar-amended soils is greatly affected by fertiliser type \n(Nelissen et al., 2014), biochar C/N ratio and the amount of organic carbon in soil \n(Troy et al., 2013; Zhu et al., 2014), biochar porosity, surface area and particle \nsize (Jeong et al., 2015; Martin et al., 2015) and the response of denitrifiers (Van \nZwietenet et al., 2014). Agricultural lands also remove CH4 from the atmosphere \nby oxidation, but this effect is small when compared with other GHG fluxes (Smith \nand Conen, 2004). Moreover, biochar can promote indirect carbon-sequestration \nby increasing crop yield, while potentially reducing carbon mineralisation (Kerr\u00e9 \net al., 2016).\n\n\n\nSince air temperature in south-western Iran is high and rainfall has recently \ndecreased, using organic matter can have a positive effect on crops but the use \nof suitable organic matter can play a positive role in reducing GHGs. The net \nemission of CO2 equivalents from farming activities can potentially be decreased \nby land management changes to increase soil organic matter content (Follett, \n2001). Studies have shown that biochar addition to soil may also influence \nmethane (CH4) emissions. Soil CH4 emissions have also been reported to decrease \nfollowing biochar addition to soil as it reduces anaerobic conditions and increases \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 81\n\n\n\nGreenhouse Gas Emmissions from Organic Matter Amended Semi-arid Soil\n\n\n\nsoil aeration (Rondon et al., 2005). In this study, the amount of GHGs emission \nfrom different sources of organic matter was compared with each other to find \nout which of these materials would have a more effective role in preventing GHG \nemissions. \n\n\n\nMATERIALS AND METHODS\nThis study was conducted at the Shavoor farm area (32o02\u201958.3\u201d_N, \n48o17\u201936.4\u201d_E). The site has an arid climate and is 65 m above sea level. Mean \nannual precipitation and temperature are 213 mm and 23o C, respectively. The soil \n(fine-clayey, mixed, hyperthermic, ustifluvents based on Keys to Soil Taxonomy \n(2006)), formed on the alluvial sediments of the Shavoor river, was initially low \nin organic matter (<0.5%). The experimental setup was a randomised complete \nblock design with different rates of five sources of organic matter (filter mud \nand bagasse from sugarcane waste, manure from livestock, poultry manure from \nchicken, biochar from citrus) and control (without organic matter) with three \nreplications conducted over two years. Then, farmyard manure at rates of 0 for \ncontrol and 20 t/ha of a different type of OM were applied as soil amendment \non the surface and mixed to 15 cm depth. The distance between blocks was 4 m \nand blocks size was 100 m \u00d7 10 m. Wheat (cultivar D79) was cultivated using \nflood-irrigation method and corn (cultivar 704) was cultivated using furrow \nirrigation method. Planting of wheat and corn was mechanically done using a \ntractor. At the beginning of wheat cultivation, 150 kg ha-1 urea was added to \nprevent the immobilisation process, followed by the application of 50 kg ha-1 \ntriple superphosphate and 100 kg ha-1 potassium sulfate in order to increase \nfertility in December 2011. For corn cultivation, 50 kg ha-1 urea was added to \nprevent the immobilisation process, followed by the application of 50 kg ha-1 \ntriple superphosphate and 50 kg ha-1 potassium sulfate in order to increase fertility. \nIn the second wheat season, no fertiliser was added. This study was conducted in \nthree-crop cycles (wheat-corn-wheat rotation) and during summer, field moisture \nwas supplied with irrigation at 10- day intervals. Measuring of GHGs was carried \nout from December 2011 to May 2013.\n\n\n\nIn this study, sampling of gas emissions from soil was performed using static \nchambers. This method has already been used by many researchers. Chambers \nwere made of polyethylene pipe (20 cm in diameter, 1mm thick and of 1 m height) \nand sealed by adhesive tape. In the middle of the chamber body, three-way pipes \nfor sampling and a thermometer were installed.  Each month, 36 chambers were \ninstalled in the field for 2 days per month at 15-day intervals, from December \n2011 to May 2013. The chambers were placed about 5 to 7 cm deep in the soil \nand after ensuring no gas leakage, samples were taken by a 60 ml syringe in a \nclosed chamber for a duration of 5 h. In order to calculate the differences and \nthe presence of gas within the chamber, concurrent to the sampling chamber, \nfresh air samples from a height of 2 m were taken as control. Control reading \nwas subtracted from the gas measurements taken from the chamber, to obtain net \ngas emission from the soil. In cases where gas was absorbed by the soil and the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201682\n\n\n\namount of gas in the chamber was lower than in the air where negative emissions \nwere obtained, it represented gas absorption immobilisation by the soil. After 1h \nof sampling, the sample gas was transported to the laboratory, where type and \namounts of gas in the chamber were read in mg cm-3 volume. Gas analysis in this \nstudy was done using the GC model UNICAM series 610. Then the readings were \ncorrected based on the temperature of the chamber. Intervention chamber volume \nand the duration of the installation chambers, and the amount of gas emissions \nwere calculated based on the emission of carbon dioxide gas in the form desired, \nthat is, mass per unit area per unit time using Excel software. Bulk density (BD; \ng cm-3) was calculated using\n\n\n\n BD= Ms/V\nwhere Ms is the mass of dry soil (g), and V (cm3) is the total volume of the soil \ncore.\n\n\n\nStatistical analysis was performed based on the data obtained from the gas reading \nsoftware. Data analysis was performed with SPSS 20 and Tukey\u2019s test was used \nfor overall comparison of data using one-way ANOVA and means of treatments. \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nEffects of Soil Temperature and Moisture on Carbon Dioxide and Nitrous Oxide \nFluxes\nThe statistical analyses of the results (Tables 1 and 2) shows significant differences \n(p<0.01) between treatment mean values for CH4, CO2 and N2O parameters. The \nbulk density for treatments is shown in Table 3 while percentage of soil moisture \nand soil temperature is shown in Figure 1. The maximum and minimum daily \ntemperature and mean monthly rainfall are shown in Figure 3. In April 2012, there \nwas high soil moisture and temperature and this case was repeated in the following \nyear (Figure 1). Based on the figures and tables in this study, the emission of N2O \nand CO2 increased in treatments compared to control after adding different types \nof organic matter to the soil. The increase in N2O and CO2 is due to increased \nmicroorganism activity in decomposing organic matter. Some studies show that \nincreased microbial biomass and root biomass are responsible for the greater CO2 \nemission in organic matter amended soils (Qingyan et al., 2015). In addition, N2O \nemission rate was found to increase due to urea fertiliser being added in order \nto prevent the immobilisation process. Some findings show that the addition of \norganic matter to soil increases N2O emissions (Qingyan et al., 2015). Organic \nwastes, including animal manure and municipal wastes and their composts, and \ncrop residues enhance CO2 and N2O emissions to the air compared with inorganic \nfertilisers (Hadas et al., 2004; Jones et al., 2005; Ding et al., 2007; Johnson et al., \n2007). During the wheat planting season that started from December 2011, 150 kg \nha-1 of urea fertiliser were added to the soil during the first three months in order to \nprevent immobilisation, leading to the emission of N2O (Figure 4). Nitrous oxide \n\n\n\nDezfuli et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 83\n\n\n\nemissions from agriculture are largely the result of N fertiliser additions (Morgan \net al., 2010). Most of the N2O emission is for bagasse treatment with 1.56 mg \nN2O-N m-2.day-1 (Figure 4). Biochar has the lowest N2O emission in the wheat \nseason with 0.474 mg N2O-N m-2.day-1. Also, among the treatments, biochar had  \nthe minimum emission of N2O for the three cycles of crop rotation (Table 1). \nVarious studied soils indicate that the use of biochar significantly reduces the N2O \nemissions (Singh et al., 2010; Taghizadeh-Toosi et al., 2011; Wang et al., 2011a; \nZhang et al., 2010, 2011). For example, the incorporation of biochar into pasture \nsoil that contained ruminant urine reduced N2O emissions up to 70% (Taghizadeh-\nToosi et al., 2011). Zhang et al. (2010, 2011) also reported that biochar additions \nsignificantly lower the N2O emissions from both paddy and upland soils. The \ndifference between biochar and soil matrix in physical properties leads to an \noverall change in soil density and aggregation, hydraulic conductivity and gas \ntransportation, which in turn impacts chemical properties and microbial activity \nin soil (Lehmann et al., 2011).\n \n\n\n\nTABLE 1\nThe effect of different types of organic matter on (GHGs) emission (mg CH4-C m-2 \nday-1,mg CO2-C m-2 day-1, mg N2O-N m-2 day-1) in wheat-corn-wheat rotation from \n\n\n\nDecember 2011 to May-2013.\n\n\n\nMeans in the same column followed by the same letters (A, B, C, D, E, F) are not significantly \ndifferent at 1% level of significance; CV: coefficient of variation; C/N: Carbon/ Nitrogen ratio; \nM: manure.\n\n\n\nTABLE 2\nCombined analysis of variance for emission of GHGs in different type of organic\n\n\n\nmatter treatments\n\n\n\n*significant at P<0.01 \n\n\n\n2 \n \n\n\n\n \nTABLE 1 \n\n\n\n        The effect of different  type of organic matter on (GHGS) emission (mg CH4- C m-2 day-1, mg CO2- C m-2 day-1 , mg N2O- N m-2 \n\n\n\nday-1) in Wheat-Corn-Wheat rotation from Dec-2011 to May-2013. \n\n\n\n                                                       Wheat                       Corn                    Wheat  \n                         C/N      CH4    CO2 N2O   CH4 CO2 N2O   CH4 CO2 N2O \nFilter Mud 31 -0.011C 332.62 B 1.37E -0.057CD 375.44B 0.844B -0.028B 341.83B 0.608D \n\n\n\nBagasse 52 -0.008C 387.19 C 1.52 F -0.042E 465.75D 1.385E -0.0108C 435.36C 0.927F \n\n\n\nManure 32 -0.005C 458.44D 1.15C -0.049DE 596.08E 1.22D -0.002D 351.44B 0.506C \nPoultry M 11 -0.037B 566.16 E 1.27 D -0.064BC 769.08F 1.526F -0.01E 361.41B 0.695E \nBiochar 10 -0.055 A 180.9 A 0.474 A -0.089 A 248.3A 0.298A -0.032 A 238.72A 0.131A \nControl  -0.043B 347.08 B 0.926 B -0.07B 423.94C 1.11C -0.01E 262.5A 0.19B \nCV %  -1.33 0.45 0.41 -0.51 0.50 0.71 -3.73 0.27 0.62 \n\n\n\nMeans in the same column followed by the same letters (A, B, C, D, E, F) are not significantly different at 1% level of \nsignificance, CV: coefficient of variation. C/N: Carbon/ Nitrogen ratio. M: manure \n\n\n\n3 \n \n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nCombined analysis of variance for (GHGs) emission in different type of organic matter treatments.  \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**significant at P<0.01  \n\n\n\n Df            Sum of square             Mean Square \n  CH4 CO2 N2O CH4 CO2 N2O \nIntercept 1 0.691 102027176 524.32 0.691 102027176 524.32 \nRotation (R) 2 0.316 2467646 49.49 0.158** 1233823** 24.74** \nMonth (M) 5 0.05 965218 17.133 0.01** 193043** 3.42** \nTreatments (T) 5 0.112 7555339 65.10 0.022** 1511067** 13.02** \nR*M 10 0.297 4671351 47.33 0.030** 467135** 4.73** \nR*T 10 0.08 2321319 12.46 0.008** 232131** 1.24** \nM*T 25 0.029 943406 13.31 0.001** 37736** 0.533** \nR*M*T 50 0.057 2428094 20.34 0.001** 48561** 10.76** \n\n\n\nGreenhouse Gas Emmissions from Organic Matter Amended Semi-arid Soil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201684\n\n\n\nFigure 1: Soil moisture (%) and soil temperature (\u02daC) measured in wheat-corn- wheat \nrotation.\n\n\n\nFigure 2: Mean rainfall (mm) from Safi-Abad meteorology station from  December 2011 \nto June 2013 in 5 cm of soil.\n\n\n\nFigure 3. Maximum and minimum temperature (\u02daC) from Safi-Abad meteorology station \nfrom  December 2011 to June 2013 in 5 cm of soil.\n\n\n\nDuring the corn season, irrigation and the addition of nitrogen fertiliser, the \nhighest emission of N2O was observed at the beginning of the cultivation in July \n2012. Meanwhile, in March 2012, the highest soil temperature was observed, and \nbecause of irrigation, soil moisture was also high. As a result, the emission of N2O \nwas significantly high (Figure 4). 4 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1. Soil moisture (%) and soil temperature (\u02daC) measured in Wheat- Corn- Wheat \n\n\n\nrotation.    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \nFigure 2. Mean rainfall (mm) from Safi-Abad meteorology station from  Dec- 2011 to \nJun- 2013 in 5 cm of soil. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Maximum and minimum temperature (\u02daC) from Safi-Abad meteorology station \nfrom  Dec- 2011 to Jun- 2013 in 5 cm of soil. \n \n\n\n\n\n\n\n\n\n\n\n\n4 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1. Soil moisture (%) and soil temperature (\u02daC) measured in Wheat- Corn- Wheat \n\n\n\nrotation.    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \nFigure 2. Mean rainfall (mm) from Safi-Abad meteorology station from  Dec- 2011 to \nJun- 2013 in 5 cm of soil. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Maximum and minimum temperature (\u02daC) from Safi-Abad meteorology station \nfrom  Dec- 2011 to Jun- 2013 in 5 cm of soil. \n \n\n\n\n\n\n\n\n\n\n\n\n4 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1. Soil moisture (%) and soil temperature (\u02daC) measured in Wheat- Corn- Wheat \n\n\n\nrotation.    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \nFigure 2. Mean rainfall (mm) from Safi-Abad meteorology station from  Dec- 2011 to \nJun- 2013 in 5 cm of soil. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Maximum and minimum temperature (\u02daC) from Safi-Abad meteorology station \nfrom  Dec- 2011 to Jun- 2013 in 5 cm of soil. \n \n\n\n\n\n\n\n\n\n\n\n\nDezfuli et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 85\n\n\n\nFigure 4. N2O emission (mg N2O- N m-2. day-1) from soil in different months from different \ntreatments. F: filter mud; Ba: bagasse; M: manure; P: poultry; Bi: biochar; C: control.\n\n\n\nFriedl et al., (2016) showed that the effect of fertiliser application on N2O \nemission is more than the natural soil nitrogen pool and that greater denitrification \nrates are exhibited at higher soil moisture content. In addition, Dhadli et al., (2016) \nshowed that in wheat and maize seasons, peaks in N2O fluxes coincided with the \nrainfall or irrigation and N-fertilisation events.\n\n\n\nPoultry treatment has the maximum value with 1.526 mg N2O-N m-2 day-1  \n\n\n\nbut is not significantly different from other treatments except for biochar with \n0.298 mg N2O-N m-2 day-1. In the second season of wheat, biochar had the lowest \nN2O emission with 0.131 mg N2O-N m-2 day-1, but was not significantly different \nfrom control which had a mean value of 0.19 mg N2O-N m-2 day-1. However, it \nwas significantly different from other treatments (Table 1). In the second season \nof wheat, the maximum mean value of N2O was obtained for bagasse with 0.927 \nmg N2O-N m-2 day-1 (Figure 4).\n\n\n\nDue to microorganism activity in decomposing organic matter added to \nthe soil, a high rate of CO2 emission was observed in January 2012. With the \npassage of time and the beginning of the spring season (March and April, 2012), \nmicroorganism activity  increased due to rising soil temperature; CO2 emission \n\n\n\n5 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4. N2O emission (mg N2O- N m-2. day-1) from soil in different months from \n\n\n\ndifferent treatments. F: Filter mud, Ba: Bagasse, M: Manure, P: poultry, Bi: Biochar, C: \n\n\n\nControl. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGreenhouse Gas Emmissions from Organic Matter Amended Semi-arid Soil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201686\n\n\n\nalso increased  during this time (Figure 5). In July 2012, due to the beginning of \nmaize cultivation and land irrigation, microorganism activity reached its maximum \nagain leading to increased rates of CO2 emission. But with the passage of time \nand the beginning of the autumn season, microorganism activity decreased due \nto the falling soil temperature resulting in decreased CO2 emission.  This trend \nof reduction continued during the  winter of 2013 and with the beginning of the \nspring season (March and April, 2013), the increasing trend of CO2 emission was \nobserved again (Figure 5). Brito et al., (2015) found that seasonal variation of soil \ncarbon dioxide emission to be directly related to variations in precipitation and \nsoil temperature. Soil CO2 emission is found to be higher in summer and lower in \nwinter. Data variability in carbon dioxide emission is higher in rainy, hot summers \nthan in dry, cold winters. A positive linear association between carbon dioxide \nemission and soil temperature is observed in summer and autumn.\n\n\n\nFigure 5:  CO2 emission (mg CO2- C m-2 day-1) from soil in different months from \ndifferent treatments. F: filter mud; Ba: bagasse; M: manure; P: poultry; Bi: biochar; \n\n\n\nC: Control.\n\n\n\nFrom Table 1, it can be seen that CO2 is low in biochar treatment with the \nmean value being 180.9 mg CO2-C m-2 day-1. In the first season of wheat and corn \nseason, the mean value of CO2 for biochar is 248.3mg CO2-C m-2 day-1which \nis significantly different compared to other treatments. In the second season of \n\n\n\n6 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5. CO2 emission (mg CO2- C m-2. day-1) from soil in different months from \n\n\n\ndifferent treatments. F: Filter mud, Ba: Bagasse, M: Manure, P: poultry, Bi: Biochar, C: \n\n\n\nControl. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDezfuli et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 87\n\n\n\nwheat, the lowest mean value for CO2 is for biochar with 238.72 mg CO2-C m-2.\nday-1 which is also significantly different from other treatments with the exception \nof the control which reads 262.5mg CO2-C m-2 day-1 (Table 1). The contour \ngraphs for N2O and CO2 (Figures 4 and 5) show that maximum CO2 fluxes are \nobserved during summer (July 2012) with 1157 mg CO2-C m-2 day-1. For poultry \ntreatment when the soil is hot and wet, and the field is tilled for corn cultivation \nthe minimum level of CO2 emission at this time was for biochar with 251 mg \nCO2-C m-2day-1 (Figure 5). In these three cycles of crop rotation, the lowest level \nof CO2 emission was from biochar application (Table 1). Thus it can be seen that \nwith increasing temperature and moisture, N2O and CO2   production increased \nas microbial activities increased at high temperature and moisture. Similar \nresults have been reported by other researchers which state that temperature \nfluctuations and seasonal soil moisture, dominated by rainfall events, affect soil-\natmosphere exchange of GHG. Vegetation type (McLain and Martens, 2006) \nas well as irrigation (Mariko et al., 2007) and other agricultural management \npractices (Mosier et al., 2006) can be controlled and the extent of GHG emissions \ndetermined, particularly in semi-arid regions. Based on Figures 4 and 5, N2O \nand CO2 emissions were highest in spring and lower in winter since precipitation \nwas less in 2013 (Figure 2). As a result, the emission level of N2O and CO2 was \nreduced in 2013 compared to 2012 (Figures 4 and 5). Therefore, the findings show \nthat GHGs emissions change in amounts according to seasons. The high fluxes of \nCO2 throughout the corn seasons for the plots with corn plants originated from \nthe contribution of root respiration and root turnover, as shown by Chen et al., \n(2005).  Figure 4 shows that in the first season of wheat cultivation, which began \nin December 2012, the rate of N2O emission decreased as spring approached. This \nis due to the low soil moisture as well as the lowest rate of inorganic nitrogen in \nthe soil compared to the first months of wheat cultivation. This decreasing trend \nreached its minimum in the months of May and June 2012, during which the soil \nmoisture was also at its minimum level. At the beginning of summer (July 2012), \nwhen maize cultivation season began, the high temperature of the soil, the use of \nfertilisers N: 50 kg ha-1, P: 50 kg ha-1 and K: 50 kg ha-1 especially nitrogen,  and \nirrigation and land preparation for maize cultivation, resulted in increased N2O \nemission. However, the N2O emission rate in autumn (October 2012) gradually \nreduced due to reducing irrigation and soil moisture and reduced soil temperature \n(Figure 4). Ekoungoulou et al. (2015) shows that the processes of carbon and \nnitrogen mineralisation leading to N2O and CO2 emissions from soils are affected \nby variability in moisture, temperature, fertiliser application and organic carbon \navailability.\n\n\n\nGreenhouse Gas Emmissions from Organic Matter Amended Semi-arid Soil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201688\n\n\n\nTable 3\nBulk density of organic matter amended soil in the beginning and ending of crop seasons.\n\n\n\nMeans in the same column followed by the same letters (A, B, C, D, E, F) are not significantly \ndifferent at 1% level of significance. CV: coefficient of variation; OM: Organic Matter; W: \nwaste; M: manure.\n\n\n\nEffects of Soil Temperature and Moisture on Methane Uptake\nThe mean immobilisation of methane in the first season for wheat under the biochar  \ntreatment was -0.055 mg CH4-C m-2 day-1.  With the application of organic matter \n(January 2012), soil bulk density decreased (Table 3) and the process of oxidation \nwith the help of microorganisms occurred leading to CH4 immobilisation in the \nsoil. Soil pores affect CH4 emission (Liu et al., 2015). However, due to the high \nrate of rainfall in this season and high moisture of the soil, CH4 immobilisation \nby the soil was low. Moreover, in the months of April and May 2012, because \nof increased soil bulk density (Table 3), rainfall and high temperature (Figures 1 \nand 2), a revival process occurred leading to an increase in CH4 emission while \nat the end of spring (June, 2012), CH4 immobilisation rate increased again due to \na reduction in soil temperature and in July 2012, at the beginning of the maize \ncultivation season, when land preparation begin, soil bulk density rate decreased \nagain (Table 3). Although soil moisture was high, the revival process had not \noccurred and therefore CH4 immobilisation in the soil  increased (Figure 6).\n\n\n\nIn the corn season, the mean immobilisation of CH4 was  -0.089 mg CH4-C \nm-2 day-1 for the biochar treatment which was significantly different compared \nto other treatments. The mean immobilisation value of CH4 was also -0.032 \nmg CH4-C m-2 day-1 for biochar in the second season of wheat. There was no \nsignificant difference between treatments in the first and second seasons of wheat \nfor CH4 emission but there was a significant difference between biochar and \nother treatments in the corn season. Based on Figure 6, maximum immobilisation \nof CH4 occurred in October 2012 with -0.145 mg CH4-C m-2 day-1 for biochar \ntreatment; the value of immobilisation of CH4 in summer was generally maximum \nfor biochar in comparison with other seasons. Other studies have reported that \nbiochar amendment reduces CH4 emissions or has no significant effect on CH4 \nemissions as compared to the control (Castaldi et al., 2011; Karhu et al., 2011; \nRondon et al., 2005;  Scheer et al., 2011; Zhang et al., 2011). Biochar addition \nmay increase soil aeration resulting in the oxidation of CH4 and the high porosity \n\n\n\n7 \n \n\n\n\nTABLE 3 \n \n\n\n\nBulk density of organic matter amended soil in the beginning and ending of crop seasons. \n\n\n\n \n \nMeans in the same column followed by the same letters (A, B, C, D, E, F) are not \nsignificantly different at 1% level of significance, CV: coefficient of variation. OM: \nOrganic Matter. W: waste. M: manure. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n                                Bulk density ( g.cm-3 )  \nTreatments Source of OM  Jan 2012 May 2012 July 2012 Nov 2012 Jun 2013 \nFilter mud Sugarcane W   0.8B 1.28D 0.9B 1B 1.2C \n\n\n\nBagasse Sugarcane W  0.65A 0.91A 0.77A 0.76A 1A \n\n\n\nManure Livestock  0.97C 1.01B 1C 0.96AB 1.18BC \n\n\n\nPoultry M Chicken  1.1D 1.45E 1.2E 1.23C 1.54E \n\n\n\nBiochar citrus  0.77B 1.1C 0.84AB 0.90AB 1.13B \n\n\n\nControl   1.03CD 1.6F 1.1D 1B 1.4D \n\n\n\nCV%   0.18 0.20 0.15 0.15 0.14 \n\n\n\nDezfuli et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 89\n\n\n\nand large surface area of aerated soil may enhance CH4 immobilisation (Karhu et \nal., 2011; Rondon et al., 2006; Yanai et al., 2007; Zhang et al., 2011), both leading \nto a reductions in CH4 emission from soils.\n\n\n\nCONCLUSION\nThe findings of this research show the significant effect of organic matter in \nthe emission of GHGs from soil in different seasons and crops. The increase in \nCO2 emission at the beginning of the wheat season is attributed to the activity of \nmicroorganisms in decomposing organic matter added to the soil.\n\n\n\nThe reduction in N2O emission at the end of the maize season is due to a \nreduction in the soil temperature, irrigation rate and soil moisture. The increase in \nCO2 emission at the beginning of cultivation is also due to nitrogen fertiliser being \nadded. Moreover, it is observed that the rate of CH4 absorbed by the soil increases \ndue to the reduced rate of soil bulk density after organic matter is added to the soil \nas well as to the activity of microorganisms in oxidising CH4. In some months, the \nrevival process occurred due to high rainfall and weakness in drainage and in such \nmonths, CH4 emission can be observed. \n\n\n\n8 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6. CH4 emission (mg CO2- C m-2. day-1) from soil in different months from \n\n\n\ndifferent treatments. F: Filter mud, Ba: Bagasse, M: Manure, P: poultry, Bi: Biochar, C: \n\n\n\nControl.   \n\n\n\n\n\n\n\nACKNOWLEDGMENTS \n\n\n\nGreenhouse Gas Emmissions from Organic Matter Amended Semi-arid Soil\n\n\n\nFigure 6: CH4 emission of mg CO2- C m-2.day-1 from soil in different months for different \ntreatments. F: filter mud; Ba: bagasse; M: manure; P: poultry; Bi: biochar; C: Control.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201690\n\n\n\nBiochar  application appears to have a better role in soil and GHGs emission as \nthe application of  biochar results in lower GHGs emission compared to other \norganic matters. Since global warming is of increasing concern to the world, \nwe recommend biochar application in soils for reducing GHGs emission and \nimproving soil fertility.\n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to thank Qatre Sazan Khouzestan Co for  providing \nfinancial support to this study. We would also like to thank Dr. R.Eslamizade  of \nthe  Agriculture and Research Center of Safi Abad Dezful and Mr. R.Khoshnood, \nfor their help in the execution of this research.\n\n\n\nREFERENCES\nBleakley, B. H. and Tiedje, J. M. 1982. Nitrous-oxide production by organisms other \n\n\n\nthan nitrifiers or denitrifiers. Appl. Environ. 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"\n\n___________________\n*Corresponding author : E-mail: gs48959@student.upm.edu.my\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 159-169 (2021)                          Malaysian Society of Soil Science\n\n\n\nNet Rainfall Components under Various Ages of the Oil Palm \n\n\n\nFarmanta, Y.1, Sung, C.T.B.1*, Giap, S.E.G.2, Paing, T.N.1, \nHandoko3 and Impron3\n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, Malaysia\n\n\n\n2Department of school of ocean engineering, Faculty of Ocean Engineering \nTechnology and Informatics, Universiti Malaysia Terengganu (UMT),\n\n\n\nTerengganu, Malaysia\n\n\n\n3Department of Geophysics and Meteorology, Faculty of Mathematics and Natural \nSciences, Bogor Agricultural University,Bogor, Indonesia\n\n\n\nABSTRACT\nThis study analysed the net rainfall components under different ages and leaf area \nindex (LAI) of the oil palm to determine the contribution of net rainfall towards \nwater requirements of the palm. We hypothesised that older palms, with their \nhigher LAI, would have lower net rainfall due to higher interception of rain. The \nstudy was conducted in oil palm plantations in Mendis village, Bayung Lincir \nDistrict, Sumatra. Tipping-bucket rain gauges connected to data loggers were used \nto measure the rainfall components (throughfall, stemflow, interception and gross \nrainfall) at ten-minute intervals for four months. The proportions of throughfall, \nstemflow, and interception for the oil palm aged between 5 to 20 years were 92.2% - \n58%, 2.4 \u2013 0.7%, and 5.4% - 41%, respectively.  The equations relating throughfall \n(Tf), stemflow (Sf), and net rainfall (Pn) to LAI were Tf = -8.5032 LAI + 116.74, \nSf = -0.469 LAI + 3.8808, and Pn = -8.9722 LAI + 120.62, respectively. Under \nvarious oil palm ages, net rainfall (Pn) had an inverse linear relationship with an \nincrease in LAI by 3.5 and 7.2 with a decrease in net rainfall from 94.6% to 58.8%. \nThe results from this study should serve as a guide to the water management  of \noil palm plantations.\n\n\n\nKeyword:    oil palm, interception, throughfall, stemflow, leaf area index.\n\n\n\nINTRODUCTION\nOil palm  requires about  2000 - 2500 mm year-1  of water a year and this amount \nof large water is required uniformly throughout the year without a prolonged \ndry season. The annual total water deficit ideally should not exceed 250 mm \n(Fauzi et al. 2002). For almost all oil palm plantations, the only input of water is \nfrom precipitation (Kerkides et al. 1996), as irrigation is rarely used in oil palm \nplantations.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021160\n\n\n\n Water management in oil palm plantations is very important. Corley and \nTinker (2016) estimated that with every 100 mm water deficit, oil palm yield \nwould decline by 10%. Rain interception, stemflow and throughfall are rainfall \ncomponents in the hydrologic process that are important in the management of \nwater resources (Arnell 2002). The part of the rain retained on the surface of \nleaves and branches is called interception, while stemflow is the portion of gross \nrainfall that flows down along the surface of the tree trunk to reach the ground. \nAnd the rainfall reaching the ground, having fallen through the gaps between \ncanopies, unintercepted, or as leaf drips is known as throughfall (Figure 1).\n \n\n\n\nFigure 1. Net rainfall components in oil palm plantations (Pg \u2013 precepitation gross,\nIc \u2013 interception, Sf \u2013 stemflow, Tf \u2013 throughfall)\n\n\n\n Redistribution of throughfall and stemflow by canopies modifies \nevaporation, which plays an important role in water balance on local and \ncatchment scales (Herbst et al. 2006; 2007). The relationships between rainfall \nand tree morphologies were also studied by Van Stan et al. (2011) who reported \nthat F. grandifolia trees with broader vertical canopy depth enhanced stemflow \nproduction for inclined rainfall, whereas L. tulipifera with its broader horizontal \ncanopy enabled stemflow production for non-inclined rainfall.\n Rainfall interception is of paramount importance as far as hydrological cycle \nis concerned, in that as rain falls onto the oil palm canopy, it is retained for some \ntime, afterwhich a part of it is lost to the atmophere through evaporation. (Gomez \net al. 2000). Canopy interception loss is influenced by canopy architecture and \nmeteorological properties (Crockford and Richardson 1990). Canopy interception \nloss ranges from 10 to 40% of gross rainfall in natural forests and may even exceed \n50% (Calder 1990). Leaf area index ( LAI) also plays a role in the process of \nrainfall interception (P\u00e9rez et al. 2017). LAI for oil palm depends on the number \nof fronds, leaflet area and planting density (Noor and Harun 2004). \n Chong et al. (2018), Ahmadi et al. (2009), Bentley (2007),  Murtilaksono \net al. (2007), and Marin et al. (2000) have measured the net rainfall components \nfor oil palm, but all these studies were done for mature oil palm with full canopies. \nThere have been no studies on net rainfall under various ages of oil palm, for \ninstance, young or maturing oil palms. Consequently, there is a knowledge gap \non how the relative proportions of the net rainfall components would change \naccording to the age of the palm age or degree of canopy cover. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 161\n\n\n\n This research is important in estimating the potential net rainfall required \nby the oil palm in deciding the need for  irrigation. Older, more mature oil palm \nwith higher LAI, for instance, would intercept more rainfall than younger trees. \nThe amount of  irrigation would therefore have to be adjusted for these differences \nin net rainfall components under various oil palm ages. Thus, the objective of this \nstudy was to measure and analyse the net rainfall components under different ages \nof the oil palm, \n\n\n\nMATERIALS AND METHODS \nThis studywas conducted in oil palm plantations located in the Mendis village, \nBayung Lincir District, Musi Banyuasin, South Sumatra Province, Indonesia at \ncoordinates of 2\u00b015\u201900\u201d to 2\u00b030\u201900\u201d N  and 103\u00b045\u2019 00\u201d to 104\u00b000\u201900\u201dE.\n Custom-built tipping-bucket rain gauges with a minimum resolution of 0.5 \nmm of rain were used for rainfall measurement. The rain collector had an opening \ndiameter of 400mm, with the rain gauge being connected to a data logger for \nrecording rainfall parameters (throughfall, stemflow, and gross rainfall) at ten-\nminute intervals. For throughfall measurement, three rain gauges were arranged \nalong a straight line in a North-South direction at a 9-m distance between every \ntwo gauges, while for the stemflow measurements, three sampled trees were \nselected randomly.\n The old pruned fronds on the selected tree trunks were removed to fix a \nalumunium collar and sealed with nails and bitumen to direct stemflow into the \nrain gauge. Finally, for collecting gross precipitation above the canopies, another \nrain gauge was installed in a nearby open area that was not hindered by tall plants \nand buildings. It was regarded as representative of the gross precipitation (above \ncanopies) at the experiment site. \n LAI measurements were carried out in the field using three palms replicating \nper tree age class. The palm age classes were 5, 10, 15, and 20 years old. LAI was \nestimated using the equation of Hardon et al. (1969) namely, \n\n\n\n LAI = a x N x D x 0.55     (1)\n\n\n\nwhere a is the  total area of frond 17 (both sides of the frond in m2), N is the \nnumber of fronds per palm, D is the planting density (148 palms/ha), and 0.55 \nis a correction factor (relative leaf area conversion). LAI was determined based \non frond 17 from each palm tree. Frond 17  was selected as representative of the \nwhole of the crown (Tailliez and Koffi 1992). A total of three fronds were cut \nand the leaflet area was measured manually. This manual method is known as \nthe direct LAI method, and is widely used for crops and adapted for vegetation \nin small-scale studies (Br\u00e9da 2003; P\u00e9rez et al. 2017). This method is useful in \nagriculture and ecological studies where the plant is harvested to measure the leaf \narea (Klingberg et al. 2017). \n In order to measure the gross precipitation, a tipping bucket was erected \nfrom the ground at 90\u00b0, while stemflow water was collected via rubber collars \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021162\n\n\n\nfastened in a downward spiral manner around the oil palm trunk into the tipping \nbucket. As for throughfall, six plastic hoses were connected and channelled into \nthe tipping bucket, slanted at a 10% slope from the tipping bucket. Each tipping \nbucket was equipped with a sensor connected to a data logger (Figure 2).\n  Similarly, the measurements using the tipping bucket method were \nautomatically recorded by the data logger during the rainfall period event at \nintervals of 10 min. Interception was calculated as follows:\n\n\n\nFigure 2. Aerial view of interception scheme for measurement \nNotes : 1 - tipping bucket to measure gross rainfall (put on top of trees); 2: tipping bucket \nto measure stemflow in the bottom of tree; 3: - tipping bucket to measure throughfall \n(among trees, selected randomly); 4: plastic hose; 5: oil palm trees.)\n\n\n\n Ic = Pg \u2013 Sf \u2013 Tf      (2)\n\n\n\nwhere Ic is interception, Pg is gross precipitation, Tf is throughfall, and Sf is \nstemflow (all in %).\n\n\n\nRESULTS AND DISCUSSION\nTable 1 shows that with increasing LAI, interception increased by 5.4 to 41%. \nThis study showed that the proportion of canopy interception to rainfall is very \nsmall (Ic = 0) for LAI <2.3, as determined from the equation in Figure 3.\n Interception loss was between 32.3 to 41 % of total gross precipitation for \nmature oil palm (>10 years). This result was very similar to a Malaysian study that \ndetermined interception loss as being between 32 to 41%  (Chong et al. 2018). \nThroughfall accounted for about 64.9% of gross rainfall for 15-year-old trees \nwhich was also similar to that reported by Chong et al. (2018) and Kee et al. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 163\n\n\n\n(2000) who found 70-78% of gross rainfall being accounted for by throughfall. \nThe proportion of stemflow in this study was 2.0-2.7% similar to that  reported by \nChong et al. (2018) and Bentley (2007). \n Figure 3 shows a strong linear relationship between tree age and LAI (R2 \n\n\n\n= 0.99). Awal et al. (2008) also reported a similar strong correlation between tree \nage and LAI at 2 years for the oil with the range of  LAI being between 0.57 to \n0.79 which increased to 3.5 to 5.02 for tree aged 16 years. \n  A similar trend was observed between throughfall and stemflow with \nLAI as shown in Figures 4 and 5. As LAI  increased, its effect was a consequent \nreduction in throughfall and stemflow with increasing rainfall interception by the \nleaves. \n \n\n\n\nTABLE 1 \nPrecipitation proportion, LAI, throughfall (Tf), stemflow (Sf) and interception (Ic) of \n\n\n\nrainfall in the upper oil palm canopy (Pg) at different ages.\n\n\n\n7 \n \n\n\n\nRESULTS AND DISCUSSION \n\n\n\nTable 1 shows that with increasing LAI, interception increased by 5.4 to 41%. \n\n\n\nThis study showed that the proportion of canopy interception to rainfall is very \n\n\n\nsmall (Ic = 0) for LAI <2.3, as determined from the equation in Figure 3. \n\n\n\n\n\n\n\nTABLE 1  \nPrecipitation proportion, LAI, throughfall (Tf), stemflow (Sf) and interception (Ic) \n\n\n\nof rainfall in the upper oil palm canopy (Pg) at different ages. \n\n\n\nAge  \nGross \n\n\n\nprecipitation LAI Tf/Pg Sf/Pg Ic/Pg \nMax Min \n\n\n\n(years) (mm d-1)  (%) (%) (%) \n5 0.7 42.1 3.5 92.2 2.4 5.4 \n\n\n\n10 0.5 56.3 4.9 66.3 1.4 32.3 \n\n\n\n15 0.2 85.1 6.2 64.9 0.8 34.4 \n\n\n\n20 0.7 67.7 7.2 58.2 0.7 41 \n\n\n\nStd error 0.1 9.1 0.80 7.48 0.39 7.85 \n\n\n\n\n\n\n\nInterception loss was between 32.3 to 41 % of total gross precipitation for \n\n\n\nmature oil palm (>10 years). This result was very similar to a Malaysian study \n\n\n\nthat determined interception loss as being between 32 to 41%  (Chong et al. \n\n\n\n2018). Throughfall accounted for about 64.9% of gross rainfall for 15-year-old \n\n\n\ntrees which was also similar to that reported by Chong et al. (2018) and Kee et al. \n\n\n\n(2000) who found 70-78% of gross rainfall being accounted for by throughfall. \n\n\n\nThe proportion of stemflow in this study was 2.0-2.7% similar to that  reported by \n\n\n\nChong et al. (2018) and Bentley (2007).  \n\n\n\n\n\n\n\n8 \n \n\n\n\n \nFigure 3. The relationship between tree age and Leaf Area Index (LAI) \n\n\n\n\n\n\n\n Figure 3 shows a strong linear relationship between tree age and LAI (R2 = \n\n\n\n0.99). Awal et al. (2008) also reported a similar strong correlation between tree \n\n\n\nage and LAI at 2 years for the oil with the range of  LAI being between 0.57 to \n\n\n\n0.79 which increased to 3.5 to 5.02 for tree aged 16 years.  \n\n\n\nFigure 3. The relationship between tree age and Leaf Area Index (LAI)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021164\n\n\n\nFigure 5. Relationship between LAI and stemflow\n\n\n\nFigure 4. Relationship between LAI and throughfall\n\n\n\n9 \n \n\n\n\n \nFigure 4. Relationship between LAI and throughfall \n\n\n\n \nFigure 5. Relationship between LAI and stemflow \n\n\n\n A similar trend was observed between throughfall and stemflow with LAI \n\n\n\nas shown in Figures 4 and 5. As LAI  increased, its effect was a consequent \n\n\n\n9 \n \n\n\n\n \nFigure 4. Relationship between LAI and throughfall \n\n\n\n \nFigure 5. Relationship between LAI and stemflow \n\n\n\n A similar trend was observed between throughfall and stemflow with LAI \n\n\n\nas shown in Figures 4 and 5. As LAI  increased, its effect was a consequent \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 165\n\n\n\n Figure 6 shows a positive linear trend between LAI and interception as an  \nincreasing LAI led to a greater likelihood of the gross rainfall being intercepted \nby the canopy of the palm. In his study, Farmanta (2012) noted that interception \nincreased with an increase in LAI which was also similar to the results of the \npresent study. Likewise,  Pramono and Ginting (1997) found that a high density \nof canopies have a higher intercepted amount of rain, while rain intensity showed \nan inverse relation to interception capacity (Siregar et al. 2006).\n\n\n\nFigure 6. Relationship between LAI and interception\n\n\n\n10 \n \n\n\n\nreduction in throughfall and stemflow with increasing rainfall interception by the \n\n\n\nleaves.  \n\n\n\n \nFigure 6. Relationship between LAI and interception \n\n\n\n \nFigure 6 shows a positive linear trend between LAI and interception as an  \n\n\n\nincreasing LAI led to a greater likelihood of the gross rainfall being intercepted by \n\n\n\nthe canopy of the palm. In his study, Farmanta (2012) noted that interception \n\n\n\nincreased with an increase in LAI which was also similar to the results of the \n\n\n\npresent study. Likewise,  Pramono and Ginting (1997) found that a high density of \n\n\n\ncanopies have a higher intercepted amount of rain, while rain intensity showed an \n\n\n\ninverse relation to interception capacity (Siregar et al. 2006). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Figure 7 presents the inverse linear relationship between LAI and net \nrainfall, that is, as LAI decreases net rainfall increases. Net rainfall is expressed \nas the amount of stemflow and throughfall. Therefore, the decreasing trend of \nnet rainfall follows an increasing trend of higher rain water interception  with \nincreasing LAI (Banabas 2007). Dinata (2007) found a negative relationship \nbetween throughfall and  age of tree. \n Net rainfall declines with increasing tree age and LAI due to increasing \ncanopy density. Net rainfall is relatively higher for young oil palm because the \ncanopies that are formed are relatively sparse, as indicated by their smaller LAI. \nConsequently, more rain water falls to the ground via throughfall and stemflow. \nBranch angle, LAI and canopy gap fractions play important roles in rainfall \npartitioning (Crockford and Richardson 2000; Johnson and Lehmann 2006).\n The texture of the oil palm trunk is fibrous and its unique frond arrangement \nand structure allows for the  intercepted rainfall to be collected and channeled \ndown into the tree trunk. Andre et al. (2008) and Ahmadi et al. (2009) documented \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021166\n\n\n\nFigure 7. Relationship between LAI and net rainfall\n\n\n\n11 \n \n\n\n\n \nFigure 7. Relationship between LAI and net rainfall \n\n\n\n \nFigure 7 presents the inverse linear relationship between LAI and net \n\n\n\nrainfall, that is, as LAI decreases net rainfall increases. Net rainfall is expressed as \n\n\n\nthe amount of stemflow and throughfall. Therefore, the decreasing trend of net \n\n\n\nrainfall follows an increasing trend of higher rain water interception  with \n\n\n\nincreasing LAI (Banabas 2007). Dinata (2007) found a negative relationship \n\n\n\nbetween throughfall and  age of tree.  \n\n\n\nNet rainfall declines with increasing tree age and LAI due to increasing \n\n\n\ncanopy density. Net rainfall is relatively higher for young oil palm because the \n\n\n\ncanopies that are formed are relatively sparse, as indicated by their smaller LAI. \n\n\n\nConsequently, more rain water falls to the ground via throughfall and stemflow. \n\n\n\nBranch angle, LAI and canopy gap fractions play important roles in rainfall \n\n\n\npartitioning (Crockford and Richardson 2000; Johnson and Lehmann 2006). \n\n\n\nthat stemflow is generally higher for trees with funnel-like canopy shapes, larger \ncrown projection area, smoother bark, and a lower growth of canopy lichens and \nmosses. \n Interception is also a factor affecting net rainfall. Interception strongly \ncorrelates with rainfall above the canopy because interception increases with \nincreasing LAI. According to Ward and Robinson (2000), rainfall duration is the \nimportant factor in interception loss after canopy storage capacity. It influences \ninterception by reducing the amount of water stored on the vegetation and increases \nevaporative losses. Farmanta (2012) documented that because the mature oil palm \nhave higher LAI than younger ones, intercepted rain is retained longer in the \ncanopies of older trees and more of the retained water in older trees will be lost as \nevaporation compared to younger trees. The larger surface area of the tree trunk \nfor older palms also means that the oil palm trunk would have a larger capacity to \nabsorb the stemflow than the trunks of younger trees.\n\n\n\nCONCLUSIONS\nThe proportions of throughfall, stemflow, and interception of rainfall for oil palm \ntrees between ages 5  to  20 years were 92.2% to 58%, 2.4 to 0.7%, and 5.4% to \n41%, respectively. The linear equations between throughfall stemflow, and net \nrainfall with LAI were Tf = -8.5032 LAI + 116.74, Sf = -0.469 LAI + 3.8808, and \nPn = -8.9722 LAI + 120.62, respectively.  Our study results  should serves as a \nguide in in water management of the oil palm of various ages.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 167\n\n\n\nACKNOWLEDGEMENTS\nThis study was funded by the Ministry of Education Malaysia PRGS fund (No. \nPRGS/1/2019/WAB01/UPM/02/1).\n\n\n\nREFERENCES\nAhmadi, M.T., P. Attarod, M.R.M. Mohajer, R. Rahmani and J. Fathi. 2009. Partitioning \n\n\n\nrainfall into throughfall, stemflow and interception loss in an oriental beech \n(Fagus orientalis Lipsky) forest within the growing season. Turkey Journal of \nAgriculture and Forestry 33: 557\u2013568. \n\n\n\nAndre, F., J. Mathieu and Q. Ponette. 2008. Influence of species and rain event \ncharacteristics on stemflow volume in a temperate mixed oak-beech stand. \nHydrological Process 20: 3651-3663.\n\n\n\nArnell, N. 2002. Hydrology and Global Environmental Change. Town???? Prentice \nHall. \n\n\n\nAwal, M.A., W.W.I. Ishak and S.M.B. Gevao. 2008. 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"\n\n___________________\n*Corresponding author : latifah.ghani@umt.odu.my\n\n\n\nINTRODUCTION\nNitrogen is an important element needed to complete environmental sustainability \nfrom a micro perspective. Nitrogen which comprises 78% of atmospheric gases is \nalso the most important element in protein sources for earth\u2019s life and has provided \nmajor support to the productivity of the food chain (Ghaly and Ramakrishnan \n2015). However, increasing nitrogen concentrations in the environment can lead \nto catastrophic crises such as surface water eutrophication, nitrate pollution in \nsoil, soil acidity and greenhouse gas emissions (Gu et al. 2015; Nettles et al. 2016; \nNguyen et al. 2016). The soil containing enough nitrogen molecule markers is \nimportant to complete the nutrient cycle, organic matter decomposition, residual \ndegradation, nutrient transformation, atmospheric composition, water quality and \nplant productivity (Alvares-Martin et al. 2016; Zheng et al. 2016; Coleman et al., \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 117-131 (2018)                          Malaysian Society of Soil Science\n\n\n\nPotential Use of Substance Flow Analysis to Recount the \nNitrogen Flux in Agriculture Soils System in Terengganu\n\n\n\nLatifah, Abdul Ghani\n\n\n\nSchool of Social and Economic Development, University Malaysia Terengganu, \nMalaysia\n\n\n\nABSTRACT\nThe Substance Flow Analysis (SFA) concept has been widely used for the \nsustainability of material flow management in agriculture of many South-east Asia \nstudies including Malaysia. Focusing on agricultural land, this paper emphasises \nthe dynamic analysis of nitrogen flux from sectors, processes and flows that \nenter, leave, circulate and also accumulate in the metabolism of agricultural land. \nSubSTance Flow Analysis software (STAN 2.5) and Microsoft Excel have been \nused to complete the nitrogen equilibrium calculation in four selected subsystems \nwhich are subsystem market use, crop production, livestock production and \nenvironment, in six districts in Terengganu. In the present study, intensive use of \nnitrate fertilisers (5835 tons N per year), crop harvesting (3645 tonnes per year), \nand water absorbing into underground systems (1969 tons per year), have been \nidentified as the major contributors to environmental degradation. The results of \nthe present study found that the SFA method is very practical to estimate the amount \nof agricultural nutrient load, to identify the contribution of agricultural activity, the \nlevel of land use, and also to analyse the pattern of agricultural nutrient release \nspace in Terengganu. Some innovative proposals are also offered and hopefully \nthis study can be used as a reference for any sustainable management plan  for \nother scientific studies. \n\n\n\nKeywords: Substance Flow Analysis (SFA), nitrogen (N), agriculture \nsoil, Terengganu, sustainability.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018118\n\n\n\n2004). Leilei et al. (2018) found soil respiration, both autotrophic respiration and \nheterotrophic respiration, to be the major contributor to controlling carbon dioxide \nin the atmosphere. Galloway et al (2008) and Poschenrieder et al. (2008), on the \nother hand, found that atmospheric deposition, due to fossil fuel burning since \nthe industrial revolution, has contributed to soil acidity and directly to toxicity \nin soil organisms and plant roots. However, the allocation of nitrogen reduction \nunder the soil ecosystem can also be affected by cation toxicity, loss of mineral \ncation and ammonium toxicity (Van den Berg et al.2005 Spelling in ref different) \nBowman et al. 2008; Tian et al. 2015; Tian and Niu 2015). In response to these \nabove-mentioned factors, the investigation of application of synthetic nitrogen \nfertilisers  and leguminous cultivation at the initial release of the material is crucial  \nto understand its relation to the entire chain of agricultural land ecosystems. \nThis requirement is in line with the discovery by Andrian (2017) who stated the \nfertiliser application of synthetic nitrogen fertiliser and manure and the increase \nin anthropic pressure in the agro-system should be understood on a detailed \nmolecular scale. Explanation on the existence of nitrogen flow underground and \non the ground organs is essential to improve sustainable agriculture.\n\n\n\nSubstance Flow Analysis (SFA) is a tool used to measure the flow and stock \nof materials in different and changing spatial space units (Senthilkumar et al. \n2012 and Zhang et al. 2017). SFA detects nutrients that flow and manages the \nnutrients flow in a more sustainable, efficient and systematic recovery system. \nHowever, Li et al. (2013) found that SFA techniques have limitations in measuring \ntrajectory of long-term changes and include external control factors such as \ntemperature regimes, crop rotations, rainfalls, trade tariffs, farm inputs, fertiliser \nsubsidies and others. The efficiency of SFA utilisation can be further enhanced \nthrough a longer period of regular inspection and  by taking into consideration \nmore suitable variables depending on the socio-economic changes in a study area. \nSFA calculates the nutrient outflow mechanism at the release point based on the \nspatial and the non-spatial inventory databases at village, city, local, national and \nglobal agglomeration scale (Chen et al. 2010; Bouwman et al. 2013). Although \nthe uniqueness of the SFA approach is often expressed in industrial ecology \n(Montangero et al. 2007; Cencic and Rechberger 2008; Schaffner et al. 2009), \nsome researchers have successfully expanded their research into nitrogen chain in \nagricultural land. Most of the studies illustrate the excessive, or the decrease and \nstagnant nitrogen flows in the study environment (Bashkin et al. 2002; Chen et al. \n2010; Gronman et al. 2016). Some quantitative assessments of the nitrogen cycle \nhave been carried out by Jiang and Yuan (2015) and Liu et al. (2016). However, \nthere is still no local study recorded yet that provides an integrated understanding \nof the nitrogen flow in agricultural land and its environment using SFA techniques. \nTherefore, the SFA\u2019s role in investigating the nitrogenous traces that enter, are \nlost  and leave the environment, can lead to the formation of a potential nitrogen \npollution index in Terengganu. Ma et al. (2010) calculated nitrogen using the \nSFA method and successfully developed Model NUFER (Nutrient Flow in the \nFood Chain, Resource and Environmental Management). York et al. (2003) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 119\n\n\n\nand Wang et al. (2013) also successfully developed Model INFA (Integrated \nNutrient Flow Analysis) and Model STRIPAT (STochastic Impacts by Regression \non Population), Affluence and Technology, which proves that there is nitrogen \nchange in agricultural land due to human destruction. The objective of this study \nis to measure and to interpret changes of nitrogen flow in the nutrient management \nsystem and subsequently present its results for the state of Terengganu, Malaysia. \n\n\n\nMATERIALS AND METHODS\nIn situ and ex situ data collection was conducted over a continuous 12-month period \nusing resources from six agricultural areas of  Kemaman, Dungun, Marang, Setiu, \nBesut, Hulu Terengganu, Terengganu with the data being  primary, secondary \nand tertiary. The study focused on crop production and livestock systems. The \noverall study applied the Substance Flow Analysis (SFA) method introduced by \nBrunner and Rechberger (2004) to calculate nitrogen inputs, outputs and stocks. \nMeanwhile, STAN, short for subSTance flow ANalysis software, has been used \nto create a system of material flow in the form of substance and mass to nutrien \nmanagement in this research study. Besides this, there are five important steps in \ngenerating the material flow modeling framework, which are as follows: System \nAnalysis, Model Approach, Data Acquisition, and Simulation of the Results and \nUncertainty Analysis. The detailed explanation for the implementation of the SFA \nmethod for some important variables is shown in Table 1. Basically, the calculation \nof nitrogen flow is derived from the value of the material flow rate multiplied \nby the nitrogen concentration. Most of the material and the nitrogen inventory \ndatabases were collected from several sources such as statistical databases at \ngovernment, state and private agencies, theses, journal articles, government \nreports, consultations and interviews with experts and farm entrepreneurs as well \nas raw data collection in the study area.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nRecounting the Nitrogen Loads in Soil System\nFrom the SFA method, a total of 9,817 tons of N per year has been recorded in the \nsubsystem of agricultural land use. It is estimated that 75% of nitrogen is in the \nsubsystem of crop land use. The remaining 25% is identified in the subsystem of \nfarming activities. The results for the nitrogen balance are based on the current \ninput rate of nitrogen at the initial stage of the subsystem. The findings from the \nseveral official interviews and data collection indicate that  most of the agricultural \nland in the study area is of BRIS Land Series which can be divided into several \nsub series :  Baging, Rhu Tapai, Rudua, Jambu, Rusila and Merchang (Armanto et \nal. 2013; Usman et al. 2013; Paramananthan, 2000; 2017 (Not in ref list)). Much \nof the land use for commercial agricultural cultivation is carried out in peat and \nlowland soil. Figure 1 shows the nitrogen flow in the agricultural land of the study \narea.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018120\n\n\n\nTABLE 1\n Mathematical equations for nitrogen input and output\n\n\n\nLatifah, A.G. \n\n\n\n134 Malaysian Journal of Soil Science Vol. 19, 2015 \n\n\n\nWet deposition (IN3_Rn) \n\n\n\nIN3_Rn = 0.14 \u20f0 (rainfall) \u00bd \n\n\n\nIN3_rainfall in kg/ha/yr and rainfall in mm/yr.  \n\n\n\nNo specific data could be obtained on the atmospheric nitrogen-\ndeposition for research area. The calculation to determine the total \natmospheric deposition for TAS ecosystem was done based on the \nformula given above by Stoorvogel et al. (1993), and multiplied by \nthe size of agricultural land in each district. Due to the long distance \nbetween the Terengganu region and heavy industries as well as the \npower plant, the atmospheric nitrogen deposition was assumed to be \nvery low. Dividing this input with the size of TAS (60,520 ha), the \nN input from atmospheric deposition was estimated to be 0.05 g/ha. \nUsing the above formula, the rainfall intensity in TAS was 1800 \nmm.  \n\n\n\nLeaching losses (OUT3_Ll) \n  \nQ = (PR-0.2S)2 - (R+ 0.8S)  \nwhere  \n\n\n\nQ = Runoff (inches),  \nR = Rainfall (inches), \n S = Potential maximum retention (inches) after runoff begins \n \nThe calculation for the potential of nitrogen concentration \nloss in leachate was done using the formula given above  by \nSmaling and Janssen(1993). The magnitude of leachate \ncoefficient was 0.25 for clay and 0.5 for sandy soil in TAS \n(DOA-TRG, 2017). The average annual rainfall in the area \nbeing studied was about 1800 mm. The results of calculation \nare shown in the Table c below. \n\n\n\n\n\n\n\nTable c : The coefficient for N losses by each crops,  \nCode Crop N losses (runoff+erosion) in kg/ha/yr\n\n\n\nOUT3_Llpaddy Paddy 6.5\nOUT3_Llrubber Rubber 11.9\nOUT3_Llopalm Oil Palm 6.5\nOUT3_Llmaize Maize 8.9\nOUT3_Llsugarcn Sugarcane 15.2\nOUT3_Llvegetables Vegetables 4.5\nOUT3_Llocrops Other Crops 11.9\nOUT3_Llfruits Fruits 4.5  \n\n\n\nNitrogen-fixation (IN4_Bnf)  \n \nIN4_Bnf = 0.043 \u00d7 E    \nIN4_Bnf = Amount of biological N fixation \nE = Economic yield of the leguminosae \nIN4_Bnfnfixed =  0.5 + 0.1 \u00d7 \u221arainfall  \nIN4_Bnfnonsymbiotic = \u221arainfall   \n \nThe determination of symbiotic N-fixation rate by leguminous crops \nin TAS was done using the formula given above (Smaling et al. \n1993). It was estimated that the percentage of N supply available in \nsoil was 50%. The rest was diverted by the P availability in soil in \nTAS, which limited the N-fixation. The calculation for the N-\nfixation by soy, nuts, and legumes was done based on the size of \narea harvested for these crops, and then multiplied by average \nannual rainfall in the area, assuming that the rainfall in TAS was > \n1300 mm and the average N-fixation by bacteria or algae was 30 \nkg/ha/yr. \n\n\n\nGasses losses (OUT4_Gl)  \nN2O(AWMS) = \u03a3 [N(T) x Nex(T) x AWMS(T) x EF(AWMS,T)]  \n \nwhere:  \nAWMS = Animal Waste Management System \nN2O(AWMS) = N2O emission from a particular AWMS \nN(T) = Total number of animals of type T \nNex(T) = N excretion from animals of type T (kg \nN/animal/yr) \nAWMS(T) = Fraction of Nex(T) for the type of AWMS \ncontaining animals of type T \nEF(AWMS,T) = N2O emission factor for an AWMS (kg N2O-\nN/kg Nex in the AWMS) of animals of type T. \n \nThe nitrogen denitrification process was calculated using \nthe guideline and formula developed by IPCC (1996a,b) \nbased on the function of basis transfer introduced by \nStoorvogel et al. (1993). The nitrous oxide emission \nconcentration coefficient from fertiliser and manure \naccording to the types of crops are shown in the table \nbelow. It was assumed that the average N range was 1.20\u2013\n78.48 g N2O-N /ha/day. The results of calculation are \nshown in the Table below. \n\n\n\nTable d: Results of calculation of N gas losses by crop \nproducts in TAS \n\n\n\nCode Crop NOx Emission from F+M\nOUT3_Llpaddy Paddy 1.25\nOUT3_Llrubber Rubber 1.25\nOUT3_Llopalm Oil Palm 1.25\nOUT3_Llmaize Maize 1.25\nOUT3_Llsugarcn Sugarcane 1.25\nOUT3_Llvegetables Vegetables 1.25\nOUT3_Llocrops Other Crops 1.25\nOUT3_Llfruits Fruits 1.25  \n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\nRecounting the Nitrogen Loads in Soil System \nFrom the SFA method, a total of 9,817 tons of N per year has been recorded in the \nsubsystem of agricultural land use. It is estimated that 75% of nitrogen is in the subsystem \nof crop land use. The remaining 25% is identified in the subsystem of farming activities. \nThe results for the nitrogen balance are based on the current input rate of nitrogen at the \ninitial stage of the subsystem. The findings from the several official interviews and data \n\n\n\n\n\n\n\n Malaysian Journal of Soil Science  133 \n\n\n\nnitrogen inputs, outputs and stocks. Meanwhile, STAN, short for subSTance flow ANalysis \nsoftware, has been used to create a system of material flow in the form of substance and \nmass to nutrien management in this research study. Besides this, there are five important \nsteps in generating the material flow modeling framework, which are as follows: System \nAnalysis, Model Approach, Data Acquisition, and Simulation of the Results and \nUncertainty Analysis. The detailed explanation for the implementation of the SFA method \nfor some important variables is shown in Table 1. Basically, the calculation of nitrogen \nflow is derived from the value of the material flow rate multiplied by the nitrogen \nconcentration. Most of the material and the nitrogen inventory databases were collected \nfrom several sources such as statistical databases at government, state and private agencies, \ntheses, journal articles, government reports, consultations and interviews with experts and \nfarm entrepreneurs as well as raw data collection in the study area. \n \n \n\n\n\nTABLE 1 \n Mathematical equations for nitrogen input and output \n\n\n\nMathematical equations for nitrogen input Mathematical equations for nitrogen \noutput \n\n\n\nInorganic fertiliser (IN1_Fer ) \n \nIN1_Ferallcrop = FN + \u00a31Fni + \u00a32Fnc  \nIN1_Ferallcrop = Total amount of N fertiliser in the fields \nFN = Amount of original accumulation of N in the fields \nFni = N applying \nFnc = Amount of employed compound fertiliser \n\u00a31, \u00a32 = Proportion of pure N in the Fni and Fnc \n \n\n\n\nThe N input from chemical fertilisers for every crop was estimated \nbased on the formula given above. Examples of fertilisers considered \nwere ammonium sulphate, potassium nitrate, urea (NPK), etc. The \nvalue of N input from fertilisers was multiplied by two seasons \nbecause the farmers normally applied fertilisers during these times. \nThe data for the proposed fertilisation based on crop types were \nobtained from the Department of Agriculture Terengganu, as \npresented in Table a below.  \n\n\n\nTable a: Series of guidelines for N fertiliser \n \n\n\n\nVariables Mean of N Concentration (kg/ha/yr)\nPaddy 139\n\n\n\nRubber 191\n\n\n\nPalm Oil 230\n\n\n\nSugarcane 100\n\n\n\nMaize 130\nOther Crops 120\nFruits 60\nVegetables 80  \n\n\n\n\n\n\n\nCrop Harvested (OUT1_Hp)  \n\n\n\nHpallcrop = FN + \u00a31Fni + \u00a32Fnc \n\n\n\n  \nOUT1_Hpallcrop = Total amount of N fertiliser in the fields \nFN = Amount of original accumulation of N in the fields \nFni = N applying \nFnc = Amount of employed compund fertiliser \n\u00a31, \u00a32 = Proportion of pure N in the Fni and Fnc \n \n\n\n\nN loss from harvesting in TAS was calculated using the \nformula given above. The quantities of harvested crops \n(e.g. grains, root crop, legumes, fruit crops, vegetables, and \nindustrial crops) were multiplied by respective nitrogen \nuptake coefficient. \n\n\n\nOrganic fertiliser (IN2_mn) \n \nIN2_allmn = \u2211i=1 ni \u20f0 pi \u20f0 ai \u20f0 365   \nIN2_allmn =  Volume of N in livestock manure \nni = Amount of each species of livestock \npi = Volume of excretion of each species per day \nai = t Rate  of collection and utilisation \n \nThe N coefficient can be seen in Table b above. The calculation for \norganic fertiliser application rate based on the animal manure only \ntook into account the manure left on the field during grazing. The \nratio of organic fertiliser frequently used by farmers in research area \nconsisted of cattle manure (81%) and chicken manure (19%). To \nformulate this input, the field of input manure = 1.7 kg/ha/yr was \nadded into attribute Table b.  \n\n\n\nTable b: Guidelines series for N fertilisation  \nCode Species N concentration (kg/head/day)\n\n\n\nIN2_mnbuffalo Buffalo 0.0020\nIN2_mncattle Cattle 0.0020\nIN2_mngoat Goat 0.0024\nIN2_mnsheep Sheep 0.0020\nIN2_mnpoultry Poultry 0.0023  \n\n\n\nCrop Residue (OUT2_Bcr) \n\n\n\nOUT2_Bcrallcrop = m\u2211j=1 OUT2,j \u20f0 Cj  \n\n\n\n \nOUT2_Bcrallcrop = Total biomass crop straw/residue output \nOUT2,j = Quantity of the type of biomass \nm = Number of different type of crops \nCj = Proportion of N per unit of a type of straw \n \nNitrogen loss from harvested agricultural wastes disposal \nwas calculated using the formula given above. The \nagricultural wastes were left in the field where they \ndecomposed or grazed by cattle, buffalos, or goats. Only \nthe straws are assumed to have left the case system through \nexternal use.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 121\n\n\n\nThe Framework of the Nitrogen Flux Model\nThe nitrogen input and output flows that were taken into account in Terengganu\u2019s \nagriculture system are illustrated in Figure 2. The total nitrogen load recorded \nin 2017 was 15.81 kiloton N/yr. The nitrogen deficit in agricultural soil was \nestimated to be 2305 ton N/yr. The comparison between crop production and \nlivestock production revealed that  nitrogen loss was four times higher in the crop \nproduction system with 3109 ton N/yr compared with the livestock production \nsystem with 804 ton N/yr. This result correlated with the initial reading of the \nstatistics, which only considered the nitrogen flow aspect in agricultural wastes \nsuch as crop residues and animal wastes. \n\n\n\nFor the nitrogen outflow from the crop production system, the findings \nshow that the percentage of nitrogen accumulation was higher in the soil stock, \nwhich was 73% with 7495 ton N/yr, compared with the 26% with 2683 ton N/\nyr of nitrogen released into the water body. The nitrous gases emission into the \natmosphere only accounted for 1% with 47 ton N/yr of the nitrogen outflow. In \nthe crop production system, the highest nitrogen input was contributed by the \n\n\n\nLatifah, A.G. \n\n\n\n134 Malaysian Journal of Soil Science Vol. 19, 2015 \n\n\n\nWet deposition (IN3_Rn) \n\n\n\nIN3_Rn = 0.14 \u20f0 (rainfall) \u00bd \n\n\n\nIN3_rainfall in kg/ha/yr and rainfall in mm/yr.  \n\n\n\nNo specific data could be obtained on the atmospheric nitrogen-\ndeposition for research area. The calculation to determine the total \natmospheric deposition for TAS ecosystem was done based on the \nformula given above by Stoorvogel et al. (1993), and multiplied by \nthe size of agricultural land in each district. Due to the long distance \nbetween the Terengganu region and heavy industries as well as the \npower plant, the atmospheric nitrogen deposition was assumed to be \nvery low. Dividing this input with the size of TAS (60,520 ha), the \nN input from atmospheric deposition was estimated to be 0.05 g/ha. \nUsing the above formula, the rainfall intensity in TAS was 1800 \nmm.  \n\n\n\nLeaching losses (OUT3_Ll) \n  \nQ = (PR-0.2S)2 - (R+ 0.8S)  \nwhere  \n\n\n\nQ = Runoff (inches),  \nR = Rainfall (inches), \n S = Potential maximum retention (inches) after runoff begins \n \nThe calculation for the potential of nitrogen concentration \nloss in leachate was done using the formula given above  by \nSmaling and Janssen(1993). The magnitude of leachate \ncoefficient was 0.25 for clay and 0.5 for sandy soil in TAS \n(DOA-TRG, 2017). The average annual rainfall in the area \nbeing studied was about 1800 mm. The results of calculation \nare shown in the Table c below. \n\n\n\n\n\n\n\nTable c : The coefficient for N losses by each crops,  \nCode Crop N losses (runoff+erosion) in kg/ha/yr\n\n\n\nOUT3_Llpaddy Paddy 6.5\nOUT3_Llrubber Rubber 11.9\nOUT3_Llopalm Oil Palm 6.5\nOUT3_Llmaize Maize 8.9\nOUT3_Llsugarcn Sugarcane 15.2\nOUT3_Llvegetables Vegetables 4.5\nOUT3_Llocrops Other Crops 11.9\nOUT3_Llfruits Fruits 4.5  \n\n\n\nNitrogen-fixation (IN4_Bnf)  \n \nIN4_Bnf = 0.043 \u00d7 E    \nIN4_Bnf = Amount of biological N fixation \nE = Economic yield of the leguminosae \nIN4_Bnfnfixed =  0.5 + 0.1 \u00d7 \u221arainfall  \nIN4_Bnfnonsymbiotic = \u221arainfall   \n \nThe determination of symbiotic N-fixation rate by leguminous crops \nin TAS was done using the formula given above (Smaling et al. \n1993). It was estimated that the percentage of N supply available in \nsoil was 50%. The rest was diverted by the P availability in soil in \nTAS, which limited the N-fixation. The calculation for the N-\nfixation by soy, nuts, and legumes was done based on the size of \narea harvested for these crops, and then multiplied by average \nannual rainfall in the area, assuming that the rainfall in TAS was > \n1300 mm and the average N-fixation by bacteria or algae was 30 \nkg/ha/yr. \n\n\n\nGasses losses (OUT4_Gl)  \nN2O(AWMS) = \u03a3 [N(T) x Nex(T) x AWMS(T) x EF(AWMS,T)]  \n \nwhere:  \nAWMS = Animal Waste Management System \nN2O(AWMS) = N2O emission from a particular AWMS \nN(T) = Total number of animals of type T \nNex(T) = N excretion from animals of type T (kg \nN/animal/yr) \nAWMS(T) = Fraction of Nex(T) for the type of AWMS \ncontaining animals of type T \nEF(AWMS,T) = N2O emission factor for an AWMS (kg N2O-\nN/kg Nex in the AWMS) of animals of type T. \n \nThe nitrogen denitrification process was calculated using \nthe guideline and formula developed by IPCC (1996a,b) \nbased on the function of basis transfer introduced by \nStoorvogel et al. (1993). The nitrous oxide emission \nconcentration coefficient from fertiliser and manure \naccording to the types of crops are shown in the table \nbelow. It was assumed that the average N range was 1.20\u2013\n78.48 g N2O-N /ha/day. The results of calculation are \nshown in the Table below. \n\n\n\nTable d: Results of calculation of N gas losses by crop \nproducts in TAS \n\n\n\nCode Crop NOx Emission from F+M\nOUT3_Llpaddy Paddy 1.25\nOUT3_Llrubber Rubber 1.25\nOUT3_Llopalm Oil Palm 1.25\nOUT3_Llmaize Maize 1.25\nOUT3_Llsugarcn Sugarcane 1.25\nOUT3_Llvegetables Vegetables 1.25\nOUT3_Llocrops Other Crops 1.25\nOUT3_Llfruits Fruits 1.25  \n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\nRecounting the Nitrogen Loads in Soil System \nFrom the SFA method, a total of 9,817 tons of N per year has been recorded in the \nsubsystem of agricultural land use. It is estimated that 75% of nitrogen is in the subsystem \nof crop land use. The remaining 25% is identified in the subsystem of farming activities. \nThe results for the nitrogen balance are based on the current input rate of nitrogen at the \ninitial stage of the subsystem. The findings from the several official interviews and data \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018122\n\n\n\napplication of nitrate-based fertilisers in cropping activities with 5954 ton N/yr, \ncrop residues with 715 ton N/yr, and bacterial fixation with 648 ton N/yr.\n\n\n\nThe highest nitrogen load percentage from the livestock production \nsubsystem released into the  \u2018Environment\u2019 was  into the soil stock especially \nplantation soil,  at  around 55% with 444 ton N/yr. Next came water and air at 39% \nwith 315 ton N/yr and 6% with 43 ton N/yr, respectively. The release of animal \nwaste and wastewater directly into the environment has emerged as the highest \nnitrogen flow input in the livestock production system, with the total amount \nbeing 749 ton N/yr. Based on the observation during the sampling activities in \nfeedlot farms and traditional grazing farms, much of the nitrogen in livestock \nmanure, carcasses, and slaughter wastes was not being reused and only 5 to 7% \nwas recycled.\n\n\n\nHowever, the production of nitrogen from import, export, household, \nand the food processing industry are excluded from this study. Further, inputs \non atmospheric deposition, seed, human excreta, and irrigation that involve \nammonification as well as nitrification, subsequently leading  to denitrification, \n\n\n\nFigure 1: Nitrogen input and output in the soil system \n\n\n\n\n\n\n\n Malaysian Journal of Soil Science  135 \n\n\n\ncollection indicate that  most of the agricultural land in the study area is of BRIS Land \nSeries which can be divided into several sub series :  Baging, Rhu Tapai, Rudua, Jambu, \nRusila and Merchang (Armanto et al. 2013; Usman et al. 2013; Paramananthan, 2000; \n2017(Not in ref list)). Much of the land use for commercial agricultural cultivation is \ncarried out in peat and lowland soil. Figure 1 shows the nitrogen flow in the agricultural \nland of the study area. \n \n\n\n\n \nFigure 1: Nitrogen input and output in the soil system  \n\n\n\n  \nThe Framework of the Nitrogen Flux Model \nThe nitrogen input and output flows that were taken into account in Terengganu\u2019s \nagriculture system are illustrated in Figure 2. The total nitrogen load recorded in 2017 was \n15.81 kiloton N/yr. The nitrogen deficit in agricultural soil was estimated to be 2305 ton \nN/yr. The comparison between crop production and livestock production revealed that  \nnitrogen loss was four times higher in the crop production system with 3109 ton N/yr \ncompared with the livestock production system with 804 ton N/yr. This result correlated \nwith the initial reading of the statistics, which only considered the nitrogen flow aspect in \nagricultural wastes such as crop residues and animal wastes.  \n\n\n\nFor the nitrogen outflow from the crop production system, the findings show that the \npercentage of nitrogen accumulation was higher in the soil stock, which was 73% with \n7495 ton N/yr, compared with the 26% with 2683 ton N/yr of nitrogen released into the \nwater body. The nitrous gases emission into the atmosphere only accounted for 1% with 47 \nton N/yr of the nitrogen outflow. In the crop production system, the highest nitrogen input \nwas contributed by the application of nitrate-based fertilisers in cropping activities with \n5954 ton N/yr, crop residues with 715 ton N/yr, and bacterial fixation with 648 ton N/yr. \n\n\n\n\n\n\n\nFigure 2: The SFA model for nitrogen flow in the agriculture system in Terengganu\n\n\n\n\n\n\n\n Malaysian Journal of Soil Science  135 \n\n\n\ncollection indicate that  most of the agricultural land in the study area is of BRIS Land \nSeries which can be divided into several sub series :  Baging, Rhu Tapai, Rudua, Jambu, \nRusila and Merchang (Armanto et al. 2013; Usman et al. 2013; Paramananthan, 2000; \n2017(Not in ref list)). Much of the land use for commercial agricultural cultivation is \ncarried out in peat and lowland soil. Figure 1 shows the nitrogen flow in the agricultural \nland of the study area. \n \n\n\n\n \nFigure 1: Nitrogen input and output in the soil system  \n\n\n\n  \nThe Framework of the Nitrogen Flux Model \nThe nitrogen input and output flows that were taken into account in Terengganu\u2019s \nagriculture system are illustrated in Figure 2. The total nitrogen load recorded in 2017 was \n15.81 kiloton N/yr. The nitrogen deficit in agricultural soil was estimated to be 2305 ton \nN/yr. The comparison between crop production and livestock production revealed that  \nnitrogen loss was four times higher in the crop production system with 3109 ton N/yr \ncompared with the livestock production system with 804 ton N/yr. This result correlated \nwith the initial reading of the statistics, which only considered the nitrogen flow aspect in \nagricultural wastes such as crop residues and animal wastes.  \n\n\n\nFor the nitrogen outflow from the crop production system, the findings show that the \npercentage of nitrogen accumulation was higher in the soil stock, which was 73% with \n7495 ton N/yr, compared with the 26% with 2683 ton N/yr of nitrogen released into the \nwater body. The nitrous gases emission into the atmosphere only accounted for 1% with 47 \nton N/yr of the nitrogen outflow. In the crop production system, the highest nitrogen input \nwas contributed by the application of nitrate-based fertilisers in cropping activities with \n5954 ton N/yr, crop residues with 715 ton N/yr, and bacterial fixation with 648 ton N/yr. \n\n\n\n \nFigure 2: The SFA model for nitrogen flow in the agriculture system in Terengganu \n\n\n\nThe highest nitrogen load percentage from the livestock production subsystem released into \nthe  \u2018Environment\u2019 was  into the soil stock especially plantation soil,  at  around 55% with \n444 ton N/yr. Next came water and air at 39% with 315 ton N/yr and 6% with 43 ton N/yr, \nrespectively. The release of animal waste and wastewater directly into the environment has \nemerged as the highest nitrogen flow input in the livestock production system, with the total \namount being 749 ton N/yr. Based on the observation during the sampling activities in \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 123\n\n\n\nplant absorption, and eutrophication, are not explained in this study because these \ninputs present insignificant and slow changes within this region.\n\n\n\nLimitations\nThere are obviously some limitations  to the data collected and calculated. \nSome factors such as changes in nutrient concentrations, changes in dry weight \nof livestock feeds and possibly the lack of statistical information from various \nsources do have an influence on the results of this study. For example, complexity \noccurs in the calculation of nitrogen in relation to fertilisation, soil decomposition, \nmanure management and wastewater treatment because of nitrogen\u2019s tendency to \nescape into the air. Also, nitrogen calculation through leachate of agricultural soil \nis difficult to determine because there are differences in factors such as soil types, \ngradients and methods of cultivation.\n\n\n\nThe Tornado diagram in Figure 3 with the calculated results in Table 1 shows \nthe variation of sensitivity towards nitrogen flow, based on four main variables: \non fertilisers into soil, leaching of soil, crop yield, and crop residue. This nitrogen \nsensitivity analysis was able to identify that the nitrate chemical fertiliser \napplication by farmers is the first critical factor influencing the nitrogen load in \nthe study area. A \u00b150% change in the value would lead to a change of 2977 ton \nN/yr in the import trade. The second factor is nitrogen leaching from agricultural \nsoil. Modifying soil fertility by applying compost and planting grass could lead \nto a change of \u00b127% nitrogen emission from soil and \u00b15% N2O decomposition \ninto the atmosphere. Other variables such as  nitrogen in crop yield and N in crop \nresidue did not contribute to negative changes in  the agriculture system\n\n\n\nFigure 3: Limitations of the simulation results for nitrogen loads into Terengganu\u2019s \nagriculture system \n\n\n\nLatifah, A.G. \n\n\n\n136 Malaysian Journal of Soil Science Vol. 19, 2015 \n\n\n\nfeedlot farms and traditional grazing farms, much of the nitrogen in livestock manure, \ncarcasses, and slaughter wastes was not being reused and only 5 to 7% was recycled. \n\n\n\nHowever, the production of nitrogen from import, export, household, and the food \nprocessing industry are excluded from this study. Further, inputs on atmospheric \ndeposition, seed, human excreta, and irrigation that involve ammonification as well as \nnitrification, subsequently leading  to denitrification, plant absorption, and eutrophication, \nare not explained in this study because these inputs present insignificant and slow changes \nwithin this region. \n\n\n\nLimitations  \n \nThere are obviously some limitations  to the data collected and calculated. Some factors \nsuch as changes in nutrient concentrations, changes in dry weight of livestock feeds and \npossibly the lack of statistical information from various sources do have an influence on the \nresults of this study. For example, complexity occurs in the calculation of nitrogen in \nrelation to fertilisation, soil decomposition, manure management and wastewater treatment \nbecause of nitrogen's tendency to escape into the air. Also, nitrogen calculation through \nleachate of agricultural soil is difficult to determine because there are differences in factors \nsuch as soil types, gradients and methods of cultivation \n \nThe Tornado diagram in Figure 3 with the calculated results in Table 1 shows the variation \nof sensitivity towards nitrogen flow, based on four main variables: on fertilisers into soil, \nleaching of soil, crop yield, and crop residue. This nitrogen sensitivity analysis was able to \nidentify that the nitrate chemical fertiliser application by farmers is the first critical factor \ninfluencing the nitrogen load in the study area. A \u00b150% change in the value would lead to a \nchange of 2977 ton N/yr in the import trade. The second factor is nitrogen leaching from \nagricultural soil. Modifying soil fertility by applying compost and planting grass could lead \nto a change of \u00b127% nitrogen emission from soil and \u00b15% N2O decomposition into the \natmosphere. Other variables such as  nitrogen in crop yield and N in crop residue did not \ncontribute to negative changes in  the agriculture system \n \n \n \n \n \n \n \n\n\n\n \n \nFigure 3: Limitations of the simulation results for nitrogen loads into Terengganu\u2019s agriculture \nsystem  \n \n\n\n\nVariables Mean \nvalues \n\n\n\n \n(%) \n\n\n\n\n\n\n\nNitrogen \nleaching of \nsoil \n\n\n\n2.3 kton \nN/yr \n\n\n\n\u00b155 \u00b11.3 kton N/yr \n\n\n\nNitrogen in \ncrop yield \n\n\n\n4.7 kton \nN/yr \n\n\n\n\u00b145 \u00b12.1 kton N/yr \n\n\n\nNitrates soil-\nfertiliser \n\n\n\n6.0 kton \nN/yr \n\n\n\n\u00b160 \u00b13.6 kton N/yr \n\n\n\nCrop \nresidues \n\n\n\n0.7 kton \nN/yr \n\n\n\n\u00b150 \u00b10.4 kton N/yr \n\n\n\nUncertainties Uncertainties\n\n\n\n0 20 40 60 80 100 120 140\n\n\n\nLV Wastewater discharged\n\n\n\nCrop residue discarded\n\n\n\nFeedstuff & Fodder\n\n\n\nCrop respiration NPP\n\n\n\nDISCUSSION AND RECOMMENDATIONS\nThe regional variation estimate for nitrogen balance in the Terengganu agriculture \nsystem revealed several crucial factors and variables that might cause nitrogen \nloss into the environment. The application of nitrate fertilisers, crop produce, and \nleachate were the three key variables that influenced the nitrogen accumulation \nin the soil and water systems. Based on the area of land use in the seven districts, \nBesut and Kemaman were the areas which had the highest nitrogen accumulation \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018124\n\n\n\nfrom the agriculture waste system. Each of the  nitrogen loads in the agriculture \nsystem had its respective identity that was clearly influenced by the different \nagricultural soil management and agricultural practices.\n\n\n\nThe extent of  nitrogen in the crop production system revealed that the \napplication of nitrogen fertilisers and the disposal of crop residue were the main \ninputs that contributed to the nitrogen accumulation in the agricultural soil system \nin Terengganu. Almost 92% of the fertilisers used to increase productivity in the \nagriculture soil system in Terengganu, especially in intensive plantation, depended \nhighly on non-organic fertilisers (FOA-TRG 2017). According to FAMA-\nTRG (2017), all nitrate-based fertilisers except urea were entirely from sources \nimported from outside the case system. This perpetual trend of importing raw \nmaterials is likely to cause a substantial loss in foreign exchange. Meanwhile, the \nnitrogen dioxide emission from fertiliser application is considered insignificant \nin anthropogenic global warming. However, the processes involved in intensive \nagriculture such as transportation, production, and the use of equipment or \ntechnologies that produce nitrogen dioxide, are considered to have an impact on \nozone destruction and climate change. This is due to the fact that the potential \nwarming effect of nitrous oxide gases (N2O) is 300 times higher than carbon \ndioxide (CO2) (Foster, 2004). \n\n\n\nThe nitrogen dioxide in crop residue disposed into the  soil system in the \nstudy area also received attention, especially in the context of nutrient management \npractice, because it contributed an approximate ly 42.3% of nitrogen loss into the \nsurrounding water body. According to interviews and records obtained at several \nharvesting sites (e.g. maize farm in Kuala Berang, oil palm plantation in Air Putih, \nKemaman, rubber farm in Manir and paddy field in Kenak, Besut), the average \nproduction of crop residues was more than 49 kg N/ha/yr. Most of the local farmers \nin Terengganu removed agriculture waste from the field or burned the residue \nat a ratio of 0.5 to 1. Therefore, if 3 to 4 kg of nitrogen is discharged into the \nenvironment without control, 1/6 of this nitrogen would go directly into the water \nbody because plants have a higher tendency to discharge the unused nitrogen into \na water source compared to phosphate or sodium (DOE-TRG 2017). In addition, \nthe high nitrogen concentration in the surface runoff  is an important indicator of  \nproblems, especially as a proxy to evaluate eutrophication. However, it is possible \nfor nitrogen enrichment in agricultural soil to change according to factors such as \nclimate, morphology, nutrient ratio, climate, water resistance time, and ecosystem \nendurance. In the  agriculture system in Terengganu, the high rainfall distribution \npredicts that the accumulated nitrogen concentration in soil is probably not much. \nHowever, balancing the nitrogen flow at the non-point pollution source should \ntake priority in the study area.\n\n\n\nSome measures to increase  nitrogen efficiency in the management of the \nstudy area are as follows:\n\n\n\n\u2022 Practice of open burning of the paddy straw and other agricultural \nwastes should cease. A better option would be to let those wastes \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 125\n\n\n\nremain at ground level with a deeper ploughing into the agricultural \nsoil.\n\n\n\n\u2022 Expanding the planting area by planting cover crops to fortify the soil \nstructure, thus reducing soil erosion and preventing nutrient leaching. \nAntikaninen et al. (2005) proposed bioenergy crop planting such as \nwillow to minimise nitrogen loss through leaching from the agricultural \nsoil. This crop could also be used as animal feed.\n\n\n\n\u2022 Practice of a fertilising technique based on the accurate compositional \nneeds of each crop, according to the standard stipulated by the \nDepartment of Terengganu Agriculture (DOA-TRG 2017).\n\n\n\n\u2022 Promotion of organic farming initiatives by local authorities of \nTerengganu by setting fertiliser-free areas or zones, especially near to \nthe water sources.\n\n\n\n\u2022 Decision makers, especially from the private sector and experienced \nindividuals  should  be encouraged to contribute ideas in matters \nrelated to nitrogen management in the agricultural system such as \neutrophication, leaching NO3-N, climate change, and so on.\n\n\n\nFor the livestock farming sector, the highest annual nitrogen surplus in \nTAS was contributed by variables such as animal feedstuff, animal manure and \nanimal wastewater. Some 66% of nitrogen came from a combination of these \nthree variables. In TAS, the harvested N consumed by farm animals in the form of \nstraw, grasses, grains, and by-products of industrial processing was very high, up \nto 283 k ton of feed. Kuipers et al. (1999) and Tunney et al. (2010) proposed that \nthe N composition in animal feed could be switched to a mixture of grasses with \nlower N-silage feed added with roughage such as corn. The results also show that \nthe average reuse rate of organic fertilisers including compost from agriculture \nwastes as well as chicken, goat, and cattle manure was around 23\u201334% equivalent  \nto 133 ton N/yr. Animal wastes including manure, bones, and ash were frequently \nused by local farmers to fertilise their agricultural land.\n\n\n\nThis study also verified that Terengganu agriculture region was basically \nfree from water pollution caused by nitrogen originating from pig manure (TVSD \n2018). In Malaysia, the health limit specified by WHO for nitrogen in water \nsource should not exceed 50 mg NO3/Liter. Camargo et al. (2005) set the nitrogen \ncritical limit for ecological and toxicological effects related to non-organic \npollution in aquatic system to be 0.5 - 1.0 mg N/Liter. The local authorities were \nmore focused on the issues of leachate and slurry from other species such as \ncow, goats and poultry. The huge nitrogen loss in water was clearly occurring in \nthe traditional free-range farming compared to the feedlot farming system in the \nresearch area. Almost 96% of the animal excreta was discharged in liquid and \nsolid forms into the environment without first undergoing any waste treatment. \nAccording to Deng Xiang (2010), about 5 kg N/ha/yr could be converted from \nthe pasture in the grazing system to animal products. Hence, concerted actions \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018126\n\n\n\nincluding policy making, technological development, and treatment plants with \naerobic and anaerobic ponds are highly needed in this region.\n\n\n\nIn general, several approaches can be adopted in nitrogen management in the \nlivestock sector in the study area, as listed below:\n\n\n\n\u2022 Adopting more advanced animal wastes recovery approaches\n\u2022 Controlling the time and dose for fertilisation. The limit proposed by \n\n\n\nTen Berge and van Dijk (2009) was 10%.\n\u2022 Covering the animal manure stored in the farm storage to reduce \n\n\n\nvolatilisation.\n\u2022 Preparing a reservoir pond to hold manure slurry and leachate for at \n\n\n\nleast nine months.\n\u2022 Applying animal wastes during ploughing within 12 hours after land \n\n\n\nspreading.\n\n\n\nAlthough the use of nitrogen in this region was balanced and under control, \nthis is not sustainable if no follow-up measures are taken. Due to the fact that \nnitrate-based raw supplies for agricultural systems involves a high cost, the \ncollaboration among the regional community, farmers\u2019 attitude, and enforcement \nof local regulations, is complex and requires continuous study. This study of \nnitrogen flow and flux in agricultural produce using SFA is useful in providing \nsupport to matters related to the calculation of influential nitrogen flow and \nreactive nitrogen in certain parts of the environment. This SFA study findings \nshow that improved strategies in agricultural wastes management and efficient \nuse of nutrients could reduce the negative impact of the nitrogen balance within \nthe study area.\n\n\n\nCONCLUSIONS\nUsing the  SFA has successfully allowed us to analyse nutrient interactions in \nagricultural land and their impact on the environment. The excess and loss of \nnitrogen are the best indicators of the  efficiency level of nutrient utilisation and \nthe level of dependence on inputs and outputs of external or internal materials. \nConsequently, a sustainable integrated farming system can be implemented \nin the study area to minimise material and resource losses and environmental \ndegradation.\n\n\n\nACKNOWLEDGEMENT\nThe authors would like to thank Universiti Malaysia Terengganu (The Incentives \nScheme of UMT Publication TAPE 2017/2018: vote 55123) for supporting this \nproject. 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"\n\n___________________\n*Corresponding author : E-mail: bernasmasreah@yahoo.com\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 45-58 (2021)                          Malaysian Society of Soil Science\n\n\n\nEffects of Placement and Application Rates of Briquette \nCompost on Soil, Plant Nitrogen Content and  Yield of Red \n\n\n\nBrown Rice in the  Swampland of South Sumatra\n\n\n\nBernas, S.M.1*, A. Wijaya2, E.P. Sagala3 and S.N.A. Fitri1\n\n\n\n1Department of Soil Science, Agricultural Faculty and Lowland Environment \nManagement of Postgraduate Study, University of Sriwijaya, Indonesia\n\n\n\n2Department of Agronomy, Agricultural Faculty and Lowland Environment \nManagement of PostgraduateStudy, University of Sriwijaya, Indonesia\n\n\n\n 3Department of Biology, Faculty of Mathematical and Natural Sciences  \nUniversity of Sriwijaya, Indonesia\n\n\n\nABSTRACT\nBriquette compost (BC) was made from water mimosa (Neptunia prostrate Lam.) \nwhich grows in the swampland rice fields. Factorial Randomised Block Design \n(FRBD) was used with the placement of BC as the first factor and BC dosage as \nthe second factor. The placement of compost was one BC at 1 dosage for 1 plant  \nclump with the BC being placed inside soil and rice seedling planted above it \n(BC1) and the second placement was one BC applied at the middle of four plant \nclumps, so 4 dosages were combined into one BC because this was for 4 clumps as \n(BC2). The second factor was application of  BC at rates of 0, 10, 20 and 30 ton ha-1 \nfor both treatments. Regression analysis showed that placement and dose of BC \nsignificantly correlated with absorbed N, tillers, productive tillers and rice yield.  \nPlacement of BC under plant roots gave better results than placement of BC in the \nmiddle of four plant clumps. The best  combination was between BC1 and a dosage \nof  20 ton ha-1 which produced  a rice yield of 1,014 g m-2 (or 10.14 ton ha-1). This \nyield was 175% higher compared with the control plot (586.73 g m-2 or 5.867 ton \nha-1).  The higher rice yield in BC1 compared to BC2 was due to plants being better \nable to absorb nutrients from BC straight away compared to BC2 where the plant \nroots needed a longer time to elongate and reach the BC placed in the middle of \nthe four plant clumps. Meanwhile,  nitrogen could have been lost before the roots \nreached the location where the BC was placed. Our study suggests that it is better \nto form the compost into a BC and  insert it into the soil under a plant clump for \ndirect absorption by the roots.\n\n\n\nKeyword:    Briquette compost, nitrogen, dosage, rice, swampland.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202148\n\n\n\nINTRODUCTION\nAs there is no proper irrigation system in paddy fields of the swamplands in South \nSumatra, Indonesia, rice fields depend on rain and flooding from rivers, but this \nmeans the water level cannot be controlled. The swampland paddy fields flood \nduring the moonson (from November to April) and are dry during summer (from \nMay to October).  Farmers grow rice by following the depth of water in the paddy \nfield; they start to plant rice when the water level is about 10 cm above ground.  \nIf compost needs to be applied, it has to be buried, otherwise it would be floating \non the water.  Incorporation of compost in the form of crumbs into the soil is also \ndifficult because of the water depth. That is why the compost needs to be in the \nform of  a briquette which   can ben inserted directly into the soil.  \n This briquette compost (BC) was made of water mimosa, Neptunia \nprostrate Lam, a dominant weed that grows in swampland paddy fields during \nflooding. This weed compost contained high nitrogen (4%), P (1675 mg kg-1),  \nand K (5.6%) while the C/N was 8.32. The process of making compost briquette \nhas been patented by Bernas et al. (2018). Bernas et al. (2017) had applied this \nbriquette compost for rice growing on the raft as  a floating system by inserting the \nBC into the soil and planting the rice crops above it. BC application of  20 ton ha-1 \n\n\n\nincreased the rice yield from 1.58 ton ha-1 (without compost) to 4.55 ton ha-1 with \ncompost. The higher yield in the BC treated plot was caused by the better contact \nbetween plant roots and the compost which caused the roots to grow vigorously \ninside the BC thus increasing their absorption of water and nutrients. As the BC \nwas high in nitrogen (Bernas et al. 2015), inserting it into the soil decreased N \nloss. Compost application could decrease net global warming potential by 25% \n(especially in releasing CH4 and N2O) in rice cultivation as reported by Seung \net al. (2018). When compost is applied as BC and inserted into the soil, global \nwarming could be reduced even more due to less oxidation of compost inside the \nsoil than if compost was on the soil surface. The purposes of this research were to \ndetermine the best combination of placement and dose of BC into the swampland \nsoils planted with rice and the effect of its placement and dose combinations on \nnitrogen content, growth and yield of rice.\n\n\n\nMATERIALS AND METHODS \nThis research was carried out on a paddy field at a swampland in Keramasan \nDistrict, Palembang City, South Sumatra, Indonesia. This experiment was done \non 18 plots with each plot being 2 m x 2 m in size with the distance between \nplant clumps being 20 cm x 25 cm. Soil used in this research was a potential Acid \nSulphate soil with a pH of 4.46 and a pyrite depth >80 cm, N content of 0.25%, \nC-organic content of  2.61%, and clay content of 64.33%. However, the pH of \nwater could be about 6.0 during flooding due to dilution and flushing (Bernas et \nal. 2015).\n The experiment treatments were as follows: In treatment 1, BC application \nat one dose, where BC was inserted into the soil and rice seedling was planted \nabove BC as (BC1). In treatment 2, one BC applied at the middle of four plant \n\n\n\n\n\n\n\n\nclumps, so 4 dosages were combined into one BC because this was for 4 clumps \nas (BC2). The BC doses were 0 g (D1), 28 g (D2), 56 g (D3), 74 g (D4) in weight \nper crop which was equivalent to 0 (control), 10, 20 and 30 ton ha-1, respectively.\n This experiment was conducted as  a Factorial Randomised  Block Design, \nBC placement as the first factor and compost dosage as the second factor. If there \nwas any significant difference between treatments, then they were analysed using \nLSD procedure (SAS University Edition 2.8 9.4 M6, USA).\n Data were collected on soil and plant N contents (Kjeldahl Method), plant \nheight, tiller, productive tiller, filled spikelet and rice yield. Rice yield was taken \nfrom 36 rice clumps from 1 m x 1 m fields. \n\n\n\nRESULTS AND DISCUSSION\nEffects of BC Placement and Dosage on Soil and Plant N Content and\nPlant Absorbed N \nBased on the ANOVA results, BC placement and dosage did not have a significant \neffect on soil and plant N content, but  affected significantly the N absorbed by \nthe crops. However, N dosage had the effect of increasing N in soil and plant up \nto application of  20 ton ha-1 but decreased when  dose was increased to 30 ton ha-1 \n\n\n\nwhen one BC was placed either under one plant clump (BC1) or under four  plant \nclumps (BC2) (Figures 1, 2 and 3). \n According to Ngo and Cavagnaro (2018), the impact of compost on soil \nN content is less consistent because of the highly dynamic nature of N cycling \nwhich is affected by soil/water content and this research was carried out in a \nflooded paddy field. Soil and plant N content tended to decrease at a dose of 30 \nton BC ha-1 both in BC1 and BC2. This could be attributed to the high amount of N \nin BC (4%). Further, as the soil used in this research contained medium C-organic \nand total N, the application of 30 ton BC ha-1 might have created excessive N.  \nBut in the case of BC2 treatment, the higher dose of compost resulted in higher \nN content in soil and plants (Figures 1 and 2). This was caused by lost N in the \ncompost in the form of ammonia that leached or evaporated before plant roots \nabsorbed it, as the plant roots needed time to reach the BC in the centre of the \nfour plants. According to Zhenping et al. (1991), under waterlogged conditions, \nN could convert to ammonia and ammonia loss is higher under waterlogged than \nin aerobic conditions. Xiaowei et al. (2016) reported that this is similar to the use \nof mineral fertilisers such as nitrogen that is applied once as basal fertiliser into 10 \ncm deep holes, positioned at 5 cm from the roots. He observed a 19.9% decrease \nin N compared with broadcast application which saw a decrease of 62.6%. In the \ncase of BC1, roots could absorb N in the compost straight away because the BC \nwas just under the crop. These plants grew well and produced more shoots as \nwell as absorbed more nitrogen (Figure 3) compared to BC2. In the case of BC2, \nthe placement of BC in the middle of four plant clumps led to more N loss than \nin BC1, perhaps due to stamping during weeding and measuring of plant growth \ncausing some BC2 to be exposed to the soil surface.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202150\n\n\n\n5 \n \n\n\n\n\n\n\n\nFigure 1.  Soil N content at heading stage. The values are the means of \nthree replicates and vertical lines are standard errors.  \n\n\n\nNote: BC1 is the placement of 1 BC under one plant clump; BC2 is the placement \nof 1 BC under and middle of  four plant clumps. \n\n\n\n\n\n\n\n \n \nFigure 2.  Plant N content at heading stage. Values are the means of three  \n\n\n\nreplicates and vertical lines are  standard errors. \nNote: BC1 is the placement of 1 BC under under one plant clump and BC2 is the \n\n\n\nplacement of 1 BC under and middle of four plant clumps. \n \n\n\n\n5 \n \n\n\n\n\n\n\n\nFigure 1.  Soil N content at heading stage. The values are the means of \nthree replicates and vertical lines are standard errors.  \n\n\n\nNote: BC1 is the placement of 1 BC under one plant clump; BC2 is the placement \nof 1 BC under and middle of  four plant clumps. \n\n\n\n\n\n\n\n \n \nFigure 2.  Plant N content at heading stage. Values are the means of three  \n\n\n\nreplicates and vertical lines are  standard errors. \nNote: BC1 is the placement of 1 BC under under one plant clump and BC2 is the \n\n\n\nplacement of 1 BC under and middle of four plant clumps. \n \n\n\n\nFigure 1.  Soil N content at heading stage. The values are the means of three\nreplicates and vertical lines are standard errors. \n\n\n\nFigure 2.  Plant N content at heading stage. Values are the means of three  replicates \nand vertical lines are  standard errors.\n\n\n\nNote: BC1 is the placement of 1 BC under under one plant clump and BC2 is the placement \nof 1 BC under and middle of four plant clumps.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 51\n\n\n\nEffect of Placement and Dosage of BC on Plant Height, Tiller and Rice Yield\nOur results showed that placement and dosage of BC influenced significantly \nplant height from 21 to 35 days after transplanting (Table 1). Plant height was \nalso recorded before 21 days after transplanting but the results did not show \nany  significant difference between treatments. Plant height was highest in BC1 \ntreatment of 30 ton ha-1 at 43.75 cm followed by 43.34 cm at 10 ton ha-1 and \n42,69 cm at 20 ton ha-1) but there was no significant difference between these  \nthree combination treatments. However, there was a significant difference with \ncontrol and BC2 treatment. Plant height appears to have responded quickly to the \ntreatment of BC placement than to dosage of compost, with BC1 being better than \nBC2. This could be attributed to roots absorbing nutrients faster in BC1 than in \nBC2, and being able to grow vigorously as the  BC was high in available  N, P, and \nK and low C/N (8.33) (Bernas et al. 2018).  Meanwhile, in BC2 treatment the plant \nroots  had to grow longer and needed more time to reach the BC.\n Results showed that placement and dosage of BC influenced significantly \nrice tillers, starting from 21 to 56 days after transplanting (Table 2). Rice tillers \nwere also recorded before 21 days after transplanting but showed no significant \ndifference between treatments. From 21 to 28 days after tranplanting, BC1 \ntreatment at 20 and 30 ton ha-1 rates significantly differed from control but not in \nthe case of BC2 treatment. Further, 35 days after tranplanting, the rice tillers started \nto increase significantly in the BC2 treatment at rates of 30 ton ha-1 (26.7 clump-1) \nbut were not significantly different from BC1 rates of 10, 20, and 30 ton ha-1 \nat 28.8, 35, and 30.17 clump-1 respectively. It appears that rice tillers responded \nmore quickly to BC placement than to dosage with BC1 being better than BC2.  \nThe highest rice tiller was recorded in the BC1 treatment of 20 ton ha-1 at a height \nof 43.53 cm followed by 30 ton ha-1 and BC2 at 35.13 clump-1. Though, there was \n\n\n\n6 \n \n\n\n\n \n \nFigure 3.  Absorbed  N at heading stage. Values are the means of three \n\n\n\nreplicates and vertical lines are standard errors. Notations \ndiffered significantly between treatments, where LSD (p<0.05) \n= 36.46 \n\n\n\nNote: BC1 is the placement of 1 BC under one plant clump; BC2 is the placement \nof 1 BC under and middle of four plant clumps.  \n\n\n\nEffect of Placement and Dosage of BC on Plant Height, Tiller and Rice Yield \n\n\n\nOur results showed that placement and dosage of BC influenced significantly \n\n\n\nplant height from 21 to 35 days after transplanting (Table 1).  Plant height was \n\n\n\nalso recorded before 21 days after transplanting but the results did not show any  \n\n\n\nsignificant difference between treatments.  Plant height was highest in BC1 \n\n\n\ntreatment of 30 ton ha-1 at 43.75 cm followed by 43.34 cm at 10 ton ha-1 and 42,69 \n\n\n\ncm at 20 ton ha-1) but there was no significant difference between these  three \n\n\n\ncombination treatments.  However, there was a significant difference with control \n\n\n\nand BC2 treatment.  Plant height appears to have responded quickly to the \n\n\n\ntreatment of BC placement than to dosage of compost, with BC1 being better than \n\n\n\nBC2. This could be attributed to roots absorbing nutrients faster in BC1 than in \n\n\n\nBC2, and being able to grow vigorously as the  BC was high in available  N, P, \n\n\n\nand K and low C/N (8.33) (Bernas et al. 2018).  Meanwhile, in BC2 treatment the \n\n\n\nplant roots  had to grow longer and needed more time to reach the BC. \n\n\n\nFigure 3.  Absorbed  N at heading stage. Values are the means of three replicates and \nvertical lines are standard errors. Notations differed significantly between treatments, \n\n\n\nwhere LSD (p<0.05) = 36.46\nNote: BC1 is the placement of 1 BC under one plant clump; BC2 is the placement of 1 BC \nunder and middle of four plant clumps.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202152\n\n\n\nno significant difference between these two combination treatments, there was a \nsignificant difference between these two treatments and other treatments. Thus, \ninserting the compost into the soil close to the plant roots was found to be a better \noption because it could increase plant N content and absorb N as reported by \nLiu et al. (2016). They found that when N organic fertiliser was inserted into the \nsoil at 20 cm depth and close to the root at about 5 cm, plant N content increased \nup to 0.95%. Similar results were found by Nurrahma and Melati (2013) with \nthe application of 5 ton ha-1 compost combined with 15 ton ha-1 chicken manure \nwhich resulted in a 0.95% increase in plant N content. Garib et al. (2008) also \nreported that compost placed under the plant was better at increasing N absorption \nand plant shoots .\n Placement of BC and dosage resulted in maximum tillers, productive tillers \nand significant yields. Our study showed that all combination treatments differed \nsignificantly with control (Table 3). The highest maximum tiller was achieved by \na combination of  BC1 and a rate of 20 ton ha-1 with 44 tillers clump-1 compared to \ncontrol of 20 tillers clump-1.  BC1 treatment at a rate of 30 ton ha-1 decreased tillers \nsigificantly (29 tillers clump-1) compared to the rate of 20 ton ha-1 which saw an \nincrease in tillers (44 tillers clump-1).  The application of  BC2 at a rate of 30 ton \nha-1 significantly increased tillers (35 tillers clump-1) compared to other rates and \ncontrol. However, it must be noted that the application of BC1 recorded higher \nmaximum tillers than BC2.  \n\n\n\n8 \n \n\n\n\nother rates and control. However, it must be noted that the application of BC1 \n\n\n\nrecorded higher maximum tillers than BC2.   \n\n\n\nTABLE  1 \n\n\n\nEffect of placement and dosage of briquette compost on plant height \n\n\n\n\n\n\n\nTreatments \n\n\n\nPlant height (cm) \nDays after transplanting \n\n\n\n21st 28th 35th 42nd 49th \nBC1D1 \n\n\n\n34.34 \u00b1 1.63  a 43.32 \u00b1 1.51 a \n48.75 \u00b1 \n1.32 a 50.87 \u00b1 3.09 53.86 \u00b1 0.90 \n\n\n\nBC1D2 43.34 \u00b1 2.08 b 54.08 \u00b1 2.86 b 58.97 \u00b1 6.12 c 62.91 \u00b1 9.63 65.93 \u00b1 12.03 \nBC1D3 \n\n\n\n42.69 \u00b1 2.96 b 51.43 \u00b1 1.37 b \n54.98 \u00b1 2.01 \n\n\n\nbc 60.70 \u00b1 4.25 67.04 \u00b1 1.95 \nBC1D4 \n\n\n\n43.75 \u00b1 1.36 b 52.18 \u00b1 1.78 b \n58.20 \u00b1 1.69 \n\n\n\nbc 61.25 \u00b1 2.09 61.04 \u00b1 5.38 \nBC2D1 36.96 \u00b1 6.12 a 44.24 \u00b1 6.31 a 48.36 \u00b1 7.95 a 53.76 \u00b1 8.17 53.76 \u00b1 9.16 \nBC2D2 \n\n\n\n36.10 \u00b1  1.29 a \n45.08 \u00b1  0.29 \n\n\n\na \n50.02 \u00b1 1.32  \n\n\n\na 54.37 \u00b1 0.94 54.37 \u00b1 2.14 \nBC2D3 \n\n\n\n35.87 \u00b1 2.39  a 45.56 \u00b1 2.55 a \n52.88 \u00b1 1.91 \n\n\n\nabc 57.32 \u00b1 1.3 57.32 \u00b1 2.85 \nBC2D4 \n\n\n\n35.47 \u00b1 1.27  a \n45.55 \u00b1  3.37 \n\n\n\na \n51.57 \u00b1 4.01 \n\n\n\nab 59.20 \u00b1 4.4 59.20 \u00b1 1.25 \nLSD0.05 5.04 5.40 7.09 ns ns \nNote: Different letters in a similar column indicate statistically significant different values \n\n\n\namong treatments in LSD (p<0.05); ns = not significant. \nBC1: placement of 1 BC under one plant clump; BC2: placement of 1 BC under  \n\n\n\nand middle of four plant clumps \nBC doses were 0 g (D1), 28 g (D2), 56 g (D3),and 74 g (D4) \n \n\n\n\nCombination of  BC placement and dosage gave significant effects on \n\n\n\nplant height and tillers starting from the  21st day after transplanting (Tables 1 and \n\n\n\n2).  However, application of BC1 had  significantly different  results from BC2 and \n\n\n\ncontrol. Plant height and tiller were higher compared to control and BC2. In BC1 \n\n\n\ntreatment, the plant roots grew inside the compost,  absorbed nutrients straight \n\n\n\naway and grew vigorously; plant height and rice tiller therefore increased \n\n\n\nsignificantly.    But in the case of BC2 treatment, the plant roots needed time to \n\n\n\nreach the BC placed in the middle of four plant clumps. So plant height and tillers \n\n\n\ngrew and developed slowly compared to BC1 treatment. On the 35th day following \n\n\n\ntransplanting BC2treatment began to have a  similar  effect as in the application of \n\n\n\nTABLE  1\nEffect of placement and dosage of briquette compost on plant height\n\n\n\nNote: Different letters in a similar column indicate statistically significant different \n values among treatments in LSD (p<0.05); ns = not significant.\n BC1: placement of 1 BC under one plant clump; BC2: placement of 1 BC under  \n and middle of four plant clumps \n BC doses were 0 g (D1), 28 g (D2), 56 g (D3),and 74 g (D4)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 53\n\n\n\n Combination of BC placement and dosage gave significant effects on \nplant height and tillers starting from the 21st day after transplanting (Tables 1 \nand 2). However, application of BC1 had significantly different results from BC2 \nand control. Plant height and tiller were higher compared to control and BC2. \nIn BC1 treatment, the plant roots grew inside the compost, absorbed nutrients \nstraight away and grew vigorously; plant height and rice tiller therefore increased \nsignificantly. But in the case of BC2 treatment, the plant roots needed time to \nreach the BC placed in the middle of four plant clumps. So plant height and tillers \ngrew and developed slowly compared to BC1 treatment. On the 35th day following \ntransplanting BC2 treatment began to have a  similar  effect as in the application of \nBC1; presumably by this time plant roots  were long enough to reach BC and create \nmore contact between roots and BC and  plants were able to absorb nutrients. It \nappears that plant roots need up to 35 days to grow in clay before reaching BC \nwhich was at a distance of about 16 cm (Phytagoras equation calculation) from \nthe crop. The slow growth of the roots could be caused by high clay content in \nthis soil. There was a difference of about 14 days before the plant roots could \ngrow long enough to reach the compost in the middle in BC2 treatment. Yoshida \net al. (1982) reported that rice plant roots could grow to a length of 105 cm at the \ntime of flowering, with the length of root growth dependent on variety of rice. \nThat is why it is important to place the fertilser close to the roots, as reported by \nDrew (1975) in Wild (1988). According to Wild (1988), when part of the roots \nare exposed to a high concentration of nitrate, it causes initiation and vigorous \nin situ extension of primary and secondary lateral roots within the exposed zone. \nPlacement of BC was found to be more important than dosage, because there was \ndirect contact between roots and BC resulting in better and immediate absorption \nof  nutrients from BC. The roots did not need to grow longer as in the case of BC2 \ntreatment. Though roots can grow up to 95 cm in length (Yoshida et al. 1982), \nthey do not grow straight and therefore take some time to reach the BC which was \nonly 16 cm away from the roots.\n The best plant height of 54.98 cm and highest amount of rice tillers of \n43.57 clump-1 were achieved by BC1 at a rate of 20 ton ha-1 BC. On the other hand, \nBC2 treatment at 20 ton ha-1 BC recorded a plant height of 52.88 cm and rice tillers \nof 24.60 clump-1. Thus placement of BC under a plant clump was better than in the \nmiddle of four plant clumps. \n Correlating BC with dosage showed similar pattern effects on productive \ntillers and maximum tillers (Table 3) where placement and dosage of BC had a \nsignificant effect on productive tillers. The treatment of  BC1 at a rate of 20 ton ha-1 \n\n\n\nproduced the highest productive tillers (22 tillers clump-1) and was significantly \ndifferent from other treatments and control (12 tillers clump-1). The highest \nproductive tillers (22 clump-1) and rice yield (10.3 ton ha-1) were reached by the \ntreatment of  BC1 at a rate of 20 ton ha-1 and this differed significantly compared \nto other treatments.  These results are rather similar to a study done by Zhou et al. \n(2017) where the rice  plant produced 33 productive tillers and 10.18 ton ha-1 rice \ngrain at a compost dosage of 439,2 g per clump and plus NPK fertilizer. This was \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202154\n\n\n\n10 \n \n\n\n\nTABLE 2 \n\n\n\nEffect of placement and dosage of briquette compost on rice tiller \n\n\n\n\n\n\n\nTreatments \nTillers per rice clump \n\n\n\nDays after transplanting \n \n\n\n\n21st \n \n\n\n\n28th \n \n\n\n\n35th \n \n\n\n\n42nd \n \n\n\n\n49th \n \n\n\n\n56th \nBC1D1 4.93 \u00b1 2.10 ab 9.67 \u00b1 2.84 \n\n\n\na \n16.07 \u00b1 \n3.53 a \n\n\n\n19.53 \u00b1 \n2.66  a \n\n\n\n19.80 \u00b1 \n5.29 ab \n\n\n\n20.00 \u00b1 4.20 \na \n\n\n\nBC1D2 7.26 \u00b1 0.61 \nabc \n\n\n\n17.80 \u00b1 \n3.02 bc \n\n\n\n28.80 \u00b1 \n8.74 cd \n\n\n\n30.33 \u00b1 \n12.19  abc \n\n\n\n26.20 \u00b1 \n8.27 abcd \n\n\n\n26.67 \u00b1 9.00 \nabc \n\n\n\nBC1D3 7.53 \u00b1 0.61 bc 23.80 \u00b1 \n3.30 c \n\n\n\n35.00 \u00b1 \n5.70 d \n\n\n\n43.27 \u00b1 \n8.69 d \n\n\n\n43.53 \u00b1 \n4.41 e \n\n\n\n35.27 \u00b1  \n11.32 c \n\n\n\nBC1D4 8.06 \u00b1 1.63 c 24.87 \u00b1 \n6.18 c \n\n\n\n30.27 \u00b1 \n7.00 cd \n\n\n\n34.27 \u00b1 \n6.49 cd \n\n\n\n28.93 \u00b1 \n6.55 bcd \n\n\n\n29.20 \u00b1 4.70 \nbc \n\n\n\nBC2D1 \n5.93 \u00b1 2.40 \n\n\n\nabc \n11.13 \u00b1 \n4.11 ab \n\n\n\n16.53 \u00b1 \n4.50 ab \n\n\n\n20.60 \u00b1 \n4.70 ab \n\n\n\n19.27 \u00b1 \n3.11 a \n\n\n\n19.67 \u00b1 2.69 \na \n\n\n\nBC2D2 6.06 \u00b1 1.22 \nabc \n\n\n\n16.00 \u00b1 \n2.43 ab \n\n\n\n25.13 \u00b1 \n2.76 abc \n\n\n\n32.07 \u00b1  \n4.03  bcd \n\n\n\n29.27 \u00b1 \n4.35 cd \n\n\n\n29.93 \u00b1 4.62 \nbc \n\n\n\nBC2D3 4.80 \u00b1 1.83 a 12.00 \u00b1 \n6.09 ab \n\n\n\n18.73 \u00b1 \n3.31 ab \n\n\n\n24.60 \u00b1  \n3.89 abc \n\n\n\n23.47 \u00b1 \n3.56 abc \n\n\n\n22.67 \u00b1 4.59 \nab \n\n\n\nBC2D4 5.06 \u00b1 1.90 ab 15.53 \u00b1 \n3.14 ab \n\n\n\n26.07 \u00b1 \n5.30 bcd \n\n\n\n32.40 \u00b1 \n4.89 bcd \n\n\n\n35.13 \u00b1 \n5.69 de \n\n\n\n34.00 \u00b1  \n7.64 bc \n\n\n\nLSD0.05 2.71 7.08 9.93 12.09 9.19 11.87 \nNote:  Different letters in a similar column indicate statistically significant different \n\n\n\nvalues among treatment in LSD (p<0.05). \nBC1: placement of 1 BC under one plant clump; BC2: placement of 1 BC under \n\n\n\nand middle of four plant clumps  \nBC doses were 0 g (D1), 28 g (D2), 56 g (D3),and 74 g (D4) \n\n\n\n\n\n\n\nTABLE 3 \nCombination effects of placement and dosage of briquette compost on \n\n\n\nproductive rice tillers and yield \n \n\n\n\nDosage of BC \n(ton ha-1) \n\n\n\nMaximum tillers per rice clump \n1 briquette (1 dosage) \n\n\n\nfor 1 plant clump (BC1) \n1 briquette (4 dosages) \n\n\n\nfor 4 plant clumps (BC2) \n0 (D1) 20 \u00b1  5.29a 20 \u00b1 0.00a \n\n\n\n10 (D2) 27 \u00b1 9.00b 30 \u00b1 0.16b \n20 (D3) 44 \u00b1 4.41d 23 \u00b1 0.30a \n30 (D4) 29 \u00b1 4.70b 35 \u00b1 0.35c \n\n\n\nLSD0.05 4.83 \nDosage of BC \n\n\n\n(ton ha-1) \nProductive tiller per clump \n\n\n\n1 briquette (1 dosage) \nfor 1 plant clump (BC1) \n\n\n\n1 briquette \n(4 dosages) \n\n\n\nfor 4 plant clumps (BC2) \n0 (D1) 12 \u00b1 1.25 a  13 \u00b1 0.52ab \n\n\n\n10 (D2) 17 \u00b1 2.69bc  17 \u00b1 2.58bc \n\n\n\n10 \n \n\n\n\nTABLE 2 \n\n\n\nEffect of placement and dosage of briquette compost on rice tiller \n\n\n\n\n\n\n\nTreatments \nTillers per rice clump \n\n\n\nDays after transplanting \n \n\n\n\n21st \n \n\n\n\n28th \n \n\n\n\n35th \n \n\n\n\n42nd \n \n\n\n\n49th \n \n\n\n\n56th \nBC1D1 4.93 \u00b1 2.10 ab 9.67 \u00b1 2.84 \n\n\n\na \n16.07 \u00b1 \n3.53 a \n\n\n\n19.53 \u00b1 \n2.66  a \n\n\n\n19.80 \u00b1 \n5.29 ab \n\n\n\n20.00 \u00b1 4.20 \na \n\n\n\nBC1D2 7.26 \u00b1 0.61 \nabc \n\n\n\n17.80 \u00b1 \n3.02 bc \n\n\n\n28.80 \u00b1 \n8.74 cd \n\n\n\n30.33 \u00b1 \n12.19  abc \n\n\n\n26.20 \u00b1 \n8.27 abcd \n\n\n\n26.67 \u00b1 9.00 \nabc \n\n\n\nBC1D3 7.53 \u00b1 0.61 bc 23.80 \u00b1 \n3.30 c \n\n\n\n35.00 \u00b1 \n5.70 d \n\n\n\n43.27 \u00b1 \n8.69 d \n\n\n\n43.53 \u00b1 \n4.41 e \n\n\n\n35.27 \u00b1  \n11.32 c \n\n\n\nBC1D4 8.06 \u00b1 1.63 c 24.87 \u00b1 \n6.18 c \n\n\n\n30.27 \u00b1 \n7.00 cd \n\n\n\n34.27 \u00b1 \n6.49 cd \n\n\n\n28.93 \u00b1 \n6.55 bcd \n\n\n\n29.20 \u00b1 4.70 \nbc \n\n\n\nBC2D1 \n5.93 \u00b1 2.40 \n\n\n\nabc \n11.13 \u00b1 \n4.11 ab \n\n\n\n16.53 \u00b1 \n4.50 ab \n\n\n\n20.60 \u00b1 \n4.70 ab \n\n\n\n19.27 \u00b1 \n3.11 a \n\n\n\n19.67 \u00b1 2.69 \na \n\n\n\nBC2D2 6.06 \u00b1 1.22 \nabc \n\n\n\n16.00 \u00b1 \n2.43 ab \n\n\n\n25.13 \u00b1 \n2.76 abc \n\n\n\n32.07 \u00b1  \n4.03  bcd \n\n\n\n29.27 \u00b1 \n4.35 cd \n\n\n\n29.93 \u00b1 4.62 \nbc \n\n\n\nBC2D3 4.80 \u00b1 1.83 a 12.00 \u00b1 \n6.09 ab \n\n\n\n18.73 \u00b1 \n3.31 ab \n\n\n\n24.60 \u00b1  \n3.89 abc \n\n\n\n23.47 \u00b1 \n3.56 abc \n\n\n\n22.67 \u00b1 4.59 \nab \n\n\n\nBC2D4 5.06 \u00b1 1.90 ab 15.53 \u00b1 \n3.14 ab \n\n\n\n26.07 \u00b1 \n5.30 bcd \n\n\n\n32.40 \u00b1 \n4.89 bcd \n\n\n\n35.13 \u00b1 \n5.69 de \n\n\n\n34.00 \u00b1  \n7.64 bc \n\n\n\nLSD0.05 2.71 7.08 9.93 12.09 9.19 11.87 \nNote:  Different letters in a similar column indicate statistically significant different \n\n\n\nvalues among treatment in LSD (p<0.05). \nBC1: placement of 1 BC under one plant clump; BC2: placement of 1 BC under \n\n\n\nand middle of four plant clumps  \nBC doses were 0 g (D1), 28 g (D2), 56 g (D3),and 74 g (D4) \n\n\n\n\n\n\n\nTABLE 3 \nCombination effects of placement and dosage of briquette compost on \n\n\n\nproductive rice tillers and yield \n \n\n\n\nDosage of BC \n(ton ha-1) \n\n\n\nMaximum tillers per rice clump \n1 briquette (1 dosage) \n\n\n\nfor 1 plant clump (BC1) \n1 briquette (4 dosages) \n\n\n\nfor 4 plant clumps (BC2) \n0 (D1) 20 \u00b1  5.29a 20 \u00b1 0.00a \n\n\n\n10 (D2) 27 \u00b1 9.00b 30 \u00b1 0.16b \n20 (D3) 44 \u00b1 4.41d 23 \u00b1 0.30a \n30 (D4) 29 \u00b1 4.70b 35 \u00b1 0.35c \n\n\n\nLSD0.05 4.83 \nDosage of BC \n\n\n\n(ton ha-1) \nProductive tiller per clump \n\n\n\n1 briquette (1 dosage) \nfor 1 plant clump (BC1) \n\n\n\n1 briquette \n(4 dosages) \n\n\n\nfor 4 plant clumps (BC2) \n0 (D1) 12 \u00b1 1.25 a  13 \u00b1 0.52ab \n\n\n\n10 (D2) 17 \u00b1 2.69bc  17 \u00b1 2.58bc \n\n\n\nTABLE 2\nEffect of placement and dosage of briquette compost on rice tiller\n\n\n\nNote:  Different letters in a similar column indicate statistically significant different \n values among treatment in LSD (p<0.05).\n BC1: placement of 1 BC under one plant clump; BC2: placement of 1 BC \n under and middle of four plant clumps \n BC doses were 0 g (D1), 28 g (D2), 56 g (D3),and 74 g (D4)\n\n\n\nTABLE 3\nCombination effects of placement and dosage of briquette compost on\n\n\n\nproductive rice tillers and yield\n\n\n\nNote: Different letters in a similar column and row indicate statistically significant \n different values among treatments in LSD (p<0.05).\n BC1: placement of 1 BC under one plant clump; BC2 : placement of 1 BC\n under and middle of four plant clumps \n BC doses were 0 g (D1), 28 g (D2), 56 g (D3),and 74 g (D4)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 55\n\n\n\nmuch higher compared to the study of Hasanuzzaman et al. (2010)  which used \ngreen manure at 15 ton ha-1 which resulted in only 5.6 productive tillers and 2.32 \nton ha-1 rice grain. This briquette compost was better than green manure because \nBC was made of legume and was high in N, P and K and  had a low C/N (8.32). \nNutrients were therefore readily available for plants (Bernas et al. 2015). \n The placement of BC and dosage had a  significantly different effect on dried \nrice grain (Table 3). The highest was reached by the treatment of BC1 at a rate of \n20 ton ha-1 (1030.81 g m-2 or equal to 10.3081 ton ha-1) and this dried rice was not \nsignificantly different from other BC1 rates (10 and 30 ton ha-1) but significantly \ndifferent from control (586.73 g m-2 or 5.867 ton ha-1).  Thus a combination of BC1 \nand a rate of 20 ton ha-1 produced about 175% higher than control (0 compost).   \nOn the other hand, the application of BC2 at 10, 20 and 30 ton  rates did not result \nin any  significantly different yields. This means the BC1 treatment was better than \nBC2 treatment. BC made of water mimosa was even better than poultry manure \nas reported by Hidayatullah (2016). In her study, poultry manure applied at a \nrate equal to 120 N resulted in 9.3 ton ha-1 rice grain. This was caused by a large \namount of plant nutrients being supplied by poultry manure at a low CN ratio \n(12.0). This also indicates that BC characteristics are similar to poultry manure as \nthe CN ratio of BC was 8.32 with a 4% N content. This high N content was due to \ncompost being made of water legume plants as reported by Bernas et al. (2018).  \nOrganic matter incorporated into the soil could increase the ability of plants to \nabsorb water through roots proliferation (Curtis and Claassen 2005), as shown by \nthe vigorous growth of roots inside BC (Appendix 1).  BC as compost was able to \nstimulate plant growth, root development and nutrient uptake (Walker and Bernal \n2008) thus increasing rice yield. \n\n\n\nRelationship between BC Placement and Dosage and  Rice Growth and \nYield \nIn the case of BC1 treatment, the regression analysis showed that BC dosage \nsignificantly correlated with productive tillers (r2 = 0.54*; p=0.03) and rice \ndry weight (r2 = 0.71*; p=0.03). But BC2 treatment showed a very significant \ncorrelation only between rate of BC and rice dry weight (r2 = 0.70**; p=0.01).  In \nBC1 treatment, the different rates showed a curve with the optimum application \nbeing reached at 20 ton ha-1 on BC1. Any further increase in rate would result \nin lower rice tillers and dry rice yield. On the other hand, the different rates of \nBC2 treatment showed a linear correlation. This means the optimum rate was not \nreached yet. So when BC was applied in the middle of four plant clumps (BC2), it \nwould need a higher rate than in BC1 treatment. Thus BC1 treatment allowed the \nroots to grow inside BC vigorously and absorb the nutrients straight away without \nthe need to grow longer compared to he BC2  treatment (see Appendix 1). \n A rate of 30 ton ha-1 in BC1 treatment showed a decrease  in rice dry weight, \nwhich might be due to excessive N, as BC contains 4% N. Dong et al. (2011) \nfound that excessive N decreased grain yield. Another study by Zhu et al. (2017) \nfound that excessive nitrogen application reduced rice yield; they suggest that a \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202156\n\n\n\nsuitable rate would be 270 kg ha-1.  Thus, it is suggested that compost  be applied \njust around the rice roots, especially under flooded conditions where the efficiency \nof N is low due to  NH3+ volatilisation (Bouwman et al. 2002).\n\n\n\n14 \n \n\n\n\n \nFigure 4.  Relationship between different BC rates and productive tillers \nNote: BC1 : placement of 1 BC under one plant clump; BC2 : placement of 1 BC under \n\n\n\nand middle of  four plant clumps  \n \n \n\n\n\n\n\n\n\nFigure  5.  Relationship between  different BC rates and rice dried weight dosages \nNote: BC1 : placement of 1 BC under one plant clump; BC2 : placement of 1 BC under \n\n\n\nand middle of four plant clumps   \n \n \n\n\n\nCONCLUSSION \n  \n\n\n\n14 \n \n\n\n\n \nFigure 4.  Relationship between different BC rates and productive tillers \nNote: BC1 : placement of 1 BC under one plant clump; BC2 : placement of 1 BC under \n\n\n\nand middle of  four plant clumps  \n \n \n\n\n\n\n\n\n\nFigure  5.  Relationship between  different BC rates and rice dried weight dosages \nNote: BC1 : placement of 1 BC under one plant clump; BC2 : placement of 1 BC under \n\n\n\nand middle of four plant clumps   \n \n \n\n\n\nCONCLUSSION \n  \n\n\n\nFigure 4.  Relationship between different BC rates and productive tillers\n\n\n\n    Note:  BC1 : placement of 1 BC under one plant clump; BC2 : placement of 1 BC under \n and middle of  four plant clumps\n\n\n\nFigure  5.  Relationship between  different BC rates and rice dried weight dosages\n\n\n\n   Note:   BC1 : placement of 1 BC under one plant clump; BC2 : placement of 1 BC under \n and middle of four plant clumps  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 57\n\n\n\nCONCLUSSION\nPlacement of BC under the rice plant clumps (BC1) gave better results than \nplacement of BC in the under and middle of four rice plant clumps (BC2). The \noptimum value was achieved by application of  20 ton ha-1 for BC1 with  a  yield \nof 1,014 g m-2 or 10.14 ton ha-1. This high rice yield in BC1 might be due to \nroots being  able to absorb nutrients and water from BC straight away resulting \nin stimulation of root development and plant growth at an early stage. In BC2 \ntreatment, crop roots needed more time and greater length to reach BC in the \nmiddle of four plant clumps and utilise the  nutrients in the compost; also several \nnutrients in the compost might be lost before the roots could reach it. So it is better \nto apply briquette compost by inserted into the soil and rice seedling was planted \nobove it.\n\n\n\nACKNOWLEDGEMENTS \nAuthors would like to thank \u201cSuperior Research Unsri\u201d from Ministry of research, \nTechnology and Higher Education of  The Republic of Indonesia for sponsoring \nthis research and Weko, Bibit, and Widia for helping in the field work.\n\n\n\nREFERENCES\nBernas, S.M., A. Wijaya, E.P. Sagala and S.N.A. Fitri. 2015. Identification and  \n\n\n\ndecomposition of  five dominant wild plants from acid swampland in South \nSumatra. J. Tropical Soils 20 (3): 67-73.\n\n\n\nBernas, S.M., A. Wijaya, E.P. Sagala, S.N.A. Fitri and  A. Napoleon. 2017. 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Hu, D. Zhu, Z. Chen and Y. Liu. 2016. Effect of N \nfertilization pattern on rice yield, N use efficiency and fertilizer-N fate in the \nYangtze river basin, China. PLoS ONE 11(11): e0166002.\n\n\n\nNgo, H. T. T. and T. R. Cavagnaro. 2018.  Interactive effects of compost and pre-\nplanting soil moisture on plant biomass, nutrition and formation of mycorrhizas: \na context dependent response. Scientific Reports  8: 1509. www.nature.com/\nscientificreports. DOI:10.1038/s41598-017-18780-2.\n\n\n\nNurrahma, A. H. I. and M. Melati. 2013. The influence of fertilizer types and \ndecomposer on organic rice growth and yield. Bul. Agrohorti. 1(1): 149\u2013155.\n\n\n\nWalker, J.D. and M.P. Bernal. 2008. The effect of olive mill waste compost and \npoultry manure on availability and plant uptake of nutrients in a highly saline \nsoil. Bioresource Technology 99(2): 396\u2013403.\n\n\n\nWild, A. and L.P.H. Jones. 1988. Mineral nutrition of crop plants. In Russell\u2019s \nSoil Conditions and Plant Growth, ed. A. Wild (11th ed.). United Kingdom: \nLongman.\n\n\n\nXiaowei Liu, Huoyan Wang, Jianmin Zhou, Fengqin Hu, Dejin Zhu, Zhaoming Chen \nand Yongzhe Liu. 2016. Effect of N fertilization pattern on rice yield, N use \nefficiency and fertilizer \u00b1 N fate in the Yangtze river basin, China. PLoS ONE \n| DOI:10.1371/journal.pone.0166002.\n\n\n\nYoshida, S., D.P. Bhattacharjee and G.S. Cabuslay. 1982.  Relationship between plant \ntype and root growth in rice. Soil Science and Plant Nutrition 28(4): 473-482.\n\n\n\nZhenping, W., O. Cleemput, P. Demeyer and L. Baert.  1991. Effect of urease inhibitors \non urea hydrolysis and ammonia volatilization. Biol. Fert. Soils 11: 43-47.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 59\n\n\n\nZhu Da-wei, Zhang Hong-cheng, Guo Bao-wei, Xu Ke, Dai Qi-gen, Wei Hai-yan, Gao \nhui, Hu Ya-jie, Cui Pei-yuan and Huo Zhong-yang. 2017. Effects of nitrogen \nlevel on yield and quality of japonica soft super rice. Journal of Integrative \nAgriculture 16(5): 1018\u20131027.\n\n\n\nZhou Qun, Ju Cheng-xin, Wang Zhi-qin, Zhang Hao, Liu Li-jun, Yang Jian-chang, \nand Zhang Jian-hua.  2017.  Grain yield and water use efficiency of super rice \nunder soil water deficit and alternate wetting and drying irrigation. Journal of \nIntegrative Agriculture 16(5): 1028\u20131043.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202160\n\n\n\n17 \n \n\n\n\nsuper rice under soil water deficit and alternate wetting and drying \nirrigation. Journal of Integrative Agriculture 16(5): 1028\u20131043. \n\n\n\n \n \n \n \n \n \n \n \n \nAppendix 1.  Development of rice plant after BC1 and BC2 treatments (Figure A, \n\n\n\nB, C and D). \n \n\n\n\nA.  Rice roots after BC1 treatment: 1 \n\n\n\nBC under 1 plant clump  \n\n\n\n \nB.  Rice roots after BC2 treatment: 1 BC \nunder & middle of 4 plant clumps  \n\n\n\n \nC.  Rice crop under BC1 treatment \nwith 20 ton ha-1  \n\n\n\n \nD. Rice crop under BC2 treatment with \n20 ton ha-1  \n\n\n\n\n\n\n\nAppendix 1.  Development of rice plant after BC1 and BC2 treatments\n (Figure A, B, C and D).\n\n\n\n\n\n"
"\n\nINTRODUCTION\nUltisols occupy approximately 45.8 million ha of the total land area in Indonesia \n(Subagyo et al. 2004; Tan 2008). Based on distribution and size, these soils are \nperhaps the next highest in land area compared to Inceptisols (the most important \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 115-123 (2014)                        Malaysian Society of Soil Science\n\n\n\nBacterial Inoculants to Increase the Biomass and Nutrient \nUptake of Tithonia Cultivated as Hedgerow Plants in Ultisols\n\n\n\nNurhajati Hakim*, Rina Alfina, Agustian, Hermansah and \nYulnafatmawita\n\n\n\nSoil Science Department, Faculty of Agriculture, University of Andalas, \nPadang, Indonesia \n\n\n\nABSTRACT\nUltisols require greater amounts of fertilizer application compared to other soils. \nUnfortunately, the price of synthetic fertilizers has increased over time during \nthe years, making them unaffordable for most Indonesian farmers. Over the last \ncentury, efforts to reduce reliance on synthetic agro-chemicals have recently \nfocused on Tithonia diversifolia as a green manure alternative. Generally known \nby its common name of tree marigold or Mexican sunflower, this plant has \nattracted considerable attention for its prolific production of green biomass, rich \nin nitrogen, phosphorous and potassium (NPK). This outstanding feature and the \nplant\u2019s capacity to solubilize soil P have recently been capitalized for improving \nthe fertility of highly leached soils in Africa and particularly in Kenya. As \nmicroorganisms are expected to play an important role in biomass production and \nhigh nutrient uptake of this plant, this issue of importance was pursued further in the \nfollowing investigation.  The aim of this study was to determine the type of bacteria \nsuitable for Tithonia cultivation as hedgerow plants in Ultisols which have higher \nbiomass production and nutrient content. The field experiment was conducted \nwith 5 treatments in a randomized block design (RBD) using 3 replications. The \ntreatments were: without microorganisms inoculation or control (K); phosphate \nsolubilizing bacteria (PSB) (L); Azospirillium (M);PSB + Azospirillium (N); and \nPSB + Azospirillium + Azotobacter (O). The bacterial substrates were inoculated \ninto the Tithonia rhizosphere in the nursery. The young Tithoniaplants were then \nplanted as hedgerow on Ultisols in the experimental field for 8 months, and \npruned once every 2 months. The differences between treatments were statistically \nsignificant by HSD test at the 95% level of probability.  Treatment L (phosphate \nsolubilizing bacteria) was found to be the most effective, followed by treatment N \n(PSB +Azospirillum).\n\n\n\nKey words: Azospirillium, azotobacter, green manure, PSB, Tithonia \nhedgerow plants\n\n\n\n___________________\n*Corresponding author : E-mail: nhakimsa@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014116\n\n\n\nsoils in Indonesia), a clear indication of the great potential of Ultisols for expanding \nagricultural operations in the country (Tan 2008). However, as Ultisols are acidic \nand highly leached, they exhibit several constraints such as Al-toxicity and low \nnutrient content for plant growth. These soils, therefore, require large amounts \nof fertilizer applications for crops to produce high yields. As prices of synthetic \nfertilizers have risen considerably, they are becoming unfortunately unaffordable \nfor the common farmers in Indonesia. Therefore, an alternative fertilizer, which \nis less expensive and easier to produce, is urgently needed to replace synthetic \nfertilizers without harming the growth and yield of crops grown in Ultisols.\n\n\n\nPrevious research involving Tithonia diversifolia, a plant generally known \nby its common name of tree marigold or Mexican sunflower in its native country, \nMexico, and locally known in Indonesia as kembang bulan or kembang matahari, \nshows great potential for cultivation and useas green manure for crop production \n(Figure 1).\n\n\n\nThe plant, belonging to the aster family (Asteracea), exhibits a capacity \nfor producing huge amounts of green biomass, rich in NPK; it is also said to be \ncapable of solubilizing soluble P compounds in soil. The NPK content of tithonia \nfoliage is reported to be greater than cow or stable manure. According to Jama et \nal. (2000), Tithonia contains approximately 3.5 to 4.0 % N, 0.35 to 0.38% P, 3.5 \nto 4.1% K, 0.59% Ca and 0.27% Mg. Tithonia plants have recently been used \nextensively by African farmers in Kenya for improving the fertility level of their \nhighly leached soils (Nziguheba et al. 2002; Waniku and Kimenye 2006; Sanchez \nand Jama 2000) have also cited reports of Tithonia being used by farmers in \nKenyaas mulch providing NPK for growing corn on their nutrient deficient soils.\n\n\n\nHakim (2002) noted that Tithonia grew abundantly at all elevations, even \nnear highways and streets in West Sumatra, Indonesia.  However, it has often \nbeen considered as a weed and has not found application as an organic fertilizer.  \nHakim and Agustian (2003) believe that although Tithonia does not belong to the \nleguminous family, the NPK rich biomass qualifies it to be used as green manure. \n\n\n\nNurhajati Hakim, Rina Alfina, Agustian, Hermansah and Yulnafatmawita\n\n\n\nFigure 1: Blooming Tithonia (left) and as hedgerow plants (right)\n\n\n\n              ISSN: 1394-7990    \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                        Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n \nFigure 1: Blooming Tithonia (left) and as hedgerow plants (right) \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 117\n\n\n\nTithonia has been shown to reduce the use of synthetic N and K fertilizers up to \n50% for the cultivation of chili, ginger, tomatoes, and melons grown in Ultisols \nin pot experiments by Hakim et al. (2003; 2004). Based on results of field studies \nconducted over two years, Hakim and Agustian (2004; 2005a; 2005b) concluded \nthat growing Tithonia as hedgerows was more effective as an in situ or in vivo \nfertilizer alternative in intercropping systems.\n\n\n\nThe Tithonia plant can be reproduced easily by vegetative means, using \nstems cuttings, and planted at a spacing of 50 cm x 50 cm in 100 cm wide \nhedgerows. The appropriate system pattern of Tithonia cultivation is by growing \nseveral hedgerows, separated by a distances of 5 m between rows (20 rows = 2000 \nm2 row ha-1). Pruning is required once every two months. With this cultivation \ntechnique, Tithonia is noted to produce 6.6 to 6.8 metric tons of dry matter (DM) \nor about 40 metric tons of fresh biomass, with nutrient contents of 150-240 kg N \nand 156-245 kg K ha-1 annually.  The amount of nutrients released from Tithonia \nis sufficient to provide 50% of N and K needed by crops grown within each \nalley. Hakim and Agustian (2004) reported that the amounts of synthetic N and K \nfertilizer applications for chili and ginger crops could be reduced by as much as \n50% under field trials by intercropping with Tithonia hedges.  Results from field \nstudies for corn in Ultisols also suggest that application of N and K fertilizers \ncould be reduced by 50% with Tithonia treatment as green manure. Tithonia \nbiomass used as green manure, containing 100 kg N and 100 kg K per 4 ton of dry \nmatter, appear to be sufficient for growth of corn. Corn yield from plants receiving \ntreatments with Tithonia +50% synthetic fertilizers, was significantly higher than \nthat treated with 100% synthetic fertilizers.\n\n\n\n Hakimet et al. (2007) reported that the high DM yield and nutrient uptake \nof Tithonia was due to the activities of microorganism in the rhizosphere. They \nfound three isolates of arbuscular mycorhizal fungi, three isolates of phosphate \nsolubilizing fungi, three isolates of Azotobacter, four isolates of phosphate \nsolubilizing bacteria, and three isolates of the bacterial phytohormone producer. \nAsman et al. (2008) who conducted port experiments found four from the ten \ntreatments produced equalamounts of DM yields and nutrients, namely: (1) \nphosphate-solubilizing bacteria (PSB), (2) Azospirillium, (3) PSB + Azosprillium, \nand (4) PSB + Azotobacter + Azospirillium.  Based on the results of pot experiments, \nthe four bacterial inoculants treatments were tested in the field on an Ultisol. The \naim of our study was to determine suitable bacteria for use in cultivating Tithonia \nas hedgerow, and consequently produce higher biomass and nutrients for Ultisols. \n\n\n\nMATERIALS AND METHODS\nA field experiment was conducted on an Ultisol at the experimental farm of the \nUniversity of Andalas, Limau Manis, Padang, located at 250 m a.s.l and 30 km to \nthe east of Minangkabau International Airport, West Sumatra, Indonesia. Selected \nsoil chemical analyses were as follows: pH (H2O) 5.15, exchangeable-Al1.43 \ncmol kg-1, total-N 0.27%, available-P 15 mg kg-1, and exchangeable-K 0.78 cmol \nkg-1. The mean annual temperature was approximately 26o C and the annual \n\n\n\nBacterial Inoculants for Biomass Production of Tithonia\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014118\n\n\n\nprecipitation was 5546 mm. The treatments were based on data of Asman et al. \n(2008) with the experimental factors being arranged in a randomized block design \n(RBD) with 5 treatments and 3 replications. The treatments were inoculation \nof microbial substrates into Tithonia rhizospheres of plants grown in the \nnursery, namely: without microorganisms inoculation or control (K); phosphate \nsolubilizing bacteria (PSB) (L); Azospirillium (M); PSB + Azospirillium (N); and \nPSB +  Azospirillium + Azotobacter (O). \n\n\n\nSterilized soil (1 kg) was transferred into a small plastic pot, and then planted \nwith Tithonia cutting stem, each having three buds. One bud was buried into the \nsoil, while the others were above the soil surface. After 2 weeks, when leaves and \nroots had emerged, 10 ml of microbial inoculants of a specific treatment were \ninjected into the root zone as mentioned earlier. The microbial inoculants were \nprepared in Nutrient Broth containing 108 bacteria cells per ml. For acclimatization \nof microbial inoculants with sterilized media for growing Tithonia, they were left \nincubated in the media for one week. The young plants were then fertilized and \nwatered daily with red Hyponex solution (5g red Hyponex in 1 litre of sterile \ndistilled water). After one month in the nursery, the Tithonia seedlings were ready \nto be transplanted into the field.\n\n\n\nThe size of each experimental plot of hedgerow was 2 m x 0.8 m (1.6 m2) \nwith a planting distance of 50 cm x 50 cm, with 8 seedlings being planted per plot. \nTo stimulate early growth, each planting hole was fertilized with 0.5 kg cow dung \nmanure, 2.5 g N + 0.25 g P + 2.5 g K + 0.25 g Mg as recommended by Hakim and \nAgustian (2005b). Plants were maintained and observed for 8 months, and pruned \nevery 2 months, (altogether 4 prunes). The biomass of each prune was weighed, \nand sampled for moisture content determination and dry weight by oven drying at \n60oC. Chemical analysis was performed for N, P, and K contents. The differences \nbetween treatments were statistically analyzed by HSD test at the 95% level of \nprobability.\n\n\n\nRESULTS AND DISCUSSION\nThe effects of bacterial inoculation on DM yield of Tithonia planted in Ultisols as \nhedgerow with 4 prunes over a growing period of eight months compared to the \nestimated yield with six prunes annually are presented in Table 1.\n\n\n\nThe data showed that treatments with bacterial inoculants increased DM \nyield of Tithonia significantly compared to the control (treatment K). The highest \nincrease was obtained by treatment L (inoculation with PSB), showing a total DM \nyield of 3.76 kg m-2, which translates into an increase of 47% with an average of 4 \nprunes. The frequency or cycle of pruning seems to affect the yield of DM, which \nshows an increase of 85% at the first pruning (Pruning I), to reach maximum DM \nyields of 116% compared to the control at the second pruning (Pruning II). The \ndry matter yield decreases in the third pruning (Pruning III), but again reaches \nhigher values of DM yield at the last pruning (Pruning IV). It appears that DM \nproduction and yield, in the presence of PSB, increases with successive pruning.\n\n\n\nNurhajati Hakim, Rina Alfina, Agustian, Hermansah and Yulnafatmawita\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 119\n\n\n\nBased on the average DM yield of 4 prunes (Table 1), it can be concluded \nthat inoculation of Tithonia rhizosphere with PSB was the best treatment for \npromoting plant growth as it provided the highest DM yield of 3.76 kg m-2, an \namount equal to 7.52 metric ton ha-1 (20 lines of hedgerow = 2000 m2 ha-1). With \n6 prunes a year, as recommended by Hakim and Agustian (2005b), the yield was \n11.3 metric ton DM ha-1y-1, whereas the inoculation by PSB + Azospirilliumonly \nproduced 7.6 metric ton DM ha-1 y-1.\n\n\n\nThe yield of 7.6 to 11.3 t DM ha-1 y-1 was high compared to the yield of \nplants without inoculation (6.8 t ha-1y-1) as reported by Hakim and Agustian \n(2005b). It showed that inoculation with PSB played an important role in the \ngrowth of Tithonia to induce higher amounts of DM. The results were clearly due \nto improved nutrient uptake by crops. Statistical analysis showed that nutrient \nuptake by Tithonia as hedgerow for 4 prunes was significantly influenced by \nbacterial inoculation (Table 2). The increase in P uptake was sufficiently high \nto stimulate better root growth, which then allowed higher nutrient uptake. \nConsequently, all the above resulted in improved growth and DM yield.\n\n\n\n In this study, the uptake of N, P, and K was observed for 8 months from the \ntime of bacterial inoculation to plants subjected to 4 prunes. Hakim and Agustian \n(2005b) have shown previously that Tithonia could be pruned up to  6 times a \n\n\n\nBacterial Inoculants for Biomass Production of Tithonia\n\n\n\nTABLE 1\nDry matter (DM) of Tithonia shoots as influenced by bacterial inoculation re-inoculation  \n\n\n\ninto Tithonia rhizosphere in Ultisols, Padang Indonesia\n\n\n\nTABLE 2\nTotal N, P, and K of Tithonia shoots as influenced by bacterial inoculants re-inoculation \n\n\n\ninto Tithonia rhizosphere in Ultisols, Padang, Indonesia\n\n\n\n              ISSN: 1394-7990    \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                        Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \n \n\n\n\nDry matter (DM) of Tithoniashootsas influenced by bacterial inoculation of Tithonia \nrhizosphere in Ultisols, Padang Indonesia \n\n\n\n \nTreatments \n\n\n\n \nPruning \n\n\n\nI \nPruning \n\n\n\nII \nPruning \n\n\n\nIII \nPruning \n\n\n\nIV \nTotal 4 \nprunes \n\n\n\n*If 6 \nprunes     \na year \n\n\n\n............................(kg m-2)............................. t. ha-1y-1 \n(K) Without bio-agent 0.40 c 0.62 b 0.71 a 0.83 b 2.56 7.68 \n(L) Phosphate-solubilizing bacteria (PSB) 0.74 a 1.34 a 0.47 c 1.21 a 3.76 11.28 \n(M) Azospirillum (Azos) 0.55 b 0.64 b 0.54bc 0.66 c 2.39 7.17 \n(N) PSB + Azos 0.49 b 0.64 b 0.59 b 0.81 b 2.53 7.59 \n(O) PSB + Azos+Azotobacter (Azot) 0.50 b 0.69 b 0.74 a 0.89 b 2.82 8.46 \n\n\n\nNote:  Means in the same column followed by different letters are significantly different according to HSD \n           At 5% probability level \n          * If pruned 6 times a year on a 2000m2 hedgerow ha-1 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \nTotal N, P, and K of Tithonia shoots as influenced by microbial inoculantre-inoculation into Tithonia \n\n\n\nrhizosphere in Ultisols, Padang, Indonesia \n \n\n\n\nTreatments \n \n\n\n\nTotal yield of N, P, dan K for 4 prunes (g. m-2), \nand estimation for 6 prunes a year (kg.2000 m2.ha-1.y-1)* \n\n\n\nN(g. m-2) (kg.ha-1) \n* \n\n\n\nP(g. m-2) kg.ha-1 \n\n\n\n* \nK(g. m-2) (kg.ha-1) \n\n\n\n* \n(K)Without bio-agent 55.0 b 165.0 6.3 b 18.9 30.0 c 90.0 \n(L)Phosphate-solubilizing bacteria (PSB) 71.9 a 215.7 10.0 a 30.0 84.4 a 253.2 \n(M) Azospirillum (Azos) 63.1 b 189.3 8.8 a 26.4 40.6 b 121.8 \n(N)PSB + Azos 75.0 a 225.0 10.0 a 30.0 47.5 b 142.5 \n(O)PSB + Azos+Azotobacter (Azot) 72.5 a 217.5 8.8 a 26.4 40.6 b 121.8 \n\n\n\nNote: Figures in the same column followed by different letters are significantly different according to HSD at   \n          5% probability level \n          * If pruned 6 times a year on a 2000m2 hedgerow ha-1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n              ISSN: 1394-7990    \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                        Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \n \n\n\n\nDry matter (DM) of Tithoniashootsas influenced by bacterial inoculation of Tithonia \nrhizosphere in Ultisols, Padang Indonesia \n\n\n\n \nTreatments \n\n\n\n \nPruning \n\n\n\nI \nPruning \n\n\n\nII \nPruning \n\n\n\nIII \nPruning \n\n\n\nIV \nTotal 4 \nprunes \n\n\n\n*If 6 \nprunes     \na year \n\n\n\n............................(kg m-2)............................. t. ha-1y-1 \n(K) Without bio-agent 0.40 c 0.62 b 0.71 a 0.83 b 2.56 7.68 \n(L) Phosphate-solubilizing bacteria (PSB) 0.74 a 1.34 a 0.47 c 1.21 a 3.76 11.28 \n(M) Azospirillum (Azos) 0.55 b 0.64 b 0.54bc 0.66 c 2.39 7.17 \n(N) PSB + Azos 0.49 b 0.64 b 0.59 b 0.81 b 2.53 7.59 \n(O) PSB + Azos+Azotobacter (Azot) 0.50 b 0.69 b 0.74 a 0.89 b 2.82 8.46 \n\n\n\nNote:  Means in the same column followed by different letters are significantly different according to HSD \n           At 5% probability level \n          * If pruned 6 times a year on a 2000m2 hedgerow ha-1 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \nTotal N, P, and K of Tithonia shoots as influenced by microbial inoculantre-inoculation into Tithonia \n\n\n\nrhizosphere in Ultisols, Padang, Indonesia \n \n\n\n\nTreatments \n \n\n\n\nTotal yield of N, P, dan K for 4 prunes (g. m-2), \nand estimation for 6 prunes a year (kg.2000 m2.ha-1.y-1)* \n\n\n\nN(g. m-2) (kg.ha-1) \n* \n\n\n\nP(g. m-2) kg.ha-1 \n\n\n\n* \nK(g. m-2) (kg.ha-1) \n\n\n\n* \n(K)Without bio-agent 55.0 b 165.0 6.3 b 18.9 30.0 c 90.0 \n(L)Phosphate-solubilizing bacteria (PSB) 71.9 a 215.7 10.0 a 30.0 84.4 a 253.2 \n(M) Azospirillum (Azos) 63.1 b 189.3 8.8 a 26.4 40.6 b 121.8 \n(N)PSB + Azos 75.0 a 225.0 10.0 a 30.0 47.5 b 142.5 \n(O)PSB + Azos+Azotobacter (Azot) 72.5 a 217.5 8.8 a 26.4 40.6 b 121.8 \n\n\n\nNote: Figures in the same column followed by different letters are significantly different according to HSD at   \n          5% probability level \n          * If pruned 6 times a year on a 2000m2 hedgerow ha-1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014120\n\n\n\nyear, and its yield projected or estimated using data from Tithonia DM production \npruned 4 times annually as a base (Table 2). In the case of 6 pruning cycles, the \narea planted with Tithonia was 2000 m2 ha-1 (20 hedgerow ha-1 having 1 m width).\n\n\n\nThe data in Table 2 shows that Tithonia inoculated with PSB, pruned 6 times \na year produced approximately 216 kg N, 30 kg P, and 253 kg K ha-1y-1. On the \nother hand, inoculation with PSB + Azospirillium resulted in higher N nutrient \ncontent, amounting to 225 kg N, 30 Kg P, and 143 kg K ha-1 y-1.\n\n\n\nThe results from this study were better than that reported earlier by Hakim \nand Agustian (2005b), where lower nutrient content was observed with about 150 \nkg N and 156 kg K ha-1 y-1 in biomass of Tithonia, pruned 6 times a year, but \nwithout microbial inoculation to Tithonia rhizospheres grown as hedgerows at 5 \nm distance. Thus, it is clear from the above that inoculation with PSB or PSB in \ncombination with Azospirillium was effective in increasing N, P, and K uptake by \nTithonia grown as hedgerows. The increased nutrient uptake improved Tithonia \ngrowth, resulting in high DM production, which can be used as an organic fertilizer \nor an alternative fertilizer, replacing or reducing synthetic fertilizer application.\n\n\n\nHigher N, P, and K content of Tithonia due to inoculation of PSB is supported \nby results of previous studies.  Alexander 1977;  Rao 1982; Illmer et al. 1995 \nreported that the presence of PSB is due to the production of organic acids such \nas citric acid, glutamate, succinate, lactate, oxalate, glycosilate, malic, fumaric, \ntartaric, and ketoglutarate. It is believed that the organic acids are effective in \nsolubilizing P, making it more available to plants. The phosphate solubilizing \nmechanism by PSB can be explained in several ways: (1) changing the solubility \nof inorganic P compounds, (2) enhancing organic and inorganic P release, and (3) \nencouraging the reduction-oxidation of inorganic P compounds (Alexander 1977). \nWith PSB, Premono (1994) found that P derived from fertilizer and fertilizer use \nefficiency increased as much as 60-135%. Andriani (1997) reported that the use \nof PSB increased P availability and nutrient uptake of corn in acid soils. The \nmechanism of increasing P availability and absorption due to PSB also occurred \nin the inoculation of Tithonia with PSB.\n\n\n\nThe results of bacterial inoculants inoculated into rhizosphere of Tithonia \nin this field trial seem to be in line with the results of a pot trial in the greenhouse \n(Asman et al. 2008). They reported that inoculation of PSB or PSB mixed with \nAzospirillum into the rhizosphere of Tithonia resulted in higher N, P, and K than \nthat from a mixture of other bacterial inoculants. Venkateswarlu and Rao (1983) \nreported that N fixing bacteria such as Azospirillium was commonly found in \nthe area around the roots, the root surface and in the root. Berg et al. (1980) \nsuggest that Azospirillium is able to penetrate the epidermis and the root cortex of \nplants by forming a capsule fixing at mospheric nitrogen and establishing a close \nassociation with its host. Beside N fixation, Azospirillium also produced plant \ngrowth promoters or phytohormones that are very important for root and above \nroot plant growth. \n\n\n\nBased on these experimental results, it could be stated that the PSB inoculation \nof Tithonia rhizosphere either singly, or in combination, improved uptake of plant \n\n\n\nNurhajati Hakim, Rina Alfina, Agustian, Hermansah and Yulnafatmawita\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 121\n\n\n\nnutrients, produced high DM, and high N, P, and K content.  Kloepper et al. (2004) \nand Glick (1995) suggest that the effect of soil microorganisms on plant growth is \nvery important to improve productivity of plants and maintain soil fertility.\n\n\n\nCONCLUSION\nPhosphate-solubilizing bacteria, and/or its combination with Azospirillum \n(N-fixing bacteria) were found to be the most suitable bacterial inoculants for \ncultivating Tithonia as hedgerow in Ultisols to obtain high dry matter yield and \nhigh N, P, and K content. Inoculation of phosphate-solubilizing bacteria into \nTithonia rhizosphere produced 11.3 t dry matter, 215 kg N; 30 Kg P, and 253 \nkg K/2000 m2 ha-1 y-1, whereas the-phosphate solubilizing bacteria+ Azospirillum \ninoculation produced about 7.6 t dry matter, 225 kg N, 30 kg P, and 142 kg K/2000 \nm2 ha-1 y-1.The phosphate-solubilizing bacteria (PSB) and /or PSB combined \nwith Azospirillium bacterial inoculants is recommended as bacterial inoculant \nfor cultivating Tithonia diversifolia as hedgerow for  production of  high in situ \norganic fertilizer in Ultisols.\n\n\n\nAKNOWLEDGEMENT\nThis study was supported by the Research Grant of the Post Graduate Team of the \nMinistry of National Education of the Republic of Indonesia.\n\n\n\nREFERENCES\nAlexander, M. 1978. Introduction to Soil Microbiology.  Canada: John Willey and \n\n\n\nSons.\n\n\n\nAndriani. 1997. 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"\n\n\uf0d7\uf0cd\uf0cd\uf0d2\uf0e6\uf020\uf0ef\uf0ed\uf0e7\uf0ec\uf0f3\uf0e9\uf0e7\uf0f0\uf0f0\n\n\n\n\uf0d1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0d3\uf0bf\uf0ac\uf0ac\uf0bb\uf0ae\uf0f4\uf020\uf0d2\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0d0\uf020\uf0dc\uf0a7\uf0b2\uf0bf\uf0b3\uf0b7\uf0bd\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0da\uf0b7\uf0b2\uf0bb\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0dd\uf0b1\uf0bf\uf0ae\uf0ad\uf0bb\uf020\n\n\n\n\uf0ce\uf0b1\uf0b1\uf0ac\uf0ad\uf020\uf0b7\uf0b2\uf020\uf0bf\uf020\uf0d8\uf0ab\uf0b3\uf0b7\uf0bc\uf020\uf0cd\uf0ab\uf0be\uf0ac\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0da\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf020\uf0db\uf0bd\uf0b1\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\uf020\uf0db\uf0a8\uf0b0\uf0b1\uf0ad\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\n\n\n\n\uf0dc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bf\uf0b2\uf0bd\uf0bb\uf020\uf0b7\uf0b2\uf020\uf0d3\uf0bb\uf0b9\uf0b8\uf0bf\uf0b4\uf0bf\uf0a7\uf0bf\uf0f4\uf020\uf0d2\uf0b1\uf0ae\uf0ac\uf0b8\uf0bb\uf0bf\uf0ad\uf0ac\uf020\uf0d7\uf0b2\uf0bc\uf0b7\uf0bf\uf020\n\n\n\n\uf0d5\uf0f2\uf020\uf0cb\uf0b0\uf0bf\uf0bc\uf0b8\uf0bf\uf0a7\uf0bf\uf0ef\uf0f6\uf0f4\uf020\uf0d8\uf0f2\uf0d2\uf0f2\uf020\uf0d0\uf0bf\uf0b2\uf0bc\uf0bb\uf0a7\uf0ee\uf0f4\uf020\uf0cd\uf0f2\uf0d5\uf0f2\uf020\uf0de\uf0bf\uf0ae\uf0b7\uf0b5\uf0ee\uf020\uf0fa\uf020\uf0ce\uf0f2\uf0cd\uf0f2\uf020\uf0cc\uf0ae\uf0b7\uf0b0\uf0bf\uf0ac\uf0b8\uf0b7\uf0ed\n\n\n\n\uf0ef\uf0dc\uf0bb\uf0b0\uf0bf\uf0ae\uf0ac\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b1\uf0ba\uf020\uf0de\uf0bf\uf0ad\uf0b7\uf0bd\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf0ad\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0cd\uf0b1\uf0bd\uf0b7\uf0bf\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf0ad\uf0f4\uf020\uf0cd\uf0bd\uf0b8\uf0b1\uf0b1\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cc\uf0bb\uf0bd\uf0b8\uf0b2\uf0b1\uf0b4\uf0b1\uf0b9\uf0a7\uf0f4\uf020\n\n\n\n\uf0d2\uf0b1\uf0ae\uf0ac\uf0b8\uf020\uf08a\uf0db\uf0bf\uf0ad\uf0ac\uf0bb\uf0ae\uf0b2\uf020\uf0d8\uf0b7\uf0b4\uf0b4\uf020\uf0cb\uf0b2\uf0b7\uf0aa\uf0bb\uf0ae\uf0ad\uf0b7\uf0ac\uf0a7\uf0f4\uf020\uf0cd\uf0b8\uf0b7\uf0b4\uf0b4\uf0b1\uf0b2\uf0b9\uf0f3\uf0e9\uf0e7\uf0ed\uf0f0\uf0ee\uf0ee\uf0f4\uf020\uf0d7\uf0b2\uf0bc\uf0b7\uf0bf\n\n\n\n\uf0ee\uf020\uf0dc\uf0bb\uf0b0\uf0bf\uf0ae\uf0ac\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b1\uf0ba\uf020\uf0de\uf0b1\uf0ac\uf0bf\uf0b2\uf0a7\uf0f4\uf020\uf0cd\uf0bd\uf0b8\uf0b1\uf0b1\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0d4\uf0b7\uf0ba\uf0bb\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf0ad\uf0f4\uf020\uf0d2\uf0b1\uf0ae\uf0ac\uf0b8\uf020\uf08a\uf0db\uf0bf\uf0ad\uf0ac\uf0bb\uf0ae\uf0b2\uf020\uf0d8\uf0b7\uf0b4\uf0b4\uf020\n\n\n\n\uf0cb\uf0b2\uf0b7\uf0aa\uf0bb\uf0ae\uf0ad\uf0b7\uf0ac\uf0a7\uf0f4\uf020\uf0cd\uf0b8\uf0b7\uf0b4\uf0b4\uf0b1\uf0b2\uf0b9\uf0f3\uf0e9\uf0e7\uf0ed\uf0f0\uf0ee\uf0ee\uf020\uf0d7\uf0b2\uf0bc\uf0b7\uf0bf\n\n\n\n\uf0ed\uf020\uf0d2\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0bf\uf0b4\uf020\uf0de\uf0b1\uf0ac\uf0bf\uf0b2\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0ce\uf0bb\uf0ad\uf0bb\uf0bf\uf0ae\uf0bd\uf0b8\uf020\uf0d7\uf0b2\uf0ad\uf0ac\uf0b7\uf0ac\uf0ab\uf0ac\uf0bb\uf0f4\uf020\uf0d4\uf0ab\uf0bd\uf0b5\uf0b2\uf0b1\uf0a9\uf0f3\uf0ee\uf0ee\uf0ea\uf0f0\uf0f0\uf0ef\n\n\n\n\uf0df\uf0de\uf0cd\uf0cc\uf0ce\uf0df\uf0dd\uf0cc\n\n\n\n\uf0bc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0bf\uf020\uf0ad\uf0ab\uf0be\uf0ac\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0b8\uf0ab\uf0b3\uf0b7\uf0bc\uf020\uf0ba\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf020\uf0ac\uf0b1\uf020\uf0bb\uf0a8\uf0bf\uf0b3\uf0b7\uf0b2\uf0bb\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ae\uf0bb\uf0ad\uf0b0\uf0b1\uf0b2\uf0ad\uf0bb\uf020\uf0b1\uf0ba\uf020\uf0be\uf0bb\uf0b4\uf0b1\uf0a9\uf0f3\n\n\n\n\uf0b9\uf0ae\uf0b1\uf0ab\uf0b2\uf0bc\uf020\uf0b0\uf0bf\uf0ae\uf0ac\uf0ad\uf020\uf0ac\uf0b1\uf020\uf0b3\uf0b7\uf0b4\uf0bc\uf020\uf0bc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bf\uf0b2\uf0bd\uf0bb\uf020\uf0bd\uf0bf\uf0ab\uf0ad\uf0bb\uf0bc\uf020\uf0be\uf0a7\uf020\uf0bf\uf0be\uf0b1\uf0aa\uf0bb\uf0f3\uf0b9\uf0ae\uf0b1\uf0ab\uf0b2\uf0bc\uf020\uf0aa\uf0bb\uf0b9\uf0bb\uf0ac\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0be\uf0a7\uf020\uf0b8\uf0ab\uf0b3\uf0bf\uf0b2\uf0ad\uf0f2\uf020\uf020\uf020\n\n\n\n\uf0f3\uf0ee\uf0f7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0bd\uf0b1\uf0bf\uf0ae\uf0ad\uf0bb\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf020\n\n\n\n\uf0f8\uf0ed\uf0f0\uf0e9\uf0f3\uf0ed\uf0e8\uf0e9\uf020\uf0b9\uf0b3\uf0f3\uf0ee\n\n\n\n\uf0f3\uf0ee 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\uf0f8\uf0df\uf0b4\uf0b4\uf0bb\uf0b2\uf020 \uf0bb\uf0ac\uf020 \uf0bf\uf0b4\uf0f2\n\n\n\n\uf0ae\uf0bf\uf0b0\uf0b7\uf0bc\uf020 \uf0ac\uf0b7\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020 \uf0b3\uf0bb\uf0ac\uf0b8\uf0b1\uf0bc\uf020 \uf0f8\uf0df\uf0b4\uf0b4\uf0bb\uf0b2\uf020 \uf0bb\uf0ac\uf020 \uf0bf\uf0b4\uf0f2\n\n\n\n\uf0a9\uf0bf\uf0ad\uf020 \uf0bb\uf0ad\uf0ac\uf0b7\uf0b3\uf0bf\uf0ac\uf0bb\uf0bc\uf020 \uf0be\uf0a7\uf020 \uf0bc\uf0b7\uf0b9\uf0bb\uf0ad\uf0ac\uf0b7\uf0b2\uf0b9\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0bf\uf0b7\uf0ae\uf020 \uf0bc\uf0ae\uf0b7\uf0bb\uf0bc\uf020 \uf0ad\uf0b1\uf0b7\uf0b4\uf020 \uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0bb\uf0ad\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\uf0d8\n\uf0ee \uf0ec\n\n\n\n\uf020 \uf0ba\uf0b1\uf0b4\uf0b4\uf0b1\uf0a9\uf0bb\uf0bc\uf020 \uf0be\uf0a7\uf020\n\n\n\n\uf0bc\uf0b7\uf0ad\uf0ac\uf0b7\uf0b4\uf0b4\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0bf\uf0b2\uf0bc\uf020 \uf0ac\uf0b7\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0f8\uf0df\uf0b4\uf0b4\uf0bb\uf0b2\uf020\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\uf020\uf0ef\uf0e7\uf0e9\uf0ec\uf0f7\uf0f2\uf020\uf0df\uf0b3\uf0b3\uf0b1\uf0b2\uf0b7\uf0ab\uf0b3\uf020\uf0b2\uf0b7\uf0ac\uf0ae\uf0b1\uf0b9\uf0bb\uf0b2\uf020\uf0f8\uf0d2\uf0d8\n\uf0ec\uf0f5\n\uf0f3\uf0d2\uf0f7\uf020\uf0a9\uf0bf\uf0ad\uf020\n\n\n\n\uf0bc\uf0bb\uf0ac\uf0bb\uf0ae\uf0b3\uf0b7\uf0b2\uf0bb\uf0bc\uf020\uf0be\uf0a7\uf020 \uf0b7\uf0b2\uf0bc\uf0b1\uf0f3\uf0b0\uf0b8\uf0bb\uf0b2\uf0b1\uf0b4\uf020\uf0be\uf0b4\uf0ab\uf0bb\uf020\uf0b3\uf0bb\uf0ac\uf0b8\uf0b1\uf0bc\uf020 \uf0bf\uf0ba\uf0ac\uf0bb\uf0ae\uf020 \uf0bb\uf0a8\uf0ac\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0b2\uf0b9\uf020 \uf0ba\uf0ae\uf0bb\uf0ad\uf0b8\uf020 \uf0ad\uf0b1\uf0b7\uf0b4\uf020 \uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0bb\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\n\n\n\n\uf0ed\n\uf0f3\n\n\n\n\uf0bf\uf0ba\uf0ac\uf0bb\uf0ae\uf020\uf0bb\uf0a8\uf0ac\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0b2\uf0b9\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\uf0bc\uf0bb\uf0b7\uf0b1\uf0b2\uf0b7\uf0ad\uf0bb\uf0bc\uf020\uf0a9\uf0bf\uf0ac\uf0bb\uf0ae\uf0f2\uf020\uf0df\uf0aa\uf0bf\uf0b7\uf0b4\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0b0\uf0b8\uf0b1\uf0ad\uf0b0\uf0b8\uf0b1\uf0ae\uf0b1\uf0ab\uf0ad\uf020\uf0a9\uf0bf\uf0ad\uf020\uf0bc\uf0bb\uf0ac\uf0bb\uf0ae\uf0b3\uf0b7\uf0b2\uf0bb\uf0bc\uf020\uf0be\uf0a7\uf020\n\n\n\n\uf0ed\n\uf020\n\n\n\n\uf0f8\uf0df\uf0b4\uf0b4\uf0bb\uf0b2\uf020\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\uf020\uf0ef\uf0e7\uf0e9\uf0ec\uf0f7\uf0f2\uf020\n\n\n\n\uf0ce\uf0b1\uf0b1\uf0ac\uf020\uf0cd\uf0bf\uf0b3\uf0b0\uf0b4\uf0b7\uf0b2\uf0b9\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0df\uf0b2\uf0bf\uf0b4\uf0a7\uf0ad\uf0b7\uf0ad\n\n\n\n\uf0d3\uf0b1\uf0b2\uf0ac\uf0b8\uf0b4\uf0a7\uf020\uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0b7\uf0b2\uf0b9\uf020\uf0b1\uf0ba\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf0ad\uf020\uf0a9\uf0bf\uf0ad\uf020\uf0bc\uf0b1\uf0b2\uf0bb\uf020\uf0bc\uf0ab\uf0ae\uf0b7\uf0b2\uf0b9\uf020\uf0d6\uf0ab\uf0b2\uf0bb\uf020\uf0ee\uf0f0\uf0f0\uf0f0\uf020\uf0ac\uf0b1\uf020\uf0d3\uf0bf\uf0a7\uf020\uf0ee\uf0f0\uf0f0\uf0ef\uf020\uf0b7\uf0b2\uf020\uf0bb\uf0bf\uf0bd\uf0b8\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\uf020\n\n\n\n\uf0d8\uf0ab\uf0b3\uf0b7\uf0bc\uf020\uf0cd\uf0ab\uf0be\uf0ac\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0da\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf020\uf0db\uf0bd\uf0b1\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\uf020\uf0db\uf0a8\uf0b0\uf0b1\uf0ad\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bf\uf0b2\uf0bd\uf0bb\n\n\n\n\uf020\nFig. 1: Monthly variation in rainfall (mm), mean maximum and minimum temperature (0C) \n\n\n\nduring the study period (June 2000 \u2013 May 2001) \n\n\n\n\n\n\n\n\n\uf0ec\uf0e8 \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0be\uf0bf\uf0b9\uf0ad\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ad\uf0ac\uf0b1\uf0ae\uf0bb\uf0bc\uf020\uf0b7\uf0b2\uf020\uf0bc\uf0bb\uf0bb\uf0b0\uf020\uf0ba\uf0ae\uf0bb\uf0bb\uf0a6\uf0bb\uf020\uf0bf\uf0ac\uf020\uf0f3\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0a9\uf0bb\uf0ae\uf0bb\uf020 \uf0ae\uf0bb\uf0ac\uf0ae\uf0b7\uf0bb\uf0aa\uf0bb\uf0bc\uf020 \uf0be\uf0a7\uf020 \uf0a9\uf0bb\uf0ac\uf0f3\uf0ad\uf0b7\uf0bb\uf0aa\uf0b7\uf0b2\uf0b9\uf020\uf0b3\uf0bb\uf0ac\uf0b8\uf0b1\uf0bc\uf020 \uf0f8\uf0de\uf0b1\uf0b8\uf0b3\uf020 \uf0ef\uf0e7\uf0e9\uf0e7\uf0f7\uf020 \uf0bf\uf0b2\uf0bc\uf020 \uf0b0\uf0ae\uf0b1\uf0bd\uf0bb\uf0ad\uf0ad\uf0b7\uf0b2\uf0b9\uf020 \uf0b1\uf0ba\uf020 \uf0bf\uf0b4\uf0b4\uf020 \uf0ac\uf0b8\uf0bb\uf020\n\n\n\n\uf0bc\uf0bb\uf0bf\uf0bc\uf020\uf0f8\uf0b2\uf0bb\uf0bd\uf0ae\uf0b1\uf0b3\uf0bf\uf0ad\uf0ad\uf0f7\uf020\uf0b1\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0be\uf0bf\uf0ad\uf0b7\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0b0\uf0b4\uf0b7\uf0bf\uf0be\uf0b7\uf0b4\uf0b7\uf0ac\uf0a7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0bc\uf0bb\uf0b9\uf0ae\uf0bb\uf0bb\uf020\uf0b1\uf0ba\uf020\uf0bd\uf0b1\uf0b8\uf0bb\uf0ad\uf0b7\uf0b1\uf0b2\uf020\uf0be\uf0bb\uf0ac\uf0a9\uf0bb\uf0bb\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\n\n\n\n\uf0bd\uf0b1\uf0ae\uf0ac\uf0bb\uf0a8\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b0\uf0bb\uf0ae\uf0b7\uf0bc\uf0bb\uf0ae\uf0b3\uf0f2\uf020\uf0d4\uf0b7\uf0aa\uf0bb\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf0ad\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0b1\uf0ba\uf0ac\uf0bb\uf0b2\uf020\uf0ad\uf0b3\uf0b1\uf0b1\uf0ac\uf0b8\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b4\uf0b7\uf0b9\uf0b8\uf0ac\uf020\uf0bd\uf0b1\uf0b4\uf0b1\uf0ab\uf0ae\uf0bb\uf0bc\uf020\uf0bd\uf0b1\uf0b3\uf0b0\uf0bf\uf0ae\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\n\n\n\n\uf0ef\uf0ed\uf0ac\uf0b8\n\n\n\n\uf0d6\uf0ab\uf0b2\uf0bb\uf020\uf0ee\uf0f0\uf0f0\uf0f0\uf020\uf0a9\uf0bf\uf0ad\uf020\uf0ab\uf0ad\uf0bb\uf0bc\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0d6\uf0ab\uf0b2\uf0bb\uf020\uf0ee\uf0f0\uf0f0\uf0ef\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0bd\uf0bf\uf0b4\uf0bd\uf0ab\uf0b4\uf0bf\uf0ac\uf0b7\uf0b2\uf0b9\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0bf\uf0b2\uf0b2\uf0ab\uf0bf\uf0b4\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf020\uf0b0\uf0ae\uf0b1\uf0bc\uf0ab\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf0f2\uf020\uf0cc\uf0b8\uf0b7\uf0ad\uf020\n\n\n\n\uf0a9\uf0bf\uf0ad\uf020\uf0bc\uf0b1\uf0b2\uf0bb\uf020\uf0be\uf0bf\uf0ad\uf0bb\uf0bc\uf020\uf0b1\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0bf\uf0ad\uf0ad\uf0ab\uf0b3\uf0b0\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ac\uf0b8\uf0bf\uf0ac\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ad\uf0ac\uf0ab\uf0bc\uf0b7\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\uf0ad\uf020\uf0bf\uf0ae\uf0bb\uf020\uf0b1\uf0b4\uf0bc\uf020\uf0b9\uf0ae\uf0b1\uf0a9\uf0ac\uf0b8\uf020\uf0ba\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf0ad\uf020\n\n\n\n\uf0ee\uf0f0\uf0f0\uf0ef\uf0f2\uf020\n\n\n\n\uf0cc\uf0b8\uf0bb\uf020 \uf0bc\uf0bf\uf0ac\uf0bf\uf020 \uf0b1\uf0b2\uf020 \uf0ae\uf0b1\uf0b1\uf0ac\uf020 \uf0b3\uf0bf\uf0ad\uf0ad\uf020 \uf0a9\uf0bb\uf0ae\uf0bb\uf020 \uf0b0\uf0b1\uf0b1\uf0b4\uf0bb\uf0bc\uf020 \uf0ac\uf0b1\uf020 \uf0b1\uf0be\uf0ac\uf0bf\uf0b7\uf0b2\uf020 \uf0bf\uf0aa\uf0bb\uf0ae\uf0bf\uf0b9\uf0bb\uf020 \uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020 \uf0ba\uf0b1\uf0ae\uf020 \uf0a9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae\uf020\n\n\n\n\uf0bf\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2\uf020\uf0f8\uf0cd\uf0bb\uf0b0\uf0ac\uf0bb\uf0b3\uf0be\uf0bb\uf0ae\uf020\uf0ac\uf0b1\uf020\uf0d2\uf0b1\uf0aa\uf0bb\uf0b3\uf0be\uf0bb\uf0ae\uf0f7\uf020\uf0ad\uf0bb\uf0bf\uf0ad\uf0b1\uf0b2\uf0ad\uf0f2\uf020\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0bc\uf0ae\uf0b7\uf0bb\uf0bc\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf020\uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0bb\uf0ad\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0b9\uf0ae\uf0b1\uf0ab\uf0b2\uf0bc\uf020\n\n\n\n\uf0f8\uf0df\uf0b4\uf0b4\uf0bb\uf0b2\uf020\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\n\n\n\n\uf0b3\uf0b1\uf0b4\uf0a7\uf0be\uf0bc\uf0bb\uf0b2\uf0ab\uf0b3\uf020 \uf0be\uf0b4\uf0ab\uf0bb\uf020 \uf0b3\uf0bb\uf0ac\uf0b8\uf0b1\uf0bc\uf020 \uf0bf\uf0ba\uf0ac\uf0bb\uf0ae\uf020 \uf0bc\uf0b7\uf0b9\uf0bb\uf0ad\uf0ac\uf0b7\uf0b2\uf0b9\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0bb\uf0ad\uf020 \uf0a9\uf0b7\uf0ac\uf0b8\uf020 \uf0ac\uf0ae\uf0b7\uf0f3\uf0bf\uf0bd\uf0b7\uf0bc\uf020 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\uf0b3\n\n\n\n\uf0b3\uf0b7\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf020\uf0bb\uf0b4\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf0ad\uf0f2\uf020\uf0cc\uf0ab\uf0ae\uf0b2\uf0b1\uf0aa\uf0bb\uf0ae\uf020\uf0ac\uf0b7\uf0b3\uf0bb\uf020\uf0f8\uf0cc\uf0f7\uf020\uf0a9\uf0bf\uf0ad\uf020\uf0bd\uf0bf\uf0b4\uf0bd\uf0ab\uf0b4\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0bf\uf0ad\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ae\uf0bb\uf0bd\uf0b7\uf0b0\uf0ae\uf0b1\uf0bd\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0ab\uf0ae\uf0b2\uf0b1\uf0aa\uf0bb\uf0ae\uf020\n\n\n\n\uf0cd\uf0ac\uf0bf\uf0ac\uf0b7\uf0ad\uf0ac\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0df\uf0b2\uf0bf\uf0b4\uf0a7\uf0ad\uf0b7\uf0ad\n\n\n\n\uf0ba\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\uf0ad\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0ad\uf0ab\uf0be\uf0b6\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\uf0ac\uf0f3\uf0ac\uf0bb\uf0ad\uf0ac\uf020\uf0be\uf0b1\uf0ac\uf0b8\uf020\uf0b7\uf0b2\uf020\uf0d7\uf0bf\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ce\uf0bf\uf0b4\uf0b7\uf0bf\uf0b2\uf0b9\uf020\uf0ba\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf0ad\uf020\uf0f8\uf0c6\uf0bf\uf0ae\uf020\uf0ef\uf0e7\uf0e9\uf0ec\uf0f7\uf0f2\uf020\uf020\n\n\n\n\n\n\n\n\n\uf0ec\uf0e7\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0ce\uf0db\uf0cd\uf0cb\uf0d4\uf0cc\uf0cd\uf020\uf0df\uf0d2\uf0dc\uf020\uf0dc\uf0d7\uf0cd\uf0dd\uf0cb\uf0cd\uf0cd\uf0d7\uf0d1\uf0d2\n\n\n\n\uf0ca\uf0bb\uf0b9\uf0bb\uf0ac\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0dd\uf0b8\uf0bf\uf0ae\uf0bf\uf0bd\uf0ac\uf0bb\uf0ae\uf0b7\uf0ad\uf0ac\uf0b7\uf0bd\uf0ad\n\n\n\n\uf0ad\uf0bf\uf0b0\uf0b4\uf0b7\uf0b2\uf0b9\uf0ad\uf020 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\uf0ec\uf0ea\uf0ea\uf0f2\uf0f0\uf0ec\uf020\n\n\n\n\uf0ca\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\uf0bc\uf0b7\uf0ad\uf0ad\uf0b7\uf0b3\uf0b7\uf0b4\uf0bf\uf0ae\uf020\uf0ad\uf0ab\uf0b0\uf0bb\uf0ae\uf0ad\uf0bd\uf0ae\uf0b7\uf0b0\uf0ac\uf020\uf0f8\uf0d7\uf0bf\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0bf\uf0f4\uf020\uf0be\uf0f4\uf020\uf0bd\uf0f4\uf020\uf0bc\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ce\uf0bf\uf0b4\uf0b7\uf0bf\uf0b2\uf0b9\uf020\uf0bb\uf0f4\uf020\uf0ba\uf0f4\uf020\uf0b9\uf0f4\uf020\uf0b8\uf0f7\uf020\uf0bf\uf0ae\uf0bb\uf020\uf0ad\uf0b7\uf0b9\uf0b2\uf0b7\uf0ba\uf0b7\uf0bd\uf0bf\uf0b2\uf0ac\uf0b4\uf0a7\uf020\uf0bc\uf0b7\uf0ba\uf0ba\uf0bb\uf0ae\uf0bb\uf0b2\uf0ac\uf020\uf0f8\uf0d0\uf0e4\uf0f0\uf0f2\uf0f0\uf0ef\uf0f7\uf020\uf0be\uf0bb\uf0ac\uf0a9\uf0bb\uf0bb\uf0b2\uf020\uf0b0\uf0ae\uf0b1\uf0ac\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020\n\n\n\n\uf0bf\uf0b2\uf0bc\uf020\uf0bc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\uf0ad\uf020\uf0bf\uf0ac\uf020\uf0d7\uf0bf\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ce\uf0bf\uf0b4\uf0b7\uf0bf\uf0b2\uf0b9\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0ba\uf0b7\uf0b2\uf0bb\uf020\uf0f8\uf0bf\uf0f4\uf020\uf0be\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0bb\uf0f4\uf020\uf0ba\uf0f7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0bd\uf0b1\uf0bf\uf0ae\uf0ad\uf0bb\uf020\uf0f8\uf0bd\uf0f4\uf020\uf0bc\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b9\uf0f4\uf020\uf0b8\uf0f7\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf0ad\uf0f2\uf020\n\n\n\n\n\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0d8\uf0ab\uf0b3\uf0b7\uf0bc\uf020\uf0cd\uf0ab\uf0be\uf0ac\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0da\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf020\uf0db\uf0bd\uf0b1\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\uf020\uf0db\uf0a8\uf0b0\uf0b1\uf0ad\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bf\uf0b2\uf0bd\uf0bb\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0ed\uf0f0\uf0f0\n\n\n\n\uf0ec\uf0f0\uf0f0\n\n\n\n\uf0eb\uf0f0\uf0f0\n\n\n\n\uf0ea\uf0f0\uf0f0\n\n\n\n\uf0e9\uf0f0\uf0f0\n\n\n\n\uf0e8\uf0f0\uf0f0\n\n\n\n\uf020\n\n\n\n\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0ed\uf0f0\uf0f0\n\n\n\n\uf0ec\uf0f0\uf0f0\n\n\n\n\uf0eb\uf0f0\uf0f0\n\n\n\n\uf0ea\uf0f0\uf0f0\n\n\n\n\uf0e9\uf0f0\uf0f0\n\n\n\n\uf0e8\uf0f0\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0ed\uf0f0\uf0f0\n\n\n\n\uf0ec\uf0f0\uf0f0\n\n\n\n\uf0eb\uf0f0\uf0f0\n\n\n\n\uf0ea\uf0f0\uf0f0\n\n\n\n\uf0e9\uf0f0\uf0f0\n\n\n\n\uf0e8\uf0f0\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0ed\uf0f0\uf0f0\n\n\n\n\uf0ec\uf0f0\uf0f0\n\n\n\n\uf0eb\uf0f0\uf0f0\n\n\n\n\uf0ea\uf0f0\uf0f0\n\n\n\n\uf0e9\uf0f0\uf0f0\n\n\n\n\uf0e8\uf0f0\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0ed\uf0f0\uf0f0\n\n\n\n\uf0ec\uf0f0\uf0f0\n\n\n\n\uf0eb\uf0f0\uf0f0\n\n\n\n\uf0ea\uf0f0\uf0f0\n\n\n\n\uf0e9\uf0f0\uf0f0\n\n\n\n\uf0e8\uf0f0\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0ed\uf0f0\uf0f0\n\n\n\n\uf0ec\uf0f0\uf0f0\n\n\n\n\uf0eb\uf0f0\uf0f0\n\n\n\n\uf0ea\uf0f0\uf0f0\n\n\n\n\uf0e9\uf0f0\uf0f0\n\n\n\n\uf0e8\uf0f0\uf0f0\n\n\n\n\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0ed\uf0f0\uf0f0\n\n\n\n\uf0ec\uf0f0\uf0f0\n\n\n\n\uf0eb\uf0f0\uf0f0\n\n\n\n\uf0ea\uf0f0\uf0f0\n\n\n\n\uf0e9\uf0f0\uf0f0\n\n\n\n\uf0e8\uf0f0\uf0f0\n\n\n\n\uf0cd\uf0bb\uf0bf\uf0ad\uf0b1\uf0b2\uf0ad\n\n\n\n\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ee\uf0f0\uf0f0\n\n\n\n\uf0ed\uf0f0\uf0f0\n\n\n\n\uf0ec\uf0f0\uf0f0\n\n\n\n\uf0eb\uf0f0\uf0f0\n\n\n\n\uf0ea\uf0f0\uf0f0\n\n\n\n\uf0e9\uf0f0\uf0f0\n\n\n\n\uf0e8\uf0f0\uf0f0\n\n\n\n\uf020\n\n\n\nFig. 2: Seasonal variation of fine (<2mm) and coarse (2-15mm) root biomass and necromass in \n0-20 cm soil layer in protected and disturbed stands \n\n\n\n\uf0b4\uf0bf\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0b0\uf0ae\uf0b1\uf0ac\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc \uf0dd\uf0b1\uf0bf\uf0ae\uf0ad\uf0bb\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf0ad\n\n\n\n\uf0de\uf0b7\uf0b1\uf0b3\uf0bf\uf0ad\n\uf0d2\uf0bb\uf0bd\uf0ae\uf0b1\uf0b3\uf0bf\uf0ad\uf0ad\n\n\n\n\uf0b4\uf0bf\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0bc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\n\n\n\n\uf0ce\uf0bf\uf0b4\uf0b7\uf0bf\uf0b2\uf0b9\uf020\uf0b0\uf0ae\uf0b1\uf0ac\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\n\n\n\n\uf0ce\uf0bf\uf0b4\uf0b7\uf0bf\uf0b2\uf0b9\uf020\uf0bc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\n\n\n\n\uf0da\uf0b7\uf0b2\uf0bb\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf0ad\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\n\n\n\n\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf020\n\n\n\nFig. 3: Seasonal variation in N and P concentration in fine and coarse root biomass and \nnecromass in protected and disturbed stands \n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae 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\uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0eb\n\n\n\n\uf0ea\n\n\n\n\uf0e9\n\uf0d7\uf0bf\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0d0\uf0ae\uf0b1\uf0ac\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\n\n\n\n\uf0da\uf0b7\uf0b2\uf0bb\uf020\uf0ae\uf0b1\uf0b1\uf0ac\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac\uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd\uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0eb\n\n\n\n\uf0ea\n\n\n\n\uf0e9\n\n\n\n\uf0dd\uf0b1\uf0bf\uf0ae\uf0ad\uf0bb\uf020\uf0ae\uf0b1\uf0b1\uf0ac\uf020 \uf020\uf020\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac\uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd\uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0ef \uf0f0\uf0f0\n\n\n\n\uf0ef \uf0ee\uf0f0\n\n\n\n\uf0ef \uf0ec\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b4\uf0b7\uf0bf\uf0b2\uf0b9\uf020\uf0d0\uf0ae\uf0b1\uf0ac\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\n\n\n\n\uf0d7\uf0bf\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac \uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd \uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0ef \uf0f0\uf0f0\n\n\n\n\uf0ef \uf0ee\uf0f0\n\n\n\n\uf0ef \uf0ec\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac\uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd\uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0eb\n\n\n\n\uf0ea\n\n\n\n\uf0e9\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac\uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd\uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0eb\n\n\n\n\uf0ea\n\n\n\n\uf0e9\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac\uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd\uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0ef \uf0f0\uf0f0\n\n\n\n\uf0ef \uf0ee\uf0f0\n\n\n\n\uf0ef \uf0ec\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac \uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd \uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0ef \uf0f0\uf0f0\n\n\n\n\uf0ef \uf0ee\uf0f0\n\n\n\n\uf0ef \uf0ec\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac\uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd\uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0eb\n\n\n\n\uf0ea\n\n\n\n\uf0e9\n\n\n\n\uf0ce\uf0bf\uf0b7 \uf0b2\uf0a7\uf0df \uf0ab\uf0ac\uf0ab\uf0b3 \uf0b2\uf0c9 \uf0b7\uf0b2 \uf0ac\uf0bb\uf0ae\uf0cd\uf0b0\uf0ae\uf0b7 \uf0b2\uf0b9\n\uf0f0\n\n\n\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0eb\n\n\n\n\uf0ea\n\n\n\n\uf0e9\n\n\n\n\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0ef \uf0f0\uf0f0\n\n\n\n\uf0ef \uf0ee\uf0f0\n\n\n\n\uf0ef \uf0ec\uf0f0\n\n\n\n\uf0ce\uf0bf\uf0b4\uf0b7\uf0bf\uf0b2\uf0b9\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ab\uf0ae\uf0be\uf0bb\uf0bc\uf020\uf0ad\uf0ac\uf0bf\uf0b2\uf0bc\n\n\n\n\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0ef \uf0f0\uf0f0\n\n\n\n\uf0ef \uf0ee\uf0f0\n\n\n\n\uf0ef \uf0ec\uf0f0\n\n\n\n\uf0f0\n\n\n\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0eb\n\n\n\n\uf0ea\n\n\n\n\uf0e9\n\n\n\n\uf0f0\n\n\n\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0eb\n\n\n\n\uf0ea\n\n\n\n\uf0e9\n\n\n\n\uf0cd\uf0bb\uf0bf\uf0ad\uf0b1\uf0b2\uf0ad\n\uf020\n\n\n\nFig. 4: Mean seasonal variation in N and P accumulation in fine and coarse root biomass and \nnecromass in protected and disturbed stands \n\n\n\n\uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9 \uf0ce\uf0bf\uf0b7\uf0b2\uf0a7 \uf0df\uf0ab\uf0ac\uf0ab\uf0b3\uf0b2 \uf0c9\uf0b7\uf0b2\uf0ac\uf0bb\uf0ae \uf0cd\uf0b0\uf0ae\uf0b7\uf0b2\uf0b9\n\n\n\n\n\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\uf020\n\n\n\n\uf0f3\uf0ef 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"\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nRelationship between Metals in Vegetables with Soils in Farmlands of Kuching, Sarawak\n\n\n\n57\n\n\n\nISSN: 1394-7990\nMalaysian Society of Soil ScienceMalaysian Journal of Soil Science Vol.11 : 57-69 (2007)\n\n\n\nRelationship between Metals in Vegetables with Soils\nin Farmlands of Kuching, Sarawak\n\n\n\nDevagi Kanakaraju*, Nur Aa\u2019in Mazura\n&  Awang Khairulanwar\n\n\n\nDepartment of Chemistry, Faculty of Resource Science and Technology\nUniversity of Malaysia Sarawak\n\n\n\n94300 Kota Samarahan, Sarawak, Malaysia\n\n\n\nABSTRACT\nThe  accumulation of Fe, Zn, Cu, Mn, Co and Pb in vegetables was investi-\ngated in two farmlands, Siburan and Beratok at Kuching, Sarawak. Leafy\nand fruit vegetable samples were collected and analysed for metal content\nusing the acid wet digestion method. Topsoil samples (0-30 cm) were\ncharacterised for pH, organic matter, particle size, nitrogen content, phos-\nphorus  content  and heavy metals. Leafy vegetables accumulated  higher\namount of metals compared to fruit vegetables. Metals were determined at\nthe highest concentrations in leaves of kale at Beratok compared to other\nplant parts. No clear pattern of metal uptake in different parts was observed\nfor green mustard and white mustard. Essential metals, Fe, Zn and Cu, were\ngenerally high in the vegetables. Pb levels in the vegetables analysed (dry\nweight basis) exceeded slightly the level recommended by the Malaysian\nFood Act 1983. Atmospheric deposition and gas emissions from traffic\nwere the contributing factors for Pb contamination since Pb displayed a\ntendency to accumulate in leaves compared to other parts of the vegetables.\nElement concentrations in the soils differed between sampling sites. Corre-\nlation analysis yielded a significant relationship between Zn concentra-\ntions in soils and vegetables (n = 15, r = 0.86, P = 0.001) and moderate\ncorrelation for Cu (r =0.55, P<0.05) and Pb (r = 0.65, P<0.05).\n\n\n\nKeywords: Metals, vegetables, soil, correlation\n\n\n\nINTRODUCTION\nVegetables  are  vital to human diet as they contain essential components needed\nby  the human body such as carbohydrates, proteins, vitamins, minerals and also\ntrace elements (Itanna 2002). Consumption of vegetables is one of the pathways\nby  which  heavy metals enter the food chain (Wang et al. 2004). Excessive\naccumulation of dietary heavy metals such as Cd, Pb and Cr can lead to serious\nhealth problems.  Heavy metals persist in the environment, are non-biodegrad-\nable and have the potential to accumulate in different body organs (Radwan and\nSalama 2006).  The process of plant growth depends on the cycle of nutrients\nincluding trace elements from soil to plant (Mohamed et al. 2003).\n\n\n\n* Corresponding author: Email: kdevagi@frst.unimas.my\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM57\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200758\n\n\n\nDevagi Kanakaraju, Nur Aa\u2019in Mazura & Awang Khairulanwar\n\n\n\nVarious studies have been conducted on metal assessment in edible veg-\netables, crops and also soils. These studies have shown the ability of crops to\ntake up heavy metals (Stalikas et al. 1997; Mohamed et al. 2003; Sharma et al.\n2007).  A study done on edible green vegetables grown along sites of the Sinza\nand Msimbazi rivers of Tanzania revealed that some of the vegetables contained\nhigh levels of metals beyond the levels designated by FAO/WHO (Bahemuka\nand Mubofu. 1999).  Alam et al. (2002) showed that vegetables in Samta Vil-\nlage in Bangladesh exhibited the potential to accumulate high levels of Pb that\nwould pose a health hazard  for  human  consumption.  A study by Cui et al.\n(2004) reported that vegetables and soils from villages located at 1500 m and\n500 m from a smelter in Naning, China were heavily contaminated compared to\na village located at 50 km from the smelter. A study was carried out in Lagos\ncity, Nigeria to compare metal pollution levels in vegetables and soils from\nindustrial and residential areas (Yusuf et al. 2003). The study indicated that\nmetal pollution was higher in industrial areas and discouraged vegetable planting\nin industrial areas.\n\n\n\nMetal accumulation in plants depends on the plant species, types of soil,\nenvironment  and  agricultural  practice  (Alloway 1990). Some  plants may\naccumulate toxic metals at levels which may be harmless to the plant but could\nbe harmful to humans if ingested (Radojevic and Bashkin 1998). The total metal\ncontent in soil is the result of parent materials, fertilisers, atmospheric deposi-\ntion, agrichemicals, and organic wastes (Alloway 1990).  Metals like zinc, cobalt\nand copper are essential group of metals required for some metabolic activities\n(Awofolu et al. 2005). Lead and cadmium are very toxic and excessive content\nof these elements can lead to cardiovascular, kidney and nervous system diseases\n(WHO 1995).\n\n\n\nThe  Malaysian dietary guidelines recommend five servings of fruits and\nvegetables for adults to maintain good health (Khairiah et al. 2004).  Largely,\ndue to awareness of vegetable consumption, large quantities of vegetables are\nconsumed to obtain sufficient nutrients as body supplements and this has resulted\nin human beings being exposed to risk from contaminated soils and vegetables.\nHence, this study aimed to investigate heavy metal uptake in cultivated leafy and\nfruit vegetables from two farmlands in Siburan and Beratok. Beratok is located\nnearer to the roadside compared to Siburan which is located far from the road-\nside.  Higher metal concentrations were anticipated to occur in Beratok. Metal\nanalyses in the edible parts of vegetables were carried out. In order to have a\nclear picture of metal distribution in each edible portion of the vegetables, the\nleaves, stems  and roots were also analysed separately. The characteristics of the\nsoil samples from the farmlands were also determined. Attempts were made to\nrelate the metal contents in both compartments and the findings  were compared\nwith the Malaysian Food Act 1983 and FAO/WHO guidelines.\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM58\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nRelationship between Metals in Vegetables with Soils in Farmlands of Kuching, Sarawak\n\n\n\n59\n\n\n\nMATERIALS AND METHODS\n\n\n\nSample Collection\nSamples were taken from agricultural farms at Siburan and Beratok in Kuching,\nSarawak (Fig. 1). These farmlands were chosen for this study mainly because\nthe vegetables harvested from these farmlands are supplied to the residents in\nthese areas and also marketed to the nearby markets for public consumption.\nVegetable samples including kale, green mustard, white mustard, long bean and\ncucumber were collected from three plots and samples were harvested in a 1m x\n1 m quadrant. Vegetable species that were collected at each area are shown in\nTable 1. These vegetables are commonly consumed by the locals and can be\neasily cooked. Topsoil samples at depth of 0-30 cm (n = 35) were also collected\nfrom these plots. Two composite soil samples which comprised four sub-samples\nwere taken randomly at each plot. Control soil samples were collected from\nnearby plots which were not planted with vegetables.\n\n\n\nX- Sampling site\nFig. 1:  Location of Siburan and Beratok\n\n\n\nTABLE 1\nVegetable species (n= 30) collected at Siburan and Beratok\n\n\n\nSampling area Types Common name Scientific name\n\n\n\nBeratok Leafy Kale Brassica alboglabra\nGreen mustard Brassica chinensis\nWhite mustard Brassica rapa\n\n\n\nSiburan Leafy Green mustard Brassica chinensis\nMustard Brassica sp.\n\n\n\nFruit Cucumber Cucumis sativus\nLong bean Vigna sinensis\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM59\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200760\n\n\n\nDevagi Kanakaraju, Nur Aa\u2019in Mazura & Awang Khairulanwar\n\n\n\nSample Preparation and Plant Analysis\nThe  vegetables  were washed with tap water followed by distilled water to\neliminate attached soil particulates.  Samples were divided for application of\ntwo types of analysis.  Some samples were used for metal analysis of all the\nedible parts.  For metal analysis in different parts of the vegetables, leaves, stems\nand roots were separated and sliced into smaller pieces.  Subsequent to this, the\nsamples were freeze-dried to reduce the risk of losing volatile elements and\nground into powder using pestle and mortar.  Two types of dissolution methods\nwere applied to assess metals recovery in the samples, namely wet digestion and\ndry ashing. In the wet digestion method, 1 g of sample was digested with a\nmixture of HClO4 and HNO3, while for dry ashing 1 g of sample was placed in a\ncrucible and heated at 450oC in a muffle furnace for 2 hours. The ash was dis-\nsolved in 5 mL of concentrated HCl solution. Recovery analysis was done based\non amount recovered  from  the spiked concentrations. Table 2 represents the\nrecovery percentage obtained for the dissolution methods. It can be seen that the\nwet digestion method produced better recovery results  for the metals. Hence,\nthis method was employed to test the samples. Metals, Fe, Co, Zn, Mn, Cu and\nPb were analysed using Flame Atomic Absorption Spectrometry (FAAS) (Model\nPerkin \u2013 Elmer 3110).\n\n\n\nSoil Analysis\nSoil samples were analysed for organic matter content, pH, soil texture, total\nphosphorus  and  total  Kjeldahl nitrogen (TKN). Total  organic matter was\ndetermined using loss of ignition method at 550oC (APHA 1995), while soil pH\nwas measured using 1:5 soil water ratio.  Particle-size analysis was done based\non Stoke\u2019s law using the pipette method (USDA 1984).  Total Kjeldahl nitrogen\nwas measured by employing the micro Kjeldahl procedure with sulfuric acid and\ndigestion catalyst.  Total phosphorus was determined by molybdenum blue\nmethod.  For heavy metal analysis, 1 g of soil sample was digested with 10 mL\nHNO3 and 3 mL of H2O2. For Cd, Zn, Cu, Mn, Co and Pb analysis, a supernatant\nwas used  with flame atomic absorption spectrometry (FAAS).  All metal con-\ntents are expressed in mg kg-1 dry weight.\n\n\n\nStatistical Analysis\nStatistical analysis was performed using Two-Way ANOVA without replicate\nanalysis to compare the parameters analysed and metal concentrations between\n\n\n\nTABLE 2\nRecovery percentage obtained for dry ashing and wet digestion method\n\n\n\nMethod Recovery percentage (%)\n\n\n\nFe Zn Co Cu Cd Pb M n\n\n\n\nWet digestion 90.3 97.0 103.9 92.4 97.6 94.2 95.5\nDry ashing 77.2 90.1 87.5 85.6 79.8 86.7 93.1\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM60\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nRelationship between Metals in Vegetables with Soils in Farmlands of Kuching, Sarawak\n\n\n\n61\n\n\n\nsampling  sites  and  vegetables. A correlation study was done to investigate the\nexistence of any relationships between metal concentration in vegetables with\nsoils.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nHeavy Metals in Vegetables\nThe mean concentrations of metals found in the edible portion of the vegetable\nsamples  are  presented  in Table 3. Wide variations were observed in the accu-\nmulation  of  metals  by  the  vegetable samples which represented the selectivity\nof plants towards metals and other contributing factors such as the locations  of\nthe farmlands and types of fertilisers applied to the vegetables.  Generally, leafy\ntype vegetables accumulated higher level of metals compared to fruit type and\nthis was further proven by statistical analysis (p<0.05).  A previous study has\n\n\n\nTABLE 3\nMean concentrations of heavy metals in edible parts of vegetables at\n\n\n\nBeratok and Siburan\n\n\n\nArea Sample Concentration (mg/kg)\n\n\n\nFe Co Zn Mn Cu Pb\n\n\n\nBeratok Kale 88 3.44 53.78 13.78 9.78 1.73\n(73.00 \u2013 (2.33- (33.33- (6.00- (6.33- (1.93-\n108.67) 4.67) 81.00) 25.00) 15.33) 2.90)\n\n\n\nGreen mustard 338.67 4.28 71.06 26.71 8.61 1.22\n(75.33- (2.33- (47.67- (12.00- (3.33- (1.50-\n421.67) 6.67) 139.00) 40.00) 14.33) 2.37)\n\n\n\nWhite mustard 138.50 2.94 59.83 20.11 6.38 1.49\n(70.67- (2.33- (28.00- (12.67- (3.00- (1.50-\n209.00) 3.67) 92.33) 33. 00) 12.33) 2.90)\n\n\n\nSiburan Green mustard 174.39 4.61 66.39 55.67 5.89 1.34\n(50.33- (3.33- (47.67- (21.33- (4.00- (1.27-\n303.33) 6.33) 90.33) 106.67) 10.33) 2.30)\n\n\n\n Mustard 240 3.67 47.33 38.05 4.00 1.24\n(58.33- (3.00- (26.33- (9.00- (2.33- (1.57-\n569.33) 5.00) 91.33) 80.3) 8.67) 2.13)\n\n\n\nCucumber 62.5 3.83 33.67 18.67 5.00 1.40\n(61.33- (3.00- (33.00- (16.00- (2.45- (1.57-\n63.67) 4.67) 34.33) 21.33) 10.58) 2.03)\n\n\n\nLong bean 72 3.67 48.84 59.17 7.84 1.15\n(65.33- (3.33- (36.67- (46.00- (7.67- (1.73-\n78.67) 4.00) 61.00) 72.33) 8.00) 2.18)\n\n\n\nValues in parentheses are minimum and maximum concentrations\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM61\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200762\n\n\n\nDevagi Kanakaraju, Nur Aa\u2019in Mazura & Awang Khairulanwar\n\n\n\nshown that leafy vegetables accumulate more metals compared to other veg-\netables (Jinadasa et al. 1997).\n\n\n\nFe, Zn, Mn and Cu were found to be present in high concentrations. It is\nknown that Fe, Zn, Cu and Mn are essential elements in plant nutrition. Fe\nindicated an elevated concentration in leafy vegetables at Beratok (88 - 338.67\nmg/kg  dry weight) and  Siburan (174.39 to 240 mg/kg) compared to fruit\nvegetables (62.5 - 72 mg/kg at Siburan). The mean concentration of Mn varied\nbetween 13.78 \u2013 26.71 mg/kg at Beratok and 18.67 - 59.17 mg/kg at Siburan.\nGreen mustard and long bean at Siburan recorded the highest amounts of Mn.\nMn plays a vital role in mucopolysaccharide metabolism and is also related to\nsuperoxide dismutase (Vandeecasteele and Block 1993). Greater accumulation\nof Cu was found in kale at Beratok (9.78 mg/kg). This could be due to translo-\ncation of Cu from roots to leaves in which micronutrients are required by plants\nfor metabolism (Hopkins 1999).\n\n\n\nIn general, the least abundant metals in the vegetables were Pb and Co. The\nlevels of Co in Siburan and Beratok were similar.  The mean level of Pb in\nseveral vegetables of Beratok exceeded slightly the permitted level of the Malay-\nsian Food Act 1983 for Pb which is 1.5 mg/kg.  However, comparing the level\nof Pb with FAO/WHO guidelines (Pb, 2.0 mg/kg) the mean level of Pb com-\nplied with the standard.  The potential source of Pb could be due to gas emission\nfrom traffic as the Beratok farmland is located near the road way.  This finding\nwas in accordance with a study done on leafy vegetables that were grown near\nthe road side in Addis Ababa which was also found to contain a higher level of\nthe Pb element (Itanna 2002). Nabulo et al. (2006) reported that Pb concentra-\ntion in vegetable weed, Amaranthus dubius leaves, decreased with increasing\ndistance from the road  edge.  This  study  concluded that the  dominant  pathway\nfor  Pb contamination was from atmospheric deposition. Sharma et al. (2006)\nstudied the variations in metal concentrations during different seasons where Pb\nand Ni concentrations were higher than in the Indian standard in both summer\nand winter seasons in the edible portions of Beta vulgaris.\n\n\n\nFigs. 2-4 show the concentrations of metals in different parts of vegetables\nfor  kale, green mustard and white mustard. Metals in different parts varied\nbetween the types of vegetables. This may be attributed to the variation in heavy\nmetals uptake by different types of vegetables. High concentrations of essential\nelements (Fe, Co, Cu, Mn and Zn) and non-essential metal (Pb) were found in\nthe leaves of kale at Beratok (Fig. 2).  Pb showed a tendency to concentrate in\nleaves of the vegetables compared to other parts at Siburan and Beratok (Figs. 3\nand 5).  Metal levels in different parts of green mustard and white mustard\ngrown at both places did not show any difference. Literature on metal distribu-\ntion in different parts of vegetables is  insufficient.\n\n\n\nConcentrations  of metals in the vegetables at Siburan and Beratok were\ninfluenced by the water used for irrigation, spraying methods and also the fertilisers\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM62\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nRelationship between Metals in Vegetables with Soils in Farmlands of Kuching, Sarawak\n\n\n\n63\n\n\n\nFig. 2: Concentrations of Fe, Cu, Zn and Mn in different parts of vegetables at Beratok\n\n\n\nFig. 3: Concentrations of Co and Pb in different parts of vegetables at Beratok\n\n\n\nFig. 4: Concentrations of Fe, Cu, Zn and Mn in different parts of vegetables at Siburan\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM63\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200764\n\n\n\nDevagi Kanakaraju, Nur Aa\u2019in Mazura & Awang Khairulanwar\n\n\n\nused. At Siburan, irrigation water from nearby Sungai(river) Ribit was applied\nto the  vegetables.  The  accumulation  effect  depends  strongly on the crop\u2019s\nphysiological properties, the mobility of the metals, and the availability of met-\nals in soils (Liu et al. 2005) and surface deposits on the parts of vegetables\nexposed to polluted air (Buchaver 1973).\n\n\n\nSoil Properties\nThe physico-chemical properties of soils collected from the plots are tabulated\nin Table 4. The soil samples in Siburan and Beratok were loamy soils. The pH\nvalues of the soils from the plots of Siburan and Beratok ranged from 6.46 - 6.65\nwhich reflect mild acidic soils. This was similar to soil pH values found in\nLinyi, Yntai and Xinxiang, China which ranged from 6.32 - 6.65 due to waste-\nwater irrigation (Wang et al. 2004).  Phosphatic fertilisers such as superphos-\nphate can have considerable acidifying effect on soils. It is known that pH af-\nfects the bioavailability of soil metals but there were no major differences in pH\nbetween the sites. High rainfall during the sampling months could have lead to\nleaching, eventually resulting in low soil pH.  However, the control plots were\nfound to be moderately alkaline (pH 6.98 \u2013 7.21) due to less wastewater irriga-\ntion presence and less usage of fertilisers and compost. It is known that the\nalkaline range of soils is known to restrict the mobilisation of heavy metals and\nthus reduce the uptake of heavy metals (Sharma et al. 2007).\n\n\n\nOrganic matter did not show major distinction between the plots and the\nareas. Soil texture, the content of sand, silt and clay showed differing fraction\nconcentrations. Statistical analysis showed significant difference (p<0.05) in the\nmeans of sand, silt and clay fractions. The sand fraction was found to be domi-\nnant, followed by clay and silt. The movement of air and  water to plant roots\nthrough soil is affected by soil texture.  A good network of soil pores would\nallow rapid exchange of air and water with plant roots.  Plant growth depends on\nrapid rates of exchange. Total phosphorus exhibited  high levels in the soil samples.\nHigh amounts of phosphorus might be sourced from the use of compost as\nfertilisers in the farmlands.  The percentage of total Kjeldahl nitrogen (TKN) is\n\n\n\nFig. 5: Concentrations of Co and Pb in different parts of vegetables at Siburan\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM64\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nRelationship between Metals in Vegetables with Soils in Farmlands of Kuching, Sarawak\n\n\n\n65\n\n\n\na reflection of of ammonia-nitrogen and organic nitrogen content in soils. Nitro-\ngen content varied between 0.16-0.20 and 0.10-0.20% at Beratok and Siburan,\nrespectively, indicating no differences.\n\n\n\nElevated  concentrations of Mn and Cu were found in the soils. The concen-\ntrations of Cu and Mn were higher than in the vegetables species. Mn occurs\nnaturally in rock, whereas Cu levels in soil could be affected by soil and crop\ntreatment such as fungicides, fertilisers and also chicken dung (Alloway 1990).\nThe levels of Cu obtained at Siburan and Beratok ranged from 120.52 \u2013 147.85\nmg/kg and 123.34 \u2013 146.70 mg/kg, respectively.  Similar levels of Cu was found\nin  soils  under vegetables in Harare, Zimbabwe which showed total concentra-\ntions of Cu ranging from 7.0 to 145 mg/kg (Mapanda 2005).\n\n\n\nComparably, the concentrations of Pb in soils were much higher than those\nfound in the vegetables.  The levels of Pb in Siburan and Beratok were 20.33-\n25.33 mg/kg and 10.00-22.10 mg/kg, respectively.  The concentrations of Pb in\nuncontaminated  soils were <20 mg/kg.  Due to low solubility and relative free-\ndom from microbial degradation, Pb tends to accumulate in soils and sediments\nand remain bioavailable.  The maximum allowable limits (MAL) in soils sug-\ngested by Kabata-Pendias and Pendias (1992) are 5 mg/kg for Cd, 50 mg/kg for\nCo, 100 mg/kg for Cu and Pb and 300 mg/kg for Zn.  The concentrations of Co,\nZn, Cu and Pb in the soils of Siburan and Beratok were below the MAL and can\nbe classified as uncontaminated soils.\n\n\n\nA correlation study was carried out to ascertain if there is any relationship\nbetween Cu, Zn and Pb in vegetables and that of soils.  A moderate correlation\nwas observed between plant Pb and soil Pb (r = 0.65) at Siburan and Beratok. A\n\n\n\nTABLE 4\nSoil characteristics of Beratok and Siburan\n\n\n\nParameters Beratok Siburan\n\n\n\nPlot1 Plot2 Plot3 Control Plot1 Plot2 Plot3 Control\n\n\n\npH 6.48 6.46 6.52 7.21 6.53 6.65 6.55 6.98\nOM(%) 3.20 3.80 4.00 4.00 3.80 4.20 5.60 3.40\nClay (%) 36.4 32.4 35.5 27.8 41.9 40.8 40.4 51.8\nSilt (%) 13.1 17.6 16.5 18.6 22.3 28.2 23.5 22.2\nSand (%) 50.5 50.0 48.0 53.6 35.8 31.0 36.1 26.0\nPhosphorus 193.2 123.2 159.2 188.7 108.4 176.5 163.3 137.1\n(ppm)\nNitrogen(%) 0.16 0.17 0.20 0.17 0.15 0.12 0.20 0.10\nCd (mg/kg) 1.00 1.24 2.00 1.33 2.67 2.00 3.17 0.80\nCo(mg/kg) 1.98 2.56 2.46 1.67 11.00 5.00 4.10 5.33\nZn(mg/kg) 66.67 63.33 66.58 85.45 76.70 63.33 78.35 93.50\nMn(mg/kg) 127.05 120.65 183.33 155.00 137.67 157.67 100.25 143.34\nCu(mg/kg) 123.34 146.70 126.80 126.70 120.52 123.36 147.85 60.00\nPb(mg/kg) 22.10 10.00 17.67 20.67 25.33 22.00 21.33 20.33\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM65\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200766\n\n\n\nDevagi Kanakaraju, Nur Aa\u2019in Mazura & Awang Khairulanwar\n\n\n\nsignificant degree of correlation was observed between Zn level in vegetables\nand Zn level in soils (r = 0.86; P=0.001) and a moderate correlation occurred\nbetween vegetable Cu and Cu in soil (r = 0.55). The soil metal concentrations\nappear to influence the uptake of Zn and to a lesser extent Cu. There have also\nbeen reports to show the relationship between metals in soils and vegetables\nduring different seasons. Sharma et al (2006) concluded that Zn in soil had a\npositive significant relationship with vegetable contamination during winter, while\nconcentrations of Cd, Cu, and Mn in soil and plant showed significant positive\nrelationships only during summer.\n\n\n\nSoil-plant Transfer Coefficients\nThe transfer coefficient is the metal concentration in plant tissue above the ground\ndivided by the total metal concentration in the soil. Table 5 shows the transfer\ncoefficients of metals at Siburan and Beratok. The transfer coefficients as sug-\ngested by Kloke et al. (1994) for Cu and Pb (0.01-0.1), and Zn (1-10) were used\nfor comparison. Zn and Pb showed the highest transfer coefficients with no\nsamples exceeding the Zn and Pb transfer coefficients limits. Samples exceeded\nthe coefficient range suggested for Cu. This could have  resulted from elevated\nconcentrations of Cu in the soils.\n\n\n\nKachenko and Singh (2004) reported that soil properties may have influ-\nenced the soil-plant transfer of Cu because at Boolaroo (Pb-Zn smelter), 82% of\nsamples exceeded the suggested coefficient range of Cu and 63% exceeded the\nsuggested Pb coefficient range. The transfer coefficients differed between the\nvegetables species and sampling locations due to soil properties. Mohamed et al\n(2003) also reported different concentration factors (CF) from vegetables to\nvegetables and the magnitudes of the CF values reflect that the uptake of the\nmajor elements (K, Mg and Ca) are more pronounced than those of the trace\nelements (Co, Cu, Fe, Ni and Zn).\n\n\n\nTABLE 5\nTransfer coefficients of Cu, Zn and Pb from soil to vegetables\n\n\n\nArea Cu Zn Pb\n\n\n\nSiburan Kale 0.33 5.8 4.7\nGreen mustard 0.27 3.6 7.5\nWhite mustard 0.08 5.4 6.8\n\n\n\nBeratok Green mustard 0.67 7.1 5.2\nMustard 0.45 2.5 4.1\nCucumber 0.35 4.8 5.5\nLong bean 0.60 7.5 4.6\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM66\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nRelationship between Metals in Vegetables with Soils in Farmlands of Kuching, Sarawak\n\n\n\n67\n\n\n\nCONCLUSION\nThe assessment of metals in vegetables showed that leafy vegetables such as kale,\nwhite mustard and green mustard exhibited higher element concentrations than\nthe fruit types. The toxic element, Pb was found in the least concentrations\ncompared to other metals analysed. However, the level of Pb exceeded the Ma-\nlaysian Food Act 1983 guidelines. The levels of metals in similar vegetable\nspecies differed between the sampling sites at Siburan and Beratok. It is sug-\ngested that soil properties between the sampling sites contributed to this varia-\ntion.\n\n\n\nThere are various sources that could attribute to metal contamination and\ncontamination process which could have taken place during pre-harvest and post\nharvest process. Sources during pre-harvest include usage of compost, fertilisers\nand pesticides, chicken dung and water used for irrigation, while sources during\npost harvest include contamination through air and also during transport to mar-\nket. Future research should consider the effects of irrigation water and compost\nor fertilisers on the availability of metals to different species of vegetables and\nsoils. Microbiological studies also should be undertaken to evaluate the presence\nof pathogens.\n\n\n\nREFERENCES\nAlam, M. G. M., E.T. Snow and A. Tanaka. 2002. Arsenic and heavy metal contami-\n\n\n\nnation of vegetables grown in Samta village, Bangladesh. Sci. Total Environ.\n308: 83-96.\n\n\n\nAlloway, B. J. 1990. Heavy  Metals in Soil.  Glascow: UK Blackie Academic and\nProfesional.\n\n\n\nApha.1995.  Standard Methods for Water and Wastewater. 19th ed.  American Wash-\nington, DC, USA: Public Health Association/ American Water Works Associa-\ntion Water Environment Federation.\n\n\n\nAwofolu, O.R., Z. Mbolekwa, V. Mtshemla and O.S. Fatoki. 2005. Levels of trace\nmetals in water and sediment from Tyume River and its effects on an irrigated\nfarmland. Water South Africa 31: 87-94.\n\n\n\nBahemuka, T. E. and E.B. Mubofu. 1999. Heavy metals in edible green vegetables\ngrown along the sites of the Sinza and Msimbazi rivers in Dar er Salaam, Tanza-\nnia. 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Print. Office.\n\n\n\nVandeecasteele, C. and C.B. Block.  1993. Modern Methods for Trace Element Determination.\nChichester: John Wiley & Sons.\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM68\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nRelationship between Metals in Vegetables with Soils in Farmlands of Kuching, Sarawak\n\n\n\n69\n\n\n\nWang, X. P., X.P. Shan, S.Z. Zhang and B. Wen. 2004. A model for evaluation of phytoavailability\nof trace elements to vegetables under field conditions. Chemosphere 55: 811-822.\n\n\n\nWHO. 1995. Lead. Environmental Health Criteria. Geneva. Vol 165.\n\n\n\nYusuf, A. A., T.A. Arowolo and O. Bamgbose. 2003. Cadmium, copper, and nickel levels in\nvegetables from industrial and residential areas of Lagos City, Nigeria. Food. Chem. Toxicol.\n41:375-378.\n\n\n\nMJ of Soil Science 057-069.pmd 08-Apr-08, 10:46 AM69\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : henrynek34@gmail.com, nnaemeka.okoli@futo.edu.ng\n\n\n\nINTRODUCTION\nSoil and water bodies are particularly polluted with toxicants from food \nprocessing and allied industries (Salami and Egwin 2007). Major pollutants from \nfood processing include hydrocarbons, palm oil mill effluent, human and animal \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 101-115 (2018)                          Malaysian Society of Soil Science\n\n\n\nLong-Term Impact of Cassava Mill Effluent on Some \nChemical and Biological Properties of Soils\n\n\n\n*N. H. Okoli, N. N. Oti, I. I. Ekpe and S. A. Mbawuike\n\n\n\nDepartment of Soil Science and Technology, Federal University of Technology, \nOwerri, Nigeria\n\n\n\nABSTRACT\nEffluent generated from cassava processing, when discharged to the soil, alters the \nnature of soil properties. Hence this study was carried out to evaluate the impact \nof long-term discharge of cassava mill effluent on soil chemical and biological \nproperties. Using a target sampling technique, four micro pits were dug, two at the \npolluted site and two 50 m away from the polluted site which served as the control. \nSoil samples were collected from the pits at varying depths of 0-5 cm, 5-10 cm, 10-\n15 cm, 15-20 cm and 20-50 cm. Soil samples collected were analysed for chemical \nand biological properties and data generated were subjected to t-test analysis to \nassess the impact of cassava effluent on some selected soil chemical properties \nand biological properties. The results of the chemical properties indicated that the \npolluted site had higher organic matter (mean=25.97 g kg-1) relative to the control \nsite (mean= 15.42 g kg-1). Total nitrogen was higher in the polluted site (mean = 1.29 \ng kg-1) relative to the control site (mean = 0.74 g kg-1). Available phosphorus was \nhigher in the polluted site (mean = 13.5 mg kg-1) relative to the control site (mean \n= 8.67 mg kg-1). Total exchangeable bases (TEB) was higher in the polluted site \n(mean= 6.7 cmolckg-1) relative to the control site (mean=4.05 cmolc kg-1). Effective \ncation exchange capacity (ECEC) was higher in the polluted site (mean = 8.25 \ncmolckg-1) relative to the control site (mean=4.78 cmolckg-1) whereas the pH was \nlower in the polluted site (mean=5.71) relative to the control site (mean=6.8). The \nresults of the biological properties showed that the Total Fungal Count was higher \nin the polluted site (mean=1.12 x 105 CFU g-1) relative to the control (mean=0.33 \nx 105 CFUg-1) whereas Total Heterotrophic Bacterial Count was lower in the \npolluted site (mean=2.29 x105 CFUg-1) relative to the control (mean=5.72 x 105 \nCFUg-1). The t-test analysis result revealed that cassava effluent had a significant \npositive impact on organic matter, total nitrogen, available phosphorus, ECEC and \ntotal fungal population whereas it had a significant negative impact on soil pH and \ntotal bacterial population. \n\n\n\nKeywords: Cassava mill effluent, soil chemical properties, soil biological \nproperties, soil pollution.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018102\n\n\n\nwastes, wood waste, waste water from agro-allied industries as well as cassava \nmill effluent from cassava processing activities (Wade et al. 2002; Arimoro and \nOsakwe 2006).\n\n\n\nCassava (Manihotes culentus Crantz) which belongs to the family \nEuphorbiaceae (Nwaugo et al. 2008) is widely cultivated in the tropical and \nsubtropical regions of the world for its starchy tuberous roots with more than 200 \ncalories/day of food value (FAO 2004). It is a staple food of nearly one billion \npeople in Africa, South America, Asia and Pacific (ANU 2007) and in Nigeria, \nthe estimated cassava production is approximately over 34 million metric tons \n(FAO 2004).\n\n\n\nThe highly perishable nature of harvested cassava roots and the presence \nof cyanogenic glucosides in bitter cultivars call for immediate processing of \nthe storage roots into more stable and safer products. Cassava can be processed \nby either washing, exposure to air, heating or pressing. Consequently, a lot of \nprocessing equipment and technology has been developed by various governmental \nand private organisations in Nigeria to facilitate the processing of cassava roots to \nreduce losses (IITA 2005).\n\n\n\nIn Nigeria, cassava is processed into traditional delicacies which include \ngarri, fufu, lafun flour etc, some of which are fermented products (Oti 2002). \nAmong all the products obtained from cassava, garri is the most common in \nNigeria and its production is done in varying scales, small, medium and large \n(Uzoije et al. 2011). \n\n\n\nDuring cassava processing, much effluent and solid wastes are generated \nand released into the environment. One of the major recipients of this effluent \nis the soil (Orhue et al. 2014). On the average, 2.62 m3 ton-1 of solid residue and \n3.68 m3 ton-1of water residues are generated via cassava processing in Nigeria. \n(Horsfall et al. 2006; Isabirye et al. 2007).\n\n\n\nCassava effluents when discharged into the soil result in changes in soil \nproperties (Nwakaudu et al. 2012). For intance, Eghoaye and Dada  (2004) \ninvestigated cassava effluent polluted soils and observed an increase in soil \nacidity, potassium, sodium, phosphorous, and organic carbon and a decrease in \ncalcium nitrogen and magnesium. Akpan et al. (2011) reported increased pH, N, \norganic carbon, exchangeable acidity and decreased Mg, K, P in soil treated with \ncassava mill effluent. In another study (Ogboghodo et al. 2001), cassava effluent \nwas found to increase the number of organisms in the soil ecosystem which may \nbe associated with an increase in soil pH, organic carbon and total nitrogen.\n\n\n\nThe Nigeria government is working towards increased cassava cultivation \nwith a harvest target of 150 million tons annually (IITA 2011). Consequently, in \nthe past few years, there has been a great upsurge in the production of cassava \nand establishment of more cassava processing mills in the southern parts of the \ncountry with the consequence of an extensive ecological pollution associated with \nthe effluent discharge into the soil (Igbinosa and Igiehon 2015).\n\n\n\nPrevious studies conducted in Nigeria to evaluate the impact of cassava mill \neffluent on soil properties indicated conflicting findings and were primarily based \n\n\n\nOkoli\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 103\n\n\n\non short-term impact, thus necessitating a more intensive and accurate study. \nTherefore the major objective of this study was to evaluate the long-term impact \nof cassava mill effluent on soil chemical and biological properties.\n\n\n\nMATERIALS AND METHODS\nStudy Area\nThe study was conducted at an active 11-year-old cassava mill located in Iho-\nDimeze, Ikeduru in Imo State Southeastern Nigeria which lies between latitude \n4o 451 N to 7o 151 N and longitude 6\u00b0 501 E to 7o 251 E. Soils of the study area \nare derived from coastal plain sands (Onweremadu et al. 2007). The study area \nfalls within the humid tropical climate with a mean annual rainfall, higher than \n2500 mm, and temperature ranging from 26 \u00b0C to 30 \u00b0C while relative humidity \nis about 70%. The rainfall pattern is bimodal with peaks in the month of July \nand September (Ihem et al. 2014). Secondary vegetation dominates the area \nand farming is a major socio-economic activity in the area with the main crops \ncultivated being maize (Zea mays), oil palm (Elaeis guineensis), cassava (Manihot \nesculenta), plantain (Musa sapientum) etc.\n\n\n\nCharacterisation of Cassava Mill Effluent\nCassava mill effluent (CME) is a colloidal of fine particles of cassava starch \nin water (Sackey and Bani 2007) with total suspended and dissolved solids of \nabout 789 and 799 mgL-1, respectively (Orhue et al., 2014).  It is high in organic \nmatter and highly acidic in nature (Sackey and Bani, 2007) with a pH of 4.6 and \na range of 2.5-4.20 reported by Adejumo and Ola (2011) and Rim-Rukeh (2012), \nrespectively. An investigation into the elemental content of CME revealed 0.19, \n0.18, 1.48, 0.58 and 0.82 mg L-1 nitrogen, phosphorus, calcium, potassium and \nmagnesium, respectively (Orhue et al. 2014). Further studies on the chemical \nproperties of the effluent showed biological oxygen demand (BOD) in the range \nof 13.0-73.0 mg L-1, chemical oxygen demand (COD) ranging from 320 to 365 \nmgL-1, dissolved oxygen ranging from 1.10 to 2.60 mgL-1 and hydrocyanic \nacid ranging from 54.10 to 63.20 (Rim-Rukeh 2012).  Studies on the biological \nproperties of CME indicated presence of Streptococcus spp, Bacillus spp, \nStaphylo coccus aureus, Lactobacillus spp, Micrococcus spp and Pseudomonas \nspp bacterial species (Ehiagbonare et al. 2009; Rim-Rukeh 2012) while  Mucor \nspp, Aspergillus spp, Penicillium spp and Saccharomyces cerevisae fungal species  \nhave been identified (Ehiagbonare et al. 2009).\n\n\n\nField Studies and Sample Collection\nGuided by the target sampling technique, two micro profile pits were dug in the \npolluted site and another two pits 50 m away from the polluted site which served \nas the control. Composite soil samples were collected from the profile pits dug at \nfive varying depths of 0-5 cm, 5-10 cm, 10-15 cm, 15-20 cm and 20 cm-50 cm, \nsumming to a total of 20 samples used for the study. The soil samples collected \nwere air-dried, sieved using a 2-mm sieve and subjected to laboratory analyses. \n\n\n\nLong-Term Impact of Cassava Mill Effluent\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018104\n\n\n\nLaboratory Analyses\nChemical Properties\nSoil pH was determined in 1:2.5 solute/suspension ratio using glass electrode \nof a pH meter (Thomas 1996). Exchangeable bases (Ca2+, Mg2+, K+, Na+) were \nextracted with NH4OAc buffered at pH 7.0 (Thomas 1982). Exchangeable K+ and \nNa+ content of extracts were read on flame photometer while exchangeable Ca2+ and \nMg2+ were determined using atomic absorption spectrophotometer. Exchangeable \nacidity (Al3+ and H+) was extracted with 1 N KCl (Thomas 1982) and determined \nby titrating with 0.5 N NaOH using phenolphthalein as an indicator. Effective \ncation exchange capacity (ECEC) was obtained by summation of basic and acidic \ncations; organic matter (OM) was determined by wet oxidation method (Nelson \nand Sommers 1982) and total nitrogen by micro Kjeldahl apparatus method \n(Bremner and Mulvaney 1982) while available phosphorus was determined using \nBray II solution (Olson and Sommers 1982).\n\n\n\nBiological properties\nThe total aerobic heterotrophic bacterial (THC) and total fungal count (TFC) \nexpressed in colony forming unit per gram soil (CFUg-1) were ascertained by a \nstandard pour plate method as described by Igbinosa and Igiehon (2015). Using \nthis method, 1 g of the soil samples was measured into a sterile test tube and 9 mL \nof sterile distilled water was added to make a stock solution.  The 10-1 suspension \nwas subsequently serially diluted to 10-10 dilution and the diluted samples were \nused for microbial analysis. Heterotrophic bacteria were isolated using nutrient \nagar amended with 0.015 % (w/v) nystatin to inhibit fungal growth. The nutrient \nagar plates were incubated at 28 \u00b1 2 0C for 24 - 48 h. Potato dextrose agar containing \n0.05 % (w/v) Chloramphenicol was used to isolate fungi upon incubation at 28 \n\u00b1 2 0C for 72 h. After incubation, total counts of fungi and heterotrophic bacteria \nwere determined using the colony counter.  The identification of bacteria species \nwas based on their morphological characteristics and biochemical tests carried \nout on the isolates using gram staining technique of Fawole and Oso (2004) while \nthe fungal isolates were further characterised based on their morphological and \nmicroscopic features using  lacto phenol cotton blue staining techniques (Hunter \nand Bamett 2000).\n\n\n\nStatistical Analysis\nData obtained from laboratory analyses were subjected to t-test analysis at 5% \nlevel of probability to assess the impact of cassava mill effluent on selected soil \nchemical and biological (bacteria and fungi) properties.  \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nImpact of Cassava Mill Effluent on Some Chemical Properties of the Soils\nFigures 1-6 show the results of chemical properties of the soils. The results \nindicated higher soil pH in the control site relative to the polluted site, with the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 105\n\n\n\nformer and latter having mean values of 6.60 and 5.71, respectively (Figure 1). \nOrhue et al. (2014) investigated the properties of cassava effluent and noted a \nlow pH value of 5.07. Hence lower soil pH values recorded in the polluted site \ncould be due to the acidic nature of the cassava mill effluent occasioned by the \npresence of hydrogen cyanide, resulting in decreased soil pH (Ogboghod\u00f3 et al. \n2001).  Soils of the polluted site had more organic matter (OM) content than \nthat of the control site with values from 12.37-35.07 g kg-1 in polluted site and \n11.51-20.27 g kg-1 in the control site (Figure 2) and these values decreased with \nsoil depth. Higher organic matter values observed in soils of the polluted site are \nchiefly due to the organic nature of cassava effluent (Aquino et al. 2015). These \nfindings are in agreement with the report of Akpan et al. (2011) who observed \na higher organic matter level in cassava effluent polluted soils relative to the \ncontrol site soils. Cassava mill effluent contributed positively to total nitrogen \ncontent of the  soils as the values were higher in polluted sites (0.61 g kg-1- 1.76 \ng kg-1) than in the control sites (0.55 g kg-1- 0.99 g kg-1) with values decreasing \nwith depth (Figure 3). Available P followed a similar distribution trend with total \nnitrogen in the soils as it was more in the polluted sites (17.32 mg kg-1) than in \nthe control sites (7.39 mg kg-1) and decreased with depth (Figure 4). The highest \nvalue (32.58 mg kg-1) of available P observed in the surface soil (0-5cm) of the \npolluted site could be due to a higher level of organic matter in the surface soil as \nincreasing organic matter increases P availability (Brady and Weil 2010). Soils of \nthe polluted site had more ECEC than soils of the control site, with values ranging \nfrom 5.69-12.67 cmolc kg-1 and 4.28-5.58 cmolc kg-1 in the former and the latter, \nrespectively. The higher ECEC observed in the polluted site could be due to its \nhigher organic matter content (Figure 5). It has been reported that organic matter \ncontributes to ECEC of soils (Havlin et al. 2012). It was also observed that ECEC \ndecreased with depth in the two sites. TEB of the polluted and control sites soils \ndiffered with the polluted soils having higher values relative to the control site \nsoils. the values varied from 5.07 cmolc kg-1 -11.89 cmolc kg-1 and 3.28 cmolc kg-1 \n- 5.16 cmolc kg-1 in the polluted and control site soils, respectively. These findings \nsuggest that cassava mill effluent has positive effects on TEB of soils (Figure 6) \nwhich supports the report of Isitekhale and Adamu (2016) who observed higher \nexchangeable bases in cassava mill effluent treated soils. \n\n\n\nImpact of Cassava Mill Effluent on Total Heterotrophic Bacterial Count (THC) \nof the Soils\nFigure 7 shows the impact of cassava mill effluent on the total heterotrophic \nbacterial count in the soils. Cassava mill effluent negatively affected total \nheterotrophic bacterial count as a lower count was observed in the polluted soils \ncompared to the control soils.  Mean values of 5.72\u00d7 105 CFUg-1 and 2.29\u00d7105 \nCFUg-1 were recorded in the control and polluted sites, respectively. The ratio of \nbacterial count of the control soils to the polluted soils was high at lower depths \nand varied from 1.42- 33.07, which reveals the high impact of cassava effluent on \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018106\n\n\n\ntotal heterotrophic bacterial population in soils and could be due to lower pH of \nthe polluted soils occasioned by the presence of cassava mill effluent. In a related \nstudy, Ibe et al. (2014) as well as Obueh  and Odesiri-Eruteyan (2016) made a \nsimilar observation and attributed their findings to the acidic nature of the effluent \ndue to the presence of cynogenic glucocide in cassava mill effluent. The results \nfurther revealed a decreasing total heterotrophic bacteria count with depth in both \n\n\n\nFigure 1: Impact of cassava effluent on\nsoil pH\n\n\n\nFigure 2: Impact of cassava effluent on \nsoil organic matter (OM)\n\n\n\nFigure 3: Impact of cassava effluent on\nsoil total nitrogen        \n\n\n\nFigure 4: Impact of cassava effluent on\nsoil available phosphorus\n\n\n\nFigure 5: Impact of cassava effluent on\nsoil ECEC\n\n\n\nFigure 6: Impact of cassava effluent on\nsoil TEB\n\n\n\n6 \n \n\n\n\n\n\n\n\nFigure 1: Impact of cassava effluent on soil pH  Figure 2: Impact of cassava effluent on soil organic matter (OM) \n\n\n\n\n\n\n\nFigure 3: Impact of cassava effluent on soil total nitrogen         Figure 4: Impact of cassava effluent on soil available phosphorus \n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Impact of cassava effluent on soil ECEC            Figure 6: Impact of cassava effluent on soil TEB \n\n\n\n\n\n\n\n0\n1\n2\n3\n4\n5\n6\n7\n8\n\n\n\npH\n \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n\n\n\nO\nM\n\n\n\n( g\n k\n\n\n\ng-1\n) \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n0.2\n0.4\n0.6\n0.8\n\n\n\n1\n1.2\n1.4\n1.6\n1.8\n\n\n\n2\n\n\n\nTo\nta\n\n\n\nl N\n (m\n\n\n\n k\ng-1\n\n\n\n) \n\n\n\ncontrol\n\n\n\npolluted\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n\n\n\nAv\nai\n\n\n\nla\nbl\n\n\n\ne \nP \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n)  \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n2\n4\n6\n8\n\n\n\n10\n12\n14\n\n\n\nEC\nEC\n\n\n\n(c\nm\n\n\n\nol\nc \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n2\n4\n6\n8\n\n\n\n10\n12\n14\n\n\n\nTE\nB(\n\n\n\ncm\nol\n\n\n\nc \nkg\n\n\n\n-1\n) \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n6 \n \n\n\n\n\n\n\n\nFigure 1: Impact of cassava effluent on soil pH  Figure 2: Impact of cassava effluent on soil organic matter (OM) \n\n\n\n\n\n\n\nFigure 3: Impact of cassava effluent on soil total nitrogen         Figure 4: Impact of cassava effluent on soil available phosphorus \n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Impact of cassava effluent on soil ECEC            Figure 6: Impact of cassava effluent on soil TEB \n\n\n\n\n\n\n\n0\n1\n2\n3\n4\n5\n6\n7\n8\n\n\n\npH\n \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n\n\n\nO\nM\n\n\n\n( g\n k\n\n\n\ng-1\n) \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n0.2\n0.4\n0.6\n0.8\n\n\n\n1\n1.2\n1.4\n1.6\n1.8\n\n\n\n2\n\n\n\nTo\nta\n\n\n\nl N\n (m\n\n\n\n k\ng-1\n\n\n\n) \n\n\n\ncontrol\n\n\n\npolluted\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n\n\n\nAv\nai\n\n\n\nla\nbl\n\n\n\ne \nP \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n)  \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n2\n4\n6\n8\n\n\n\n10\n12\n14\n\n\n\nEC\nEC\n\n\n\n(c\nm\n\n\n\nol\nc \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n2\n4\n6\n8\n\n\n\n10\n12\n14\n\n\n\nTE\nB(\n\n\n\ncm\nol\n\n\n\nc \nkg\n\n\n\n-1\n) \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n6 \n \n\n\n\n\n\n\n\nFigure 1: Impact of cassava effluent on soil pH  Figure 2: Impact of cassava effluent on soil organic matter (OM) \n\n\n\n\n\n\n\nFigure 3: Impact of cassava effluent on soil total nitrogen         Figure 4: Impact of cassava effluent on soil available phosphorus \n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Impact of cassava effluent on soil ECEC            Figure 6: Impact of cassava effluent on soil TEB \n\n\n\n\n\n\n\n0\n1\n2\n3\n4\n5\n6\n7\n8\n\n\n\npH\n \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n\n\n\nO\nM\n\n\n\n( g\n k\n\n\n\ng-1\n) \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n0.2\n0.4\n0.6\n0.8\n\n\n\n1\n1.2\n1.4\n1.6\n1.8\n\n\n\n2\n\n\n\nTo\nta\n\n\n\nl N\n (g\n\n\n\n k\ng-1\n\n\n\n) \n\n\n\ncontrol\n\n\n\npolluted\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n\n\n\nAv\nai\n\n\n\nla\nbl\n\n\n\ne \nP \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n)  \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n2\n4\n6\n8\n\n\n\n10\n12\n14\n\n\n\nEC\nEC\n\n\n\n(c\nm\n\n\n\nol\nc \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n0\n2\n4\n6\n8\n\n\n\n10\n12\n14\n\n\n\nTE\nB(\n\n\n\ncm\nol\n\n\n\nc \nkg\n\n\n\n-1\n) \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 107\n\n\n\nsites which could be due to a reduced organic matter level since organic matter is \nknown to contribute to soil biomass (Maloney et al. 1997). \n\n\n\nImpact of Cassava Mill Effluent on Total Fungal Count (TFC) of the Soils\nThe results of the impact of cassava mill effluent on fugal population of the \nsoils are presented in Figure 8. Total fungal count was better in the polluted site \n(0.15\u00d7105 - 1.25\u00d7105 CFUg-1) relative to the control site (0.05\u00d7105 - 0.85\u00d7105 \n\n\n\nCFUg-1) which suggests that cassava mill effluent favours fungi diversity and \ncould be due to the starchy nature of the cassava effluent which serves as source \nof energy to the fungi as well as higher nitrogen content of the polluted site which \nfavours microbial diversity. It could also be attributed to lower pH of the polluted \nsite. Das (2011) noted that yeast grows readily at low pH. These findings are \nin agreement with the report of Igbinosa and Igiehon (2015). The varying ratio \n(0.25- 0.68) of total fungal count of the control site to the polluted site (C:P) site \nindicates that cassava mill effluent impacted on total fungal count of soils. The \ndecreasing total fungal count with depth observed in the two sites could be due \nto decreasing organic matter content of the soils with depth. It has been reported \nthat increasing organic matter content of soils, enhances microbial growth (Brady \nand Weil 2010). The decrease with depth could be also due to a decrease in soil \naeration with depth since fungi are aerobes (Das 2011).\n\n\n\nIdentification of Bacterial Isolates\nThe results of identification of bacteria isolated are presented in Table 1. \nStaphylococcus aureus, Escherichia coli, Baccilus spp, Pseudomonas spp, \nStreptococcus spp and Conyne bacterium spp. bacteria species were identified \nin the soil samples investigated, similar to the assertions of Igbinosa and Igiehon \n(2015). The results revealed that Escherichia coli was observed in the polluted \nsite only, an indication that cassava mill effluent enhances the proliferation of \n\n\n\nFigure 7: Impact of cassava mill effluent on total heterotrophic bacterial count\n\n\n\n7 \n \n\n\n\nImpact of Cassava Mill Effluent on Total Heterotrophic Bacterial Count (THC) of the Soils \n\n\n\nFigure 7 shows the impact of cassava mill effluent on the total heterotrophic bacterial count in \nthe soils. Cassava mill effluent negatively affected total heterotrophic bacterial count as a lower \ncount was observed in the polluted soils compared to the control soils.  Mean values of 5.72\u00d7 105 \nCFUg-1 and 2.29\u00d7105 CFUg-1 were recorded in the control and polluted sites, respectively. The \nratio of bacterial count of the control soils to the polluted soils was high at lower depths and \nvaried from 1.42- 33.07, which reveals the high impact of cassava effluent on total heterotrophic \nbacterial population in soils and could be due to lower pH of the polluted soils occasioned by the \npresence of cassava mill effluent. In a related study, Ibe et al.(2014) as well as Obueh  and \nOdesiri-Eruteyan (2016) made a similar observation and attributed their findings to the acidic \nnature of the effluent due to the presence of cynogenic glucocide in cassava effluent. The results \nfurther revealed a decreasing total heterotrophic bacteria count with depth in both sites which \ncould be due to a reduced organic matter level since organic matter is known to contribute to soil \nbiomass (Maloney et al. 1997).  \n \n\n\n\n\n\n\n\nFigure 7: Impact of cassava mill effluent on total heterotrophic bacterial count  \n\n\n\n0\n\n\n\n0.00001\n\n\n\n0.00002\n\n\n\n0.00003\n\n\n\n0.00004\n\n\n\n0.00005\n\n\n\n0.00006\n\n\n\n0.00007\n\n\n\n0-5 cm 5-10 cm 10-15 cm 15-20 cm 20-25 cm\n\n\n\nTH\nC \n\n\n\n(C\nFU\n\n\n\n g\n-1\n\n\n\n) \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018108\n\n\n\nthe bacterium specie which could be due to significant glucose level in cassava \neffluent (Uzochukwu et al. 2001) and the higher pH of the polluted site (Table \n1) which enhances faster growth of the bacterium (Don 2008). Streptococcus \nspp was more prominent in the control site whereas Pseudomonas spp was \nmore prominent in the polluted site. These findings suggest that cassava mill \neffluent promotes the proliferation of Pseudomonas spp but inhibits the growth \nof Streptococcus spp. However, cassava effluent did not influence the growth of \nBaccilus spp, Staphylococcus aureus and Conynebacterium spp (Table 1) as the \naforementioned bacteria species were identified in the both sites.  The findings \nfurther revealed that most of the bacteria species identified were observed at the \nupper depths which could be due to the high organic matter of the depths. It has \nbeen reported that organic matter is particularly important as the prime habitat for \nimmense numbers and variety of soil fauna and microflora (Bullock 2005).\n \nIdentification of Fungal Isolates\nTable 2 shows the various fungi species identified in the soil samples studied.  A \ntotal of six fungi species were identified namely, Candida spp, Penicillum spp, \nRhizopus spp, Aspergillus niger, Rhodonturula spp and Fusarium spp. Findings \nsuggest that cassava mill effluent promoted the proliferation of Candida spp and \nFusarium spp but inhibited the proliferation of Rhodonturula spp as the former \nwere identified in the polluted site only whereas the latter was identified in the \ncontrol site only. Penicillum spp was more prominent in the polluted site relative \nto the control site, an indication that the cassava effluent favours the growth of \nthe fungus which could be due to a higher quantity of decaying organic matter \nin the polluted site occasioned by the deposition of cassava effluent into the soil. \nBullerman (2003) reported that the presence of decaying organic materials in an \n\n\n\nFigure 8: Impact of the cassava mill effluent on total fungal count\n\n\n\n8 \n \n\n\n\nImpact of Cassava Mill Effluent on Total Fungal Count (TFC) of the Soils \n \nThe results of the impact of cassava mill effluent on fugal population of the soils are presented in \nFigure 8. Total fungal count was better in the polluted site (0.15\u00d7105 \u2013 1.25\u00d7105 CFUg-1) relative \nto the control site (0.05\u00d7105-0.85\u00d7105 CFUg-1) which suggests that cassava mill effluent favours \nfungi diversity and could be due to the starchy nature of the cassava effluent which serves as \nsource of energy to the fungi as well as higher nitrogen content of the polluted site which favours \nmicrobial diversity. It could also be attributed to lower pH of the polluted site. Das (2011) noted \nthat yeast grows readily at low pH. These findings are in agreement with the report of Igbinosa \nand Igiehon (2015). The varying ratio (0.25- 0.68) of total fungal count of the control site to the \npolluted site (C:P) site indicates that cassava mill effluent impacted on total fungal count of soils. \nThe decreasing total fungal count with depth observed in the two sites could be due to decreasing \norganic matter content of the soils with depth. It has been reported that increasing organic matter \ncontent of soils, enhances microbial growth (Brady and Weil 2010). The decrease with depth \ncould be also due to a decrease in soil aeration with depth since fungi are aerobes (Das 2011). \n \n \n\n\n\n \n \nFigure 8: Impact of the cassava mill effluent on total fungal count \n \n \n \nIdentification of Bacterial Isolates \nThe results of identification of bacteria isolated are presented in Table 1. Staphylococcus aureus, \nEscherichia coli, Baccilus spp, Pseudomonas spp, Streptococcus spp and Conyne bacterium spp. \nbacteria species were identified in the soil samples investigated, similar to the assertions of \n\n\n\n0\n\n\n\n0.000002\n\n\n\n0.000004\n\n\n\n0.000006\n\n\n\n0.000008\n\n\n\n0.00001\n\n\n\n0.000012\n\n\n\n0.000014\n\n\n\n0-5cm 5-10cm 10-15cm15-20cm20-25cm mean\n\n\n\nTF\nC(\n\n\n\nCF\nU\n\n\n\n g\n-1\n\n\n\n) \n\n\n\ndepth \n\n\n\ncontrol\n\n\n\npolluted\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 109\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nId\nen\n\n\n\ntifi\nca\n\n\n\ntio\nn \n\n\n\nof\n th\n\n\n\ne \nba\n\n\n\nct\ner\n\n\n\nia\nl i\n\n\n\nso\nla\n\n\n\nte\ns\n\n\n\n10\n \n\n\n\n No\nte\n\n\n\ns: \nN\n\n\n\n.A\n=n\n\n\n\nut\nrie\n\n\n\nnt\n a\n\n\n\nga\nr, \n\n\n\nS=\nslo\n\n\n\npe\n c\n\n\n\nol\nou\n\n\n\nra\ntio\n\n\n\nn,\n B\n\n\n\n=b\nut\n\n\n\nt c\nol\n\n\n\nou\nra\n\n\n\ntio\nn,\n\n\n\n G\n=g\n\n\n\nas\n p\n\n\n\nro\ndu\n\n\n\nct\nio\n\n\n\nn,\n H\n\n\n\n2S\n= \n\n\n\nhy\ndr\n\n\n\nog\nen\n\n\n\n s\nul\n\n\n\nph\nid\n\n\n\ne \npr\n\n\n\nod\nuc\n\n\n\ntio\nn,\n\n\n\n Y\n=y\n\n\n\nel\nlo\n\n\n\nw\nis\n\n\n\nh \nco\n\n\n\nlo\nur\n\n\n\nat\nio\n\n\n\nn \n(a\n\n\n\nci\ndi\n\n\n\nc)\n, \n\n\n\nR\n=r\n\n\n\ned\ndi\n\n\n\nsh\n c\n\n\n\nol\nou\n\n\n\nra\ntio\n\n\n\nn \n(a\n\n\n\nlk\nal\n\n\n\nin\ne)\n\n\n\n, m\not\n\n\n\n= \nm\n\n\n\not\nili\n\n\n\nty\n, I\n\n\n\nnd\n= \n\n\n\nIn\ndo\n\n\n\nle\n, C\n\n\n\nat\n= \n\n\n\nca\nta\n\n\n\nla\nse\n\n\n\n, C\nit=\n\n\n\n c\nitr\n\n\n\nat\ne,\n\n\n\n C\noa\n\n\n\n=c\noa\n\n\n\ngu\nla\n\n\n\nse\n, A\n\n\n\n1-\n 0\n\n\n\n-5\n c\n\n\n\nm\n P\n\n\n\nol\nlu\n\n\n\nte\nd,\n\n\n\n A\n2-\n\n\n\n 5\n-1\n\n\n\n0 \ncm\n\n\n\n p\nol\n\n\n\nlu\nte\n\n\n\nd,\n A\n\n\n\n3-\n10\n\n\n\n-2\n0 \n\n\n\ncm\n p\n\n\n\nol\nlu\n\n\n\nte\nd,\n\n\n\n A\n4-\n\n\n\n 2\n0-\n\n\n\n50\n c\n\n\n\nm\n p\n\n\n\nol\nlu\n\n\n\nte\nd,\n\n\n\n B\n1-\n\n\n\n0-\n5 \n\n\n\ncm\n c\n\n\n\non\ntro\n\n\n\nl, \nB\n\n\n\n2-\n5-\n\n\n\n10\n c\n\n\n\nm\n c\n\n\n\non\ntro\n\n\n\nl, \nB\n\n\n\n3-\n 1\n\n\n\n0-\n20\n\n\n\n c\non\n\n\n\ntro\nl, \n\n\n\nB\n4-\n\n\n\n20\n-5\n\n\n\n0 \ncm\n\n\n\n c\non\n\n\n\ntro\nl \n\n\n\n  \nT\n\n\n\nA\nB\n\n\n\nL\nE\n\n\n\n 1\n \n\n\n\nId\nen\n\n\n\ntif\nic\n\n\n\nat\nio\n\n\n\nn \nof\n\n\n\n th\ne \n\n\n\nba\nct\n\n\n\ner\nia\n\n\n\nl i\nso\n\n\n\nla\nte\n\n\n\ns \n \n\n\n\nSa\nm\n\n\n\npl\nes\n\n\n\n \nM\n\n\n\ned\nia\n\n\n\n \nm\n\n\n\nor\nph\n\n\n\nol\nog\n\n\n\nic\nal\n\n\n\n  \ngr\n\n\n\nam\n re\n\n\n\nac\ntio\n\n\n\nn \n   \n\n\n\n   \nO\n\n\n\nX\n  \n\n\n\n\n\n\n\nM\nO\n\n\n\nT \n \n\n\n\n   IN\nD\n\n\n\n  \nSP\n\n\n\nO\nR\n\n\n\nE \n \n\n\n\n   C\nA\n\n\n\nT \n \n\n\n\n\n\n\n\nC\nIT\n\n\n\n  \n C\n\n\n\nO\nA\n\n\n\n  \nSu\n\n\n\nga\nr f\n\n\n\ner\nm\n\n\n\n te\nst\n\n\n\n \nPo\n\n\n\nss\nib\n\n\n\nle\n b\n\n\n\nac\nte\n\n\n\nria\n \n\n\n\n\n\n\n\nch\nar\n\n\n\nac\nte\n\n\n\nris\ntic\n\n\n\ns \n \n\n\n\n\n\n\n\nTe\nst\n\n\n\n\n\n\n\n \nTe\n\n\n\nst\n \n\n\n\n   Te\nst\n\n\n\n \nSt\n\n\n\nai\nni\n\n\n\nng\n \n\n\n\n   Te\nst\n\n\n\n\n\n\n\n  \nTe\n\n\n\nst\n \n\n\n\n te\nst\n\n\n\n \nS \n\n\n\n\n\n\n\nB \n   \n\n\n\n   \nG\n\n\n\n   \nH\n\n\n\n2S\n \n\n\n\n \n A\n\n\n\n1,\nA\n\n\n\n2,\nA\n\n\n\n3,\nB1\n\n\n\n,B\n2,\n\n\n\nB3\n \n\n\n\nN\n.A\n\n\n\n \nM\n\n\n\nilk\nis\n\n\n\nh \nra\n\n\n\nis\ned\n\n\n\n n\non\n\n\n\n  \nG\n\n\n\nra\nm\n\n\n\n p\nos\n\n\n\niti\nve\n\n\n\n c\noc\n\n\n\nci\n \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n +\n \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\nR\n   \n\n\n\n   \n Y\n\n\n\n\n\n\n\n  -\n   \n\n\n\n\n\n\n\n- \nSt\n\n\n\nap\nhy\n\n\n\nlo\nco\n\n\n\ncc\nus\n\n\n\n a\nur\n\n\n\neu\ns \n\n\n\n\n\n\n\nm\nuc\n\n\n\noi\nd \n\n\n\nco\nlo\n\n\n\nni\nes\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n B\n\n\n\n1,\nB2\n\n\n\n,B\n3,\n\n\n\nB4\n \n\n\n\nN\n.A\n\n\n\n \nM\n\n\n\nilk\nis\n\n\n\nh \nfla\n\n\n\nt m\nuc\n\n\n\noi\nd \n\n\n\nG\nra\n\n\n\nm\n n\n\n\n\neg\nat\n\n\n\niv\ne \n\n\n\nro\nd \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n  \n+ \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\nY\n   \n\n\n\n   \n  Y\n\n\n\n\n\n\n\n +\n   \n\n\n\n\n\n\n\n- \nEs\n\n\n\nch\ner\n\n\n\nic\nhi\n\n\n\na \n c\n\n\n\nol\ni \n\n\n\n\n\n\n\nco\nlo\n\n\n\nni\nes\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n A\n1,\n\n\n\nA\n2,\n\n\n\nB1\n,B\n\n\n\n2,\nB3\n\n\n\n \nN\n\n\n\n.A\n \n\n\n\nM\nilk\n\n\n\nis\nh \n\n\n\nra\nis\n\n\n\ned\n n\n\n\n\non\n  \n\n\n\nG\nra\n\n\n\nm\n p\n\n\n\nos\niti\n\n\n\nve\n c\n\n\n\noc\nci\n\n\n\n\n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \nR\n\n\n\n\n\n\n\n  R\n   \n\n\n\n   \n  -\n\n\n\n   \n- \n\n\n\nSt\nre\n\n\n\npt\noc\n\n\n\noc\ncu\n\n\n\ns  \nsp\n\n\n\np \n\n\n\n\n\n\n\nm\nuc\n\n\n\noi\nd \n\n\n\nco\nlo\n\n\n\nni\nes\n\n\n\n \nin\n\n\n\n c\nha\n\n\n\nin\ns \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nw\n\n\n\nith\n z\n\n\n\non\ne \n\n\n\nof\n c\n\n\n\nle\nar\n\n\n\nan\nce\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n A\n1,\n\n\n\nA\n2,\n\n\n\nA\n3,\n\n\n\n \nA\n\n\n\n4,\nB1\n\n\n\n,B\n2,\n\n\n\nB3\n,B\n\n\n\n4 \nN\n\n\n\n.A\n \n\n\n\nM\nilk\n\n\n\nis\nh \n\n\n\nfla\nt n\n\n\n\non\n m\n\n\n\nuc\noi\n\n\n\nd \nG\n\n\n\nra\nm\n\n\n\n p\nos\n\n\n\niti\nve\n\n\n\n ro\nd \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n  +\n \n\n\n\n\n\n\n\n   \n+ \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\nR\n   \n\n\n\n   \n  R\n\n\n\n\n\n\n\n  -\n   \n\n\n\n\n\n\n\n- \nBa\n\n\n\nci\nllu\n\n\n\ns  \nsp\n\n\n\np \n\n\n\n\n\n\n\nco\nlo\n\n\n\nni\nes\n\n\n\n w\nith\n\n\n\n ro\nug\n\n\n\nh \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ned\nge\n\n\n\ns \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n A\n1,\n\n\n\n A\n2,\n\n\n\nA\n3,\n\n\n\n B\n1,\n\n\n\n B\n2,\n\n\n\n \nN\n\n\n\n.A\n \n\n\n\nBl\nui\n\n\n\nsh\n g\n\n\n\nre\nen\n\n\n\n ra\nis\n\n\n\ned\n  \n\n\n\nG\nra\n\n\n\nm\n n\n\n\n\neg\nat\n\n\n\niv\ne \n\n\n\nro\nd \n\n\n\n\n\n\n\n\n\n\n\n+ \n   \n\n\n\n\n\n\n\n \n+ \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n+ \n\n\n\n\n\n\n\n   \n- \n\n\n\n\n\n\n\n   \n- \n\n\n\nR\n   \n\n\n\n   \n  R\n\n\n\n\n\n\n\n  -\n   \n\n\n\n\n\n\n\n- \nPs\n\n\n\neu\ndo\n\n\n\nm\non\n\n\n\nas\n  s\n\n\n\npp\n \n\n\n\n\n\n\n\npi\ngm\n\n\n\nen\nte\n\n\n\nd \nco\n\n\n\nlo\nni\n\n\n\nes\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nA\n\n\n\n1,\nA\n\n\n\n2,\nA\n\n\n\n3,\nA\n\n\n\n4,\nB1\n\n\n\n,B\n2,\n\n\n\n \nB3\n\n\n\n,B\n4 \n\n\n\nN\n.A\n\n\n\n \nM\n\n\n\nilk\nis\n\n\n\nh \nra\n\n\n\nis\ned\n\n\n\n n\nee\n\n\n\ndl\ne \n\n\n\nG\nra\n\n\n\nm\n p\n\n\n\nos\niti\n\n\n\nve\n ro\n\n\n\nd \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n+ \n   \n\n\n\n\n\n\n\n- \n   \n\n\n\n\n\n\n\n- \nR\n\n\n\n\n\n\n\n  R\n   \n\n\n\n   \n  -\n\n\n\n\n\n\n\n \n- \n\n\n\nC\non\n\n\n\nyn\neb\n\n\n\nac\nte\n\n\n\nri\num\n\n\n\n sp\np \n\n\n\n\n\n\n\npo\nin\n\n\n\nte\nd \n\n\n\nco\nlo\n\n\n\nni\nes\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018110\n\n\n\narea promotes the proliferation of Penicillum spp. A comparison of the presence \nof Rhizopus spp and Fusarium spp in the polluted and control sites suggests \nthat cassava mill effluent does not influence the proliferation of the two fungi \nspecies.  Irrespective of the sites, most of the fungi species were identified in the \nupper depths with higher organic matter and soil aeration, revealing the positive \ncontribution of organic matter and soil aeration to fungi proliferation (Zhang et \nal. 2013; Das 2011).\n \n\n\n\nTABLE 2\nIdentification of fungal Isolates\n\n\n\n12 \n \n\n\n\nTABLE 2 \nIdentification of fungal Isolates \n\n\n\n\n\n\n\nNotes: A1=  0-5 cm polluted;  A2= 5-10 cm polluted; A3=10-20 cm polluted; A4-=20-50 cm polluted; B1=0-5 cm \ncontrol; B2=5-10 cm control; B3= 10-20 control; B4=20-50 cm control \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSample(s) Morphological characteristics Microscopic characteristics Possible fungi \n\n\n\nA1, A2,A3,A4 Creamy raised non mucoid colonies Budded yeast cells Candida  spp \n\n\n\nA1,A2A3,A4,B\n1,B2 \n\n\n\nWhitish broom-like cottony colonies with greenish \ncentre \n\n\n\nSeptate hyphae with Candida borne on \nSterigmata \n\n\n\nPenicillium spp \n\n\n\nA2, B2,  Whitish broom-like cottony colonies Unbranched hyphae with terminal spores Rhizopus spp \n\n\n\nA1,B1 Whitish broom-like cottony colonies with \nyellowish green centre \n\n\n\nSeptate hyphae with candida borne on \nsterigmata \n\n\n\nAspergillus niger \n\n\n\nA1 Whitish broom-like cottony colonies turning \npurple \n\n\n\nSeptate hyphae with spores Fusarium spp \n\n\n\nB2 Orange broom-like cottony colony Non Septate hyphae with spores Rhodonturula spp \n\n\n\nNotes: A1=  0-5 cm polluted;  A2= 5-10 cm polluted; A3=10-20 cm polluted; A4-=20-50 cm \npolluted; B1=0-5 cm control; B2=5-10 cm control; B3= 10-20 control; B4=20-50 cm control\n\n\n\nTABLE 3\nComparison of the chemical and biological properties of soils of the polluted and control \n\n\n\nsites using t-Test\n\n\n\n14 \n \n\n\n\nTABLE 3Comparison of the chemical and biological properties of the polluted and control sites using t-\nTest  \n\n\n\nNotes: ECEC=Effective cation exchange capacity; THC=Total heterotrophic bacterial count; \nTFC= Total fungal count  \n\n\n\nSoil property Polluted  Control T-test value (p<0.05) Remark \n\n\n\nSoil pH (H2O) 5.716  6.601 0.002 Significant(-) \n\n\n\nSoil organic matter 15.059  8.951 0.030 Significant(+) \n\n\n\nTotal nitrogen 1.29  0.728 0.028 Significant(+) \n\n\n\nAvailable phosphorus 17.32  7.392 0.012 Significant(+) \n\n\n\nTotal  exchangeable bases 7.651  4.059 0.014 Significant (+) \n\n\n\nECEC 8.159  5.091 0.036 Significant(+) \n\n\n\nTHC 229200  535100 0.0043 Significant(-) \n\n\n\nTFC 146700  33000 0.0977 Significant(+) \n\n\n\nNotes: ECEC=Effective cation exchange capacity; THC=Total heterotrophic\nbacterial count; TFC= Total fungal count\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 111\n\n\n\nComparison of the Chemical and Biological Properties of Soils of the Polluted \nand Control Sites Using t-test Analysis.\nThe results of the t-test statistical analysis conducted to compare some chemical \n(pH, organic matter, total nitrogen, available phosphorus, total exchangeable \nbases and ECEC) and biological properties (total heterotrophic bacterial count \nand total fungal count)of the cassava mill effluent in polluted and control sites are \npresented in Table 3. For chemical properties, the analysis indicated significant \n(p< 0.05) and positive impact of cassava effluent on organic matter, total nitrogen, \navailable phosphorus and ECEC. The implication of these findings is that \nincreasing the concentration of cassava effluent in the soils will significantly result \nin an increase in the values of the aforementioned chemical properties. However, \ncassava effluent significantly and negatively impacted on the soil pH as there was \na negative significant (p< 0.05) difference between the polluted and control sites \nwhich indicates that increasing the concentration of cassava effluent in the soils \nwill significantly decrease the pH of the soils. For biological properties, whereas \nsignificant negative (p<0.05) difference was observed between the total fungal \ncount of the polluted and control sites, there was significant positive (p< 0.05) \ndifference between the total heterotrophic bacterial count of the polluted and \ncontrol sites. From the findings, it can be inferred that long-term discharge of \ncassava into the soil will result in a significant decrease in bacterial population of \nthe soils but will result in an increase in the fungi population of the soils.\n\n\n\nCONCLUSIONS\nThe results of this study revealed that long-term discharge of cassava mill effluent \ninto the soil significantly increased soil organic matter, total nitrogen, available \nphosphorus, total exchangeable bases and effective cation exchange capacity \nbut significantly decreased soil pH. Generally, long-term discharge of cassava \neffluent into the soil increased total fungal count but decreased total heterotrophic \nbacterial count. Specifically, the long-term discharge of the effluent promoted the \nproliferation of Escherichia coli and Pseudomonas spp bacteria species as well \nas Candida spp, Fusarium spp and Penicillium spp fungi species but inhibited \nthe growth of Streptococcus spp bacterium specie and Rhodonturula spp fungus \nspecie.\n\n\n\nREFERENCES\nAdejumo, B.A. and F.A. Ola. 2011. The effect of cassava effluent on the chemical \n\n\n\ncomposition of agricultural soil, pp. 220\u2013226. Retrived from http://iworx5.\nwebxtra.net/~istroorg/downloadNigeria_conf_downloads/SWE/Adejumo_\nOlla%20.pdf. \n\n\n\nAkpan, J.F., M.G. Solomon and O.S. Bello. 2011. Effects of cassava mill effluent \non some chemical and microbiological properties of soils in Cross River State, \nNigeria. Global Journal of Agricultural Science 10 (2): 19-25\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018112\n\n\n\nANU (Australian National University). 2007. New methods of cyanide removal to \nhold millions. 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Lechevalier (Eds.)(4th \n\n\n\nedn.), Boca Raton, FL.: CRC Press, pp: 1-234.\n\n\n\nIbe, I.J., J.N.Ogbulie, D.C.Odum, C. Onyirioha, P. Peter-Ogu and R.N.Okechi. \n2014. Effects of cassava mill effluent on some groups of soil bacteria and soil \nenzymes. Int. J. Curr. Microbiol.App. Sci.  3(10): 284-289.\n\n\n\nIgbinosa, E.O. and O.N. Igiehon. 2015. The impact of cassava effluent on the \nmicrobial and physicochemical characteristics on soil dynamics and structure. \nJordan Journal of Biological Sciences 8(2): 107 \u2013 112.\n\n\n\nIhem, E.E.,  G.E. Osuji,  E.U. Onweremadu, S. U. Onwudike,  U.N. Nkwopara,  B. \nN. Ndukwu and B.U. Uzoho. 2014. Variability in properties of soils under \nthree land use types in a humid tropical environment.  International Journal of \nDevelopment and Sustainability 3 (4): 923-930.\n\n\n\nIITA (International Institute of Tropical Agriculture). 2005. The uses of cassava. \nRome:Integrated Cassava Project of the International Institute of Tropical \nAgriculture.\n\n\n\nIITA (International Institute of Tropical Agriculture). 2011. Cassava processing in \nNigeria\u2019s research for development, Issue 7. http://www.iita.com\n\n\n\nIsabirye, M., G., L. Ruysschaert, J.Van-Linden, M.K. Maguada and  J. Deckers. 2007. \nSoil losses due to cassava and sweet potatoes harvesting: a case study from low \ninput traditional agriculture. Soil Tillage Resources 92: 96-103.\n\n\n\nIsitekhale, H.H.E and  B. Adamu. 2016. Effects of effluents on soil chemical \nproperties in forest derived savanna transition . Journal of Enviromental \nScience, Toxicology and Food Technology 10(6): 30-34.\n\n\n\nMaloney P.E.,  A.H.C. Van-Bruggen and  S. Hu. 1997. Bacteria community structure \nin relation to the carbon environments in lettuce and tomato rhizospheres and in \nbulk Soil. Microb Ecol. 34: 109-117.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018114\n\n\n\nNelson. D.W. and L.E. Sommers. 1982. Total carbon, organic carbon and organic \nmatter. In: Methods of Soil Analysis, Part 2, ed. A.L. Page, R.H. Miller and D.R. \nKeeney, pp.539-579.  Madison, Wisconsin: American Society of Agronomy. \n\n\n\nNwakaudu, M.S., F.L. Kamen,  G. Afube, A.A. Nwakaudu and I.S. Ike. 2012. Impact \nof cassava processing effluent on agricultural soil: a case study of maize growth. \nJournal of Emerging Trends in Engineering and Applied Sciences (JETEAS) \n3(5): 881-885.\n\n\n\nNwaugo VO, G.N. Chima, C.A. Etok and C.E. Ogbonna. 2008. Impact of cassava mill \neffluent (CME) on soil physicochemical and microbial community structure \nand functions. Nig J Microbiol. 22:1681-1688.\n\n\n\nObueh H.O. and E.Odesiri-Eruteyan. 2016. A study on the effects of cassava \nprocessing wastes on the soil environment of a local cassava mill. J Pollut. Eff. \nCont. 4: 177-182\n\n\n\nOgboghodo I.A., I.O. Osenweota, S.O. Eke and A.E.  Iribhogbe. 2001. Effect of \ncassava (Manihot esculanta Crantz) mill grating effluent on the textual, chemical \nand biological properties of surrounding soils. World Journal of Biotechnology \n2: 292-301.\n\n\n\nOlson, S.R. and L.E. Sommers. 1982. Phosphorus. In: Methods of Soil Analysis, Part \n2,  ed. A.L. Page A.L., R.H. Miller and D.R. Keeney, pp. 403 -430.  Madison, \nWisconsin: American Society of Agronomy. \n\n\n\nOnweremadu, E.U.,  F.O.R. Akamigbo and  C.A. Igwe.  2007. Physical shrinkage \nrelationship in soils of dissimilar lithologies in Central Southeastern Nigeria. \nJournal of Applied Science 7: 2495 \u2013 2499.\n\n\n\nOrhue, E.R, E.E. Imasuen and D.E. Okunima 2014. Effect of cassava mill effluent \non some soil chemical properties and the growth of fluted pumpkin (Telfairia \noccidentalis Hook F.). Journal of Applied and Natural Science 6 (2): 320-325\n\n\n\nOti, N. N. 2002. Discriminant functions for classifying erosion of degraded lands of \nOtamiri, Southeastern, Nigeria.  Agroscience 3(1): 24-40.\n\n\n\nRim-Rukeh, A. 2012. Microbiologically influenced corrosion of S45c mild steel in \ncassava mill effluent. Research Journal in Engineering and Applied Sciences \n1(5): 284-290.\n\n\n\nSackey, I. S. and R. J. Bani. 2007. Survey of waste management practices in cassava \nprocessing to gari in selected districts of Ghana. Journal of Food, Agriculture \nand Environment, 5(2): 325 \u2013 328. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 115\n\n\n\nSalami S.J. and I.N. Egwin. 2007. Impact of tannery effluents on the quality of \nreceiving stream. Afri J Nat Sci. 2: 17-20.\n\n\n\nThomas, G.W. 1982. Exchangeable bases. In: Methods of Soil Analysis, Part 2,  ed. \nA.L. Page, R.H. Miller and D.R. Keeney, pp. 159-165. Madison, Wisconsin: \nAmerican Society of  Agronomy. \n\n\n\nThomas, G.W. 1996. Soil pH and soil acidity, In: Methods of Soil Analysis. Part \n3.Chemical Methods, ed. D.L.Sparks, A.L. Page, P.A. Helmke, R.H. Loeppert, \nP.N. Soltanpour, M.A. Tabatabai, C.T. Johnson and M.E. Summer, pp. 475 \u2013 \n490. Maidson, WI.USA: Soil Science Society of America, Inc, and American \nSociety of Agronomy.\n\n\n\nUzochukwu, S.V.A., R. Oyede and O. Atanda, 2001. Utilization of garri industry \neffluent in the preparation of a gin. Nigerian J. Microbiol. 15: 87-92.\n\n\n\nUzoije A.P, N. Egwuonwu and A.A.Onunkwo. 2011. Distribution of cyanide in a \ncassava mill effluent polluted eutrictropofluvent soils of Ohaji Area, South-\nEastern Nigeria. Journal of Soil Science and Environmental Management 2(2):  \n49-57.\n\n\n\nWade J.W., E. Omoregie and  I. Ezenwata.2002. Toxicity of cassava (Manihot \nesculenta Crantz) effluent on the Nile Tilapia, Oreochromis niloticus(L) under \nlaboratory condition. J Aqua Sci. 17: 89-94.\n\n\n\nZhang, N., O. Lilje and P. McGee. 2013. Quantification of the proliferation of \narbuscular mycorrhizal fungi in soil. Geophysical Research Abstracts Vol. 15, \nEGU2013-1081-1, 2013 EGU General Assembly 2013.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n8 \n\n\n\n Landscape, Geology and Soils of the Malay Peninsula  \n\n\n\n \nShamshuddin, J.1, Shafar, J.M.2* and Mohd Firdaus, M.A.1  \n\n\n\n \n1Department of Land Management, Faculty of Agriculture, 43400 UPM Serdang, Selangor, Malaysia \n\n\n\n2Department of Crop Science, Faculty of Agriculture, 43400 UPM Serdang, Selangor, Malaysia \n\n\n\n \nCorrespondence: shafarjefri@upm.edu.my  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nIntermittent climate change episodes since the Late Pleistocene resulted in decreasing or increasing \n\n\n\nthe earth\u2019s temperature causing the global sea levels to drop or rise accordingly. These episodes had \n\n\n\na remarkable impact on landscape and soils in the Malay Peninsula. The peninsula is characterised by \n\n\n\nthe presence of steep highlands in the central region with the rest occupied by upland undulating \nterrains and flat alluvial areas. Major soils in the upland regions are formed from igneous, sedimentary \n\n\n\nand metamorphic rocks ranging in age from Mesozoic to Paleozoic. Most soils developed from those \n\n\n\nrocks are classified as Ultisols or Oxisols. Both soil types are acidic in nature, having low basic cations \ninsufficient to sustain crop production. Three levels of riverine terraces are scattered sporadically in \n\n\n\nthe peninsula. The age of the sediments forming the highest terraces is 40,000 years, while the lowest \n\n\n\nterraces are found in the present flood plains. The fluvial characters of the terraces are preserved in \nthe sediments that can be observed and studied. Marine deposits are located along the low-lying coastal \n\n\n\nplains. The alluvium is divided into clayey sediments found mainly in the West coast and the sandy ones \n\n\n\nin the East coast of the peninsula. The former contains pyrite at certain locations that produces acidity \n\n\n\non oxidation, while the latter have very high sand content. The pyritization of the sediments took place \n4,300 years ago when the sea level in the peninsula rose by 3-5 m above the present level.  \n\n\n\n \nKey words: climate change, river capture, soil fertility, tropical weathering, Sundaland \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nThe Late Pleistocene (50,000-11,000 BP) was the geological epoch that registered intermittent \n\n\n\nclimatic fluctuations in Southeast Asia. The decrease and increase in earth\u2019s temperature during \n\n\n\nthe period had a remarkable influence or impact on the landscape, soil and agriculture in the \n\n\n\nregion. It was during that time that the sea level in Southeast Asia was 100 m below, followed \n\n\n\nby a rise of 50 m above the present level (Molengraaff 1921; Scrivenor 1931). When the sea \n\n\n\nlevel was at its lowest, a large area which hitherto had been flooded by sea water was exposed \n\n\n\nto atmospheric conditions. The new land area resulting from the sea level drop was called \n\n\n\n\u2018Sundaland\u2019 by Molengraaff (1921). \n\n\n\n\n\n\n\nThe intermittent change in climate during the era impacted the Malay Peninsula in terms of \n\n\n\nlandscape, geology, soil, agriculture and human civilization. According to Bird et al. (2005), \n\n\n\nduring the Late Pleistocene, Southeast Asia was covered by Savanna Forest i.e., the area was \n\n\n\ndominated by a mixed woodland-grassland ecosystem. The objectives of this review paper are: \n\n\n\n1) to explain the formation of and/or changes in landscape in the Malay Peninsula since the \n\n\n\nLate Pleistocene as affected by global climate change; and 2) to describe in detail the various \n\n\n\ngeological processes and pedological attributes that influenced the formation of soils and their \n\n\n\nphysico-chemical properties that impacted agricultural production.  \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nmailto:shafarjefri@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n9 \n\n\n\nGLOBAL DROP AND RISE IN TEMPERATURE \n\n\n\n\n\n\n\nGeologists and soil scientists know with certainty that there was a period of global drop in \n\n\n\nearth\u2019s temperature, followed by a rise throughout the Late Pleistocene. The former is known \n\n\n\nas the Ice Age era (glacial), while the latter is the interglacial period. Studies conducted over \n\n\n\n100 years ago reported that the Late Pleistocene was the geological epoch that registered \n\n\n\nintermittent climate fluctuations in Southeast Asia. The geological episode had a striking \n\n\n\ninfluence on landscape, geology and vegetation of the region (Bird et al. 2005).  \n\n\n\n\n\n\n\nIntermittent changes in climate during the Late Pleistocene had significant impact on the \n\n\n\nlandform of the Malay Peninsula in terms of topography (due to deposition of riverine and \n\n\n\nmarine sediments, erosion, river capture, etc.), soil development/formation and agriculture. The \n\n\n\nshallow seas surrounding the Malay Archipelago in ancient Southeast Asia, often regarded as \n\n\n\n\u2018Maritime Continent\u2018 by scholars and historians, had a rather warm climate. According to Bird \n\n\n\net al. (2005), the area was then covered by a mixed woodland-grassland ecosystem.    \n\n\n\n\n\n\n\nTHE EMERGENCE OF SUNDALAND \n\n\n\n\n\n\n\nThe Last Glacial Maximum (LGM) i.e., the Ice Age, was the most recent glacial period that \n\n\n\noccurred during the Late Pleistocene. One of the most extensive glaciations during the above-\n\n\n\nmentioned era that significantly impacted Southeast Asia took place 50,000-40,000 BP (Bird \n\n\n\net al. 2005). Molengraaff (1921), a Dutch geologist working in the region, investigated and \n\n\n\nsubsequently published a landmark paper on the ancient submerged system of the Pleistocene \n\n\n\nage in South China Sea and the adjoining landmasses.  \n\n\n\n\n\n\n\nIt was postulated by Molengraaff (1921) and later confirmed by Bird et al. (2005) that during \n\n\n\nthe Late Pleistocene, the sea level in Southeast Asia dropped to more than 100 m below the \n\n\n\npresent level. The episode of sea level drop was closely related to the decrease in the earth\u2019s \n\n\n\ntemperature that caused the Ice Age. The phenomenal drop of sea level in Southeast Asia at \n\n\n\nthat time is consistent with the findings of studies conducted by a British geologist, working in \n\n\n\npre-independent Malaya (Scrivenor 1931; Scrivenor 1949). The amazing geological event \n\n\n\nresulted in the exposure of a new large landmass in the region with a combined size of a small \n\n\n\ncontinent which Molengraaff (1921) termed \u2018Sundaland\u2019 (Figure 1).  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1.  Sundaland during the Late Pleistocene with its complete river system \n\n\n\n(Courtesy of Reddit) \n(https://www.reddit.com/r/worldbuilding/comments/4xjhvr/sundaland_looks_like_a_fantastic_invented_world/)  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n10 \n\n\n\nIn a paper on the geology of Sundaland, Bird et al. (2005) reiterated that glacio-eustatic \n\n\n\ndepression of the sea level by 100 m during the LGM fully exposed the Sunda Shelf, joining \n\n\n\nmainland Southeast Asia and Sumatra, Java and Borneo (Figure 1). Thus, Malaya (the Malay \n\n\n\nPeninsula), Sumatra, Java and Borneo, Thailand, Cambodia and Vietnam were connected. At \n\n\n\nthat time, humans from the North migrated South to the regions of Southeast Asia looking for \n\n\n\na better life. The main rivers that provided drainage for the region during that period of the \n\n\n\ngeological history are shown in the above-mentioned figure.    \n\n\n\n\n\n\n\nA RIVER CAPTURE IN THE MALAY PENINSULA \n\n\n\n\n\n\n\nExcess water from the highlands of Sundaland was drained into the South China Sea or Indian \n\n\n\nOcean by an intricate system of rivers and waterways. Three main rivers in the Malay Peninsula \n\n\n\nare shown in Figure 1. The first is the Perak River located in the middle of the peninsula, which \n\n\n\ndrains its water into the Straits of Melaka. The second is the Muar River (down south), which \n\n\n\nalso drains its water into the Straits of Melaka. (The third big river, the Kelantan River flows \n\n\n\ninto the South China Sea.  \n\n\n\n\n\n\n\nAt the present time, tourists traveling in the vicinity of western Pahang (bordering Negeri \n\n\n\nSembilan) in the Malay Peninsula will notice that the Pahang River and the Muar River are \n\n\n\nnearly connected at a location near Jempol or Bahau (in the state of Negeri Sembilan). Note \n\n\n\nthat the Serting River in Negeri Sembilan flows into the Bera River in Pahang. The latter is \n\n\n\nbelieved to be a former tributary of the current Pahang River that discharges its water into the \n\n\n\nSouth China Sea. Today, we can see that the Jempol River flows into the Muar River in Johor \n\n\n\nstate, which discharges its water into the Straits of Melaka. \n\n\n\n\n\n\n\nIt is known that at Jalan Penarikan located near Jempol, the locals were hired to pull boats \n\n\n\noverland for about 300 m to get across to a tributary of the Pahang River (Bera River), which \n\n\n\nwas flowing into the South China Sea. It was a shorter route to Thailand, Cambodia, Vietnam \n\n\n\nor even China when the Melaka Empire reigned supreme in the region. \n\n\n\n\n\n\n\nCurrently, the Pahang River flows its water into the South China Sea, while the Muar River \n\n\n\nflows into the Straits of Melaka. Hutchison (1989) was convinced that a long time ago, water \n\n\n\nfrom the streams in the upper reaches of the current Pahang River could very well be flowing \n\n\n\ninto the Muar River (Figure 1). The geological episode is termed \u2018river capture\u2019. It was \n\n\n\nplausible that the rivers in the upper reaches of the Muar River were captured by the Pahang \n\n\n\nRiver at a location near Bahau town in Negeri Sembilan. The river capture had probably taken \n\n\n\nplace during the Late Pleistocene or the early Holocene. The geological phenomenon is in \n\n\n\nagreement with the results of the study conducted by Raj (2009) who provided extra field \n\n\n\nevidence. \n\n\n\n\n\n\n\nAs to when the river capture took place is uncertain. To give a convincing answer, we need to \n\n\n\nstudy the river system that existed during the Sundaland era (Figure 1). The longest river in the \n\n\n\nMalay Peninsula at that time was the Muar River, with its upper reaches in the highlands of the \n\n\n\npeninsula. Its water flowed into the Straits of Melaka, with the river mouth in Johor. As Figure \n\n\n\n2 shows, Muar River is no longer the longest river in the Malay Peninsula. That position has \n\n\n\nbeen taken over by the Pahang River, which drains into the South China Sea. \n\n\n\n \n\n\n\n\nhttps://en.wikipedia.org/wiki/Pahang_River\n\n\nhttps://en.wikipedia.org/wiki/Muar_River\n\n\nhttps://en.wikipedia.org/wiki/Negeri_Sembilan\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n11 \n\n\n\n \nFigure 2. A map showing the current major rivers that control the drainage pattern in the \n\n\n\nMalay Peninsula \n\n\n\n(Courtesy of Wikipedia Commons) \n\n\n\n(https://en.wikipedia.org/wiki/Geography_of_Malaysia#/media/File:Location_map_Peninsula_Malaysia.png) \n\n\n\n\n\n\n\nCurrently, the Muar River is just a short river, originating from northern Negeri Sembilan \n\n\n\nand/or south-western Pahang state (Figure 2). Notwithstanding, the mouth of the Muar River \n\n\n\nin the state of Johor is very big, fitting the size of a major river it once was. So far, the available \n\n\n\nfield data obtained from geological excursions in the area are not sufficient to determine the \n\n\n\napproximate date when the Muar River was captured by the Pahang River. \n\n\n\n\n\n\n\nThere is an area partly located in the state of Pahang with the rest in Negeri Sembilan that can \n\n\n\nbe used to link the river capture discussed above. The area is termed as Cenozoic Basins (Figure \n\n\n\n3). It is now covered by freshwater lakes, namely Lake Bera and Lake Chini, made well-known \n\n\n\nin Malaysia by word of mouth or legends. Perhaps, Lake Bera (a government conserved \n\n\n\nwetland) is one of the remnants of the area involved in the river capture. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n12 \n\n\n\n \nFigure 3. A geology map of the Malay Peninsula* \n\n\n\n(Courtesy of Google LLC) \n\n\n\n(*\nS-Type granitoids are over-saturated with Al, while I-Type granitoids are saturated with Si, but under-saturated with Al) \n\n\n\n(https://www.google.com/search?q=geological+map+of+peninsular+malaysia&tbm) \n\n\n\n\n\n\n\nLake Bera has been known for its beauty and splendour since the 14-15th century when the \n\n\n\nMelaka Empire in the Malay Archipelago was at its height. During the period of glory and \n\n\n\nsplendour, sailors, merchants, Islamic scholars and others from many countries of the world \n\n\n\ncame in droves to Melaka. Some could have passed through Lake Bera while on their way to \n\n\n\nthe East coast states of the Malay Peninsula or even Thailand, Indochina and China. \n\n\n\n\n\n\n\n\n\n\n\nTHE GEOLOGY OF THE MALAY PENINSULA \n\n\n\n\n\n\n\nThe physico-chemical properties of soils in the Malay Peninsula are related to a great extent to \n\n\n\nthe main geological features existing in the area (Tessens and Shamshuddin 1983). To fully \n\n\n\nunderstand the pedogenesis of the soils in the Malay Peninsula that make a difference, its \n\n\n\ngeology is briefly described in this paper. Tectonic evolution of the Malay Peninsula, \n\n\n\nexplaining major geological events in the area before and during the Pleistocene epoch, has \n\n\n\nbeen extensively studied and subsequently written by many geologists (Hutchison1989; \n\n\n\nHutchison 2007; Hutchison, 2009; Metcalfe 2013; Hutchison 2014). The tectonic activity was \n\n\n\nalready active in the area during the Mesozoic Era. In essence, the Malay Peninsula is \n\n\n\ncharacterised by three North\u2013South belts i.e., the Western, Central and Eastern belts, based on \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n13 \n\n\n\ndistinct differences in stratigraphy, structure, geophysical signatures and geological evolution \n\n\n\n(Figure 3). \n\n\n\n\n\n\n\nMetcalfe (2013) found that the Western Belt of the Malay Peninsula formed part of the \n\n\n\nSibumasu Terrane, derived from the NW Australian Gondwana margin in the early Permian. \n\n\n\nThe Central and Eastern Belts represent the Sukhothai Arc, which were constructed in Late \n\n\n\nCarboniferous\u2013early Permian on the margin of the Indochina Block.  \n\n\n\n\n\n\n\nAccording to Hutchison (1989), the Bentong-Raub suture zone formed the boundary between \n\n\n\nthe Sibumasu Terrane (Western Belt) and Sukhothai Arc of the Central and Eastern Belts. The \n\n\n\npreserved remnants of the Devonian\u2013Permian Ocean basin were destroyed by subduction \n\n\n\nbeneath the Indochina Block/Sukhothai Arc. The geological phenomenon was assumed to have \n\n\n\nproduced the Permian\u2013Triassic andesitic volcanism and I-Type granitoids observed in the \n\n\n\nCentral and Eastern Belts of the Malay Peninsula (Figure 3).  \n\n\n\n\n\n\n\nHutchison (1989) and Hutchison (2014) reiterated that collisional crustal thickening that \n\n\n\nfollowed produced the Main Range S-Type granitoids that were found to have intruded the \n\n\n\nWestern Belt as well as the Bentong-Raub suture zone (Figure 3). The S-Type granitoids are \n\n\n\ncoarse grained igneous rock, composed mostly of quartz, K-feldspar (alkali-feldspar) and \n\n\n\nplagioclase. A significant Late Cretaceous tectono-thermal event affected the Malay Peninsula \n\n\n\nwith major faulting, granitoid intrusion and re-setting of palaeomagnetic signatures.  \n\n\n\n\n\n\n\nThe Malay Peninsula is geologically part of the Sunda Shelf in the South China Sea. Yeh (1968) \n\n\n\nwrote convincingly that with its complexity and splendour, the fold mountain system of \n\n\n\nMalaysia was the southerly continuation of that extending from Myanmar through Thailand, \n\n\n\nBangka and Billiton Islands, and eastwards into Kalimantan (Indonesia) in Borneo.  \n\n\n\n\n\n\n\nAs Figure 3 shows, all strata from the Cambrian to Quaternary age are found in the Malay \n\n\n\nPeninsula. Triassic and older rocks are marine in origin and include shale, sandstone, limestone \n\n\n\nand schist. However, the post-Triassic strata are characteristically non-marine and are \n\n\n\npresented by Riverine Alluvium of the Late Pleistocene and/or the early Holocene.  \n\n\n\n\n\n\n\nFigure 3 further shows that intrusive granite occupies almost half of the total land surface of \n\n\n\nthe Malay Peninsula. As such, soils formed on granite occupy a large area of the peninsula. \n\n\n\nSuch being the case, the soils are very crucial for agriculture in the region, especially for the \n\n\n\ncultivation of oil palm, rubber, cocoa and other tropical crops.  \n\n\n\n\n\n\n\nIn essence, granite forms the compelling topographic highs of the Malay Peninsula, the largest \n\n\n\nof which is the Main Range (otherwise known as Central Range). The Main Range is about \n\n\n\n480 km long, with an average width of 64-80 km and rising to more than 2,000 m above sea \n\n\n\nlevel at some places. As shown in Figure 3 more basic intrusions are also present in the Malay \n\n\n\nPeninsula, which are represented by basalt, gabbro and volcanic tuffs.  \n\n\n\n\n\n\n\nThe oldest sedimentary formations are rocks of the Machinchang Formation, which is found in \n\n\n\nthe North coast of the Langkawi Islands in the state of Kedah (northern region of the West \n\n\n\ncoast of the Malay Peninsula). The sediments are predominantly arenaceous in character with \n\n\n\nminor beds of shale, sandstones and conglomerates.  \n\n\n\n\n\n\n\nLimestone beds are found as isolated hills in many parts of the Malay Peninsula, most \n\n\n\nparticularly in Kedah-Perlis (northern), Ipoh in Perak and Kuala Lumpur (central) as well as \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n14 \n\n\n\nthe southern regions of Kelantan (northeast). Riverine and marine alluvial deposits of \n\n\n\nQuaternary age, with thickness varying from a few m to 150 m (Singh 1978), cover a large \n\n\n\nportion of the coastal plains in the Malay Peninsula. These are the areas where food crops such \n\n\n\nas rice, vegetables and fruit trees are mainly produced.  \n\n\n\n\n\n\n\nFor all intents and purposes, Hutchison (1977) subdivided the Malay Peninsula into four \n\n\n\ntectonic regions. The proposed tectonic regions are:  \n\n\n\n\n\n\n\n1. The Western Stable Shelf \n\n\n\n2. The Main Belt (Main Range) \n\n\n\n3. The Central Graben  \n\n\n\n4. The Eastern Belt \n\n\n\n\n\n\n\nYeh (1968) believed that there were at least four major episodes of granitic emplacement in \n\n\n\nthe Malay Peninsula. Much of the known mineralization in the peninsula occurred in the later \n\n\n\nepisodes, commonly associated with faulting. Regional metamorphism is widespread and most \n\n\n\nof the Paleozoic and Mesozoic rocks show slight to moderate deformation. By and large, the \n\n\n\nolder rocks show a greater degree of metamorphism than those of the younger ones.  \n\n\n\n\n\n\n\nLarge granitic batholiths of predominantly Permian to Triassic age characterize the Main Belt \n\n\n\nand Eastern Belt of the Malay Peninsula. According to Hutchison (1977), granite of the Main \n\n\n\nBelt of the peninsula is described as mesozonal, which is coarsely porphyritic in nature. On the \n\n\n\nother hand, granite in the Eastern Belt is epizonal and is primarily characterized by an \n\n\n\nequigranular to weakly porphyritic texture. As such, the texture of the soils formed from the \n\n\n\ntwo granite types differs slightly. \n\n\n\n\n\n\n\nGranite in the Malay Peninsula has undergone extensive physico-chemical weathering because \n\n\n\nof the long exposure to the tropical conditions, forming soils with sandy clay texture \n\n\n\n(Paramananthan 2000; Soil Survey Staff 2018). According to Paramananthan (1977) and \n\n\n\nTessens and Shamshuddin (1983), the mineralogy of the clay fraction is dominated by kaolinite \n\n\n\nand sesquioxides (i.e., gibbsite, goethite and hematite).  \n\n\n\n\n\n\n\nMost of the soils in the Malay Peninsula formed on granite is classified as Ultisol (e.g., Rengam \n\n\n\nSeries), which is found to be suitable for oil palm and rubber cultivation (Shamshuddin et al. \n\n\n\n2018; Soil Survey Staff 2018). Rengam Series soil is dominantly scattered in the well-drained \n\n\n\nupland regions, having undulating to steep landscapes. Note that granite soils are acidic in \n\n\n\nnature, but with proper agro-management practices, they can be very productive.  \n\n\n\n\n\n\n\nAccording to Tjia (1973), the drainage system in the Malay Peninsula has morphologically \n\n\n\nevolved during the Cretaceous. The main rivers controlling the drainage pattern of the \n\n\n\npeninsula (Figure 2) are Pahang River (420 km), Perak River (350 km) and Kelantan River \n\n\n\n(280 km). Currently, the Kelantan River (located in the northeast) and Pahang River (middle) \n\n\n\nflow into the South China Sea, while the Perak River (middle) flows into the Straits of Melaka. \n\n\n\nA long time ago, water from the upper reaches of the present Pahang River is believed to have \n\n\n\ndrained into the Straits of Melaka via the Muar River i.e., before a river capture took place.  \n\n\n\n\n\n\n\nAggradation on the East coast shorelines seems to be predominant, particularly in the vicinity \n\n\n\nof large rivers such as the Pahang River and Kelantan River. The storm waves of the South \n\n\n\nChina Sea account partly for the development of the conspicuous ridged beach landscapes in \n\n\n\nthe East coast states of the Malay Peninsula (Shamshuddin et al. 2021a). The weaker wave \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n15 \n\n\n\naction in the Straits of Melaka is reflected by the occurrence of the smaller beach ridges and \n\n\n\nmuddy beaches along the west coast. Extensive drowning of the coastal regions is indicated by \n\n\n\nswamps at several places (Shamshuddin et al. 2021b). It appears that the longshore currents \n\n\n\nhave a remarkable influence on the general direction of the river mouth.  \n\n\n\n\n\n\n\nThe geomorphology of the West coast of the Malay Peninsula is characterized by flatland in \n\n\n\nthe coastal regions. This is evidenced by the presence  of  coastal plains in the peninsula which \n\n\n\nare widest in the vicinity of the Perak River mouth (Figure 3), where they are  about 45 km \n\n\n\nwide (Tjia 1973). On average, the coastal plains in the West coast have a width varying between \n\n\n\n20 and 30 km. Alluvial terraces at various levels are found in this area (Shamshuddin 1982). \n\n\n\nThe main constituents of the alluvial terraces are granite clasts and sand grains with some \n\n\n\nclayey materials.  \n\n\n\n\n\n\n\nAccording to Tjia (1973), the prevailing West-Northwest to Northwest longshore currents \n\n\n\nbetween 0\u00b0 and 6\u00b0N latitude had resulted in the deflection of most river mouths to the \n\n\n\nNorthwest. Beyond the 6\u00b0N, the resultant longshore current is towards the Southeast. So, the \n\n\n\nKedah River (up north) has deflected in the same direction. The phenomenon of the movement \n\n\n\nof river mouths, resulting from the longshore current, was confirmed by Raj (2009). Koopmans \n\n\n\n(1964) estimated the rate of coastal accretion to be about 100 m in 80 years.  \n\n\n\n\n\n\n\nThe eastern shoreline of the Malay Peninsula from Kelantan (Northeast of the peninsula, near \n\n\n\nThailand) to Johor (South, close to Singapore) is about 640 km long. The occurrence of beach \n\n\n\nridges and deltas in the coastal regions indicate pro-grading of the shoreline (Tjia 1973). These \n\n\n\nbeach ridges formed about 160,000 ha of very sandy soils in the East coast. This landscape is \n\n\n\nfound extensively in the East coast of the Malay Peninsula (Panton 1958; Panton 1960; \n\n\n\nSmallwood 1967; Abdul Halim 1978; Lim 1991) while on the west coast of the peninsula, it \n\n\n\noccurs as isolated ridges (Soo, 1968, 1976), except on the main island of Langkawi where it is \n\n\n\nwell expressed (Lim et al. 1984; Lim 1991).   \n\n\n\n\n\n\n\nAccording to Roslan et al. (2010), the very sandy soils located along the coastlines are either \n\n\n\nclassified as Entisols (young soils fringing the shorelines, without pedogenetic horizon) or \n\n\n\nSpodosols (soils having a diagnostic spodic horizon). The former is found extensively in the \n\n\n\nareas close to the shoreline, while the latter is located further inland (about 0.5 to a few km \n\n\n\naway from the coastline). The very sandy soils are locally known as BRIS soils \u2013 the term \n\n\n\nBRIS is the acronym for \u2018Beach Ridges Interspersed with Swales\u2019. The first ever recorded \n\n\n\nmentioned term \u2018BRIS\u2019 is credited to Willimot (1948) in the Geological Map of Malaya (Lim \n\n\n\n1991). BRIS is one of the most conspicuous landscapes in the East coast states of the Malay \n\n\n\nPeninsula (Shamshuddin et al. 2021a).  \n\n\n\n\n\n\n\nTjia (1970) and Tjia (1973) noted that from the Thailand border down to Terengganu, the \n\n\n\ngeneral shift of the river courses in the coastal plains is towards the Northwest (NW) or West \n\n\n\nNorthwest (WNW), as indicated by a 35 km shift of the Kelantan River mouth towards the left \n\n\n\nof the former river. Figure 4 shows the current location of the Kelantan River. The shift in the \n\n\n\ncourse of the river mouth (from Pantai Senok) towards the Thailand border (to Pantai Mek \n\n\n\nMas) is an ongoing process. From Kuantan (Pahang) to Pontian (Johor), the general shift is \n\n\n\ntowards the right, following the direction of the longshore current (Tjia 1973; Raj 2009). This \n\n\n\nphenomenon has a great impact on soil formation and agriculture in the East coast states of the \n\n\n\nMalay Peninsula.  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n16 \n\n\n\n\n\n\n\nFigure 4. A Google map of the East coast of the Malay Peninsula bordering \n\n\n\nThailand showing the current position of the Kelantan River \n\n\n\n(Courtesy of Google LLC) \n\n\n\n\n\n\n\nSOIL TYPE DISTRIBUTION IN THE MALAY PENINSULA \n\n\n\n\n\n\n\nSoils in the Upland Regions \n\n\n\n\n\n\n\nOn weathering under a tropical environment, basalt and andesite in the Malay Peninsula \n\n\n\nnormally give rise to Oxisols, while granite produces Ultisols (Tesssens and Shamshuddin \n\n\n\n1983; Paramananthan 2000). Shale and schist, on the other hand, are weathered to form either \n\n\n\nUltisols or Oxisols, depending on the local conditions where the rocks are located. \n\n\n\nNotwithstanding the close similarity in the climatic conditions, the formation of both soil types \n\n\n\nrequire a well-drained environment. As such, Ultisols and Oxisols are mostly found in the \n\n\n\nupland regions. These two types of soils occupy about 70 % of the country\u2019s land surface \n\n\n\n(Shamshuddin and Fauziah 2010). Typical profiles of the highly weathered soils are depicted \n\n\n\nin Figure 5 i.e., Rengam Series formed from granite and Segamat Series from andesite.  \n\n\n\n\n\n\n\nThe type of shale, which is a function of the geological deposit that includes factors such as the \n\n\n\namount of quartz, iron or carbon in the sediment together with physiographic position and the \n\n\n\ndepth of the ground water table, will give rise to several types of soils (Noordin 1975). In \n\n\n\ngeneral, illite and mica are typically more abundant in the clay fraction of soils formed from \n\n\n\nshale than in soils derived from other parent materials, especially at the inceptic stage \n\n\n\n(Inceptisols). However, with the progress of pedogenesis, there is a marked disappearance of \n\n\n\nillite and micaceous clay. The amount of kaolinite, gibbsite and goethite increases with the \n\n\n\nprogress of pedogenesis. Hence, minerals dominate the entire clay fraction of the soils at the \n\n\n\nlater stage of pedogenesis i.e., argillic (Ultisols) and oxic stage (Oxisols) (Noordin and Shafar \n\n\n\n2017a). \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n17 \n\n\n\n\n\n\n\nFigure 5. Typical Ultisol (Rengam Series, left) and Oxisol (Segamat Series, right) \n\n\n\nprofiles found in the Malay Peninsula \n\n\n\n\n\n\n\nOxisols are usually assumed to be more weathered than the soils of the Ultisols (Shamshuddin \n\n\n\net al. 2018; Soil Survey Staff 1999). The notion is based on the mineralogical composition of \n\n\n\nthe soils and/or rocks from which they are formed. Basalt containing ferro-magnesium silicates \n\n\n\n(olivine and pyroxenes) plus feldspars weathers faster (and so does andesite) compared to \n\n\n\ngranite, which is dominated by alumino-silicates (quartz, mica and feldspars). As such, \n\n\n\nweathering of basalt or andesite in a tropical environment results in the formation of Oxisols, \n\n\n\nwhile most granite forms Ultisols (Figure 5). The more weathered soils contain higher amounts \n\n\n\nof oxides of Fe (reddish hematite or goethite) and/or Al (gibbsite) as shown by the Segamat \n\n\n\nSeries (Figure 5). Such being the case, Ultisols and Oxisols have significantly different \n\n\n\nphysico-chemical properties.  \n\n\n\n\n\n\n\nHaving being exposed to a tropical environment for a long period of time (since Late \n\n\n\nPleistocene), rocks in the upland regions of the Malay Peninsula are weathered to form highly \n\n\n\nleached, acidic Ultisols and Oxisols (Tessens and Shamshuddin 1983; Shamshuddin and \n\n\n\nFauziah,2010). Soil pH is low and basic cations are usually insufficient for crop requirements. \n\n\n\nKey characteristics of Ultisols and Oxisols in the peninsula are summarized in Table 1. The \n\n\n\nsoils are mainly utilized for oil palm and rubber cultivation, with some areas reserved for cocoa. \n\n\n\nAs the first two crops are known to be acid-tolerant, they are able to grow well below pH 5. \n\n\n\nBesides, physical properties of soil are of greater importance in soil evaluation because they \n\n\n\nare more essential and permanent than chemical properties which can be modified by \n\n\n\nmanagement practices (Chan 1977; Noordin, 2013; Shamshuddin et al. 2018). That could be \n\n\n\none of the reasons why most of the agricultural land in Malaysia is dominated by plantation \n\n\n\ncrops (oil palm, rubber, cocoa and pepper), which account for about 84% of the total area \n\n\n\n(Noordin and Shafar 2017b).  \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n18 \n\n\n\nTABLE 1 \n\n\n\n Common characteristics of Ultisols and Oxisols in the Malay Peninsula \n\n\n\n\n\n\n\nSoil            Diagnostic      Soil pH         Clay Mineral*                 CEC                    Colour \n\n\n\n                    Horizon                                                                 cmolc kg-1 clay \n\n\n\nUltisols       Bt                   4.0-4.5           kn, gb, gt                         >16                   Yellowish \n\n\n\nOxisols       Bo                  4.5-5.0        kn, gb, gt, ht                       <16                   Reddish \n\n\n\n* kn = kaolinite; gb = gibbsite; gt = goethite; ht = hematite \n\n\n\n\n\n\n\nIn plantations, prolonged application of ammonium sulfate on Ultisols or Oxisols results in \n\n\n\nincreased soil acidity that is known to affect crop productivity. However, an increase in soil \n\n\n\nacidity can, to a certain extent, be offset by the hydrolysis of Ca2+ from dissolution of applied \n\n\n\nphosphate rocks that produce some hydroxyls (Shamshuddin 2022). To increase soil pH to \n\n\n\nsustain crop production (e.g., corn) on Malaysian Ultisols, ground magnesium limestone \n\n\n\n(GML) can be applied at an appropriate rate and time (Shamshuddin et al. 1991). According to \n\n\n\nShamshuddin and Fauziah (2010), the ameliorative impact of applying GML at 4 t/ha on \n\n\n\nUltisols and Oxisols in the Malay Peninsula could last up to four years.  \n\n\n\n\n\n\n\nSoils in the Riverine Alluvial Plains \n\n\n\n\n\n\n\nThe phenomenal sea level drop in the Malay Peninsula during the Late Pleistocene resulted in \n\n\n\nthe development of fluvial (riverine) terraces at different levels (Shamshuddin 1982). \n\n\n\nGopinathan (1968) identified, studied and described in detail the characteristics of the above-\n\n\n\nmentioned fluvial terraces. The Malaysian pedologist divided fluvial terraces into: (i) High \n\n\n\nterraces; (ii) Intermediate terraces; and (iii) Low terraces of the present flood plains. A pictorial \n\n\n\nillustration of the existence of the fluvial terraces in the Malay Peninsula is shown in Figure 6, \n\n\n\ndrawn according to the elevation and age of the alluvial deposits observed/recorded during the \n\n\n\nfield work. In physiographic terms, they are known in the peninsula as T3, T2 and T1 terraces, \n\n\n\nfollowing the order of decreasing age (Figure 6). \n\n\n\n\n\n\n\n \nFigure 6. Artist impression of the fluvial terraces in the Malay Peninsula formed   \n\n\n\nfrom the Late Pleistocene to the present time \n\n\n\n[Source: Shamshuddin (1982); Shamshuddin and Tessens (1983)] \n\n\n\n\n\n\n\nThe Late Pleistocene sediments of varying thickness were identified and characterized in the \n\n\n\nalluvial plains of the Malay Peninsula by geologists and/or soil scientists in the 1960s. The \n\n\n\nalluvial sediments located in Johor state situated at an elevation of 45-70 msl were classified \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n19 \n\n\n\nas Old Alluvium by Burton (1964); this is otherwise known as T3 Terrace (Figure 6). According \n\n\n\nto Sivam (1969), the age of the Old Alluvium in Kinta Valley, Perak (in the middle of the \n\n\n\nMalay Peninsula) is about 40,000 years.  \n\n\n\n\n\n\n\nA detailed study on the pedological characteristics of the soils on T2 terraces was carried out \n\n\n\nby Shamshuddin (1982), with the aim of determining their genesis and physico-chemical \n\n\n\nproperties. Sivam (1969) reported that primary sedimentary structures of fluvial character were \n\n\n\nfound abundantly in the Young Alluvium of the present floodplains. The age of the Young \n\n\n\nAlluvium (T1 terrace) in the same vicinity is less than 3,000 years.  \n\n\n\n\n\n\n\nThe older deposits are usually characterized by a regular decrease in organic carbon in relation \n\n\n\nto soil depth. In recent deposits, the distribution of organic carbon is erratic with depth (Shafar \n\n\n\n2017). Another notable feature in the older deposits is that clay has moved with the formation \n\n\n\nof an argillic horizon. This feature is lacking in recent deposits, indicating that insufficient time \n\n\n\nhas elapsed for clay illuviation (Noordin 1980; Soil Survey Staff 1999). \n\n\n\n\n\n\n\nSoils in the Marine Alluvial Plains \n\n\n\n\n\n\n\nPyritization of the coastal plains \n\n\n\n\n\n\n\nFigure 3 shows the aerial distribution of the Marine Alluvial Deposits of the Quaternary age in \n\n\n\nthe Malay Peninsula. A significant area of the marine alluvium contains pyrite (FeS2), which \n\n\n\nis a prerequisite to the development of acid sulfate soils. The mineralization of pyrite in the \n\n\n\nsediments of the coastal plains of the countries in Southeast Asia can be used as solid evidence \n\n\n\nfor the sea level rise (or higher sea level) during the Holocene (Shamshuddin 2017; \n\n\n\nShamshuddin et al. 2017). This notion was confirmed by Enio et al. (2011) who studied pyrite \n\n\n\ndistribution in the coastal plains of Kelantan in the Malay Peninsula (Figure 4). \n\n\n\n\n\n\n\nA detailed study of the Perak plains by Noordin (1980) found new evidence of deposits on \n\n\n\ncoastal plains originating from the sea where he found diatoms, sponge spicules and phytolite \n\n\n\nsea microorganisms. Diatoms are characteristically absent in the soils on riverine deposits.  \n\n\n\n\n\n\n\nThe areas in the Kelantan Plains where pyrite was identified/found and those without were \n\n\n\ndelineated by Enio et al. (2011); the line separating them is shown in Figure 7. The said line \n\n\n\nrepresents the predicted shoreline when the sea level was at its highest during the mid-Holocene \n\n\n\ni.e., about 4,300 years ago. The coastline in the Kelantan Plains 4,300 BP was a few km inland. \n\n\n\nWhen the sea level successively dropped to the present level due to climate change, the sea-\n\n\n\nwater flooded lands were exposed to the atmosphere, forming the present conspicuous BRIS \n\n\n\nlandscape. This landscape will be explained/discussed later. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n20 \n\n\n\n \nFigure 7. Map showing the predicted coastline in the Kelantan Plains of the Malay \n\n\n\nPeninsula when the sea level was 3-5 m above the present level \n\n\n\n[Courtesy of Enio et al. (2011)] \n\n\n\n\n\n\n\nAccording to Tjia et al. (1977), the sea level about 4,300 years ago was 3-5 m above the present \n\n\n\nlevel, The Malay Peninsula was seemingly smaller than what it is today. The sea level in \n\n\n\nSoutheast Asia of 3-5 m above the present level was confirmed by a study conducted in \n\n\n\nThailand (Sathiamurthy and Voris 2006). The rise in sea level during the mid-Holocene is \n\n\n\nconsistent with the results of a study conducted by Azmi (1982). He found that rice fields in \n\n\n\nthe Kedah-Perlis Plains (in the northern region of the peninsula) were once under the sea, as \n\n\n\nevidenced by sporadic occurrence of pyrite in the sediments/soils (Figure 8). \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n21 \n\n\n\n \nFigure 8. Major landforms in the Kedah-Perlis coastal plains \n\n\n\nof the Malay Peninsula \n\n\n\n[Source: Azmi (1982)] \n\n\n\n\n\n\n\nMarine alluvium in the Kedah-Perlis Plains was found to have extended >10 km inwards i.e., \n\n\n\ninto the upland region (Figure 8). Elsewhere in the peninsula, marine alluvium is also found a \n\n\n\nfew km inland (Figure 3). According to Enio et al. (2011) and Shamshuddin et al. (2021b), \n\n\n\nmarine alluvium had cut across the Quaternary sediments as in the case of the Kelantan Plains \n\n\n\nor Kedah-Perlis Plains. When the sea level was higher than that of the present level, much of \n\n\n\nthe landmass then in the Malay Peninsula was inundated by sea water. This inundation accounts \n\n\n\nfor the mineralization of pyrite in areas found many km away from the present coastline. It was \n\n\n\nthe time when pyrite in the soils far away from the present coastline in the Kedah-Perlis Plains \n\n\n\nwas formed or mineralized. Soils containing pyrite are classified as acid sulfate soils, which \n\n\n\nare not suitable for crop cultivation without undergoing alleviation.  \n\n\n\n\n\n\n\nA study in Kedah-Perlis Plains by Lim (1991) reported some differences in soil characteristics \n\n\n\nthat developed on marine and riverine alluvium. The CEC of the marine alluvial soils is higher \n\n\n\nthan that of the riverine derived soils, indicating the presence of 2:1 clay mineral (smectite) in \n\n\n\nthe former. The presence of the 2:1 clay minerals result in the formation of cracks and gypsum \n\n\n\nduring a distinct dry season that normally extends from December to March.  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n22 \n\n\n\nThe coastal plains in the Malay Peninsula are partly occupied by swampy depressions, with a \n\n\n\npermanent high water table level. These are the areas where organic materials have \n\n\n\naccumulated over the years, forming vast peatlands, sometimes containing pyrite. Peatlands \n\n\n\nare sporadically distributed in the state of Selangor, Johor, Perak, Pahang, Terengganu and \n\n\n\nKelantan in the Malay Peninsula. The area covered by peatlands in Malaysia is about 2,457,730 \n\n\n\nha, with 642,918 ha (26.16%) in the peninsula, 116,965 ha (4.76%) in Sabah and 1,697,847 ha \n\n\n\n(69.08%) in Sarawak (Wetland International 2010). \n\n\n\n\n\n\n\nPeat soils are organic soils containing more than 65% organic matter with a minimum thickness \n\n\n\nof 50 cm to 100 cm, or with more than half of it being lithic/paralithic or terric layer. According \n\n\n\nto Wong (1986), the capability of peatlands to support agriculture is very low. This is due \n\n\n\namong others to the problems of a high water table level, high acidity, insufficient nutrients \n\n\n\nand the presence of wood in the soil profile and above the ground. However, with proper soil, \n\n\n\nagronomic and water management, numerous economic crops such as oil palm and pineapple \n\n\n\ncan be grown with satisfactory yields (As\u2019ari et al. 2016; Paramananthan 2016). \n\n\n\n\n\n\n\nDue to drainage or other reasons, pyrite in the sediments (Figure 9) is oxidized, producing a \n\n\n\nhigh level of acidity that significantly affects crop growth and/or production (Shamshuddin et \n\n\n\nal. 2014; Shamshuddin et al. 2021b). Under a low pH environment, alumino-silicates present \n\n\n\nin the coastal sediments are disintegrated and eventually dissolve releasing acid metals (Al \n\n\n\nand/or Fe) that cause toxicity to crops such as rice, cocoa or even oil palm.  Therefore, in \n\n\n\npreparing the soils for cultivation, it is recommended that the drains be deepened gradually to \n\n\n\nwash away the excess acids formed to avoid sudden changes in acidity, which may have \n\n\n\ndetrimental effects on plant growth (Chan et al. 1977; Noordin, 1980). \n\n\n\n\n\n\n\n \n                     Figure 9. Oxidation of pyrite (left) results in the formation of jarosite (right) \n\n\n\n\n\n\n\nAccording to Shamshuddin et al. (2014) and Shamshuddin et al. (2017), pyrite (Figure 9, left) \n\n\n\noxidation produces a mineral called jarosite which is characteristically straw-yellow in colour \n\n\n\n(Figure 9, right). Al and Fe released into the soils will seriously pollute the environment in the \n\n\n\nvicinity of the acid sulfate soil areas as observed in the Kelantan Plains (Figure 7) and the \n\n\n\nKedah-Perlis Plains (Figure 8).  \n\n\n\n\n\n\n\nA high level of Fe in the acid sulfate soils of the Kelantan Plains is often observed when land \n\n\n\nis flooded for rice cultivation. The presence of a high amount of Fe in the water of the rice \n\n\n\nfields is evidenced by its red colour. The low pH of <4 is consistent with the presence of a high \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n23 \n\n\n\nconcentration of Fe and Al in the water. It has been established that Fe2+ and Al3+ are toxic to \n\n\n\nrice plants growing in the fields (Shamshuddin et al. 2014; Shamshuddin et al. 2021b). The \n\n\n\nproblem of Fe2+ and/or Al3+ toxicities can be alleviated effectively by applying an adequate \n\n\n\namount of lime (Elisa et al. 2014) or lime in combination with a bio-fertilizer, fortified with \n\n\n\nphosphate-solubilizing bacteria (Panhwar et al. 2014a; Panhwar et al. 2014b).   \n\n\n\n\n\n\n\na) Development of BRIS landscape \n\n\n\n\n\n\n\nDuring the mid-Holocene, a large areas of the coastal plains in the countries of Southeast Asia \n\n\n\nwere inundated by sea water from the South China Sea. At that time of the geological history, \n\n\n\nthe coastal zones in the region had undergone progressive progradation (Tjia et al. 1977). A \n\n\n\nstudy by Bird and Teh (1990) concluded that the coastal zone progradation led to the formation \n\n\n\nof plains with wide beach ridges scattered sporadically along the coast, which are commonly \n\n\n\nobserved in the Malay Peninsula, Thailand, Indonesia and even Cambodia.     \n\n\n\n\n\n\n\nOccurrence of the conspicuous sandy beach ridges in the East coast of the Malay Peninsula is \n\n\n\nindicative of a recession of the sea, beginning about 6,000 years ago. Based on geological and \n\n\n\npedological evidence, the formation of the beach ridges at various elevations is related to the \n\n\n\nprogressive lowering of sea level of many meters since then (Roslan et al. 2010; Shamshuddin \n\n\n\net al. 2021a). Using field evidence gathered by Roslan et al. (2010) in the Kelantan-Terengganu \n\n\n\nPlains and the laboratory investigations that followed, it was concluded that the younger ridges \n\n\n\nwere subsequently created as the sea level fell, with their elevation getting lower. The notion \n\n\n\non the occurrence and distribution of the beach ridges is consistent with the results of the studies \n\n\n\nconducted by Raj et al. (2007) and Raj (2009).  \n\n\n\n\n\n\n\nNossin (1965) believed that the whole length of the low-lying coastal regions in the East coast \n\n\n\nof the Malay Peninsula has recently been added to the old land; this was confirmed by Tjia et \n\n\n\nal. (1977). As mentioned earlier, the conspicuous landscape formed in this manner is termed \n\n\n\nas \u2018Beach Ridges Interspersed with Swales, with BRIS as the acronym. Soils formed from the \n\n\n\nsandy beach ridges are called BRIS Soils (Shamshuddin et al. 2021a).  \n\n\n\n\n\n\n\nMost of the soils utilized for agriculture in the BRIS landscape occur on the three ridges, \n\n\n\nrunning parallel to the present shoreline, but having different elevations. The ridge closest to \n\n\n\nthe shoreline is about 1 m above the present sea level, while the next ridge is slightly higher, \n\n\n\nmostly at about 2 m (Figure 10). At specific locations in the Kelantan-Terengganu Plains, the \n\n\n\nformer is about 2 km wide, while the latter is about 0.5 km (Roslan et al.  2010). In between \n\n\n\nthe conspicuous sandy ridges occur slightly depressed areas (mostly at 1-2 m above the present \n\n\n\nsea level), which are usually under a permanent high water table. These are the so-called swales \n\n\n\n(lakes) that dominate the littoral landscape of the East coast states of the Malay Peninsula. \n\n\n\nAccording to Shamshuddin et al. (2021a), soils of this nature are also found extensively in \n\n\n\nother parts of Southeast Asia such as Thailand and West Kalimantan (Indonesia).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n24 \n\n\n\n \nFigure 10. Spatial distribution of BRIS Soils in the Kelantan-Terengganu coastal plains (P1, \n\n\n\nP2 and P3 occurs at 0-50, 50-100 and > 100 cm below soil surface, respectively) \n\n\n\n[Source: Roslan et al. (2010)] \n\n\n\n\n\n\n\nSoils on another ridge can occasionally be identified, occurring at an elevation of 5-7 m above \n\n\n\nthe present sea level. This ridge is exclusively located farthest away from the present shoreline. \n\n\n\nAccording to Raj et al. (2007) and Raj (2009), the ridge (which is mostly about 0.5 km wide) \n\n\n\nis located some 4 km away from the present shoreline. Whenever this happens, a second swale \n\n\n\nis almost certainly found, which also runs parallel with the other ridges. It appears that every \n\n\n\ntime the sea level drops, a new ridge adjacent to the shoreline would be formed. As three sets \n\n\n\nof sandy ridges can be observed in the Kelantan-Terengganu Plains, the sea level in the Malay \n\n\n\nPeninsula could have dropped at the least three times during the Holocene.  \n\n\n\n\n\n\n\nThe evidence so mentioned seemingly suggests that there is no doubt that the sea level at the \n\n\n\nKelantan-Terengganu Plains in the past was higher than at the present level. This is in \n\n\n\nagreement with the findings of the studies conducted by Haile (1970), Haile (1971), Tjia et al. \n\n\n\n(1977) and Enio et al. (2011). Thus, the sea level in Southeast Asia during the mid-Holocene \n\n\n\nwas a few meters above the present level. Other studies conducted in the coastal regions of the \n\n\n\nMalay Peninsula (Nossin 1961; Nossin 1964) and Thailand (Sathiamurthy and Voris 2006) \n\n\n\nprovide further evidence on the sea level rise in the region during the period.  \n\n\n\n\n\n\n\nIt is schematically shown in Figure 10 that the oldest ridge (R3 where P3 is sited), located at \n\n\n\nthe farthest distance from the shoreline, is sitting at the highest elevation of 5-7 meters above \n\n\n\nthe present sea level. The youngest ridge (R1 where P1 is sited), the one closest to the \n\n\n\nshorelines, is at the lowest elevation of about 1 m above the sea level.  \n\n\n\n\n\n\n\nIn between the ridges occur swales either at 1-2 or 2-3 m above the present sea level. The \n\n\n\ndepression can either be occupied by acid sulfate soils, peat soils or even normal soils. The \n\n\n\nhydromorphic soils are sometimes drained to make way for agriculture such as for the \n\n\n\ncultivation of rice and/or vegetables. Figure 10 shows that the Jambu, Rudua and Rhu Tapai \n\n\n\nSeries occurring in the BRIS landscape are taxonomically classified as Spodosols, while the \n\n\n\nBaging Series is an Entisol. The sandy nature of the soils makes it impossible for farmers to \n\n\n\nsustain crop production without undergoing proper or innovative agro-tech practices. \n\n\n\n\n\n\n\n\n\n\n\nJambu Series Rhu Tapai Series \n\n\n\nRudua Series \n\n\n\nRusila Series \n\n\n\nBaging Series \n\n\n\nRusila Series \n\n\n\nSpodic layer \n\n\n\nP3 \n\n\n\nSea \n\n\n\nEntisols Spodosols \n\n\n\nP2 \nP1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 8-28 \n\n\n\n\n\n\n\n \n25 \n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nThe present landscape, geology and soils in the Malay Peninsula are partly a consequence of \n\n\n\nclimate change that took place in the region since the Late Pleistocene. During that era, the \n\n\n\nearth\u2019s temperature intermittently decreased or increased, causing global sea levels to drop and \n\n\n\nrise, respectively. This had a profound impact on soils in the peninsula. The landscape of the \n\n\n\npeninsula is characterised by the presence of steep highlands in the central region with upland \n\n\n\nundulating terrains and flat alluvial areas along the coast.  \n\n\n\n\n\n\n\nMajor soils in the upland regions are formed from igneous, sedimentary and metamorphic \n\n\n\nrocks, with their age ranging from Mesozoic to Paleozoic. Under a tropical environment, \n\n\n\nsedimentary rocks are usually weathered to form soils classified as Ultisols or Oxisols, while \n\n\n\nthose developed from igneous rocks are Oxisols. Both soil types are acidic in nature, having \n\n\n\nlow basic cations, and are mostly insufficient in nutrients to sustain crop production.  \n\n\n\n\n\n\n\nClimate change resulted in the development of three levels of riverine terraces, found \n\n\n\nsporadically in the Malay Peninsula. Sediments forming the highest terrace are 40,000 years, \n\n\n\nold while the lowest terrace is found in the present flood plains. The riverine terraces are \n\n\n\noccupied by soils with slightly different physico-chemical characteristics compared to those \n\n\n\nformed from igneous, sedimentary or metamorphic rocks.  \n\n\n\n\n\n\n\nMarine alluvial deposits are found abundantly along the low-lying coastal regions of the Malay \n\n\n\nPeninsula. The alluvium is divided into clayey sediments (sometimes containing pyrite) mostly \n\n\n\ndeposited in the West coast and the sandy sediments occur predominantly in the East coast. \n\n\n\nThe pyritization of the coastal plains took place about 4,300 years ago when the sea level in \n\n\n\nthe peninsula rose by 3-5 m above the present level. Pyrite in the sediments produces acidity \n\n\n\non oxidation that negatively impacts crop production. The lands so formed are occupied by \n\n\n\nacid sulfate soils. The progressive drop in sea level since mid-Holocene resulted in the \n\n\n\ndevelopment of the conspicuous BRIS landscape, having three beach ridges, with different \n\n\n\nelevations, running parallel to the present coastlines \u2013 i.e., the youngest ridge is located closest \n\n\n\nto the beach. Acid sulfate soils and BRIS Soils are not productive without proper agro-\n\n\n\nmanagement practises derived from innovative research. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nThe authors wish to acknowledge Universiti Putra Malaysia and the government of Malaysia \n\n\n\nfor providing the financial and technical support during the conduct of the research in Malaysia \n\n\n\nand overseas to obtain the necessary data required to write this paper. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\n \nAbdul Halim, A.G. 1978. Semi-detailed Soil Map of Kemasin-Semarak Development Project. Kuala \n\n\n\nLumpur: Department of Agriculture. \n\n\n\nAs\u2019ari, H., H.T. Frederick, D.S. Ngab, M. Elizabeth, M. Roslan, M.S. Noranizam, W.I. Wan Mohd \nRusydan. and COMSSSEM. 2016. Characterization and Classification of Peat Soils in \n\n\n\nMalaysia. In Proceedings of 15th International Peat Congress 2016 (pp. 474-475). Finland: \n\n\n\nInternational Peat Society. \n\n\n\nAzmi, M.A., 1982. 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"\n\n\uf0d7\uf0cd\uf0cd\uf0d2\uf0e6\uf020\uf0ef\uf0ed\uf0e7\uf0ec\uf0f3\uf0e9\uf0e7\uf0f0\uf0f0\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\uf020\uf0e6\uf020\uf0ef\uf0ef\uf0ed\uf0f3\uf0ef\uf0ee\uf0ec\uf020\uf020\uf0f8\uf0ee\uf0f0\uf0f0\uf0e8\uf0f7\uf020 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\uf0ad\uf0ac\uf0ab\uf0bc\uf0b7\uf0bb\uf0bc\uf020 \uf0b8\uf0bf\uf0aa\uf0bb\uf020 \uf0b7\uf0b2\uf0bc\uf0b7\uf0bd\uf0bf\uf0ac\uf0bb\uf0bc\uf020 \uf0ac\uf0b8\uf0bf\uf0ac\uf020 \uf0ac\uf0b8\uf0bb\uf020\uf0c6\uf0b2\uf0f3\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0b3\uf0bf\uf0ac\uf0ac\uf0bb\uf0ae\uf020 \uf0bf\uf0ad\uf0ad\uf0b1\uf0bd\uf0b7\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b3\uf0bf\uf0a7\uf020 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\uf0b7\uf0b2\uf020 \uf0ac\uf0b8\uf0b7\uf0ad\uf020 \uf0ba\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf020 \uf0bf\uf0ae\uf0bb\uf020 \uf0bb\uf0a8\uf0b0\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020 \uf0ac\uf0b1\uf020 \uf0be\uf0bb\uf020 \uf0bd\uf0b8\uf0bb\uf0b3\uf0b7\uf0bd\uf0bf\uf0b4\uf0b4\uf0a7\uf020 \uf0ad\uf0ac\uf0bf\uf0be\uf0b4\uf0bb\uf020 \uf0bf\uf0b2\uf0bc\uf020 \uf0be\uf0b7\uf0b1\uf0b4\uf0b1\uf0b9\uf0b7\uf0bd\uf0bf\uf0b4\uf0b4\uf0a7\uf020\n\n\n\n\uf0b7\uf0b2\uf0bf\uf0bd\uf0ac\uf0b7\uf0aa\uf0bb\uf0f2\n\n\n\n\uf0d8\uf0bb\uf0bf\uf0aa\uf0a7\uf020\uf0b3\uf0bb\uf0ac\uf0bf\uf0b4\uf0ad\uf020\uf0b7\uf0b2\uf020\uf0bf\uf0aa\uf0bf\uf0b7\uf0b4\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0ba\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ad\uf0ab\uf0bd\uf0b8\uf020\uf0bf\uf0ad\uf020\uf0bb\uf0a8\uf0bd\uf0b8\uf0bf\uf0b2\uf0b9\uf0bb\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0bd\uf0bf\uf0ae\uf0be\uf0b1\uf0b2\uf0bf\uf0ac\uf0bb\uf020\uf0ba\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf020\n\n\n\n\uf0b3\uf0bf\uf0a7\uf020\uf0b7\uf0b2\uf0bc\uf0b7\uf0bd\uf0bf\uf0ac\uf0bb\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b0\uf0b1\uf0ac\uf0bb\uf0b2\uf0ac\uf0b7\uf0bf\uf0b4\uf020\uf0ba\uf0b1\uf0ae\uf0b3\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0b3\uf0bb\uf0ac\uf0bf\uf0b4\uf0ad\uf020\uf0ac\uf0b8\uf0bf\uf0ac\uf020\uf0bd\uf0bf\uf0b2\uf020\uf0be\uf0bb\uf020\uf0bf\uf0bd\uf0bd\uf0ab\uf0b3\uf0ab\uf0b4\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b0\uf0b4\uf0bf\uf0b2\uf0ac\uf0f2\uf020\n\n\n\n\uf0bb\uf0a8\uf0ac\uf0ae\uf0bf\uf0bd\uf0ac\uf0bb\uf0bc\uf020\uf0f8\uf0bb\uf0a8\uf0bd\uf0b8\uf0bf\uf0b2\uf0b9\uf0bb\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0ba\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf0f7\uf020\uf0bf\uf0ae\uf0bb\uf020\uf0ab\uf0ad\uf0ab\uf0bf\uf0b4\uf0b4\uf0a7\uf020\uf0bc\uf0bb\uf0b0\uf0bb\uf0b2\uf0bc\uf0bb\uf0b2\uf0ac\uf020\uf0ab\uf0b0\uf0b1\uf0b2\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0b0\uf0d8\uf0f2\uf020\uf0d4\uf0b7\uf0b3\uf020\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\uf020\n\n\n\n\uf0bd\uf0b1\uf0b2\uf0bc\uf0b7\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b4\uf0bf\uf0ae\uf0b9\uf0bb\uf0ae\uf020\uf0bc\uf0bb\uf0bd\uf0ae\uf0bb\uf0bf\uf0ad\uf0bb\uf020\uf0b1\uf0bd\uf0bd\uf0ab\uf0ae\uf0ae\uf0bb\uf0bc\uf020\uf0bf\uf0ac\uf020\uf0b0\uf0d8\uf020\uf0e9\uf0f2\uf020\uf0ce\uf0bb\uf0ad\uf0b7\uf0bc\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ac\uf0b7\uf0b3\uf0bb\uf020\uf0bf\uf0b4\uf0ad\uf0b1\uf020\uf0bc\uf0b7\uf0ae\uf0bb\uf0bd\uf0ac\uf0b4\uf0a7\uf020\uf0ae\uf0bb\uf0b4\uf0bf\uf0ac\uf0bb\uf0ad\uf020\n\n\n\n\uf0ac\uf0b1\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ae\uf0bb\uf0bf\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf0ad\uf020\uf0be\uf0bb\uf0ac\uf0a9\uf0bb\uf0bb\uf0b2\uf020\uf0b3\uf0bb\uf0ac\uf0bf\uf0b4\uf020\uf0b7\uf0b1\uf0b2\uf0ad\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0f2\uf020\uf0cd\uf0ac\uf0ab\uf0bc\uf0b7\uf0bb\uf0ad\uf020\uf0bc\uf0b1\uf0b2\uf0bb\uf020\uf0be\uf0a7\uf020\uf0b0\uf0ae\uf0bb\uf0aa\uf0b7\uf0b1\uf0ab\uf0ad\uf020\uf0ae\uf0bb\uf0ad\uf0bb\uf0bf\uf0ae\uf0bd\uf0b8\uf0bb\uf0ae\uf0ad\uf020\n\n\n\n\n\n\n\n\n\uf0ef\uf0ef\uf0e7\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf020\uf020\uf020\n\n\n\n\uf0ef\uf0f2\uf0e9\uf0ec\n\n\n\n\uf0ed\uf0f2\uf0f0\uf0e7\n\n\n\n\uf0ee\uf0f2\uf0ef\uf0ec\n\n\n\n\uf0ea\uf0f2\uf0ee\uf0ef\n\n\n\n\uf0e8\uf0ea\uf0f2\uf0e8\uf0ef\n\n\n\n\uf020 \uf020\uf020\uf020\uf020\n\n\n\n\uf0ef\uf0f2\uf0e8\uf0e9\n\n\n\n\uf0ed\uf0f2\uf0ee\uf0e7\n\n\n\n\uf0ee\uf0f2\uf0ed\uf0ec\n\n\n\n\uf0ec\uf0f2\uf0ea\uf0ee\n\n\n\n\uf0e8\uf0e9\uf0f2\uf0e8\uf0e9\n\n\n\n\uf020\n\uf0dd\uf0b1\uf0b0\uf0b0\uf0bb\uf0ae\uf020\uf0f8\uf0bd\uf0ab\uf0b4\uf0ac\uf0b7\uf0aa\uf0bf\uf0ac\uf0bb\uf0bc\uf0f7\uf020 \uf0dd\uf0b1\uf0b0\uf0b0\uf0bb\uf0ae\uf020\uf0f8\uf0be\uf0bf\uf0bd\uf0b5\uf0b9\uf0ae\uf0b1\uf0ab\uf0b2\uf0bc\uf0f7 \uf020\n\n\n\n\uf020\n\n\n\n\uf020\uf0fb\uf0da\uf0ef\uf020\n\uf0db\uf0a8\uf0bd\uf0b8\uf0bf\uf0b2\uf0b9\uf0bb\uf0bf\uf0be\uf0b4\uf0bb\uf020\n\n\n\n\uf0fb\uf0da\uf0ee\uf020\n\uf0dd\uf0bf\uf0ae\uf0be\uf0b1\uf0b2\uf0bf\uf0ac\uf0bb\uf020\n\n\n\n\uf020\uf020\uf0fb\uf0da\uf0ed\uf020\n\uf0da\uf0bb\uf0f3\uf0d3\uf0b2\uf020\uf0b1\uf0a8\uf0b7\uf0bc\uf0bb\uf020\n\n\n\n\uf020\uf020\uf0fb\uf0da\uf0ec\uf020\n\uf0d1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\n\n\n\n\uf020\uf0fb\uf0da\uf0eb\uf020\n\uf0ce\uf0bb\uf0ad\uf0b7\uf0bc\uf0ab\uf0bf\uf0b4\uf020\n\n\n\n\uf020\n\n\n\nFig.  1: Copper Fractions in Cultivated and Background Soil Expressed as \n                         Percentage of Sum of all Fractions (%)       \n              \n\n\n\n\uf020\n\uf020\n\uf020\n\n\n\n\uf020\uf020\uf020\uf020\n\n\n\n\uf0ef\uf0ea\uf0f2\uf0ea\uf0ef\n\n\n\n\uf0ec\uf0f2\uf0e8\uf0e7\n\n\n\n\uf0e8\uf0f2\uf0ee\uf0ec\n\n\n\n\uf0ef\uf0e9\uf0f2\uf0ec\uf0ec\n\n\n\n\uf0eb\uf0ee\uf0f2\uf0e8\uf0ee\n\n\n\n\uf020 \uf020\uf020\uf020\uf020\uf020\uf020\n\n\n\n\uf0ef\uf0ed\uf0f2\uf0eb\uf0e7\n\n\n\n\uf0e9\uf0f2\uf0e8\uf0e9\n\n\n\n\uf0ef\uf0ee\uf0f2\uf0e9\uf0e9\n\n\n\n\uf0ef\uf0ec\uf0f2\uf0e7\uf0ed\n\n\n\n\uf0eb\uf0f0\uf0f2\uf0e8\uf0ec\n\n\n\n\uf020\n\uf0d4\uf0bb\uf0bf\uf0bc\uf020\uf0f8\uf0bd\uf0ab\uf0b4\uf0ac\uf0b7\uf0aa\uf0bf\uf0ac\uf0bb\uf0bc\uf0f7\uf020 \uf0d4\uf0bb\uf0bf\uf0bc\uf020\uf0f8\uf0be\uf0bf\uf0bd\uf0b5\uf0b9\uf0ae\uf0b1\uf0ab\uf0b2\uf0bc\uf0f7 \uf020\n\n\n\n\uf020\n\n\n\n\uf020\uf0fb\uf0da\uf0ef\uf020\n\uf0db\uf0a8\uf0bd\uf0b8\uf0bf\uf0b2\uf0b9\uf0bb\uf0bf\uf0be\uf0b4\uf0bb\uf020\n\n\n\n\uf0fb\uf0da\uf0ee\uf020\n\uf0dd\uf0bf\uf0ae\uf0be\uf0b1\uf0b2\uf0bf\uf0ac\uf0bb\uf020\n\n\n\n\uf020\uf020\uf0fb\uf0da\uf0ed\uf020\n\uf0da\uf0bb\uf0f3\uf0d3\uf0b2\uf020\uf0b1\uf0a8\uf0b7\uf0bc\uf0bb\uf020\n\n\n\n\uf020\uf020\uf0fb\uf0da\uf0ec\uf020\n\uf0d1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\n\n\n\n\uf020\uf0fb\uf0da\uf0eb\uf020\n\uf0ce\uf0bb\uf0ad\uf0b7\uf0bc\uf0ab\uf0bf\uf0b4\uf020\n\n\n\n \nFig.  2: Lead Fractions in Cultivated and Background Soil Expressed as  \n                         Percentage of Sum of all Fractions (%)       \n \n\n\n\n\n\n\n\n\n\uf0ef\uf0ee\uf0f0 \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf020\uf020\uf020\uf020\uf020\n\n\n\n\uf0ec\uf0f2\uf0ed\uf0e7\n\n\n\n\uf0eb\uf0f2\uf0ee\uf0eb\n\n\n\n\uf0ed\uf0f2\uf0ef\uf0e8\n\n\n\n\uf0f0\uf0f2\uf0ea\uf0e8\n\uf0e8\uf0ea\uf0f2\uf0eb\uf0ef\n\n\n\n\uf020 \uf020\uf020\uf020\uf020\uf020\n\n\n\n\uf0ec\uf0f2\uf0f0\uf0e7\n\n\n\n\uf0ec\uf0f2\uf0e8\uf0e7\n\n\n\n\uf0ed\uf0f2\uf0ef\uf0ec\n\n\n\n\uf0f0\uf0f2\uf0e9\uf0e8\n\n\n\n\uf0e8\uf0e9\uf0f2\uf0ef\uf0f0\n\n\n\n\uf020\n\n\n\n\uf0c6\uf0b7\uf0b2\uf0bd\uf020\uf0f8\uf0bd\uf0ab\uf0b4\uf0ac\uf0b7\uf0aa\uf0bf\uf0ac\uf0bb\uf0bc\uf0f7\uf020 \uf0c6\uf0b7\uf0b2\uf0bd\uf020\uf0f8\uf0be\uf0bf\uf0bd\uf0b5\uf0b9\uf0ae\uf0b1\uf0ab\uf0b2\uf0bc\uf0f7 \uf020\n\n\n\n\uf020\n\n\n\n\uf020\uf0fb\uf0da\uf0ef\uf020\n\uf0db\uf0a8\uf0bd\uf0b8\uf0bf\uf0b2\uf0b9\uf0bb\uf0bf\uf0be\uf0b4\uf0bb\uf020\n\n\n\n\uf0fb\uf0da\uf0ee\uf020\n\uf0dd\uf0bf\uf0ae\uf0be\uf0b1\uf0b2\uf0bf\uf0ac\uf0bb\uf020\n\n\n\n\uf020\uf020\uf0fb\uf0da\uf0ed\uf020\n\uf0da\uf0bb\uf0f3\uf0d3\uf0b2\uf020\uf0b1\uf0a8\uf0b7\uf0bc\uf0bb\uf020\n\n\n\n\uf020\uf020\uf0fb\uf0da\uf0ec\uf020\n\uf0d1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\n\n\n\n\uf020\uf0fb\uf0da\uf0eb\uf020\n\uf0ce\uf0bb\uf0ad\uf0b7\uf0bc\uf0ab\uf0bf\uf0b4\uf020\n\n\n\n\uf020\n\n\n\nFig.  3: Zinc Fractions in Cultivated and Background Soil Expressed as \n                         Percentage of Sum of all Fractions (%)   \n\n\n\n\uf020\n\n\n\n\uf020\n\n\n\n\uf020\n \n\n\n\n\uf020\uf020\uf020\uf020\n\n\n\n\uf0ec\uf0f2\uf0ea\uf0e9\n\n\n\n\uf0ea\uf0f2\uf0ee\uf0ed\n\n\n\n\uf0ed\uf0f2\uf0e7\uf0ea\n\n\n\n\uf0ee\uf0f2\uf0e8\uf0ee\n\n\n\n\uf0e8\uf0ee\uf0f2\uf0ed\uf0ef\n\n\n\n\uf020\n\uf020\uf020\uf020\uf020\n\n\n\n\uf0eb\uf0f2\uf0f0\uf0ec\n\n\n\n\uf0e9\uf0f2\uf0e9\uf0ef\n\n\n\n\uf0ec\uf0f2\uf0eb\uf0ec\n\n\n\n\uf0ed\uf0f2\uf0ef\uf0e8\n\n\n\n\uf0e9\uf0e7\uf0f2\uf0eb\uf0ed\n\n\n\n\uf020\n\n\n\n\uf0d2\uf0b7\uf0bd\uf0b5\uf0bb\uf0b4\uf020\uf0f8\uf0bd\uf0ab\uf0b4\uf0ac\uf0b7\uf0aa\uf0bf\uf0ac\uf0bb\uf0bc\uf0f7\uf020 \uf0d2\uf0b7\uf0bd\uf0b5\uf0bb\uf0b4\uf020\uf0f8\uf0be\uf0bf\uf0bd\uf0b5\uf0b9\uf0ae\uf0b1\uf0ab\uf0b2\uf0bc\uf0f7 \uf020\n\n\n\n\uf020\n\n\n\n\uf020\uf0fb\uf0da\uf0ef\uf020\n\uf0db\uf0a8\uf0bd\uf0b8\uf0bf\uf0b2\uf0b9\uf0bb\uf0bf\uf0be\uf0b4\uf0bb\uf020\n\n\n\n\uf0fb\uf0da\uf0ee\uf020\n\uf0dd\uf0bf\uf0ae\uf0be\uf0b1\uf0b2\uf0bf\uf0ac\uf0bb\uf020\n\n\n\n\uf020\uf020\uf0fb\uf0da\uf0ed\uf020\n\uf0da\uf0bb\uf0f3\uf0d3\uf0b2\uf020\uf0b1\uf0a8\uf0b7\uf0bc\uf0bb\uf020\n\n\n\n\uf020\uf020\uf0fb\uf0da\uf0ec\uf020\n\uf0d1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\n\n\n\n\uf020\uf0fb\uf0da\uf0eb\uf020\n\uf0ce\uf0bb\uf0ad\uf0b7\uf0bc\uf0ab\uf0bf\uf0b4\uf020\n\n\n\n\uf020\n\n\n\nFig. 4: Nickel Fractions in Cultivated and Background Soil Expressed as \n                        Percentage of Sum of all Fractions (%)       \n\n\n\n\n\n\n\n\n\uf0ef\uf0ee\uf0ef\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0b7\uf0b2\uf0bc\uf0b7\uf0bd\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0ac\uf0b8\uf0bf\uf0ac\uf020\uf0b8\uf0bb\uf0bf\uf0aa\uf0a7\uf020\uf0b3\uf0bb\uf0ac\uf0bf\uf0b4\uf0ad\uf020 \uf0b7\uf0b2\uf020\uf0da\uf0bb\uf0f3\uf0d3\uf0b2\uf020\uf0b1\uf0a8\uf0b7\uf0bc\uf0bb\uf0ad\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0bd\uf0b1\uf0b3\uf0b0\uf0b1\uf0ab\uf0b2\uf0bc\uf0ad\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0aa\uf0bb\uf0ae\uf0a7\uf020\n\n\n\n\uf0b4\uf0b1\uf0a9\uf020\uf0b7\uf0b2\uf020\uf0ad\uf0b1\uf0b4\uf0ab\uf0be\uf0b7\uf0b4\uf0b7\uf0ac\uf0a7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b8\uf0b7\uf0b9\uf0b8\uf020\uf0ad\uf0ac\uf0bf\uf0be\uf0b7\uf0b4\uf0b7\uf0ac\uf0a7\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0be\uf0b7\uf0b1\uf0b4\uf0b1\uf0b9\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0bf\uf0bd\uf0ac\uf0b7\uf0aa\uf0b7\uf0ac\uf0a7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0a9\uf0b1\uf0ab\uf0b4\uf0bc\uf020\uf0b2\uf0b1\uf0ac\uf020\uf0b8\uf0bf\uf0aa\uf0bb\uf020\uf0bc\uf0b7\uf0ae\uf0bb\uf0bd\uf0ac\uf020\n\n\n\n\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0ba\uf0b1\uf0ab\uf0b2\uf0bc\uf020\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ae\uf0bb\uf0ad\uf0b7\uf0bc\uf0ab\uf0bf\uf0b4\uf020\uf0ba\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf0ad\uf0f2\uf020\n\n\n\n\uf0bd\uf0b1\uf0b2\uf0ad\uf0b7\uf0bc\uf0bb\uf0ae\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\uf0be\uf0bb\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ad\uf0ac\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0ba\uf0b1\uf0ae\uf0b3\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ac\uf0b8\uf0ab\uf0ad\uf020\uf0b2\uf0b1\uf0ac\uf020\uf0bf\uf0aa\uf0bf\uf0b7\uf0b4\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0ac\uf0b1\uf020\uf0b0\uf0b4\uf0bf\uf0b2\uf0ac\uf0ad\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0bf\uf020\uf0ae\uf0bb\uf0bf\uf0ad\uf0b1\uf0b2\uf0bf\uf0be\uf0b4\uf0bb\uf020\n\n\n\n\uf0d2\uf0b7\uf0f2\n\n\n\n\uf0dd\uf0d1\uf0d2\uf0dd\uf0d4\uf0cb\uf0cd\uf0d7\uf0d1\uf0d2\n\n\n\n\uf0cc\uf0b1\uf0ac\uf0bf\uf0b4\uf020\uf0b8\uf0bb\uf0bf\uf0aa\uf0a7\uf020\uf0b3\uf0bb\uf0ac\uf0bf\uf0b4\uf020\uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0ad\uf020\uf0b7\uf0b2\uf020\uf0bd\uf0ab\uf0b4\uf0ac\uf0b7\uf0aa\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0cb\uf0b4\uf0ac\uf0b7\uf0ad\uf0b1\uf0b4\uf0ad\uf020\uf020\uf0ba\uf0bf\uf0b4\uf0b4\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ac\uf0a7\uf0b0\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0ae\uf0bf\uf0b2\uf0b9\uf0bb\uf020\n\n\n\n\uf0bb\uf0a8\uf0ac\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf020 \uf0b0\uf0ae\uf0b1\uf0bd\uf0bb\uf0bc\uf0ab\uf0ae\uf0bb\uf020 \uf0ad\uf0b8\uf0b1\uf0a9\uf0bb\uf0bc\uf020 \uf0ac\uf0b8\uf0bf\uf0ac\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0b9\uf0bb\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf020 \uf0ac\uf0ae\uf0bb\uf0b2\uf0bc\uf020 \uf0b7\uf0b2\uf020 \uf0cb\uf0b4\uf0ac\uf0b7\uf0ad\uf0b1\uf0b4\uf0ad\uf020 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"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: nurulain@mardi.gov.my\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 171-193 (2021)                          Malaysian Society of Soil Science\n\n\n\nIndirect Estimation of Agricultural Nitrous Oxide Emission \nin Malaysia \n\n\n\nNurul Ain A.B.1*, Mohammad Hariz, A.R.1, Shaidatul Azdawiyah, \nA.T.1, Azizi A.A, Mardhati, M.2, Mohd Fairuz, M.S.3, \n\n\n\nMohd Saufi, B.2, and Fauzi J.1\n\n\n\n1Agrobiodiversity and Environmental Research Center\n2Animal Science Research Center\n3Paddy and Rice Research Centre\n\n\n\nMalaysian Agricultural Research and Development Institute (MARDI),\nSerdang, Selangor\n\n\n\nABSTRACT\nAgricultural activities cause high nitrous oxide (N2O) emissions through nitrification \nand denitrification processes. Nitrous oxide has a global warming potential of \napproximately 298 times higher than carbon dioxide on a 100-year time scale. The \nexcess of N2O gas causes ozone layer depletion, leading to increased UV radiation \nof the earth\u2019s surface. The estimation of N2O can provide a basis for developing \npotential mitigation strategies as it is the most significant contributor to greenhouse \ngas emissions. This research aimed to estimate N2O emission from the agricultural \nsector caused by anthropogenic activities in Malaysia from 1994 to 2014. The \ninventory was prepared and calculated using the IPCC Tier 1 methodology. Input \ndata were collected from national data inventories, literature research, surveys, \nand expert judgement reports. An increasing trend of N2O emissions, ranging from \n4.9 to 12 kt, was observed due to agricultural activities. The increment was mainly \nassociated with synthetic fertiliser use due to the expansion in oil palm cultivation \nacreage. Synthetic fertiliser consumption contributed to 78% of these emissions, \nfollowed by crop residue application to the soil (13%) and organic amendments \n(9%). The increased trend in emission and contribution from fertiliser  input \nindicate that appropriate mitigation strategies are needed since it is the largest \nanthropogenic activity contributor to N2O emissions.\n\n\n\nKeyword:    greenhouse gases, nitrous oxide, fertilisers, crop residue.\n\n\n\nINTRODUCTION\nNitrous oxide (N2O) is one of the main contributors to greenhouse gas emissions \nfrom agricultural sectors, which in turn contribute to global warming and various \nenvironmental issues. Nitrogen dioxide is produced when nitrogen (N) input is \nadded to soils (Stehfest and Bouwman 2006). The nitrogen undergoes nitrification \nand denitrification processes with the latter releasing N2O gas to the atmosphere. \nNitrous oxide emissions produced from these activities could either be direct \nor indirect. Direct N2O emission occurs due to N input to the soil. In contrast, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021172\n\n\n\nindirect N2O emission occurs when the N molecules move from the area where \nthey are deposited to other locations by volatilisation and leaching/run-off (IPCC \nGuidelines for National Greenhouse Gas Inventories 2006).\n Agricultural activities inevitably result in N2O production mainly due to \nthe application of synthetic fertilisers, crop residue and organic N to the soil and N \nsources from animal waste. The use of fertilisers, especially chemical fertilisers, is \ncrucial for the production and maintenance of better-quality industrial crops (such \nas oil palm and paddy) and to improve crop yields in the agricultural sector (Lai \net al. 2019). In 2014, oil palm cultivation covered 5,000,000 hectares (ha) while \npaddy cultivation was estimated at 300,000 ha (MESTECC 2018). To sustain the \ngrowth of these industrial and food crops, a large amount of fertilisers is applied.\n The oil palm plantation industry and paddy cultivation generate an abundant \namount of biomass residue. Palm oil biomass includes empty fruit bunches (EFB), \nmesocarp fibres (MF), palm shells (PS), oil palm fronds (OPF), and oil palm \ntrunks (OPT). Meanwhile, paddy plantation generates paddy straw, rice husk and \nrice bran as biomass. The N available from paddy and oil palm biomass that is \nreturned to the soil was also considered for N2O emissions estimation in our study \n(IPCC Guidelines for National Greenhouse Gas Inventories 2006).\n Other sources of N2O production from agricultural soils are N from organic \ninputs applied as fertiliser and N in urine and manure deposited by grazing animals \non pasture. Projection by the Food and Agriculture Organization Corporate \nStatistical Database (FAOSTAT) suggests that the national cattle (dairy and non-\ndairy) and poultry population will increase by 15% and 5%, respectively in 2030 \nand 19% and 15%, respectively by 2050 (FAO 2007). The growing population \nwill contribute to the increment of the production of N2O since N sources from \nanimal manure will be incorporated into the soils as organic N. \n As a signatory of UNFCCC, Malaysia is obligated to account for \nanthropogenic emissions and removals corresponding to its nationally determined \ncontributions under the Paris Agreement. The agricultural sector contributes \nsignificant amounts of carbon dioxide (CO2), methane (CH4), and nitrous oxide \n(N2O) to the atmosphere (Smith et al. 2007). According to the Third National \nCommunication (NC3) and Second Biennial Update Report (BUR2) of Malaysia \nto the UNFCCC in 2018, direct N2O emission from the soil is the critical category \nand one of the main GHG contributors in the agricultural sector. \n The objective of this study was to estimate N2O emissions from Malaysian \nagriculture soils. Activity data were collected to observe N2O emissions from 1995 \nto 2014 on a yearly basis. Malaysia\u2019s greenhouse gas emission from 1995 to 2014 \n(19 years) were estimated to determine the contribution of N2O emissions from \nagricultural practices and the trend in these two decades. This emissions inventory \ncan be used to assess the impact of specific human activities and the primary \nsources responsible for such emissions besides developing and evaluating the \nresults of particular mitigation strategies (Winiwarter et al. 2009). The calculation \nand analysis of the data presented in this study constitute primary data collected \nfor Malaysia  under the Biennial Update Report for the UNFCCC.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 173\n\n\n\n \nMETHODOLOGY\n\n\n\nMalaysia\u2019s N2O emissions from agricultural practices were estimated based on the \nmethodology of IPCC. Tier 1 method was used since country-specific emissions \nfactor (EF) was not available. Two types of EF values accounted for these \nactivities: EF1 and EF3 (Table 1).\n\n\n\n EF1 is EF values used to estimate N2O emissions from various sources, \nwhich in this study includes synthetic fertiliser application, organic N application \n\n\n\n4 \n \n\n\n\nMETHODOLOGY \n\n\n\nMalaysia\u2019s N2O emissions from agricultural practices were estimated based on the methodology \n\n\n\nof IPCC. Tier 1 method was used since country-specific emissions factor (EF) was not available. \n\n\n\nTwo types of EF values accounted for these activities: EF1 and EF3 (Table 1). \n\n\n\n \nTABLE 1 \n\n\n\nCalculation of emissions factor values by activity for estimation of N2O emissions  \n \n\n\n\nType of emission \nfactor used in \n\n\n\ncalculating direct \nnitrous oxide \n\n\n\nemissions \n\n\n\nEmission factor \nvalue used \n\n\n\nUncertainty range Reference \n\n\n\nEF1 for N additions \nfrom mineral \nfertilisers, organic \namendments, and \ncrop residue \n\n\n\n0.01 \n 0.003 - 0.03 Stehfest and \n\n\n\nBouwman (2006) \n\n\n\nEF1 for flooded rice \nfields (FR) \n \n\n\n\n0.003 0.000 - 0.006 Akiyama et al. \n(2005) \n\n\n\nEF3 for cattle, swine, \nand chicken  \n \n\n\n\n0.02 0.007 - 0.06 \nde Klein (2004) \n\n\n\nEF3 for sheep and \ngoat  0.01 0.003 - 0.03 \n\n\n\n\n\n\n\nEF1 is EF values used to estimate N2O emissions from various sources, which in this study \n\n\n\nincludes synthetic fertiliser application, organic N application to soils, and crop residue, \n\n\n\ndepending on the type of soils (managed or flooded rice soils, Table 1). EF3 is EF values used to \n\n\n\nestimate N2O emissions from urine and dung N deposited by grazing animals on pastures, \n\n\n\nranges, and paddocks, depending on the type of animals (Table 1, IPCC Guidelines for National \n\n\n\nGreenhouse Gas Inventories 2006). \n\n\n\nThe N2O emissions were assumed to occur in the year N was added to the soil. All input data for \n\n\n\nthe estimation of N2O emissions were collected from data reported in surveys, expert judgement \n\n\n\nreports, national depository, and technical articles following the good practice guidance of the \n\n\n\nGHG inventory. Details on the data sources are presented in each section of soil emissions. \n\n\n\nTABLE 1\nCalculation of emissions factor values by activity for estimation of N2O emissions \n\n\n\nto soils, and crop residue, depending on the type of soils (managed or flooded rice \nsoils, Table 1). EF3 is EF values used to estimate N2O emissions from urine and \ndung N deposited by grazing animals on pastures, ranges, and paddocks, depending \non the type of animals (Table 1, IPCC Guidelines for National Greenhouse Gas \nInventories 2006).\n The N2O emissions were assumed to occur in the year N was added to the \nsoil. All input data for the estimation of N2O emissions were collected from data \nreported in surveys, expert judgement reports, national depository, and technical \narticles following the good practice guidance of the GHG inventory. Details on \nthe data sources are presented in each section of soil emissions. Figure 1 illustrates \nthe data source of direct N2O from soil. Default EF values from IPCC Emission \nFactor Database (EFDB) or 2006 IPCC Guidelines were used for all activity data \ncollection. However, country-specific EFs were also adopted for some manure \nmanagement systems based on animal types linked to the emissions from the \norganic amendment. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021174\n\n\n\nFigure 1. Activity data source for estimation of direct N2O emissions \n\n\n\nParameters Used to Estimate Nitrous Oxide (N2O) Emissions \nEmissions from synthetic fertilisers\nFertilisers are the major input applied to soil. Fertilisers are categorised into \ntwo clusters - synthetic and organic. To estimate the emissions, only synthetic \nfertilisers were considered due to its massive usage in agriculture compared to \norganic fertilisers. Synthetic N fertilisers applied to soil were calculated using the \ntotal amount of synthetic fertilisers utilised annually. The calculation was based \non the equation below:\n\n\n\n Consumption  = Production + Import \u2013 Export of Total N\n\n\n\nFertiliser data were collected from the official country statistics, recorded as \ndomestic, import, and export synthetic fertilisers. Urea is the only fertiliser \nproduced in Malaysia, and data were obtained from the Food and Agriculture \nOrganisation of the United Nations (FAO 2007). Import and export data on \nfertilisers under harmonised systems code were categorised into different groups \n(Table 2). The balance of the production, import and export data was summed up \nas fertiliser consumptions (not shown). \n The conversion factor used to calculate N2O emissions is shown in Table \n2 which represents N content availability (FAO 2007). The N2O emissions were \nexpressed as the nitrogen content percentages from each fertiliser multiplied by \nfertiliser application to soil. Table 3 gives an example of a data worksheet to \ncalculate N fertiliser consumption in 2014.\n A separate calculation was done for fertiliser applied in paddy fields due \nto the differences in EF values which was 0.3% default value for flooded rice \nfields. In contrast, a value of 1% was used for managed soils. In this calculation, \nmanaged soils were accounted for by oil palm and other areas under agriculture.\n\n\n\n5 \n \n\n\n\nFigure 1 illustrates the data source of direct N2O from soil. Default EF values from IPCC \n\n\n\nEmission Factor Database (EFDB) or 2006 IPCC Guidelines were used for all activity data \n\n\n\ncollection. However, country-specific EFs were also adopted for some manure management \n\n\n\nsystems based on animal types linked to the emissions from the organic amendment.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1. Activity data source for estimation of direct N2O emissions  \n\n\n\n\n\n\n\n\n\n\n\nDirect N2O \nemissions \n\n\n\nSynthetic \nfertiliser \n\n\n\napplication \non soil \n\n\n\nOrganic \namendments \n(from animal \ndung) to soil \n\n\n\n \nCrop residue \napplied on \n\n\n\nsoil \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 175\n\n\n\nTABLE 2 \nTypes of synthetic fertiliser and N conversion factors\n\n\n\n7 \n \n\n\n\nTABLE 2  \nTypes of synthetic fertiliser and N conversion factors \n\n\n\nType of synthetic fertiliser Conversion factor \n\n\n\nAmmonium nitrate 33% N \n\n\n\nAmmonium sulphate 21% N \n\n\n\nCalcium ammonium nitrate 21% N \n\n\n\nUrea 46% N \n\n\n\nUrea and ammonium nitrate \n\n\n\nsolutions \n\n\n\n32% N \n\n\n\nDiammonium phosphate (DAP) 18% N \n\n\n\nMonoammonium phosphate \n\n\n\n(MAP) \n\n\n\n11% N \n\n\n\nOther NP compounds 20% N \n\n\n\nNPK complex 15% N \n\n\n\nNPK blends 15% N \n\n\n\nPotassium nitrate 13% N \n\n\n\nSource: FAO (2007)  \n\n\n\n The annual amount of animal manure was determined by adjusting the \namount of manure N available (NMMSAvb) with the fraction of animal manure used \nfor feed (FracFEED). Only the data mentioned above were used for the calculation \nof FAM and was summarised as follows:\n\n\n\n FAM  = NMMSAvb  \u00d7 0.4\n\n\n\n The calculation for NMMSAvb falls under livestock sector emissions. This \ninformation is available in Chapter 10 on emission from livestock and manure \nmanagement of the IPCC Guidelines for National Greenhouse Gas Inventories \n(2006). Animal population numbers are needed to calculate NMMSAvb (Table 4). \nAnimal population data were verified by the Department of Veterinary Services, \nMalaysia.\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021176\n\n\n\nTABLE 3\nA sample data worksheet to calculate N fertiliser consumption in 2014\n\n\n\n8 \n \n\n\n\nTABLE 3 \nA sample data worksheet to calculate N fertiliser consumption in 2014 \n\n\n\n\n\n\n\nTypes of fertiliser A: \nConsumption B: N % kg N (AxB) Item codes* \n\n\n\nAmmonium nitrate 39,033,621.60 0.33 12,881,095.13 2814 \n\n\n\nAmmonium sulphate 792,224,772.86 0.21 166,367,202.30 310230 \n\n\n\nCalcium ammonium nitrate 7,287,426.00 0.21 1,530,359.46 310221 \n\n\n\nDiammonium phosphate \n(DAP) 79,768,765.12 0.18 14,358,377.72 310240 \n\n\n\nMonoammonium phosphate \n(MAP) 49,854,413.66 0.11 5,483,985.50 310530 \n\n\n\nNPK complex  0.15 - 310540 \n\n\n\nNPK complex <=10kg 12,949,120.99 0.15 1,942,368.15 310610 \n\n\n\nNPK complex >10kg 109,393,895.01 0.15 16,409,084.25 310510 \n\n\n\nOther N and phosphate \ncompounds 7,600,800.00 0.2 1,520,160.00 310520 \n\n\n\nOther N and phosphorus \ncompounds 154,598,564.74 0.2 30,919,712.95 310551 \n\n\n\nUrea 784,445,695.89 0.46 360,845,020.11 310210 \n\n\n\nUrea and ammonium nitrate \nsolutions 26529.8 0.32 8,489.54 310280 \n\n\n\nTotal   612,265,855.11  \nNote: Data on N from fertiliser application will be used to estimate the N2O released. \n\n\n\n*Harmonised systems code for each type of fertiliser \n\n\n\n\n\n\n\nA separate calculation was done for fertiliser applied in paddy fields due to the differences in EF \n\n\n\nvalues which was 0.3% default value for flooded rice fields. In contrast, a value of 1% was used \n\n\n\nfor managed soils. In this calculation, managed soils were accounted for by oil palm and other \n\n\n\nareas under agriculture. \n\n\n\n\n\n\n\n\n\n\n\nEmissions from organic amendments\nEmissions from organic amendments were estimated based on three sources: \nthe N percentage of the total amount of animal manure applied to the soil, N \npercentage from total sewage applied to the soil, and the N percentage from \ncompost applied to the soil. However, since the total sewage and compost applied \nto soil were insignificant, only animal manure was taken as an organic amendment \nfor calculation purposes. EF of 1% N in the form of N2O emissions from the \norganic amendment was used. The equation for N from animal manure applied to \nsoil was as follows: \n\n\n\n FAM  = NMMSAvb  \u00d7 [1 - (FracFEED  + FracFUEL  + FracCNST )]  \n\n\n\nwhere  \nFAM = annual amount of animal manure N applied to soils\nNMMSAvb = amount of managed manure N available for soil application, feed, \n     fuel, or construction\nFracFEED = fraction of managed manure used for feed\nFracFUEL = fraction of managed manure used for fuel\nFracCNST = fraction of managed manure used for construction\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 177\n\n\n\nTABLE 4\nTotal population of livestock\n\n\n\n10 \n \n\n\n\nTABLE 4 \nTotal population of livestock \n\n\n\nYear Chicken Swine \nDairy \n\n\n\ncattle \n\n\n\nBeef \n\n\n\ncattle \nSheep Goats \n\n\n\n1995 83,196,223 3,110,440 35,626 676,898 219,337 280,398 \n\n\n\n1996 105,981,241 3,089,820 34,993 664,865 192,136 259,321 \n\n\n\n1997 117,537,784 2,983,735 34,977 664,567 173,531 244,034 \n\n\n\n1998 123,521,566 2,600,326 35,333 671,336 161,606 238,397 \n\n\n\n1999 119,285,674 2,184,893 36,052 684,996 154,221 237,014 \n\n\n\n2000 123,596,784 1,894,438 44,339 689,553 145,257 237,113 \n\n\n\n2001 148,990,824 1,972,532 34,666 707,505 129,108 247,338 \n\n\n\n2002 170,395,132 2,047,176 34,805 679,349 125,836 233,017 \n\n\n\n2003 183,345,888 2,070,686 34,734 717,766 115,131 246,977 \n\n\n\n2004 191,655,949 2,110,847 35,517 751,867 115,498 264,394 \n\n\n\n2005 174,694,165 2,035,647 34,592 755,473 115,922 287,670 \n\n\n\n2006 179,226,276 2,029,119 38,675 747,526 116,387 349,427 \n\n\n\n2007 188,383,841 2,020,117 38,813 803,373 125,988 428,263 \n\n\n\n2008 198,924,820 1,988,889 36,950 814,277 131,278 477,480 \n\n\n\n2009 201,967,963 1,831,308 41,368 819,123 136,285 514,223 \n\n\n\n2010 217,227,467 1,931,207 43,821 793,038 123,475 498,385 \n\n\n\n2011 229,142,007 1,816,557 44,330 724,380 126,412 479,444 \n\n\n\n2012 251,157,340 1,851,842 42,870 701,507 131,923 462,510 \n\n\n\n2013 272,451,321 1,842,953 42,976 708,521 141,918 434,202 \n\n\n\n2014 288,304,256 1,844,103 44,567 702,216 142,435 429,398 \n\n\n\nSource: Livestock Statistics (for various years) (Department of Veterinary Services, Malaysia) \n\n\n\n  Emissions from crop residue\nNitrogen added to the soil from crop residue was estimated using a combination of \ndata from IPCC defaults and country-specific data. The procedure for estimation \nfollowed the equation provided in the Tier 1 method of the IPCC Guidelines \nfor National Greenhouse Gas Inventories (2006). As both oil palm and paddy \ncultivation occupy large areas in the country, only the residues of these crops in \nsoil were calculated. Calculation of paddy residue was as follows:\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021178\n\n\n\nThe annual amount of N in crop residues (FCR) =\n \n\u2211{ Crop(T)  \u00d7 FracRenew(T)  \u00d7 [(Area(T)  - Area burnt(T)\u00d7 Cf) \u00d7 RAG(T)  \u00d7 NAG(T)  \u00d7 (1- \nFRACRemove(T)) + Area(T)  \u00d7 RBG(T)  \u00d7 NBG(T)]}\n\n\n\nwhere \nCrop(T) = harvested annual dry matter yield for paddy, kilogram dry matter/\n                 hectare\nArea(T) =   total annual area harvested of paddy, hectare/year\nArea burnt(T) = annual area of paddy burnt, hectare/year\nCf               =  combustion factor \nFracRenew(T) = fraction of total area under paddy that is renewed annually\nRAG(T)         = ratio of above-ground residue dry matter (AGDM(T) to harvested \n                    yield for paddy kilogram dry matter/hectare\nNAG(T)        = N content of above-ground residue dry matter for paddy, kilogram N\nFRACRemove(T) = fraction of above-ground residue of paddy crop removed annually \n                        for purposes such as feed, bedding, and construction, kilogram N\nRBG(T)           = ratio of below-ground residue to harvested yield for paddy, kilogram \n                     dry matter\nNBG(T)         = N content of below-ground residue for paddy, kilogram N\n\n\n\n The estimation of N added to the soil from rice straw was based on the \ndefault factors from Table 11.2, Chapter 11 in the IPCC Guidelines for National \nGreenhouse Gas Inventories (2006). The percentage of dry matter yield for paddy \nwas 89% (Table 11.2, IPCC 2006). The assumption for area burnt was 10% and \nthe combustion was assumed to be wholly burnt during the land management \nperiod. The overall paddy area was renewed annually. Rice straw incorporated in \nsoil was calculated based on N on the above and below-ground residue. N content \nfor the above and below ground residue ratio was 0.4% and 0.7%, respectively \n(Azza Ebid et al. 2007). Although some of the above-ground paddy residue was \nremoved from the field after harvesting, the amount was insignificant. Therefore, \nit was not included in the calculation.\n Oil palm cultivation produces a huge amount of biomass residue. However, \nN added to the soil was only calculated from EFB, OPT and OPF dry weight as \nthese parts are left on the ground and applied to the soil. The total area under \noil palm cultivation is significant in the calculation of emissions from oil palm \nresidue. OPT and OPF dry weight availability in Malaysia from 1995 to 2014 was \nbased on Ng et al. (2011).\n OPT and OPF biomass from replanting was also included for the calculation \nof N from oil palm residue. N percentages for OPF and OPT was 0.38% (Rahman \net al. 2014) and 0.169% (Loh 2016), respectively. It is estimated that around \n50% of oil palm waste is left for direct decaying on the site (Loh 2016) and was \nconsidered for calculating the emission. In contrast, the remaining 50% removed \nfrom the fields was not considered in the calculation. The value of 0.249% N \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 179\n\n\n\nfor dry weight of the EFB was calculated from the amount of per tonne EFB \nreceived by mills (Loh 2016). Data on EFB were obtained from the Malaysian \nPalm Oil Board (2015). The input of the overall residue from EFB, OPT, and OPF \nin kilogram N was used to estimate N2O emissions from managed soils.\n \n\n\n\nRESULTS AND DISCUSSION\nTotal Nitrous Oxide (N2O) Emissions from the Agricultural Sector\nAntropogenic activities from agriculture contribute to changes in atmospheric N2O \nlevel. N2O emission levels from these activities are estimated in the study. Figure \n2 shows the percentage of N2O emissions from agricultural land. The highest \nN2O emissions were produced from the application of synthetic fertilisers at 78%, \nfollowed by crop residue at 13%, and organic amendments at 9%.  Approximately \nmore than 6 million hectares of land area in the country are classified as \nagricultural land, including industrial and food cropland (Olaniyi et al. 2013). \nN fertiliser is an essential input for plant growth and development (Liu et al. \n2014). The application of synthetic N fertiliser is crucial for maintaining yields \nin the agricultural sector. However, applying excessive amounts of N fertilisers \ncauses severe environmental problems such as air and water pollution and climate \nchange issues. Excess N will be lost to the atmosphere in the form of gas through \nleaching, volatilisation and denitrification process (Tamme et al. 2009). Further \ndetails on N2O emissions are discussed in the next sections.\n Figure 3 shows the production of N2O emissions from 1995 to 2014. The \nlowest and highest N2O emissions from anthropogenic activities were recorded \nin 1996 (4.9 kt) and 2008 (12.0 kt), respectively. Overall, the emissions show an \nincreasing trend over the years. From 1955, as Malaysia began moving towards \nindustrialisation, land use for cultivation of food crops decreased to 16.3% of \nthe total land in Malaysia by the year 2005 (Olaniyi et al. 2013). Nevertheless, \nthe demand for palm oil as a major vegetable oil has continued to grow over the \nyears. Between 2007 to 2010, the high demand for palm oil led to the expansion \nof areas of oil palm plantations, with Sarawak and Riau having a quarter of the \n\n\n\n13 \n \n\n\n\nRESULTS AND DISCUSSION \n\n\n\nTotal Nitrous Oxide (N2O) Emissions from the Agricultural Sector \n\n\n\nAntropogenic activities from agriculture contribute to changes in atmospheric N2O level. N2O \n\n\n\nemission levels from these activities are estimated in the study. Figure 2 shows the percentage of \n\n\n\nN2O emissions from agricultural land. The highest N2O emissions were produced from the \n\n\n\napplication of synthetic fertilisers at 78%, followed by crop residue at 13%, and organic \n\n\n\namendments at 9%.  Approximately more than 6 million hectares of land area in the country are \n\n\n\nclassified as agricultural land, including industrial and food cropland (Olaniyi et al. 2013). N \n\n\n\nfertiliser is an essential input for plant growth and development (Liu et al. 2014). The application \n\n\n\nof synthetic N fertiliser is crucial for maintaining yields in the agricultural sector. However, \n\n\n\napplying excessive amounts of N fertilisers causes severe environmental problems such as air \n\n\n\nand water pollution and climate change issues. Excess N will be lost to the atmosphere in the \n\n\n\nform of gas through leaching, volatilisation and denitrification process (Tamme et al. 2009). \n\n\n\nFurther details on N2O emissions are discussed in the next sections. \n\n\n\n\n\n\n\nFigure 2.  Distribution of N2O emission from agricultural land practices \n\n\n\n\n\n\n\nSynthetic \nfertiliser \n\n\n\n78% \n\n\n\nCrop residue \n13% \n\n\n\nOrganic \namendment \n\n\n\n9% \n\n\n\nFigure 2.  Distribution of N2O emission from agricultural land practices\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021180\n\n\n\n2.1 Mha at a rate of 190,000 ha/year (Miettinen et al. 2012). Fertiliser application \nincreased as the plantation areas increased to supply nutrients for plant growth \nand maximise crop yield and productivity. This has resulted in an increasing trend \nin anthropogenic emissions over the recent decades (Figure 3).\n \nGreenhouse gas emissions from synthetic fertilisers\nSynthetic fertilisers are commonly used in agriculture. The main synthetic fertilisers \nused for crops are urea, ammonium sulphate, calcium ammonium nitrate, nitrogen-\nphosphorus-potassium fertilisers (NPK), and compound fertilisers. In order to \ncalculate N2O emissions, the estimation of synthetic fertilisers used in flooded \nrice fields was done separately from managed soils as management practices are \ndifferent. Thus, their EFs values are different. A total of 8,916 tonnes of N2O per \nyear was produced from the use of synthetic fertilisers in 2014 (Figure 4). This is \nin line with studies of other countries which also reported high N2O emissions due \n\n\n\nFigure 3. Increasing trend of nitrous oxide emission from agricultural activities in \nMalaysia\n\n\n\n14 \n \n\n\n\nFigure 3 shows the production of N2O emissions from 1995 to 2014. The lowest and highest N2O \n\n\n\nemissions from anthropogenic activities were recorded in 1996 (4.9 kt) and 2008 (12.0 kt), \n\n\n\nrespectively. Overall, the emissions show an increasing trend over the years. From 1955, as \n\n\n\nMalaysia began moving towards industrialisation, land use for cultivation of food crops \n\n\n\ndecreased to 16.3% of the total land in Malaysia by the year 2005 (Olaniyi et al. 2013). \n\n\n\nNevertheless, the demand for palm oil as a major vegetable oil has continued to grow over the \n\n\n\nyears. Between 2007 to 2010, the high demand for palm oil led to the expansion of areas of oil \n\n\n\npalm plantations, with Sarawak and Riau having a quarter of the 2.1 Mha at a rate of 190,000 \n\n\n\nha/year (Miettinen et al. 2012). Fertiliser application increased as the plantation areas increased \n\n\n\nto supply nutrients for plant growth and maximise crop yield and productivity. This has resulted \n\n\n\nin an increasing trend in anthropogenic emissions over the recent decades (Figure 3). \n\n\n\n \nFigure 3. Increasing trend of nitrous oxide emission from agricultural activities in Malaysia \n\n\n\n\n\n\n\n0\n\n\n\n2000\n\n\n\n4000\n\n\n\n6000\n\n\n\n8000\n\n\n\n10000\n\n\n\n12000\n\n\n\n14000\n\n\n\n19951996199719981999200020012002200320042005200620072008200920102011201220132014\n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\niss\nio\n\n\n\nn \n(t\n\n\n\non\nne\n\n\n\ns)\n \n\n\n\nYear \n\n\n\nto synthetic fertiliser application. For example, China reported a rapid increase in \nfertiliser application over time with the emission being at 73.7% (Xing and Yan \n1999). A study by Ambarita et al. (2018) in Sumatera Utara Province reported \nthat 3.47% of emissions from the agricultural sector is contributed by the oil palm \nindustry, fertiliser, and livestock sectors. \n Total N2O emissions from synthetic fertiliser application demonstrated \nan increased pattern over the years due to the growth of cultivation area for oil \npalm and paddy (Figure 4). An expansion in area under cultivation inevitably \nleads to increased fertiliser use since fertilisers are needed to improve soil fertility \nand crop productivity (Rahman et al. 2014). As shown in Figure 4, the highest \nemission was recorded in 2008. The increasing trend from 2006 till 2008 might \nbe due to the increase in urea production and a rise in fertiliser price. The cost of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 181\n\n\n\nurea increased to USD 493 t-1 in 2008 compared to USD 309 t-1 in 2007 and USD \n223 t-1 in 2006 (Mohd Arif 2010). According to the Scottish Agricultural College \n(SAC 2008), the increase in biofuel demand boosted agricultural production and \nincreased the need for chemical fertilisers from emerging economies.\n In this study, it was assumed that emission values were from the application \n\n\n\nFigure 4. Total N2O emissions from synthetic fertiliser application for years 1995 to 2014\n\n\n\n15 \n \n\n\n\nGreenhouse gas emissions from synthetic fertilisers \n\n\n\nSynthetic fertilisers are commonly used in agriculture. The main synthetic fertilisers used for \n\n\n\ncrops are urea, ammonium sulphate, calcium ammonium nitrate, nitrogen-phosphorus-potassium \n\n\n\nfertilisers (NPK), and compound fertilisers. In order to calculate N2O emissions, the estimation \n\n\n\nof synthetic fertilisers used in flooded rice fields was done separately from managed soils as \n\n\n\nmanagement practices are different. Thus, their EFs values are different. A total of 8,916 tonnes \n\n\n\nof N2O per year was produced from the use of synthetic fertilisers in 2014 (Figure 4). This is in \n\n\n\nline with studies of other countries which also reported high N2O emissions due to synthetic \n\n\n\nfertiliser application. For example, China reported a rapid increase in fertiliser application over \n\n\n\ntime with the emission being at 73.7% (Xing and Yan 1999). A study by Ambarita et al. (2018) \n\n\n\nin Sumatera Utara Province reported that 3.47% of emissions from the agricultural sector is \n\n\n\ncontributed by the oil palm industry, fertiliser, and livestock sectors. \n\n\n\n\n\n\n\nFigure 4. Total N2O emissions from synthetic fertiliser application for years 1995 to 2014 \n\n\n\n \nTotal N2O emissions from synthetic fertiliser application demonstrated an increased pattern over \n\n\n\nthe years due to the growth of cultivation area for oil palm and paddy (Figure 4). An expansion \n\n\n\nin area under cultivation inevitably leads to increased fertiliser use since fertilisers are needed to \n\n\n\nimprove soil fertility and crop productivity (Rahman et al. 2014). As shown in Figure 4, the \n\n\n\nhighest emission was recorded in 2008. The increasing trend from 2006 till 2008 might be due to \n\n\n\n0.00\n\n\n\n2000.00\n\n\n\n4000.00\n\n\n\n6000.00\n\n\n\n8000.00\n\n\n\n10000.00\n\n\n\n12000.00\n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\nis\nsi\n\n\n\non\ns \n\n\n\n(to\nnn\n\n\n\nes\n)/y\n\n\n\nea\nr \n\n\n\nYear \n\n\n\nof fertilisers produced in Malaysia and used for agriculture.  Hence, the calculation \nof N2O emissions was based on synthetic fertiliser application on two crops, \noil palm and rice, which take up the major cultivation areas in Malaysia. Other \ncultivated crops were distributed along with oil palm plantations under managed \nsoils. N2O production from managed soils where  N was applied ranged between \n4,300 and 9,800 tonnes per year (Figure 5). \n In 2014, oil palm cultivation area accounted for total land use of 5,392,235 \nha (MPOB 2015). Oil palm planting area expands every year to meet the \nincreasing demand for palm oil. Fertiliser application is based on soil conditions, \nthe environment, and the age of the plant. The use of N fertiliser in oil palm \nplantation is usually in the range of   108-134 kg N/ha, with a higher amount of \nfertiliser being applied for plants aged between 5-20 years than others (Faradiella \net al. 2015). As the oil palm plantation area increases, the amount of N fertiliser \nrequired to maintain productivity increases significantly. A study by Fitri et al. \n(2015) in Indonesia found that the production of N2O emissions is significant \nbased on fertiliser application rather than on land-use conversion. They also \nshowed that the emissions rate from synthetic fertiliser application in oil palm \nplantations is high even after 44 days of fertiliser application. \n Conventional rice cultivation in Malaysia relies heavily on synthetic \nfertilisers for crop productivity and yield. The area under paddy cultivation \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021182\n\n\n\nshows an increasing trend from 1994, with 679,239 ha in 2014 (DOA, 2015). \nPaddy cultivation in Malaysia requires 104 kg N/ha. Data on acreage of paddy \ncultivation was obtained to determine the use of synthetic fertiliser in paddy fields. \nThe balance from the production, import and export of fertiliser was assumed \nto be N applied to managed soils. Fertilisation in paddy planting involves the \napplication of urea, compound NPK (17.5N: 15.5P: 10K), and additional NPK \n(17.5N: 15.5P: 10K) in three phases: vegetative, reproductive, and ripening \nphase (Mohammad Hariz et al. 2019). In 2014, fertiliser application in paddy \nplanting released approximately 301.8 tonnes N2O while the rest of agriculture \n\n\n\nFigure 5. Total N2O emissions from synthetic fertiliser application on managed soils\n\n\n\n17 \n \n\n\n\n \n \nFigure 5. Total N2O emissions from synthetic fertiliser application on managed soils \n \n\n\n\nConventional rice cultivation in Malaysia relies heavily on synthetic fertilisers for crop \n\n\n\nproductivity and yield. The area under paddy cultivation shows an increasing trend from 1994, \n\n\n\nwith 679,239 ha in 2014 (DOA,2015). Paddy cultivation in Malaysia requires 104 kg N/ha. Data \n\n\n\non acreage of paddy cultivation was obtained to determine the use of synthetic fertiliser in paddy \n\n\n\nfields. The balance from the production, import and export of fertiliser was assumed to be N \n\n\n\napplied to managed soils. Fertilisation in paddy planting involves the application of urea, \n\n\n\ncompound NPK (17.5N: 15.5P: 10K), and additional NPK (17.5N: 15.5P: 10K) in three phases: \n\n\n\nvegetative, reproductive, and ripening phase (Mohammad Hariz et al. 2019). In 2014, fertiliser \n\n\n\napplication in paddy planting released approximately 301.8 tonnes N2O while the rest of \n\n\n\nagriculture under managed soils released 8615.1 tonnes N2O. Figure 6 shows the effect of N \n\n\n\nfertiliser application in an irrigated paddy field based on paddy productivity (yield/ha). We \n\n\n\nobserved an increasing trend in the yield of rice with the amount of fertiliser used. Over the \n\n\n\nyears, improved technology and high yield varieties have been introduced (Adedoyin et al. \n\n\n\n2016). Furthermore, through the Economic Transformation Programme, the Malaysian \n\n\n\ngovernment has listed scaling up and strengthening paddy productivity to increase self-\n\n\n\nsufficiency and reduce dependency on fertiliser subsidies (Nordin et al. 2014). \n\n\n\n\n\n\n\n0.00\n\n\n\n2000.00\n\n\n\n4000.00\n\n\n\n6000.00\n\n\n\n8000.00\n\n\n\n10000.00\n\n\n\n12000.00\n\n\n\n19951996199719981999200020012002200320042005200620072008200920102011201220132014\n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\nis\nsi\n\n\n\non\ns (\n\n\n\nto\nnn\n\n\n\nes\n)/y\n\n\n\nea\nr \n\n\n\nYear \n\n\n\nunder managed soils released 8615.1 tonnes N2O. Figure 6 shows the effect of \nN fertiliser application in an irrigated paddy field based on paddy productivity \n(yield/ha). We observed an increasing trend in the yield of rice with the amount \nof fertiliser used. Over the years, improved technology and high yield varieties \nhave been introduced (Adedoyin et al. 2016). Furthermore, through the Economic \nTransformation Programme, the Malaysian government has listed scaling up \nand strengthening paddy productivity to increase self-sufficiency and reduce \ndependency on fertiliser subsidies (Nordin et al. 2014).\n It is estimated that the N2O emissions from synthetic fertiliser applied \nto paddy are in the range of 280-300 tonnes N2O/year (Figure 7). Direct N2O \nemissions from the paddy field are considered lower compared to methane (CH4) \nemissions. Yodkhum et al. (2017) estimated total field emission of 1.34 kg CO2eq/\nkg of paddy in northern Thailand. This amount is similar for Malaysian paddy \nfield emission with a value of 1.39 kg CO2eq/kg paddy rice (Mohammad Hariz et \nal. 2019). A lower emission of 0.48 kg CO2eq/kg of paddy could be achieved by \neliminating fertiliser application and adopting organic practices (Yodkhum et al. \n2017).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 183\n\n\n\nGreenhouse gas emissions by organic amendment\nEmissions from organic amendments in Malaysia were calculated for the \napplication of animal manure on soil under agriculture (excluding pasture range \nand paddock). Animal manure from swine, dairy cattle, cattle, sheep, chicken, and \ngoat were summed up for the calculation.\n An increasing trend of N2O emissions from organic amendments was \nobserved from 1995 with the range being 480 to 970 tonnes N2O (Figure 8). \nThis trend corresponds to the total chicken population which adopted the manure \nmanagement system with or without litter.\n\n\n\nFigure 6. Effect of synthetic fertiliser application (kg N) on paddy production (yield/ha)\n\n\n\n18 \n \n\n\n\n \n \nFigure 6. Effect of synthetic fertiliser application (kg N) on paddy production (yield/ha) \n \n \nIt is estimated that the N2O emissions from synthetic fertiliser applied to paddy are in the range \n\n\n\nof 280-300 tonnes N2O/year (Figure 7). Direct N2O emissions from the paddy field are \n\n\n\nconsidered lower compared to methane (CH4) emissions. Yodkhum et al. (2017) estimated total \n\n\n\nfield emission of 1.34 kg CO2eq/kg of paddy in northern Thailand. This amount is similar for \n\n\n\nMalaysian paddy field emission with a value of 1.39 kg CO2eq/kg paddy rice (Mohammad Hariz \n\n\n\net al. 2019). A lower emission of 0.48 kg CO2eq/kg of paddy could be achieved by eliminating \n\n\n\nfertiliser application and adopting organic practices (Yodkhum et al. 2017). \n\n\n\n\n\n\n\n0\n\n\n\n500\n\n\n\n1000\n\n\n\n1500\n\n\n\n2000\n\n\n\n2500\n\n\n\n3000\n\n\n\n3500\n\n\n\n4000\n\n\n\n4500\n\n\n\n60000000\n\n\n\n60500000\n\n\n\n61000000\n\n\n\n61500000\n\n\n\n62000000\n\n\n\n62500000\n\n\n\n63000000\n\n\n\n63500000\n\n\n\n64000000\n\n\n\n64500000\n\n\n\nPa\ndd\n\n\n\ny \nyi\n\n\n\nel\nd(\n\n\n\nkg\n/h\n\n\n\na)\n \n\n\n\nkg\n N\n\n\n\n fe\nrti\n\n\n\nlis\ner\n\n\n\n a\npp\n\n\n\nlic\nat\n\n\n\nio\nn \n\n\n\nYear \n\n\n\nPaddy Yield (kg/ha) Fertliser (kg N)\n\n\n\nFigure 7. Estimation of N2O emissions from synthetic fertiliser application on paddy \nfields\n\n\n\n19 \n \n\n\n\n \n \nFigure 7. Estimation of N2O emissions from synthetic fertiliser application on paddy fields  \n \n\n\n\nGreenhouse gas emissions by organic amendment \n\n\n\nEmissions from organic amendments in Malaysia were calculated for the application of animal \n\n\n\nmanure on soil under agriculture (excluding pasture range and paddock). Animal manure from \n\n\n\nswine, dairy cattle, cattle, sheep, chicken, and goat were summed up for the calculation. \n\n\n\nAn increasing trend of N2O emissions from organic amendments was observed from 1995 with \n\n\n\nthe range being 480 to 970 tonnes N2O (Figure 8). This trend corresponds to the total chicken \n\n\n\npopulation which adopted the manure management system with or without litter. \n\n\n\n280.00\n\n\n\n285.00\n\n\n\n290.00\n\n\n\n295.00\n\n\n\n300.00\n\n\n\n305.00\n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\nis\nsi\n\n\n\non\ns \n\n\n\n(t\non\n\n\n\nne\ns)\n\n\n\n/y\nea\n\n\n\nr \n\n\n\nYear \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021184\n\n\n\n  The main source of N2O emissions from organic amendments is from \nthe population of these animals (swine, dairy cattle, cattle, sheep, chicken and \ngoat). The poultry industry in Malaysia accounts for more than 50% of the total \nlivestock sector (Abdurofi et al. 2017). Poultry meat in Malaysia was 120% self-\nsufficient in 2007 to 2012 (DVS 2012). This in turn, has resulted in higher and \nabundant quantity of poultry litter available from this industry. Poultry litter \nand manure serve as organic fertilisers for crops as they contain 3-5% N, 1.5-\n3.5% phosphorus, and 1-3% potassium and micro-nutrients available for crop \nuptake (Amnullah et al. 2010). Dikinya and Mufwanzala (2010) also showed that \nchicken manure applied to soil acts as a potential source of plant nutrients that can \n\n\n\nFigure 8. N2O emissions from organic amendments\n\n\n\n20 \n \n\n\n\n\n\n\n\nFigure 8. N2O emissions from organic amendments  \n \n\n\n\nThe main source of N2O emissions from organic amendments is from the population of these \n\n\n\nanimals (swine, dairy cattle, cattle, sheep, chicken and goat). The poultry industry in Malaysia \n\n\n\naccounts for more than 50% of the total livestock sector (Abdurofi et al. 2017). Poultry meat in \n\n\n\nMalaysia was 120% self-sufficient in 2007 to 2012 (DVS 2012). This in turn, has resulted in \n\n\n\nhigher and abundant quantity of poultry litter available from this industry. Poultry litter and \n\n\n\nmanure serve as organic fertilisers for crops as they contain 3-5% N, 1.5-3.5% phosphorus, and \n\n\n\n1-3% potassium and micro-nutrients available for crop uptake (Amnullah et al. 2010). Dikinya \n\n\n\nand Mufwanzala (2010) also showed that chicken manure applied to soil acts as a potential \n\n\n\nsource of plant nutrients that can improve soil fertility and increase plant yield. According to \n\n\n\nTruong et al. (2018), N2O emission from livestock farming in Vietnam is at 5.5 kt CO2 eq/year \n\n\n\nand poultry husbandry is estimated to contribute approximately 27% of total GHG emissions in \n\n\n\nthe country.  However, of all livestock in Vietnam, pig farming is the most significant \n\n\n\ncontributor to emissions (Truong et al. 2018).  \n\n\n\n\n\n\n\n\n\n\n\n0\n\n\n\n200\n\n\n\n400\n\n\n\n600\n\n\n\n800\n\n\n\n1000\n\n\n\n1200\n\n\n\n19951996199719981999200020012002200320042005200620072008200920102011201220132014\n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\nis\nsi\n\n\n\non\ns \n\n\n\n(to\nnn\n\n\n\nes\n)/y\n\n\n\nea\nr \n\n\n\nYear \n\n\n\nimprove soil fertility and increase plant yield. According to Truong et al. (2018), \nN2O emission from livestock farming in Vietnam is at 5.5 kt CO2 eq/year and \npoultry husbandry is estimated to contribute approximately 27% of total GHG \nemissions in the country.  However, of all livestock in Vietnam, pig farming is the \nmost significant contributor to emissions (Truong et al. 2018). \nGreenhouse gas emissions by crop residues\nCrop residue is usually left to decay on agricultural land after the harvest. This \nprocess improves soil fertility and increases organic matter in the soil for plant \nuptake (Singh et al. 2008). However, the presence of the residue in soil initiates \nthe denitrification process, thus releasing N2O (Henderson et al. 2010). We \ncalculated N2O emissions from paddy and oil palm, the major crops cultivated \nin Malaysia, and by far the largest fertiliser-consuming crops in Malaysia due to \nextensive plantation areas.\n Paddy planting in Malaysia is done two times a year: main-season and off-\nseason. The main-season period occurs when paddy is grown without dependence \non any irrigation system, usually between August and February of the following \nyear (DOA 2004). Off-season known as dry season occurs between March and July \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 185\n\n\n\nof the same year where paddy planting usually depends on the irrigation system \n(DOA 2004). An abundance of rice straw is produced from the rice field after \nplanting seasons. During the off-season, rice straw burning is commonly practised \nto manage paddy residue; however, the incorporation of rice straw usually occurs \nduring the main season. N2O emission from the incorporation of rice straw into \nthe fields was calculated as the nitrification and denitrification processes occur \nduring this event.\n Data on incorporation of rice straw to soil is presented as kilogram N per \nyear (tonnes N/yr). N2O emission from paddy crop residue and the amount of rice \nstraw (N) incorporated into the soil are shown for 1995 to 2014 (Figure 9).\n In Malaysia, the ratio of grain harvested to rice straw was reported as 1:1 \n(Lembaga Kemajuan Pertanian Muda 2010). This indicates that the figures for \nrice yield and the amount of straw produced in the field are similar.  Production \nof N2O emissions from rice straw ranges from 90 to 110 tonnes N2O/year (Figure \n9) depending on the amount of N from paddy residue incorporated into the soil. \nInsignificant amounts of N from paddy residue incorporated into the soil have \nbeen recorded each year, ranging between 20,000 and 24,000 tons per year. The \namount presented was based on the assumption that all straw produced were \nincorporated back into the soil. This study could be improved by conducting a \nfield survey to determine the amount of straw used for animal feed or other uses. \nA study by Feng et al. (2018) showed that crop residue removal could reduce N2O \nemissions by 10.9% in no-tillage paddy cultivation. However, they observed that \nthe N2O emissions produced from no-tillage paddy and conventional tillage paddy \n\n\n\nFigure 9. Application of paddy straw residue (N) incorporated into soil on N2O\nemissions production\n\n\n\n22 \n \n\n\n\n \n \nFigure 9. Application of paddy straw residue (N) incorporated into soil on N2O emissions \n\n\n\nproduction \n \n\n\n\nIn Malaysia, the ratio of grain harvested to rice straw was reported as 1:1 (Lembaga Kemajuan \n\n\n\nPertanian Muda 2010). This indicates that the figures for rice yield and the amount of straw \n\n\n\nproduced in the field are similar.  Production of N2O emissions from rice straw ranges from 90 to \n\n\n\n110 tonnes N2O/year (Figure 9) depending on the amount of N from paddy residue incorporated \n\n\n\ninto the soil. Insignificant amounts of N from paddy residue incorporated into the soil have been \n\n\n\nrecorded each year, ranging between 20,000 and 24,000 tons per year. The amount presented was \n\n\n\nbased on the assumption that all straw produced were incorporated back into the soil. This study \n\n\n\ncould be improved by conducting a field survey to determine the amount of straw used for \n\n\n\nanimal feed or other uses. A study by Feng et al. (2018) showed that crop residue removal could \n\n\n\nreduce N2O emissions by 10.9% in no-tillage paddy cultivation. However, they observed that the \n\n\n\nN2O emissions produced from no-tillage paddy and conventional tillage paddy are insignificantly \n\n\n\ndifferent when paddy residue is not removed.  In rice planting systems, CH4 is the main gas \n\n\n\nproduced due to crop residue incorporation as it acts as a substrate for the complex microbial \n\n\n\ncommunity (Yavinder-singh et al. 2005). The presence of N2O in rice fields is due to the \n\n\n\nnitrification-denitrification process during periods of alternate wetting and drying (Yavinder-\n\n\n\n0\n\n\n\n5000\n\n\n\n10000\n\n\n\n15000\n\n\n\n20000\n\n\n\n25000\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\nTo\nnn\n\n\n\nes\n  N\n\n\n\n a\npp\n\n\n\nlie\nd \n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\nis\nsi\n\n\n\non\ns/\n\n\n\nye\nar\n\n\n\n\n\n\n\nYear \n\n\n\nTonnes N applied N2O emissions\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021186\n\n\n\nare insignificantly different when paddy residue is not removed.  In rice planting \nsystems, CH4 is the main gas produced due to crop residue incorporation as it \nacts as a substrate for the complex microbial community (Yavinder-singh et al. \n2005). The presence of N2O in rice fields is due to the nitrification-denitrification \nprocess during periods of alternate wetting and drying (Yavinder-singh et al. \n2005). Therefore, N2O emissions are usually low in an irrigated rice field. In \ncomparison to palm oil, paddy produces quite a high level of N2O emission from \nadding residue to the soil.\n Oil palm produces an abundance of biomass waste. For our calculation, \nthree categories of residue from oil palm plantations incorporated into the soil are \nEFB, OPT, and OPF. The calculation of oil palm biomass produced was based \non planted area and replanting area. According to Loh (2016), 50% of biomass \nfrom plantations will be retained in the fields as part of soil management. A factor \nconsidered necessary in estimating N2O from crop residue is the amount of N \ncontent of the residue from the different plant parts. The amount of N from each \nplant part is different. In this study, the amount of N was 0.38% for OPF (Rahman \net al. 2014), 0.17% for OPT, and 0.25% for EFB (Loh 2016).\n Table 5 shows the production of total tonnes in dry weight of OPT, EFB, \nand OPF used to estimate N2O emissions. OPF biomass is the main contributor to \nthe production of N2O emissions (Figure 10). Emissions emitted from crop residue \nof managed soils ranged from 698 to 1400 tonnes N2O. The emissions increased \nthroughout the year due to expanding oil palm plantation acreage (MPOB 2018). \nIn 2014, the main source of soil N2O emissions was from OPF (21,728,501.74 \ntonnes in dry weight) (Figure 10). \n\n\n\nMitigation approach in agriculture\nMalaysia is committed to reducing greenhouse gas emission as it is a signatory \nto the Paris Agreement under the United Nations Framework Convention on \nClimate Change (UNFCCC). Strategies to decrease and/or optimise synthetic \nfertiliser rates have been developed to reduce emissions and shift towards a \nsustainable agricultural environment. One of the approaches is organic farming or \nthe utilisation of organic fertilisers. Malaysia has acknowledged organic farming \nthrough Malaysia Organic Certification (SOM), which was launched in 2003 \n(Kala et al. 2011). The use of synthetic fertilisers has been banned in organic \nfarming, therefore minimising the loss of N through leaching, volatilisation, and \ndenitrification. Organic farming contributes to less emission than conventional \nfarming, as reported by Skinner et al. (2019), who observed that N2O emissions \nare 40.2% lower in organic farming than in conventional farming.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 187\n\n\n\nTABLE 5\n Estimated total tonnes in dry weight of EFB, OPT and OPF. Crop residue from replanted \n\n\n\nareas has been included in this estimation\n\n\n\n24 \n \n\n\n\nTABLE 5 \n Estimated total tonnes in dry weight of EFB, OPT and OPF. Crop residue from replanted \n\n\n\nareas has been included in this estimation \n \n\n\n\nTotal dry weight (tonnes) \n\n\n\nYear EFB OPF OPT \n\n\n\n1995 3,702,456.21 10,641,440.48 3,783,713.60 \n\n\n\n1996 3,928,449.12 11,279,062.97 4,010,429.23 \n\n\n\n1997 4,254,865.99 12,120,307.06 4,309,545.37 \n\n\n\n1998 3,787,498.61 12,895,459.17 4,585,161.59 \n\n\n\n1999 4,913,828.09 13,881,128.63 4,935,630.21 \n\n\n\n2000 4,765,857.34 13,459,185.45 1,493,696.40 \n\n\n\n2001 5,156,773.91 14,076,766.77 2,216,487.56 \n\n\n\n2002 5,078,478.54 14,899,700.54 3,014,987.64 \n\n\n\n2003 5,559,456.95 15,213,711.73 1,985,562.32 \n\n\n\n2004 5,550,243.33 15,513,357.12 2,056,727.96 \n\n\n\n2005 5,889,725.47 16,154,273.10 1,821,669.08 \n\n\n\n2006 6,286,142.48 16,642,625.25 2,050,062.00 \n\n\n\n2007 6,084,088.78 17,291,414.40 2,585,200.80 \n\n\n\n2008 6,817,116.08 17,887,333.80 1,978,077.08 \n\n\n\n2009 6,666,749.40 18,956,947.70 3,404,480.80 \n\n\n\n2010 6,461,695.47 19,775,024.80 4,351,121.60 \n\n\n\n2011 7,223,783.64 20,121,875.43 3,198,729.80 \n\n\n\n2012 7,181,412.24 20,443,149.49 3,310,300.84 \n\n\n\n2013 7,371,101.35 21,045,670.63 3,344,077.52 \n\n\n\n2014 7,397,140.52 21,728,501.74 3,596,788.16 \n\n\n\n\n\n\n\n  The adoption of precision agriculture will allow for more precise amounts \nof fertiliser and manure application in farming. The new technologies and \ntechniques adopted in precision farming could reduce the number of inputs for \nGHG contribution, thus minimising emissions from agriculture and increasing \nagricultural productivity (Balafoitus et al. 2017). Numerous studies in Malaysia \nhave developed precision farming models for optimum use of inputs in agriculture \n(Chan et al. 2002; Aimrun et al. 2007; Razak et al. 2014). The implementation of \nprecision farming in agriculture offers much potential to farmers for sustainable \nand optimum production of crops. \n Another approach to reducing the production of N2O from crop residue is \nby optimising the use of crop biomass. The oil palm industry has been shifting \ntowards green technology approaches such as composting, leading to zero \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021188\n\n\n\nFigure 10. Effect of incorporation of oil palm residue (N) into soil on N2O production in \noil palm plantations\n\n\n\n25 \n \n\n\n\n \nFigure 10. Effect of incorporation of oil palm residue (N) into soil on N2O production in oil palm \n\n\n\nplantations \n \n\n\n\nMitigation approach in agriculture \n\n\n\nMalaysia is committed to reducing greenhouse gas emission as it is a signatory to the Paris \n\n\n\nAgreement under the United Nations Framework Convention on Climate Change (UNFCCC). \n\n\n\nStrategies to decrease and/or optimise synthetic fertiliser rates have been developed to reduce \n\n\n\nemissions and shift towards a sustainable agricultural environment. One of the approaches is \n\n\n\norganic farming or the utilisation of organic fertilisers. Malaysia has acknowledged organic \n\n\n\nfarming through Malaysia Organic Certification (SOM), which was launched in 2003 (Kala et al. \n\n\n\n2011). The use of synthetic fertilisers has been banned in organic farming, therefore minimising \n\n\n\nthe loss of N through leaching, volatilisation, and denitrification. Organic farming contributes to \n\n\n\nless emission than conventional farming, as reported by Skinner et al. (2019), who observed that \n\n\n\nN2O emissions are 40.2% lower in organic farming than in conventional farming. \n\n\n\nThe adoption of precision agriculture will allow for more precise amounts of fertiliser and \n\n\n\nmanure application in farming. The new technologies and techniques adopted in precision \n\n\n\nfarming could reduce the number of inputs for GHG contribution, thus minimising emissions \n\n\n\nfrom agriculture and increasing agricultural productivity (Balafoitus et al. 2017). Numerous \n\n\n\n -\n\n\n\n 10,000.00\n\n\n\n 20,000.00\n\n\n\n 30,000.00\n\n\n\n 40,000.00\n\n\n\n 50,000.00\n\n\n\n 60,000.00\n\n\n\n 70,000.00\n\n\n\n 80,000.00\n\n\n\n 90,000.00\n\n\n\n 100,000.00\n\n\n\n0\n\n\n\n200\n\n\n\n400\n\n\n\n600\n\n\n\n800\n\n\n\n1000\n\n\n\n1200\n\n\n\n1400\n\n\n\n1600\n\n\n\nTo\nnn\n\n\n\nes\n N\n\n\n\n a\npp\n\n\n\nlie\nd \n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\nis\nsi\n\n\n\non\ns/\n\n\n\nye\nar\n\n\n\n\n\n\n\nTonnes N applied N2O emissions\n\n\n\ndischarge (Teo et al. 2010). Another approach to maximising oil palm biomass \nuse is by its conversion to three subcategories: biofuel, bioproduct, and biopower \n(Tueku Muerah et al. 2019). Furthermore, exploring conversion of lignocellulosic \nbiomass to a value-added product could resolve the disposing and management of \nbiomass and reduce waste treatment costs (Goh et al. 2010).\n Malaysia has developed the National Biomass Strategy 2020 as one of its \nefforts to utilise all crop residue as a renewable resource. With the new development \nin research and technologies, Malaysia is determined to create wealth and value-\nadded products from biomass for the nation. One such value-added product from \nbiomass is biofuel and towards this, National Biofuel Malaysia was established \nin 2006.  Malaysia is also looking towards converting biomass into high-value \nproducts such as furniture (Lim et al. 2000) and biofertilisers (Abas et al. 2011), \nwhile research is also being carried out on the use of biomass for electricity \ngeneration (Shafie et al. 2014) and as feedstock (Mayulu 2014).\n\n\n\nCONCLUSION\nNitrous oxide is emitted mainly from soils applied with synthetic fertilisers. \nIncreased use of  synthetic fertilisers is a consequence of increasing  plantation \nacreage and the need to maintain and improve crop production. Meanwhile, \nit is observed that the lowest contributor of N2O emissions is from organic \namendments. Therefore, an approach to mitigating emissions from agricultural \npractices is by reducing the use of synthetic fertilisers. The plantations industry \nshould be encouraged to work towards improving nutrient use efficiency of the \nsynthetic fertilisers to reduce excessive application. 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"\n\nINTRODUCTION\nPeatlands have been used extensively for agriculture with approximately 1.7 \nmillion ha of the total of 14.9 million ha of Indonesian peatland under  oil \npalm cultivation (Tropenbos International Indonesia 2012; Ritung et al. 2011). \nCurrently there is increasing concern about the conversion of peatland into oil \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 73- 88 (2017)                         Malaysian Society of Soil Science\n\n\n\nOrganic Acids Exudates and Enzyme Activities in the \nRhizosphere based on Distance from the Trunk of Oil Palm \n\n\n\nin Peatland \n\n\n\nMimien Harianti1, Atang Sutandi2, Rasti Saraswati3, Maswar3, \nSupiandi Sabiham2\n\n\n\n1Andalas University, Faculty of Agriculture Department of Soil Science, Padang\n2Department of Soil Science and Land Resource, Bogor Agriculture University, \n\n\n\nBogor 16680, Indonesia\n3Indonesian Soil Research Institute, Bogor 16114, Indonesia.\n\n\n\nABSTRACT\nEnzyme activity in oil palm rhizosphere could be used as a quality indicator of \npeatland. Roots play an important role in producing exudates of organic acids \nthat are deposited in the rhizosphere. This research aims to study root exudates \nand enzyme activities in oil palm rhizosphere based on the distance from the tree \ntrunk. The research was done in an  oil palm plantation at Koto  Gasib,  Siak  \nDistrict,  Riau  Province,  Indonesia  (0.74\u00b0\u20130.77\u00b0 N  and 101.77\u00b0\u2013101.74\u00b0 E) \nusing the explorative method. The observation of oil palm rhizospheres was done \nby dismantling the root zone of the selected oil palm tree trunk.  Oil palm root \nwas collected at distances of    0\u20131, 1\u20132, and 3\u20134 m from the tree trunk while the \nadhered peat samples were taken at the surface layer of 0\u201325 cm depth within a \nquarter circle area of the canopy. The results showed that enzyme activities in \noil palm rhizosphere decreased with increasing distance from the tree trunk. This \ndecrease is attributed  to the increase in organic acid root exudates and water \ncontent and a  decrease in soil pH.  Thickness of peat did not influence enzyme \nactivity and organic acid content. Enzyme activities in the rhizosphere of severely \ndegraded forest and shrubs were lower than those in oil palm rhizosphere. The \norganic acid exudates in the rhizosphere of oil palm, forest and shrubs consisted \nmainly of aliphatic compounds.   Fertiliser application at 1\u20132 m from oil palm \ntrees decreased  organic  acid  content in the exudates. Results from this study \nalso showed that the large N uptake by plants mainly originated from fertiliser \napplication. Thus, nutrient  supply  for  oil  palm  growth  originated  from  fertiliser \napplication rather than from peat decomposition.\n\n\n\nKeywords: Root exudates,  rhizosphere, enzyme activities, oilpalm, peatland. \n\n\n\n___________________\n*Corresponding author : E-mail: mimienferdinal@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201774\n\n\n\npalm plantation due to carbon dioxide (CO2) emissions as a result of land drainage \nand peat decomposition. \n\n\n\nThe changes in some chemical properties in oil palm plantations as a result of \npeat decomposition can be understood  by studying rhizosphere enzyme activities. \nThe changes in enzyme activity, especially the extracellular enzymes, are actually \nmicrobiota responses to environmental factors (Nannipieri et al. 2011). Enzyme \nactivity is affected by plant root exudates in the form of organic acids (malic, \nacetic, succinic, citric and maleic acids) (Gianfreda 2015). Thus,  the process of \ndecomposition  of organic matter  caused  by conversion of   peatland  into  oil  \npalm plantations can be investigated through the activity of enzymes affected by \nplant roots in the rhizosphere.\n\n\n\nThe enzyme often used as an indicator of organic carbon (C) mineralization \nin the soil is \u03b2-glucosidase (Stott et al. 2010). Some enzymes, such as urease, acid \nphosphatase, \u03b2-glucosidase and laccase serve as indicators of the decomposition \nprocess of organic matter in peatland. Sabiham et al. (2014) found that fertiliser \napplication at the position nearest to the oil palm trees increased the nutrient \nlevels in peat soil. According to Aon and Colaneri (2001), soil enzyme activities \ninhibited by N fertiliser are  promoted by P and K fertilisers. Plant roots stimulate \nenzyme activity due to their positive effects on microbial activity and production \nof exudates (USDA 2010). Fertiliser application and  the distribution and root \nmass of oil palm roots are influenced by enzyme activity. The aim of this study \nwas to determine organic acid exudates and enzyme activities in the rhizosphere \nof peatland based on distance from the oil palm tree trunk.\n\n\n\nMETHODS\nThe research was done in an oil palm plantation in Pangkalan Pisang  Village,  \nKoto  Gasib,  Siak  District,  Riau  Province,  Indonesia  (0.74\u00b0\u20130.77\u00b0 N  and \n101.77\u00b0\u2013101.74\u00b0 E).  The peat material analysis was conducted at the Laboratory \nof Chemistry and  Soil  Fertility,  Department  of  Soil  Science  and  Land  \nResources,  Bogor  Agricultural University (IPB), Bogor and at the Laboratory \nof Agrochemical Material Residue, Agricultural Environment Research Institute, \nCenter for Agricultural Land Resource, Bogor Indonesia.\n\n\n\nThe research was an exploratory study in the form of field observation \nactivities. Sites were selected based on peat thickness of <3 and >3 m and plant \nage of oil palm <6 years, 6\u201315 years and >15 years. The observations were done \non transects perpendicular to the drainage channel (collection drain). In each \ntransect two oil palm trees were dismantled 50 m and 100 m from the collection \ndrain. \n\n\n\nThe observation of oil palm rhizosphere was done by dismantling the root \nzone of the selected oil palm tree trunk on each transect on the frond windrow, that \nis, space between two rows of plants. Oil palm roots were collected at distances \nof  0\u20131, 1\u20132, and 3\u20134 m  from the tree trunk within a quarter circle area of the \ncanopy (Fig. 1). Adhered peat samples were taken at the surface layer of 0\u201325 cm \ndepth within 1-2 mm from the roots. Differences in root distribution and fertiliser \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 75\n\n\n\napplication in the oil palm circle served as benchmark changes in the rhizosphere \nof oil palm on peatland (Fig. 1). Peat samples were collected 3-4 months after the \nlast regular fertiliser application (for nutrients N, P, K) by plantation management. \nThe amount of N and P fertilisers applied by plantation management was as much \nas 19 (N), 15 (P) kg/tree (for oil palm <6 years); 6 (N), 1(P) kg/tree (for oil palm \n6-15 years) and 7 (N), 3(P) kg/tree (for oil palm >15 years) before soil sampling \nwas done. For comparison purposes, samples were also taken from peat soil \nprofiles (0-25 cm) of severely degraded forest and shrub vegetation in peatland.\n\n\n\nWater content in the peat was determined by gravimetric method. The pH \nvalue measurement(1:2) of peat material (10 g of material: 20 ml of deionised \nwater) was performed using a pH meter (2700 Autech Instrument). Total N and \nP detemination by digestion method with 98% H2SO4 solution. Total P  was \ndetermined  by 60 % HClO4 solution and blue colour intensity  was  measured by \nSpectrofotometer 490 nm Shimadzu UV 1280 (Page et al. 1982). Organic acid \nexudates were extracted with deionised water (5 g peat matter: 10 ml deionised \nwater);  the mixture was ????  for about 30 min and centrifuged at 4000 rpm. \nOrganic acids in the supernatant were measured by high-performance liquid \nchromatography (HPLC) Shimadzu 20A.\n\n\n\nFig. 1:  Peat composite sampling in rhizosphere based on (a) distance from the trunk of \noil palm and (b) fertiliser application around of oil palm tree\n\n\n\n\n\n\n\n\n\n\n\nFigure 1.  Peat composite sampling in rhizosphere based on distance from the trunk of oil \npalm (a); and  fertilizer application around of oil palm tree (b).  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201776\n\n\n\nActivities of enzymes  urease, phosphatase, \u03b2-glucosidase, and laccase \nactivities were measured In the case of urease, the release of ammonium by the \nnon-buffered method was measured (Schinner et al. 1996). Phosphatase activity \nwas determined by measuring the release of phosphorus (P) from organic P by \nthe p-nitrophenyl phosphate buffer method (Schinner et al. 1996). \u03b2-glucosidase \nactivity was determined to measure the breakdown of cellulose into glucose using \nthe \u03b2-glucosido-saligenin (salicin) method (Schinner et al. 1996). Finally, laccase \nactivity was determined by measuring lignin degradation activity using the 2, \n2-azinobis (3-ethyl-benzothiazoline-6-sulphonate) method (Eichlerov\u00e1 et al. \n2012).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nTotal Weight of Oil Palm Roots \nHigher root biomass was consistent with palm age and increased soil CO2 flux, \nsuggesting that root respiration and microbial activity are associated with root \nexudates as a major component of soil respiration in tropical peatland under oil \npalm (Melling et al. 2014). The older the oil palm, the higher the weight of the \nmain roots (root diameter (\u00d8) >0.5 mm) and feeding roots (\u00d8 <0.5 mm) at a depth \nof 0\u201325 cm (Table 1). The total weight of roots and feeding roots decreased with \nincreasing distance from the tree. The total weight of roots in peat (thickness <3 \nm) was higher compared to the peat (thickness >3 m) (Table 1). Sabiham et al. \n(2014) found that in the 0\u201315 cm peat layer, the quaternary roots had the highest \ndensity compared to primary, secondary, and tertiary roots.  \n\n\n\npH Value and Water Content\nThe water content of the peat in the rhizosphere of oil palm ranged from 150%\u2013\n450%, and this was found to increase with increasing distance from the tree trunk \nfor all treatments (Fig. 2).  This might be due to the rapid water absorption in \nthe root zone, which consisted of  more than 50 % of the total weight of roots. \nThe water content was negatively correlated with enzyme activity, indicating that \nincreasing soil water content might lead to decreasing enzyme activity (Table \n2). Water content of peat soils from Bibai marsh,  Hokkaido in Japan was found \nto vary from about 200% to more than 2000% of dry weight (Hamamoto et al. \n2010). \n\n\n\nThe pH of the rhizosphere was in the range of 3.5\u20134.0. The pH value of peat \ndecreased with increasing distance from the palm tree, which might be due to \nthe increase in water content, root activities and the release of organic acids. In \nthe rhizosphere of severely degraded forests, peat moisture content was higher \ncompared to shrub vegetation because the stand in the severely degraded forest  \nmaintained a higher water content than that of the shrub vegetation (dominated \nby ferns). The pH values in the rhizosphere of severely degraded forest and shrub \nvegetation tended to be similar (within the range of 3\u20133.5), but lower than the pH of \npeat in oil palm rhizosphere. This is because the peat in oil palm rhizosphere held \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 77\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nW\nei\n\n\n\ngh\nts\n\n\n\n o\nf o\n\n\n\nil \npa\n\n\n\nlm\n m\n\n\n\nai\nn-\n\n\n\nro\not\n\n\n\ns (\n\u00d8\n\n\n\n >\n0.\n\n\n\n5m\nm\n\n\n\n) a\nnd\n\n\n\n fe\ned\n\n\n\nin\ng-\n\n\n\nro\not\n\n\n\ns (\n\u00d8\n\n\n\n <\n 0\n\n\n\n.5\nm\n\n\n\nm\n) i\n\n\n\nn \nre\n\n\n\nla\ntio\n\n\n\nn \nto\n\n\n\n o\nil \n\n\n\npa\nlm\n\n\n\n a\nge\n\n\n\ns, \npe\n\n\n\nat\n th\n\n\n\nic\nkn\n\n\n\nes\ns a\n\n\n\nnd\n \n\n\n\ndi\nst\n\n\n\nan\nce\n\n\n\n fr\nom\n\n\n\n o\nil \n\n\n\npa\nlm\n\n\n\n tr\nee\n\n\n\n tr\nun\n\n\n\nk.\n \n\n\n\nTa\nbl\n\n\n\ne \n1.\n\n\n\n W\nei\n\n\n\ngh\nts\n\n\n\n o\nf \n\n\n\noi\nl p\n\n\n\nal\nm\n\n\n\n m\nai\n\n\n\nn-\nro\n\n\n\not\ns \n\n\n\n(\u00d8\n >\n\n\n\n0.\n5m\n\n\n\nm\n) a\n\n\n\nnd\n f\n\n\n\nee\ndi\n\n\n\nng\n-ro\n\n\n\not\ns \n\n\n\n(\u00d8\n <\n\n\n\n 0\n.5\n\n\n\nm\nm\n\n\n\n) \nin\n\n\n\n r\nel\n\n\n\nat\nio\n\n\n\nn \nto\n\n\n\n o\nil \n\n\n\npa\nlm\n\n\n\n a\nge\n\n\n\ns, \npe\n\n\n\nat\n th\n\n\n\nic\nkn\n\n\n\nes\ns \n\n\n\nan\nd \n\n\n\ndi\nst\n\n\n\nan\nce\n\n\n\n \nfr\n\n\n\nom\n o\n\n\n\nil \npa\n\n\n\nlm\n tr\n\n\n\nee\n tr\n\n\n\nun\nk.\n\n\n\n  \n O\n\n\n\nil \npa\n\n\n\nlm\n a\n\n\n\nge\n \n\n\n\n(y\nea\n\n\n\nrs\n o\n\n\n\nld\n) \n\n\n\n \nTo\n\n\n\nta\nl w\n\n\n\nei\ngh\n\n\n\nt o\nf r\n\n\n\noo\nts\n\n\n\n (k\ng/\n\n\n\nm\n3 ) \n\n\n\n \nFe\n\n\n\ned\nin\n\n\n\ng \nro\n\n\n\not\n w\n\n\n\nei\ngh\n\n\n\nt  \n(k\n\n\n\ng/\nm\n\n\n\n3 ) \nD\n\n\n\nist\nan\n\n\n\nce\n fr\n\n\n\nom\n tr\n\n\n\nee\n tr\n\n\n\nun\nk \n\n\n\n(m\n) \n\n\n\n \nD\n\n\n\nist\nan\n\n\n\nce\n fr\n\n\n\nom\n tr\n\n\n\nee\n tr\n\n\n\nun\nk \n\n\n\n(m\n) \n\n\n\n0-\n1 \n\n\n\n1-\n2 \n\n\n\n2-\n3 \n\n\n\n3-\n4 \n\n\n\nTo\nta\n\n\n\nl \n0-\n\n\n\n1 \n1-\n\n\n\n2 \n2-\n\n\n\n3 \n3-\n\n\n\n4 \nTo\n\n\n\nta\nl \n\n\n\nn \nPe\n\n\n\nat\n th\n\n\n\nic\nkn\n\n\n\nes\ns <\n\n\n\n3 \nm\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nPe\n\n\n\nat\n th\n\n\n\nic\nkn\n\n\n\nes\ns <\n\n\n\n3 \nm\n\n\n\n\n\n\n\n<6\n \n\n\n\n4 \n1.\n\n\n\n96\n \u00b1\n\n\n\n 0\n.5\n\n\n\n9 \n2.\n\n\n\n14\n \u00b1\n\n\n\n 0\n.2\n\n\n\n8 \n0.\n\n\n\n82\n \u00b1\n\n\n\n 0\n.0\n\n\n\n81\n \n\n\n\n0.\n15\n\n\n\n \u00b1\n 0\n\n\n\n.0\n5 \n\n\n\n  5\n.0\n\n\n\n6 \n\u00b1 \n\n\n\n0.\n99\n\n\n\n \n1.\n\n\n\n33\n \u00b1\n\n\n\n 0\n.4\n\n\n\n3 \n1.\n\n\n\n27\n \u00b1\n\n\n\n 0\n.1\n\n\n\n7 \n0.\n\n\n\n58\n  \u00b1\n\n\n\n 0\n.0\n\n\n\n57\n \n\n\n\n0.\n11\n\n\n\n \u00b1\n 0\n\n\n\n.0\n3 \n\n\n\n3.\n39\n\n\n\n \u00b1\n 0\n\n\n\n.6\n87\n\n\n\n \n6-\n\n\n\n15\n \n\n\n\n4 \n4.\n\n\n\n36\n \u00b1\n\n\n\n 0\n.7\n\n\n\n8 \n2.\n\n\n\n18\n \u00b1\n\n\n\n 0\n.2\n\n\n\n6 \n3.\n\n\n\n11\n \u00b1\n\n\n\n 1\n.5\n\n\n\n5 \n1.\n\n\n\n50\n \u00b1\n\n\n\n 1\n.0\n\n\n\n1 \n11\n\n\n\n.1\n3 \n\n\n\n\u00b1 \n3.\n\n\n\n59\n \n\n\n\n2.\n99\n\n\n\n \u00b1\n 0\n\n\n\n.5\n3 \n\n\n\n1.\n74\n\n\n\n \u00b1\n 0\n\n\n\n.2\n1 \n\n\n\n1.\n12\n\n\n\n \u00b1\n0.\n\n\n\n55\n \n\n\n\n1.\n40\n\n\n\n \u00b1\n 0\n\n\n\n.9\n4 \n\n\n\n7.\n25\n\n\n\n \u00b1\n 2\n\n\n\n.2\n3 \n\n\n\n>1\n5 \n\n\n\n4 \n8.\n\n\n\n28\n \u00b1\n\n\n\n5.\n53\n\n\n\n \n4.\n\n\n\n04\n \u00b1\n\n\n\n 3\n.4\n\n\n\n7 \n2.\n\n\n\n26\n \u00b1\n\n\n\n 0\n.2\n\n\n\n7 \n1.\n\n\n\n23\n \u00b1\n\n\n\n 0\n.6\n\n\n\n7 \n15\n\n\n\n.8\n2 \n\n\n\n\u00b1 \n9.\n\n\n\n94\n \n\n\n\n3.\n00\n\n\n\n \u00b1\n 2\n\n\n\n.0\n1 \n\n\n\n2.\n38\n\n\n\n \u00b1\n 2\n\n\n\n.0\n4 \n\n\n\n 1\n.3\n\n\n\n \u00b1\n 0\n\n\n\n.1\n5 \n\n\n\n \n1.\n\n\n\n08\n \u00b1\n\n\n\n 0\n.5\n\n\n\n9 \n7.\n\n\n\n76\n \u00b1\n\n\n\n 4\n.7\n\n\n\n9 \n \n\n\n\n \nPe\n\n\n\nat\n th\n\n\n\nic\nkn\n\n\n\nes\ns >\n\n\n\n3 \nm\n\n\n\n\n\n\n\nPe\nat\n\n\n\n th\nic\n\n\n\nkn\nes\n\n\n\ns >\n3 \n\n\n\nm\n \n\n\n\n<6\n \n\n\n\n4 \n2.\n\n\n\n55\n \u00b1\n\n\n\n 0\n.3\n\n\n\n3 \n2.\n\n\n\n70\n \u00b1\n\n\n\n 0\n.8\n\n\n\n4 \n1.\n\n\n\n40\n \u00b1\n\n\n\n 0\n.2\n\n\n\n4 \n0.\n\n\n\n57\n \u00b1\n\n\n\n 0\n.6\n\n\n\n7 \n7.\n\n\n\n24\n \u00b1\n\n\n\n 2\n.1\n\n\n\n0 \n0.\n\n\n\n96\n \u00b1\n\n\n\n 0\n.1\n\n\n\n2 \n2.\n\n\n\n33\n \u00b1\n\n\n\n 0\n.7\n\n\n\n3 \n0.\n\n\n\n92\n\u00b1 \n\n\n\n0.\n16\n\n\n\n \n0.\n\n\n\n55\n\u00b1 \n\n\n\n0.\n65\n\n\n\n \n4.\n\n\n\n76\n \u00b1\n\n\n\n 1\n.6\n\n\n\n6 \n6-\n\n\n\n15\n \n\n\n\n4 \n5.\n\n\n\n00\n \u00b1\n\n\n\n 4\n.7\n\n\n\n4 \n1.\n\n\n\n82\n \u00b1\n\n\n\n 0\n.2\n\n\n\n0 \n0.\n\n\n\n89\n \u00b1\n\n\n\n 0\n.2\n\n\n\n4 \n0.\n\n\n\n69\n \u00b1\n\n\n\n 0\n.7\n\n\n\n3 \n8.\n\n\n\n42\n \u00b1\n\n\n\n 5\n.9\n\n\n\n2 \n3.\n\n\n\n58\n \u00b1\n\n\n\n 3\n.3\n\n\n\n9 \n1.\n\n\n\n47\n \u00b1\n\n\n\n 0\n.9\n\n\n\n7 \n0.\n\n\n\n65\n \u00b1\n\n\n\n 0\n.1\n\n\n\n7 \n0.\n\n\n\n58\n \u00b1\n\n\n\n 1\n.1\n\n\n\n5 \n6.\n\n\n\n28\n \u00b1\n\n\n\n 5\n.6\n\n\n\n8 \n>1\n\n\n\n5 \n4 \n\n\n\n5.\n32\n\n\n\n \u00b1\n 3\n\n\n\n.5\n6 \n\n\n\n2.\n82\n\n\n\n \u00b1\n 0\n\n\n\n.4\n4 \n\n\n\n2.\n02\n\n\n\n \u00b1\n 1\n\n\n\n.1\n2 \n\n\n\n0.\n89\n\n\n\n \u00b1\n 0\n\n\n\n.4\n1 \n\n\n\n7.\n23\n\n\n\n \u00b1\n 5\n\n\n\n.5\n5 \n\n\n\n2.\n41\n\n\n\n \u00b1\n 1\n\n\n\n.6\n1 \n\n\n\n2.\n12\n\n\n\n \u00b1\n 0\n\n\n\n.3\n3 \n\n\n\n0.\n96\n\n\n\n \u00b1\n 0\n\n\n\n.5\n3 \n\n\n\n \n0.\n\n\n\n68\n \u00b1\n\n\n\n 0\n.3\n\n\n\n1 \n \n\n\n\n6.\n17\n\n\n\n \u00b1\n 3\n\n\n\n.0\n9 \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201778\n\n\n\nalmost twice the water content compared to that of the severely degraded forest \nand shrub vegetation. The pH of the peat was very low (2.68\u20133.74)  but was \nrelatively high in all of the peat layers sampled at the agricultural site in central \nKalimantan, Indonesia (K\u00f6n\u00f6nen et al. 2015). \n\n\n\nThe increase in organic acid content decreased the pH of peat due to the \ndonation of protons from the organic acid group. This is associated with the \nconcentration of soil nitrogen (N) and phosphorous (P), where low availability of \nP triggers the release of citric and oxalic acids into the rhizosphere to solubilise \nP and promote NH4\n\n\n\n+ uptake by plants, thus decreasing the pH. pH is  positively \ncorrelated with urease and phosphatase in which the decrease in pH suppresses \nenzyme activities. Blonska (2010) observed the same results for urease and \ndehydrogenase activities which were found to increase with increasing pH in \npeatland.\n\n\n\nFig. 2:  Water content and pH in rhizospheres of oil palm based on distance from oil palm tree \ntrunk, oil palm age and peat thickness and rhizosphere of degraded forest and\n\n\n\nshrub in peatland.\n\n\n\n\n\n\n\nFigure  2.   Water content and pH in rhizospheres of oil palm based on distance from oil palm \ntree trunk, oil palm age and peat thickness and rhizosphere of degraded forest and \nshrub in peatland.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 79\n\n\n\nTotal N and P Content\nTotal N content in the oil palm rhizosphere (0.5%\u20131.5%) was lower than in the \nrhizosphere of severely degraded forest and shrub vegetation (1%\u20132%). This \nmight be due to the high application of Nitrogen fertiliser by  oil palm plantations \n(average of 2.3% to 2.7% N). The large N uptake by plants  is largely due to \nfertiliser application.  \n\n\n\nThe total N content of peat in young (lesss than 6 year old) and old (more \nthan 15 year old) oil palm rhizosphere for peat thicknesses of <3 and >3 m tended \nto decrease with increasing distance from the tree trunk (Fig. 3). In contrast, total \nN content significantly decreased, at a distance of 1\u20132 m from the tree trunk and  \nincreased at a distance of 3\u20134 m from the tree trunk in 6\u201315- year-old oil palm. \nThis is because of the large N uptake by roots of oil palm at the 1\u20132 m distance,  \nalthough  fertiliser was  applied  further  away from  the tree trunk,  and  the \nhigh  N requirement of 6\u201315-year-old oil palm (Fig.3). The total N content of \npeat correlated positively with citric and butyric acids (Table 2). The increase in \ntotal N correlated with increased organic acid content, especially at a distance of \n3\u20134 m from the oil palm tree (Fig. 3). The high root activity at a distance of 3\u20134 \nm,   due to high root exudates, leads to high organic acid  content,  triggering  an  \nincrease  in  microbial  abundance  and  microbial  activities, thereby increasing \nthe total N content.\n\n\n\nTotal P content of peat in oil palm rhizosphere ranged from 100\u2013300 ppm, \nwhereas P contents in the rhizosphere of severely degraded forest and shrub \nvegetation were only around 100 ppm. The high P content in oil palm rhizosphere \nwas due to the application of large amounts of P fertiliser. The P contents in \n<6- and >15-year-old oil palm rhizosphere tended to decrease with decreasing \ndistance from the tree trunk. For 6\u201315-year-old oil palm with peat thickness of >3 \nm, the total P content increased at a distance of 3-4 m from the tree trunk because \nthe organic acid exudates increased P solubility. The release of high organic acids \ncontent from carboxylic groups, especially citric acid (Fig. 4) shows the low \nsolubility of P in the rhizosphere, while oil palm needs very high available P. \nAccording to Neumann et al. (2000), the plant roots secrete a number of organic \nacids, especially citric acid (in mature plants), to acidify the rhizosphere.\n\n\n\nOrganic Acid Content of Root Exudates\nOrganic acids are part of the root exudates released into the rhizosphere; they \nare the source of energy for microbes in enzyme synthesis, which affect physical \nand chemical properties of peat (Carvalhais et al. 2010). The carboxylic acid-\ncontaining organic acid of peat material in oil palm rhizosphere aged <6 years at \npeat depths of <3 m and >3 m tended to decrease with increasing distance from \nthe tree trunk, while carboxylic acid containing organic acid content for oil palm \naged 6\u201315 years and >15 years decreased at a distance of 2 m and increased at a \ndistance of 3\u20134 m from the tree trunk (Fig. 3). This could be due to oil palm roots \nnot releasing root exudates at a distance of 1\u20132 m from the tree trunk as an effect \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201780\n\n\n\nof intensive fertiliser application in the oil palm tree circle at a distance of 1\u20132 m \n(Fig. 1). The increase in carboxylic acid content at a distance of 3\u20134 m from the \ntree might be due to an increase in root activity because of increasing plant  age \n(i.e., in 6\u201315-and  >15-year-old plants),  root hairs (feeding roots) and decreased \npH (Fig. 2). The expansion of the root total weight by root hairs reached >50% \nof the total weight of the roots (Table 1). According to Badri and Vivanco (2009), \nroot hair cells are involved in root secretion of organic compounds. Garcia et \nal. (2001) showed that root exudation is positively correlated with root growth; \nactively growing root systems secrete more exudates.\n\n\n\npH value was significantly negatively correlated with oxalic and citric acid \ncontents and positively correlated with enzyme activities (urease and phosphatase; \nTable 2). The organic acid content in the oil palm rhizosphere showed a positive \ncorrelation with each group of organic acids (malic, acetic, citric, oxalic and \n\n\n\nFig. 3:  Total Nitrogen and Phosphorous contents in rhizosphere of oil palm based on distance \nfrom oil palm tree trunk, oil palm age and peat thickness and rhizosphere of degraded forest \n\n\n\nand shrub in peatland.\n\n\n\n\n\n\n\nFigure 3.  Total Nitrogen and Phosphorous contents in rhizosphere of oil palm based on \ndistance from oil palm tree trunk, oil palm age and peat thickness and rhizosphere \nof degraded forest and shrub in peatland.  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 81\n\n\n\nTA\nB\n\n\n\nLE\n 2\n\n\n\nPe\nar\n\n\n\nso\nn\u2019\n\n\n\ns c\nor\n\n\n\nre\nla\n\n\n\ntio\nn \n\n\n\nco\nef\n\n\n\nfic\nie\n\n\n\nnt\n te\n\n\n\nst\n b\n\n\n\net\nw\n\n\n\nee\nn \n\n\n\nrh\niz\n\n\n\nos\nph\n\n\n\ner\nes\n\n\n\n p\nH\n\n\n\n, w\nat\n\n\n\ner\n c\n\n\n\non\nte\n\n\n\nnt\n fo\n\n\n\nr t\not\n\n\n\nal\n N\n\n\n\n a\nnd\n\n\n\n P\n, r\n\n\n\noo\nt e\n\n\n\nxu\nda\n\n\n\nte\n o\n\n\n\nrg\nan\n\n\n\nic\n a\n\n\n\nci\nds\n\n\n\n, a\nnd\n\n\n\n e\nnz\n\n\n\nym\ne \n\n\n\nac\ntiv\n\n\n\niti\nes\n\n\n\n in\n p\n\n\n\nea\ntla\n\n\n\nnd\n.\n\n\n\nTa\nbl\n\n\n\ne \n2.\n\n\n\n  P\nea\n\n\n\nrs\non\n\n\n\n\u2019s\n c\n\n\n\nor\nre\n\n\n\nla\ntio\n\n\n\nn \nco\n\n\n\nef\nfic\n\n\n\nie\nnt\n\n\n\n te\nst\n\n\n\n b\net\n\n\n\nw\nee\n\n\n\nn \nrh\n\n\n\niz\nos\n\n\n\nph\ner\n\n\n\nes\n p\n\n\n\nH\n, w\n\n\n\nat\ner\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n, c\non\n\n\n\nte\nnt\n\n\n\n fo\n to\n\n\n\nta\nl N\n\n\n\n a\nnd\n\n\n\n P\n, r\n\n\n\noo\nt e\n\n\n\nxu\nda\n\n\n\nte\n o\n\n\n\nrg\nan\n\n\n\nic\n a\n\n\n\nci\nds\n\n\n\n, a\nnd\n\n\n\n \nen\n\n\n\nzy\nm\n\n\n\ne \nac\n\n\n\ntiv\niti\n\n\n\nes\n in\n\n\n\n p\nea\n\n\n\ntla\nnd\n\n\n\n.  \n\n\n\n \npH\n\n\n\n \nW\n\n\n\nat\ner\n\n\n\n \nC\n\n\n\non\nte\n\n\n\nnt\n To\n\n\n\nta\nl N\n\n\n\n \nco\n\n\n\nnt\nen\n\n\n\nt \nTo\n\n\n\nta\nl P\n\n\n\n \nco\n\n\n\nnt\nen\n\n\n\nt \nM\n\n\n\nal\nic\n\n\n\n \nac\n\n\n\nid\ns \n\n\n\nA\nce\n\n\n\ntic\n \n\n\n\nac\nid\n\n\n\ns \nO\n\n\n\nxa\nlic\n\n\n\n \nac\n\n\n\nid\ns \n\n\n\nC\nitr\n\n\n\nic\n \n\n\n\nac\nid\n\n\n\ns \nB\n\n\n\nut\nyr\n\n\n\nic\n \n\n\n\nac\nid\n\n\n\ns \nU\n\n\n\nre\nas\n\n\n\ne \nA\n\n\n\nci\nd \n\n\n\nPh\nos\n\n\n\nph\nat\n\n\n\nas\ne \n\n\n\n\u03b2-\ngl\n\n\n\nuc\nos\n\n\n\nid\nas\n\n\n\ne \nLa\n\n\n\ncc\nas\n\n\n\ne \n\n\n\npH\n \n\n\n\n1 \n-0\n\n\n\n.3\n42\n\n\n\n \n-0\n\n\n\n.2\n06\n\n\n\n \n0.\n\n\n\n21\n0 \n\n\n\n-0\n.0\n\n\n\n42\n \n\n\n\n-0\n.3\n\n\n\n19\n \n\n\n\n-0\n,5\n\n\n\n36\n* \n\n\n\n-0\n.5\n\n\n\n24\n* \n\n\n\n-0\n.2\n\n\n\n17\n \n\n\n\n0.\n78\n\n\n\n0*\n* \n\n\n\n0.\n49\n\n\n\n0*\n \n\n\n\n0.\n13\n\n\n\n9 \n0.\n\n\n\n17\n9 \n\n\n\nW\nat\n\n\n\ner\n c\n\n\n\non\nte\n\n\n\nnt\n \n\n\n\n \n1 \n\n\n\n-0\n.0\n\n\n\n98\n \n\n\n\n-0\n.0\n\n\n\n49\n \n\n\n\n-0\n.0\n\n\n\n57\n \n\n\n\n-0\n.0\n\n\n\n49\n \n\n\n\n0.\n13\n\n\n\n0 \n-0\n\n\n\n.0\n95\n\n\n\n \n-0\n\n\n\n.2\n45\n\n\n\n \n-0\n\n\n\n.3\n24\n\n\n\n \n-0\n\n\n\n.5\n32\n\n\n\n* \n-0\n\n\n\n.6\n90\n\n\n\n**\n \n\n\n\n-0\n.5\n\n\n\n59\n* \n\n\n\n T\not\n\n\n\nal\n N\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n\n\n\n\n \n1 \n\n\n\n0.\n23\n\n\n\n4 \n0.\n\n\n\n11\n1 \n\n\n\n0.\n25\n\n\n\n7 \n0.\n\n\n\n24\n4 \n\n\n\n0.\n56\n\n\n\n0*\n \n\n\n\n0.\n49\n\n\n\n0*\n \n\n\n\n0.\n14\n\n\n\n7 \n0.\n\n\n\n07\n1 \n\n\n\n0.\n10\n\n\n\n6 \n-0\n\n\n\n.0\n53\n\n\n\n\n\n\n\n T\not\n\n\n\nal\n P\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n\n\n\n\n\n\n\n\n1 \n-0\n\n\n\n.3\n12\n\n\n\n \n-0\n\n\n\n.0\n37\n\n\n\n \n-0\n\n\n\n.0\n67\n\n\n\n \n-0\n\n\n\n.0\n16\n\n\n\n \n-0\n\n\n\n.1\n30\n\n\n\n \n0.\n\n\n\n43\n6 \n\n\n\n0.\n25\n\n\n\n9 \n0.\n\n\n\n09\n4 \n\n\n\n-0\n.0\n\n\n\n32\n \n\n\n\n M\nal\n\n\n\nic\n a\n\n\n\nci\nds\n\n\n\n\n\n\n\n\n\n\n\n \n1 \n\n\n\n0.\n55\n\n\n\n1*\n \n\n\n\n0.\n45\n\n\n\n8 \n0.\n\n\n\n31\n3 \n\n\n\n0.\n73\n\n\n\n4*\n* \n\n\n\n0.\n05\n\n\n\n2 \n0.\n\n\n\n13\n7 \n\n\n\n-0\n.0\n\n\n\n19\n \n\n\n\n0.\n10\n\n\n\n0 \n\n\n\nA\nce\n\n\n\ntic\n a\n\n\n\nci\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1 \n   \n\n\n\n0.\n78\n\n\n\n0*\n* \n\n\n\n0.\n64\n\n\n\n1*\n* \n\n\n\n0.\n66\n\n\n\n1*\n* \n\n\n\n-0\n.1\n\n\n\n48\n \n\n\n\n-0\n.3\n\n\n\n29\n \n\n\n\n-0\n.0\n\n\n\n98\n \n\n\n\n0.\n26\n\n\n\n1 \n\n\n\nO\nxa\n\n\n\nlic\n a\n\n\n\nci\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n1 \n\n\n\n0.\n61\n\n\n\n7*\n* \n\n\n\n0.\n41\n\n\n\n4 \n-0\n\n\n\n.2\n60\n\n\n\n \n0.\n\n\n\n41\n6 \n\n\n\n0.\n04\n\n\n\n1 \n0.\n\n\n\n06\n2 \n\n\n\nC\nitr\n\n\n\nic\n a\n\n\n\nci\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1 \n0.\n\n\n\n75\n9*\n\n\n\n* \n0.\n\n\n\n15\n5 \n\n\n\n-0\n.1\n\n\n\n15\n \n\n\n\n0.\n25\n\n\n\n8 \n0.\n\n\n\n37\n3 \n\n\n\nB\nut\n\n\n\nyr\nic\n\n\n\n a\nci\n\n\n\nds\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1 \n-0\n\n\n\n.0\n19\n\n\n\n \n0.\n\n\n\n12\n2 \n\n\n\n0.\n19\n\n\n\n1 \n0.\n\n\n\n32\n7 \n\n\n\nU\nre\n\n\n\nas\ne \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n1 \n\n\n\n0.\n57\n\n\n\n1*\n \n\n\n\n0.\n15\n\n\n\n8 \n0.\n\n\n\n14\n5 \n\n\n\nA\nci\n\n\n\nd \nPh\n\n\n\nos\nph\n\n\n\nat\nas\n\n\n\ne \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n1 \n\n\n\n0.\n33\n\n\n\n9 \n0.\n\n\n\n22\n4 \n\n\n\n\u0392\n-g\n\n\n\nlu\nco\n\n\n\nsid\nas\n\n\n\ne \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1 \n   \n\n\n\n 0\n.8\n\n\n\n42\n**\n\n\n\n\n\n\n\nLa\ncc\n\n\n\nas\ne \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1 \n\n\n\n* \n  C\n\n\n\nor\nre\n\n\n\nla\ntio\n\n\n\nn \nis\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\n a\n\n\n\nt t\nhe\n\n\n\n 0\n.0\n\n\n\n5 \nle\n\n\n\nve\nl \n\n\n\n**\n C\n\n\n\nor\nre\n\n\n\nla\ntio\n\n\n\nn \nis\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\n a\n\n\n\nt t\nhe\n\n\n\n 0\n.0\n\n\n\n1 \nle\n\n\n\nve\nl \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201782\n\n\n\nbutyric acids; Table 2). The carboxylic acid containing organic acid in the \nrhizosphere of severely degraded forest and shrub vegetation was lower than \nin the oil palm rhizosphere at various ages, which indicates that the root of oil \npalm actively secretes organic acid exudates that might affect peat conditions. \nThe active secretion of root exudates by plant roots indicates the low solubility of \nnutrients needed by plants (Carvalhais et al. 2010). The high amounts of organic \nacids (malic and citric acid) released into the rhizosphere is a response to low \nP uptake by plants so that these acids can solubilise the phosphate, making it \nmore available for plant use (Neumann and Martinoia 2002). A similar trend was \nobserved with, the content of aromatic organic acid (phenolic acid) (Fig. 3). The \nsynthesis of carboxylic anions and their ultimate exudation in the rhizosphere also \ndepend upon the cation-anion balance (Hinsinger et al. 2003)(Not in ref list???).\n\n\n\nFig. 4:  Aliphatic acids (carboxylic acids) concentrations (ppm) in  rhizosphere of oil palm \nbased on distance from oil palm tree trunk, oil palm age and peat thickness and rhizosphere of \n\n\n\ndegraded forest and shrub in peatland.\n\n\n\n\n\n\n\nFigure 4.  Aliphatic acids (carboxylic acids) concentrations (ppm) in  rhizosphere of oil palm \nbased on distance from oil palm tree trunk, oil palm age and peat thickness and \nrhizosphere of degraded forest and shrub in peatland. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 83\n\n\n\nIn the oil palm aged <6 years, the decrease in phenolic organic acid content \nwas consistent with the distance from the tree trunk (Fig. 5), while in the oil palm \nof 6\u201315 years and >15 years, phenolic acid content increased with increasing \ndistance from the tree trunk because of the large contribution of hair root exudates \nin the release of organic acids. Phenolic acids  content was very low in the \nrhizosphere of severely degraded forest and shrub vegetation compared with oil \npalm rhizosphere. Aliphatic organic acid (carboxylic acid) content was higher \nin the rhizosphere of peat compared to that  of aromatic organic acids (phenolic \nacids), where carboxylic acids were at concentrations of <20 \u03bclL-1   and phenolic \nacids at <2 \u03bclL-1 . This finding is similar to the observation by Tuason and Arocena \n(2008) that the concentrations of low molecular weight aliphatic organic acids \nin soil samples and root exudates in the rhizosphere are higher than those of \n\n\n\nFig. 5: Aromatic acids (phenolic acids) concentration (ppm)  in  rhizosphere of oil palm based \non distance from oil palm tree trunk, oil palm age and peat thickness and rhizosphere of \n\n\n\ndegraded forest and shrub in peatland.\n\n\n\n\n\n\n\nFigure 5.   Aromatic acids (phenolic acids) concentration (ppm)  in  rhizosphere of oil palm \nbased on distance from oil palm tree trunk, oil palm age and peat thickness and \nrhizosphere of degraded forest and shrub in peatland.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201784\n\n\n\naromatic acids. In contrast to the tropical peatland in Kalimantan and Jambi, the \nconcentrations of phenolic acid (ferulyc and synapic acids) in our study were \nhigher than those of carboxylic acids (Sabiham 1997(not in ref list???); Mario \nand Sabiham 2002). \n\n\n\nEnzyme Activity\nEnzyme and microbial activities are related to and associated with decomposition \nof organic matter. Peat thickness did not significantly influence enzyme activity. \nUrease activity in the rhizosphere of the peatland was very low (<0.5 \u03bcg g\u22121  \nh\u22121) because of the low pH of peat (Fig. 2); there was a significant negative \ncorrelation between peat pH and urease activity. The nitrogen supply to meet the \nplant requirement was largely derived from the fertiliser applied. This indicated \nthat the availability of N and P were not associated with peat decomposition \nbut from the high N fertiliser application in the circle of the tree. According to \nAon and Colaneri. (2001) a decrease in urease activity could be explained by \nthe activation of nitrification and denitrification which causes the suppression of \nurease production. \n\n\n\nPhosphatase activity ranged from 2\u20136 \u03bcg g\u22121 h\u22121 and was higher than urease \nactivity, indicating that phosphate requirement by plants is  not met by the fertiliser \naddition. Phosphatase activity in peat of <3 m thickness was lower than that in \npeat of >3 m thickness and tended to decrease with increasing age of the plant. \nThis is because the P fertiliser applied (which was sufficient at age <6 years) was \ninsufficient due to low solubility. Plants can survive under low P conditions by \nsecreting phosphohydrolase (phosphatase) to solubilise organic phosphate into \nsoluble inorganic phosphate in  the  rhizosphere (Lefebvre et al.1990; Duff et \nal. 1994). The increase in phosphatase activity in the rhizosphere  of oil palm \nseedlings suppresses the production of oxalic acid (Widiastuti et al. 2015)(2003 \nin ref list).\n\n\n\n\u03b2-glucosidase and laccase activities are the activities of enzymes involved in \ndecomposing peat derived from cellulose\u2013hemicellulose and lignin. \u03b2-glucosidase \nand laccase activities in the oil palm rhizosphere tended to decrease with increasing \ndistance from the tree trunk (Fig. 6). The enzyme activities of \u03b2-glucosidase and \nlaccase were higher than those of phosphatase and urease, which indicates that \nthe nutrient supply from the peat decomposition process (lignocellulolytic) is in \nthe rhizosphere. This is due to the high root activity involved in releasing organic \nacid exudates to dissolve organic matter and absorb nutrients and the low water \ncontent of peat.\n\n\n\nThe high enzyme activity at adistance of 0\u20121 m from the tree trunk was \ncaused by the high activity of roots and low water content of peat, which led to \nan oxidative condition that increases the microbial activities in the rhizosphere. \nHowever, application of fertilisers a ta distance of 1\u20132 m from the tree trunk led \nto reduced enzyme activities and decreased organic acids content (Fig. 4 and 5), \nwhereas a decrease in enzyme activities at a distance of 3\u20134 m from the trunk was \ncaused by an increase in water content and low pH of peat. \u03b2-glucosidase and \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 85\n\n\n\nlaccase activities were significantly negatively correlated with the water content \n(Table 2). The enzyme activities in the rhizosphere of severely degraded forest \nand shrub vegetation tended to be similar as in the rhizosphere of oil palm trees \nin peat of different thickness, indicating that peat decomposition also occurs in \nunproductive peatland of different thickness. \n\n\n\nCONCLUSION\nThe enzyme activity in the oil palm rhizosphere decreased with increasing \ndistance from the tree trunk due to an increase in water content, adecrease in \npH, and an increase in organic acid root exudates, especially carboxylic organic \nacids. Aliphatic organic acids (carboxylic group) released into the rhizosphere \n\n\n\nFig. 6: Enzyme activities in  rhizosphere of oil palm based on distance from oil palm tree \ntrunk, oil palm age and peat thickness and rhizosphere of degraded forest and shrub in \n\n\n\npeatland.\n\n\n\n\n\n\n\nFigure 6.  Enzyme activities in  rhizosphere of oil palm based on distance from oil palm tree \ntrunk, oil palm age and peat thickness and rhizosphere of degraded forest and shrub \nin peatland. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201786\n\n\n\nwere higher than aromatic organic acids. The thickness of peat did not influence \nenzyme activity and organic acid content. Fertiliser application in the circle of \nthe oil palm tree trunk decreased the content of organic acid exudate and enzyme \nactivity at a distance of 1\u20132 m from the tree trunk, while the nutrient supply for \nplants was derived from fertiliser and not from peat decomposition.\n\n\n\nACKNOWLEDGEMENTS\nWe thank the management of Oil Palm Plantation in Koto Gasib, at Siak  District \nRiau Province who gave us access to their land for this research. The soil \nanalysis for this study was done by friends and colleagues in the laboratory of the \nDepartment of Soil Science and Land Resource, Bogor   Agriculture   University   \nand   the   Laboratory   of   Agrochemical   Material   Residue, Agricultural \nEnvironment Research Institute, Center for Agricultural Land Resource,  We wish \nto express our deep gratitude to them.\n\n\n\nREFERENCES\nAon M.A., and Colaneri A.C. 2001. Temporal and spatial evolution of enzymatic \n\n\n\nactivities and physico-chemical properties in an agricultural soil. Appl. Soil \nEcol. 18: 255\u2013270. \n\n\n\nBadri D.V. and J.M. Vivanco . 2009. Regulation and function of root exudates. Plant \nCell and Environ. 32: 66\u2013681. \n\n\n\nBlonska E. 2010. Enzyme activity in forest peat soils. Folia Forestalia Polonica. \nSeries A 52 (1): 20\u201325.\n\n\n\nCarvalhais L.C., R.H.Dennis, D.Fedoseyenk, M.R. Hajirezaei, R.Borriss and N.von \nWir\u00e9n. 2010. 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New York (USA) (ID): Springer-Verlag Berlin Heidelberg. \n\n\n\nStott DE, S.S. Andrews, M.M. Liebig B.J. Wienhold and D.L.Karlen 2010. Evaluation \nof \u03b2-glucosidase activity as a soil quality indicator for the soil management \nassessment framework. Soil Sci. Soc. Am. J. 74:107\u2013119.\n\n\n\nTuason, M. M. S. and M. Arocena. 2009. Root organic acid exudates and properties \nof rhizosphere soils of white spruce (Picea glauca) and subalpine fir (Abies \nlasiocarpa). Can. J. Soil Sci. 89: 287_300.\n\n\n\nTropenbos  International  Indonesia.  2012.  Kajian  Penggunaan  Lahan  Gambut  \ndi  Indonesia. Disampaikan pada seminar \u201cLahan Gambut: Maslahat atau \nMudharat\u201d. 15 Maret 2012. Jakarta, Indonesia. Forum Wartawan Pertanian.\n\n\n\nUSDA, Natural Resources Conservation Service. 2010. Soil Quality Indicators. Soil \nEnzyme. United State of America.\n\n\n\nWidiastuti H., N. Sukarno, L.K. Darusman, D.H. Goenadi, S. Smith, and E. Guhardja. \n2003. Aktivitas fosfatase dan produksi asam organik di rhizosfer dan hifosfer \nbibit kelapa sawit bermikoriza. Menara Perkebunan 71(2): 70-81. 2015 in \ntext??? \n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : jeyanny@frim.gov.my\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 161-173 (2018)                          Malaysian Society of Soil Science\n\n\n\nAssessing Soil Quality of a Regenerating Mangrove Forest \nUsing Geospatial Modelling Approach\n\n\n\nJeyanny, V.*1, Siva Kumar B.2, Ne\u2019ryez, S.R.3, Fakhri M.I.1, Daljit, S.K.2, \nMaisarah, M.Z.4, Wan Rasidah, K.1 and Husni, M.H.A.2\n\n\n\n1Forest Plantation Programme, Forest Biotechnology Division, Forest Research \nInstitute Malaysia, 52109 Kepong Selangor Malaysia.\n\n\n\n2Faculty of Agriculture, Universiti Putra Malaysia, 43400 Serdang,\nSelangor, Malaysia.\n\n\n\n3No. 48, Jalan Wirawati 5, Taman Maluri, 55100 Kuala Lumpur.\n4No. 28, Jalan Terasek Empat, Bangsar Baru, 59100 Kuala Lumpur Malaysia.\n\n\n\nABSTRACT\nMangrove forest plays an important role in our ecosystems. The functions of \nmangroves include coastline protection, aquaculture, firewood source, charcoal \nproduction and  the conservation of floral and faunal species. The decline in \nmangrove coastlines over the years has raised the need to investigate and document \nthe soil quality of microsites within regenerating and established mangrove \nstands. This paper aims to showcase the important changes that took place in a \nnewly regenerating mangrove using geospatial tools as a preliminary guideline \non rehabilitating mangroves. An established mangrove stand (back portion) and a \nnewly regenerating mangrove stand (front portion) were selected in the west coast \nof Peninsular Malaysia. Systematic sampling with 40 quadrants each measuring \n5 m2 were established in both plots. Soil sampling at 15 cm depths were carried \nout and geo-referenced using a GPS receiver. Electrical conductivity, soil pH and \nsoil organic carbon (C) were analysed using standard laboratory practices. The \nvariables were  first explored using univariate statistics. This was followed by \nvariography and kriging analyses to quantify spatial variability of soil variables. \nSoil variables exhibited a strong to moderate spatial dependence. Surface maps \nof the test variables displayed spatial clustering and acceptable accuracy of \ninterpolated values. Values for soil C and soil EC were significantly lower and soil \npH was near neutral in the regenerating site but the continuous improvement of \nsoil structure and vegetative proliferation may promise a successful rehabilitation \nmodel of coastline mangroves in time. Site-specific management of mangrove \nforest based on soil quality is necessary for future rehabilitation initiatives \nthat incorporate selection of suitable species for planting, application of soil \namendments and innovative planting methods to boost survival of seedlings.\n  \nKeywords: Coastline ecosystem, spatial variability, soil carbon, soil \n\n\n\npH, soil electrical conductivity, rehabilitation \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018162\n\n\n\nINTRODUCTION\nThe term mangrove is used to describe a group of floristically diverse trees and \nshrubs which characterise the intertidal vegetation of many tropical and subtropical \nareas. Mangroves are an integral part of the ecosystem serving various functions \nsuch as coastline protection, impeding salt water intrusion, aquaculture, fodder \nfor pole, firewood, charcoal production and conserving floral and faunal species \n(Walter et al. 2008; Spalding 2004). However, this unique ecosystem is under \ntremendous stress due to erosion, excessive anthropogenic activities and natural \ndisasters such as a tsunami. The coastlines of Malaysia have witnessed drastic \ndecline in recent years with 29% of the Malaysian coastal areas being reported to \nbe vulnerable to serious erosion (Wan Rasidah et al. 2015).\n\n\n\nMost mangroves species are influenced by soil salinity and soil pH which \naffect survival, adaptation and growth. Electrical conductivity (EC) has been \nwidely used in agriculture and forestry to describe within-field spatial variability \nin some soil properties (Lund et al. 1999; Shaner et al. 2008), and is considered a \nrapid, easy, reliable, and cheap method for mapping within field heterogeneity in \nsoil properties (Mondal et al. 2011). As a strong relationship exists between soil \nproperties and soil EC (Kitchen et al. 2003; Sudduth et al. 2005),  variability in \nsoil biophysical characters such as soil texture, organic matter, soil moisture, soil \ntemperature and cation exchange capacity can affect soil EC readings. \n\n\n\nSoil pH measurement is useful because it is a predictor of various chemical \nactivities within the soil. Soil pH significantly affects plant growth and species, \nprimarily due to the change in availability of essential elements such as phosphorus \n(P), as well as non-essential elements such as aluminium (Al) that can be toxic \nto plants at elevated concentrations (Joshi and Goshe, 2003. The importance \nof both soil salinity and pH for the growth of mangroves has been emphasised \nby Wakushima et al. (1994) and Joshi and Goshe (2003) whereby distribution \nof mangrove species differs with varying levels of pH and EC. This serves as \na useful tool in making management decisions concerning the type of plants \nsuitable for which location and the amount of soil amelioration needed to enhance \ngrowth. Litter is abundant in mangroves, being derived primarily from leaves, \ntwigs, bark and dead roots. They cover the soil surface and slowly decompose \nto form organic matter that increases microbial biomass and supports nutrient \ntransfer. Soil organic matter (C), may control soil quality which determines the \nsustainability and productivity of soils (Swezey et al. 1998) of mangroves and its \nabundance or absence alters the soil health dynamics. \n\n\n\nPrecision Agriculture is a tool that has been widely used in recent times \nboth in developed and the developing nations. Its purpose is to build a farming \nmanagement system, with the capability of expanding area profitability by \ntaking into account precise data about spatial variability in natural traits that can \npossibly cause yield variety (Mondal et al. 2011). It is quick, easy and efficient. \nThe implementation of precision agriculture requires the adoption of spatial \ntechnologies such as global positioning system (GPS), geographic information \nsystem (GIS) and remote sensing mapping. This is also the case with precision \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 163\n\n\n\nforestry which utilises similar concepts for forest management. Precision forestry \nis defined by Taylor et al. (2002), as planning and conducting site-specific \nforest management activities and operations to improve wood product quality \nand utilisation, reduce waste, increase profits and maintain the quality of the \nenvironment. Precision forestry provides real time data on variability maps, in \nterms of tree growth, tree nutrition and harvest. Spatial variability occurs when a \nquantity that is measured at different spatial locations exhibits values that differ \nacross the locations. Spatial variability can be assessed using spatial descriptive \nstatistics such as range. Precision forestry or site-specific management is aimed at \nmanaging soil spatial variability by applying inputs in accordance with the site-\nspecific requirements of a specific soil and tree (Mzuku et al. 2005) in order to \nsave cost, time and labour.\n\n\n\nThe aim of our study was to detect and compare spatial variability of soil pH, \nsoil salinity (EC) and soil carbon of a regenerating and an established mangrove \nbelt using geostatistical tools. \n\n\n\nSince concerted on-going efforts are being carried out in order to rehabilitate \nthe mangrove belts on the West Coast of Peninsular Malaysia, we hope that the \nresults can indicate the soil status that can be further utilised by forest managers \nin deciding on management strategies such as species selection, soil ameliorations \nand innovative planting techniques that will assure the survival of introduced \nmangrove seedlings via the rehabilitation efforts. \n\n\n\nMATERIALS AND  METHODS\nThe mangrove forest in Sungai Haji Dorani, Sungai Besar Selangor (3\u00b0 38\u2019N, \n101\u00b0 01\u2019E) is mainly dominated by the Avicennia and Rhizophora species. The \nannual average temperature here is about 26.9 \u00b0C, with the highest being in \nOctober (27.7 \u00b0C) and the lowest being in July (26.2 \u00b0C). Annual rainfall and \nrelative humidity are approximately 130 mm and 70\u201395%, respectively (Jeyanny \net al. 2009). In 2007, 4 units of geotubes measuring 50 m each were installed \nas a wave breaker to impede the effects of erosion and the impact of waves \non the mangrove coastline.  By 2016, the installation of geotubes allowed the \nregeneration of mangrove forests overlooking the sea. An established mangrove \nstand (back portion) and a newly regenerating mangrove stand (front portion) \nwere selected for comparison. The range for diameter at breast height (DBH) for \nthe newly regenerating mangrove stand ranged from 3.7 \u2013 10.3 cm whereas the \nvalues for established mangrove stands, the DBH ranged from 2.8 \u2013 11.5 cm, \nrespectively. Plots of 5 x 5 m quadrants with 40 quadrants each were established \nfor both mangrove stands. The length and width of systematic sampling for \neach block (i.e. mangrove type) was 25 m x 20 m, respectively. Thus, the total \nsampling area for each block was 0.05 ha. Soil samples were collected at 0-15 \ncm depth in each quadrant and transported to FRIM for the determination of soil \npH (Crison micro pH 2001, Crison Instruments, Spain), electrical conductivity \n(Mettler-Toledo Seven Easy, Mettler Toledo, Switzerland) and soil organic \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018164\n\n\n\ncarbon using Walkley and Black method. Soil pH, soil EC and soil C were \nanalysed using T-test and the mean comparison computed using Duncan Multiple \nRange Test (DMRT) in Statistical Analysis System (SAS). The variables were \nalso subjected to descriptive statistics, normality check and non-spatial outlier \ndetection in Statistix version 8.1. Spatial analyses for variables were carried out \nusing variography and interpolation techniques (Balasundram et al. 2008). The \nvariogram is a tool that quantifies spatial correlation. Variography and kriging \nwere computed using GS+ 7.0 (2004). Measured and kriged values were mapped \nusing Surfer 11 (2012). Spatial dependence of the data was computed using the \nnugget to sill ratio according to Cambardella et al. (1994) as shown in Table 1. \n\n\n\nTABLE 1\nClassification of spatial dependence\n\n\n\n13 \n \n\n\n\nTABLE 1 1 \n\n\n\nClassification of spatial dependence 2 \n\n\n\nRatio        Inference 3 \n\n\n\nNugget:Sill< 0.25     Strong spatial dependence 4 \n\n\n\n0.25 <Nugget:Sill<0.75     Moderate spatial dependence 5 \n\n\n\nNugget: Sill>0.75     Weak spatial dependence 6 \n\n\n\n 7 \n\n\n\nTABLE 2 8 \nTotal soil C, soil pH and soil EC of the study site 9 \n\n\n\n 10 \n\n\n\nVariables  Regenerating \nmangrove \n\n\n\nEstablished \nmangrove   \n\n\n\nC (%) 2.45a** 3.62b** \n\n\n\n \n0.05\u00a5 0.14 \n\n\n\npH 7.63a** 6.59b** \n\n\n\n \n0.02 0.04 \n\n\n\nEC (ms/cm) 11.99a** 20.92b** \n\n\n\n \n0.26 0.86 \n\n\n\n\u00a5 : Standard error  11 \n\n\n\n*  significant at p< 0.05 ; ** significant at p< 0.01 12 \n\n\n\n 13 \n\n\n\nTABLE 3 14 \n\n\n\nDescriptive statistics of parameters at study site 15 \n\n\n\n \nParameters n1 Mean Median CV(%) Skewness2 Kurtosis2 Normality3 \n\n\n\nRegenerating \nmangrove \n\n\n\n\n\n\n\n       C 37 2.45 2.39 12.40 0.25 -0.12 0.21 \npH 37 7.63 7.63 1.35 -0.67 0.10 0.12 \nEC 38 11.99 12.08 13.47 -0.006 -0.40 0.10 \n\n\n\n        \nEstablished \nmangrove \n\n\n\n\n\n\n\n   \nC 39 3.62 3.67 24.94 0.25 0.67 0.52 \n\n\n\npH 40 6.60 6.67 4.67 -0.68 -0.23 0.07 \nEC  39 20.92 21.40 25.61 -0.89 1.43 0.06 \n\n\n\n        \nCV: Coefficient of Variation 16 \n 17 \n\n\n\nKriged values were cross-validated to assess accuracy of the interpolated \nvalues using the following three accuracy measurements procedure. \n\n\n\nFirst, the interpolated Mean Error (ME) should be close to zero and is calculated \nas follows:\n                                                                                          \n\n\n\nwhere\nn  =  the number of sample points\n(z \u0305 )  =  is the predicted value of the variable at point x_i and \nz(x_i )       =  is the measured value of the variable at point x_i\n\n\n\nSecond, the Mean Squared Error (MSE) should be less than the sample variance. \nThe MSE is given by\n\n\n\n                                                                                              5 \n \n\n\n\ncomparison. The range for diameter at breast height (DBH) for the newly regenerating mangrove 1 \n\n\n\nstand ranged from 3.7 \u2013 10.3 cm whereas the values for established mangrove stands, the DBH ranged 2 \n\n\n\nfrom2.8 \u2013 11.5 cm, respectively. Plots of 5 x 5 m quadrants with 40 quadrants each were established 3 \n\n\n\nfor both mangrove stands. The length and width of systematic sampling for each block (i.e. mangrove 4 \n\n\n\ntype) was 25 m x 20 m, respectively. Thus, the total sampling area for each block was 0.05 ha. Soil 5 \n\n\n\nsamples were collected at 0-15 cm depth in each quadrant and transported to FRIM for the 6 \n\n\n\ndetermination of soil pH (Crison micro pH 2001, Crison Instruments, Spain), electrical conductivity 7 \n\n\n\n(Mettler-Toledo Seven Easy, Mettler Toledo, Switzerland) and soil organic carbon using Walkley and 8 \n\n\n\nBlack method. Soil pH, soil EC and soil C were analysed using T-test and the mean comparison 9 \n\n\n\ncomputed using Duncan Multiple Range Test (DMRT) in Statistical Analysis System (SAS). The 10 \n\n\n\nvariables were also subjected to descriptive statistics, normality check and non-spatial outlier 11 \n\n\n\ndetection in Statistix version 8.1. Spatial analyses for variables were carried out using variography 12 \n\n\n\nand interpolation techniques (Balasundram et al. 2008). The variogram is a tool that quantifies spatial 13 \n\n\n\ncorrelation. Variography and kriging were computed using GS+ 7.0 (2004). Measured and kriged 14 \n\n\n\nvalues were mapped using Surfer 11 (2012). Spatial dependence of the data was computed using the 15 \n\n\n\nnugget to sill ratio according to Cambardella et al. (1994) as shown in Table 1.  16 \n\n\n\n 17 \n\n\n\nKriged values were cross-validated to assess accuracy of the interpolated values using the following 18 \n\n\n\nthree accuracy measurements procedure.  19 \n\n\n\n 20 \n\n\n\nFirst, the interpolated Mean Error (ME) should be close to zero and is calculated as follows: 21 \n\n\n\n 22 \n\n\n\n    \n \u2211[ \u0305(  )   (  )]\n\n\n\n\n\n\n\n   \n                                                                   ( ) \n\n\n\nwhere 23 \n\n\n\nn  =  the number of sample points 24 \n\n\n\n \u0305  =  is the predicted value of the variable at point    and  25 \n\n\n\n (  )       =  is the measured value of the variable at point    26 \n\n\n\n6 \n \n\n\n\nSecond, the Mean Squared Error (MSE) should be less than the sample variance. The MSE is given 1 \n\n\n\nby 2 \n\n\n\n     \n \u2211[ \u0305(  )   (  )]\n\n\n\n\n\n\n\n   \n                                                                  (  ) \n\n\n\nThird, the ratio of the theoretical and calculated variance, called the Standardized Mean Squared Error 3 \n\n\n\n(SMSE), should be approximately close to one. The SMSE is given by: 4 \n\n\n\n\n\n\n\n   \u2211 [ \u0305(  )   (  )] \n \n   \n\n\n\n                                                       (   ) \n\n\n\n 5 \nwhere    is the theoretical variance. 6 \n 7 \n\n\n\n 8 \n\n\n\n 9 \n\n\n\n 10 \n\n\n\nRESULTS AND DISCUSSION 11 \n\n\n\nOur results showed that the amount of soil carbon in the established mangroves was 47% significantly 12 \n\n\n\nhigher compared to the regenerating mangroves (Table 2). The soil pH for the established site was at 13 \n\n\n\nleast 1 unit lower compared to the newly regenerating area which recorded near neutral (pH 7) values. 14 \n\n\n\nThe salinity of the established mangroves were 1.7 fold significantly higher compared with the newly 15 \n\n\n\nregenerating mangroves. This study showed that the established mangroves had slightly higher C 16 \n\n\n\ncontent (organic matter) compared to a well-established mangrove forest (2.64%) in the Kelantan 17 \n\n\n\nDelta (Jeyanny and Wan Rasidah, 2015). Soil pH at 6.5 to 7.0 is considered ideal as it directly 18 \n\n\n\nincreases the solubility of the plant nutrients in soil solutions (Brady and Weil 2008). The higher EC 19 \n\n\n\nvalues in the established area indicate the tolerance of mangrove species and adaptability to grow well 20 \n\n\n\nas Avicennia were reported to withstand EC levels up to 39 ms/cm (Chan and Baba 2009).  21 \n\n\n\n 22 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 165\n\n\n\nThird, the ratio of the theoretical and calculated variance, called the Standardized \nMean Squared Error (SMSE), should be approximately close to one. The SMSE \nis given by:\n\n\n\nwhere \u03c32 is the theoretical variance.\n\n\n\nRESULTS AND DISCUSSION\nOur results showed that the amount of soil carbon in the established mangroves \nwas 47% significantly higher compared to the regenerating mangroves (Table 2). \nThe soil pH for the established site was at least 1 unit lower compared to the \nnewly regenerating area which recorded near neutral (pH 7) values. The salinity \nof the established mangroves were 1.7 fold significantly higher compared with the \nnewly regenerating mangroves. This study showed that the established mangroves \nhad slightly higher C content (organic matter) compared to a well-established \nmangrove forest (2.64%) in the Kelantan Delta (Jeyanny and Wan Rasidah, 2015). \nSoil pH at 6.5 to 7.0 is considered ideal as it directly increases the solubility of the \nplant nutrients in soil solutions (Brady and Weil 2008). The higher EC values in \nthe established area indicate the tolerance of mangrove species and its adaptability \nto grow well as Avicennia were reported to withstand EC levels up to 39 ms/cm \n(Chan and Baba 2009). \n\n\n\n6 \n \n\n\n\nSecond, the Mean Squared Error (MSE) should be less than the sample variance. The MSE is given 1 \n\n\n\nby 2 \n\n\n\n     \n \u2211[ \u0305(  )   (  )]\n\n\n\n\n\n\n\n   \n                                                                  (  ) \n\n\n\nThird, the ratio of the theoretical and calculated variance, called the Standardized Mean Squared Error 3 \n\n\n\n(SMSE), should be approximately close to one. The SMSE is given by: 4 \n\n\n\n\n\n\n\n   \u2211 [ \u0305(  )   (  )] \n \n   \n\n\n\n                                                       (   ) \n\n\n\n 5 \nwhere    is the theoretical variance. 6 \n 7 \n\n\n\n 8 \n\n\n\n 9 \n\n\n\n 10 \n\n\n\nRESULTS AND DISCUSSION 11 \n\n\n\nOur results showed that the amount of soil carbon in the established mangroves was 47% significantly 12 \n\n\n\nhigher compared to the regenerating mangroves (Table 2). The soil pH for the established site was at 13 \n\n\n\nleast 1 unit lower compared to the newly regenerating area which recorded near neutral (pH 7) values. 14 \n\n\n\nThe salinity of the established mangroves were 1.7 fold significantly higher compared with the newly 15 \n\n\n\nregenerating mangroves. This study showed that the established mangroves had slightly higher C 16 \n\n\n\ncontent (organic matter) compared to a well-established mangrove forest (2.64%) in the Kelantan 17 \n\n\n\nDelta (Jeyanny and Wan Rasidah, 2015). Soil pH at 6.5 to 7.0 is considered ideal as it directly 18 \n\n\n\nincreases the solubility of the plant nutrients in soil solutions (Brady and Weil 2008). The higher EC 19 \n\n\n\nvalues in the established area indicate the tolerance of mangrove species and adaptability to grow well 20 \n\n\n\nas Avicennia were reported to withstand EC levels up to 39 ms/cm (Chan and Baba 2009).  21 \n\n\n\n 22 \n\n\n\nTABLE 2\nTotal soil C, soil pH and soil EC of the study site\n\n\n\n13 \n \n\n\n\nNugget: Sill>0.75     Weak spatial dependence 1 \n\n\n\n 2 \n\n\n\nTABLE 2 3 \nTotal soil C, soil pH and soil EC of the study site 4 \n\n\n\n 5 \n\n\n\nVariables  Regenerating \nmangrove \n\n\n\nEstablished \nmangrove   \n\n\n\nC (%) 2.45a** 3.62b** \n\n\n\n \n0.05\u00a5 0.14 \n\n\n\npH 7.63a** 6.59b** \n\n\n\n \n0.02 0.04 \n\n\n\nEC (ms/cm) 11.99a** 20.92b** \n\n\n\n \n0.26 0.86 \n\n\n\n\u00a5 : Standard error *  significant at p< 0.05 ; ** significant at p< 0.01 6 \n\n\n\nTABLE 3 7 \n\n\n\nDescriptive statistics of parameters at study site 8 \n\n\n\n \nParameters n1 Mean Median CV(%) Skewness2 Kurtosis2 Normality3 \n\n\n\nRegenerating \nmangrove \n\n\n\n\n\n\n\n       C 37 2.45 2.39 12.40 0.25 -0.12 0.21 \npH 37 7.63 7.63 1.35 -0.67 0.10 0.12 \nEC 38 11.99 12.08 13.47 -0.006 -0.40 0.10 \n\n\n\n        \nEstablished \nmangrove \n\n\n\n\n\n\n\n   \nC 39 3.62 3.67 24.94 0.25 0.67 0.52 \n\n\n\npH 40 6.60 6.67 4.67 -0.68 -0.23 0.07 \nEC  39 20.92 21.40 25.61 -0.89 1.43 0.06 \n\n\n\n        \nCV: Coefficient of Variation 9 \n 10 \n\n\n\nTABLE 4 11 \nCross validation statistics of kriged values at study site 12 \n\n\n\n 13 \nParameters Model Sample variance    ME MSE SMSE \n\n\n\nRegenerating \nmangrove            \n\n\n\nC Spherical 0.09 0.004 0.05 0.60 \npH Exponential 0.01 -0.001 0.01 1.08 \n\n\n\nEC  Exponential 2.61 -0.003 2.60 1.02 \n      \n\n\n\n\u00a5 : Standard error *  significant at p< 0.05 ; ** significant at p< 0.01\n\n\n\nSoil pH and soil salinity (EC) values for the regenerating site (front) \nconcurred with a previous study carried out by Mohamad Fakhri et al. (2017) \nin the mudflats. Previous reports in the mudflats have shown that severe soil \ndegradation, soil erosion, structureless substrates and soil depletion (Jeyanny \nand Wan Rasidah, 2015; Jeyanny et al. 2009) have occurred. However, with \ngeotubes placement, it was observed that the area cordoned off for restoration \nhave transitioned from bare mudflats to newly regenerating vegetation.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018166\n\n\n\nFor the regenerating site, all the variables were normally distributed (Table \n3). The mean for soil pH was at the neutral zone. All the variables displayed \nnegative skewness except for soil C. Negative kurtosis was displayed by the \nvariables C and EC, implying a rather flat frequency distribution. The coefficient \nof variance (CV) was 1.35% for soil pH and 13.47% for soil EC. A slight degree \nof variation was detected in the test variables inferring minimal variability for test \nvariables.\n\n\n\nFor the established site, all the variables were normally distributed (Table \n3). The mean for soil pH was acidic. Soil pH and EC at the back portion displayed \nnegative skewness. Only soil pH displayed negative kurtosis, showing variable \nfrequency distribution. CV was 4.67% for soil pH and 25.61% for soil EC. \n\n\n\nTABLE 3\nDescriptive statistics of parameters at study site\n\n\n\n13 \n \n\n\n\nNugget: Sill>0.75     Weak spatial dependence 1 \n\n\n\n 2 \n\n\n\nTABLE 2 3 \nTotal soil C, soil pH and soil EC of the study site 4 \n\n\n\n 5 \n\n\n\nVariables  Regenerating \nmangrove \n\n\n\nEstablished \nmangrove   \n\n\n\nC (%) 2.45a** 3.62b** \n\n\n\n \n0.05\u00a5 0.14 \n\n\n\npH 7.63a** 6.59b** \n\n\n\n \n0.02 0.04 \n\n\n\nEC (ms/cm) 11.99a** 20.92b** \n\n\n\n \n0.26 0.86 \n\n\n\n\u00a5 : Standard error *  significant at p< 0.05 ; ** significant at p< 0.01 6 \n\n\n\nTABLE 3 7 \n\n\n\nDescriptive statistics of parameters at study site 8 \n\n\n\n \nParameters n1 Mean Median CV(%) Skewness2 Kurtosis2 Normality3 \n\n\n\nRegenerating \nmangrove \n\n\n\n\n\n\n\n       C 37 2.45 2.39 12.40 0.25 -0.12 0.21 \npH 37 7.63 7.63 1.35 -0.67 0.10 0.12 \nEC 38 11.99 12.08 13.47 -0.006 -0.40 0.10 \n\n\n\n        \nEstablished \nmangrove \n\n\n\n\n\n\n\n   \nC 39 3.62 3.67 24.94 0.25 0.67 0.52 \n\n\n\npH 40 6.60 6.67 4.67 -0.68 -0.23 0.07 \nEC  39 20.92 21.40 25.61 -0.89 1.43 0.06 \n\n\n\n        \nCV: Coefficient of Variation 9 \n 10 \n\n\n\nTABLE 4 11 \nCross validation statistics of kriged values at study site 12 \n\n\n\n 13 \nParameters Model Sample variance    ME MSE SMSE \n\n\n\nRegenerating \nmangrove            \n\n\n\nC Spherical 0.09 0.004 0.05 0.60 \npH Exponential 0.01 -0.001 0.01 1.08 \n\n\n\nEC  Exponential 2.61 -0.003 2.60 1.02 \n      \n\n\n\nFor the regenerating site, almost all variables fitted the cross-validation \nassumptions (Table 4). The SMSE values for C were slightly lower than 1 but \nwere acceptable. The SMSE for soil pH at the established mangrove did not fit \nthe criteria, implying that the cross-validation assumption was poorly recognised.   \nHowever, ME and MSE were acceptable (Table 4). This could be due to the very \nsmall range of distribution for soil pH at the back portion that gave a CV of 4.6%, \nimplying that soil pH did not vary greatly. \n\n\n\nFor the regenerating site, the semi-vari 0 grams corresponding to soil C, EC \nand pH were constructed with the active lag distance of 51 m, but the uniform \ninterval was set within the range of 5.1 \u2013 9 m [Figures 1(a)-(c)]. For soil C, the \nbest model was spherical, whereas pH and EC displayed exponential models \n[Figures 1(a)-(c)]. The semi-variance value at the shortest distance is defined as \nthe sill. The nugget shows the analytical error while the sill expresses the amount \nof spatial structural variance (Liu et al. 2010). Spatial dependence was calculated \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 167\n\n\n\nby dividing nugget with sill and was found to range from <0 to 0.5. Strong spatial \ndependence was shown by soil C, while all other variables displayed moderate \nspatial dependence [Figures 1(a)-(c)]. The effective range (ER) values were \nmoderate to distant, 19 to 632 m. Sampling points which are distanced greater \nthan the ER is spatially independent, and will no longer demonstrate spatial \ncorrelation (Balasundram et al. 2008).\n\n\n\nFor the established mangrove, the active lag distance was computed at 36.35 \nwhereas the uniform interval was set within the range of 3.64 \u2013 7.5 [Figures 1(d)-\n(f)]. Soil pH and EC displayed a spherical model whereas soil C displayed an \nexponential model. Soil EC had the highest nugget and sill value, 18.77 and 39.88 \nrespectively. Strong spatial dependence was shown by soil pH, whereas C and EC \nshowed moderate spatial dependence. The longest ER was shown by C (158.3 m) \nand shortest by pH (75.0 m) [Figures 1(d)-(f)]. Soil C is known to have longer ER \n(Jeyanny et al. 2016; Law et al. 2009) due to the large spatial variations in soil C \n(Zhang and McGrath 2004).\n\n\n\nThe distribution and trends of both measured and kriged values for each \nvariable are represented as surface maps in Figures 2. Almost all variables \nexhibited spatial clustering of test values. For soil C, the north-west direction \nhad the highest amount of C, gradually reducing towards the south-west direction \n[Figure 2(a)]  the regenerating mangrove. The soil pH at the north-west region \nat the regenerating mangrove showed the highest values but reached a plateau \nat the south-west region [Figure 2(b)]. We believe that the amount of soil C \naccumulation was more profound in the north-west region due to the swirling \neffects of wave action that may have deposited organic rich sediments at this \narea that assisted in the soil aggregation processes. The increase in soil pH at \n\n\n\nTABLE 4\nCross validation statistics of kriged values at study site\n\n\n\n14 \n \n\n\n\nTABLE 4 1 \nCross validation statistics of kriged values at study site 2 \n\n\n\n 3 \nParameters Model Sample variance    ME MSE SMSE \n\n\n\nRegenerating \nmangrove            \n\n\n\nC Spherical 0.09 0.004 0.05 0.60 \npH Exponential 0.01 -0.001 0.01 1.08 \n\n\n\nEC  Exponential 2.61 -0.003 2.60 1.02 \n      \n\n\n\nEstablished \nmangrove          \n\n\n\nC Exponential 0.81 -0.00 0.83 1.05 \npH Spherical 0.09 0.005 0.04 0.44 \n\n\n\nEC  Exponential 28.71 0.14 23.94 0.86 \n            \n\n\n\n 4 \n\n\n\nME: Mean Error; MSE: Mean Squared Error; SMSE: Sum of Mean Squared Error;  5 \n\n\n\nME = 0; MSE \u2264   ; SMSE = 1  6 \n\n\n\n 7 \n\n\n\n 8 \n\n\n\n \na) a) Total Carbon (%), Regenerating mangrove \n\n\n\n    Model: Spherical; Spatial dependence:Strong;  \n    Nugget: 0.0001; Sill: 0.09; ER: 19.0 m \n \n\n\n\n\n\n\n\n \nd) Total Carbon (%), Established mangrove \n    Model: Exponential; Spatial dependence:   \n    Moderate; Nugget: 0.70; Sill: 1.4; ER: 158.3 m \n \n\n\n\n \nb) Soil  pH, Regenerating mangrove \n\n\n\n \ne) Soil pH, Established mangrove \n\n\n\nME: Mean Error; MSE: Mean Squared Error; SMSE: Sum of Mean Squared Error; \nME = 0; MSE \u2264 \u03c3^2; SMSE = 1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018168\n\n\n\nthis location was due to the dynamic water percolation and seawater intrusion \n(Jeyanny et al. 2018), which is more neutral compared to the coastal belt. Soil \nEC at the south-western area displayed the highest values whereas values dipped \ntowards the north-west boundary [Figure 2(c)] for the same site. There is a clear \nindication that the relationship between soil pH and soil EC is antagonistic in this \narea and it may be correlated with other soil chemical properties which are not \ndescribed here. 15 \n\n\n\n\n\n\n\n \na) a) Total Carbon (%), Regenerating mangrove \n\n\n\n    Model: Spherical; Spatial dependence:Strong;  \n    Nugget: 0.0001; Sill: 0.09; ER: 19.0 m \n \n\n\n\n\n\n\n\n \nd) Total Carbon (%), Established mangrove \n    Model: Exponential; Spatial dependence:   \n    Moderate; Nugget: 0.70; Sill: 1.4; ER: 158.3 m \n \n\n\n\n \nb) Soil  pH, Regenerating mangrove \n  Model: Exponential; Spatial dependence: \n Moderate; Nugget: 0.01; Sill: 0.02; ER:632.7 m \n \n\n\n\n \ne) Soil pH, Established mangrove \n    Model: Spherical; Spatial dependence: Strong;  \n    Nugget: 0.02; Sill: 0.23; ER: 75.0 m \n \n \n\n\n\n \nc) Electrical Conductivity, Regenerating mangrove \n    Model: Exponential; Spatial dependence: \n   Moderate; Nugget: 2.24; Sill: 4.48; ER: 616.8 m \n \n\n\n\nf)Electrical Conductivity, Established mangrove \n   Model: Spherical; Spatial dependence:  \n   Moderate; Nugget: 18.77; Sill: 39.88; ER: 81.0 m \n \n\n\n\n\n\n\n\nFigures 1 (a-f): Spatial structure and attributes of selected soil variables at the regenerating and \nestablished mangroves in Sungai Haji Dorani, Sungai Besar \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigures 1 (a-f): Spatial structure and attributes of selected soil variables at the \nregenerating and established mangroves in Sungai Haji Dorani, Sungai Besar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 169\n\n\n\nSoil C surged in the eastern region but levelled off towards the west [Figure \n2(d)] at the established mangroves. Soil pH at the western side had the highest \nvalues but took a dip towards the east [Figure 2(e)]. Our observations at site show \nthat the mangrove belt is close to outlets that transport residues from agricultural \nand human activities in the east. This may have affected the surge in soil C \nand acidic conditions although these assumptions need further investigations. \nElectrical conductivity has the highest values around the north-east region and the \n\n\n\n16 \n \n\n\n\n 1 \n\n\n\n 2 \n\n\n\n \na) Total Carbon (%), Regenerating \n\n\n\nmangrove \n\n\n\n \nd)  Total Carbon (%), Established mangrove  \n\n\n\n \nb) Soil pH, Regenerating mangrove \n\n\n\n \ne) Soil pH, Established mangrove \n\n\n\n \nc) Electrical Conductivity, Regenerating \n\n\n\nmangrove \n\n\n\n \nf) Electrical Conductivity, Established mangrove \n\n\n\n 3 \nFigures 2 (a-f): Spatial variability of selected soil variables (based on measured and kriged values) at 4 \nthe regenerating and established mangroves in Sungai Haji Dorani, Sungai Besar. 5 \n 6 \n 7 \n\n\n\n 8 \n\n\n\n 9 \n\n\n\nFigures 2 (a-f): Spatial variability of selected soil variables (based on measured and \nkriged values) at the regenerating and established mangroves in\n\n\n\nSungai Haji Dorani, Sungai Besar.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018170\n\n\n\nlowest values around the south-west region, indicative of the abundant Avicennia \ntrees which are more tolerant to higher EC levels (Chan and Baba 2009) [Figure \n2 (f)]. \n\n\n\nCONCLUSION \nSoil total C, soil pH and soil EC exhibited spatial variability in the established \nand regenerating mangroves. Spatial structure of test variables varied and mostly \nfitted spherical or exponential models. Variables exhibited a strong to moderate \nspatial dependence. The established mangrove showed shorter ER, implying that \nspacing of samples should be closer in the established mangroves. The majority \nof surface maps of the test variables showed distinct spatial clustering and \ndisplayed acceptable accuracy of interpolated values, suggesting that total C, soil \npH and soil salinity can be estimated reliably via geo-spatial analysis. Although \nwe acknowledge that coastal mangroves are dynamic and constantly changing, \nthis study was able to elucidate the varying soil qualities of an established and a \nnewly regenerating mangrove forest using spatial maps. The gradual increase in \nsoil properties (soil C and EC) in the regenerating mangroves may indicate the \ncapacity of soils in decomposition and nutrient cycling processes in supporting \nthe evolving mudflat zone for vegetative productivity. Soil restoration with wave \nbreakers may benefit the degrading coastline in the long run, provided that the \nsoil environment is conducive for mangrove forest re-establishment. These \nmaps can be utilised by forestry managers in understanding the natural evolving \nenvironment of the mudflats as well as managing the area site-specifically for \nfuture rehabilitation strategies and monitoring of coastal mangroves. Since most \nrehabilitation strategies are labour and cost intensive, a rapid assessment of the \ncoastline using geospatial tools will provide baseline data that can be utilised for \nsuitable planting techniques (i.e. soil ameliorations, species selection) and further \nmonitored for spatial and temporal changes. \n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to thank the FRIM Steering Committee, Department \nof Forestry Malaysia and the Ministry of Natural Resources and Environment \nMalaysia for the funding (Vot No: 23410207001). The field and technical \nassistance of the Soil Management Branch of FRIM is also duly acknowledged. \n\n\n\nREFERENCES \nBalasundram S.K., M.H.A. Husni and O.H. Ahmed.2008. Application of geostatistical \n\n\n\ntools to quantify spatial variability of selected soil chemical properties from a \ncultivated tropical peat. Journal of Agronomy 7(1): 82-87.\n\n\n\nBrady, N.C. and  R.R. Weil. 2008. The Nature and Properties of Soils. 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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 186-195 \n\n\n\n186 \n \n\n\n\nAnalysis of Chemical Soil Properties and Social Economic Study of \nSwampland Rice Productivity \n\n\n\n \nHandayani, Etik Puji1*, Rakhmiati1, Zulkarnain2, Gusmiatun3,  \n\n\n\nIsnaini, Soni2, and Maryati2 \n \n\n\n\n1Department of Agrotechnology, Dharma Wacana College of Agricultural Sciences Metro,  \nLampung, Indonesia \n\n\n\n2Department of Agribusiness, Dharma Wacana College of Agricultural Sciences Metro, \nLampung, Indonesia \n\n\n\n3Department of Agrotecnology, Faculty of Agriculture, Muhammadiyah Palembang University, \nPalembang, Indonesia \n\n\n\n \n*Correspondence:etikpuji68@gmail.com  \n\n\n\n \nABSTRACT \n\n\n\nSoil characteristics are a crucial matter in relation torice production in a swampland area.   A study \non chemical soil characteristics and economics of swampland rice production was carried out at a \nsubdistrict of Rambutan and Banyuasin 1, Banyuasin, South Sumatera.  Evaluation of soil organic \nmatter in both locations foundcarbon content in Rambutan and Banyuasinto bemedium to high and \nmedium to very high, respectively. In respect to nitrogen content, Rambutan had very low to medium, \nwhile Banyuasin 1 fell into the low to medium categories.  Based on the average C/N ratio, both \nlocations were considered to have a high accumulation of organic matter in the soil.  In regard to \nmacronutrient content, especially P and K, Rambutan and Banyuasin 1 fell into the low to medium \ncategory for P and low for K. Cost analysis of swampland rice production in both locations revealed \nthe production processto be economically feasible.  Linear regression analysis among pertinent factors \nin production improvement presented the positive impacts on rice productivity to be derived from \nAmount of planted seeds, phosphate fertilizer, return of straw to soil/organic matter and knowledge \nof the recommended fertilizer dosage. \n\n\n\n \nINTRODUCTION \n\n\n\nA freshwater swampland is naturally occurring soils or sediment formed between two rivers or \nlakes in a lowland.  It is seasonally submerged and critically dependent upon rainwater and \nseasonal flooding to maintain natural water level fluctuation (Dodds et al. 2019).  In Indonesia, \nthe swampland area covers 34.12 million ha and is mostly located in Sumatera, Kalimantan \nand West Papua.  Of this area, 25.2 million ha are non-tidal swamps while around 8.92 million \nha are tidal swamps.  According to records, the area of freshwater swampland utilised for \nagricultural activities is 7.52 million ha with the breakdown being 5.12 million ha for \nflooded/paddy rice, 1.47 million ha for horticulture and 0.93 million ha for perennial crops \n(Sulaiman et al. 2019).  In Sumatera island, the largest swamp land area of 2.98 million ha is \nlocated in South Sumatera Province, of which only 368,690 ha have been utilised for rice which \nis cultivated in 70,908 ha of shallow swamp land, 129,103 ha in mid-swamp land and 168,670 \nha in deep swamp land (Muhakka et al. 2019). \n \nIn many parts of the world, most swamplands do not actually function as an ideal land resource \nfor agriculture.  According to Susilawati et al.(2017), low optimisation of swamp land is due \nto its physical condition, mainly its fragile nature. Swamplands pose several obstacles to \ncultivation. These are suboptimal physical and chemical conditions with high acidity and \n\n\n\nKey words: Swampland rice production, soil chemical characteristics, cost analysis,   \nregression equation \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 186-195 \n\n\n\n187 \n \n\n\n\nerratic water logging and flooding in the rainy season withdrought in the dry season. Wetlands \nare also characterised by high concentrations of toxic elements such as iron (Fe), aluminium \n(Al), sulphur (S) and sodium (Na).  Nutrient deficiency has also been reported, especially \nphosphorus (P), potassium (K), zinc (Zn) copper (Cu) and boron (B).  Further, biological \nproblems of a high level of weeds, pests and diseases are also present (Sulaiman et al. 2019). \nThe continuing scarcity of arable land in Indonesia has led to wetlands being utilised as \nproductive agricultural lands.  Swamplands do offer advantages for agriculture such as \nabundant water, resilience to seasonal drought and support for longer cropping periods.  \nSwamplands have great potential for an integrated farming system (food crops, estate crops \nand animal husbandry) (Wildayana and Armanto 2017).  Based on these considerations, \nagriculture in swamplands should be directed to sustainability in terms of production and being \nenvironment friendly. The approaches to develop sustainable production should involve natural \nresources mapping, land suitability to crops, soil amelioration and improvement of the \nirrigation network, development of specific technology, empowerment of local communities \nand development of infrastructure and agribusiness (Ar-Riza and Alkasuma 2008).  \n \nInformation related to soil characteristics and suitable crops in specific wetland locations are \nimportant considerations in the development of sustainable wetland productive areas.  The \nprovince of South Sumatera has quite a large swampland area and several parts have been \ncultivated, mainly with rice/paddy (Purbiyanti et al. 2019). Rice cultivation starts in the dry \nseason, when the submerged land is still under shallow conditions with the vegetative growth \nand harvest period occurring in the rainy season.  Various varieties are observed to be cultivated \nin these areas, from local to improved varieties indicating farmers do select adaptive varieties \n(Irmawati et al. 2015; Kodir et al. 2016). Improvements in rice productivity in swampland \nareas could also be conducted through the implementation of specific procedures such as \nnutrient management.  Proper evaluation of soil chemical properties is needed to determine \nlevel of soil fertility, nutrient status and other pertinent features that might directly or indirectly \ninvolve the availability of macro and micro nutrients and absorption capacity of the rice plant \nto the required nutrients. This paper elucidates the chemical soil properties of swampland in \nBanyuasin, South Sumatera in relation to rice productivity in the area.  This information should \nserve as the basis for recommendations in nutrient management, a vital component of \nsustainable production strategy to improve and sustain rice production.  An economic study of \nexisting rice production systems was also carried out to obtain a socio-economic overview of \nswampland rice production in Banyuasin, South Sumatera. \n \n\n\n\nMATERIALS AND METHODS \nThe study was carried out at the Subdistrict of Rambutan and Banyuasin 1, Banyuasin, South \nSumatera (02o43'48\"- 03o09'00\" Sand 104o10'48\" - 105o07'12\" E)in 2012. The acreage of the  \nfreshwater swamplands  was recorded as 624.55 km2 in Rambutan, and 701.38 km2 on \nBanyuasin 1. Administratively, the Banyuasin District is bounded by Muara Jambi Regency, \nJambi Province and Bangka Straits in the North, East by Ogan Komering Ilir Regency, West \nby Musi Banyuasin Regency and South by Ogan Komering Ilir Regency, Palembang City, and \nMuara Enim Regency. \n\n\n\nCollection of Soil Samples and Analysis \nEach sub district of the study site was divided into six locations based on availability of \ncultivated swamplands.  In each location, a composite soil sample was taken using a 5- point \nsampling technique from a depth of \u00b120 cm from moist to wet soil conditions.  Subsequently, \nthe soil samples were stirred evenly in a plastic bucket,cleared of  roots, plants parts and animal \ndebris. The soil samples were then brought to the laboratory for further assessment. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 186-195 \n\n\n\n188 \n \n\n\n\nThe soil analysis was conducted at the laboratory of Soil and Agroclimate Research Center, \nBogor, Indonesia. The soil analysis included (1) pH extracted by H2O and 1 M KCl measured \nwith the pH electrode; (2) C-organic, wet digestion of K2Cr2O7 using Walkley and Black \nmethod; (3) total-N content with Kjeldahl method; (4) available-P extracted by Bray-1 and \ntotal-P extracted by HCl 25% measured with a spectrophotometer; and (5) total-K extracted by \nHCl 25% measured with a flame photometer. Soil analysis data describes the current nutrient \nstatus of the freshwater swampland of the targeted locations. \n \nEconomic Assessment of Rice Production \nThe budgetary technique used for cost and return analysis is the gross margin. In 1 ha, the gross \nmargin (GM) is the difference between Total Revenue (TR) and Total Variable Costs (TVC), \nexpressed through the equation : GM =TR \u2013 TVC, and NFI = GM \u2013 TFC \nwhere, \n\n\n\nTR = Total Revenue (IDR/ha)  \nTVC = Total Variable Cost (IDR/ha)  \nTFC= Total Fixed Cost (IDR/ha)  \nNFI = Net Farm Income (IDR/ha)  \n\n\n\n \nTotal Revenue (TR) is derived from TR = \u2211QyPy - \u2211XiPxi \nwhere \n\n\n\nTR =  Total Revenue  \nQy = Qutput (kg/ha)  \nPy = Unit price of the output (N)  \nQyPy = Total revenue derived ha-1 \nXi = Quantity of the ith input ha-1 \nPxi = Price unit-1 of the ith input ha-1 \nXiPxi = Total cost associated with input ha-1 \n\n\n\n\n\n\n\nThe relationship between input and output was analysed using linear regression equation \n(Paltasingh and Goyari 2018), Y = a + bX, \nwhere  \n\n\n\nY = Revenue, production costs, farm income  \na = Constant  \nb = Regression coefficient  \nX = Planting area \nThe significance of regression coefficient was carried out by F test with \u03b1 = 5% \n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\n \nOrganic Matter Content and Soil Acidity \nOrganic matter  content represented by C/N ratio and soil pH from the sites of Rambutan and \nBanyuasin 1 sub districts are presented in Table 1.  Carbon content in the soil from Rambutan \nranged from 2.21 to 5.61%, while in Banyuasin 1,it ranged from 2.31 to 8.42%.  Nitrogen \ncontent in Rambutan varied from 0.08 to 0.41% and in Banyuasin 1, it was found to be  0.14 \nto 0.48%.  Based on the study by Prabowo and Subantoro (2008),the carbon content in \nRambutan was categorised as medium to high, while in Banyuasin 1, it was categorised as \nmedium to very high.  In regard to nitrogen content, Rambutan was classified as very low to \nmedium, while Banyuasin 1 fell into low to medium (Rusdiana and Lubis 2012). Based on the \naverage C/N ratio, both locations were considered to have a high accumulation of organic \nmatter in the soil (Rahmi and Biantary 2014). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 186-195 \n\n\n\n189 \n \n\n\n\n \nTABLE 1 \n\n\n\nC/N ratio and pH of soil samples taken from six location points from sub-districts of \nRambutan and Banyuasin 1, South Sumatera \n\n\n\nLocation Organic matter (%) pH (1:5) \nC N C/N H2O KCl \n\n\n\nRambutan      \n1 4.58 0.24 19 3.73 3.45 \n2 2.52 0.09 26 4.04 3.75 \n3 3.80 0.23 17 3.61 3.32 \n4 5.62 0.08 27 3.68 3.37 \n5 2.21 0.41 14 3.83 3.55 \n6 2.76 0.12 22 3.81 3.44 \nAverage 3.58 0.20 20.83 3.78 3.48 \n      \nBanyuasin 1      \n1 8.42 0.48 18 3.42 3.40 \n2 5.26 0.27 19 3.51 3.28 \n3 3.20 0.36 15 3.90 3.45 \n4 5.24 0.14 23 4.12 3.49 \n5 2.31 0.29 18 3.94 3.59 \n6 7.94 0.31 25 3.55 3.38 \nAverage 5.40 0.31 19.67 3.74 3.43 \n\n\n\nSource : Processed primary data (2022) \n \nSoil pH at Rambutan ranged from 3.61 to 4.04 when extracted using H2O and 3.32 to 3.75 \nwhen extracted using KCl.  Meanwhile in Banyuasin 1, soil pH when extracted with H2O was \nrecorded at 3.55 to 4.12  and 0.28 to 3.59 when extracted with KCI (Handayani et al. 2021).The \nsoil pH range at both locations was considered low (very acidic) according to Prabowo and \nSubantoro (2008). Freshwater swampland as well as swampy tidal and peatland have high \nacidity with pH ranging from  2.9 - 3.9 when extracted with H2O and  2.23 - 3.07 when extracted \nwith KCI (Handayani and Maswar 2019). The acidic conditions are due to a high concentration \nof H+ in the soil as a result of  oxidation of the pyrite compound which is rich in iron, aluminum \nand sulphur (Stevanus et al. 2017; Priatmadi and Haris 2009). Several studies have indicated \nthat high Fe2+ solubility, Al and S in soil solution might be toxic to plants (Shamshuddin et al. \n2016; Elisa et al. 2016), restricting root growth and resulting in a stunted root system (Zhang \net al. 2018), thus decreasing the capability of nutrient uptake (Marashi 2018). \n \nAnother adverse characteristics of acidic soils is the low availability of cations, especially P \nand K. These cations are known as macro nutrients essentially needed by plants (Han et al. \n2019).  The limited availability of these macro nutrients and toxicity effect of soluble Fe, Al \nand S have been reported to be the main obstacles to rice productivity in swampy acidic areas \n(Rusdiansyah and Saleh 2017; Halim et al. 2018).  According to Masulili et al. (2016), the \neffect of Fe, Al and S toxicity and  the limited availability of macro nutrients in swampy acidic \nsoils might decrease rice production from  30-100% depending on the variety and level of \npoisoning. \n \nPhosphorus and Potassium Content \nThe P and K content of soil samples from the six studied sites of Rambutan and Banyuasin 1 \nsub districts are presented in Table 2.  Total P content of Rambutan using HCl 25% method \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 186-195 \n\n\n\n190 \n \n\n\n\nwas observed at 11 \u2013 66 mg/100 g soil and 3 \u2013 25.01 mg/kg using Bray 1 methods. In average, \nthe sub district of Rambutan hada  total P of 23 mg/100 g soil (HCl method) and 11.08 using \nBray 1 (Handayani et al. 2021), categorised as low to medium according to Bai et al. (2013) \nand Melese et al. (2015). The sub districts also have an average K2O content of  11.67 mg/100 \ng soil, classified as low based on  the study of Prabowo and Subantoro (2008).  As in the case \nof Rambutan sub district, total P content at Banyuasin 1 sub district was observed at 29.17 \nmg/100 g soil and 25.29 mg/kg using HCl 25% and Bray1 methods, respectively,  Meanwhile, \nK content in these areas averaged 13.17 mg/100 g soil. Therefore soil samples collected from \nBanyuasin 1 sub district were classified as low to medium for P content and low for K content \n(Bai et al. 2013; Melese et al. 2015; Prabowo and Subantoro 2008). \n \n\n\n\nTABLE 2 \nTotal P and K from soil samples taken from Rambutan and Banyuasin 1 sub districts \n\n\n\nLocation \nTotal P and K (HCl 25%) \n\n\n\n(mg/100 g soil) \nP-available P2O5  \n\n\n\n(Bray 1) \n(mg/kg) P2O5 K2O \n\n\n\nRambutan    \n1 11 14 5.75 \n2 12 10 3.51 \n3 14 11 25.01 \n4 12 12 9.79 \n5 66 12 19.44 \n6 23 11 3.00 \n\n\n\nAverage 23.00 11.67 11.08 \n    \nBanyuasin 1    \n\n\n\n1 62 19 56.16 \n2 7 9 1.92 \n3 9 11 4.62 \n4 8 11 2.73 \n5 30 18 26.66 \n6 59 11 59.65 \n\n\n\nAverage 29.17 13.17 25.29 \nSource : Processed primary data (2022) \n \nThe low level of P and K content in the soil of Rambutan and Banyuasin 1 sub-districts could \nbe the limiting factors in increasing rice productivity. Phosphorus (P) or phosphate exists in \nthe soil in the form of calcium phosphate, iron phosphate, aluminum phosphate and organic \nphosphate, etc. In swamp areas, rice plants are grown mostly in water-logged conditions in \nalmost all planting cycles.  The water logged conditions induce a  reduction in soil quality \n(Nishigaki et al. 2019). When the soil pH is neutral, ferric phosphate is hydrolysed to form \nferrous phosphate and the solubility of phosphorus is increased (Penn and Camberato 2019). \nIn acidic soils, however, the process of Fe-phosphate hydrolysis is inhibited and the \nconcentration of Fe2+ increases.  These conditions might lead to reduced phosphate released \ninto the soil solution, with less being available for plant  uptake (Amirrullah and Prabowo \n2017). \n \n\n\n\nAs in the case of  phosphate, the availability of potassium (K) is affected by soil pH under \nacidic soil conditions. Acidification of rhizosphere can dissolve several low soluble \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 186-195 \n\n\n\n191 \n \n\n\n\nmacronutrients and micronutrients.  H+ is released from roots when plants take up lower anions \nthan cations.  This can promote the transformation  of non-exchangeable K to exchangeable K \nand increase K+ leaching in submerged conditions (Han et al. 2019).  The loss of K from the \nrhizosphere due to leaching could be very high, especially in soils with porous drainage.  The \nloss of K due to leaching might be as much as the amount of K in the harvested plants or equal \nto 25 kg per hectare or even more (Mendes et al. 2016).    \n\n\n\nSocial Economic Characteristics of Swamp Rice Farmers \nSurveys conducted on two study sites revealed the characteristics of respondents representing \nthe swampland rice farmers.  The data showed that farmers aged more than 45 yearswere more \ndominant and comprised 51.7% of all swamp rice farmers.  In terms of formal education most \nfarmers had only only elementary education followed by junior high school.  Only 2% of the \nfarmers had a bachelor\u2018s degree.  Slightly more than  two-thirds (67.2%) of the farmers  had a \nfamily size of more than 5 members. From these figures, we can conclude that swamp rice \nfarmers in Rambutan and Banyuasin 1 generally had less formal education and were of less \nproductive age but had to financially support many family members. \n \nThe lack of young farmers in the rice production area indicates that productive-aged labour \nwas less interested in the business of agriculture. A less formal education of the farmers could \nalso be a constraint to  technology adoption for improvements in rice production.  Another \nconstraining factor is that a large family size leads to financial support of family members \nwhich poses a limitation to the  farmer\u2019s ability to save or even access capital. This scenario of \nthe farming community in the swampland area should become an important consideration in \nthe dissemination of specific technology to the  respektive areas.  According to Alam (2015) \nand Sjakir et al. (2015), age, education, and the distance of agricultural land to the source of \ninformation technology are the key determinants of the adoptiveability of farmers to \ngovernment programs in accelerating productivity of swamp rice cultivation. \n \nIn the case of production cost analysis of the farmers, the annual expenses for rice production \nin the swamp area was around 4.2 to 4.8 million IDR per hectare with the revenue reaching \n12.2 to 17.7 million IDR (Table 3).  Based on the annual scheme, farmers still get a net income \nof around 8.05  to 12.87 million IDR from a total production of 3,452 and 4,542 kg ha-1 in \nBanyuasin 1 and Rambutan sub-districts, respectively.  The B/C ratio for swamp rice \nproduction was observed to be 1.9 in Banyuasin 1 and 2.65 in Rambutan, indicating an \neconomically feasible production process (Suparwoto 2019).  The profit margin of farming was \nderived from the higher ceiling price of  6,176 IDR kg-1 milled grains.  \n\n\n\nAgro-climate factors, biological (weed problems, pests, and diseases), agro-inputs and the cost \nof procuring agro-inputs, lack of credit facilities, and poor price incentives were found to be \nthe factors affecting improvement in rice production in freshwater swampland areas.  Linear \nregression analysis conducted on these factors revealed that positive impacts on rice \nproductivity were derived from Amount of planted seeds (Seed), phosphate fertilizer (P2O5), \nthe return of straw to soil/organic matter (Straw) and knowledge of the recommended fertilizer \ndosage (Dosage).  The negative coefficient resulted from land area (Area), choice of variety \n(Variety), dose of nitrogen fertilizer(N), potassium fertilizer (K2O), education level (Edu), \nfarmers' preference for compound fertilizer (Com-fer), climate disturbance (Climate) and plant \ndisease bearing pests (Pest). \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 186-195 \n\n\n\n192 \n \n\n\n\nTABLE 3 \n Financial analysis of rice farming based on research locations \n\n\n\nDescription Districts Average Banyuasin I Rambutan \nProduction cost (IDR/hectare) 1,519,678 1,315,775 1,417,726 \n  -Seed 387,705 389,537 388,621 \n  -fertilizer 717,829 711,772 714,801 \n  -Pesticide 414,144 214,466 314,305 \nHired labour (IDR/hectare) 2,457,878 2,667,778 2,562,828 \nOther costs (IDR/hectare) 266,846 876,321 571,583 \nTotal production costs 4,244,402 4,859,873 4,552,138 \nQutput (kg/ hectare) 3,452 4,542 3,997 \nUnit price of the output (N) 3,562 3,905 3,733 \nRevenue (IDR/hectare) 12,296,204 17,738,899 15,017,551 \nIncome (IDR/hectare) 8,051,802 12,879,025 10,465,413 \nB/C ratio 1.90 2.65 2.30 \nDescription  Favorable Favorable Favorable \n\n\n\nSource : Processed primary data (2022) \n \nThe regression equation obtained is as follows: \nY = 3,573.3 -517.2 Area + 16.2 Seeds - 667.6 Variety - 1.4 N + 8.5 P2O5-16 K2O - 76.5 Edu -\n305 Com-fer + 832.8 Dosage + 252 Straw- 507.5 Climate - 69.2 Pest, with R2 = 38.8%, F-\ncount = 2.375 at \u03b1 = 1.8%. \nwhere \n\n\n\nY=Rice production \nArea= Land area  \nSeeds = Amount of planted seeds  \nVariety= Impact of choice of existing variety  \nN = Nitrogen fertilizer  \nP2O5= Phosphate fertilizer  \nK2O = Potassium fertilizer,  \nEdu = Education level of farmers  \nCom-fer = Compound fertilizer  \nDosage = Knowledgeofrecommended fertilizer dosage  \nStraw = Return of straw to soil/organic matter  \nClimate= Climate disturbance  \nPest = Pest and diseases \n\n\n\n \nBased on the regression equation, rice productivity in the freshwater swampland could be \nimproved through the addition of seeds, phosphate fertilizer, straw as organic matter, and \nincreased knowledge on fertilizer recommendation. Improving capacity of farmers, reducing \npesticide use, changing rice varieties and adapting to the effects of climate change were the \nfactors that could be improved in the scale of farm management to increase productivity of rice \ncultivation.  The increase in total planted seeds and straw return are dependant on the \navailability of resources.  While the increase in phosphate fertilizer application could improve \nrice production and not potassium, though concentration of  both nutrients was observed to be \nlow in the studied area.  Given the seconditions, improvement in rice productivity could be \nimplemented through long and short term programs.  Long term programs include the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 186-195 \n\n\n\n193 \n \n\n\n\nimprovement of soil characteristics through the return of straw to the soil, increased farmer \ncapacity and knowledge, including adapting to the effects of climate change, and government \npolicy.  While the short term program can be achieved through an increase in phosphate content \nin the soil.  In increasing phosphate content, efficient and effective methods as well as the cost \nof new techniques of application should be thoroughly examined to ensure a significant \nincrease not only in  productivity, but farmers\u2018 income as well (Tashikalma et al. 2014). \n \n\n\n\nThe element P is a macronutrient needed by plants in large quantities but in smaller amounts \ncompared to N, and K. Phosphate uptake by the rice plant through the  root system ranges from \n0.01 to 0.08%, and the optimal level of P in plants during vegetative growth ranges from 0.3 to \n0.5% (Lan et al. 2012). The strategy to improve P content in swampland should involve  various \ntechnological innovations. These includes the application of lime combined with organic \nfertilizer or other ameliorants (Azman et al. 2014), compost application (Barus 2012) or even \nphosphate fertilizers (Hubert 2018).  The implication of these methods is that knowledge \nrelated to symptoms of nutrient deficiency and toxicity should be disseminated to farmers.  It \nshould include the standard fertilizer applications in specific locations, like dosage, time, \napplication technique and type of fertilizers needed. Through these programs,  farmer will learn \nhow to manage fertilizer applications and other cultural practices based on their own production \nschedule (Arifin et al. 2018). \n\n\n\n \nCONCLUSION \n\n\n\nThe carbon content in Rambutan was classified as medium to high, while in Banyuasin 1, it \nwas medium to very high. With regard to nitrogen content, it was very low to medium in \nRambutan, while it was low to medium in Banyuasin 1.  Both locations also have almost similar \nsoil acidity characteristics, with soil pH recorded inthe range of 3.48 \u2013 3.78 in Rambutan and \n3.43 \u2013 3.74 in Banyuasin 1. Based on the average C/N ratio, both locations were considered to \nhave high accumulation of organic matter in the soil.  For P and K content, Rambutan fell in \nthe low to medium category for P and low for K content.  In the case of Banyuasin 1, P content \nwas classified as low to medium and low for K. Cost analysis for swampland rice production \nin both locations revealed that the B/C ratio was 1.9 in Banyuasin 1 and 2.65 in Rambutan, \nindicating that the production process is economically feasible.  Linear regression analysis \namong pertinent factors in production improvement showed that positive impacts on rice \nproductivity could be achieved by a combination of (i) total seeds, (ii) phosphate fertilizer, (iii) \nreturn of straw to soil/organic matter, and (iv) knowledge of the recommended fertilizer dosage. \n \n\n\n\nREFERENCES \nHalim, N.S.A., R. Abdullah, S.A. Karsani, N. Osman, Q.A. Panhwar and C.F. Ishak. 2018. 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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n29 \n\n\n\n\n\n\n\nInfluence of Agronomic Practices on the Yield of Oil Palm (Elaeis \n\n\n\nGuineensis Jacq.) Grown on Various Soil Management Groups \n \n\n\n\n Kayondo, Bonny1*, Jalloh, Mohamadu Boyie 1, Binti Hasbullah, Nur Aainaa1,  \n\n\n\nIImas, Abdurofi 1, Selliah, Paramananthan 2\n \n\n\n\n \n1Faculty of Sustainable Agriculture, Universiti Malaysia Sabah, Mile 10, Jalan Sungai Batang, \n\n\n\n90000, Sandakan, Sabah \n2Param Agricultural Soil Surveys (M) Sdn. Bhd. \n\n\n\n \n*Correspondence: bonnykayondo@gmail.com  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nCompared to yields of 25-30 t ha-1 per year obtained elsewhere, the annual fresh fruit bunch (FFB) yields \n\n\n\nof 11 t ha-1 from oil palm plantations in Uganda are considered very low. Therefore, this study \ninvestigated the yield performance of oil palm progenies grown on various soil management groups \n\n\n\n(SMGs) in a large commercial plantation.   A factorial randomized complete block design with two oil \n\n\n\npalm progenies planted in blocks of three replicates on six SMGs was used. Initially, the semi-detailed \nsoil survey report of the plantation provided details about the SMGs. Rainfall records of 2012 to 2021 \n\n\n\nperiod were also documented. Site-specific agronomic techniques were implemented because of \n\n\n\nvariations in the physicochemical sufficiency of the SMGs. Fresh fruit bunch data were collected every \n10 days between 2016 and 2021 and subjected to analysis of variance using SPSS software version 20.0. \n\n\n\nResults showed that a change in the soil pH and cation exchange capacity (CEC) enhanced FFB yields \n\n\n\nacross SMGs though in preceding years with uneven rainfall distribution, declines were experienced. \n\n\n\nHowever, the highest average yields were obtained from SMGs B (20.21 t ha-1), and A (19.46 t ha-1) and \nthe lowest from Ait (18.03 t ha-1) and Bi (17.79 t ha-1). Also, the two progenies responded differently with \n\n\n\nthe Deli x Ghana average yield being 19.98 t ha-1 and 17.60 t ha-1 for Guthrie D x P. Lastly, the highest \n\n\n\naverage yield of 21.46 t ha-1 was obtained in 2021 in contrast to that of 2016, which was only 16.12 t ha-\n\n\n\n1. Therefore, site-specific agronomic techniques contributed to an increase in FFB output from the \n\n\n\nplantation in 2021.   This study provides a guide tool to managers to evaluate the influence of site-specific \n\n\n\nagronomic techniques for yield enhancement in the plantation towards attaining higher profit margins. \n\n\n\n\n\n\n\nKey words: Progenies, soil management groups, soil fertility, fresh fruit bunch yield, rainfall \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nThe tropics have a diversity of soil types that support oil palm (Elaeis guineensis Jacq.) \n\n\n\ncultivation (Corley & Tinker, 2016; Woittiez et al., 2017). However, drought and low inherent \n\n\n\nsoil fertility affect oil palm yields in several African plantations (Rhebergen et al., 2016).  The \n\n\n\ncontinuous weathering of soils, intense leaching, and erosion brought on by high temperatures \n\n\n\nand excessive rains result in the formation of diverse marginal soils in the tropics \n\n\n\n(Paramananthan 2003; Shamshuddin et al. 2015; Shamshuddin and Wan 2011). The first oil \n\n\n\npalm plantation in Uganda is located in an area that receives the highest rainfall in Uganda, ideal \n\n\n\nfor oil palm growth and fresh fruit bunch (FFB) production (KDLG, 2005). Initial reports from \n\n\n\nthe pilot trials in this area showed FFB yields of 11 t ha-1 per year from 6\u20138 year-old oil palm \n\n\n\nplantations treated with no fertilizer (MAAIF/VODP, 2003).  These yields are remarkably low \n\n\n\nin comparison to  25-30 t ha-1 year FFB from well-managed commercial plantations located in \n\n\n\n\nmailto:bonnykayondo@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n30 \n\n\n\n\n\n\n\noptimum biophysical environments (Donough et al. 2009; Pupathy and Sundian 2020). Studies \n\n\n\n(Manishimwe 2018; Muwanga 2019)show that the problem of low FFB productivity in Uganda \n\n\n\nhas led to a low vegetable oil supply. In 2018 while the annual demand for vegetable oil \n\n\n\nwas120,000 metric tonnes, the nation was only able to generate about 40,000 metric tonnes. \n\n\n\nThere are claims  that the oil palm trees of the commercial plantation in Uganda were planted on \n\n\n\nmarginal soils (Wakabi, 2021). Consequently, failure to appreciate the variability that exists \n\n\n\namong soils poses a financial risk in agricultural investment in Africa.  (Rushemuka et al., 2014).  \n\n\n\nThis is because different soils in the plantation possess unique physicochemical characteristics \n\n\n\nthat influence the performance of oil palm progenies and, as a result, create variation in the FFB \n\n\n\nyields (Harahap et al., 2019). In the case of Malaysian plantations, adoption of site-specific \n\n\n\nagronomic practices tailored to the limitations found in different soil management groups \n\n\n\n(SMGs) increased oil palm FFB productivity (Oberth\u00fcr et al., 2017).  Therefore, the objective \n\n\n\nof this study was to investigate how site-specific agronomic techniques influence FFB yield \n\n\n\nproductivity in Ugandan plantations after its zoning into SMGs.  The investigation was carried \n\n\n\nout from the pre-soil evaluation period in the year 2016 and compared with data from year 2021. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nLocation and Description of the Study Area \n\n\n\nThis study was conducted in a privately-owned mature commercial plantation situated on Bugala \n\n\n\nIsland, Kalangala District in Lake Victoria, Uganda between latitudes 0\u00b010\u2019S and 0\u00b035\u2019S and \n\n\n\nlongitudes 32\u00b004\u2019E and 32\u00b020\u2019E. Figure 1 shows the location of the study area. This Uganda \n\n\n\nplantation consists of scattered parcels of land totalling about 6,324.23 ha with 5,956.16 ha \n\n\n\nplanted with oil palm. The elevation of the plantation is between 1,000 to over 1,500 meters \n\n\n\nabove sea level. The area has a mean annual maximum and minimum temperature of 250C and \n\n\n\n17. 0C respectively, with an annual rainfall ranging from1,125to 2,250 mm (NEMA, 1998). It \n\n\n\nexperiences two distinct rainy seasons: first rain (March to June) and second rain (August to \n\n\n\nDecember).  \n\n\n\n \nFigure 1. Location of Bugala Island, Kalangala District, Lake Victoria Uganda. \n\n\n\nSource: ACAPS \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n31 \n\n\n\n\n\n\n\nRainfall Data \n\n\n\nBecause oil palm requires a year-round supply of moisture to develop its yield potential, rainfall \n\n\n\nis taken into account in this study (Von Uexkull & Fairhurst, 1991). For the period of 2012 to \n\n\n\n2021, the total monthly precipitation (mm) and rain days (see Table 1) data of the Uganda \n\n\n\nplantation was documented. Months with rainfall below 100 mm are marked red to indicate \n\n\n\nperiods of moisture deficit for oil palm (Paramananthan, 2003). \n\n\n\n\n\n\n\nTABLE 1  \n\n\n\nData on collected rainfall (mm) and rain days for the oil palm plantation in Uganda (2012-\n\n\n\n2021) \n\n\n\n\n\n\n\nExperimental Design \n\n\n\nPrior to the study, a soil survey was conducted in the plantation in collaboration with Param \n\n\n\nAgricultural Soil Surveys in 2017, which generated a report about soil management groups \n\n\n\n(SMGs). The SMGs were characterized as per criteria by Paramananthan (2010). The 2016 soil \n\n\n\nnutrient evaluation data used was extracted from this soil survey report (Table 2) with the same \n\n\n\npoints being sampled in 2021 (Table 3). Plantation management provided data about the oil palm \n\n\n\nprogenies and their coverage on the plantation as shown in Table 4. From the aforementioned \n\n\n\ninformation, SMGs and progenies represented largely on the plantation were selected for this \n\n\n\nstudy. Figure 2 shows the identified SMGs on the Uganda plantation. This study was arranged \n\n\n\nin a factorial randomized complete block design.  The factors in this study were two tenera oil \n\n\n\npalm progenies (Deli x Ghana and Guthrie D x P) and six SMGs (A, Ai, Ait, B, Bi and Gi (Table \n\n\n\n5). The progenies were 11 years old and planted in an equilateral triangular pattern at a density \n\n\n\nof 148 palms per hectare. Each of the factor combinations were replicated three times. This \n\n\n\nresulted in 36 experimental blocks. The blocks were sized between 13 to 30 hectares. The year \n\n\n\n2016 was considered a pre-study period. Site-specific agronomic techniques were implemented \n\n\n\nfrom 2017 to 2021. The fertilizer regime for the Uganda oil palm plantation for the years 2016\u2013\n\n\n\n2021 are shown in Table 6.  Based on annual leaf analysis results, agronomists developed \n\n\n\nfertilizer regimes. Data on FFB were gathered annually at intervals of 10 days. Using SPSS \n\n\n\nsoftware (version 20.0), analysis of variance was performed on this yield data.  \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n32 \n\n\n\n\n\n\n\nTABLE 2  \n Soil nutrient concentrations of representative series in 2017 \n\n\n\n\n\n\n\nParameter \n\n\n\nSoil Series \n\n\n\nBungor/red  \n\n\n\n(Pedon 1) \n\n\n\nJitra/red \n\n\n\n(Pedon 2) \n\n\n\nLaka \n\n\n\n (Pedon3) \n\n\n\nDepth (cm) 0-12 12-39 0-12 12-42 0-10 10-30 \n\n\n\npH High Low Low Low Low Low \n\n\n\nOrganic C (%) Very High Very High Very High High Very High High \n\n\n\nTotal N (%) Very High High Very High Low High High \n\n\n\nTotal P (\u00b5g g-1) Very High Very High Very Low Very Low Low Very Low \n\n\n\nAvailable P (\u00b5g g-1) Very High Very High Very Low Very Low Very High Very High \n\n\n\nExchangeable K (cmol kg-1) soil Very High Very High Very High Very High High Very High \n\n\n\nExchangeable Mg (cmol kg-1) soil Very High Very High Very High Very High Very High Very High \n\n\n\nCEC (cmol kg-1) soil Moderate Low High Very High Very Low Very Low \n\n\n\n \nTABLE 3   \n\n\n\nSoil nutrient concentrations of representative series in 2021 \n\n\n\n\n\n\n\nParameter \n\n\n\nSoil Series \n\n\n\nBungor/red  \n\n\n\n(Pedon 1) \n\n\n\nJitra/red \n\n\n\n(Pedon 2) \n\n\n\nLaka  \n\n\n\n(Pedon 3) \n\n\n\nDepth (cm) 0-12 12-39 0-12 12-42 0-10 10-30 \n\n\n\npH High High High High High High \n\n\n\nOrganic C (%) High High High Moderate High Moderate \n\n\n\nTotal N (%) High High High High High High \n\n\n\nTotal P (\u00b5g g-1) Very High Very High Very Low Very High Very High Very High \n\n\n\nAvailable P (\u00b5g g-1) Very High Very High Very High Very High Very High Very High \n\n\n\nExchangeable K (cmol kg-1) soil Very High Very High Very High Very High Very High Very High \n\n\n\nExchangeable Mg (cmol kg-1) soil Very High Very High Very High Very High Very High Very High \n\n\n\nCEC (cmol kg-1) soil Moderate High Moderate Moderate Moderate Moderate \n\n\n\n \nTABLE 4  \n\n\n\n Oil palm progenies and their coverage at Uganda plantation \n \n\n\n\nProgeny \nCoverage \n\n\n\nHectare (ha) Percentage (%) \n\n\n\n1.Deli x Compacta 8.28 0.14 \n\n\n\n2.Deli x Ghana 501.25 8.42 \n\n\n\n3. Deli x Nigeria 26.13 0.44 \n\n\n\n4. Tanzania x Ekona 290.43 4.88 \n\n\n\n5. Bamenda x Ekona 52.64 0.88 \n\n\n\n6. Guthrie DxP 4,343.66 72.92 \n\n\n\n7. IOPRI 245.54 4.12 \n\n\n\n8. Deli x La Me 488.23 8.2 \n\n\n\nTOTAL 5956.16 100 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n33 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Map of the soil management groups found on the Uganda Plantation \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n34 \n\n\n\n\n\n\n\nTABLE 5 \nSoil management groups (SMGs) and implemented soil practices \n\n\n\nSoil \n\n\n\nGroups \n\n\n\nSoil types  \n\n\n\n(Classification \n\n\n\naccording to Soil \n\n\n\nTaxonomy) \n\n\n\nMain Characteristic/ \n\n\n\nLimitation \n\n\n\nImplemented Agronomic \n\n\n\nPractices \n\n\n\n\n\n\n\nPeak Yield \n\n\n\nPotential \nExtent \n\n\n\nmt/ha/yr Ha % \n\n\n\nA \n\n\n\n\n\n\n\n\n\n\n\nTypic Paleudult \n\n\n\nTypic Kandiudult \n\n\n\n\u2022 Deep (>100 cm) to \n\n\n\nmoderately deep (50-\n\n\n\n100 cm) fine sandy \n\n\n\nclay to clay (>35% \n\n\n\nclay) textured soils   \n\n\n\n\u2022 Low fertility \n\n\n\n\u2022   Understory vegetation. \n\n\n\n\u2022 AS, MOP, NK Mixture, \n\n\n\nNPK compound and \n\n\n\nGypsum \n\n\n\n\u2022 U-shape frond stacking \n\n\n\nand Mucuna spp \n\n\n\nmaintained \n\n\n\n28-32 94.4 1.6 \n\n\n\nAi \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRhodic Paleudult \n\n\n\n\u2022 Deep (>100 cm) fine \n\n\n\nsandy clay to clay \n\n\n\n(>35% clay) textured \n\n\n\nsoils   \n\n\n\n\u2022 Low fertility \n\n\n\n\u2022 High iron content \n\n\n\n\u2022 High P-fixation \n\n\n\n\n\n\n\n\u2022 AS, MOP, NK Mixture, \n\n\n\nNPK compound, RP, \n\n\n\nKieserite & Gypsum \n\n\n\n\u2022 Terracing \n\n\n\n\u2022 U-shape frond stacking \n\n\n\n\u2022 EFB mulch (40 t ha-1) \n\n\n\n\u2022 Understory vegetation \n\n\n\nand Tapak kuda \n\n\n\n28-32 1,946.8 32.6 \n\n\n\nAit \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRhodic Paleudult \n\n\n\n\u2022 Deep (>100 cm) fine \n\n\n\nsandy clay to clay \n\n\n\n(>35% clay) textured \n\n\n\nsoils \n\n\n\n\u2022 Low fertility \n\n\n\n\u2022 High iron content \n\n\n\n\u2022 High P-fixation \n\n\n\n\u2022 Soils on hilly to steep \n\n\n\nslopes \n\n\n\n\u2022 High soil erosion \n\n\n\n\u2022 AS, MOP, NK Mixture, \n\n\n\nNPK compound, RP, \n\n\n\nKieserite & Gypsum \n\n\n\n\u2022 Terracing \n\n\n\n\u2022 U-shape frond stacking \n\n\n\n\u2022 EFB mulch (40 t ha-1) \n\n\n\n\u2022 Understory vegetation \n\n\n\nand Tapak kuda \n\n\n\n26-30 240.3 4.0 \n\n\n\nB \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTypic Kandiudult \n\n\n\nTypic Hapludult \n\n\n\n\u2022 Deep (>100 cm) to \n\n\n\nmoderately deep (50-\n\n\n\n100 cm) well drained \n\n\n\nsoils.  Texture sandy \n\n\n\nclay loam (18-35% \n\n\n\nclay) \n\n\n\n\u2022 Moisture stress and \n\n\n\nyield fluctuations \n\n\n\n\u2022 Low fertility \n\n\n\n\u2022 MOP, NK Mixture, NPK \n\n\n\ncompound, RP, Kieserite \n\n\n\n& Dolomite \n\n\n\n\u2022 Terracing \n\n\n\n\u2022 Decanter cake (200 \n\n\n\nkg/palm) \n\n\n\n\u2022 U-shape frond stacking. \n\n\n\n\u2022 EFB mulch (40 t ha-1) \n\n\n\n\u2022 Understory vegetation \n\n\n\nand Tapak kuda \n\n\n\n26-30 566.1 9.5 \n\n\n\nBi Rhodic Kandiudult \n\n\n\n\u2022 Deep (>100 cm) to \n\n\n\nmoderately deep (50-\n\n\n\n100 cm) well drained \n\n\n\nsoils.  Texture sandy \n\n\n\nclay loam (18-35% \n\n\n\nclay) \n\n\n\n\u2022 Moisture stress and \n\n\n\nyield fluctuations \n\n\n\n\u2022 Low fertility \n\n\n\n\u2022 High iron content \n\n\n\n\u2022 High P-fixation \n\n\n\n\u2022 MOP, NK Mixture, NPK \n\n\n\ncompound, RP, Kieserite \n\n\n\n& Dolomite \n\n\n\n\u2022 U- shape frond stacking \n\n\n\n\u2022 EFB mulch (40 t ha-1) \n\n\n\n\u2022 Understory vegetation \n\n\n\nand Tapak kuda \n\n\n\n26-30 1,315.6 22.1 \n\n\n\n\n\n\n\nGi \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRhodic Kandiudult \n\n\n\n\u2022 Shallow (<50 cm) \n\n\n\nwell drained soils.  \n\n\n\nGravelly sandy clay \n\n\n\ntextures.  Shallow soil \n\n\n\ndepth. Dense \n\n\n\nlateritic/gravel layer \n\n\n\n(<50 cm) \n\n\n\n\u2022 Low fertility. \n\n\n\n\u2022 Poor rooting. \n\n\n\n\u2022 Moisture stress \n\n\n\n\u2022 Wind damage \n\n\n\n\u2022 High iron content \n\n\n\n\u2022 High P-fixation \n\n\n\n\u2022 AS, MOP, NK Mixture, \n\n\n\nNPK compound, RP, \n\n\n\nKieserite & Dolomite \n\n\n\n\u2022 U-shape frond stacking \n\n\n\n\u2022 EFB mulch (40 t ha-1) \n\n\n\n\u2022 Understory vegetation \n\n\n\nand Mucuna spp  \n\n\n\n\u2022   Decanter cake \n\n\n\n(200kg/palm) \n\n\n\n22-28 532.2 6.0 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n35 \n\n\n\n\n\n\n\nTABLE 6 \n\n\n\nFertilizer regime of the Uganda oil palm plantation for the 2016-2021 period \n\n\n\n\n\n\n\nFertilizer and liming materials \n\n\n\n\n\n\n\nAnnual application dosages (kg/palm) \n\n\n\n \n2016 \n\n\n\n\n\n\n\n \n2017 \n\n\n\n\n\n\n\n \n2018 \n\n\n\n\n\n\n\n \n2019 \n\n\n\n\n\n\n\n \n2020 \n\n\n\n\n\n\n\n \n2021 \n\n\n\n \nAmmonium sulphate  \n\n\n\n2.0 \n\n\n\n\n\n\n\nMOP (60%K2O)  \n\n\n\n2.0 \n\n\n\n\n\n\n\nNK Mix 10.5/30 \n\n\n\n(50%SOA,50%MOP) \n\n\n\n\n\n\n\n2.0 \n\n\n\n\n\n\n\n2.0 \n\n\n\n\n\n\n\n2.0 \n\n\n\n\n\n\n\n2.0 \n\n\n\nNPK  \n\n\n\n( 8.5-6-28-4.5+0.72 B) \n\n\n\n\n\n\n\n2.0 \n\n\n\n\n\n\n\n3.0 \n\n\n\n\n\n\n\nNPK  \n\n\n\n( 8 - 4 -23-4 + 0.48 B) \n\n\n\n\n\n\n\n2.0 \n\n\n\n\n\n\n\nNK Mix 15.75/15 \n\n\n\n(75%SOA,25%MOP) \n\n\n\n   2.0   \n\n\n\nNPK  \n\n\n\n(0-8-28-6.5+1.44) \n\n\n\n   2.0   \n\n\n\nNPK \n\n\n\n(10-6-24-5+0.96B) \n\n\n\n\n\n\n\n2.5 \n\n\n\n\n\n\n\n2.0 \n\n\n\n\n\n\n\n2.0 \nEgyptian RP (30%P2O5,46% CaO)     2.0 2.0 \n\n\n\nNK Mix 8.4/36 \n\n\n\n (40% SOA / 60% MOP) \n\n\n\n\n\n\n\n1.5 \n\n\n\n\n\n\n\n1.5 \n\n\n\n\n\n\n\n2.0 \n\n\n\nKieserite (27%MgO,22%S) 1.0   1.0 1.0 1.0 \n\n\n\n\n\n\n\nAverage application per tree \n\n\n\n\n\n\n\n7.0 \n\n\n\n\n\n\n\n7.0 \n\n\n\n\n\n\n\n9.0 \n\n\n\n\n\n\n\n11.0 \n\n\n\n\n\n\n\n11.0 \n\n\n\n\n\n\n\n9.0 \n\n\n\nGypsum (32% CaO,18%S)  2.0 2.0 2.0 2.0  \n\n\n\nDolomite (30% CaO,18%MgO)  3.0  3.0 3.0  \n\n\n\n\n\n\n\nNote:  MOP-Muriate of Potash, RP-Rock phosphate, NPK-Nitrogen, Phosphorus, Potassium \n\n\n\n\n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nRainfall Distribution from 2012 to 2021  \n\n\n\nThe collected rainfall data over a period of 10 years is presented in Table 1. The average rainfall \n\n\n\nand rain days are 1,604 mm and 143 respectively. However, there is a wide variation between the \n\n\n\nlowest rainfall of 1,396 mm and the highest of 2,059 mm.  The Uganda oil palm plantations face \n\n\n\na long dry season from June to September. Another secondary dry spell is experienced in some \n\n\n\nmonths through December to February that sometimes extends to March but more frequently to \n\n\n\nJanuary or February. From 2016 to 2021, rainfall was well distributed except for years 2016 and \n\n\n\n2019 where four consecutive dry months were recorded, starting June to September. It is noted \n\n\n\nthat there is no month with zero rainfall. However, current highest and lowest  rainfall values differ \n\n\n\nfrom those earlier reported, in the range of 1,125 and 2,250 mm on Bugala Island (KDLG, 2005; \n\n\n\nNEMA, 1998). These differences could probably be due to climate change effect causing \n\n\n\nvariations in metrological factors in tropical Africa (Tamara, 2021), hence the  longer drought \n\n\n\nperiods  in the plantation. Continuous supply of rainfall is required by oil palm to counterbalance \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n36 \n\n\n\n\n\n\n\nwater lost through evapotranspiration (Paramananthan, 2003). Furthermore, rainfall is important \n\n\n\nto support various physiological and developmental functions such as flowering, bunch \n\n\n\nproduction, translocation of nutrients and photo-assimilates. Therefore, oil palm requires a high \n\n\n\nannual well- distributed rainfall of at least 2,000-2,500 mm with no dry month recording less than \n\n\n\n100 mm (Corley & Tinker, 2003; Paramananthan, 2003). In  the presence of a dry month, yields \n\n\n\ndecline even where the total annual rainfall exceeds the required 2000 mm (Hartley, 1988). \n\n\n\nConsequently,  the rainfall of the Uganda oil palm plantation is typical of the moderate type that \n\n\n\nranges between 1,450-1,700 mm as discussed by Paramananthan (2011) and Paramananthan et al. \n\n\n\n(2000). \n\n\n\n\n\n\n\n\n\n\n\nFFB yields of Deli x Ghana and Guthrie D x P Progenies in the Uganda Plantation \n\n\n\n\n\n\n\nStatistical analysis results indicate a significant interaction P= 0.038, between SMGs and \n\n\n\nprogenies for FFB yields in years 2016 and 2021 (Figure 3). It is seen that the overall mean \n\n\n\nproductivity of FFB yields of both progenies (M=18.79, SD=3.31) increased annually across all \n\n\n\nSMGs after implementation of site-specific agronomic practices. This is justified by the significant \n\n\n\n(P<0.05) difference in FFB productivity between years 2016 and 2021. The mean yield in 2016 \n\n\n\nwas M=16.12, SD=2.06 and in 2021 it was M=21.46, SD=1.81. In other words, FFB yields of the \n\n\n\nplantation increased by 33% or 5.34 t ha-1 in 2021.  \n\n\n\n\n\n\n\n \nFigure 3: Interaction effect of soil management groups and progenies for mean FFB yield \n\n\n\nbetween years 2016 and 2021; error bars represent standard deviation and n=3. \n\n\n\n\n\n\n\nFigures 4 and 5 show the yield trends for the DelixGhana and Guthrie DxP progenies respectively \n\n\n\nfrom 2016 to 2021. The mean FFB yield of M=17.56, SD=1.42 for DelixGhana in 2016 increased \n\n\n\nby 28% or 4.85 t ha-1 (M=22.41, SD=1.61) in 2021. The mean yield for Guthrie DxP in 2016 was \n\n\n\nM=14.69, SD=1.54, an increase of 40% or 5.83 t ha-1 (M=20.52, SD=1.52) in 2021. However, \n\n\n\nDelixGhana's FFB mean yields declined by 14% (2.45 t ha-1 to 15.11 t ha-1) in 2017 and again by \n\n\n\n13% (2.59 t ha-1 to 17.70 t ha-1) in 2020. It is also observed that the mean yield of Guthrie DxP \n\n\n\ndeclined by 23% (3.37 t ha-1 to 11.32 t ha-1) in 2017 and again by 10% (1.86 t ha-1 to 17.07 t ha-1) \n\n\n\nin 2020 before increasing again in 2021. Statistically, there is a significant (P<0.05) difference \n\n\n\nbetween the mean FFB yield of DelixGhana (M=19.98, SD=2.88) and Guthrie DxP (M=17.60, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n37 \n\n\n\n\n\n\n\nSD=3.32) progenies. The highest yield in 2021 of M=23.17, SD=1.69 and M=22.89, SD=1.89 was \n\n\n\nobserved on SMGs A and B respectively and the lowest of M=20.10, SD=0.85 on Gi. However, \n\n\n\nDelixGhana's mean FFB yields on A and B were M=24.43, SD=0.94 and M=23.94, SD=0.60, \n\n\n\nrespectively. On SMGs A and B, the Guthrie DxP mean yields were M=21.92, SD=3.63 and \n\n\n\nM=21.83, SD=2.26, respectively. DelixGhana yields were lowest on Gi (M=20.31, SD=0.5), while \n\n\n\nGuthrie DxP yields were lowest on Bi (M=19.45, SD=2.34). Therefore, the productivity of \n\n\n\nDelixGhana was higher by 13% or 2.37 t ha-1 than that of the Guthrie D x P cultivar. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 4: Yield trend of DelixGhana on the \n\n\n\nsix SMGs from 2016-2021 \n\n\n\n\n\n\n\nFigure 5: Yield trend of Guthrie DxP on the six \n\n\n\nSMGs from 2016-2021 \n\n\n\n\n\n\n\nFigure 6 shows the significant (P<0.05) difference in the mean (M) FFB yields of the six SMGs. \n\n\n\nSMG B yields were highest (M=20.21, SD=3.34) closely followed by SMG A (M=19.46, \n\n\n\nSD=4.13). However, mean yields of sub-groups of SMG A, Ai (M=18.90, SD=2.86) and Gi \n\n\n\n(M=18.37, SD=2.11) do not differ much. Mean yields of sub-groups Ait (M=18.03, SD=3.74) and \n\n\n\nBi (M=17.79, SD=3.55) were the lowest and did not differ much. \n\n\n\n\n\n\n\n \nFigure 6: Mean FFB Yields of the various soil management groups (2016-2021)). Bars with \n\n\n\ndifferent letters are significantly different (p<0.05) and error bars represent standard deviation  \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n\n\n\n\n38 \n\n\n\n\n\n\n\nThe observed increase in 2021 FFB yields justifies the effectiveness of adopting site-specific \n\n\n\nsoil agronomic techniques on the SMGs. This is because soil management altered the physico-\n\n\n\nchemical properties of the soils. Hereafter, the two progenies positively adapted to the changes \n\n\n\nand responded by producing increasing FFB yields as illustrated in Figures 4 and 5. The soils \n\n\n\nof the Uganda plantation initially showed a low soil pH and poor cation exchange capacities \n\n\n\n(CEC), two problematically prevalent traits of tropical soils (see Table 2). These soil traits \n\n\n\nprobably contributed to the initial low FFB yields across SMGs. Though oil palm tolerates a \n\n\n\nlow soil pH, acidic conditions cause severe chemical imbalances resulting from toxic levels of \n\n\n\nAl3+ and H+ at exchangeable sites This is compounded by nutrient deficiencies of N, P, K, Ca, \n\n\n\nMg, Mo and B. Additionally, as the soil CEC is pH-dependent, its low levels point to leaching \n\n\n\nof K, Mg, and Ca. This implies that the Uganda plantation soils have a limited capacity to retain \n\n\n\nthe added cations from mineral fertilizers.  \n\n\n\n\n\n\n\nThe applied organic fertilisers increased the capacity of soils store added K, Ca and Mg from \n\n\n\ninorganic fertilizers, which improved the CFC of soils and subsequently increased FFB output. \n\n\n\nThis clearly indicated that fertilizers aided the physiological functions of the cultivars thereby \n\n\n\ncausing a significant increase in FFB yields (Sundram et al. 2019). Furthermore, split fertiliser \n\n\n\napplication encouraged the roots of the progenies to efficiently absorb nutrients from the soil \n\n\n\nand reduce nutrient leaching (Reetz, 2016).Because of the acidic nature of the soils in the \n\n\n\nplantation, rock phosphate dissolved more quickly, releasing phosphorus and reducing the \n\n\n\neffects of iron fixation (Uwumarongie-Ilori et al., 2012).  Also, Kieserite released magnesium \n\n\n\nin a form that was easily absorbed by oil palm roots. Further, the long-lasting ameliorating \n\n\n\neffects of dolomite which releases exchangeable cations Ca2+ and Mg2+ also enhanced available \n\n\n\nphosphorus levels in the soil and a rise in soil pH as shown in Table 3.  \n\n\n\n\n\n\n\nThe improved CEC (Table 3) from the pruned fronds increased the initial low pH of the soil \n\n\n\n(Formaglio et al. 2021) allowing for the availability of nutrients (Kotowska et al. 2016). \n\n\n\nAdditionally, pruned fronds had the effect of increasing OC, total N, exchangeable K, Ca, and \n\n\n\nMg while exchangeable Al3+ decreased in the soil with the addition of organic matter (Comte \n\n\n\net al. 2013).  In the moisture-stress year of 2020 compared to 2017, organic matter also \n\n\n\nimproved the soil's structural stability and reduced the consequences of the drought. The \n\n\n\ndecanter improved soil quality and moisture storage which promoted the growth of the oil palm \n\n\n\nroots  (Sahad et al. 2014). Additional soil nutrients were also extracted from decanter wastes \n\n\n\nby oil palm roots (Rahman et al., 2021). The Mucuna bracteata fixed nitrogen in the soil, \n\n\n\ndecreased soil erosion, and enhanced water infiltration into the soil (Arifin et al. 2015; Wawan \n\n\n\net al. 2019). Additionally, it strengthened the structural stability of the soil. The individual \n\n\n\nplanting platform maximized the effectiveness of retaining the fertilizer as well as conserving \n\n\n\nsoil moisture (Goh et al. 2016; Hidayat 2017). On the other hand, the understory vegetation \n\n\n\nimproved soil and water conservation by halting surface runoff and soil erosion (Corley and \n\n\n\nTinker 2016; Pardon et al.2016).  \n\n\n\n\n\n\n\nThe constructed terraces minimized soil erosion, intercepted surface run-off and increased \n\n\n\ninfiltration. However, it is conceivable that the physical constraints of SMGs Ait and Bi \n\n\n\nlessened the effectiveness of the practices that were put in place, resulting in low FFB yields. \n\n\n\nThe prevalence of iron oxides in these SMGs could have resulted in the formation of pseudo-\n\n\n\nsands and pseudo-silts. These enhanced the porosity of the soil, resulting in the loss of water \n\n\n\nand nutrients through infiltration and leaching respectively.  Hence yields were affected.  This \n\n\n\nis because they create conditions of moisture stress even after the application of high-quality \n\n\n\ninorganic fertilizer (Hoffmann et al. 2014).   \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n\n\n\n\n39 \n\n\n\n\n\n\n\nThe temporal and spatial diversity in topography and physicochemical soil factors was another \n\n\n\nfactor contributing to the observed FFB yield differences seen in Figure 6. This is consistent \n\n\n\nwith the earlier observations from the research by Aniku (2001) that crop yields vary across \n\n\n\nthe catena, indicating differences in the soil's physicochemical properties. Because of the \n\n\n\nseverity of the limitations related to steep topography, shallowness, and high iron content, \n\n\n\nSMGs Ait and Bi did not respond well to soil management approaches. This accounts for the \n\n\n\nlow FFB yields.  \n\n\n\n\n\n\n\nHowever, in contrast, the two progenies across SMGs differ in how they responded to the soil \n\n\n\nagronomic techniques. This is an indication of their differential preference for soil conditions.  \n\n\n\nThe higher FFB yields of progenies SMGs A and B indicate sufficiency of their \n\n\n\nphysicochemical characteristics after modification contrary to those of Bi and Gi.  On shallow \n\n\n\nGi, the Deli x Ghana's low FFB yields were caused by the inability of the roots to fully absorb \n\n\n\nnutrients and moisture because of the stones and gravel, but Guthrie's DxP was limited by low \n\n\n\nmoisture on Bi. The yield troughs shown in years 2017 and 2020 (Figures 4 and 5) were a \n\n\n\nresult of the consecutive four dry-month periods that were experienced in the preceding years \n\n\n\nof 2016 and 2019 respectively. This indicates that the years with consecutive four dry months \n\n\n\n(Table 1) are responsible for the low and fluctuating yields. Surprisingly, despite having a good \n\n\n\ntexture and being well drained, SMG A was also affected by the drought.   \n\n\n\n\n\n\n\nThis observation supports studies by Padi and Ehlers (2008) that drought impact occurs on \n\n\n\nwell-drained soils without moisture replenishment from rainfall. The observed reduction in \n\n\n\nFFB yields after a 12-month lag period was a result of abortion of female inflorescences and \n\n\n\nbunch rotting signalled by dominance of the male inflorescences. Similar findings in an oil \n\n\n\nplantation were reported by Adam et al.( 2011) and Carr (2011). Higher FFB yields were \n\n\n\nachieved in the years 2018, 2019, and 2021 with judicious fertilizer application and moisture \n\n\n\navailability.  This study results are consistent with those of Goh et al. (2016) that an even \n\n\n\ndistribution of rainfall is required for soils with low water-holding capacity and/or where root \n\n\n\ndevelopment is restricted. The fact that both cultivars in this study were impacted by drought \n\n\n\nalso supports the findings that FFB yield output is a non-heritable trait predominantly \n\n\n\ninfluenced by changes in the environment, as described by Arolu et al. (2016).   \n\n\n\n\n\n\n\nThe yield decline ranging from 10% to 23% shown in Figures 4 and 5 further justify the effect \n\n\n\nof the occasional drought faced by the Uganda plantation. This is consistent with the findings \n\n\n\nof  a study that drought reduces FFB yields by up to 20% on poor soils and by 10% to 15% on \n\n\n\nexcellent soils (Caliman and Southworth, 1998). The persistent yield of the two progenies \n\n\n\ndespite the drought prevalence in this study justifies tolerance of Tenera cultivars under harsh \n\n\n\nenvironmental conditions as per reports by Almeida et al. (2020) and  Corley and Tinker \n\n\n\n(2003). However, the observed difference in yields  between the two progenies is an indication \n\n\n\nof their variation in the genotypes and origins (Arolu et al., 2016; Swaray et al., 2020). The \n\n\n\ndrought  and cold  tolerant DelixGhana progeny is bred from ASD Costa Rica (Escobar et al. \n\n\n\n2006). In contrast, the Guthrie D x P progeny which originates from Malaysia (SDSAS 2011) \n\n\n\nhas  high yields under optimal conditions of rainfall distribution and a good fertilizer supply \n\n\n\n(Yong et al., 1997; Yong and Chan 1992).  \n\n\n\n\n\n\n\nThis partly explains why it responded well to well-managed soil groups A and B in this study. \n\n\n\nThis finding is similar to that of Nodichao et al. (2011), that a genetically superior material \n\n\n\nproduces high and stable FFB yields under drought conditions. This study therefore confirms \n\n\n\nthat DelixGhana is better adapted to the Uganda plantation condition. This is also consistent \n\n\n\nwith other findings that DelixGhana yields are favourable even  under suboptimal \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 29-44 \n\n\n\n\n\n\n\n40 \n\n\n\n\n\n\n\nenvironments of low fertility and rainfall(Aye et al. 2005). Based on Figure 3, the mean FFB \n\n\n\nyields of SMGs A, Ai, Ait, B, Bi, and Gi, increased by 47%, 26%, 38%, 31%, 38%, and 21%, \n\n\n\nrespectively, in the year 2021. Our findings show that events that result in changes to oil palm \n\n\n\nyields or stress-related effects occur within a period of one to three years (Haniff et al., 2016).  \n\n\n\n\n\n\n\nComparing the Uganda plantation mean potential yield of 28.17 t ha-1 and the actual mean of \n\n\n\n18.79 t ha-1 shows a yield gap of 33% or 9.37tha-1. Based on Figure 7 below, the yield gaps on \n\n\n\nsoil management groups A, Ai, Ait, B, Bi and Gi are 35%, 37%, 36%, 28%, 36% and 27% \n\n\n\nrespectively.  These are within the range of 25% to 35% reported by Palat et al. (2008) in a \n\n\n\nThailand  oil palm plantation that experienced  a dry period of 3 to 4 dry months. Also the \n\n\n\nactual FFB yields of between 17.1 t ha-1 and 21 t ha-1 (Figure 6) are similar to those reported \n\n\n\nin Peninsular Malaysia state of Kelantan State  faced with extreme weather of 2 to 3 dry months \n\n\n\n(Chan 2005). Therefore, the yield gaps of the Uganda plantation SMGs confirm that drought \n\n\n\nalso significantly contributes to the potential for low site yield. \n\n\n\n\n\n\n\n \nFigure 7: Mean comparison between potential and actual FFB yields for the six-soil \n\n\n\nmanagement groups. \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nThe moderate rainfall, defined by a four-month dry spell (between June and September), \n\n\n\nresulted in an FFB yield decline of between 10% and 23%. However, altering the fertilizer \n\n\n\nregime and utilizing both organic and inorganic fertilizers, as governed by annual leaf analysis, \n\n\n\nincreased the FFB yields under soil moisture availability.  The use of decanter solid at a rate of \n\n\n\n200 kg palm-1, EFB mulch at a rate of 40 t ha-1, terracing, planting leguminous cover crops, \n\n\n\nmaintaining understory vegetation, and construction of Tapak kuda resulted in the best FFB \n\n\n\nyields of 20.21 t ha-1 and 19.46 t ha-1 on SMGs B and A, respectively. For the Uganda plantation \n\n\n\nto fulfil its yield potential, these techniques should be maintained or further optimized.  \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nWe wish to thank Wilmar International for financial assistance and permission to conduct \n\n\n\nresearch on their oil palm plantation. We also appreciate the technical assistance provided by \n\n\n\nthe Faculty of Sustainable Agriculture (FSA) Sandakan, Universiti Malaysia Sabah. 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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 87-98 (2019)                         Malaysian Society of Soil Science\n\n\n\nQuality Estimation of the Western Algeria Forest Soils \n\n\n\nM. Zouidi*1, A.H. Borsali1, A. Allam1 and R. Gros2 \n\n\n\n1Water Resources and Environment Laboratory, Department of Biology, Faculty of \nScience, University Dr Moulay Tahar, 20 000 Sa\u00efda, Algeria.\n\n\n\n2Mediterranean  Institute of Biodiversity and Ecology, UMR CNRS IRD 7263, Team \nVulnerability of Microbial Systems, Service 452, Faculty of Sciences and Techniques \n\n\n\nof Saint-J\u00e9r\u00f4me, Aix-Marseille University,13397 Marseille Cedex 20, France\n\n\n\nABSTRACT\nIn recent years, there has been significant regression of the Aleppo pine \nforest massif in the semi-arid areas of Algeria which is the last barrier against \ndesertification. Several studies on the effects of climate and anthropogenic \npractices have been undertaken to identify the limiting factors but no study in \nthe region deals with the effects of soil properties. In this work, we studied the \nquality of soils in a pine forest of Aleppo, Western Algeria by comparing their \nphysico-chemical and biological parameters in order to characterise these soils \nand to identify the main limiting and degrading factors of their quality. The results \nof this study showed that the forest soils in this area were alkaline but not salty \nwith a presence of limestone. They had a balanced texture homogeneous moisture \nwith the colour varying from reddish brown to reddish maroon. The C/N ratio was \nmoderately low indicating that these soils release some nitrogen despite being \nrich in organic matter. Microbial activity in these soils was moderately low as a \nfunction of nitrogen availability to ensure good carbon mineralisation. This study \nhas shown that the soils of Aleppo pine forests in semi-arid zones are fragile and \ngenerally characterised by heterogeneous properties that are very sensitive to the \ninfluence of environmental factors (climate and human). This may result in the \ndeterioration of physico-chemical and biological quality of the soils over a long-\nterm consequently changing them into arid soils.\n\n\n\nKeywords: Soil properties, Aleppo pine, quality, degradation, forest, semi-\narid\n\n\n\n___________________\n*Corresponding author : bio.zouidi1991@hotmail.com \n\n\n\nINTRODUCTION\nThe Mediterranean basin is one of the most important hotspots of global \nbiodiversity, given its floristic richness of terrestrial plant communities and its \nhigh level of endemism (M\u00e9dail and Quezel 1999; Myers et al. 2000; M\u00e9dail \nand Myers 2004).  According to Seigue (1985), the Mediterranean forest covers \n65 million hectares of which 45 million of forests proper and 19 million hectares \nof forest formations have become a fragile natural environment disturbed by \nmultiple uses, the origins of which date back to the beginning of the Neolithic \nperiod. However, the aggression the Mediterranean forest has undergone \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201988\n\n\n\nconsiderably varied in frequency and intensity over the ages as a function of \nhuman demography which has determined regression or progression phases of \ntheir surface (Tillier 2011).The issue of degradation of natural resources is still \nassociated with climate, which largely determines the natural characteristics of \nthe environment. Indeed, plants and some soil properties are strongly correlated \nwith the climatic conditions of the area.\n Soils are formed over time as the climate and vegetation act on the material \nof the mother rock. Important aspects of soil formation in an arid climate are \nsignificant daily changes in temperature which cause the mechanical or physical \ndecomposition of the rocks, and the sands transported by wind or water erosions \n(FAO 1992). The knowledge of soil constituents, their composition and their \nmain physico-chemical and biological properties, is in any case a prerequisite \nfor the study of the soil. This fundamental knowledge makes it possible to \nundertake the study of soil formation processes (pedogenesis process) in relation \nto environmental conditions, which leads to the classification of world soils and \ntheir mapping (Drouet 2010). The objective of this work is to study the physico-\nchemical and microbiological quality of forest soils dominated by Aleppo pine in \na semi-arid zone characterised by strong anthropogenic pressure. This research \nwas carried out at the forest of mountain Sid Ahmed Zeggai located in the Saida \nregion of Western Algerian. This research is the first field study conducted in the \narea. In the present study, we aim to determine the physico-chemical properties \nof soils in this area to better characterise them and to build a data bank which will \nhelp the managers and the foresters to better manage this very fragile ecosystem.\n\n\n\nMATERIALS AND METHODS\n\n\n\nThe Study Area\nThe mountain of Sid Ahmed Zeggai located 4.5 km west of Saida (north-west of \nAlgeria) was our study area. The Saida region is called a hybrid territory, neither \ntruly steppe, nor truly Tellian (ANAT 2008). The Sid Ahmed Zeggai mountain \nis a part of the Saida mountains which are the eastern extension of the denny \nmountains forming part of the Atlas Tellian as shown in Figure 1. It is characterised \nby geological formations containing groundwaters and a geomorphological \nframework characterised by terrain and slopes. The region has fauna and flora \nspecific to the area and its forest encompasses a multitude of landscapes and \nvaried environments, with the Aleppo pine and lentisk as the main tree species \npresent on the mountain of Sid Ahmed Zeggai.\n From a climatic point of view, the forest benefits from a semi-arid climate \n(M = 3\u00b0C, P = 344.6 mm) located on the upper floor of the Mediterranean \nvegetation (M > 3\u00b0C, 200 < P < 400 mm). It has a dry period of almost 6 months \nfrom May to October and 38 ice days on average per year (ONM 2016).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 89\n\n\n\nSelection of Stations and Sampling  \nFive sampling stations of 400 m2 were set up in the study area (Table 1). For each \nstation, five samples of one kilogram soil were randomly collected with a set of 25 \nsoil samples, after the litter was eliminated at a depth of 0 to 20 cm. The composite \nsamples were sieved to 2 mm; the samples were air-dried before subjecting them \nto physico-chemical analyses, or stored at 4\u00b0C pending microbiological assays\n\n\n\nPhysico-chemical and Biological Analyses \nA particle size analysis was carried out using the Robinson pipette method \n(Aubert 1978). The soil colours were determined by the Munsell Soil Color Chart \n(Delaunois 2006). Gravimetric water content was determined by subtracting \nthe mass of a sample of dried soil (105\u00b0C, 24 h) from that of the fresh sample. \nThe apparent particle density was determined by the method of drying and \nweighing the cylinders while the actual particle density was determined by the \npycnometer method (Aubert 1978). The permeability of a soil was determined \nby the measurement of water height per centimetre that infiltrated per unit of \ntime in the soil (Mathieu et al. 1998). The water retention capacity was measured \nby weighing a sample saturated with water after decanting for 24 h at 4\u00b0C and \nthe soil pH was measured in a soil and distilled water suspension (1:2.5). The \nmeasurement was performed after two hours of stabilisation at room temperature \nusing a M\u00e9trom pH meter (Herisau, Switzerland). Calcium carbonate (CaCO3) \ncontent was determined by decomposition to CO2 by HCl (Aubert 1978).  Total \n\n\n\nFig. 1: The study area\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201990\n\n\n\nnitrogen level was determined by the Kjeldhal method (Aubert 1978). Organic \nmatter content was measured by mass loss of a dry sample during calcination at \n550\u00b0C for 16 h. Basal respiration and microbial biomass were measured according \nto the protocol described by Anderson and Domsch (1978).\n\n\n\nStatistical Analysis\nThe results of analyses of physico-chemical and microbiological properties \nbetween the different stations studied were subjected to one-way ANOVA by the \nStatistica 8.0 software. \n\n\n\nRESULTS\n\n\n\nPhysical Properties\nThe physical parameters of the soils are recorded in Table 2.  On the basis of \naverage particle size composition and according to the texture triangle, the soils of \nour study area were categorised as sandy-loam. The soil colour generally varied \nfrom reddish brown (10R3/3) to reddish black (10R2/1). We noted a moderately \nlow level of soil moisture in the stations ranging from13.29% and 24.12% which \nwas not significant (p> 0.05); the S2 station had the lowest percentage of humidity \nat 13.29%. The experimentation indicates that stations S3, S5, S2 had a higher \nretention capacity rate (74%, 70%, 69%) per contribution compared to the S1 \nstation with a water storage rate of 60%. The observation that the S4 station had a \nretention capacity not exceeding 49% was probably due to a significant recovery \nby the vegetation that used this water. Retention capacity remained moderately \nsignificant between study stations (p< 0.05). Apparent density was lower than \nactual density at the five stations and varied from 0.15 g cm-3 to 0.25g cm-3 while \n\n\n\nTABLE 1 \nGeographic coordinates and characterisation of the study stations\n\n\n\n5 \n\n\n\n\n\n\n\nTABLE 1  \nGeographic coordinates and characterisation of the study stations \n\n\n\n\n\n\n\nPhysico-chemical and Biological Analyses  \n\n\n\nA particle size analysis was carried out using the Robinson pipette method (Aubert 1978). \n\n\n\nThe soil colours were determined by the Munsell Soil Color Chart (Delaunois 2006). \n\n\n\nGravimetric water content was determined by subtracting the mass of a sample of dried \n\n\n\nsoil (105\u00b0C, 24 h) from that of the fresh sample. The apparent particle density was \n\n\n\ndetermined by the method of drying and weighing the cylinders while the actual particle \n\n\n\ndensity was determined by the pycnometer method (Aubert 1978). The permeability of a \n\n\n\nsoil was determined by the measurement of water height per centimetre that infiltrated per \n\n\n\nunit of time in the soil (Mathieu et al. 1998). The water retention capacity was measured \n\n\n\nby weighing a sample saturated with water after decanting for 24 h at 4\u00b0C and the soil pH \n\n\n\nwas measured in a soil and distilled water suspension (1:2.5). The measurement was \n\n\n\nperformed after two hours of stabilisation at room temperature using a M\u00e9trom pH meter \n\n\n\n(Herisau, Switzerland). Calcium carbonate (CaCO3) content was determined by \n\n\n\ndecomposition to CO2 by HCl (Aubert 1978).  Total nitrogen level was determined by the \n\n\n\nKjeldhal method (Aubert 1978). Organic matter content was measured by mass loss of a \n\n\n\nStations Altitude \n\n\n\n(m) \n\n\n\nLongitude  \n\n\n\nX \n\n\n\nLatitude  \n\n\n\nY \n\n\n\nExposition Slope \n\n\n\n(%) \n\n\n\nVegetation \n\n\n\ncover (%) \n\n\n\nStation \n\n\n\n01 \n\n\n\n1092 34\u00b0 52' 13'' N 00\u00b0 04' 26'' E North 5 70 \n\n\n\nStation \n\n\n\n02 \n\n\n\n1097 34\u00b0 51' 30'' N 00\u00b0 04' 56'' E South 10 40 \n\n\n\nStation \n\n\n\n03 \n\n\n\n1146 34\u00b0 50' 48'' N 00\u00b0 04' 40'' E All 0 60 \n\n\n\nStation \n\n\n\n04 \n\n\n\n1160 34\u00b0 50' 11'' N 00\u00b0 05' 22'' E East 10 60 \n\n\n\nStation \n\n\n\n05 \n\n\n\n1081 34\u00b0 49' 29'' N 00\u00b0 04' 53'' E North 5 55 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 91\n\n\n\nactual density varied from 1.30g cm-3 to 2.70g cm-3. Permeability, porosity and \nreal density were found to exhibit a highly significant difference in means (p> \n0.001) between the stations. The permeability average ranged from 44.21 cm h-1 \nto 50.91cm h-1. Porosity is an essential concept for all who are concerned with \nwater conservation, the circulation of liquids and gases, and rooting capacity of \nplants. Table 2 shows that the total porosity varied from one station to another \nfrom 12 % to 54 % with Station 4 having the lowest rate of porosity.\n\n\n\nChemical properties\nThe Soil chemical properties show that the concentrations of organic carbon and \ntotal nitrogen varied from one station to another and exhibited a highly significant \ndifference (p< 0.001).  However, we recorded average concentrations of significant \ncarbon and total nitrogen in the S5 station (7.19 g kg-1;   0.31 g kg-1) while S2 \nrecorded the lowest carbon and S4 the lowest nitrogen rate (1.33 g.kg-1 and 0.081 \ng kg-1, respectively). The total nitrogen content was low in all stations compared \nto the carbon content. The C/N ratio is widely used to classify the evolution and \ntypes of organic matter in a soil. It is an indicator of residue degradation in soil \nand remains moderately significant (p< 0.01) between soils with an average \npercentage ranging from 16% to 23%. Organic matter averages exhibited a highly \nsignificant difference between the stations (p< 0.001) and correlated with carbon \naverages. Station S5 was richest in organic matter (12.37%) while S4 station had \nthe poorest percentage (2.29%).\n  As the soil pH varied from 7.67 to 8.91, the results confirmed that the soil \nin all the stations were alkaline. The electrical conductivity varied from 0.37dS \nm-1, and 0.55 dS m-1, and based on these results, all stations are characterised by \nunsalted soil (Aubert 1978). The stations studied exhibited a variable limestone \nrate with a small difference (p< 0.05). Station S5 had the maximum value with \n\n\n\nTABLE 2 \nPhysical properties of soils\n\n\n\n7 \n\n\n\n\n\n\n\nvaried from one station to another from 12 % to 54 % with Station 4 having the lowest \n\n\n\nrate of porosity. \n\n\n\n\n\n\n\nTABLE 2  \nPhysical properties of soils \n\n\n\n\n\n\n\n\n\n\n\n \nChemical properties \n\n\n\nThe Soil chemical properties show that the concentrations of organic carbon and total \n\n\n\nnitrogen varied from one station to another and exhibited a highly significant difference \n\n\n\n(p< 0.001).  However, we recorded average concentrations of significant carbon and total \n\n\n\nnitrogen in the S5 station (7.19 g.kg-1;   0.31 g.kg-1) while S2 recorded the lowest carbon \n\n\n\nand S4 the lowest nitrogen rate (1.33 g.kg-1 and 0.081 g.kg-1, respectively). The total \n\n\n\nnitrogen content was low in all stations compared to the carbon content. The C/N ratio is \n\n\n\nwidely used to classify the evolution and types of organic matter in a soil. It is an \n\n\n\nindicator of residue degradation in soil and remains moderately significant (p< 0.01) \n\n\n\nbetween soils with an average percentage ranging from 16% to 23%. Organic matter \n\n\n\nPhysical properties Stations F value and \nsignificance S1 S2 S3 S4 S5 \n\n\n\nHumidity (%) \n \n\n\n\n16.47 \n \n\n\n\n13.29 \n \n\n\n\n20.90 \n \n\n\n\n18.00 \n \n\n\n\n24.12 \n \n\n\n\nF=2.47 ns \n\n\n\nRetention.capacity (%) 62.86 \n \n\n\n\n73.81 \n \n\n\n\n74.91 \n \n\n\n\n67.69 \n \n\n\n\n77.40 \n \n\n\n\nF=4.57** \n\n\n\nPermeability (cm.h-1) 46.21 \n \n\n\n\n50.09 \n \n\n\n\n44.21 \n \n\n\n\n47.55 \n \n\n\n\n50.91 \n \n\n\n\nF=11.87*** \n\n\n\nPorosity (%) 74.30 \n \n\n\n\n74.81 \n \n\n\n\n81.53 \n \n\n\n\n72.61 \n \n\n\n\n84.54 \n \n\n\n\nF=8.73*** \n\n\n\nApparent.density (g.cm-3) 1.15 \n \n\n\n\n1.03 \n \n\n\n\n1.03 \n \n\n\n\n1.29 \n \n\n\n\n0.98 \n \n\n\n\nF=3.70* \n\n\n\nReal density (g.cm-3) 4.53 \n \n\n\n\n4.12 \n \n\n\n\n4.64 \n \n\n\n\n4.87 \n \n\n\n\n4.42 \n \n\n\n\nF=3.96* \n\n\n\nThis table records the average values; Physical properties, the p value of independent test is presented \nwith its threshold of significance (*: p<0.05; **: p<0.01; ***: p<0.001; ns: not significant). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201992\n\n\n\n39.26%, with Station 4 having the minimum limestone rate at 19.99% as shown \nin Table 3.\n\n\n\nTABLE 3 \nChemical properties of soils\n\n\n\n8 \n\n\n\n\n\n\n\naverages exhibited a highly significant difference between the stations (p< 0.001) and \n\n\n\ncorrelated with carbon averages. Station S5 was richest in organic matter (12.37%) while \n\n\n\nS4 station had the poorest percentage (2.29%). \n\n\n\n\n\n\n\n As the soil pH varied from 7.67 to 8.91, the results confirmed that the soil in all the \n\n\n\nstations were alkaline. The electrical conductivity varied from 0.37dS.m-1, and 0.55 dS.m-\n\n\n\n1, and based on these results, all stations are characterised by unsalted soil (Aubert 1978). \n\n\n\nThe stations studied exhibited a variable limestone rate with a small difference (p< 0.05). \n\n\n\nStation S5 had the maximum value with 39.26%, with Station 4 having the minimum \n\n\n\nlimestone rate at 19.99% as shown in Table 3. \n\n\n\nTABLE 3  \nChemical properties of soils \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChemical properties Stations F value and \nsignificance S1 S2 S3 S4 S5 \n\n\n\nCarbon (g.kg-1) 3.31 5.60 3.80 1.33 7.19 F=378.82*** \n\n\n\nNitrogen (g.kg-1) 0.18 \n \n\n\n\n0.27 \n \n\n\n\n0.22 \n \n\n\n\n0.081 \n \n\n\n\n0.31 \n \n\n\n\nF=50.36*** \n\n\n\nC/N ration 18.30 \n \n\n\n\n20.90 \n \n\n\n\n17.06 \n \n\n\n\n16.45 \n \n\n\n\n23.54 F=5.98** \n\n\n\nOrganic matter (%) 5.70 \n \n\n\n\n9.64 \n \n\n\n\n6.54 \n \n\n\n\n3.29 \n \n\n\n\n12.37 \n \n\n\n\nF=382.40*** \n\n\n\npH 7.67 \n \n\n\n\n8.76 \n \n\n\n\n7.73 \n \n\n\n\n8.21 \n \n\n\n\n7.93 \n \n\n\n\nF=108.53*** \n\n\n\nConductivity (dS.m-1) 0.45 \n \n\n\n\n0.55 \n \n\n\n\n0.52 \n \n\n\n\n0.37 \n \n\n\n\n0.47 \n \n\n\n\nF=8.66*** \n\n\n\nTotal limestone (%) 27.40 27.03 21.48 \n \n\n\n\n19.99 \n \n\n\n\n39.26 \n \n\n\n\nF=4.18* \n \n\n\n\nThis table records the average values; for chemical properties, the p value of independent test is \npresented with its threshold of significance (*: p<0.05; **: p<0.01; ***: p<0.001; ns: not \nsignificant). \n\n\n\nBiological Properties\nThe averages of the soil microbial parameters are recorded in Figure 2. The \nstatistical data show that the biomass is homogeneous throughout the study area \nand is not significantly different (p > 0.05). Station S5 had the highest microbial \nbiomass (50.25 \u03bcgC-CO2 g\n\n\n\n-1) while Station S2 had the lowest microbial biomass \nwith an average of 22.65 \u03bcgC-CO2 g\n\n\n\n-1. \n Basal respiration averages were low compared to microbial biomass in \nall stations. The lowest value was recorded in station S3 (2.06 \u03bcg C-CO2 h\n\n\n\n-1.g-1) \nand the maximum for station S1 (4.13\u03bcg C-CO2 h-1.g-1). The statistical results \nshowed a small significant difference between the stations (p<0.05). A moderately \nsignificant difference was measured between stations for metabolic quotient \n(qCO2) (p< 0.01).\n\n\n\nDISCUSSION\nSoils are a major component of forest ecosystems with their qualities being closely \ndependent on vegetation, climate and human action. In this context, it is important \nto be able to monitor soil quality in the short and medium terms (INRA 2006). \nHowever, it is quite widely accepted that there is no single, universal indicator of \nthe quality of a forest floor (Fox 2000). The degradation of soil resources results \nfrom the synergistic effects of climate, the aggressiveness of certain natural \nconditions, and above all human activities carried out on generally fragile and \ninfertile soils. All agricultural, forestry and pastoral activities must contribute to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 93\n\n\n\nthe maintenance of this natural capital, to the improvement of its productivity and \nto the preservation of the environment (AEE 2014). \n The results show that the soils of the different stations have a sandy-\nloam texture. The soils have a red colour due to the oxidation of iron which is \npresent in large amounts in these soils while the brown and maroon colours are \ndue to brunification (formation of iron hydroxide) (Birot 1966; Delaunois 2006). \nHumidity is moderately low due to high evaporation and evapotranspiration \nand irregular precipitation. Borsali (2013) shows that the gravimetric water \ncontent of soils depends primarily on the climatic conditions (temperatures and \nprecipitation) preceding sampling and that this decrease in water content could be \na consequence of the impacts of the past events of forest fire on some physico-\nchemical properties of soils and on vegetation.\n Soils have a good capacity for water retention due to the presence of \norganic matter (Borsali 2013; Zouidi and Borsali 2017). Several authors have \nshown that the retention capacity decreases if the organic matter is destroyed by \nfire, which induces a decrease in the aggregate stability leading to a reduction \nin the soil capacity to conserve water (Rigolot 1992; Borsali et al. 2012).  The \nsoils have good porosity due to the presence of sand which has high infiltration \nand percolation rates. The size and number of pores are closely dependent on \n\n\n\n9 \n\n\n\n\n\n\n\nBiological Properties \n\n\n\nThe averages of the soil microbial parameters are recorded in Figure 2. The statistical \n\n\n\ndata show that the biomass is homogeneous throughout the study area and is not \n\n\n\nsignificantly different (p > 0.05). Station S5 had the highest microbial biomass (50.25 \n\n\n\n\u03bcgC-CO2.g-1) while Station S2 had the lowest microbial biomass with an average of 22.65 \n\n\n\n\u03bcgC-CO2.g-1.  \n\n\n\nBasal respiration averages were low compared to microbial biomass in all stations. The \n\n\n\nlowest value was recorded in station S3 (2.06 \u03bcg C-CO2.h-1.g-1) and the maximum for \n\n\n\nstation S1 (4.13\u03bcg C-CO2.h-1.g-1). The statistical results showed a small significant \n\n\n\ndifference between the stations (p<0.05). A moderately significant difference was \n\n\n\nmeasured between stations for metabolic quotient (qCO2) (p< 0.01). \n\n\n\n\n\n\n\n \n \n \n \n \nFigure 2. Microbiological properties of soil (A. Microbial biomass; B. Basal  respiration; \n\n\n\nC.  Metabolic quotient (qCO2) (*: p < 0.05; * *: p < 0.01; * * *: p < 0.001; ns: not \nsignificant). \n\n\n\n\n\n\n\nFig. 2: Microbiological properties of soil (A. Microbial biomass; B. Basal  respiration; \nC.  Metabolic quotient (qCO2) (*: p < 0.05; * *: p < 0.01; * * *: p < 0.001; \n\n\n\nns: not significant).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201994\n\n\n\ntexture (balanced texture) and soil structure which also influence its permeability. \nThe finer the soil texture, the lower the permeability (FAO 1985). The soil in the \nstudy area is well structured and fine with good (wide pores); its apparent density \nwill be low compared to that of the same compact soil (Laborier 1994). The \ndegradation of the physical properties of a soil can lead to a significant reduction \nof its chemical and biological qualities.\n Soil quality assessment is based on chemical properties (Singer and Ewing \n2011). The results of the chemical properties show that the soils of the pinewoods \nat Saida exhibit highly significant variation in organic carbon richness and organic \nmatter. This difference is explained by the rate of plant recovery which differs \nfrom one station to another and which ensures the production of litter (the fall of \nplants, mainly the needles of Aleppo pine). Nitrogen is low relative to carbon in the \nstations, which induced a C/N ratio that varied from (15<C/N<20). This is a sign \nthat  required nitrogen cover to allow good decomposition of the carbonaceous \nmaterial  is found in all stations except for station S5 where the ratio C/N > 20, \nindicating a significant carbon rate but  not enough nitrogen to allow for carbon \ndecomposition. Soils at the level of our forest are generally alkaline (pH 7.5 \u2013 8.5). \nThe presence of carbonates strongly influences soil reaction, with carbonate soils \ndistinguished by a pH that is higher than 7 (Drouet 2010). Based on the salinity \nscale (Aubert 1978), the conductivity of the soils studied in our area shows that \nthey are classified in the category of non-saline soils. This is due to a low level \nof mineral salts and also because the fragmented chemical elements have been \nleached by the rains and transported by erosion (Aubry 2007). Determination of \ntotal limestone shows that the soils in the study area are moderately calcareous \nexcept for the S5 station, which has a highly calcareous soil. According to (Nieder \nand Benbi 2008), carbon in the soil occurs largely in carbonaceous minerals such \nas calcium carbonate (CaCO3) that develops on calcareous parent materials and \nin arid or a semi-arid climate. The chemical properties of a soil condition its \nbiological quality. Any disturbance of these properties (natural or anthropogenic) \ncan lead to a reduction in the biological quality of a soil and consequently induce \na malfunction of the terrestrial ecosystem (Gros 2002).\n Soil microbial activity is largely controlled by the physical and chemical \nconditions prevailing in these soils (Doran and Linn 1994). At the level of our \nstudy area, the rate of microbial biomass is homogeneous in all stations. This \nhomogeneity depends in the first place on water content that is similar in all the \nstations and also on the pH, limestone, salinity, texture and temperature that \ndoes not change much between the study sites, as reported by several authors \n(Lavelle and Spain 2001; Salducci 2007; Briat and Job  2017). Soil microbial \nactivity is largely controlled by management practices such as tillage (Doran \nand Linn 1994) and in parallel, biological fertility influences the physical state \nof the soil, the amount of organic matter, and the availability of nutrients. Our \nresults show a weakly significant difference in basal breathing between stations \n(p< 0.05) although microbial biomass is homogeneous, and this is probably due \nto dormancy of certain bacteria under the influence of soil-climatic conditions. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 95\n\n\n\nMicrobial activity is mainly dependent on soil conditions (aeration, moisture, \nporosity, microbial life). The activation of microbial life requires the presence \nof easily degradable organic matter that will serve as food. It is the interactions \nbetween the different soil properties that give it its ability to feed the plant in \nthe long term (Huber and Schaub 2011).  Return dynamics are therefore highly \nvariable and depend on the amount of resources available, the adaptation of \ncommunities, and the stressful climatic conditions that may limit microbial \nrecolonisation (Borsali 2013), including decreased water content and an increase \nin recalcitrant carbon levels for microbial biodegradation (Borsali et al. 2012). \nThe metabolic quotient (qCO2) indicates a low efficacy of microbial activities that \nuse available soil carbon for biosynthesis; this can be explained by the variation \nin daily and seasonal temperatures in semi-arid areas, and by the age and types \nof microbial communities in this area that can affect energy efficiency (Anderson \nand Domsch 1978; 1986).\n During this experiment a large number of physical, chemical and \nmicrobiological parameters were studied in order to best identify the quality of \nthe soils subjected to different stresses. Field observations have highlighted that \ndifferent microbial responses and physico-chemical characteristics related to \nlocal environmental stresses (drought, heat wave, salinity etc.) which are either \ninternal or external factors to the ecosystem can affect the forest traits (growth, \nreproduction, longevity, etc.) and the behaviour of organisms. All these factors \naffect all levels of ecological organisation (individuals, populations, communities \nand processes) (Barrett et al. 1976). The stress imposed on an active system \ndevelops an adaptive or attenuation response which intensity depends on the state \nof the system (stability and vulnerability). Smithers and Smit (1997) indicated that \nin the case of a natural stimulus such as the climate, adaptation differs according \nto frequency, duration, suddenness and amplitude of the phenomena (Gu\u00e9non et \nal. 2011).\n\n\n\nCONCLUSION\nThe study showed the existence of a semi-arid bioclimatic stage imprint on the \nphysico-chemical and biological properties of forest soils at different spatial \nscales by integrating the specific stress effect into a semi-arid environment such \nas climate, terrain and vegetation. To our knowledge, there is no data on soil \ncharacteristics in the semi-arid area of western Algeria. These soils generally have \na balanced texture favorable to the life of the roots and micro-organisms because \nit has good aeration, drainage, water retention and nutrient status. This study \nshows that variation for many distinct parameters (texture, actual and apparent \ndensity, holding capacity, moisture, bacterial biomass and basal respiration) \namong the soils is very minimal. A small difference exists for the organic matter \ndecomposition rate as a result of the difference in the slope and recovery rate.  \nThe C/N ratio indicates moderately low biological activity in the soils due to the \npresence of low nitrogen which does not support the decomposition of the highly \ncarbonaceous material. Soils in this region are alkaline but not salty and contain \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201996\n\n\n\na significant amount of limestone resulting from the weathering of calcimagnesic \nparent materials which contains high amounts of limestone. Soil microbial \nproperties show only a significant presence of microbial biomass due to the high \ntemperature and low humidity of this area. \n\n\n\nREFERENCES\nAEE. 2014. Horizon 2020 Report on the Mediterranean Annex 04: European \n\n\n\nEnvironment Agency NO 6/2014.\n\n\n\nANAT. 2008. Land Use Plan of the Saida region, Phase I, Territorial Evaluation, \n150p.\n\n\n\nAnderson, J.P.E. and K.H. Domsch. 1978. A physiological method for the quantitative \nmeasurement of microbial biomass in soils. Soil biology and biochemistry \n10(3): 215-221.\n\n\n\nAnderson, J.P.E. and K.H.Domsch. 1986. Carbon assimilation and microbial activity \nin soil. Zeitschriftf\u00fcr Pflanzenern\u00e4hrung und Bodenkunde 149(4): 457-468.\nAubert,  G .1978. Methods of Soil Analysis. Marseille : CRDP, 189 p.\n\n\n\nAubry, C. 2007. Technical management of farms: component of agronomic theory. \nMemory of empowerment to direct research. Toulouse: National Polytechnic \nInstitute of Toulouse, 101p.\n\n\n\nBarrett GW., G.M. van Dyne and E.P. Odum. 1976. Stress ecology. BioScience 26: \n192-194.\n\n\n\nBirot, P. 1966. New soil classifications after P. Duchaufour. 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Soil organic \nmatter quality and microbial catabolic functions along a gradient of wildfire \nhistory in a Mediterranean ecosystem. Applied Soil Ecology 48(1): 81-93.\n\n\n\nHuber G. and C. Schaub. 2011. Soil Fertility: The Importance of Organic Matter. \nAgriculture and territory Chamber of agriculture Bas-Rhin. Environment-\nInnovation Service 46p. Available at https://agriculture-de-conservation.com.\n\n\n\nINRA. 2006. Maintenance of the soil quality of forest ecosystems: Use of sustainable \nmanagement indicators in the Landes de Gascogne forest, 1-19p.\n\n\n\nLaborier, J. 1994. Compacted soils or layered topsoil: Developing reasoned \nprograms of mechanical work. Green-Keeper Review 27: 7.Lavelle, P. and A.V. \nSpain.2001.  Soil Ecology. Amsterdam: Kluwer Scientific, 678p.\n\n\n\nMathieu, C., F. Pieltain., Asseline Jean., J.C. Chossat. and C. Valentin. 1998. Physical \nAnalysis of Soils: Selected Methods. Paris: Lavoisier, 275 p.\n\n\n\nM\u00e9dail, F. and N, Myers. 2004. Mediterranean Basin. Hotspots revisited: Earth\u2019s \nbiologically richest and most endangered terrestrial ecoregions (ed. by \nR.A. Mittermeier, P. Robles Gil, M. Hoffmann, J. Pilgrim, T. Brooks, C.G. \nMittermeier, J. Lamoreaux and G.A.B. da Fonseca), pp. 144\u2013147. CEMEX, \nMonterrey, Conservation International, Washington and Agrupacio\u00b4n Sierra \nMadre, Mexico.\n\n\n\nM\u00e9dail, F. and P. Qu\u00e9zel. 1999. Biodiversity hotspots in the Mediterranean Basin: \nsetting global conservation priorities. Conservation biology 13(6): 1510-1513.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201998\n\n\n\nMyers, N., R.A. Mittermeier, C.G. Mittermeirer, G.A.B. Da Fonseca and J. Kent. \n2000. Biodiversity hotspots for conservation priorities. Nature 403: 853\u2013858.\nNieder, R. and  D. K. Benbi. 2008, Carbon and Nitrogen in the Terrestrial \nEnvironment, Springer, New York: Springer. DOI : 10.1007/978-1-4020-8433-1\n\n\n\nONM.  2016. National Office of Meteorology. Collection of climatic data of the \nwilaya of Saida. Daily surveys leaves of the period 1985\u20132015. \n\n\n\nRigolot, E. 1992. Burning in the French Mediterranean region. Meetings forest-\nresearchers in Mediterranean forest, la Grande-Motte (34), 6-7 October 1992. \nEd. INRA (the symposia No. 63), pp.223 \u2013 250. \n\n\n\nSalducci, X. 2007. Reasoned fertilization and soil analysis: what\u2019s new in 2007. 8 \ndays of reasoned fertilization and soil analysis GEMAS-COMIFER, Blois 20-\n21 November 2007, 9p.\n\n\n\nSeigue, A. 1985. The circum Mediterranean forest and its problems. Agricultural \ntechniques and Mediterranean productions. G.-P. Maisonneuve and Larousse. \n502 p.\n\n\n\nSinger, M.J. and S. Ewing . 2011. Soil quality. In: Handbook of Soil Sciences: Resource \nManagement and Environmental Impacts, ed. Pan Ming Huang, Yuncong Li, \nMalcolm E. Sumner, Boca Raton, Florida, CRC Press 830 p.\n\n\n\nSmithers, J., and B. Smit. 1997. 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"\n\n___________________\n*Corresponding author : \n\n\n\nINTRODUCTION\nSulphur (S) is an essential plant nutrient. A S deficiency slows down the formation \nof all amino acids which are required for optimal plant growth and final maximum \ncrop yield.  In recent years, soil S deficiency has become a major problem for crop \nproduction in many countries due to the extensive and popular use of high-analysis \nNP fertilisers, e.g., urea, mono-ammonium phosphate (MAP), di-ammonium \nphosphate (DAP), and triple superphosphate (TSP), which contain little or no \nS nutrient (Chien et al., 2009). Reduction of SO2 emissions from industry to the \natmosphere by the environmental laws also has a significant effect on S deposition \nfrom air to soils. The major S fertiliser sources have been gypsum (CaSO4) in \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 1-11 (2017)                          Malaysian Society of Soil Science\n\n\n\nA Review on the Oxidation of Granular Fertilisers \nContaining Elemental Sulphur with or without Ammonium \n\n\n\nSulphate in Soils \n\n\n\nS. H. Chien, B. Guertala and M. M Gearhartb\n\n\n\nFormerly with International Fertilizer Development Center (IFDC),\n Muscle Shoals, AL, USA\n\n\n\naAuburn University, Auburn, AL, USA\nbAdvanSix Inc., Hopewell, VA, USA\n\n\n\nABSTRACT\nSulphur (S) is an essential plant nutrient. S deficiency slows down the formation \nof all amino acids which are required for optimal plant growth and final maximum \ncrop yield.  Recent research has shown that many agricultural soils worldwide are \nincreasingly suffering (S) nutrient deficiency for crop production. Various types of \ngranular fertilisers containing elemental sulphur (ES) with or without ammonium \nsulphate (AS) have been commercialised. Since plants cannot absorb ES directly, \nES oxidation to SO4-S by soil microbes must occur to enable ES to provide plants \navailable S. In this paper, the results of several literature reports from laboratory \nsoil incubation studies with granular \n\n\n\n(ES\u00b1AS) products were extracted and reviewed critically. Granulation of \nmicronised ES particles result in a locality effect on dispersion of ES particles \nafter granule disintegration, limiting contact between ES surface and ES oxidising \nmicrobes in the soil. It is concluded that ES oxidation of these granular S fertilisers \noften is too slow or inadequate to provide initial available SO4-S. Therefore, \ngranular ES products are generally inferior to SO4-S fertilisers for agronomic \neffectiveness.\n\n\n\nKeywords: Granular elemental Sulphur, oxidation of elemental \nSulphur, available Sulphur, Sulphur response \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 20172\n\n\n\nSSP, ammonium sulphate (AS), and elemental S (ES). Natural gypsum and \nphosphogypsum, a by-products of H3PO4 production, are used as soil amendments \nand supply S nutrient as well. Recently, flue gas deSulphurised (FGD) gypsum, a \nby-product from coal-generated power plants, has been introduced to the farmers. \n\n\n\nGiven that  powdered ES particles are dusty, difficult to apply to soils and \npotentially explosive, the use of ES as an S source normally is in granular form. \nFor example, a granulated product of powdered ES with10% bentonite has been \nintroduced to farmers for some time.  Recently, several fertiliser companies \nhave developed and marketed high-analysis granular NP fertilisers such as TSP, \nMAP and DAP containing ES.  Since ES is almost 100% S, N and P contents are \nnot significantly reduced compared to incorporating AS or gypsum.  However, \nES is not plant available unless it is oxidised to SO4-S by soil microbes. Since \nthe oxidation rate of ES particles increases with decreasing particle size of ES \n(Boswell and Friesen, 1993), some fertiliser companies have developed and \nmarketed granular ES or NP fertilisers containing micronised ES particles (< 100 \n\u00b5m) (Table 1). It has been assumed that once the fertiliser granules disintegrate \nand the ES particles are released back to the original very fine particle size, the \nrate of ES oxidation in the soils may be rapidly enhanced. In order to make up for \na potentially slow ES oxidation from granular ES and NP-ES fertilisers to provide \navailable SO4-S early in the first crop, products containing a mixture of (ES + \nAS) at various S ratios have been marketed. It is assumed that AS of granular \nNP-(ES+AS) products supplies initial available S while ES provides available S \nat later stages of crop growth up to maturity for season-long or first crop nutrition \ncompared to sulphate-based S fertilisers within the year of application.  However, \nmanufacturers of these products have not provided scientific evidence that \ngranular ES products can provide available S as effectively as SO4-based sources \nduring the first cropping season (Chien et al., 2016a).\n\n\n\nTABLE 1\nSome commercial-grade granular fertilisers containing elemental S (ES)\n\n\n\nwith or without ammonium sulfate (AS)\n\n\n\n15 \n \n\n\n\nAsia & South Pacific Aagriculture, ed. G.L. Blair and R.T., . Uni. New England, \n\n\n\nAustralia: University New England, . pPp..301-314.  \n\n\n\nSchultz, J.J. 1998. Compound fertilizers. In: UNIDO; IFDC eds. Fertilizer Manual. \n\n\n\nKluwer Academic Publishers, P. O. Box 322, 3300 AH Dordrecht, The \n\n\n\nNetherlandsCompound fertilizers. In: Fertilizer Manual, ed. UNIDO,IFDC. Kluwer \n\n\n\nAcademic Publishers, AH Dordrecht, The Netherlands,. pPp..432-455.      \n\n\n\nZhao C., F. Degryes, V. Gupta and M.J.  McLaughlin. MJ. 2016. Low effective surface \n\n\n\narea explains slow oxidation of co-granulated elemental sulfur. Soil Sci. Soc. Am. J. \n\n\n\n80: 911-918. \n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nSome commercial-grade granular fertiliszers containing elemental S (ES) \n\n\n\nwith or without ammonium sulfate (AS) \n\n\n\nChemical Composition Total S \n\n\n\n(%)  \n\n\n\nES-S \n\n\n\n(%)  \n\n\n\nAS-S \n\n\n\n(%) \n\n\n\n(Bentonite + ES)  \n\n\n\n(Bentonite + ES)  \n\n\n\n(Bentonite + ES) + (AS)  \n\n\n\n(MAP + ES + AS)  \n\n\n\n90 \n\n\n\n85 \n\n\n\n46 \n\n\n\n9 \n\n\n\n90 \n\n\n\n85 \n\n\n\n32 \n\n\n\n4.5 \n\n\n\n0 \n\n\n\n0 \n\n\n\n14 \n\n\n\n4.5 \n\n\n\n(MAP + ES + AS)  10 5 5 \n\n\n\n(MAP + ES + AS)  15 7.5 7.5 \n\n\n\n(TSP + ES)  12 12 0 \n\n\n\n\n\n\n\nFormatted: Font: Italic\n\n\n\nFormatted: Font: Italic\n\n\n\nFormatted: Centered,  Indent: Left:  0\"\n\n\n\nFormatted: Centered,  Indent: Left:  0.2\"\n\n\n\nFormatted: Centered\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 3\n\n\n\nWhile several reviews of factors affecting oxidation of powdered ES in soils \nare reported in literature (Johnson, 1975; Palmer et al., 1983; Hagstrom, 1986), \nplus a comprehensive one by Boswell and Friesen (1993), there are no reviews \nor discussions available on the issue of ES oxidation of granular ES products in \nsoils. The purpose of this paper is to examine and discuss some published results \nof the ES oxidation of granular fertilisers containing (ES\u00b1AS). The review will \nbe focus mainly on the results of ES oxidation from laboratory soil incubation \nstudies reported in literature. A review of agronomic results from the greenhouse \nand field studies that have shown little or inadequate available S from oxidation \nof granular (ES\u00b1AS) products compared to AS for crop growth can be found in \nChien et al. (2016a).\n\n\n\nES Oxidation in Laboratory Soil Incubation Study without Leaching\nBiological ES oxidation requires colonisation of soil oxidising bacteria on the \nsurface of the ES particles (Janzen and Bettany, 1987). Therefore, the rate of ES \noxidation increases with the specific surface area of ES particles which increases \nwith decreasing ES particle size (Boswell and Friesen, 1993). While very fine \nmicronised ES particles (< 100 \u00b5m) can oxidise rapidly in soils, granulation of \nthe same fine micronised fine ES particles may not have the same oxidisation \neffect as claimed by some researchers and fertiliser companies. This is due to the \nso-called negative \u201clocality effect\u201d on the ES oxidation as discussed by Chien \net al. (2009;, 2011). The concept implies that when the ES granules disintegrate \nand release micronised ES particles in the soil, the very fine ES particles still in \ncluster form, localise with limited dispersion around the applied granule site due \nto the fact that ES is water-insoluble. Furthermore, ES is hydrophobic and the \nreleased micronised ES particles tend to coalesce to form larger aggregates that \nfurther decrease ES oxidation (Friesen, 1996). Consequently, little S oxidation \nof granular (ES + bentonite) and MAP-(ES+AS) products occurrs during soil \nincubation compared to the powdered ES which goes on up to 10 weeks (Chien \net al., 2016b; Fig. 1).  \n\n\n\nJanzen (1990) used a unique technique to demonstrate the importance \nof dispersion of ES particles (mean 87 \u00b5m) and the minimum soil volume (or \nequivalent soil weight) that is required to mix the ES particles to begin the ES \noxidation in a Chernozemic and Luvisolic soil. The ES particles (64 mg S) were \nthoroughly mixed with varying amounts of moist soil ranging from 0 to 64 g \n(oven-dry basis). Untreated moist soil was then placed in the bottom of incubation \nvials, the ES-soil mixture was placed in an indentation in this layer, and additional \nuntreated soil was placed on the surface. In this way, the ES-soil mixture was \nplaced in the centre of a volume of soil weighing a total of 64 g. The uncovered \nvials were incubated at 80% of field capacity in a sealed, humidified incubation \nchamber at room temperature. During the course of the incubation, the samples \nwere aerated and watered to weight as required. After 35 days, the ES concentration \nin the soils was determined by extraction with acetone and colorimetric assay.   \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 20174\n\n\n\nThe study by Janzen (1990) showed that the non-mixed ES particles with soil \ncould be considered to mimic the disintegrated ES granule without dispersion. \nThe only contact between ES particles with soil would be limited to a very small \nsurface area of outer ES particles.  In this study, ES oxidation rate was < 8 \u00b5g S \ncm-2 day-1 in the two soils. The ES oxidation rate did not increase with increasing \nsoil volume (or weight) until ES particles were mixed with soil weight higher than \n50 g (Fig. 2). Assuming the soil bulk density was 1.25 Mg m-3, it suggests that \n1 g of ES particles must be mixed with a minimum of 40 cm3 of soil to initiate \nan increase in ES oxidation. After that, a steady, almost linear increase in ES \noxidation rate to 40 \u2013 50 \u00b5g S cm-2 day-1 was observed with 1 g of ES particles \nmixed with 1 kg or 800 cm3 of the soils (Fig. 2b). For commercial-grade granular \nfertilisers containing ES with or without AS, the granule sizes generally passed \na sieve opening of 3.35 mm but we retained on an opening of 1.00 mm with \nan average diameter = 2.22 mm or radius (r) = 1.11 mm and granule volume = \n(4/3) x \u03c0 r3 = (4/3) x 3.1416 x (1.11)3 = 5.73 mm3. The specific gravity of solid \nES is 1.92 \u2013 2.07 Mg m-3 with an average of 2 Mg m-3 (or 2 g cm-3). Therefore, \n1 g of granular ES has a volume of 0.5 cm3 (or 500 mm3) with about 500/5.73 \n\u2248 87 granules. After disintegration, the ES particles must disperse within a soil \nvolume equivalent to at least (40 x 103) / (5.73 x 87) \u2248 80 times the volume of \n1 g of ES granules before a significant ES oxidation could begin to occur in 35 \ndays. In commercial application, unless the soil is mixed with the ES particles \nby ploughing, this dispersion of ES particles from ES granules is limited even \nwhen granulated with bentonite. According to the manufacturers, the bentonite-\n\n\n\nFig. 1: Oxidation of ES of granular MAP-(5% ES + 5% AS-S) and (Bentonite + 85% ES) \nand their same but finely ground powder (< 0.15 mm) in a sandy loam soil\n\n\n\n(pH 6.4) during incubation (Chien et al., 2016b). \n\n\n\n18 \n \n\n\n\n\n\n\n\nFigure 1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 5\n\n\n\nS\n\n\n\nES granules could expand up to a range of 5-7 (mean = 6) times higher than \nits volume (no supported data). For example, a volume of 1 g of bentonite-ES \ngranules could expand to 0.5 cm3 x 6 = 3 cm3. This is way below the minimum 40 \ncm3 of soil volume that is required for 1 g of bentonite-ES granules to begin ES \noxidation as shown in Fig.. 2b. The theoretical calculation thus supports the lack \nof oxidation of granular (ES\u00b1AS) products due to the localised ES particles even \nafter the disintegration of ES granules, whereas the ground ES particles rapidly \noxidised in the sandy loam soil as shown in Fig. 1.   \n\n\n\nES Oxidation in Laboratory Soil Incubation Study with Leaching\nIn another study, Degryse et al. (2016a) applied a column study to minimisze \n\n\n\npossible immobiliszation of SO4-S by soil organic matter after ES oxidation \nby mixing three soils with granular (ES\u00b1AS) sources at 24 mg total S per soil \ncolumn. The soil columns were immediately leached to remove SO4-S and then \nthe soil columns were incubated at 25 \u00baC and leached at regular time intervals to \nremove SO4-S that was produced by ES oxidation. Three S sources used were: (1) \nES particles, (2) granulated bentonite-ES (ES pastille) containing 90% ES and \n10% bentonite, and (3) co-granulated MAP-(ES+AS) containing (5% ES + 5% \nAS-S). The results with the Edmonton soil (Fig. 3) show that the ES oxidation \nincreased in the order of ES particles > granular MAP-(ES+AS) > (bentonite+ \nES) pastille. Although ES particles of both (bentonite +ES) pastille and MAP-\n(ES+AS) were in granular form, ES oxidation of ES pastille was much lower than \nMAP-(ES+AS) (Fig. 3).\n\n\n\nIt should be pointed out that percentage of S recovered in leachate for SO4-S \nfor MAP-(ES+AS) granules was based on 12 mg ES per column because MAP-\n(ES+AS) contained only 50% of total S as ES whereas it was based on 24 mg ES \n\n\n\nFig. 2: Oxidation rate of fine ES particles (87 \u00b5m) mixed with various amounts of two \n soils during soil incubation (a) regular S rate and (b) log scale of S rate (Janzen, \n\n\n\n 1990).\n\n\n\n19 \n \n\n\n\nFIG 2 \n \n \n \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 20176\n\n\n\nper column for ES particles and ES pastilles as shown in Fig. 3. This is important \nwhen comparing ES oxidation of ES particles with that of MAP-(ES+AS) \ngranules. For example, about 90% of 24 mg of ES added for ES particles and \n50% of 12 mg of ES added for MAP-(ES+AS) granules were oxidised after 20 \nweeks (Fig. 3). The actual amount of ES oxidation from ES particles was 0.90 x \n24 = 21.6 mg SO4-S whereas the amount of ES oxidation from MAP-(ES+AS) \ngranules was 0.50 x 12 = 6.0 mg SO4-S. Therefore, it is incorrect to compare 90% \nof ES oxidation from ES particles with 50% of ES oxidation from MAP-(ES+AS) \ngranules at 20 weeks. The best unbiased comparison should be based on the same \namount of ES applied. In this case, 48 mg of total S per soil column of granular \nMAP-(ES+AS) that provided 24 mg ES per soil column should be compared with \nthe same amount of 24 mg of ES particles per soil column in terms of ES oxidation \nso that both S sources provided the same amount of 24 mg ES per column in order \nto study the effect of particle-size or granule-size on ES oxidation in soils. \n\n\n\nThe poorer ES oxidation of granular MAP-(ES+AS) than that of ES particles \nshown in Fig. 3 is due to the negative locality effect on ES oxidation as discussed \npreviously. The granular ES pastille that had the least ES oxidation was due to, \nin addition to the locality effect, the physical hardness of the ES pastilles that did \nnot significantly break down or disintegrate into the ES particles as revealed by \nnearly intact pastilles in the soil columns at the end of incubation (Degryse et al., \n2016a). The data of % ES oxidation with the three soils treated with S sources at \n20 weeks are shown in Table 2. ES pastilles were found  to have very poor ES \noxidation to SO4-S (0-5%), similar to the results shown in Fig. 3. Oxidation of \n\n\n\nFig. 3: Recovered SO4-S in leachate from ES oxidation of ES particles, granular ES \npastilles and granular MAP- (5%ES+5%AS-S) in soil columns under leaching            \n\n\n\nconditions (adapted from Degryse et al., 2016a).   \n\n\n\n20 \n \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 7\n\n\n\nS\n\n\n\ngranular MAP-(ES+AS) products in the soils at 20 weeks (equivalent to 5 months \nof maximum general annual crop growth to maturity) was much lower (10-50%) \nthan that of ES fine particles (70-90%). This suggests that these granular MAP-\n(ES+AS) products are not effective as SO4-S sources for the initial S effect on \ncrop growth.\n\n\n\nZhao et al. (2016) estimated that the rates of ES oxidation after 20 weeks \nin an incubated sandy soil (90% sand) mixed with 200 mg S kg-1 were 36% and \n95%, for co-granulated (DAP+ES) and powdered ES products, respectively. They \nexplained that the slow oxidation of co-granulated ES was due to the limited \naccess of S oxidisers to the interior of the granule because of inadequate dispersion \nof ES particles in the soil. Therefore, the findings of Degryse et al. (2016a) and \nZhao et al. (2016) suggest that the oxidation of granular NP-ES products could \nhardly provide adequate available S at the latter stages of crop growth to maturity \nand put in at risk the strategy of using granular NP-(ES\u00b1AS) to supply S for the \nwhole cycle.\n\n\n\nGeneral Discussion on ES Oxidation\nSoil leaching may confound the evaluation of the degree of ES oxidation for \n\n\n\nthe granular NP-(ES+AS) products in soil incubation. Under leaching conditions, \n\n\n\nTABLE 2\nES-S recovered in the form of SO4-S in leachate from soil columns treated with different\n\n\n\nsoils and S sources following incubation for 20 weeks (estimated from Degryse et al.,\n2016a)\n\n\n\n17 \n \n\n\n\nTABLE 2 \n\n\n\nES-S recovered in the form of SO4-S in leachate from soil columns treated with different \n\n\n\nsoils and S sources following incubation for 20 weeks (estimated from Degryse et al., \n\n\n\n2016a) \n\n\n\nSoil S Source ES-S Recovered in \n\n\n\nLeachate, % of ES \n\n\n\nEdmonton, Canada ES Particles 90 \n\n\n\n Granular MAP-(5%ES+5%AS-S)  50 \n\n\n\n ES Pastilles 5 \n\n\n\n\n\n\n\nBeardstown, USA ES Particles 95 \n\n\n\n Granular MAP-(5%ES+5%AS-S)  10 \n\n\n\n ES Pastilles 0 \n\n\n\n\n\n\n\nSorriso, Brazil ES Particles 95 \n\n\n\n Granular MAP-(4.5%ES+4.5%AS-S)  40 \n\n\n\n Granular MAP-(7.5%ES+7.5%AS-S)  40 \n\n\n\n ES Pastilles 5 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFormatted: Font: Italic\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 20178\n\n\n\nmass flow of water may carry small clay particles which may intermix with very \nfine ES particles from top to bottom in the soil columns. Thus, ES particles may be \ndispersed wider under leaching than that of the same soil without leaching. If so, \nthe degree of ES oxidation of the granular NP-(ES+AS) products may be higher \nwith leaching than without leaching. Research is needed to test this hypothesis so \nthat a proper comparison of ES oxidation may be made.\n\n\n\nIt has been claimed by manufacturers that granular ES and NP-ES products \ncan provide available S through ES oxidation during the season of application. \nRegarding granular NP-(ES+AS) products, it has been claimed that AS provides \navailable S during the crop vegetative stages while ES oxidation provides available \nS during the later reproductive stages to maturity. For cereal crops such as maize, \nthe average vegetative growth to VT stage (tassel fully extended) is around 65-72 \ndays after seeding and around 128-135 days to maturity for harvest at R6 stage. \nTherefore, oxidation of granular ES would be expected to provide available S \nat least before 20 weeks to maturity. However, data from Degryse et al. (2016a) \nin Table 2 indicate that the ES oxidation of different granular bentonite-ES or \nMAP-(ES+AS) products at 20 weeks in different soils under favourable leaching \nconditions for ES oxidation ranged only between 0 and 5% or 10 and 50% of \nES applied, respectively. They estimated that with yearly applications, 40% of \nES applied in the current year would be expected to oxidise under these soil and \nclimate conditions, but also circa 30% of ES added in the previous year and circa \n20% of ES applied in the year before that, i.e., after three to four years, the ES that \noxidises within a growing season would be expected to approach the added ES \nrate. In other words, it is questionable that the ES oxidation of granular bentonite-\nES or NP-(ES+AS) products would be able to provide adequate available S to \nthe first season or in the first year after application. The limited agronomic field \nresults presented by Chien et al. (2016a) tend to confirm this suggestion. \n\n\n\nThe lack of a significant increase in powdered ES oxidation from 6 to 10 \nweeks in the sandy loam soil (Fig. 1) is probably, in part, due to an increased \nsoil acidity induced by ES oxidation. It is known that the rate of ES oxidation \ndecreases with increasing soil acidity as described by Barrow (1971). In the a \nsoil incubation study with the sandy texture soil at  pH 6.7 (Zhao et al., 2016), \nsoil pH values after 20 weeks were: control = 6.3, granular (DAP+ES) = 5.8, \nand powdered (DAP+ES) = 5.0. To remove excess salt and protons generated \nby the ES oxidation, leaching was performed at 5 weeks by adding artificial rain \nwater. Despite the lower pH with the powdered ES than the granular ES, % of ES \noxidation of powdered ES (95%) was much higher than that of granular ES (36%). \nThus the extent of ES oxidation of the granular ES products may be influenced \nby the pH-buffering capacity of the soils (sandy versus clayey) used, in addition \nto other factors such as temperature, aeration, moisture and biological activity. \n\n\n\nDegryse et al. (2016b) developed a conceptual model to predict the \u201ceffective \ndiameter\u201d for the ES oxidation of granular NP-(ES\u00b1AS) products including MAP-\n(ES+AS) discussed in the present article. The \u201ceffective diameter\u201d was defined as \nan imagine(imagined??) diameter of ES particles mixed through soil that would \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 9\n\n\n\noxidise at the same rate as the granulated ES by taking into account the effect of \ngranulation on the effective surface area available to the ES in the granule cavity \nafter the soluble macronutrient compound in the granule has diffused. \n\n\n\nOne application of the model to commercial products is to predict the effect \nof granule size on ES oxidation (Degryse et al., 2016b). For example, comparing \nsmall and large granules of ES with diameter = 1.8 mm (10-mesh) and 3.4 mm \n(6-mesh), the predicted \u201ceffective diameters\u201d are 81 and 153 \u00b5m, respectively, \nbased on the same ES particle-size (25 \u00b5m) and mass fraction of ES (0.05 or \n5%) in the MAP-(ES+AS) granule. After 140 days (20 weeks), the estimated ES \noxidation rates of the smaller and larger granules would be about 90% and 50% of \nES applied, respectively. Therefore, the MAP-(5%ES+5%AS-S) product with a \ngranule size of 3.4 mm would have only 50% of applied ES oxidised, which may \nnot provide adequate available SO4-S during the later stages of crop growth to \nreach maturity, or the S rate would have to be increased to compensate the partial \noxidation of ES. \n\n\n\nIt is noted that large granular MAP/DAP products are often bulk-blended \nwith other large granular N (e.g., urea) and K (e.g., KCl) sources to form popular \nN-P-K compound fertilisers (Schultz, 1998). Thus smaller granular MAP/DAP-\n(ES+AS) products (e.g., 1.8-mm granule size), which may increase ES oxidation \nrate, may be incompatible with other larger granular N and K fertilisers (e.g., \n3.4-mm granule size) for bulk-blending because of potential segregation problem.\n  \n\n\n\nCONCLUSIONS\nOxidation of ES from the granular NP-ES or NP-(ES+AS) products was nil (0-\n5%) or low (40-50%) for up to 20 weeks in laboratory studies using procedures \nof static soil incubation or soil columns under leaching conditions. The results of \ngreenhouse studies also showed very low or inadequate ES oxidation of granular \nNP-ES or NP-(ES+AS) products to provide available S to first crops compared \nwith SO4-S sources such as gypsum and AS. The nil or inadequate ES oxidation \nfrom the granular NP-ES or NP-(ES+AS) products is due to locality effect, which \nsignificantly reduces surface area of ES particles in contact with ES oxidising \nmicrobes.  The reason is that following granule disintegration and release of \nthe fine ES particles, these clusters in large aggregates around the granule\u2019s \napplication site within the soil.  These granular ES products, however, may have \nlong-term residual S effect, especially under field conditions. In this review \narticle, the initial, not residual, S effect is the main issue for discussion since the \nfertiliser producers have claimed that these ES products are as effective as SO4-S \nsources even in the first year for the season-long or first crops. It is concluded \nthat ES oxidation of these granular S fertilisers is often is too slow or inadequate \nto provide initial available SO4-S. Therefore, granular ES products are generally \ninferior to SO4-S fertilisers in agronomic effectiveness.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201710\n\n\n\nREFERENCES\nBarrow N.J. 1971. Slowly available sulfur fertilizers in south-western Australia.  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Sulfur oxidation of granular \nfertilizers containing elemental sulfur with/without ammonium sulfate in soil. \nAgron. Abstr.  #99858, ASA meet., ASA, WI. USA \n\n\n\nDegryse F., AjIboye B, R. Baird, R.C. da Silva and M.J. McLaughlin. 2016a. \nOxidation of elemental sulfur in granular fertilizers depends on the soil-exposed \nsurface area. Soil Sci. Soc. Am. 80: 294-305.\n\n\n\nDegryse F., R.C. da Silva, R. Bair and  M.J. McLaughlin. 2016b. Effect of co-\ngranulation on oxidation of elemental sulfur: Theoretical model and experimental \nvalidation. Soil Sci. Soc. Am. J. 80: 294-305.      \n\n\n\nFriesen D.K. 1996. Influence of co-granulated nutrients and granule size on plant \nresponse to elemental sulfur in compound fertilizers. Nutr. Cycl. Agroecosyst. \n46: 81\u201390.\n\n\n\nHagstrom G.R. 1986. Fertilizer sources of sulfur and their use. In: Tabatabai MA, \neditor. Sulfur in Agriculture, ed.. M.A. Tabatabai, Agron. 27, ASA, Madison, \nWI, USA.; pp.567-581.\n\n\n\nJanzen H.H. and J.R. Bettany. 1987.  Measurement of sulfur oxidation in soils. Soil \nSci. 143: 444-452.\n\n\n\nJanzen H.H. 1990. Elemental sulfur oxidation as influenced by plant growth and \ndegree of dispersion within soil. Can. J. Soil Sci. 70: 499-502.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 11\n\n\n\nJohnson A. 1975. Prescription of the types of sulfur fertilizers: Currently needed \nin agriculture. In: Sulfur in Australian Aagriculture, ed. K.D. McLachlan. \nAustralia: Sydney University Press, pp..242-248.\n\n\n\nPalmer B., M. McCaskil, D.K. Friesen and L.L. Hammond. 1983. Sulfur containing \nfertilizers, past, present and future. In: Sulfur in South-East Asia & South Pacific \nAgriculture, ed. G.L. Blair and R.T., Australia: University New England, pp. \n301-314.\n\n\n\nSchultz, J.J. 1998. Compound fertilizers. In: Fertilizer Manual, ed. UNIDO,IFDC. \nKluwer Academic Publishers, AH Dordrecht, The Netherlands,. pp. 432-455.     \n\n\n\nZhao C., F. Degryes, V. Gupta and M.J.  McLaughlin. 2016. Low effective surface \narea explains slow oxidation of co-granulated elemental sulfur. Soil Sci. Soc. \nAm. J. 80: 911-918.\n\n\n\n\n\n"
"\n\n___________________\n\n\n\n*Corresponding author : E-mail: samsudin@agri.upm.edu.my\n\n\n\nINTRODUCTION\n\n\n\nCurrently, the main agricultural exports of Malaysia are palm oil, rubber and cocoa. \n\n\n\nApparently, these plantation crops are mostly grown on acid and highly weathered \n\n\n\nsoils which are classified as Ultisols and Oxisols. IBSRAM (1985) estimates that \n\n\n\nthese soils cover approximately 72% of the country\u2019s land surface. It is known \n\n\n\nthat the clay fraction of the soils is dominated by kaolinite, gibbsite, goethite and \n\n\n\nhematite (Tessens and Shamshuddin 1983; Anda et al. 2008; Shamshuddin and \n\n\n\nAnda 2008), with charges on the mineral surfaces that change with changing pH \n\n\n\n(Uehara and Gillman 1981; Shamshuddin and Ismail 1995).\n\n\n\n       These soils are usually used for maize production during the early phase \n\n\n\nof rubber and oil palm cultivation. But the yield of maize is limited by low pH, \n\n\n\nhigh Al and Ca and/or Mg deficiencies in the topsoil (Shamshuddin et al. 1991). \n\n\n\nLikewise, in the cultivation of cocoa, subsoil acidity affects growth of the plant \n\n\n\n(Noordiana et al. 2007).\n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 14: 1-13 (2010)                          Malaysian Society of Soil Science\n\n\n\nAlleviating Acid Soil Infertility Constrains Using Basalt, \n\n\n\nGround Magnesium Limestone and Gypsum\n\n\n\nin a Tropical Environment \n\n\n\nJ. Shamshuddin* & I. Che Fauziah\n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nUltisols and Oxisols in the tropical regions are often acidic, with high Al but \n\n\n\ndeficient in Ca and/or Mg.  This limits maize production. Studies were conducted \n\n\n\nto investigate the efficacy of basalt, ground magnesium limestone (GML) and \n\n\n\ngypsum as acid soil ameliorants. Results showed that basalt improved soil fertility \n\n\n\nby increasing soil pH, cation exchange capacity (CEC) and exchangeable Ca, Mg \n\n\n\nand K and available P, with a concomitant lowering of exchangeable Al. In the \n\n\n\nsoils treated with GML, Ca remained in the zone of incorporation. When GML \n\n\n\nwas applied together with gypsum, Ca moved deeper into the soil profile. Sulfate, \n\n\n\nSO\n4\n\n\n\n2-, adsorption onto the surfaces of oxides resulted in an increase in pH and \n\n\n\nnegative charge. The increase in pH was due to the replacement of OH- by SO\n4\n\n\n\n2-. \n\n\n\nBeneficial effects of GML application at the rate of 4 t ha-1 lasted for about 8 years \n\n\n\nwith the effect  being comparable to application of 1 t GML ha-1 annually.\n\n\n\nKeywords: Basalt, ground magnesium limestome, gypsum, Oxisols, soil\n\n\n\n                    acidity, Ultisols \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 20102\n\n\n\n       Subsoil acidity has been a common phenomenon in highly weathered soils of \n\n\n\nthe tropics as Ca from lime application usually accumulates in the topsoil (Pavan \n\n\n\net al. 1984; Gillman et al. 1989). This has led to plant roots restricting their growth \n\n\n\nto the plow layer where lime is originally incorporated. During the dry season, the \n\n\n\nplant suffers from water stress because its functional roots are unable to tap the \n\n\n\nunderground water.   \n\n\n\n       Aluminum toxicity is a major factor in acid soil fertility. It has been identified \n\n\n\nthat Al3+ inhibits root growth either by inhibition of cell division, cell elongation \n\n\n\nor both (Marshner 1991). Deficiency in Ca and/or Mg in the soil aggravates the \n\n\n\nlimitation of root growth by Al toxicity. These acid soil infertility constrains \n\n\n\ncan be ameliorated effectively by applying amendments. Among amendments \n\n\n\navailable in Malaysia are limestone, basalt and gypsum. This study was aimed \n\n\n\nat investigating the efficacy of basalt, ground magnesium limestone (GML) and \n\n\n\ngypsum as acid soil ameliorants.  \n\n\n\nMATERIALS AND METHODS\n\n\n\nSoils and the Ameliorants\n\n\n\nRelevant chemical properties of the soils used in the study are given in Table 1. The \n\n\n\nsoils of Bungor (Ultisol) and Prang Series (Oxisol) were used for pot and leaching \n\n\n\nexperiments, while the Rengam soil (Ultisol) was used for the field trial. The three \n\n\n\nsoils were selected on the basis of their mineralogy and charge properties that \n\n\n\nwere determined prior to the conduct of the study proper. The ameliorants tested \n\n\n\nin the study were ground basalt, GML and gypsum. The elemental composition \n\n\n\nof the GML and gypsum are given in Table 2. Basalt, obtained from a private \n\n\n\ncompany in Australia, contained 216,000 mg/kg Si, 65,400 mg/kg Ca, 64,400 mg/\n\n\n\nkg, 12,500 mg/kg K, 3,030 mg/kg P and 2,150 mg/kg S. \n\n\n\nPot Study \n\n\n\nBasalt treatment. Soil of Bungor Series was treated with ground basalt at rates \n\n\n\nof 0, 5, 10 and 20 t ha-1 for 6 months. The soils were maintained under moist \n\n\n\ncondition (at the matric suction of 10 kPa) throughout the experimental period. \n\n\n\nSub-samples were taken every 2 months for analyses.\n\n\n\nGML and Gypsum Treatments. \n\n\n\nAir-dried surface soil (0 \u2013 15 cm depth) from each of the Bungor and Prang series \n\n\n\nwere mixed with GML, gypsum and their combinations. Application rates were 0, \n\n\n\n0.5, 1.0, 2.0, and 4.0 t ha-1. The response of maize (Zea Mays) to the ameliorants \n\n\n\nwas assessed in a glasshouse trial, using a complete randomized experimental \n\n\n\ndesign with three replications. Pots were filled with 5 kg air-dried soil and allowed \n\n\n\nto equilibrate with the ameliorants and basic fertilizers (180 kg N ha-1 as urea, 150 \n\n\n\nkg P\n2\nO\n\n\n\n5\n ha-1 as triple super phosphate and 75 kg K\n\n\n\n2\nO ha-1 as muriate of potash) for \n\n\n\n30 days prior to seeding of maize. Plants were watered 3 times daily with 250 mL \n\n\n\nH\n2\nO each time and grown for 40 days, after which they were harvested. The water \n\n\n\nJ. Shamshuddin & I. Che Fauziah\n\n\n\n\n\n\n\n\nM\nalay\n\n\n\nsian\n Jo\n\n\n\nu\nrn\n\n\n\nal o\nf S\n\n\n\no\nil S\n\n\n\ncien\nce V\n\n\n\no\nl. 1\n\n\n\n4\n, 2\n\n\n\n0\n1\n0\n\n\n\n3\n\n\n\nA\nllev\n\n\n\niatin\ng\n A\n\n\n\ncid\n S\n\n\n\no\nil In\n\n\n\nfertility\n\n\n\nSoil\n\n\n\nCations\n\n\n\nDepth\n\n\n\ncm\n\n\n\npH\n\n\n\n(H  O)\n2 Ca Mg K Na Free Fe O.C ClayAl ECEC* Al Saturation\n\n\n\nPrang\n\n\n\nBungor\n\n\n\nRengam\n\n\n\ncmol  kgc\n-1 % g kg -1\n\n\n\n0 - 15\n\n\n\n30 - 45\n\n\n\n0 - 15\n\n\n\n30 - 45\n\n\n\n0 - 15\n\n\n\n30 - 45\n\n\n\n4.29\n\n\n\n4.76\n\n\n\n4.86\n\n\n\n5.04\n\n\n\n4.83\n\n\n\n4.43\n\n\n\n1.05\n\n\n\n0.83\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n1.05\n\n\n\n0.72\n\n\n\n0.30\n\n\n\n0.18\n\n\n\n0.07\n\n\n\n0.03\n\n\n\n0.17\n\n\n\n0.14\n\n\n\n0.22\n\n\n\n0.06\n\n\n\n0.08\n\n\n\n0.04\n\n\n\n0.08\n\n\n\n0.05\n\n\n\n0.02\n\n\n\n0.02\n\n\n\n0.02\n\n\n\n0.01\n\n\n\n0.02\n\n\n\n0.01\n\n\n\n4.02\n\n\n\n3.98\n\n\n\n1.62\n\n\n\n1.08\n\n\n\n2.68\n\n\n\n2.83\n\n\n\n5.16\n\n\n\n5.07\n\n\n\n2.19\n\n\n\n1.77\n\n\n\n4.00\n\n\n\n3.74\n\n\n\n19.5\n\n\n\n8\n\n\n\n5.17\n\n\n\n18.2\n\n\n\n21.3\n\n\n\n12.1\n\n\n\n72\n\n\n\n79\n\n\n\n74\n\n\n\n62\n\n\n\n67\n\n\n\n76\n\n\n\n36\n\n\n\n38\n\n\n\n91\n\n\n\n117\n\n\n\n35\n\n\n\n39\n\n\n\n250\n\n\n\n300\n\n\n\n540\n\n\n\n590\n\n\n\n400\n\n\n\n450\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 20104\n\n\n\nwas allowed to drain out of the pot and the  leachates collected and analyzed. \n\n\n\nAfter harvest, the soils in the pot were air-dried, well mixed and sub-sampled for \n\n\n\nlaboratory analysis. Plant tops were sampled and dried in an oven at 600C and \n\n\n\nrelative top weight calculated. \n\n\n\nLeaching Experiment in PVC Columns \n\n\n\nThe Bungor and Prang soils were sampled in the field at depth intervals of 15 cm \n\n\n\nto a depth of 90 cm. The soil samples were air-dried, ground, and carefully packed \n\n\n\ninto PVC columns according to their depths. Gypsum, GML and combinations of \n\n\n\nGML and gypsum were incorporated into the soil at 0 \u2013 15 cm depth. A complete \n\n\n\nrandomized experimental design with three replications was used. The treatments \n\n\n\nwere 0, 0.5, 1.0, 2.0, 4.0, and 8.0 t ha-1 of GML and gypsum in all combinations. \n\n\n\nThe treated soils were watered twice weekly at a rate equivalent to a rainfall of \n\n\n\n2500 mm year-1. Leachates were collected and analyzed every 30 days. The soils \n\n\n\nin the PVC columns were sampled at a depth interval of 15 cm after 180 days. \n\n\n\nField Experiment \n\n\n\nA field experiment was established within the Chembong Department of \n\n\n\nAgriculture Complex, Negeri Sembilan, Malaysia. The area receives an annual \n\n\n\nrainfall of 2300 mm. The plots were originally treated with GML at the rate of 0 \n\n\n\n(without fertilizer), 0, 0.5, 1.0, 2.0, 4.0, and 8.0 t GML ha-1 incorporated into 0 \u2013 \n\n\n\n30 cm depth (henceforth referred to respectively as T\n1\n, T\n\n\n\n2\n, T\n\n\n\n3\n, T\n\n\n\n4\n, T\n\n\n\n5\n, T\n\n\n\n6\n and T\n\n\n\n7\n). \n\n\n\nThe current experiment started 5 years later with yearly GML application at the \n\n\n\nrate of 0, 0.5, 1.0 and 2.0 t ha-1 in plots T\n1\n, T\n\n\n\n2\n, T\n\n\n\n3\n and T\n\n\n\n4\n, respectively.  Plots T\n\n\n\n5\n, \n\n\n\nT\n6 \nand T\n\n\n\n7\n received no further treatment and were regarded as residual plots. Each \n\n\n\ntreatment was replicated 4 times and arranged in a randomized complete block \n\n\n\ndesign with each plot measuring 4 x 4 m.\n\n\n\nMaize seeds (Zea mays var. Mas Madu) were planted 30 days after treatments. \n\n\n\nFertilizers at the rate of 120 kg N, 100 kg P and 150 kg K ha-1 were applied prior \n\n\n\nto planting of maize. A composite sample of 5 cores was taken from each of the \n\n\n\nJ. Shamshuddin & I. Che Fauziah\n\n\n\nTABLE 2\n\n\n\nElemental composition of GML and gypsum\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 5\n\n\n\nexperimental plots subsequent to the maize harvest at depths of 0 \u2013 15 and 15 \u2013 30 \n\n\n\ncm. The experiment was continued up to 4 crops of maize. \n\n\n\nAnalyses of Soil and Soil Solution\n\n\n\nSoil pH was measured in suspensions of soil samples in water (1:2.5) after 1 \n\n\n\nhour of intermittent shaking and left standing overnight. Basic exchangeable \n\n\n\ncations were extracted with 1M NH\n4\nOAC buffered at pH 7. Calcium, Mg, K and \n\n\n\nNa were determined by atomic absorption spectrophotometry. Cation exchange \n\n\n\ncapacity was determined by an unbuffered solution of NH\n4\nCl. Exchangeable Al \n\n\n\nwas extracted with 1M KCl and determined colorimetrically. Charge properties \n\n\n\nof the untreated soils were determined by the method of Gillman and Sumpter \n\n\n\n(1986) where negative charge as measured by Ca adsorption is termed CEC\nB\n.\n\n\n\nThat measured by Ca and Al adsorption is termed CEC\nT\n while positive charge as \n\n\n\nmeasured by Cl- adsorption is termed AEC. \n\n\n\n Soil solutions were extracted from the moist soils by centrifugation at 2000 \n\n\n\nrpm for 1 hour. The air-dried soils from the PVC columns and the pot experiments \n\n\n\nwere made moist by incubation for 1 day at a matric suction of 10 kPa. Soil solution \n\n\n\npH and EC were determined immediately from 2 mL sub-samples. The remaining \n\n\n\nsolution was stored at 50C to determine metals by Inductively Coupled Plasma \n\n\n\nAtomic Emission Spectroscopy (ICPAES) and ligands by High Performance \n\n\n\nLiquid Chromatography (HPLC). The activities of Al species and other ions in \n\n\n\nthe soil solution were calculated by the GEOCHEM \u2013PC (Version 2.0) computer \n\n\n\nprogram (Parker et al. 1990). \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nEffects of Basalt Application\n\n\n\nWithin 6 months of basalt application, pH had increased significantly (Table \n\n\n\n3). The increase in pH resulted in the reduction of exchangeable Al. As the soil \n\n\n\ncontained variable charge minerals, the increase in pH resulted in an increase in \n\n\n\nCEC. The CEC increase may also be due to the lowering of pHo as the  reaction \n\n\n\nof silicate in soils is known to lower pHo (Uehara and Gillman 1981). The term \n\n\n\npHo is the pH at which net charge on the surface of variable charge colloids \n\n\n\nis zero. Cation Exchange capacity is dependent on the difference between soil \n\n\n\npH and pHo. When pHo is decreased, the difference is widened causing CEC to \n\n\n\nincrease. The dissolution of basalt released Ca, Mg, K and P into the soil, causing \n\n\n\nexchangeable Ca, Mg and K, and available P to increase significantly. This means \n\n\n\nthat acid soil infertility can be somewhat ameliorated by basalt application.\n\n\n\nMovement of Ca\n\n\n\nThe movement of Ca in the PVC columns was studied in the leaching experiment \n\n\n\nof the GML treated soils. It was observed that Ca remained in the zone where the \n\n\n\nlime was originally incorporated, that is in the topsoil (0 \u2013 15 cm depth). This \n\n\n\nfinding is similar to that of other studies that have been reported (Pavan et al. \n\n\n\nAlleviating Acid Soil Infertility\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 20106\n\n\n\n1984; Gillman et al. 1989). The accumulation of the Ca in the topsoil can probably \n\n\n\nbe explained in the following manner: the highly weathered soils of Malaysia are \n\n\n\ndominated by kaolinite and sesquioxides (Tessens and Shamshuddin 1983), and \n\n\n\nthe charge on the exchange complex of these minerals increases with increasing \n\n\n\npH (Uehara and Gillman 1981). When the GML was applied, pH increased, \n\n\n\nfollowed by an increase in CEC. Hence, Ca was held by the negatively-charged \n\n\n\nsurfaces. As long as the pH remained high, the Ca would not move down the soil \n\n\n\nprofile. The increase in negative charge and the decrease in positive charge as a \n\n\n\nresult of the pH increase are depicted in Fig. 1. \n\n\n\nWhen GML was applied together with gypsum, the Ca moved deeper into \n\n\n\nthe soil profile. In this case, the amounts of Ca in the soil were higher than those \n\n\n\nrequired to neutralize the CEC raised by the increase in soil pH. The excess Ca \n\n\n\nnaturally moved into the subsoil. The recommended rate of application is 2 t GML \n\n\n\n+ 1 t gypsum ha-1. This caused GML to detoxify Al in the topsoil, while the Ca that \n\n\n\nmoved downwards alleviated Ca deficiency in the subsoil.\n\n\n\nAdsorption of SO\n4\n\n\n\nWhen gypsum was applied, SO\n4\n\n\n\n2- from the gypsum was adsorbed specifically onto \n\n\n\nthe oxide surfaces. As a consequence, the pH and negative charge on the oxides \n\n\n\nincreased. The mechanism of charge development on the oxides is illustrated in \n\n\n\nFig. 2. However, the resultant increase in pH was only observed in the Oxisol of \n\n\n\nthe Prang soil series (Table 4). In the Ultisol of the  Bungor series,  the pH tended \n\n\n\nto decrease.\n\n\n\nAn opposing reaction took place simultaneously along with SO\n4\n\n\n\n2- adsorption \n\n\n\nwhen gypsum was applied. The second reaction was the replacement of Al on \n\n\n\nthe exchange complex by Ca. In this case, the replaced Al went into the solution \n\n\n\nand pH was lowered accordingly. Both SO\n4\n\n\n\n2- adsorption and Al replacement by \n\n\n\nCa occurred in the Oxisol and Ultisol, but the former was more dominant in \n\n\n\nthe Oxisol as the soil contains high amounts of oxides. On the other hand, as \n\n\n\nJ. Shamshuddin & I. Che Fauziah\n\n\n\nLSD\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 7\n\n\n\nexchangeable Al was high in the Ultisol, Al replacement was dominant. Thus, \n\n\n\nthe beneficial effect of gypsum application is only observed in the Oxisol. This \n\n\n\nsuggests that gypsum can be used to ameliorate Oxisols, but not Ultisols. In fact, \n\n\n\nthe Ultisol becomes more acidic with gypsum application.\n\n\n\nThe presence of SO\n4\n\n\n\n2- in the soil implies that other anions can be exchanged \n\n\n\nand subsequently removed out of the system. This is  especially true for NO\n3\n\n\n\n-. It \n\n\n\nwas observed that SO\n4\n\n\n\n2- that was replaced in the Oxisol, in turn, moved downwards \n\n\n\nand accumulated in the subsoil. In the subsoil, the NO\n3\n\n\n\n- was attracted and adsorbed \n\n\n\nonto the positive charge sites on the surfaces of the oxides. The NO\n3\n\n\n\n-  is therefore \n\n\n\nnot lost to the groundwater but retained in the soils. \n\n\n\nFig. 1: Relationship between pH and CEC\nT\n, CEC\n\n\n\nB\n and AEC in the \n\n\n\ntopsoil of Bungor and Prang soils.\n\n\n\n\n\n\n\n\n\n\n\nAlleviating Acid Soil Infertility\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 20108\n\n\n\nFig. 2; Development of negative charge resulting from adsorption of SO\n4\n2- \n\n\n\nonto oxide of Fe\n\n\n\nAlleviation of Acid Soil Infertility\n\n\n\nAluminum toxicity is one of the major factors limiting crop growth in the \n\n\n\ntropics. It inhibits root growth and consequently reduces crop yield. A past study \n\n\n\n(Shamshuddin et al. 1991) and the current study indicate that Al toxicity can be \n\n\n\novercome by basalt application at the rate of 10 t ha-1. It can also be overcome by \n\n\n\nGML application at the rate of 2 t ha-1 or to a limited extent by gypsum application \n\n\n\nat an appropriate rate. It appears that a good agronomic option is to apply GML \n\n\n\ntogether with gypsum in the topsoil. Unlike GML, basalt takes time to dissolve \n\n\n\ncompletely in soil and therefore offers long-term beneficial effects\n\n\n\nIn the Oxisol, SO\n4\n\n\n\n2- adsorption increases pH and negative charge which \n\n\n\nconsequently reduces Al, rendering soil conditions more conducive for plant \n\n\n\ngrowth. Aluminum toxicity can also be reduced by gypsum application via another \n\n\n\nmechanism. The SO\n4\n\n\n\n2- from gypsum forms soluble AlSO\n4\n\n\n\n+ ion pairs which find \n\n\n\ntheir way into the groundwater. \n\n\n\nIt is known that Ca is able to detoxify Al to a certain extent. When GML and/\n\n\n\nor gypsum are applied onto the soils, Ca is made available in large quantities, \n\n\n\nand consequently reduces Al toxicity. As such, there exists a Ca/Al concentration \n\n\n\nratio,  above which crop yield is  no longer affected by Al toxicity.  Relative top \n\n\n\nmaize weight % was plotted against soil solution Ca/Al concentration ratio (data \n\n\n\nnot shown). A 10% drop in relative top maize weight corresponds to a Ca/Al \n\n\n\nconcentration ratio of 79. It shows that Ca needs to be considerably high in the \n\n\n\nsoil solution of Ultisols and Oxisols in order to alleviate Al toxicity. The Ca/Al \n\n\n\nconcentration can, therefore, be used as an index of soil acidity. \n\n\n\nLong-term Effect of GML Application\n\n\n\nThe changes of exchangeable Ca with time and depth are shown in Table 5.   Note \n\n\n\nthat plots T\n5 \n\u2013 T\n\n\n\n7\n were started 5 years earlier and that no further GML was applied, \n\n\n\nwhereas in T\n1\n \u2013 T\n\n\n\n4,\n  GML was applied yearly according to the rates mentioned \n\n\n\nearlier.\n\n\n\n\n\n\n\nJ. Shamshuddin & I. Che Fauziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 9\n\n\n\nIn the residual treatments (T\n5\n \u2013 T\n\n\n\n7\n), the exchangeable Ca in July 1991 remained \n\n\n\nreasonably high although GML was applied 5 years earlier (Table 5). After 1991, \n\n\n\nthe exchangeable Ca began to decrease. In the case of T\n5\n, where 2 t GML ha-1 was \n\n\n\napplied, the exchangeable Ca was reduced to the level of the untreated soil (Table \n\n\n\n1). However, in the T\n6\n and T\n\n\n\n7\n where GML application rates were 4 and 8 t ha-1, \n\n\n\nrespectively, the exchangeable Ca was considered within the range suitable for \n\n\n\nmaize growth. This means that the beneficial effect of GML at the rates of 4 t ha-1 \n\n\n\nor higher can last up to 8 years. \n\n\n\nIn T\n3\n (1 t GML ha-1 was applied annually), the amount of exchangeable Ca \n\n\n\nwas reasonably high. The exchangeable Ca was higher in T\n4\n (2 t GML ha-1 were \n\n\n\napplied annually) than that of T\n3\n. But, as it is too costly to apply GML at 2 t ha-1 \n\n\n\nannually, it is reasonable to recommend liming at a rate of 1 t ha-1 be applied \n\n\n\nannually. The beneficial effect of liming at this rate is comparable to those of T\n6\n \n\n\n\nand T\n7\n. Applying GML at a rate lower than 1 t ha-1 annually is not effective for \n\n\n\nalleviation of  Al toxicity. \n\n\n\nThe change in exchangeable Al (Table 6) is consistent with those of the \n\n\n\nexchangeable Ca shown in Table 5. The amount of exchangeable Al present in T\n6\n \n\n\n\nand T\n7\n is below the toxic level for maize growth. Maize yield (data not shown) \n\n\n\nwas not affected by Al toxicity in these treatments. The exchangeable Al was \n\n\n\nlow throughout the experiment period in T\n4\n but in T\n\n\n\n3\n, the value either increased \n\n\n\nor decreased depending on whether the soils were sampled before or after GML \n\n\n\napplication.\n\n\n\nSoil pH (data not shown) followed the changing trend of the exchangeable \n\n\n\nAl. When the exchangeable Al increased, pH decreased and vice versa. In T\n7\n, the \n\n\n\ntopsoil pH (H\n2\nO) remained above 5.0 up to 1992, after which it decreased to less \n\n\n\nthan 5.0.\n\n\n\nTABLE 4\n\n\n\nEffects of gypsum application on the pH of the Bungor and Prang topsoils\n\n\n\nAlleviating Acid Soil Infertility\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201010\n\n\n\nTABLE 5\n\n\n\nChanges in exchangeable Ca in Rengam soil with time and depth as affected\n\n\n\nby GML application\n\n\n\nJ. Shamshuddin & I. Che Fauziah\n\n\n\n\n\n\n\nDates of soil sampling  \n\n\n\nTreatment  Depth (cm)  \n    \n\n\n\nJULY 91  AUG 9 2 FEB 93  FEB 94  \n\n\n\n\n\n\n\nExch Ca (cmolc kg \n-1\n\n\n\n) \n\n\n\nT1 0 - 15 1.35 0.69 0.36 0.67\n\n\n\n15 - 30 0.89 0.80 0.15 0.12\n\n\n\nT2 0 - 15 1.87 0.86 0.48 1.24\n\n\n\n15 - 30 1.28 0.16 0.14 0.35\n\n\n\nT3 0 - 15 1.86 1.35 0.73 0.98\n\n\n\n15 - 30 1.16 0.31 0.22 0.43\n\n\n\nT4 0 - 15 3.10 1.64 1.50 1.21\n\n\n\n15 - 30 2.15 0.90 1.07 1.28\n\n\n\n\n\n\n\nT5 0 - 15 1.17 0.53 0.58 0.43\n\n\n\n15 - 30 1.44 0.32 0.53 0.38\n\n\n\n\n\n\n\nT6 0 - 15 2.33 1.16 1.12 1.15\n\n\n\n15 - 30 1.71 0.80 0.99 0.84\n\n\n\n\n\n\n\nT7 0 - 15 3.88 1.64 0.85 1.24\n\n\n\n15 - 30 2.73 1.27 1.38 0.75\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 11\n\n\n\nTABLE 6\n\n\n\nChanges in exchangeable Al in Rengam soil with time and depth as affected\n\n\n\nby GML application\n\n\n\nCONCLUSION\n\n\n\nGround magnesium limestone application is only able to alleviate topsoil acidity. \n\n\n\nIn order to alleviate subsoil Ca deficiency of Ultisols and Oxisols, GML has to be \n\n\n\napplied together with gypsum. Gypsum can be used to ameliorate Oxisols with \n\n\n\na high oxide content, but is not to be applied in isolation in Ultisols with high \n\n\n\nexchangeable Al. Sulfate, SO\n4\n\n\n\n2-, adsorption of oxides increases pH and negative \n\n\n\ncharge via replacement of OH- by SO\n4\n\n\n\n2-. Liming at a rate of 4 t ha-1 or higher \n\n\n\nis effective for about 8 years with the beneficial effect being comparable to the \n\n\n\napplication of 1 t GML ha-1 annually.\n\n\n\nAlleviating Acid Soil Infertility\n\n\n\n\n\n\n\n\n\n\n\nTreatment  Depth  (cm) JULY 91  AUG 92  FEB 93  FEB 94  \n\n\n\n(Maize 1)  (Maize 2)  (Maize 3)  (Maize 4)  \n\n\n\n\n\n\n\nExch Al (cmolc kg \n-1\n\n\n\n)  \n\n\n\n\n\n\n\n\n\n\n\nT1 0 - 15 2.18 0.83 1.1 2.12 \n\n\n\n 15 - 30 2.15 1.08 1.1 2.31 \n\n\n\nT2 0 - 15 1.45 1.03 1.09 1.77 \n\n\n\n 15 - 30 1.94 1.34 1.32 1.94 \n\n\n\nT3 0 - 15 1.61 0.54 1.34 1.58 \n\n\n\n 15 - 30 2.22 1.12 1.04 1.97 \n\n\n\n\n\n\n\nT4 0 - 15 0.48 0.06 0.52 0.49 \n\n\n\n 15 - 30 1.17 0.82 0.58 1.27 \n\n\n\n\n\n\n\nT5 0 - 15 1.55 1.37 1.55 2.51 \n\n\n\n 15 - 30 1.79 1.09 1.05 2.6 \n\n\n\nT6 0 - 15 1.62 1.05 1.02 1.64 \n\n\n\n 15 - 30 1.92 1.04 0.8 2.01 \n\n\n\nT7 0 - 15 0.37 0.29 0.53 0.9 \n\n\n\n 15 - 30 0.94 0.53 0.53 1.51 \n\n\n\nDates of soil sampling\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201012\n\n\n\nACKNOWLEDGEMENTS\n\n\n\nThe authors would like to thank Universiti Putra Malaysia, University of \n\n\n\nQueensland and the Australian Center for International Agricultural Research for \n\n\n\nfinancial and technical support during the conduct of the research and preparation \n\n\n\nof this paper. \n\n\n\nREFERENCES\nAnda, M., J. Shamshuddin, I. Fauziah and S.R. Syed Omar. 2008. Mineralogy and \n\n\n\nfactors controlling charge development of three Oxisols developed from \n\n\n\ndifferent parent materials. Geoderma 143:153-167.\n\n\n\nIBSRAM. 1985. Report of the inaugural workshop and proposal for implementation \n\n\n\nof the acid tropical soil management network. International Board for Soil \n\n\n\nResearch and Management, Bangkok, Thailand.\n\n\n\nGillman, G.P. and E.A. Sumpter. 1986. Surface charge characteristics and lime \n\n\n\nrequirements of soils derived from basaltic, granitic and metamorphic rocks \n\n\n\nin high-rainfall tropical Queensland. Australian Journal of Soil Research 24: \n\n\n\n173-192.\n\n\n\nGillman, G.P., K.L. Bristow and M.J. Hallman. 1989. Leaching of applied calcium \n\n\n\nand potassium from Oxisol in humid tropical Queensland. Australian Journal \n\n\n\nof Soil Research 27: 183-198.\n\n\n\nMarschner, H. 1991. Mechanisms of adaptation of plants to acid soils. Plant and Soil \n\n\n\n134: 1-20.\n\n\n\nNoordiana, N., S.R. Syed Omar, J. Shamshuddin and N.M. Nik Aziz. 2007. Effects of \n\n\n\norganic-based and foliar fertilizers on cocoa (Theobroma cocoa L.) grown on an \n\n\n\nOxisol in Malaysia. Malaysian Journal of  Soil Science 11:29-43.\n\n\n\nParker, D.R., W.A. Norvell and R.L. Chaney. 1990. Geochem-PC. Version 2.0. \n\n\n\nDepartment of Soil and Environmental Sciences. University of California, \n\n\n\nRiverside, USA.\n\n\n\nPavan, M.A, F.T. Bingham and P.F. Pratt. 1984. Redistribution of exchangeable \n\n\n\ncalcium, magnesium, and aluminum following lime or gypsum applications to \n\n\n\na Brazilian Oxisol. Soil Science Society of America Journal 48: 33-38.\n\n\n\nShamshuddin, J., I. Che Fauziah and H.A.H. Sharifuddin. 1991. Effects of limestone \n\n\n\nand gypsum applications to a Malaysian Ultisol on soil solution composition \n\n\n\nand yield of maize and groundnut. Plant and Soil 134: 45-52\n\n\n\nShamshuddin, J and H. Ismail 1995.  Reactions of ground magnesium limestone and \n\n\n\ngypsum in soils with variable-charge minerals.  Soil Science Society of America \n\n\n\nJournal 59: 106-112.\n\n\n\n\n\n\n\nJ. Shamshuddin & I. Che Fauziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 13\n\n\n\nShamshuddin, J. and M. Anda 2008. Charge properties of soils in Malaysia dominated \n\n\n\nby  kaolinite,  gibbsite, goethite and hematite.  Bull. Geol Soc. Malaysia  54: \n\n\n\n27-31. \n\n\n\n\n\n\n\nTessens, E. and J. Shamshuddin. 1983. Quantitative Relationship between Mineralogy \n\n\n\nand Properties of Tropical Soils. UPM Press, Serdang, Selangor, Malaysia.\n\n\n\nUehara, G. and G.P. Gillman. 1981. The Mineralogy, Chemistry and Physics of \n\n\n\nTropical Soils with Variable Charge Minerals. Westview Press, Boulder, CO.\n\n\n\n\n\n\n\nAlleviating Acid Soil Infertility\n\n\n\n\n\n"
"\n\nINTRODUCTION\nChemical fertilisers have been a major nutrient source to promote and sustain \ncrop production. However, continuous use of chemical fertilisers has contributed \nto environmental problems. Poultry manure and biofertilisers are nutrient sources \nthat can substitute the use of chemical fertilisers. Nutrients from poultry manure \nare gradually released into the soil as decomposition of the manure progresses. \nThe slow process of decomposition improves soil fertility and extends nitrogen \navailability for plant uptake as well as minimises nutrient losses through leaching \nor runoff after rainfall (Zublena et al. 1997). Biofertilisers are now of interest \nas they contain live microorganisms that can help to increase availability and \nuptake of mineral nutrients for plants through nitrogen fixation, solubilisation \nof soil minerals, organic matter decomposition, and organic waste degradation. \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 83-93  (2020)                         Malaysian Society of Soil Science\n\n\n\nDry Matter Yield and Growth of Mixed Forage in Corn-\nSoybean Intercropping Systems Affected by Different \n\n\n\nFertiliser Types\n\n\n\nNoorhanin, D.1*, Halim R.A.1 and Radziah, O.2\n\n\n\n1Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, \n43400 UPM, Serdang, Selangor, Malaysia\n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 UPM, Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nFertilisation plays a major role in growth and dry matter yield of forage. A field \nexperiment was conducted over two planting seasons on the same site to determine \nthe effect of different fertilisers on growth of corn and soybean and dry matter yield \nof mixed forage. Six fertiliser treatments with three replications were arranged in \nrandomised complete block design. The treatments were 100% chemical fertiliser \n(NPK), 100% poultry manure (PM), 50% PM, combined application of 50% PM \nwith biofertiliser, sole biofertilser and untreated (control). The results in the first \nseason showed that 100% NPK  produced the highest dry matter yield (13.83 t \nha-1) but in the second season, 100% PM produced a similar dry matter yield (9.91 \nt ha-1) with 100% NPK (9.84 t ha-1). Sole biofertiliser produced the same yield as \n50% PM. The results indicate that 100% PM at the rate of 6.3 t ha-1 significantly \nincreased dry matter yield of mixed forage.\n \nKey words: Poultry manure, biofertiliser, chemical fertiliser, dry matter  \n       yield, mixed forage.\n\n\n\n___________________\n*Corresponding author : E-mail: noorhanin@gmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202084\n\n\n\nBiofertilisers promote root growth and morphology and build up long term soil \nproductivity and crop health. Integration of poultry manure and biofertilisers has \nbeen suggested as a soil fertiliser management strategy especially to increase \nproductivity and long-term maintenance of soil health (Wu et al. 2015; Adesemoye \nand Kloepper 2009). The potential of poultry manure and biofertilisers has not \nbeen fully utilised due to lack of knowledge in handling manure systems and \nappropriate application rates for biofertilisers under field conditions. Therefore, \nthe objective of the experiment was to determine the effect of poultry manure and \nbiofertilisers on growth of corn and soybean and dry matter yield of mixed forage \n(corn-soybean) in comparison with chemical fertilisers.\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Site\nThis study was conducted at Field 2, Faculty of Agriculture, Universiti Putra \nMalaysia (UPM), Malaysia at a latitude of 3\u00b00\u201929.53\u201dN and a longitude of \n101\u00b042\u201914.54\u201dE. The experiments were carried out from March to May 2014 and \nrepeated from November 2014 to January 2015. The area experiences a hot and \nhumid climate with an annual rainfall of 2911 mm. Figure 1 shows the distribution \nof rainfall from January 2014 to January 2015. The temperature ranges from \n24.3\u00b0C to 35.1\u00b0C and relative humidity  from 91.5% to 94.7% (Figure 2). The \nsamples for soil analysis were taken from the top soil layer (0 to 30 cm) at random \nby walking in a zig-zag pattern in the entire plot before the experiment. All the soil \nsamples were mixed thoroughly for a composite lab sample. The soil texture of \nthe experimental site was sandy loam. The details of soil chemical characteristics \nfor both seasons are shown in Table 1\n \n\n\n\nFigure 1. Distribution of rainfall in Serdang, Malaysia\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 85\n\n\n\nFigure 2. Temperature and relative humidity in Serdang, Malaysia.\n\n\n\nTABLE 1\nTwo-season analysis of chemical characteristics of soil at experimental sites \n\n\n\n The effect of poultry manure, biofertilizer and chemical fertilizer   \n \n\n\n\n3 \n \n\n\n\n\n\n\n\n \nFigure 1. Distribution of rainfall in Serdang, Malaysia \n \n\n\n\n \nFigure 2. Temperature and relative humidity in Serdang, Malaysia. \n \n\n\n\nTABLE 1 \nTwo-season analysis of chemical characteristics of soil at experimental \n\n\n\nsites  \nSoil characteristics Season 1 Season 2 \n\n\n\nOrganic carbon (%) 2.03 \u00b1 0.07 3.02 \u00b1 0.09 \npH 6.43 \u00b1 0.39 6.03 \u00b1 0.16 \nEC (ds m-1) 3.01 \u00b1 0.14 2.75 \u00b1 0.23 \nK (ppm) 13.04 \u00b1 0.14 14.55\u00b1 0.53 \nN (%) 0.17 \u00b1 0.03 2.05  \u00b1 0.21 \nP (ppm) 22.12 \u00b1 0.68 24.87\u00b1 0.64 \n \n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n300\n\n\n\n350\n\n\n\n400\n\n\n\nR\nai\n\n\n\nnf\nal\n\n\n\nl (\nm\n\n\n\nm\n) \n\n\n\nMonth \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\nR\nel\n\n\n\nat\niv\n\n\n\ne \nhu\n\n\n\nm\nid\n\n\n\nity\n (%\n\n\n\n)  \nan\n\n\n\nd \nte\n\n\n\nm\npe\n\n\n\nra\ntu\n\n\n\nre\n (\u00b0\n\n\n\nC\n) \n\n\n\nMonth \n\n\n\nRelative Humidity\n\n\n\nMax temperature\n\n\n\nMin temperature\n\n\n\n Fertiliser treatments consisted of chemical fertiliser, organic fertiliser, \nbiofertiliser and untreated plot as control (Table 2). The required levels of N, P \nand K to support the yield goals for mixed forage production were 140 kg N ha-1, \n130 kg P ha-1 and 80 kg K ha-1. The first treatment (T1) was chemical fertiliser that \nsupplied N, P and K in the form of urea, triple super phosphate (TSP) and muriate \nof potash (MOP), respectively. The whole of P and K and half dosage of N fertiliser \nwere applied at planting time. Another half of N fertiliser was given at the eighth \nleaf stage of corn. The second and third treatments were based on organic fertiliser \napplication. The organic fertiliser used in this study was processed poultry manure. \nPoultry manure in powder form was applied 10 days before planting. Based on \nthe chemical analysis, every 40 kg of the manure contained 4.5% N, 2.5% P, 2% \nK2O, 2% CaO and 1% MgO. The second treatment (T2) was poultry manure at \nthe rate of 6.3 t ha-1 which supplied 140 N kg ha-1. The third treatment (T3) was \npoultry manure at the rate of 3.15 t ha-1 where the N rate was reduced by half \nof T2. This treatment was included to determine whether the use of biofertiliser \n(T4) can supplement nutrients when poultry manure is reduced by half the rate. \nThe half rate of poultry manure was applied 10 days before the planting time. \nThe biofertiliser consisted of a mixture of six bacterial strains without additional \nnutrients. These z bacterial strains were nitrogen-fixing bacteria and a phosphorus \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202086\n\n\n\nsolubilising bacteria. The biofertiliser mixture in solution form was prepared in \nthe Soil Microbiological Laboratory, Department of Land Management, UPM and \napplied twice. The first application was prior to planting where corn and soybean \nseeds were inoculated with the biofertiliser mixture as seed treatment. The washed \nseeds were immersed in a biofertiliser solution for 60 min after which they were \nsown in the field. The second biofertiliser application was on soil surface at 7 days \nafter seed emergence. The biofertiliser was sprayed at a rate of 10 ml plant-1. The \nfifth treatment (T5) comprised of solely biofertiliser without chemical fertiliser or \npoultry manure. The sixth treatment (T6) was an untreated plot that served as the \ncontrol. \n\n\n\nStatistical Data Analysis\nAll data were analysed with analysis of variance (ANOVA). SAS Software \nPackage (Version 9.4) was used to perform an analysis of variance appropriate \nfor randomised complete block design with three replications. The Tukey Honest \nSignificant Differences (HSD) test was used to compare treatment means at 0.05 \nprobability levels.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nDry Matter Yield\nThere were significant effects of season, fertiliser and interaction between seasons \nand fertilisers on dry matter yield of corn, soybean and the mixture of corn and \nsoybean (mixed forage) (Table 2). \n\n\n\nTABLE 2\nMean square of fertiliser effects and season effects on dry matter yield of corn,\n\n\n\nsoybean and mixed forage\n\n\n\n The effect of poultry manure, biofertilizer and chemical fertilizer   \n \n\n\n\n5 \n \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\nDry Matter Yield \n\n\n\nThere were significant effects of season, fertiliser and interaction between seasons \nand fertilisers on dry matter yield of corn, soybean and the mixture of corn and \nsoybean (mixed forage) (Table 2).  \n\n\n\nTABLE 2 \nMean square of fertiliser effects and season effects on dry matter yield of corn, \n\n\n\nsoybean and mixed forage \nSource of  variance df DM yield corn DM yield soybean DM yield mixed forage \nSeason 1 1.36* 1.31* 21.76* \nBlock 2 0.07 0.12 0.21 \nSeason*block 2 0.07 0.06 0.16 \nFertilizer 5 11.77* 8.13* 46.09* \nSeason*fertiliser 5 1.24* 0.74* 2.73* \nError 20 0.04 0.03 0.05 \ncv (%)  0.32 0.34 0.37 \nNotes: *significant at P\u22640.05. DM- Dry matter \n \n\n\n\nTABLE 3 \nEffects of different fertilisers on dry matter yield of corn, soybean and mixed forage \n\n\n\nfor both seasons \nTreatment Corn (t ha-1) Soybean (t ha-1) Mixed forage (t ha-1) \n\n\n\n Season 1 Season 2 Season 1 Season 2 Season 1 Season 2 \n100% NPK 7.70 \u00b1 0.14a 5.52 \u00b1 0.06a 6.14 \u00b1 0.14a 4.36 \u00b1 0.08a 13.83 \u00b1 0.07a 9.84 \u00b1 0.06a \n100% PM 5.59 \u00b1 0.04b 5.70 \u00b1 0.21a 4.23 \u00b10.13b 4.44 \u00b1 0.24a 9.82 \u00b1 0.16b 9.91 \u00b1 0.08a \n50% PM 3.85 \u00b1 0.02c 3.70 \u00b1 0.17b 2.90 \u00b10.09d 2.81 \u00b1 0.11b 6.75 \u00b1 0.07c 5.13 \u00b1 0.22b \n50% PM + \nBIO \n\n\n\n3.61\u00b10.15cd 3.98 \u00b1 0.15b 3.22 \u00b1 0.11c 3.07 \u00b1 0.02b 6.83 \u00b1 0.24c 5.37 \u00b1 0.14b \n\n\n\nBIO 3.85 \u00b1 0.18c 3.53 \u00b1 0.06b 2.87 \u00b10.08d 2.56 \u00b10.07bc 6.71 \u00b1 0.25c 5.24 \u00b1 0.16b \nControl 3.03 \u00b1 0.08d 2.86 \u00b1 0.05c 2.20 \u00b1 0.10e 2.03 \u00b1 0.06c 5.23 \u00b1 0.17d 4.35 \u00b1 0.07c \nP Value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 \n\n\n\nNotes: Means values \u00b1 S. E. followed by the same letter in the same column are not \nsignificantly different based on Tukey HSD test at 5% level.  NPK - Chemical fertiliser; PM - \nPoultry manure; BIO - Biofertiliser \n \nDry matter yield of corn was significantly affected by fertiliser applications and \nseasons. In season 1, the highest DM yield of corn was found from 100% NPK \ntreatment compared to other treatments. This was followed by application of poultry \nmanure at the rate of 6.3 t ha-1. Reducing poultry manure to half reduced the DM of \ncorn. Applications of biofertiliser individually or in combination with poultry manure \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 87\n\n\n\n Dry matter yield of corn was significantly affected by fertiliser \napplications and seasons. In season 1, the highest DM yield of corn was found \nfrom 100% NPK treatment compared to other treatments. This was followed by \napplication of poultry manure at the rate of 6.3 t ha-1. Reducing poultry manure \nto half reduced the DM of corn. Applications of biofertiliser individually or in \ncombination with poultry manure were not able to improve the plant biomass. \nIn season 2, greater dry matter of corn was found in 100% NPK and 100% PM \ntreatments compared to other treatments. No differences in DM were observed in \ntreatments 50% PM, 50% PM + BIO and sole BIO. The DM yield of corn from \ncontrol plot was significantly lower than other fertiliser applications. Dry matter \nyield of soybean was significantly affected by fertiliser applications and season. \nIn season 1, the highest yield was obtained from NPK fertiliser followed by 100% \nPM. Combination of 50% PM with BIO produced greater DM yield than 50% \nPM and sole BIO. In season 2, greater dry matter of soybean was found in 100% \nNPK and 100% PM treatments compared to other treatments. The DM yields of \nsoybean were not significantly different among 50% PM, 50% PM with BIO and \nsole BIO. The lowest DM yield of soybean was obtained from control plots. \n Dry matter yield of mixed forage was significantly affected by fertiliser \napplications and season. In season 1, DM yield of mixed forage followed the trend \nof 100% NPK > 100% PM > 50% + BIO, sole BIO and 50% PM > control. In \nseason 2, the highest plant biomass was found in 100% PM followed by 100% \nNPK treatments compared to other treatments. Applications of biofertiliser \nindividually or in combination with poultry manure were not able to improve the \nplant biomass. The DM yield of mixed forage from control plot was significantly \nlower than other fertiliser applications. \n These results demonstrate the effect of fertilisers and interaction between \nfertilisation and season. In season 1, NPK gave the highest DM yields for corn, \nsoybean and mixed forage. NPK application was expected to show excellent results \n\n\n\nTABLE 3\nEffects of different fertilisers on dry matter yield of corn, soybean and mixed forage for \n\n\n\nboth seasons\n\n\n\n The effect of poultry manure, biofertilizer and chemical fertilizer   \n \n\n\n\n5 \n \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\nDry Matter Yield \n\n\n\nThere were significant effects of season, fertiliser and interaction between seasons \nand fertilisers on dry matter yield of corn, soybean and the mixture of corn and \nsoybean (mixed forage) (Table 2).  \n\n\n\nTABLE 2 \nMean square of fertiliser effects and season effects on dry matter yield of corn, \n\n\n\nsoybean and mixed forage \nSource of  variance df DM yield corn DM yield soybean DM yield mixed forage \nSeason 1 1.36* 1.31* 21.76* \nBlock 2 0.07 0.12 0.21 \nSeason*block 2 0.07 0.06 0.16 \nFertilizer 5 11.77* 8.13* 46.09* \nSeason*fertiliser 5 1.24* 0.74* 2.73* \nError 20 0.04 0.03 0.05 \ncv (%)  0.32 0.34 0.37 \nNotes: *significant at P\u22640.05. DM- Dry matter \n \n\n\n\nTABLE 3 \nEffects of different fertilisers on dry matter yield of corn, soybean and mixed forage \n\n\n\nfor both seasons \nTreatment Corn (t ha-1) Soybean (t ha-1) Mixed forage (t ha-1) \n\n\n\n Season 1 Season 2 Season 1 Season 2 Season 1 Season 2 \n100% NPK 7.70 \u00b1 0.14a 5.52 \u00b1 0.06a 6.14 \u00b1 0.14a 4.36 \u00b1 0.08a 13.83 \u00b1 0.07a 9.84 \u00b1 0.06a \n100% PM 5.59 \u00b1 0.04b 5.70 \u00b1 0.21a 4.23 \u00b10.13b 4.44 \u00b1 0.24a 9.82 \u00b1 0.16b 9.91 \u00b1 0.08a \n50% PM 3.85 \u00b1 0.02c 3.70 \u00b1 0.17b 2.90 \u00b10.09d 2.81 \u00b1 0.11b 6.75 \u00b1 0.07c 5.13 \u00b1 0.22b \n50% PM + \nBIO \n\n\n\n3.61\u00b10.15cd 3.98 \u00b1 0.15b 3.22 \u00b1 0.11c 3.07 \u00b1 0.02b 6.83 \u00b1 0.24c 5.37 \u00b1 0.14b \n\n\n\nBIO 3.85 \u00b1 0.18c 3.53 \u00b1 0.06b 2.87 \u00b10.08d 2.56 \u00b10.07bc 6.71 \u00b1 0.25c 5.24 \u00b1 0.16b \nControl 3.03 \u00b1 0.08d 2.86 \u00b1 0.05c 2.20 \u00b1 0.10e 2.03 \u00b1 0.06c 5.23 \u00b1 0.17d 4.35 \u00b1 0.07c \nP Value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 \n\n\n\nNotes: Means values \u00b1 S. E. followed by the same letter in the same column are not \nsignificantly different based on Tukey HSD test at 5% level.  NPK - Chemical fertiliser; PM - \nPoultry manure; BIO - Biofertiliser \n \nDry matter yield of corn was significantly affected by fertiliser applications and \nseasons. In season 1, the highest DM yield of corn was found from 100% NPK \ntreatment compared to other treatments. This was followed by application of poultry \nmanure at the rate of 6.3 t ha-1. Reducing poultry manure to half reduced the DM of \ncorn. Applications of biofertiliser individually or in combination with poultry manure \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202088\n\n\n\nover PM and BIO because it is a fast-release fertiliser and a highly water-soluble \nmacronutrient  that immediately meets the plant nutrient requirements compared \nto PM and BIO. Decomposition of organic matter from PM usually needs a longer \ntime to fulfil the nutrient requirement for plant growth. Although PM used was \nbased on nitrogen crop requirement, the proportion of other major nutrients in PM \nwas not ready to be taken by plant and still waited for the decomposition process \nto be accomplished. However, application of 100% PM showed improvements \nin DM yield of corn, soybean and mixed forage in season 2 which were not \nsignificantly different from 100% NPK. The previous application of PM from \nseason 1 is believed to have created residual effects in the experimental site. This \nwas due to the content of N, P and K of the experimental site prior to season 2 \nbeing greater than  in season 1 (Table 1). Higher availability of nutrients for plant \nuptake in the following season was associated with mineralisation of manure \nand the consequent release of nutrient elements during microbial decomposition \n(Witkamp 1971). Savithri et al. (1991) reported that 6.25 t ha-1 of poultry manure \nwas used in the first season of sorghum. They found a significant residual effect on \nsucceeding crop yields with increasing content of soil nutrients. Another study by \nLiu et al. (2011) reported that organic manure cumulatively increased the content \nof organic matter in the soil when fully decomposed. They found the highest DM \nof stevia during seedling stage to be influenced by chemical fertilisers but noted \nthat organic manure was capable of  producing higher DM yield than chemical \nfertilisers at the harvest stage. Moreover, Nsa et al. (2013) found that poultry \nmanure application gave a similar yield for sweet potato as chemical fertiliser \napplication. In this case, sweet potato is a perennial shrub and the process of \norganic matter decomposition may have enough time to enhance soil fertility \nand result in increased crop yield. In addition, other researchers have reported \nthe benefit of poultry manure on rice production. Nitrogen uptake in rice was \nincreased under poultry manure application thus producing an equal yield as \nchemical fertilisers (Xu et al. 2008). \n In both seasons, application of BIO was found to be inadequate to meet \nthe nutritional needs of crops. BIO did not directly supply any nutrients to the \nplant but enhanced nutrient availability by fixing the atmospheric nitrogen, \nsolubilising soil minerals and decomposing the organic matter in the soil. These \nnatural processes take time to release the nutrients and might be too slow to \nmeet crop requirements within a short time, hence some nutrient deficiency may \noccur. Our results revealed that sole BIO application produced DM yields of \ncorn, soybean and mixed forage below that of NPK and 100% PM applications, \nbut produced similar DM yields with 50% PM application in both seasons. This \nshowed that bacterial inoculants in BIO application were capable of supporting \nplant growth nutrients, similar to the half rate application  of PM. However, no \nfurther increase in DM yield was observed when BIO was combined with 50% PM \n(50% PM + BIO). This finding is associated with the ability of bacterial inoculants \nto be active when combined with PM application. Competition for available \nsubstrate resources between bacterial inoculants with indigenous microorganisms \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 89\n\n\n\noriginating from PM may possibly affect bacterial activity. According to Ho and \nKo (1985), the  ability of biofertilisers to improve the yield is dependent on the \nactivity of bacteria in fixing atmospheric nitrogen, decomposing organic matter, \nmineralising and solubilising minerals in the soil for plant uptake. This is  in line \nwith Veen et al. (1997) who noted that bacteria activity increased when they have \nthe capability to compete with other microorganisms for limited resources such as \norganic carbon, inorganic nitrogen and phosphorus as well as biological space for \nmicrobial growth. Araujo et al. (2015) reported that the performance of inoculant \nbacteria in biofertilisers differ depending on the condition of the cultivation area.  \nTheir activity will be slow if the soil contains too much competition due to limited \nresources and space for microbial growth in the plant rhizophere (Baghdadi 2015).\n\n\n\nGrowth\nSignificant effects were found for fertilisers, season and their interactions on plant \nheight and leaf area index (LAI) of corn and soybean (Table 4). \n\n\n\nTABLE 4\nMean square from ANOVA of  plant height and LAI of corn and soybean\n\n\n\n Noorhanin, D., Halim, R. A. and Radziah, O.  \n\n\n\nresources such as organic carbon, inorganic nitrogen and phosphorus as well as \nbiological space for microbial growth. Araujo et al. (2015) reported that the \nperformance of inoculant bacteria in biofertilisers differ depending on the condition \nof the cultivation area.  Their activity will be slow if the soil contains too much \ncompetition due to limited resources and space for microbial growth in the plant \nrhizophere (Baghdadi 2015). \n\n\n\nGrowth \n\n\n\nSignificant effects were found for fertilisers, season and their interactions on plant \nheight and leaf area index (LAI) of corn and soybean (Table 4).  \n \n\n\n\nTABLE 4 \nMean square from ANOVA of  plant height and LAI of corn and soybean \n\n\n\nSource of variance df \nPlant height \n\n\n\ncorn \nPlant height soybean LAI \n\n\n\ncorn \nLAI \n\n\n\nsoybean \nSeason 1 1871.57* 96.86* 0.13* 0.16* \nBlock 2 3.60 0.60 0.00 0.00 \nSeason*block  2 1.60 0.73 0.00 0.00 \nFertilizer 5 3356.23* 1191.17* 0.37* 0.19* \nSeason*fertiliser 5 184.07* 55.00* 0.02* 0.04* \nError  20 3.74 1.87 0.00 0.00 \ncv (%)  0.15 0.16 0.12 0.07 \n*significant at P<0.05 \n \n \n \n\n\n\nTABLE 5 \nPlant height of corn and soybean as affected by different fertiliser treatments \n\n\n\nTreatment  Season 1 Season 2 \n Corn (cm)  Soybean (cm) Corn (cm) Soybean (cm) \n\n\n\n100% NPK 197.0 \u00b1 0.58a 109.0 \u00b1 1.00a 170.6 \u00b1 0.22a 94.5 \u00b1 1.04a \n100% PM 184.0 \u00b1 0.58b 98.7 \u00b1 0.33b 174.0 \u00b1 1.15a 96.8 \u00b1 0.44a \n50% PM 153.0 \u00b1 1.15c 78.0 \u00b1 0.58c 147.2 \u00b1 0.44b 75.6 \u00b1 0.71b \n50% PM+ BIO 152.3 \u00b1 0.33c 78.3 \u00b1 0.67c 146.6 \u00b1 2.34b 76.2 \u00b1 0.22b \nBIO 152.3 \u00b1 1.76c 76.3 \u00b1 0.88c 121.8 \u00b1 0.42c 73.5 \u00b1 0.76b \nControl 127.0 \u00b1 1.15d 64.0 \u00b1 1.15d 119.0 \u00b1 0.63c 68.0 \u00b1 0.50c \nP Value <.0001 <.0001 <.0001 <.0001 \nNotes: Means values \u00b1 S. E. followed by the same letter in the same column are not \nsignificantly different based on Tukey HSD test at 5% level. NPK- Chemical fertiliser; PM- \nPoultry manure; BIO- Biofertiliser \n \n\n\n\n Noorhanin, D., Halim, R. A. and Radziah, O.  \n\n\n\nresources such as organic carbon, inorganic nitrogen and phosphorus as well as \nbiological space for microbial growth. Araujo et al. (2015) reported that the \nperformance of inoculant bacteria in biofertilisers differ depending on the condition \nof the cultivation area.  Their activity will be slow if the soil contains too much \ncompetition due to limited resources and space for microbial growth in the plant \nrhizophere (Baghdadi 2015). \n\n\n\nGrowth \n\n\n\nSignificant effects were found for fertilisers, season and their interactions on plant \nheight and leaf area index (LAI) of corn and soybean (Table 4).  \n \n\n\n\nTABLE 4 \nMean square from ANOVA of  plant height and LAI of corn and soybean \n\n\n\nSource of variance df \nPlant height \n\n\n\ncorn \nPlant height soybean LAI \n\n\n\ncorn \nLAI \n\n\n\nsoybean \nSeason 1 1871.57* 96.86* 0.13* 0.16* \nBlock 2 3.60 0.60 0.00 0.00 \nSeason*block  2 1.60 0.73 0.00 0.00 \nFertilizer 5 3356.23* 1191.17* 0.37* 0.19* \nSeason*fertiliser 5 184.07* 55.00* 0.02* 0.04* \nError  20 3.74 1.87 0.00 0.00 \ncv (%)  0.15 0.16 0.12 0.07 \n*significant at P<0.05 \n \n \n \n\n\n\nTABLE 5 \nPlant height of corn and soybean as affected by different fertiliser treatments \n\n\n\nTreatment  Season 1 Season 2 \n Corn (cm)  Soybean (cm) Corn (cm) Soybean (cm) \n\n\n\n100% NPK 197.0 \u00b1 0.58a 109.0 \u00b1 1.00a 170.6 \u00b1 0.22a 94.5 \u00b1 1.04a \n100% PM 184.0 \u00b1 0.58b 98.7 \u00b1 0.33b 174.0 \u00b1 1.15a 96.8 \u00b1 0.44a \n50% PM 153.0 \u00b1 1.15c 78.0 \u00b1 0.58c 147.2 \u00b1 0.44b 75.6 \u00b1 0.71b \n50% PM+ BIO 152.3 \u00b1 0.33c 78.3 \u00b1 0.67c 146.6 \u00b1 2.34b 76.2 \u00b1 0.22b \nBIO 152.3 \u00b1 1.76c 76.3 \u00b1 0.88c 121.8 \u00b1 0.42c 73.5 \u00b1 0.76b \nControl 127.0 \u00b1 1.15d 64.0 \u00b1 1.15d 119.0 \u00b1 0.63c 68.0 \u00b1 0.50c \nP Value <.0001 <.0001 <.0001 <.0001 \nNotes: Means values \u00b1 S. E. followed by the same letter in the same column are not \nsignificantly different based on Tukey HSD test at 5% level. NPK- Chemical fertiliser; PM- \nPoultry manure; BIO- Biofertiliser \n \n\n\n\nTABLE 5\nPlant height of corn and soybean as affected by different fertiliser treatments\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202090\n\n\n\nTABLE 6\nLAI of corn and soybean as affected by different fertiliser treatments\n\n\n\n The effect of poultry manure, biofertilizer and chemical fertilizer   \n \n\n\n\n9 \n \n\n\n\n\n\n\n\nTABLE 6 \nLAI of corn and soybean as affected by different fertiliser treatments \n\n\n\nTreatment Season 1 Season 2 \n Corn Soybean Corn Soybean \n100% NPK 2.6 \u00b1 0.03a 3.2 \u00b1 0.01a 2.4 \u00b1 0.02a 2.8 \u00b1 0.01a \n100% PM 2.4 \u00b1 0.02b 3.1 \u00b1 0.04b 2.4 \u00b1 0.02a 2.8 \u00b1 0.01a \n50% PM 2.1 \u00b1 0.01c 2.8 \u00b1 0.01c 2.0 \u00b1 0.01b 2.8 \u00b1 0.02a \n50% PM+ BIO 2.1 \u00b1 0.01c 2.8 \u00b1 0.01c 2.1 \u00b1 0.01b 2.8 \u00b1 0.02a \nBIO 2.2 \u00b1 0.01c 2.8 \u00b1 0.02c 1.9 \u00b1 0.01c 2.8 \u00b1 0.02a \nControl 1.8 \u00b1 0.01d 2.5 \u00b1 0.001d 1.7 \u00b1 0.02d 2.4 \u00b1 0.02b \nP Value <.0001 <.0001 <.0001 <.0001 \nNotes: Mean values \u00b1 S. E. followed by the same letter in the same column are not \nsignificantly different based on Tukey HSD test at 5% level. NPK- Chemical fertiliser; PM- \nPoultry manure; BIO- Biofertiliser \n\n\n\n \nThe study results showed that the highest plant heights of corn and soybean were \nobtained from NPK followed by 100% PM in season 1. Reducing the application of \nPM by half resulted in decreased plant height of corn and soybean compared to 100% \nPM. Application of BIO individually or in combination with PM did not result in \nincreased plant height. In season 2, the highest plant heights of corn and soybean \nwere found from the application of 100% NPK and 100% PM. No differences in \nplant height were observed in 50% PM, 50% PM + BIO and sole BIO treatments.  \nThe plant height of both crops from the control plot was significantly lower than \nother fertiliser applications. The results for LAI also showed that NPK produced \nsuperior mean values compared to other fertilisers for both crops in season 1. This \nwas followed by the application of 100% PM. However, LAI of corn from 100% \nNPK and 100% PM was greater compared to other treatments in season 2. No \ndifferences in LAI were observed in 50% PM, 50% PM + BIO treatments. The \ncontrol plot produced the lowest LAI compared to all   treatments. For soybean, no \nsignificant difference among fertiliser treatments was observed with all treatments \nsignificantly producing higher LAI than control.   \n \nThe trend of plant height was similar with DM yield where the   highest plant heights \nwere produced by NPK in season 1. Application of NPK was better than 100% PM \nbecause NPK provided all three major nutrients in water soluble form and was readily \navailable for plant uptake. According to Bilal et al. (2015), plant height was most \nresponsive to nutrients especially nitrogen and each successive increase in nitrogen \ndose significantly produced taller plants. In season 2, there was no difference in plant \n\n\n\n The study results showed that the highest plant heights of corn and \nsoybean were obtained from NPK followed by 100% PM in season 1. Reducing the \napplication of PM by half resulted in decreased plant height of corn and soybean \ncompared to 100% PM. Application of BIO individually or in combination with \nPM did not result in increased plant height. In season 2, the highest plant heights \nof corn and soybean were found from the application of 100% NPK and 100% \nPM. No differences in plant height were observed in 50% PM, 50% PM + BIO \nand sole BIO treatments.  The plant height of both crops from the control plot \nwas significantly lower than other fertiliser applications. The results for LAI also \nshowed that NPK produced superior mean values compared to other fertilisers \nfor both crops in season 1. This was followed by the application of 100% PM. \nHowever, LAI of corn from 100% NPK and 100% PM was greater compared to \nother treatments in season 2. No differences in LAI were observed in 50% PM, \n50% PM + BIO treatments. The control plot produced the lowest LAI compared to \nall   treatments. For soybean, no significant difference among fertiliser treatments \nwas observed with all treatments significantly producing higher LAI than control.  \n The trend of plant height was similar with DM yield where the   highest \nplant heights were produced by NPK in season 1. Application of NPK was \nbetter than 100% PM because NPK provided all three major nutrients in water \nsoluble form and was readily available for plant uptake. According to Bilal et \nal. (2015), plant height was most responsive to nutrients especially nitrogen and \neach successive increase in nitrogen dose significantly produced taller plants. In \nseason 2, there was no difference in plant height between 100% PM and NPK. \nPM worked quite differently from NPK. Nutrients from PM are derived from \nthe natural process of organic matter decomposition which takes time to release \nthe nutrients for growth of corn and soybean. By the time these nutrients change \nto water soluble forms, plant roots are ready to absorb all the soluble nutrients \n(Adeola et al. 2011). The current study showed that the macronutrient content in \nthe soil prior to season 2 was greater than in season 1. The contents of N, P and \nK were increased from 0.17%, 0.0022% and 0.0013% to 2.05%, 0.0025% and \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 91\n\n\n\n0.0015% respectively. PM ensured a long supply season of nutrients compared to \nNPK. Application of NPK and 100% PM did not only enhance plant height but \nalso produced a similar trend for LAI. This finding is in agreement with earlier \nstudies conducted on various crops (Bondada et al. 1996; Gardner and Tucker \n1967; Muharam et al.  2014; Shahin et al. 2013). Nitrogen is a nutrient essential \nto meristematic and photosynthetic activity by regulating cell division, stem \nelongation and leaf expansion (Bakry et al. 2009; Bilal et al. 2015). An increasing \nrate of nitrogen tends to produce more leaves, therefore generating a high value of \nLAI (Bilal et al. 2015). Treatments receiving BIO (either alone or in combination  \nwith PM) produced significantly shorter plants and lower LAI compared to NPK \nand 100% PM. This could be attributed to BIO being a slow-release fertiliser \nacross growth stages (Chen 2006). The major nutrients may not exist in BIO to \nsustain optimum crop growth in the field, but the bacterial inoculants in BIO are \nstill able to enhance the availability of nutrients in the soil of the control plot,  \nresulting in a  similar crop performance to the plot treated with half rate of PM. \n\n\n\nCONCLUSIONS\nApplication of 100% PM showed almost a similar effect to conventional fertiliser \n(100% NPK) which significantly increased DM yield of mixed forage and \nenhanced growth characteristics in corn and soybean. PM at the rate of 6.3 t ha-1 \n\n\n\nsupplying 140 N kg ha-1 has the potential to support good growth of both crops; it \nis available in the local market and is affordable compared to chemical fertilisers. \nThus, application of PM is recommended for a corn-soybean intercropping \nsystem. Applications of BIO individually or in combination with half rate of PM \ndid not improve DM yield of mixed forage, compared to 6.3 t ha-1 of PM. \n\n\n\nACKNOWLEDGEMENTS\nThe authors gratefully extend their appreciation to the staff of the Department \nof Land Management and Crop Science, Faculty of Agriculture, Universiti Putra \nMalaysia for their support throughout this research.\n\n\n\nREFERENCES\nAdeola, R. G., H. Tijani-Eniola and E.A. Makinde. 2011. Ameliorate the effects of \n\n\n\npoultry   manure and NPK fertilizer on the performance of pepper relay \ncropped with two cassava varieties. Global Journal of Science Frontier \nResearch 11(9): 6\u20139.\n\n\n\nAdesemoye, A. O. and J.W Kloepper.  2009. Plant-microbes interactions in enhanced \nfertilizer-use efficiency. Applied Microbiology and Biotechnology 85(1): \n1\u201312.\n\n\n\nAraujo, J., C.A. D\u00edaz-Alc\u00e1ntara, E.Vel\u00e1zquez, B.Urbano and F. Gonz\u00e1lez-Andr\u00e9s. \n2015. 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In: Proceedings of Seminar on Utilization of Coirpith in Agriculture, \n20th November,  TNAU, Coimbatore, pp. 1-11.\n\n\n\nShahin, M. G., R.T. Abdrabou, W.R. Abdelmoemn and M.M. Hamada. 2013. Response \nof growth and forage yield of pearl millet (Pennisetum galucum) to nitrogen \nfertilization rates and cutting height. Annals of Agricultural Sciences 58(2): \n153\u2013162.\n\n\n\nVeen, J. A. V., L.S.V. Overbeek and J.D.V. Elsas. 1997. Fate and activity of \nmicroorganisms introduced into soil. Microbiology and Molecular Biology \nReviews 61(2): 121\u2013135.\n\n\n\nWitkamp, M. 1971. Productivity of forest ecosystems. In: Forest Soil Microflora and \nMineral Cycling. Paris, pp. 413\u2013424.\n\n\n\nWu, Y., C. Zhao, J. Farmer and J. Sun. 2015. Effects of bio-organic fertilizer on pepper \ngrowth and Fusarium wilt biocontrol. Scientia Horticulturae 193: 114\u2013120.\n\n\n\nXu, M., D. Li, J. Li, D. Qin, Y. Kazuyuki and Y. Hosen. 2008. Effects of organic \nmanure application with chemical fertilizers on nutrient absorption and yield \nof rice in Hunan of Southern China. Agricultural Sciences in China 7(10): \n1245\u20131252.\n\n\n\nZublena, J. P., J. C. Barker and T.A. Carter.1997. Poultry manure as a fertilizer source. \nNorth Carolina Cooperative Extension Service. Retrieved 23 January 2017 \nfrom http://www.soil.ncsu.edu/publications/Soilfacts/AG-439-05/ \n\n\n\n\n\n\n\n\n\n"
"\n\nINTRODUCTION\nThe paddy fields in the Kelantan Plains, Peninsular Malaysia, are chemically \n\n\n\ndegraded due to acidity released by the oxidation of pyrite (FeS2) when the area is \ndrained. Oxidation of pyrite also results in the formation of straw-yellow jarosite, \n[KFe3(SO4)2(OH)6], present as mottles in the soil profiles (Shamshuddin et al. \n2004). Pyrite was formed when the Plains, were inundated with seawater some \n6,000 years ago when the sea level was 3-5 m above the present level (Roslan \net al. 2010; Enio et al. 2011). Pyrite-bearing soils are collectively called acid \nsulfate soils (Shamshuddin 2006). Some of the paddy fields are located in acid \nsulfate soils, which are not only low in pH (< 3.5), but also contain high amounts \nof Al and/or Fe (Shamshuddin 2006). It is known that the critical pH and Al \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 87-102 (2014)                         Malaysian Society of Soil Science\n\n\n\nEffects of Applying Ground Basalt with or without Organic \nFertilizer on the Fertility of an Acid Sulfate Soil and \n\n\n\nGrowth of Rice\n\n\n\nShazana, M.A.R.1, J. Shamshuddin1, 2*, C.I. Fauziah, Q.A. \nPanhwar1, 3 and U.A. Naher2\n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\n2Institute of Tropical Agriculture, Universiti Putra Malaysia, 43400 \nSerdang, Selangor, Malaysia\n\n\n\n3Soil Chemistry Section, Agriculture Research Institute Tandojam, \n70060 Sindh, Pakistan\n\n\n\nABSTRACT\nRice yield grown on acid sulfate soils is very low because of Al3+ and/or Fe2+ \ntoxicity. A study was conducted to determine the effects of applying ground basalt \nwith or without organic fertilizer on the growth of rice. Results showed clear \nbenefits of ground basalt as an amendment for acid sulfate soil infertility. The \nameliorative effects were comparable with that of applying 4 t ground magnesium \nlimestone (GML) ha-1; however, basalt had an additional advantage over GML as \nit contained K and P besides Ca and Mg. But as basalt needs time to disintegrate \nand dissolve completely in the acid sulfate soil under submerged conditions, the \nbest option is to apply ground basalt in combination with organic fertilizers a few \nmonths ahead of transplanting rice in the field. The organic fertilizers would then \nbe able to partly reduce Al and/or Fe in the soil via the chelation process. \n\n\n\nKeywords:  Acid sulfate soil, aluminum toxicity, basalt, iron toxicity, organic \n       fertilizer, rice production\n\n\n\n___________________\n*Corresponding author : E-mail: shamshud@upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201488\n\n\n\nShazana, M.A.R., J. Shamshuddin, C.I. Fauziah, Q.A. Panhwar and U.A. Naher\n\n\n\nconcentration in water for rice growth is 6 and 15 \u00b5M,  respectively (Elisa et al. \n2011), a clear indication that rice plants are sensitive to H+ and Al3+ stress.        \n\n\n\nThe rice plant shows symptoms of Fe2+ toxicity during its reproductive stage \ncausing roots to die (Hanhart and Duong 1993). However, the rice plant has a \nspecial mechanism to reduce the effects of Fe2+ toxicity. According to Moormann \nand van Breemen (1978), the rice plant can do so by pumping O2 downwards \nvia its root, creating an oxidized area around it where Fe(OH)3 is precipitated, \npreventing further uptake of toxic Fe2+.\n\n\n\nMost of the rice fields on acid sulfate soils in the Kemasin-Semerak, \nIntegrated Agricultural Development Area (IADP), Kelantan, produce rice \nyields below the national average of 3.8 t ha-1; this has led to some farms being \nabandoned by the farming community (Shamshuddin 2006). Studies conducted \nearlier using GML as soil amendments have shown promising results (Suswanto \net al. 2007). The increase in rice yield resulting from the treatment is probably \ndue to pH increase and/or the increasing availability of macronutrients such as Ca \nand Mg originating from the dissolving limestone. Calcium, to a certain extent, \nalleviates Al3+ toxicity (Alva et al. 1986). The critical exchangeable Ca in soil \nfor rice growth is 2 cmolc kg-1 soil (Doberman and Fairhurst 2000). Hence, it is \njustified that the infertility of acid sulfate soils is ameliorated by using appropriate \namendments that increase soil pH and supply Ca to the growing rice plants in the \nfield.\n\n\n\nGround basalt is an alternative to GML for increasing soil pH and consequently \neliminating Al in soil solution (Anda et al. 2009). It not only increases soil pH, \nbut also supplies Ca, Mg, K and P to the growing crops in the field (Shazana et al. \n2013). Shamshuddin and Kapok (2010) have shown that ground basalt releases \nthese nutrients into soils under glasshouse conditions. In the upland soils of \nMalaysia with pH of 4-5, basalt takes time to disintegrate and dissolve completely \n(Anda et al. 2009). But under acid sulfate soil conditions (pH< 3.5), basalt is          \nexpected to dissolve much faster (Shazana et al. 2013). Fe is abundant in the \nwater of the paddy fields in the Kelantan Plains as indicated by the red coloration \nof the water before rice sowing (Shamshuddin 2006). As a result of proton \nconsumption, the pH of the water will be increased, and consequently Al will \nbe precipitated as inert Al-hydroxides. This reduction process can be accelerated \nby adding organic matter (Muhrizal et al. 2006). Low pH soils, especially acid \nsulfate soils, contain low total microorganisms. The addition of organic fertilizers \nto the rice crop stimulates the microbes into the soil. Microbes contained in the \nfertilizers increase plant growth either by supplying essential nutrient elements or \nincreased availability of nutrient elements to the plant roots (Panhwar et al. 2013). \nThis study was conducted to determine the effects of applying ground basalt with \nor without organic fertilizers on the chemical properties of an acid  sulfate soil and \nthe growth of rice in pots under flooded conditions.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 89\n\n\n\nMATERIALS AND METHODS\n\n\n\nLocation and Soil Sampling\nSoil samples were taken from paddy fields within the Kemasin-Semerak \nIntegrated Agricultural Development Area (IADP), Kelantan, and the soil was \ntaxonomically classified as Typic Sulfaquepts (Soil Survey Staff 2010). Samples \nfor soil characterization were taken at 0-15, 15-30, 30-45 and 45-60 cm depths \nusing an auger. Soils for the pot experiment were taken from the surface horizon \n(0-15 cm depth). \n\n\n\nExperimental\nMoist soils taken from the field were mixed with the amendments and placed in \n1 m\u00d71 m pots. The experiment was conducted using randomized complete block \ndesign (RCBD) with 4 replications. The treatments were: T1 = control; T2 = 4 t \nGML ha-1; T3 = 4 t ground basalt ha-1; T4 = 0.25 t organic fertilizer ha-1; and T5 \n= 4 t ground basalt ha-1 + 0.25 t organic fertilizer ha-1. The organic fertilizer used \nwas JITU\u2122, a rice husk-based commercial compost currently available in the \nmarketplace.\n\n\n\nThe rice variety (Oryza sativa L.) used was MR 219. All treatments received \nstandard fertilizer rates, recommended for rice production in Malaysia: 90- 120 \nkg N, 12-18 kg P, and 90- 120 kg K ha-1, using urea and ammonium sulfate, single \nsuper phosphate and muriate of potash, respectively, as the nutrient sources. Vita-\ngrow\u2122 and Robust\u2122 were sprayed as micronutrient foliar fertilizers on 15, 40 \nand 60 days after transplanting. Vita-grow\u2122 enhance the growth of rice plants in \nthe pots.\n\n\n\nThe experiment was conducted over 120 days using transplanting technique \nwith a planting density of 15 cm \u00d7 25 cm (20 points in each tank; one point \ncontaining approximately 3 rice plants). The soils were kept moist with the \namendments added and mixed at three weeks before transplanting was carried \nout. Water in the pots was sampled at regular interval in order to determine pH, \nAl, Fe and other chemical properties. At harvest, soils for chemical analyses were \nsampled and yield component measurements were recorded. Roots were sampled \nfor examination under scanning electron microscope.\n\n\n\nMineralogical Analysis\nClay was separated from the rest of the soil by mechanical analysis and this clay \nfraction was used for the identification of minerals by X-ray diffraction analysis. \nThe samples for the XRD analysis were treated with Mg, Mg-glycol, K and K \nheated at 550oC. They were then X-rayed using a diffractometer, Philips PW \n3040/60 X\u2019Pert PRO (Philip Analytical B.V., AA Almelo, The Netherlands).\n\n\n\nAnalysis of Water Samples\nWater collected from the pots containing soils under treatments was immediately \nanalyzed for chemical properties after centrifugation so as to remove the \n\n\n\nBasalt and Organic Fertilizer Effect on Acid Sulfate Soil and Rice\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201490\n\n\n\nparticulates floating in the water. pH of the water was determined, followed by \nthe determination of the concentration of cations (Ca, Mg, K, Al and Fe) in the \nsolutions using atomic absorption spectrophotometer (AAS).\n\n\n\nAnalysis of Soil Samples\nSoil pH was determined in water at a soil-to-solution ratio of 1:2.5 using a pH meter. \nBasic cations were extracted using 1 M NH4OAc, buffered at pH 7. The cations \n(Ca, Mg, K, Na) in the NH4OAc solution were determined by AAS. Exchangeable \nAl was extracted by 1 M KCl and the Al in the extract was determined by AAS. \nExtractable Fe (double acid method) was also determined by AAS. Total carbon \nwas determined by the Carbon Analyzer Leco CR-412 (Leo Corporation, St. \nJoseph, MI). Available P was determined by the method of Bray and Kurt (1945) \nwith the extracted P determined by an auto analyzer (AA).\n\n\n\nAnalysis of Tissue\nLeaf samples were collected using quadrate 25 cm \u00d7 25 cm with 5 tillers being \nselected for plant tissue analysis. The samples were separated into \u2018above ground \nplant parts\u2019 (leaves stems) and \u2018below ground plant parts\u2019 (roots). The fresh \nsamples were cleaned and dried in an oven set at 70\u00b0 C until dry. The samples \nwere ground and digested following Benton (2001). The cations in the solutions \n(Ca, Mg, K, Al and Fe) were determined by AAS, while N and P were determined \nby AA.\n\n\n\nGrain Yield and Yield Component Parameters\nAt harvest, all plant parts were harvested and grain yields were measured. Plant \ngrowth parame-ters were measured using the same 25 cm \u00d725 cm quadrate. In this \nexercise, twenty tillers were selected randomly to count the number of panicles \nwith at least one filled grain per hectare, number of filled grain per panicle and \n1000-grain weight.\n\n\n\nElectron Microscopic Study\nThe roots of the rice plants for electron microscopic investigation were freeze-\ndried. The roots for this study were especially selected from the control treatment, \ntreated with ground basalt and observed under scanning electron microscope \n(JEOL JSM-7600F, Field Emission Scanning    Microscope, Japan). The elemental \ncomposition (Al, Fe, Si, K, Mg, etc.) was determined by energy dispersive X-ray \n(EDX) attached to the microscope. \n\n\n\nStatistical Analysis\nStatistical analysis for means comparison was carried out by Tukey\u2019s test using \nSAS version 9.2 (SAS Institute, Inc., Cary, N.C., USA). \n\n\n\nShazana, M.A.R., J. Shamshuddin, C.I. Fauziah, Q.A. Panhwar and U.A. Naher\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 91\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nChemical Properties of the Untreated Soil\nThe soil under field conditions was very acidic as evidenced by the presence of \njarosite in the topsoil. Pyrite was certainly present in this soil as it is the precursor \nof jarosite. Exchangeable Al in the topsoil was 5.36cmolckg-1, increasing in value \nwith depth, indicating the higher acidity in the subsoil. Basic cations were low, \nbut available P was within the sufficient range for rice growth. This is a true \nacid sulfate soil which under normal circumstances would not be suitable for rice \nproduction (Table 1).\n\n\n\nMineralogy of the Clay Fraction\nThe mineral that controls the chemical properties of the soil is pyrite and its \nproduct of oxidation, jarosite. The presence of jarosite in the topsoil was observed \nduring the field work. However, pyrite only occurred in the subsoil below the \nwater table. Other minerals present in the soil (clay fraction) were identified by \nX-ray diffraction analysis (Figure 1). The most common minerals were mica \n(10 and 4.98 \u00c5), kaolinite (7.1 and 3.57 \u00c5) and quartz (4.25 and 3.3\u00c5). A small \namount of gibbsite was detected in the XRD diffractogram, indicated by the weak \nreflection at 4.83\u00c5. Part of the mica had been weathered to form smectite, its \npresence being indicated by the diffractogram of Mg-glycolated sample (15.60\u00c5). \nThe presence of the above minerals was the main factor controlling the change in \nthe chemical properties of the soil as affected by the     treatments. Similar findings \nwere reported by Auxtero (199), Enio et al. (2011) and Shazana et al. (2011).\n\n\n\nChanges in Solution pH and Al Concentration with Time\nWater pH and Al were monitored till the pots dried up just before rice harvest. \nChanges in pH with time are shown in Figure 2a, while changes in Al are \nshown in Figure 2b. The pH of water increased to a value higher than 6 at day \n6, for all treatments due to consumption of proton during the reduction process. \nSubsequently, pH decreased to about 4 except in the case of the lime treatment.  \n\n\n\nBasalt and Organic Fertilizer Effect on Acid Sulfate Soil and Rice\n\n\n\nTABLE 1\nChemical properties of the untreated soil taken from the field\n\n\n\n11 \n \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nChemical properties of the untreated soil taken from the field \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepth pH \n\n\n\nTotal \n\n\n\nC Exchangeable cations Ext. Fe Avail. P \n\n\n\n(cm) H2O (%)                                    (cmolckg-1) (mg kg-1) \n\n\n\n   Ca  Mg  K  Na  Al   \n\n\n\n0 - 15cm 3.44 1.45 0.30 0.25 0.21 0.28 5.36 0.91 24.4 \n\n\n\n15 - 30cm 3.43 1.44 0.20 0.23 0.14 0.17 6.98 0.57 20.7 \n\n\n\n30 - 45cm 3.44 0.66 0.22 0.24 0.14 0.25 8.67 0.37 22.8 \n\n\n\n45 - 60cm 3.47 0.46 0.17 0.24 0.12 0.54 8.96 0.28 22.6 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201492\n\n\n\nAl concentration in the water did not change much during the first 10 days. For \nreasons unknown, Al concentration increased significantly in T1, T2 and T3. \nThe changes in pH and Al concentration as observed in this study would have \nprofound effects on the growth of rice.\n\n\n\nShazana, M.A.R., J. Shamshuddin, C.I. Fauziah, Q.A. Panhwar and U.A. Naher\n\n\n\n15 \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: XRD diffractograms of the clay fraction of the soil treated with Mg, Mg-glycol, K and \nK-heated at 550o \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: XRD diffractograms of the clay fraction of the soil treated with Mg,\nMg-glycol, K and K-heated at 550o\n\n\n\nFigure 2: Change in solution pH (a) and Al (b) with time\n\n\n\n16 \n \n\n\n\n\n\n\n\nFigure 2: Change in solution pH (a) and Al (b) with time \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 93\n\n\n\nRice variety MR 219 was able to grow well in water at a pH of about 6 \n(Elisa et al. 2011). This means that rice grown on acid sulfate soils with pH < \n3.5 would produce uneconomic yields. This finding is consistent with the results \nof other studies conducted elsewhere in Malaysia (Suswanto et al. 2007). The \ncurrent study showed that Al in the soils can be significantly reduced by applying \n4 t basalt ha-1, which resulted in an acceptable rice yield. Moreover, if the same \nsoil was used for the second cycle of rice cultivation, the pH could have been \nhigher as clearly shown by the study of Shazana et al. (2013). At pH 5, Al in soil \nsolution will start to precipitate as inert Al-hydroxides. This reaction occurs when \nGML or basalt is applied onto the acid sulfate soil causing the lime to immediately \ndisintegrate and subsequently dissolve to release hydroxyls.\n\n\n\n \nEffect of Treatment on Chemical Properties of Soil at Harvest\nChemical properties of the soils at harvest are shown in Table 2. According to \nHSD, all the chemical properties of the soil showed significant difference between \ntreatments. Soil pH was below 3.5 except for the soil treated with 4 t GML ha-1.  \nThis means that the soil was still acidic although it was treated with ground basalt \nover a period of more than 120 days. The low soil pH is consistent with the high \nexchangeable Al and Fe. Exchangeable Al in the control treatment was 4.18 cmolc \nkg-1 soil, which was far too high for rice growth. As GML takes a shorter time to \nreact completely with the soil compared to ground basalt, exchangeable Ca and \nMg in the lime treatment showed high values during the time of harvest. This \nwas not the case for the basalt treatment where the values were not significantly \ndifferent from the control treatment.\n\n\n\nBasalt and Organic Fertilizer Effect on Acid Sulfate Soil and Rice\n\n\n\nTABLE 2\nChemical properties of the soil at harvest\n\n\n\n12 \n \n\n\n\n\n\n\n\nTABLE 2 \nChemical properties of the soil at harvest \n\n\n\n\n\n\n\n \nNotes: Means followed by the same letter within a column are not significantly different (HSD P<0.05). Where T1= \n\n\n\nControl, T2= GML 4 t ha-1, T3= ground basalt 4 t ha-1; T4 = 0.25 organic fertiliser t ha-1; and T5 = ground \nbasalt 4 t ha-1 + 0.25 organic fertiliser t ha-1. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTreatments \n\n\n\npH  Exchangeable cations Avail. P Fe \n\n\n\nH2O \n\n\n\n\n\n\n\ncmolc kg-1 mg kg-1 \n\n\n\nCa Mg K Al     \n\n\n\nT1 3.42 b 0.15 b 0.82 b 0.15 a  4.18 a 16.28 a 252.09 ab \n\n\n\nT2 4.37 a 3.10 a 4.30 a 0.08 b 0.69 b 13.60 a 274.33 a \n\n\n\nT3 3.34 b 0.27 b 1.71 bc 0.09 ab 3.19 a 14.28 a 227.01 ab \n\n\n\nT4 3.18 b 0.11 b 0.38 c 0.09 ab 4.01 a 16.55 a 103.50 b \n\n\n\nT5 3.49 b 0.37 b 1.97 b 0.07 b 2.92 a 15.73 a 125.38 ab \n\n\n\nHSD <0.001 <0.001 <0.001 0.02 <0.01  0.27 0.02 \n\n\n\nNotes: Means followed by the same letter within a column are not significantly different (HSD \nP<0.05). Where T1= Control, T2= GML 4 t ha-1, T3= ground basalt 4 t ha-1; T4 = 0.25 \norganic fertiliser t ha-1; and T5 = ground basalt 4 t ha-1 + 0.25 organic fertiliser t ha-1.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201494\n\n\n\nThere was a significant increase in exchangeable Ca in the soil treated with 4 \nt basalt ha-1. Ca, to certain extent, could detoxify Al (Alva et al. 1986), resulting in \nbetter rice growth as reflected in the higher rice yield. The highest exchangeable \nCa was more than 3.5 cmolc kg-1 soil. Doberman and Fairhurst (2000) found that \nthe critical exchangeable Ca level for rice production was 2 cmolc kg-1. Thus, \napplying ground basalt at 4 t ha-1 succeeded in increasing exchangeable Ca level \nabove the critical value for rice growth. \n\n\n\nEffects of Treatment on the Growth of Rice \nThe effect of basalt application on the growth of rice was significant (Table 3). \nIn the control treatment, rice grew poorly. On the other hand, basalt treatment \nresulted in better growth  compared to that of the control. Table 3 shows the results \nof rice yield and its components. Sig-nificant differences were noted for yield and \nrice components among the treatments. Application of ground basalt improved \nsoil fertility somewhat, giving a yield of 441.43 g pot-1 (4.41 t ha-1), which is \nconsidered good for infertile soils like the acid sulfate soil used in this study, while \nfor the control treatment, it was very low, 47.69 g pot-1 (<1 t ha-1). The highest \nyield [472.82 g pot-1 (4.7 t ha-1)] was obtained by treating the soil with basalt \nin combination with organic fertilizers. Treating the soil with organic fertilizers \nalone did not help as the yield was still very low. The pattern for the spikelet \nnumber per panicle was similar to that of the grain yield. According to HSD, \nall treatments showed an increase in panicle number. The mean comparison of \ntreatments showed that basalt with organic fertilizers gave the highest percentage \nof filled spikelets at more than 90%. Similar findings were reported by Panhwar et \nal. (2014) where the application of    basalt with a combination of organic sources \n(biofertilizers) on  the acid sulfate soil improved rice yield and growth.\n\n\n\nShazana, M.A.R., J. Shamshuddin, C.I. Fauziah, Q.A. Panhwar and U.A. Naher\n\n\n\nTABLE 3\nData on rice yield components\n\n\n\n13 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nData on rice yield components \n\n\n\nTreatments \n\n\n\n\n\n\n\n\n\n\n\nGrain \n\n\n\nyield \n\n\n\n(g pot-1) \n\n\n\nYield Component \n\n\n\nPanicle num-\n\n\n\nber \n\n\n\nSpikelet num-\n\n\n\nber  Filled spikelet \n\n\n\n1000 grain \n\n\n\nweight \n\n\n\n(104 ha-1) (panicle-1) (%)  (g) \n\n\n\nT1 47.69 b 252 b 58.25 b 74.58 b 20.10 c \n\n\n\nT2 421.31 a 760 a 116.75 a 94.87 a 20.40 a \n\n\n\nT3 441.43 a 784 a 99.75 a 93.26 a 23.68 a \n\n\n\nT4 109.97 b 704 a 65.00 b 80.99 ab 20.33 bc \n\n\n\nT5 472.82 a 796 a 109.75 a 93.53 a 23.55 ab \n\n\n\nHSD0.05 <0.01 <0.01 <0.01 <0.01 <0.01 \n\n\n\n \nNotes: Means followed by the same letter within a column are not significantly different (HSD P<0.05). Where \n\n\n\nT1= Control, T2= GML 4 t ha-1, T3= ground basalt 4 t ha-1; T4 = 0.25 organic fertiliser t ha-1; and T5 = \nground basalt 4 t ha-1 + 0.25 organic fertiliser t ha-1. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNotes: Means followed by the same letter within a column are not significantly different (HSD \nP<0.05). Where T1= Control, T2= GML 4 t ha-1, T3= ground basalt 4 t ha-1; T4 = 0.25 \norganic fertiliser t ha-1; and T5 = ground basalt 4 t ha-1 + 0.25 organic fertiliser t ha-1.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 95\n\n\n\nEffects of pH, Exchangeable Al and Exchangeable Ca on Rice Yield\nRice variety MR 219 grows well in water with a pH of about 6. Hence, at a lower \npH, rice yield islow,  clearly shown in Figure 3a.  The relative rice yield was \npositively correlated with soil pH and the relationship is given by the equation, \ny= 32.65x \u2013 63.85 (r = 0.22). This means that rice grown on acid sulfate soils with \npH < 3.5 would produce uneconomic yields. It is known that excess Al in a soil is \ntoxic to the rice plant. As exchangeable Al in the soil increased, relative rice yield \nlinearly decreased (Figure 3b). To reduce Al completely in the soil, pH has to be \nraised to a value above 5 (the pKa of Al is 5). The current study showed that Al in \nthe soils can be significantly reduced by applying 4 t basalt ha-1, which resulted in \nan acceptable rice yield (Table 3).\n\n\n\nTable 4 gives the elemental composition in the upper part of the rice plant \nand the root at harvest time. No significant difference was observed for N, P, K, \nCa, Mg, Al and Si. However, there was a significant difference in Fe concentration. \nThe Fe concentration in the upper part decreased from 0.07 to 0.05% due to an \napplication of 4 t GML ha-1. Mean comparison showed that soils treated with \nbasalt had a lower concentration of Fe in the rice plants.  \n\n\n\nAs basalt contains Ca, application of basalt increased Ca in the soil. There \nwas a significant increase in exchangeable Ca in the soil treated with 4 t basalt \nha-1. Ca, to certain extent, could detoxify Al, resulting in better rice growth as \nshown by the higher rice yield (Figure 4a). The best rice yield was reported for \ntreatment with 4 t basalt ha-1 in combination with 0.25 t organic fertilizers ha-1. \nThis is because organic fertilizers are partly responsible for the reduction in Al3+ \n\n\n\ntoxicity via the chelation process. Furthermore, organic matter such as organic \nfertilizers used in the current study hasten the reduction of Fe resulting in a more \n\n\n\nBasalt and Organic Fertilizer Effect on Acid Sulfate Soil and Rice\n\n\n\nFigure 3: Relationship between relative rice yield and (a) pH and (b) exchangeable Al in \nthe soil at harvest\n\n\n\n17 \n \n\n\n\n \nFigure 3: Relationship between relative rice yield and (a) pH and (b) exchangeable Al in the soil \n\n\n\nat harvest \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201496\n\n\n\nrapid increase in pH (Muhrizal et al. 2006). Basalt itself increases the pH of \nwater resulting in precipitation of some Al as inert   Al-hydroxides, rendering it \nunavailable for uptake by rice plant (Anda et al. 2009). \n\n\n\nEffect of Organic Fertilizer on Yield\nThe organic fertilizer with basalt proved better results with because of increased \nwater pH that precipitated Al for rice plant uptake (Anda et al. 2010).The critical \nexchangeable Al for rice growth of 0.5 cmolc kg-1 was obtained (Figure 4). A sonly \n0.25 t ha-1 of as organic fertilizer had been applied, no significant differences were \nobserved among the treatments. But beneficial effects observed were plant growth \nenhancement and induced soil microbial activity. Ultimately, itenhanced plant \ngrowth and reduced the toxicity of Fe and Al in the acidic soil. Similar findings \nwere reported by Raja Namasivayamand Bharani (2012) who stated that organic \nfertilizers contain several beneficial bacteria, fungi and actinomycetes. In general, \nthese soils have a low microorganisms population and the type of microbes found \ndiffers significantly according to the vegetation and nutrients available. It is \nknown that a high number of microbes is found in the plant rhizosphere which \nenhances crop growth by supplying phytohormones (Naher et al. 2009). \n\n\n\nA study was conducted using acid sulfate soils of the Kelantan \nPlains, Malaysia, to prove the occurrence of the bacteria. Different types of \n\n\n\nTABLE 4\nConcentration of nutrients in the plant above ground and roots\n\n\n\nNotes: Means followed by the same letter within a column are not significantly different (HSD \nP<0.05). Where T1= Control, T2= GML 4 t ha-1, T3= ground basalt 4 t ha-1; T4 = 0.25 \norganic fertiliser t ha-1; and T5 = ground basalt 4 t ha-1 + 0.25 organic fertiliser t ha-1.\n\n\n\n14 \n \n\n\n\n\n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nConcentration of nutrients in the plant above ground and roots \n\n\n\nNotes: Means followed by the same letter within a column are not significantly different (HSD P<0.05). Where T1= \nControl, T2= GML 4 t ha-1, T3= ground basalt 4 t ha-1; T4 = 0.25 organic fertiliser t ha-1; and T5 = ground \nbasalt 4 t ha-1 + 0.25 organic fertiliser t ha-1. \n\n\n\n\n\n\n\nTreatments \n\n\n\n\n\n\n\nMetal concentration in the upper parts (%) \n\n\n\nAl Fe Ca Mg N P K Si \n\n\n\nT1 0.03 a 0.07 a 0.09 a 0.52 a 1.83 a 0.17 a 2.04 a 0.17 a \n\n\n\nT2 0.03 a 0.05 b 0.15 a 0.57 a 2.63 a 0.18 a 1.90 a 0.28 a \n\n\n\nT3 0.02 a 0.03 ab 0.14 a 0.75 a 2.12 a 0.18 a 1.29 a 0.34 a \n\n\n\nT4 0.03 a 0.05 ab 0.13 a 0.59 a 2.54 a 0.20 a 1.87 a 0.49 a \n\n\n\nT5 0.04 a 0.05 ab 0.14 a 0.60 a 2.66 a 0.17 a 1.58 a 0.40 a \n\n\n\nHSD0.05 0.55 0.08 0.33 0.61 0.47 0.8 0.59 0.1 \n\n\n\n\n\n\n\nTreatments \n\n\n\n\n\n\n\nMetal concentration in the root (%)  \n\n\n\nAl Fe \n\n\n\n\n\n\n\nCa \n\n\n\n\n\n\n\nMg N \n\n\n\n\n\n\n\nP K \n\n\n\nT1 0.55 b 1.33 a 0.0007 a 0.03 a 1.53 a 0.11 a 0.28 a \n\n\n\nT2 0.63 ab 1.21 a 0.0006 a 0.05 a 1.43 a 0.15 a 0.20 a \n\n\n\nT3 0.46 b 0.93 a 0.0009 a 0.04 a 1.27 a 0.10 a 0.19 a \n\n\n\nT4 0.90 a 1.53 a 0.0006 a 0.03 a 1.43 a 0.16 a 0.27 a \n\n\n\nT5 0.60 ab 1.28 a 0.0006 a 0.04 a 1.35 a 0.12 a 0.14 a \n\n\n\nHSD0.05 0.01 0.48 0.42 0.83 0.12 0.45 0.20  \n\n\n\nShazana, M.A.R., J. Shamshuddin, C.I. Fauziah, Q.A. Panhwar and U.A. Naher\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 97\n\n\n\nmicroorganisms (bacteria, fungi and actinomycetes) were found in these soils \nbut comparatively, the population of these microorganisms was lower than in \nthe normal soils (Shamshuddin et al. 2014). Organic fertilizers like JITUTM have \nmicroorganisms and the application of such fertilizers on these soils may improve \nnutrient availability and lessen Al toxicity. Phosphate-solubilising bacteria \nproduce large amounts of organic acids (Panhwar et al. 2012) that result in P \nbinding by chelation and this could serve as a possible mechanism for reducing \nAl toxicity of plant roots (Ma and Furukawa 2003).\n\n\n\nRelationship between Relative Rice Yields with Al concentration\nRice plants take in Al through the roots if a sufficient amount is present in the \nsolution. Its presence in the roots may have damaged the cells that significantly \naffect rice growth, as  reflected by poor yields. As the Al in the roots increased, rice \nyield decreased (Figure 4c). This clearly shows that Al3+ affects rice production, \nand therefore needs to be eliminated from the soil at all costs in order to achieve \nagricultural sustainability. Figure 4c shows the relationship between the linear \n\n\n\nBasalt and Organic Fertilizer Effect on Acid Sulfate Soil and Rice\n\n\n\n18 \n \n\n\n\n\n\n\n\nFigure 4: Relationship between relative rice yield with (a) exchangeable Ca (b) \nAl in the root (c) Fe in the root \n\n\n\n\n\n\n\nFigure 4: Relationship between relative rice yield with (a) exchangeable Ca (b)\nAl in the root (c) Fe in the root\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201498\n\n\n\ndecrease in relative yield and increasing Fe in the root. This is shown by the \nequation y= -443.2x +100.2 (R2 = 0.28). In the control treatment, much of the Fe \nwas taken up by the roots, but Fe was not detected in the roots of the basalt-treated \nrice plants. \n\n\n\nWhen rice plant roots are coated with Fe oxides, the capacity of roots to \nabsorb nutrients from the soil is reduced, causing stunted growth (Doberman and \nFairhurst 2000). According to Panhwar et al. (2014), the addition of microbes \nimproves root growth and volume by reducing Al toxicity and improving dry \nweight of the inoculated plants, an indication of the potential of microbes in \nbiofertilizer formulations for rice cultivation on acid sulfate soils\n\n\n\nSEM Investigation of the Roots \nRice roots were studied under SEM to investigate their structure and to determine \nAl, Fe, Si and other elements (Figure 5). In this investigation, roots from the \ncontrol and those from basalt treatment were compared. A SEM micrograph of the \nroots of the control treatment is given in Figure 5a. The Al concentration in the \nroots at spectrum 1 was 1.06%. Treating the soil with basalt at 4 t ha-1 reduced the \nAl concentration to 0.86% (Figure 5b, spectrum 3). Essentially, this means that \nless Al had been taken up by the rice as a result of basalt treatment as less Al was \navailable in the solution because of an increase in pH.\n\n\n\nIn the control treatment, much Fe was taken up by the roots, but Fe was not \ndetected in the roots of basalt treated plants. As the pH increased due to basalt \napplication, pH of the water too increased, resulting in the precipitation of Fe as \nFe-hydroxides. Rice roots can only take in Fe in soluble form, not as Fe(OH3). \nTherefore, in order to reduce the effects of Fe2+ toxicity under field conditions, \nthe pH of water needs to be raised as soon as the paddy field is flooded for rice \n\n\n\nFigure 5: SEM micrographs of the root with a1 which is an X-ray spectrum 1 for electron \nimage a (control); b1 which is an X-ray spectrum 3 for electron image b\n\n\n\n(basalt treatment)\n\n\n\n19 \n \n\n\n\n\n\n\n\nFigure 5: SEM micrographs of the root with a1 which is an X-ray spectrum 1 for electron image \na (control); b1 which is an X-ray spectrum 3 for electron image b (basalt treatment) \n\n\n\n\n\n\n\nShazana, M.A.R., J. Shamshuddin, C.I. Fauziah, Q.A. Panhwar and U.A. Naher\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 99\n\n\n\nBasalt and Organic Fertilizer Effect on Acid Sulfate Soil and Rice\n\n\n\ncultivation. This can be done by liming or applying basalt at the appropriate rate \nand time; about 4 t ha-1 is probably sufficient for this purpose. Adding organic \nfertilizers into a flooded acid sulfate soil intensifies the reduction process and \nincreases microbial activity, resulting in the release of Fe2+, which is toxic to rice \nplants (Muhrizal et al. 2003; Tran and Vo 2004). It is also proven that microbes \nhave the potential to produce large amounts of organic acids which result in P \nbinding chelation which may be a possible mechanism for reducing Al toxicity \nof roots (Sudhakar et al. 2000).  However, this is compensated by a pH increase \nthat consequently precipitates Fe as inert Fe-hydroxides. In the presence of \nhigh quality organic matter in the soil, an immediate reduction could take place \nreducing Fe3+ to Fe2+ (Muhrizal et al. 2003). \n\n\n\n \nCONCLUSION\n\n\n\nThis study has clearly shown the efficacy of ground basalt as an amendment for \nalleviating the infertility of chemically degraded acid sulfate soils. The low pH \nand high aluminum of acid sulfate soils can be effectively ameliorated by the \napplication of 4 t ground basalt ha-1. This is comparable to applying 4 t GML ha-1, \nwhich is the standard lime requirement of acid sulfate soils in Malaysia. However, \nbasalt has an additional advantage over GML because besides containing plant \nnutrients such as P and K, it also contains Si. The only problem with basalt is \nthat it takes time to disintegrate and dissolve completely in soil under submerged \nconditions. In this regard, organic matter application may enhance microbial \nactivity for the dissolution of    basalt and enhance plant growth. It is, therefore, \na good option for long-term sustainability of yields. The best option is to apply \nbasalt in combination with organic fertilizers, way ahead of transplanting rice in \nthe field.\n\n\n\nACKNOWLEDGEMENTS\nThe authors would to thank Universiti Putra Malaysia and Ministry of Education \nMalaysia for the Long term Research Grant Scheme (LRGS)-Food Security for \nfinancial and technical support during the conduct of this research. \n\n\n\nREFERENCES\nAlva, A.K., C.J. Asher and D.G. Edwards. 1986. The role of calcium in alleviating \n\n\n\naluminum toxicity. Australian Journal of Soil Research. 37:375-383.\n\n\n\nAnda, M., J. Shamshuddin, C.I. Fauziah and S.R. Syed Omar. 2009. Dissolution of \nground basalt on an Oxisol and its effect on chemical properties and cocoa \ngrowth. Soil Science. 174:264-271.\n\n\n\nAnda, M., J. Shamshuddin, C.I. Fauziah and S.R. Syed Omar. 2010. 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Journal of Agricultural Sciences. 134: \n227\u2013234.\n\n\n\nSuswanto, T., J. Shamshuddin, S.R. Syed Omar, Peli Mat and C.B.S. Teh. 2007. \nAlleviating an acid sulfate soil cultivated to rice (Oryza sativa) using ground \nmagnesium limestone and or-ganic fertilizer. Journal of Soil and Environment. \n9(1): 1-9.\n\n\n\nTran, K.T. and T.G. Vo. 2004. Effect of mixed organic and inorganic fertilizers \non rice yield and soil chemistry of the 8th crop on heavy acid sulfate soil \n(HydraquenticSulfaquepts) in the Mekong Delta of  Vietnam. In: 6th International \nSymposium on Plant Soil at Low pH. 1-5   August,  2004: Sendai: Japan.\n\n\n\n\n\n\n\nShazana, M.A.R., J. Shamshuddin, C.I. Fauziah, Q.A. Panhwar and U.A. Naher\n\n\n\n\n\n"
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"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n27 \n\n\n\n\n\n\n\n\n\n\n\nAmelioration of Soil Hydrological Performance and Erosion Rate on a \n\n\n\nRevegetated Cut Slope \n \n\n\n\nAimee Halim1*, Ismail Yusoff2, Normaniza Osman1 and Noer El Hidayah Ismail2\n \n\n\n\n \n1Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, \n\n\n\nMalaysia. \n2Department of Geology, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia  \n\n\n\n \n*Corresponding author: aimeehalim@um.edu.my \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nMost of the uncovered slope surfaces in tropical climates are highly susceptible to shallow, rainfall-\ninduced soil erosion. The practice of re-vegetation is known to hold promise for a sustainable and long-\n\n\n\nterm solution. Hence this study aimed to evaluate slope hydrological performance associated with \n\n\n\nvegetation and identify correlations among the parameters. Three experimental plots were set up \ncomprising three density treatments; control (C; without the addition of plants), less dense (LD; 0.7 \n\n\n\nplant/m2), and dense (D; 1 plant/m2). The vegetated plots were grown with potential pioneers, namely \n\n\n\nLantana camara, Melastoma malabathricum, and Bauhinia pupurea in a mixed species composition. \nA significant decrease in soil bulk density and an increase in soil total porosity, hydraulic conductivity, \n\n\n\nmoisture content, organic matter, and organic carbon were found in D plot. These positive changes \n\n\n\nboosted plant growth, resulting in higher community-plants aboveground biomass and root length \n\n\n\ndensity resulting in the erosion rate being alleviated in LD and D plots by 50.1% and 74.04%, \nrespectively. Soil infiltration capacity, soil structural dynamics, and soil water retention capacity \n\n\n\nexplained the first three components of the principal component analysis (PCA). Thus, we suggest that \n\n\n\nthe promising observations could improve our understanding of differential plant density responses to \ncut slope restoration performance, particularly for the eroded cut slopes in the tropics.  \n\n\n\n\n\n\n\nKey words: Soil bioengineering, vegetation density, mixed species composition, soil hydrology, \n\n\n\nsoil loss  \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nOver decades, the expanding conversion of hillslopes into agricultural lands and infrastructure \n\n\n\nprojects has intensified the geomorphological activities on sloping lands including erosion. \n\n\n\nGenerally, erosion involves a movement and transport of soil by the force of natural agents like \n\n\n\nwind and water. Persistent exposure of a sloping soil surface without protection cover may \n\n\n\nstrongly accelerate erosion and degrade soil quality (Borrelli et al. 2017; Celentano et al. 2017). \n\n\n\nEventually, these disturbances may affect the slope ecosystem functioning and services such \n\n\n\nas carbon storage and hydrological cycle (Dorairaj and Osman 2021; Buechel et al. 2022).  \n\n\n\n\n\n\n\nWater erosion is one of the most severe types of soil degradation, owing to runoff and \n\n\n\nraindrop impact (Ketema and Dwarakish 2019). It comprises three processes that act in \n\n\n\nsequence, namely, soil detachment, transport of soil particles, and soil mass deposition (Wang \n\n\n\net al. 2022). Moreover, water erosion can be classified into several forms including the sheet, \n\n\n\nrill, and gully erosions. Sheet and rill erosions develop as a result of shallow overland flows \n\n\n\nand small channels formed by the runoff, respectively. Gully erosion is different from rill \n\n\n\nerosion by the presence of larger channels (>0.3 m in width and >0.3 m in depth) (Vanmaercke \n\n\n\net al. 2021). As erosion threatens ecosystem productivity, tropical landscapes which have the \n\n\n\n\nmailto:aimeehalim@um.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n28 \n\n\n\n\n\n\n\nlargest diversity of species on Earth attract much attention, particularly because the large \n\n\n\namount and high intensity of rainfall in these regions can potentially exacerbate the occurrence \n\n\n\nof erosion (Rahman and Mapjabil 2017; Meng et al. 2021). \n\n\n\n\n\n\n\nTropical climates have no distinct season except for the interchanging warm tropical \n\n\n\nseason and rainy monsoon throughout the year. In the face of catastrophic climate change and \n\n\n\nextreme rates of land clearing, the tropical slopes are exposed to increasing temperature and \n\n\n\nhigh rainfall erosivity, an important input parameter that reflects the hydrological and erosive \n\n\n\nprocesses on slopes. On record, tropical countries have the highest annual mean rainfall \n\n\n\nerosivity of about 5,000 MJ mm ha/h/yr, more than double of the global average (Panagos et \n\n\n\nal. 2015). Thus, abundant rainfall could contribute to the depletion of organic matter and \n\n\n\nnutrients that are usually rich in tropical topsoils and lead to nutrient leaching and critical soil \n\n\n\nloss with  subsequent negative impacts on ecosystem productivity and sustainability as well as \n\n\n\neconomic losses (Garc\u00eda-Ruiz et al. 2017; Elbasiouny et al. 2022).  \n\n\n\n\n\n\n\nFurthermore, the combined effects of year-long intense rainfall, high temperature, and \n\n\n\ngeological characteristics in tropical areas result in rapid chemical weathering and formation \n\n\n\nof thick residual soil profiles (Huat et al. 2008). The process of drying and wetting triggered \n\n\n\nby high temperatures and dense precipitation will affect the strength and carrying capacity of \n\n\n\nsoils and push the residual soils to move (Wibawa et al. 2018). Thus, improper designing and \n\n\n\nprotection planning disturbs the inherent balance of the hydrological and mechanical networks \n\n\n\nof the natural and cut slopes causing instability and sliding of soil (Zhu and Xiao 2020). To \n\n\n\ncurb further slope damage, slope stability assessment is a fundamental step towards designing \n\n\n\napproaches aimed at reducing slope erosion (Zhu and  Qi 2017; Liu et al. 2022). More recently, \n\n\n\ndue to increasing  environmental sustainability concerns, integration of vegetation components \n\n\n\nis a prerequisite to alleviate slope erosion problems.  \n\n\n\n\n\n\n\nSlope bioengineering techniques can be defined as the application of vegetation \n\n\n\ncomponents for slope protection either on their own or in tandem with mechanical methods \n\n\n\n(Dorairaj and Osman 2021; Gayathiri et al. 2022). This technique has been observed by several \n\n\n\nresearchers to be a suitable tool for site rehabilitation (Halim et al. 2021; Osman et al. 2021) \n\n\n\nparticularly in increasing strength and functionality of  slope soil (Osman et al. 2014; Bella et \n\n\n\nal., 2017). Moreover, re-vegetation is a common practice in slope bioengineering for \n\n\n\ncontrolling erosion that can contribute to developing a new ecosystem that has more ecological \n\n\n\nvalue; it is also cost effective and provides  long-term soil stability and successful restoration \n\n\n\n(Boldrin et al. 2017; Chau and Chu 2018). However, it is noted that not all plants species are \n\n\n\nsuitable for use in re-vegetaion of slopes. It is imperative to select the optimal vegetation type \n\n\n\nthat not only effectively controls soil erosion but can also maintain sustainable vegetation \n\n\n\nrestoration (Huang et al. 2022; Liang et al. 2022).  \n\n\n\n\n\n\n\nIn view of the fact that the effects of bioengineering techniques on slopes vary from \n\n\n\ncase to case depending on the design, choice of species, type of composition, as well as \n\n\n\nmicroclimatic conditions of the slope, the aim of this study is to quantify the dynamics of soil \n\n\n\nhydrological reinforcement that control erosion through the practice of slope re-vegetation in \n\n\n\na tropical climate. It is anticipated that the results of the present study can be leveraged to other \n\n\n\nregions with the same soil and climatic conditions as well as be of  relevance towards  \n\n\n\nimproving the success of future slope restoration efforts. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n29 \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nStudy Area \n\n\n\n\n\n\n\nThe study area is located along the Guthrie Corridor Expressway, Selangor, Malaysia (N \n\n\n\n03\u00b013\u201924.1\u201d and E 101\u00b030\u201950.2\u201d) with the land use encircling this expressway being  mainly \n\n\n\noil palm plantations. The geological formation of this area is Kenny Hill Formation, consisting \n\n\n\nof low grade meta sedimentary rock, mainly the inter-bedded sequences of sandstone and shale \n\n\n\nwhich metamorphosed into quartzite and phyllite (Lee 2001; Mohamed et al. 2007; Yin 2011) \n\n\n\n(Figure 1). This region has a tropical climate with no distinct season except for the \n\n\n\ninterchanging warm tropical season and rainy monsoon throughout the year. The average \n\n\n\ntemperature is 27 \u00b0C, with an average annual precipitation of 2114.5 mm, and relative humidity \n\n\n\nof between 60 to 75 %. \n\n\n\n\n\n\n\nExperimental Setup \n\n\n\n\n\n\n\nThe experimental plots were established on similar berm slopes to avoid the influence of slope \n\n\n\naspect such as the distribution of soil organic matter and soil mineralogical and micro-\n\n\n\nmorphological properties. The weathering grade of the experimental site was classified as grade \n\n\n\nIII to IV (International Society for Rock Mechanics) i.e. varying from partially to highly \n\n\n\nweathered materials, reflecting mixed ground behaviour and partially decomposed rock mass \n\n\n\n(Brown 1981). Soil pH recorded ranged from pH 3.78 to 5.37 with the soil type being sandy \n\n\n\nclay loam, based on the USDA Textural Soil Classification. The presence of sheet and gully \n\n\n\nerosions was a result of runoff formation. Based on electrical resistivity survey, the highly \n\n\n\nconductive characteristic in the upper part of the soil (0-5 m of depth) and a weak zone due to \n\n\n\nthe presence of water in the lower part of the soil (5-14.8 m of depth) could pose a threat to the \n\n\n\nslope and may contribute to further erosion especially during the rainy season (Abidin et al. \n\n\n\n2017; Hussin et al. 2017) (Figure 2). \n\n\n\n\n\n\n\nThree experimental plots were set up and assigned to three different vegetation densities which \n\n\n\nwere, control (C; without the addition of plant), less dense (LD; 0.7 plant/m2), and dense (D; 1 \n\n\n\nplant/m2) with the size of each plot being 8 m x 8 m. Three l slope plants with potential--\n\n\n\nLantana camara, Melastoma malabathricum, and Bauhinia purpurea-were selected for their \n\n\n\nefficacy to withstand slope conditions (Halim and Normaniza 2015; Normaniza et al. 2018; \n\n\n\nHalim et al. 2021). These plants were transplanted onto the slope under a mixed species \n\n\n\ncomposition regime, using a Microclimate Plant Propagation Technique with modified soil \n\n\n\ndepth (Osman and Barakbah 2011). The mixed species composition approach where plants \n\n\n\nvary in  species, structures and functions, is acclaimed as a more environmentally sustainable \n\n\n\nvegetation system compared to monoculture (Warner et al. 2022) as it maximises the use of \n\n\n\nresources, and consequently increases carbon sequestration (Xiang et al. 2022). The basic \n\n\n\ninformation of each experimental plot is shown in Table 1. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n30 \n\n\n\n\n\n\n\n\n\n\n\n \n(a) Control   (b) Less dense         (c) Dense \n\n\n\n\n\n\n\nFigure 1. Geological map of Selangor, Malaysia and the experimental plots \n\n\n\n\n\n\n\n \nFigure 2. 2D inversion l resistivity model of the study area \n\n\n\n \nTABLE 1 \n\n\n\nGeneral information of the experimental plots \nPlot Slope angle(%) Elevation(m) Plant density (plant m-2) Number of plants \n\n\n\nC 53 63.5 0 0 \n\n\n\nLD 55 64.1 0.7 36 \n\n\n\nD 57 64.5 1 64 \n\n\n\n    Note: C \u2013 Control (control soil); LD \u2013 Less dense; D \u2013 Dense \n\n\n\nVery high resistivity value \n\n\n\nWater zone \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n31 \n\n\n\n\n\n\n\nPlant Species Selection \n\n\n\n\n\n\n\nThe selection of slope plants, Lantana camara, Melastoma malabathricum, and Bauhinia \n\n\n\npurpurea  was  based on their desirable traits that might enhance the levels of resilience and \n\n\n\nresistance on the harsh condition of the slope. Apart from having root systems that are capable \n\n\n\nof enhancing soil shear strength and preventing soil movement (Normaniza et al. 2018; \n\n\n\nSaifuddin et al. 2022), these species are fast-growing and display a high process of accelerating \n\n\n\nearly phase natural succession (Chang et al. 2019; Dorairaj and Osman 2021). \n\n\n\n\n\n\n\nPrevious studies revealed that L. camara could improve the fertility of soil tolerate a \n\n\n\nlong period of drought (Kumar et al. 2011) and exhibited a significantly higher photosynthetic \n\n\n\ncapacity at high temperature (Carri\u00f3n-Tacuri et al. 2011). Studies show that M. malabathricum \n\n\n\nhas  outstanding capacity to survive in polluted soil conditions and is  known as an aluminum \n\n\n\naccumulating plant (Patek-Mohd et al. 2018; Mahmud and Burslem 2020; Master et al. 2020). \n\n\n\nA higher planting density of this species has been known to contribute towards  alleviating the \n\n\n\nerosion rate of slope soils (Halim et al. 2021). B. purpurea, a legume,  offers beneficial effects \n\n\n\nof replenishing soil nitrogen while  the larger surface area of its big-heart shaped leaf may \n\n\n\nexhibit a higher photosynthetic rate (Rawat et al. 2022). \n\n\n\n\n\n\n\n Measurement of Soil Properties \n\n\n\n\n\n\n\nSoil properties were measured every six-month intervals for twenty-four months to identify \n\n\n\ntheir changes as affected by different vegetation density. Seven soil samples were taken \n\n\n\nrandomly from each plot in a zigzag pattern by using a metal auger (Edelman, Eijkelkamp, The \n\n\n\nNetherlands) to 1 m depth for measuring soil bulk density, total porosity, organic matter, and \n\n\n\norganic carbon. The soil samples were oven-dried at 105\u00b0C for three days and the value of bulk \n\n\n\ndensity and total porosity were determined. For soil organic matter, a loss on ignition (LOI) \n\n\n\nmethod (Buurman et al. 1996) was used, whilst total organic carbon was determined by \n\n\n\nadopting Walkley and Black (1934) method. \n\n\n\n\n\n\n\nMeanwhile, in situ field assessment was conducted to measure soil moisture content \n\n\n\nusing a portable Delta-T soil moisture (HH2 Moisture Meter, Delta -T Devices Ltd., England), \n\n\n\nwhich was installed at a depth of 10 cm from the soil surface between 1130 h to 1230 h. Soil \n\n\n\nhydraulic conductivity (K) was performed using the Inverted Auger-Hole Method (Van Hoorn \n\n\n\n1979) and the value of hydraulic conductivity was calculated as  \n\n\n\n\n\n\n\nHydraulic conductivity (K) = 1.15r [log10(ho + r/2) - log10(h1 + r/2) / t \u2013 to]  \n\n\n\n*r=radius hole, ho= water depth in auger hole at time t=0, h1= water depth in auger hole \n\n\n\nat time t=t \n\n\n\n\n\n\n\nErosion performance was determined by adopting the Gerlach sampling field method (Gerlach \n\n\n\n1967). The experimental plots were enclosed with 25-cm height wooden barriers and the eroded \n\n\n\nsoils were collected, air dried and weighed in g/m2. \n\n\n\n\n\n\n\n\n\n\n\nMeasurement of Plant Performance \n\n\n\n\n\n\n\nIn understanding plant-soil feedback in a mixed-species composition, the aboveground \n\n\n\nbiomass of the community was estimated at the end of experiment by randomly  placing three \n\n\n\n1 \u00d7 1 m quadrats in each plot. The plant samples were oven-dried at 80 \u00b0C until constant weight \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n32 \n\n\n\n\n\n\n\nand the dry weights were obtained by using an electrical weighing balance (Model-PJ3000, \n\n\n\nMettler Toledo, Japan). Meanwhile, root samples taken from the soil coring were gently washed \n\n\n\nwith tap water to remove soil and root length was determined using WinRHIZO Pro Software \n\n\n\n(Version 2008a, Regent Instruments Inc., Canada). Root length density (RLD) was calculated \n\n\n\nby dividing the total root length by the volume of soil sample.  \n\n\n\n\n\n\n\nStatistical Analysis \n\n\n\n\n\n\n\nA one-way analysis of variance (ANOVA) was used to test the effects of different vegetation \n\n\n\ndensity on soil properties and performance of community plants. Correlations between soil \n\n\n\nproperties and plant traits were tested using Pearson\u2019s correlation coefficients, and principal \n\n\n\ncomponent analysis (PCA) was carried out to analyse the overall pattern of correlations. \n\n\n\nStatistical analyses were performed using the SPSS software (Version 20, IBM, U.S.A) and \n\n\n\nXLSTAT software (Version 2020.5. Addinsoft, U.S.A). \n\n\n\n\n\n\n\nRESULTS \n\n\n\n\n\n\n\nChanges in  Soil Properties  \n\n\n\n\n\n\n\nOverall, soil properties displayed changes in vegetated plots across the 2-year observation \n\n\n\n(Figure 3). Soil bulk density (BD) was almost similar during the early stage of the experiment \n\n\n\nbut was  significantly reduced in vegetated plots by 11.4% and 13% in less dense (LD) and \n\n\n\ndense (D) plots, respectively (Figure 3a). Throughout the experiment, the establishment of \n\n\n\nvegetation positively increased total porosity (TP) value and the highest TP was determined in \n\n\n\nD plot with the maximum value of 48.23% at the end of the experiment and was 14.34% and \n\n\n\n90.63% higher than that in LD and C plots, respectively (Figure 3b). Nonetheless, no increment \n\n\n\nof TP was found in control soil (C).  \n\n\n\n\n\n\n\nGenerally, an increase in total porosity promotes the generation of macropores, \n\n\n\nsubsequently contributing to enhancing soil hydraulic conductivity (K). In this study, the \n\n\n\nmeasured K was similar during the first year of observation but was significantly changed after \n\n\n\na year following re-vegetation (Figure 3c). The D plot exhibited a positive increment of soil \n\n\n\nK, followed by LD plot with  a total increment of 73.8% and 30.85% respectively. Likewise, \n\n\n\nsoil moisture content (MC) value showed the highest increment in D plot and the lowest was \n\n\n\nrecorded in C plot (Figure 3d). D plot was higher as compared to LD and C plots by 27.44% \n\n\n\nand 68.37%, respectively. It was also observed that soil MC in all treatments did not change \n\n\n\nmarkedly during the 0-, 6-, and 12-month periods, indicating that the  rainfall factor  had a \n\n\n\ngreater  influence than the effects of vegetation.  \n\n\n\n\n\n\n\nWe compared the organic matter contents (OM) between the vegetation densities.  As \n\n\n\nthe plants grew on the vegetated plots, OM value increased with time and was drastically \n\n\n\nenhanced at the end of the experiment with nearly fourfold total increment in the D plot (Figure \n\n\n\n3e). However, no change of OM was found in the C plot with the cumulative average of OM \n\n\n\n0.17\u00b10.01. Furthermore, the re-vegetation practice also contributed to reducing the carbon \n\n\n\nemissions from soil disturbance by capturing the carbon from the atmosphere to store in the \n\n\n\nsoils. At the end of the experiment, a six-fold (645%) increase in total organic carbon (OC) \n\n\n\nvalue was estimated within two years of the study period in D plot and four-fold (400%) \n\n\n\nincrease in LD plot. C plot, however, exhibited a constant trend with time (Figure 3f).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n33 \n\n\n\n\n\n\n\n \n(a) Bulk density      (b) Total porosity \n\n\n\n \n(c) Hydraulic conductivity   (d) Moisture content \n\n\n\n\n\n\n\n \n(e) Organic matter    (f) Organic carbon \n\n\n\n\n\n\n\nFigure 3. Variation in soil properties at vegetation densities throughout the two years of \n\n\n\nobservation. (a) Bulk density, (b) Total porosity, (c) Hydraulic conductivity, (d) Moisture \n\n\n\ncontent, (e) Organic matter (f) Organic carbon.  \nNote: Vertical bars indicate the standard deviation, * and ** denote significant differences at p\u2264 0.05 \n\n\n\nand p\u2264 0.001, respectively. C -control soil (Control); LD- less dense plot; D- dense plot. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nResponse of Soil Properties to Performance of Community Plants  \n\n\n\n\n\n\n\nThe vegetation induced changes in soil properties, resulting in reciprocal effects which \n\n\n\ninfluenced the growth performance of community plants in the vegetated plots (Table 2). With \n\n\n\ntime, plant litter accumulated in soils and gradually transformed into organic matter through \n\n\n\n0.0\n\n\n\n0.5\n\n\n\n1.0\n\n\n\n1.5\n\n\n\n2.0\n\n\n\n0 6 12 18 24\n\n\n\nB\nu\nlk\n\n\n\n d\nen\n\n\n\nsi\nty\n\n\n\n (\ng\n/c\n\n\n\nm\n3\n)\n\n\n\nMonth\n\n\n\nC\n\n\n\nLD\n\n\n\nD\n\n\n\n* **\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n0 6 12 18 24\n\n\n\nT\no\nta\n\n\n\nl \np\no\nro\n\n\n\nsi\nty\n\n\n\n (\n%\n\n\n\n)\n\n\n\nMonth \n\n\n\nC\n\n\n\nLD\n\n\n\nD\n\n\n\n* * **\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n45\n\n\n\n0 6 12 18 24\n\n\n\nH\nyd\n\n\n\nra\nu\nli\n\n\n\nc \nco\n\n\n\nn\nd\nu\nct\n\n\n\niv\nit\n\n\n\ny\n(m\n\n\n\nm\n/h\n\n\n\n)\n\n\n\nMonth\n\n\n\nC\n\n\n\nLD\n\n\n\nD\n\n\n\n****\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n0 6 12 18 24\n\n\n\nM\no\nis\n\n\n\ntu\nre\n\n\n\n c\no\nn\nte\n\n\n\nn\nt \n\n\n\n(%\n)\n\n\n\nMonth\n\n\n\nC\n\n\n\nLD\n\n\n\nD\n\n\n\n* * **\n\n\n\n0.0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\n\n\n\n0 6 12 18 24\n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n m\nat\n\n\n\nte\nr \n\n\n\n(%\n)\n\n\n\nMonth\n\n\n\nC\n\n\n\nLD\n\n\n\nD\n\n\n\n* ****\n\n\n\n0.0\n\n\n\n1.0\n\n\n\n2.0\n\n\n\n3.0\n\n\n\n4.0\n\n\n\n5.0\n\n\n\n0 6 12 18 24\n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n c\nar\n\n\n\nb\no\nn\n (\n\n\n\n%\n)\n\n\n\nMonth\n\n\n\nC\n\n\n\nLD\n\n\n\nD\n\n\n\n* ** **\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n34 \n\n\n\n\n\n\n\nthe decomposition process (Middleton 2020). As a result, the total above ground biomass \n\n\n\n(AGB) of vegetated plots of  D and LD were significantly greater than that of C plot. \n\n\n\nCommunity plants in D plot achieved dry matter values of 14.2% higher than that of LD plot. \n\n\n\nApart from that, at the end of the experiment, root length density (RLD) of D plot was \n\n\n\nsignificantly higher by 34.3% compared to LD plot, indicating that higher plant density had led \n\n\n\nto a more enhanced spatial occupation of the soil (Haque and Sakimin 2022). This may have \n\n\n\nsubsequently contributed to enhancing underground environment activities (e.g. root and soil \n\n\n\nmicrobial biomass) and alleviating soil erosion. \n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nMeans of above ground biomass and root length density at different vegetation densities \n\n\n\nVariable Control Less dense Dense \n\n\n\nAboveground biomass \n\n\n\n(AGB) 0.23c 76.83\u00b12.77b 89.52\u00b19.75a \n\n\n\n(g/m2) \n\n\n\nRoot length density \n\n\n\n(RLD) 0.31c 119\u00b19.12b 181\u00b118.62a \n\n\n\n(cm/m3) \n\n\n\nNote: \u00b1 indicates the standard deviation and letters indicate significant differences between \n\n\n\ntreatments at p\u02c20.05. \n\n\n\n\n\n\n\nResponse of Soil Respiration and Erosion Rate \n\n\n\n\n\n\n\nIn two years, significant differences were observed in soil respiration rate (RES) and erosion \n\n\n\nrate (ER) between the experimental plots (Figure 4). RES exhibited an outstanding increment \n\n\n\nin vegetated plots with  the total increment in D plot being more than twice  that of the 6-month \n\n\n\nperiod (212.75%) (Figure 4a). At the end of the experiment, the RES ranged widely from 0.32 \n\n\n\nto 3.19 g[Co2] m-2 h-1, with the  D plot exhibiting a significantly higher value by 31.82% over \n\n\n\nthat  in  the LD plot. The increased rate of RES is attributed  to decomposition activities of \n\n\n\nplant litter, a good sign of microbial abundance in soil. Figure 4b shows a decreased ER with \n\n\n\nincreased vegetation densities, which further confirms the effectiveness of re-vegetation in \n\n\n\nimproving soil properties. At the end of the experiment, the vegetated plots saved about 10 to \n\n\n\n15 t h-1 of total soil loss per year. The ER of the D plot decreased by 74.04%, whilst the total \n\n\n\nreduction of the LD plot was 50.1%. Moreover, the D plot showed a significantly lower value \n\n\n\nof  47.6% and 76.17% compared to the values of  the LD and C plots, respectively. The C plot \n\n\n\nvalues tended to fluctuate with a much lower of ER being observed.  \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n35 \n\n\n\n\n\n\n\n \n(a) Respiration rate     (b) Erosion rate \n\n\n\n \nFigure 4. Changes in (a) Respiration rate, and (b) Erosion rate at different vegetation densities. Line \ngraph represents rainfall intensity during the study period.  \n\n\n\nNote: Vertical bars indicate standard deviation, * and ** denote significant differences at p\u2264 0.05 and \n\n\n\np\u2264 0.001, respectively. C -Control; LD- less dense plot; D- dense plot. \n \n\n\n\nRelationships between Soil Properties and Community Plant Parameters \n\n\n\n\n\n\n\nBoth soil properties and community plant parameters exhibited a significant correlation with \n\n\n\nrespiration rate and erosion rate (Table 3). In this study, all components were significantly \n\n\n\ninversely related with ER except for BD, indicating that mixed-species establishment in sloping \n\n\n\nland effectively enhanced soil properties and reduced soil erosion. Also there was a significant \n\n\n\nco-relation between OC and RES due to high development of the C cycle in terrestrial \n\n\n\necosystems which increased the RES from both roots and microbes (Tang et al. 2022). \n\n\n\nFurthermore, principal component analysis (PCA) revealed that the first three principal \n\n\n\ncomponents explained 90.15% of the total variance, implying that these components express \n\n\n\nmost of the soil properties and performance information of community plants (Table 4). The \n\n\n\nBD, MC, and K had positive weights on PC1 and their contributions were larger than other \n\n\n\nvariables. RLD, OC, and TP had positive weights and larger contribution rates than that of \n\n\n\nother variables on PC2. Whereas, OM and AGB exhibited larger contribution rates in PC3 with \n\n\n\npositive and negative weights, respectively. As shown in Figure 5, the principal component \n\n\n\nbiplot displayed the two first explanatory axes (PC1: 44.57% of variation; PC2: 34.44% of \n\n\n\nvariation), indicating that the first axis was mostly determined by soil infiltration capacity while \n\n\n\nthe second axis was mostly explained by  soil structural dynamics. \n \n\n\n\nTABLE 3 \n\n\n\nCorrelation matrix between soil properties and community plant parameters with soil respiration and \nerosion rates \n\n\n\nVariables BD TP OM OC K MC AGB RLD RES ER \n\n\n\nBD 1 -0.894** -0.682** -0.886** -0.779** -0.799** -0.956** -0.935** -0.932** 0.945** \n\n\n\nTP  1 0.681** 0.919** 0.906** 0.847** 0.941** 0.949** 0.932** -0.946** \n\n\n\nOM   1 0.869** 0.751** 0.669** 0.732** 0.797** 0.724** -0.736** \n\n\n\nOC    1 0.918** 0.874** 0.944** 0.968** 0.940** -0.958** \n\n\n\nK     1 0.924** 0.846** 0.885** 0.849** -0.884** \n\n\n\nMC      1 0.818** 0.875** 0.846** -0.889** \n\n\n\nAVB       1 0.963** 0.952** -0.968** \n\n\n\nRLD        1 0.952** -0.975** \n\n\n\nRES         1 -0.975** \n\n\n\nER          1 \n\n\n\nNote: ** indicates significant differences at p\u2264 0.001. \n\n\n\n0.0\n\n\n\n0.5\n\n\n\n1.0\n\n\n\n1.5\n\n\n\n2.0\n\n\n\n2.5\n\n\n\n3.0\n\n\n\n3.5\n\n\n\n4.0\n\n\n\n4.5\n\n\n\n0 6 12 18 24\n\n\n\nR\nes\n\n\n\np\nir\n\n\n\nat\nio\n\n\n\nn\n r\n\n\n\nat\ne \n\n\n\n(g\n[C\n\n\n\nO\n2\n]/\n\n\n\nm\n2\n/h\n\n\n\n)\n\n\n\nMonth\n\n\n\nC\n\n\n\nLD\n\n\n\nD\n\n\n\n** ** * \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n0 6 12 18 24\n\n\n\nE\nro\n\n\n\nsi\no\nn\n\n\n\n r\nat\n\n\n\ne \n(t\n\n\n\n/h\n/y\n\n\n\nr)\n\n\n\nMonth\n\n\n\nC\n\n\n\nLD\n\n\n\nD\n\n\n\n******\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n36 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nTotal variance explained and principal component matrix of the principal component \n\n\n\nanalysis. \n Component matrix \n\n\n\n\n\n\n\nComponent Eigen- \nvalue \n\n\n\nVariance \n(%) \n\n\n\nCumulative \nVariance \n(%) \n\n\n\nBD TP OM OC K MC AGB RLD \n\n\n\nFirst \nprincipal \ncomponent \n\n\n\n3.57 44.57 44.57 0.81 0.66 0.49 0.53 0.87 0.89 0.55 0.34 \n\n\n\nSecond \nprincipal \ncomponent \n\n\n\n2.75 34.44 79.01 -0.48 0.7 0.22 0.84 -0.36 -0.42 -0.43 0.88 \n\n\n\nThird \nprincipal \n\n\n\ncomponent \n\n\n\n0.89 11.14 90.15 -0.16 -0.02 0.71 -0.1 0.19 0.05 -0.48 -0.3 \n\n\n\n\n\n\n\n \nFigure 5. Biplot projection of soil properties and plant-community parameters on the plane \n\n\n\nrepresented by the first two components of principal component analysis (PCA) (PC1: 44.57% \n\n\n\nof variation; PC2: 34.44%of variation). \n\n\n\n\n\n\n\nDISCUSSION \n\n\n\n\n\n\n\nThe soil hydrological process is one of the main factors that shapes all other systems in sloping \n\n\n\nlands. The expanding development at hilly areas has long been recognized as disturbing the \n\n\n\nnatural balance of the hydrological network of the slopes. Slopes with exposed soil surface or \n\n\n\ninsufficient surface protection cause a reduction in water holding capacity, have poor soil \n\n\n\nstructure, and are  thus, susceptible to failure (Wang et al. 2021; Lawrence et al. 2022). At our \n\n\n\nstudy area, the cut slope for highway development had resulted in the exposure of fresh rock \n\n\n\nto the weathering process. The process of drying and wetting transformed the hard rock facies \n\n\n\nof the Kenny Hill to weathered materials, behaving more like soil in the surface zone (Komoo \n\n\n\n1995; Mohamed et al., 2004). Moreover, the coarse and poorly structured type of sandy soil \n\n\n\nassociated with an exposed surface subjected to abundant precipitation in the study area \n\n\n\ncontributed to soil compaction, indicating that this condition tends to heighten pore pressure \n\n\n\npropagation (Igor et al. 2020; Khodadadi et al. 2021). This also implies that the pore space of \n\n\n\nBD\n\n\n\nTP\n\n\n\nOM\n\n\n\nOC\n\n\n\nK\nMCAGB\n\n\n\nRLD\n\n\n\n-1\n\n\n\n-0.75\n\n\n\n-0.5\n\n\n\n-0.25\n\n\n\n0\n\n\n\n0.25\n\n\n\n0.5\n\n\n\n0.75\n\n\n\n1\n\n\n\n-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1\n\n\n\nP\nC\n\n\n\n2\n (\n\n\n\n3\n4\n.4\n\n\n\n4\n %\n\n\n\n)\n\n\n\nPC1 (44.57 %)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n37 \n\n\n\n\n\n\n\nthe soil was irregular and non-uniform, posing a complex fluid dynamics of the study area. \n\n\n\nTherefore, we suggest that the practice of re-vegetation be adopted as a key measure for slope \n\n\n\nhydrological improvement and erosion control (Mishra et al. 2019; Emeka et al. 2021). \n\n\n\n\n\n\n\nCollectively, within a 24-month monitoring period of our study, soil parameters and \n\n\n\ntheir interaction with plants were found to be highly dynamic.  Introduction of selected pioneers \n\n\n\nin high density (D plot) (i.e. Lantana camara, Melastoma malabathricum, and Bauhinia \n\n\n\npurpurea) positively affected soil properties (Figure 3 & 4) and this in turn improved the \n\n\n\nperformance of community plants (Table 2). Based on correlation analysis, we found that soil \n\n\n\ninfiltration capacity, soil structural dynamics, and soil water retention capacity were the main \n\n\n\nfactors influencing the hydrological condition and erosion reduction in the study area (Table 3, \n\n\n\nFigure 5).  \n\n\n\n\n\n\n\nNotice that the plant root system easily penetrated through the medium-coarse texture \n\n\n\nof sandy clay loam of the study area. However, the soil BD was greatly affected in the early \n\n\n\npart of the experiment due to the natural compaction of the control slope surface and high \n\n\n\nrainfall intensity. As the vegetation covers were introduced and the root system embedded in \n\n\n\nthe soil, they positively influenced the lowest soil BD achieved at the end of the experiment \n\n\n\n(Figure 3a). Moreover, a higher soil MC was also observed in re-vegetated plots (Figure 3d), \n\n\n\nimplying that the species studied had successfully reduced evaporation losses through the plant \n\n\n\ncanopies as well as restraining both horizontal and vertical water flows in the soil via root \n\n\n\ndistribution (Dong et al. 2022; Hoek van Dijke et al. 2022). Although the presence of \n\n\n\nvegetation might increase soil water consumption, our findings found that soil MC increased \n\n\n\nin comparison to control (C plot), indicating an ongoing process of hydraulic lift (Lee et al. \n\n\n\n2021; Guerreiro et al. 2022) which could have a mitigating effect on soil drying in the \n\n\n\nsubsurface (Bayala and Prieto 2020). Furthermore, as the root system of plants and organic \n\n\n\nresidues facilitated in re-organizing soil aggregation, soil K was subsequently enhanced in the \n\n\n\nvegetated plots (Figure 3c) (Wang et al. 2017; Kalhoro et al. 2017; Zhu et al. 2022). Therefore, \n\n\n\nwe postulate that the soil BD, MC, and K are indicators that reflect soil infiltration capacity, \n\n\n\nwhich help to initiate preferential flow paths and convey water more efficiently in the slope. \n\n\n\n\n\n\n\nBesides soil water flow, structural properties of soil are also important in soil hydrology \n\n\n\nof the slope. Root is that part of the  plant organ that is closely related to the soil, and we found \n\n\n\nthat the high RLD of the slope pioneers (Table 2) promoted the generation of macropores and \n\n\n\nchannels through root penetration, hence  improving the TP value of the slope soil (Figure 3b). \n\n\n\nMoreover, the coarse roots formed a soil-root matrix to reinforce soils, whilst the mucilage \n\n\n\nfrom the fine roots and soil microorganisms chemically adhered to the soil particles, thus \n\n\n\ncontributing to enhance the soil-root bonding and channel longevity (Garcia et al. 2019; Hao \n\n\n\net al. 2020). Further, a high amount of litter and MC in soil corresponded to a high OC \n\n\n\nconcentration of the vegetated plots (Figure 3f), which accelerated the transport of organic \n\n\n\ncarbon to deep soil layers and led to an increase in soil carbon stock (Zhao et al. 2019; \nCastellano et al. 2022). Thus, RLD, TP, and OC affected the slope not only through lowering \n\n\n\nthe density of the solid phase, but also enabled the slope to build a   soil structure to combat \n\n\n\nerosion. \n\n\n\n\n\n\n\nFurthermore, enhancement of soil structure aids vegetation growth (AGB, Table 2), and \n\n\n\nin turn, vegetation performance feeds back into the quality of the soil (Furey and Tilman 2021). \n\n\n\nInitially, soil fertility of the study area was low due to the effects of slope cutting which was \n\n\n\nless suitable for plant growth, Interestingly, plant establishment in our study successfully \n\n\n\nproduced a high amount of litter composition, thus, enhancing OM content (Figure 3e) as well \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 27-43  \n\n\n\n\n\n\n\n38 \n\n\n\n\n\n\n\nas RES rate (Figure 4a) (signalling the increase in decomposition process by microbial \n\n\n\nactivities) (Habtewold et al. 2020; Paranychianakis et al. 2021). Thus, from the perspective of \n\n\n\nhydrological performance, given that  sandy soils have difficulty retaining water and nutrients, \n\n\n\na greater amount of OM alters soil structure and positively influences soil water retention \n\n\n\n(Panagea et al. 2021; Araya et al. 2022). Subsequently, the subsurface soil acts as a buffer zone \n\n\n\nto capture a large amount of water especially during intense rainfall before it infiltrates deep \n\n\n\ninto the soil, and diminishes slope runoffs and erosion, ultimately. \n\n\n\n\n\n\n\nIn our view, the advantages  of our selected pioneers in this study responded to the \n\n\n\nchanges in soil water availability and continuously served the slope hydrological system via \n\n\n\nenhancement of soil infiltration capacity, soil structural dynamics, and soil water retention \n\n\n\ncapacity, leading to a reduced soil erosion rate (Li et al. 2014; Zhang et al. 2021). Moreover, \n\n\n\nwe found that all the parameters were inextricably linked to each other (Table 3), suggesting \n\n\n\nthat the inclusion of these parameters in slope hydrological studies is practical and  effective in \n\n\n\nsoil hydrological performance in alleviating erosion in slopes \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nOur study showed that dense re-vegetation with suitable pioneers on tropical compacted and \n\n\n\ninfertile sandy textured soils has a  strong positive impact on water storage and fluxes in these \n\n\n\nsoils. Based on the correlation analysis, three main components were found to influence the \n\n\n\nsoil hydrological condition and erosion performance of the studied area: soil infiltration \n\n\n\ncapacity, soil structural dynamics, and soil water retention capacity. Our findings provide \n\n\n\nhelpful inputs that may enable slope operators to explore and predict the outcome of re-\n\n\n\nvegetation efforts aimed at enhancing slope hydrological functions and erosion reduction. \n\n\n\nFinally, since the identified key components in this study are specific to the investigated slope \n\n\n\nand the outputs might differ due to slope condition variations (e.g. parent material, topography, \n\n\n\nand microclimatic conditions), further studies should be conducted on a broader range of slope \n\n\n\nconditions such as variable slope angles and different plant types to understand their \n\n\n\ninvolvement in enhancing cut slope restoration and erosion prevention. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nAll authors have no conflict of interest to disclose. This work was supported by the University \n\n\n\nof Malaya Research Grant (UMRG-RP005A-13SUS & RP005D-13SUS). 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"\n\n___________________\n*Corresponding author : E-mail: szaharah@upm.edu.my \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 15-27 (2021)                          Malaysian Society of Soil Science\n\n\n\nImpact of Soil Compaction on Soil Physical Properties and \nPhysiological Performance of Sweet Potato\n\n\n\n(Ipomea batatas L.)\n\n\n\nUmaru, M.A.1,2, Adam, P.1, Zaharah, S.S.1,3 and Daljit, S.K.4\n\n\n\n1Department of Crop Science, Faculty of Agriculture, UPM Serdang, Selangor, \nMalaysia\n\n\n\n2Department of Agriculture, Hassan Usman Katsina Polytechnic, PMB 5026 \nKatsina, Nigeria \n\n\n\n3Institute of Tropical Agriculture & Food Security (ITAFoS), UPM Serdang, \nSelangor, Malaysia\n\n\n\n4Department of Land Management, Faculty of Agriculture, UPM Serdang, Selangor, \nMalaysia\n\n\n\nABSTRACT\nSweet potato is the most important food crop after wheat, rice, maize and \ncassava. Soil compaction degrades soil by altering its structure and aggregate, \nthereby causing poor plant-water relationship. This study aimed to determine the \neffect of soil compaction on some soil physical properties and eco-physiological \ncharacteristics of sweet potato. Prior to planting and after harvest, soil bulk density \nand moisture content were determined.  For the eco-physiological measurements, \nthe treatments tested were assembled in a factorial combination of three levels of \nsoil compaction as main plots and three varieties in the sub-plots. The treatments \nwere arranged in a split plot design and replicated four times. Gas exchange \nparameters, leaf area index and chlorophyll content were subsequently determined. \nThe results showed that soil compaction significantly decreased plant chlorophyll \ncontent, leaf area index and gas exchange parameters. On tropical sandy loam \nsoils, tilling the soil once was sufficient for optimum emergence and establishment \nof a sweet potato. Gendut proved to be a tolerant variety, suitable to be planted in \nenvironments prone to compaction stress.\n\n\n\nKeyword:    Bulk density, compaction, physiology, soil and sweet potato.\n\n\n\nINTRODUCTION\nSweet potato (Ipomoea batatas L.) is a root crop grown in the tropics and subtropical \nregions. The crop does best in well-drained sandy loam or loamy soils and does \npoorly on clay soils especially when flooded (Neduchezhiyan and Raya, 2010). \nUnder water logged conditions, the roots might rot and problems of aeration may \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202118\n\n\n\narise thereby leading to poor plant growth and a significant reduction in yield.  \nConsidering the environmental requirements of the crop, Peninsular Malaysia is \nsuitable for the commercial production of this commodity; surprisingly however, \nsweet potato is being considered among the minor crops in the area (Tan et al., \n2007).\n An ideal soil has sufficient water content, adequate aeration, easy root \npenetration, good anchorage and is rich enough to provide sufficient nutrients \nto the plant. Soil is said to be compacted when it undergoes changes owing to \nalteration of soil structure and soil aggregate. Soil compaction is an abiotic stress \nthat restricts plant growth and is a serious menace globally (Ramazan et al., 2012). \nWhen soil experiences compression from external forces (farm machinery, farm \nworkers, animals etc.) the distance/volume of the pore spaces becomes reduced  \n(Grzesiak et al. 2013). The exerted force then causes an increase in soil density, a \nphenomenon referred to as soil compaction (Abu-Hamdeh 2003). Advancements \nin mechanisation and increased usage of chemical fertilisers coupled with heavy \nrains and intensive cropping practices are the main causes of soil compaction. \nThe consequence is alteration in soil structure, thereby leading to increased soil \nbulk density, penetration resistance and decreased total porosity, all of  which are \nsuggested indices of soil compaction (H\u00e5kansson and Lipiec 2000; Abu-Hamdeh \n2003; Huang et al. 2012). \n Soil bulk density (SBD) is a basic physico-chemical characteristic of the \nsoil and this includes soil texture and structure, presence of organic matter etc. \nChaudhari et al. (2013) reported normal ranges of soil bulk density as 1.0 to 1.6 \nmg/m3 and 1.2 to 1.8 mg/m3 for clay and sand soil particles, respectively and \u2265 \n1.4 mg/m3 and \u2265 1.6 mg/m3 as potential values capable of restricting root growth \nin the respective mediums. Sand has been reported to significantly and positively \ncorrelate with soil bulk density while clay negatively (Chaudhari et al. 2013). \nConsequent to high levels of compaction, slow seed emergence, poor seedling \nestablishment, thin stands, uneven growth and poor yield in terms of quantity and \nquality occur in plants (Grzesiak et al. 2013).\n Limitations in plant growth and physiological performance mainly occur \nowing to retardation of plant root growth by compaction. Restriction in root growth \nand penetration and subsequent retarded physiological activities of the plant is \nattributed to poor plant-water relationship and shoot development. Changes in \nroot architecture under varying levels of compaction are always accompanied \nby changes in physiological parameters. Leaf water potential (LWP) of maize \nand triticale has been reported to decrease with increased soil compaction. Water \ndeficit in leaf owing to compaction inhibits various physiological processes. Soil \ncompaction is also responsible for low stomatal conductance and consequently \nreduced carbon supply. Several studies shown that changes in LWP in response to \nstress prompt stomatal closure and results in rising LWP and slower transpiration \n(Cornic and Massacci, 1991; Chen and Weil, 2011; Grzesiak et al., 2013). Stomatal \nclosure is responsible for water content in leaf tissue in a short stress period. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 19\n\n\n\nHowever, when stress is prolonged, changes in chlorophyll, chloroplast and in the \naccumulation and distribution of assimilates occur (Medrano et al. 2002). \n Advancements in mechanization and increased usage of chemical \nfertilizers, not only by the large  but also small scale farmers, coupled with year-\nround rains and practice of intensive cropping makes Malaysia more prone to soil \ncompaction problems. This problem therefore indicates the need for continuous \nsoil compaction studies in the area. The effect of soil compaction on bulk density \nand water content of the soil has been studied but only a few were found in \nrelation to sweet potato production. In Malaysia, there exists, however, quite \na few field studies on response of physiological performance of sweet potato \nvarieties to abiotic stress of soil compaction. As research in the management \nof soil physical properties in sweet potato field is lacking, this study therefore \naimed to compare the temporal variation of soil bulk density, and soil moisture \ncontent with varying levels of soil compaction and its subsequent influence on \nphysiological performance of sweet potato. \n\n\n\nMATERIALS AND METHODS\nExperimental Site\nAn experiment was conducted at Field 15 (L15) and University Agriculture Park \n(TPU), Universiti Putra Malaysia (UPM). The climate of the area is equatorial in \nnature, being hot and humid throughout the year. The average annual temperature, \nrelative humidity and rainfall are 27\u00b0C, 90% and 250 cm respectively. The \nprevailing wind patterns are South-east monsoon and North-east monsoon that \nblow from May to September and November to March and these influence light \nand heavy rainfall in the areas respectively (Malaysian Meteorology Department, \n2016).\n Prior to the set-up of the experiment, soil samples were taken from both \nexperimental sites at a depth of 0 to 30 cm before and after land preparation \n(according to the dictates of the experimental design). A tubular auger was driven \nmanually into the soil by using a hammer. Samples collected were wrapped \nin plastic bags and taken to the laboratory for analysis. In the laboratory the \nsamples were bulked, air dried, sieved (using 2-mm mesh) and analyzed for its \ncomposite (type) and physical and chemical properties using standard procedures \nas described by Carter (1993). \n Prior to sample collection, a tractor (Massey Ferguson 5340 MODEL) \nwas run four times so as to increase the compaction intensity in the experimental \nsites. Three levels of soil compaction (no till, tilled once and tilled twice) as \ndictated by the experimental design were performed during land preparation. For \npurposes of this study, soil bulk density was used as soil compaction index; this \nwas arrived at by decreasing soil mass for the same volume. Hence, tillage was \nused in modifying and varying the degree of compactness. The treatments were \narranged in a completely randomised design with four replications. A total of nine \nsamples were taken at depths of 0-15cm and 15-30 cm per replication i.e. three \nsamples per plot. Unit plot size was 4.8 m x 3 m (14.4 m2). The samples collected \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202120\n\n\n\nwere used in the determination of soil bulk density by using gravimetric method \n(Materechera et al., 1991). The same procedure was repeated after harvest. \nSoil moisture content was determined by using soil moisture sensor attached to \n(Eijkelkamp penetrometer 06.15.31) Eielkamp Agricserch Equipment, 6987EM \nGiesbee. Moisture values were automatically generated from a chart recording \non the Eijkelkamp Stiboka penetrograph when the penetrometer was held in a \nvertical position and pressure was manually applied to the penetrometer handle.\n For the eco-physiological measurements, the treatments tested were \nassembled in a factorial combination of three levels of soil compaction (untilled, \ntilled once and tilled twice) on three varieties of sweet potatoes (Gendut, Vitato \nand Kedudut). The treatments were arranged in a split plot design and replicated \nfour times. In these experiments, tillage was used in modifying and varying \nthe degrees of compactness. Photosynthesis, gas exchange rate (carbon dioxide \nand oxygen) and stomata resistance and conductance were determined using a \nportable photosynthesis system (LI-6400XT Li-cor, Nebraska, USA). This was \ndone at 9 WAP at a day when the sky was cloud clear. Leaf area index (LAI) was \ndetermined by using a plant canopy analyser (LI-COR 2200) that has a probe \nconnected to a meter. For each sampled plant, the probe was held at four locations \naround it and measurements taken. At same period the chlorophyll content of \nthe leaf was measured using a portable chlorophyll meter (SPAD 502-MINOLTA \nInc.). From the sampled plants, two mature leaves were selected and clamped into \na hand held probe that gave the estimated values of chlorophyll presence in the \nplant. The mean values of the physiological measurements were calculated and \nrecorded. \n\n\n\nData Analysis \nThe generated data from this experiment were subjected to Analysis of Variance \n(ANOVA) using SAS version 9.4. The mean values were compared using the \nLeast Significant Difference (LSD) test of mean separation at 95% significance \nlevel (Gomez and Gomez, 1984). Linear correlations were calculated to determine \nthe existing relationships between the variables.\n\n\n\nRESULTS AND DISCUSSION\nSoil Compaction and Temporal Variation of Soil Bulk Density (SBD) and Moisture \nContent\nAt both sampling periods (before planting and after harvesting) and both locations, \nthe results showed that soil compaction had a significant effect on SBD (Table  and \n2). It is pertinent to note that there was an increase in SBD with time. Locations \nsubjected to treatments of one and two tilling saw 15.4% and 24.2% decrease in \nbulk density. The low SBD values (ranging from 1.49 to 1.59 g/cm3) observed at \nL15 could be the result of formation of good soil aggregation caused by relatively \nhigh (1.85%) organic matter content and a high (80.15%) silt proportion. The \nhigher values of SBD at TPU could possibly be attributed to filling up of pore \nspaces by migrating clay particles in the soil horizon. The decreased soil bulk \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 21\n\n\n\ndensity in tilled treated plots as observed in this experiment could be as the result \nof loosening effect of tillage. This observation is in conformity with Otto et al. \n(2011) and Huang et al. (2012) who reported decreased soil bulk density with \nvarying compaction treatments using tillage practices. The increased SBD (ranging \nfrom 1.59 to 1.62 g/cm3) at/after harvest could be the result of frequent rains \nimpacting the soil surface and the movement of the farm workers. The increased \ncompaction was quite  obvious as earlier reported by several researchers that a \nforce as low as 1.81 kg/m2 and trampling of soil by grazing animals in the course \nof movement can cause significant soil compaction (Maciej, 2009; Adekiya et al., \n2011;  Ramazan et al., 2013).\n Soil compaction treatment had a significant effect on soil moisture content \n(SMC) (Table 2). The SMC at L15 was significantly higher (8.66% and 12.12% \nbefore and after planting respectively) than that at TPU. At both locations, it was \nobserved that the SMC of the first sampling period was higher than that of the \nsecond sampling period (Table 2). The no till treatments had the highest SMC \n(ranging from 13.64% to 63.64%) before planting which, however, declined (in \nthe range of 59.46% to 68.35%) at harvest. However, the SMC of tilled once \nand tilled twice treatments were at par after harvest. The presence of higher soil \nmoisture content prior to planting in no till plots could possibly be due to the \nprotective covering it received from the topmost layer of the soil surface. The \nupper layer in most cases is dry soil with low conductivity and hence, capable of \nreducing evaporation losses (Adekiya et al., 2011). Unlike the tilled soils which \nwere exposed to evaporation losses, the no till plots had their organic matter \nintact on the soil surface and thus acted as mulch by reducing evaporation losses. \nHowever, the decrease in soil moisture content values in no till plots, at/after \nharvest could be due to moisture uptake by the growing crop, coupled with slow \ninfiltration of water into the soil to replace the quantity already absorbed by the \n\n\n\nTABLE 1 \n\n\n\nPysico-chemical properties of the experimental sites at UPM, Serdang, Selangor, Malaysia \n\n\n\n Location \nLocation Ladang 15 TPU \nProperties   \nSand (%) 34.5 \u00b1 0.42b 71.1 \u00b1 1.06a \nSilt (%) 15.23 \u00b1 0.38b 17.83 \u00b1 0.34a \nClay (%) 50.54 \u00b1 1.03a 10.03 \u00b1 0.03b \nPH 4.67 \u00b1 0.02a 5.20 \u00b1 0.02a \nOrganic matter content 1.51 \u00b1 0.02a 1.50 \u00b1 0.02a \nCarbon (%) 1.38 \u00b1 0.02a 1.40 \u00b1 0.01a \nNitrogen (%) 0.13 \u00b1 0.06a 0.10 \u00b1 0.04b \nPhosphorus (\u00b5g/g) 8.3a \u00b1 0.19 18.13 \u00b1 0.07b \nPotassium  (\u00b5g/g) 41.27 \u00b1 1.62a 28.74 \u00b1 0.94b \nCalcium  (\u00b5g/g) 26.82 \u00b1 1.12b 62.52 \u00b1 0.88a \n   \nNote: Means with similar letters are not significantly different (p<0.05); NS = not significant at p \n(0.005) \n \n\n\n\n\n\n\n\nTABLE 1\nPysico-chemical properties of the experimental sites at UPM, Serdang, Selangor, \n\n\n\nMalaysia\n\n\n\nNote: Means with similar letters are not significantly different (p<0.05); \n          NS = not significant at p (0.005)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202122\n\n\n\nplant owing to compaction pressure. The reduction in soil moisture content on \ntilled plots before planting could possibly be attributed to exposure of the soil \nto evaporation activities. The rapid moisture loss could be due to the resultant \nincrease in turbulent movement of atmospheric air into the soil (Ojeniyi and \nDexter, 1979). \n   \nEffect of Soil Compaction and Variety on Physiological Characteristics of Sweet \nPotato\nThis study showed that soil compaction had a significant effect on the entire \nobserved physiological characters of sweet potato. At TPU, the net photosynthetic \nrate (NPR) and chlorophyll content were significantly reduced, ranging from 6.52% \n\n\n\nTABLE 2\nTemporal variation of soil bulk density and moisture content with compaction at L15 \n\n\n\nand TPU, UPM, Sri Serdang, Selangor, Malaysia\n                                                               Location \nParameter Treatment Ladang  15 TPU Combined \n\n\n\n  Before \nplanting \n\n\n\nAfter \nharvest \n\n\n\nBefore \nplanting \n\n\n\nAfter \nharvest \n\n\n\nBefore \nplanting \n\n\n\nAfter \nharvest \n\n\n\nSoil bulk \ndensity \n\n\n\nLocation(L)       \n\n\n\n Ladang 15     1.30b 1.39b \n TPU     1.35a 1.44a \n P-value     0.0246 0.0001 \n No till 1.49a 1.59a 1.50a 1.62a 1.50a 1.61a \n Tilled once 1.28b 1.34b 1.33b 1.44b 1.31b 1.39b \n Tilled twice 1.14c 1.23c 1.21c 1.25c 1.17c 1.24c \n P-value \u22640.0001 \u22640.0001 \u22640.0001 \u22640.0001 \u22640.0001 0.0137 \n SE \u00b10.10 \u00b10.11 \u00b10.08 \u00b10.11 \u00b10.10 \u00b10.11 \n  \n\n\n\n(L*T) \n      \n\n\n\n    0.2978 0.1447 \n CV (%) 3.01 2.20 3.68 2.52 3.27 3.005 \n        \nSoil \nmoisture \ncontent \n\n\n\n\n\n\n\n Ladang 15     31.75a  33a \n TPU     29.00b 29b \n P-value     0.0482 0.0123 \n No till 44.00a 15.50b 39.00a 12.50c 42.00a 14.0b \n Tilled once 38.00b 37.00a 32.00b 34.75b 35.00b 35.88a \n Tilled twice 16.00c 42.75a 15.00c 39.5a 16.00c 14.25b \n P-value \u22640.0001 0.0001 \u22640.0001 0.0001 \u22640.0001 0.0001 \n SE \u00b18.51 \u00b18.29 \u00b17.13 \u00b18.32 \u00b17.77 \u00b17.25 \n (L*T)       \n      0.5216 0.9628 \n CV (%) 11.19 12.59 8.41 8.488 10.31 9.63 \n\n\n\n Note: Means with similar letters are not significantly different (p<0.05); NS = not \n           significant at p (0.005)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 23\n\n\n\nto 16.31% and 30.29% to 57.45% with increased soil compaction respectively; \nwhereas transpiration rate increased (Table 3). Water vapour potential decreased \nsignificantly (ranging from 35.58% to 36.79%) owing to an increased level of \ncompaction; however, this was only at L15 (Table 4). At both locations, soil \ncompaction had a significant effect on stomata conductance and LAI (Tables 3 \nand 4 respectively). These were reduced in the range of 12.50% to 36.79% and \n22.93% to 24.34% respectively. Transpiration rate was however, significantly \ndecreased (from 5.50% to 12.67%) with increased soil compaction.  The decrease \nin NPR could be associated with reduced carbon supply due to a decrease in \nstomata conductance and decreased transpiration (Tun and Tan,1988). Reduced \nmoisture supply affects LAI negatively and this could be another reason for lower \nNPR. The lower chlorophyll content observed could be due to lower uptake of \nnitrogen in the soil horizon by the restricted roots. Decreased chlorophyll might \nbe one of the factors responsible for the observed decrease in NPR. Insufficient \nchlorophyll has been earlier reported (Liu, 2011; Kobaissi et al., 2013) to limit the \nphotosynthetic potentials of a plant. From the correlation studies, it was observed \nthat soil compaction had a strong but negative relationship with leaf area index \n(-0.92), chlorophyll content (-0.95), water vapour potential (-0.90), NPR (-0.46) \nand stomata conductance (-0.45) (Table 5). The observed decrease in these \nparameters could subsequently affect plant productivity negatively. However, soil \ncompaction and transpiration rate correlated positively (Table 5). This implies \nthat transpiration rate tends to increase with increased soil compaction. Despite \nthe fact that photosynthesis and stomatal conductance declined with increased \nsoil compaction, they had no significant relationship with compaction in the \nsoil (Table 5). Contrary to this finding, Kobaissi et al. (2013)  observed a strong \nrelationship between soil compaction on one hand and photosynthesis and \nstomatal conductance on the other. However, this was in crops other than sweet \npotato and on soils with higher values of SBD than our experimental sites. \n Plant variety had a significant effect on NPR, LAI (Table 3), WVP and \nchlorophyll content (Table 4). The highest and the lowest mean values of NPR \nand LAI were observed in Gendut and Kedudut respectively. Gendut could \npossibly perform better than Kedudut due to higher values of  LAI and NPR. \nVitato, however, had the highest mean chlorophyll content. The effects of variety \non stomatal conductance and TR were, however, not significant at both locations \n(Table 4). This indicates that the ability of carbon uptake and maintenance of \nthe leaf water potential of the tested varieties are similar. However, there was no \nsignificant interaction effect between soil compaction and variety on chlorophyll \ncontent, stomatal conductance and transpiration rate (Tables 3 and 4 respectively). \nOn the other hand, there was a significant interaction effect between soil compaction \nand variety on NPR and LAI at only TPU (Table 3 and 4, respectively). At L15, \nhowever, soil compaction and variety had a significant interaction effect on water \nvapour potential (Table 4). This means that the performance of the observed \ncharacteristics were a result of the combined effect between soil compaction and \nvarietal treatments.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202124\n\n\n\nTABLE 3\nEffect of soil compaction and variety on photosynthesis, stomatal conductance and \n\n\n\ntranspiration rate of three varieties of sweet potato at L15 and TPU, UPM, Sri Serdang, \nSelangor, Malaysia\n\n\n\nNote: Means with similar letters are not significantly different (p<0.05); NS = not \n          significant at p (0.005)\n\n\n\nParameters Photosynthesis Stomatal conductance Transpiration rate \n\n\n\nLocation L15 TPU Combined L15 TPU Combined L15 TPU Combined       \n\n\n\nL15   24.28a   0.93   4.70 \nTPU   15.71b   1.26   5.64 \nP-value   0.0020   0.2520   0.1150 \nLSD   0.403   NS   NS \nSE   \u00b14.29   \u00b10.93   \u00b10.47 \nTillage(T)          \n\n\n\n  No till 23.73 14.34c 19.07b 0.67b 0.98b 1.09 4.73 6.00a 5.20 \n Tilled once 25.64 17.14a 20.49a 1.06a 1.38a 1.17 4.83 5.67a 5.14 \n Tilled twice 23.47 15.34b 20.44a 1.04a 1.12a 1.03 4.54 5.24b 5.17 \n P-Value 0.1225 \u22640.0001 0.0126 \u2264.0001 0.0535 0.6393 0.6132 0.0516 0.9786 \n LSD NS 0.03 1.04 0.05 0.09 NS NS 0.57 NS \n SE \u00b10.68 \u00b10.82 \u00b10.47 \u00b10.13 \u00b10.12 \u00b10.04 \u00b10.09 \u00b10.22 \u00b10.02 \nVariety (V)          \n\n\n\n Gendut 23.18b 16.97a 20.08a 1.23 0.89 1.13ab 5.47 4.58 5.20 \n Kedudut 26.36a 15.62b 20.99a 1.11 0.86 1.27a 5.76 4.93 5.41 \n Vitato 23.30b 14.54c 18.92b 1.22 0.93 0.88b 5.69 4.58 4.89 \n P-Value \n \n\n\n\n0.0088 \u2264.0001 0.0011 0.0569 0.4234 0.0312 0.1672 0.3211 0.2458 \n\n\n\nSE \u00b11.06 \u00b10.70 \u00b10.33 \u00b10.04 \u00b10.02 \u00b10.04 \u00b10.09 \u00b10.12 \u00b10.15 \n\n\n\nLSD 2.15 0.03 1.04 NS NS 0.32 NS NS NS \n T*V          \np-value 0.1598 \n\n\n\n \n\u22640.0001 \n \n\n\n\n\u22640.0001 0.3605 0.3489 0.0997 0.2542 0.1762 0.6830 \n\n\n\nLSD  NS * NS NS NS NS NS NS NS \nCV (%) 10.32 0.20 8.86 16.87 17.74 45.44 12.51 52.90 20.44 \n\n\n\n \nNote: Means with similar letters are not significantly different (p<0.05); NS = not significant at p \n(0.005) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 25\n\n\n\nTABLE 4\nEffect of soil compaction and variety on water vapour potential, leaf area index and \nchlorophyll of three varieties of sweet potato at L15 and TPU, UPM, Sri Serdang, \n\n\n\nSelangor, Malaysia\n\n\n\nNote: Means with similar letters are not significantly different (p<0.05); NS = not \n          significant at p (0.005)\n\n\n\nParameters Water vapour potential \n\n\n\n\n\n\n\nLeaf Area Index Chlorophyll content \n\n\n\nLocation L15 TPU Combine\nd \n\n\n\nL15 TPU Combined L15 TPU combined  Combine\nd \n\n\n\n\n\n\n\nL15   0.50b   8.33a   24.75 \nTPU   0.76a   6.49b   25.92 \nP-value   0.0063   \u22640.0001   0.5617 \nLSD   0.13   0.34   NS \nSE   \u00b10.13   \u00b10.92   \u00b10.59 \nTillage(T)          \n\n\n\n No till 0.67b 1.21 0.67 6.87b 5.41b 6.14b 18.67 16.25b 17.46c \nTilled once 1.06a 1.47 0.56 9.06a 7.03a 8.04a 26.54 23.31b 24.92b \n Tilled \ntwice \n\n\n\n1.04a 1.09 0.66 9.08a 7.02a 8.05a 29.05 38.19a 33.62a \n\n\n\n P-Value \u2264.0001 0.3489 0.0717 0.0003 0.0042 \u2264.0001 0.0722 0.0130 0.0009 \nLSD 0.05 NS NS 0.66 0.82 0.47 NS 12.41 6.86 \nSE \u00b10.13 \u00b10.11 \u00b10.04 \u00b10.73 \u00b10.54 \u00b10.64 \u00b13.13 \u00b16.47 \u00b14.67 \nVariety (V)          \n Gendut 0.98a 1.21 0.66ab 8.72a 7.12a 7.92a 24.40a 28.50a 27.45a \n Kedudut 0.93a 1.35 0.56b 7.77b 5.28b 6.53b 20.19b 20.46b 20.33b \n Vitato 0.86b 1.21 0.68a 8.50a 7.07a 7.78a 27.67a 28.78a 28.23a \nP-Value 0.0424 0.8457 0.0544 0.0023 \u22640.0001 \u22640.0001 0.0037 \u22640.0001 \u22640.0001 \nLSD 0.10 NS 0.11 0.50 0.22 0.26 4.28 3.27 2.60 \nSE \u00b10.03 \u00b10.05 \u00b10.04 \u00b10.29 \u00b10.61 \u00b10.44 \u00b12.16 \u00b12.73 \u00b12.51 \nT*V          \np-value \u2264.0001 0.4554 0.3617 0.4407 0.0299 0.2402 0.7868 0.2638 0.3227 \nLSD  * NS NS NS * NS NS NS NS \nCV (%) 12.51 52.90 27.05 7.02 3.93 6.09 20.15 14.72 17.53 \n\n\n\nNote : Means with similar letters are not significantly different (p<0.05); NS = not significant at p \n(0.005) \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202126\n\n\n\nTA\nB\n\n\n\nLE\n 5\n\n\n\n\n\n\n\nC\nor\n\n\n\nre\nla\n\n\n\ntio\nn \n\n\n\nco\nef\n\n\n\nfic\nie\n\n\n\nnt\n b\n\n\n\net\nw\n\n\n\nee\nn \n\n\n\nso\nil \n\n\n\nco\nm\n\n\n\npa\nct\n\n\n\nio\nn \n\n\n\nan\nd \n\n\n\nph\nys\n\n\n\nio\nlo\n\n\n\ngi\nca\n\n\n\nl c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\ns o\n\n\n\nf t\nhr\n\n\n\nee\n v\n\n\n\nar\nie\n\n\n\ntie\ns o\n\n\n\nf \nsw\n\n\n\nee\nt p\n\n\n\not\nat\n\n\n\no \nat\n\n\n\n L\n15\n\n\n\n a\nnd\n\n\n\n T\nPU\n\n\n\n fi\nel\n\n\n\nds\n, U\n\n\n\nni\nve\n\n\n\nrs\niti\n\n\n\n P\nut\n\n\n\nra\n M\n\n\n\nal\nay\n\n\n\nsi\na \n\n\n\n  \nSo\n\n\n\nil \nbu\n\n\n\nlk\n \n\n\n\nde\nns\n\n\n\nity\n \n\n\n\nPh\not\n\n\n\nos\nyn\n\n\n\nth\nes\n\n\n\nis\n \n\n\n\nra\nte\n\n\n\n\n\n\n\nSt\nom\n\n\n\nat\nal\n\n\n\n \nco\n\n\n\nnd\nuc\n\n\n\nta\nnc\n\n\n\ne \nTr\n\n\n\nan\nsp\n\n\n\nira\ntio\n\n\n\nn \n \n\n\n\nR\nat\n\n\n\ne \n\n\n\nW\nat\n\n\n\ner\n \n\n\n\nva\npo\n\n\n\nur\n \n\n\n\npo\nte\n\n\n\nnt\nia\n\n\n\nl \nLe\n\n\n\naf\n a\n\n\n\nre\na \n\n\n\nin\nde\n\n\n\nx \nC\n\n\n\nhl\nor\n\n\n\nop\nhy\n\n\n\nll \nco\n\n\n\nnt\nen\n\n\n\nt \nSo\n\n\n\nil \nbu\n\n\n\nlk\n \n\n\n\nD\nen\n\n\n\nsi\nty\n\n\n\n \n1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPh\not\n\n\n\nos\nyn\n\n\n\nth\nes\n\n\n\nis\n \n\n\n\nR\nat\n\n\n\ne \n-0\n\n\n\n.4\n57\n\n\n\n* \n \n\n\n\n\n\n\n\n\n\n\n\n \nSt\n\n\n\nom\nat\n\n\n\nal\n \n\n\n\nC\non\n\n\n\ndu\nct\n\n\n\nan\nce\n\n\n\n \n-0\n\n\n\n.4\n50\n\n\n\n* \n0.\n\n\n\n99\n9*\n\n\n\n* \n1 \n\n\n\n\n\n\n\n\n\n\n\nTr\nan\n\n\n\nsp\nira\n\n\n\ntio\nn \n\n\n\nra\nte\n\n\n\n \n0.\n\n\n\n98\n2*\n\n\n\n* \n-0\n\n\n\n.2\n80\n\n\n\nN\nS \n\n\n\n-0\n.2\n\n\n\n73\nN\n\n\n\nS \n1 \n\n\n\n\n\n\n\n \nW\n\n\n\nat\ner\n\n\n\n v\nap\n\n\n\nor\n \n\n\n\npo\nte\n\n\n\nnt\nia\n\n\n\nl \n-0\n\n\n\n.8\n99\n\n\n\n**\n \n\n\n\n0.\n80\n\n\n\n1*\n* \n\n\n\n0.\n79\n\n\n\n6*\n* \n\n\n\n-0\n.7\n\n\n\n99\n**\n\n\n\n \n1 \n\n\n\n\n\n\n\nLe\naf\n\n\n\n a\nre\n\n\n\na \nin\n\n\n\nde\nx \n\n\n\n-0\n.9\n\n\n\n20\n**\n\n\n\n \n0.\n\n\n\n77\n0*\n\n\n\n* \n0.\n\n\n\n76\n5*\n\n\n\n* \n-0\n\n\n\n.8\n28\n\n\n\n**\n \n\n\n\n0.\n99\n\n\n\n8*\n* \n\n\n\n1 \n \n\n\n\nC\nhl\n\n\n\nor\nop\n\n\n\nhy\nll \n\n\n\nco\nnt\n\n\n\nen\nt \n\n\n\n-0\n.9\n\n\n\n50\n**\n\n\n\n \n0.\n\n\n\n15\n7N\n\n\n\nS \n0.\n\n\n\n14\n8N\n\n\n\nS \n-0\n\n\n\n.9\n92\n\n\n\n**\n \n\n\n\n0.\n71\n\n\n\n6*\n* \n\n\n\n0.\n75\n\n\n\n0*\n* \n\n\n\n1 \nN\n\n\n\not\ne:\n\n\n\n *\n a\n\n\n\nnd\n *\n\n\n\n* \nco\n\n\n\nrr\nel\n\n\n\nat\ne \n\n\n\nat\n p\n\n\n\n<0\n.0\n\n\n\n5 \nan\n\n\n\nd \np<\n\n\n\n 0\n.0\n\n\n\n1 \nre\n\n\n\nsp\nec\n\n\n\ntiv\nel\n\n\n\ny \n\n\n\nC\nO\n\n\n\nN\nC\n\n\n\nL\nU\n\n\n\nSI\nO\n\n\n\nN\n \n\n\n\n A\ns \n\n\n\nth\ne \n\n\n\nso\nil \n\n\n\nat\n T\n\n\n\nPU\n w\n\n\n\nas\n m\n\n\n\nor\ne \n\n\n\nco\nm\n\n\n\npa\nct\n\n\n\ned\n a\n\n\n\nnd\n d\n\n\n\nrie\nr, \n\n\n\nit \nha\n\n\n\nd \nhi\n\n\n\ngh\ner\n\n\n\n m\nec\n\n\n\nha\nni\n\n\n\nca\nl i\n\n\n\nm\npe\n\n\n\nda\nnc\n\n\n\ne \nth\n\n\n\nan\n th\n\n\n\nat\n o\n\n\n\nf \n\n\n\nL1\n5.\n\n\n\n T\nill\n\n\n\ned\n tr\n\n\n\nea\nte\n\n\n\nd \npl\n\n\n\not\ns \n\n\n\ndi\nd \n\n\n\nno\nt o\n\n\n\nnl\ny \n\n\n\nre\ndu\n\n\n\nce\n s\n\n\n\noi\nl c\n\n\n\nom\npa\n\n\n\nct\nio\n\n\n\nn \nbu\n\n\n\nt a\nls\n\n\n\no \npr\n\n\n\nom\not\n\n\n\ned\n g\n\n\n\noo\nd \n\n\n\nro\not\n\n\n\n p\nen\n\n\n\net\nra\n\n\n\ntio\nn \n\n\n\nan\nd \n\n\n\nw\nat\n\n\n\ner\n in\n\n\n\nfil\ntra\n\n\n\ntio\nn \n\n\n\nra\nte\n\n\n\ns \nw\n\n\n\nith\nin\n\n\n\n th\ne \n\n\n\npo\nre\n\n\n\n p\nar\n\n\n\ntic\nle\n\n\n\ns. \nTh\n\n\n\ne \nph\n\n\n\nys\nio\n\n\n\nlo\ngi\n\n\n\nca\nl p\n\n\n\ner\nfo\n\n\n\nrm\nan\n\n\n\nce\n o\n\n\n\nf \nsw\n\n\n\nee\nt p\n\n\n\not\nat\n\n\n\no \n\n\n\nw\nas\n\n\n\n a\ndv\n\n\n\ner\nse\n\n\n\nly\n in\n\n\n\nflu\nen\n\n\n\nce\nd \n\n\n\nby\n s\n\n\n\noi\nl c\n\n\n\nom\npa\n\n\n\nct\nio\n\n\n\nn.\n H\n\n\n\now\nev\n\n\n\ner\n, t\n\n\n\nhe\n e\n\n\n\nff\nec\n\n\n\nt w\nas\n\n\n\n n\not\n\n\n\n c\non\n\n\n\nsi\nst\n\n\n\nen\nt w\n\n\n\nith\n v\n\n\n\nar\nie\n\n\n\ntie\ns. \n\n\n\nTh\ne \n\n\n\nG\nen\n\n\n\ndu\nt v\n\n\n\nar\nie\n\n\n\nty\n h\n\n\n\nad\n th\n\n\n\ne \nhi\n\n\n\ngh\nes\n\n\n\nt a\nbi\n\n\n\nlit\ny \n\n\n\nto\n a\n\n\n\nda\npt\n\n\n\n to\n c\n\n\n\nom\npa\n\n\n\nct\ned\n\n\n\n s\noi\n\n\n\nls\n. I\n\n\n\nn \nor\n\n\n\nde\nr t\n\n\n\no \npr\n\n\n\nom\not\n\n\n\ne \ngo\n\n\n\nod\n c\n\n\n\nro\np \n\n\n\nTA\nB\n\n\n\nLE\n 5\n\n\n\nC\nor\n\n\n\nre\nla\n\n\n\ntio\nn \n\n\n\nco\nef\n\n\n\nfic\nie\n\n\n\nnt\n b\n\n\n\net\nw\n\n\n\nee\nn \n\n\n\nso\nil \n\n\n\nco\nm\n\n\n\npa\nct\n\n\n\nio\nn \n\n\n\nan\nd \n\n\n\nph\nys\n\n\n\nio\nlo\n\n\n\ngi\nca\n\n\n\nl c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\ns o\n\n\n\nf t\nhr\n\n\n\nee\n v\n\n\n\nar\nie\n\n\n\ntie\ns o\n\n\n\nf s\nw\n\n\n\nee\nt p\n\n\n\not\nat\n\n\n\no \nat\n\n\n\n L\n15\n\n\n\n a\nnd\n\n\n\n T\nPU\n\n\n\n fi\nel\n\n\n\nds\n, \n\n\n\nU\nni\n\n\n\nve\nrs\n\n\n\niti\n P\n\n\n\nut\nra\n\n\n\n M\nal\n\n\n\nay\nsi\n\n\n\na\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 27\n\n\n\nCONCLUSION\nAs the soil at TPU was more compacted and drier, it had higher mechanical \nimpedance than that of  L15. Tilled treated plots did not only reduce soil compaction \nbut also promoted good root penetration and water infiltration rates within the \npore particles. The physiological performance of sweet potato was adversely \ninfluenced by soil compaction. However, the effect was not consistent with \nvarieties. The Gendut variety had the highest ability to adapt to compacted soils. \nIn order to promote good crop establishment, there is therefore a need to carry out \ntillage on agricultural lands before planting. On tropical sandy loam soils, tilling \nthe soil once is sufficient for optimum emergence and establishment. Gendut \nwhich proved to be a better tolerant variety should be planted in environments \nprone to compaction stress.  \n\n\n\nREFERENCES\nAbu-hamdeh, N.H. 2003. Soil compaction and root distribution for okra as affect by \n\n\n\ntillage  and vehicle parameters. Soil Till. Res. 74: 25-35.\n\n\n\nAdekiya, O.A., S.O. Ojeniyi and T.M. Agbede. 2011. 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Olivera and P.C.O. Trivelin. 2011. High \nsoil penetration resistance reduces sugarcane root system development. Soil & \nTillage Research 117: 201-210\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 29\n\n\n\nRamazan, M., D. Khan, M.  Hanif and S. Ali. 2013. Impact of soil compaction on \nlength   and yield of corn (Zea mays) under irrigated conditions.  Middle East \nJournal of Scientific Research II (3):382-385.\n\n\n\nTan, S.L., A.M. Abdul Aziz and A. Zahara. 2007. Selection of sweet potato clones \nfor flour production. Journal of Tropical Agriculture and Food Science 35(2): \n205-212.\n\n\n\nTun, J.C. and S.C. Tan.1988. Soil compaction effect on photosynthesis, root rot \nseverity and growth of white beans. Canadian Journal of Soil Science 68: 455-\n459\n\n\n\n\n\n"
"\n\nINTRODUCTION\nTangkuban Perahu volcano, located in West Java, is one of the active volcanoes \nin Indonesia. Tangkuban Perahu is a post-caldera volcano situated in the eastern \nrim of the Sunda caldera. A number of research   studies have been conducted on \nTangkuban Perahu volcano. However, they only observed the geologic condition \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 65-81  (2020)                         Malaysian Society of Soil Science\n\n\n\nAccelerated Weathering of Secondary Minerals on Ratu \nCrater Toposequences of Tangkuban Parahu Volcano,\n\n\n\nWest Java\n\n\n\nHakim, D.L.1,*, McDaniel, P.2 and Kamarudin, K.R.3\n\n\n\n1Faculty of Agriculture, Universitas Galuh, District of Ciamis,\nWest Java 46215, Indonesia\n\n\n\n2College of Agricultural and Life Sciences, University of Idaho,\nMoscow, ID 83844, USA\n\n\n\n3Faculty of Applied Sciences and Technology, UTHM, 84600 Muar,\nJohor, Malaysia\n\n\n\nABSTRACT\nThe crater environment of Ratu Crater, Tangkuban Parahu Volcano was largely \nshaped by chemical processes that occurred in the geothermal centre in the form \nof  fumarole and solfatar. A range of five representative profiles were identified \nin the toposequences of the crater i.e. A (toeslope), B, D (backslope), G and J \n(summit). Soil samples were physically, chemically, and mineralogically analysed. \nMineralogical analysis showed that the sand fraction of heavy minerals (specific \ngravity> 2.87) were opaque, augite, and hipersten, while light minerals (specific \ngravity< 2.87) were volcanic glass, zeolite, andesin, labradorite, bitownite and \nrock fragments. Extraction with oxalate and pyrophosphate showed Profile \nD (backslope) to contain the highest mineral content of allophane (1.414 %), \nimogolite (0.391 %), and ferrihydrite (2,091 %). The lowest content was found \nin Profile A (toeslope), which had a smaller content than Profile J (summit). XRD \nanalysis results (no treatment) showed that all profiles of A, B, D, G, J had almost \nthe same reflection pattern consisting of calcite (3.03 \u00c5), cristobalite (4.04 \u00c5), \nfeldspar (3.1-3.25 \u00c5, gibbsite (4.85 \u00c5), kaolinite (7.1 \u00c5) and quartz (3.34, 4.27 \n\u00c5). XRD analysis (Mg+glycol) of the profiles showed each profile to be mostly \ndominated by non-crystalline minerals (amorphous); however Profile J (Summit) \nand Profile A (toeslope) were dominated by crystalline minerals that had been \ndeveloped from amorphous minerals, i.e. mineral 2:1 (smectite and chlorite) and \nmineral 1:1 (halloysite and kaolinite).\n \nKey words: Crater, secondary minerals, geothermal, toposequences, \n       volcanic ash.\n\n\n\n___________________\n*Corresponding author : E-mail: danijudge@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202066\n\n\n\nof the volcano and the soil genesis developed in the external system. A crater is a \nvolcanic depression formed as a result of explosion and is integral to the volcano\u2019s \ninternal system. The mineral development inside the crater remains unclear to \ndate. \n The clay mineralogy of the soils formed in volcanic materials varies \nwidely depending on factors such as the composition of the parent material, stage \nof soil formation, pH, soil moisture regime, and the accumulation of organic matter \n(Shoji 1985). Poorly ordered materials such as allophane, imogolite, ferryhydrite, \nand Al- and Fe-humus complexes often dominate the clay size fraction of volcanic \nsoils.\n Generally, climatic conditions and their effects on degree of leaching and \nsoil solution chemistry also play an important role in volcanic material weathering \npathways and secondary mineral neogenesis. Volcanic materials may weather \ndirectly to short range order (SRO) materials or kaolin, depending on the amount \nof rainfall and silica solution activity (Parfitt et al. 1983). Kaolin minerals can \nshow a wide range of structural disorder (Churchman 1990; Soma et al. 1992) due \nprimarily to Al-vacancy displacements in the octahedral sheet (Soma et al. 1992). \nThese vacancies may originate from non-stoichiometric substitution of Fe3+ for \nAl3+ in the octahedral sheet (Soma et al. 1992). Indeed, some studies have shown \nthat crystalline clays, such as halloysite, form initially without a SRO precursor \nin weathering systems that exhibit high solution silica activity (McIntosh 1979; \nSingleton et al. 1989). Variable effects of hydration might also add to the degree \nof disorder in halloysite. Low rainfall or leaching promote a high solution of \nsilica activities and facilitate halloysite formation, whereas high precipitation or \nleaching promote low silica activities, favoring SRO minerals (Parfitt et al. 1983). \nLikewise precipitation and temperature also play a role in the formation of SRO \nor crystalline minerals, which promotes crystallisation as the soil climate gets \nwarmer and drier (Talibudeen and Goulding 1983; Schwertmann 1985). SRO \nminerals are more persistent under low soil temperatures as crystallisation is \nhindered by a low input of thermal energy. Therefore, we hypothesised that thermal \nenergy radiated by sulphide existing in the crater affected the transformation of \nthe minerals surrounding the crater, and the translocation process. This study \nwas done to investigate the formation of secondary minerals across Ratu Crater \ntoposequences of Tangkuban Parahu volcano, West Java.\n\n\n\nMATERIALS AND METHODS\n\n\n\nEnvironmental Setting\nA series of volcanic ash material were examined on the topographic gradient of \nRatu Crater (toposequences). The transect spun a broad environmental gradient \nwith variations in slope level. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 67\n\n\n\nField Methods\nIn this study, five representative sample profiles (A, B, D, G, J) were taken along \nthe path of the Ratu Crater topographic gradient (toposequences) of Tangkuban \nParahu volcano (Figure 1) with a steep to very steep slope grade (Van Zuidam \n1986). The observation and discussion  focus only on the data taken from  five \nprofiles i.e. Profile A-toeslope, Profile B , Profile D-backslope, Profile G and \nProfile J-summit (Figure 2). Samples from each profile were analysed for physical, \nchemical, and mineralogical properties. All samples were dominated by volcanic \nash parent material, released by Tangkuban Parahu volcano eruption.\n\n\n\nFigure 1. 3D illustration of sample profile distribution on Ratu Crater toposequences. \nThe image was taken from the eastern top of Ratu Crater: Profile A (toeslope), Profile D \n\n\n\n(backslope), and Profile J (summit)\n\n\n\nFigure 2. Cross-section illustration of sample profile distribution on Ratu Crater \ntoposequences: Profile A (toeslope), Profile D (backslope), and Profile J (summit)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202068\n\n\n\nLaboratory Methods\nPhysical Analysis \nSamples were air-dried and crushed to pass a 2-mm sieve. Coarse (2.0\u20130.2 mm), \nfine-sand (0.20\u20130.02 mm), silt (0.020\u20130.002 mm), and clay (0.002 mm) fractions \nwere separated by pipette and sieving following pretreatment with H2O2 to oxidise \norganic matter and dispersion aided by sodium hexa-metaphosphate. Water \ncontent at 1.5 MPa was determined on air-dried and field-moist 2-mm soil (Soil \nSurvey Staff 2014). \n\n\n\nChemical Analysis \nSoil pH was measured by potentiometry in soil/solution suspensions of 1:2.5 \nH2O and 1:2.5 1\u2212MKCl. Organic C (OC) was estimated by wet digestion with a \nmodified Walkley-Black procedure (Tan, 2010). \n\n\n\nMineralogical Analysis\nX-ray diffraction (XRD) was performed on the clay (<2 \u03bcm), silt (2\u201353 \u03bcm), \nand very fine sand (53\u2013100 \u03bcm) fractions for each horizon of the some pedon \nin the observation line. Clays and silts were collected by repeated mixing and \ncentrifugation with dilute Na2CO3. X-ray analyses were done with a Diano XRD \n8000 diffractometer (Diano, Woburn, MA). \n Clays and silts were oriented on glass slides with the following standard \ntreatments: Mg saturation, Mg saturation and glycerol solvation (Whittig and \nAllardice 2018). Halloysite was distinguished from kaolinite by the presence of a \npeak near 1.0 nm after intercalation with formamide (Churchman 1990). Very fine \nsands were analysed using random powder mounts. \n Selective dissolution was performed on the fine-earth fraction by non-\nsequential extractions using sodium pyrophosphate, acid ammonium oxalate, and \ncitrate\u2013dithionite  (Soil Survey Staff 2014). Samples were shaken for 15 h with \n0.1\u2212M sodium pyrophosphate at pH 10 and a soil/liquid ratio of 1:100 to extract \nAl (Alp) bound in organo-metal complexes. Samples were shaken for 4 h in the \ndark with a soil/oxalate ratio of 1:100 with 0.2-Mammonium oxalate adjusted to \npH 3.0 with oxalic acid to extract Al, Fe, and Si (Alo, Feo, and Sio) from organic \ncomplexes and SRO Fe oxyhydroxides (e.g. ferrihydrite) and aluminosilicates \n(e.g. allophane and imogolite).\n Citrate-dithionite extraction consisted of shaking 4g of soil for 15 h with \n2g of  sodium dithionite and 100 mL of 0.3-M sodium citrate to extract Fe and Al \n(Fed and Ald) from organic complexes, some SRO aluminosilicates, and secondary \nforms of Fe oxyhydroxides (Parfitt and Childs 1988; Dahlgren 1994). Aluminum \nand Fe concentrations were determined by atomic absorption spectrophotometry, \nand Si was determined by colorimetry (Weaver et al. 1968). \n All laboratory analyses were carried out at Pedology Laboratory, \nUniversity of Idaho (Moscow, USA); Soil Research Institute, Bogor; Geology \nLaboratories, Bandung; and Laboratory of Soil Chemistry and Plant Nutrition, \nPadjadjaran University.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 69\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Physical Properties\nTexture\nThe texture analysis showed that the sand and silt fractions dominated almost \nall sample profiles (Table 1). A small amount of clay fraction was found. Some \nprofiles had clay content of below 5%. Interestingly, allophane in the clay fraction \nwas the main mineral of the soil even though the clay fraction was only below 5%. \n Though the results of the texture analysis may not reflect the actual \nconditions, the texture at the location was dominated by sand and silt fractions, \nthus the class textures were from loam to sandy loam. The composition order of \nsoil fractions in each profile was sand>silt> clay, while the clay composition in \neach profile was J> D> A> B> G.\n\n\n\nTABLE 1\nSoil texture analysis on each sample profile\n\n\n\n5 \n \n\n\n\nwere from loam to sandy loam. The composition order of soil fractions in each profile \nwas sand>silt> clay, while the clay composition in each profile was J> D> A> B> G. \n \n\n\n\n\n\n\n\nTABLE 1 \nSoil texture analysis on each sample profile \n\n\n\n\n\n\n\nProfile \nSand Silt Clay \n\n\n\nTotal fraction \n(%) \n\n\n\nA 61.62 32,.01 6.37 Sandy loam \nB 60.44 35.80 3.76 Sandy loam \nD 73.49 20.09 6.42 Loamy sand \nG 81.54 15.09 3.37 Loamy sand \nJ 42.96 45.10 11.94 Loam \n\n\n\n \n \nSoil Chemical Properties \npH Value \nThe pH values of soil in H2O ranged between 2.71 and 5.51 and showed a distinct trend \nwith increasing elevation (Table 2). This was in contrast to similar elevation gradients on \ngranite and basalt parent materials ranges, where pH steadily declined with increasing \nelevation (Dahlgren et al. 1997). Besides, the pH values in KCl ranged between 2.33 and \n4.16 showing a similar trend with the pH values in H2O. \n \n\n\n\nTABLE 2 \npH values of soil (in H2O and KCl) in sample profiles along the Ratu Crater \n\n\n\ntoposequences \n \n\n\n\nProfile H2O KCl \u0394 pH \nA 2.71 2.33 -0.38 \nB 3.65 3.05 -0.60 \nD 3.68 3.05 -0.63 \nG 4.80 4.16 -0.64 \nJ 5.51 4.16 -1.35 \n\n\n\n \n The geothermal system must have played an important role in the trend due to the \npresence of rich sulfuric acid, indicating major amounts of exchangeable H+  (Tan 2010). \nDelta pH values [\u0394pH = pH(KCl) \u2013 pH(H2O)] ranged between -0.38 and -1.35, indicating \nthat all sites were dominated by a net negative surface charge (Soil Survey Staff 2014). \nThe unexpectedly high pH in the upper elevation sites may be a result of decreased \nleaching, probably because the leaching process by precipitation may not have infiltrated \nthe soil profile. \n \nOrganic Carbon \n\n\n\nSoil Chemical Properties\npH Value\nThe pH values of soil in H2O ranged between 2.71 and 5.51 and showed a distinct \ntrend with increasing elevation (Table 2). This was in contrast to similar elevation \ngradients on granite and basalt parent materials ranges, where pH steadily declined \nwith increasing elevation (Dahlgren et al. 1997). Besides, the pH values in KCl \nranged between 2.33 and 4.16 showing a similar trend with the pH values in H2O.\n\n\n\nTABLE 2\npH values of soil (in H2O and KCl) in sample profiles along the Ratu\n\n\n\nCrater toposequences\n\n\n\n5 \n \n\n\n\nwere from loam to sandy loam. The composition order of soil fractions in each profile \nwas sand>silt> clay, while the clay composition in each profile was J> D> A> B> G. \n \n\n\n\n\n\n\n\nTABLE 1 \nSoil texture analysis on each sample profile \n\n\n\n\n\n\n\nProfile \nSand Silt Clay \n\n\n\nTotal fraction \n(%) \n\n\n\nA 61.62 32,.01 6.37 Sandy loam \nB 60.44 35.80 3.76 Sandy loam \nD 73.49 20.09 6.42 Loamy sand \nG 81.54 15.09 3.37 Loamy sand \nJ 42.96 45.10 11.94 Loam \n\n\n\n \n \nSoil Chemical Properties \npH Value \nThe pH values of soil in H2O ranged between 2.71 and 5.51 and showed a distinct trend \nwith increasing elevation (Table 2). This was in contrast to similar elevation gradients on \ngranite and basalt parent materials ranges, where pH steadily declined with increasing \nelevation (Dahlgren et al. 1997). Besides, the pH values in KCl ranged between 2.33 and \n4.16 showing a similar trend with the pH values in H2O. \n \n\n\n\nTABLE 2 \npH values of soil (in H2O and KCl) in sample profiles along the Ratu Crater \n\n\n\ntoposequences \n \n\n\n\nProfile H2O KCl \u0394 pH \nA 2.71 2.33 -0.38 \nB 3.65 3.05 -0.60 \nD 3.68 3.05 -0.63 \nG 4.80 4.16 -0.64 \nJ 5.51 4.16 -1.35 \n\n\n\n \n The geothermal system must have played an important role in the trend due to the \npresence of rich sulfuric acid, indicating major amounts of exchangeable H+  (Tan 2010). \nDelta pH values [\u0394pH = pH(KCl) \u2013 pH(H2O)] ranged between -0.38 and -1.35, indicating \nthat all sites were dominated by a net negative surface charge (Soil Survey Staff 2014). \nThe unexpectedly high pH in the upper elevation sites may be a result of decreased \nleaching, probably because the leaching process by precipitation may not have infiltrated \nthe soil profile. \n \nOrganic Carbon \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202070\n\n\n\n The geothermal system must have played an important role in the trend \ndue to the presence of rich sulfuric acid, indicating major amounts of exchangeable \nH+  (Tan 2010). Delta pH values [\u0394pH = pH(KCl) \u2013 pH(H2O)] ranged between \n-0.38 and -1.35, indicating that all sites were dominated by a net negative surface \ncharge (Soil Survey Staff 2014). The unexpectedly high pH in the upper elevation \nsites may be a result of decreased leaching, probably because the leaching process \nby precipitation may not have infiltrated the soil profile.\n\n\n\nOrganic Carbon\nSoil organic C content (on a mass percentage basis) on the upper horizon showed \nno clear pattern (Table 3). Soil C content variation across the gradient was probably \ndue to the presence of SRO materials that provided numerous adsorption sites for \nC coupled with the Al-humus complex that inhibits biodegradation of organic C \n(Parfitt and Kimble 1989;  Rasmussen et al. 2007). \n\n\n\nTABLE 3\nThe chemical composition of soils along the Ratu Crater toposequence\n\n\n\n6 \n \n\n\n\nSoil organic C content (on a mass percentage basis) on the upper horizon showed no clear \npattern (Table 3). Soil C content variation across the gradient was probably due to the \npresence of SRO materials that provided numerous adsorption sites for C coupled with \nthe Al-humus complex that inhibits biodegradation of organic C (Parfitt and Kimble \n1989;  Rasmussen et al. 2007).  \n \n\n\n\n  \nTABLE 3 \n\n\n\nThe chemical composition of soils along the Ratu Crater toposequence \n \n\n\n\nProfile S Fe Pyrite (FeS2) C Org \n\n\n\n (%) \n\n\n\nA 0.10 0.02 0.04 0.73 \nB 0.09 3.04 0.17 0.63 \nD 0.14 2.13 0.26 0.40 \nG 0.22 0.07 0.14 0.43 \nJ 0.16 2.18 0.30 2.65 \n\n\n\n\n\n\n\nSoil Mineralogical Properties \n \nQuantitative Analysis of Sand Fraction Minerals \nMineralogical analysis of the sand fraction showed that the heavy minerals (specific \ngravity > 2.87) were opaque, augite, and hyperstein at various percentages (Table 4). \nLight minerals (specific gravity < 2.87) found were volcanic glass, zeolite, andesine, \nlabradorite, bitownite and rock fragments. \n \n\n\n\nTABLE 4  \nThe mineral composition of soils along the Ratu Crater toposequence \n\n\n\n\n\n\n\nProfile \nHeavy mineral (%) Light mineral (%) \n\n\n\nLM \nOp Aug Hip GV Lab Bit FB \n\n\n\nA 0 0 0 96 0 0 3 1 \n\n\n\nB 3 6 2 63 16 2 4 4 \nD 2 6 1 61 22 1 5 2 \nG 0 0 0 98 0 0 0 2 \nJ 3 7 0 64 20 0 2 4 \n\n\n\nOp= opaque  Lab = labradorite     \nAug= augite  Bit= bitownite    \nHip = hypersteine  FB = rock fragment \nGV = volcanic glass LM = altered mineral \n \nVolcanic Glass \nVolcanic glass content varied substantially across the gradient (Figure 3) and the pattern \nmay be due to a function of differential weathering environments or parent material \nvariation, or both. This variation in glass content  suggests that glass was accumulated by \n\n\n\nSoil Mineralogical Properties\n\n\n\nQuantitative Analysis of Sand Fraction Minerals\nMineralogical analysis of the sand fraction showed that the heavy minerals (specific \ngravity > 2.87) were opaque, augite, and hyperstein at various percentages (Table \n4). Light minerals (specific gravity < 2.87) found were volcanic glass, zeolite, \nandesine, labradorite, bitownite and rock fragments.\n\n\n\nVolcanic Glass\nVolcanic glass content varied substantially across the gradient (Figure 3) and the \npattern may be due to a function of differential weathering environments or parent \nmaterial variation, or both. This variation in glass content  suggests that glass was \naccumulated by the leaching process from the top to bottom gradient, due either \nto precipitation or gravitational energy. Glass content increased significantly in \nProfile A, suggesting a possible accumulation threshold related to the form of \nslope gradient. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 71\n\n\n\n Figure 3. Volcanic glass content in the very fine sand (VFS) fraction of surface horizon \non gradient from toeslope (Profile A) to summit (Profile J)\n\n\n\nProfiles B, D, and J showed less glass content compared to Profiles A and B, \nprobably a result of enhanced leaching due to the degree of the gradient slope. \nIt is also possible that some volcanic glasses in the lower slope originated from \nthe latest volcanic eruptions as the glass also showed resistance to chemical \nweathering (Shoji 1985).  The linear line in Figure 3 suggests that the content of \nvolcanic glasses decreased from lower to higher gradient slope.\n\n\n\nXRD on Silt and Very Fine Sand Fraction\nSamples were selected from three profiles representing extreme different gradients \ni.e. Profile A as toeslope, Profile D as backslope, and Profile J as summit. XRD \nanalysis (without treatment) on silt and very fine  sand fraction (Figure 4) showed \nthat all profiles contained almost similar reflection patterns showing the presence \nof calcite (3.03 \u00c5), cristobalite (4.04 \u00c5), feldspar (3.1-3.25 \u00c5, gibbsite (4.85 \u00c5), \nkaolinite (7.1 \u00c5) and quartz (3.34, 4.27  \u00c5) (Table 5).\n\n\n\n6 \n \n\n\n\nSoil organic C content (on a mass percentage basis) on the upper horizon showed no clear \npattern (Table 3). Soil C content variation across the gradient was probably due to the \npresence of SRO materials that provided numerous adsorption sites for C coupled with \nthe Al-humus complex that inhibits biodegradation of organic C (Parfitt and Kimble \n1989;  Rasmussen et al. 2007).  \n \n\n\n\n  \nTABLE 3 \n\n\n\nThe chemical composition of soils along the Ratu Crater toposequence \n \n\n\n\nProfile S Fe Pyrite (FeS2) C Org \n\n\n\n (%) \n\n\n\nA 0.10 0.02 0.04 0.73 \nB 0.09 3.04 0.17 0.63 \nD 0.14 2.13 0.26 0.40 \nG 0.22 0.07 0.14 0.43 \nJ 0.16 2.18 0.30 2.65 \n\n\n\n\n\n\n\nSoil Mineralogical Properties \n \nQuantitative Analysis of Sand Fraction Minerals \nMineralogical analysis of the sand fraction showed that the heavy minerals (specific \ngravity > 2.87) were opaque, augite, and hyperstein at various percentages (Table 4). \nLight minerals (specific gravity < 2.87) found were volcanic glass, zeolite, andesine, \nlabradorite, bitownite and rock fragments. \n \n\n\n\nTABLE 4  \nThe mineral composition of soils along the Ratu Crater toposequence \n\n\n\n\n\n\n\nProfile \nHeavy mineral (%) Light mineral (%) \n\n\n\nLM \nOp Aug Hip GV Lab Bit FB \n\n\n\nA 0 0 0 96 0 0 3 1 \n\n\n\nB 3 6 2 63 16 2 4 4 \nD 2 6 1 61 22 1 5 2 \nG 0 0 0 98 0 0 0 2 \nJ 3 7 0 64 20 0 2 4 \n\n\n\nOp= opaque  Lab = labradorite     \nAug= augite  Bit= bitownite    \nHip = hypersteine  FB = rock fragment \nGV = volcanic glass LM = altered mineral \n \nVolcanic Glass \nVolcanic glass content varied substantially across the gradient (Figure 3) and the pattern \nmay be due to a function of differential weathering environments or parent material \nvariation, or both. This variation in glass content  suggests that glass was accumulated by \n\n\n\nTABLE 4 \nThe mineral composition of soils along the Ratu Crater toposequence\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202072\n\n\n\nTABLE 5\nMineral composition of each profile based on the results of XRD analysis on silt and \n\n\n\nvery fine sand fraction\n\n\n\n7 \n \n\n\n\n \nFigure 3. Volcanic glass content in the very fine sand (VFS) fraction of surface horizon \n\n\n\non gradient from toeslope (Profile A) to summit (Profile J) \n \n\n\n\nProfiles B, D, and J showed less glass content compared to Profiles A and B, \nprobably a result of enhanced leaching due to the degree of the gradient slope. It is also \npossible that some volcanic glasses in the lower slope originated from the latest volcanic \neruptions as the glass also showed resistance to chemical weathering (Shoji 1985).  The \nlinear line in Figure 3 suggests that the content of volcanic glasses decreased from lower \nto higher gradient slope. \n \n\n\n\nTABLE 5 \nMineral composition of each profile based on the results of XRD analysis on silt and very \n\n\n\nfine sand fraction \n \n\n\n\nProfile Mineral \n\n\n\nA Feldspar (KAlSi3O8 \u2013 NaAlSi3O8 \u2013 CaAl2Si2O8) \nCristobalite (SiO2) \nQuartz (SiO2) \n\n\n\nB Feldspar (KAlSi3O8 \u2013 NaAlSi3O8 \u2013 CaAl2Si2O8) \nCristobalite (SiO2) \nQuartz (SiO2) \n\n\n\nD Feldspar (KAlSi3O8 \u2013 NaAlSi3O8 \u2013 CaAl2Si2O8) \nQuartz (SiO2) \n\n\n\nG Feldspar (KAlSi3O8 \u2013 NaAlSi3O8 \u2013 CaAl2Si2O8) \nGibbsite (Al (OH)3) \nQuartz (SiO2) \n\n\n\nJ Feldspar (KAlSi3O8 \u2013 NaAlSi3O8 \u2013 CaAl2Si2O8) \nCalcite (CaCO3) \nGibbsite (Al (OH)3) \nKaolinite (Al2O3 2SiO4.2H2O) \nCristobalite (SiO2) \nQuartz (SiO2) \n\n\n\n\n\n\n\n(a)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 73\n\n\n\nFigure 4. Images from XRD analysis (no treatment) on silt and very fine sand fractions \nfrom surface horizons of each profile; (a) Profile A (toeslope), (b) Profile D (backslope), \n\n\n\nand (c) Profile J (summit)\n\n\n\n(b)\n\n\n\n(c)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202074\n\n\n\nMicroscopic Analysis\n\n\n\nPolarisation Microscope\nPolarisation microscopic observations on very fine sand fractions showed that \nall sample profiles had fresh volcanic glass indicated by bright colours, while \nother parts had weathered indicated by dark colours (Figure 5). However, most \nweathered minerals were found at A (toeslope) point, and D (backslope) point, \nand least amounts at J point (summit). \n\n\n\nFigure  5.  Images from polarisation microscopic observations on very fine sand with 40x \nmagnification; (A), (B), (D), (G), and (J) indicate respective profiles\n\n\n\nIn fact, A point was a place of accumulated materials. Furthermore, A point had \nspecific environmental characteristics in terms of temperature and pH due to \ngeothermal activities of the crater. The volcanic glass content of the soil was very \ndependent on the content of the initial volcanic glass and the level of weathering. \nThe higher level of weathering in soil had resulted in less volcanic glass content \ndue to its transformation into both crystalline minerals and secondary minerals \n(Van Ranst et al. 2016).\n\n\n\nQuantitative Analysis with Oxalate and Pyrophosphate Selective Solutions\nThe results of the analyses with oxalate and pyrophosphate acid are shown in \nTable 6. Silica, aluminum and iron extracted with oxalate and pyrophosphate acid \nwere symbolised as Sio, Alo, Feo, Sip, Alp, and Fep. The percentages of allophane, \nimogolite and ferihydrite were calculated based on the amounts of Si, Al, and Fe \nextracted with oxalate and pyrophosphate acid with the equation proposed by \nShoji (1985) as follows: (1) % allophane = % Sio x 7.14; (2) % imogolite = % \n(Alo\u2013 Alp) x 1.7; (3) % ferihydrite = % Feo x 1.7. The Alo value was higher than \nthat of Alp because oxalic acid extracted Al derived from allophane, imogolite \nand Al-humus complex while pyrophosphate only extracted Al from the Al-\nhumus complex. The presence of non-inorganic crystalline Al was estimated \nfrom the difference in Alo-Alp. Table 6 shows that Alo-Alp  values ranged from \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 75\n\n\n\n0.10-0.23%,  suggesting the tendency of the profile on the upper slope to have a \nhigher value than the profile on the lower slope in the internal environment of the \ncrater. This result indicates that the proportion of Al-humus complexes decreases \nand the Al-inorganic form increases, corresponding to the depth of the crater. An \nopposite relationship was observed in the case of the formation of Al-humus with \nAl-inorganic (allophane, imogolite, ferihydrite). The presence of humus inhibited \nallophane formation (non allophanic reaction) if the content of organic matter was \nhigh.\n\n\n\nTABLE 6 \nExtraction results with oxalate and pyrophosphate acid and percentage estimation of \n\n\n\nallophane, imogolite, and ferihydrite (%)\n\n\n\n10 \n \n\n\n\nmagnification; (A), (B), (D), (G), and (J) indicate respective profiles \n \n\n\n\nIn fact, A point was a place of accumulated materials. Furthermore, A point had \nspecific environmental characteristics in terms of temperature and pH due to geothermal \nactivities of the crater. The volcanic glass content of the soil was very dependent on the \ncontent of the initial volcanic glass and the level of weathering. The higher level of \nweathering in soil had resulted in less volcanic glass content due to its transformation into \nboth crystalline minerals and secondary minerals (Van Ranst et al. 2016). \n \nQuantitative Analysis with Oxalate and Pyrophosphate Selective Solutions \nThe results of the analyses with oxalate and pyrophosphate acid are shown in Table 6. \nSilica, aluminum and iron extracted with oxalate and pyrophosphate acid were \nsymbolised as Sio, Alo, Feo, Sip, Alp, and Fep. The percentages of allophane, imogolite and \nferihydrite were calculated based on the amounts of Si, Al, and Fe extracted with oxalate \nand pyrophosphate acid with the equation proposed by Shoji (1985) as follows: (1) % \nallophane = %Sio x 7.14; (2) % imogolite = %(Alo\u2013 Alp) x 1.7; (3) % ferihydrite = %Feo x \n1.7. The Alo value was higher than that of Alp because oxalic acid extracted Al derived \nfrom allophane, imogolite and Al-humus complex while pyrophosphate only extracted Al \nfrom the Al-humus complex. The presence of non-inorganic crystalline Al was estimated \nfrom the difference in Alo-Alp. Table 6 shows that Alo-Alp  values ranged from 0.10-\n0.23%,  suggesting the tendency of the profile on the upper slope to have a higher value \nthan the profile on the lower slope in the internal environment of the crater. This result \nindicates that the proportion of Al-humus complexes decreases and the Al-inorganic form \nincreases, corresponding to the depth of the crater. An opposite relationship was observed \nin the case of the formation of Al-humus with Al-inorganic (allophane, imogolite, \nferihydrite). The presence of humus inhibited allophane formation (non allophanic \nreaction) if the content of organic matter was high. \n \n\n\n\n \nTABLE 6  \n\n\n\nExtraction results with oxalate and pyrophosphate acid and percentage estimation of \nallophane, imogolite, and ferihydrite (%) \n\n\n\n\n\n\n\nProfil Sio Alo Feo Alp Fep (%) Alo + \u00bd Feo Alo-Alp al im fer \n\n\n\nA 0,003 0,02 0,02 0,01 0,02 0,030 0,01 0,021 0,017 0,034 \n\n\n\nB 0,074 0,15 0,03 0,05 0,12 0,165 0,10 0,528 0,170 0,051 \n\n\n\nD 0,198 0,34 1,23 0,11 0,04 0,955 0,23 1,414 0,391 2,091 \n\n\n\nG 0,001 0,03 0,04 0,01 0,02 0,050 0,02 0,007 0,034 0,068 \n\n\n\nJ 0,070 0,20 1,55 0,08 0,06 0,975 0,12 0,500 0,204 2,635 \nal = allophone; im = imogolite; fer = ferihydrite \n  \nXRD on Clay Fraction \nThe XRD analysis (Mg+glycol) on clay fraction showed that each profile was dominated \nby amorphous materials. Profile J (Summit) and Profile A (toeslope) were dominated by \ncrystalline minerals that could have been developed from the amorphous materials i.e. \nmineral 2: 1 (smectite and chlorite) and mineral 1:1 (halloysite and kaolinite). \n\n\n\nBased on the analysis results, this study showed that crystalline clays such as \nhalloysite were formed initially without a SRO precursor in weathering systems that \n\n\n\nXRD on Clay Fraction\nThe XRD analysis (Mg+glycol) on clay fraction showed that each profile was \ndominated by amorphous materials. Profile J (Summit) and Profile A (toeslope) \nwere dominated by crystalline minerals that could have been developed from \nthe amorphous materials i.e. mineral 2: 1 (smectite and chlorite) and mineral 1:1 \n(halloysite and kaolinite).\n Based on the analysis results, this study showed that crystalline clays \nsuch as halloysite were formed initially without a SRO precursor in weathering \nsystems that exhibited high solution silica activity (McIntosh 1979; Singleton \net al. 1989). Variable effects of hydration might also contribute to disorder in \nhalloysite. Low rainfall or leaching promotes high solution-silica activity and \nfacilitates halloysite formation, whereas high precipitation or leaching promotes \nlow silica activity, favouring SRO material formation (Parfitt and Kimble 1989). \nLike precipitation, temperature also plays a role in the formation of SRO or \ncrystalline minerals, with crystallisation promoted by higher temperatures \n(Talibudeen and Goulding 1983; Schwertmann 1985). SRO minerals are more \npersistent under a low soil temperature as crystallisation is hindered by a low \ninput of thermal energy. Therefore, we hypothesised that thermal energy radiated \nby sulphide existing in the crater and the translocation process affected the \ntransformation of the minerals in the crater. Climatic conditions and their effects \non the degree of leaching and soil solution chemistry (toposequences) play an \nimportant role in volcanic material weathering pathways and secondary mineral \nneogenesis.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202076\n\n\n\n(a)\n\n\n\n(b)\n\n\n\nFigure 6. X-ray diffractograms for random powder mounts of Mg2+ saturation and \nglycerol solvation, clay fractions from surface horizons of each profile: (a) Profile A \n\n\n\n(toeslope), (b) Profile D (backslope), and (c) Profile J (summit)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 77\n\n\n\nScanning Electron Microscope (SEM)\nFurther observations focused on three sample points on very fine sand fractions, \ni.e. A profile (toeslope) with 121x and 365x magnification in 20 \u00b5m size fractions, \nD profile (backslope) with 131x and 358x magnification in 20 \u00b5m size fractions, \nand J profile (summit) with 159x and 292x magnification in 30 \u00b5m size fraction, \nas shown in Figure 7.\n\n\n\nFigure 7. SEM images of sample profiles (A), (D), and (J) : A profile observed with \n121x and 365 magnification, D profile observed with 131x and 368 magnification, and J \n\n\n\nprofile observed with 159x and 292 magnification\n\n\n\n        (121x)      (131x)        (159x)\n\n\n\n        (365x)      (358x)        (292x)\n\n\n\n  The observation showed both fresh and weathered materials with Profile \nA being dominated by weathered materials due to deposit accumulation from \nthe top sequence. Otherwise, the high temperature in the crater (as geothermal \nactivity) also played an important role in the acceleration of  materials weathering.\n\n\n\nVisual Observations\nVisual observations of surface rock along Ratu Crater toposequence are shown in \nFigure 8. The high temperature fluctuations due to geothermal activities swelled \nit vertically and horizontally, creating a crack and splitting the rock as shown in \nimages A and B. Otherwise, the extreme temperature fluctuations split the top \nlayer of the rock faster than the under layer, creating radial weathering shown in \nimages C and D. Buol et al. (2011) stated that weathering is physical and chemical \ndisintegration and decomposition of rocks, which occur due to the minerals not  \nbeing in balance under conditions of extreme temperature, pressure, and humidity. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202078\n\n\n\nFigure 8. Rock weathering on Ratu Crater toposequences of Tangkuban Parahu volcano\n\n\n\nCONCLUSION\nGeothermal activities played a role primarily in creating specific conditions \nat the geothermal and surrounding location of the study, mainly at a very high \nambient temperature and  very acidic pH. Meanwhile, the topographic gradient \n(toposequence) played a role mainly in the process of mineral leaching. \nGeothermal activities and crater toposequence affected the composition of \nsecondary minerals resulting in the toeslope profile being dominated by crystalline \nminerals (type 1: 1 and 2: 1). This was caused by high deposit accumulation \nfrom the leaching process, extremely fluctuating temperatures, very acidic pH \nand specific chemical content in the geothermal environment of the crater. The \nbackslope was dominated by amorphous (non crystalline) minerals due to high \nintensity of leaching while the summit was dominated by amorphous and type \n1: 1 minerals due to relatively lower temperatures that supported the formation \nof amorphous minerals. Mineralogical analysis showed that the sand fractions \nof heavy minerals (specific gravity > 2.87) were opaque, augite, and hipersten \nwhile the light minerals (specific gravity < 2.87) were volcanic glass, zeolite, \nandesin, labradorite, bytownite and rock fragments. Extraction with oxalate \nand pyrophosphate showed the highest mineral content of allophane (1.414 %), \nimogolite (0.391 %), and ferrihydrite (2,091 %) was in Profile D (backslope). The \nlowest content was found in Profile A (toeslope), which had a smaller content than \nProfile J (summit). XRD analysis results (no treatment) showed that all profiles \nA, B, D, G, J had almost the same reflection pattern consisting of calcite (3.03 \u00c5), \ncristobalite (4.04 \u00c5), feldspar (3.1-3.25 \u00c5, gibbsite (4.85 \u00c5), kaolinite (7.1 \u00c5) and \nquartz (3.34, 4.27  \u00c5). The mechanism and formation of secondary minerals in the \nRatu Crater of TangkubanParahu volcano are summarised in Figure 10.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 79\n\n\n\nFigure 10. Mechanism and formation of secondary minerals on Ratu Crater \ntoposequences of Tangkuban Parahu volcano\n\n\n\nACKNOWLEDGEMENT\nWe would like to thank soil laboratory staff of Idaho University, all supporting \nacademic staff and the anonymous reviewers of this paper for their constructive \ncomments and suggestions. This research was funded by Sandwich-Like Program \nfrom The Ministry of Higher Education and Technology, Indonesia.\n\n\n\nREFERENCES\nBuol, S. W., Southard, R. J., Graham, R. C., & McDaniel, P. A. (2011). Soil Genesis \n\n\n\nand Classification: Sixth Edition. Soil Genesis and Classification: Sixth \nEdition. Wiley-Blackwell. https://doi.org/10.1002/9780470960622\n\n\n\nChurchman, G. J. (1990). Relevance of different intercalation tests for distinguishing \nhalloysite from kaolinite in soils. Clays & Clay Minerals. 38(6), 591-599. \nRetrieved from www.clays.org/journal/archive/volume 38/38-6-591.pdf. \nhttps://doi.org/10.1346/CCMN.1990.0380604.\n\n\n\nDahlgren, R. A. (1994). Weathering, Soils & Paleosols. Journal of   Environmental  \nQuality, 23(2), 389\u2013390. https:/ /doi.org /10.2134/jeq1994. \n00472425002300020031x.\n\n\n\nDahlgren, R. A., Boettinger, J. L., Huntington, G. L., & Amundson, R. G. (1997). \nSoil development along an elevational transect in the western Sierra Nevada, \nCalifornia. Geoderma, 78(3\u20134), 207\u2013236. https://doi.org/10.1016/S0016-\n7061(97)00034-7\n\n\n\nMcIntosh, P. D. (1979). Halloysite in a New Zealand tephra and paleosol less than \n2500 years old. New Zealand Journal of Science, 22(1), 49\u201354.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202080\n\n\n\nParfitt, R. L., & Childs, C. W. (1988). Estimation of forms of fe and al: A review, \nand analysis of contrasting soils by dissolution and moessbauer methods. \nAustralian Journal of Soil Research, 26(1), 121\u2013144. https://doi.org/10.1071/\nSR9880121\n\n\n\nParfitt, R. L., & Kimble, J. M. (1989). Conditions for Formation of Allophane in \nSoils. Soil Science Society of America Journal,  53(3), 971\u2013977. https://doi.\norg/10.2136/sssaj1989.03615995005300030057x\n\n\n\nParfitt, R. L., Russell, M., & Orbell, G. E. (1983). Weathering sequence of soils from \nvolcanic ash involving allophane and halloysite, New Zealand. Geoderma, \n29(1), 41\u201357 https://doi.org/10.1016/0016-7061(83)90029-0\n\n\n\nRasmussen, C., Matsuyama, N., Dahlgren, R. A., Southard, R. J., & Brauer, N. (2007). \nSoil Genesis and Mineral Transformation Across an Environmental Gradient \non Andesitic Lahar. Soil Science Society of America Journal, 71(1), 225\u2013\n237. https://doi.org/10.2136/sssaj2006.0100\n\n\n\nSchwertmann, U. (1985). Formation of Secondary Iron Oxides in Various \nEnvironments. In The Chemistry of Weathering (pp. 119\u2013120). Springer \nNetherlands. https://doi.org/10.1007/978-94-009-5333-8_7\n\n\n\nShoji, S. (1985). Genesis and properties of non-allophanic andisols in Japan. Applied \nClay Science, 1(1\u20132), 83\u201388. https://doi.org/10.1016/0169-1317(85)90564-2\n\n\n\nSingleton, P. L., McLeod, M., & Percival, H. J. (1989). Allophane and halloysite \ncontent and soil solution silicon in soils fromrhyolitic volcanic material, \nnew zealand. Australian Journal of Soil Research, 27(1), 67\u201377. https://doi.\norg/10.1071/SR9890067\n\n\n\nSoil Survey Staff. (2014). Kellogg Soil Survey Laboratory Methods Manual. Soil \nSurvey Investigations Report No. 42, Version 5.0. R. Burt and Soil Survey \nStaff (ed.). U.S. Department of Agriculture, Natural Resources Conservation \nService.\n\n\n\nSoma, M., Churchman, G. J., & Theng, B. K. G. (1992). X-ray photoelectron \nspectroscopic analysis of halloysites with different composition and particle \nmorphology. Clay Minerals, 27(4), 413\u2013421. https://doi.org/10.1180/\nclaymin.1992.027.4.02\n\n\n\nTalibudeen, O., & Goulding, K. W. T. (1983). Apparent charge heterogeneity in \nkaolins in relation to their 2:1 phyllosilicate content. Clays & Clay Minerals, \n31(2), 137\u2013142. https://doi.org/10.1346/CCMN.1983.0310208\n\n\n\nTan, K. H. (2010). Principles of Soil Chemistry. Principles of Soil Chemistry. CRC \nPress. https://doi.org/10.1201/9781439894606\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 81\n\n\n\nLal, R. (2017). Variable Charge Soils: Mineralogy and Chemistry. In Encyclopedia \nof Soil Science, Third Edition. (pp. 2432\u20132439). CRC Press. https://doi.\norg/10.1081/e-ess3-120053756\n\n\n\nVan Zuidam, R. A. (1986). Aerial photo-interpretation in terrain analysis and \ngeomorphologic mapping. Aerial Photo-Interpretation in Terrain Analysis \nand Geomorphologic Mapping. Smits Publishers, The Hague. https://doi.\norg/10.1016/0012-8252(88)90100-6\n\n\n\nWeaver, R. M., Syers, J. K., & Jackson, M. L. (1968). Determination of Silica \nin Citrate-Bicarbonate-Dithionite Extracts of Soils. Soil Science \nSociety of America Journal. 32(4), 497\u2013501. https://doi.org/10.2136/\nsssaj1968.03615995003200040023x\n\n\n\nWhittig, L. D., & Allardice, W. R. (2018). X-Ray Diffraction Techniques (pp. 331\u2013\n362). https://doi.org/10.2136/sssabookser5.1.2ed.c12\n\n\n\n\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 167-172 (2019)                         Malaysian Society of Soil Science\n\n\n\nEffect of Azolla Compost and Biofertiliser on Phosphate \nSolubilising Bacteria, Available-P and Dry Weight \n\n\n\n of Rice Cultivated in Saline Soil \n\n\n\nBetty Natalie Fitriatin*, Zaky Abdul Haris, Nadia Nuraniya \nKamaluddin, Mieke Rochimi Setiawati, Pujawati Suryatmana, \nReginawanti Hindersah, Anne Nurbaity and Tualar Simarmata\n\n\n\nDepartment of Soil Science and Land Resource, Faculty of Agriculture, \nUniversitas Padjadjaran, Jatinangor, 45363 Sumedang, West Java, Indonesia\n\n\n\nABSTRACT\nSaline soils can be developed for the cultivation of food crops such as rice. Despite \noffering this potential, the utilisation of saline soils is limited by its physical, \nchemical and biological properties. These barriers can be amended through the \napplication of azolla compost and biofertilisers. In this research, combinations \nof azolla compost and biofertiliser consortia were assessed to identify the best \ntreatment for rice plants grown in soil at various salinity levels. showed that the \napplication of azolla compost and biofertiliser increased the phosphate solubilising \nbacteria (PSB) population and the dry weight of rice dry cultivated in a saline soil. \nThe salinity treatment of 2-6 mm hos cm-1 influenced PSB population and rice \ndry weight.  An ncreasing level of salinity decreased PSB population and rice dry \nweight.\n\n\n\nKeywords: Azolla compost, biofertiliser, saline soil.\n\n\n\n___________________\n*Corresponding author : betty.natalie@unpad.ac.id    \n\n\n\nINTRODUCTION\nOver 13.2 million of the 40 million hectares of degraded land in Indonesia consists \nof saline soil. The land is commonly found in coastal areas, river estuaries, and \ndeltas affected by seawater intrusion (Setiawati et al. 2007). One stretch of \nsaline land with a salinity level of 6,59 mmhos cm-1 in West Java is located in \nPanyingkiran Lor Village, Cantigi District, Indramayu.. \n Indonesia\u2019s saline soils have the potential to be utilised for the cultivation \nof food crops.In addition, rice demand throughout Indonesia is continually \nincreasing despite a government policy on staple food substitutes. This can be \nseen from the consumption of rice in 2015 which was estimated at 35.123 million \ntons, assuming a per capita consumption of 139 kg / year and a population of 260 \nmillion. By 2020, it is forecasted that there will be a shortage of 1.09 million tons \nof rice, with the deficit increasing to 12.25 million tons by 2045 (Ali et al. 2013). \nSalinity decreases potassium (K) and phosphorus (P) availability and also leads \nto low K+/ Na+ ratios. Salinity in soil will significantly reduce the absorption of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019168\n\n\n\nmineral nutrients, especially P, since phosphate ions binds with Ca2+, Mg2+ and \nZn2+ ions. This will result in the unavailability of these elements for plant uptake. \nP deficiency in soil will lower energy in the form of Adenosine triphosphate (ATP) \n(Lacerda et al., 2003). \n In order to solve the complex problems posed by saline soils, technology \nis being constantly developed to enhance land productivity.  A known method of \nsoil enhancement is through the application of biological fertilisers (biofertiliser) \nand organic matter. Organic matter such as azolla compost can be applied to \nremediate soil salinity and increase crop production.  Azolla pinnata is a type \nof fern known for its symbiotic association with the N2-fixing blue-green algae \nAnabaena azollae.  Azolla pinnata can be degraded easily by soil microorganisms; \ntherefore, its addition is expected to increase the  nitrogen needed by plants \n(Rosiana et al. 2013).\n A biofertiliser is an inoculant with living organisms that serves as an \nactive ingredient, and its addition can inhibit or facilitate the availability of certain \nnutrients in the soil for plants (Simanungkalit et al. 2006). Biofertilisers aid \nbalanced nutrient supply for plants, and reduce the necessity of inorganic fertiliser \napplication. It is also beneficial for the environment through the reduction of \npollution caused by inorganic fertilisers. In this research, we studied the effect \nof a biofertliser consortia on a phosphate solubilising bacteria (PSB) population, \navailable phospohorus, and dry weight of rice (Oryza sativa L.) grown at different \nsalinity levels. \n\n\n\nMATERIALS AND METHODS\nThe experiment was done in a randomised complete block design. Treatments \nconsisted of various salinity levels, biofertiliser consortium consisting of \nAzotobacter sp, Azospirillum sp., phosphate solubilising bacteria, endophytic \nbacteria with a population  of 107 cfu mL-1, mycorhiza with a density of 10 spores/\ngr) and azolla compost (Table 1). \n The soil used was Inceptisols from Jatinangor, West Java, Indonesia \ntaken from 0-20 cm depth. Azolla compost (10 g pot-1) was mixed with 1 kg soil \n\n\n\n       \uf06e          ISSN: 1978-1520 \n\n\n\nIJCCS  Vol. x, No. x,  July 201x :  first_page \u2013 end_page \n\n\n\n4 \n\n\n\n\n\n\n\nTABLE 1 \nTreatment of salinity, biofertiliser and azolla compost \n\n\n\n \nCode Salinity Biofertiliser Azolla  \n\n\n\ncompost \nA 0 mmhos cm-1 - - \nB 0 mmhos cm-1 + + \nC 2 mmhos cm-1 - - \nD 2 mmhos cm-1 + + \nE 4 mmhos cm-1 - - \nF 4 mmhos cm-1 + + \nG 6 mmhos cm-1                   -                 - \nH 6 mmhos cm-1               +                 + \n\n\n\n\n\n\n\nThe soil used was Inceptisols from Jatinangor, West Java, Indonesia taken \n\n\n\nfrom 0-20 cm depth. Azolla compost (10 g pot-1) was mixed with 1 kg soil and \n\n\n\nincubated for one week. The biofertiliser consortium (10 ml per plant) was \n\n\n\napplied during the rice cultivation.    \n\n\n\n\n\n\n\nRESULT AND DISCUSSION \n\n\n\nPhosphate Solubilising Bacteria Population \n\n\n\nObservation data and statistical analysis are presented in Table 2 which shows the \n\n\n\nincrease in PSB after biofertiliser consortium and azolla compost application.  \n\n\n\nTABLE 2 \nApplication effects of biofertiliser consortium and azolla compost on the  \n\n\n\nphosphate solubilising bacteria population \n\n\n\nTreatments PSB population \n(107 CFU g-1)* \n\n\n\nA = 0 mmhos cm-1 of saline soil (negative control) 2.13 a \nB = 0 mmhos cm-1 of saline soil+ biofertliser + azolla \n\n\n\ncompost \n7.33 c \n\n\n\nC = 2 mmhos cm-1 of saline soil 1.38 a \nD = 2 mmhos cm-1 +biofertiliser + azolla compost 5.25 b \nE = 4 mmhos cm-1 of saline soil 1.50 a \n\n\n\nTABLE 1\nTreatment of salinity, biofertiliser and azolla compost\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 169\n\n\n\nand incubated for one week. The biofertiliser consortium (10 ml per plant) was \napplied during the rice cultivation.   \n\n\n\nRESULT AND DISCUSSION\nPhosphate Solubilising Bacteria Population\nObservation data and statistical analysis are presented in Table 2 which shows \nthe increase in PSB after biofertiliser consortium and azolla compost application. \n Table 2 explains that the highest bacterial populations are found in the \nconsortium fertiliser and azolla compost treatment with salinity 0 mmhos cm-1. \nAlong with increased salinity, the bacterial population also decreased  while the \nbiofertiliser consortium and azolla compost increased the population of phosphate \nsolubilising bacteria by 244% at 0 mmhos cm-1, 280% at 2 mm hos cm-1, 342% at \n4 mmhos cm-1 and 74% at 6 mmhos cm-1. \n Widawati (2015) reported that PSB can survive salinity for up to 2 mmhos \ncm-1, but cannot withstand higher salt concentrations. Microorganisms that can \ntolerate higher levels of salinilty have the ability to balance the intracellular and \nextracellular osmotic pressure by increasing the amount of solubilised compound \ninside of their cells (Cherif-Silini et al. 2013).\n\n\n\n       \uf06e          ISSN: 1978-1520 \n\n\n\nIJCCS  Vol. x, No. x,  July 201x :  first_page \u2013 end_page \n\n\n\n4 \n\n\n\nG 6 mmhos cm-1                   -                 - \nH 6 mmhos cm-1               +                 + \n\n\n\n\n\n\n\nThe soil used was Inceptisols from Jatinangor, West Java, Indonesia taken \n\n\n\nfrom 0-20 cm depth. Azolla compost (10 g pot-1) was mixed with 1 kg soil and \n\n\n\nincubated for one week. The biofertiliser consortium (10 ml per plant) was \n\n\n\napplied during the rice cultivation.    \n\n\n\n\n\n\n\nRESULT AND DISCUSSION \n\n\n\nPhosphate Solubilising Bacteria Population \n\n\n\nObservation data and statistical analysis are presented in Table 2 which shows the \n\n\n\nincrease in PSB after biofertiliser consortium and azolla compost application.  \n\n\n\nTABLE 2 \nApplication effects of biofertiliser consortium and azolla compost on the  \n\n\n\nphosphate solubilising bacteria population \n\n\n\nTreatments PSB population \n(107 CFU g-1)* \n\n\n\nA = 0 mmhos cm-1 of saline soil (negative control) 2.13 a \nB = 0 mmhos cm-1 of saline soil+ biofertliser + azolla \n\n\n\ncompost \n7.33 c \n\n\n\nC = 2 mmhos cm-1 of saline soil 1.38 a \nD = 2 mmhos cm-1 +biofertiliser + azolla compost 5.25 b \nE = 4 mmhos cm-1 of saline soil 1.50 a \nF = 4 mmhos cm-1 +biofertilizer + azolla compost 6.63 bc \nG = 6 mmhos cm-1 of saline soil 1.50 a \nH = 6 mmhos cm-1 +biofertiliser + azolla compost 2.63 a \n*Numbers followedby the same letter are not significantly different according to Duncan\u2019s \nMultiple Range Test (confidence level 5%) \n\n\n\nTable 2 explains that the highest bacterial populations are found in the consortium \n\n\n\nfertiliser and azolla compost treatment with salinity 0 mmhos cm-1. Along with \n\n\n\nincreased salinity, the bacterial population also decreased  while the biofertiliser \n\n\n\nconsortium and azolla compost increased the population of phosphate solubilising \n\n\n\nTABLE 2\nApplication effects of biofertiliser consortium and azolla compost on the  phosphate \n\n\n\nsolubilising bacteria population\n\n\n\nAvailable P\nObservational data and statistical analysis in Table 2 shows that the biofertiliser \nconsortium and azolla compost did not have any significant effect on available \nP, but increased the availability of P by 23.5% in 0 mmhos cm-1 and 3.9% in \n6 mmhos cm-1.  This was due to available P in this soil being high and thus \npreventing the biofertiliser (especially phospahate solubilising microorganisms) \nfrom functioning optimally. Phosphate solubilising microorganisms increase \nsolubilising phosphate in the P-deficient soil. This finding is in line with the study \nof Pereira and Castro (2014) who observed that phosphate solubilising bacteria \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019170\n\n\n\n(Pseudomonas sp.) are able to increase available P concentrations in a P-deficient \nsoil.\n P availability in soil is influenced by several factors, including pH, Fe, Al \nand Ca cations and fertilisation intensity. The range of soil pH that supports the \nhighest P availability is 6.0 to 7.0 (Hardjowigeno 2010). Based on the provisions \nof the Indonesian Soil Research Institute (2009), available P above 20 ppm is \nconsidered to be very high. Table 3 shows that all treatments can be categorised \nas high in available P. \n\n\n\n       \uf06e          ISSN: 1978-1520 \n\n\n\nIJCCS  Vol. x, No. x,  July 201x :  first_page \u2013 end_page \n\n\n\n6 \n\n\n\nconsidered to be very high. Table 3 shows that all treatments can be categorised as \n\n\n\nhigh in available P.  \n\n\n\nTABLE 3 \nEffect of biofertiliser consortium and azolla compost on available P \n\n\n\nTreatments Available P \n(ppm)* \n\n\n\nA = 0 mmhos cm-1 of saline soil (negative control) 67.50 a \nB = 0 mmhos cm-1 of saline soil+ bio-fertlizer + azolla compost 83.39 a \nC = 2 mmhos cm-1 of saline soil 103.83 a \nD = 2 mmhos cm-1 +biofertilis+ azolla compost 83.56 a \nE = 4 mmhos cm-1 of saline soil 115.63 a \nF = 4 mmhos cm-1 +biofertiliser + azolla compost 89.55 a \nG = 6 mmhos cm-1 of saline soil 116.90 a \nH = 6 mmhos cm-1 +biofertilizs+ azolla compost 121.48 a \n*Numbers followed by  the same letter are not significantly different according to \nDuncan\u2019s Multiple Range Test (confidence level 5%) \n\n\n\nPlant Dry Weight \n\n\n\nObservation data and statistical analysis in Table 4 show that there is an increase \n\n\n\nin dry weight of plants due to the biofertiliser and azolla compost application at \n\n\n\nvarious levels of salinity.  \n\n\n\nTABLE 4 \nEffect of biofertiliser consortium and azolla compost on plant dry weight \n\n\n\nTreatments Plant dry weight \n(gr/pot)* \n\n\n\nA = 0 mmhos cm-1 of saline soil (negative control) 0.170 c \nB = 0 mmhos cm-1 of saline soil+ bio-fertlizer + azolla compost 0.213 d \nC = 2 mmhos cm-1 of saline soil 0.130 b \nD = 2 mmhos cm-1 +biofertiliser + azolla compost 0.173 c \nE = 4 mmhos cm-1 of saline soil 0.125 ab \nF = 4 mmhos cm-1 +biofertiliser + azolla compost 0.133 b \nG = 6 mmhos cm-1 of saline soil 0.100 a \nH = 6 mmhos cm-1 +biofertiliser + azolla compost 0.153 bc \n*Numbers followed wby the same letterare not significantly different according to Duncan\u2019s \nMultiple Range Test (confidence level 5%) \n \n\n\n\nTable 4 shows that the highest dry weight value was achieved with \n\n\n\ntreatment 0 mmhos cm-1 of saline soil with biofertliser and azolla compost at \n\n\n\nPlant Dry Weight\nObservation data and statistical analysis in Table 4 show that there is an increase \nin dry weight of plants due to the biofertiliser and azolla compost application at \nvarious levels of salinity. \n Table 4 shows that the highest dry weight value was achieved with \ntreatment 0 mmhos cm-1 of saline soil with biofertliser and azolla compost at \n0.213 gr / pot. Statistical analysis showed this treatment to be different to from the \ncontrol. This shows that biofertiliser and azolla compost give the same yield and \ngrowth potential in 2 mmhos cm-1 salinity as in the case of a non-saline condition. \n This study showed that the application of biofertiliser consortium and \nazolla compost have a positive effect on dry weight at all salinity levels. The \ntreatment without the biofertiliser consortium and azolla compost resulted in less \ndry weight compared to the treatment with biofertiliser consortium and azolla \ncompost in all treatments. The biofertiliser consortium and azolla compost could \nincrease the dry weight of rice by 25.3% in 0 mmhos cm-1 salinity. The increase \nin dry weight at each salinity level ranged from 0.075 to 0.525 gr per pot or \nequivalent to 6 - 50%. \n The increase in plant dry weight was due to phytohormone production \nby root colonising bacteria. These bacteria can stimulate plant growth, resulting \nin higher dry weight.  Bacteria in the biofertiliser can provide additional \nphytohormones and stimulate growth to a certain degree of salinity (Egamberdieva \n\n\n\nTABLE 3\nEffect of biofertiliser consortium and azolla compost on available P\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 171\n\n\n\n2012). Good plant growth will result in higher growth weight, and vice versa \n(Subowo 2015). \n\n\n\nCONCLUSION\nOur study showed that biofertiliser consortium and azolla compost had a positive \neffect on PSB population and dry weight of rice plants at some level of salinity \nbut had no effect on available P. Biofertilizer and azolla compost fertiliser at 2 \nmmhos cm-1 gave an equivalent result in dry weight of rice plant and increased the \npopulation of PSB by 146% compared with 0 mmhos cm-1 without the addition \nof biofertiliser and azolla compost. A high level of salinity affected the PSB \npopulation resulting in a decline which eventually influenced plant dry weight.  \nFurther assesment on the increased availability of other soil macro- nutrients \nafter  the application of a biofertiliser consortium in saline soil is needed to obtain \nsuperior isolates. \n\n\n\nACKNOWLEDGMENTS\nThis research was funded by Academic Leadership Grant (ALG) from Universitas \nPadjadjaran. We thank to staff Laboratory of Soil Biology and Laboratory of Soil \nFertility and Plant Nutrition Faculty of Agriculture, Universitas Padjadjaran for \ntheir cooperation We are also thankful to our students for supporting us during \nexperiment.\n \n\n\n\nTABLE 4\nEffect of biofertiliser consortium and azolla compost on plant dry weight\n\n\n\n       \uf06e          ISSN: 1978-1520 \n\n\n\nIJCCS  Vol. x, No. x,  July 201x :  first_page \u2013 end_page \n\n\n\n6 \n\n\n\nconsidered to be very high. Table 3 shows that all treatments can be categorised as \n\n\n\nhigh in available P.  \n\n\n\nTABLE 3 \nEffect of biofertiliser consortium and azolla compost on available P \n\n\n\nTreatments Available P \n(ppm)* \n\n\n\nA = 0 mmhos cm-1 of saline soil (negative control) 67.50 a \nB = 0 mmhos cm-1 of saline soil+ bio-fertlizer + azolla compost 83.39 a \nC = 2 mmhos cm-1 of saline soil 103.83 a \nD = 2 mmhos cm-1 +biofertilis+ azolla compost 83.56 a \nE = 4 mmhos cm-1 of saline soil 115.63 a \nF = 4 mmhos cm-1 +biofertiliser + azolla compost 89.55 a \nG = 6 mmhos cm-1 of saline soil 116.90 a \nH = 6 mmhos cm-1 +biofertilizs+ azolla compost 121.48 a \n*Numbers followed by  the same letter are not significantly different according to \nDuncan\u2019s Multiple Range Test (confidence level 5%) \n\n\n\nPlant Dry Weight \n\n\n\nObservation data and statistical analysis in Table 4 show that there is an increase \n\n\n\nin dry weight of plants due to the biofertiliser and azolla compost application at \n\n\n\nvarious levels of salinity.  \n\n\n\nTABLE 4 \nEffect of biofertiliser consortium and azolla compost on plant dry weight \n\n\n\nTreatments Plant dry weight \n(gr/pot)* \n\n\n\nA = 0 mmhos cm-1 of saline soil (negative control) 0.170 c \nB = 0 mmhos cm-1 of saline soil+ bio-fertlizer + azolla compost 0.213 d \nC = 2 mmhos cm-1 of saline soil 0.130 b \nD = 2 mmhos cm-1 +biofertiliser + azolla compost 0.173 c \nE = 4 mmhos cm-1 of saline soil 0.125 ab \nF = 4 mmhos cm-1 +biofertiliser + azolla compost 0.133 b \nG = 6 mmhos cm-1 of saline soil 0.100 a \nH = 6 mmhos cm-1 +biofertiliser + azolla compost 0.153 bc \n*Numbers followed wby the same letterare not significantly different according to Duncan\u2019s \nMultiple Range Test (confidence level 5%) \n \n\n\n\nTable 4 shows that the highest dry weight value was achieved with \n\n\n\ntreatment 0 mmhos cm-1 of saline soil with biofertliser and azolla compost at \n\n\n\nREFERENCES\nAli A., U.K. Anggoro, M.M. Machfoedz, D.H. Darwanto, F. Radhy, A. Sasono, E. \n\n\n\nHarmayani, U. Santosa, Darmanto, B.S Wigyosuryanto, M. Astuti, Subejo, \nRustiadi, S. Das and H.N. Kamiso. 2013. Improve food security: Inspiration \nfrom Bulaksumur. Yogyakarta: Gadjah Mada University Press.\n\n\n\nCherif-Silini, H., A. Silini, M. Ghoul, B.Yahiaoui and F.Arif. 2013. Solubilization of \nphosphate by the bacillus under salt stress and in the presence of osmoprotectant \ncompounds. African Journal of Microbiology Research 3(37): 4562- 4571. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019172\n\n\n\nEgamberdieva, D. 2012. Bacteria in agrobiology: stress management, Chap 2. In: The \nManagement of Soil Quality and Plant Productivity in Stressed Environment \nwith Rhizobacteria. Tashkent: National University of Uzbekistan.\n\n\n\nHardjowigeno, S. 2010. Soil Science. Jakarta: Akademika Pressindo.\n\n\n\nLacerda, C. F., J. Canbrain, M. A. Cano, H. A. Ruiz and J.T. Prisco. 2003. 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Bogor: Center for Agricultural Land Resources \nResearch and Development.\n\n\n\nSoil Research Institute. 2009. Technical Guidelines for Analysis of Soil, Plants, Water \nand Fertilizers. Soil Research Institute. Accessed 20 August 2017 from https://\ndoi.org/10.1007/s13398-014-0173-7.2 \n\n\n\nSubowo. Y. B. 2015. Application of biofertilizer as support for the growth of rice \n(Oryza sativa L.) on saline soil. Proceedings of the National Seminar on \nIndonesian Biodiversity Society 1(1): 150-154.\n\n\n\nWidawati, S. 2015. The effect of salinity to activity and effectivity phosphate \nsolubilizing bacteria on growth and production of paddy. Knowledge Publishing \nService 2: 609-612.\n\n\n\nWidawati, S., Suliasih and Muharam. 2015. Effect of seawater on the biofertilizer \npopulation, P available in the soil, and the growth of spinach (Amaranthus sp.). \nJ. Hort. 25(3): 222\u2013228.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: hashembefoul@yahoo.com \n\n\n\nINTRODUCTION\nArbuscular mycorrhizal fungi (AMF) are widespread in nature and are a \nfundamental component of the agro-ecosystem. They are stable, form mutually \nbeneficial plant-fungus associations, in which the fungus is partly inside and partly \noutside the host and form a living link between root and soil (Bethlenfalvay et al., \n1997). One of the most dramatic effects of infection by mycorrhizal fungi on the \nhost plant is the increase in phosphorus (P) uptake by the plant (Koide, 1991), \nmainly due to the capacity of mycorrhizal fungi to absorb phosphate from soil and \ntransfer it to the roots of the host (Asimi et al., 1980). Additionally, mycorrhizal \ninfection results in an increase in uptake of copper (Gildon and Tinker, 1983), zinc \n(Lambert et al., 1979), and sulphate (Buwalda et al., 1983). Sharma (2003) argued \nthat the resistance against biotic and abiotic stresses is due to the effects of AMF \ninducing the production of plant hormones.\n\n\n\nYeasts are a common component of the rhizosphere in all geographic zones \n(Sl\u00e1vikova and Vadkertiova, 2003) but little is known about their function in \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 157-168 (2015)                          Malaysian Society of Soil Science\n\n\n\nEffect of Arbuscular Mycorrhizal Fungus (Glomus Mosseae) \nand Soil Yeasts Interaction on Root Nodulation, N-Fixation \n\n\n\nand Growth of Faba Bean (Vichia faba)\n\n\n\nMohamed, H.M.*\n\n\n\n1Department of Soils & Water, Faculty of Agriculture, University of Assiut,\n71526, Assiut, Egypt.\n\n\n\nABSTRACT\nInteractions between the arbuscular mycorrhizal fungus (AMF) Glomus \nmosseae and two soil yeasts (Saccharomyces cerevisiae and Candida sake) and \ntheir effects on faba bean plants were studied in a pot experiment in sterile, \nphosphorus (P) deficient soil. These organisms interacted synergistically when \nadded consecutively at 2-week intervals, where sporulation, root infection with \nG. mosseae and the populations of either soil yeast species were significant with \ndual inoculation, especially when soil yeast species were inoculated for two weeks \nprior to sowing. Plant shoot dry weight, uptake of nitrogen (N) and P by the shoots, \nas well as nodulation and nitrogenase activity of faba bean roots were improved \nby inoculation with either G. mosseae or the two soil yeast species. Soil yeast \nspecies S. cerevisiae was more effective than C. sake. Dual inoculation was more \neffective on growth, nutrition nodulation and nitrogenase activity than individual \ninoculation.  \n\n\n\nKeywords: Glomus Mosseae, Candida sake, Saccharomyces cerevisiae, \nglasshouse experiment, inoculation, nodulation, Vichia faba\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015158\n\n\n\nnutrient cycling (Sl\u00e1vikova et al., 2002) and their interactions with other soil \nmicroorganisms. Yeasts have been found in different soils and rhizosphere of \nvarious plants (Morais et al., 1995; Ganter, 2006). Although the numbers of yeasts \nare low in comparison with other microorganisms, many investigators claimed \nthat this group of organisms appear to play an important role in soil fertility and \nare capable of producing certain growth promoting substances such as hormones, \namino acids, vitamins, proteins, organic acids, and soluble and volatile exudates \n(Sampedro et al., 2004; Boby et al., 2007). Nonetheless, despite the known ability \nof yeasts to produce organic acids, there have been very few reports on their \nability to solubilise inorganic phosphate (Kanti and Sudiana, 2002; Vassileva et \nal., 2000; Hesham and Mohamed, 2011). Only a few studies have investigated \nAMF interactions with soil yeasts (Fracchia et al., 2003; Sampedro et al., 2004; \nGollner et al., 2006).\n\n\n\nInteractions between mycorrhizal fungi and other soil microorganisms may \noccur widely. Shifts in the presence or abundance of microbial species occuring \nin the rhizosphere of mycorrhizal plants (Linderman and Paulitz, 1992) and \ninteraction between mycorrhizal fungi and other rhizosphere inhabitants can be \ndetrimental to the mycorrhizal fungi and certain rhizosphere microorganisms \n(Posta et al., 1994). Hence, this study was conducted to investigate the interactions \nbetween the AMF and yeast species, and their effects on faba bean plants. \n\n\n\nMATERIALS AND METHODS\n\n\n\nPreparation of Microbial Inoculums\nRhizobium leguminosarum, locally isolated from root nodules of common bean \nplants, was used in this study. The bacterium was cultured on yeast extract-\nmannitol broth (Trinick and Parker, 1982), at 28\u00baC on a rotary shaker at a speed \nof 150 RPM. Bacterial cells were harvested at the late logarithmic growth phase, \ncentrifuged, washed with sterile distilled water, and diluted with sterile distilled \nwater to a cell density of approximately 105 cells per ml. During seeding, 10 ml \nof the bacterial suspension was inoculated into each pot.\n\n\n\nYeast species Saccharomyces cerevisiae and Candida sake were previously \nisolated from a composite sample of clay soil (Hesham and Mohamed, 2011). The \nstrains were maintained on malt-yeast-glucose-peptone agar (YM) slants at \n4\u00baC. One-week old YM slant were scraped into sterile water to give a suspension \nof 106 cells per ml, 10 ml of which was added per treated pot. Each soil yeast \nspecies (S. cerevisiae and C. sake) were inoculated at 2-week intervals before or \nafter sowing.\n\n\n\nThe spores of the AMF Glomus mosseae were isolated by wet sieving the \nsoil (Gerdeman and Nicolson, 1963) from a stock culture where onion was the \nhost plant. This species was reproduced by onion pot culture within 4 months in \nsterilised clay loam soil, by autoclaving twice at 121\u00baC for one hour. The amount \nof inoculum was adjusted to give about 104 spores per pot at sowing. The number \n\n\n\nMohamed, H.M.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 159\n\n\n\nof spores in the soil sample was determined by Gerdeman and Nicolson\u2019s (1963) \nwet sieving method. \n\n\n\nGreenhouse Experiment\nThe trial was carried out in the greenhouse of the Soils and Water Department, \nFaculty of Agriculture, Assiut University, Egypt. A pot experiment was conducted \nin the 2013 season to study the interactions between the AMF, G. mosseae and \ntwo soil yeasts (S. cerevisiae and C. sake) and their effects on faba bean plants \nin calcareous soil collected from the El-Gorahib Experimental Farm of Assiut \nUniversity. The physical and chemical properties of the soil used in this study are \npresented in Table 1. The experimental design used was a complete randomised \nblock design with six replicates of each treatment. The experiment was established \nwith 6 treatments and one control: uninoculated control (C); inoculation with AM \nfungus (G. mosseae) (Gm); inoculation with S. cerevisiae (Sc); inoculation with \nC. sake (Cs); G. mosseae and S. cerevisiae (Gm + Sc); G. mosseae and C. sake \n(Gm + Cs). Yeast species (S. cerevisiae and C. sake) were inoculated at 2-week \nintervals before or after sowing.\n\n\n\nFaba bean seeds (Vichia faba) cv. Assiut-115 were surface sterilised by \nshaking in 7% calcium hypochlorite for 10 minutes, rinsed with sterile distilled \nwater and sown (4 seeds per pot) in 30-cm  diameter plastic pots containing 5 \nkg sieved calcareous soil. The soil was autoclaved twice at 1.2 kg cm2 pressure \nand 121\u00baC for one hour at a time. The pots were irrigated to field capacity (47%) \nduring the experimental period under greenhouse conditions. After emergence, \nthe seedlings were thinned to two uniform plants per pot. Plants were harvested \n45 days after sowing. The root systems of each six pot replicates per treatment \nwere divided into three batches. In the first batch, mycorrhizal root infection \n\n\n\nEffect of Mycorrhizal and Yeasts on Faba Bean \n\n\n\nTABLE 1\nPhysical and chemical characteristics of soil used.\n\n\n\n\n\n\n\n 9 \n\n\n\n \nTABLE 1 \n\n\n\nPhysical and chemical characteristics of soil used. \n \n\n\n\nProperties Values \nClay (%) 9.3 \nSilt   (%) 30.5 \nSand (%) 60.2 \nTexture grade Sand loam \nTotal CaCO3 (%) 16.18 \nEC dS cm-1 (1:1) 1.12 \npH (1:1 suspension) 8.3 \nTotal nitrogen (%) 0.005 \nOrganic matter (%) 0.28 \nAvailable (P mg g-1 soil) 5.60 \nExchangeable cation (cmolc kg-1): \n  Ca2+ \n  Mg2+ \n  Na+ \n\n\n\n \n0.51 \n0.26 \n0.33 \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015160\n\n\n\nwas measured after clearing and staining 1 cm root segments with tryphan blue \n(Philips and Hayman, 1970). In the second batch, the number, fresh and dry \nweights of the nodules and the dry weight of the roots were determined. In the \nthird batch, nitrogenase activity of the nodulating faba bean root was assayed by \nthe acetylene reduction method (Hardy et al., 1973). Unwashed plant roots were \nplaced immediately in a canning jar fitted with a serum stopper for gas sampling. \nTo prevent drying, the roots in the sampling jars were covered with some of the \nsoil that had been removed from them earlier. Also, control jars contained only \nsoil from each treatment. Ten percent of the gaseous atmosphere in the jar was \nremoved and replaced by acetylene (C2H2). The jars were then tightly sealed \nwith Parafilm  M\u00ae and incubated at 30\u00baC for 24 hours. A volume of 0.1 ml gas \nsample from each jar was removed and injected into a Pye Unicome 104-inch \ngas chromatograph containing a flame ionisation detector and a 5 ft. x 118-inch \nglass column of activated alumina (80-100 mesh). The oven temperature was set \nat 150\u00ba C, and the carrier gas was nitrogen (N) at a flow rate of 30 ml/min. Dried \nshoots were ground and submitted to the acid-digestion using a 2:1 HNO3: HClO4 \nacid mixture for determination of N and P uptake. The population of each soil \nyeast (S. cerevisiae and C. sake) in the rhizosphere soil was measured by dilution \nplate count on malt-yeast-glucose-peptone agar medium at 2, 4 and 6 weeks after \nsowing, The number of G. mosseae spores in the soil was determined using the \nwet sieving and decanting method (Gerdeman and Nicolson, 1963).\n\n\n\nStatistical Analysis\nThe data reported in this paper are the mean values based on the six replicates. \nDifferences among treatments were tested by ANOVA and mean values among \ntreatments were compared using Duncan\u2019s multiple range test at P = 0.05. \nStatistical analyses of the data were performed using the statistical computer \nprogram (Statsoft, 1995). \n\n\n\nRESULTS\n\n\n\nInteractions Between G. mosseae and Soil Yeast Species \nSoil yeast species S. cerevisiae or C. sake significantly stimulated sporulation \nand mycorrhizal infection when inoculated with G. mosseae, either pre-sowing \nor at sowing of faba bean seeds (Table 2). The response was very apparent when \ninoculation was carried out 2 weeks prior to sowing and prominently so when S. \ncerevisiae was applied. Post-sowing inoculation of yeast species did not affect \nsporulation or mycorrhizal infection.\n\n\n\nDual inoculation with G. mosseae and soil yeast species in the rhizosphere \nsignificantly increased the population density when the latter was inoculated 2 \nweeks prior to sowing (Table 2). This response did not occur with inoculation \n2 weeks post-sowing. Irrespective of application time, the population density of \neither soil yeast species doubled every 2 weeks. The population of S. cerevisiae \nwas higher than that of  C. sake, especially 2 weeks after sowing.\n\n\n\nMohamed, H.M.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 161\n\n\n\nNodulation and Nitrogenase Activity \nThe effect of inoculation with G. mosseae and two soil yeast species on nodulation \nand nitrogenase activity varied with the treatments (Table 3). Under all inoculation \nconditions, neither soil yeast species significantly affected the number of nodules, \nfresh and dry weight per plant, nor nitrogenase activity per unit nodule dry \nweight. The dual inoculation with G. mosseae significantly increased nodulation \nand weight as well as nitrogenase activity per unit nodule dry weight. G. mosseae \nalone had no effect on nitrogenase activity, but the coupling of either soil yeast \nspecies stimulated nitrogenase activity per unit nodule dry weight, almost to the \nsame extent, regardless of time of application of these organisms.\n\n\n\nPlant Growth and Uptake of Nutrients \nThere was a significant interaction effect between soil yeast species and \nmycorrhizal inoculation on plant growth (Table 4). Soil yeast species affected \nthe dry weight of either shoots or roots of faba bean plants, except when S. \ncerevisiae was inoculated 2 weeks before sowing. Whilst inoculation with G. \nmosseae increased the dry weight of either shoots and roots of faba bean plants, \ndual inoculation with G. mosseae and yeast species significantly increased the dry \nweight above that of the G. mosseae treatment alone. Maximum weights were \nachieved when inoculation was performed 2 weeks before sowing.\n\n\n\nEffect of Mycorrhizal and Yeasts on Faba Bean \n\n\n\nTABLE 2\nInteraction between G. mosseae and soil yeast strains (S. cerevisiae and C. sake) in the \n\n\n\nrhizosphere soil of faba bean plants.\n\n\n\n\n\n\n\n 10 \n\n\n\nTABLE 2 \nInteraction between G. mosseae and soil yeast strains (S. cerevisiae and C. sake) in the \n\n\n\nrhizosphere soil of faba bean plants. \n \n\n\n\nInoculation \ntime for \nsoil yeasts \n\n\n\nInoculation* \ntreatment \n\n\n\nG. \nmosseae \nspore g-1 \ndry soil \n\n\n\nInfection \nby G. \n\n\n\nmosseae \n(%) \n\n\n\nSoil yeasts population \nc.f.u. x 103 g-1 dry soil \nWeeks after sowing \n\n\n\n2 4 6 \nAt sowing C \n\n\n\nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n0d \n\n\n\n27c \n\n\n\n0d \n\n\n\n0d \n\n\n\n52a \n\n\n\n39b \n\n\n\n0d \n\n\n\n46c \n\n\n\n0d \n\n\n\n0d \n\n\n\n60a \n\n\n\n52b \n\n\n\n0c \n\n\n\n0c \n\n\n\n2.53b \n\n\n\n2.70b \n3.56a \n3.32a \n\n\n\n0e \n\n\n\n0e \n\n\n\n4.56d \n\n\n\n5.83c \n\n\n\n7.30a \n\n\n\n6.80b \n\n\n\n0e \n\n\n\n0e \n\n\n\n5.86d \n\n\n\n6.96c \n\n\n\n9.76a \n\n\n\n8.90b \n\n\n\n2 weeks \npre-sowing \n\n\n\nC \nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n0c \n\n\n\n33b \n\n\n\n0c \n\n\n\n0c \n\n\n\n56a \n\n\n\n45b \n\n\n\n0d \n\n\n\n46c \n\n\n\n0d \n\n\n\n0d \n\n\n\n71a \n\n\n\n62b \n\n\n\n0d \n0d \n\n\n\n3.63b \n2.83c \n4.80a \n3.90b \n\n\n\n0d \n\n\n\n0d \n5.90b \n4.63c \n7.70a \n5.80b \n\n\n\n0e \n\n\n\n0e \n\n\n\n10.26b \n\n\n\n7.63d \n\n\n\n12.66a \n\n\n\n8.46c \n\n\n\n2 weeks \npost-\nsowing \n\n\n\nC \nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n0c \n\n\n\n32b \n\n\n\n0c \n\n\n\n0c \n\n\n\n34a \n\n\n\n31b \n\n\n\n0c \n\n\n\n46b \n\n\n\n0c \n\n\n\n0c \n\n\n\n50a \n\n\n\n47b \n\n\n\n0c \n\n\n\n0c \n0.76ab \n\n\n\n0.73ab \n\n\n\n0.90a \n\n\n\n0.56b \n\n\n\n0d \n\n\n\n0d \n\n\n\n2.36b \n\n\n\n2.03c \n\n\n\n3.96a \n\n\n\n3.80a \n\n\n\n0d \n\n\n\n0d \n\n\n\n5.11c \n\n\n\n4.90c \n\n\n\n7.72a \n\n\n\n6.80b \n\n\n\n* C: uninoculated soil; Gm: Glomus mosseae; Sc: Saccharomyces cerevisiae; Cs: Candida sake. \nThe values in columns followed by the same letter (s) are not significant at 5% significance level by \nDuncan's multiple range test. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015162\n\n\n\nMohamed, H.M.\n\n\n\nTABLE 3\nEffect of inoculation with G. mosseae or/and soil yeast strains (S. cerevisiae and C. sake) \n\n\n\non nodulation and nitrogenase activity in faba bean plants.\n\n\n\nTABLE 4\nEffect of inoculation with G. mosseae or/and soil yeast strains (S. cerviceae and C. sake) \n\n\n\non plant growth, nitrogen and phosphorus uptake of faba bean plants.\n\n\n\n\n\n\n\n 11 \n\n\n\nTABLE 3 \nEffect of inoculation with G. mosseae or/and soil yeast strains (S. cerevisiae and C. sake) \n\n\n\non nodulation and nitrogenase activity in faba bean plants. \n \n\n\n\nInoculation \ntime for \nsoil yeasts \n\n\n\nInoculation* \ntreatment \n\n\n\nNodulation Nitrogenase \nactivity \n\n\n\nnm C2H2 h-1  \nper plant \n\n\n\nNumber \nper plant \n\n\n\nFresh wt. \nmg-1 plant \n\n\n\nDry wt. \nmg-1 plant \n\n\n\nAt sowing C \nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n36e \n\n\n\n50c \n41d \n55b \n63a \n47c \n\n\n\n476.5b \n607.4ab \n545.2b \n596.4ab \n715.9a \n602.8ab \n\n\n\n72.3e \n96.0c \n82.3d \n80.3d \n109.4a \n101.5b \n\n\n\n1.63b \n1.90ab \n1.86ab \n1.80ab \n2.26a \n2.00ab \n\n\n\n2 weeks \npre-sowing \n\n\n\nC \nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n36e \n53c \n44d \n43d \n65a \n60b \n\n\n\n542.5b \n644.7ab \n609.5ab \n680.5ab \n746.9a \n627.2ab \n\n\n\n73.4e \n99.8c \n90.9d \n97.7c \n113.9a \n106.3b \n\n\n\n1.76b \n1.86ab \n1.96ab \n2.06ab \n2.50a \n1.90ab \n\n\n\n2 weeks \npost-\nsowing \n\n\n\nC \nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n35d \n51b \n42c \n38cd \n60a \n56a \n\n\n\n549.7bc \n633.6ab \n494.3c \n547.5bc \n720.3a \n605.1b \n\n\n\n75.8d \n97.4b \n74.5d \n83.0c \n108.8a \n91.5b \n\n\n\n1.66b \n1.83b \n1.73b \n1.66b \n2.13a \n1.90b \n\n\n\n* C: uninoculated soil; Gm: Glomus mosseae; Sc: Saccharomyces cerevisiae; Cs: Candida sake. \nThe values in columns followed by the same letter (s) are not significant at 5% significance level \nby Duncan's multiple range test. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n 12 \n\n\n\nTABLE 4 \nEffect of inoculation with G. mosseae or/and soil yeast strains (S. cerviceae and C. sake) \n\n\n\non plant growth, nitrogen and phosphorus uptake of faba bean plants. \n \n\n\n\nInoculation \ntime for \nsoil yeasts \n\n\n\nInoculation* \ntreatment \n\n\n\nDry weight \ng per plant \n\n\n\nN-uptake \nmg per plant \n\n\n\nP-uptake \nmg per plant \n\n\n\nShoots Roots \n\n\n\nAt sowing C \nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n0.97e \n1.41bc \n1.23cd \n1.07de \n1.98a \n1.62b \n\n\n\n0.68e \n0.89c \n0.79d \n0.76d \n1.09a \n0.99b \n\n\n\n27.87c \n28.56abc \n28.80abc \n29.60a \n29.33ab \n28.30cb \n\n\n\n1.59bc \n2.12a \n1.60c \n1.63b \n2.09a \n2.15a \n\n\n\n2 weeks \npre-sowing \n\n\n\nC \nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n0.98e \n1.44c \n1.26d \n1.08e \n2.31a \n1.89b \n\n\n\n0.67e \n0.90c \n0.80d \n0.69e \n1.16a \n1.05b \n\n\n\n28.36b \n28.64b \n30.23b \n29.26b \n34.40a \n30.06b \n\n\n\n1.53e \n2.14a \n1.71c \n1.61d \n2.20b \n2.18b \n\n\n\n2 weeks \npost-\nsowing \n\n\n\nC \nG m \nSc \nCs \nGm + Sc \nGm + Cs \n\n\n\n1.00cd \n1.47b \n1.13c \n0.95d \n2.04a \n1.37b \n\n\n\n0.68b \n0.85b \n0.74b \n0.76b \n0.97a \n0.90b \n\n\n\n28.3a \n28.7a \n27.5a \n29.3a \n32.3a \n29.8a \n\n\n\n1.53d \n2.12b \n1.55d \n1.65c \n1.97a \n1.93ab \n\n\n\n* C: uninoculated soil; Gm: Glomus mosseae; Sc: Saccharomyces cerevisiae; Cs: Candida sake. \nThe values in column followed by the same letter (s) are not significant at 5% significance level by \nDuncan's multiple range test. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 163\n\n\n\nWith regard to nutrient-uptake, the inoculation of soil yeast species, alone \nor in the presence of G. mosseae, did not affect faba bean shoot N-uptake (Table \n4). Whereas, the P-uptake was significantly affected by the presence of either \nsoil yeast species except for S. cerevisiae inoculated 2 weeks before sowing. G. \nmosseae, alone or coupled with soil yeasts, increased P accumulation in faba bean \nshoots. \n\n\n\nDISCUSSION\nThis study showed a significant increase in infection and spore production of G. \nmosseae as a result of its association with either soil yeast species (S. cerevisiae or \nC. sake), with the former being more effective than the latter. The results obtained \nin the present study were consistent with the findings of Boby et al. (2008) \nwho investigated all the yeasts (Rhodotorula mucilaginosa, Metschnikowia \npulcherrima, Trichosporon cutaneum var. cutaneum, S. cerevisiae, Cryptococcus \nlaurentii, and Debaryomyces occidentalis var. occidentalis) to show a synergistic \ninteraction with the G. mosseae with significant increases in mycorrhizal root \ncolonisation and spore numbers. Singh et al. (1991) reported an increase in the \nproduction of vesicles, arbuscular and spores of native AMF due to inoculation \nwith S. cerevisiae in legumes. Furthermore, Fracchia et al. (2003) reported the \nenhancement of arbuscular mycorrhizal colonisation of soybean and red clover with \nthe application of the yeast R. mucilaginosa to the soil. In contrast, Gollner et al. \n(2006) showed that presence of soil yeasts did not significantly affect mycorrhizal \ncolonisation of maize roots, but negatively affected the length of AMF extraradical \nmycelium. The yeasts may enhance AMF development by supplying vitamin B12 \nto the rhizosphere, as AMF have been shown to be stimulated by this vitamin \n(Singh et al., 1991). Thus, vitamin B12 produced by the soil yeasts might have \nresulted in better plant growth and yield in plants treated with both G. mosseae and \nsoil yeasts. The observations of Boby et al. (2008) show the effect of inoculation \nwith S. cerevisiae on non-mycorrhizal plants to be negligible, while it did increase \nthe root colonisation and spore count of mycorrhizal plants. This suggests that the \nyeasts specifically stimulate arbuscular mycorrhizal development rather than the \nhost plant, which upholds the observation made by Larsen and Jacobsen (1996). \nIt is quite possible that Vitamin B12 production by soil yeasts could be the main \nreason for the stimulation of mycorrhizal development observed in this study \nand needs further investigation. Time of soil yeasts inoculation played a role in \nsporulation and infection of mycorrhizal fungus. Two weeks post sowing seemed \nto have a lower effect. This might be attributed to the age of soil yeasts (being \nonly 4 weeks old) compared with inoculation 2 weeks pre-sowing (8 weeks old). \nUnder the latter condition, the soil yeast count was double that under the former \ncondition. Soil yeasts seemed to secrete moderately harmful metabolites which \nhinder, to a certain extent, spore formation by G. mosseae.\n\n\n\nThe population of soil yeasts in the root zone soil was stimulated in plants \ninoculated with G. mosseae + soil yeasts. The stimulatory effects of G. mosseae \non the activity of either test soil yeast species may be attributed to alterations in the \n\n\n\nEffect of Mycorrhizal and Yeasts on Faba Bean \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015164\n\n\n\nroot exudates. AMF can significantly influence the microflora in the rhizosphere \ndirectly through fungal exudates or indirectly through altering root exudation \npatterns (Linderman, 1992). Boby et al., (2008) found that soil yeasts in the root \nzone soil were stimulated in plants inoculated with G. mosseae + soil yeasts. \nThe plants inoculated with G. mosseae alone also had a higher yeast population \nin the root zone soil compared to uninoculated plants. These results contradict \nthose of Sampedro et al., (2004) who found no effect due to the presence of the \nAMF G. mosseae on populations of soil yeasts (R. mucilaginosa, C. laurentii and \nS. kunashirensis) in the rhizosphere of soybean. Fracchia et al., (2003) observed \na similar population of Rhodotorula mucilaginosa in rhizosphere of soybean \ninoculated with G. mosseae and in rhizosphere of red clover inoculated with \nGigaspora rosea. \n\n\n\nThe results further showed that nodule number, fresh and dry weights and \nnitrogenase activity were highly apparent due to treatment with G. mosseae. \nCoupling either yeast species with G. mosseae, in the soil under the test \ncondition, increased the efficacy of the latter organism to initiate nodulation, \nbetter nitrogenase activity and growth of faba bean plants. Singh  et al., (1991) \nreport that the increases in nodule numbers and dry weights of legumes due to \ninoculation with yeasts were caused by stimulation of the legumes\u2019 indigenous \nmicroflora. Similarly, the increases in nodulation and other symbiotic parameters \nof forage legumes (Trifolium alexandrium and Medicago sativa) due to combined \ninoculation of yeasts (S. cerevisiae and Candida torpicalis) and specific \nRhizobium sp. have been reported earlier (Tuladhur and Sub Rao, 1985) and were \nalso attributed to the stimulatory action of yeasts on the multiplication of native \nrhizobia (Tuladhur, 1983).\n\n\n\nSimilarly, G. mosseae stimulated growth and the uptake of N and P in the \nfaba bean shoot system, regardless of inoculation time, whereas C. sake alone \nshowed no significant effect. The same applied to S. cerevisiae when inoculated 2 \nweeks before sowing indicating the difference in metabolic activity of these two \norganisms. The former seemed to produce metabolic activators (bio-regulators) \nthat stimulated the activity of faba bean plants. Many investigators claimed \nthat yeasts seemed to play an important role in soil fertility and are capable of \nproducing certain growth promoting substances such as hormones, amino acids, \nvitamins, proteins, organic acids and soluble and volatile exudates (Sampedro et \nal., 2004; Boby et al., 2007; Hesham and Mohamed, 2011). Dual inoculation with \nG. mosseae and two soil yeast species highly stimulated the growth of the faba \nbean regardless of the type of treatment, indicating the synergistic effect between \nthe two types of organisms. However, the efficacy of these organisms for N-uptake \nin faba bean shoot was unaffected by either type of treatment, except when \ninoculated 2 weeks pre-sowing, indicating that the soil yeasts had a significant \nrole in the synergistic efficacy of these organisms. The efficacy of G. mosseae \nfor P accumulation was highly apparent when either soil yeast was coupled with \nG. mosseae. AMF are known to improve P nutrition of plants, especially in P \n\n\n\nMohamed, H.M.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 165\n\n\n\ndeficient soil, and can translocate phosphate by scavenging a larger volume of soil \nwith extensive hyphae (Kothari et al., 1990; Ortas et al., 2002). \n\n\n\nSome studies reveal that interactions between soil microorganisms and \nAMF are important for plant growth (Azco\u00b4n-Aguilar et al., 2002; Barea et al., \n2002). Gollner et al., (2006) showed that inoculation with soil yeasts and AMF \ncan significantly affect the shoot dry weight of maize. Moreover, this study \nfound specific effects of certain combinations of mycorrhizal inoculation and \nyeast species on plant biomass. In plants not inoculated with AMF, only C. sake \nincreased plant growth compared to plants inoculated with Glomus intraradices \nwhere all two yeasts showed positive effects on shoot dry weight. Similar results \nwere obtained by Bhowmik and Singh (2004) when inoculation of Chloris guyana \nwith the yeast S. cerevisiae alone did not affect plant growth. However, inoculation \nwith the yeast and the AMF G. mosseae resulted in a significant increase in plant \nbiomass.\n\n\n\nCONCLUSION\nIn conclusion, the results indicated that the soil yeasts (S. cerevisiae and C. \nsake) and AMF (G. mosseae), generally exhibit positive mutual relationships. \nDual inoculation of faba bean with soil yeast and AMF increased shoot and \nroot biomass, nodulation, nitrogen-fixing activity, and uptake of N and P. 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Interactions between a mycophagus Collembola, \ndry yeast and the external mycelium of an arbuscular mycorrhizal fungus. \nMycorrhiza 6: 259\u2013264.\n\n\n\nLinderman, R.G. 1992. Vesicular-arbuscular mycorrhizae and soil microbial \ninteractions. In G.J. Bethlenfalvay and R.G. Linderman (Eds.), Mycorrhizae \nin Sustainable Agriculture, ed. C.A. Rosa and G. Peter. American Society \nAgronomy special publication No. 54. Madison, Wisconsin, pp. 45\u201370.\n\n\n\nLinderman, R.G. and Paulitz, T.C. (1990) Mycorrhizal-rhizobacterial interactions. \nIn: D. Homby (ed) Biological Control of Soil-Borne Plan Pathogens, CAB \nInternational, Wallingford.\n\n\n\nMorais, P.B., M.B. Martins, A.N., Hagler, L.C. Mendon\u00e7a-Hagler and L.B. Klaczko. \n1995. Yeast succession in the Amazon fruit Parahancornia amapa as resource \npartitioning among Drosophila spp. Applied and Environmental Microbiology \n61: 4251-4257. \n\n\n\nOrtas, I., D. Ortakci, Z. Kaya, A. Cinar and N. Onelge. 2002.  Mycorrhizal dependency \nof sour orange in relation to phosphorus and zinc nutrition. J. Plant Nutr. 25: \n1263\u20131279.\n\n\n\nPhilips, J.M. and D.S. Hayman. 1970. Improved procedures for clearing roots and \nstaining parasite and VA mycorrhizal fungi for rapid assessment of infection. \nTrans. Br. Mycol. Soc. 55: 158- 161.\n\n\n\nPosta, K., H. Marschner and V. R\u00f6mheld. 1994. Manganese reduction in the \nrhizosphere of mycorrhizal and nonmycorrhizal maize. Mycorrhiza 5: 119-124.\n\n\n\nSampedro, I., E. Aranda, J. M. scervino, S. Fracchia, I. Garc\u00eda-Romera,  J. A. Ocampo \nand S. Godeas. 2004. Improvement by soil yeasts of arbuscular mycorrhizal \nsymbiosis of soybean (Glycine max) colonized by Glomus mosseae. Mycorrhiza \n14: 229-234.  \n\n\n\nSharma, A.K. 2003. Biofertilizers for Sustainable Agriculture. India: Agro-Bios.\n\n\n\nSingh, C.S., A. Kapoor and S.S. Wange. 1991. The enhancement of root colonization \nof legumes by vesicular-arbuscular mycorrhizal (VAM) fungi through the \ninoculation of the legume seed with commercial yeast (Saccharomyces \ncerevisiae). Plant Soil 131: 129\u2013133.\n\n\n\nSl\u00e1vikov\u00e1, E., B. Ko\u015b\u0131kov\u00e1 and M. Mikula\u015b\u03ccv\u00e1. 2002. Biotransformation of waste \nlignin products by the soil-inhabiting yeast Trichosporon pullulans. Can. J. \nMicrobiol. 48(3): 200\u2013203.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015168\n\n\n\nSl\u00e1vikov\u00e1, E. and R. Vadkertiov\u00e1. 2003. The diversity of yeasts in the agricultural soil. \nJ. Basic Microbiol. 43: 430\u2013436.\n\n\n\nStatSoft, 1995. Statistica for Windows (Computer Program manual). StatSoft, Inc., \nTulsa, Oklahoma, USA.\n\n\n\nTrinick, M.J. and CA. Parker. 1982. Self inhibition of Rhizobial strains and the \ninfluence of cultural conditions on microbial interactions. Soil Biol. Biochem.14 \n79-86. \n\n\n\nTuladhur, K.D.Y. 1983. Interaction of Soil Microorganisms with Rhizobium. Ph.D. \nThesis, Indian Agricultural Research Institute, Pusa University, New Delhi, \nIndia.\n\n\n\nTuladhur, K.D.Y. and N.S. Subba Rao, 1985.  Interaction of yeasts and some nitrogen-\nfixing bacteria on nodulation of legumes. Plant Soil 84: 287\u2013291.\n\n\n\nVassileva, M,, R. Azcon, J. Barea and N. Vassilev. 2000. Rock phosphate solubilization \nby free and encapsulated cells of Yarrowia lipolytica. Process Biochem. 35: \n693-697.\n\n\n\nMohamed, H.M.\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : Email : toufiq_iqbal@yahoo.com\n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 13: 105-117                               Malaysian Society of Soil Science\n\n\n\nEffects of Nitrogen and Phosphorous Fertilisation on Nitrous \n\n\n\nOxide Emission and Nitrogen Loss in an Irrigated Rice Field\n\n\n\nM.T. Iqbal\n\n\n\nDepartment of Agronomy and Agricultural Extension,\n\n\n\nUniversity of Rajshahi, Rajshahi-6205, Bangladesh\n\n\n\nINTRODUCTION\n\n\n\nNitrous oxide is an important greenhouse gas and contributes to the destruction of \n\n\n\nthe ozone layer (Bowden 1986; Graedel and Crutzen 1994). Considerable amouns \n\n\n\nof this trace gas are emitted from natural and cultivated soils through microbial \n\n\n\nprocess, the most important being nitrification and denitrification (Bowden 1986; \n\n\n\nTidje 1988). Fertiliser application in rice soil leads to increased N\n2\nO emissions \n\n\n\n(Eicher 1990). China is the most densely populated country in the world. It \n\n\n\ncontains 26% of  the world\u2019s total crop harvested area and is responsible for \n\n\n\nABSTRACT\n\n\n\nNitrous oxide is an important greenhouse gas which contributes to stratospheric \n\n\n\nozone destruction, but still little is known about emission of this trace gas from \n\n\n\npaddy rice fields treated with N and P fertilisation and how it is affected by \n\n\n\nirrigation. Therefore, a field experiment was conducted to measure nitrous oxide \n\n\n\n(N\n2\nO) emissions and nitrogen loss through the emission from irrigated rice \n\n\n\nfields treated with different nitrogen and phosphorous fertilisers. Emissions of \n\n\n\nnitrous oxide (N\n2\nO) were measured by the closed chamber method during the \n\n\n\nvegetative period (6 July to 8 August) of the paddy plant in ShuangQiao farm \n\n\n\nin the northern part of Zhejiang Province in the Southeast coastal area of China. \n\n\n\nJia-9312 rice variety was used for rice cultivation. Treatments of five nitrogen \n\n\n\nrates (0, 90, 180, 270, 360 kg N ha-1) and three phosphorous rates (0, 40 and \n\n\n\n60 kg P ha-1) were laid out in a randomised block design with 3 replications \n\n\n\nin 45 plots. Submerging the rice field by continuous flooding irrigation at 7 \n\n\n\ncm depth up to maturity caused a remarkable reduction in N\n2\nO emission. First \n\n\n\nand second peaks of emission were observed immediately after basal and top \n\n\n\ndressing of fertiliser addition due to nitrification and denitrification process.\n\n\n\nThe study indicates that 180 kg N ha-1 incorporation with 40 kg P ha-1 may \n\n\n\nbe practised in mitigation of N\n2\nO emissions from irrigated paddy rice fields. \n\n\n\nThe amount of total N\n2\nO emission from different N and P treatments ranged \n\n\n\nfrom 431.89 to 1181.21 g N ha-1 which was a N loss of 0.10  to 1.18% through \n\n\n\nemission of applied nitrogen. \n\n\n\nKeywords: Denitrification, nitrification, submerged soil, vegetative \n\n\n\n      period\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009106\n\n\n\nmore than half of the total global chemical N fertiliser consumption, as of 1999 \n\n\n\n(FAOSTAT 2002). Production and harvest area of rice in these regions account for \n\n\n\nalmost 50% of the total world rice production and harvest (FAOSTAT, 2002). Urea \n\n\n\nand ammonium bicarbonate account for 85% of the total chemical N fertilisers, \n\n\n\nwhich is unlike the situation in the rest of the world (Xings 1998). Therefore, \n\n\n\ngreat efforts should be taken to understand the processes and key regulating \n\n\n\nfactors of N\n2\nO emissions from rice paddy fields (Zheng et al. 1999). Nitrous oxide \n\n\n\nemission from fertilised rice has been reported by several other authors (Smith et \n\n\n\nal. 1982; Buresh and DeDatta, 1990: Lindau et al. 1990; Cai et al. 1997;1999). \n\n\n\nNitrous oxide emissions from lowland rice fields, although small compared with \n\n\n\nthose from upland systems, represent a substantial source of atmospheric N\n2\nO \n\n\n\n(Hasegawa et al. 1998; Xing 1998). According to IPCC (1995) and Houghton et \n\n\n\nal. (1995), anthropogenic emissions of N\n2\nO needs to be reduced by 50%. Nitrous \n\n\n\noxide emission is stimulated by nitrogen fertiliser application. Nitrous oxide \n\n\n\nemission from nitrogen fertiliser application in rice fields of Taiwan ranged from \n\n\n\n0.05 to 0.28% (Chao 1997). The quantification of N\n2\nO and N\n\n\n\n2\n losses is uncertain \n\n\n\nbecause of the large spatial and temporal variability. By reviewing data for N\n2\nO \n\n\n\nemissions from agricultural soils, emission of N\n2\nO was found to range from 0.20 \n\n\n\nto 41.8 kg N ha-1 per year (Eichner 1990). Calculated as a percentage of the N \n\n\n\nfertiliser applied, N losses vary from about 0.07  to 5.3% for N\n2\nO (Granli and \n\n\n\nBockman 1994) and about 0-25% for N2 (Ryden 1983; Svensson et al. 1991; \n\n\n\nBarraclough et al. 1992; Khalil et al. 2002).\n\n\n\n There have been few studies on N\n2\nO emission and N losses through emission \n\n\n\nfrom rice fields to which both nitrogen and phosphorous fertilisers have been \n\n\n\napplied under irrigated conditions.\n\n\n\n The aim of this study was to investigate various treatments of nitrogen and \n\n\n\nphosphorous fertiliser rates that could accelerate or amplify the N\n2\nO emissions and \n\n\n\nN loss during vegetation period from rice fields and determine possible mitigation \n\n\n\nof N\n2\nO emission with P fertiliser and water management.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSite, Climate and Soil \n\n\n\nThe field experiment was carried out in ShuangQiao farm of Zhejiang University \n\n\n\nin the northern part of Zhejiang Province (120\u00b040\u2019E, 30\u00b0 50\u2019N), in the Southeast \n\n\n\ncoastal area of China. The annual precipitation of the area was 1205.5mm, of \n\n\n\nwhich 1006.7mm occurred from April to August. According to USDA, the soil \n\n\n\nis silt and blue soil that comprises 29.5% sand, 50.5% silt and 20.2% clay. The \n\n\n\nsoil in the plot trail contained 0.06-0.08% total N, 0.05-0.06% total P, 1.2-1.4% \n\n\n\norganic matter, pH 7.6-7.8. The mean air temperature during the experimental \n\n\n\nperiod was 28.1oC and the average maximum temperature was 34oC.\n\n\n\nM.T. Iqbal\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 107\n\n\n\nCrop\n\n\n\nRice (Oryza sativa L.) was grown in July \u2013 October of 2007, which was the \n\n\n\nsummer season in Southern China where rice is s cultivated in almost all rice \n\n\n\ngrowing regions of that season. The rice cultivar JIA-9312 with a growing period \n\n\n\nof 120-125 days under such conditions was used because it is the most widespread \n\n\n\ncropping system in Zhejiang province, with an approximate rice cultivated area \n\n\n\nof 1.88 x 106 hectares. The summer season is characterised by overcast sky, \n\n\n\nrainfall, high humidity and temperature. Thirty-day-old paddy rice seedlings were \n\n\n\ntransplanted in different main plots after ploughing and puddling. \n\n\n\nTreatments\n\n\n\nThe experimental field consisted of 45 plots each having dimensions of 4m \u00d7 \n\n\n\n5m. A strip of 0.3 m land was left between the plots. The experiment was laid \n\n\n\ndown in a completely randomised block design with three replicates. Factors \n\n\n\nwere N and P fertilisers. Table 1 shows the nitrogen and phosphorous fertiliser \n\n\n\napplication rates in each experimental plot in different forms. Urea was applied \n\n\n\nas N fertiliser and P\n2\nO\n\n\n\n5\n was added in the form of single super phosphate as a P \n\n\n\nfertiliser. Cai et al. (1997) reported that the form of N fertiliser did not cause \n\n\n\nsignificant differences in the N\n2\nO flux. Application of N fertiliser up to 100 kg N \n\n\n\nha-1 also did not significantly affect N\n2\nO flux, compared with the unfertilised plot, \n\n\n\nuntil it was increased to 300 kg N ha-1. This study consisted of higher N fertiliser \n\n\n\ntreatment. Granule urea is applied by farmers in China on their fields. This type \n\n\n\nof urea causes quick release of ammonia and emission of N\n2\nO and formation of \n\n\n\nnitrate throughout the paddy growing season. For this reason, rates of N higher \n\n\n\nthan the typical N application rate were considered to investigate the magnitude \n\n\n\nof nitrogen loss by N\n2\nO emission. The 180 kg N ha-1 as urea was the typical N \n\n\n\napplication rate (TNAR) in the region. There were five treatments:(1) N0, no N \n\n\n\napplication (control); (2) N1, 90 kg N ha-1 as urea (i.e. 50 % t of the TNAR); (3) \n\n\n\nN2, 180 kg N ha-1 (i.e. 100%t of the TNAR); (4) N3, 270 kg N ha-1 (i.e. 150% \n\n\n\nof the TNAR) and (5) N4, 360 kg N ha-1 (i.e 200 % of the TNAR). Sixty percent \n\n\n\nof the N fertiliser was applied as basal fertiliser and 40% as top dressing. Also, \n\n\n\nthere were three treatments for phosphorous fertiliser: (1) P0, no P application \n\n\n\n(control); (2) P1, 286 kg ha-1 as super phosphate or 40 kg P ha-1 P\n2\nO\n\n\n\n5 \n(3) P2, 429 \n\n\n\nkg ha-1 as super phosphate or 60 kg  ha-1 P\n2\nO\n\n\n\n5.\n All P applications were made as \n\n\n\nbasal dressings incorporated into the surface soils. Each plot received the same \n\n\n\nfertiliser treatment throughout the experiment, that is, incorporated in the plough \n\n\n\nlayer before flooding. The rice seedlings were transplanted the same day when \n\n\n\nbasal fertiliser was applied. All fields were ploughed with a tractor and harrowed \n\n\n\nthree times in a dry condition to about 15 cm depth.\n\n\n\nNitrous Oxide Emission from Irrigated Rice\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009108\n\n\n\nTABLE 1\n\n\n\nNitrogen and phosphorus fertiliser sources, amounts and treatments for the experiment \n\n\n\nIrrigation in the Field\n\n\n\nThere were three rainfalls during the growing season of rice but as total amount \n\n\n\nof rainfall was too low, the field was irrigated. Irrigation water was applied as \n\n\n\nflood irrigation from an overhead tank via flexible plastic pipes to the rice field \n\n\n\nthat maintained a certain water depth.  Irrigation was provided from time to time \n\n\n\nto maintain a 7-cm depth of water above ground level. The same water depth was \n\n\n\nmaintained in a flooded condition continuously throughout the vegetative period \n\n\n\nof the npaddy plant in each of the 15 plots. Also, irrigation was given at different \n\n\n\nintervals up to physical maturity (20 days before harvesting) of the crop.\n\n\n\nSampling and Measurements of N\n2\nO Emission\n\n\n\nGas sampling for nitrous oxide emission was initiated by the closed chamber \n\n\n\nmethod. The chamber was made using Plexiglas with dimensions of 90 cm \u00d7 60 \n\n\n\ncm \u00d7 100 cm. Before sampling, boxes were kept on the rice plants for 2 hours \n\n\n\ncontinuously during the daytime (10:00-12:00 hours) on the days of sampling. \n\n\n\nGas samples were collected through sampling ports fitted at the top of the \n\n\n\nchamber using gas-tight syringes. The headspace gas was sampled during the \n\n\n\nvegetative period (6 July to 8 August) of the paddy plant. A gas-tight syringe was \n\n\n\nused to transfer air samples into evacuated headspace vials which were sealed \n\n\n\nwith stopcock and stored in room temperature (23-300C) until analysis. Before \n\n\n\nsampling, the headspace gas was mixed by withdrawing and injecting headspace \n\n\n\ngas three times using a 25-ml gas-tight syringe with a stopcock. Then a 12-ml \n\n\n\nvial sample of headspace gas was collected from the closed box for N\n2\nO analysis. \n\n\n\nAfter sampling, the same volume of air was injected into the jars to maintain \n\n\n\nconstant pressure inside the jars. The concentration of N\n2\nO in each of the 12-ml \n\n\n\nvial was determined by automated IMRS (Stevens et al. 1993). Automation of \n\n\n\nthe valve switching and source setting enabled the analysis of the N\n2\nO emission. \n\n\n\nA Europa Scientific 20-20 Stable Isotope Analyzer was interfaced to a Europa \n\n\n\nScientific Trace Gas Preparation System with Gilson auto-sampler and was used \n\n\n\nfor N\n2\nO analysis. Also, the ion currents at m/z 44 (44I), 45 (45I) and 46 (46I) enabled \n\n\n\nconcentrations, 45R (45I/44I) and 46R (46I/44I) to be calculated for N\n2\nO emissions. \n\n\n\nEmissions were reported as N\n2\n-N g d-1 ha-1. Total emissions of N\n\n\n\n2\nO-N during \n\n\n\nthe entire period were estimated by multiplying the average emissions of two \n\n\n\nconsecutive sampling dates by the number of days in between.\n\n\n\nM.T. Iqbal\n\n\n\n\n\n\n\nNitrogen \ntreatment \n\n\n\nUrea \n(kg ha\n\n\n\n-1\n) \n\n\n\nNitrogen \n(kg ha\n\n\n\n-1\n) \n\n\n\nPhosphorus \ntreatment \n\n\n\nSuper \nphosphate \n\n\n\n(kg ha\n-1\n\n\n\n) \n\n\n\nPhosphorous \n(kg ha\n\n\n\n-1\n) \n\n\n\nN0 0 0 P0 0 0 \n\n\n\nN1 196 90 P1 286 40 \n\n\n\nN2 392 180 P2 429 60 \nN3 588 270 \n\n\n\nN4  360 \n \n\n\n\n\n\n\n\n784\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 109\n\n\n\nData Analysis\n\n\n\nDifferences between treatments were determined through analysis of variance \n\n\n\n(ANOVA) using the General Linear Model (GLM) procedures of the Statistical \n\n\n\nAnalysis System software 8.1 (SAS Institute, Inc., Cray, NC, USA).  Statistical \n\n\n\ncomparisons were considered significant at P<0.05. In order to evaluate the effects \n\n\n\nof the treatments and combination of treatments on N\n2\nO emission, the variance of \n\n\n\ndaily data was analysed (ANOVA) followed by the Least Significant Difference \n\n\n\n(LSD) and Least Squares Means (LSM).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nIrrigation and its Effect on N\n2\nO Emissions\n\n\n\nFig.1 describes the daily decrease of surface water from the paddy field. Daily \n\n\n\nmeasurements of the depth of standing water in the plots were taken to know \n\n\n\nthe daily decrease in surface water after the first fertiliser application. Water was \n\n\n\napplied as flood irrigation from an overhead tank and maintained at a depth of 7 cm \n\n\n\nin the field. Water was applied a number of time during the dry spells. Application \n\n\n\nof irrigation water was made just a day after the standing water had almost vanished \n\n\n\nfrom the field. Maximum decreasing value of surface water was 17.5 mm after \n\n\n\n47 days of planting and minimum was 2.5 mm after 40 days of planting. From \n\n\n\nplanting to harvest, three rainfalls occurred in the paddy field. Generally, this \n\n\n\nroutine work of water management practices was to control weeds as is currently \n\n\n\ndone in Chinese rice farming practices and depress N\n2\nO emissions from the field. \n\n\n\nDuring the rice-growing period, soil moisture is usually over-saturated so that \n\n\n\nalmost no change of soil moisture or O\n2\n availability happens when rain events \n\n\n\noccur. To maintain water levels in the rice fields, they were frequently irrigated. \n\n\n\nWhen fields were irrigated with fresh water, it brought substantial amount of \n\n\n\ndissolved oxygen and the soil remained partially aerobic for sometime even after \n\n\n\nsubmergence. Similar observations were found in other reported experiments. \n\n\n\nChen et al. (1996) found that rice fields in China emitted little N\n2\nO while the fields \n\n\n\nwere flooded, but when fields were drained, substantial N\n2\nO was emitted. On an \n\n\n\nannual basis only 0.04 kg N\n2\nO ha-1 was emitted while the fields were flooded \n\n\n\ncompared to 1.7 kg N\n2\nO ha-1 during non-flooded periods. Cheng et al. (1998) \n\n\n\nfound that little N\n2\nO was emitted from the soil to the atmosphere when the soil \n\n\n\nwas saturated. Soil moisture is of great importance for mitigation of N\n2\nO emission \n\n\n\nfrom irrigated rice fields because it is the most sensitive factor to regulate N\n2\nO \n\n\n\nemission and is easy to to be artificially controlled (Zheng et al. 1996). As soil is \n\n\n\ngenerally flooded, the condition becomes unfavorable to nitrification which leads \n\n\n\nto low N\n2\nO production through the denitrification process. Early studies reported \n\n\n\nthat N\n2\nO emission from paddy field was negligible. Under submergence, N\n\n\n\n2\nO \n\n\n\nemission is low even though its formation in soil may be high, as the pressure \n\n\n\nof standing water prevents N\n2\nO from being released into atmosphere, and also \n\n\n\nbecause it gets denitrified to N\n2\n within the soil (Granli and Bockman 1994).\n\n\n\nNitrous Oxide Emission from Irrigated Rice\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009110\n\n\n\nFig. 1. Amount of water  applied in the field\n\n\n\nFertilisation and its Effects on N\n2\nO Emission:\n\n\n\nFig. 2(a) shows nitrous oxide emission for control P and different nitrogen fertiliser \n\n\n\ntreatments during the vegetative period (6 July to 8 August) of the paddy plant. \n\n\n\nA basal dose of fertiliser was applied on 6 July. After that, N\n2\nO emissions were \n\n\n\nhigher in all nitrogen treatments. Up to two weeks (6 to 19 July), a fluctuation in \n\n\n\nemissions was observed because at that time more nitrogen existed in submerged \n\n\n\nsoil. As water was available in the field, less emission occurred from 19 to 30 July.  \n\n\n\nAgain N\n2\nO increased in all treatments due to second dose of N fertiliser  applied \n\n\n\non 1 August in field plots. N\n2\nO emission was higher in N4 treatment, but later \n\n\n\nshowed less emission from all the treatments. However, for N3 treatment, initially \n\n\n\nthere was less emission and then higher emission later. \n\n\n\n Fig. 2(b) reveals the N\n2\nO emission from interaction of 40 kg P ha-1 with \n\n\n\ndifferent N fertiliser treatments under irrigation and natural rainfall conditions. \n\n\n\nThe highest emission was 113.27 g d-1ha-1 on 10 July and the lowest emission was \n\n\n\n2.45 g d-1ha-1 on 5 August for N\n0\nP\n\n\n\n1\n treatment. From 19 to 30 July, the fluctuation \n\n\n\ntrend of N\n2\nO flux was almost similar in all treatments. Prior to that, a wide variation \n\n\n\nwas observed from day to day and treatment to treatment. Also, less emission was \n\n\n\nobserved in N2P1 treatment, that is,  180 kg N ha-1 and 40 kg P ha-1.\n\n\n\n Fig. 2 (c) shows nitrous oxide emission during the vegetative period (6 July \n\n\n\nto 8 August) of the paddy plants for different nitrogen treatments with 60 kg P \n\n\n\nha-1. From 11 to 14 July, emissions were higher in all treatments. The highest peak \n\n\n\nemission value was observed for N\n4\nP\n\n\n\n2\n treatment on July and the second highest \n\n\n\npeak emission was observed for N\n2\nP\n\n\n\n2\n treatment on 12 July. N\n\n\n\n2\nO fluxes (i.e. rate \n\n\n\nof emission per hectare and day) in N\n0\nP\n\n\n\n2\n treatment was low throughout the study \n\n\n\nperiod, never exceeding 50 g d-1 ha-1. \n\n\n\nM.T. Iqbal\n\n\n\n0,0\n\n\n\n5,0\n\n\n\n10,0\n\n\n\n15,0\n\n\n\n20,0\n\n\n\n1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 32 33 34 35 36 37 38 39 40 47 49 51 53 56 57 59 61 63\n\n\n\nA\nm\n\n\n\no\nu\nn\nt \n\n\n\no\nf \n\n\n\nw\nat\n\n\n\ner\n (\n\n\n\nm\nm\n\n\n\n)\n\n\n\nDays of sampling\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 111\n\n\n\nFig. 2:  Nitrous oxide emission during the vegetative period for the different N fertilising \n\n\n\ntreatments with a P addition of 0 (a), 40 (b) and 60 (c) kg P ha-1.\n\n\n\nNitrous Oxide Emission from Irrigated Rice\n\n\n\n\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n140\n\n\n\n160\n\n\n\n180\n\n\n\n200\n\n\n\n06-Jul 08-Jul 11-Jul 14-Jul 17-Jul 19-Jul 22-Jul 24-Jul 30-Jul 04-Ogos 07-Ogos\n\n\n\n\n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n06-Jul 08-Jul 11-Jul 14-Jul 17-Jul 19-Jul 22-Jul 24-Jul 30-Jul 04-Ogos 07-Ogos\n\n\n\n\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n140\n\n\n\n160\n\n\n\n180\n\n\n\n200\n\n\n\n6-Jul 8-Jul 11-Jul 14-Jul 17-Jul 19-Jul 22-Jul 24-Jul 30-Jul 4-Aug 7-Aug\n\n\n\nN1P2 N2P2\n\n\n\nN4P2\n\n\n\nN\nit\n\n\n\nro\nu\n\n\n\ns\n o\n\n\n\nx\nid\n\n\n\ne\n e\n\n\n\nm\nis\n\n\n\ns\nio\n\n\n\nn\n\n\n\n(g\nN\n\n\n\n/h\na\n  \n\n\n\n  \n )\n\n\n\n-1\nN\n\n\n\nit\nro\n\n\n\nu\ns\n o\n\n\n\nx\nid\n\n\n\ne\n e\n\n\n\nm\nis\n\n\n\ns\nio\n\n\n\nn\n\n\n\n(g\nN\n\n\n\n/h\na\n  \n\n\n\n  \n )\n\n\n\n-1\nN\n\n\n\nit\nro\n\n\n\nu\ns\n o\n\n\n\nx\nid\n\n\n\ne\n e\n\n\n\nm\nis\n\n\n\ns\nio\n\n\n\nn\n\n\n\n(g\nN\n\n\n\n/h\na\n  \n\n\n\n  \n )\n\n\n\n-1\n\n\n\nDays after transplanting\n\n\n\nDays after transplanting\n\n\n\nDays after transplanting\n\n\n\nN0P0\n\n\n\nN3P0\n\n\n\nN1P0\n\n\n\nN4P0\nN2P0\n\n\n\nN0P1\n\n\n\nN3P1\n\n\n\nN1P1\n\n\n\nN4P1\n\n\n\nN2P1\n\n\n\nN0P2\n\n\n\nN3P2\n\n\n\n2 (c)\n\n\n\n2 (b)\n\n\n\n2 (a)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009112\n\n\n\n  As Fig. 2(a) - (c) obviously demonstrate, variation patterns of N\n2\nO \n\n\n\nemissions from N and P fertiliser treatments were quite similar. Applied nitrogen \n\n\n\nwas an immediate source of N\n2\nO formation through nitrification and denitrification \n\n\n\nunder strongly anaerobic conditions and that was why the peak appeared earlier \n\n\n\nwhen nitrogen was applied as basal fertiliser and time of top dressing. This \n\n\n\nobservation was consistent with the results of other experiments. Nitrification and \n\n\n\ndenitrification are not a separate process. NO\n3\n\n\n\n- produced during nitrification can \n\n\n\nbe utilised by nitrifiers. Nitrification and denitrification can take place in soils \n\n\n\nwhere favourable conditions for both nitrification and denitrification are present in \n\n\n\nneighbouring microhabitats (Arah 1997). In a study of these microhabitats, Khdyer \n\n\n\nand Cho (1983) investigated the degree of nitrification and denitrification which \n\n\n\noccurred after the addition of urea in soil. Nitrification take place in the aerobic \n\n\n\nsurface layer, wheareas the anaerobic zone was dominated by denitrification. \n\n\n\nN\n2\nO was mainly produced at the aerobic-anaerobic interface from where it could \n\n\n\ndiffuse into the soil surface. This suggests that the production of N\n2\nO is highest \n\n\n\nwhen conditions are suboptimal for both nitrifiers and denitrifiers. Comparable \n\n\n\nmechanisms are active in natural soils. Here, nitrification can take place in aerobic \n\n\n\nsurface layers or cracks. Denitrification is mostly confined to anaerobic deeper \n\n\n\nlayers, waterlogged areas or the interior of soil aggregates (Tiedje et al. 1984; \n\n\n\nLeffelaar, 1986). In rice paddy fields treated with nitrogen fertilisers, including \n\n\n\nurea, emission of N\n2\nO was suppressed when the plots were flooded and again \n\n\n\nreached its peak when fertiliser was applied due to nitrification and denitrification \n\n\n\nprocess (Cai et al. 1997; Xu et al. 1997).\n\n\n\n ANOVA results showed significant difference in emission over the days of \n\n\n\ngas sampling (P<0.05) (Table 3). Also, N and N*P was significant (P \u2264 0.001 \n\n\n\n0.001) on N\n2\nO emission as well as phosphorous (P) treatment was significant (P \n\n\n\n\u22640.05) for N\n2\nO emission (Table 2). The results showed that emission significantly \n\n\n\nincreased over the first week of fertilisation and decreased in the second week and \n\n\n\nincreased again in the third week at which fertiliser was applied in the field (Fig.2 \n\n\n\n(a)-(c)). \n\n\n\nTABLE 2\nSignificance levels for the main and interactive effects of N and P on N\n\n\n\n2\nO emission\n\n\n\n The variance in  daily emission was analysed up to day 22, because after that \n\n\n\nthe plant height was high and no nitrogen fertiliser was added; also comparison on \n\n\n\nemission was no longer valid (Table 3). It was found that N\n2\nO emission depends \n\n\n\nM.T. Iqbal\n\n\n\n\n\n\n\nSource  of variance                         N2O emission \n\n\n\nNitrogen (N)                               \n\n\n\nPhosphorous (P)                             \n\n\n\nN\u00d7P                                       \n\n\n\n\n\n\n\nWhere  * and *** represent probability of  \u2264 0.05 and \u2264 0.001, respectively. \n\n\n\n*\n\n\n\n***\n\n\n\n***\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 113\n\n\n\non amount and time of nitrogen fertiliser application. Similar results have been \n\n\n\nreported from other studies. The amount of N\n2\nO emission would then depend \n\n\n\non the amount of fertiliser or nitrate transferred to the denitrifying layer and \n\n\n\nirrigation water level. The more urea applied, the more nitrate available from the \n\n\n\nnitrification of ammonia. Likewise, with more frequent flooding, more nitrogen \n\n\n\ngas (N\n2\n) and not N\n\n\n\n2\nO would be produced (Buresh and Austin 1988).\n\n\n\nNitrogen Loss through N\n2\nO Emission \n\n\n\nNitrogen loss through N\n2\nO emission for nitrogen and phosphorous fertiliser \n\n\n\ntreatment is shown in Table 4. Losses were calculated from each plot during total \n\n\n\nvegetative period of paddy rice plant. Nitrogen loss for 0 and 40 kg P ha-1 treatments  \n\n\n\ndecreased linearly with respect to different nitrogen treatments. But for 60 kg P \n\n\n\nha-1, no relationship was observed with different nitrogen treatments. Total N2O \n\n\n\nemission for N0P0, N2P0, N2P2, N3P2, N4P2 were the same, that is, 998.54 g \n\n\n\nha-1. The highest amount of N\n2\nO emission was 1181.28 in N0P1 treatment and \n\n\n\nlowest amount of N\n2\nO emission was 431.89 g ha-1 in N0P2 treatment. Nitrogen \n\n\n\nloss through N\n2\nO emission for N1P0, N2P0, N3P0, N4P0 was 1.18%, 0.55%, \n\n\n\n0.37%, 0.27%, respectively, and for N1P1, N2P1, N3P1, N4P1,  was 1.15%, \n\n\n\n0.43%, 0.40% and 0.25% and for N1P2, N2P2, N3P2, N4P2, it was 0.10%, \n\n\n\n0.55%, 0.37%, 0.28%, respectively. From this value, it was evaluated that N\n2\nO \n\n\n\nemission loss through nitrogen appears to be lower for phosphorous treatments, \n\n\n\nwith the exception of N3P1, and, N2P2 and N4P2. This was consistent with other \n\n\n\nstudies which indicated that 3.1% of the total N applied is lost as N\n2\nO (Hansen \n\n\n\net al.1993). The losses of N\n2\nO-N emissions were reported to vary between 0.01 - \n\n\n\n0.55% of the total nitrogen applied in rice (Smith et al. 1983; Minami, 1987; Cai \n\n\n\net al. 1997).\n\n\n\nTABLE 3\n\n\n\nLeast Significant Difference (LSD) of mean N\n2\nO emission\n\n\n\nNitrous Oxide Emission from Irrigated Rice\n\n\n\n\n\n\n\nSource of variance                   Mean N2O emission (g/ N ha\n- 1\n\n\n\n)  \n\n\n\n\n\n\n\n Days                 3               4       5            6           9              18                19    \n\n\n\nN2O emission   34.29\nbcde\n\n\n\n   47.28\nab\n\n\n\n  45.21\nabc\n\n\n\n  63.06\na\n  32.28\n\n\n\nbcdef\n  31.83\n\n\n\n bcdef \n    38.10\n\n\n\nbcd\n                                              \n\n\n\n\n\n\n\nValues within a row followed by different letters are significantly different from other values (P<0.05 ). \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009114\n\n\n\nTABLE 4\n\n\n\nNitrogen loss by N fertilizer through N\n2\nO emission during sampling period.\n\n\n\nCONCLUSIONS\n\n\n\nIn the experimental design of this study, the highest amount of nitrogen and \n\n\n\nphosphorous fertiliser doses were considered to detect N\n2\nO emission from the \n\n\n\ninteraction of N and P fertilisers under an irrigated system. An optimum rate of \n\n\n\n180 kg N ha-1 and 40 kg P ha-1 was effective in reducing N losses through N\n2\nO \n\n\n\nemission and maintain crop yields compared to the traditionally high N rates (240 \n\n\n\nand 360 kg N ha-1). The main effect on emission is significant (P<0.05) for days \n\n\n\nas well as N (P\u2264 0.001, P (P\u2264 0.05)  and interaction between them was significant \n\n\n\n(P\u2264 0.001). This emission was significant (P<0.05) at 3,4,5,6,9,18 and 19 days \n\n\n\nduring the emission measurement. Applied nitrogen was an immediate source of \n\n\n\nN\n2\nO emission through denitrification under strongly anaerobic conditions and that \n\n\n\nwas why the peak appeared earlier when nitrogen was applied as basal fertilizer \n\n\n\nand time of top  dressing. Total N\n2\nO emissions varied from 431.89 to 1181.28 g \n\n\n\nha-1 and  nitrogen loss through N\n2\nO emission varied from 0.10 to 1.18%.\n\n\n\n Our study showed that N\n2\nO emissions may not be a serious concern from \n\n\n\nthe economic point of view considering the low percentage of applied N lost \n\n\n\nthrough N\n2\nO emissions. However, its high global warming potential and total \n\n\n\nannual emission load to the atmosphere by widespread rice cultivation in China, \n\n\n\nmay add significantly to enhance the green house effect. This illustrates that high \n\n\n\ndoses of N and P fertiliser application to soils in China is a waste of resources that \n\n\n\ncould lead eventually to water pollution and loss of income to farmers. So, efforts \n\n\n\nare needed to mitigate N\n2\nO emission, as agriculture needs increasingly higher \n\n\n\nfertiliser N to meet production demands.\n\n\n\nM.T. Iqbal\n\n\n\n\n\n\n\nTreatment P treatment Emmission amount ( g ha\n-1\n\n\n\n) Loss percentage  (%) \n\n\n\n0 0 998.54 - \n\n\n\n1 0 1059.82  1.18 \n\n\n\n2 0 998.54 0.55 \n\n\n\n3 0 998.54 0.37 \n\n\n\n4 0 933.31 0.26 \n\n\n\n0 1 1181.28  - \n\n\n\n1 1 1031.12  1.15 \n\n\n\n2 1 777.06 0.43 \n\n\n\n3 1 1078.4 0.40 \n\n\n\n4 1 888.08 0.25 \n\n\n\n0 2 431.89 - \n\n\n\n1 2 897.19 0.10 \n\n\n\n2 2 998.54 0.55 \n\n\n\n3 2 998.54 0.37 \n\n\n\n4 2 998.54 0.28 \n\n\n\nData were mean values of different treatment with three replications. Mean sharing different letter(s) differ significantly \nprobability.\n\n\n\n \nat 5% level of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 115\n\n\n\nACKNOWLEDGEMENT\n\n\n\nMd. Toufiq Iqbal is thankful to UNESCO and China Scholarship Council (CSC) \n\n\n\nfor financial assistance under UNESCO and the China Great Wall fellowship \n\n\n\nprogramme. 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"\n\nMalaysian Journal of Soil Science 2023, Vol. 23: 204-217 \n \n\n\n\n204 \n \n\n\n\nEffects of Arbuscular Mycorrhizal Fungi combined with P Fertilizer  \non the System of Rice Intensification Cultivation \n\n\n\n \nElita, N., Yanti, R.*, Susila, E., Karmaita, Y.,  \n\n\n\nAndam, D.S. and Kurnia, A.I. \n \n\n\n\nFood Crops Production Technology Study Program and Master Program in Applied Food Security, \nPoliteknik Pertanian Negeri Payakumbuh, Tanjung Pati, Km 7, Kecamatan Harau,  \n\n\n\nKabupaten 50 Kota West Sumatera, Indonesia, 26271 \n \n\n\n\n*Correspondence: rinda_yanti@yahoo.co.id  \n \n\n\n\nABSTRACT \n\n\n\nThis paper aimed to obtain appropriate indigenous arbuscular mycorrhizal fungi types and determine  the \nright dosage of phosphate fertilizer to increase rice yields under the System of Rice Intensification \nCultivation. The research design used a factorial randomised block design with three replications. Factor \nI was a type of arbuscular mycorrhizal fungi (Glomus sp. 2, Glomus sp. 3, and Sclerocystis sp) on Agam \nriver sand media with the number of spores being 120 spores per 100 grams of sand media. The dose of \narbuscular mycorrhizal fungi was1.6 tons/ha or 15 g/plant. Factor II was the dose of phosphate fertilizer \n(P = 0%, P1 = 25%, P2 = 50%, and P3 = 75%) of the recommended dose which was applied for each \ntreatment at these rates:: P = 0 g/plot, P1 = 20 g/plot, P2 = 40 g/plot, and P3 = 60 g/plot. The data was \nanalsed for variance using the SAS program. The results showed that Glomus sp. 3 with 50% phosphate \nfertilizer efficiency had statistically significant different results (p <0.05) for plant height, number of tillers, \nnumber of panicles, number of filled grains, weight of 1000 seeds, and yield per hectare compared to the \nother treatments. The nutrient content of paddy fields  pH, CN-ratio, total N , P available and CEC showed \na statistically significant difference between the arbuscular mycorrhizal fungi types and the P fertilizer \nefficiency treatment. No statistically significant difference was found in the CN ratio between Glomus sp. 3 \nand Sclerocystis sp., but there was a statistically significant difference from Glomus sp. 2. \n\n\n\nKeywords: Glomus sp3, indigenous rice, SRI, Sclerocyctis sp \n \n\n\n\nINTRODUCTION \nRice cultivation under the System of Rice Intensification (SRI) method uses an environmental \nsustainability approach. This system seeks to minimise the use of chemical fertilizers and \npesticides. The SRI method integrates conventional approaches with more environment friendly \nmethods such as the application of microorganisms (Farrar et al.2014). Research results of several \nstudies(Elita et al. 2018; Elita et al. 2020; Elita et al. 2021) show that microorganism application \nin rice cultivation using the SRI method increases rice yield and soil nutrient content in paddy \nfields. \n\n\n\nAs SRI is based on a sustainable farming system, it is said to produce higher-quality rice, and \nincreases yields by reducing water requirements. SRI offers environmental benefits and gains of > \n60%, as it  reduces groundwater use by 60%. SRI based rice cultivation reduces costs per hectare \n(ha) of cultivation significantly (Gathorne-Hardy et al. 2016). The SRI method aids crop \nproductivity by enhancing plant root growth and xylem exudation rate, leaf area index, light \n\n\n\n\n\n\n\n\n\n\n\n\n205 \n \n\n\n\ninterception by the plant canopy, and photosynthetic rate at the grain filling stage which result in \nhigher seed yields, thereby increasing production by about  56%  (Thakur et al. 2018). \n\n\n\nThe SRI method in the vegetative phase of groundwater under aerobic conditions, which allows \ndecomposing microorganisms to live actively and develop properly and make them available in \nabundance to the plant. Among the microorganisms are arbuscular mycorrhizal fungi (AMF). \nRhizobia in the rhizosphere of rice plants increase protein content and yield per hectare through \nthe production of auxins and other growth-stimulating substances. Root exudate is a key factor in \nthe various processes of the SRI method, which results in a larger root system and more rice tillers   \n\n\n\n(Uphoff et al. 2015) . \n\n\n\nAMF offers a  symbiotic mutualism between fungi and the roots of higher plants. Plant roots are \nassociated with AMF to obtain phosphorus (P) from the soil, including as a phosphate solvent. \nImprovement of soil health can take place quickly by utilising AMF as a bioremediator because \nAMF proliferates in unfavorable (marginal) environmental conditions. The presence of AMF in \nthe roots of rice plants can increase the variety and amount of nutrients that can be absorbed by \nroots, especially P biologically, while also increasing the availability of soil P  (Sarabia et al. 2018). \n\n\n\nIndigenous AMF is more adaptive and effective in promoting plant development, as its ability to \nabsorb nutrients is higher, thereby enhancing the speed of plant growth. According to Basu et al. \n(2018), the success of the association of AMF with plant roots is strongly influenced by the \nsuitability of AMF species for the type of host plant. This association helps increase the supply of \nnutrients like nitrogen, phosphorus, and water to the plants, and in turn, the fungus gets 20% of \nthe fixed carbon from the plants.  Xu et al. (2018) added that there were differences in the \nbehaviour of each AMF genus in obtaining nutrients from different plants. Therefore, their \ndecomposition results in different bacterial and fungal communities. This effect is modulated by \nsoil P availability which affects plant growth and production. \n\n\n\nFurthermore, according to Sosa Hern\u00e1ndez et al. (2017), in different soil types, the sources of \nAMF inoculants lead to different AMF communities. Their study indicated that AMF subsoil has \na potential role as a reservoir of biodiversity. Double inoculation of AMF with rhizobium bacteria \nincreased root biomass and nodule growth (Budiastuti et al. 2021). \n \nResearch on AMF has been carried out on various commodities, but the use of indigenous AMF \nspecies isolated from the rice rhizosphere and applied to rice using the SRI method lacks \ninformation. Therefore, a study was conducted on the effect of the type of AMF and the right dose \nof P fertilizer in increasing the yield of rice using the SRI method. The purpose of this study was \nto obtain the appropriate type of indigenous AMF and the right dose of P fertilizer to increase the \nyield of rice using the SRI method. \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n206 \n \n\n\n\nMATERIALS AND METHODS \n\n\n\nTime and Place \n\n\n\nThe research was carried out on farmers' rice fields in Taram, Harau District, Limapuluh Kota \nRegency, Indonesia. The research was conducted from September 2022\u2013January 2023. \n\n\n\nResearch Design \n\n\n\nThe field research used a factorial randomized block design with three replications. Factor I: \nIndigenous AMF  of type Glomus sp.3, Sclerocystis sp, and Glomus sp.2 on Batang Agam river \nsand media with the number of spores being 120 spores per 100 grams of media. AMF was given \nat a dose of 1.6 tons/ha or 15 g/plant. \n\n\n\nFactor II: For the treatment, based on the recommended dose of P fertilizer where P = 0%, P1 = \n25%, P2 = 50%, and P3 = 75%) P fertilizer was applied  to the plots as follows: P = 0 g/plot, P1 = \n20 g/plot, P2 = 40 g/plot, and P3 = 60 g/plot. \n\n\n\nTo cultivate rice based on the SRI method, the soil had to be prepared. For the initial soil analysis, \nsoil samples were collected and thoroughly mixed to prepare a composite sample of approximately \n250 g as the working sample. The paddy fields were sanded with a tractor. The experimental plots \nmeasured 2.1 x 2.1 m and totalled 36 plots. The distance between plots in blocks was 0.5 m while \nthe distance between replicates was 1 m. Each experimental plot was treated and eight weeks after \nplanting, soil nutrient analysis was carried out by taking 250 g of soil samples from each \nexperimental plot. Inoculant-type indigenous AMF was given according to the treatment at \nplanting at a dose of 15 g/clump or 1.6 tons/ha (120 spores/100 grams). \n\n\n\nNurseries are mainly made up of manure. The seeds are soaked for 12 h, ripened for 12 h, and then \nsown in the nursery. Rice seedlings, aged 12 days, are ready for planting in the plot. Seedlings are \nplanted one stem per planting point, at  a distance of 25 x 25 cm. One plot will have 49 clumps of \nplants. Fertilization is given at half the recommended dose with urea (150 kg/ha or 63 g/plot) and \nKCl (50 kg/ha or 21 g/plot) at the time of planting, and urea is given twice (at the beginning and \nat 30 days old). SP-36 fertilizer was given based on  the treatment. Water conditions were kept  \nmoist during the vegetative phase. In the flowering phase, the water level was  maintained up to 5 \ncm above the soil surface. Ten days before harvesting, the soil was left to dry. Weeding was done \nby removing weeds and immersing them using a weeding tool to ensure a better soil air system. \nHarvesting was done at 110 days. After harvest, the grain was dried in the sun to attain a moisture \ncontent of 14%. \n\n\n\nThe following were observed: (i). plant height (cm), (ii) number of tillers, (iii) number of panicles \nper clump (panicle), (iv) number of pithy grains per panicle, (v) weight of 1000 seeds (g), (vi) \nproduction of dry grains per hectare (ton), (vii) analysis of soil nutrients before and after treatment, \nand (viii) development of AMF in the rhizosphere of rice plants. \n\n\n\nLaboratory observations included root colonisation and spore density. To calculate spore density \nat the end of the observation, 10 g of AMF formula were inserted into 500 ml cup glass with 200 \n\n\n\n\n\n\n\n\n\n\n\n\n207 \n \n\n\n\nml of sterile distilled water, stirred and left for 30 sec. Spore suspensions were filtered at sizes of \n500 \u03bcm, 250 \u03bcm, 106 \u03bcm and 50 \u03bcm. The remaining were collected on a 50-\u03bcm sieve and \ntransferred to a centrifuge tube to which was added 60% glucose and centrifuged for 10 min at \n1000 rpm. The spores in the centrifuged tubes were poured into a 50-\u03bcm sieve and carefully \nwashed with water to remove the glucose. The sponges retained on the sieve were transferred into \na Petri dish and observed under a microscope. \n \nRoot samples were taken in the rhizosphere of rice plants after screening to observe root \ncolonisation, including: \n1. Percentage of mycorrhizal colonisation on plant roots, calculated using   \n                               \u03a3 field of view (+) \n% colonisation = --------------------------- x 100 % \n                                \u03a3 field of view \n2. The intensity of infection andclassification of root infection rate are based on the classification \nof the Institute of Mycorrhizal Research and Development, USDA:   \nClass  1: Infection is  0-5 % (very low , +) \nClass 2: Infection is 6-25 % (low, ++) \nClass 3: Infection is 26-50% (medium, +++) \nClass  4: Infection  is 51-75% (high, ++++) \nClass  5: Infection  is >75 % (very high, +++++) \n \nThe higher the category of infection intensity, the higher the intensity of AMF infection in plant \nroots, an indicator of the effectiveness of AMF on plants. That is, the higher the rate of colonisation \nbetween AMF and plants, the higher the level of symbiosis between the two. \n \nStatistical Analysis \nTo test the effect of treatment on the observed variable responses, analysis of variance was carried \nout using the Statistical Analysis System (SAS) program. The Duncan New Multiple Range Test \n(DNMRT) was conducted to see the difference in treatment at the 5% level. \n \n\n\n\nRESULTS AND DISCUSSION \n \nVegetative Growth of Rice Plants \nThe results of the field experiment after statistical analysis showed no interaction between the type \nof AMF and the dose of P.  The results of testing the type of AMF and the efficiency of P fertilizer \non the vegetative growth of rice plants using the SRI method are presented in Figure 1. \n \n\n\n\n\n\n\n\n\n\n\n\n\n208 \n \n\n\n\n  \nFigure 1. Vegetative observations of indigenous AMF inoculation and efficiency of P fertilizer in \n\n\n\nrice cultivation using the SRI method \n \nFigure 1 shows that the highest plant height and number of tillers were found in Glomus sp3, with \nthe difference not being statistically significant P < 0.05) with Sclerocystis sp, but compared to \nGlomus sp.2, the difference was statistically significant. Among the three types of AMF isolates, \ncompatibility differences were reflected in plant height and number of tillers. Glomus sp. 3 and \nSclerocystis sp. species provided better nutrients than Glomus sp. 2. \n \nPrevious studies have reported the degree of compatibility of AMF isolates with plants to \ndetermine the effectiveness of AMF on plant growth  decompose litter, thus accelerating the \nprovision of nutrients from litter and organic matter Xu et al. (2018)  .  AMF can enhance plant \nnutrition with mineral nutrients, and vice versa while plant growth and development are triggered \nby organic carbon supplied from host plants (Moradi et al. 2017). Our study showed that the AMF \ntype Glomus sp.3 on Batang Agam river sand carrier material was more suitable in that it could \naffect plant height and number of tillers in rice plants cultivated underthe SRI method. A study by \nOmomowo et al. (2018) found that Glomus species were able to increase the growth and yield of \ncowpea. \n\n\n\nWith regard to the study on the efficiency of P fertilizer, the highest plant height and number of \ntillers were obtained at 50% fertilization efficiency (P2). The results of this study indicate that a \nmaximum amount of P fertilizer is absorbed by rice plants in the presence of AMF. Rice plants \nunder the SRI method go into a  vegetative phase under dry conditions. In the presence of AMF \nsymbiosis, rice plants have  improved tolerance to drought stress and can be grown efficiently with  \nthe absorption of fertilizers, especially P (Quiroga et al. 2018). \n\n\n\nAMF can suppress the loss of nutrients from the soil by enlarging the nutrient interception zone \nand preventing the loss of nutrients due to the leaching process by percolation of water which \nefficiently absorbs nutrients especially P (Cavagnaro et al. 2015). The results of this study indicate \nthat Glomus sp3 and Scleroscystis sp are mycorrhizal isolates that are suitable for vegetative \ngrowth of rice plants under the SRI method. \n\n\n\nArbuscular mycorrhizal fungi (Phylum glomeromycota) are important components of the soil \nmicrobial community. AMF forms mutualistic associations with the roots of terrestrial plants, \nincluding many agricultural crops. In many agricultural crops, these mutualistic associations have \n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 128.28 131.22 129.72 127.52 128.74 132.81 129.89\n\n\n\nB\n\n\n\nA\nA\n\n\n\nc\nbc\n\n\n\na\n\n\n\nb\n\n\n\n122\n124\n126\n128\n130\n132\n134\n\n\n\nPl\nan\n\n\n\nt h\nei\n\n\n\ngh\nt (\n\n\n\ncm\n)\n\n\n\nIndigenous AMF types and P fertilizer\n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 35.69 51.81 46.14 34.67 40 56.81 46.7\n\n\n\nC\n\n\n\nA\nB\n\n\n\nd\nc\n\n\n\na\nb\n\n\n\n0\n10\n20\n30\n40\n50\n60\n70\n\n\n\nN\num\n\n\n\nbe\nr o\n\n\n\nf t\nill\n\n\n\ner\ns\n\n\n\nIndigenous AMF types and P fertilizer\n\n\n\n\n\n\n\n\n\n\n\n\n209 \n \n\n\n\ndemonstrated the potential to increase crop productivity, thereby playing a key role in the \nfunctioning and sustainability of agroecosystems (Cosme 2023; Birhane et al. 2023). \n\n\n\nThe Generative Growth of Rice Plants \nField test results after statistical analysis, showed no interaction between AMF species and P dose. \nThe results of inoculation of AMF types and efficiency of P fertilizer on the generative growth of \nrice plants using the SRI method are presented in Figure 2. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 2. Generative observations of inoculation of indigenous AMF and efficiency of P fertilizer \nunder SRI method of rice cultivation \n\n\n\nInoculation of Glomus sp.3 in rice plants under the SRI method showed a statistically significant \nincrease (P < 0.05) in the number of panicles compared to Sclerocystis sp and Glomus sp.2. Glomus \nsp. 3 had more spores with an average size of > 250 m and a higher colonisation rate  (Elita et al. \n2018). Glomus sp.3 was found to to provide better nutrients, sufficient for panicle formation. \n\n\n\nIn terms of the number of pithy grains, statistically there was no significant difference between \nGlomus sp3 and Sclerocystis sp, but between Glomus sp2 and Sclerocystis sp, a statistically \nsignificant difference was observed. This result is related to the levels of the C/N ratio, where the \ndifference was not statistically significant (Figure 3). These results are a clear indication that the \n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 32.28 46.14 35.64 31.96 34.26 45.26 40\n\n\n\nC\n\n\n\nA\n\n\n\nB\nd c\n\n\n\na\nb\n\n\n\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n45\n50\n\n\n\nN\num\n\n\n\nbe\nr o\n\n\n\nf p\nan\n\n\n\nic\nle\n\n\n\ns\n\n\n\nIndigenous AMF types and P fertilizer  \n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 120.6 142.6 125.4 105.4 123.5 160.6 134.3\n\n\n\nB\nA\n\n\n\nA\nd\n\n\n\nc\n\n\n\na\n\n\n\nb\n\n\n\n0\n20\n40\n60\n80\n\n\n\n100\n120\n140\n160\n180\n\n\n\nN\num\n\n\n\nbe\nr o\n\n\n\nf f\nill\n\n\n\ned\n g\n\n\n\nra\nin\n\n\n\nIndigenous  AMF types and P fertilizer  \n\n\n\nG2 GS SC P0 P1 P2 P3\nSeries1 19.0420.5419.8218.5319.1421.2620.27\n\n\n\nC\n\n\n\nA\nB\n\n\n\nd\nc\n\n\n\na\nb\n\n\n\n16\n17\n18\n19\n20\n21\n22\n\n\n\nW\nei\n\n\n\ngh\nt o\n\n\n\nf 1\n00\n\n\n\n0 \nse\n\n\n\ned\ns (\n\n\n\ng)\n\n\n\nIndigenous AMF types and P fertilizer  \n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 6.4 8.25 7.05 4.75 6.43 10.3 7.53\n\n\n\nB\nA\n\n\n\nB\n\n\n\nd\nc\n\n\n\na\n\n\n\nb\n\n\n\n0\n2\n4\n6\n8\n10\n12\n\n\n\nY\nie\n\n\n\nld\n p\n\n\n\ner\n h\n\n\n\nec\nta\n\n\n\nre\n (t\n\n\n\non\n)\n\n\n\nIndigenous AMF types and P fertilizer  \n\n\n\n\n\n\n\n\n\n\n\n\n210 \n \n\n\n\nsoil organic matter had decomposed well and was widely available, thus leading to a high amount \nof pithy grain. \n\n\n\nThe weight gain of 1000 seeds from plants with Glomus sp.3 type was significant (P<0.05).  The \nweight of 1000 seeds is  a description of the results of good photosynthesis resulting in a larger \ngrain size. Glomus sp.3 is able to provide better nutrition at a pH  that has reached a normal level \n(6.05) resulting in organic matter and organic C being available for uptake of nutrients by the  rice \nplants (Figure 3). \n\n\n\nThe highest production/ha was obtained with Glomus sp. 3. The ability of Glomus sp. 3 to provide \ngood plant nutrients (Figure 4), as can be seen by the CEC value which increased from low  to \nmoderate (Table 1). This increase in CEC value facilitated the smooth transport (translocation) of \ncarbohydrates from leaves to other plant parts, allowing photosynthesis to accumulate in the grain. \nThese results indicate that Glomus sp.3 is able to increase the yield component under the SRI rice \nmethod. P2 fertilizer (50% efficiency) showed the highest yield for the component parameters of \nrice yields. \n\n\n\nThe most important function of this symbiotic association involves the transfer of nutrients such \nas organic C in the form of sugars and lipids (Jiang et al.2017) (Luginbuehl et al. 2017)   for fungi \nby plants and the transfer of P and N to plants by fungi (Smith-Ramesh et al., 2017)  The interaction \nbetween AMF and plant roots is one of the symbiotic relationships that makes a major and \nconsistent contribution to the production of agricultural crops. Plant growth will depend on \nincreased nutrient flow through the AMF network (Hamel and Plenchette 2017). According to \nCosme et al. (2018), AMF play an important role in increasing agricultural yields and productivity \nwith low inputs. Bernaola et al. (2018) state that in addition to the benefits accorded to plants, \nAMF can improve soil structure, reduce drought and salinity stress, and affect the diversity of plant \ncommunities. \n\n\n\nResearch results by  Hidayati et al.(2016)  state that the high yield of rice plants under the SRI \nmethod is  a result of the significantly higher rate of photosynthesis, chlorophyll content, and N \nand P uptake. Rice plants under the SRI method in the generative phase (especially in the seed \nfilling phase) were found to have the highest photosynthesis and lowest transpiration rates. The \nrice yield under the  SRI method is higher (about 24%) compared to the conventional method of \ncultivation. \n\n\n\nRice fields are open  to various types of beneficial microorganisms such as the following bacterial \nspecies: Lactobacillus spp., Klebsiella aerogenes, Bacillus subtilis, Escherichia coli, \nPseudomonas fluorescens, Azospirillum brasilense, Bacillus subtilis, Staphylococcus aureus, \nEnterobacter cloacae, and Micrococcus sp., especially cultivated under the  SRI method. Research \nresults by Okonji et al. (2018)  state that AMF inoculation in the root zone of rice plants will have \nbeneficial interaction with one of the above stated bacteria on soil fertility, which  increases P \navailability, neutralises soil pH, and encourages C activity. Yield components of rice significantly \nincrease due to the behaviour of two interacting microorganisms that encourage P uptake. \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n211 \n \n\n\n\nNutrient Content of Paddy Fields before and after Application of AMF and P Fertilizer \nAn analysis of soil nutrient content was carried out before the soil was treated. The results of the \ninitial soil analysis before treatment application are presented in Table 1. \n \n\n\n\nTABLE 1 \nResults of the initial soil analysis of the study \n\n\n\nParameter Results Criteria \npH 1:1 5.71 Currently \nC-Organic (%) 1.10 Very low \nC/N 7.33 Low \nN-Total (%) 0.15 Low \nP (ppm) 9.2 Very low \nCEC (me 100 g-1 soil) 12.70 Low \n \n       \n\n\n\nSoil analysis  results subsequent to the inoculation of AMF type and the efficiency of P fertilizer \non pH, C-organic, and CN-soil ratio showed no interaction between the two treatments. With \nregard  to the main effect of AMF type, the results were significantly different, and in  terms of  P \nfertilizer efficiency, the results showed an increase in pH and CN-ratio, that was significantly \ndifferent (Figure 3). \n\n\n\n\n\n\n\n  \nFigure 3. Analysis of soil nutrients after inoculation of AMF and the efficiency of P fertilizer on \n\n\n\npH and CN-ratio\nIn Figure 3, it can be seen that the main effect of AMF was on soil pH which was significantly \ndifferent; the highest pH was obtained in the treatment of Glomus sp. 3. In terms of the efficiency \nof P fertilizer, the observations of the highest soil pH at P2 were not significantly different from \nP3, but significantly different from P1 and P0. When compared with the initial soil pH (5.71), there \nwas an increase in pH for all treatments; the highest increase in pH was obtained in Glomus sp. 3 \ntreatment.  \n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 5.78 6.05 5.9 5.76 5.9 6 5.98\n\n\n\nC\n\n\n\nA\n\n\n\nB\n\n\n\nc\n\n\n\nb\n\n\n\na a\n\n\n\n5.5\n\n\n\n5.6\n\n\n\n5.7\n\n\n\n5.8\n\n\n\n5.9\n\n\n\n6\n\n\n\n6.1\n\n\n\n6.2\n\n\n\npH\n\n\n\nIndigenous AMF types and P fertilizer  \n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 8.82 9.5 9.97 11.46 8.71 8.4 9.14\n\n\n\nB A A\na\n\n\n\nab d c\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\nC\n/N\n\n\n\n ra\ntio\n\n\n\nIndigenous AMF types and P fertilizer\n\n\n\n\n\n\n\n\n\n\n\n\n212 \n \n\n\n\nThis shows that the application of Glomus sp3 is the most suitable for increasing soil pH for \nlowland rice under the SRI method, with further effects on root absorption, root development, and \nhigher metabolic activity. The role of AMF is to increase the nutrients N and P in the soil, coupled \nwith P fertilization, so that it results in an  increase in soil pH (Wu et al. 2021). Acidic soil pH has \na detrimental effect on rice plant growth, reducing root length by up to 31%. Decrease in \nchlorophyll content which affects the process of photosynthesis (Awasthi et al. 2022). \n\n\n\nThe C/N value of the application effect of Glomus sp. 3 and Sclerocystis sp. was not significantly \ndifferent, but significantly different from that of the Glomus sp. 2 fungus, indicating that these two \ntypes of fungi have strong activity in decomposing soil organic matter. The main effect of the \nefficiency of P fertilizer is shown by the highest C/N value at P0, which indicates lower AMF \nactivity without P fertilizer mineralising organic matter. According to Sun et al. (2021), \nrhizosphere microorganisms interact with plant roots to accelerate the mineralisation of soil \norganic matter to obtain nutrients. Root-mediated changes in soil organic matter mineralisation are \nhighly dependent on root-derived carbon inputs and soil nutrient status. Root morphology, rather \nthan root biomass, is  positively related to C/N mineralisation. There was interaction between the \napplication of AMF and the efficiency of P fertilizer on organic C, total N, CEC, and P-available \npaddy soil (Figure 4). \n\n\n\n\n\n\n\n \nFigure 4. Interaction of AMF species with efficiency of P fertilizer on organic C, total N,  \n\n\n\n                     P available and CEC \n\n\n\nP0 P1 P2 P3\nGlomus sp 2 2.58 3.74 3.87 3.60\nGlomus sp 3 2.36 4.99 7.35 6.75\nSclerocytis sp 2.81 3.97 6.93 6.25\n\n\n\n0.00\n1.00\n2.00\n3.00\n4.00\n5.00\n6.00\n7.00\n8.00\n\n\n\nC\n-O\n\n\n\nrg\nan\n\n\n\nic\n (%\n\n\n\n) \n\n\n\nP Fertilizer \n\n\n\nP0 P1 P2 P3\nGlomus sp 2 0.132 0.255 0.43 0.36\nGlomus sp 3 0.127 0.35 0.51 0.37\nSclerocytis sp 0.133 0.25 0.46 0.38\n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\nN\n\n\n\n -\nTo\n\n\n\nta\nl (\n\n\n\n%\n)\n\n\n\nP Fertilizer \n\n\n\nP0 P1 P2 P3\nGlomus sp 2 12.1 12.25 12.52 12.52\nGlomus sp 3 12.33 13.6 13.95 13.59\nSclerocytis sp 12.06 12.32 12.81 12.33\n\n\n\n11\n\n\n\n11.5\n\n\n\n12\n\n\n\n12.5\n\n\n\n13\n\n\n\n13.5\n\n\n\n14\n\n\n\n14.5\n\n\n\nP \n-A\n\n\n\nva\nila\n\n\n\nbl\ne \n\n\n\n (p\npm\n\n\n\n)\n\n\n\nP Fertilizer \n\n\n\nP0 P1 P2 P3\nGlomus sp 2 12.95 13.26 16.63 13.61\nGlomus sp 3 16.02 16.75 17.95 17.33\nSclerocytis sp 13.81 13.57 17.13 13.9\n\n\n\n0\n2\n4\n6\n8\n\n\n\n10\n12\n14\n16\n18\n20\n\n\n\nC\nat\n\n\n\nio\nn \n\n\n\nex\nch\n\n\n\nan\nge\n\n\n\n c\nap\n\n\n\nac\nity\n\n\n\n(m\ne \n\n\n\n10\n0 \n\n\n\ng-\n1 \n\n\n\n)\n\n\n\nP Ferilizer \n\n\n\n\n\n\n\n\n\n\n\n\n213 \n \n\n\n\n \nFungus Glomus sp 2, Glomus sp 3 and Sclerocystis sp interacted with organic C, Total N, available \nP and Cation Exchange Capacity (CEC) at doses of P2. The highest interaction was given by \nGlomus sp 3 at doses of P2. Competition for the fungus Glomus sp 3 is more beneficial in \nincreasing soil nutrients compared to Glomus sp2 and Sclerocystis sp. According to Wu et al (2021) \ninterspecific competition of fungi is more beneficial than intraspecific competition for increasing \nsoil nutrients. \n \nThe average N-total soil applied with Glomus sp 3 was the highest (0.34%), whereas in \ncombination with P2 it was the highest and significantly different from the others (0.42%). The \ncombination of Glomus sp3 and P2 has the highest total soil N-value (0.51%), this indicates that \nGlomus sp 3 and P2 can provide N nutrients so that they are suitable for plant needs, especially \nlowland rice with the SRI method which requires high N. The impact of this condition has a further \neffect on increasing root absorption, root development and higher metabolic activity. Tan et al \n(2021) stated that the mycelium of the AMF fungus can increase litter decomposition and \naccelerate the release of N nutrients through the decomposition of nitrogen compounds in litter. \nYang et al (2019) stated that competition for specific fungal species greatly intensified plant \nnutrition. \n \nThe highest interaction in Glomus sp. 3 with a dose of P2 fertilizer with a value of 17.95 increased \nthe CEC status of the soil at the medium criteria level that was higher than the initial CEC (Table \n1) of 12.70 (medium). The increase in CEC value isa result of AMF, especially the Glomus sp. 3 \nspecies at P2 dose, resulting in a faster rate of  ion exchange for potassium calcium and magnesium \nwhich have an impact on increasing soil pH (Figure 3). An increase in soil pH has an impact on \nroot absorption of soil nutrients leading to an  increase in the number of pithy rice grains (Figure \n2). This encourages an improvement in soil quality and better absorption of plant roots. \n\n\n\nAMF variety of Glomus sp. 2, Glomus sp. 3 and Sclerocystis sp. interact with the application of P \nfertilizer so as to increase available P. The increase in available P from very low levels (see Table \n1) had a positive impact on the availability of P. AMF can help the hyphae absorb phosphorus far \nfrom the reach of the roots and increase metabolic energy to form a higher number of  seeds, \nresulting in an increase in  the number of pithy seeds and production.  \n\n\n\nThe Development of AMF in the Rice Rhizosphere \nAMF application increases soil microbial activity, especially microbes around the rhizosphere of \nrice plants. This can be seen from the observations shown in Figure 5.  \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n214 \n \n\n\n\n  \nFigure 5. Comparison of AMF colonisation and AMF spore density with P. fertilizer efficiency \n \n\n\n\nAll plant treatments showed mycorrhizal colonisation and spore density. The colonisation effect \nand the highest spore density were found in  Glomus sp. 3 and P2 treatment (p < 0.05), which \nsignificantly increased rice yield. Colonisation and density of mycorrhizal spores increased soil \nnutrients, including organic C, N-total, CEC, and P-available soil (Figure 5). In addition to the \nnutritional effects, AMF fungi also have an effect on biotic resistance in host plants (Campo et al. \n2020) and  provide basic resistance to plants which enables better  plant growth (Wang et al. 2021). \n\n\n\nAMF are important microorganisms in rice fields and several other wetlands (Wang et al. 2015). \nThe development of AMF fungus is not depressed in flooded conditions but is very responsive to \nthe growth of rice plants in non-flooded rice fields (Vallino et al. 2014). \n\n\n\nVaried growth response of rice plants to the inoculation of AMF was obtained in several studies \nranging from positive to negative. Most studies reported that AMF increased rice plant biomass, \ngrain yield, and P uptake under flooded conditions  (Gewaily 2019).  According to Wang et al. \n(2021),  AMF colonisation triggers a strong defense response in rice plants in shaded conditions \nand has a good influence on rice plant metabolism. In contrast, several other studies found that \nAMF inoculation resulted in a reduction in the amount of dry matter and rice production under \nflooded conditions (Bao et al. 2019). In line with the results of this study, in the SRI method of \nrice cultivation, the vegetative phase of groundwater conditions was not flooded. This unstressed \ngrowth conditions led to a good development of AMF, which could have had the effect of \nincreasing growth, rice plant production, and soil nutrients. \n \n\n\n\nCONCLUSION \n\n\n\nGlomus sp. 3 and 50% P fertilizer efficiency increased the highest  rate of  vegetative and \ngenerative growth in rice cultivation under the SRI method and the pH of paddy soil. The Glomus \nsp. 3 type increased rice yields up to 8.25 tons/ha, with 50% P fertilizer efficiency, reaching 10.27 \ntons/ha. It also increased the nutrient content of paddy soil planted with rice using the SRI method. \nThe best CN ratio was obtained from Glomus sp. 3 and Sclerocystis sp. The fungi Glomus sp.2, \nGlomus sp.3, and Sclerocystis sp. interacted on organic C, total N, available P, and CEC at doses \nof P2. The highest rate of  interaction was given by Glomus sp. 3 at a dose of P2. Competition for \nthe fungus Glomus sp. 3 was found to be  more beneficial in increasing soil nutrients compared to \n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 61.71 77.44 71.61 48.55 68.23 87.54 76.69\n\n\n\nC\n\n\n\nA\nB\n\n\n\nc\n\n\n\nb\n\n\n\na\na\n\n\n\n0\n10\n20\n30\n40\n50\n60\n70\n80\n90\n\n\n\n100\nC\n\n\n\nol\non\n\n\n\niz\nat\n\n\n\nio\nn\n\n\n\nIndigenous AMF types and P fertilizer\n\n\n\nG2 G3 SC P0 P1 P2 P3\nSeries1 65.83 94.28 72.71 51.35 70.68 105.9 82.5\n\n\n\nC\n\n\n\nA\n\n\n\nB\n\n\n\nd\n\n\n\nc\n\n\n\na\n\n\n\nb\n\n\n\nSp\nor\n\n\n\ne \nde\n\n\n\nns\nity\n\n\n\nIndigenous AMF types and P fertilizer\n\n\n\n\n\n\n\n\n\n\n\n\n215 \n \n\n\n\nGlomus sp. 2 and Sclerocystis sp. 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Arbuscular mycorrhizal fungi \ninfluence decomposition and the associated soil microbial community under different soil phosphorus \navailability. Soil Biology and Biochemistry 120: 181\u2013190. https://doi.org/10.1016/j.soilbio.2018.02.010 \n\n\n\nYang, X.; Zhang, W.; He, Q. Effects of intraspecific competition on growth, architecture and biomass allocation \nof Quercus liaotungensis. J. Plant Interact. 2019, 14, 284\u2013294. \nhttps://doi.org/10.1080/17429145.2019.1629656 \n \n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: agimchimaobi@yahoo.com\n\n\n\nINTRODUCTION\nUltisols, characterised by high leaching of base\u2013forming cations, very acidic \nB-horizon, and low fertility (Brady and Weil,1999), are highly weathered but \nprone to degradation due to excessive rains (Lal, 1987).  However, Ultisols do \nrespond to good management (Landon, 1991). Soils are a natural resource of great \nimportance in agriculture, especially for providing crops with nutrients, rooting \nspace and anchorage. Soil, as a scarce commodity, has competition in terms of \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 1-15 (2015)                          Malaysian Society of Soil Science\n\n\n\nSeasonal Variability and Land Use Effects on Aggregate \nStability, Shear Strength and Organic Matter Content of an \n\n\n\nUltisol\n\n\n\nAgim, L.C.1, G.E. Osuji1, C.A. Igwe2, I.I Ekpe1 andS. Ikeh3\n\n\n\n1Department of Soil Science, Federal University of Technology Owerri, Imo State \n2Department of Soil Science, University of Nigeria, Nsukka Enugu State\n\n\n\n3Department of Agricultural Technology, Imo State Polytechnic, Umuagwo Owerri, \nImo State\n\n\n\nABSTRACT\nUltisols of the tropics are characterised bylow crop productivity, severe degradation \nand variability in their properties due to inappropriate land use practices and \nseasonal changes. Knowledge of variability in soil properties is important for \nprecision farming, adequate food production and environmental modeling. The \nmajor objective of this study was to evaluate the effect of season and land use \non the studied properties. The experiment was factorially arranged in randomised \ncomplete block design (RCBD), with season, month of sampling and land use, \nserving as factors. Data were analysed using analysis of variance (ANOVA) \nand significant means were separated using least significant difference at 5% \nprobability level. Bare fallow had the lowest shear strength (119.62 kN m-2), water \nstable aggregates (WSA) (33.34 %), soil organic matter (13.95 g kg-1), and bulk \ndensity (1.40 g cm-3). Soil under bush fallow had the highest shear strength (136.95 \nkN m-2) andWSA (38.00 %), and the least silt content (89.35 g kg-1).The shear \nstrength,organic matter and aggregate stability varied moderately (C.V=16.89, \n20.26 and 38.43%, respectively). Significant interactions between the season and \nland use were noted in organic matter content only. Seasonal variations affected \nshear strength, organic matter, and bulk density significantly (P=0.05). \n\n\n\nKeywords: Bare fallow, bush fallow, continuos cassava cultivation, dry \nand rainy seasons, Ultisols\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 20152\n\n\n\nAgim, L.C, G.E. Osuji, C.A. Igwe, I.I Ekpe and S. Ikeh\n\n\n\nusage other than agriculture (Oluwole,  2011) as a result of population pressure, \ngovernment policies, market demand, climate change and urbanisation (Valentin \net al., 2008). Soil properties are highly variable, show complex interaction, and \nare sensitive to human activities and agricultural intervention. Variability in soil \nresources affects patterns of soil process rates (Ettema et al., 1998; Corstanje \net al., 2006), resulting in low crop yield, increased cost, land devaluation and \ndegradation (Lal, 1987) .  \n\n\n\nIn Sub-Saharan Africa, soils are characterised by low crop productivity \n(Eswaran, 1997), and are subject to severe degradation due to inappropriate land \nuse practices (Igwe, 2003; Lal, 2009) and seasonal changes (Singer and Munns, \n1999). Several researchers (Geeves et al., 1995; Abbasi et al., 1988; Jirku et \nal., 2010;  Mosayeb et al., 2011) have documented that land use practices have \nled to changes in the soil physico-chemical properties, especially aggregate \nstability, soil organic carbon, cation exchange capacity (CEC), and shear strength. \nThe effects of land use on soil properties have also been documented by other \nresearchers. Whilst studying the effects of land use on soil, Aluko and Fagbenro \n(2000), observed increased pH and organic matter for soils under Gmelina aborea \ncompared to soils under Pinus canaborea, Treculia Africana, agroforestry and \nfallow. They also observed increased phosphorous (P) in fallow compared to \nother land use types. Akamigbo and Asadu (2001) reported marked changes in \nmorphological, physical and chemical properties, which accelerates pedogenic \nprocesses, and decline in fertility of soil under traditional use when contrasted \nwith forest land. Agim (2010), Uzoho (2011),  Ahukaemere et al., (2012) and  \nAgim et al., (2012) also found significant differences is soil properties.\n\n\n\nAggregate stability, soil organic matter and shear strength are important soil \nproperties that can be used in the study of soil erosion (Brady and Weil, 1999). Soil \naggregate stability is an important indicator of the soil\u2019s physical quality (Castro \nFilho et al., 2002) and can be affected by land use (Bergkamp and Jongejans, \n1988; Cerda 2000). A loss of aggregate stability leads to disintegration, slaking \nand ultimately soil erosion (Oti, 2002). Shear strength, as an important measure of \nsoil strength, is the ultimate state of stress that a soil or material can sustain before \nit fails (Singer and Munns, 1999). It is used to describe the maximum strength of \nsoil at which point significant plastic deformation or yielding occurs due to applied \nshear stress (Atkinson, 1993). It is also a quantitative measure of a soil\u2019s internal \nresistance to externally applied forces before the soil fails. Igwe (2003) noted \nthat in low strength soils, soil erosion, soil loss, surface sealing, crusting, nutrient \ndepletion are prevalent. Chukwuezi (1986) reported that soil detachability directly \nrelates to low shear strength. Soil organic matter is comprised of the product of \nplant and animal materials that have undergone decomposition processes (Bot \nand Benites, 2005). Soil organic matter improves the ability of the soil to resist \nerosion and enables the soil to hold more water.\n\n\n\nThe driving factor for this study was the lack of knowledge in the critical \nimportance of variability in soil properties, especially in precision farming, and \nenvironmental modeling in the area studied. The objectives of this study were to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 3\n\n\n\nSeasonal and Land use Effects on an Ultisol\n\n\n\ndetermine the effects of seasons and land use on aggregate stability, shear strength, \nand organic matter on the studied soil. \n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area\nThis study was carried out at the Federal University of Technology Teaching and \nResearch Farm Owerri, Imo State in South-eastern Nigeria. The farm is located \non latitude 05o22\u201955.5\u201d N and longitude 06o59\u2019 39.3\u2019\u2019 E, and is 61 m above sea \nlevel. The soils were derived from coastal plain sands (Benin formation) (Orajaka, \n1975). The topography is almost flat having a slope gradient of between 0 and \n2% (Onweremadu and Anikwe, 2007). The existing vegetation is secondary \nforest (Igbozuruike, 1975). The area lies in the humid tropics with two seasons \n(dry and rainy/wet), minimum and maximum ambient temperatures of 20oC and \n32oC, respectively, and is characterised by an annual rainfall of about 2500 mm \nbimodially distributed with peaks in the months of July through September and  a \nshort dry season in August known as August break (Department of Land Survey \nand Imo State of Nigeria,1984)\n\n\n\nExperimental Design\nThe experiment was a three-factor factorial experiment arranged in a randomised \ncomplete block design (RCBD); the four land usetypes constituted factor A, the \nseason of sampling factor B, whilst the six sampling periods (months) constituted \nfactor C.\n\n\n\nSoil Sampling\nRandom sampling technique was used in collecting soil samples. Soil augers were \nused to collect soil samples at depths of 0-20 cm. Collected soil samples were \nair dried, passed through a 2 mm sieve for routine laboratory analysis. Sample \ncollections were carried out at two-monthly intervals from October 2008 to \nDecember 2009 (viz. October/November, December/ January, February/March, \nApril/ May, June/July, August/September). There were 6 sampling periods to \ncollect the 72 soil samples used for this study. Core rings were used to collect \nundisturbed soils for the determination of bulk density.\n\n\n\nLand Use Types Studied\nThere were four land use types included in this study. The first was (i) soil under \ncontinuous cultivation (SCC) with cassava (Manihot spp.) This soil has been under \ncultivation for more than 10 years, (ii) Soil under bush fallow (SBF) for more than \nten (10) years, (iii) Soil under a pineapple (Ananas comosus) orchard (SPO). The \norchard was about 10-years old, and (iv) Bare fallow soil (BF) without vegetation. \nThe BF soil wasthe control for this study and was maintained by the constant hand \npicking of weeds throughout the sampling period. The selection of the different \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 20154\n\n\n\nland use types from the same area was to avoid variations introduced by different \nparent materials in the soils.\n \nLaboratory Analysis\nParticle size distribution was determined by the hydrometer method according \nto the procedure of Gee and Or (2002). Bulk density was measured by the core \nmethod (Grossman and Reinsch, 2002). Aggregate stability of water stable \naggregates (WSA) larger than 0.5 mm was measured by the wet sieving method of \nKemper and Rosenau (1986). Shear strength of the soil  was determined by  direct \nshear tests as described by Head (1982). Soil pH was measured potentiometrically \nin a suspension with a soil to water ratio of 1:2.5 (Hendershot et al., 1993).  \nOrganic carbon was determined by the procedure of Nelson and Sommers (1982).\nSoil organic matter was calculated by multiplying organic carbon with a factor of \n1.724.\n\n\n\nData Analyses\nData were analysed using analyses of variance (ANOVA). Least significant \ndifference (LSD) was used to separate significant means at 5% probability. \nCorrelation and regression were carried out using Microsoft Excel 2007. Ranking \nof the coefficients of variation was done according to the method of Aweto (1982).\n\n\n\nRESULTS AND DISCUSSION\nResults showed that all the studied soils had sandy loam texture irrespective of \nland use and season (Table 1), typical of soils in the study area (Enwezor et al., \n1990).  The sandiness of the studied soils reflected the parent materialfrom which \nthey were formed, namely coastal plain sand (Enwezor et al., 1990). Generally the \nstudied soils had low silt to clay ratio (SCR) ranging from 0.55 to 0.65 (Table 1). \nSCRs as low as those found in this study indicate soils that are highly weathered \n(Wambeke, 1962).\n\n\n\nShear Strength\nSBF had the highest shear strength (136.95 kN m-2), whilst BF had the lowest \nstrength of 119.62 kN m-2 (Table 1). The high value of shear strength found \nfor SBF could be attributed to its increased bulk density, and moisture content. \nAdditionally, Poulos (1989) reported that shear strength of a soil is a result of the \nbasic soil composition (i.e., shape of particles, soil water content, and particle size \ndistribution), state of the soil (i.e., effective normal and shear stresses, void ratio, \nloose, dense or over consolidated, etc.), soil structure, and loading conditions. The \nvarying climatic factors (Table 6), land use management and parent material of \nthe studied area could have also contributed to the change. The result for BFwas \nin line with those obtained by Osuji (1985) and Chukwuezi (1986). Shear strength \nshowed moderate variation (CV = 33.80%) (Table 1) in all the studied landuse \ntypes and related positively (r2 = 0.11) with organic matter and aggregate stability \n(Table 5).\n\n\n\nAgim, L.C, G.E. Osuji, C.A. Igwe, I.I Ekpe and S. Ikeh\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 5\n\n\n\nSeasonally, shear strength did not differ significantly at the 5% probability \nlevel; however higher values occurred in dry rather than in rainy seasons in all \nthe studied landuse types, except for BF (Table 2). Vidrih and Hopkins (1996) \nstate that dryness increases soil strength by reducing the water film between soil \nparticles, which increases inter-particle attraction. Zimbone et al. (1996) and \nSinger and Munns (1991) noted that shear strength decreases with increasing \nmoisture content. The increase in soil shear strength during the dry season has \nalso been attributed to past landuse history, and compaction of near surface of \nthe soils as a result of wetting and drying during the late dry season (Achmad et \nal., 2003). The percentage reduction of shear strength between the dry and wet \nseasons were 14.05%, 5.99%, and 5.25%  and 10.26% for SCC, SPO, SBF, and BF \nlanduse types, respectively. Additionally, the shear strength reduction in the rainy \nseason is also attributed to the direct impact of rain drops splashing on already \nsaturated soils, affecting its cohesion (Chukwuezi, 1986) and changes in other \nclimatic variables (Table 6). There were no patterns in the monthly variations \nin shear strength for the various landuse types (Table 3). However, the months \n\n\n\nTABLE 1\nEffect of land use on  physico-chemical properties of studied soil\n\n\n\nTABLE 2\nEffect of season on selected physico-chemical properties of studied soil\n\n\n\nSeasonal and Land use Effects on an Ultisol\n\n\n\n\n\n\n\n11 \n \n\n\n\nTABLE 2 \nEffect of season on selected physico-chemical properties of studied soil \n\n\n\n \n \nLand use Sand Silt Clay T.C SCR \u2113b VMV  WSA>0.5mm   pH(H2O)   SOM \n\n\n\n g kg-1 g kg-1 g kg-1   g cm-1 g kg-1 kN m-2 %   \nDry Season           \n\n\n\nSCC 710.17 113.77 203.93 SL 0.49 1.23 87.48 149.77 34.39 5.51 20.56 \nSPO 749.84 77.56 72.60 SL 0.58 1.29 55.14 129.04 38.82 5.47 21.42 \nSBF 753.30 82.64 164.02 SL 0.51 1.33 78.52 140.64 37.07 5.60 20.06 \nBF 741.63 86.17 193.32 SL 0.48 1.41 69.12 113.44 32.27 5.49 16.63 \n\n\n\nRainy Season           \nSCC 664.11 139.35 192.10 SL 0.75 1.37 124.86 110.33 39.54 4.73 16.24 \nSPO 632.99 143.53 218.36 SL 1.06 1.33 116.22 121.30 30.29 4.91 10.87 \nSBF 695.03 96.06 201.66 SL 0.58 1.37 119.36 133.26 39.54 4.51 9.73 \nBF 697.77 120.17 192.56 SL 0.74 1.39 131.34 125.80 34.40 4.64 11.26 \n\n\n\nLSD \n(P=0.05) \n\n\n\n21.62* 23.56* NS   0.11* 0.04* NS NS NS 0.09* \n\n\n\n \nSCC=Soil under continuous cultivation, SOP= Soil under pineapple orchard, SBF=Soil under bush fallow, BF= \nBare fallow, LSD=Least significant difference, TC=Textural class, SL=Sandy loam, SCR=Silt clay ratio, \nV.M.C= Volumetric moisture content, SOM=Soil organic matter, \u2113b=bulk density, =Shear strength, \nWSA=Water stable aggregates,**=Highly significant, NS=Not significant,*=significant  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n10 \n \n\n\n\n\n\n\n\nTABLE 1 \nEffect of land use on  physico-chemical properties of studied soil \n\n\n\n \nLand use Sand Silt Clay T.C SCR \u2113b VMV  WSA>0.5mm   pH(H2O)   SOM \n\n\n\n g kg-1 g kg-1  g kg-1   g cm-1 g kg-1 kN m-2 %   \nSCC 687.14 112.63 198.02 SL 0.62 1.30 128.86 130.15 37.02 5.12 16.95 \nSPO 735.47 110.55 195.45 SL 0.61 1.31 116.22 125.17 34.56 5.19 16.15 \nSBF 724.17 89.35 182.84 SL 0.55 .35 119.36 136.95 38.30 5.06 14.89 \nBF 697.77 120.17 192.56 SL 0.65 1.40 131.34 119.62 33.40 5.07 13.95 \n\n\n\nLSD \n(P=0.05) \n\n\n\nNS 15.23* 20.57**  NS 0.05* NS NS NS 0.13* 0.16* \n\n\n\nC.V (%)   11.01 51.68 27.85  44.23 8.76 41.60 16.89 16.89 11.15 38.43 \n \nSCC=Soil under continuous cultivation, SOP= Soil under pineapple orchard, SBF=Soil under bush fallow, BF= \nBare fallow, LSD=Least significant difference, TC=Textural class, SL=Sandy loam, SCR=Silt clay ratio, \nV.M.C= Volumetric moisture content, SOM=Soil organic matter, \u2113b=bulk density, =Shear strength, \nWSA=Water stable aggregates,**=Highly significant, NS=Not significant,*=significant  \nCV= coefficient of variation; (C.V results were ranked as follows: 50-100% = High variation , 20-49 % = \nModerate variation, 1-19% = Little variation, respectively). \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 20156\n\n\n\nof November to April witnessed increased strength in all the soils studied. The \nlowest and highest shear strength values for SCC (94.91 and 186.69 kN m-2), SPO \n(92.90  and 152.85  kN m-2), SBF (101.32 and  155.16 kN m-2) and BF (65.04 and \n148.06 kN m-2) occurred in July /August and November/December, November/\nDecember and January/February, September/October and January/February, and \nNovember/December and January/February, respectively (Table 3). Achmad et \nal., (2003) also observed a decrease in shear strength from November to January, \nan increase from January to April and a decrease again from April to July. These \nresults accounted for the significant difference (P<0.01) (Table 3) that existed \nbetween shear strength and the month of sampling.  The results also showed that \nthe interaction between the season and month affected the result of shear strength \nsignificantly (P=0.05) compared to the interaction effects of other factors (Table 4).\n\n\n\nBulk Density\nResults showed that bulk density was highest in BF (1.40 g cm-3). This \nwas  followed by SBF (1.35g cm-3), SPO (1.31g cm-3) and, lastly, SCC (1.30 \ng cm-3) (Table 1). The lower bulk density found in SCC was in line with the \nfindings of Landon (1991).The higher bulk densities found in bare soil was not \nsurprising since bare soil receives the direct impact of rain which leads to surface \nsealing. deGeus (1973) and Koorevaar et al., (1983), both reported that at high \n\n\n\nTABLE 3\nDistribution of  bulk density, shear strength, water  stable aggregates and soil organic \n\n\n\nmatter with respect to months of sampling\n\n\n\n\n\n\n\n12 \n \n\n\n\nTABLE 3 \nDistribution of  bulk density, shear strength, water  stable aggregates and soil organic matter with \n\n\n\nrespect to months of sampling \n \n\n\n\nNov/Dec Jan/Feb March/April May/June            July/Aug Sep/Oct   LSD \n(P=0.05)          \n\n\n\nBulk density g cm-3      \nSCC 1.24 1.29 1.15 1.43 1.37  \nSPO 1.13 1.47 1.26 1.39 1.36  \nSBF 1.42 1.30 1.27 1.39 1.43  \nBF 1.43 1.47 1.32 1.40 1.43 0.05* \n\n\n\n \nShear strength (kN m-2) \n\n\n\n\n\n\n\nSCC 189.69 127.50 135.12 94.91 105.15  \nSPO 92.90 152.85 141.36 124.16 99.22  \nSBF 155.57 155.16 151.20 140.84 101.32  \nBF 65.04 148.06 127.22 134.84 95.97 15.40* \n\n\n\n \nWater  Stable Aggregates >0.5 mm ( %) \n\n\n\n\n\n\n\nSCC 21.92 53.19 28.80 42.34 43.44  \nSPO 31.00 39.74 45.72 35.35 21.73  \nSBF 39.47 33.63 38.08 32.29 4.16  \nBF 27.48 36.35 32.98 27.99 8.05 NS \n\n\n\n \nSoil organic matter (g  kg-1) \n\n\n\n\n\n\n\nSCC 20.29 23.73 17.65 9.75 19.60  \nSPO 19.78 27.10 20.94 6.30 11.01  \nSBF 12.04 16.40 19.72 6.37 8.48  \nBF 5.56 20.23 16.63 9.70 8.50 0.64* \n\n\n\n  \nLSD=Least significant difference, MOS=month of sampling, SCC= Soil under continuous cultivation,  \nSPO= Soil under pineapple orchard, SBF=Soil under bush fallow, BF= bare fallow,  \n* Significant at 5% probability level. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nAgim, L.C, G.E. Osuji, C.A. Igwe, I.I Ekpe and S. Ikeh\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 7\n\n\n\nbulk densities, pore space, soil compaction and runoff increase whereas water \ninfiltration, root growth and seed emergence are reduced. Bulk densities with \nrespect to months of this study, ranged from 1.15 to 1.43 g cm-3, 1.13 to 1.47 g \ncm-3, 1.27 to 1.43 g cm-3, and 1.32 to 1.47 g cm-3 for SCC, SPO, SBF and BF, \nrespectively (Table 3). These showed that the studied soil groups were not very \ncompact (Landon, 1991).This could be attributed to parent material and climate. \nResults showed that bulk density increased towards the rainy season in all the \nstudied soils except for BF, where different resultswere found (Table 2). This may \naccount for the lower shear strengths found in this study. Bulk densities varied \nminimally (CV =8.76%)  (Table1) with respect to land use and season of study. \nLal (1987) attributes variation in bulk density to variation in particle size and the \nmethod adopted in sampling. Organic matter and bulk density have a negative \nrelationship (R= -0.29) (Table 5). Agim et al., 2012 found similar results. This \nimplies that an increase in soil organic matter content decreases soil bulk density\n\n\n\nTABLE 4\nInteraction effects on studied soil properties\n\n\n\nTABLE 5\nRealationship between  soil organic matter, shear strength and aggregate stability\n\n\n\n\n\n\n\n13 \n \n\n\n\n \nTABLE 4 \n\n\n\nInteraction effects on studied soil properties \n \n \n\n\n\nSoil property Source of variation DF LSD (P=0.05) \nShear strength Season x month  2 20.12** \n Season x land use  3 1.12NS \n Month x land use  6 0.57NS \n Season x month x land use  6 0.46NS \n Error 48 32319.7 \n Total  71 83794.90 \nWSA >0.5mm Season x month  2 0.48NS \n Season x land use  3 1.71NS \n Month x land use  6 1.29NS \n Season x month x land use  6 4.49** \n Error 48 79.54 \n Total  71 7467.17 \nOrganic matter Season x month  2 20.43** \n Season x land use  3 6.02* \n\n\n\n Month x land use  6 1.86NS \n Season x month x land use  6 1.93NS \n Error 48 645.48 \n Total  71 2769.79 \n\n\n\n \nD.F=Degree of freedom, **=Highly significant, *=Significant, NS=Not significant. \n\n\n\n\n\n\n\n\n\n\n\n13 \n \n\n\n\n\n\n\n\n\n\n\n\nTABLE 5 \nRealationship between  soil organic matter, shear strength and aggregate stability \n\n\n\n \nSoil property  R r2 \nAggregate stability  %  0.17 0.03NS \nShear strength    (kN m-2) 0.31 0.09* \nAggregate stability and shear strength   0.34 0.11* \nBulk density    (g cm-3 ) -0.29 0.08NS \npH(water) 0.21 0.01 \n\n\n\n\n\n\n\nR=Correlation coefficient, *=significant, NS=not significant. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nSeasonal and Land use Effects on an Ultisol\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 20158\n\n\n\nAggregate stability using water stable aggregates showed significantly (P= \n0.05) higher values in SBF (38.30) followed by SCC (37.02%), SPO (34.56%) \nand BF (33.34%). This trend was also true for the period of sampling except for \nMay/June, where the highest aggregate stability was found in SCC. The highest \nvalue of 53.1% occurred in SCC in Jan/Feb whilst the lowest value of 21.73 % for \nSPO occurred during July/Aug. Mbagwu et al., (1993) pointed out that soils with \nhigh WSA percentage values of >0.5 mm are more stable than those with lower \nWSA >0.5 mm. This therefore meant that SBF soil was more stable than the others \nsoils. Aggregate breakdown of soils is attributed to direct impact of raindrops, \nclay mineralogy of soils, and vegetation differences (Nwadialo and Mbagwu, \n1991). Availability of intact roots without tillage could be another reason for the \nhigher percentage of WSAin SBF. Achmad et al., (2003) found similar results. \nHowever, the monthly variations in aggregate stability for each landuse type did \nnot follow a particular trend (Table 3).  Achmad et al., (2003) found similar results \nin wheat and corn farms. Past landuse history and changes in climatic elements \nof the studied area (Table 6) could also be responsible for the variation. Season, \nmonth and land use interactions affected aggregate stability significantly (Table 4).\n\n\n\nAgim, L.C, G.E. Osuji, C.A. Igwe, I.I Ekpe and S. Ikeh\n\n\n\nTABLE 6\nMean maximum rainfall and temperature data in Imo State for a 5-year period\n\n\n\n(2005-2009)\n\n\n\n\n\n\n\n14 \n \n\n\n\n \nTABLE 6 \n\n\n\nMean maximum rainfall and temperature data in Imo State for a 5-year period (2005-2009) \n \n\n\n\nElements Year Jan. Feb. Mar. April May June July Aug. Sep. Oct. Nov. Dec. \nRainfall (mm) 2009 38.6 71.4 40.1 71.2 273.3 371.2 311.1 423.7 392.4 60.0 293.3 10.0 \n 2008 0.9 0.0 256.4 238.2 165.4 230.8 328.8 362.8 446.3 183.3 14.8 10.1 \n 2007 Trace 7.4 57.7 62.1 260.9 397.3 485.4 5090 3030 180.2 42.7 9.6 \n 2006 89.8 18 167.0 81.9 358.2 454.7 802.5 286.7 479.4 3606 9.2 0.0 \n 2005 38.3 84.3 103.1 182.2 469.8 500.7 260.0 190.5 490.6 1943 21.5 10.5 \nTemp.( 0C) 2009 33.8 34.3 33.8 34.60 30.50 30.10 30.90 30.50 30.30 30.30 30.20 29.10 \n 2008 33.5 36.9 35.00 32.20 31.10 30.20 29.10 29.40 29.90 31.30 33.00 33.10 \n 2007 34.10 35.9 35.10 33.30 33.10 31.60 30.80 30.70 30.80 31.50 32.40 34.00 \n 2006 34.10 34.8 34.30 35.10 32.20 32.20 29.10 29.50 30.00 31.60 34.06 34.80 \n 2005 33.90 35.2 34.00 34.30 31.40 31.40 30.80 30.20 31.00 32.10 34.60 34.30 \n\n\n\n \n29.70Source: NIMET 2009, Lagos, Nigeria. \n\n\n\n\n\n\n\nSoil Organic Matter\nSoil organic matter varied moderately (CV= 38.43%) (Table 1) and was \nsignificantly (P=0.05) higher in SCC. The order was CCS (16.95 g kg-1) > SPO \n(16.15 g kg-1 ) >SBF (14.89 g kg-1) >BF (13.95 g kg-1) (Table 1). This accounted \nfor the low bulk density exhibited by the SCC. Higher values of organic matter \nin SCC could be attributed to the addition of organicmanure to the soil whilst \ncultivation was taking place. It thus implied that the mineralisation of essential \nminerals will occur to a greater extent in SCC compared to the others. Seasonally, \nsoil organic matter was significantly (P=0.05)  higher in the dry season than in \nrainy season (Table 2) with the percentage decreases being 21%, 49.26%,43.55% \nand 20.71% in SCC, SPO, SBF and BF, respectively. Increased soil organic matter \nfound during the dry season is attributable to increased temperature, and decreased \nsoil moisture content in the soil during the dry season (Table 5) which invariably \naffects decomposition, and further mineralisation (Singer and Munns, 1999). \nAlexandra and Jose (2005) reported high temperature as a key factor that controls\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 9\n\n\n\nthe rate of decomposition of plant residues.  This result is also corroborated by \nthe positive significant relationship between soil organic matter and temperature \ndistribution of the studied location (Figures 1-4). This could also be attributed to \nenvironmental conditions and the quality of the residue materials added to the soils \n\n\n\nFigure1: Relationship between temperature and soil organic matter in soil under \ncontinuous cultivation\n\n\n\nFigure 2: Relationship between temperature and soil organic matter in soil under  \npineapple cultivation\n\n\n\nFigure 3: Relationship between temperature and soil organic matter in soil under bush \nfallow\n\n\n\n\n\n\n\n14 \n \n\n\n\n\n\n\n\n\n\n\n\n \nFigure1: Relationship between temperature and soil organic matter in soil under continuous \n\n\n\ncultivation \n \n \n \n\n\n\nSOM= 0.071temperature + 29.78\nr\u00b2 = 0.052\n\n\n\n28\n29\n30\n31\n32\n33\n34\n35\n\n\n\n0 5 10 15 20 25 30\n\n\n\nSO\nM\n\n\n\n\n\n\n\n(g\n k\n\n\n\ng-1\n)\n\n\n\nTemperature (0C) \n\n\n\n\n\n\n\n15 \n \n\n\n\n \nFigure 2: Relationship between temperature and soil organic matter in soil under  pineapple \n\n\n\ncultivation \n \n \n \n \n \n \n \n\n\n\n \n \nFigure 3: Relationship between temperature and soil organic matter in soil under bush fallow \n\n\n\n\n\n\n\nSOM = 0.057 temperature + 30.16\nr\u00b2 = 0.054\n\n\n\n28\n\n\n\n29\n\n\n\n30\n\n\n\n31\n\n\n\n32\n\n\n\n33\n\n\n\n34\n\n\n\n35\n\n\n\n0 5 10 15 20 25 30\n\n\n\nSO\nM\n\n\n\n   \n(g\n\n\n\n k\ng-1\n\n\n\n)\n\n\n\nTemperature (0C)\n\n\n\nSOM = 0.100 temperature + 29.73\nr\u00b2 = 0.090\n\n\n\n28\n29\n30\n31\n32\n33\n34\n35\n\n\n\n0 5 10 15 20 25 30\n\n\n\nSO\nM\n\n\n\n   \n(g\n\n\n\n k\ng-1\n\n\n\n)\n\n\n\nTemperature (0C)\n\n\n\n\n\n\n\n15 \n \n\n\n\n \nFigure 2: Relationship between temperature and soil organic matter in soil under  pineapple \n\n\n\ncultivation \n \n \n \n \n \n \n \n\n\n\n \n \nFigure 3: Relationship between temperature and soil organic matter in soil under bush fallow \n\n\n\n\n\n\n\nSOM = 0.057 temperature + 30.16\nr\u00b2 = 0.054\n\n\n\n28\n\n\n\n29\n\n\n\n30\n\n\n\n31\n\n\n\n32\n\n\n\n33\n\n\n\n34\n\n\n\n35\n\n\n\n0 5 10 15 20 25 30\n\n\n\nSO\nM\n\n\n\n   \n(g\n\n\n\n k\ng-1\n\n\n\n)\n\n\n\nTemperature (0C)\n\n\n\nSOM = 0.100 temperature + 29.73\nr\u00b2 = 0.090\n\n\n\n28\n29\n30\n31\n32\n33\n34\n35\n\n\n\n0 5 10 15 20 25 30\n\n\n\nSO\nM\n\n\n\n   \n(g\n\n\n\n k\ng-1\n\n\n\n)\n\n\n\nTemperature (0C)\n\n\n\nSeasonal and Land use Effects on an Ultisol\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201510\n\n\n\n(Anikwe, 2006; Brady and Weil, 1999). Other contributing factors are cultivation, \nclimate, and land use history.  Mbagwu et al., (2003) reported that the higher the \ncoarse sand and organic matter, the more stable the soil aggregates. Following \nthis, SCC and SBF should be better aggregated compared to the order two land use \ntypes. Results showed significant variations in soil organic matter with the highest \nvalue between January/February and July/August in all the studied land use types, \nexcept BF where the November/December samples had lower organic matter \ncontent (Table 3). Lower values of soil organic matter found during July/August \nwhen rainfall was at its peak is attributed to near soil saturation. This condition \nfavours anaerobic conditions (Bot and Benites, 2005). The interaction beween \nthe season and the period of sampling affected soil organic matter significantly \n(Table 4). Soil organic matter had significant (P=0.05) positive relationship  with  \naggregate stability and shear strength (r2 = 0.11) (Table 5).\n\n\n\nCONCLUSION\nThis study revealed that season and land use type significantly affected soil organic \nmatter, aggregate stability and shear strength. Among the studied land use types, \nBF had the lowest value for shear strength (119.62 kN m-2), organic matter (13.95 \ng kg-1) and percentage WSA >0.5mm (33.34 %), buthad the highest values of silt \nfraction (120.17 g kg-1) and bulk density (1.40 g cm-3), indicating high erosion \npotential. SBF had the highest value for shear strength (130.15 kN m-2), WSA(38.30 \n%) and lowest valuefor silt content indicating low erodibility. SCC contained the \nhighest value of soil organic matter. Soil organic matter had positive relationships \nwith pH (water) (r2= 0.01), shear strength (r2=0.09), and the combination of shear \nstrength and WSA (r2= 0.11). Based on the results where SBF recorded significant \nimprovements in soil properties, this study recommends bush fallowing, but \nwhere land scarcity excludes the fallow option, regular application of organic soil \namendments will help to improve the physical condition of the soil. Additionally, \npractices such as agro-forestry and mulching that minimises soil exposure to \nrainfall are recommended.\n\n\n\nFigure 4: Relationship between temperatureand soil organic matter in bare soil\n\n\n\n\n\n\n\n16 \n \n\n\n\n \nFigure 4: Relationship between temperatureand soil organic matter in bare soil \n\n\n\nSOM= -0.095 temperature + 32.31\nr\u00b2 = 0.111\n\n\n\n28\n\n\n\n29\n\n\n\n30\n\n\n\n31\n\n\n\n32\n\n\n\n33\n\n\n\n34\n\n\n\n35\n\n\n\n0 5 10 15 20 25\n\n\n\nSO\nM\n\n\n\n   \n(g\n\n\n\n k\ng-1\n\n\n\n)\n\n\n\nTemperature (0C)\n\n\n\nAgim, L.C, G.E. Osuji, C.A. Igwe, I.I Ekpe and S. 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Chaplot.  2008. \nRunoff and sediment losses from 27 upland catchments in Southeast Asia: \nImpact of rapid land use changes and conservation practices. Agric. Ecosyst. \nEnviron. 128(4): 225-238. DOI:10.1016/j.agee.2008.06.004.\n\n\n\nAgim, L.C, G.E. Osuji, C.A. Igwe, I.I Ekpe and S. Ikeh\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 15\n\n\n\nVidrih T. and  A. Hopkins . 1996. The Effect of Soil Environment on White Clover \nPersistence and Productivity under Grazing. http:www.fao.org/docrep/v9968e/\nv9968eo3.htm#Top up page.\n\n\n\nVan Wambeke, A.R. 1962. Criteria for classifying tropical soils by age. J. Soil Sci. \n13(1):124-132.\n\n\n\nZimbone S.M., A. Vickers, R.P.C. Morgan and P. Vella. 1996. Field investigations of \ndifferent techniques for measuring surface soil shear strength. Soil Technology \n9(1-2): 101-111.\n\n\n\nSeasonal and Land use Effects on an Ultisol\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n45 \n\n\n\n\n\n\n\nSoil Fertility Status in Relation to Insidious Fruit Rot Incidence at \n\n\n\nHarumanis Mango Orchard in Perlis: A Case Study \n \n\n\n\nKamarudin, K. N.1*, Abdul Rahman, M. H.1, Mohamad, M.1, Mohamad, M.1,  \n\n\n\nAbd Rashid, N. F.1, Shahidin, N. M.1, Roslan, N. and Khairun, N.M.2 \n\n\n\n \n1 Faculty of Plantation and Agrotechnology, Universiti Teknologi MARA Perlis Branch Arau Campus, \n\n\n\n02600 Arau, Perlis \n2 Bank Pertanian Malaysia Berhad (Agrobank), Leboh Pasar Besar, P.O. Box 10815, 50726 Kuala \n\n\n\nLumpur \n\n\n\n \nCorrespondence: *irunkha@hotmail.com  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nMango is one of the six important fruits crops besides banana, watermelon, apple, orange, and grapes \n\n\n\nin the world. Of late, insidious fruit rot (IFR) has been of increasing concern among mango growers in \n\n\n\nMalaysia as this disease does not exhibit visible symptoms but has the potential to reduce the quality of \nthe fruit. Therefore, the objectives of this study were to evaluate and correlate soil fertility status \n\n\n\nbetween mango orchards with and without the IFR incidence by comparing content of selected soil \n\n\n\nnutrients. The soil samples were collected from orchards planted with Harumanis, a mango variety,  \nwith and without IFR incidence. The samples were taken at a depth of 0\u201330 cm, crushed and then sieved \n\n\n\nusing a 2-mm mesh size prior to laboratory analysis. The analyses followed standard practice. The \n\n\n\nresults showed a significant difference between EC, total C, total N, C/N ratio, and exchangeable bases \n\n\n\n(Ca, Mg, K and Na) between the soil samples from Harumanis orchards with and without IFR \nincidences. Although soil nutrient content was higher than the optimal range for Harumanis cultivation \n\n\n\nin both sites, synergistic and antagonistic effects were discovered mostly in soil samples from \n\n\n\nHarumanis orchard with IFR incidence. Studies show that application of Ca and K fertilizers can \nsuppress IFR incidence.    \n\n\n\n\n\n\n\nKey words: internal fruit breakdown, MA128, Mangifera indica, physiological disorder, soil \n\n\n\nnutrient content \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nMango (Mangifera indica) is one of the top six fruit crops in the world (UNCTAD 2016). It \n\n\n\nhas been cultivated mainly in tropical and sub-tropical countries including Malaysia. Common \n\n\n\ncommercial mango varieties in Malaysia and their registered names with the Department of \n\n\n\nAgriculture (DOA) in Malaysia are Harumanis (MA128), Golek/Foo Fatt (MA162), Maha 65 \n\n\n\n(MA165), Malele (MA204), and Chokanan (MA224). These varieties are mainly cultivated by \n\n\n\nsmallholder growers with less than 2 ha of land while large mango cultivated areas are managed \n\n\n\nby government agencies like the DOA and the Muda Agricultural Development Authority \n\n\n\n(MADA). Harumanis is one of the most popular mango varieties cultivated in parts of northern \n\n\n\nPeninsular Malaysia, namely Perlis and Kedah. The weather conditions in this region, with \n\n\n\nlong periods of drought (about two months), are suitable for the cultivation of the Harumanis \n\n\n\nvariety. Although the Harumanis cultivation is labour-intensive and requires high maintenance \n\n\n\nand operational costs, the fruits can be traded at a higher price, up to four times higher than \n\n\n\nother mango varieties sold domestically. As a result, many rice farmers have converted part of \n\n\n\ntheir paddy field into Harumanis orchards due to a higher profit margin. The planted area of \n\n\n\nHarumanis has increased by 336.5%, from 323 ha in 2011 to 1,410 ha in 2019, accounting for \n\n\n\nabout 5.5% of the agricultural land area in Perlis.                    \n\n\n\n\nmailto:*irunkha@hotmail.com\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n46 \n\n\n\n\n\n\n\nInsidious fruit rot (IFR) or yeasty fruit rot is currently a problem faced by Harumanis growers \n\n\n\nin northern Peninsular Malaysia. IFR incidence has been reported in the Harumanis cultivar in \n\n\n\nMalaysia from as early as 1985 (Lim and Khoo 1985) and is characterized by dissolution of \n\n\n\nflesh tissue in the sinus region, specifically the ventral towards the beak. The tissues become \n\n\n\nwatery, soft, and yellowish-brown with a yeasty odour (Shivashankar 2014). This disorder has \n\n\n\nbeen reported to be identical to soft nose (Tarmizi et al. 1993). IFR incidence results in poor \n\n\n\nfruit quality which naturally affects the marketability of the fruit (Tarmizi et al. 1993). \n\n\n\nAccording to Shivashankar (2014), IFR is caused by physiological disturbance from metabolic \n\n\n\nimbalance caused by pre- and/or post-harvest factors leading to cell collapse. The main cause \n\n\n\nof this physiological disorder has yet to be identified but is likely due to environment and poor \n\n\n\nmanagement practices, including soil and nutrient management (Sauco 2009; Shivashankar \n\n\n\n2014). IFR incidence in Harumanis has been noted with respect to nitrogen (N), calcium (Ca) \n\n\n\nand N/Ca ratio imbalance in both fruits and leaves during the fruiting stage (Tarmizi et al. \n\n\n\n1993). Although some studies have been conducted on the soil-leaf relationship, there is \n\n\n\nrelatively insufficient information to understand IFR incidence.  \n\n\n\n\n\n\n\nSoil fertility is a major limitation of agriculture in Malaysia. According to the Soil Taxonomy \n\n\n\n(Teh et al. 2018), Ultisol and Oxisol orders make up 70% of Malaysian soils. These soils are \n\n\n\nacidic in nature, with pH values ranging from pH 4 to 5, and are very deficient in available \n\n\n\nphosphorus (P) due to high sesquioxides fixation within the soil system. These soils also have \n\n\n\na very low basic cation status and effective cation exchange capacity (CEC). Therefore, \n\n\n\nsustainable soil management is necessary under these conditions. Mango trees can survive \n\n\n\ncultivation in marginal soils, but poor management might affect yield and quality of the fruits. \n\n\n\nA poor understanding of soil characteristics by local mango growers does not help in resolving \n\n\n\nthis problem. Therefore, the aims of this study were to compare and correlate selected soil \n\n\n\nnutrient content and evaluate soil fertility status between Harumanis mango orchards with and \n\n\n\nwithout IFR incidence. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nStudy Site \n\n\n\nTo compare soil nutrient content of Harumanis orchards with and without IFR incidence, two \n\n\n\norchards were selected for the study: (1) Harumanis orchard with IFR incidence (6o 23\u2019 N, 100o \n\n\n\n17\u2019 E) located at the Perlis-Kedah border and (2) Harumanis orchard without IFR incidence (6o \n\n\n\n27\u2019 N, 100o 15\u2019 E) located in Arau, Perlis (Figure 1).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n47 \n\n\n\n\n\n\n\n \nFigure 1. Location of Harumanis orchards with IFR (square) and without IFR (triangle) \n\n\n\nincidence  \n\n\n\n\n\n\n\nAt the time of the study, the orchard with IFR incidence was 0.25 in acreage with 70 nine-year-\n\n\n\nold trees. The trees had been planted in a standard 9 m \u00d7 9 m square system with some trees \n\n\n\nrandomly planted without specific distance especially around the fence. The land had \n\n\n\npreviously being a paddy field. As IFR incidence had first surfaced in the orchard in 2019, an \n\n\n\nattempt had being made to rehabilitate the soil with the addition of dolomite. During the study \n\n\n\nperiod, IFR incidence was in stage 1 and/or 2 with the black spot observed clearly when the \n\n\n\nfruits were cut. Generally, NPK fertilizer (12:12:17+2+TE) was used during the vegetative \n\n\n\nstage while foliar fertilizers were used during flowering and fruiting stages. Other \n\n\n\nagrochemicals like pesticide, insecticide and herbicides were used to control pest, disease, and \n\n\n\nweeds. Standard cultural practices in mango cultivation such as the application of paclobutrazol \n\n\n\nand pruning were followed.  \n\n\n\n\n\n\n\nMeanwhile, the Harumanis trees in the orchard without the IFR incidence were 12 and 38 years \n\n\n\nold. The trees had been planted in a standard 9 m \u00d7 9 m square system. This orchard had a long \n\n\n\nhistory of fruit and rubber cultivation before it was fully planted with Harumanis mango. This \n\n\n\norchard had experienced IFR incidence in 2007 and recovered fully after three years of \n\n\n\nrehabilitation. Control release NPK fertilizer (12:12:17+2+TE) was applied once every three \n\n\n\nmonths during the vegetative stage while foliar fertilizers were used once every two weeks \n\n\n\nduring the flowering and fruiting stages. Cultural practices were almost similar to the \n\n\n\nHarumanis orchard with IFR incidence.         \n\n\n\n\n\n\n\nPerlis state has a tropical monsoon (Am) climate according to the K\u00f6ppen-Geiger Classification \n\n\n\nSystem, with an average annual temperature of 27.4oC and a mean annual precipitation of \n\n\n\n1,893.7 mm over the past 30 years (1990\u20132021) (MMD 2022).  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nN\n\n\n\nArau\n\n\n\nKangar\n\n\n\nPadang \n\n\n\nBesar\n\n\n\n5 km\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n48 \n\n\n\n\n\n\n\nField Survey and Soil Sampling \n\n\n\nThe field survey and soil sampling were done in April 2021. A total of 22 soil samples were \n\n\n\ncollected with 10 and 12 samples being collected from Harumanis orchards with and without \n\n\n\nIFR incidence, respectively. One soil sample per tree was collected using a soil auger at a depth \n\n\n\nof 30 cm under the tree canopy. All selected trees had a canopy diameter of about 2 to 3 m. \n\n\n\nThe soil samples were brought back to the laboratory, air-dried, ground using mortar and pestle \n\n\n\nand sieved to pass through a 2-mm mesh for laboratory analysis.  \n\n\n\n\n\n\n\nLaboratory Analysis \n\n\n\nSoil pH was determined using a pH meter at a soil-water ratio of 1:2.5 while soil electrical \n\n\n\nconductivity (EC) was measured using an EC meter at a soil-water ratio of 1:5. Total carbon \n\n\n\n(C) and total N were simultaneously determined by CHNS Elemental Analyzer (Perkin Elmer \n\n\n\n2400 Series II, Waltham, Massachusetts). Available P was extracted using Bray & Kurtz No. \n\n\n\n2 and determined by the molybdenum blue method using an UV-visible spectrophotometer \n\n\n\n(Varian Cary 50 Scan, California, United States). Exchangeable bases such as calcium (Ca), \n\n\n\nmagnesium (Mg), potassium (K), and sodium (Na) were extracted with 1 M ammonium acetate \n\n\n\nbuffered at pH 7, and their concentrations were determined by inductively coupled plasma\u2013\n\n\n\noptical emission spectrometry (Perkin Elmer Optima, Waltham, Massachusetts). Exchangeable \n\n\n\nacidity was measured by alkaline titration after extraction with 1 M potassium chloride. \n\n\n\nEffective CEC was calculated by the summation of exchangeable bases and effective CEC. \n\n\n\nBase saturation was defined as the ratio of the sum of exchangeable bases to effective CEC in \n\n\n\npercentage (%).     \n\n\n\n\n\n\n\nStatistical Analysis \n\n\n\nAll statistical analyses i.e. mean, median, minimum, maximum, standard deviation (SD), and \n\n\n\ncoefficient of variation (CV) were done using R software version 4.2.0 (R Core Team, R \n\n\n\nFoundation for Statistical Computing). CV was categorized into three classes: high (CV > \n\n\n\n90%), intermediate (CV = 90\u201310%) and low (CV < 10%) (Kamarudin et al. 2019). Pearson \n\n\n\ncorrelation was used to identify the relationship among the variables in each Harumanis orchard \n\n\n\nwhile the Welch T-test was run to compare the measured soil parameters in both the Harumanis \n\n\n\norchards.  \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nThe descriptive statistics of soil samples taken from Harumanis orchards with and without the \n\n\n\nIFR incidence are shown in Table 1. Soil samples from the Harumanis orchard with IFR \n\n\n\nincidence had a slightly acidic to slightly alkaline condition (pH 6.93 \u00b1 0.82) with exchangeable \n\n\n\nacidity ranging from 0.3 to 1.9 cmol kg\u20131 while the soil samples from the Harumanis orchard \n\n\n\nwithout IFR incidence was moderately acidic to neutral (pH 6.48 \u00b1 0.84) with exchangeable \n\n\n\nacidity ranging from 0.4 to 3.4 cmol kg\u20131. The pH at both sites was suitable for Harumanis \n\n\n\ncultivation as mango cultivation requires soil with a pH ranging from 5.5 to 6.5 (MDOA 2009). \n\n\n\nThough this mildly acidic condition is suitable for tree root development, it is recommended \n\n\n\nthat soil pH be maintained in the range of 6.6 to 7.5 as most of the nutrients are available within \n\n\n\nthis range for plant uptake (Nyi et al. 2017).  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n49 \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nDescriptive statistics of soil samples from Harumanis orchards with and without IFR \n\n\n\nincidence \n\n\n\n\n\n\n\n\n\n\n\nBoth sites had a low content of total C (<25 g/kg), EC (<1 mS cm\u20131) (MDOA 2009), and C/N \n\n\n\nratio (<10) (Kirkby et al. 2011). It is well known that low total C content indicates low soil \n\n\n\norganic materials. This low EC value is suitable for Harumanis cultivation as  mango is one of \n\n\n\nthe crops  sensitive to saline (0\u20132 mS cm\u20131) soil condition. The general C/N ratio of soils ranged \n\n\n\nfrom 10\u201312 which is often referred to as humus (Kirkby et al. 2011) while the low C/N ratio \n\n\n\nat both sites suggest that the amount of N available for microbes (i.e, C/N ratio 25) is more \n\n\n\nthan than sufficient. A medium content of total N (1.5\u20132.6 g kg\u20131) (Nyi et al. 2017) was \n\n\n\nobserved in both sites which was below the optimum range required for Harumanis cultivation \n\n\n\n(2.7\u20134.0 g kg\u20131) (MDOA unpublished). Meanwhile, a high content of available P (>15 mg kg\u2013\n\n\n\n1) (Nyi et al. 2017) was observed in both sites which was higher than the optimum range \n\n\n\nrequired for Harumanis cultivation (16\u201325 mg kg\u20131) (MDOA unpublished).    \n\n\n\n\n\n\n\nLow contents of exchangeable Ca (<5 cmol kg\u20131) and exchangeable K (<2.3 cmol kg\u20131) were \n\n\n\nobservered in the soil samples from Harumanis orchard with IFR incidence. Meanwhile,  \n\n\n\nmedium contents of exchangeable Ca (5\u201310 cmol kg\u20131) and exchangeable K (2.3\u20133.8 cmol kg\u2013\n\n\n\n1) were observed in the soil samples from Harumanis orchard without IFR incidence (Marx et \n\n\n\nal. 1999; Nyi et al. 2017). Although exchangeable Ca content was low in the soil samples from \n\n\n\nthe Harumanis orchard with IFR incidence, this value is within the optimum range (2.5\u20134.0 \n\n\n\ncmolc kg\u22121) required for Harumanis cultivation while exchangeable K at both sites is higher \n\n\n\nthan optimum level (0.8\u20131.4 cmolc kg\u22121) (MDOA unpublished). Meanwhile, a high (>1.5 cmol \n\n\n\nkg\u20131) and medium (0.5\u20131.5 cmol kg\u20131) exchangeable Mg content (Marx et al. 1999) was \n\n\n\nmeasured in the soil samples from Harumanis orchard with and without IFR incidence, \n\n\n\nrespectively. However, exchangeable Mg content in the soil samples from the Harumanis \n\n\n\norchard without IFR incidence is lower than the optimum range (3.0\u201310.0 cmolc kg\u22121) for \n\n\n\nHarumanis cultivation. Meanwhile, exchangeable Na in the soil samples from both sites was \n\n\n\nhigher than the optimum level (0.7\u20132.0 cmolc kg\u22121) required for Harumanis cultivation \n\n\n\n(MDOA unpublished).  \n\n\n\n\n\n\n\nA high (>12 cmol kg\u20131) (Nyi et al. 2017) content of effective CEC was noted for soil samples \n\n\n\ntaken from both orchards. Generally, soils with effective CEC > 7 cmolc kg\u22121 (or CEC > 10 \n\n\n\nVariable pH EC Total C Total N\nC/N \n\n\n\nratio\n\n\n\nAvai.     \n\n\n\nP\n\n\n\nExch. \n\n\n\nCa\n\n\n\nExch. \n\n\n\nMg\n\n\n\nExch.    \n\n\n\nK\n\n\n\nExch. \n\n\n\nNa\n\n\n\nExch. \n\n\n\nacidity\n\n\n\nEffective \n\n\n\nCEC\nBS\n\n\n\nMean 6.93 0.09 16.67 2.56 6.52 80.92 4.01 4.61 2.06 3.51 0.57 14.76 96.23\n\n\n\nSD 0.82 0.05 6.49 0.58 2.07 32.61 2.76 1.95 1.41 2.42 0.48 6.03 1.57\n\n\n\nMedian 7.16 0.08 14.30 2.50 5.89 75.60 3.16 4.63 1.62 2.76 0.40 12.14 96.44\n\n\n\nMinimum 5.00 0.04 10.00 1.60 4.38 44.80 1.03 1.91 0.53 0.90 0.32 8.68 92.63\n\n\n\nMaximum 7.68 0.17 28.40 3.90 11.36 134.40 8.74 7.40 4.48 7.65 1.92 26.07 98.62\n\n\n\nCV 11.81 53.88 38.91 22.64 31.82 40.30 68.76 42.43 68.76 68.76 84.62 40.88 1.63\n\n\n\nMean 6.48 0.05 4.71 1.54 3.02 116.40 6.63 0.81 3.40 5.80 1.03 17.66 93.04\n\n\n\nSD 0.84 0.02 2.23 0.63 0.74 67.47 2.23 0.17 1.14 1.95 0.94 4.88 7.98\n\n\n\nMedian 6.57 0.05 4.60 1.70 2.89 102.20 7.37 0.80 3.78 6.45 0.72 19.05 96.33\n\n\n\nMinimum 5.21 0.02 1.70 0.70 2.29 42.00 3.66 0.56 1.88 3.21 0.40 9.83 73.48\n\n\n\nMaximum 7.93 0.08 8.20 2.40 4.67 257.60 9.86 1.06 5.06 8.63 3.36 25.28 98.11\n\n\n\nCV 13.00 38.70 47.37 40.69 24.42 57.95 33.61 21.23 33.61 33.61 91.89 27.62 8.58\n\n\n\nHarumanis orchard with IFR incidence (n = 10)\n\n\n\nHarumanis orchard without IFR incidence (n = 12)\n\n\n\nAbbreviation: SD = standard deviation, CV = coefficient of variation, EC = electrical conductivity, Effective CEC = effective cation exchange \n\n\n\ncapacity, and BS = base saturation\n\n\n\nUnit: EC = mS cm\n-1\n\n\n\n, Total C = g kg\n-1\n\n\n\n, Total N = g kg\n-1\n\n\n\n, Avail. P = mg kg\n-1\n\n\n\n, Exch. bases (Ca, Mg, K, Na) = cmol kg\n-1\n\n\n\n, Exch. Acidity = cmol kg\n-1\n\n\n\n, \n\n\n\nECEC = cmol kg\n-1\n\n\n\n, and BS = % \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n50 \n\n\n\n\n\n\n\ncmolc kg\u22121) are suitable for mango cultivation (MDOA 2009) but the optimum range for \n\n\n\nHarumanis cultivation is 12\u201317 cmolc kg\u22121 (or CEC = 15\u201320 cmolc kg\u22121) (MDOA \n\n\n\nunpublished). This value indicates that the capacity of the soil to hold the nutrients is higher \n\n\n\nwhen a higher effective CEC value is obtained. Meanwhile, a higher BS (>80.0%) value was \n\n\n\nobserved in the soil samples taken from both sites indicating the percentage of the effective \n\n\n\nCEC occupied by the basic cations such as Ca2+, Mg2+, K+ and Na2+. Several published soil \n\n\n\nstandards are available to help in interpreting analytical results. The recommended optimal \n\n\n\nranges for soil analysis should only be considered as a general guideline (Bally 2009). For a \n\n\n\nbetter interpretation, selected soil standards and previous soil analysis should be compared and \n\n\n\nread together with the previous season\u2019s fertilization history.    \n\n\n\n\n\n\n\nIn this study, all the measured soil parameters in both sites show an intermediate CV indicating \n\n\n\nintermediate variation except for BS and exchangeable acidity. The BS in both sites show a \n\n\n\nlow CV while soil exchangeable acidity in soil samples from the Harumanis orchard without \n\n\n\nIFR incidence shows a high CV indicating low and high variation, respectively (Kamarudin et \n\n\n\nal. 2019). \n\n\n\n\n\n\n\nThe correlation between the measured parameters of soil samples from Harumanis orchards \n\n\n\nwith and without IFR incidences is shown in Table 2. Soil pH of samples from Harumanis \n\n\n\norchard with IFR incidence had a moderate (R = -0.40\u20130.69) and high (R = -0.70\u20130.89) \n\n\n\nnegative correlation with total N and exchangeable acidity, respectively and a high positive \n\n\n\ncorrelation with BS. In the same orchard, the soil EC and total C were found to have a high \n\n\n\npositive correlation  with the C/N ratio. Total N was found to have a moderate positive \n\n\n\ncorrelation with exchangeable Ca, exchangeable K, exchangeable Na, exchangeable acidity \n\n\n\nand effective CEC. Available P was found to have a moderate positive correlation with total C \n\n\n\nand a high postive correlation with the C/N ratio and exchangeable Mg. Meanwhile, \n\n\n\nexchangeable Ca was found to have a very high positive (R = 0.90\u20131.00) correlation with \n\n\n\nexchangeable K, exchangeable Na and effective CEC. Exchangeable K was found to have a \n\n\n\nvery high positive correlation with exchangeable Na and effective CEC while exchangeable \n\n\n\nNa had a very high positive correlation with effective CEC. A moderate positive correlation \n\n\n\nwas found between exchangeable acidity and effective CEC whileexchangeable acidity had \n\n\n\nhigh negative correlation with BS.       \n\n\n\n\n\n\n\nThe pH  of soil samples  from the Harumanis orchard without IFR incidence had a high positive \n\n\n\ncorrelation  with exchangeable Ca, exchangeable K, exchangeable Na, effective CEC and BS. \n\n\n\nExchangeable acidity was found to have a moderate negative correlation with pH, EC, \n\n\n\nexchangeable Ca, exchangeable K and exchangeable Na. Total C was found to have a high \n\n\n\npositive correlation with total N. Exchangable Ca was found to have a very high positive \n\n\n\ncorrelation with exchangeable K, exchangeable Na and effective CEC. Exchangeable K was \n\n\n\nfound to have a very high positive correlation with exchangeable Na and effective CEC. A very \n\n\n\nhigh positive correlation was also found between exchangeable Na  and effective CEC while a \n\n\n\nvery high negative (R = 0.90\u20131.00) correlation was found between exchangeable acidity and \n\n\n\nBS. Meanwhile, BS was found to have a moderate positive correlation with EC, exchangeable \n\n\n\nCa, exchangeable K and exchangeable Na.       \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n51 \n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nPearson correlation coefficient among the measured parameters of soil samples from \n\n\n\nHarumanis orchards with and without IFR incidence \n\n\n\n\n\n\n\n\n\n\n\nIn comparing the soil samples from both sites, significant differences (p < 0.05) were found in \n\n\n\nthe EC, total C, total N, C/N ratio and exchangeable bases (Figure 2). Soil samples from the  \n\n\n\nHarumanis orchard with the IFR incidence showed significantly higher content in EC, total C, \n\n\n\ntotal N, C/N ratio and exchangeable Mg at 0.04 mS cm\u20131, 11.96 g kg\u20131, 1.02 g kg\u20131, 3.5, and \n\n\n\n3.80 cmol kg\u20131, respectively as compared with the orchard without  IFR incidence. Meanwhile, \n\n\n\nsoil samples from the Harumanis orchard without IFR incidence showed significantly higher \n\n\n\ncontent of exchangeable Ca, exchangeable K and exchangeable Na at 2.62 cmol kg\u20131, 1.34 \n\n\n\ncmol kg\u20131, and 2.29 cmol kg\u20131, respectively, compared to the Harumanis orchard with IFR \n\n\n\nincidence. \n\n\n\n\n\n\n\npH EC Total C Total N\nC/N \n\n\n\nratio\nAvail. P\n\n\n\nExch.   \n\n\n\nCa\n\n\n\nExch. \n\n\n\nMg\n\n\n\nExch.     \n\n\n\nK\n\n\n\nExch.   \n\n\n\nNa\n\n\n\nExch. \n\n\n\nacidity\n\n\n\nEffective \n\n\n\nCEC\nBS\n\n\n\npH 1.00\n\n\n\nEC 0.40 1.00\n\n\n\nTotal C -0.32 0.23 1.00\n\n\n\nTotal N -0.65\n* -0.6 0.60 1.00\n\n\n\nC/N ratio 0.11 0.74\n*\n\n\n\n0.79\n** -0.01 1.00\n\n\n\nAvail. P 0.11 0.55 0.64\n* -0.01 0.83\n\n\n\n** 1.00\n\n\n\nExch. Ca -0.44 -0.58 0.31 0.73\n* -0.18 -0.31 1.00\n\n\n\nExch. Mg 0.28 0.61 0.18 -0.44 0.58 0.80\n** -0.56 1.00\n\n\n\nExch. K -0.44 -0.58 0.31 0.73\n* -0.18 -0.31 1.00\n\n\n\n*** -0.56 1.00\n\n\n\nExch. Na -0.44 -0.58 0.31 0.73\n* -0.18 -0.31 1.00\n\n\n\n*** -0.56 1.00\n*** 1.00\n\n\n\nExch. acidity -0.87\n*** -0.39 0.54 0.82\n\n\n\n* 0.02 0.02 0.62 -0.30 0.62 0.62 1.00\n\n\n\nEffective CEC -0.47 -0.46 0.44 0.72\n** -0.01 -0.08 0.96\n\n\n\n*** -0.31 0.96\n***\n\n\n\n0.96\n***\n\n\n\n0.66\n* 1.00\n\n\n\nBS 0.82\n** 0.22 -0.26 -0.51 0.09 0.06 -0.15 0.26 -0.15 -0.15 -0.83\n\n\n\n** -0.14 1.00\n\n\n\npH 1.00\n\n\n\nEC 0.29 1.00\n\n\n\nTotal C 0.31 -0.06 1.00\n\n\n\nTotal N 0.18 0.12 0.87\n*** 1.00\n\n\n\nC/N ratio 0.30 -0.27 0.56 0.11 1.00\n\n\n\nAvail. P -0.13 0.44 -0.14 0.00 -0.25 1.00\n\n\n\nExch. Ca 0.85\n*** 0.44 0.35 0.36 0.01 0.05 1.00\n\n\n\nExch. Mg 0.10 0.28 -0.34 -0.11 -0.52 0.00 0.30 1.00\n\n\n\nExch. K 0.85\n*** 0.44 0.35 0.36 0.01 0.05 1.00\n\n\n\n*** 0.30 1.00\n\n\n\nExch. Na 0.85\n*** 0.44 0.35 0.36 0.01 0.05 1.00\n\n\n\n*** 0.30 1.00\n*** 1.00\n\n\n\nExch. acidity -0.65\n*\n\n\n\n-0.61\n* -0.48 -0.50 -0.26 -0.16 -0.59\n\n\n\n* -0.16 -0.59\n*\n\n\n\n-0.59\n* 1.00\n\n\n\nEffective CEC 0.81\n** 0.38 0.27 0.29 -0.06 0.02 0.99\n\n\n\n*** 0.33 0.99\n***\n\n\n\n0.99\n*** -0.45 1.00\n\n\n\nBS 0.73\n**\n\n\n\n0.59\n* 0.51 0.51 0.28 0.11 0.69\n\n\n\n* 0.18 0.69\n*\n\n\n\n0.69\n*\n\n\n\n-0.99\n*** 0.57 1.00\n\n\n\n*, **, *** indicate a significant level of p  < 0.05, 0.01 and 0.001, respectively\n\n\n\nHarumanis orchard with IFR incidence (n = 10)\n\n\n\nHarumanis orchard without IFR incidence (n = 12)\n\n\n\nAbbreviation: EC = electrical conductivity, Effective CEC = effective cation exchange capacity, BS = base saturation\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n52 \n\n\n\n\n\n\n\n \nFigure 2. Comparison of Harumanis mango orchards with IFR (dark grey) and without IFR \n\n\n\n(light grey) incidence \n\n\n\n\n\n\n\nInsidious fruit rot in Harumanis is associated with plant nutrient imbalance especially N and \n\n\n\nCa. The antagonistic effects of N and Ca accumulation in mango leaves and fruit has been very \n\n\n\nwell established and the N/Ca ratio has been used widely to indicate the balance between these \n\n\n\ntwo nutrients (Tarmizi et al. 1993). IFR incidence is higher when the N/Ca ratio of \u2265 0.5 is \n\n\n\nfound in Harumanis leaves. Nitrogen plays an important role in mango growth, yield, and fruit \n\n\n\nquality, while Ca is important for pection polymers that strengthen the cell walls (Bally 2009). \n\n\n\nThus, when Harumanis trees take in high N and less Ca, the cell walls become thinner and \n\n\n\nweaker, leading to cell damage. However, calculating this N/Ca ratio from soil nutrient content \n\n\n\nis less practical. This is because both N and Ca can be removed from the soil system by crops \n\n\n\nand as well as the leaching process. In fact, soil analysis can help to determine the availability \n\n\n\nof essential minerals required by mango trees and ensure their levels are in the optimal range \n\n\n\n(Bally 2009).  \n\n\n\n\n\n\n\nSoil nutrient availability differed in both sites (see Table 2). The availability of soil nutrients \n\n\n\ncan be affected by the synergistic and antagonistic effects of each nutrient (Rietra et al. 2017; \n\n\n\nJakobsen 1993). The former and latter effects may increase or decrease the availability of other \n\n\n\nnutrients for uptake by the plant (Rietra et al. 2017). A moderate positive correlation between \n\n\n\ntotal N and exchangeable Ca, exchangeable K, exchangeable Na, exchangeable acidity or \n\n\n\neffective CEC indicates synergistic effect. This condition shows that besides the application of \n\n\n\nN fertilizer, of Ca, K and Na fertilizer application is required as well. Without a good balance \n\n\n\nof Ca, K and Na fertilizers, mango trees tend to absorb more N, resulting in this physiological \n\n\n\ndisorder (Singh et al. 2013). Concurrently, application of N fertilizer will increase soil acidity, \n\n\n\npH\nEC\n\n\n\n(mS cm\u20131)\n\n\n\nTotal C\n\n\n\n(g kg\u20131)\n\n\n\nTotal N\n\n\n\n(g kg\u20131)\nC/N ratio\n\n\n\nAvail. P\n\n\n\n(mg kg\u20131)\n\n\n\nExch. Ca\n\n\n\n(cmol kg\u20131)\n\n\n\nExch. Mg\n\n\n\n(cmol kg\u20131)\n\n\n\nExch. K\n\n\n\n(cmol kg\u20131)\n\n\n\nExch. Na\n\n\n\n(cmol kg\u20131)\n\n\n\nExch. acidity\n\n\n\n(cmol kg\u20131)\n\n\n\nEffective CEC\n\n\n\n(cmol kg\u20131)\n\n\n\nBS\n\n\n\n(%)\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n110\n\n\n\n* * *\n\n\n\n* * * *\n\n\n\n*\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n53 \n\n\n\n\n\n\n\nwhich can lead to soil acidification (R = 0.82; p < 0.05) due to a decrease in soil pH (R = -0.65; \n\n\n\np < 0.05) although the current soil pH was at the optimum level. This can be explained by the \n\n\n\ndecomposition process of fertilizers which produce hydrogen ions and increase soil acidity \n\n\n\n(Barak et al. 1997).      \n\n\n\n\n\n\n\nFurther a high content of exchangeable Mg and/or available P in the soil samples from \n\n\n\nHarumanis orchard with IFR incidence might show a synergistic effect. This is supported by a \n\n\n\nvery high positive correlation between both nutrients. A high content of exchangeable Mg or \n\n\n\navailable P may increase the availability of one of them, which may cause cell tissues to \n\n\n\nbreakdown. This is supported by a previous study that observed a high content of Mg and P in \n\n\n\nthe broken tissue (Selvaraj et al. 2000). At the same time, exchangeable Mg and available P \n\n\n\nhave an antagonistic effect through a negative correlation with exchangeable Ca (Rietra et al. \n\n\n\n2017). Although no significant differences were found in these antagonistic effects, it may have \n\n\n\nan influence if the condition is not corrected. This is because the antagonistic effect can be \n\n\n\ncaused by precipitation of less soluble calcium phosphates at the vicinity of nutrient-absorbing \n\n\n\nroots (Jakobsen 1993). In contrast, the soil sample from the Harumanis orchard without IFR \n\n\n\nincidence showed an optimal reaction for a healthy soil. The synergistic effects can be observed \n\n\n\nbetween soil pH, exchangeable bases (Ca, K and Na), effective CEC and/or BS.             \n\n\n\n\n\n\n\nThe significant difference in soil parameters between both sites may be caused by differences \n\n\n\nin soil texture (Shahidin et al. 2022). Soil samples from the Harumanis orchard with IFR \n\n\n\nincidence had a sandy clay loam texture (clay = 31.1%; silt = 20.4%; coarse sand = 25.0%; fine \n\n\n\nsand = 23.5%), while the soil samples from the Harumanis orchard without IFR incidence had \n\n\n\na fine sandy loam texture (clay = 14.8%; silt = 17.5%; coarse sand = 10.2%; fine sand = 57.5%). \n\n\n\nSoil texture may strongly influence nutrient availability and retention (Brady and Weil 2008), \n\n\n\nparticularly in highly weathered soils as in Malaysia and consequently may affect nutrient \n\n\n\nuptake by mango trees. Soil nutrients are available for plant uptake in the form of ammmonium \n\n\n\n(NH4\n+), monovalent phosphate anion (H2OP4\n\n\n\n\u2013; pH < 7), divalent anion (HOP4\n2\u2013; pH > 7), Ca \n\n\n\nions (Ca+), and Mg ions (Mg2+) which are immobile in the soil, and K ions (K+) which are less \n\n\n\nimmobile while nitrate (NO3\n+) is highly mobile in the soil. The highly mobile nutrients in the \n\n\n\nsoil are prone to loss though the leaching process. For instance, soil N often limits mango tree \n\n\n\ngrowth especially in sandy soil because N is easily leached from the soil (Musvoto et al. 2000). \n\n\n\nAlthough Ca+ is immobile in the soil, the exchangeable Ca is weakly bound and capable of \n\n\n\nrapid exchange with the soil solutions. Therefore, it is prone to leaching (Mengel and Kirkby \n\n\n\n2001). \n\n\n\n\n\n\n\nA significantly low content of N and a significantly high content of Ca and K in the soil samples \n\n\n\nfrom the Harumanis orchard without IFR incidence could suggest that IFR incidence can be \n\n\n\nsuppressed through application of Ca and K fertilizers (Singh et al. 2013). This suggestion is \n\n\n\nalso supported by Lim and Khoo (1985) who state in their study that the IFR incidence can be \n\n\n\ntreated with a well-balanced, split application fertilization program containing adequate \n\n\n\namounts of N, P, K and Ca. However, the type and frequency of fertilizer application should \n\n\n\nbe considered based on the soil texture. In a leachable soil environment, a slow release fertilizer \n\n\n\nadopting inhibitors technology or controlled release fertilizers should be more suitable. This \n\n\n\nhas being practised by the owner of Harumanis orchard without IFR incidence. Moreover, this \n\n\n\ngrower also applied dolomite after the harvesting stage in order to maintain the soil pH by \n\n\n\nadding Ca and Mg since 2007 which has been imitated later by the owner of Harumanis orchard \n\n\n\nwith IFR incidence.       \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 45-55  \n\n\n\n54 \n\n\n\n\n\n\n\nDue to low total C in the soil samples from both Harumanis orchards, it is suggested that soil \n\n\n\norganic matter be added to increase C content. The function and capability of soil can be \n\n\n\nsustained by adding organic matter (optimum level = 3\u20135%) (MDOA unpublished) like \n\n\n\ncompost and manure to increase nutrient retention by increasing soil CEC (Nyi et al. 2017; \n\n\n\nShahidin et al. 2022). In a tropical environment, organic matter can be highly decomposed and \n\n\n\ncan therefore be added to the soil after each harvest as the standard practice.    \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nIn conclusion, significant differences were found  in EC, total C, total N, C/N ratio and \n\n\n\nexchangeable bases (Ca, Mg, K, Na) in the soil samples from Harumanis orchards with and \n\n\n\nwithout IFR incidence. Although soil nutrient contents were higher than optimal range for \n\n\n\nHarumanis cultivation in both sites, synergistic and antagonistic effects were mostly discovered \n\n\n\nfrom soil samples from the Harumanis orchard with IFR incidence. Application of Ca and K \n\n\n\nfertilizers can suppress IFR incidence (Lim and Khoo 1985; Singh et al. 2013).  \n\n\n\n\n\n\n\nIt  is suggested  that a further  study be carried out on soil micronutrients like boron, manganese, \n\n\n\ncopper, molydbenum and zinc. This is because access to these micronutrients in the soil can \n\n\n\nalso influence the availability of other nutrients through synergistic and antagonistic effects.       \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nSpecial thanks to Mr. Ramli Abu Seman and Mr. Hasni Halim for permission to conduct this \n\n\n\nstudy in their mango orchard and for the information they shared with the authors. The authors \n\n\n\nwould like to extend their gratitude to the Dana Pembudayaan Penyelidikan Dalaman (600-\n\n\n\nUiTMPs (PJIM&A/PI-DPPD 08) and the students for their assistance during the field survey.   \n\n\n\n\n\n\n\nREFERENCES \n\n\n\n \nAitken, R.L. and B.J. Scott. 1999. Magnesium. In: Soil Analysis: an Interpretation Manual ed. K.I. \n\n\n\nPeverill, L.A. Sparrow and D.J. Reuter, (pp. 255\u2013262).  Melbourne: Commonwealth Scientific \n\n\n\nand Industrial Research Organization (CSIRO). \nBally, I.S.E. 2009. Crop production: mineral nutrition. 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Biochemical studies on internal breakdown: a ripening \ndisorder in mango fruits. Indian Journal of Horticulture 57: 183\u2013189. \n\n\n\nShahidin, N.M., I. Roslan, S.S. Zaharah, S.H. Kang, A.A. Elisa, M.N. Malisa, K.N. Kamarudin, H. \n\n\n\nMurano and S.S. Abe. 2022. Soil spatial variation in a sloping mango orchard of northern \nPeninsular Malaysia. Malaysian Journal of Soil Science 26: 104\u2013119. \n\n\n\nShivashankar, S. 2014. Physiological disorder of mango fruit. In: Horticultural Reviews, Volume 42 \n\n\n\n(1st ed.) ed. J. Janick (pp. 313\u2013347). New Jersey: John Wiley & Sons, Inc.  \nSingh, D.K., R.B. Ram and L.P. Yadava. 2013. Preharvet treatment of Ca, K, and B reduces softening \n\n\n\nof tissue in \u2019Dashehari\u2019 mango. International Journal of Fruit Science 3: 299\u2013311. \n\n\n\nTarmizi, A.S., T.A.T.M. Malik, M. Pauziah and T. Zahrah. 1993. Incidence of insidious fruit rot as \n\n\n\nrelated to mineral nutrients in Harumanis mangoes. MARDI Research Journal 21: 43\u201349.   \nTeh, C.B.S., C.F. Ishak, R. Abdullah, R. Othman, Q.A. Panhwar and M.M.A. Aziz. 2018. Soil \n\n\n\nproperties (physical, chemical, biological, mechanical). In Soils of Malaysia, ed. M.A. Ashraf, \n\n\n\nR. Othman and C.F. Ishak (pp. 103\u2013154). Boca Raton: CRC Press. \nUNCTAD (United Nations Conference on Trade and Development). 2016. Mango: An Infocomm \n\n\n\nCommodity Profile. Geneva: United Nations. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nCharacterisation and Composting of Tannery Sludge\n\n\n\n71\n\n\n\nISSN: 1394-7990\nMalaysian Society of Soil ScienceMalaysian Journal of Soil Science Vol.11: 71-80 (2007)\n\n\n\nCharacterisation and Composting of Tannery Sludge\n\n\n\nMahdi Haroun*1, Azni Idris 1 & S.R. Syed Omar2\n\n\n\n1Department  of  Chemical and Environmental Engineering\nFaculty of Engineering, Universiti Putra Malaysia\n\n\n\n43400 UPM, Serdang, Malaysia\n\n\n\n2Department of Land Management, Faculty of Agriculture\n Universiti Putra Malaysia, 43400 UPM, Serdang, Malaysia\n\n\n\nABSTRACT\nTannery industries create serious environmental problems especially in\nterms of polluting organic effluent and hazardous solid waste as a result of\nhides and skin processing. It is very important that tannery waste in the\nform of sludge is managed in an environmentally sound manner. This study\nfocuses on the characterisation of tannery sludge and its development as a\ncomposting material. The results show that electrolytic conductivity (EC) of\nthe compost was 2.0 mS cm-1, pH 6.6 and C/N ratio of 16. Total concentrations\nof chromium, zinc, copper, lead, and cadmium in dry compost were reduced\nand complied with the standards of the Canadian limits, thus classifying\nthem excellent for making the compost suitable for use as a fertiliser and soil\nconditioner. The compost characteristics indicated that it was mature, and\nthe germination index for Chinese cabbage was 82.5 %, which may suggest\nabsence of phytotoxic compounds.\n\n\n\nKeywords: Composting, tannery sludge, pathogens, heavy metals, ger-\nmination\n\n\n\nINTRODUCTION\nThe necessity to preserve natural resources and the optimisation of the use of\nnon-renewable energy has encouraged recycling and recovery of organic waste\nas  an  alternative  to dumping and incineration (ADEME 1994). Among the\norganic waste recycled in agriculture, residual sludge generated by wastewater\ntreatment is a source of organic matter rich in both phosphorus and nitrogen. It\ncan contribute to the rehabilitation of degraded soils by its fertilising and other\nsoil-improving qualities. Sludge from leather processing, a major industry that\nproduces up to 600 tons of sludge annually (Kenny Leather Sdn Bhd, Melaka-\nMalaysia), contains large concentrations of inorganic nitrogen (N) and N-rich\norganic residues (INE-DGMRAR 1999).  Nevertheless, direct agricultural use of\ntannery  sludge is limited by the presence of pathogens, fermentation of any\nunstable organic matter and the organic and inorganic pollutants it contains (Dudka\nand Muller 1999). To overcome the risks incurred by the direct use of this waste\n\n\n\n* Corresponding author: Email -  Mahdiupm@hotmail.com\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM71\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200772\n\n\n\nMahdi Haroun, Azni Idris and S.R. Syed Omar\n\n\n\nin agriculture, treatment is required to minimise and eliminate the undesirable\neffects and to optimise the efficiency of the materials once applied to the soil.\n\n\n\nThe process of tanning consists of the transformation of animal skin to\nleather.  Animal  skin  (cow, goat, sheep and other animals) is submitted to\ndifferent processes to eliminate meat, fat and hair in which different chemicals\nsuch as sodium hydroxide, sodium hypochlorite, enzymes, lime, chlorides, sul-\nfuric acid, formic acid, ammonium salts, kerosene, and other compounds are\nused (Semarna 1995).  The  obtained hide is then treated with Cr+3 or tannins,\nmineral salts and colours to obtain leather.\n\n\n\nComposting has long been recognised as one of the most cost effective and\nenvironmentally  sound alternatives for organic waste recycling.  It is considered\nto be the best pretreatment for overcoming these problems (Ouatmane et al.\n2000), and the high temperatures reached, 50\u201370oC, destroy almost all patho-\ngens (Dumontet et al. 1999).  Numerous bacteria degrade the readily  available\norganic components or transform them into stable humic components (Garcia et\nal. 1992).\n\n\n\nA  decrease  in  the  content of organic pollutants and a reduction in the bio-\navailability of metal trace elements during the composting of tannery sludge has\nalso been reported (Lau et al. 2003). The success of composting is linked to the\nquality of  final product, especially its stability. Spreading immature or unstable\ncompost can generate serious problems of hygiene and phytotoxicity (Pascual\net al. 1997).\n\n\n\nThe reliability of individual indicators for the determination of compost ma-\nturity is debatable, so several parameters are often considered together. Nu-\nmerous researchers (Ouatmane  et al. 2000; Tomati et al. 2000) have suggested\nthe use of different maturity indices (C/N ratio, humification indices, and germi-\nnation indices).\n\n\n\nThe objective of this study was to determine some of the physico-chemical\ncharacteristics of tannery sludge during its composting.  The evolution of com-\npost maturity was assessed from measurement of various types of humification\nindices.\n\n\n\nMATERIALS AND METHODS\n\n\n\nComposting Sampling and Preparation of Compost Samples\nThe tannery sludge used in this study was collected from the Kenny Leather Sdn\nBhd in Melaka, Malaysia. The sludge (100kg) was mixed with sawdust (50kg),\nchicken manure (30kg), beneficial organisms (1 litre), and rice bran (20kg) in a\npile 2.5 m long and 1.5 m high in a composting windrow type of production. The\nmixture was prepared in order to optimise the composting parameters, that is,\n60% humidity and a C/N ratio of about 30.  Table 1 shows the main chemical\ncharacteristics  of  the  raw materials.  With  the aim of maintaining aerobic\nconditions during the process, the pile was turned over manually every 10 days.\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM72\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nCharacterisation and Composting of Tannery Sludge\n\n\n\n73\n\n\n\nTemperature was measured daily at a depth of 50 cm at different positions\ninside the pile. The composting cycle lasted for sixty days. Subsequently, 10\nsamples were taken systematically, that is, before composting (T0), and after 20\n(T20), 40 (T40) and 60 (T60) days of composting. Each sample was air-dried for a\nperiod of ten days. The dried sample was ground down into a fine powder.\n\n\n\nChemical Analysis\nA representative sample was taken from the homogenised compost pile for heavy\nmetals and other analyses. Samples (250 g) were taken from 10 different points\nof the compost heap (bottom, surface, side, and centre) at each stage of\ncomposting, that is, 0, 20, 40, and 60 days of composting.\n\n\n\nPhysico-chemical analyses were conducted on the samples. The pH was\ndetermined in a suspension of 10 g sample in 15 mL water.  Total organic carbon\n(TOC) was measured according to the ANNE method (Aubert 1978), while total\nnitrogen (Kjeldahl method) and inorganic nitrogen were measured by the method\nof  Bremner  (1965). Humic carbon extracted by 0.1 M NaOH solution was\nmeasured  after  oxidation  by  KMnO4 (Bernal et al. 1996).  The rate of decom-\n\n\n\nTABLE 1\nChemical Properties of raw materials used in composting\n\n\n\n(results expressed on dry basis)\n\n\n\nCharacteristics Tannery sludge Sawdust Chicken manure Rice bran\n\n\n\nMoisture 60.6 80.7 50.6 66.9\npH 7.36 5.9 7.93 7.2\nE.C. (mS cm-1)  9 15 7 6\nOrganic \u2013C (%) 20.03 57 30.4 49.33\nTotal nitrogen (%) 0.9996 0.3  4 1.1\nC/ N 20.022 190  7.6 45.72\nAsh 65  80  45 30\n\n\n\nMacronutrients\nPotassium (%) 0.415 0.02 1.23 0.99\nPhosphorus (%) 0.097 1.17 3.02 0.23\nCalcium (%) 7.7 0.02 1.99 0.30\nMagnesium (mgkg-1) 1190 0.004 1.05 236.33\nSodium (mgkg-1) 1006 64 123 98\n\n\n\nHeavy metals\nIron (mgkg-1) 1062  402    1738  142.33\nChromium (mgkg-1) 350 14.6    16.6  6.3\nLead (mgkg-1) 15 16    1.3  1.2\nCadmium (mgkg-1) 3.23 6.5    0.5   0.2\nCopper (mgkg-1) 60 4.8    329.67   24.33\nZinc (mgkg-1) 180 8.2    634.67   127\nManganese (mgkg-1)  70 4.6    34    24\n\n\n\nE.C. = Electrical conductivity\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM73\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200774\n\n\n\nMahdi Haroun, Azni Idris and S.R. Syed Omar\n\n\n\nposition was calculated after ignition of the dry sample at 550oC (16 h).  Available\nP was determined according to Olsen method (Hafidi et al. 1994). Exchangeable\nCa, Na, K, Mg were determined using ammonium acetate. Total P, Ca, Na, K,\nMg, Fe and Mn were determined after ashing. Phosphurus was measured colo-\nrimetrically and other elements in the extracts were analysed using atomic ad-\nsorption and flame photometry (De Souza 1998). Total Cr, Zn, Cu, Pb, and Cd\nwere analyzed by the method of French Association of Normalizations (AFNOR\n1993). One gram of each sample was mineralised for 4 h at 550oC, and then\ndissolved  in  5 mL  of  hydrofluoric acid. The solution obtained was evaporated\nto dryness and the residue was then dissolved with concentrated HNO3/HCl (1:1)\nsolution and the acid solution was diluted for analysis.\n\n\n\nGermination Index\nThe germination index was used to determine the inhibitory potential of the com-\npost water extract. Seed germination test was carried out with Chinese cabbage\nusing compost substrate extract. Two g of oven-dried compost was placed in a\ntest tube with screw cap and 20 mL of distilled water was added; the tube was\nthen placed on an electric rotator at 125 rpm for 1 hour. The supernatant was\ndecanted and centrifuged at 10000 rpm for 10 minutes and filtered through\nWhatman paper. Two mL of filtrate was diluted with one mL of distilled water\nand sprayed over a sheet of filter paper kept inside the petri dish. Ten seeds of\nChinese cabbage were then placed on the filter paper; another filter paper was\nmoistened with 3 mL distilled water and 10 seeds and was used as a control The\npercentage of germination was measured after incubating the covered petri dishes\nin the dark at 28oC for 4 days (Matheur et al. 1993).\n\n\n\nRESULTS  AND DISCUSSION\n\n\n\nCharacterisation of Tannery Sludge\nThe results of the chemical characterisation of the sludge are presented in Table\n1.The sludge showed that it was low in C/N ratio (20) and high in nitrogen\ncontent (0.99). The sludge was rich in organic matter and had a high content of\nsodium and calcium.\n        Apart from the plant nutrients, the analysis of the sludge showed that it\ncontained high amounts of trace elements like chromium, cadmium, and lead, all\nof which have a negative impact on plant growth (Lisk et al.  1992).  From the\nabove results, it appears that the sludge from the tannery was at an acceptable\nlevel according to Canadian limits (CCME 1995), except for chromium and cad-\nmium (Table 2).\n       The tannery sludge was alkali in nature (pH, 7.36) (Table1). More than 83%\nof the sludge had a particle size fraction <50\u00b5. The wet bulk density was close to\nthat of mineral soil but when dry, it was relatively light with a bulk density equal\nto 0.14g cm \u20133 (Table 3).\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM74\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nCharacterisation and Composting of Tannery Sludge\n\n\n\n75\n\n\n\nCharacterisation of Compost\nThe main physico-chemical properties of the composting mixtures at different\ntimes of the process are presented in Table 4. The pH values were within the\noptimal range for the development of bacteria 6\u20137.5 and fungi 5.5\u20138.0 (Zorpas et\nal. 2003). Two phases of the composting process were recorded: a phase of\nstabilisation (about 30 day), where temperature peaked at 64oC after 30 days of\nprocessing and pH was slightly increased (7.5); a phase of maturation (about 30\ndays), characterised by a temperature plateau at 35oC and slight acidification of\nthe medium (6.6). The change in the C/N ratio from 23 to 16 and the amount of\nash reflect microbial decomposition of organic matter and stabilisation during\ncomposting.\n\n\n\nTABLE 2\nTotal heavy metal contents in the final compost (60 day) and allowable limit\n\n\n\nfor different class compost according to Canadian limit\n(CCME 1995) (results expressed in dry basis)\n\n\n\nHeavy Raw Tannery Final mature Allowable limit Allowable\nmetal Sludge compost content (mgkg-1dry wt) limit (mgkg-1drywt)\n\n\n\n(mgkg-1) (mgkg-1) Class A Class B\n\n\n\nCr 350 100 210 1060\nZn 180 148 500 1850\nCu 60 54 100 757\nPb 15  2.2 150 500\nCd 3.23  1.6 3 20\n\n\n\nClass A compost (which have no restrictions in use).\nClass B Compost (which can be used on forest lands and roadsides and for other landscaping\npurposes).\n\n\n\nTABLE 3\nPhysical properties of the tannery sludge\n\n\n\nParameters Sludge Typical tropical\nsoil *\n\n\n\nBulk density 0.140 1.3\nTotal pore space 94.72 -\nAvailable water 8.93   5.3\n\n\n\nWater retention at pressure (Kpa) (%w/w, dry basis):\n\n\n\n0 153.41 33.40\n1 117.5 32.20\n10 89.50 22.80\n33 78.50 21.20\n1500 69.57 15.90\n\n\n\n* Mokhtaruddin et al. ( 2001)\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM75\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200776\n\n\n\nMahdi Haroun, Azni Idris and S.R. Syed Omar\n\n\n\nThe increase in total nitrogen during composting was caused by the de-\ncrease in substrate carbon resulting from the loss of CO2 (Soumare et al. 2002;\nZorpas et al.  2003). Inorganic nitrogen, N\u2013NH4 and N\u2013NO3 are usually affected\nby the action of proteolytic bacteria and partly incorporated into stable organic\nforms such as amide and heterocyclic nitrogen. Organic matter is decomposed\nand transformed to stable humic compounds (Amir et al. 2004, in press).  Hu-\nmic substances (HS) have a capacity to interact with metal ions, and the ability\nto buffer pH and to act as a potential source of nutrients for plants. Electrical\nconductivity in a water extract of final product did not exceed the salinity limit\nvalue of 3 mScm-1 to be used in good fertilisers (Soumare et al.  2003).  Available\nand total P, Ca, K, Mg, Na as well Fe and Mn were more important for use in this\nmaterial as mineral fertilisers (Soumare et al. 2003). Therefore, application of\nthis material will increase the stable organic N and humic carbon and improve\nmineral elements necessary for plant growth.\n\n\n\nTABLE 4\nPhysico-chemical properties of composting mixture at different times\n\n\n\n(results expressed in dry basis)\n\n\n\nProperties Day 0 Day 20 Day 40 Day 60\n\n\n\nMoisture 58.4 68.6 64.1 60.1\npH 7.3 7.50 6.9 6.6\nE.C. (mS cm-1) 2.0 1.5 1.7 2.0\nTOC % 19.6 17.2 6.0 14.8\nOM % 33.8 27.6 23.6 19.8\nTotal nitrogen (%) 0.80 0.90 0.92 0.95\nAsh (%) 63 75 83 88\nC/N 23 19.11 17.4 16\nHS 19.3 20.6 23.5 26.5\nNH+4-N(mgkg-1) 3.7 2.8 2.0 1.4\nNO3-N(mgkg-1) 3.4 2.6 1.5 1.5\nN-org. (mgkg-1) 5.4 6.1 6.8 8.2\nP total (mgkg-1) 7.5 5.9 5.0 3.9\nP available (mgkg-1) 4.2 3.2 2.6 1.9\nCa total (mgkg-1) 720 530 300 150\nCa exchangeable (mgkg-1) 105 70 59 45\nK total (mgkg-1) 38 18 12 8\nK available (mgkg-1) 12 8 5.2 2.5\nNa total (mgkg-1) 980 540 290 140\nNa available (mgkg-1) 250 120 80 50\nMg total (mgkg-1) 990 530 310 130\nMg available (mgkg-1) 100 60 30 10\nMn (mgkg-1) 85 76 50 60\nFe (mgkg-1) 1200 2876 3850 5674\n\n\n\nOM= Organic Matter, HS= Humic Substances\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM76\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nCharacterisation and Composting of Tannery Sludge\n\n\n\n77\n\n\n\nFig. 1 shows the temperature profile with time for compost development. It\nwas characterised by increasing the temperature up to 64oC, and then decreasing\nit to 35oC, which indicated the end of the composting process. It was found that\ntannery sludge compost was able to reach high thermophilic temperatures due to\nits high content of nutrients from organic materials (Mays and Giordano 1989).\nTo maintain a high temperature within the windrow, the compost heap should be\nlarge enough to allow heat generated by metabolic processes to exceed the heat\nloss at the exposed surfaces (Eweis et al. 1998).\n\n\n\nHeavy Metals Content of Compost\nComposting can concentrate or dilute heavy metals present in tannery sludge\n(Zorpas et al. 2003).  Lowering the amounts of heavy metal depends on metal\nloss through leaching.  The increase in metal level is due to weight loss in the\ncourse of composting following organic matter decomposition, release of car-\nbon dioxide and water and the mineralisation processes (Canarutto et al. 1991).\n\n\n\nFig. 2 shows the total concentration of metals (Cr, Zn, Cu, Pb, Cd) during\ncomposting.  The order of total metal content in the final composted sludge was\n\n\n\nFig. 1: Temperature profile throughout the composting process\n\n\n\nFig. 2: Total amount of heavy metals during composting of tannery sludge\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM77\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200778\n\n\n\nMahdi Haroun, Azni Idris and S.R. Syed Omar\n\n\n\nZn > Cr > Cu > Pb>Cd.  During composting, all total metal content decreased.\nThis could be explained by metal loss through leaching in the course of composting.\nThis loss mainly occurred during the thermophilic phase which could be related\nto metal release from decomposed organic matter, an increase in moisture from\n58.4 to 73.5%, and  change of other oxidic and anionic conditions in the medium\nincreasing so too the solubility of metals (Hsu and Lo 2001; Soumare et al. 2003;\nZorpas et al. 2003).  Although some researchers suggest that when the potential\ntoxic metal concentrations of compost are high, the leachability of metal associ-\nated with compost is of concern (Hsu and Lo 2001).\n\n\n\nThe determination of the total content of heavy metals in the tannery sludge\ncompost showed values significantly lower than those of compost authorised\nfor agricultural use as determined by the Canadian limits, thus classifying them\nas excellent (CCME 1995)(Table 2). However, knowledge of the total content of\nheavy metals remains insufficient to estimate the mobility risk, and metal\nbioavailability for plants. The results of the metals analysis on the compost at\ndifferent stages of composting show that the main part of the metal elements\nconcentrated in the most resistant fractions are either not bioavailable or weakly\nbioavailable to plants, and only a weak proportion represents the unstable frac-\ntion that is easily bioavailable (exchangeable + soluble).\n       Aqueous compost extracts had a germination index of 82.5 %. A germina-\ntion index value of above 50% indicates that maturity is sufficient (Zucconi et\nal.  1981; Mathur et al.  1993) and phytotoxic compounds such as acetic, propi-\nonic, butyric and isobutyric acid might have not been metabolised, inhibiting\ngermination (Epstein 1997).\n\n\n\nCONCLUSION\nThe study concludes that the initial concentrations of chromium, cadmium, lead,\ncopper and zinc decreased as the composting process progressed. The total\nconcentrations of heavy metals in the compost complied with the standards of\nthe Canadian Limits (CCME 1995), making the compost suitable for use as a\nfertiliser and soil conditioner. The composting process significantly produced\nstable  and  mature  compost and the germination index of Chinese cabbage\nsignificantly encourages the utilisation of the compost.\n\n\n\nACKNOWLEDGEMENTS\nThe authors wish to express their appreciation to Mr. Rosallein, the Director of\nKenny Leather Sdn. Bhd. for financial support of this research.\n\n\n\nREFERENCES\nAdeme 1994. La collecte et le traitement des de\u00b4 chets, Guide pratique pour les e\u00b4 lus\n\n\n\nde Midi\u2014 pp 145-156. Pyr\u00e9n\u00e9es : Edition C.R.D.P.\n\n\n\nAfnor.1993. Sols\u2014Se\u00b4diments\u2014Boues de stations De\u00b4puration. Mise en solution des\ne\u00b4lements m\u00e9talliques traces (Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn) par attaques acides,\npp. 139\u2013145. Pyr\u00e9n\u00e9es. Edition C.R.D.P\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM78\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nCharacterisation and Composting of Tannery Sludge\n\n\n\n79\n\n\n\nAmir, S., M. Hafidi, G. Merlina, H. Hamdi and J.C. Revel. 2004. Elemental analysis,\nFTIR, 13C-NMR of humic acids from sewage sludge composting. Agronomie 24\n: 13\u201318.\n\n\n\nAubert, G. 1978. Me\u00b4thodes d\u2019Analyse des Sols. pp. 213. Marseille : Edition C.R.D.P\n\n\n\nBernal, M.P., A.F. Navarro, A. Roig, J. Cegarra and D. Garcia.1996. Carbon and nitro-\ngen transformation during composting of sweet sorghum bagasse. Biol. Fert.\nSoil. 22: 141-148.\n\n\n\nBremner, J.M.1965. Inorganic forms of nitrogen. In Methods of Soil Analysis, Part 2,\ned. C.A. Black, J.L. Evans De wite, L.E. Ensminger and F.E. Clark, F.E.  Agronomy,\nVol. 9, 1179\u20131237. Madison: American Society Agronomy.\n\n\n\nCanarutto, S., G. Petruzzelli, L. Lubrano and G.B. Guidi.1991. How composting affects\nheavy metal content. BioCycle 32 : 48\u201350.\n\n\n\nCanadian Council of Ministers of the Environment, CCME: 1995. Proposed compost\nstandards for Canada, 1993. Cited in the Composting Council of Canada, pp 1235-\n1245. Canada : Composting Technologies and Practices.\n\n\n\nDe Souza, E.K.1998. Contribution a\u2018 la valorisation de la bagasse par transformation\nbiologique et chimique. Valeur agronomique des composts et proprie\u00b4te\u00b4s\nsuppressives vis-vis du chambignons phytopathoge\u00b4nes Fusarium solanium.\nThe\u2018se de troisie\u2018me cycle, pp.386-392. Marseille : Institut National Polytechnique\nde Toulouse.\n\n\n\nDudka, S. and W.P. Muller.1999. Accumulation of potentially toxic elements in plants\nand their transfer to human food chain. J. Environ. Sci . & Health 34:681\u2013708.\n\n\n\nDumontet, S., H. Dinel and S.B. Baloda.1999. Pathogen reduction in sewage sludge by\ncomposting and other biological treatment: a review. Bio. Agri. & Hort.. 16: 409\u2013\n430.\n\n\n\nEpstein, E.  1997. The Science of Composting, 430 p.  London: Technomic Publishing\nCompany.\n\n\n\nEweis, J.B., S.J. Ergas, D.P.Y. Chang and E.D. Schroeder. 1998. Bioremediation\nPrincipals, 200p.  United States: McGraw-Hill Companies.\n\n\n\nGarcia, C., T. Hernandez and F. Costa. 1992. Characterization of humic acids from\nuncomposted and composted sewage sludge by degradative and non-degradative\ntechniques. Bioresource Technology  41: 53\u201357.\n\n\n\nHafidi, M., W. Badri, M. Kaemmerer and J.C. Revel.1994. Comparaison des me\u00b4thodes\nd_extraction chimique et biologique pour la de\u00b4termination du phoshore assimi-\nlable. Actes Inst.  Agron. Vet. (Maroc). 14 (3): 43\u201348.\n\n\n\nHSU, J.H. and S.L. LO. 2001. Effect of composting on characterization and leaching of\ncopper, manganese, and zinc from swine manure. Environ. Pollut. 114 : 119\u2013127.\n\n\n\nIne-Dgmrar. 1999. Manual de procedimientos para el manejo adecuado de los residuos\nde la curtidur. Direccion General de Materiales, Residuos y Actividades Riesgosas.\npp.122-133. Mexico, D.F: Instituto Nacional de Ecologia.\n\n\n\nLau, K.L., Y.Y. Tsang and S.W. Chiu. 2003. Use of spent mushroom compost to\nbioremediate PAH-contaminated samples. Chemosphere 52: 1539\u20131546.\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM79\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200780\n\n\n\nMahdi Haroun, Azni Idris and S.R. Syed Omar\n\n\n\nLisk, D.J., W.H. Gutenmann M. Rutzke, H.T. Kuntz and G. Chu. 1992. Toxic materials in\nthe soil. Arch. Environ. Contam. Toxicol. 22:190-194.\n\n\n\nMathur, S.P., G. Owen, H. Dinel and M. Schnitzer. 1993. Determination of compost\nmaturity. I. Literature review. Biol. Agric. Hort. 10:65-86.\n\n\n\nMays, D.A. and P.M. Giordano. 1989. Land spreading municipal waste compost.\nBioCycle 30 (3):37-39.\n\n\n\nMokhtaruddin, A.M., S.R. Syed Omar, H.M. Ahmed, Z.D. Siti, M.S. Halimi and E.S.\nFazrul. 2001. Utilization of Sewage Sludge as Soil Amendment. Final Report, 50 p.\nUPM, Serdang: Joint Research UPM/IWK.\n\n\n\nOuatmane, A., M.R. Provenzano, M. Hafidi and N. Senesi. 2000. Compost maturity\nassessment using calorimetry, spectroscopy and chemical analysis. Compost\nScience and Utilization 8: 135\u2013146.\n\n\n\nPascual, J.A., M. Ayuso, C. Garcia and T. Hernandez. 1997. Characterization of urban\nwastes according to fertility and phytotoxicity parameters. Waste Management\nand Research 15: 103\u2013112.\n\n\n\nSemarna, P. 1995. Informe tecnico. Mortandad de aves acuaticas en la Presa de Silva\nGto. pp. 45-56. Instituto Nacional de Ecologia,\n\n\n\nSoumare, M., A. Demeyer, F.M.G. Tack and M.G. Verloo. 2002. Chemical characteristics\nof Malian and Belgian solid waste composts. Bioresour. Technol. 81: 97\u2013101.\n\n\n\nSoumare, M., F.M.G. Tack and M.G. Verloo. 2003. Characterization of Malian and Bel-\ngian solid waste composts with respect to fertility and suitability for land applica-\ntion. Waste Management 23: 517\u2013522.\n\n\n\nTomati, U., E. Madejon and E. Galli. 2000. Evaluation of humic acid molecular weight as\nan index of compost stability. Compost Science and Utilization 8: 108\u2013115.\n\n\n\nZorpas, A.A., D. Arapoglou and K. Panagiotis. 2003. Waste paper and clinoptilolite as\na bulking material with dewatered anaerobically stabilized primary sewage sludge\n(DASPSS) for compost production. Waste Management 23: 27\u201335.\n\n\n\nZucconi, F., A. Pera, M. Forte and M. De Bertoldi. 1981. Evaluating toxicity of imma-\nture compost. Biocycle 22: 54-57.\n\n\n\nMJ of Soil Science 071-080.pmd 08-Apr-08, 10:48 AM80\n\n\n\n\n\n"
"\n\nINTRODUCTION\n\n\n\nhave low productivity (Ouattara et a1\nagriculture continues to diminish inexorably as a result of increased erosion and \npopulation growth. Incidentally, it is in this region that demand for agricultural \nproducts is continually rising; hence, the need to intensify land use to generate \nhigher yield per land area. Due to the poor inherent capacity of the soils to provide \nplant nutrients, it is imperative that the plant nutrients be augmented through \n\n\n\nof foreign exchange needed to purchase chemical fertilizers as well as low \n\n\n\nThe fact that fossil fuel is being consumed at a prodigious rate for the \n\n\n\nEffects of Household Compost on the Chemical Properties of \na Typic Paleudult in Nigeria\n\n\n\nObi Clementine Obiamaka\n\n\n\nDepartment of Agriculture Engineering, Federal Polytechnic, \nOko, Anambra State, Nigeria\n\n\n\nABSTRACT\nEffects of soil amendments, tillage practices and season on the yield of amaranthus \n\n\n\nPaleudult from Okpuno, South-eastern Nigeria, during dry and rainy seasons and \n-1, \n\n\n\n-1 -1 -1 \nwere laid out in a randomized complete block design, and replicated thrice for \nthe two tillage practices and seasons. Results indicated that treatment and tillage \n\n\n\n-1. Treatment \n\n\n\nKeywords: Typic Paleudult, C sequestration, oil palm empty fruit bunch ash,\n                    household compost, tillage.\n\n\n\n___________________\n*Corresponding author : E-mail: obiamaka4mary@yahoo.com\n\n\n\n\n\n\n\n\n36\n\n\n\nObi Clementine Obiamaka\n\n\n\nfurther, environmental hazards associated with it have necessitated the search \nfor an environment-friendly alternative source of nutrients for growing plants. \nOrganic fertilizers have recently been used as cheap and abundant biological \nwastes for the bioremediation of these degraded soils for crop production. The \ndisposal of household waste, a form of biological waste, is becoming problematic \nfor large towns and cities in most developing countries. Since these organic wastes \ncontain substantial amounts of plant nutrients, they have to be harnessed through \ncomposting to produce useful organic fertilizers.\n\n\n\nto improvement in soil nutrients content and chemical properties (Kekong et al. \net al et al\n\n\n\nof soil productivity for continuous cropping of highly weathered soil can be \n\n\n\nIt is also known that crop yield increase that accompany organic fertilizer \n\n\n\nthrough repeated amendments has been useful in the reclamation of polluted soils \net al.\n\n\n\nsoils are associated with the depletion of soil organic matter. Soil organic matter, \nto a large extent controls the chemical properties of highly weathered soils, such \nas those of South-eastern Nigeria. This has necessitated this study which is aimed \n\n\n\nMATERIALS AND METHODS\n\n\n\nup to four leaves were transplanted to plots laid out in a randomized complete \n\n\n\nprepared by Indore method (Inkle et al.\n-1 -1 -1 ash and fertilizer NPK 15:15:15 \n\n\n\n-1\n\n\n\nexperiments were carried out during the rainy and dry seasons of two consecutive \nyears. Irrigation was used to sustain the dry season crops and also during dry \nspells in the rainy season.\n\n\n\nWeeding was done manually; no pesticide or herbicide was used in accordance \n\n\n\n\n\n\n\n\n37\n\n\n\nEffect of Organic Amendment on an Ultisol\n\n\n\nharvested after measuring plant height with a metre rule and stem girth by tying \na thread round the base of the stem and measuring the length of the string. The \n\n\n\nto constant weight before obtaining the dry weight.\nThe soil samples collected from the site were bulked air-dried and analyzed \n\n\n\nchemically before planting. At the end of each harvest, soil samples were collected \nfrom each plot, air-dried and analyzed. Soil pH in 1:1 water was determined with \na Pye unicam meter, organic C by dichromate oxidation method (Walkley and \n\n\n\nin the extract were determined by EDTA complexometric titration method while \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nMain Effects of Treatment\nThe chemical properties of the soil and that of compost used for amendment are \nsummarized in Table 1. The soil had low nutrients reserve and was highly acidic \n\n\n\nThe soil was a degraded, acidic Typic Paleudult from Okpuno, an area in the \nrainforest ecological zone, but due to urbanization, had been transformed into a \n\n\n\nis highly leached, acidic in reaction, and has low organic matter and low nutrients \nreserve. The compost was not acidic. It was prepared from household waste under \nshade, thus, it was not exposed to either intense rainfall or sunshine. Moreover \nsome of the kitchen wastes were products of richer soils from other parts of the \ncountry.\n\n\n\n-1 -1 ash, being 6.61 and 5.64 for the \ntwo years. The presence of ash complimented the effect of compost in raising \nsoil pH. Increase in soil pH was accompanied by an increase in exchangeable Ca \n\n\n\nincreased as the values for exchangeable Ca and Mg increased (Ano and Agwu \n\n\n\nbut declined in the second year. In both years the t effect of treatment was not \n\n\n\n-1 \n-1 compost. The same rate of compost also generated higher \n\n\n\n\n\n\n\n\n38\n\n\n\nObi Clementine Obiamaka\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nPr\ne-\n\n\n\npl\nan\n\n\n\ntin\ng \n\n\n\nch\nem\n\n\n\nic\nal\n\n\n\n p\nro\n\n\n\npe\nrti\n\n\n\nes\n o\n\n\n\nf s\noi\n\n\n\nl a\nnd\n\n\n\n c\nom\n\n\n\npo\nst\n\n\n\ncm\nol\n\n\n\nc k\ng-1\n\n\n\n \nSa\n\n\n\nm\npl\n\n\n\ne \npH\n\n\n\n \nO\n\n\n\nrg\nan\n\n\n\nic\n C\n\n\n\n \ng \n\n\n\nkg\n-1\n\n\n\n \nTo\n\n\n\nta\nl N\n\n\n\n \ng \n\n\n\nkg\n-1\n\n\n\n \nA\n\n\n\nva\nila\n\n\n\nbl\ne \n\n\n\nP \nm\n\n\n\ng \nkg\n\n\n\n-1\n \n\n\n\nEx\nch\n\n\n\n. C\na \n\n\n\nM\ng  \n\n\n\nEx\nch\n\n\n\n. K\n \n\n\n\nEx\nch\n\n\n\n. N\na \n\n\n\nSo\nil \n\n\n\nde\npt\n\n\n\nh \n0-\n\n\n\n15\ncm\n\n\n\n \n4.\n\n\n\n26\n \n\n\n\n1.\n29\n\n\n\n \n1.\n\n\n\n11\n \n\n\n\n2.\n00\n\n\n\n \n0.\n\n\n\n80\n \n\n\n\n3.\n48\n\n\n\n \n0.\n\n\n\n12\n \n\n\n\n0.\n06\n\n\n\n \nSo\n\n\n\nil \nde\n\n\n\npt\nh \n\n\n\n15\n-3\n\n\n\n0c\nm\n\n\n\n \n4.\n\n\n\n42\n \n\n\n\n1.\n40\n\n\n\n \n0.\n\n\n\n11\n \n\n\n\n2.\n14\n\n\n\n \n1.\n\n\n\n15\n \n\n\n\n3.\n52\n\n\n\n \n0.\n\n\n\n16\n \n\n\n\n0.\n06\n\n\n\n \nC\n\n\n\nom\npo\n\n\n\nst\n \n\n\n\nD\nry\n\n\n\n S\nea\n\n\n\nso\nn \n\n\n\n1st\n Y\n\n\n\nea\nr \n\n\n\n7.\n20\n\n\n\n \n36\n\n\n\n.5\n \n\n\n\n3.\n40\n\n\n\n \n12\n\n\n\n.4\n2 \n\n\n\n16\n.8\n\n\n\n0 \n10\n\n\n\n.3\n0 \n\n\n\n0.\n58\n\n\n\n \n2.\n\n\n\n58\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\n \nR\n\n\n\nai\nny\n\n\n\n S\nea\n\n\n\nso\nn \n\n\n\n1st\n Y\n\n\n\nea\nr \n\n\n\n7.\n30\n\n\n\n \n35\n\n\n\n.5\n \n\n\n\n3.\n20\n\n\n\n \n12\n\n\n\n.3\n5 \n\n\n\n16\n.1\n\n\n\n0 \n10\n\n\n\n.4\n2 \n\n\n\n0.\n60\n\n\n\n \n2.\n\n\n\n56\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\n \nD\n\n\n\nry\n S\n\n\n\nea\nso\n\n\n\nn \n2nd\n\n\n\n Y\nea\n\n\n\nr \n7.\n\n\n\n35\n \n\n\n\n3.\n60\n\n\n\n \n3.\n\n\n\n30\n \n\n\n\n12\n.3\n\n\n\n0 \n16\n\n\n\n.4\n0 \n\n\n\n11\n.2\n\n\n\n0 \n0.\n\n\n\n52\n \n\n\n\n2.\n40\n\n\n\n\n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\n \nR\n\n\n\nai\nny\n\n\n\n S\nea\n\n\n\nso\nn \n\n\n\n3rd\n Y\n\n\n\nea\nr \n\n\n\n7.\n15\n\n\n\n \n36\n\n\n\n.1\n0 \n\n\n\n3.\n10\n\n\n\n \n11\n\n\n\n.9\n0 \n\n\n\n15\n.9\n\n\n\n0 \n9.\n\n\n\n90\n \n\n\n\n0.\n56\n\n\n\n \n2.\n\n\n\n22\n \n\n\n\n\n\n\n\nEx\nch\n\n\n\n.\n\n\n\n\n\n\n\n\n39\n\n\n\nEffect of Organic Amendment on an Ultisol\n\n\n\ncm\nol\n\n\n\nc k\ng-1\n\n\n\n \nTr\n\n\n\nea\ntm\n\n\n\nen\nt \n\n\n\nSo\nil \n\n\n\npH\n \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n C\n \n\n\n\ng \nkg\n\n\n\n-1\n \n\n\n\nTo\nta\n\n\n\nl N\n \n\n\n\ng \nkg\n\n\n\n-1\n \n\n\n\nA\nva\n\n\n\nila\nbl\n\n\n\ne \nP \n\n\n\nm\ng \n\n\n\nkg\n-1\n\n\n\n \nEx\n\n\n\nch\n. C\n\n\n\na \nEx\n\n\n\nch\n. M\n\n\n\ng \nEx\n\n\n\nch\n. K\n\n\n\n \nEx\n\n\n\nch\n. N\n\n\n\na \n20\n\n\n\n t \nha\n\n\n\n-1\n \n\n\n\n5.\n58\n\n\n\n \n8.\n\n\n\n43\n \n\n\n\n0.\n79\n\n\n\n \n3.\n\n\n\n49\n \n\n\n\n3.\n61\n\n\n\n \n3.\n\n\n\n56\n \n\n\n\n0.\n60\n\n\n\n \n0.\n\n\n\n24\n \n\n\n\n40\n t \n\n\n\nha\n-1\n\n\n\n \n6.\n\n\n\n01\n \n\n\n\n11\n.5\n\n\n\n0 \n0.\n\n\n\n87\n \n\n\n\n4.\n36\n\n\n\n \n4.\n\n\n\n29\n \n\n\n\n3.\n80\n\n\n\n \n0.\n\n\n\n63\n \n\n\n\n0.\n26\n\n\n\n \n60\n\n\n\n t \nha\n\n\n\n-1\n \n\n\n\n5.\n88\n\n\n\n \n13\n\n\n\n.2\n8 \n\n\n\n1.\n01\n\n\n\n \n5.\n\n\n\n03\n \n\n\n\n4.\n95\n\n\n\n \n4.\n\n\n\n47\n \n\n\n\n0.\n66\n\n\n\n \n0.\n\n\n\n36\n \n\n\n\n30\n t \n\n\n\nha\n-1\n\n\n\n pl\nus\n\n\n\n 2\n0 \n\n\n\nt h\na-1\n\n\n\n as\nh \n\n\n\n6.\n61\n\n\n\n \n10\n\n\n\n.3\n8 \n\n\n\n0.\n85\n\n\n\n \n4.\n\n\n\n46\n \n\n\n\n4.\n75\n\n\n\n \n4.\n\n\n\n18\n \n\n\n\n0.\n78\n\n\n\n \n0.\n\n\n\n39\n \n\n\n\nFe\nrti\n\n\n\nliz\ner\n\n\n\n \n4.\n\n\n\n22\n \n\n\n\n4.\n48\n\n\n\n \n0.\n\n\n\n88\n \n\n\n\n3.\n18\n\n\n\n \n3.\n\n\n\n26\n \n\n\n\n3.\n26\n\n\n\n \n0.\n\n\n\n44\n \n\n\n\n0.\n20\n\n\n\n \nC\n\n\n\non\ntro\n\n\n\nl \n4.\n\n\n\n41\n \n\n\n\n8.\n43\n\n\n\n \n0.\n\n\n\n56\n \n\n\n\n0.\n97\n\n\n\n \n0.\n\n\n\n89\n \n\n\n\n2.\n04\n\n\n\n \n0.\n\n\n\n45\n \n\n\n\n0.\n11\n\n\n\n \nLS\n\n\n\nD\n (p\n\n\n\n<0\n.0\n\n\n\n5)\n \n\n\n\n0.\n74\n\n\n\n6 \n3.\n\n\n\n27\n \n\n\n\nns\n \n\n\n\n0.\n80\n\n\n\n \n0.\n\n\n\n48\n \n\n\n\n0.\n48\n\n\n\n \nns\n\n\n\n \n0.\n\n\n\n15\n \n\n\n\nns\n- n\n\n\n\not\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n \n\n\n\n\n\n\n\n\nObi Clementine Obiamaka\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\ncm\nol\n\n\n\nc k\ng-1\n\n\n\n \nTr\n\n\n\nea\ntm\n\n\n\nen\nt \n\n\n\nSo\nil \n\n\n\npH\n \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n C\n \n\n\n\ng \nkg\n\n\n\n-1\n \n\n\n\nTo\nta\n\n\n\nl N\n \n\n\n\ng \nkg\n\n\n\n-1\n \n\n\n\nA\nva\n\n\n\nila\nbl\n\n\n\ne \nP \n\n\n\nm\ng \n\n\n\nkg\n-1\n\n\n\n \nEx\n\n\n\nch\n. C\n\n\n\na \nEx\n\n\n\nch\n. M\n\n\n\ng \n E\n\n\n\nxc\nh.\n\n\n\n K\n \n\n\n\nEx\nch\n\n\n\n. N\na \n\n\n\n20\n t \n\n\n\nha\n-1\n\n\n\n \n5.\n\n\n\n06\n \n\n\n\n10\n.5\n\n\n\n3 \n0.\n\n\n\n96\n \n\n\n\n19\n.2\n\n\n\n6 \n4.\n\n\n\n52\n \n\n\n\n4.\n28\n\n\n\n \n0.\n\n\n\n28\n \n\n\n\n0.\n58\n\n\n\n \n40\n\n\n\n t \nha\n\n\n\n-1\n \n\n\n\n5.\n32\n\n\n\n \n15\n\n\n\n.6\n2 \n\n\n\n1.\n37\n\n\n\n \n27\n\n\n\n.0\n7 \n\n\n\n5.\n00\n\n\n\n \n4.\n\n\n\n57\n \n\n\n\n0.\n24\n\n\n\n \n0.\n\n\n\n37\n \n\n\n\n60\n t \n\n\n\nha\n-1\n\n\n\n \n5.\n\n\n\n42\n \n\n\n\n20\n.9\n\n\n\n7 \n1.\n\n\n\n95\n \n\n\n\n36\n.1\n\n\n\n2 \n5.\n\n\n\n78\n \n\n\n\n5.\n62\n\n\n\n \n0.\n\n\n\n38\n \n\n\n\n0.\n53\n\n\n\n \n30\n\n\n\n t \nha\n\n\n\n-1\n pl\n\n\n\nus\n 2\n\n\n\n0 \nt h\n\n\n\na-1\n as\n\n\n\nh \n5.\n\n\n\n64\n \n\n\n\n11\n.1\n\n\n\n6 \n1.\n\n\n\n16\n \n\n\n\n23\n.5\n\n\n\n5 \n6.\n\n\n\n49\n \n\n\n\n5.\n77\n\n\n\n \n0.\n\n\n\n40\n \n\n\n\n0.\n42\n\n\n\n \nFe\n\n\n\nrti\nliz\n\n\n\ner\n \n\n\n\n4.\n49\n\n\n\n \n6.\n\n\n\n41\n \n\n\n\n1.\n96\n\n\n\n \n16\n\n\n\n.0\n7 \n\n\n\n2.\n34\n\n\n\n \n3.\n\n\n\n01\n \n\n\n\n0.\n28\n\n\n\n \n0.\n\n\n\n27\n \n\n\n\nC\non\n\n\n\ntro\nl \n\n\n\n4.\n70\n\n\n\n \n6.\n\n\n\n75\n \n\n\n\n0.\n13\n\n\n\n \n0.\n\n\n\n68\n \n\n\n\n0.\n59\n\n\n\n \n2.\n\n\n\n05\n \n\n\n\n0.\n16\n\n\n\n \n0.\n\n\n\n26\n \n\n\n\nLS\nD\n\n\n\n (p\n<0\n\n\n\n.0\n5)\n\n\n\n \n0.\n\n\n\n38\n \n\n\n\n1.\n55\n\n\n\n \n0.\n\n\n\n46\n \n\n\n\n6.\n53\n\n\n\n \n0.\n\n\n\n28\n \n\n\n\n0.\n40\n\n\n\n \nns\n\n\n\n \nns\n\n\n\n \nns\n\n\n\n \u2013\n n\n\n\n\not\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n \n\n\n\n\n\n\n\n\n41\n\n\n\nEffect of Organic Amendment on an Ultisol\n\n\n\nmean total N of 1.48 g kg-1 -1. Higher positive \n\n\n\net al.\nof organic matter. The pH and soil organic matter content of plots that received \ninorganic fertilizer were lower than those of the control because inorganic \nfertilizers are known to encourage mineralization of organic matter. Fertilizer \ntreatment also slightly increased soil chemical properties measured as they were \nabove the values for the control but below those of the compost treatments.\n\n\n\nImprovements in all the measured crop yield parameters relative to control \nwere due to the increase in the fertility status of the amended plots (Tables 4 and \n\n\n\nnutrients availability to the crop. The ability of household compost, applied at \n-1 -1, to increase plant growth and yield relative \n\n\n\nto NPK fertilizer indicates that these wastes, harnessed as compost, serve as a \nsuitable fertilizer for growing crops.\n\n\n\nThe better crop performance obtained in the second year can be attributed to \nimproved soil chemical and physical conditions resulting from mineralization of \nfreshly applied and residual soil organic matter. Moreover, organic matter may \nhave reduced nematode infestation of the amaranthus roots in plots that received \ncompost amendment since the root of amaranthus in control plots had knots, unlike \n\n\n\nthe rhizosphere. It is also known that mineralization of compost produced humid \n\n\n\nAlso, in the second year also, the fertilizer treatment had higher fresh and dry \n\n\n\nwas lower than that obtained from compost treatments in both years. Adeniyan \n\n\n\na short period of time because of leaching, which removes nutrients from the \nrhizosphere. This could be responsible for the lower yield obtained from NPK \nfertilizer treatment relative to those of compost treatment. Inorganic fertilizers \naccelerate the decomposition of soil organic matter, which is responsible for \n\n\n\nmicrobial growth.\n\n\n\n-1\n\n\n\n-1\n\n\n\n-1\n\n\n\nha-1. Mineralization of organic matter has been found not to be proportional to \net al\n\n\n\n-1 -1 \n\n\n\nrate produced crops with higher dry weight in the second year, though the value \n-1. The crops generally grew \n\n\n\n\n\n\n\n\nObi Clementine Obiamaka\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nTr\nea\n\n\n\ntm\nen\n\n\n\nt \nPl\n\n\n\nan\nt H\n\n\n\nei\ngh\n\n\n\nt \n(c\n\n\n\nm\n) \n\n\n\nSt\nem\n\n\n\n g\nro\n\n\n\nw\nth\n\n\n\n \n(c\n\n\n\nm\n) \n\n\n\nFr\nes\n\n\n\nh \nW\n\n\n\nei\ngh\n\n\n\nt \n(g\n\n\n\n/p\nlo\n\n\n\nt) \nD\n\n\n\nry\n W\n\n\n\nei\ngh\n\n\n\nt \n(g\n\n\n\n/p\nlo\n\n\n\nt) \nFr\n\n\n\nes\nh \n\n\n\nSe\ned\n\n\n\n \nW\n\n\n\nei\ngh\n\n\n\nt (\ng)\n\n\n\n \n20\n\n\n\n t \nha\n\n\n\n-1\n \n\n\n\n53\n.0\n\n\n\n1 \n2.\n\n\n\n99\n \n\n\n\n96\n6 \n\n\n\n24\n2 \n\n\n\n0.\n90\n\n\n\n \n40\n\n\n\n t \nha\n\n\n\n-1\n \n\n\n\n60\n.5\n\n\n\n3 \n3.\n\n\n\n59\n \n\n\n\n16\n59\n\n\n\n \n27\n\n\n\n1 \n1.\n\n\n\n01\n \n\n\n\n60\n t \n\n\n\nha\n-1\n\n\n\n \n60\n\n\n\n.3\n9 \n\n\n\n3.\n51\n\n\n\n \n10\n\n\n\n49\n \n\n\n\n22\n7 \n\n\n\n1.\n45\n\n\n\n \n30\n\n\n\n t \nha\n\n\n\n-1\n p\n\n\n\nlu\ns 2\n\n\n\n0 \nt h\n\n\n\na -\n1 \n\n\n\nas\nh \n\n\n\n59\n.1\n\n\n\n5 \n3.\n\n\n\n74\n \n\n\n\n13\n71\n\n\n\n \n23\n\n\n\n5 \n1.\n\n\n\n28\n \n\n\n\nFe\nrti\n\n\n\nliz\ner\n\n\n\n \n49\n\n\n\n.2\n5 \n\n\n\n2.\n13\n\n\n\n \n97\n\n\n\n8 \n18\n\n\n\n8 \n1.\n\n\n\n51\n \n\n\n\nC\non\n\n\n\ntro\nl \n\n\n\n49\n.1\n\n\n\n0 \n2.\n\n\n\n83\n \n\n\n\n50\n6 \n\n\n\n64\n \n\n\n\n0.\n52\n\n\n\n \nLS\n\n\n\nD\n (p\n\n\n\n<0\n.0\n\n\n\n5)\n \n\n\n\n2.\n23\n\n\n\n \n0.\n\n\n\n62\n \n\n\n\nns\n \n\n\n\nns\n \n\n\n\n0.\n14\n\n\n\n4 \nns\n\n\n\n \u2013\n n\n\n\n\not\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n \n\n\n\n\n\n\n\n\n43\n\n\n\nEffect of Organic Amendment on an Ultisol\n\n\n\nTA\nB\n\n\n\nLE\n 5\n\n\n\nC\nro\n\n\n\np \nyi\n\n\n\nel\nd \n\n\n\nas\n a\n\n\n\nffe\nct\n\n\n\ned\n b\n\n\n\ny \ntre\n\n\n\nat\nm\n\n\n\nen\nt i\n\n\n\nn \nth\n\n\n\ne \nse\n\n\n\nco\nnd\n\n\n\n y\nea\n\n\n\nr\n\n\n\nTr\nea\n\n\n\ntm\nen\n\n\n\nt \nPl\n\n\n\nan\nt H\n\n\n\nei\ngh\n\n\n\nt \n(c\n\n\n\nm\n) \n\n\n\nSt\nem\n\n\n\n g\nro\n\n\n\nw\nth\n\n\n\n \n(c\n\n\n\nm\n) \n\n\n\nFr\nes\n\n\n\nh \nW\n\n\n\nei\ngh\n\n\n\nt \n(g\n\n\n\n/p\nlo\n\n\n\nt) \nD\n\n\n\nry\n W\n\n\n\nei\ngh\n\n\n\nt \n(g\n\n\n\n/p\nlo\n\n\n\nt) \nFr\n\n\n\nes\nh \n\n\n\nSe\ned\n\n\n\n \nW\n\n\n\nei\ngh\n\n\n\nt (\ng)\n\n\n\n \n20\n\n\n\n t \nha\n\n\n\n-1\n69\n\n\n\n.0\n \n\n\n\n4.\n51\n\n\n\n \n17\n\n\n\n89\n \n\n\n\n23\n7 \n\n\n\n37\n \n\n\n\n40\n t \n\n\n\nha\n-1\n\n\n\n \n75\n\n\n\n.7\n \n\n\n\n5.\n75\n\n\n\n \n21\n\n\n\n96\n \n\n\n\n29\n9 \n\n\n\n47\n \n\n\n\n60\n t \n\n\n\nha\n-1\n\n\n\n76\n.2\n\n\n\n \n5.\n\n\n\n33\n \n\n\n\n20\n86\n\n\n\n \n31\n\n\n\n8 \n57\n\n\n\n \n30\n\n\n\n t \nha\n\n\n\n-1\n pl\n\n\n\nus\n 2\n\n\n\n0 \nt h\n\n\n\na-1\n as\n\n\n\nh \n73\n\n\n\n.2\n \n\n\n\n4.\n82\n\n\n\n \n15\n\n\n\n42\n \n\n\n\n25\n8 \n\n\n\n50\n \n\n\n\nFe\nrti\n\n\n\nliz\ner\n\n\n\n \n70\n\n\n\n.7\n \n\n\n\n4.\n71\n\n\n\n \n16\n\n\n\n01\n \n\n\n\n24\n5 \n\n\n\n46\n \n\n\n\nC\non\n\n\n\ntro\nl \n\n\n\n61\n.3\n\n\n\n \n3.\n\n\n\n50\n \n\n\n\n83\n8 \n\n\n\n16\n0 \n\n\n\n27\n \n\n\n\nLS\nD\n\n\n\n (p\n<0\n\n\n\n.0\n5)\n\n\n\n \nns\n\n\n\n \n0.\n\n\n\n90\n \n\n\n\n51\n7 \n\n\n\n35\n \n\n\n\n18\n \n\n\n\nns\n \u2013\n\n\n\n n\not\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\n \n\n\n\n\n\n\n\n\n44\n\n\n\nObi Clementine Obiamaka\n\n\n\nTA\nB\n\n\n\nLE\n 6\n\n\n\nEf\nfe\n\n\n\nct\ns o\n\n\n\nf t\nill\n\n\n\nag\ne \n\n\n\non\n p\n\n\n\nos\nt h\n\n\n\nar\nve\n\n\n\nst\n so\n\n\n\nil \nch\n\n\n\nem\nic\n\n\n\nal\n p\n\n\n\nro\npe\n\n\n\nrti\nes\n\n\n\n fo\nr a\n\n\n\nm\nar\n\n\n\nan\nth\n\n\n\nus\n p\n\n\n\nlo\nts\n\n\n\n1st\n Y\n\n\n\nea\nr \n\n\n\n2nd\n Y\n\n\n\nea\nr \n\n\n\nTr\nea\n\n\n\ntm\nen\n\n\n\nt \nTi\n\n\n\nlle\nd\n\n\n\nU\nnt\n\n\n\nill\ned\n\n\n\nTi\nlle\n\n\n\nd\nU\n\n\n\nnt\nill\n\n\n\ned\nSo\n\n\n\nil \npH\n\n\n\n \n5.\n\n\n\n60\n \n\n\n\n5.\n30\n\n\n\n \nns\n\n\n\n \n4.\n\n\n\n97\n \n\n\n\n5.\n25\n\n\n\n \nsi\n\n\n\ng \nO\n\n\n\nrg\nan\n\n\n\nic\n C\n\n\n\n (g\n k\n\n\n\ng \n-1\n\n\n\n) \n8.\n\n\n\n74\n \n\n\n\n10\n.0\n\n\n\n8 \nns\n\n\n\n \n11\n\n\n\n.7\n6 \n\n\n\n12\n.0\n\n\n\n5 \nns\n\n\n\n \nTo\n\n\n\nta\nl N\n\n\n\n (g\n k\n\n\n\ng-1\n) \n\n\n\n3.\n47\n\n\n\n \n3.\n\n\n\n70\n \n\n\n\nns\n \n\n\n\n19\n.3\n\n\n\n5 \n21\n\n\n\n.5\n4 \n\n\n\nns\n \n\n\n\nA\nva\n\n\n\nila\nbl\n\n\n\ne \nP \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n) \n0.\n\n\n\n78\n \n\n\n\n0.\n88\n\n\n\n \nns\n\n\n\n \n1.\n\n\n\n27\n \n\n\n\n1.\n25\n\n\n\n \nns\n\n\n\n \nEx\n\n\n\nch\nan\n\n\n\nge\nab\n\n\n\nle\n \n\n\n\ncm\nol\n\n\n\n  k\ng\n\n\n\n-1\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nC\na \n\n\n\n3.\n86\n\n\n\n \n3.\n\n\n\n39\n \n\n\n\nsi\ng \n\n\n\n4.\n33\n\n\n\n \n3.\n\n\n\n91\n \n\n\n\nsi\ng \n\n\n\nM\ng \n\n\n\n3.\n66\n\n\n\n \n3.\n\n\n\n45\n \n\n\n\nsi\ng \n\n\n\n4.\n38\n\n\n\n \n4.\n\n\n\n06\n \n\n\n\nsi\ng \n\n\n\nK\n \n\n\n\n0.\n28\n\n\n\n \n0.\n\n\n\n24\n \n\n\n\nns\n \n\n\n\n0.\n28\n\n\n\n \n0.\n\n\n\n30\n \n\n\n\nns\n \n\n\n\nN\na \n\n\n\n0.\n58\n\n\n\n \n0.\n\n\n\n62\n \n\n\n\nns\n \n\n\n\n0.\n33\n\n\n\n \n0.\n\n\n\n48\n \n\n\n\nns\n \n\n\n\n \ns\n\n\n\n\n\n\n\n\n\n\n\nc\n\n\n\nns\n \u2013\n\n\n\n n\not\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\nig\n\n\n\n\u2013\nsi\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nt\n\n\n\n\n\n\n\n\n45\n\n\n\nEffect of Organic Amendment on an Ultisol\n\n\n\nTA\nB\n\n\n\nLE\n 7\n\n\n\nEf\nfe\n\n\n\nct\n o\n\n\n\nf s\nea\n\n\n\nso\nn \n\n\n\non\n p\n\n\n\nos\nt h\n\n\n\nar\nve\n\n\n\nst\n so\n\n\n\nil \nch\n\n\n\nem\nic\n\n\n\nal\n p\n\n\n\nro\npe\n\n\n\nrti\nes\n\n\n\n in\n 1\n\n\n\nst\nnd\n\n\n\n y\nea\n\n\n\nr\n\n\n\n1st\n Y\n\n\n\nea\nr \n\n\n\n2nd\n Y\n\n\n\nea\nr \n\n\n\nTr\nea\n\n\n\ntm\nen\n\n\n\nt \nD\n\n\n\nry\n \n\n\n\nR\nai\n\n\n\nny\n \n\n\n\n \nD\n\n\n\nry\n \n\n\n\nR\nai\n\n\n\nny\n \n\n\n\n \nSo\n\n\n\nil \npH\n\n\n\n \n5.\n\n\n\n43\n \n\n\n\n5.\n47\n\n\n\n \nns\n\n\n\n \n4.\n\n\n\n89\n \n\n\n\n5.\n38\n\n\n\n \nsi\n\n\n\ng \nO\n\n\n\nrg\nan\n\n\n\nic\n C\n\n\n\n (g\n k\n\n\n\ng \n-1\n\n\n\n) \n12\n\n\n\n.9\n8 \n\n\n\n8.\n85\n\n\n\n \nsi\n\n\n\ng \n11\n\n\n\n.7\n2 \n\n\n\n12\n.0\n\n\n\n9 \nns\n\n\n\n \nTo\n\n\n\nta\nl N\n\n\n\n (g\n k\n\n\n\ng-1\n) \n\n\n\n3.\n96\n\n\n\n \n3.\n\n\n\n21\n \n\n\n\nsi\ng \n\n\n\n19\n.3\n\n\n\n7 \n21\n\n\n\n.5\n4 \n\n\n\nsi\ng \n\n\n\nA\nva\n\n\n\nila\nbl\n\n\n\ne \nP \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n) \n1.\n\n\n\n02\n \n\n\n\n0.\n63\n\n\n\n \nsi\n\n\n\ng \n1.\n\n\n\n11\n \n\n\n\n1.\n41\n\n\n\n \nsi\n\n\n\ng \nEx\n\n\n\nch\nan\n\n\n\nge\nab\n\n\n\nle\n \n\n\n\ncm\nol\n\n\n\n  k\ng\n\n\n\n-1\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nC\na \n\n\n\n3.\n72\n\n\n\n \n3.\n\n\n\n53\n \n\n\n\nns\n \n\n\n\n4.\n35\n\n\n\n \n4.\n\n\n\n22\n \n\n\n\nns\n \n\n\n\nM\ng \n\n\n\n3.\n99\n\n\n\n \n3.\n\n\n\n59\n \n\n\n\nns\n \n\n\n\n4.\n21\n\n\n\n \n4.\n\n\n\n22\n \n\n\n\nsi\ng \n\n\n\nK\n \n\n\n\n0.\n27\n\n\n\n \n0.\n\n\n\n20\n \n\n\n\nns\n \n\n\n\n0.\n10\n\n\n\n \n0.\n\n\n\n48\n \n\n\n\nsi\ng \n\n\n\nN\na \n\n\n\n0.\n64\n\n\n\n \n0.\n\n\n\n55\n \n\n\n\nns\n \n\n\n\n0.\n19\n\n\n\n \n0.\n\n\n\n62\n \n\n\n\nsi\ng \n\n\n\nns\n \u2013\n\n\n\n n\not\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\n \n\n\n\nsi\ng\n\n\n\n \n\u2013\n\n\n\n \nsi\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nt\n \n\n\n\nc\n\n\n\n\n\n\n\n\n46\n\n\n\nObi Clementine Obiamaka\n\n\n\ntaller and bigger in the second year due to higher nutrients availability. Plants with \n-1 -1 ash in both years, \n\n\n\na trend which can be associated with better roots development and rhizosphere \nvolume arising from improved soil pH. The complimentary effects of the mixture \n\n\n\nMain Effect of Tillage\n\n\n\nplot for tilled and untilled plots. Generally, tillage raised the other yield parameters \n\n\n\nencouraged higher organic matter mineralization which was responsible for \nbetter crop performance. Soil pH, exchangeable Ca and Mg were higher for tilled \nplots, due to higher mineralization of organic matter. The interaction between \n\n\n\nha-1 compost.\n\n\n\nMain Effect of Season\n-1, \n\n\n\nrainy season encouraged higher concentration of plant nutrients due to higher \n\n\n\nCONCLUSION\nThis study found that soil chemical properties were improved by compost \n\n\n\n-1 was \n-1\n\n\n\n-1 due to the increased ability of organic matter to \n\n\n\nresponsible for the increase in crop yield. Highest fresh matter production was \n-1 compost. It was also found that the dry season and zero \n\n\n\ntillage had the effect of improving chemical properties of the soil.\n\n\n\nREFERENCES\n\n\n\nnematodes of cowpea. Journal of Agriculture and Environment. \n\n\n\nA\nand combination of their reduced levels on maize growth and soil chemical \nproperties. Nigerian Journal of Soil Science. 15: 34-41.\n\n\n\nNigerian Journal \nof Soil Science. \n\n\n\n\n\n\n\n\n47\n\n\n\nEffect of Organic Amendment on an Ultisol\n\n\n\nA\nphosphate sorption by Cerrado soils from Brazil. Soil Science\n\n\n\nAk\n\n\n\nrock. World Journal of Agricultural Science\n\n\n\nproperties. Nigerian Journal of Soil Science. 15: 14-19.\n\n\n\nBray, R.H. and L.T. Kurtz. 1945. Determination of total organic and available forms \nof phosphorus in soils. Soil Science. 59: 35-45.\n\n\n\nBremmer, J.M. 1965. Nitrogen availability indexes in C.A. Black ed. Methods of Soil \n\n\n\nD\nAfrica; Ed. Romain H. Racmackers. CIP Royal Library Albert Karmelictenstraat. \n\n\n\nFagbenro, J.A. and A.A. Agboola. 1993. Effect of different levels of humid acid on \nthe growth and nutrient uptake of Teak seedings. Journal of Plant Nutrition. 16: \n1465-1483.\n\n\n\nGra\n\n\n\nInkel, M., P. De Smet, T. Tersmette and T. Valdkamp. 1994. The preparation and use \nof compost. Agrodock Series. No. 8\n\n\n\nKe\n\n\n\nhumid guinea Savanna and rainforest belt of Nigeria. Nigerian Journal of Soil \nScience\n\n\n\nM\n\n\n\nBioresource Technology\n\n\n\nOb\n\n\n\nNigerian Journal of Soil Science\n\n\n\nrainforest area of Nigeria. Applied Tropical Agriculture\n\n\n\nO\nnutrients composition, growth and yield of amaranthus. Nigerian Agriculture \nJournal. 33: 46-49.\n\n\n\n\n\n\n\n\n48\n\n\n\nObi Clementine Obiamaka\n\n\n\nOuttara, S., M. Lupta, I. Kilinwiko, G. Garuku and F. Ajayi. 1991. Natural fertilizers \n\n\n\nP Advance in \nAgronomy\n\n\n\nPimentel, D. and L.E. Hurd. 1973. Food production and the energy crisis. Science \nCompost \n\n\n\nScience. November- December 1973.\n\n\n\nRoy, A.H and G. Harris. 1995. Meeting crop nutrient challenges. Farm Chemical \n\n\n\nThomas, G.W., G.R. Haszler and R.L. Blevins. 1996. The effects of organic matter \nand tillage on maximum compatibility of soils using the Proctor Test. Soil \nScience\n\n\n\nUt\necosystem.  Journal of Environmental Quality.\n\n\n\nWalkley A. and L.A Black. 1934. An examination of the degtiareff method for \n\n\n\nacid titration method. Soil Science\n\n\n\nZan\nevolution of three paddy soil from South China and the temperature dependence. \nJournal of Environment Sciences\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 149-166 (2019)                         Malaysian Society of Soil Science\n\n\n\nEffects of gamma-irradiated Acinetobacter calcoaceticus on \nnitrogen and phosphorus uptake of green mustard (Brassica \n\n\n\nchinensis) \n\n\n\nPhua Choo Kwai Hoe1, Halimi Mohd Saud1, Khairuddin Abdul \nRahim2,Che Fauziah Binti Ishak3 and Puteri E. Megat Wahab4\n\n\n\n1Universiti Putra Malaysia, Department of Agriculture Technology, Faculty of \nAgriculture, 43300 UPM Serdang, Selangor, Malaysia\n\n\n\n2Malaysian Nuclear Agency, Agrotechnology and Bioscience Division, Bangi,\n43000 Kajang, Selangor, Malaysia\n\n\n\n3Universiti Putra Malaysia, Department of Land Management, Faculty of \nAgriculture, 43300 UPM Serdang, Selangor, Malaysia\n\n\n\n4Universiti Putra Malaysia, Department of Crop Science, Faculty of Agriculture, \n43300 UPM Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nSeveral biofertiliser microorganisms are subjected to gamma-irradiation for \nmutagenesis to improve capabilities of N2 fixation and P solubilisation, and \nalso to satisfy biofertiliser market demands. The effects of gamma-irradiated \nAcinetobacter calcoaceticus on N and P uptake of choy sum or green mustard \n(Brassica chinensis) were investigated in a greenhouse experiment. Eight bacteria \nisolated from compost, soil and plants which were to be used as biofertilisers \nwere gamma irradiated at 50\u2013400 Gy and screened for the best N-fixing and \nP-solubilising mutants. The gamma-irradiated A. calcoaceticus M100/200 mutant \nshowed higher N2 fixation and phosphate solubilisation than those of the wild-type \nin vitro. The selected mutant (M100/200) and wild-type (M100) A. calcoaceticus  \nwere then tested on green mustard in a greenhouse experiment. N, P and K contents \nin soil, as well as pH, were determined using a soil nutrient analyser. Urea and rock \nphosphate were used as nitrogen and phosphate sources, respectively. Two-week-\nold seedlings were treated with biofertiliser (mutant or wild type) with either N or \nP source alone or in combination. The control treatments comprised of biofertiliser \nor N or P source alone (positive control) and without treatment (negative control). \nCrops were harvested after 2 months. Fresh and dry weight, height, chlorophyll \ncontent, leaf area and total N and P in the tissue of the crops were measured. \nMutant M100/200 with N and P source treatment or with P source only showed \nsuperior growth and nutrient uptake in comparison to those of the samples given \nother treatments in the greenhouse experiment. Nitrogen input did not enhance \ngrowth, whereas phosphorus input and administration of phosphate solubiliser \nincreased plant growth indirectly through a better rooting system. Green mustard \ninoculated with mutant strain demonstrated better nutrient uptake in a greenhouse \nexperiment than those inoculated with the wild type. \n\n\n\n___________________\n*Corresponding author : \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019150\n\n\n\nINTRODUCTION\nA biofertiliser is a substance composed of living microorganisms that colonise \nthe rhizosphere or the interior parts of plants and promote growth by increasing \nthe supply or availability of primary nutrients to the host plant (Vessey 2003).  \nBiofertilisers with arbuscular mycorrhiza fungi and nitrogen fixing bacteria (NFB) \nwere developed in Malaysia in the late 1980s and early 1990s. Since then, phosphate \nsolubilising microorganisms (PSM) and plant growth promoting microorganisms \n(PGPR) have been used as biofertiliser inoculants. In 2012, bioinoculants mainly \nnitrogen fixing bacteria (NFB) such as rhizobia, which constitute 79% of the global \ndemand of biofertiliser application (Transparency Market Research, 2014). This \nshows that biofertiliser products target limited activities. Thus, it is prudent for \nmultifunctional biofertilisers to be developed. Multifunctional activities include \nN2 fixation, phosphate solubilisation, potassium solubilisation and plant growth \npromotion in a single application. Current practices to produce multifunctional \nbiofertilisers involve mixing of microorganisms. As a consequence, this approach \nmay cause other problems such as contamination, antagonistic effects among \nmicroorganisms, and decreased biofertiliser shelf life. Mutagenesis or genetic \nrecombination of biofertiliser microorganisms may improve their multifunctional \nactivities to satisfy market demands. Gamma-irradiation is widely used for plant \nmutagenesis (Anna et al. 2008; Ibrahim et al. 2013 and Sikder et al. 2013). \nThe microbial mutation method by gamma irradiation has been applied in the \nfermentation industry (Huma et al. 2012 and Mehdikhani et al. 2011), whereas \ndisease-control mutants have been utilised in the agriculture industry (Haggag \n2002; Haggag and Mohamed 2002). To date, mutagenesis of biofertiliser microbes \nhas not been conducted in Malaysia. In this study, A. calcoaceticus was gamma-\nirradiated to enhance N2 fixation and phosphate solubilisation relative to those of \nthe wild-type and ultimately satisfy biofertiliser market demands. \n Nitrogen and phosphorus are two important plant nutrients. Biological \nnitrogen fixation (BNF) by N2-fixing bacteria is considered to be important \nin determining the nitrogen balance in soil systems (Lugtenberg et al. \n2013). Meanwhile, phosphate solubilisers comprise a group of heterotrophic \nmicroorganisms known to solubilise inorganic phosphorus from insoluble \nsources. Applying phosphate-solubilising microorganisms substantially increases \nthe abundance of active and effective microorganisms at the root rhizosperic zone \nand leads to an increases in plant nutrient uptake (Mehrvarz et al. 2008; Shen et \nal. 2011). \n Vegetable and cash crop statistics in Malaysia have shown that green \nmustard has the highest production (15,522.6 ha of harvested area and 275,732.4 \nMt) of vegetables in Malaysia (DOA 2014). Green mustard (Brassica chinensis)  \nhas been inoculated with biofertilisers, such as mycorrhiza, Rhizobium sp., \nAzotobacter sp., Azospirillum  . Aspergillus niger and Bacillus sp. (Kalay et al. \n\n\n\nKeywords: Acinetobacter baumannii, Acinetobacter calcoaceticus, gamma- \nirradiation, multifunctional biofertiliser\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 151\n\n\n\n2015; Halim et al. 2016; Andhika and Augustini 2017; Widnyana et al., 2018), \nand positive results in terms of growth and yield have been obtained.\n The effects of gamma-irradiated A. calcoaceticus on the nitrogen and \nphosphorus uptake of green mustard were evaluated in a greenhouse experiment. \n\n\n\nMATERIALS AND METHODS\n\n\n\nMutagenesis of A. calcoaceticus through gamma-irradiation\nA. calcoaceticus was isolated from paddy soil in MADA (Muda Agricultural \nDevelopment Authorities) field (Kedah). The bacterial strains were identified \nby using the 16S rRNA method (Tahir et al. 2013). The radiation mutagenesis \nprocedure was performed with gamma irradiation (50\u2013400 Gy) from the gamma \ncell in Malaysian Nuclear Agency as described by Rugthaworn et al. (2007) but \nwith slight modifications. Serial dilution and plate count methods were employed \nto determine the lethal dosage (LD50) and to obtain the survival curve. N2 fixing \nactivity was screened by culturing the isolates on yeast extract\u2013mannitol agar \n(YMA) containing 25 \u00b5g/ml bromothymol blue (BTB) (Swelim et al. 2010). \nN2-fixing bacteria produced a blue zone on this agar plate.  The colonies that \nproduced larger blue zones than those of the non-irradiated colonies were selected. \nPhosphate-solubilising activities were screened on phosphate agar plates (PDYA) \n(Freitas et al. 1997). Phosphate-solubilising bacteria produced halo zones on this \nagar plate.  The colonies that produced larger halo zones than those of the non-\nirradiated colonies were selected. Stable mutants (stable N2-fixing and phosphate \nsolubilisation activities) were selected after five screening times. Data were \nanalysed by ANOVA, with the means separated by Duncan\u2019s test (P \u2264 0.05).\n\n\n\nNitrogen and Phosphorus Uptake of Choy Sum (B. chinensis) in a Greenhouse \nExperiment\nA. calcoaceticus M100 (wild-type) and M100/200 (mutant) were prepared by \nculturing on nutrient broth for 24 h. Then, 2-wk-old green mustard seedlings \nwere transplanted into pots containing 5 kg soil mixture of soil, peat and sand \nat a ratio of 2:1:1.  The soil was then gamma sterilised at 50 kGy. N, P, K and \npH were determined by using a soil nutrient analyser. Urea (120 kg ha-1 of N) \nand phosphate rock (80 kg ha-1 of P) were applied as N and phosphate sources, \nrespectively. Treatments are listed in Table 1.  Plants were harvested after two \nmonths. Fresh and dry weights, height, root length, SPAD, chlorophyll content, \nleaf area, and total N and P were measured. Total chlorophyll (chlorophyll a and b) \nwere extracted and measured as described by Coombs et al. (1985). The total N in \nthe samples was determined by Kjeldahl digestion and titration (Jones, 2001). The \ntotal P in the samples was extracted by the wet digestion method (Jones , 2001) \nand measured by inductively coupled plasma (ICP) optical emission spectrometry.  \nData were analysed by ANOVA, with means separated by Duncan\u2019s Multiple \nRange test (P \u2264 0.05).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019152\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nMutagenesis of A. calcoaceticus Through Gamma Irradiation\nThe wild-type (M100) and mutant (M100/200) strains were identified as A. \ncalcoaceticus by the 16S rRNA method. Table 2 shows that the LD50 for M100 was \n488.50 Gy (50% of log cfu/ml bacteria were killed at the dose of 488.50 Gy, where \ncfu stands for colony-forming units). On N2 fixing agar media, M100/200 formed \na 2.20 cm zone, which was larger than those of the other irradiated isolates (Figure \n1). The diameters of the clear zones after 0, 50, 100, 300 and 400 Gy irradiations \nwere 2.05, 1.95, 1.88, 2.02 and 2.00 cm, respectively. On phosphate-solubilising \nagar media, M100/200 showed the clear zone with the largest diameter (2.38 cm). \nOther irradiated isolates exhibited clear zones with diameters of 1.98, 2.025, 1.88, \n\n\n\nTABLE 1\nTreatments of green mustard in greenhouse experiment\n\n\n\n6 \n \n\n\n\nNitrogen and Phosphorus Uptake of Choy Sum (B. chinensis) in a \nGreenhouse Experiment \nA. calcoaceticus M100 (wild-type) and M100/200 (mutant) were prepared by \n\n\n\nculturing on nutrient broth for 24 h. Then, 2-wk-old green mustard seedlings were \n\n\n\ntransplanted into pots containing 5 kg soil mixture of soil, peat and sand at a ratio \n\n\n\nof 2:1:1.  The soil was then gamma sterilised at 50 kGy. N, P, K and pH were \n\n\n\ndetermined by using a soil nutrient analyser. Urea (120 kg/ha of N) and phosphate \n\n\n\nrock (80 kg/ha of P) were applied as N and phosphate sources, respectively. \n\n\n\nTreatments are listed in Table 1.  Plants were harvested after two months. Fresh \n\n\n\nand dry weights, height, root length, SPAD, chlorophyll content, leaf area, and \n\n\n\ntotal N and P were measured. Total chlorophyll (chlorophyll a and b) were \n\n\n\nextracted and measured as described by Coombs et al. (1985). The total N in the \n\n\n\nsamples was determined by Kjeldahl digestion and titration (Jones, 2001). The \n\n\n\ntotal P in the samples was extracted by the wet digestion method (Jones , 2001) \n\n\n\nand measured by inductively coupled plasma (ICP) optical emission spectrometry.  \n\n\n\nData were analysed by ANOVA, with means separated by Duncan\u2019s Multiple \n\n\n\nRange test (P \u2264 0.05). \n\n\n\nTABLE 1 \n\n\n\nTreatments of green mustard in greenhouse experiment \n\n\n\nTreatments N \n(4.3 g/pot) \n\n\n\nP \n(3.85 g/pot) \n\n\n\nN (2.15 g/pot) and P \n(1.93 g/pot) \n\n\n\nWithout N \nand P  \n\n\n\nM100  \n(50 mL/pot) \n \n\n\n\nT1 T4 T7 T10 \n\n\n\nM100/200 \n(50 mL/pot) \n \n\n\n\nT2 T5 T8 T11 \n\n\n\nControl T3 T6 T9 T12 \n\n\n\n8 \n \n\n\n\n\n\n\n\nFigure 1. Determination of the N2-fixing activity of the gamma-irradiated and \n\n\n\nnon-irradiated (A) A. calcoaceticus (M100) on yeast extract mannitol \n\n\n\nAgar (YMA) with bromothymol blue (BTB)  \n\n\n\nNotes: All values are expressed as means of four replications with one plate per \n\n\n\nreplication. Means followed by the same letter are insignificantly different \n\n\n\nfrom each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test .  \n\n\n\n\n\n\n\n\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n0 50 100 200 300 400Zo\nne\n\n\n\n S\niz\n\n\n\ne \n(c\n\n\n\nm\n) f\n\n\n\nor\n N\n\n\n\nitr\nog\n\n\n\nen\n fi\n\n\n\nxin\ng \n\n\n\nac\ntiv\n\n\n\nity\n \n\n\n\nfo\nr A\n\n\n\nci\nne\n\n\n\nto\nba\n\n\n\nct\ner\n\n\n\n c\nal\n\n\n\nco\nac\n\n\n\net\nic\n\n\n\nus\n  \n\n\n\nGamma irradiated dose (Gy) \n\n\n\nbc ab a \n\n\n\nc \nbc bc \n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n3\n\n\n\n0 50 100 200 300 400\n\n\n\nZo\nne\n\n\n\n Si\nze\n\n\n\n (c\nm\n\n\n\n) f\nor\n\n\n\n P\nho\n\n\n\nsp\nha\n\n\n\nte\n \n\n\n\nso\nlu\n\n\n\nbi\nlis\n\n\n\nin\ng \n\n\n\nac\ntiv\n\n\n\nity\n fo\n\n\n\nr A\ncin\n\n\n\net\nob\n\n\n\nac\nte\n\n\n\nr \nca\n\n\n\nlco\nac\n\n\n\net\nicu\n\n\n\ns \n\n\n\nGamma irradiated dose (Gy)\n\n\n\nab abc a\n\n\n\nd\nc bc\n\n\n\nFig. 1: Determination of the N2-fixing activity of the gamma-irradiated and non-\nirradiated (A) A. calcoaceticus (M100) on yeast extract mannitol Agar (YMA) with \n\n\n\nbromothymol blue (BTB) \nNotes: All values are expressed as means of four replications with one plate per \nreplication. Means followed by the same letter are insignificantly different from each \nother (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test .\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 153\n\n\n\n2.15 and 2.05 cm after irradiation with 0, 50, 100, 300 and 400 Gy, respectively \n(Figure 2). These findings reveal that Acinetobacter sp. could be improved as \nmultifunctional biofertiliser inoculants through gamma irradiation. \n  The plant growth-promoting activity of Acinetobacter sp. has been \nwidely reported recently. The Acinetobacter rhizosphaerae strain BIHB 723 from \nthe cold deserts of the Himalayas exhibited plant-growth-promoting attributes, \nnamely, inorganic and organic phosphate solubilisation, auxin production, \n1-aminocyclopropane-1-carboxylate deaminase activity, ammonia generation and \nsiderophore production. This isolate significantly increased the growth of pea, \nchickpea, maize and barley. In the field tests, the growth and yield of the pea \nplants significantly increased (Gulati et al. 2009). Sarode et al. (2009) reported \nthat the siderophore-producing A. calcoaceticus SCW 1, which was isolated \nfrom wheat rhizospheres of black cotton soils of the North Maharashtra region, \nimproved wheat plant growth in pot and field studies. The PGPR activities of \nthis isolate were phosphate solubilisation and IAA production. Endophytic \nA. calcoaceticus from coffee plant tissue produced phosphatase and IAA in \nvitro (Silva et al. 2012).  Acinetobacter schindleri nbri 05 tolerates arsenic-\ncontaminated environments by producing the plant growth-promoting substance \nIAA and siderophores (Srivastava and Singh 2014). A. calcoaceticus has also \nbeen reported as a gibberellin-producing species that promotes cucumber plant \ngrowth (Kang et al. 2012). These reports demonstrate that Acinetobacter sp. \nexhibits various plant-growth-promoting activities, such as plant growth hormone \nproduction, biological control effect, phosphate solubilisation, siderophore \nproduction or phytoremediation. 9 \n\n\n\n\n\n\n\n \nFigure 2. Determination of the phosphate-solubilising activity of gamma-\n\n\n\nirradiated and non-irradiated (A) A. calcoaceticus (M100) on phosphate-\n\n\n\nsolubilising yeast extract agar (PDYA) \n\n\n\nNotes: All values are expressed as means of four replications with one plate per \n\n\n\nreplication. Means followed by the same letter are insignificantly different \n\n\n\nfrom each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test .  \n\n\n\n \nThe plant growth-promoting activity of Acinetobacter sp. has been widely \n\n\n\nreported recently. The Acinetobacter rhizosphaerae strain BIHB 723 from the \n\n\n\ncold deserts of the Himalayas exhibited plant-growth-promoting attributes, \n\n\n\nnamely, inorganic and organic phosphate solubilisation, auxin production, 1-\n\n\n\naminocyclopropane-1-carboxylate deaminase activity, ammonia generation and \n\n\n\nsiderophore production. This isolate significantly increased the growth of pea, \n\n\n\nchickpea, maize and barley. In the field tests, the growth and yield of the pea \n\n\n\nplants significantly increased (Gulati et al. 2009). Sarode et al. (2009) reported \n\n\n\nthat the siderophore-producing A. calcoaceticus SCW 1, which was isolated from \n\n\n\nwheat rhizospheres of black cotton soils of the North Maharashtra region, \n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n3\n\n\n\n0 50 100 200 300 400\n\n\n\nZo\nne\n\n\n\n Si\nze\n\n\n\n (c\nm\n\n\n\n) f\nor\n\n\n\n P\nho\n\n\n\nsp\nha\n\n\n\nte\n \n\n\n\nso\nlu\n\n\n\nbi\nlis\n\n\n\nin\ng \n\n\n\nac\ntiv\n\n\n\nity\n fo\n\n\n\nr A\ncin\n\n\n\net\nob\n\n\n\nac\nte\n\n\n\nr \nca\n\n\n\nlco\nac\n\n\n\net\nicu\n\n\n\ns \n\n\n\nGamma irradiated dose (Gy)\n\n\n\nab abc a\n\n\n\nd\nc bc\n\n\n\nFig. 2: Determination of the phosphate-solubilising activity of gamma-irradiated \nand non-irradiated (A) A. calcoaceticus (M100) on phosphate-solubilising \n\n\n\nyeast extract agar (PDYA)\nNotes: All values are expressed as means of four replications with one plate per replication. \nMeans followed by the same letter are insignificantly different from each other (P \u2264 0.05) \nas determined by Duncan\u2019s Multiple Range test . \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019154\n\n\n\nNitrogen and Phosphorus Uptake of Choy Sum (B. chinensis) in a Greenhouse \nExperiment\nSoil N, P and K elements were 18.27, 27.51 and 190 mg kg-1 respectively while \nsoil pH was approximately 7.7. Highest plant heights were obtained from plants \ninoculated with T5 (mutant M100/200 with N source) and T8 (mutant M100/200 \nwith a combination of N and P sources) (Figure 3).  The T5 and T8 significantly \ndiffered from other treatments (P \u2264 0.05).  The plant height for T5 was 29.5 cm \nand for T8, it was 28.75 cm.  The plant heights for other treatments were in the \nrange of 18.25\u201325.5 cm. \n\n\n\n10 \n \n\n\n\nimproved wheat plant growth in pot and field studies. The PGPR activities of this \n\n\n\nisolate were phosphate solubilisation and IAA production. Endophytic A. \n\n\n\ncalcoaceticus from coffee plant tissue produced phosphatase and IAA in vitro \n\n\n\n(Silva et al. 2012). Acinetobacter schindleri nbri 05 tolerates arsenic-\n\n\n\ncontaminated environments by producing the plant growth-promoting substance \n\n\n\nIAA and siderophores (Srivastava and Singh 2014). A. calcoaceticus has also been \n\n\n\nreported as a gibberellin-producing species that promotes cucumber plant growth \n\n\n\n(Kang et al. 2012). These reports demonstrate that Acinetobacter sp. exhibits \n\n\n\nvarious plant-growth-promoting activities, such as plant growth hormone \n\n\n\nproduction, biological control effect, phosphate solubilisation, siderophore \n\n\n\nproduction or phytoremediation.  \n\n\n\nTABLE 2 \n\n\n\nBiofertiliser identification by 16S RNA, isolation sources and LD50 \n\n\n\nIsolates Isolation source Organism \nidentification \n\n\n\nGen Bank \naccession no. \n\n\n\nSimilarity \n(%) \n\n\n\nLD50 \nvalue \n(Gy) \n \n\n\n\nM100 Soil from paddy field: \nMADA, Kedah. \n\n\n\nAcinetobacter \ncalcoaceticus \n \n\n\n\nJF681282 97 448.5 \n\n\n\nM100/200 Radiated mutant at dose \n200 Gy. Nuclear \nMalaysia \n\n\n\nAcinetobacter \ncalcoaceticus  \n\n\n\nJF681282 94  \n\n\n\n \nNitrogen and Phosphorus Uptake of Choy Sum (B. chinensis) in a \nGreenhouse Experiment \n \nSoil N, P and K elements were 18.27, 27.51 and 190 mg kg-1 respectively while \n\n\n\nsoil pH was approximately 7.7. Highest plant heights were obtained from plants \n\n\n\ninoculated with T5 (mutant M100/200 with N source) and T8 (mutant M100/200 \n\n\n\nTABLE 2\nBiofertiliser identification by 16S RNA, isolation sources and LD50\n\n\n\n11 \n \n\n\n\nwith a combination of N and P sources) (Figure 3).  The T5 and T8 significantly \n\n\n\ndiffered from other treatments (P \u2264 0.05).  The plant height for T5 was 29.5 cm \n\n\n\nand for T8, it was 28.75 cm.  The plant heights for other treatments were in the \n\n\n\nrange of 18.25\u201325.5 cm.  \n\n\n\n\n\n\n\nFigure 3. Effects of A. calcoaceticus on green mustard height in a greenhouse \n\n\n\nexperiment \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \nFigures 4 and 5 show the top fresh and dry weights of the green mustard.  \n\n\n\nOnly T8 showed the highest (P \u2264 0.05) fresh (66.33 g) and dry (3.83 g) weights \n\n\n\namong treatments, followed by T5 with fresh and dry weights of 53.25 and 3.52 g, \n\n\n\nrespectively.  The lowest fresh weight was observed in T12 (no treatment), \n\n\n\nwhereas the lowest dry weight was observed in T3 (only N treatment). \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\nT1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12\n\n\n\nHe\nig\n\n\n\nht\n (c\n\n\n\nm\n) \n\n\n\nTreatments \n\n\n\nbc \nc \n\n\n\nbc \n\n\n\na \n\n\n\nbc \nab \n\n\n\na \n\n\n\nab \nbc bc \n\n\n\nc \nbc \n\n\n\n Fig. 3: Effects of A. calcoaceticus on green mustard height in a greenhouse experiment\n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \nper replication. Means followed by the same letter are insignificantly different from each \nother (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 155\n\n\n\n  Figures 4 and 5 show the top fresh and dry weights of the green mustard.  \nOnly T8 showed the highest (P \u2264 0.05) fresh (66.33 g) and dry (3.83 g) weights \namong treatments, followed by T5 with fresh and dry weights of 53.25 and 3.52 \ng, respectively.  The lowest fresh weight was observed in T12 (no treatment), \nwhereas the lowest dry weight was observed in T3 (only N treatment).\n\n\n\n12 \n \n\n\n\n\n\n\n\nFigure 4. Fresh weights of top part of green mustard in a greenhouse experiment \n\n\n\n \nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n\n\n\n\nFig. 4: Fresh weights of top part of green mustard in a greenhouse experiment.\n\n\n\n Fig. 5: Dry weights of the top parts of green mustard plants in a greenhouse experiment\n\n\n\n12 \n \n\n\n\n\n\n\n\nFigure 4. Fresh weights of top part of green mustard in a greenhouse experiment \n\n\n\n \nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n\n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \nper replication. Means followed by the same letter are insignificantly different from \neach other  (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test. \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each other \n(P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019156\n\n\n\n Similar results were obtained for root length.  The longest root lengths \nwere achieved by T5 and T8 (Figures 6).  The T5 (13.13 cm) and T8 (13.38 \ncm) achieved significantly longer (P \u2264 0.05) root lengths than those of the other \ntreatments (7.13 cm to 10.25 cm).  Figure 7 shows the highest (P \u2264 0.05) fresh \nweight of 4.9 g.  Dry weight results (Figure 8) of roots showed insignificant \ndifference (P \u2264 0.05) between T5 and T8.  The dry weights for T5 and T8 were \n0.75 and 0.97 g, respectively. \n \n\n\n\n13 \n \n\n\n\nFigure 5. Dry weights of the top parts of green mustard plants in a greenhouse \n\n\n\nexperiment \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n\n\n\n\nSimilar results were obtained for root length.  The longest root lengths \n\n\n\nwere achieved by T5 and T8 (Figures 6).  The T5 (13.13 cm) and T8 (13.38 cm) \n\n\n\nachieved significantly longer (P \u2264 0.05) root lengths than those of the other \n\n\n\ntreatments (7.13 cm to 10.25 cm).  Figure 7 shows the highest (P \u2264 0.05) fresh \n\n\n\nweight of 4.9 g.  Dry weight results (Figure 8) of roots showed insignificant \n\n\n\ndifference (P \u2264 0.05) between T5 and T8.  The dry weights for T5 and T8 were \n\n\n\n0.75 and 0.97 g, respectively.  \n\n\n\n\n\n\n\nFigure 6. Root length of green mustard plants in a greenhouse experiment \n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\n16\n\n\n\nT1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12\n\n\n\nRo\not\n\n\n\n le\nng\n\n\n\nth\n (c\n\n\n\nm\n) \n\n\n\nTreatments \n\n\n\nbcd \nab \n\n\n\ncd \n\n\n\nbc \n\n\n\na \n\n\n\ncd \n\n\n\nbc \n\n\n\na \n\n\n\ncd \nbcd \n\n\n\ncd \nd \n\n\n\nFig. 6: Root length of green mustard plants in a greenhouse experiment\n\n\n\n14 \n \n\n\n\n \nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \n \nFigure 7. Fresh weights of the roots of green mustard plants in a greenhouse \nexperiment \n\n\n\nFig. 7: Fresh weights of the roots of green mustard plants in a greenhouse experiment.\nNotes: All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each other \n(P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test. \n\n\n\nNotes: All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each other\n (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 157\n\n\n\n  Figure 9 shows the largest leaf area of 938.62 cm2 for T8.  The second \nand third largest leaf areas were observed for T5 and T7 (M100 with N and P), \naccounting for 808.21 and 761.99 cm2, respectively.  The smallest leaf area was \nobserved for T12 (335.29 cm2).\n\n\n\n15 \n \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \nFigure 8. Dry weights of the roots of green mustard plants in a greenhouse \n\n\n\nexperiment \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \nFigure 9 shows the largest leaf area of 938.62 cm2 for T8.  The second and \n\n\n\nthird largest leaf areas were observed for T5 and T7 (M100 with N and P), \n\n\n\naccounting for 808.21 and 761.99 cm2, respectively.  The smallest leaf area was \n\n\n\nobserved for T12 (335.29 cm2). \n\n\n\nFig. 8: Dry weights of the roots of green mustard plants in a greenhouse experiment.\nNotes: All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each \nother (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test.\n\n\n\n16 \n \n\n\n\n\n\n\n\nFigure 9. Leaf areas of green mustard plants in a greenhouse experiment \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \nFigure 10 shows that an insignificant difference (P \u2264 0.05) in SPAD \n\n\n\nreading was detected among T5 (38.30), T7 (38.33) and T8 (40.48).  These \n\n\n\ntreatments showed a higher SPAD reading than in other treatments.  The lowest \n\n\n\nSPAD reading was observed for T12 (27.38) followed by T3 (30.13).  Total \n\n\n\nchlorophyll obtained followed almost the same trend as the SPAD reading results.  \n\n\n\nThe chlorophyll contents for T7 (1.93 mg cm\u22122) and T8 (1.97 mg cm\u22122) did not \n\n\n\nsignificantly differ (Figure 11).  The lowest total chlorophyll was observed for \n\n\n\nT12 (0.76 mg cm\u22122), followed by T3 (1.01 mg cm\u22122). \n\n\n\nFig. 9: Leaf areas of green mustard plants in a greenhouse experiment.\n\n\n\nNotes: All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each \nother (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019158\n\n\n\n Figure 10 shows that an insignificant difference (P \u2264 0.05) in SPAD \nreading was detected among T5 (38.30), T7 (38.33) and T8 (40.48).  These \ntreatments showed a higher SPAD reading than in other treatments.  The lowest \nSPAD reading was observed for T12 (27.38) followed by T3 (30.13).  Total \nchlorophyll obtained followed almost the same trend as the SPAD reading results.  \nThe chlorophyll contents for T7 (1.93 mg cm-2) and T8 (1.97 mg cm-2) did not \nsignificantly differ (Figure 11).  The lowest total chlorophyll was observed for \nT12 (0.76 mg cm-2), followed by T3 (1.01 mg cm\u22122).\n\n\n\n17 \n \n\n\n\n\n\n\n\nFigure 10.  SPAD reading of green mustard plants in a greenhouse experiment \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \nFigure 11. Total chlorophyll contents of green mustard plants in a greenhouse \n\n\n\nexperiment \n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\nT1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12\n\n\n\nTo\nta\n\n\n\nl c\nhl\n\n\n\nor\nop\n\n\n\nhy\nll \n\n\n\n(m\ng \n\n\n\ncm\n-2\n\n\n\n) \n\n\n\nTreatments \n\n\n\nab \n\n\n\ncde \nef \n\n\n\ncd \n\n\n\nab \n\n\n\nde \n\n\n\na a \n\n\n\nbc \nab \n\n\n\na \n\n\n\nf \n\n\n\nFig. 11: Total chlorophyll contents of green mustard plants in a greenhouse experiment\n\n\n\n17 \n \n\n\n\n\n\n\n\nFigure 10.  SPAD reading of green mustard plants in a greenhouse experiment \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \nFigure 11. Total chlorophyll contents of green mustard plants in a greenhouse \n\n\n\nexperiment \n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\nT1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12\n\n\n\nTo\nta\n\n\n\nl c\nhl\n\n\n\nor\nop\n\n\n\nhy\nll \n\n\n\n(m\ng \n\n\n\ncm\n-2\n\n\n\n) \n\n\n\nTreatments \n\n\n\nab \n\n\n\ncde \nef \n\n\n\ncd \n\n\n\nab \n\n\n\nde \n\n\n\na a \n\n\n\nbc \nab \n\n\n\na \n\n\n\nf \n\n\n\nNotes: All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each \nother (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test.\n\n\n\nNotes: All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each \nother (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test.\n\n\n\nFig. 10:  SPAD reading of green mustard plants in a greenhouse experiment\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 159\n\n\n\n  An insignificant difference (P \u2264 0.05) in the total N content of the oven-\ndried samples for T4 (4.17 %), T5 (4.69 %), T6 (4.20%) and T7 (4.30 %) was \nobserved.  The difference in the total P of T5 (10.10 mg/L), T6 (9.72 mg/L), T7 \n(11.29 mg/L), T8 (9.82 mg/L) and T11 (9.59 mg/L) was insignificant (P \u2264 0.05).  \nFigure 12 shows that T5 attained the highest total N content, whilst Figure 13 \nshows that T7 achieved the highest total P content of 11.29 mg/L. \n\n\n\nNotes: All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each \nother (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test.\n\n\n\nNotes: All values are expressed as the means of four replications with one seedling per \nreplication. Means followed by the same letter are insignificantly different from each \nother (P \u2264 0.05) as determined by Duncan\u2019s Multiple Range test.\n\n\n\n18 \n \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \nAn insignificant difference (P \u2264 0.05) in the total N content of the oven-\n\n\n\ndried samples for T4 (4.17 %), T5 (4.69 %), T6 (4.20%) and T7 (4.30 %) was \n\n\n\nobserved.  The difference in the total P of T5 (10.10 mg/L), T6 (9.72 mg/L), T7 \n\n\n\n(11.29 mg/L), T8 (9.82 mg/L) and T11 (9.59 mg/L) was insignificant (P \u2264 0.05).  \n\n\n\nFigure 12 shows that T5 attained the highest total N content, whilst Figure 13 \n\n\n\nshows that T7 achieved the highest total P content of 11.29 mg/L.  \n\n\n\n\n\n\n\nFigure 12. Total N of green mustard plants in a greenhouse experiment \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\nFig. 12: Total N of green mustard plants in a greenhouse experiment.\n\n\n\n19 \n \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n \nFigure 13: Total P contents of green mustard plants in a greenhouse experiment \n\n\n\nNotes:  All values are expressed as the means of four replications with one seedling \n\n\n\nper replication. Means followed by the same letter are insignificantly \n\n\n\ndifferent from each other (P \u2264 0.05) as determined by Duncan\u2019s Multiple \n\n\n\nRange test .  \n\n\n\n\n\n\n\nOverall, T8 (mutant A. calcoaceticus M100/200 with N and P sources) and \n\n\n\nT5 (mutant A. calcoaceticus M100/200 with P source only) exhibited superior \n\n\n\ngrowth and nutrient uptake compared with those of the other treatments.  \n\n\n\nAmongst the functions of biofertilisers, N2 fixation and phosphate \n\n\n\nsolubilisation play important roles in plant growth. Studies on the effect of \n\n\n\nphosphate solubilisation on N2 fixation (Afzal and Bano 2008; Linu et al. 2009; \n\n\n\nFig. 13: Total P contents of green mustard plants in a greenhouse experiment.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019160\n\n\n\n Overall, T8 (mutant A. calcoaceticus M100/200 with N and P sources) \nand T5 (mutant A. calcoaceticus M100/200 with P source only) exhibited superior \ngrowth and nutrient uptake compared with those of the other treatments. \n Amongst the functions of biofertilisers, N2 fixation and phosphate \nsolubilisation play important roles in plant growth. Studies on the effect of \nphosphate solubilisation on N2 fixation (Afzal and Bano 2008; Linu et al. 2009; \nArgaw 2012 and Wahid et al. 2016) have demonstrated that phosphate solubilising \nmicroorganisms influence nutrient uptake and root zone biodiversity. \n The effects of wild-type and mutant A. calcoaceticus strains on N2 and \nphosphorus uptake of green mustard were tested in a greenhouse experiment in \nthis study. A similar report observes that a combination of N2 fixing bacteria and \nphosphate solubiliser increases the chlorophyll content, leaf area index, total root \nlength and root volume of rice grown under aerobic conditions than those of the \ncontrol (Nur\u2019ain et al. 2017). A. calcoaceticus used in this study has both N2 \nfixing and phosphate-solubilising activities. Overall, treatment with T8 (mutant \nM100/200 with the combination of N and P sources) and T5 (mutant M100/200 \nwith P sources) was found to lead to better plant heights, top fresh and dry weights, \nroot and leaf growth and chlorophyll contents compared to other treatments. \n Chlorophyll content and leaf area were correlated with the nutrient uptake \nof N. The majority of leaf N accumulated in the chloroplast where photosynthesis \noccurs, resulting in a strong association between plant photosynthesis and leaf \nN status. Leaf N can be rapidly estimated by using an SPAD chlorophyll meter \n(Zakeri et al. 2015), a non-destructive indirect method for evaluating plant N \nstatus in real time. However, the relationship between chlorophyll concentration \nand SPAD values is not always linear (Ferreira et al. 2015). Conversely, other \nstudies have demonstrated that the relationship amongst SPAD reading, \nchlorophyll content and leaf N content per leaf area is linear. In some cases, this \nrelationship is affected by environmental factors and leaf features of crop species \n(Xiong et al. 2015). In our study, total chlorophyll content was linearly related to \nSPAD reading. Leaf area was also linearly associated with chlorophyll content \nand SPAD reading. This linear result could be due to the growth of green mustard \nin a greenhouse which was a controlled environment. Both wild-type  (M100) and \nmutant (M100/200) showed significant chlorophyll content, SPAD reading and \nleaf area compared with those in the other treatments, indicating that wild-type  \nand mutant treatment had N2 fixation activities that enhanced the growth of leafy \nparts. \n The root systems of the wild-type and the mutant supplied with P had \nsignificant growth. However, these treatments provided additional N but promoted \ninsignificant growth. The results revealed that phosphate solubilisation with P \nsource played an important role. Phosphate is essential for root growth. Good \nroot systems enhance leaf growth, thereby increasing chlorophyll and N uptake. \nAfzal and Bano (2008) also reported that a single inoculation of Rhizobium \n(symbiotic N) cannot increase grain yield, but the dual inoculation of Rhizobium \nand phosphate solubilising bacteria increases the yield by 29% and 25% with \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 161\n\n\n\nand without fertiliser, respectively. Seed inoculation of cowpea by phosphate \nsolubilisers improves its nodulation, root and shoot biomasses, straw and grain \nyields and P and N uptakes (Linu et al. 2009). \n A. calcoaceticus enhanced the growth of hazelnut seedlings and increased \nN, P, K, Ca, Mg, Fe, Cu, Mn, Zn, B and Al concentrations in plant tissues (Erturk \net al. 2011). In our study, A. calcoaceticus increased N and P uptake. Overall, \nthe total N and P of the plant and soil samples treated with the mutant strain and \nphosphate rock (T5) or the wild-type strain with urea and phosphate rock (T7) \nwere significantly high. Thus, the combination of biofertiliser with additional \nnutrient sources could enhance plant nutrient uptake. The treatment with the \nmutant strain and urea mixed with phosphate rock (T8) was the best combination \ntreatment as shown by physical measurements.\n\n\n\nCONCLUSIONS\nIn conclusion, Acinetobacter sp. could be improved as a multifunctional \nbiofertiliser inoculant through gamma irradiation.   The mutant from gamma-\nirradiation, A. calcoaceticus M100/200, showed superior N2-fixation and \nphosphate solubilisation in comparison to the wild-type in vitro and under a \ngreenhouse experiment on green mustard plants. For future investigations, 15N \nisotope could be considered for quantitative N determination to elucidate the \ninfluence of P on N utilisation and plant development. \n\n\n\nACKNOWLEDGEMENTS\nThe authors wish to express their gratitude to Universiti Putra Malaysia, Malaysian \nNuclear Agency (Nuclear Malaysia), Ministry of Energy, Science, Technology, \nEnvironment and Climate Change (MESTECC) and the Public Service \nDepartment (JPA) for technical and financial support. Technical assistance from \nDr. Ahmad Zainuri B. Mohd Dzomir, Latiffah Norddin, Abdul Razak Ruslan, \nHazlina Abdullah, Shuhaimi B. Shamsudin, Sharpudin Md Nor and Nur Fariz Bt. \nHj Sulaiman is greatly appreciated.\n\n\n\nREFERENCES\nAfzal, A. and A. Bano. 2008.  Rhizobium and phosphate solubilizing bacteria improve \n\n\n\nthe yield and phosphorus uptake in wheat (Triticum aestivum). International \nJournal of Agriculture and Biology 10: 85-88.\n\n\n\nAndhika, P. and R. Agustini.  2017.  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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n111 \n\n\n\n\n\n\n\nPlant Growth-Promoting Rhizobacteria (PGPR) and Humic Acid \n\n\n\nAmendment Improves N-use Efficiency in Sweet Potato  \n \n\n\n\nBuraq Musa Sadeq\n1\n, Ali Tan Kee Zuan\n\n\n\n1\n*, Nur Maizatul Idayu Othman\n\n\n\n2\n, \n\n\n\nJawadyn Talib Alkooranee\n3\n, Wong Mui Yun\n\n\n\n4\n, Susilawati Kasim\n\n\n\n1\n, Amaily Akter\n\n\n\n1\n, \n\n\n\nSayma Serine Chompa\n1\n and Md Ekhlasur Rahman\n\n\n\n1,5 \n\n\n\n \n1Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400 UPM \n\n\n\nSerdang, Selangor, Malaysia  \n2Faculty of Plantation and Agrotechnology, Universiti Teknologi Mara, Melaka, Kampus Jasin 77300 \n\n\n\nMerlimau, Melaka \n3Department of Plant Protection, Faculty of Agriculture, University of Wasit, Wasit, Iraq  \n\n\n\n4Department of Plant Protection, Faculty of Agriculture, Universiti Putra Malaysia, 43400 UPM \n\n\n\nSerdang, Selangor, Malaysia  \n\n\n\n5Divisional Laboratory, Soil Resource Development Institute, Krishi Khamar Sharak, Farmgate, \nDhaka-1215, Bangladesh  \n\n\n\n\n\n\n\n*Correspondence: tkz@upm.edu.my \n\n\n\n\n\n\n\nABSTRACT \n\n\n\nThis study aimed to verify the effect of Plant Growth-Promoting Rhizobacteria (PGPR) and humic acid \n\n\n\namendment with different N-fertilizer rates on sweet potato. The results showed that inoculation of \n\n\n\nUPMRB9 in combination with 50% fertilizer-N produced significantly higher dry matter (DM) in \n\n\n\nleaves, storage, and fibrous roots, and in the whole plant. Similar trends were observed for nitrogen \n\n\n\nuse efficiency (NUE), in which 75% N+UPMB10 and 50% N+UPMRB9 treatments significantly \nproduced higher efficiency by 44.89% and 40%, respectively. In addition, the highest OD of \u03b2-carotene, \n\n\n\n0.71 and 0.68 mg g-1, were observed in 50% N+UPMRB9 and 75% N+UPMB10, respectively. Sweet \n\n\n\npotato plants obtained greater NUE and N uptake when lower rates of N were used with the microbial \ninoculations. These findings show the ability of PGPR-HA to fix nitrogen and thereby increase N \n\n\n\navailability of soils, reducing the need to provide mineral nitrogen to crops. Thus, applying biofertilizer \n\n\n\ncontaining PGPR amended with humic acid could be a sustainable approach to improving the NUE \n\n\n\nand total N concentration of sweet potato plants. Higher N use efficiency will lead to savings in the \namount of N fertilizer needed, thus reducing costs and promoting an eco-friendly approach at the same \n\n\n\ntime. \n\n\n\n\n\n\n\nKey words: PGPR, humic acid, NUE, sweet potato, and \u03b2-carotene \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\nThe sweet potato [Ipomoea batatas (L.) Lam] is an essential tuberous crop grown in Central \n\n\n\nand South America. Aside from cassava being a primary source of starch, the sweet potato \n\n\n\nplant has a long legacy of being farmed as a starch food in Malaysia (Karim et al. 2022). Sweet \n\n\n\npotato output in Malaysia has decreased owing to land scarcity and conversion of agricultural \n\n\n\nland to industrial purposes, higher labour expenses, marketing challenges, disease outbreaks, \n\n\n\nand pricey inputs such as fertilizer (Yasmin et al. 2020). The sweet potato plant is a short-\n\n\n\nseason crop that demands inorganic fertilizers to promote quicker nitrogen release into the \n\n\n\nsoil (Singh et al. 2022). An increase in world food supply depends on a massive use of chemical \n\n\n\nfertilizers with the unfortunate consequence of environmental degradation (Zago et al. 2019). \n\n\n\nExcess nitrogen is inefficient and may result in several problems: environmental degradation; \n\n\n\nincreased agricultural production costs; loss of vital soil microorganisms; and water \n\n\n\neutrophication. A strategy for increasing output is to use friendly microbes such as plant \n\n\n\ngrowth-promoting rhizobacteria (PGPR) (Zhao et al. 2022). The application of beneficial \n\n\n\nbacteria as biofertilizers is becoming crucial in agricultural production because of their \n\n\n\n\nmailto:tkz@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n112 \n\n\n\n\n\n\n\npotential to enhance consumer safety and sustainable crop production (Ammar et al. 2022). \n\n\n\nThese PGPRs are collected due to their beneficial biochemical and morphological \n\n\n\ncharacteristics like N2-fixing ability and solubilising phosphate and potassium, siderophores, \n\n\n\nand pectinase. Additionally, it has been noted that these PGPR strains increase the levels of N, \n\n\n\nP, and K in shoots and storage roots (Ali-Tan et al. 2017; Shultana et al. 2021). \n\n\n\n\n\n\n\nHumic acid (HA) is a naturally occurring chemical in soil. It is a bioproduct of organic matter \n\n\n\nbreakdown that has been effectively used in crop cultivation (Tang et al. 2022). Ekin\u2019s study \n\n\n\n(2020) reported on several direct effects of HA on plant growth. These are: (i) increased \n\n\n\nmacronutrient and micronutrient uptake and root expansion; (ii) improved microbial growth by \n\n\n\nproviding a carbon source that serves as food for microbes; and(iii) increased water retention. \n\n\n\nA study by Chen et al. (2017) showed that applying humic acid nitrogen fertilizer (HA-N) \n\n\n\nincreases the number of storage roots per plant, total fresh weight per storage root, and the yield \n\n\n\nby 30%.  The inclusion of HA-PGPRs might potentially aid in the colonisation of roots by local \n\n\n\nmycorrhizal fungi. Sweet potatoes are one of the most important root crops worldwide, as they \n\n\n\nprovide food and feed for people and animals and various raw materials for the agricultural \n\n\n\nsector (Neela and Fanta 2019). \n\n\n\n\n\n\n\nNitrogen usage efficiency (NUE), describes how well a plant uses applied or fixed nitrogen to \n\n\n\nproduce biomass. It further describes the proportion of crop output to the quantity of nitrogen \n\n\n\nreceived through soil roots or from the atmosphere through bacterial fixation (Ullah et al. \n\n\n\n2019). There are several ways to improve nitrogen use efficiency in plants. One way is to use \n\n\n\nnitrogen fertilizers with bacteria that can fix nitrogen from the atmosphere and make it \n\n\n\navailable to plants. Another is to use slow-release fertilizers that release nitrogen over a longer \n\n\n\nperiod, reducing the amount of fertilizer needed and reducing the environmental impact of \n\n\n\nnitrogen fertilizer use (Sharma and Bali 2017). Crop rotation and intercropping can also help \n\n\n\nimprove nitrogen use efficiency by reducing the amount of nitrogen lost from the soil and \n\n\n\nincreasing the amount of nitrogen available to plants (Zhu et al. 2023). Tolessa (2019) states   \n\n\n\nthat NUE is principally dependent on the presence of soil nitrogen, its uptake and integration, \n\n\n\nphotosynthetic and nutrient supply, carbon-nitrogen flow, nitrate transmission, and regulation \n\n\n\nby light and hormones. It is possible to calculate NUE by using either the nitrogen taken in or \n\n\n\nthe nitrogen used to build tubers. NUE may additionally be determined by physiological and \n\n\n\nagronomic criteria based on apparent nitrogen recovery. The crop, harvest product, and \n\n\n\ntechniques influence the best approach to determining NUE (Di Gioia et al. 2017). \n\n\n\n\n\n\n\nIt is well known that sweet potato plants, especially the tubers, produce the pigment \n\n\n\ncarotenoids. One of the carotenoids is the \u03b2-carotene, which has antioxidant capabilities, crucial \n\n\n\nfor human nutrition and health (Atmini et al. 2022). \u03b2-carotene, known as provitamin A, is the \n\n\n\nmost common carotenoid in fruits with the highest vitamin A activity. UV-Vis spectroscopy \n\n\n\nhas been found to be the best method to determine the content of \u03b2-carotene, in terms of cost-\n\n\n\neffectiveness, availability, versatility, simplicity, and speed (Safdarian et al. 2021). In this \n\n\n\nmethod, determining the analyte concentration in the unknown sample is very simple and fast. \n\n\n\nHowever, measuring the analytes in complex samples is difficult, and sample preparation, \n\n\n\nextraction, and pre-concentration steps are required before measurement (Drapal and Fraser \n\n\n\n2019). Although the amount of \u03b2-carotene in commercial fruit juices may be higher than the \n\n\n\ndetection limit of a UV-Vis spectroscopy method, the complexity of the matrix in these samples \n\n\n\ndoes not allow for its direct measurement (Biswas et al. 2011). Methods of extraction used \n\n\n\nbefore the final spectrophotometric determination of analytes include solvent extraction and \n\n\n\npartitioning. In the present study, beneficial bacteria addition with humic acid (HA-PGPR) and \n\n\n\nN-fertilizer aimed to support N concentration in soil and plant tissue, N uptake, NUE, and the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n113 \n\n\n\n\n\n\n\ncontent of b-carotene in sweet potato plants. This method can potentially increase food security, \n\n\n\nparticularly in developing countries. This study describes a pot trial conducted with two locally \n\n\n\nisolated bacteria (Bacillus tequilensis and Bacillus subtilis) with 0.1 % humic acid amendment \n\n\n\nand three different rates of N-fertilizer (0, 50, and 75 %) under glasshouse conditions. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nExperimental Site  \n\n\n\nThis glasshouse pot trial was conducted at an experimental site, located at field 15 (03 \u030a00\u0301 12.6 \u030b\n\n\n\nN; 101\u030a 47\u0301 22.4\u030b E and 56.8m above sea level), in the Agriculture Faculty, UPM, on the West \n\n\n\ncoast of Peninsular Malaysia.  Throughout the experiment, the site experienced a humid and \n\n\n\nhot climate, with an average lowest temperature of about 23.5\u00b0C, average high temperature of \n\n\n\n37\u00b0C and a relative humidity of 76.67 %.  \n\n\n\n\n\n\n\nPGPR and Humic Acid Collection and Preparation   \nBacillus tequilensis (UPMRB9) and Bacillus subtilis (UPMB10) are two locally isolated plant \n\n\n\ngrowth-promoting rhizobacteria (PGPR) collected from the Microbiology Laboratory, \n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra Malaysia. \n\n\n\nTechnical humic acid was purchased from the Sigma-Aldrich Swiss brand products (code \n\n\n\n102098564 and 53680-50G). Humic acid, which is black in colour, solubilises slowly in water, \n\n\n\nand has a pH of less than 6; it is soluble in alkali but insoluble in acid and can be adjusted to \n\n\n\npH 7 by using tryptic soy broth media (TSB) which has a pH of 7.23. The characterisation of \n\n\n\nthe physical and chemical properties of humic acid (HA) are as shown in Table 1. \n\n\n\nApproximately 0.1 g of HA was weighed and transferred into 500-mL Erlenmeyer flasks. TSB \n\n\n\nmedia was used to prepare the liquid formulation. About 100 ml of TSB was transferred and \n\n\n\nautoclaved for 15 min at 121\u00b0C. One full loop (approximately 1\u00d7106 CFU ml-1) was taken from \n\n\n\nBacillus tequilensis and Bacillus subtilis strains and dipped into broth media, then incubated \n\n\n\nunder a constant shaker at 150 rpm for 48 h at 30\u00b0C. Each sample was replicated three times. \n\n\n\n\n\n\n\nSoil Collection and Plant Preparation \n\n\n\nMineral soil was collected from Ladang Kongsi, Taman Pertanian Universiti, Universiti Putra \n\n\n\nMalaysia, Seri Kembangan, Selangor, Malaysia, located at 30 02\u2032 N latitude, 1010 42\u2032 E \n\n\n\nlongitude, and 31 m above sea level. The soil was classified as sandy-clay-textured. Ten grams \n\n\n\nof air-dried soil samples were weighed and ground to pass through a 2-mm sieve. Physical and \n\n\n\nchemical soil properties were characterised as shown in Table 1. Thirty-six plastic containers \n\n\n\n(23 cm high and 26 cm in inner diameter) were filled with 7 kg of soil. Four holes were drilled \n\n\n\nat the bottom of each container to permit flow-out of excess water. Before planting, Sepang \n\n\n\nOren cuttings of the sweet potato variety (30 cm long with 8 nodes) were thoroughly cleaned \n\n\n\nusing sterilized distilled water and soaked in 48-h-old PGPRs-HA formulations for 6-7 h. At \n\n\n\nplanting and every 2-week intervals, 20 mL (approximately 109 CFUmL-1) of PGPRs-HA \n\n\n\nformulations were inoculated onto each plant. The control plants received the same volume of \n\n\n\nsterile media but without bacteria. NPK fertilizers were applied following the recommendation \n\n\n\nof Pedram (Kashiani 2012). Urea for nitrogen fertilizer with three levels (0, 50, and 75%), triple \n\n\n\nsuperphosphate (TSP), and muriate of potash (MOP) were used based on the recommended \n\n\n\nrates; urea was applied at 2.16g (50%), and 3.24g (75%). Weeding of pots was handled \n\n\n\nmanually as soon as the weeds appeared, and Mapa Malathon 57 was applied twice during the \n\n\n\ncultivation period to control the insects on sweet potato plants. The experiment was conducted \n\n\n\nfrom March 2022 to June 2022. The sweet potato plants were harvested after 110 days. \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n114 \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nSelected physical and chemical properties of experimental soil and humic acid. \n\n\n\n\n\n\n\nExperimental Layout and Treatments \n\n\n\nAn incubation experiment was arranged in a randomized complete block design (RCBD) with \n\n\n\ntwelve treatments and three replicates. The detailed treatments are as follows: \n\n\n\nT1 =   0%   Nitrogen fertilizer (uninoculated) \n\n\n\nT2 =   0%   Nitrogen fertilizer + UPMB10 \n\n\n\nT3 =   0%   Nitrogen fertilizer + UPMRB9 \n\n\n\nT4= 50%   Nitrogen fertilizer (uninoculated) \n\n\n\nT5 = 50%   Nitrogen fertilizer + UPMB10 \n\n\n\nT6= 50%   Nitrogen fertilizer + UPMRB9 \n\n\n\nT7 = 75%   Nitrogen fertilizer (uninoculated) \n\n\n\nT8= 75%   Nitrogen fertilizer + UPMB10 \n\n\n\nT9 = 75%   Nitrogen fertilizer + UPMRB9 \n\n\n\n\n\n\n\nPlant Measurements and Sampling \n\n\n\nAfter harvest, the plants were washed and cleaned under running tap water and separated into \n\n\n\nshoot, fibrous, and storage roots. The plant parts were separately dried in an oven with forced-\n\n\n\nair circulation at 70\u00b0C for 3 days until constant weight was reached. After drying, the samples \n\n\n\nwere weighed using a digital weighing machine to determine the amount of DM accumulated \n\n\n\nin each plant part and the whole plant. Plant storage roots were harvested, brushed, counted, \n\n\n\nand classified according to the Brazilian classification proposed by Da Silva et al. (1995) \n\n\n\nStorage roots were then weighed to determine storage root yield. \n\n\n\n\n\n\n\nNitrogen Determination of the Post-Harvest Soil and Plant Tissues \n\n\n\nThe micro-Kjeldahl method was used to determine total N content in sweet potato soil, leaves \n\n\n\nand storage roots. This was followed by adding concentrated H2SO4, K2SO4, and catalyst, \n\n\n\nfollowed by digestion for 1 h at 360\u00b0C using block digestion following Bowman et al. (1988). \n\n\n\n\n\n\n\nN Uptake Efficiency by Plant and N Removal  \n\n\n\nN uptake efficiency of the storage root yield was calculated for each cover crop treatment by \n\n\n\ndividing the storage root yield with the total amount of N taken up by the plants. Nitrogen \n\n\n\nremoval was determined by multiplying N concentration of storage roots by the amount of DM \n\n\n\nin the storage roots (Fernandes et al. 2018). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProperties Soil Properties Humic acid \n\n\n\nTextural Class Sandy clay Color Black \n\n\n\n% Sand 67.7 % Solubility Slow \n\n\n\n% Silt 5.5% Molecular Formula C9H9NO6 \n\n\n\n% Clay 27.6% Molecular Weight 227.17 \n\n\n\npH 5.17 pH 3.5- 6.0 \n\n\n\nEC (mS/cm) 17.78 Moisture 15 % \n\n\n\nCEC (cmolc kg\u22121) 5.82 CEC (cmolc kg\u22121) 70-166 \n\n\n\nTotal C (%) 1.75% Organic C 30-50% \n\n\n\nTotal N (%) 0.05% Nitrogen 3% \n\n\n\nTotal S (%) 0.04% Hydrogen 5% \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n115 \n\n\n\n\n\n\n\nN-fertilizer use efficiency (NUE)  \nN-fertilizer use efficiency of the sweet potato plant was calculated after obtaining data on \n\n\n\nproductivity and N uptake for each treatment, according to the methodology of Oliver and \n\n\n\nAlmeida (2018) based on the following formulae: \n\n\n\n\n\n\n\nNitrogen use efficiency (NUE) of the applied nutrient is the relation between total N uptake \n\n\n\nby the whole plant from fertilized plots and the amount of applied N (g kg-1). \n\n\n\n\n\n\n\nNUE = (NFP - NC) / R \n\n\n\n\n\n\n\nwhere NFP is the total N uptake by whole plant from N-fertilized plots, NC is the uptake of N \n\n\n\nfrom control pots, and R is N-fertilizer dose applied in the treated pots. \n\n\n\n\n\n\n\nPartial factor of productivity (PFP) of the applied nutrient is the relation between productivity \n\n\n\n(g kg-1) and the amount of applied N (g kg-1). \n\n\n\n\n\n\n\nPFP = P/R \n\n\n\n\n\n\n\nwhere P is productivity of treated pots and R is N-fertilizer rates applied on treated pots. \n\n\n\n\n\n\n\nEfficiency of recovery (ER) of the applied nutrient is the relation between N uptake in tubers \n\n\n\n(g kg-1) and the amount of applied N (g kg-1). \n\n\n\n\n\n\n\nER = (NFT - NC) / R \n\n\n\n\n\n\n\nwhere NFT is N uptake of tubers in the treated pots, NC is uptake of N from control pots, and R \n\n\n\nis N fertilizer dose applied in the treated pots. \n\n\n\n\n\n\n\nN-Utilisation Efficiency (UtE) of the applied nutrient is the relation between the yield (g kg-\n1) and the amount of N uptake by the whole plant from fertilized plots (g kg-1). \n\n\n\n\n\n\n\nUtE = Y/NFP \n\n\n\n\n\n\n\nwhere Y is fresh yield of root tubers in treated pots and NFP is amount of N uptake by whole \n\n\n\nplant in treated pots. \n\n\n\n\n\n\n\nPhysiological efficiency (PE) of the applied N is the relation between the increase in \n\n\n\nproductivity (g kg-1) and the increase in N uptake by the whole plant (g kg-1). \n\n\n\n\n\n\n\nPE = (PF - PC) / (NFP - NC) \n\n\n\n\n\n\n\nwhere PF is productivity of treated pots, PC is productivity of control pots, NFP is N uptake of \n\n\n\ntreated pots, and NC is N uptake of control pots. \n\n\n\n\n\n\n\nHarvest index (HI) is the relation between the amount of N uptake by the tuber (g kg-1) and \n\n\n\nthe amount of N uptake by the whole plant (g kg-1). \n\n\n\n\n\n\n\nHI = (NFT / NFP) x 100 \n\n\n\nwhere NFT is amount of N uptake by tuber in treated pots and NFP is amount of N uptake by \n\n\n\nwhole plant in treated pots. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n116 \n\n\n\n\n\n\n\n\u03b2-Carotene Content Detection  \n\u03b2-carotene content was determined using a UV-Vis spectrophotometer to measure the optical \n\n\n\ndensity, according to Biswas et al. (2011). Acetone solution was used to prepare the \u03b2-carotene \n\n\n\nsample concentration. Working standard solutions were prepared daily in the same solvent and \n\n\n\nused to spike sweet potato samples. Frozen samples were thawed overnight in a refrigerator (4 \n\n\n\n\u00b1 1\u00b0C). The outer, thinner layer (skin or cuticle) was removed, and the remaining portions were \n\n\n\nsliced. The components obtained were then ground separately in a spice grinder until they \n\n\n\nbecame a fine paste. For extraction, a representative portion of this sample, 1 g, was accurately \n\n\n\nweighed in a glass test tube. Then 5 ml of chilled acetone was added, and the tube was held for \n\n\n\n15 min with occasional shaking at 4 \u00b1 1\u00b0C, vortexed at high speed for 10 min, and centrifuged \n\n\n\nat 1370 x g for 10 min. The supernatant was collected into a separate test tube, and the \n\n\n\ncompound was re-extracted with 5 ml of acetone, followed by centrifugation as above. Both \n\n\n\nsupernatants were pooled and then passed through Whatman filter paper No. 42. The \n\n\n\nabsorbance of the extract was determined at a 449 nm wavelength. The pure solvent (acetone)-\n\n\n\nbased calibration curve of \u03b2-carotene was generated by plotting the O.D. value versus the \n\n\n\nanalyte concentration, and linear regression analysis was performed using Microsoft Excel. \n\n\n\n\n\n\n\nStatistical Analysis  \n\n\n\nSAS 9.4 Statistical Analysis System was used to analyse the data using the Least Significant \n\n\n\nDifference (LSD) comparison technique at p = 0.05. Differences between treatment means \n\n\n\nwere identified using the analysis of variance procedure (ANOVA). \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nEffect of Treatments on Sweet Potato Dry Matter and Yield-Contributing Parameters \nInoculation of PGPRs-HA formulations and application of differing nitrogen fertilization rates \n\n\n\nresulted in significant differences (P\u2264 0.05) in sweet potato contributing parameters (Table 2). \n\n\n\nDry matter (DM) amounts of the leaves, fibrous roots, storage roots, and the whole sweet potato \n\n\n\nplant were significantly affected. The highest amounts of DM in shoots, fibrous roots, and the \n\n\n\nwhole plant were observed in T6 at 30.57 g kg-1, 9.5 g kg-1, and 83.63 g kg-1, respectively, \n\n\n\nwhile the lowest amount was found in T1 (control) compared with all other treatments. \n\n\n\nStatistically, significant differences were not found in T6 and T8 treatments with regard to DM \n\n\n\nof storage roots, which saw a significant increase in both treatments at 43.58 g kg-1 and 40.34 \n\n\n\ng kg-1, respectively. Treatment T1 (control) showed the lowest values of DM in storage roots. \n\n\n\nThe highest significant increase was observed in T6 at (542 g kg-1), followed by T8 at (455 g \n\n\n\nkg-1), in storage root yield compared to the lowest treatment, T1 (control)(Table 2). \n\n\n\n\n\n\n\nEffect of Treatments on Nitrogen Nutritional Status, N Uptake, and Removal  \n\n\n\nTreatments had a significant (P\u2264 0.05) impact on total N concentration in post-harvest soil and \n\n\n\nplant tissue after the inoculation of PGPR-HA formulations (Figure 1). Results of UPMRB9 \n\n\n\n+50% N-treated sweet potato plants showed a higher concentration of N, followed by treatment \n\n\n\nof UPMB10 +75% N at 0.28% and 0.26% for soil and at 1.86% and 1.85% for storage roots, \n\n\n\nrespectively. Control 0% N which had the lowest treatment had no tubers growing at 0% and \n\n\n\n0.05%. Comparing UPMRB9 + 50% N to the control treatments, total N in soil and storage \n\n\n\nroots was found to be highest, with an increase of 82.14% and 100%, respectively, Leaf N \n\n\n\ncontent was significantly higher with inoculation of UPMB10 (75% N) at 3.1% compared to \n\n\n\nthe lowest control treatments (0% N) at 0.19%. Highest total N content in the leaf increased by \n\n\n\n93.87% from UPMRB9 + 50% N compared to the control treatments. There was a significant \n\n\n\ndifference in the plant\u2019s nitrogen uptake of sweet potatoes, as represented in Figure 2. The \n\n\n\naddition of PGPR-HA and N-rate fertilization influenced the uptake. The highest uptake of N \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n117 \n\n\n\n\n\n\n\ne\n\n\n\nd\nd\n\n\n\nd\n\n\n\nc\n\n\n\na\n\n\n\nd\n\n\n\nab\n\n\n\nbc\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n3\n\n\n\n3.5\n\n\n\n0 % 5 0 % 7 5 %\n\n\n\nN\n c\n\n\n\no\nn\nte\n\n\n\nn\nt \n\n\n\n(%\n)\n\n\n\nN Levels\n\n\n\nL e af  N  c onte nt  \n\n\n\ncontrol UPMB10 UPMRB9\n\n\n\nf\n\n\n\ne cd\n\n\n\nd\n\n\n\nab\n\n\n\na\n\n\n\ncde\n\n\n\na\n\n\n\nbc\n\n\n\n0\n\n\n\n0.05\n\n\n\n0.1\n\n\n\n0.15\n\n\n\n0.2\n\n\n\n0.25\n\n\n\n0.3\n\n\n\n0.35\n\n\n\n0 % 5 0 % 7 5 %\n\n\n\nN\n c\n\n\n\no\nn\nte\n\n\n\nn\nt \n\n\n\n(%\n)\n\n\n\nN Levels\n\n\n\nSoi l  N  c onte nt  \n\n\n\ncontrol UPMB10 UPMRB9\n\n\n\ne\n\n\n\nc\nbcd\n\n\n\nc\n\n\n\na\n\n\n\nc\n\n\n\na\n\n\n\nb\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n0 % 5 0 % 7 5 %\n\n\n\nN\n c\n\n\n\no\nn\nte\n\n\n\nn\nt \n\n\n\n(%\n)\n\n\n\nN Levels   \n\n\n\nStor age  r oot  N  c onte nt  \n\n\n\ncontrol UPMB10 UPMRB9\n\n\n\n(2.0027 g plant\u22121) was obtained from UPMRB9 +50% N followed by 2.0012 g plant\u22121 from \n\n\n\nUPMB10 +75% N, respectively. \n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nEffects of treatments on dry matter of sweet potato \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Effect of treatments on soil, storage root and leaf N content. Means with the same letters \nare not significantly different based on LSD (0.05) \n\n\n\nTreatments Shoot DM  \n\n\n\nyield \n\n\n\nFibrous root \n\n\n\nDM yield \n\n\n\nStorage root  \n\n\n\nDM yield \n\n\n\nWhole plant \n\n\n\nDM yield \n\n\n\nStorage root \n\n\n\nyield \n\n\n\nT1 17.62 \u00b1 0.57 e 5.1 \u00b1 0.16 d 0 e 22.79 \u00b1 0.5 h 0 g \n\n\n\nT2 19.89 \u00b1 0.57 de 7.8 \u00b1 0.12 c 4.33   \u00b1 0.6 de 32.02 \u00b1 0.2 g 82 \u00b1 11.8 f \n\n\n\nT3 21.30 \u00b1 0.48 cd 7.7 \u00b1 0.15 c 8.93   \u00b1 0.8 d 37.90 \u00b1 1.1 f 117 \u00b1 0.8 ef \n\n\n\nT4 20.17 \u00b1 0.94 de 7.8 \u00b1 0.20 c 18.14 \u00b1 0.5 c 46.07 \u00b1 1.1 e 123 \u00b1 1.8 ef \n\n\n\nT5 24.04 \u00b1 2.40 bc 8.1 \u00b1 0.12 c 18.98 \u00b1 0.6 c 51.14 \u00b1 2.6 d 251 \u00b1 3.7 d \n\n\n\nT6 30.57 \u00b1 2.40 a 9.5 \u00b1 0.28 a 43.58 \u00b1 1.1 a 83.63 \u00b1 0.6 a 542 \u00b1 22.9 a \n\n\n\nT7 21.45 \u00b1 0.30 cd 7.8 \u00b1 0.03 c 20.35 \u00b1 0.09 c 49.61 \u00b1 0.2 de 156 \u00b1 3.8 e \n\n\n\nT8 26.80 \u00b1 0.33 b 8.9 \u00b1 0.05 b 40.34 \u00b1 0.3 a 76.00 \u00b1 0.6 b 455 \u00b1 3.1 b \n\n\n\nT9 25.75 \u00b1 0.58 b 8.2 \u00b1 0.17 c 34.27 \u00b1 34.27 b 68.15 \u00b1 2.9 c 332 \u00b1 1.5 c \n\n\n\n\n\n\n\nNotes: Means within the same column followed by the same letter are not significantly different at p \u2264 0.05 \n\n\n\nLeast Significant Difference (LSD test). The columns represent the mean values \u00b1 standard error. T1 = 0%   \n\n\n\nNitrogen fertilizer (uninoculated), T2 = 0%   Nitrogen fertilizer + UPMB10, T3 = 0%   Nitrogen fertilizer + \n\n\n\nUPMRB9, T4= 50% Nitrogen fertilizer (uninoculated), T5 = 50%   Nitrogen fertilizer + UPMB10, T6= 50%   \n\n\n\nNitrogen fertilizer + UPMRB9, T7 = 75%   Nitrogen fertilizer (uninoculated), T8= 75%   Nitrogen fertilizer \n\n\n\n+ UPMB10, T9 = 75%   Nitrogen fertilizer + UPMRB9. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n118 \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Effect of treatments on N uptake by plant and N removal. Means with the same letters \n\n\n\nare not significant different based on LSD (0.05) \n\n\n\n\n\n\n\nEffect of Treatments on Nitrogen Use Efficiency (NUE) \n\n\n\nFor NUE, the best results were found for the levels of 50% nitrogen associated with UPMRB9 \n\n\n\ninoculant and 75% nitrogen associated with UPMB10. These treatments resulted in \n\n\n\nsignificantly higher NUE, by 44.89% and 40%, respectively, compared to controls and other \n\n\n\ntreatments (Table 3). Based on the PFP index, a greater amount of productivity was found in \n\n\n\nUPMRB9 inoculation with 50% N with the partial factor of productivity being significantly \n\n\n\nhigher by 58.5 g kg-1 PFP in relation to the treatment that received 50% N without inoculation. \n\n\n\nThe best outcomes in terms of ER were achieved from UPMRB9 inoculation with 50% N and \n\n\n\nUPMB10 inoculation with 75% N at 1.86 and 1.85 g kg-1, respectively. The increments were \n\n\n\n12.36% and 8.64%, respectively, compared to the uninoculated treatments. In relation to UtE, \n\n\n\nthe best findings, shown in Table 3, were obtained from the same inoculation with different N-\n\n\n\nfertilization rates. Both UPMRB9 with 50% N and 75% N treatments gave the highest results \n\n\n\nof UtE at 9.6 and 8.7 g kg-1, respectively, compared to the controls and other treatments. In \n\n\n\ncontrast, the lowest effect was observed from UPMB10 with 0% N at 1.7 g kg-1. For the PE \n\n\n\n(the relation between the increase in productivity and the increase in N uptake by the whole \n\n\n\nplant), the highest value was observed from UPMRB9 with 50% N (23.3 g kg-1) with, the \n\n\n\nlowest value observed from UPMB10 with 0% N (2.7 g kg-1). In relation to the HI (the relation \n\n\n\nbetween the amount of N uptake by the tuber and the amount of N uptake by the whole plant), \n\n\n\nthe maximum value obtained was from 0% of N associated with UPMB10 inoculation at 62%, \n\n\n\nwhile the minimum value was from 75% of N associated with UPMB10 inoculation at 37.4%. \n\n\n\n\n\n\n\nEffect of Treatments on the Concentration of \u03b2-Carotene  \n\n\n\nThe concentration of \u03b2-carotene in the sweet potato plant was determined using solvent acetone \n\n\n\nand checked at 449\u2009nm through a UV-Vis spectrophotometer. The concentration was found to \n\n\n\nbe significantly affected by PGPRs-HA addition. Results (Figure 3) show that the inoculation \n\n\n\nwith treatments of UPMRB9 and 50% N and UPMB10 with 75% N gave the highest optical \n\n\n\ndensity (OD) value at 0.71 and 0.68 \u03bcg g-1, respectively, compared to the controls and other \n\n\n\ntreatments. There were no significant differences among treatments (UPMRB9 + 0% N, \n\n\n\nUPMB10 + 0% N, and the control + 50% N) at 0.36, 0.33, and 0.35 \u03bcg g-1, respectively, with \n\n\n\nthe lowest OD value being observed in control (+0 N%). The standard curves of the math \n\n\n\nequations were as follows: y = 0.2071x + 1.2107 (R2 = 0.99) \n\n\n\n\n\n\n\nPGPR combined with humic acid as an amendment formulation had a greater effect on the \n\n\n\ngrowth and content of N in the leaves of sweet potato plants than in the storage roots because \n\n\n\nDM and N uptake by the plant\u2019s leaves increased with increasing N, more than they did in the \n\n\n\nstorage roots (Table 2). Fernandes et al. (2018) found that nitrogen fertilization had a bigger \n\n\n\ne\nc\n\n\n\nc\n\n\n\ne\n\n\n\nc\n\n\n\na\n\n\n\ne\n\n\n\na\n\n\n\nb\n\n\n\n-0.5\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n0 % 5 0 % 7 5 %\n\n\n\nN\n u\n\n\n\np\nta\n\n\n\nk\ne \n\n\n\nb\ny\n p\n\n\n\nla\nn\nt \n\n\n\n(g\n p\n\n\n\nla\nn\nt-\n\n\n\n1\n)\n\n\n\nN Levels \n\n\n\nN  uptak e  \n\n\n\ncontrol UPMB10 UPMRB9\n\n\n\ne\n\n\n\nc\n\n\n\nc\n\n\n\nde\n\n\n\nc\n\n\n\na\n\n\n\nd\n\n\n\na\n\n\n\nb\n\n\n\n-20\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n0 % 5 0 % 7 5 %\n\n\n\nN\n r\n\n\n\nem\no\nv\nal\n\n\n\n (\ng\n k\n\n\n\ng\n-1\n\n\n\n)\n\n\n\nN Levels \n\n\n\nN  r e m ova l\n\n\n\ncontrol UPMB10 UPMRB9\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n119 \n\n\n\n\n\n\n\ninfluence on sweet potato crop development and N accumulation in shoots than in tubers \n\n\n\nbecause DM and N accumulation in shoots grew linearly with increasing nitrogen levels than \n\n\n\nin tubers. Other studies on the sweet potato have found that higher mineral N rates encourage \n\n\n\nthe development of vegetative plant parts rather than storage roots (Motsa et al. 2015; Ellong \n\n\n\net al. 2014). Okpara et al. (2009) state that a high yield is associated with restricted leaf \n\n\n\ndevelopment of the sweet potato crop during the storage root bulking stage. Treatments with \n\n\n\nlow N fertilizer rates and PGPRs-HA formulation resulted in uptake of greater amounts of N \n\n\n\nby sweet potato plants than in treatments with non-PGPRs-HA formulation. These results show \n\n\n\nthat the ability of the PGPRs-HA formulation to fix atmospheric N increases the availability of \n\n\n\nN in the soil, reducing the need to supply mineral N to plants grown in succession. Previous \n\n\n\nstudies demonstrated that sugarcane treated with beneficial microbes and half the required \n\n\n\namount of N (50 kg ha-1) achieved production levels comparable to plants with a maximum \n\n\n\namount of N and no inoculation. Previously, the same investigators detected a higher \n\n\n\npopulation of PGPR under 75 kg N ha-1 compared to treatments without fertilization and under \n\n\n\n150 kg N ha-1 (Oliver and Almeida 2018). \n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nEffect of treatments on NUE and productivity data \n\n\n\n\n\n\n\nThe application of PGPRs-HA could increase the N content of the soil. In this study, N content \n\n\n\nin the soil was influenced by applying 50% N+UPMRB9 and UPMB10 +75% N, which \n\n\n\nshowed the highest significant measurements at 0.28% and 0.26%, respectively. Ding et al. \n\n\n\n(2020) showed that high urea levels in the soil reduced sweet potato yield and could raise soil \n\n\n\nacidity, preventing plants from converting ammonia to nitrates. At lower N rates, the treatments \n\n\n\nwith PGPRs-HA increased sweet potato storage roots and the uptake of N in relation to \n\n\n\ntreatments with no PGPRs-HA because PGPR contributed more to nitrogen supply. Ahmad et \n\n\n\nal. (2016) suggest that combining HA and PGPR is a better technique for increasing canola \n\n\n\nnutrition and production. The same researchers found that HA and PGPR increased N uptake \n\n\n\nin canola seed, improved plant nutrition, enhanced nutrient availability, and improved root \n\n\n\ngrowth. By chelating, humic acid increases the availability of nutrients and the amount \n\n\n\nabsorbed (Moradzadeh et al. 2021). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nN rates Bacterial \n\n\n\ntreatments \n\n\n\nNUE PFP ER UtE PE HI \n\n\n\n0 % Control ____ ____ ____ ____ ____ ____ \n\n\n\nUPMB10 ____ ____ ____ 1.7 de 2.7 de 62 a \n\n\n\nUPMRB9 ____ ____ ____ 3.1 d 5.3 d 59.3 ab \n\n\n\n50 % Control 2.7 c 8.3 d 1.63 d 6.2 c 13.5 c 57 ab \n\n\n\nUPMB10 3.6 b 8.7 d   1.66 cd 5.1 c 11.3 c 44.9 c \n\n\n\nUPMRB9 4.5 a       20 a 1.86 a 9.6 a 23.3 a 41.2 cd \n\n\n\n75 % Control   3.08 c 6.2 e   1.69 bc 6.4 bc 11.9 c 54.1 b \n\n\n\nUPMB10 4.9 a   12.4 b 1.85 a 8.1 ab 21.7 ab 37.4 d \n\n\n\nUPMRB9 3.9 b   10.5 c 1.72 b 8.7 a 19.8 b 43.7 c \n\n\n\nNotes: Nitrogen use efficiency (NUE), Partial factor of productivity (PFP), Efficiency of recovery (ER), N-\n\n\n\nutilization efficiency (UtE), physiological efficiency (PE) and Harvest index (HI) of the sweet potato, under \nUPMRB9 and UPMB10 strains and different N doses. Means with the same letters are not significantly different \n\n\n\nbased on LSD test (p \u2264 0.05). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n120 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Preparation process of \u03b2-carotene extraction and analysis by using acetone solvent \n\n\n\n\n\n\n\nAn improved N content is positively connected with improved storage root development \n\n\n\nbecause it allows plants to absorb nutrients from the soil. This study found noticeable variations \n\n\n\nin the nitrogen content of the soil, sweet potato leaves, and storage roots with and without HA \n\n\n\nand PGPR (Figure 1). PGPR is an environmentally acceptable alternative technology to \n\n\n\npromote crop nutrient uptake, growth, and agricultural productivity. PGPR application \n\n\n\nimproved onion mineral nutrition, with these plants having the maximum mineral content in \n\n\n\nthe leaves and bulbs (Laftah et al. 2022). PGPR also increases nutritional availability by \n\n\n\nreleasing nutrients from organic matter through the production of enzymes. Moreover, the \n\n\n\ncombination of humic acid and bio-inoculants (PGPR) demonstrated a considerable rise in leaf \n\n\n\nand storage of root N contents (Ashwini et al. 2023). Similarly, Ahmad et al. (2016) and Zahid \n\n\n\net al. (2015) reported improved N content and uptake in several crops following PGPR \n\n\n\ntreatment. Additionally, the structure and function of the microbial population in the soil, \n\n\n\nparticularly in the rhizosphere area, are positively influenced by humic compounds (Yuan et \n\n\n\nal. 2022). This could be the cause of our study finding that the interaction between humic acid \n\n\n\nand PGPR is more effective for nutrient absorption. \n\n\n\n\n\n\n\nPGPR can help increase N- use efficiency in plants by fixing nitrogen from the atmosphere and \n\n\n\nmaking it available to plants. Some bacteria form symbiotic relationships with plants, living in \n\n\n\nnodules on the roots and providing the plant with nitrogen in exchange for carbohydrates. Other \n\n\n\nbacteria can fix nitrogen in the soil and make it available to plants. In this study, N-use \n\n\n\nefficiency increased with the addition of PGPRs-HA formulations with lower N fertilizer rates \n\n\n\nof 50% and 75%. These improvements will make it possible to optimise and lower the amount \n\n\n\nof nitrogen fertilizer required. Previous studies have shown that plant growth-promoting \n\n\n\nbacteria (PGPB) play a role in improving nitrogen-use efficiency in plants. Di Benedetto et al. \n\n\n\n(2017) focused on the interaction between PGPB and wheat to improve nitrogen-use efficiency. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n121 \n\n\n\n\n\n\n\nPseudomonas and Bacillus, which could oxidise ammonia to NO2 and ultimately to NO3, were \n\n\n\nfound to be the probable PGPB. These PGPBs can promote beneficial mycorrhizal-plant \n\n\n\ninteractions, affect biological nitrogen fixation, solubilise phosphate, create phytohormones \n\n\n\nand other compounds, and protect plants from harmful bacteria (Mataranyika et al. 2022; Di \n\n\n\nBenedetto et al. 2017). Leite et al. (2020) demonstrated that humic acids enhance plant ability \n\n\n\nto absorb nutrients and thrive. An alternate method to improve NUE is to apply urea together \n\n\n\nwith humic compounds and humic acids. The findings were similar to those of Kong et al. \n\n\n\n(2022) who found that utilising humic acid and urea greatly boosted the yield and NUE of \n\n\n\nwheat and maize. Plants that received 50% N-fertilizer associated with UPMRB9 inoculation \n\n\n\npromoted a higher partial factor of productivity (PFP) than controls and other treatments. \n\n\n\nSuman et al. (2008) showed that the sugarcane plant associated with PGPR inoculation and \n\n\n\nwith the required N dose (50 kg ha-1) achieved production at levels comparable to those \n\n\n\nachieved by those receiving the full N recommendation without inoculation. \n\n\n\n\n\n\n\nThe straightforward and speedy estimation of \u03b2-carotene content was effectively accomplished \n\n\n\nusing acetone solvent as the extraction medium and UV-Vis spectrophotometry detection. \n\n\n\nResults showed that the highest significant optical density (OD) measurements were observed \n\n\n\nfrom the UPMRB9+50%N and UPMB10+75%N treatments (0.71 mg g-1 and 0.68 mg g-1, \n\n\n\nrespectively). These results showed that PGPRs-HA inoculation significantly increased the \u03b2-\n\n\n\ncarotene content of sweet potato storage roots compared to the control. With regard to this \n\n\n\neffect, Abd-Alrahman et al. (2021) observed that applying humic acid consistently enhanced \n\n\n\nantioxidants such as tocopherol, \u03b2-carotene, superoxide dismutase, and ascorbic acid \n\n\n\nconcentrations in crops. Moreover, Aremu et al. (2022) found that the application of PGPR \n\n\n\nsignificantly promoted the \u03b2-carotene content in Abelmoschus esculentus genotypes. The same \n\n\n\nresearchers demonstrated that after adding PGPR inoculations, the chemical composition of \u03b2-\n\n\n\ncarotene varied with the genotypes; for instance, the Cannabis sativa genotype had \n\n\n\nsignificantly higher b-carotene content than the Tygra genotype. According to Biswas et al. \n\n\n\n(2011), acetone is a suitable solvent for extracting the \u03b2-carotene compound in the samples. \n\n\n\nSince \u03b2-carotene is practically a non-polar molecule acetone, a moderately polar solvent \n\n\n\nenhanced beta-carotene extraction. It was discovered that this solvent has benefits over a \n\n\n\nnumber of organic solvents. The proposed UV-Vis spectrophotometry with the acetone solvent \n\n\n\nmethod is environment-friendly, inexpensive, and easily performed (Yilmaz and Soylak 2018). \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\nBased on the findings of the current investigation, the inoculation of PGPR-HA with an \n\n\n\noptimum nitrogen fertilization rate significantly increased N-use efficiency, N uptake, and \u03b2-\n\n\n\ncarotene content in sweet potatoes. Humic acid amendments could support the microbial \n\n\n\nenvironment, promoting beneficial bacteria and health of crops. Plant development functions, \n\n\n\nincluding cell division, hormone management, and energy generation, depend on a number of \n\n\n\nenzymes that humic acid boosts in plants. Therefore, N-use efficiency can be improved by \n\n\n\nusing nitrogen fertilizers with bacteria that can fix nitrogen from the atmosphere and make it \n\n\n\navailable to plants. This can help reduce the amount of nitrogen fertilizer needed and the \n\n\n\nenvironmental impact of excessive nitrogen fertilizer use. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe authors sincerely acknowledge the research grant (6300406) by a private Malaysian \n\n\n\ncompany (Hj Mat Hj Jantan Pvt. Ltd). \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 111-124 \n\n\n\n\n\n\n\n122 \n\n\n\n\n\n\n\nREFERENCES \nAbd-Alrahman, H. A., N.M. Marzouk, S.M. El-Sawy and S.D. Abou-Hussein. 2021. Improving growth, \n\n\n\nyield and quality of onion plants by amino and humic acids under sandy soil conditions. Middle \n\n\n\nEast Journal of Applied Sciences 11 (3): 637-648. \n\n\n\nAhmad, S., I. Daur, S.G. Al-Solaimani, S. Mahmood, A.A. Bakhashwain, M.H. Madkour and M. 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Isolation and identification of indigenous \n\n\n\nplant growth promoting rhizobacteria from Himalayan region of Kashmir and their effect on \n\n\n\nimproving growth and nutrient contents of maize (Zea mays L.). Front. Microbial 17(6):207.  \nZhao, C., G. Liu, Y. Chen, Y. Jiang, Y. Shi, L. Zhao and Z. Huo. 2022. Excessive nitrogen application \n\n\n\nleads to lower rice yield and grain quality by inhibiting the grain filling of inferior \n\n\n\ngrains. Agriculture 12(7): 962. \nZhu, X., G.H. Ros, M. Xu, Z. Cai, N. Sun, Y. Duan and W. de Vries. 2023. Long-term impacts of \n\n\n\nmineral and organic fertilizer inputs on nitrogen use efficiency for different cropping systems \n\n\n\nand site conditions in Southern China. European Journal of Agronomy 1;146:126797. \n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 51-66 (2020)                         Malaysian Society of Soil Science\n\n\n\nRelationship between in-situ spatial soil resistivity and \nselected soil physical properties \n\n\n\nEluwole, A.B.1,* and Olorunfemi, M.O.2\n\n\n\n1Federal University Oye-Ekiti, Oye-Ekiti, Nigeria\n  2Obafemi Awolowo University, Ile-Ife, Nigeria\n\n\n\nABSTRACT\nStatistical and spatial approaches have been used to determine the relationship \nbetween in-situ resistivity, soil moisture content and pH. These are done to establish \nthe extent to which resistivity measurements can be used in mapping selected soil \nproperties. Soil resistivity measurements were taken via a Wenner array platform \nhaving an electrode spacing of 8 cm.  Soil samples were taken at 0\u20133 cm depth. \nThe samples were analysed for moisture content and pH. Clayey sand and sand \ntextural classes were deduced from resistivity values (210\u2013750 ohm-m and >750 \nohm-m respectively). Moisture content values were classified in conjunction with \ntexture as unavailable water (UW) and available water (AW). pH values were \nclassified as moderately acidic (5.2 \u2013 6.0), slightly acidic (6.1 \u2013 6.5) and neutral \n(6.6 \u2013 6.9).  A moderate inverse relationship was observed between resistivity \nand moisture content. The spatial correlation between resistivity-derived textural \nclasses and moisture content classes was 62%. Resistivity was also found to be \ninversely correlated with pH. Spatially, higher resistivity zones were abundantly \nassociated with moderately acidic pH zones, while lower resistivities correlated \nwith slightly acidic to neutral pH zones \u2013 thereby bringing the spatial association \nbetween resistivity and pH to 60%. The pilot/plot-scale study concluded that to \na satisfactory extent, in-situ soil resistivity measurements can be adopted as a \ncomplimentary tool for selected soil properties mapping.\n\n\n\nKeywords: in-situ  resistivity, moisture content, pH, spatial association, \nmapping.\n\n\n\nINTRODUCTION\nThe soil is inherently variable in its physical, chemical and biological properties \nthat determine crop yield potential. To achieve sustainable agriculture, precision \nfarming which ensures efficient use of resources is needed. Precision farming \ninvolves accurate measurement of within field variations in soil physico-chemical \nproperties through efficient methods. \n Geophysical methods have been adopted as valuable tools in precision \nfarming. They have assisted farmers in making informed decisions on \u201cwhat to \nplant where, and when to plant what\u201d on their farmlands. The electromagnetic \ninduction (EMI) method has been used in mapping pesticide penetration \ncoefficients (Kd) (Jaynes et al. 1994). Soils have been characterised from electrical \n___________________\n*Corresponding author : akinola.eluwole@fuoye.edu.ng\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202052\n\n\n\nconductivity mapping and in-situ resistivity measurements (Johnson et al. 2001; \nPozdnyakova et al. 2004; Eluwole et al. 2018). Corwin et al. (2003) delineated \nsite-specific management units from electrical conductivity (EC)-directed soil \nsampling approach. Continuous measurement of near-surface water dynamics \nare also possible (Serbin and Or 2004; Gish et al. 2005). The advantages of \ngeophysical methods in precision farming cannot be overemphasised. They have \nbeen found to be efficient in delineating soil variability in less laborious, non-\ninvasive and relatively inexpensive manner compared to traditional sampling \nmethods.\n The soils of Ekiti State, Nigeria (the study area) have been evaluated, \ncharacterised and classified using the traditional pedological methods of taking \ndisturbed samples and analysing the same (Ogunkunle 1988; Shittu and Fasina \n2004; Fasina and Adeyanju 2007; Fasina et al. 2009; Fasina 2013; Ogunkunle \n2009). The complimentary potential of in-situ, non-invasive, relatively fast and \ncost effective geophysical measurements in soil mapping, land quality assessment \nand management have not been adequately embraced in the study area. This \nstudy was conducted to determine the relationship between in-situ soil electrical \nresistivity, moisture content and pH.  Moisture content and pH are considered to \nbe extremely important indices for establishing the relationship between the way \na soil behaves and the availability of nutrients (Jones and Jacobsen 2001; Reddy \n2002 ; and McCauley et al. 2005). This was with a view to establishing the extent \nto which in-situ resistivity can be adopted as an index for the determination of \nselected soil properties so as to serve as a fast guide in farmers\u2019 decision making.\n\n\n\nMATERIALS AND METHODS\n\n\n\nLocation\nThis study was conducted on a 676-m2 arable plot of land in the Teaching and \nResearch farm of the Ekiti State University, Ado-Ekiti, South-western Nigeria \n(7\u00b0 42\u2032 40\u2032\u2032 N and 5\u00b0 14\u2032 53\u2032\u2032 E). Soils at this site, according to the Soils Science \nDivision Staff (2017) textural triangle, are sandy loam and loamy sand (Eluwole \net al. 2018). Both soils are members of the Iwo Association (Smyth and \nMontgomery 1962) (Figure 1). Under the FAO-UNESCO (1988) classification, \nthe Iwo Association is equivalent to the Plinthic Luvisol classification (Fagbami \nand Shogunle 1995). \n\n\n\nMeasurements\nIn-situ soil resistivity measurements were taken on a1 \u2013 by \u2013 1 m grid at 729 \nstations (Figure 2) with the aid of a calibrated four-electrode Wenner array \nplatform (Plate 1). The platform is made up of two outer electrodes (C1 and \nC2) through which current is sent into the soil; and two inner electrodes (P1 \nand P2) from which potential difference arising from the transmitted current \nis measured via the Ohmega resistivity meter.  A similar arrangement has been \nreported by Pozdnyakova et al. (2004). The depth of probe of the array is a \n\n\n\n\n\n\n\n\nFigure 1. Location and soil association map of the area around the study area\n(adapted from Smyth and Montgomery (1962).\n\n\n\nPlate 1. The Wenner Array platform\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202054\n\n\n\nfunction of the soil type and the distances between the electrodes (known as \ngeometry).  The depth of interest of this study was the topmost (0\u20133 cm thickness) \nof the topsoil described by Eluwole et al. (2018) as the surface layer \u2013 the so-\ncalled O-horizon in agricultural parlance. A constant electrode spacing of 8 cm \nwas adopted. Eighty-one (81) sampling locations were distributed with a spacing \nof 3 m (Figure 2). Soil samples were also taken at 0\u20133 cm depth on the same \nday of resistivity measurements and under the same climatic conditions \u2013 more \nimportantly 24 h after rainfall in order to establish Field Capacity (FC). Soil \nsamples were analysed for moisture content and pH using the gravimetric method \n(Black, 1965) and glass electrode pH meter respectively.\n\n\n\nData Analysis\nElectrical resistivity values obtained from stations parametric to soil sample \nstations were compared with measured soil properties through cross plots, cross-\ntabulations and spatial pattern inspection. The resistivity, moisture content and pH \ndata were classified using relevant tables and charts from Eluwole et al. (2018) \n(Figure 4), Gurevitch et al. (2002) (Figure 5) and Horneck et al. (2011) (Table 1).\n \n\n\n\nFigure 2. Site layout showing resistivity measurement stations and soil sample locations.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nResistivity Classification\nEluwole et al. (2018) have classified the soils of the topmost layer (O-horizon) of \nthe site in terms of resistivity as clayey sand (210\u2013750 ohm-m) and sand (>750 \nohm-m) (Figure 3). The figure shows that about 60% of the study area is underlain \nby sandy soils, while the remaining 40% is underlain by clayey sand.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 55\n\n\n\n Figure 3. Resistivity map of the O-horizon (Eluwole et al. 2018) \n\n\n\nMoisture Content Classification\nThe chart presented in Figure 4 formed the basis for the classification of the \nmoisture content of the O-horizon. Field capacity is the amount of water remaining \nin the soil after all gravitation water has drained. The amount of capillary water \nthat is available to plants is the soil\u2019s available water (AW) and the point at which \nthere is no water for plant uptake is referred to as the unavailable water (UW).\n\n\n\nFigure 4. Relationship of soil texture with soil water content\n(Adapted from Gurevitch et al. 2002)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202056\n\n\n\n The moisture content obtained from the 81 soil samples collected from \nthe surface layer of the pilot plot varied between 4.11% and 12.6% (Figure 5). \nZones characterised by relatively low moisture content (ranging between 4.1% \nand 7.3%) belong to the unavailable water (UW) class and they are present around \nthe north-eastern, southern and the north-western/western parts of the pilot plot. \nThe other class which covers about 62% of the pilot plot represents areas with \nmoisture contents greater than 7.3%, and is classified as available water (AW) \nareas.\n\n\n\nFigure 5. Soil moisture content map\n\n\n\npH Classification\nSoil pH expresses soil acidity and most crops grow best when the soil pH is \nbetween 6.0 and 8.2 (Horneck et al. 2011). \n The pH obtained from the soil samples ranged between 5.1 and 6.9. \nBased on the pH classification in Table 1, the pH characteristics of the soils of the \npilot plot falls within the moderately to slightly acidic and neutral pH categories \n(Figure 6). The moderately acidic category dominates the layer, having 61% \ncoverage, while the slightly acidic category has 36.6% coverage. On the other \nhand, the neutral pH category which covers the remaining 2.4% manifests itself \naround the south-western region of the pilot plot.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 57\n\n\n\nResistivity Versus Moisture Content \nRegression Analysis: The cross-plot graph of resistivity and moisture content \n(Figure 7) shows that an inverse relationship exists between soil resistivity and \nmoisture content. The coefficient of correlation (r) of 0.33 is an indication of \n\n\n\n6 \n \n\n\n\nThe moisture content obtained from the 81 soil samples collected from the surface layer of \nthe pilot plot varied between 4.11% and 12.6% (Figure 5). Zones characterised by relatively \nlow moisture content (ranging between 4.1% and 7.3%) belong to the unavailable water \n(UW) class and they are present around the north-eastern, southern and the north-\nwestern/western parts of the pilot plot. The other class which covers about 62% of the pilot \nplot represents areas with moisture contents greater than 7.3%, and is classified as available \nwater (AW) areas. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 5. Soil moisture content map \n\n\n\npH Classification \nSoil pH expresses soil acidity and most crops grow best when the soil pH is between 6.0 and \n8.2 (Horneck et al. 2011).  \n\n\n\n                      TABLE 1  \nSoil pH classifications (Horneck et al. 2011) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6. Soil pH map Comparative Analyses \n\n\n\nSoil pH Category \n\n\n\n< 5.1 Strongly acidic \n\n\n\n5.2 \u2013 6.0 Moderately acidic \n\n\n\n6.1 \u2013 6.5 Slightly acidic \n\n\n\n6.6 \u2013 7.3 Neutral \n\n\n\n7.4 \u2013 8.4 Moderately alkaline \n\n\n\n> 8.5 Strongly alkaline \n\n\n\nTABLE 1 \nSoil pH classifications (Horneck et al. 2011)\n\n\n\nFigure 6. Soil pH map Comparative Analyses\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202058\n\n\n\na moderate relationship between the two variables. The regression curve which \nshows that soil resistivity increases with a decrease in moisture satisfies the \nempirical equation of Archie (1942) which indicates that resistivity of rocks \ndecreases with increasing water saturation. \n\n\n\nFigure 7. Linear regression analysis on soil resistivity and moisture content\n\n\n\n Cross-tabulation and Chi-square Tests: The cross-tabulation report \n(Tables 2a and b) describes the relationship between soil moisture content and \nresistivity in terms of resistivity-derived soil classification. The percentage \noccurrence of the available water-moisture content regime within the clayey sand \nand sand resistivity-derived soil classes is 33.3% and 29.6% respectively. It can \ntherefore be inferred that a greater percentage of the zones within the available \nwater-moisture content regime falls within the clayey sand which is characterised \nby lower resistivity values.\n On the other hand, comparing the percentage occurrence ratio \n(12.3:24.7%) of the unavailable water-moisture class within the clayey sand and \nsand soil classes, it is evident that the unavailable water has greater association \nwith the sand soil class. In Table 2b, the significance probability of 0.087, which \nis not too distant from the 0.05 standard significance level portrays the possibility \nof a moderately statistically significant relationship between soil moisture content \nand resistivity. \n Spatial Pattern Analysis: The relationship between the spatial distribution \nof soil moisture content classes and resistivity-derived soil classes was assessed \nfrom the spatial association map of soil moisture content and resistivity (Figure 8). \nThe black colored portion on the spatial association map represents areas where \nmoisture content classes are inversely related to resistivity-derived soil classes. \nThe areas account for 62% of the entire plot, while the remaining 38% with no \ncolor are areas where there are no spatial relationships between soil moisture \ncontent and resistivity.\n The spatial association map was used to evolve a composite map (Figure \n9) that was partitioned based on the relationships between moisture content \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 59\n\n\n\n8 \n \n\n\n\nCross-tabulation and Chi-square Tests: The cross-tabulation report (Tables 2a and b) \ndescribes the relationship between soil moisture content and resistivity in terms of resistivity-\nderived soil classification. The percentage occurrence of the available water-moisture content \nregime within the clayey sand and sand resistivity-derived soil classes is 33.3% and 29.6% \nrespectively. It can therefore be inferred that a greater percentage of the zones within the \navailable water-moisture content regime falls within the clayey sand which is characterised \nby lower resistivity values. \n\n\n\n \nOn the other hand, comparing the percentage occurrence ratio (12.3:24.7%) of the \nunavailable water-moisture class within the clayey sand and sand soil classes, it is evident \nthat the unavailable water has greater association with the sand soil class. In Table 2b, the \nsignificance probability of 0.087, which is not too distant from the 0.05 standard significance \nlevel portrays the possibility of a moderately statistically significant relationship between soil \nmoisture content and resistivity.  \n\n\n\n \nSpatial Pattern Analysis: The relationship between the spatial distribution of soil moisture \ncontent classes and resistivity-derived soil classes was assessed from the spatial association \nmap of soil moisture content and resistivity (Figure 8). The black colored portion on the \nspatial association map represents areas where moisture content classes are inversely related \nto resistivity-derived soil classes. The areas account for 62% of the entire plot, while the \nremaining 38% with no color are areas where there are no spatial relationships between soil \nmoisture content and resistivity. \n\n\n\n   Resistivity-derived soil \nclassification \n\n\n\n               Total \n\n\n\n   Clayey \nsand \n\n\n\nSand \n\n\n\nMoisture content \nclassification \n\n\n\nAW  Count 27 24 51 \n % of total 33.3% 29.6% 63.0% \n\n\n\nUW  Count 10 20 30 \n % of total 12.3% 24.7% 37.0% \n\n\n\nTotal   Count 37 44 81 \n% of total 45.7% 54.3% 100.0% \n\n\n\n* AW = Available water, UW = Unavailable water \n \n\n\n\nTABLE 2b \nChi-square and symmetric measures \n \n Value Significance \n\n\n\nprobability \nPearson Chi-\nsquare \n\n\n\n2.927 0.087 \n\n\n\nPhi 0.20  \nNo. of samples 81  \n\n\n\nFigure 8. Spatial pattern association map of moisture content and resistivity-derived  soil \nclasses.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202060\n\n\n\nclasses and soil resistivity. The areas identified by stars on the composite map \ndepicts areas within the unavailable water class that are characterised by the \nresistivity-derived sand soil class which is associated with higher resistivities. \nThe black colored areas describe the zones having available water; these zones \nare also associated with lower resistivities, in the range of the clayey sand soil \ncategory. Areas covered by the white colour are areas where the relationship \nbetween moisture content and resistivity is overlapping. In such areas, high \nmoisture content does not necessarily translate to lower resistivity, and neither \ndoes low moisture content translate to higher resistivity.   \n\n\n\nResistivity Versus pH \nRegression Analysis: The weak coefficient of correlation of 0.22 depicts a poor \nrelationship between resistivity and soil pH (Figure 10). The regression trend \nshows that both parameters are inversely correlated, i.e. soil acidity increases with \nincreasing resistivity but tends towards neutrality when the resistivity reduces. \nMurad (2012) reported that soils within the acidic pH range have coarse sand \nparticles as their main constituents and are characterised by high resistivity. \n\n\n\nFigure 9. Composite map of soil moisture content and resistivity-derived soil class\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 61\n\n\n\n Cross-tabulation and Chi-square Tests: The cross-tabulation results of \nthe comparison of surface layer pH and resistivity presented in Table 3a show \nthat 53.1% of the resistivity data are characterised by the moderately acidic \npH category. The percentage ratio of this pH category within the clayey sand \nand sand resistivity-derived soil types is 19.8:33.3%. The neutral pH category \nis only present within the clayey sand, while the slightly acidic pH category, \nwhich represents 42% of the sample data, is present within the clayey sand and \nsand resistivity-derived soil types in equal proportions. The high occurrence of \nthe moderately acidic pH category (characterised by pH values of between 5.2 \nand 6.0) within sand, and the association of the neutral pH category with clayey \nsand only, further explains the inverse relationship between pH and resistivity. \nThe equal distribution of the slightly acidic pH within the clayey sand and sand \nsoil types may have been a result of the overlapping resistivity phenomenon. The \nchi-square value of 6.26 and a significance probability of 0.044 (4.4%) which \nis significant at the conventional cut-off of 5% can be regarded as evidence that \nthere is an association between soil pH and resistivity (Table 3b).\n\n\n\nFigure 10. Linear regression analysis on resistivity and pH\n\n\n\n Spatial Pattern Analysis: The spatial association map of soil pH and \nresistivity (Figure 11) shows that soil pH is related to resistivity to an extent \nof about 60%, owing to the coverage of the spatial association indicated by the \nshaded portions. Areas where there are no distinct relationships between soil pH \nand resistivity constitute the white colour band. Areas under identified by square \npatterns on the composite map (Figure 12) fall within the neutral pH category and \nare characterised by clayey sand. The shaded portions having star patterns are \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202062\n\n\n\ncharacteristic of the slightly acidic/clayey sand category, while the moderately \nacidic/sand category is the shaded portion without patterns.\n\n\n\n11 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 10. Linear regression analysis on resistivity and pH \n\n\n\nSpatial Pattern Analysis: The spatial association map of soil pH and resistivity (Figure 11) \nshows that soil pH is related to resistivity to an extent of about 60%, owing to the coverage of \nthe spatial association indicated by the shaded portions. Areas where there are no distinct \nrelationships between soil pH and resistivity constitute the white colour band. Areas under \nidentified by square patterns on the composite map (Figure 12) fall within the neutral pH \ncategory and are characterised by clayey sand. The shaded portions having star patterns are \ncharacteristic of the slightly acidic/clayey sand category, while the moderately acidic/sand \ncategory is the shaded portion without patterns. \n \n\n\n\nTABLE 3a \nCross-tabulation of surface layer pH and resistivity-derived soil  classification \n\n\n\n \n   Resistivity-derived soil \n\n\n\nclassification \n              Total \n\n\n\n       Clayey     \nsand \n\n\n\n               Sand \n\n\n\npH \ncategory \n\n\n\nMA  Count 16 27 43 \n % of total   19.8% 33.3% 53.1% \n\n\n\nSA  Count 17 17 34 \n % of total 21.0% 21.0% 42% \n\n\n\nN  Count 4 0 4 \n % of total  4.9% 0% 4.9% \n\n\n\nTotal  Count 37 44 81 \n % of total 45.7% 54.3% 100.0% \n\n\n\n   MA = Moderately acidic \n   SA  = Slightly acidic,   N = Neutral \n\n\n\nR\u00b2 = 0.0629 \ny = -208.7x + 2062.9 \n\n\n\n1\n\n\n\n10\n\n\n\n100\n\n\n\n1000\n\n\n\n10000\n\n\n\n5 5.5 6 6.5 7\n\n\n\nA\npp\n\n\n\nar\nen\n\n\n\nt R\nes\n\n\n\nis\nti\n\n\n\nvi\nty\n\n\n\n (o\nhm\n\n\n\n-m\n) \n\n\n\npH \n\n\n\nTABLE 3a\nCross-tabulation of surface layer pH and resistivity-derived soil  classification\n\n\n\nFigure 11. Spatial pattern association map of pH and resistivity-derived soil \n                   classes\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 63\n\n\n\nFigure 12. Composite map of pH and resistivity-derived soil classes.\n\n\n\nCONCLUSIONS\nThe soils were classified in terms of resistivity as clayey sand and sand with a \nresistivity range of 210 \u2013 750 ohm-m and > 750 ohm-m respectively. Moisture \ncontent varied between 4.11 and 12.6%. Using an appropriate chart, moisture \ncontent values were classified in terms of their relationships with soil texture as \nAW and UW. The pH ranged between 5.1 and 6.9 and the values were classified as \nmoderately acidic, slightly acidic and neutral. The regression analysis of moisture \ncontent and resistivity indicated that there was a moderate inverse relationship \nbetween resistivity and moisture content owing to a coefficient of correlation (r) \nof 0.33. \n The significance probability of 0.087 obtained from the chi-square tests \nshowed that a moderately statistically significant relationship existed between \nresistivity and moisture content. The extent of spatial association of resistivity \nand moisture content was estimated to be about 62%.\n Resistivity was also compared with pH. The regression analysis \ncarried out on resistivity and pH showed that resistivity varied inversely with \npH. The relationship was, however, weak, based on the low (0.22) coefficient \nof correlation. The chi-square tests however portrayed a statistically significant \nrelationship between resistivity and pH, because of the significance of probability \nof 0.044. Also a spatial pattern association of about 60% was estimated for \nresistivity and pH classes. This pilot/plot scale study has demonstrated that to a \nsatisfactory extent, in-situ soil resistivity can be considered as a relevant index in \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202064\n\n\n\nestimating soil properties such as moisture content and pH. The study showed that \nin its present scale, it can only serve as a complimentary tool to conventional soil \nmeasurements.\n\n\n\nREFERENCES\nArchie, G.E. 1942. The electrical resistivity log as an aid in determining some reservoir \n characteristics. Trans. Am. Inst. Min. Metall. Eng. 146: 54-67.\n\n\n\nBlack C.A. 1965. \u201cMethods of Soil Analysis: Part I Physical and mineralogical \nproperties\u201d. American Society of Agronomy, Madison, Wisconsin, USA.\n\n\n\nCorwin, D.L., S.M. Lesch, P.J. Shouse, R. Soppe and J. Ayars. 2003. Identifying \nsoil properties that influence cotton yield using soil sampling directed by \napparent soil    electrical conductivity. Agronomy Journal  95 (2): 352\u2013364.\n\n\n\nEluwole, A.B., M.O. Olorunfemi and O.L.Ademilua. 2018. Soil horizon mapping and \ntextural classification using micro soil electrical resistivity measurements: \ncase study from Ado-Ekiti, Southwestern Nigeria. Arabian Journal of \nGeosciences 11:315.\n\n\n\nFagbami, A.A. and  E.A.A. Shogunle.  1995 . Nigeria: Sandy reference soils of the \nmoist  lowlands near Ibadan (19 p). Soil Brief Nigeria 1. Wageningen: \nUniversity of Ibadan and International Soil Reference and Information \nCentre.\n\n\n\nFasina A.S. 2013. Can these soils sustain? The dilemma of a pedologist. 37th Inaugural \nLecture Series, Ekiti State University, Ado-Ekiti.\n\n\n\nFasina A.S. and A. Adeyanju. 2007. Comparison of three land evaluation systems \nin evaluating the predicted value of some selected soils in Ado-Ekiti, \nSouthwestern Nigeria. Nigeria Journal of Soil Science 3(1): 35-41.\n\n\n\nFasina A.S., G.O. Awe and J.O. Aruleba. 2009. Irrigation suitability evaluation and \ncrop yield. An example with Amaranthus cruenthus in Southwestern Nigeria. \nAfrican Journal of Plant Science 2(7): 61-66\n\n\n\nFAO\u2013UNESCO. 1988. Soil map of the world. Revised legend. World Soil Resources \nReport No. 60. Rome.\n\n\n\nGish, T. J., C.L. Walthall, C.S.T. Daughtry and K.J.S. Kung. 2005. Using soil moisture \nand spatial yield patterns to identify subsurface flow pathways. Journal of  \nEnvironmental Quality 34(1): 274\u2013286.\n\n\n\nGurevitch, J., S.M. Scheiner and G.A. Fox. 2002. The Ecology of Plants (523 p.) \nSunderland, Massachusetts: Sinauer Associates Inc. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 65\n\n\n\nHorneck, D.A., D.M. Sullivan, J.S. Owen  and J.M. Hart. 2011. Soil Interpretation \nGuide (12p.).  Oregon State University Extension Service. \n\n\n\nJaynes, D.B., J.M. Novak, T.B. Moorman and C.A. Cambardella. 1994. Estimating \nherbicide partition coefficients from electromagnetic induction measurements. \nJournal of Environmental Quality 24: 36\u201341.\n\n\n\nJones C. and J. Jacobsen, 2001. Plant Nutrition and Soil Fertility. Nutrition Management \nModule No. 2. Montana State University Extension Service. 4449-2.\n\n\n\nJohnson, C.K., J.W. Doran, H.R. Duke, B.J. Wienhold, K.M. Eskridge and J.F. \nShanahan. 2001. Field-scale electrical conductivity mapping for delineating \nsoil condition. Soil Science Society of America J. 65: 1829-1837.\n\n\n\nMcCauley, A., C. Jones, and J. Jacobsen. 2005. Basic Soil Properties. Montana State \nUniversity Extension Service, Bozeman. Module 1, 12 pp.\n\n\n\nMurad, O.F. 2012. Obtaining chemical properties through soil electrical resistivity.  \nJournal of Civil Engineering Research  2(6): 120 \u2013 128. \n\n\n\nOgunkunle, A.O. 1988. Relative influence of some soil properties on maize and \nsoya bean yield in Southwest Nigeria. International Journal of Tropical \nAgriculture  6(3-4): 213-220.\n\n\n\n \nOgunkunle, A.O. 2009. Management of Nigeria soil resources for sustainable \n\n\n\nagricultural productivity and food security. Proceedings of the 33rd Annual \nConference of Soil Science Society of Nigeria (pp 9-24). University of Ado-\nEkiti, Ado-Ekiti.\n\n\n\nPozdnyakova, L., P.V. Oudemans, A.I. Pozdnyakov and M.S. Kelly. 2004. 7th \nInternational Conference on Precision agriculture and other precision \nresources management. July 24 \u2013 28, Mineapolis, Minnesota, USA.\n\n\n\nReddy, K. 2002. Engineering Properties of Soils Based on Laboratory Testing. \nUniversity of Illinois, Chicago.\n\n\n\nSerbin, G. and  D. Or. 2004. Ground-penetrating radar measurement of soil water \ncontent dynamics using a suspended horn antenna. Trans. Geoscience and \nRemote Sensing 42(8):1695\u20131705.\n\n\n\nShittu O.S. and A.S. Fasina. 2004. Comparative effect of different residue management \non maize at Ado-Ekiti, Nigeria. Journal of Sustainable Agriculture (USA) \n28(2): 41-54.\n\n\n\nSmyth, A.J. and R.F. Montgomery. 1962. Soils and Land Use in Central Western \nNigeria. Ibadan: Government Printers.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202066\n\n\n\nSoil Science Division Staff. 2017. Soil survey manual. C. Ditzler, K. Scheffe, and \nH.C. Monger (eds.). USDA Handbook 18. Government Printing Office, \nWashington, D.C.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n80 \n \n\n\n\nEffects of Crop Types on the Physicochemical and Biological Properties of \nAgricultural Soils in Semi-Arid Regions (Western Algeria) \n\n\n\n \nMohamed Zouidi1*, Amine Habib Borsali2, Ayoub Allam3, Salah Hadjout1,  \n\n\n\nImane Hadjadji2, Djemaa Chikhi2 and Anne-Marie Farnet Da Silva4  \n \n\n\n\n1Centre de Recherche en Am\u00e9nagement du Territoire (CRAT), Campus Zouaghi Slimane,  \nRoute de Ain el Bey,25000 Constantine, Alg\u00e9ria \n\n\n\n2Laboratory \"Water Resources and Environment\" Dr. Tahar Moulay University, Saida, Algeria \n3Centre de Recherche en Technologies Agro-Alimentaires (CRTAA),  \n\n\n\nCampus universitaire Tergua Ouzemour, Bejaia,06000, Alegria \n4Aix Marseille University, CNRS, IRD, Avignon University, IMBE UMR 7263, Marseille, France \n\n\n\n \n*Correspondence: mohamed.zouidi@crat.dz ; zouidibiologie20@gmail.com \n\n\n\n \nABSTRACT \n\n\n\n \nThe soil is an element of the biosphere that forms the foundation for agricultural production. \nAgricultural practices can have a significant impact on the quality of soils, and therefore on the \nproductivity and sustainability of agriculture. Thus, it is crucial to evaluate the impact of different crops \non soil fertility and determine the most sustainable agricultural practices to maximize productivity while \npreserving soil quality. The present work examines the quality variability of agricultural soils due to \ncultivating different crops in a semi-arid zone in western Algeria. The research aims to compare the \nimpact of three different crops (legumes, cereal and fruit tree cultivation) on the fertility of agricultural \nsoils. To achieve this, we compared the physicochemical and biological properties of 75 soil samples \ndistributed among three types of crops (five sampling stations of 400 m2 per crop). The results show \nthat agricultural soils in the studied areas are generally characterized by a sandy texture with differences \nin some physicochemical parameters, notably high moisture content and water retention in arboriculture \n(7,87%; 53%). Soils in cereal crops are rich in carbon (0,62 g/kg), whereas soils in legumes are rich in \nnitrogen (0,10 g/kg), which ensures good mineralization of organic matter (C/N: 5,15). Biological \nproperty analysis indicates that microbial biomass and its effectiveness are generally homogenous (p> \n0.05), with a small significant difference in basal respiration (P<0.05). The diversity of microflora \n(bacteria, fungi, and rhizobium) is influenced by organic matter differentiation caused by the \nagricultural practices used for each crop and their effects on the physicochemical properties of \nagricultural soils. In conclusion, this study shows that different types of crops have a significant impact \non the quality of agricultural soils in a semi-arid zone in western Algeria. The results highlight the \nimportance of considering the effects of different crops on soil properties to optimize crop yields and \nensure the sustainability of agriculture in this region. \n \nKey words: Soil quality, physicochemical parameters, microbial biomass, microbial diversity, \naridity \n \n\n\n\nINTRODUCTION \n \nSoil represents the support of agricultural production and the interface with other biosphere \ncompartments. It fulfils many essential functions for providing ecosystem services necessary \nfor the well-being of our societies (Bourgeois 2015; Pavan and Ometto 2018). It is also a non-\nrenewable resource as its physicochemical and biological properties have been altered by the \ndevelopment of intensive agriculture (Chabert and Sarthou 2017; Douaer et al. 2021). The \ncurrent awareness of this situation has revealed the need to define new management methods \nadapted to the preservation and sustainable use of soils. This awareness has marked the entry \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n81 \n \n\n\n\nof agroecology into the agriculture sector which advocates a production model that optimizes \nthe services provided by biodiversity to reduce the use of inputs and energy (INRA 2006; \nBourgeois 2015). \n \nThe Mediterranean basin is dominated by rain-fed agriculture with a wide cultivation practice \nof winter cereals such as wheat and barley, rotated with a fallow period lasting from 16 to 18 \nmonths (Raymond et al. 2018). When moisture permits, cereals are accompanied by olive trees, \nalmond trees, and vines. With irrigation, diversification and intensification are practised, \nincluding the cultivation of fruit trees (apple, pear, peach, citrus, olive), vegetables (fava beans, \nlentils, chickpeas), forage crops (vetch, alfa), potatoes, industrial crops (sunflower, sugar beet, \ncotton, rapeseed) (Lahmar 2006). On the southern shores of the Mediterranean, especially in \narid areas, livestock farming is present in virtually all agroecosystems, with a strong interaction \nwith crops, particularly in cereal-growing regions where it has been and continues to be the \nmain, if not the only, basis for economic activity in these regions (Merdas et al. 2021). \n \nSoil is a living environment and constitutes an exceptional reservoir of different \nmicroorganisms and genes that determine various activities, which are more or less directly \nlinked to their functioning in general and some of their agronomic properties in particular \n(Timmis and Ramos 2021). These microorganisms are the foundation of the biosphere and play \nan important role in biogeochemical cycles, conditioning the efficiency and mechanisms of soil \norganic matter use (Bowles et al. 2014; Dhaliwal et al. 2019). These diverse organisms interact \nwith each other and plants and animals in the ecosystem, forming a complex network of \nbiological activities that contribute to a wide range of essential services for the sustainable \nfunctioning of all ecosystems. These services are essential for the functioning of natural \necosystems and constitute an important resource for the sustainable management of agricultural \nsystems. The biological activity and composition of the soil can be affected by the spatial \nvariability of agricultural landscapes, including variations in soil physicochemical \ncharacteristics and agroecosystem management (Schipanski and Drinkwater 2012; Vasseur et \nal. 2013). \n \nToday, inappropriate types and cultural practices and land over-exploitation that do not \ncorrespond to the pedoclimatic evolution of the environment, combined with the conventional \ntillage technique involving ploughing (cutting and turning over a strip of land), have reached \ntheir development limits in arid regions, where tilled lands are directly subject to erosion \nproblems (Abdellaoui et al. 2010; Boudiar et al. 2022). Comparing agricultural soil to a \nreference site under a native forest, carbon stocks were 50 to 75% lower in agricultural soils \n(Spaccini et al. 2004; Zouidi et al. 2019). Therefore, it is very easy to lose soil organic carbon \ndue to its use and management, but it is very difficult to reach the initial level found in natural \nforests (Pouya et al. 2013; Di Sacco et al. 2021). The effects of land use, management practices, \nand types of agriculture on the physical, chemical, and microbiological properties of soil can \nprovide essential information for assessing sustainability and environmental impact (Swarup \net al. 2019). It is nevertheless indispensable to stem the degradation of soils, which has \ncontinued to increase over the past decades (Zouidi et al. 2018; Diop et al. 2022). \n \n\n\n\nThis work aims to study the effect of crop types based on their rooting and agricultural practice \nsystems of each crop type on the physicochemical and biological quality of agricultural soils \nin the semi-arid zone. In this study, we focused on studying agricultural soils in the Saida region \n(western Algeria) of the three most commonly grown crops in this area: cereals, legumes \n(cultivation of fava beans and peas), and orchards (almond groves). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n82 \n \n\n\n\nMATERIALS AND METHODS \n \nLocalization and Description of Study Sites \nThe study area is a ten-hectare agricultural land located 45 km west of Saida upstream of the \nYoub municipality, in a semi-arid zone (34\u00b057'46.86\"N; 0\u00b012'6.71\"W; altitude 670 m). The \nagricultural land contains a one-hectare almond orchard, with the remaining area generally \ncultivated with cereal crops (wheat and barley) and food legumes (broad beans and cultivated \npeas) (Figure 1). \n\n\n\n \nFigure 1. The study area with the different sampling stations (March 2019). \n\n\n\nSoil Sampling  \nFive sampling stations were selected for each type of crop (cereal, legume, almond orchard). \nAt each station with an area of 400 m2, five soil samples were randomly collected in March \n2019, at a depth ranging from 0 to 20 cm corresponding to the organic-mineral surface horizon, \nresulting in a total of 25 samples for each crop type. The composite soil samples were sieved \nat 2 mm to perform certain physicochemical analyses, and a portion was kept at 4\u00b0C for 15 \ndays pending biological analyses. \n \n\n\n\nPhysical Analysis  \nField moisture content was measured according to the protocol of (Mathieu and Pieltain 1998). \nSoil water content was obtained by subtracting the mass of a soil sample after oven-drying \n(105\u00b0C, 24 h) from the mass of the sample before drying. Water content at field capacity was \nobtained using PVC cylinders according to the protocol described by Saetre (1998). Bulk \ndensity (Da) corresponds to the dry weight of a soil volume with an undisturbed structure and \nis measured by the cylinder method using undisturbed samples, knowing the constant dry \nweight of the samples at 105 \u00b0C and the volume of the cylinder used for sampling (Blake and \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n83 \n \n\n\n\nHartge 1986). The measurement of real density (Dr) was determined by the pycnometer \nmethod, which consists of determining the volume of benzene displaced by the solid phase of \na sample of known mass in a known volume (Petard 1993). Porosity can be determined from \nreal and bulk density. Soil permeability represents the height of water infiltrated per unit of \ntime evaluated by centimeters (Mathieu and Pieltain 1998). \n \n\n\n\nChemical Analysis \nTotal organic carbon was determined by the method of oxidation of the cold organic matter by \nan excess of potassium dichromate (K2Cr2O7) in the presence of concentrated sulfuric acid \naccording to the protocol of Anne described by Aubert (1978). Total nitrogen is measured using \nthe Kjeldhal method. Organic matter content is measured by the loss of mass of a dried sample \nupon calcination at 550\u00b0C for 16 h. pH and electrical conductivity are first measured in boiled \ndistilled water on a fine earth suspension (1:2.5) using the electrometric method with a glass \nelectrode (pH meter HI2210; conductimeter HI2300). Total limestone (CaCO3) was estimated \nusing the Bernard calcimeter. This method measures the volume of CO2 released by the soil \nsamples upon exposure to hydrochloric acid (HCl) (Aubert 1978). \n \nBiological Analysis \nBasal respiration:  Basal respiration (\u03bcg C-CO2/g dry soil) allows for the evaluation of the \nphysiological state of soil microbial communities. It is measured using the protocol described \nby Anderson and Domsch (1978) with the help of a gas chromatograph (Chrompack CHROM \n3 \u2013 CP 9001). The chromatograph was equipped with a TCD detector and a packed column \n(Porapack) through which helium flowed at a rate of 60 mL/h. The obtained values were \nadjusted to 22 \u00b0C by the ideal gas law with Q10 = 2. Ambient concentrations of CO2 were \nsubtracted from the concentrations of CO2 measured after incubation to obtain the amount of \nCO2 produced by the heterotrophic microorganisms contained in the sample. \n \nMicrobial biomass: The microbial biomass was estimated using the method of induced \nrespiration by adding a mixture of talc and glucose (1,000 \u03bcg C g-1 soil) to ten g (dry \nequivalent) of soil, after incubation to reach a maximal rate of induced respiration (Anderson \nand Domsch 1978). The CO2 concentration in the flasks was analyzed by gas chromatography \nand corrected in the same manner as described previously for basal respiration. The rates of \ninduced respiration were converted to microbial biomass values using the equation given by \n(Beare et al. 1990). \n \nEnumeration of the Microflora \nThe condition of the soil can be determined by analyzing the state of various groups of \nmicroorganisms, including bacteria, actinomycetes, fungi, algae, and rhizobia. The microflora \nof the soil is characterized by the number of distinct groups within the microbial population of \nthe soil. However, analyzing the state of different microorganisms in the soil is of great \nimportance. Measuring microbial densities using the soil suspension-dilution technique is a \ngood overall indicator. \n \nBacterial microflora: To obtain bacteria from soil, it is sufficient to suspend a few grams of \nsoil in water. After agitation and settling, we spread a few drops of the supernatant onto a \nnutrient agar medium with soil extract. This results in separate colonies, each originating from \na single bacterium (Davet 1996). The results are read by counting the colonies that appear after \nincubation for 24 h at 28\u00b0C using a colony counter. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n84 \n \n\n\n\nFungi: Fungi are cultivated on a culture medium (PDA) and inoculated with soil suspension \ndilutions at a rate of 3 drops from each dilution (10-1 to 10-6), or 0.2 ml, deposited on each plate \nand carefully spread over the entire surface. Generally, efforts are made to avoid competitive \nbacterial growth by acidifying the medium or adding citric acid to a pH of 4 (Davet 1996). The \nresults are read starting from the seventh day of incubation (28\u00b0C). \n\n\n\nRhizobia: Inoculation is done with soil suspension dilutions on a sterilized YEM (Yeast, \nExtract, Mannitol) medium autoclaved at 120\u00b0C for 20 min, which particularly favors the \ngrowth of rhizobia by inhibiting the growth of other microorganisms. Three dilution plates \nbetween 10-2 and 10-6 are inoculated. Incubation is done for 72 h at 28\u00b0C in an inverted position \n(Vincent 1970). \n\n\n\nStatistical Analysis \nStatistical analysis of the results was performed using Minitab 17 software to compare the soil \nresults based on the analytical variability of the physicochemical and biological properties \namong the soils of the study area using one-way analysis of variance (One-Way ANOVA). \nThen, the homogeneity of the groups was tested using Fisher's test. \n \n\n\n\nRESULTS AND DISCUSSION \n \nThe soil is the superficial part that forms the skin of our planet and represents only a thin layer; \nbut farmers or foresters understand its importance well because it forms a non-renewable \nresource on the scale of human life.  Hence, it is of utmost importance to know its \ncharacteristics. It is in the soil that seeds germinate and organic matter is recycled (Lal 2015; \nGirard et al. 2011). The physical, chemical, and biological characteristics of the soil condition \nthe functioning of the entire ecosystem, but conversely, climatic factors, type of vegetation, \npresence or absence of fauna, and nature of the parent rock, also influence the formation and \nevolution of soils (Bai et al. 2018; Kehal et al. 2021; Dahmani et al. 2023). Fertile soil is \nfundamental to our ability to achieve food security, but soil degradation problems are \nexacerbated by poor management. Therefore, it is necessary to better understand management \napproaches that provide multiple ecosystem services from agricultural lands (Holland et al. \n2018). \n \nRelationship Between Physical Quality of Agricultural Soils and Type of Agriculture \nPracticed \nAgricultural soils in our study area for different crop types exhibit significantly different \nphysical properties (p<0.05), except for values of bulk density and porosity (Table 1). Based \non the average particle size distribution, soils under cereals and legumes have a loamy-sandy \ntexture while soils intended for orchards have a loamy-clay texture. The cereal field has the \nlowest moisture content (4.68%) compared to other types, which shows homogeneity for \nmoisture content (6.79% - 7.87%). The presence of a proportion of clay in orchard soils \nsignificantly increases bulk density (1.67 g/cm3) and decreases porosity and permeability \n(45.3% - 37.19 cm/h). \n \n \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n85 \n \n\n\n\nTABLE 1 \n\n\n\nPhysical properties of agricultural soils under the different crop types \n\n\n\nPhysical properties Cereals Legume Arboriculture Calculated f value and \nsignificance \n\n\n\nTexture loamy-sandy loamy-sandy loamy-clay / \n \n\n\n\nMoisture (%) 4.68\u00b10.88b \n \n\n\n\n6.79\u00b1 1.05a \n \n\n\n\n7.87\u00b1 1.17 a \n \n\n\n\n12.07** \n\n\n\nWater retention (%) \n \n\n\n\n46.32\u00b1 1.27 b \n \n\n\n\n44.46\u00b1 1.86 b \n \n\n\n\n53.38\u00b1 4.21 a \n \n\n\n\n14.53** \n\n\n\nApparent density (Da) \n \n\n\n\n1.22\u00b1 0.06 b \n \n\n\n\n1.34\u00b1 0.07 b \n \n\n\n\n1.64\u00b1 0.33 a \n \n\n\n\n5.61* \n\n\n\nReal density (Dr) \n \n\n\n\n3.60\u00b1 2.33 a \n \n\n\n\n3.60\u00b1 1.15 a \n \n\n\n\n3.07\u00b1 1.08 a \n \n\n\n\n0.17ns \n \n\n\n\nPorosity (%) \n \n\n\n\n63.99\u00b112.27 a \n \n\n\n\n59.84\u00b110.53 a \n \n\n\n\n45.3\u00b1 22.7 a \n \n\n\n\n1.86ns \n \n\n\n\nPermeability (cm/h) \n \n\n\n\n41.65\u00b1 1.55 a \n \n\n\n\n41.99\u00b1 0.85 a \n \n\n\n\n37.19\u00b1 0.97 b \n \n\n\n\n26,14** \n \n\n\n\n \nKnowledge of soil texture allows for indication of trends in the physical qualities of soil that \nprimarily influence soil water regimes. According to the performed granulometric analysis, the \nchange in soil texture between arboriculture and other crops is the result of tillage that varies \naccording to the different types of crops grown. According to Roger-Estrade et al. (2014), \ntillage technique changes depend on the type of crop practised, which modifies the soil texture, \nsuch as the arrangement of voids, aggregates, pore connectivity, and soil aeration \n(Steponavi\u010dien\u0117 et al. 2022). In our study area, as the same semi-arid climatic conditions \nprevailed, significant variation in moisture is explained by the type of crop and its rooting in \nthe soil, as well as the effect of agricultural practices, particularly tillage, which can modify \nsoil physical parameters (Boiffin et al. 2020). It is therefore essential to take into account the \ncumulative effects of cropping systems to manage the conditions for tillage intervention. The \nproportion of sand present in cereal and legume soils prevents them from retaining water, \nunlike the soil in arboriculture, which contains a higher proportion of clay and promotes water \nretention. Additionally, vegetation cover plays a role in water retention as protected soil is less \nsusceptible to evaporation. Other scientists explain variations in soil moisture by the effect of \nvariations in precipitation and temperature.  \n \nThe accumulation of organic matter on the surface layers, particularly depending on the type \nof crop (cereal and legumes), contributes to improving the physical properties of the soil, \nparticularly its apparent density to provide better porosity (Brewer et al. 2014). Several studies, \nparticularly on semi-arid climates in Morocco, have shown that no-till and the passage of \nagricultural machinery make the soil more compact and less permeable, such as in the case of \narboriculture (Mrabet et al. 2001). Soils containing more clay have a very compact and dense \nstructure, which decreases their porosity and permeability, making water movement slower. \n \nChemical Quality Changes of Agricultural Soils Depend on Type of Crop \nThe determination of carbon and nitrogen levels in the different soils shows a very small \nvariation (p<0.05), with the highest carbon level recorded for cereal crops (0.62g/kg), followed \nby legume soils (0.56g/kg) and orchards (0.53g/kg). In contrast, the highest nitrogen level is \nfound in soils under legumes (0.108g/kg), followed by cereal crops (0.082g/kg). Organic matter \nlevels are correlated with carbon levels. The pH of the studied plots' soils is generally alkaline \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n86 \n \n\n\n\n(pH>8), with a significant difference between agricultural types (p<0.01). The electrical \nconductivity of soils from different crop type ranges from 0.08 ms to 0.10 ms, and according \nto Aubert's salinity scale (1978), these are non-saline soils (CE<0.6 mS). Measurements of \nlimestone content show a high level of limestone in orchard soils, which are strongly calcareous \n(27.53%), while cereal and legume soils are weakly calcareous (between 6.67% and 8.01%). \nStatistical analysis shows a highly significant difference (p<0.01). \n\n\n\n \nTABLE 2  \n\n\n\nChemical properties of agricultural soils under the different crop types \n\n\n\n\n\n\n\n \nA change or difference in soil management according to the type of agriculture practised can \nlead to changes in the physical properties of the soil and consequently affect the chemical and \nbiological properties of the soil, and thereforeits function (Chan 2001; Islam and Weil 2000). \nThe evolution of carbon levels in the soil depends on the mechanisms protecting organic matter \n(OM) from microbial biodegradation, namely: physical protection by trapping OM inside soil \naggregates, physicochemical protection by the association of OM with the mineral fraction of \nthe soil, and chemical recalcitrance of OM due to condensation of its chemical structure (Chenu \net al. 2019). Intensive agricultural practices often cause erosion, leading to soil fertility \ndegradation (Garc\u00eda-Ruiz et al. 2009; Comino et al. 2018). Soil organic carbon (SOC) \ncontributes to maintaining soil health and food security due to its important role in water \nretention and nutrient supply (Allam et al. 2021; Lessmann et al. 2022). Legumes, which are \nan important part of farming systems, have been extensively studied for their significant \necological benefits, particularly in enriching the soil with nitrogen (Aschi et al. 2017). During \ngrowth, legumes can fix N2 from the atmosphere through symbiotic associations with rhizobia, \nincreasing the nitrogen (N) content of legume biomass (Espinoza et al. 2020). The N fixed in \nthe soil by legumes and contained in their residues that enter the soil, increases the availability \nof N for the benefit of other crops grown in the farming system, thus increasing the supply of \nC to the agroecosystem (Liu et al. 2022). Furthermore, legume residues in the soil with a lower \nC/N ratio can be effectively utilized by the soil microbiome, thus reducing C losses in the \nagroecosystem (Cotrufo et al. 2013). \n \nIn contrast, cereal crops are an excellent source of organic matter for agricultural soils due to \ntheir straw, which improves carbon storage (Solberg et al. 2019). Land use type strongly affects \n\n\n\nChemical properties Cereals Legume Arboriculture Calculated f value \nand significance \n\n\n\nCarbon (C) (g/kg) \n \n\n\n\n0.62\u00b10.14a \n \n\n\n\n0.56 \u00b1 0.18ab \n \n\n\n\n0.53\u00b1 0,28b \n \n\n\n\nF=1.78* \n\n\n\nNitrogen (N) (g/kg) \n \n\n\n\n0.08\u00b10.03ab \n \n\n\n\n0.11\u00b10.04a \n \n\n\n\n0.05\u00b1 0,01b \n \n\n\n\nF=5.81* \n \n\n\n\nC/N \n \n\n\n\n8.03\u00b11.67ab \n \n\n\n\n5.15\u00b10.95b \n \n\n\n\n11.95\u00b17.91a \n \n\n\n\nF=2.63* \n \n\n\n\nOrganic matter (%) \n \n\n\n\n1.07\u00b10,25a \n \n\n\n\n0.97\u00b10,32b \n \n\n\n\n0,92\u00b1 0,48b \n \n\n\n\nF=1.18* \n\n\n\npH water 8,17 \u00b1 0,05b 8.20 \u00b1 0.03 b \n \n\n\n\n8.61\u00b1 0,05 a \n \n\n\n\nF=35.03** \n\n\n\nConductivity (mS) \n \n\n\n\n0.09\u00b1 000 \n \n\n\n\n0.10\u00b1 0.02 \n \n\n\n\n0.10\u00b1 0.01 \n \n\n\n\nF=1.03NS \n\n\n\nTotal limestone (%) \n \n\n\n\n810 \u00b13.61b \n \n\n\n\n6.66 \u00b1 1.99 b \n \n\n\n\n27.53 \u00b1 3.39 a \n \n\n\n\nF=71.59** \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n87 \n \n\n\n\nthe control of nitrogen and carbon levels in the soil and allows for control of the C/N ratio \n(Assemien 2018). According to Baise (2018), the C/N ratio serves to characterize the organic \nmatter under the type of cultivated crop, and in ploughed surfaces, the upper horizon under \ncultivation with a ratio equal to or greater than 12 indicates that mineralization encounters \ndifficulties such as anaerobic conditions in the presence of clay, as shown in the case of the \nsoil in our orchard. On the other hand, this organic matter decreases due to the sandy structure \nof the soil, which is too aerated, and thus organic matter decomposes more quickly, which is \nnot the case in clay soils that provide physical protection of organic matter (Karabi 2016). \n \nIntensive tillage deteriorates soil quality and threatens long-term agricultural production \n(Lopez-Bellido et al. 2013). Fertility, structure, and organic matter (MO) of the soil decrease \ndue to tillage and other practices (grazing, export of straw) that prevent the incorporation of \norganic material into the soil (Lopez-Bellido et al. 2013). \n \nThe studied soils are alkaline (pH>7). When soils contain a lot of carbonates, oxides, and \nhydroxides that neutralize H+ ions, the pH becomes alkaline (pH 8 and above), therefore there \nis a close relationship between the limestone content and the degree of soil acidity, which vary \ninversely (Ramade 2003). Salts are more soluble in water than gypsum. Their overall \nconcentration is generally expressed by electrical conductivity, which actually represents \nelectrolytic conductivity (Halitim 1988). The slightly elevated electrical conductivity in our \norchard soils depends on the electrolyte content (SO4, Cl, K, Na, Mg, Ca) which expresses the \nsalt concentration. According to some authors, the main soil properties determining electrical \nconductivity are soil depth and clay content (Uribeetxebarria et al. 2018). The evolution of \nlimestone is important in soil formation in semi-arid regions, which is mainly due to very \nabrupt changes in humidity during rain and intense drought for most of the year (Aubert 1951). \nAccording to Drouet (2010), CaCO3 content in carbonate soils is extremely variable, ranging \nfrom a few per cent to over 70%, and the most abundant carbonate is calcite (CaCO3). Having \nlimestone soil is often considered a calamity by gardeners. A soil is considered limestone when \nit contains 10 to 30% of lime carbonate which is always associated with clay and which makes \nthe soil rather sticky (Gerbeaud 2018). \n \nVariation of Biomass, Activity, and Microbial Diversity of Agricultural Soils According \nto Crop Types \n \nMicrobial properties of soils under different crops \nRegarding microbiological properties, statistical analyses show a weakly significant difference \nin basal respiration (p<0.05) between different soils depending on the type of crop grown. The \nhighest values of microbial activity are recorded for arboriculture soils (0.437 \u00b5g CO2-C/g \nsoil/hour), while the lowest value is recorded for legumes (0.295 \u00b5g CO2-C/g soil/hour). \n \nThe microbial biomass of the soils is homogeneous in the different types of crops (p>0.05), \nwith averages ranging between 0.069 and 0.085 \u00b5g Cmic/g. Microbial biomass varies between \n0 and 700 to 800 mg C/kg of soil in agricultural soils. \n \nThe metabolic quotient for different types of crops has an average range of between 5.04 and \n5.24 \u00b5g CO2-C/g soil/hour. The results show that the efficiency with which microorganisms \nuse the available carbon in the soil for their biosynthesis does not present any significant \ndifference, such as microbial biomass, between the soils. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n88 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure. 2. Microbial properties of agricultural soils \n\n\n\nThe soil is considered one of the most complex environments in the biosphere and is therefore \na major reservoir of microbial diversity. Living organisms in the soil include bacteria, fungi, \nalgae, the underground parts of plants, and a wide variety of animals that play a fundamental \nrole in important processes such as the regulation of biogeochemical cycles (nitrogen, carbon, \nsulfur) (Dubey et al. 2019; Yadav et al. 2021). Improving the activity and specific diversity of \nsoil fauna (micro, meso, and macro) is therefore essential for restoring and improving soil \nquality and reducing the risks of soil degradation (Moreno et al. 2008; Borsali et al. 2017; \nDouaer et al. 2021). \n \nThe harmful effects of agricultural management on the microbiological quality of soils are a \nglobal concern (Bastida et al. 2006; Lal 2015). The variation in microbial diversity and their \nactivity is correlated with the presence of organic matter, which forms the energetic state of the \nsoil. According to Delogu (2013), the quantity and quality of crop residues play an important \nrole in soil heterotrophic respiration because residues are directly decomposed by soil \nmicroorganisms. The quality of the substrate refers to the biochemical composition of tissues: \na highly woody compound will be difficult to decompose, and its residence time in the soil will \nbe very long. The quality of the substrate, which thus influences the rate of decomposition of \norganic matter, determines the short-term respiration flux of the soil and the long-term storage \n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\n\n\n\nLegume Cereals Arboriculture\n\n\n\n(\u00b5\ng \n\n\n\nC\nO\n2 -\n\n\n\nC\n/g\n\n\n\n so\nl/h\n\n\n\neu\nre\n\n\n\n)\n\n\n\nBasal respiration\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\nLegume Cereals Arboriculture\n\n\n\n(\u00b5\ng \n\n\n\nC\nO\n2 -\n\n\n\nC\n/g\n\n\n\n so\nl/h\n\n\n\neu\nre\n\n\n\n)\n\n\n\nmetabolic quotient (qCO2)\n\n\n\n0\n\n\n\n0.02\n\n\n\n0.04\n\n\n\n0.06\n\n\n\n0.08\n\n\n\n0.1\n\n\n\nLegume Cereals Arboriculture\n\n\n\n(\u00b5\ng \n\n\n\nC\nm\n\n\n\nic\n/g\n\n\n\n)\n\n\n\nMicrobial biomass \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n89 \n \n\n\n\nof carbon in the soil (Fontaine et al. 2004; Chirinda et al. 2010). These substrate-influenced \nprocesses come directly into conflict with climatic or soil texture effects. Soils in orchards are \nmore exposed to the sun compared to other crops that cover it completely, which increases the \nsoil temperature, whereas numerous studies show that soil respiration increases exponentially \nwith temperature (Marino et al. 2019). Microbial activity, i.e., the depletion of OM, is \nstimulated by cultural practices such as fertilization, irrigation, or tillage (Jansa et al. 2006). \nHowever, intensification of land use could result in a continuous decline in soil organic stock, \nas in the case of soils on large European plateaus (Van-Camp et al. 2004). According to \nLechevallier et al. (2004), this variation is linked to the crop: biomass (BM) grassland > BM \narable crops and orchards > BM market gardening > BM vineyards, and to cultural techniques: \nBM grassing > BM tillage. The metabolic quotient or specific respiration rate serves as an \nindicator of the physiological state of soil microorganisms. According to Dilly (2005), qCO2 \nin the upper horizons of cultivated soils varies from 0.5 to 10 mg C-CO2 g/Cmic/hour. High \nqCO2 values reflect poor substrate quality and low metabolic efficiency (Flie\u00dfbach et al. 2007). \n \nMicrobial density and diversity \nIn agroecosystems, the soil contains many types of microorganisms - microscopic forms of \nanimal life such as bacteria, actinomycetes, fungi, and algae. Soil microorganisms are \nimportant because they affect the physical, chemical, and biological properties of soils. For \nexample, the process of decomposition, breakdown, and disappearance of dead plant and \nanimal matter occurs due to the action of many different types of microorganisms (Wahome et \nal. 2023). There is a group of PGPR strains acting as biocontrol agents in order to suppress the \npathogens and thus prevent plants from diseases or infections. The same mode of action is \nrequired by the plant to develop resistance against bacterial, fungal, viral phytopthogens, \ninsects and nematodes. The ability of PGPR to produce and discharge metabolites which can \nameliorate pathogens\u2019 microbial loads and their activities or rhizosphere microflora that are \ndeleterious is another major type of action found in several strains of PGPR (Odelade and \nBabalola, 2019). Characterization of soil bacterial communities was done using in vitro culture \nmethods on nutrient media, but these did not allow for comprehensive characterization. Among \nthe recognized major groups of bacteria are cellulolytic bacteria which degrade cellulose and \npectinolytic bacteria that degrade pectin and its derivatives. The most abundant ones belong to \nthe genus Arthrobacter. Functional groups involved in the nitrogen cycle include ammonifying \nand nitrifying bacteria, and atmospheric nitrogen fixing bacteria, which transform it into \ncompounds usable by plants (ammonia). These are notably symbiotic bacteria located in the \nrhizosphere of cultivated plants (Rhizobium in legumes) (Riou and Prevostbourre, 2018). \n \nThe bacterial biomass has a slightly higher average in almond orchard soils (53 x106 CFU/g \ndry soil) followed by cereal crops (50.75 x106 CFU/g dry soil) and legumes (49.87 x106 CFU/g \ndry soil). The variance analysis shows the homogeneity of microbial biomass between different \ntypes of crops (p>0.05). It is also noteworthy that legume soils have a higher density of rhizobia \n(75.6 x104 CFU/g dry soil) compared to cereal and orchard soils (38.3 x104 CFU/g dry soil and \n32.3 x104 CFU/g dry soil, respectively). The statistical analysis of these results shows a \nsignificant difference (p<0.05) among the three sites. The fungal density is low compared to \nbacterial and rhizobial density. A weakly significant difference in soil fungal density according \nto the expressed crop type is observed (p<0.05) (Table 3). \n \n \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n90 \n \n\n\n\nTABLE 3 \n\n\n\nMicrobial density and diversity under the different crop types \n\n\n\n \nBacteria are both quantitatively and functionally the major group of microorganisms in the soil. \nFor example, it is estimated that 1g of soil contains between 106 and 109 bacteria (Soltner \n2003). They are relatively limited in soils that are insufficient in organic matter and water \n(Dubey et al. 2019). Bacterial densities are generally lower in cultivated soils with tillage, \nespecially in cereal crops, compared to grasslands or forest, or fruit soils (Constancias et al. \n2014). \n \nAmong the soil bacteria there is a unique group called Rhizobia that have beneficial effect on \nthe growth of legumes. Rhizobium is soil-inhabiting bacteria that form the root nodules where \nsymbiotic biological Nitrogen fixation occurs (Howieson and Dilworth 2016). The importance \nof rhizobial density in legumes is due to the rhizobial symbiosis established with a large number \nof species in the Fabaceae family (legumes), which is the third largest family after Asteraceae \nand Orchidaceae. The Rhizobium-legume symbiosis is a mutualistic association with \nreciprocal benefits between legumes and rhizobium-type bacteria. The latter allows the transfer \nof nitrogen from the air in a form assimilable by plants. In exchange, the plant provides rhizobia \nwith carbon resulting from its photosynthesis (Haag et al. 2013). During symbiosis, a new \norgan, the nodule, is formed on the roots or more rarely on the stems where atmospheric \nnitrogen is fixed by the bacteria (Haag et al. 2013; Niste et al. 2014). \n \nMabrouk et al (2018) suggest that the presence or absence of rhizobia in natural soil depends \non their growth, physical properties of the soil, and the host plant. Host specificity is one of the \nmajor characteristics of the rhizobium-legume symbiosis. Each bacterial species has a well-\ndefined host spectrum, the amplitude of which is highly variable (Dommergues 2006). \nAlkalinity is less harmful to the survival of rhizobia. The majority of these bacteria can tolerate \npH values up to 9 (Zhang et al. 2020).  \n \nThe results show that fungal density is low compared to bacterial and rhizobial density. Fungi \nare not the most numerous microorganisms in the soil, but their weight is very important due \nto their large size, compared to bacteria (Huber and Schaub 2011). The low number of fungi in \norchard soils is explained by their texture, which has a high proportion of clay. There is a \nnegative correlation between clay content and fungal biomass, while this correlation is positive \nwith bacterial biomass (Fotio et al. 2009). Moreover, fungi predominate in the decomposition \nof low-quality materials (Senn-Irlet et al. 2012). This decrease can be explained by the \nparticularity of fungi with regard to acidity. Indeed, fungi prefer acidic environments where \nthey do not encounter competition from bacteria. The alkaline pH of our soils explains the low \ndensity of fungi compared to bacteria, as fungi thrive in a low pH environment (Rousk et al. \n2009). \n \n\n\n\nMicrobial groups Cereals Legume Arboriculture Calculated f value \nand significance \n\n\n\nBacteria (x106 CFU. g-1. s. s) 50.75 \n \n\n\n\n49.87 \n \n\n\n\n53 \n \n\n\n\nF=0.02ns \n \n\n\n\nFungi (x103 CFU.g-1 .s.s) 16.66 \n \n\n\n\n25.33 \n \n\n\n\n11.33 \n \n\n\n\nF=2.07* \n\n\n\nRhizobia (x104 CFU.g-1. s.s) 38.03 \n \n\n\n\n75.66 \n \n\n\n\n32.33 \n \n\n\n\nF=5.51* \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 80-96 \n \n\n\n\n91 \n \n\n\n\nCONCLUSION \n \nCereal cultivation, arboriculture, and legume farming are the main agricultural practices in \nsemi-arid zone. This study aimed to investigate the characterization of agricultural soils based \non the type of crops grown. Analysis of physicochemical properties showed that changes in \ncrop types affect the soil water regime by modifying soil texture, density, and permeability. \nCereal and legume farming improves chemical fertility more than arboriculture through organic \nmatter and nitrogen amendment. \n \nThe microbial properties of the soil showed homogeneous microbial biomass between soils, \nwith little variation in basal respiration represented by CO2 release. These results were \nconfirmed by the enumeration of microorganisms and their diversity. This bacterial life is \nlinked to soils rich in organic matter, nitrogen, water, and agricultural practice techniques, \nmainly ploughing, depending on the crop type. Regarding the density of rhizobia in soils, we \nnote that legume soils have a higher density of rhizobia than cereal and orchard soils due to \nrhizobium-legume symbiosis. Unlike alkaline soils, a low fungal density also characterizes \nthem because fungal development requires an acidic pH. Therefore, agricultural soils in the \nsemi-arid zone are fragile and vulnerable. In this regard, not only do physicochemical and \nbiological corrections need to be considered, but much more attention must be paid to the socio-\neconomic context in which soil protection programs must be successfully carried out, such as \ncultural practices on land and the use of crop rotation systems. \n \n\n\n\nACKNOWLEDGEMENTS \n \nThis work is part of a PRFU project on agricultural soils agricultural and forestry soils in the \narid zone facing the stresses of climate change and anthropo-genic action: what indicators to \nassess their vulnerabilities and allow their restorations? The authors thank Aix Marseille \nUniversity, CNRS, IRD, Avignon University, IMBE UMR 7263, Marseille, France for the \nmicrobiological analyses. \n \n\n\n\nREFERENCES \n \nAbdellaoui, Z., H. 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"\n\n___________________\n*Corresponding author : dianatzahra31@yahoo.com\n\n\n\nINTRODUCTION\nSoil contamination has emerged as a major environmental problem in many \ncountries in recent years. With increasing population, the request for food has \nincreased enormously, resulting in the adaptation of agricultural practices that not \nonly increase agricultural production but also result in pollution of heavy metals \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 133-145 (2018)                          Malaysian Society of Soil Science\n\n\n\nFractionation of Cadmium in a Calcareous Sandy Loam Soil \nAmended with Almond Soft Husk Compost and Biochar\n\n\n\nZahra Dianat Maharlouei* and  Majid Fekri\n\n\n\nDepartment of Soil Science, College of Agriculture, Shahid Bahonar \nUniversity of Kerman, Kerman, Iran\n\n\n\nABSTRACT\nReducing the mobility of heavy metals in soils is one of the best soil remediation \nmethods to decrease the mobile fraction of the metals. This study was conducted \nto evaluate the degree of mobility and fractionation of cadmium (Cd) after the \naddition of almond soft husk compost and biochar in a sandy loam calcareous \nsoil. The efficacy of amendments was evaluated using sequential extraction. This \nresearch was factorial and based on a completely randomised design with three \nlevels of cadmium nitrate (0, 40 and 80 mg kg-1 soil) and three levels (0, 2 and 4 wt \n%) of almond soft husk compost and biochar application rate. It was conducted in \nthree replications. The application of biochar at 4 wt % to soil spiked with 40 mg \nkg-1 cadmium reduced the exchangeable Cd by 66% and the carbonate-Cd by 23% \nduring the 45 days after incubation compared to the control treatment. Meanwhile, \nthe application of biochar at 4 wt % to soil spiked with 80 mg kg-1  cadmium \nreduced the exchangeable Cd by 62% and the carbonate-Cd by 17% compared \nto the control treatment. The application of compost at 4wt% to soil spiked with \n40 mg kg-1 cadmium reduced the exchangeable Cd by 39% and the carbonate-\nCd by 16% compared to the control treatment, while the application of compost \nat 4 wt % to soil spiked with 80 mg kg-1 cadmium reduced the exchangeable \nCd by 47% and the carbonate-Cd by13.5% compared to the control treatment. \nThe application of biochar at 4 wt %  to soil spiked with 40 mg kg-1 cadmium \nreduced the exchangeable Cd by 40% and the carbonate-Cd by 7% compared to \nthe compost, while the application of biochar at 4 wt % to soil spiked with 80 \nmg kg-1 cadmium reduced the exchangeable Cd by 28% and the carbonate-Cd \nby 2% compared to the compost. The highest mobility factor in soils treated with \ndifferent concentrations of cadmium was related to control treatment, which was \ncalculated to be 57.4%. \n\n\n\nKeywords: Calcareous soil, chemical forms, mobility,sequential \nextraction,soil pollution.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018134\n\n\n\nin soil (Fernandez-Ondono et al. 2017). Anthropogenic input can increase levels \nof heavy metals in agricultural soils and reduce the quality of agricultural products \nand crop yield, degrade soil quality and easily transfer the heavy metals from soil \nto groundwater. The presence of heavy metals in groundwater can result in long \nlasting toxic effects as they cannot be degraded (Tashakor et al. 2014). Cadmium \n(Cd) is one of the heavy metals that exists in contaminated soils, causing long-\nterm danger to humans and the  environments (Liu et al. 2013). \n\n\n\nIn the last years, much research has been done in order to remediate heavy \nmetal polluted soils (Bolan et al. 2014). One such innovation is the use of \npyrogenic forms of carbon called biochar. Biochar is a carbon-rich black solid \nproduced by pyrolysis of carbon rich residues, manure or dead animals and is \nused as a soil amendment in agriculture and the environment (Guo et al. 2016; Ok \net al. 2015; Yang et al. 2016). The properties of biochar produced from different \nfeedstocks and pyrolysis conditions vary widely. Typically, most biochars have \na large surface area, a micro-porous structure, active organic functional groups, \nhigh pH and its cost is low. These are  very attractive features (Wu et al. 2012; \nBeesley et al. 2011). The primary mechanisms of metal immobilisation by biochar \nin soils include an increase in soil pH, precipitation and co-precipitation, physical \nsorption, and ion exchange (Park et al. 2011). The mobility of heavy metals in soil \ndepends on the form of the heavy metals (Zhong et al. 2011; Sahito et al. 2015), \nwhich can be measured by sequential extraction (Kubov\u00e1 et al. 2008). \n\n\n\nSequential extraction methods can predict the reactivity and availability of \ndifferent chemical forms of metals in soils and how these metals are transferred \nfrom the solid phase to the liquid phase (Altundag et al. 2016). Chemical forms \nof metals in soils include several geochemical forms such as water-soluble, \nexchangeable, carbonate-bound, Mn-oxide-bound, Fe-oxide-bound, organic-\nbound, and residue. Water-soluble and exchangeable fractions are considered as \nbioavailable, but the fractions associated with carbonates, oxides, and organic \nmatter can be potentially bioavailable. However, the residual fraction is not \navailable for plants (Rodr\u00edguez et al. 2009). Some researchers have suggested \nthat organic matter (OM) could redistribute heavy metals from soluble and \nexchangeable forms to carbonate, Fe\u2013Mn oxides, and OM-bonded fractions and \ntemporarily reduce their availability to plants (Lee et al. 2009). Thus, the aim \nof the present study was to investigate fractionation of cadmium in a calcareous \nsandy loam soil amended with almond soft husk compost and biochar.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSoil Collection and Characterisation\nThe soil sample was taken from the 0-30 cm layer of a field at the Faculty of \nAgriculture, Kerman University, Iran. The soil was air-dried and passed through \na 2 mm mesh sieve and stored in polyethylene bags prior to use. Selected \nchemical and physical properties of the soil are shown in Table 1. Particle size \nwas determined by the hydrometer method (Bouyoucos 1962); soil pH was \n\n\n\nZahra Dianat\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 135\n\n\n\ndetermined in the saturated extract of the soil using a combination electrode \n(Thomas 1996); electrical conductivity (EC) was measured in the soil saturated \nextract using a conductometer (Rhoades et al., 1996); organic matter (OM) \ncontent was determined by dichromate oxidation (Nelson and Sommers 1996); \nCEC was obtained by saturation with 1Mammonium acetate (NH4OAc) at pH \n7 (Sumner et al., 1996) calcium carbonate (CaCO3) was determined by titration \n(Loeppert and Suarez 1996); available phosphorus (P) was determined according \nto the Olsen method (Olsen et al.1954); total nitrogen by Bremner(1996) method; \nextractable potassium by ammonium acetate molar (Chapman and Pratt 1982) and \nFe, Zn, Mn, Cu and   Cd concentrations were extracted by DTPA and determined \nby atomic absorption spectrometry (AAS) (Lindsay and Norvell 1978). This \nresearch was  factorial  based on a completely randomied design with three levels \nof cadmium nitrate (0, 40 and 80 mg kg-1 soil) and three levels (0, 2 and 4 wt%) \nof either almond soft husk compost or almond soft husk biochar and conducted \nin triplicate. \n\n\n\nFractionation of Cadmium in a Calcareous Sandy Loam Soil\n\n\n\nTABLE 1\nSelected chemical and physical properties of the soil studied\n\n\n\nThe soil sample was taken from the 0-30-cm layer of a field at the Faculty of Agriculture, \nKerman University, Iran. The soil was air-dried and passed through a 2-mm mesh sieve and \nstored in polyethylene bags prior to use. Selected chemical and physical properties of the soil are \nshown in Table 1. Particle size was determined by the hydrometer method (Bouyoucos 1962); \nsoil pH was determined in the saturated extract of the soil using a combination electrode \n(Thomas 1996); electrical conductivity (EC) was measured in the soil saturated extract using a \nconductometer (Rhoades et al., 1996); organic matter (OM) content was determined by \ndichromate oxidation (Nelson and Sommers 1996); CEC was obtained by saturation with \n1Mammonium acetate (NH4OAc) at pH 7 (Sumner et al., 1996) calcium carbonate (CaCO3) was \ndetermined by titration (Loeppert and Suarez 1996); available phosphorus (P) was determined \naccording to the Olsen method (Olsen et al.1954); total nitrogen by Bremner(1996) method; \nextractable potassium by ammonium acetate molar (Chapman and Pratt 1982) and Fe, Zn, Mn, \nCu and   Cd concentrations were extracted by DTPA and determined by atomic absorption \nspectrometry (AAS) (Lindsay and Norvell 1978). This research was  factorial  based on a \ncompletely randomied design with three levels of cadmium nitrate (0, 40 and 80 mg kg-1 soil) \nand three levels (0, 2 and 4 wt%) of either almond soft husk compost or almond soft husk \nbiochar and conducted in triplicate.  \n \n \n \n \n\n\n\n TABLE1 \n\n\n\nSelected chemical and physical properties of the soil studied \nProperty (Unit) Value \nSoil texture Sandy loam \nOM (%) 0.69 \nCCE (%) 8.32 \nEc(dS m-1) 2.39 \npH 7.50 \nCEC (cmol(+) kg-1) 8.93 \nN (%) 0.03 \nK (mg kg-1) 433 \nP (mg kg-1) 14 \nMn (mg kg-1) 1.74 \nCu (mg kg-1) 0.5 \nFe (mg kg-1) 1.52 \nZn (mg kg-1) 0.72 \nCd (mg kg-1) Insignificant \n\n\n\n \nBiochar Production and Characterisation  \nThe almond soft husk was collected from an orchard in Shiraz (Fars province). Windrow method \nwas used for composting.The almond softhusk was put in a pit and covered with plastic and kept \nat temperatures of 37 to 60\u00b0C and a moisture level  of 50 to 60% for 4 months to prepare the \ncompost. The almond soft husk biochar was air-dried and packed in aluminum sheets to limit \noxygenation during the pyrolysis process. Then, the samples were placed in an oven at 500\u00b0C for \n4 h to produce the biochar (Hall et al.,  2008). Almond soft husk was air dried, ground, passed \n\n\n\nBiochar Production and Characterisation \nThe almond soft husk was collected from an orchard in Shiraz (Fars province). \nWindrow method was used for composting.The almond softhusk was put in a pit \nand covered with plastic and kept at temperatures of 37 to 60 \u00b0C and a moisture \nlevel  of 50 to 60% for 4 months to prepare the compost. The almond soft husk \nbiochar was air-dried and packed in aluminum sheets to limit oxygenation during \nthe pyrolysis process. Then, the samples were placed in an oven at 500\u00b0C for \n4 h to produce the biochar (Hall et al., 2008). Almond soft husk was air dried, \nground, passed through a 0.5-mm sieve, and analysed for some of its chemical \ncharacteristics (Table 2). To perform chemical analysis of the compost and biochar, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018136\n\n\n\n1 g of compost and biochar almond soft husk was placed in an electric furnace for \n7 h at 750\u00b0C to obtain their ashes. Then it was dissolved in HCl. pH was measured \nin 1:5 organic matter to water suspension; EC was determined in 1:5 organic \nmatter to water suspension by a conductivity meter; and P was determined by the \nvanadomolybdate phosphate method in a nitric acid system (Chapman and Pratt \n1961), total nitrogen (Bremner1996), and Fe, Zn, Mn, Cu and Cd concentrations \nwere analysed by atomic adsorption spectrometry.\n\n\n\nIncubation Study\nThe experiment was performed by adding 1 kg of soil into each pot, to which was \nadded  either almond soft husk compost or almond soft husk biochar  and mixed \ncompletely. The pots were placed in a greenhouse at 25 \u00b1 5 \u00b0C for 2 months. After \nthe 2-month incubation, either 40 or 80 mg kg-1cadmium was added to each pot \nand mixed completely; the pots were then placed in the glasshouse for another 3 \nmonths. The treatments of the  incubation study are shown in Table 3.\n\n\n\nA soil fractionation study was carried out at 45 and 90 days after incubation \nwhere different chemical forms of Cd was determined adopted from Singh et \nal.(1988) (Table 4).\n\n\n\nTABLE 2\nSelected chemical properties of the compost and biochar tested\n\n\n\nthrough a 0.5-mm sieve, and analysed for some of its chemical characteristics (Table 2). To \nperform chemical analysis of the compost and biochar, 1 g of compost and biochar almond soft \nhusk was placed in an electric furnace for 7 h at 750\u00b0C to obtain their ashes. Then it was \ndissolved in HCl. pH was measured in 1:5 organic matter to water suspension; EC was \ndetermined in 1:5 organic matter to water suspension by a conductivity meter; and P was \ndetermined by the vanadomolybdate phosphate method in a nitric acid system (Chapman and \nPratt 1961), total nitrogen (Bremner1996), and Fe, Zn, Mn, Cu and Cd concentrations were \nanalysed by atomic adsorption spectrometry. \n \n \n\n\n\nTABLE 2 \nSelected chemical properties of the compost and biochar tested \n\n\n\nProperty (Unit) Almond soft husk Almond soft husk compost Almond soft husk biochar \npH 3.21 4.83 11.61 \nEC (dS m-1) 1.15 1.61 7.93 \nN (%) 0.34 0.55 0.68 \nP (%) 0.11 0.13 0.14 \nK (%) 0.32 0.42 0.63 \nFe (mg kg-1) 68 90 140 \nZn (mg kg-1) 8.37 12.24 28.06 \nCu (mg kg-1) 3.18 4.08 8.03 \nMn(mg kg-1) 5.83 8.67 13.27 \nCd (mg kg-1) Insignificant Insignificant Insignificant \n\n\n\n\n\n\n\nIncubation Study \nThe experiment was performed by adding 1 kg of soil into each pot, to which was added  either \nalmond soft husk compost or almond soft husk biochar  and mixed completely. The pots were \nplaced in a greenhouse at 25 \u00b1 5\u00b0C for 2 months. After the 2-month incubation, either 40 or 80 \nmg kg-1cadmium was added to each pot and mixed completely; the pots were then placed in the \nglasshouse for another 3 months. The treatments of the  incubation study are shown in Table 3. \n\n\n\nTABLE 3 \n Experimental design for incubation experiment \n\n\n\ntreatment pollution with cd Level of biochar Level of compost \nCd40B0 40 mg kg-1 0 wt% ------- \nCd40B2 40 mg kg-1 2 wt% ------- \nCd40B4 40 mg kg-1 4 wt% ------- \nCd80B0 80 mg kg-1 0 wt% ------- \nCd80B2 80 mg kg-1 2 wt% ------- \nCd80B4 80 mg kg-1 4 wt% ------- \nCd40C0 40 mg kg-1 ------- 0 wt% \nCd40C2 40 mg kg-1 ------- 2 wt% \nCd40C4 40 mg kg-1 ------- 4 wt% \nCd80C0 80 mg kg-1 ------- 0 wt% \nCd80C2 80 mg kg-1 ------- 2 wt% \nCd80C4 80 mg kg-1 ------- 4 wt% \n\n\n\n\n\n\n\nthrough a 0.5-mm sieve, and analysed for some of its chemical characteristics (Table 2). To \nperform chemical analysis of the compost and biochar, 1 g of compost and biochar almond soft \nhusk was placed in an electric furnace for 7 h at 750\u00b0C to obtain their ashes. Then it was \ndissolved in HCl. pH was measured in 1:5 organic matter to water suspension; EC was \ndetermined in 1:5 organic matter to water suspension by a conductivity meter; and P was \ndetermined by the vanadomolybdate phosphate method in a nitric acid system (Chapman and \nPratt 1961), total nitrogen (Bremner1996), and Fe, Zn, Mn, Cu and Cd concentrations were \nanalysed by atomic adsorption spectrometry. \n \n \n\n\n\nTABLE 2 \nSelected chemical properties of the compost and biochar tested \n\n\n\nProperty (Unit) Almond soft husk Almond soft husk compost Almond soft husk biochar \npH 3.21 4.83 11.61 \nEC (dS m-1) 1.15 1.61 7.93 \nN (%) 0.34 0.55 0.68 \nP (%) 0.11 0.13 0.14 \nK (%) 0.32 0.42 0.63 \nFe (mg kg-1) 68 90 140 \nZn (mg kg-1) 8.37 12.24 28.06 \nCu (mg kg-1) 3.18 4.08 8.03 \nMn(mg kg-1) 5.83 8.67 13.27 \nCd (mg kg-1) Insignificant Insignificant Insignificant \n\n\n\n\n\n\n\nIncubation Study \nThe experiment was performed by adding 1 kg of soil into each pot, to which was added  either \nalmond soft husk compost or almond soft husk biochar  and mixed completely. The pots were \nplaced in a greenhouse at 25 \u00b1 5\u00b0C for 2 months. After the 2-month incubation, either 40 or 80 \nmg kg-1cadmium was added to each pot and mixed completely; the pots were then placed in the \nglasshouse for another 3 months. The treatments of the  incubation study are shown in Table 3. \n\n\n\nTABLE 3 \n Experimental design for incubation experiment \n\n\n\ntreatment pollution with cd Level of biochar Level of compost \nCd40B0 40 mg kg-1 0 wt% ------- \nCd40B2 40 mg kg-1 2 wt% ------- \nCd40B4 40 mg kg-1 4 wt% ------- \nCd80B0 80 mg kg-1 0 wt% ------- \nCd80B2 80 mg kg-1 2 wt% ------- \nCd80B4 80 mg kg-1 4 wt% ------- \nCd40C0 40 mg kg-1 ------- 0 wt% \nCd40C2 40 mg kg-1 ------- 2 wt% \nCd40C4 40 mg kg-1 ------- 4 wt% \nCd80C0 80 mg kg-1 ------- 0 wt% \nCd80C2 80 mg kg-1 ------- 2 wt% \nCd80C4 80 mg kg-1 ------- 4 wt% \n\n\n\n\n\n\n\nTABLE 3\n Experimental design for incubation experiment\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 137\n\n\n\nThe metal index of mobility(IM) was calculated based on equation (1), adapted \nfrom Kabala and Singh (2001):\n\n\n\nwhere IM is the index of mobility; EX, CAR, ORG, MnOX, AFeOX, CFeOX and \nRES are the chemical fractions of Cd in the soil as explained in Table 3. \n\n\n\nStatistical Analysis\nAll data were processed by Microsoft Excel 2010 and statistical analyses were \nconducted using the SPSS 11.5 software.\n\n\n\nRESULTS AND DISCUSSION\nThe relative amounts of sequential extraction of cadmium in the treated samples \nand the control sample  45 and 90 days after incubation, indicate that the carbonate \nforms has the largest share of the chemical fractions of cadmium (Figures 1 and \n2). Rajaie et al. (2008)(not in ref list)?? reported that in calcareous and alkaline \nsoils of Iran, the carbonate form was the dominant soil Cd in soils treated with \nenriched compost. Khanmirzaei et al. (2013)(not in ref list)?? investigated the \ndifferent form of Cd in the calcareous soils of Iran using a sequential extraction \nmethod and found the carbonate form to be the dominant form of cadmium in \nthese soils. Application of amendments at various rate (2 and 4 wt %), reduce \nthe exchangeable and carbonate forms compared to the control treatment. The \nexchangeable form can be introduced as a determinant factor in increasing \nenvironmental hazards caused by the leaching of heavy elements. Therefore, \nreducing the distribution of the exchangeable form could be an effective factor to \nreduce environmental pollution caused by the release of heavy metals. \n\n\n\nThe application of biochar at 4 wt % to soil spiked with 40 mg kg-1 cadmium \nreduced the exchangeable Cd by 66% and the carbonate-Cd by 23% during the 45 \ndays after incubation compared to the control treatment. While the application of \nbiochar at 4wt% to soil spiked with 80 mg kg-1 cadmium reduced the exchangeable \nCd by 62% and the carbonate-Cd by 17% compared to the control treatment. \nAlso, the application of compost at 4wt% to soil spiked with 40 mg kg-1 cadmium \n\n\n\nTABLE 4 \nProcedure for the sequential extraction of Cd from soil and corresponding phases\n\n\n\nCd80C4 80 mg kg-1 ------- 4 wt% \n \n\n\n\nA soil fractionation study was carried out at 45 and 90 days after incubation where different \nchemical forms of Cd was determined adopted from Singh et al.(1988) (Table 4). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 4  \nProcedure for the sequential extraction of Cd from soil and corresponding phases \n\n\n\nPhase/association Abbreviation step Operational definition \nExchangeable EX 1 4mL 1M Mg(NO3)2, shaking 2 h \nCarbonated-bound CAR 2 4mL 1M NaOAc, pH 5, shaking 5 h \nOrganic-bound ORG 3 2mL 0.7M NaOCl, pH 8.5, stir occasionally 0.5 h in  \n\n\n\nwater bath (Repeat extraction) \nMn oxide-bound MnOX 4 10mL 1M NH2OH.HCl\u200f+HNO3, pH 2, shaking 0.5 h \nAmorph Fe oxide-bound AFeOX 5 10mL 0.25M NH2OH.HCl0.25 + \u200fM HCl, shaking 0.5 h  \n\n\n\nat 50\u00b0C in water bath \nCrystalinefe oxide-bound CFeOX 6 10mL 0.2 M(NH4)2C2O40.2 +\u200fM H2C2O40.1 +\u200fM C6H8O5,  \n\n\n\npH 3, stir occasionally 0.5 h in boiling water bath \nResidual RES 7 Conc. HF, conc. HClO4 and conc. HCl in sequence \n\n\n\nThe metal index of mobility(IM) was calculated based on equation (1), adapted from Kabala and \nSingh (2001): \n\n\n\n\n\n\n\n  ( )           \n                                            \n\n\n\n(1) \n\n\n\n \nwhere IM is the index of mobility; EX, CAR, ORG, MnOX, AFeOX, CFeOX and RES are the \nchemical fractions of Cd in the soil as explained in Table 3.  \n \nStatistical Analysis \nAll data were processed by Microsoft Excel 2010 and statistical analyses were conducted using \nthe SPSS 11.5 software. \n \n\n\n\nRESULTS AND DISCUSSION \nThe relative amounts of sequential extraction of cadmium in the treated samples and the control \nsample  45 and 90 days after incubation, indicate that the carbonate forms has the largest share of \nthe chemical fractions of cadmium (Figures 1 and 2). Rajaie et al. (2008)(not in ref list)?? \nreported that in calcareous and alkaline soils of Iran, the carbonate form was the dominant soil \nCd in soils treated with enriched compost. Khanmirzaei et al. (2013)(not in ref list)?? \n\n\n\nCd80C4 80 mg kg-1 ------- 4 wt% \n \n\n\n\nA soil fractionation study was carried out at 45 and 90 days after incubation where different \nchemical forms of Cd was determined adopted from Singh et al.(1988) (Table 4). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 4  \nProcedure for the sequential extraction of Cd from soil and corresponding phases \n\n\n\nPhase/association Abbreviation step Operational definition \nExchangeable EX 1 4mL 1M Mg(NO3)2, shaking 2 h \nCarbonated-bound CAR 2 4mL 1M NaOAc, pH 5, shaking 5 h \nOrganic-bound ORG 3 2mL 0.7M NaOCl, pH 8.5, stir occasionally 0.5 h in  \n\n\n\nwater bath (Repeat extraction) \nMn oxide-bound MnOX 4 10mL 1M NH2OH.HCl\u200f+HNO3, pH 2, shaking 0.5 h \nAmorph Fe oxide-bound AFeOX 5 10mL 0.25M NH2OH.HCl0.25 + \u200fM HCl, shaking 0.5 h  \n\n\n\nat 50\u00b0C in water bath \nCrystalinefe oxide-bound CFeOX 6 10mL 0.2 M(NH4)2C2O40.2 +\u200fM H2C2O40.1 +\u200fM C6H8O5,  \n\n\n\npH 3, stir occasionally 0.5 h in boiling water bath \nResidual RES 7 Conc. HF, conc. HClO4 and conc. HCl in sequence \n\n\n\nThe metal index of mobility(IM) was calculated based on equation (1), adapted from Kabala and \nSingh (2001): \n\n\n\n\n\n\n\n  ( )           \n                                            \n\n\n\n(1) \n\n\n\n \nwhere IM is the index of mobility; EX, CAR, ORG, MnOX, AFeOX, CFeOX and RES are the \nchemical fractions of Cd in the soil as explained in Table 3.  \n \nStatistical Analysis \nAll data were processed by Microsoft Excel 2010 and statistical analyses were conducted using \nthe SPSS 11.5 software. \n \n\n\n\nRESULTS AND DISCUSSION \nThe relative amounts of sequential extraction of cadmium in the treated samples and the control \nsample  45 and 90 days after incubation, indicate that the carbonate forms has the largest share of \nthe chemical fractions of cadmium (Figures 1 and 2). Rajaie et al. (2008)(not in ref list)?? \nreported that in calcareous and alkaline soils of Iran, the carbonate form was the dominant soil \nCd in soils treated with enriched compost. Khanmirzaei et al. (2013)(not in ref list)?? \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018138\n\n\n\nreduced the exchangeable Cd by 39% and the carbonate-Cd by 16% compared to \nthe control treatment, while the application of compost at 4 wt% to soil spiked with \n80 mg kg-1 cadmium reduced the exchangeable Cd by 47% and the carbonate-Cd \nby13.5% compared to the control treatment. The application of biochar at 4wt%  \n\n\n\nFigure 1: Distribution relative Cadmium fractions, 45 days after incubation in biochar- \ntreated specimens\n\n\n\ncarbonate forms compared to the control treatment. The exchangeable form can be introduced as \na determinant factor in increasing environmental hazards caused by the leaching of heavy \nelements. Therefore, reducing the distribution of the exchangeable form could be an effective \nfactor to reduce environmental pollution caused by the release of heavy metals.  \n \n\n\n\n \nFigure 1: Distribution relative Cadmium fractions, 45 days after incubation in biochar- treated  \n\n\n\nspecimens \n\n\n\n\n\n\n\nFigure 2: Distribution relative Cadmium fractions, 45 days after incubation in compost- treated \n\n\n\nspecimens \n\n\n\ncarbonate forms compared to the control treatment. The exchangeable form can be introduced as \na determinant factor in increasing environmental hazards caused by the leaching of heavy \nelements. Therefore, reducing the distribution of the exchangeable form could be an effective \nfactor to reduce environmental pollution caused by the release of heavy metals.  \n \n\n\n\n \nFigure 1: Distribution relative Cadmium fractions, 45 days after incubation in biochar- treated  \n\n\n\nspecimens \n\n\n\n\n\n\n\nFigure 2: Distribution relative Cadmium fractions, 45 days after incubation in compost- treated \n\n\n\nspecimens \n\n\n\nFigure 2: Distribution relative Cadmium fractions, 45 days after incubation in compost- \ntreatedspecimens\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 139\n\n\n\nto soil spiked with 40 mg kg-1 cadmium reduced the exchangeable Cd by 40% and \nthe carbonate-Cd by 7% compared to the compost, while the application of biochar \nat 4 wt% to soil spiked with 80 mg kg-1 cadmium reduced the exchangeable Cd by \n28% and the carbonate-Cd by 2% compared to the compost.\n\n\n\nInvestigating the cadmium mobility factor in soil using  sequential extraction \nof cadmium can complement the positive or negative effects of amendments \nadopted. The highest mobility factor in soils treated with different concentrations \nof cadmium in Figure 4 and 5 is related to control treatment, which is calculated \nto be 57.4%.\n\n\n\nFigure 3: Cadmium mobility factor, 45 days after incubation in biochar-treated \nspecimens\n\n\n\nThe application of biochar at 4 wt % to soil spiked with 40 mg kg-1 cadmium reduced the \nexchangeable Cd by 66% and the carbonate-Cd by 23% during the 45 days after incubation \ncompared to the control treatment. While the application of biochar at 4wt% to soil spiked with \n80 mg kg-1  cadmium reduced the exchangeable Cd by 62% and the carbonate-Cd by 17% \ncompared to the control treatment. Also, the application of compost at 4wt% to soil spiked with \n40 mg kg-1 cadmium reduced the exchangeable Cd by 39% and the carbonate-Cd by 16% \ncompared to the control treatment, while the application of compost at 4 wt% to soil spiked with \n80 mg kg-1 cadmium reduced the exchangeable Cd by 47% and the carbonate-Cd by13.5% \ncompared to the control treatment. The application of biochar at 4wt%  to soil spiked with 40 mg \nkg-1  cadmium reduced the exchangeable Cd by 40% and the carbonate-Cd by 7% compared to \nthe compost, while the application of biochar at 4 wt% to soil spiked with 80 mg kg-1  cadmium \n\n\n\nreduced the exchangeable Cd by 28% and the carbonate-Cd by 2% compared to the compost. \n \nInvestigating the cadmium mobility factor in soil using  sequential extraction of cadmium can \ncomplement the positive or negative effects of amendments adopted. The highest mobility factor \nin soils treated with different concentrations of cadmium in Figure 4 and 5 is related to control \ntreatment, which is calculated to be 57.4%. \n \n \n\n\n\n \nFigure 3: Cadmium mobility factor, 45 days after incubation in biochar-treated  \n\n\n\nspecimens \n\n\n\n\n\n\n\nFigure 4: Cadmium mobility factor, 45 days after incubation in compost\u2013treated \n\n\n\n specimens \n\n\n\nBiochar application at 4 wt % to the soils spiked with Cd at 40 and 80 mgkg-1 reduced the metal \nmobility factor by 41.4 and 46.5%, respectively.The relative amounts of fractions of cadmium by \nsequential extraction by Sing et al,????Year?Not in ref list?) with different treatments 90 days \nafter incubation are shown in Figures 5 and 6. The results show a tangible change in the \nexchangeable and carbonate forms with an  increase in the residual forms compared to the soil 45 \ndays after incubation. \n\n\n\n\n\n\n\n Figure 5: Distribution of  relative cadmium fractions, 90 days after incubation  \n\n\n\nFigure 4: Cadmium mobility factor, 45 days after incubation in compost\u2013treated\n specimens\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018140\n\n\n\nThe application of biochar at 4 wt % to soil spiked with 40 mg kg-1 cadmium \nreduced the exchangeable Cd by 66% and the carbonate-Cd by 23% during the 45 \ndays after incubation compared to the control treatment. While the application of \nbiochar at 4wt% to soil spiked with 80 mg kg-1  cadmium reduced the exchangeable \nCd by 62% and the carbonate-Cd by 17% compared to the control treatment. \nAlso, the application of compost at 4wt% to soil spiked with 40 mg kg-1 cadmium \nreduced the exchangeable Cd by 39% and the carbonate-Cd by 16% compared to \nthe control treatment, while the application of compost at 4 wt% to soil spiked with \n80 mg kg-1 cadmium reduced the exchangeable Cd by 47% and the carbonate-Cd \nby13.5% compared to the control treatment. The application of biochar at 4wt%  \nto soil spiked with 40 mg kg-1 cadmium reduced the exchangeable Cd by 40% and \nthe carbonate-Cd by 7% compared to the compost, while the application of biochar \nat 4 wt% to soil spiked with 80 mg kg-1 cadmium reduced the exchangeable Cd by \n28% and the carbonate-Cd by 2% compared to the compost.\n\n\n\nInvestigating the cadmium mobility factor in soil using  sequential extraction \nof cadmium can complement the positive or negative effects of amendments \nadopted. The highest mobility factor in soils treated with different concentrations \nof cadmium in Figure 4 and 5 is related to control treatment, which is calculated \nto be 57.4%.\n\n\n\nBiochar application at 4 wt % to the soils spiked with Cd at 40 and 80 \nmg kg-1 reduced the metal mobility factor by 41.4 and 46.5%, respectively.The \nrelative amounts of fractions of cadmium by sequential extraction by Sing et \nal,????Year? Not in ref list?) with different treatments 90 days after incubation are \nshown in Figures 5 and 6. The results show a tangible change in the exchangeable \nand carbonate forms with an  increase in the residual forms compared to the soil \n45 days after incubation.\n\n\n\n\n\n\n\nFigure 4: Cadmium mobility factor, 45 days after incubation in compost\u2013treated \n\n\n\n specimens \n\n\n\nBiochar application at 4 wt % to the soils spiked with Cd at 40 and 80 mgkg-1 reduced the metal \nmobility factor by 41.4 and 46.5%, respectively.The relative amounts of fractions of cadmium by \nsequential extraction by Sing et al,????Year?Not in ref list?) with different treatments 90 days \nafter incubation are shown in Figures 5 and 6. The results show a tangible change in the \nexchangeable and carbonate forms with an  increase in the residual forms compared to the soil 45 \ndays after incubation. \n\n\n\n\n\n\n\n Figure 5: Distribution of  relative cadmium fractions, 90 days after incubation  \n\n\n\nFigure 5: Distribution of  relative cadmium fractions, 90 days after incubation \nin biochar-treated specimens\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 141\n\n\n\nThe relative amounts of fractions of cadmium in soils treated 90 day after \nincubation show that in treated samples and control samples, they were similar \nto 45 day after incubation with the carbonate form having the largest share of \nthe fractions of cadmium. The results obtained from 90-day incubation soils \nare similar to the results of 45-day incubation soils, with the difference being \na decrease in exchangeable and carbonate forms and an increase in the residual \nform.\n\n\n\nFigure 6: Distribution of  relative cadmium fractions, 90 days after incubation\nin compost- treated specimens\n\n\n\nin biochar-treated specimens \n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Distribution of  relative cadmium fractions, 90 days after incubation \n\n\n\nin compost- treated specimens \n\n\n\nThe relative amounts of fractions of cadmium in soils treated 90 day after incubation show that \nin treated samples and control samples, they were similar to 45 day after incubation with the \ncarbonate form having the largest share of the fractions of cadmium. The results obtained from \n90-day incubation soils are similar to the results of 45-day incubation soils, with the difference \nbeing a decrease in exchangeable and carbonate forms and an increase in the residual form. \n\n\n\nFigure 7: Cadmium mobility factor, 90 days after incubation in biochar-treated \nspecimens\n\n\n\n\n\n\n\nFigure 7: Cadmium mobility factor, 90 days after incubation in biochar-treated specimens \n\n\n\n\n\n\n\nFigure 8: cadmium mobility factor, 90 days after incubation in compost-treated specimens \n\n\n\n \n \nGenerally, the values of chemical forms of Cd in the soil 45 and 90 days after incubation were as \nfollows: \nCarbonate-bound<residual <Mn-oxide-bound<exchangeable<AmorphousFe- oxide-\nbound<organic-bound<CrystallineFe- oxide-bound \n \nConclusions \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018142\n\n\n\nGenerally, the values of chemical forms of Cd in the soil 45 and 90 days \nafter incubation were as follows:\nCarbonate-bound<residual<Mn-oxide-bound<exchangeable<Amorphous \nFe- oxide-bound<organic-bound<CrystallineFe- oxide-bound\n\n\n\nCONCLUSIONS\nThe relative amounts of chemical forms of cadmium were influenced by the use \nof different amendments; cadmium carbonate forms were the most observed \nchemical forms of cadmium. 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Chemical \nSpeciation and Bioavailability 26: 139\u2013147.\n\n\n\nThomas, G.W. 1996. Soil pH and soil acidity. In: Methods of Soil Analysis, Part 3 \n(Chemical Methods), ed. D.L. Sparks (3rd ed.), pp.475-490.  Madison, Wisconsin: \nSoil Science Society of America and American Society of Agronomy.\n\n\n\nWu,W., M. Yang, Q.  Feng, K. McGrouther, H. Wang, H. Lu and Y. Chen.  2012. \nChemical characterization of rice straw-derived biochar for soil amendment. \nBiomass and Bioenergy 47: 268-276.\n\n\n\nYang, X., K. Lu, K. McGrouther,  L. Che, G.  Hu, X.  Liu, L.  Shen, H.  Huang, Z. Ye \nand H. Wang.  2016. Bioavailability of Cd and Zn in soils treated with biochars \nderived from tobacco stalk and dead pigs. Journal of Soils and Sediments. \n17(3): 751-762.\n\n\n\nZhong, X., S. Zhou, Q. Zhu and Q. Zhao. 2011. Fraction distribution and bioavailability \nof soil heavy metals in the Yangtze River Delta\u2014a case study of Kunshan City \nin Jiangsu Province, China. Journal of Hazardous Materials 198:13\u201321.\n\n\n\n\n\n"
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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 109-118 (2019)                         Malaysian Society of Soil Science\n\n\n\nEffect of Triple Super Phosphate (TSP) and KH2PO4 at \nDifferent Application Rates on Biomass and Arsenic Uptake \n\n\n\nby Pteris vittata L. \n\n\n\nNurAini A.B. and I. Che Fauziah*\n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nPteris vittata L. has been demonstrated to absorb heavy metals, especially arsenic \n(As) from the soil. The interaction of As (V) and phosphate in the ferns that \nhyperaccumulate As is essential. Therefore, a glasshouse study was conducted to \ninvestigate the effect and interactions of phosphate in different forms at varying \nrates on biomass and uptake of As by Pteris vittata L. grown in naturally organic \nAs-rich soil. P.Vittata was grown in soil subjected to 9 treatments including control \n(0 P treatment) and four different rates of P in solid form of Triple Superphosphate \n(TSP) and liquid form of potassium dihydrogen phosphate (KH2PO4) at 12.5, 25, \n50 and 75 kg P ha-1. Results showed a significant (P\u22640.0001) increase in frond \nbiomass compared to non-amended soil, whereas the biomass of  roots showed \nno significant (P>0.05) difference in all treatments. The concentration of As in \nthe fronds ranged from 29 to 157 mg kg-1 with the highest uptake of As being \n0.71% in TSP, while in KH2PO4, it  was 0.331%, almost half the rate in TSP. The \napplication of P has been shown to increase the phyto-availability of arsenic in \nsoil, resulting in a positive response on P.vittata As uptake, that is, TSP performed \nbetter compared to KH2PO4 in terms of As uptake and plant growth. It is postulated \nthat the increase in As uptake was accompanied or more likely a synergism by the \naddition of phosphate. \n\n\n\nKeywords: Assisted Phyto-Remediation, Phosphate Forms, Hyper-\naccumulator Fern, Plant Uptake\n\n\n\n___________________\n*Corresponding author : fauziah@upm.edu.my   \n\n\n\nINTRODUCTION\nSoil contaminated by arsenic constitutes a fundamental problem to environmental \nand human health and this problem needs an effective and affordable technological \nsolution (Raskin et al. 1997). Conventionally, the remediation of contaminated soil \nonly focused on engineering-related methods rather than plant based techniques. \nRecently, phyto-extraction has been identified as a potential in-situ remediation \noption to traditional soil remediation methods (Watanabe 1997). Generally, there \nare several ways to enhance the phytoremediation process other than screening \nthe plants that hyper-accumulate heavy metals. The first method is by increasing \nthe biomass of hyper-accumulators or application of genetically engineered plants \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019110\n\n\n\nwhich can improve the biomass production to extract more metals from soil. The \nsecond method is by elevating the mobility of metals in soil. One example is by \nthe application of amending reagents which enable plants to accumulate more \ncontaminants. \n The discovery of Chinese brake fern (Pteris vittata L.) to hyper-\naccumulate arsenic offers the  potential for its use as a phyto-remediation \nagent of As contaminated soil. This plant was the first As hyper-accumulator to \nbe identified (Chen and Wei 2000; Ma et al. 2001). Several studies have been \ncarried out in other parts of the world, for example, United States of America \n(Wang et al. 2002) and China (Chen et al. 2002) on the effects of phosphate on \nAs accumulation rate by hyper-accumulator, Pteris vittata L. However, there is \nno report on phosphate interaction with arsenic in Pteris vittata in Malaysia to \nremediate organic As-rich soil. The interaction of As (V) and phosphate in the \nferns that hyper-accumulate As is essential since optimum phosphate fertilisation \ncould be a key factor for optimum phyto-extraction of As (Rathinasabapathi et \nal. 2006). This study was conducted to investigate the effect and interactions of \nphosphate in different forms at varying rates on plant biomass and As uptake by \nPteris vittata L. grown in naturally organic As-rich soil. \n\n\n\nMATERIALS AND METHODS\n\n\n\nSoil Sampling\nThe As-rich soil was sampled in Kampung Renal, Tangkak, Johor, Peninsular \nMalaysia (N 02.24438o, E 102.55548o). This soil is categorised as Histosol \n(Saprist) according to the USDA Soil Taxonomy (Soil Survey Staff 2014). The \nsite is under guava and papaya cultivation. The soil sample was collected from \nthe surface till 20 cm depth randomly. Then, the soil samples were placed in \nclean sacks and kept in a place which was not exposed directly to sunlight to \nretain moisture and to prevent change in the physico-chemical properties of the \norganic soil. The soil was then transferred to the laboratory and mixed well until \nit homogenised. The sample was then sieved through a 2-mm mesh to remove \nstones and  plant materials (Jones 2001). \n\n\n\nPlant Sampling\nForty young seedlings of Pteris vittata were obtained from the fern nursery of \nUniversiti Kebangsaan Malaysia (UKM) which propagates P. vittata  collected \nfrom the vicinity of the university. \n\n\n\nSoil Preparation\nThe soil was sieved through the 2.0-mm sieve for routine analysis. The pot \nexperiment was carried out in the glasshouse unit of Universiti Putra Malaysia, \nSerdang, Selangor, Malaysia. The experiment consisted of nine treatments \nincluding control which was T0=Soil without P treatment and four different rates \nwhich were T1=12.5, T2=25, T3=50 and T4=75 kg P ha-1 in solid form of Triple \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 111\n\n\n\nSuperphosphate (TSP) at four different concentrations of T5=12.5, T6=25, T7=50 \nand T8=75 kg P ha-1 in liquid form of potassium dihydrogen phosphate (KH2PO4). \nEach polybag was fertilised with a basal dose of 75 kg N ha-1 (NH4NO3) and \n105 kg K ha-1 (KCl). Before the start of the experiment, the treated soils were \nincubated for two weeks.\n\n\n\nPlant Preparation and Maintenance\nA seedling of healthy fern with 5 to 6 fronds each was transferred to each pot \ncontaining 16 kg of soil in the glasshouse and  grown for 90 days. The ferns were \nkept under shady conditions and a ventilation fan in the glasshouse was used 24 \nh to maintain the temperature. Weeding was conducted manually. Soil moisture \ncontent was maintained close to field capacity level by adding deionised water \nperiodically as required.\n\n\n\nHarvesting\nAt the end of the experiment, whole plants were harvested. The plants were \nseparated into two parts (fronds and roots including the  rhizomes) for further \nanalysis. Plant samples were washed with deionised water and kept in paper bags. \n\n\n\nSoil and Plant Analyses\nBasic properties of the soil were analysed for pH, electrical conductivity (EC), \ncation exchange capacity (CEC), total nitrogen (N), total carbon (C), available \nK, Ca, Mg and total As before the initiation of the experiment. After the plants \nwere harvested, 50 g of soil in each of the polybags was mixed well and kept in a \nziplock bag. They were brought to the laboratory for further analyses. The fresh \nweight of the plant sample was taken before drying  in the oven at 65oC. Once  \nconstant weight was achieved, the dry matter was weighed, ground into powder \nto pass a sieve of 0.1 mm and dried in an oven at 105oC to eliminate moisture. \nAnalysis of plant samples was done using dry ashing method to determine arsenic \n(As) and phosphorus (P) uptake in the fronds  and roots  of the  ferns.\n\n\n\nStatistical Analysis\nThe experiment was a factorial experiment with 4 blocks arranged in a Randomized \nComplete Block Design (RCBD). Statistical analysis was performed using SAS \nstatistical software, Version 9.4 (SAS Institute Inc., Cary, NC, USA). Evaluation \nof statistical significance was computed using analysis of variance (ANOVA) and \nthe means were compared using Tukey\u2019s Honestly Significant Different (HSD) \ntest at a significance level of P\u22640.05.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019112\n\n\n\nEffect of Phosphate on Biomass in Fronds and Roots of Ferns\nThe dry aboveground biomass of fern is presented in Figure 1. The application \nrate of T3 and T4  in TSP form resulted in a significant (P\u22640.0001) increase in \nthe biomass of fern\u2019s fronds compared with control. Pteris vittata grew quite well \nwith no apparent chlorosis and necrosis on the fronds. The plant\u2019s fronds were \ncollected throughout the experimental period for further analysis to determine \nAs accumulation in the aboveground part. Tu et al. (2002) stated that as the \nfronds aged, As concentration increased. However, Kertulis (2005) in her study \nconcluded that the biomass of P. vittata fronds should be harvested before they \nsenesce because As concentrations in live fronds were higher than in the senescing \nfronds. For the biomass of dry root, there was no significant (P>0.05) difference \nbetween treatments and control as the range was 0.495 g to 2.658 g plant-1 with \nthe highest rate of both phosphate treatments achieving the highest value. The \ninsignificant results in this experiment could be attributed to the short period of \nplanting (90 days); the response of phosphate towards the growth of root biomass \ncould therefore not be determined.   \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nThe basic properties of the soil are shown in Table 1.\n\n\n\nTABLE 1\nProperties of soil (mean value \u00b1 standard deviation of mean, n=4)\n\n\n\nStatistical Analysis \n\n\n\nRESULTS AND DISCUSSION \n\n\n\nThe basic properties of the soil are shown in Table 1. \n\n\n\nTABLE 1 \n\n\n\nProperties of soil (mean value \u00b1 standard deviation of mean, n=4) \n\n\n\n \nVariable \n\n\n\n \nValue \n\n\n\nBulk density (g/cm3) 0.30 \u00b1 0.02 \n\n\n\nOrganic matter (%) \n\n\n\npH \n\n\n\n77.73 \u00b1 0.1 \n\n\n\n6.24 \u00b1 0.04 \n\n\n\nOrganic carbon (%) \n\n\n\nTotal N (%) \n\n\n\n15.94 \u00b1 0.023 \n\n\n\n0.824 \u00b1 0.005   \n\n\n\nElectrical conductivity (mS/cm) 1.68 \u00b1 0.26 \n\n\n\nCEC (cmolc/kg) \n\n\n\nAvailable P (%) \n\n\n\n29.44 \u00b1 0.88 \n\n\n\n0.06 \u00b1 0.0003 \n\n\n\nExchangeable K (cmolc/kg) 0.195 \u00b1 0.04 \n\n\n\nExchangeable Ca (cmolc/kg) 32.29 \u00b1 1.09 \n\n\n\nExchangeable Mg (cmolc/kg) 0.25 \u00b1 0.05 \n\n\n\nTotal Fe (%) \n\n\n\nTotal As (mg/kg) \n\n\n\n2.91 \u00b1 0.18 \n\n\n\n62.07 \u00b1 1.37 \n\n\n\n\n\n\n\nEffect of Phosphate on Biomass in Fronds and Roots of Ferns \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 113\n\n\n\nArsenic Uptake by Pteris vittata\nFigure 2 shows As uptake by Pteris vittata in fronds after 90 days of planting; As \nuptake was found to significantly (P\u22640.0001) increase  at all rates of phosphate \napplication. The fronds of plants treated with the highest treatment rates from TSP \nand KH2PO4 had the highest value of As uptake compared to the control and other \ntreatments. The higher As uptake by fern applied with different phosphate sources \nindicates that these phosphates have potential to increase the availability of As in \nsoil to be taken up by plants. However, TSP works better compared with KH2PO4. \nFor phyto-extraction to be successful, the contaminants should be bioavailable or \nsubjected to absorption by plant roots and translocated to the harvestable above-\nground portions.\n The intake of arsenic in roots of Pteris vittata is presented in Figure 3. \nStatistical analysis showed a significant increase (P\u22640.05) in As intake by roots \nwith the application of TSP and KH2PO4. The highest value of As intake was \nobserved in soil amended with the highest application rate with the same amount \nof phosphorus. \n\n\n\nTranslocation and Bioconcentration Factor \nThe translocation factor (TF) was used to evaluate how much metal taken up \nwas transported to the aboveground plant parts, while the bioconcentration factor \n(BCF) was used to indicate the ratio of metal moving from soil to fronds. The \npotential of a plant species can be assessed based on these numerical factors. \nFigure 4 shows there was no significant (P=0.138) difference in the translocation \n\n\n\nFig. 1: Total biomass of fern from fronds and roots after 90 days of growing \nin a soil treated with phosphate.\n\n\n\nFigure 1. Total biomass of fern from fronds and roots after 90 days of growing in a \n\n\n\nsoil treated with phosphate. \n\n\n\nNotes: T0=control (no P treatment added); T1, T2, T3 and T4 treated with12.5, 25, 50 and 75 \n\n\n\nkg P/ha of TSP treatment in solid form, respectively and for T5, T6, T7 and T8 with 12.5, 25, \n\n\n\n50 and 75 kg P/ha of KH2PO4 in liquid form, respectively. Error bars represent the standard \n\n\n\nerror of the mean (n=4) \n\n\n\nArsenic Uptake by Pteris vittata \n\n\n\nFigure 2 shows As uptake by Pteris vittata in fronds after 90 days of planting; As \n\n\n\nuptake was found to significantly (P\u22640.0001) increase  at all rates of phosphate \n\n\n\napplication. The fronds of plants treated with the highest treatment rates from TSP \n\n\n\nand KH2PO4 had the highest value of As uptake compared to the control and other \n\n\n\ntreatments. The higher As uptake by fern applied with different phosphate sources \n\n\n\nindicates that these phosphates have potential to increase the availability of As in soil \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n45\n\n\n\n50\n\n\n\nT0 T1 T2 T3 T4 T5 T6 T7 T8\n\n\n\nD\nry\n\n\n\n b\nio\n\n\n\nm\nas\n\n\n\ns \n(g\n\n\n\n) \n\n\n\nTreatments \n\n\n\nfrond\n\n\n\nroot\n\n\n\nc \n\n\n\nbc abc \n\n\n\nab \na \n\n\n\nbc bc \nabc \n\n\n\nab \n\n\n\nNotes: T0=control (no P treatment added); T1, T2, T3 and T4 treated with12.5, 25, 50 and \n75 kg P ha-1 of TSP treatment in solid form, respectively and for T5, T6, T7 and T8 with \n12.5, 25, 50 and 75 kg P ha-1 of KH2PO4 in liquid form, respectively. Error bars represent \nthe standard error of the mean (n=4)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019114\n\n\n\nfactor (TF) of Pteris vittata with the application of all rates of phosphate fertilisers \ncompared with the control treatment. This showed that P. vittata is good in \ntranslocating As and can be classified as a hyper-accumulator plant as the value \nof TF exceeded 1. The contaminant was absorbed and then transported to the \naboveground part of the plant making it practicable to harvest the aerial part \ncontaining the contaminant.   \n Figure 5 shows values obtained for BCF of Pteris vittata based on a  ratio \nof As in the aerial part to total arsenic in soils which significantly (P\u22640.0001) \nincreased in all treatment rates compared with control. However, Prasetia et al. \n(2016) stated that a plant should be considered as a hyper-accumulator not only \nbased on TF and BCF results, but also on the ability to accumulate the contaminant.    \n\n\n\nFig. 2: Arsenic uptake in the fronds of the Pteris vittata. Error bars represent \nhe standard error of the mean (n=4)\n\n\n\n\n\n\n\nFigure 2. Arsenic uptake in the fronds of the Pteris vittata. Error bars represent the \n\n\n\nstandard error of the mean (n=4) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Arsenic intake in the roots of the Pteris vittata. Error bars represent the \n\n\n\nstandard error of the mean (n=4). \n\n\n\ny = 0.0801x + 0.4327 \nR\u00b2 = 0.9913 \n\n\n\ny = 0.0329x + 0.7484 \nR\u00b2 = 0.9576 \n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\n7\n\n\n\n8\n\n\n\n0 10 20 30 40 50 60 70 80\n\n\n\nA\ns u\n\n\n\npt\nak\n\n\n\ne \n(m\n\n\n\ng/\npo\n\n\n\nt)\n \n\n\n\nDays of Planting \n\n\n\nTSP\n\n\n\nKH2PO4\n\n\n\ny = 0.0012x2 + 0.2456x + 3.9057 \nR\u00b2 = 0.9601 \n\n\n\ny = 0.0012x2 + 0.1733x + 3.2361 \nR\u00b2 = 0.9757 \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n0 10 20 30 40 50 60 70 80\n\n\n\nA\ns i\n\n\n\nnt\nak\n\n\n\ne \n(u\n\n\n\ng/\npo\n\n\n\nt)\n \n\n\n\nDays of Planting \n\n\n\nTSP\n\n\n\nKH2PO4\n\n\n\n\n\n\n\nFigure 2. Arsenic uptake in the fronds of the Pteris vittata. Error bars represent the \n\n\n\nstandard error of the mean (n=4) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Arsenic intake in the roots of the Pteris vittata. Error bars represent the \n\n\n\nstandard error of the mean (n=4). \n\n\n\ny = 0.0801x + 0.4327 \nR\u00b2 = 0.9913 \n\n\n\ny = 0.0329x + 0.7484 \nR\u00b2 = 0.9576 \n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\n7\n\n\n\n8\n\n\n\n0 10 20 30 40 50 60 70 80\n\n\n\nA\ns u\n\n\n\npt\nak\n\n\n\ne \n(m\n\n\n\ng/\npo\n\n\n\nt)\n \n\n\n\nDays of Planting \n\n\n\nTSP\n\n\n\nKH2PO4\n\n\n\ny = 0.0012x2 + 0.2456x + 3.9057 \nR\u00b2 = 0.9601 \n\n\n\ny = 0.0012x2 + 0.1733x + 3.2361 \nR\u00b2 = 0.9757 \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n0 10 20 30 40 50 60 70 80\n\n\n\nA\ns i\n\n\n\nnt\nak\n\n\n\ne \n(u\n\n\n\ng/\npo\n\n\n\nt)\n \n\n\n\nDays of Planting \n\n\n\nTSP\n\n\n\nKH2PO4\n\n\n\nFig. 3: Arsenic intake in the roots of the Pteris vittata. Error bars represent the standard \nerror of the mean (n=4).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 115\n\n\n\nRelationship between Phosphorus Treatment and Plant Uptake in P. vittata\nCorrelation coefficients between As uptake and phosphate uptake in fronds and \nroots of P. vittata are presented in Table 2. The arsenic uptake by fronds was \nfound to be significantly correlated with phosphate uptake by fronds for both TSP \n(r = 0.7781***; P\u22640.0001) and KH2PO4 (r = 0.8036***; P\u22640.0001) treatments. \n\n\n\nFig. 4: Translocation factor of Pteris vittata grown in a soil treated with phosphate\nfor 90 days. Error bars represent the standard error of the mean (n=4).\n\n\n\n\n\n\n\nTranslocation and Bioconcentration Factor  \n\n\n\nThe translocation factor (TF) was used to evaluate how much metal taken up was  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4. Translocation factor of Pteris vittata grown in a soil treated with phosphate \n\n\n\nfor 90 days. Error bars represent the standard error of the mean (n=4). \n\n\n\nFigure 5 shows values obtained for BCF of Pteris vittata based on a  ratio of As in the \n\n\n\naerial part to total arsenic in soils which significantly (P\u22640.0001) increased in all \n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\n16\n\n\n\n18\n\n\n\nT0 T1 T2 T3 T4 T5 T6 T7 T8\n\n\n\nTr\nan\n\n\n\nsl\noc\n\n\n\nat\nio\n\n\n\nn \nFa\n\n\n\nct\nor\n\n\n\n (T\nF)\n\n\n\n\n\n\n\nTreatment \n\n\n\na \na \n\n\n\na \n\n\n\na \n\n\n\na \n\n\n\na \n\n\n\na \n\n\n\na \n\n\n\na \n\n\n\nFig. 5: Bioconcentration factor of Pteris vittata grown in a soil treated with phosphate \nfor 90 days. Error bars represent the standard error of the mean (n=4).\n\n\n\ntreatment rates compared with control. However, Prasetia et al. (2016) stated that a \n\n\n\nplant should be considered as a hyper-accumulator not only based on TF and BCF \n\n\n\nresults, but also on the ability to accumulate the contaminant.     \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5. Bioconcentration factor of Pteris vittata grown in a soil treated with \n\n\n\nphosphate for 90 days. Error bars represent the standard error of the mean (n=4). \n\n\n\n\n\n\n\nRelationship between Phosphorus Treatment and Plant Uptake in P. vittata \n\n\n\nCorrelation coefficients between As uptake and phosphate uptake in fronds and roots \n\n\n\nof P.vittata are presented in Table 2. The arsenic uptake by fronds was found to be \n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n3\n\n\n\nT0 T1 T2 T3 T4 T5 T6 T7 T8\n\n\n\nB\nio\n\n\n\nco\nnc\n\n\n\nen\ntr\n\n\n\nat\nio\n\n\n\nn \nFa\n\n\n\nct\nor\n\n\n\n (B\nC\n\n\n\nF)\n \n\n\n\nTreatment \n\n\n\nc \nbc \n\n\n\nbc \n\n\n\nb \n\n\n\na \n\n\n\nbc bc \n\n\n\nb b \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019116\n\n\n\nTABLE 2\nCorrelation coefficient between uptake/intake of As and phosphate in fronds and roots\n\n\n\nsignificantly correlated with phosphate uptake by fronds for both TSP (r= 0.7781***; \n\n\n\nP\u22640.0001) and KH2PO4 (r= 0.8036***; P\u22640.0001) treatments. This result is  linked to \n\n\n\nthe result of As intake by P.vittata roots which was also positively correlated to \n\n\n\nphosphate intake by roots (r= 0.8057***; P\u22640.0001) in TSP and (r= 0.8583***; \n\n\n\nP\u22640.0001) KH2PO4 treatments respectively. \n\n\n\nTABLE 2 \n\n\n\nCorrelation coefficient between uptake/intake of As and phosphate in fronds and \n\n\n\nroots \n\n\n\n \nPhosphate sources \n\n\n\n \nUptake / intake of \n\n\n\nPhosphate \n\n\n\nUptake / intake of Arsenic \n \n\n\n\nFrond \n \n\n\n\nRoot \n \n\n\n\nTSP  \nFrond 0.7781*** 0.5870* \nRoot 0.6706*  0.8057*** \n\n\n\n \nKH2PO4 \n\n\n\nFrond 0.8036***  0.4567*  \nRoot 0.4504*  0.8583***  \n\n\n\n \nNotes: *** significant at P\u22640.0001;    * significant at P\u22640.05 \n\n\n\n\n\n\n\nMany studies have reported on the possibility of competition between phosphorus \n\n\n\nand arsenic uptake by plant. Arsenic (V) uptake is facilitated by the phosphate \n\n\n\ntransporter, which means the mechanism of uptake is similar to phosphate. According \n\n\n\nto Koseki (1988) and Sharples et al. (2000), the initial phase of arsenate uptake \n\n\n\noccurred in an instantaneous action and evidently is an irreversible process,  followed \n\n\n\nby a slower but much more stable and steady state phase. These two different phases \n\n\n\nThis result is  linked to the result of As intake by P. vittata roots which was also \npositively correlated to phosphate intake by roots (r = 0.8057***; P\u22640.0001) in \nTSP and (r = 0.8583***; P\u22640.0001) KH2PO4 treatments respectively.\n Many studies have reported on the possibility of competition between \nphosphorus and arsenic uptake by plant. Arsenic (V) uptake is facilitated by \nthe phosphate transporter, which means the mechanism of uptake is similar to \nphosphate. According to Koseki (1988) and Sharples et al. (2000), the initial \nphase of arsenate uptake occurred in an instantaneous action and evidently is \nan irreversible process,  followed by a slower but much more stable and steady \nstate phase. These two different phases of reaction were controlled and inhibited \nby phosphate. When the concentration of phosphate was extremely high, PO4\n\n\n\n3- \n\n\n\nacts as a competitor with AsO4\n-3 in the plant membrane. Studies  on competition \n\n\n\nbetween these two elements in other plants have been published. Khattak et al. \n(1991) demonstrated that As concentration of alfalfa in shoot decreased with \nphosphorus treatment while another study also showed results of a reduction in As \nconcentration with an increase in phosphorus concentration (Meharg and Macnair \n1990). Meanwhile, Zhao et al. (2002) pointed out that the lack of interaction \nbetween As and P could be due to the phosphate system in plant roots. As the \naffinity for phosphate is higher than for arsenate, phosphate is less able to compete \nwith arsenate.\n Nevertheless, the outcome from this experiment showed a significantly \n(P\u22640.0001) positive correlation between As uptake and P uptake in frond for \nTSP at (r = 0.7781; P\u22640.0001) and KH2PO4 at (r =0.8036; P\u22640.0001). These \nresults clearly show that there is no competition between phosphorus and arsenic \nuptake in P. vittata (Chen et al. 2002). The concentration of As in soil used in \nthis experiment might have contributed to this condition. Our findings are in \nagreement with research conducted by Chen et al. (2002), which found that the \naddition of phosphate increased in As uptake. This indicates that phosphorus and \narsenic are in synergism and based on our  results, it is hypothesised that As (V) \nand phosphate are not taken up via the same uptake channel in P. vittata. It is \ntherefore concluded that phosphate and arsenic uptake mechanism in P. vittata \nshould be further investigated.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 117\n\n\n\nCONCLUSION\nIn conclusion, P.vittata has the potential to hyper-accumulate arsenic from a \nmoderate concentration of naturally As contaminated soil assisted by phosphate. \nEfficiency of uptake by P. vittata can be judged by considering the production of \nplant biomass, translocation factor and bioconcentration factor. The application \nof TSP to the soil which was in solid form performed better compared to KH2PO4 \nin terms of As uptake and plant growth of P.vittata. Further studies are needed \non the mechanisms of synergism between arsenate and phosphate on P. vittata, \nespecially in naturally As-enriched soil. \n\n\n\nREFERENCES\nChen, T., C. Wei, Z.Huang, Q. Huang, Q.Lu. and Z. Fan. 2002. Notes: Arsenic \n\n\n\nhyperaccumulator Pteris vittata L. and its arsenic accumulation. Chinese \nScience Bulletin 47 (11): 902-905.\n\n\n\nChen, T. B. and C.Y.Wei. 2000. Arsenic hyperaccumulator in some plant species in \nSouth China. Proceedings of International Conference of Soil Remediation, \nHangzhou, China, pp194-195.\n\n\n\nJones Jr, J.B. 2001. Quality assurance: Quality control in the laboratory. In Laboratory \nGuide for Conducting Soil Tests and Plant Analysis, ed. J.B. Jones Jr, pp. 285-\n292. Florida: CRC Press. \n\n\n\nKertulis, G.M. 2005. Arsenic hyperaccumulation by Pteris vittata L and its potential  \nfor phyto-remediation of arsenic-contaminated soils, PhD Thesis, University of \nFlorida.\n\n\n\nKhattak, R.A., A.L. Page, D.R. Parker and D. Bakhtar. 1991. Accumulation and \ninteractions of arsenic, selenium, molybdenum and phosphorus in alfalfa. \nJournal of Environmental Quality 20: 165-168.\n\n\n\nKoseki, K. 1988. Suppression of arsenic injury of rice plants by the application of \nhigher phosphate concentration in culture solution. Scientific Reports of the \nMiyagi Agriculture College 36:15.\n\n\n\nMa, L. Q., K.M. Komar and E.D. Kennelley. 2001. Methods for removing pollutants \nfrom contaminated soil materials with a fern plant. USA Patent US patent 6 \n(302): 942.\n\n\n\nMeharg, A. A. and M.R. Macnair. 1990. An altered phosphate uptake system in \narsenate-tolerant Holcuslanatus L. New Phytologist 116 (1): 29.\n\n\n\nPrasetia, R., F. Sinniger and S. Harii. 2016. Gametogenesis and fecundity of \nAcroporatenella (Brook 1892) in a mesophotic coral ecosystem in Okinawa, \nJapan. Coral Reefs 35: 1-10. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019118\n\n\n\nRaskin, I., R.D. Smith and D.E.Salt. 1997. Phytoremediation of metals: Using plants \nto remove pollutants from the environment. Current Opinion in Biotechnology \n8: 221-226.\n\n\n\nRathinasabaphati, B., L.Q. Ma and M. Srivastava. 2006. Arsenic hyperaccumulating  \nferns and their application to phytoremediation of arsenic contaminated sites. \nFloriculture, Ornamental and Plant Biotechnology 3 (32): 304-311.\n\n\n\nSharples, J. M., A.A. Meharg and S.M. Chambers. 2000. Evolution: symbiotic \nsolution to arsenic contamination. Nature 404: 951\n\n\n\nSoil Survey Staff. 2014. Keys to Soil (12th ed) Natural Resources Conservation \nServices, US Department of Agriculture, Washington, DC.\n\n\n\nTu, C., L.Q. Ma and B. Bondada. 2002. Arsenic accumulation in the hyperaccumulator \nChinese Brake (Pteris vittata L.) and its utilization  potential  for  \nphytoremediation. Journal of Environmental Quality 31: 1671-1675.\n\n\n\nWang, J., F.J. Zhao, A.A.  Meharg, A. Raab, J. Fieldmann and S.P. McGrath. 2002. \nMechanisms of arsenic hyper-accumulation in Pteris vittata uptake kinetics, \ninteractions with phosphate and arsenic speciation. Plant Physiology 130: 1552-\n1561.\n\n\n\nWatanabe, M.E. 1997. Phyto-remediation on the brink of commercialization.\nEnvironmental Science and Technology 31: 182-186. \n\n\n\nZhao, F.J., S.J. Dunham and S.P. McGrath. 2002. Arsenic hyperaccumulation by \ndifferent fern species. New Phytolologist 156: 27-31.\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n55 \n \n\n\n\n\n\n\n\nModelling Soil-Water Behavior in Oil Palm Plantations Using Neutron \n\n\n\nMoisture Meter (NMM) and Resistivity Imaging System (RIS) \n \n\n\n\nRajoo, K.S.1,2*, Yusop, Z.3, Mejus, L.4 and Gerusu, G.J. 1,2*\n \n\n\n\n \n1 Department of Forestry Science, Faculty of Agricultural Science and Forestry, Universiti Putra \n\n\n\nMalaysia Bintulu Sarawak Campus, 97000 Bintulu, Sarawak \n\n\n\n 2 Institute of Ecosystem Science Borneo, UPM Bintulu Sarawak Campus, Jalan Nyabau, 97000 \n\n\n\nBintulu, Sarawak. \n3 Institute of Environmental and Water Resources Management (IPASA), \n\n\n\n4 Waste and Environmental Technology Division, Malaysian Nuclear Agency, Bangi, 43000 Kajang, \n\n\n\nSelangor, Malaysia. \n\n\n\n \n*Corresponding author: keeren.rajoo@upm.edu.my  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nWater management is rarely a focus in Southeast Asian oil palm plantations, due to high rainfall all \n\n\n\nyear long. However, this climatic condition is changing. There is increasing evidence that climate \n\n\n\nchange is causing tropical regions to have reduced annual mean precipitation and increased \nprevalence of dry seasons. Thus, knowledge gaps in the area of soil-water relationship in these \n\n\n\nagricultural systems will result in inefficient water management. Therefore, this study was conducted \n\n\n\nto evaluate the potential of two approaches in monitoring and modelling soil water behaviour under \n\n\n\noil palm canopies. The first approach used Neutron Moisture Meter (NMM), to obtain a time series on \n\u04e8, including evaluating soil-rainfall relationship. The second approach used Resistivity Imaging \n\n\n\nSystem (RIS), to provide data on stratigraphy that was used to further characterize the geologic setting \n\n\n\nof soil (6 m depth) and its effects on soil moisture. The results revealed a small significant variability \nin \u04e8 values in the 20 different soil depths. \u04e8 values also showed significant increases in response to \n\n\n\nhigh rainfall events (>30.0 mm) which decreased with time. Soil-water content percentage changes \n\n\n\nranged from 9.5 to 23.8% at different depths. The resistivity imaging surveys successfully mapped the \nsoil water content underneath the oil palm catchment up to 6 m depth, revealing leakages to \n\n\n\ngroundwater flow at some study sites. Both techniques (NMM and RIS) were able to model the soil-\n\n\n\nwater relationship in oil palm plantations. These methods can be used to charter better water \n\n\n\nmanagement strategies in oil palm plantations in the future. \n\n\n\n\n\n\n\nKey words: Soil water content, soil stratigraphy pattern, Resistivity Imaging System, oil palm \n\n\n\ncatchment \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nOil palm is a vital socio-economic crop since it produces the most widely consumed \n\n\n\nvegetable oil in the world (Gonzalez-Diaz et al. 2021). This plantation crop grows well in \n\n\n\nhigh temperatures and requires large amounts of water, making it an ideal crop for tropical \n\n\n\nclimates (Rajoo et al. 2021a). It is therefore no surprise that Indonesia and Malaysia are the \n\n\n\nworld\u2019s largest producers of palm oil. In 2017, Malaysia produced 39% of the world\u2019s palm \n\n\n\noil, which came from 5.81 million hectares of oil palm plantations (Olaniyi and Szulczyk \n\n\n\n2020). Due to ever increasing demand for edible oils, it is very likely that palm oil production \n\n\n\nwill continue to rise for the foreseeable future (Saad et al. 2021).   \n\n\n\n\n\n\n\nThe demand for palm oil has fueled research interest in this field. However, these \n\n\n\nstudies usually focus on sustainability (Anyaoha et al. 2018) and managing by-products or \n\n\n\n\nmailto:keeren.rajoo@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n56 \n \n\n\n\nwaste from palm oil production (Gonzalez-Diaz et al. 2021; Saad et al. 2021; Karam et al. \n\n\n\n2022). There is also growing research interest in palm oil certification (Tey et al. 2021) and \n\n\n\nevaluating the social impacts of palm oil production (Castellanos-Navarrete et al. 2021). \n\n\n\nUnfortunately, research pertaining to water demand and soil water content in oil palm \n\n\n\nplantations is almost non-existent (Tezara et al. 2021), especially in the Southeast Asia \n\n\n\n(Subramaniam and Hashim 2018), the world\u2019s largest producer of palm oil.  \n\n\n\n\n\n\n\nAgricultural production is the biggest user of freshwater, with over 70% of global \n\n\n\nfreshwater being used in this area (Li et al. 2021). This demand for freshwater is only going \n\n\n\nto increase since by 2050, the world\u2019s population will exceed 9 billion, requiring an estimated \n\n\n\n50% increase in agricultural production (Li et al. 2021). Therefore, water management is an \n\n\n\nintegral part of agricultural production, especially in non-tropical regions. Quantification of \n\n\n\nsoil water contents (\u04e8) has long been known to be essential in agricultural systems. Rawls et \n\n\n\nal. (1973) reported that the amount of \u04e8 in the soil has an impact on vegetative yield. For \n\n\n\ninstance, in western Kenya, Radersma and Ong (2004) reported that a soil-water content \n\n\n\nreduction of 2 to 3% caused a 40% decrease in maize-production. \n\n\n\n\n\n\n\nWater management is rarely a focus in tropical agricultural systems, due to high \n\n\n\nrainfall all year long (Subramaniam and Hashim 2018; Rajoo et al. 2020). However, this \n\n\n\nclimatic condition is changing. There is increasing evidence that climate change is affecting \n\n\n\ntropical regions (Rajoo et al., 2021b), namely by reducing annual mean precipitation and \n\n\n\nincreased prevalence of dry seasons (Dinar et al. 2019). This has contributed to water \n\n\n\nscarcity, affecting oil palm production (Subramaniam and Hashim 2018; Tezara et al. 2021). \n\n\n\nIt is likely that irrigation systems will soon be required for oil palm plantations, perhaps \n\n\n\nbecause knowledge gaps in the area of soil-water relationship in oil palm plantations will \n\n\n\nresult in inefficient water management in these agricultural systems.  \n\n\n\n\n\n\n\nTherefore, this study aims to evaluate the potential of two approaches in monitoring \n\n\n\nand modelling soil water content under oil palm canopies. The first approach used Neutron \n\n\n\nMoisture Meter (NMM). NMM was used to obtain a time series on \u04e8, including evaluating \n\n\n\nthe soil-rainfall relationship in oil palm plantations. The second approach used Resistivity \n\n\n\nImaging System (RIS) to provide data on stratigraphy that was used to further characterize \n\n\n\nthe geologic setting of soil (6 m depth) and its effects on soil moisture. The availability of \n\n\n\nadequate soil water in the root zone has a large impact on plant growth and biomass \n\n\n\nproduction, thus RIS will provide essential data in this area. To date, there has been no study \n\n\n\nusing NMM and RIS to evaluate the soil-water relationship in oil palm plantations. Thus, this \n\n\n\nstudy will provide a better understanding of \u04e8 characteristics, stratigraphy pattern and their \n\n\n\neffects on soil water content in oil palm plantations. This will allow for a more reliable \n\n\n\nmethodology for future water management strategies in oil palm plantations.  \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nStudy Location \n\n\n\n\n\n\n\nThis study was carried out at three catchments in Sedenak oil palm plantation, Johor, \n\n\n\nMalaysia (Figure 1). These undulating areas have an altitude ranging from 43 to 62 m above \n\n\n\nsea level and are situated in the upstream of the Skudai River, approximately at 01o 43\u2019 35\u201d N \n\n\n\nand 103o 32\u2019 42\u201d E. The general physical characteristics of these catchments are summarized \n\n\n\nin Table 1. These sites were planted with Elaeis guineensis Jacq clones PAMOL/FELDA. \n\n\n\nThe canopy cover is quite uniform ranging from 85% to 90% of the ground area. All three \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n57 \n \n\n\n\nstudy sites had a density of 148 ha-1 (planting distance: 8 X 8 m), with leaf area index (LAI) \n\n\n\nof 5.9 to 7.1. LAI increases with palm age and reaches a stable maximum after 10 years, thus \n\n\n\nthe LAI at the study sites were at their maximum. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Location of study sites \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nSummary of physical characteristics of the study catchments \n\n\n\nCatchment C1 C2 C3 \n\n\n\nLocation Sedenak Estate, Johor Sedenak Estate, Johor Sedenak Estate, Johor \n\n\n\nGPS point approx 01o 44\u2019 37\u201d N \n\n\n\nand 103o 32\u2019 41\u201d E \n\n\n\napprox 01\u00b0 42\u2019 54\u201d N \n\n\n\nand 103\u00b0 31\u2019 47\u201d E \n\n\n\napprox 01o 43\u2019 35\u201d N \n\n\n\nand 103o 32\u2019 42\u201d E \n\n\n\nOil palm clone PAMOL/FELDA (<3 \n\n\n\nyears old) \n\n\n\nPAMOL/FELDA (5-6 \n\n\n\nyears old) \n\n\n\nPAMOL/FELDA (11 \n\n\n\nyears old) \n\n\n\nTopography undulating area undulating area undulating area \n\n\n\nSoil texture Coarse sandy clay \nloam of red yellow \n\n\n\nultisols and belongs to \n\n\n\nRengam series \n\n\n\nCoarse sandy clay \nloam of red yellow \n\n\n\nultisols and belongs to \n\n\n\nRengam series \n\n\n\nCoarse sandy clay loam \nof red yellow ultisols \n\n\n\nand belongs to Rengam \n\n\n\nseries \n\n\n\nTotal area (ha) 17.59 8.17 15.62 \n\n\n\nStream length (m) 351 153 641 \n\n\n\nStream max. elevation \n\n\n\n(masl) \n\n\n\n66.7 58.8 60.9 \n\n\n\nStream min. elevation \n\n\n\n(masl) \n\n\n\n55.4 49.0 43.5 \n\n\n\nStream slope 0.0321 0.0637 0.0271 \n\n\n\nStream slope (%) 9 7 8 \n\n\n\nCatchment length (m) 372 221 705 \n\n\n\nCatchment max. \n\n\n\nelevation (masl) \n\n\n\n67.9 63.1 62.8 \n\n\n\nCatchment min. \n\n\n\nelevation (masl) \n\n\n\n55.4 49.0 43.5 \n\n\n\nMean catchment slope \n(m/m) \n\n\n\n0.0356 0.0916 0.0300 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n58 \n \n\n\n\nRainfall Measurements \n\n\n\nAn automatic tipping bucket rain gauge (ONSET RG2-M) was installed at all study sites. \n\n\n\nONSET RG2-M rain gauge logs readings cumulatively whenever the tipping bucket is filled \n\n\n\nwith 0.2 mm rainwater. The rain gauge was installed at an open area within the catchment \n\n\n\nwith enough clearance above a 45o angle from the edge of the gauge. Rainfall data was \n\n\n\nretrieved on a weekly basis using a laptop. \n\n\n\n\n\n\n\nSoil Survey \n\n\n\nA detailed investigation on soil type and bedrock depth profile was conducted at three \n\n\n\nselected sampling points (BH1, BH2 and BH3) within Catchment 3 (Figure 2). A Mackintosh \n\n\n\nProbe was used to investigate the site. This soil sampling equipment used a 4.5-kilogram \n\n\n\nhammer to determine the number of blows required for every 30 cm penetration. The probing \n\n\n\nwas terminated when the blow count of 400 (refusal) was recorded over a penetration of 300 \n\n\n\nmm or less. For this study, the average penetration refusal or stop for BH1, BH2 and BH3 \n\n\n\nwere 1.55 m, 2.05 m and 4.95 m, respectively. \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Soil survey locations \n\n\n\n\n\n\n\nNeutron Moisture Meter (NMM) Sampling  \n\n\n\n\n\n\n\nThe NMM used in this study was manufactured by Didcot Instrument Company Limited, \n\n\n\nAbingdon, Oxon, England. The neutrons, emitted 50-mCi from its source (241Am-Be), are \n\n\n\nradioactive and reflect on collision with particles of equal mass (hydrogen nuclei). The \n\n\n\nthermalized neutrons are detected by a BF3 detector (Figure 3). In water the NMM gave ~ \n\n\n\n1200 count per second (c.p.s). Access tubes of 2-m depth for these measurements were \n\n\n\ninstalled at the three selected sampling points (BH1, BH2, BH3) in Catchment 3. Temporal \n\n\n\nchanges of soil water content (\u04e8) was carried out 39 times at these sampling points for a \n\n\n\nperiod of three months (Oct to Dec), by measuring the count rates of the neutron meter and \n\n\n\ngravimetric soil water contents at 20 different depths: 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, \n\n\n\n0.80, 0.90, 1.00, 1.10, 1.20, 1.30, 1.40, 1.50, 1.60, 1.70, 1.80, 1.90 and 2.00 m. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n59 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: The NMM probe \n\n\n\n\n\n\n\nSoil samples to determine mean value of the soil water content based on gravimetric method \n\n\n\nwere retrieved horizontally at appropriate depths in cores adjacent to neutron access tubes. \n\n\n\nCore samples of 100 cm3 from each depth were collected and sealed in plastic begs for further \n\n\n\nanalysis in the laboratory. All samples collected were weighed moist, oven-dried for 4 days at \n\n\n\n105\u00b0C, and weighed dry. The relationship between gravimetric water content and neutron \n\n\n\nmeter readings is defined as follows (Chanasyk and Naeth 1996): \n\n\n\n\ud835\udc36\ud835\udc45 = (\n\ud835\udc36\ud835\udc45\ud835\udc60\ud835\udc5c\ud835\udc56\ud835\udc59\n\n\n\n\ud835\udc36\ud835\udc45\ud835\udc64\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc5f\n) \u00d7 100(%) \n\n\n\nwhere  \n\n\n\n\ud835\udc36\ud835\udc45\ud835\udc60\ud835\udc5c\ud835\udc56\ud835\udc59 = counts s-1 in soil \n\n\n\n\ud835\udc36\ud835\udc45\ud835\udc64\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc5f  = counts s- 1 in water tank \n\n\n\n\n\n\n\nThen the gravimetric water content, W, (%) is \n\n\n\n\n\n\n\n\ud835\udc4a = \ud835\udc4e\ud835\udc36\ud835\udc45 + \ud835\udc4f(%) \n\n\n\nwhere  \n\n\n\na and b are constants. \n\n\n\n\n\n\n\nThe calibration equation retrieved from the relationship between gravimetric water \n\n\n\ncontent and neutron meter readings strongly correlated with coefficient of determination (r2) \n\n\n\nof 0.88 (Figure 4). This suggests that soil water content (\u04e8) at the study site can be measured \n\n\n\nusing NMM by using the following linear least-square regression equations: \n\n\n\n\u04e8 = 0.0582(NMMreading) \u2013 0.125;         r2 = 0.88 \n\n\n\nwhere \n\n\n\n\u04e8 \u2013 soil water content (%);NMMreading \u2013 neutron count/second \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n60 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4: Calibration curve \n\n\n\n\n\n\n\nResistivity Imaging System (RIS) Sampling  \n\n\n\nRIS sampling was conducted at all three catchments (C1. C2 and C3) using ABEM \n\n\n\nTerrameter SAS4000 connected to LUND electrode selector 464 system (ES464). This \n\n\n\nsystem uses a four electrode dipole\u2013dipole type array with 2 m electrode spacing and four \n\n\n\ndipole offset. A continuous sequence of measurements (with 1480 total measurements) was \n\n\n\nmade covering a total length of approximately 160 m, providing a maximum imaged depth of \n\n\n\nabout 6 m (Figure 5). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5: General setup of resistivity imaging monitoring system at the study sties \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n61 \n \n\n\n\nResistivity measurements were based on the principle that the distribution of electrical \n\n\n\npotential in the ground around a current-carrying electrode depends on the electrical \n\n\n\nresistivities and distribution of the surrounding soils and rocks. Subsurface resistivity is \n\n\n\nmeasured by applying an electric current through two electrodes and measuring the resulting \n\n\n\nvoltage difference between two potential electrodes. The measurement is reported as apparent \n\n\n\nresistivity (\u03c1a) \n\n\n\n\n\n\n\n\ud835\udf0c\ud835\udc4e =\n\ud835\udc58\ud835\udc49\n\n\n\n\ud835\udc3c\n                    \n\n\n\nwhere V is the measured voltage difference, I is the applied current, and k is a geometric \n\n\n\nfactor based on the electrode arrangement (Loke 2000). \n\n\n\n\n\n\n\nThe field measurements of apparent resistivity were calculated by multiplying the \n\n\n\nresistance value of geometric correction which is based on the arrangement of the current \n\n\n\nelectrode and the potential electrode in relation to each other (Ain-Llhout et al. 2016; Pan et \n\n\n\nal. 2021).  \n\n\n\nTo estimate true resistivity, the apparent resistivity data files are then inverted using \n\n\n\nthe commercial interpretation software RES2DINV (Rucker et al. 2021). The program uses a \n\n\n\nnonlinear least squares optimization technique to obtain the inversion of apparent resistivities \n\n\n\n(Pan et al. 2021). The results from this program were used to generate 2D sections of \n\n\n\nresistivity data referred to as inverted field datasets or inversion results. The inversion results \n\n\n\nwere used to characterize the resistivity changes in the vertical direction, as well as in the \n\n\n\nhorizontal direction along the survey line (Loke 2000). Resistivity is measured in ohm-m or \n\n\n\nohm-ft, and is the reciprocal of the conductivity of the material. \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nSoil Water Changes According to Time and Depth \n\n\n\n\n\n\n\nAs mentioned previously, temporal changes of soil water content (\u04e8) under oil palm canopy \n\n\n\nwere estimated from 39 individual events using the Neutron Moisture Meter (NMM). All \u04e8 \n\n\n\ndata from 20 different depths were analyzed accordingly: 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, \n\n\n\n0.70, 0.80, 0.90, 1.00, 1.10, 1.20, 1.30, 1.40, 1.50, 1.60, 1.70, 1.80, 1.90 and 2.00 m. The \n\n\n\nsummarised results retrieved from the three different access-tube locations at the study sites \n\n\n\nare shown in Table 2 and Figure 6.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n62 \n \n\n\n\nTABLE 2  \n\n\n\nSummary of \u04e8 values of BH1, BH2 and BH3 \n\n\n\nSOIL \u04e8 (%) for BH1 \u04e8 (%) for BH2 \u04e8 (%) for BH3 \n\n\n\nDEPTH \nValues \n\n\n\nrange* \nAverage \n\n\n\nValues \n\n\n\nrange* \nAverage \n\n\n\nValues \n\n\n\nrange* \nAverage \n\n\n\n200 20-27 24 20-27 23 23-28 25 \n\n\n\n190 20-27 23 21-27 22 22-28 25 \n\n\n\n180 20-26 23 20-26 22 22-28 24 \n\n\n\n170 19-25 22 20-25 22 21-27 23 \n\n\n\n160 19-25 22 20-25 22 21-28 24 \n\n\n\n150 18-25 22 20-25 22 21-28 23 \n\n\n\n140 18-25 22 20-26 21 21-27 23 \n\n\n\n130 16-24 21 19-25 21 20-27 23 \n\n\n\n120 16-24 21 19-25 21 20-26 22 \n\n\n\n110 17-26 22 19-24 21 20-25 22 \n\n\n\n100 17-26 22 18-23 21 19-25 22 \n\n\n\n90 16-26 21 16-24 20 18-25 21 \n\n\n\n80 15-25 20 16-22 20 18-24 21 \n\n\n\n70 13-24 19 17-23 20 18-24 21 \n\n\n\n60 15-24 19 15-22 20 17-24 20 \n\n\n\n50 14-24 18 15-21 19 16-23 20 \n\n\n\n40 13-22 17 14-21 19 15-22 19 \n\n\n\n30 12-20 16 13-20 18 14-22 18 \n\n\n\n20 12-19 15 12-21 17 12-21 17 \n\n\n\n10 7-14 10 8-16 12 6-17 10 \n\n\n\nAverage \n\n\n\nrange \n15.9-23.9 20 17.1-23.4 20 18.2-25.0 21 \n\n\n\n            Note : *-data ranges based on 39 measurement events \n\n\n\n\n\n\n\n The \u04e8 values obtained ranged from 7 to 27%, 8 to 27% and 6 to 28% for BH1, BH2 \n\n\n\nand BH3, respectively. The upper layers of 0.8, 0.6 and 0.5 m soil depth showed \u04e8 values \n\n\n\nranging from 20 to 28%, indicating moderate wet and wet soil conditions for BH1, BH2 and \n\n\n\nBH3, respectively. However, the overall \u04e8 average at 2.0 m depth within the study area \n\n\n\nsuggested a moderate wet soil. The general descriptive statistics of all three access tubes of \u04e8 \n\n\n\ndata are presented in Table 3. Further details of the distribution pattern of soil water content \n\n\n\ncan be observed in the box plot in Figure 7. The data reveals a tail towards the smaller value \n\n\n\nindicting it is negatively skewed. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n63 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Characteristics of \u04e8 for BH1, BH2 and BH3\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n64 \n \n\n\n\n\n\n\n\nTABLE 3  \n\n\n\nSummary of soil water content (%) descriptive analysis for bore holes 1, 2 and 3 (39 events) \n\n\n\n\n\n\n\n  \nBore Hole 1   Bore Hole 2   Bore Hole 3 \n\n\n\nOct Nov Dec \n\n\n\n\n\n\n\nOct Nov Dec \n\n\n\n\n\n\n\nOct Nov Dec \n\n\n\n\n\n\n\nNo of events \n\n\n\n \n11 \n\n\n\n \n15 \n \n\n\n\n13 \n  \n\n\n\n11 \n \n\n\n\n15 \n \n\n\n\n13 \n  \n\n\n\n11 \n \n\n\n\n15 \n \n\n\n\n13 \n \n\n\n\nR\na\nn\n\n\n\ng\ne \n\n\n\n o\nf \n\n\n\n v\na\nlu\n\n\n\nes\n \n\n\n\nMean 17.1-21.1 18.8-23.1 19.7-22.6 \n \n\n\n\n17.8-22.0 19.0-22.5 17.9-21.9 \n \n\n\n\n18.8-23.1 20.5-24.5 19.9-23.9 \n\n\n\nStandard error 0.56-0.83 0.57-1.02 0.47-0.87 \n \n\n\n\n0.37-0.81 0.41-0.84 0.31-0.76 \n \n\n\n\n0.61-0.94 0.60-1.06 0.58-0.89 \n\n\n\nMedian 17-22 20-24 20-24 \n \n\n\n\n19-23 20-23 19-22 \n \n\n\n\n20-24 21-25 20-25 \n\n\n\nMode 15-23 20-25 20-25 \n \n\n\n\n19-23 20.24 20.24 \n \n\n\n\n20-26 21-28 21-27 \n\n\n\nStandard \n\n\n\nDeviation \n2.52-3.73 2.56-4.56 2.12-3.90 \n\n\n\n \n1.67-3.63 1.82-3.76 1.39-3.42 \n\n\n\n \n2.72-4.19 2.67-4.74 2.60-3.96 \n\n\n\nSample variance 6.36-13.94 6.58-20.80 4.48-15.21 \n \n\n\n\n2.79-13.14 3.31-14.16 1.94-11.69 \n \n\n\n\n7.41-17.57 7.15-22.46 6.75-15.69 \n\n\n\nMinimum 9-10 9-12 7-14 \n \n\n\n\n9-14 9-14 8-16 \n \n\n\n\n8-12 6-12 7-17 \n\n\n\nMaximum 21-25 22-26 22-26 \n \n\n\n\n22-27 22-26 21-26 \n \n\n\n\n23-28 24-28 24-28 \n\n\n\nSum 335-421 376-462 394-451 \n \n\n\n\n355-440 380-449 357-438 \n \n\n\n\n376-462 413-490 398-478 \n\n\n\nConfidence \n\n\n\nLevel(95.0%) \n1.18-1.75 1.20-2.13 0.99-1.83   0.78-1.70 0.85-1.76 0.65-1.60   1.27-1.98 1.25-2.20 1.22-1.85 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 55-72 \n\n\n\n\n\n\n\n65 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 7: Distribution pattern of soil water content in box plot figures \n\n\n\nBH1 BH2 \n\n\n\nBH3 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Year Vol. (XX) (Issue) \n\n\n\n\n\n\n\n66 \n\n\n\n\n\n\n\nComparison of Soil Water Characteristics Between the Three Study Sites \n\n\n\n\n\n\n\nIn order to visualize the differences among the three NMM results obtained from BH1, BH2 \n\n\n\nand BH3, graphs 1:1 were plotted as shown in Figure 8. From the graphs, BH3 access tube \n\n\n\nindicated a significantly higher \u04e8 values compared to BH1 and BH2. However, \u04e8 values at \n\n\n\nBH1 and BH2 was equally distributed. As indicated in Figure 8, higher \u04e8 values occurred for \n\n\n\nBH1 and BH2 at >1.0 m and <1.0 m of soil depth, respectively. This study also revealed that \n\n\n\na steady increase of \u04e8 values occurred with increment of soil depth up to 2.0 m maximum. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 8: 1:1 graphs for BH1, BH2 and BH3 in the oil palm catchment \n\n\n\n\n\n\n\nFurther analysis revealed that an increase in \u04e8 occurred in response to occurrence of \n\n\n\n>30.0mm total rainfall. Eight events registered \u04e8w (soil water content measured within 24 h \n\n\n\nafter rainfall) greater than the average \u04e8d (soil water content measured during dry conditions) \n\n\n\nfor all three bore holes (Figure 9). Percentage \u04e8 increment ranged from 5.0-23.8% for BH1, \n\n\n\n4.3-31.3% for BH2 and 4.5-33.3% for BH3 when high rainfall occurred.  \n\n\n\n\n\n\n\nOur data also showed that the average travelling time for water to percolate from the \n\n\n\nsurface to 1.1, 1.5, and 1.6 m depths for BH1, BH2 and BH3 respectively, was about 4-10 h.  \n\n\n\nFor example, after 4 h of 46.6 mm of rainfall, the water moved 1.1 m in BH1, 1.5 m in BH2 \n\n\n\nand 1.6 m in BH3. The soil water content percentage increment ranged from 9.5 to 23.8% at \n\n\n\nthese particular depths. However, traveling period for water in the soil was dependent on the \n\n\n\namount of rainfall and the antecedent conditions (Cheng et al. 2013). Other studies also \n\n\n\nsuggest that rapid movement may occur through the upper layers and movement in deeper \n\n\n\nlayers is typically slower (Lo et al. 2020). Our study results show that the \u04e8 values decrease \n\n\n\nslowly in response to dry conditions when no high rainfall was recorded after 24 h from the \n\n\n\nprevious storm event. \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Year Vol. (XX) (Issue) \n\n\n\n\n\n\n\n67 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 9: A significant increase in \u04e8w in response to high rainfall (>30.0 mm) \n\n\n\n\n\n\n\n\n\n\n\nResistivity Imaging System Survey Results \n\n\n\n\n\n\n\nResistivity monitoring was carefully established at three study sites in accordance to RIS \n\n\n\nrequirements. From the findings, all catchments exhibited high resistivity values within 6.0 m \n\n\n\ndepths. The stratigraphy patterns are described according to the respective study sites \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Year Vol. (XX) (Issue) \n\n\n\n\n\n\n\n68 \n\n\n\n\n\n\n\nCatchment 1 (C1) Stratigraphy Pattern \n\n\n\n\n\n\n\nThe stratigraphy pattern for C1 is shown in Figure 10. The general characteristics of C1 \n\n\n\naccording to RIS sampling: \n\n\n\n\n\n\n\n1) Hard zone covered an area more than 40% of the total resistivity image. \n\n\n\n2) Stream leakage occurred contributing to ground water. \n\n\n\n3) No clear indications of shallow horizontal flow towards stream based on the \n\n\n\ngroundwater contour, but possibilities of leakage of shallow horizontal flow towards \n\n\n\nground water bearing strata. \n\n\n\n4) Clear inter-connection among existing hard layers within the survey area. \n\n\n\n5) Difficult to identify the inter-connection between horizontal flow and ground water \n\n\n\nbearing strata. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 10: Resistivity imaging with topography of Catchment 1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Year Vol. (XX) (Issue) \n\n\n\n\n\n\n\n69 \n\n\n\n\n\n\n\nCatchment 2 (C2) Stratigraphy Pattern \n\n\n\n\n\n\n\nThe stratigraphy pattern for C2 is shown in Figure 11. The general characteristics of C2 \n\n\n\naccording to RIS sampling: \n\n\n\n\n\n\n\n1) Presence of clear indications of horizontal flow towards stream \n\n\n\n2) High permeability type  of soil characteristics \n\n\n\n3) Lack of hard zone with detection of only a few boulders.  \n\n\n\n4) No leakages of stream towards ground water. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 11: Resistivity imaging with topography of Catchment 2 \n\n\n\n\n\n\n\n\n\n\n\nCatchment 3 (C3) stratigraphy pattern \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Year Vol. (XX) (Issue) \n\n\n\n\n\n\n\n70 \n\n\n\n\n\n\n\nThe stratigraphy pattern for C3 is shown in Figure 12. The RIS sampling found a shallow \n\n\n\nwater saturated strata within 2.0 to 6.0 m depth, which reaffirmed the NMM findings where \n\n\n\nthis phenomenon was observed at 1.0 to 2.0 m depth. General characteristics of C3 according \n\n\n\nto RIS sampling were: \n\n\n\n\n\n\n\n1) Flow of subsurface water zone towards the stream based on groundwater contour. \n\n\n\n2)  High porosity zone beyond 2.0 m depth, reaffirming NMM findings. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 12: Resistivity imaging with topography of Catchment 3 \n\n\n\n\n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nThe results obtained using NMM revealed a small significant variability in \u04e8 values in the 20 \n\n\n\ndifferent soil depths. Variability of soil water content also showed a significant increase in \n\n\n\nresponse to high rainfall events (>30.0 mm). \u04e8 values decreased when there was no high \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Year Vol. (XX) (Issue) \n\n\n\n\n\n\n\n71 \n\n\n\n\n\n\n\nrainfall after 24 h and this showed that time also has an influencing role. The average \n\n\n\ntravelling time for water to move from the surface to 1.1m, 1.5m, and 1.6 m for BH1, BH2 \n\n\n\nand BH3, respectively was found to be about 4-10 h. However, as measurements were not \n\n\n\nmade below 2.0 m. it was not possible to determine whether preferential flow occurred at \n\n\n\ngreater depths in oil palm catchments. The 2-D direct current resistivity surveys using 80-\n\n\n\nelectrode imaging technique was successfully used in this study to map the soil water content \n\n\n\nunderneath the oil palm catchment up to 6 m depth. The NMM and RIS analyses identified \n\n\n\nthe soil characteristics that influence water behavior in the ground. Both \u04e8 values and \n\n\n\nstratigraphy pattern showed that soil depth, rainfall intensity and antecedent conditions \n\n\n\ndirectly affected water flow. Though vegetative cover is known to have an effect on soil-\n\n\n\nwater relationship (Dinar et al. 2019), our \u04e8 values and stratigraphy pattern showed that soil \n\n\n\ndepth and antecedent conditions were a bigger influencing factor in the oil palm plantations. \n\n\n\nBoth techniques (NMM and RIS) were able to model the soil-water relationship in oil palm \n\n\n\nplantations and can be used in the future to charter better water management strategies in oil \n\n\n\npalm plantations. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nThis work was supported by Universiti Teknologi Malaysia under Research Vote No 74287. \n\n\n\nThe publication of this study was funded by Universiti Putra Malaysia, under the Geran Putra \n\n\n\nInisiatif Putra Muda (Vot 9706500) grant. The authors wish to acknowledge the Mahamurni \n\n\n\nOil Palm Plantation Sdn Bhd for access to the Sedenak oil palm catchment and to Ms Remy, \n\n\n\nMr Sekam and Mr Morris for their support and cooperation in this study. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\n\n\n\n\nAin-Lhout, F., S. Boutaleb, M. Diaz-Barradas, J.  Jauregui and M. Zunzunegui.  2016. \n\n\n\nMonitoring the evolution of soil moisture in root zone system of Argania spinosa \n\n\n\nusing electrical resistivity imaging. Agricultural Water Management 164: 158-166. \n\n\n\nAnyaoha, K., R. Sakrabani, K. Patchigolla and A. Mouazen.  2018. Critical evaluation of oil \n\n\n\npalm fresh fruit bunch solid wastes as soil amendments: Prospects and challenges. \n\n\n\nResources, Conservation and Recycling 136: 399-409. \n\n\n\nCastellanos-Navarrete, A., F. de Castro and P. Pacheco. 2021. The impact of oil palm on rural \n\n\n\nlivelihoods and tropical forest landscapes in Latin America. 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"\n\nINTRODUCTION\n\n\n\nimmediate attention. As a result of widespread industrialization, soil pollution \n\n\n\npolluted areas is necessary in order to remediate these soils and minimize the \net al\n\n\n\nthese technologies are not only costly, but also cause soil disturbance, and are not \nreadily accepted by the general public (Gardea-Torresdey et al. \n\n\n\nAssessment on the Degree of Immobilization of Heavy Metals \nin Contaminated Urban Soils by Selected Phosphate \n\n\n\nRocks of Different Particle Sizes\n \n\n\n\nSoltan, M.E.1,2*, E.M. Fawzy1 and  M.N. Rashed1 \n\n\n\n1Chemistry Department, Faculty of Science, Aswan University, Egypt.\n2Chemistry Department, Faculty of Science and Arts at Al-Rass, \n\n\n\nQassim University, KSA\n\n\n\nABSTRACT\n\n\n\nPb immobilization. Depending on phosphate solubility, the decrease in of metal \n\n\n\nmg kg-1\n\n\n\n-1\n\n\n\n-1\n\n\n\nis noted that lower dissolution of phosphate rock may be advantageous for long \nterm maintenance.\n\n\n\nKeywords: Mined PR, processed PR, biogenic PR, TCLP, sequential     \nextraction study  \n\n\n\n___________________\n*Corresponding author : E-mail: \n\n\n\n\n\n\n\n\nplays a vital role in the solubility and potential bioavailability of metals in soils \n(Tandy et al.\nin the environment, and can persist in soils for decades or even centuries. The \ncontamination of soils by metals can have long-term environmental and health \nimplications (Le\u0161tan et al. \napproaches to polluted soil, which can reduce the risk of metal contamination. The \n\n\n\nhas generally become one of the key research activities in environmental science \nand technology. In selecting the most appropriate soil remediation methods for a \nparticular polluted site, it is of paramount importance to consider the characteristics \nof the soil and the contaminants (Le\u0161tan et al\n\n\n\nmolecules, organic materials, aluminosilicates, phosphates, iron and manganese \n\n\n\nare effective in decreasing the bioavailability of metals by introducing additional \n\n\n\nplants, and their bioconcentration through the food chain is reduced (Guo et al. \n\n\n\ndust is ingested or inhaled. Many of the amendments used in soil stabilization are \n\n\n\nin large amounts. Reviews on previously successfully applied amending agents \n et al.\n\n\n\nand Puschenreiter et al.\nfocused on this topic. Several soil additives have been tested: eight substrates \n\n\n\net al. et \nal. et al et al\nSappin-Didier et al. et al et al\n\n\n\n4 6  leads to \n\n\n\net al. \net al\n\n\n\nimmobilizing metals in polluted soils.\nMany studies have been conducted to understand the mechanism of this \n\n\n\nimmobilization (Suzuki et al et al\n\n\n\nnecessary to assure that immobilization results in lower soil to plant transfer and \n\n\n\nSoltan, M.E., E.M. Fawzy and  M.N. Rashed \n\n\n\n\n\n\n\n\nPb content in three week-old-maize plants (Zea Mays\nHordeum vulgare\n\n\n\nsoils led to a strong decrease in Pb concentrations in shoots of sorghum (Sorghum \nbicolor\napplication rates of HA.\n\n\n\nsolubilization rarely occurs in non-acidic soils. However, microbial mediated \n\n\n\ninorganic P forms (Rodr\u00f5\u00c2guez et al.\n\n\n\nmetal-contaminated soils by determing the total metal concentrations in different \nphosphate sources, the tendency of these trace impurities to leach, and the apparent \namendments for stabilizing contaminants.\n\n\n\n METHODOLOGY\nQuality Assurance\nPrecautions were taken to prevent sample contamination and to ensure reliability \n\n\n\nof stainless steel and stored in non-metallic containers, such as glass bottles or \npolyethylene bags.  For all analysis, standard samples were run during and at the \nend of the measurements to ensure continued accuracy. \n\n\n\nFor analytical precision, the samples were run in triplicates. The blanks were \nrun all the time.  All chemicals used were of analytical grade. Concentrated acids \n\n\n\nSamples Collection\n\n\n\nheavy metals, including Pb, Cd, Co and Cu due to a high population density\nThree types of phosphate materials were investigated: mined, processed, \n\n\n\n5\n\n\n\nTilapia nilotica\nNasser, Egypt.\n\n\n\nHeavy Metals Immobilization in Urban Soils\n\n\n\n\n\n\n\n\nAnalysis of Soil \n\n\n\nsample suspension obtained from pH determination using conductivity meter \n\n\n\nwas determined by the micropipette method. \n\n\n\nAnalysis of Phosphate Materials\n\n\n\nreferred to as the apparent solubility of these elements. Total metal concentrations \n\n\n\n, 4 mL of H 4\n\n\n\nduplicated.\n\n\n\nSequential Extraction Study\n\n\n\nand validation of the methods used for speciation studies in soils and sediments \n(Tessier et al\n\n\n\nSoltan, M.E., E.M. Fawzy and  M.N. Rashed \n\n\n\n\n\n\n\n\nreactivity and potential bioavailability of heavy metals generally increase with \n\n\n\nform of metals in soils and the other three fractions are relatively immobile and \nmore stable, but may sometimes become mobile and bioavailable with changes \nin soil conditions.\n\n\n\nAll the steps and the operational fractions of metal are displayed in Table \n\n\n\nElemental Analysis and Instrumental Technique\n\n\n\nsamples were determined by the atomic absorption spectrophotometer (Model \n\n\n\nHeavy Metals Immobilization in Urban Soils\n\n\n\nTABLE 1\nSome chemical soil properties\n\n\n\ncorresponding phases\n\n\n\nTABLE 1 \nSome chemical soil properties \n\n\n\nItem Mechanical \nAnalysis % \n\n\n\nCaCO3 mg g-1 O.M mg g-1 CEC ( S cm-1) pH \n\n\n\n\n\n\n\n \nSand\n\n\n\n \nSilt \n\n\n\n \nClay     \n\n\n\n \nUrban Soil \n\n\n\n\n\n\n\n \n35 \n\n\n\n \n36 \n\n\n\n \n38 \n\n\n\n \n2.5 \n\n\n\n \n23 \n\n\n\n \n920 \n\n\n\n \n6.8 \n\n\n\nTABLE 2 \nProcedure of the five step sequential extraction of metals from the urban soil and corresponding \n\n\n\nphases \n\n\n\nPhase/Association Abbreviation Operational Definition \nExchangeable EX 16 ml MgCl2 , pH7.0, shaking 1h          \n\n\n\n \nOrganic-bound OB 10 ml 30 % H2O2 (pH2),                                                                                                                                   \n\n\n\nthen 10 ml 30 % H2O2 (pH2)                                                                                                                               \ncool, add 50 ml mol/1ammonium                                                                                                                       \nacetate (pH2)  \n\n\n\n \nAcidic AC 16 ml 1M NaOAC, pH5.0 with                                                                                                                                    \n\n\n\nHOAC, shaking 5h   \n \n\n\n\nAmorphous Fe/Al \noxides-bound \n\n\n\nOX 40 ml 0.175M (NH4)C2O4/0.1M                                                                                                                                \nH2C2O4, shaking 4h in the dark \n\n\n\n \n \nResidual \n\n\n\n \nRES \n\n\n\n                                                           \nAcid mixturea \n\n\n\n \na the residual from step 4 was digested in 15 ml HCl/HNO3/HF (3:1:2, v/v/v) at 150 C \n\n\n\n\n\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSequential Extraction Procedure\n\n\n\nFig. 1. Metal levels indicate relatively high \n\n\n\nhelp to investigate metal distribution in the various soil compartments. To illustrate \n\n\n\nThe leached metals concentrations in different forms are in the order: residual \n\n\n\n(Fig. 1\n\n\n\nwhich, together with the changing environmental condition (both soil and plant \n\n\n\nthat all measured metals were primarily associated with residual and amorphous \n\n\n\nmetal content (Fig. 1\n\n\n\nwhere the contaminated metals in these fractions might be moderately soluble and \n\n\n\nthe heavy metal solubility e.g. pH, organic matter, CEC, carbonate and clay \n\n\n\nFawzy et al.\n\n\n\nof the geochemistry of heavy and trace metals in soil. They concluded that the \n\n\n\nfrom electron microprobe analysis of surface soils that Pb is concentrated in the \n\n\n\nSoltan, M.E., E.M. Fawzy and  M.N. Rashed \n\n\n\n\n\n\n\n\nHeavy Metals Immobilization in Urban Soils\n\n\n\n0 20 40 60 80 100\n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nP\nb\n D\n\n\n\nis\ntr\n\n\n\nib\nu\nti\no\nn\n\n\n\n\n\n\n\n0 20 40 60 80 100\n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nC\nd\n D\n\n\n\nis\ntr\n\n\n\nib\nu\nti\no\nn\n\n\n\n\n\n\n\n0 20 40 60 80 100\n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nC\no\n D\n\n\n\nis\ntr\n\n\n\nib\nu\nti\no\nn\n\n\n\n0 20 40 60 80 100\n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nC\nu\n D\n\n\n\nis\ntr\n\n\n\nib\nu\nti\no\nn\n\n\n\nExchangeable Acidic\nOrganic Amorphous Fe/Mn bond\nResidual\n\n\n\nFig. 1:  Percentage of Pb, Cd, Co and Cu in contaminated and treated soilFig. 1:  Percentage of Pb, Cd, Co and Cu in contaminated and treated soil\n\n\n\n\n\n\n\n\nSoltan, M.E., E.M. Fawzy and  M.N. Rashed \n\n\n\n\n\n\n\n0 20 40 60 80 100\n\n\n\nT0\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\nP\nb\n  \n\n\n\nP\ne\nrc\n\n\n\ne\nn\nta\n\n\n\ng\ne\n\n\n\n\n\n\n\n0 20 40 60 80 100\n\n\n\nT0\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\nC\nd\n  \n\n\n\nP\ne\nrc\n\n\n\ne\nn\nta\n\n\n\ng\ne\n\n\n\n\n\n\n\n0 20 40 60 80 100\n\n\n\nT0\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\nC\no\n  \n\n\n\nP\ne\nrc\n\n\n\ne\nn\nta\n\n\n\ng\ne\n\n\n\n\n\n\n\n0 20 40 60 80 100\n\n\n\nT0\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\nC\nu\n  \n\n\n\nP\ne\nrc\n\n\n\ne\nn\nta\n\n\n\ng\ne\n\n\n\nExchangeable Acidic\nOrganic Amorphous Fe/Mn bond\nResidual\n\n\n\nFig. 2: Comparison of metals percentage in different phosphate sources.\nFig. 2: Comparison of metals percentage in different phosphate sources.\n\n\n\n\n\n\n\n\n111\n\n\n\nleached lead content was lower than the control, because it shifted from the \nmobile fractions to the residual fraction. Lead stabilization may have been due to \nthe formation of sparingly soluble lead phosphate mineral phases (Badawy et al. \n\n\n\net al. sp\nchloropyromorphite (log Ksp\n\n\n\net al.\n\n\n\nshowed a same trend (Fig. 1 et al. \nsoil-wash process for Cd-contaminant paddy sorts. They assumed that cadmium \n\n\n\n-\n4 , \n\n\n\n4 , organic acids, and fulvic acid. Immobilization or transformation of \nsoil metals especially Pb to low soluble species that reduces metals dissolution \nand leachability in soil system may be a remedial strategy for metal contaminated \nsoil which safeguards human and ecosystem from contamination (Yang and \n\n\n\nsoils can be assessed using a fractionation scheme (Chen et al\n\n\n\ntransferring metals from the non-residual fractions to the residual fraction, while \n\n\n\nHeavy Metals Immobilization in Urban Soils\n\n\n\n\n\n\n\nTABLE 3 \nMeasured metals percentage in the non-residual (EX+OB+AC+OX) and residual fractions (RES). \n\n\n\nTreatment Cu Co Cd Pb \n\n\n\nT0 24.8 \n\n\n\n75.20 \n\n\n\n28.3 \n\n\n\n71.73 \n\n\n\n27.2 \n\n\n\n72.8 \n\n\n\n31.86 \n\n\n\n68.15 \n\n\n\nT1 23.27 \n\n\n\n75.73 \n\n\n\n26.98 \n\n\n\n73.00 \n\n\n\n26.45 \n\n\n\n73.60 \n\n\n\n30.84 \n\n\n\n69.17 \n\n\n\nT2 22.43 \n\n\n\n77.60 \n\n\n\n26.08 \n\n\n\n74.00 \n\n\n\n26.4 \n\n\n\n73.60 \n\n\n\n29.4 \n\n\n\n70.6 \n\n\n\nT3 21.5 \n\n\n\n78.50 \n\n\n\n25.72 \n\n\n\n74.30 \n\n\n\n25.84 \n\n\n\n74.2 \n\n\n\n28.85 \n\n\n\n71.15 \n\n\n\nT4 19.6 \n\n\n\n80.43 \n\n\n\n19.13 \n\n\n\n80.88 \n\n\n\n24.3 \n\n\n\n75.7 \n\n\n\n27.53 \n\n\n\n72.5 \n\n\n\nT5 19.3 \n\n\n\n80.70 \n\n\n\n17.97 \n\n\n\n82.03 \n\n\n\n6.34 \n\n\n\n93.7 \n\n\n\n25.2 \n\n\n\n74.9 \n\n\n\nT6 16.76 \n\n\n\n83.24 \n\n\n\n14.94 \n\n\n\n85.1 \n\n\n\n5.59 \n\n\n\n94.41 \n\n\n\n23.37 \n\n\n\n72.64 \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nSoltan, M.E., E.M. Fawzy and  M.N. Rashed \n\n\n\n29%\n\n\n\n26%\n\n\n\n23%\n\n\n\n22%\nPb\n\n\n\n43%\n\n\n\n38%\n\n\n\n10%\n\n\n\n9%Cd\n\n\n\n35%\n\n\n\n24%\n\n\n\n22%\n\n\n\n19%\nCo\n\n\n\n31%\n\n\n\n24%\n\n\n\n24%\n\n\n\n21%\nCu\n\n\n\nControl  Mined<63 m  Biogenic  Processed\n\n\n\nFig. 3: Comparison of percentage of Pb, Cd, Co and Cu, respectively in the non-residual \nfraction of the unamended and treated soils\n\n\n\nFig. 3: Comparison of percentage of Pb, Cd, Co and Cu, respectively in the non-residual \nfraction of the unamended and treated soils\n\n\n\n\n\n\n\n\nof Pb, Cd, Co and Cu in PR treatments decreases with increasing rock grain size.  \n \n\n\n\nComparison of Different Phosphate Sources Suitability for Remediation of \nContaminated Soils\n\n\n\nmarked decrease in contaminant metal concentrations compared to the control \n\n\n\ndecreased in the following order: mobile < acidic organic bound < amorphous \nFig. 2\n\n\n\nphosphate is the most effective amendment for the remediation of metal \ncontaminated soil (Fig. 3\nfrom the contaminated soil may result from the capacity of induce metal-binding \n\n\n\nsources with contaminated soil, biogenic phosphates are the preferential sites to \nre-adsorbed metals from contaminated soil. Soltan et al\n\n\n\nof some bights at Lake Nasser, Egypt. They proved that Tilapia can be used as \na bioindicator species due to its abundance and accumulation capacity for heavy \nmetals. \n\n\n\nAvailability of Metals in Phosphate Sources\nSoluble impurities pose a greater hazard than strongly bound materials because \nthey readily enter soil water where they are potentially available for plant or \n\n\n\nLusatian Lignite mining sediments and internal metal distribution in Juncus \nBulbosus L. and indicated that iron in the pore waters was very likely present as \n\n\n\nform. From the monitoring results of analysis of different phosphate sources, \n\n\n\necological threat is associated with Pb mobility in the soil system (Yang and \n\n\n\nof soil Pb and their solubility (Ruby et al. et al\nof soil Pb can be achieved by formation of pyromorphite through phosphate \namendments. Based on the results, a pronounced mobilization of Pb, Cd, Co and \nCu was observed in mined phosphate compared to other phosphate sources (Table \n\n\n\nHeavy Metals Immobilization in Urban Soils\n\n\n\n\n\n\n\n\n114\n\n\n\nphase during the solubility test. The solubility of phosphate materials is important \nbecause dissolution is dependent on the contaminant, a necessary step in the \n\n\n\net al\n\n\n\nTotal Concentration of Impurities in Phosphate Sources\n\n\n\nPb and Cu concentrations in mined and processed phosphate sources than those \nin uncontaminated soils. The concentrations of metals in the mined, processed \nand biogenic phosphate sources were higher than measured concentrations in the \nsoluble form. These elevated concentrations of metals in some phosphate materials \n\n\n\net al\n\n\n\nphosphate to scavenge elements. Some of the variations among the metal contents \nof phosphate sources are likely to be due to the different geological origins of the \n\n\n\net al.\n\n\n\nphosphate fertilizers are considered the most important source of metal \n\n\n\nmagmatic phosphate tend to have only negligible concentrations of Cd, whereas \nthose from sedimentary phosphate tend to have higher Cd concentrations (Adriano \n\n\n\nSoltan, M.E., E.M. Fawzy and  M.N. Rashed \n\n\n\nTABLE 4 \nTotal, Water Soluble and TCLP Mean Concentrations ( g g-1) of Impurities in \n\n\n\nDifferent Sources of Phosphate Materials. \n\n\n\nElement TCLP \nregulatory \n\n\n\nlimit \n\n\n\nAverage \nShale, 1961 \n\n\n\nBiogenic \nMean \n\n\n\nProcessed \nMean \n\n\n\nMined \nMean \n\n\n\npH   6.98 6.41 8.89 \n \n\n\n\nWater Soluble \nPb \nCd \nCo \nCu \n\n\n\n\n\n\n\n0.02 \n0.0 \n\n\n\n0.005 \n0.017 \n\n\n\n\n\n\n\n0.05 \n0.02 \n0.22 \n2.01 \n\n\n\n\n\n\n\n0.03 \n0.011 \n1.12 \n0.005 \n\n\n\n \nTotal Digestion \n\n\n\nPb \nCd \nCo \nCu \n\n\n\n\n\n\n\n20.00 \n0.03 \n19.0 \n45.00 \n\n\n\n\n\n\n\n1.15 \n0.021 \n0.021 \n2.3 \n\n\n\n\n\n\n\n3.93 \n0.09 \n0.76 \n3.6 \n\n\n\n\n\n\n\n2.15 \n0.13 \n16.23 \n\n\n\n45 \n \n\n\n\nTCLP \nPb \nCd \nCo \nCu \n\n\n\n\n\n\n\n5.0 a \n1.0 a \n\n\n\n\n\n\n\n0.23 \n0.01 \n0.11 \n0.088 \n\n\n\n\n\n\n\n0.27 \n0.01 \n0.52 \n1.2 \n\n\n\n\n\n\n\n1.0 \n0.067 \n0.087 \n3.0  \n\n\n\nTCLP: Toxicity characteristic leaching procedure limits (a \u2014 40 CFR 261.24, U.S. EPA 1999). \n\n\n\nTABLE 4\n-1\n\n\n\nSources of Phosphate Materials.\n\n\n\n\n\n\n\n\n115\n\n\n\nstudied trace metal concentrations were lower than in natural back ground levels \n\n\n\nThe TCLP Test\nThe TCLP was designed to determine the mobility of both organic and inorganic \n\n\n\nlow pH, that could cause the release of contaminants that would otherwise be \n\n\n\nregulatory test used widely to classify solid waste materials as hazardous or non-\n\n\n\nMeasured metals in mined and processed phosphate materials showed relatively \n\n\n\nHowever, even in this case, metal contents in different phosphate sources are still \n\n\n\nremediation purpose. \net al.\n\n\n\nof contaminated soils. They concluded that rapid phosphate release would \nbe advantageous when a rapid immobilization of contaminants is necessary. \nConversely, a slow-release phosphate source may be preferred for long-term \ntreatment. Combination of phosphate sources having high, and the other, of slow \ndissolution rates, may provide a rapid immobilization of contaminants while \nproviding a slow release of phosphate for continued long-term treatment and \nmaintenance.\n\n\n\n CONCLUSION\n\n\n\nBased on these results, it can be concluded that:\n\n\n\nmore effective than larger particle sizes in transforming large amounts of Pb, \nCd, Co and Cu from non-residual fraction to residual ones.\nBiogenic apatite has lower concentrations of impurities than mined and \nprocessed phosphate.  These differences between mined, processed, and \nbiogenic phosphate are obvious in total impurities concentrations test (TCLP \n\n\n\nlevels to be used for remediation purposes.\nFurther research should be conducted on the stabilization mechanisms in \n\n\n\nthe long-term stability of the amendment process.\n\n\n\nHeavy Metals Immobilization in Urban Soils\n\n\n\n\n\n\n\n\n116\n\n\n\nSoltan, M.E., E.M. Fawzy and  M.N. Rashed \n\n\n\nREFERENCES\nTrace Elements in Terrestrial Environments: Biochemistry, \n\n\n\nBioavailability and Risks of Metals. New York: Springer.\n\n\n\nsolid-phase controls leads activity in soil solution. Journal of Environmental \nQuality.\n\n\n\nBoisson, J., A. Ruttens, M. Mencha and J. Vangronsveld.  1999. Evaluation of \n\n\n\nsoil, plant growth and plant metal accumulation. Environmental Pollution\n\n\n\nEnvironmental \nPollution.\n\n\n\nWater, Air, and \nSoil Pollution\n\n\n\namendment on metal immobilization in contaminated soils.  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Journal of Geochemical Exploration\n\n\n\nindustrial area contaminated by non-ferrous metals: in situ metal immobilization \nand revegetation. Environmental Pollution. 87: 51-59. \n\n\n\nVangronsveld, J., J.V. Colpaert and K.K.Van Tichelen.  1996. Reclamation of a \nbare industrial area contaminated by non-ferrous metals: physicochemical \nand biological evaluation of the durability of soil treatment and revegetation. \nEnvironmental Pollution\n\n\n\nsolutions. Journal of Contaminant Hydrology.\n \n\n\n\nof phosphoric acid application in lead-contaminated urban soil. Science of the \nTotal Environment\n\n\n\n \nPractice \n\n\n\nPeriodical of Hazardous, Toxic and Radioactive Waste Management.\n\n\n\ntime-dependent distribution of heavy metals in paddy crops. Chemosphere.\n\n\n\nWater Research\n\n\n\nSoltan, M.E., E.M. Fawzy and  M.N. Rashed \n\n\n\n\n\n"
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"\n\n___________________\n*Corresponding author : E-mail: mohammedkamal8@yahoo.com\n\n\n\nINTRODUCTION\nSoil salinity, a widespread environmental problem, particularly in arid and \nsemi-arid regions of the world, affects a quarter to a third of all crop producing \nagricultural land on earth (Szabolcs, 1989; Rengasamy, 2010).  Salt-affected \nsoils occupy more than 20% of global irrigated cropping areas (Szabolcs, 1994; \nGhassemi et al., 1995).\n\n\n\nAdding gypsum is the most common amendment used to overcome soil \nsodicity hazards due to its low cost, availability, and ease of handling (Siyal et \nal., 2002). Miyamoto and Enriquez (1990) pointed out that gypsum  decreased \nsodicity and salinity in a percolating solution and allowed the maintenance of a \nrelatively uniform hydraulic gradient throughout the soil profile. Abdurrahman et \nal. (2004) found that the application of gypsum followed up with the application \nof municipal solid compost restored degraded sodic soils. The application of \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 131-139 (2015)                     Malaysian Society of Soil Science\n\n\n\nPotential Use of Halophytes in Combination with Gypsum\nto Reclaim and Restore Saline-Sodic Soils in Egypt\n\n\n\nAbdel-Fattah, M.K.\n\n\n\nSoil Science Department, Faculty of Agriculture, Zagazig University,\nEgypt\n\n\n\nABSTRACT\nA pot experiment was conducted under greenhouse conditions to assess the impact \nof three halophyte species, Atriplex halimus, Atriplex lentiformis, and Atriplex \namnicola, coupled with or without gypsum, on salt accumulation from excessive \nsaline-sodic soil from the Sahl El-Tina plain, North West coast of Sinai, Egypt. \nAll halophyte species ameliorated the soil at the end of their growth. Soil salinity \nand sodicity decreased particularly when combined with gypsum. Soil salinity \ndecreased from  51.2 (initial soil salinity)  to  8.10, 12.10, 10.14, 4.10, 7.10 and 5.14 \nmS/cm using A. halimus, A. amnicola, A. lentiformis,    A. halimus + gypsum, A. \nlentiformis + gypsum, and A. amnicola + gypsum, respectively. Sodium adsorption \nratio (SAR) decreased from 31.1 to  4.10, 5.01, 4.58, 2.91, 3.84 and 3.26, whilst \nexchangeable sodium percentage (ESP)  decreased from 35.9 to  4.83, 6.19, 5.56, \n3.07, 4.44 and 3.59 using A. halimus, A. amnicola, A. lentiformis, A. halimus + \ngypsum, A. lentiformis + gypsum, and A. amnicola + gypsum, respectively. The \nmost efficient treatments that enhanced soil characteristics were A. halimus + \ngypsum, followed by A. amnicola + gypsum, A. lentiformis + gypsum, A. halimus, \nA. amnicola, and A. lentiformis.\n\n\n\nKeywords: Greenhouse study, gypsum halophytes, greenhouse study, \nreclamation, phytoremediation, saline-sodic soil \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015132\n\n\n\nAbdel-Fattah, M.K.\n\n\n\ngypsum also decreased pH, electrical conductivity (EC), exchangeable sodium \npercentage (ESP), and bulk density (Manzoor et al., 2001). Conversely, the \naddition of gypsum increased soil hydraulic conductivity and infiltration rate \n(Abdel-Fattah, 2011). These results are consistent with the findings of Gupta et al. \n(1988) who state that gypsum application to a sodic vertisol at different rates and \nfrequencies decreased soil pH, EC and ESP. Furthermore, Abou Yuossef (2001) \nstated that using phosphogypsum (PG) decreased pH, EC, ESP and decreased \nbulk density  with increasing PG supply, but increased soil hydraulic conductivity, \ntotal porosity, mean weight diameter of soil aggregates, geometric mean diameter, \nand water stable aggregates. \n\n\n\nA halophyte is a hydrophobic plant that is capable of surviving in a highly \nsaline environment. Halophytes can grow in salt marshes, live on cliffs and \ndunes near the oceans, and adapt to the desert environments. Nevertheless, \nCust\u00f3dia Gago et al. (2011) found that 20% of halophytic plant species, mainly \nglycophytes, cannot survive in saline environments. Halophytes adopt salinity \ntolerance mechanisms such as salt exclusion, uptake, compartmentalisation \nand extrusion (Holly, 2004). While the physiological uniqueness of halophytes \nis often expressed in morphological features such as salt glands, salt hairs, and \nsucculence, they evolve different mechanisms to deal with excess sodium and \nother salts in their environments (Holly, 2004; Naidoo and Naidoo, 1999). Some \nvascular halophytes accumulate high levels of salts in their above-ground parts \n(Gorham et al., 1987). Thus, functioning halophytes are ion accumulators and ion \nexcreters, to be able  to phytoremediate excess soil salinity and sodicity. Rush and \nEpstein (1981) found that, as an adaptation mechanism to saline environments, \nion accumulators (hyperaccumulators) absorb high amounts of ions. The \naccumulation of salts reduces the requirements for increased wall extensibility, \nleaf thickness, and water permeability required to maintain positive growth and \nturgor at low soil water potentials. The objective of this study was to examine \namelioration of a saline-sodic soil in Egypt using halophyte plants and gypsum.    \n\n\n\nMATERIALS AND METHODS\nA pot experiment was conducted under greenhouse conditions using clay saline-\nsodic soil collected from the 0-30 cm topsoil of the Sahl El-Tina plain, north-\nwest coast of Sinai, Egypt. Closed bottom plastic pots of internal dimensions 25 \n\u00d7 20 cm were filled with 10 kg of the collected soil. The experiment assessed \nthe  reclamation of soil using three halophyte Atriplex species of A. halimus, A. \nlentiformis, A. amnicola and the combination of each with gypsum. Therefore, \nthere were six treatments executed in a randomised complete block design, and \nthe three replicates were as follows: A. halimus, A. amnicola, A. lentiformis, A. \nhalimus + gypsum, A. lentiformis + gypsum, and A. amnicola + gypsum. Images \nand classifications of A. halimus, A. lentiformis and A. amnicola are shown in \nFigure 1. Gypsum was added until its final concentration was 33.5 g kg-1. This \nwas calculated to adapt the ESP of the soil to 10% in line with the United States \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 133\n\n\n\nHalophytes and Gypsum for Saline-Sodic Soils Reclamation\n\n\n\nof America\u2019s Department of Agriculture\u2019s (USDA) (1954) guidelines.  Gypsum \napplication was done by mixing it with soil before filling the pots. Seedlings were \ntransplanted in the pots. Water supplied to plants in the pots was maintained at \nwater holding capacity of the soils. After 6 months, at the end of the experiment, \nthe plants and soils were analysed according to the methods described by the \nUSDA (1954) and the studies of  Van Reeuvijk (2002) and Piper (1950).\n\n\n\nFigure 1: Images and classification of studied A. halimus, A. lentiformis and A. amnicola\n\n\n\n\n\n\n\n7 \n \n\n\n\n \nTABLE 2 \n\n\n\nImages and classification of studied A. halimus, A. lentiformis and A. amnicola \n\n\n\n \nImage of halophyte plant Classification of halophyte plant \n\n\n\n\n\n\n\n \nKingdom  Plantae \u2013 Plants \nSubkingdom  Tracheobionta \u2013 Vascular plants \nSuperdivision  Spermatophyta \u2013 Seed plants \nDivision  Magnoliophyta \u2013 Flowering plants \nClass  Magnoliopsida \u2013 Dicotyledons \nSubclass  Caryophyllidae \nOrder  Caryophyllales \nFamily  Chenopodiaceae \u2013 Goosefoot family \nGenus  Atriplex L. \u2013 saltbush \nSpecies Atriplex halimus L. \u2013 saltbush \n\n\n\nAtriplex halimus  \n\n\n\n\n\n\n\nKingdom  Plantae \u2013 Plants \nSubkingdom  Tracheobionta \u2013 Vascular plants \nSuperdivision  Spermatophyta \u2013 Seed plants \nDivision  Magnoliophyta \u2013 Flowering plants \nClass  Magnoliopsida \u2013 Dicotyledons \nSubclass  Caryophyllidae \nOrder  Caryophyllales \nFamily  Chenopodiaceae \u2013 Goosefoot family \nGenus  Atriplex L. \u2013 saltbush \nSpecies Atriplex lentiformis (Torr.) S. Watson \u2013 big saltbush \n\n\n\nAtriplex lentiformis  \n\n\n\n\n\n\n\n \nKingdom  Plantae \u2013 Plants \nSubkingdom  Tracheobionta \u2013 Vascular plants \nSuperdivision  Spermatophyta \u2013 Seed plants \nDivision  Magnoliophyta \u2013 Flowering plants \nClass  Magnoliopsida \u2013 Dicotyledons \nSubclass  Caryophyllidae \nOrder  Caryophyllales \nFamily  Chenopodiaceae \u2013 Goosefoot family \nGenus  Atriplex L. \u2013 saltbush \nSpecies Atriplex amnicola Paul G. Wilson \u2013 swamp saltbush \n\n\n\nAtriplex amnicola  \n\n\n\n \nSources:http://plants.usda.gov/java/ClassificationServlet?source=profile&symbol=ATRIP&display=31 \nhttps://commons.wikimedia.org/wiki/Category:Atriplex  \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015134\n\n\n\nAbdel-Fattah, M.K.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nInitial Soil Properties\nThe physical and chemical properties of soil are presented in Table 1. In accordance \nwith the guidelines of the USDA (1954), the soil was classified as saline-sodic \nas (i) the EC of its saturation extract exceeded 4 mS cm-1; (ii) ESP exceeded \n15; and (iii) pH was 8.40. This may be attributed to the extremely high soluble \nMg2+ and Na+ contents. Soluble sodium was the dominant cation, constituting \napproximately half of the soluble cations. Cations found, listed in the order of \ndecreasing propotion, were Na+ > Mg2+ > Ca2+ > K+. On the other hand, chloride \nwas the major anion. Anions found, listed in the order of decreasing proportion \nwere Cl- > SO4\n\n\n\n= > HCO3\n-. Exchangeable cations found, listed in the order of \n\n\n\ndecreasing proportion, were Na+ > Mg2+ > Ca2+ > K+. The soil had a clay texture, \nwas low in organic matter and contained 94 g CaCO3 kg-1.\n\n\n\nTABLE 1\nPhysical and chemical properties of studied soil\n\n\n\n\n\n\n\n6 \n \n\n\n\n \nTABLE 1 \n\n\n\nPhysical and chemical properties of studied soil \n \n\n\n\nProperty Value \nTexture class Clay \nOrganic matter [g kg-1] 5.3 \nCaCO3 [g kg-1] 94 \nSoluble ions, ECe and pH:   \n\n\n\n\uf0a7 EC (mS cm-1)** 51.2 \n\uf0a7 pH [Soil suspension 1:2.5] 8.68 \n\n\n\nSoluble ions (mmolc L-1)  \n\uf0a7 Na+ 588.9 \n\uf0a7 K+ 26.4 \n\uf0a7 Ca++ 151.9 \n\uf0a7 Mg++ 564.0 \n\uf0a7 Cl- 1126.1 \n\uf0a7 HCO3\n\n\n\n- 9.7 \n\uf0a7 SO4\n\n\n\n= 195.4 \n\uf0a7 SAR 31.1 \n\n\n\nExchangeable cations and CEC (cmolc kg-1)  \n\uf0a7 Na+ 11.3 \n\uf0a7 K+ 2.8 \n\uf0a7 Ca++ 7.4 \n\uf0a7 Mg++ 10.0 \n\uf0a7 CEC (cmolc kg-1)  31.5 \n\uf0a7 ESP 35.9 \n\n\n\n                              ** measured in soil paste extract  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 135\n\n\n\nHalophytes and Gypsum for Saline-Sodic Soils Reclamation\n\n\n\nSoil Properties at the End of the Experiment\nHalophytes with or without gypsum decreased soil salinity, soil pH, soluble ions, \nSAR and ESP (Table 2). Electric conductivity decreased from 51.2 (initial soil \nsalinity) to a range between 4.10 to 12.10 mS cm-1. These results were obtained \nfor A. halimus, A. amnicola, A. lentiformis, A. halimus + gypsum, A. lentiformis + \ngypsum, and A. amnicola + gypsum with reduced ECs of 8.10, 12.10, 10.14, 4.10, \n7.10 and 5.14 mS cm-1 respectively with a p value of < 0.05. Thus the efficiency \nof A. halimus + gypsum, A. amnicola + gypsum, A. lentiformis + gypsum, A. \nhalimus, A. amnicola, A. lentiformis at decreasing soil salinity were 92, 90, 86, 84, \n80 and 76 %, respectively. These results agreed with the findings of Le Hou\u00b4erou \n(1992)  who stated that A. halimus is a highly drought resistant and salt tolerant \ncrop, which can be used to ameliorate saline soils and provide periodical removal \nof extracted salts. A. halimus is characterised by the presence of vesiculated hairs \n(trichomes) on the leaf surface, which collect water from the atmosphere and \nsecrete salts, and upon bursting, the water and salts can be seen on the surface of \nits leaves (Mozafar and Goodin, 1970). Combining the halophytes with gypsum \nenhanced the soil\u2019s characteristics by liberating adsorbed Na+ on the soil exchange \ncomplex, which leave the exchange sites due to the Ca2+ from applied gypsum. As \nfor the other soluble sodium and chloride, the same trend was observed.\n\n\n\nWith regard to soil pH, soils treated with gypsum showed lower values. \nInitial soil pH was 8.68, but by the end of the experiment, this value was reduced \nto 8.63, 8.65, 8.63, 7.88, 8.12 and 8.1 for treatments of A. halimus, A. amnicola, \nA. lentiformis, A. halimus + gypsum , A. lentiformis + gypsum, A. amnicola + \ngypsum, respectively. Gypsum application led to the replacement of Na+ by Ca2+ \n\n\n\non the soil exchangeable complex, which decreased pH (Abdel-Fattah, 2011). \n\n\n\nTABLE 2\nSome chemical soil properties of soil at the end of experiment\n\n\n\n\n\n\n\n8 \n \n\n\n\nTABLE 3 \n\n\n\nSome chemical soil properties of soil at the end of experiment \n\n\n\nTreatment ECe  \n\u00b1 SE \n\n\n\npH \n \u00b1 SE \n\n\n\nCations (mmolc L-1) \n\u00b1 SE \n\n\n\nAnions (mmolc L-1) \n\u00b1 SE SAR \n\n\n\n\u00b1 SE \nESP \n\u00b1 SE \n\n\n\nNa+ K+ Ca++ Mg++ Cl- HCO3\n- SO4\n\n\n\n= \n\n\n\nT1 \n8.10c 8.63a 19.94c  13.69c   31.17c 16.20c  69.73c  3.89c  7.38bc  4.10c  4.83c  \n\n\n\n\u00b10.04 \u00b10.05 \u00b10.22 \u00b10.13 \u00b10.05 \u00b10.05 \u00b10.21 \u00b10.06 \u00b1 0.62 \u00b10.05 \u00b10.07 \n\n\n\nT2 \n12.10a 8.65a 29.78a  20.45a 46.56a 24.20a  104.16a  5.81a 11.02a  5.01a  6.19a  \n\n\n\n\u00b10.02 \u00b10.01 \u00b10.18 \u00b10.08 \u00b10.51 \u00b10.02 \u00b1 0.12 \u00b10.05 \u00b1 0.39 \u00b10.05 \u00b10.07 \n\n\n\nT3 \n10.14b 8.63a 24.96b   17.14b 39.02b  20.28b 87.29b  4.87b  9.24ab 4.58b  5.56b  \n\n\n\n\u00b10.09 \u00b10.02 \u00b10.25 \u00b10.05 \u00b10.57 \u00b10.03 \u00b1 0.02 \u00b10.05 \u00b10.94 \u00b10.02 \u00b10.03 \n\n\n\nT4 \n4.10f 7.88c 10.09f  6.93f  15.78f 8.20f  35.29f  1.97d 3.74e  2.91f  3.07f  \n\n\n\n\u00b10.08 \u00b10.06 \u00b10.32 \u00b10.05 \u00b10.36 \u00b10.03 \u00b1 0.03 \u00b10.00 \u00b1 0.73 \u00b10.07 \u00b10.10 \n\n\n\nT5 \n7.10d 8.12b 17.48d  12.00d 27.32d 14.20d  61.12d  3.4c  6.47cd  3.84d  4.44d  \n\n\n\n\u00b10.03 \u00b10.06 \u00b10.02 \u00b10.01 \u00b10.19 \u00b10.12 \u00b10.08 \u00b10.09 \u00b1 0.47 \u00b10.02 \u00b10.03 \n\n\n\nT6 \n5.14e 8.10b 12.65e  8.69e  19.78e 10.28e 44.25e  2.47d  4.68de  3.26e 3.59e  \n\n\n\n\u00b10.03 \u00b10.02 \u00b10.01 \u00b10.06 \u00b10.18 \u00b10.03 \u00b1 0.49 \u00b10.57 \u00b1 0.78 \u00b10.01 \u00b10.02 \nNotes: Means with the same letter are not significantly different, T1 = A. halimus, T2 = A. lentiformis, T3 = \nA. amnicola, T4 = A. halimus + gypsum, T5 = A. lentiformis + gypsum, T6 = A. amnicola + gypsum, and SE \nrefers to Standard Error  \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015136\n\n\n\nGypsum solubility was enhanced due to the increased activity coefficient of Ca2+ \n\n\n\nand SO4\n2- as a result of the increased ionic strength of the soil solution, and the \n\n\n\nformation of sodium sulphate ion pairs. Additionally, most of the CO2 would have \ndissolved in the ground water to form carbonic acid.\n\n\n\nWith regard to soil sodicity, ESP and SAR at the end of experiment were \nlower than their initial values. ESP at the end of the experiment varied from 3.59 to \n4.83 compared with its initial value of 35.9. The efficiency ranking of treatments, \nin decreasing order, was A. halimus + gypsum, A. amnicola + gypsum, A. \nlentiformis + gypsum, A. halimus, A. amnicola, and A. lentiformis. These results \nagreed wih the findings of Robbins (1986), Qadir and Oster (2002) and Qadir et \nal.,  (2005) that halophytes increase calcite dissolution through their root activity \nresulting in adequate Ca2+ ions in soil solution, which replace exchangeable Na+.\n\n\n\nFresh Weight, Dry Weight, Biomass and Ionic Concentration\nThe significant decrease in soil salinity was reflected in a high amount of soluble \nions that were removed through the uptake by halophytes (Table 3) especially for \nNa+ and Cl- ions. Sodium ions removed by halophytes were 13.09, 12.86, 12.97, \n13.31, 13.14 and 13.25-gram sodium per kilogram for A. halimus, A. amnicola, \nA. lentiformis, A. halimus + gypsum, A. lentiformis + gypsum, A. amnicola + \ngypsum, respectively. Removal of chloride ions showed a similar pattern in the \nplant tissues, in that their values increased and ranged from 37.5 to 38.41 gram \nchloride per kilogram.\n\n\n\nPlants\u2019 dry weights at the end of the experiment were 40.20, 71.77, 78.67, \n42.42, 73.12 and 79.98 g/pot for A. halimus, A. amnicola, A. lentiformis, A. halimus \n+ gypsum,  A. lentiformis + gypsum, A. amnicola + gypsum, respectively. A. \nhalimus produced more biomass than A. amnicola and A. lentiformis. The biomass \nreadings at the end of the experiment were 3.9, 3.5, 3.56, 3.75, 3.49 and 3.67 for \nA. halimus, A. amnicola, A. lentiformis, A. halimus + gypsum, A. lentiformis + \ngypsum, A. amnicola + gypsum, respectively. These results agreed with Munns \n(1993) and Munns et al. (1995) who found that salts taken up by halophytes do not \ndirectly control their growth by affecting turgor, photosynthesis, or the activity of \n\n\n\nTABLE 3\nFresh weight, dry weight, biomass and ionic concentration for halophytes species\n\n\n\n\n\n\n\n9 \n \n\n\n\nTABLE 4 \n\n\n\nFresh weight, dry weight, biomass and ionic concentration for halophytes species \n\n\n\nTreatment \nFW \n\n\n\ng pot-1 \n\n\n\nDW \n\n\n\ng pot-1 \n\n\n\nBiomass \n\n\n\nFW:DW \n\n\n\nIons removal from soil by plant (g kg-1) \n\n\n\nNa+ K+ Ca++ Mg++ Cl- HCO3\n- SO4\n\n\n\n= \n\n\n\nT1 156.80e 40.20f 3.90a 13.09d 0.50d 2.41d 6.57d 37.50d 0.35b 9.02cd \n\n\n\nT2 250.85d 71.77d 3.50d 12.86f 0.23f 2.11f 6.48f 36.28f 0.24d 8.85e \n\n\n\nT3 283.67b 78.67b 3.56cd 12.97e 0.36e 2.26e 6.52e 36.88e 0.29c 8.94de \n\n\n\nT4 159.10e 42.42e 3.75b 13.31a 0.76a 2.72a 6.67a 38.72a 0.47a 9.20a \n\n\n\nT5 255.23c 73.12c 3.49d 13.14c 0.56c 2.49c 6.60c 37.81c 0.38b 9.07bc \n\n\n\nT6 293.25a 79.98a 3.67bc 13.25b 0.69b 2.64b 6.64b 38.41b 0.44a 9.15ab \n\n\n\nNotes: Means with the same letter are not significantly different, FW = Fresh Weight, DW = dry Weight, T1 = A. halimus, \nT2 = A. lentiformis, T3 = A. amnicola, T4 = A. halimus + gypsum, T5 = A. lentiformis + gypsum, T6 = A. amnicola + \ngypsum and SE refers to Standard Error \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAbdel-Fattah, M.K.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 137\n\n\n\nenzymes. It was the build-up of salts in old leaves, which hastened their death, \nand loss of leaves in turn affected the supply of assimilates or hormones to the \ngrowing organs and therefore affected plant growth. Additionally, the efficiency \nof A. halimus in increasing salt removal was reported with increasing salinity, \nespecially for calcareous-sodic soils, as noted by Qadir et al. (2005) who found \nthat phytoextraction is driven by: (i) enhanced Na+ and salt uptake in shoots; and \n(ii) ability of roots to increase the dissolution rate of calcite, resulting in enhanced \nlevels of Ca2+ in soil solution to replace Na+ from the cation exchange complex. \nThis process was enhanced by the pressure of CO2 within the root zone.  \n\n\n\nCONCLUSIONS\nHalophytes were effective for ameliorating saline sodic soils. Combining gypsum \nwith  halophytes helped to decrease soil salinity and sodicity by absorbing salts \nand liberating Na+ on the soil exchange complex as a result of Ca2+ emanating \nfrom applied gypsum. The efficiency of the treatments in decreasing order was A. \nhalimus + gypsum, A. amnicola + gypsum, A. lentiformis + gypsum, A. halimus, \nA. amnicola, and A. lentiformis.\n\n\n\nREFERENCES\nAbdel-Fattah, M.K. 2011. Some Biological and Chemical Methods for Salt Affected \n\n\n\nSoils Reclamation. Ph.D. Thesis, Faculty. of Agric. Zagazig University, Egypt. \n\n\n\nAbdurrahman, H., B. Fatih, M.K. Fatih and Y.C. Mustafa. 2004. Reclamation of \nsaline-sodic soils with gypsum and MSW compost. Compost Sci. and Utilization \n12(2): 175-179. \n\n\n\nAbou Youssef, M.F. 2001. Use phosphogypsum fortified as a soil amendment for \nsaline sodic soil in El-Salhiya plain. Zagazig J. Agric. Res. 28: 889-911.\n\n\n\nGago C., A.R. Sousa, M. Juliao, G. Miguel, D.C. Antunes and T. Panagopoulos. \n2011. Sustainable use of energy in the storage of halophytes used for food. \nInternational Journal of Energy and Environment 4(5): 592-599.\n\n\n\nGhassemi, F., A.J. Jakeman and H.A Nix. 1995. Salinisation of Land and Water  \nResources: Human Causes, Extent, Management and Case Studies. Wallingford, \nUnited Kingdom: CABI Publishing.\n\n\n\nGorham, J., C. Hardy, R.G. Wyn Jones, L. R. Joppa and C.N. Law. 1987. Chromosomal \nlocation of the K/Na discriminating character in the D genome of wheat. Theor. \nAppl.Genet. 74(5): 584-588.\n\n\n\nGupta, R.K., O.P. Shaema and S.K. Dubey. 1988. Effect of dose and frequency of \ngypsum application on properties of sodic soil and on performance of rice \n(Orayza sativa L) and bread wheat (Triticum Aestivum L). Indian J. Agric. Sci. \n58 (6): 449-453.\n\n\n\nHolly, N.S. 2004. Water Use Potential and Salt Tolerance Of Riparian Species in Saline-\nSodic Environments, Thesis submitted in partial fulfillment of the requirements \n\n\n\nHalophytes and Gypsum for Saline-Sodic Soils Reclamation\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015138\n\n\n\nfor the degree of Master of Science in Land Resources and Environmental \nSciences, Montana State University-Bozeman, Bozeman, Montana.\n\n\n\nhttp://plants.usda.gov/java/ClassificationServlet?source=profile&symbol=ATRIP&di\nsplay=31\n\n\n\nhttps://commons.wikimedia.org/wiki/Category:Atriplex\n\n\n\nLe Hou\u00b4erou, H.N. 1992. The role of salt bushes (Atriplex spp.) in arid land \nrehabilitation in the Mediterranean basin: A review. Agro. for Syst. 18: 107\u2013148.\n\n\n\nManzoor, A., N. Hussain, M. Salim and B.H. Niazi. 2001. Use of chemical amendments \nfor reclamation of saline-sodic soils. Int. J. Agri. Biol. 3(3): 305-307.\n\n\n\nMiyamoto, S. and C.A. Enriquez. 1990. Comparative effects of chemical amendments \non salt and sodium leaching. Irrigation Sci. 11(2): 83-92.\n\n\n\nMozafar, A. and J.R. Goodin. 1970. Viscylated hairs: A mechanism for salt tolerance \nin Atriplex halimus L. Plant Physiol. 45: 62\u201365.\n\n\n\nMunns, R. 1993. Physiological processes limiting plant growth in saline soils: Some \ndogmas and hypotheses. Plant, Cell & Environment 16(1): 15\u201324.\n\n\n\nMunns, R., D. P. Schachtman, and A. G. Condon. 1995. The significance of a two-\nphase growth response to salinity in wheat and barley. Australian Journal of \nPlant Physiology 22(4): 561\u2013569.\n\n\n\nNaidoo, Y. and G. Naidoo. 1999. Cytochemical localisation of adenosine triphosphatase \nactivity in salt glands of Sporobolus virginicus (L.) Kunth. South African \nJournal of Botany 65: 370-373.\n\n\n\nPiper, C.S. 1950. Soil and Plant Analysis. New York: Inter-science Publishers. Inc.\n\n\n\nQadir, M. and J.D. Oster. 2002. Vegetative bioremediation of calcareous sodic soils: \nHistory, mechanisms, and evaluation. Irrig. Sci. 21: 91\u2013101.\n\n\n\nQadir, M., A.D. Noble, J.D. Oster, S. Schubert and A. Ghafoor. 2005 Driving forces \nfor sodium removed during phytoremediation of calcareous sodic and saline-\nsodic soils: A review. Soil Use and Management 21: 173-180. \n\n\n\nRengasamy, P. 2010. Soil processes affecting crop production in salt affected soils. \nFunct. Plant Biol. 37: 613-620.\n\n\n\nRobbins, C.W. 1986. Sodic calcareous soil reclamation as affected by different \namendments and crops. Agron J. 78: 916\u2013920.\n\n\n\nRush, D.W. and E. Epstein. 1981. Breeding and selecting for crop tolerance by the \nincorporation of wild germplast into a domesticated tomato. J. Amer. Soc. Hort. \nSci. 106: 669-670.\n\n\n\nAbdel-Fattah, M.K.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 139\n\n\n\nSiyal, A.A., A.G. Siyal, and Z.A. Abro. 2002. Salt affected soils their identification \nand reclamation. Pak. J. of App. Sci. 2(5): 537-540.\n\n\n\nSzabolcs, I. 1989. Salt-affected Soils. Boca Raton, FL: CRC Press.\n\n\n\nSzabolcs, I. 1994. Soils and salinization. In: Handbook of Plant and Crop Stress, ed. \nM. Pessarakli (pp 3-11), (1st edn.). New York: Marcel Dekker.\n\n\n\nUSDA, (1954) Diagnosis and Improvement of Saline and Alkali Soils. Agriculture \nHand Book No. 60 US Gov. Printing Office, Washington, 1954.\n\n\n\nVan Reeuvijk L.P. (ed.). 2002. Procedures for Soil Analysis. Wageningen, Netherlands: \nISRIC.\n\n\n\n\n\n\n\nHalophytes and Gypsum for Saline-Sodic Soils Reclamation\n\n\n\n\n\n"
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"\n\nINTRODUCTION\n\n\n\nBoron is an essential micronutrient element required for the normal growth \n\n\n\nof plants. The range between B deficiency and toxicity symptoms in plants is \n\n\n\ntypically narrow, in the range of 0.028 to 0.093 mmol L-1 for sensitive crops and \n\n\n\n0.37 to 1.39 mmol L-1 for tolerant crops (Goldberg 1997). Boron deficiency is \n\n\n\nthe most widespread of all the micronutrients deficiencies in many crop regions, \n\n\n\nfrom tropical to temperate zones. Chronic and marginal B deficiency has been \n\n\n\nreported in many crop species across large areas of field production (Shorrocks \n\n\n\n1997). Availability of B to plants is affected by a variety of soil factors including \n\n\n\nsoil solution pH, soil texture, soil moisture, temperature, oxide content, carbonate \n\n\n\ncontent, organic matter content and clay mineralogy.  Liming of acid soils also \n\n\n\ninduces B deficiency (Goldberg et al. 2000). \n\n\n\nBoron deficiency is of great concern in areas receiving heavy rainfall because \n\n\n\nof leaching losses. Compared with other micronutrients, the chemistry of B in \n\n\n\nsoils is very simple. The pH is one of the most important factors affecting the \n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 14: 83-94 (2010)                         Malaysian Society of Soil Science\n\n\n\nBoron Status of Paddy Soils in the States of Kedah \n\n\n\nand Kelantan, Malaysia\n\n\n\nM. Saleem, Y.M. Khanif, I. Che Fauziah, A.W.  Samsuri \n\n\n\n& B. Hafeez\n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nManagement of micronutrient B in soil is difficult because of its high mobility. \n\n\n\nSoil sampling and analysis is the first important step in managing the nutrients \n\n\n\nrequired by plants. This study was conducted to evaluate the B status in soils of \n\n\n\nrice growing areas in  Kedah and Kelantan  which  are the main rice growing states \n\n\n\nof the country. Soil samples were collected from 15 soil series namely Kranji, \n\n\n\nSedeka, Guar, Kundur, Tualang, Teluk Chengai, Kuala Kedah, Rotan, Sedu, \n\n\n\nKangkong, Batu Hitam, Lubok Itek, Tepus, Telemong and Chempaka to determine \n\n\n\nB status and other physico-chemical properties. The soils of paddy growing areas \n\n\n\ninvestigated were very low in available B status. All the fifteen soil series had B \n\n\n\nbelow 0.5 mg kg-1, irrespective of depth and locations. Kundur and Chempaka \n\n\n\nSeries soils had the highest B content (0.46 mg kg-1) among all the series while the \n\n\n\nTualang Series soil had the lowest B (0.22 mg kg-1). Boron status in soils differed \n\n\n\nsignificantly with depth; the upper layers had higher B concentrations compared \n\n\n\nto lower depths because of high organic carbon content. Boron showed a positive \n\n\n\ncorrelation with organic carbon content  but a negative correlation  with soil pH.  \n\n\n\nKeywords:  Correlation study, hot water extractable B, paddy soils\n\n\n\n___________________\n\n\n\n*Corresponding author : E-mail: sarkisaleem@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201084\n\n\n\navailability of B in soil. Boron becomes less available to plants with increasing \n\n\n\npH (Gupta 1993). Boron adsorption on soil constituents is very dependent on \n\n\n\nsolution pH. Available B in the soil decreases with increasing pH because of more \n\n\n\nfixation to the soil sites at high pH values. Maximum B fixation occurs at pH 6 to \n\n\n\n9 (Goldberg 1997).  \n\n\n\nBoron-containing minerals are either insoluble (tourmaline) or very soluble \n\n\n\n(hydrated B minerals) and generally do not control the solubility of B in soil \n\n\n\nsolution (Goldberg 1997). Boron  is critical for the process of cell differenciation \n\n\n\nat all growing tips of the plant (meristems) where cell division is active (Liu 2000) \n\n\n\nand when it is low,  crops experience yield reductions. \n\n\n\nThe main paddy growing areas of Malaysia are the states of Kedah and \n\n\n\nKelantan.  It is forecasted that a 50 to 60% increase in rice production will be \n\n\n\nrequired to meet the demands of the population by 2025 (IRRI Notes 2008). A rice \n\n\n\nyield increase is likely to occur through fine-tuning of crop management. \n\n\n\nBoron deficiency in crops is widespread in a number of countries including \n\n\n\nMalaysia (FFTC, 1999). The FAO global study on micronutrients conducted by \n\n\n\nSillanpaa (1982) reported that oil palm growing areas of Malaysia are deficient \n\n\n\nin B and other micronutrients. Gyul\u2019akhmedov (1984) and Domingo (1983) also \n\n\n\nreported that Malaysian soils are deficient in micronutrients. B status and other \n\n\n\nphysico-chemical properties of soils grown with paddy were determined in this \n\n\n\nstudy.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSample Collection and Analysis\n\n\n\nSoil samples were collected from the main rice growing areas of Kedah and \n\n\n\nKelantan.  Ten soil series were identified. Those developed on marine alluvium \n\n\n\nparent materials from Kedah were the Kranji, Sedeka, Guar, Kundur, Tualang, \n\n\n\nTeluk Chengai, Kuala Kedah, Rotan, Sedu and Kangkong, whilst soil series \n\n\n\ndeveloped on riverine alluvium parent materials from Kelantan were Batu Hitam, \n\n\n\nLubok Itek, Tepus, Telemong and Chempaka. At the time of sampling, the crop \n\n\n\nhad been recently harvested and the soil was drained and bare. Sampling was \n\n\n\nperformed using an auger. Samples were taken from three different locations in \n\n\n\nthe same field area of each soil series and at depths of 0 to 15, 15 to 30 and 30 to 45 \n\n\n\ncm. Three replications of each sample were maintained for chemical analysis. Soil \n\n\n\nand geographical maps of the area were used as guidelines with the location of the \n\n\n\nsampling site being recorded using GPS. Soil samples were air-dried, ground and \n\n\n\nsieved through a 2-mm sieve for laboratory analyses.\n\n\n\nBoron in the soil samples was extracted using hot water (Bingham 1982). \n\n\n\nAvailable B in the soil extracts was measured colorimetrically at 420 nm using \n\n\n\nthe azomethine-H dye. Soil pH was determined by taking 10 g of soil and mixing \n\n\n\nwith 25 ml of distilled water, shaken for 15 minutes and left to stand  overnight, \n\n\n\nafter which pH of the extract was read on PHM210 Standard pH meter. Electrical \n\n\n\nconductivity (EC) was determined by taking 10 g soil and mixing with 25 ml of \n\n\n\nM. Saleem, Y.M. Khanif, I. Che Fauziah, A.W.  Samsuri  & B. Hafeez\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 85\n\n\n\ndistilled H\n2\nO, then shaken for 15 minutes and left to stand overnight. Electrical \n\n\n\nconductivity was analyzed using the EC meter (Radiometer) at 240C.  Phosphorus \n\n\n\nwas extracted using Bray and Kurtz No. 2 extractants (Bray and Kurtz 1945). \n\n\n\nOrganic carbon in the soils was determined by using non-dispersive, infrared, \n\n\n\ndigital-controlled instrument CR-412 Carbon Analyser. Soil texture was determined \n\n\n\nusing the pipette method (Gee and Bauder 1986). For statistical analysis,  the SAS \n\n\n\nprogramme was used  while the separation of means was tested using Tukey test \n\n\n\nat the probability level of 0.05.\n\n\n\nFig. 1: Dots in the Kedah and Kelantan maps showing the soil sampling locations\n\n\n\n\n\n\n\nBoron Status of Paddy Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201086\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nBoron Status of Rice Soils of Kedah and Kelantan States\n\n\n\nThe soils from Kedah were of marine alluvium parent material while the five \n\n\n\nsoil series from Kelantan were derived from riverine alluvium (Table 1).  All \n\n\n\nthe fifteen soil series of Kranji, Sedeka, Guar, Kundur, Tualang , Teluk Chengai, \n\n\n\nKuala Kedah, Rotan, Sedu, Kangkong, Batu Hitam, Lubok Itek, Tepus, Telemong \n\n\n\nand Chempaka had available B below 0.5 mg kg-1 at all three depths irrespective \n\n\n\nof location (Tables 2a-b). Kundur and Chempaka series soils had the highest B \n\n\n\ncontent (0.46 mg kg-1) among all series while Tualang series soil had the lowest \n\n\n\nB (0.22 mg kg-1) for two out of three locations. In all other soils, the available B \n\n\n\nwas between 0.25 to 0.40 mg kg-1. As no soil series had available B of more than \n\n\n\n1 mg kg-1, all these soils may be considered as deficient in B status (Bingham \n\n\n\n1982). Soil samples were taken from three locations of each soil series and no \n\n\n\nsignificant difference was observed in available B content among locations of the \n\n\n\nsame soil series. There was significant difference in B status according to depth \n\n\n\nwith available B being significantly higher in the upper layer (0 to 15 cm) as \n\n\n\ncompared to the lower depths. \n\n\n\nThe presence of organic matter was found to be higher in the upper layer and \n\n\n\nits presence was found to have a significant positive correlation with available \n\n\n\nB (r = 0.54*, n=405) (Table 3). These results are in agreement with the findings \n\n\n\nof Shorrocks (1997) who reported that organic matter is the primary source of \n\n\n\n\u2018reserve\u2019 B. Available B had significant negative correlation (r = -0.24*, n=405) \n\n\n\nwith soil pH. Chemical analysis of soil samples showed that in high pH soils, \n\n\n\nB content was comparatively low which indicates that increasing pH reduces \n\n\n\navailable B content in soil. Keren and Bingham (1985) reported that B becomes \n\n\n\nless available with increasing solution pH due to increased B adsorption at higher \n\n\n\nsoil pH levels. The decline in available B content is correlated with increasing \n\n\n\nrainfall and paddy growing areas of Kedah state having an annual rainfall of more \n\n\n\nthan 2600 mm (MMD 2009). The high precipitation may be the major cause of \n\n\n\nprolonged B leaching from soils. It is observed that these rice growing areas have \n\n\n\nbeen under cultivation for a long period of time with continuous uptake of B by \n\n\n\ncrops. However, B fertilizer is not applied to replenish  the soils and nutrient \n\n\n\nmining is offered as one possible reason for B deficiency. All the soil series had a \n\n\n\nsilty clay and clay loam texture. Stevens et al. (2005) stated that in fine textured \n\n\n\nsoils, plant available B may be low because B is strongly held by clay surfaces. \n\n\n\nSoil critical limits of B are  categorised as follows:  <1 mg kg-1 -  insufficient for \n\n\n\nplant growth; 1 to 2 mg kg-1  - sufficient for normal growth; 2.1 to 5.0 mg kg-1 \u2013 \n\n\n\nhigh;  and >5 mg kg-1 -toxic to the plant (Bingham 1982). \n\n\n\nM. Saleem, Y.M. Khanif, I., Che Fauziah, A.W.  Samsuri  & B. Hafeez\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 87\n\n\n\nTABLE 1\n\n\n\nTaxonomic classifications of the soils collected from the rice growing areas of Kedah \n\n\n\nand Kelantan, Malaysia\n\n\n\n(Source: Paramananthan 1987; Paramananthan 2000)\n\n\n\nSoil series  Great group Parent material\n\n\n\nKranji  Sulfaquent Marine, estuarine deposits\n\n\n\nSedaka Paleudult Marine, estuarine deposits\n\n\n\nGuar  Hydraquent Brackish water deposits  \n\n\n\nKundur Fluvaquent Marine, estuarine deposits\n\n\n\nTualang  Fluvaquent Riverine deposits  \n\n\n\nTeluk Chengai  Paleudult Marine, estuarine deposits\n\n\n\nKuala Kedah Fluvaquent Marine, estuarine deposits\n\n\n\nRotan  Fluvaquent Marine, estuarine deposits\n\n\n\nSedu   \n\n\n\n\n\n\n\nKangkong  \n\n\n\n\n\n\n\nSulfaquept\n\n\n\n\n\n\n\nHapludult  \n\n\n\n\n\n\n\nMarine, estuarine deposits\n\n\n\n\n\n\n\nMarine, estuarine deposits\n\n\n\n\n\n\n\nBatu Hitam  \n\n\n\n\n\n\n\nEndoaqualf Recent  alluvium\n\n\n\nLubok Itek  \n\n\n\n\n\n\n\nFluvaquent Recent alluvium\n\n\n\nTepus  \n\n\n\n\n\n\n\nKandiaquult Recent alluvium\n\n\n\nTelemong  \n\n\n\n\n\n\n\nUdorthent Recent alluvium\n\n\n\nChempaka  \n\n\n\n\n\n\n\nPaleudult  Recent alluvium\n\n\n\n\n\n\n\n(Source : Paramananthan 1987; 2000)\n\n\n\nBoron Status of Paddy Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201088\n\n\n\nM. Saleem, Y.M. Khanif, I., Che Fauziah, A.W.  Samsuri  & B. Hafeez\n\n\n\nTABLE 2a\n\n\n\nAvailable B (mg kg-1) status of the soil series of Kedah soils at different sites and depths\n\n\n\n     Means with the same letter within the rows are not significantly different at p>0.05    \n\n\n\nSoil series GPS location \u2026\u2026\u2026\u2026..Depth (cm)\u2026\u2026\u2026\u2026..  \n\n\n\n0 -15          15 -30             30 -45  \n\n\n\nKranji      -1\u2026\u2026\u2026\u2026\u2026.mg kg \u2026\u2026\u2026\u2026\u2026\u2026 \n\n\n\nSite 1  N 06  05.363\n\n\n\nE 100  18.848\n\n\n\n0.50  0.30  0.22  \n\n\n\nSite 2  N 06  05.086\n\n\n\nE100 19.087\n\n\n\n0.25  0.12  0.08  \n\n\n\nSite 3  N 06  05.086\n\n\n\nE 100 19.309\n\n\n\n0.29  0.18  0.12  \n\n\n\nMean  0.34 a  0.20 b  0.14 c  \n\n\n\nSedaka  \n\n\n\nSite 1  N 05  53.310\n\n\n\nE 100  26.364\n\n\n\n0.20  0.15  0.10  \n\n\n\nSite 2  N 05  53.307\n\n\n\nE  100  26.313\n\n\n\n0.32  0.24  0.20  \n\n\n\nSite 3  N 05  53.307\n\n\n\nE 100  26.292\n\n\n\n0.20  0.12  0.08  \n\n\n\nMean  0.24 a  0.17 b  0.12 b\n\n\n\nGuar  \n\n\n\nSite 1  N 05  52.098\n\n\n\nE 100  27.578\n\n\n\n0.42  0.28  0.18  \n\n\n\nSite 2  N 05  52.126\n\n\n\nE 100  27.593\n\n\n\n0 .11 0.09  0.04  \n\n\n\nSite 3  N 05  52.194\n\n\n\nE 100  27.583\n\n\n\n0.34  0.20  0.12  \n\n\n\nMean  0.29 a  0.19 b  0.11 c\n\n\n\n\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\nTualang  \u2026\u2026\u2026\u2026\u2026mg kg -1 \u2026\u2026\u2026\u2026\u2026.  \n\n\n\nSite 1  N 06  02.653\n\n\n\nE 100  28.9 00  \n\n\n\n0.36  0.22  0.18  \n\n\n\nSite 2  N 06  02.625\n\n\n\nE 100  28.900\n\n\n\n0.10  0.05  0.02  \n\n\n\nSite 3  N 06  02.623\n\n\n\nE 100  28.933\n\n\n\n0.20  0.12  0.06  \n\n\n\nMean  0.22 a 0.13 b  0.08 b\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 89\n\n\n\n      Means with the same letter within the rows are not significantly different at p>0.05    \n\n\n\nBoron Status of Paddy Soils\n\n\n\nSoil series  GPS location       \u2026\u2026\u2026\u2026..Depth (cm)\u2026\u2026\u2026\u2026..  \n\n\n\n      0 -15          15 -30             30 -45  \n\n\n\nTualang  \u2026\u2026\u2026\u2026\u2026mg kg -1 \u2026\u2026\u2026\u2026\u2026.  \n\n\n\nTeluk Chengai \u2026\u2026\u2026 mg kg -1 \u2026\u2026\u2026\u2026.\n\n\n\nSite 1 N 06  05.389\n\n\n\nE 100  19.504\n\n\n\n0.35 0.20 0.12\n\n\n\nSite 2 N 06  05.369\n\n\n\nE 100  19.504\n\n\n\n0.28 0.21 0.15\n\n\n\nSite 3 N 06  05.330\n\n\n\nE 100  19.476\n\n\n\n0.14 0.09 0.04\n\n\n\nMean 0.25 a 0.16 b 0.10 c\n\n\n\nKuala Kedah \n\n\n\nSite 1  N 06  08.078  \n\n\n\nE 100  17.712  \n\n\n\n0.43 0.32 0.20\n\n\n\nSite 2  N 06  08.117  \n\n\n\nE 100  17.692  \n\n\n\n0.29 0.16 0.40\n\n\n\nSite 3  N 06  08.147  \n\n\n\nE 100  17.673  \n\n\n\n0.42 0.30 0.20\n\n\n\nMean  0.38 a 0.26 b 0.26 c\n\n\n\nRotan \n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\nKundur  \n\n\n\nSite 1  N 05  58.800\n\n\n\nE 100  26.005\n\n\n\n0.46  0.32  0.20  \n\n\n\nSite 2  N 05  58.821\n\n\n\nE 100  25.948\n\n\n\n0.42  0.22  0.12  \n\n\n\nSite 3  N 05  58.816\n\n\n\nE 100  25.881\n\n\n\n0.40  0.22  0.15  \n\n\n\nMean  0.42 a  0.25 b  0.15 c\n\n\n\nKangkong  \n\n\n\nSite 1 N 06  13.643\n\n\n\nE 100  15.698\n\n\n\n0.28  0.21  0.14  \n\n\n\nSite 2 N 06  13.643\n\n\n\nE 100  15.719\n\n\n\n0.12  0.08  0.05  \n\n\n\nSite 3 N 06  13.709\n\n\n\nE 100  15.765\n\n\n\n0.40  0.28  0.20  \n\n\n\nMean 0.26 a  0.19 b  0.15 b  \n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201090\n\n\n\nM. Saleem, Y.M. Khanif, I., Che Fauziah, A.W.  Samsuri  & B. Hafeez\n\n\n\nSoil series  GPS location       \u2026\u2026\u2026\u2026..Depth (cm)\u2026\u2026\u2026\u2026..  \n\n\n\n      0 -15          15 -30             30 -45  \n\n\n\nTualang  \u2026\u2026\u2026\u2026\u2026mg kg -1 \u2026\u2026\u2026\u2026\u2026.  \n\n\n\nRotan \n\n\n\nSite 1 N 06  08.147\n\n\n\nE 100  17.673\n\n\n\n0.22  0.12 0.09\n\n\n\nSite 2 N 06  09.492\n\n\n\nE 100  19.871\n\n\n\n0.40  0.32 0.25\n\n\n\nSite 3 N 06  09.964\n\n\n\nE 100  19.879\n\n\n\n0.34  0.20 0.16\n\n\n\nMean 0.32 a 0.21 b  0.16 b\n\n\n\nSedu  \n\n\n\nSite 1 N 06  12.733\n\n\n\nE 100  19.693\n\n\n\n0.40  0.24 0.14\n\n\n\nSite 2 N 06  12.766\n\n\n\nE 100  19.696\n\n\n\n0.32  0.24 0.12\n\n\n\nSite 3 N 06  12.793\n\n\n\nE 100 19.691\n\n\n\n0.40  0.30 0.24\n\n\n\nMean 0.37 a 0.26 b 0.16 b\n\n\n\nMeans with the same letter within the rows are not significantly different at p>0.05\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 91\n\n\n\nBoron Status of Paddy Soils\n\n\n\nTABLE 2b\n\n\n\nAvailable B (mg kg-1) status of the soil series of Kelantan at different sites and depths\n\n\n\n     Means with the same letter within the rows are not significantly different at p>0.05    \n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201092\n\n\n\nTABLE 3\n\n\n\nCorrelation study on soil chemical properties of paddy soils\n\n\n\nCONCLUSION\n\n\n\nSoil sampling and laboratory analysis from different locations of 15 soil series of \n\n\n\nrice growing areas showed that all the soils are deficient in plant available B status \n\n\n\nas no soil series had B of more than 1 mg kg-1. Boron fertilizers should be applied \n\n\n\nto these soils for higher  crop yields. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS\n\n\n\nThe authors would like to thank Agriculture Department Government of Sindh, \n\n\n\nPakistan for financial support and Universiti Putra Malaysia for providing all \n\n\n\nnecessary facilities is hereby acknowledged.\n\n\n\nM. Saleem, Y.M. Khanif, I., Che Fauziah, A.W.  Samsuri  & B. Hafeez\n\n\n\nSoil series  GPS location       \u2026\u2026\u2026\u2026..Depth (cm)\u2026\u2026\u2026\u2026..  \n\n\n\n      0 -15          15 -30             30 -45  \n\n\n\nTualang  \u2026\u2026\u2026\u2026\u2026mg kg -1 \u2026\u2026\u2026\u2026\u2026.  \u2026\u2026\u2026\u2026\u2026mg kg -1 \u2026\u2026\u2026\u2026\u2026.  Chempaka  \n\n\n\nSite 1 N 05  57.494\n\n\n\nE 102  13.455\n\n\n\n0.40 0.30 0.22\n\n\n\nSite 2 N 05  57.483  \n\n\n\nE 102  13.453\n\n\n\n0.44 0.30 0.26\n\n\n\nSite 3 N 05  57.465\n\n\n\nE 102  13.481\n\n\n\n0.46 0.42 0.40\n\n\n\nMean 0.43 a 0.34 b 0.35 c\n\n\n\n \nMeans with the same letter within the rows are not significantly different at p>0.05\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\no\n\u2018\n\n\n\n \n Boron   pH  Organic \n\n\n\ncarbon\n\n\n\nP  EC  \n\n\n\nBoron  -0.239* 0.439* 0.120 ns -0.001 ns\n\n\n\npH  -0.600* 0.047 ns -0.269 ns\n\n\n\nOrganic \n\n\n\ncarbon\n\n\n\n0.121 ns 0.237*\n\n\n\nP     0.087 ns\n\n\n\n(Pearson Correlation Coefficients, n = 405)  \n\n\n\nValue presents R -value  \nns  - non-significant at 5% level\n *    -  significant at 5% level\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 93\n\n\n\nREFERENCES\nBingham, F.T. 1982. Boron, p.431-448. In A. L. Page (ed.). Methods of Soil Analysis, \n\n\n\nPart 2: Chemical and Mineralogical Properties. American Society of Agronomy. \n\n\n\nMadison,WI, USA.   \n\n\n\nBray, R.H. and L.T. Kurtz.  1945. Determination of total, organic and available forms \n\n\n\nof phosphorus. Soil Science 59: 39-46.\n\n\n\nDomingo, L.E. and K. Kyuma. 1983. Trace elements in tropical Asian paddy soils. I. \n\n\n\nTotal trace elements status. Soil Science and Plant Nutrition 29: 439-452.\n\n\n\nFFTC, Publication. 1999. Micronutrients in crop production. In: Abstracts, \n\n\n\nInternational Seminar on Minor Elements And Their Problems, held at National \n\n\n\nTaiwan University on November 8-13. Food and Fertilizer Technology Centre \n\n\n\nTaiwan.\n\n\n\nGee, G.W. and J.W. Bauder. 1986. Particle size analysis. In Klute, A. (ed). Methods \n\n\n\nof Soil Analysis. Part 1. Physical and Mineralogical Methods. 2nd Ed. ASA- \n\n\n\nSSSA, Wisconsin, pp.383-411.\n\n\n\nGoldberg. S., M.L. Scott and D.L. Saurez.  2000. Predicting boron adsorption by soils \n\n\n\nusing soil chemical parameters in the constant capacitance  model. Soil Science \n\n\n\nSociety of Americe Journal  64: 1356-1363.\n\n\n\nGoldberg, S. 1997. Reactions of boron with soils. Plant and Soil 193: 35-48.\n\n\n\nGupta, U.C. 1993. Sources of boron. In: Gupta, U.C. (Ed.) Boron and Its Role in Crop \n\n\n\nProduction 87-104. CRP Press, Florida, USA. \n\n\n\nGyul\u2019akhmedov, A. N and O.K. Mamedov. 1984. Boron content in soils of central \n\n\n\nBurma. Izv Akad Nauk Ser Biological Journal 1: 49-51. \n\n\n\nIRRI (International Rice Research Institute) 2008. 12p. Background Paper: The rice \n\n\n\ncrisis: What needs to be done? Los Ba\u00f1os (Philippines): IRRI. \n\n\n\nKeren, R., F.T. Bingham and J.D. Rhoades 1985. Plant uptake of boron as  affected by \n\n\n\nboron distribution between liquid and solid phases in soil. Soil  Science Society \n\n\n\nof America  Journal  49: 297-302.\n\n\n\nLiu, D., W. Jiang, L. Zhang and L. Li. 2000. Effects of boron ions on root growth and \n\n\n\ncell division of broad bean (Vicia faba L.) Israel Journal of Plant  Sciences 48: \n\n\n\n47-51.\n\n\n\nMMD, Malaysian Meteorological Department, 2009. Official website of department.  \n\n\n\nhttp//: www.met.gov.my\n\n\n\nParamananthan, S. 1987. Field Legends for Soil Surveys in Malaysia. Printed by \n\n\n\nUniversity Pertanian Malaysia press.\n\n\n\nParamananthan, S.  2000. Soils of Malaysia, their Characteristics and Identification. \n\n\n\nVol: 1.  Academy of Science Malaysia press.\n\n\n\nBoron Status of Paddy Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201094\n\n\n\nShorrocks, V. M. 1997. The occurrence and correction of boron deficiency. Plant and \n\n\n\nSoil 193: 121-148.\n\n\n\nSillanpaa, M. 1982.pp: 48. Micronutrients and Nutrient Status of Soils \u2013 A global \n\n\n\nstudy. FAO  Soils Bulletin, Rome.\n\n\n\nStevens, G., D. Dunn and A. Kendig. 2005.  Crop Management, (No. February) 1-7 \n\n\n\nUniversity of Missouri, Portageville, MO 63873, USA.\n\n\n\nM. Saleem, Y.M. Khanif, I., Che Fauziah, A.W.  Samsuri  & B. Hafeez\n\n\n\n\n\n"
"\n\nINTRODUCTION\nThere has been a rapid increase in agricultural activities in Malaysia. The growth \nin agriculture has led to increased production of waste materials which can \nlead detrimental environmental impacts. There is a dire need to put in place a \nsustainable agrowaste management system. An agrowaste that is of increasing \nconcern is sago pith waste. Lai et al. (2013) estimated that 52, 000 tonnes of sago \npith waste were produced in 2011.\n\n\n\nVermicomposting is a globally popular option for converting these wastes \ninto a value added product and consequently reducing environmental problems. \nVermicomposting is a process where earthworms consume organic residue to \nproduce vermicompost that is also known as vermicast.  As vermicompost is a \ntype of soil conditioner that has high nutrient bioavailability for plant growth \nand can improve soil health for sustainable agriculture.  According to Pramanik \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 103-114 (2014)                         Malaysian Society of Soil Science\n\n\n\nComparison of Sago Pith Waste Vermicompost \nCharacteristics to Vermicomposts of Different Feedstock\n\n\n\nin Malaysia\n\n\n\nElton T.1, A.B. Rosenani 1*, C.I. Fauziah1 and J. Kadir2\n\n\n\n1Department of Land Management, 2Department of Plant Protection\nFaculty of Agriculture, Universiti Putra Malaysia\n\n\n\n43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nThe vermicompost industry in Malaysia has grown rapidly in recent years. \nHowever, there is a lack of documentation on the quality of commercially \navailable vermicomposts; moreover, there are no guidelines for the production \nof quality vermicomposts. Therefore, a trial was conducted in Universiti Putra \nMalaysia to convert sago pith waste into a value-added organic fertilizer through \nvermicomposting using cow, goat and horse dung manure as co-composting \nmaterials. Two types of sago pith waste vermicomposts were produced in Universiti \nPutra Malaysia and compared with fifteen different vermicomposts available in \nthe market. The chemical characteristics of these vermicomposts were determined. \nResults showed that pH of the vermicomposts ranged from 4.5-6.5 indicating \nsignificant difference between the different feedstock. The macronutrients of the \ndifferent vermicomposts varied greatly, that is, total N, total P and total K were \n1.5-2.16, 0.54-1.89 and 0.39-1.73%, respectively. Humic acid concentration in the \nvermicomposts ranged from 16.7- 24%. Study results indicate that the chemical \nproperties of the vermicomposts varied according to the type of initial feedstock \nand earthworms used and the vermicomposting procedure adopted.\n\n\n\nKeywords: Agricultural waste, earthworms, vermicompost.\n\n\n\n___________________\n*Corresponding author : E-mail: rosenani@upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014104\n\n\n\net al. (2007), vermicomposting offers rapid recovery of valuable resources from \nbiodegradable wastes (plant residue) within a short decomposition period and \ngenerate humus-like vermicompost.\n\n\n\nCurrently, the vermicompost industry in Malaysia is growing due to the \nincreasing demand for organic fertilizers from organic farms and also home \ngardening. The different types of feedstock currently used in Malaysia for \nvermicomposting are easily available agricultural wastes such as rice straw, \nsawdust, oil palm empty fruit bunch and spent mushroom substrate while the \nco-composting material used are cow, goat and horse dung manure.  Examples of \nfeedstock used for commercially available vermicomposts are spent mushroom \nmedia (Moore and Chiu 2001), toxic cassava peels (Mba 1996), and rice residue \n(Shak et. al. 2013) \n\n\n\nAlthough the vermicomposting industry has been in existence for several \nyears in Malaysia, there is still lack of documented information and data on the \nnutrient characteristics and quality of vermicomposts as well as guidelines for \nquality production of vermicomposts and quality control for the benefit of both \nthe producers and consumers. The lack of guidelines could lead to the production \nof low quality vermicomposts and marketing of immature vermicomposts. \nCurrently, there is no quality assurance for vermicomposts sold in the market \nalthough these composts enjoy a higher price compared to the common organic \nfertilizers. \n\n\n\nThe sago starch industry in Sarawak produces much agricultural wastes \nparticularly sago pith waste. According to Awang-Adeni et al. (2010), sago \nstarch processing residues are washed off into nearby streams along with the \nwastewater which could lead to serious environmental problems. Therefore, a \ntrial was conducted in Universiti Putra Malaysia to convert sago pith waste into a \nvalue-added organic fertilizer through vermicomposting using goat and cow dung \nmanure as co-composting materials. \n\n\n\nThis paper presents the chemical characteristics and crop growth performance \nof the sago pith waste vermicompost produced in UPM with comparisons to \nselected locally available commercial vermicomposts produced from different \nfeedstock. \n\n\n\nMATERIALS AND METHODS\n\n\n\nCollection and Analyses of Vermicomposts \nTwo sago pith waste vermicomposts were produced by UPM using cow \nmanure and goat manure, separately. To compare for chemical characteristics, \nvermicomposts of other feedstocks were also collected for this study. A total of 12 \nvermicomposts were purchased directly from producers in Selangor and another \nthree vermicomposts produced in Terengganu, Kelantan and Sarawak were \npurchased from the local markets. The locally produced vermicomposts used four \ntypes of feedstock which were sawdust (SD), spent mushroom substrate (SMS), \npaddy straw (PS), oil palm empty fruit bunch (EFB) and fresh market waste \n\n\n\nElton T., A.B. Rosenani, C.I. Fauziah and J. Kadir\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 105\n\n\n\n(FMW) while the co-composting materials were cow dung manure (CM), goat \ndung manure (GM) and horse dung manure (HM). As production procedures were \nsimilar for some of the vermicomposts, ten vermicomposts were finally selected \nand analyzed. They were (i) spent mushroom substrate with cow dung manure \nand goat dung manure (SMS+CM+GM); (ii) spent mushroom substrate with \ncow dung manure (SMS+CM); (iii) oil palm empty fruit bunch with cow dung \nmanure (EFB+CM); (iv) sawdust with cow dung manure and goat dung manure \n(SD+CM+GM); (v) sawdust with cow dung, goat dung and horse dung manure \n(SD+CM+GM+HM); (vi) goat  dung manure only (GM); (vii) paddy straw with \ncow dung manure (PS+CM); (viii) fresh market waste with cow dung manure \n(FMW+CM); (ix) sago pith waste with cow dung manure (SPW+CM), and (x) \nsago pith waste with goat dung manure (SPW+GM). \n\n\n\nIn this study, Australian Standard AS 4454 (Standard Australia, 2012) and \nOfficial Organic Fertilizer Standard of Korea (2001) were used to benchmark \nthe chemical quality characteristics of the collected vermicomposts.  The \nvermicompost were oven-dried at 70\u00b0C for 48 hours and ground for chemical \nanalysis. Each vermicompost was analyzed in triplicate. The pH of the \nvermicompost was determined in the ratio of 1:5 (vermicompost: water) using a \npH meter (Mettler MP 225). The samples were also analyzed for organic carbon \naccording to the combustion method (McKeague 1976) with a CR-412 carbon \nautoanalyser (LECO Corporation, St Joseph, USA) and total N using the Kjedahl \nmethod (Bremmer and Mulvaney 1982). The macronutrient contents (P, K, Ca and \nMg) were determined using dry-ashing method (Mitra 2003) while micronutrient \n(Fe, Cu, Mn and Zn) were determined using the (Thermo Scientific S-Series) \natomic absorption spectrophotometry. Humic acid (HA) was isolated using the \nmethod of Ahmed et al. (2005).\n\n\n\nCrop Growth Performance of Vermicomposts\nA pot experiment was carried out in a glasshouse with maize (Zea mays) as the test \ncrop to investigate growth performance using sago pith waste vermicompost in \ncomparison to a commercial spent mushroom vermicompost. The study consisted \nof four treatments: (i) chemical fertilizer, (ii) SMS+GM+CM, (iii) SPW+CM, \n(iv) SPW+GM. Twenty experimental units were laid out in randomized complete \nblock design (RCBD) with 4 treatments and 5 replications. Each pot was filled \nwith ten kg of sieved air-dried soil from Munchong series. The plants were \nharvested on the 50th day after sowing by cutting the plant 5 cm above the ground. \nTissue samples were taken, oven dried at 70\u00b0C and ground for analysis of nutrient \nconcentrations and uptake.\n\n\n\nStatistical Analysis\nVariables were statistically analyzed using ANOVA and LSD (Least significant \ndifference) to test for significant differences between treatment means. All \nstatistical analyses were performed using Statistical Analysis System (SAS 1999).\n\n\n\nCharacteristics of Vermicomposts of Different Feedstock\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014106\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nChemical Characteristics of Vermicompost\n\n\n\npH\nThe pH for SPW vermicompost was in the range of 5.8 - 6.0 (Table 3), which is \nacceptable according to the Australian Standard AS 4454 (pH 5.0 - 7.5). The pH \nrange for other commercial vermicomposts was between pH 4.5 - 6.5, with PS \nvermicompost having the lowest pH and SMS+CM vermicompost the highest \n(Figure 1). According to Ndegwa and Thomson (2000), the pH decreases during \nvermicomposting because of the bioconversion of organic materials into various \nintermediate types of organic acids. Further, the low pH in EFB + CM could be \ndue to the production of CO2 and organic acids from the microbial decomposition \nof the substrate. Another explanation for the low pH in PS + CM could be the  \nadoption of certain practices during the vermicomposting process such as the \nsoaking of the paddy straw for at least 6 weeks to soften the fibrous straw before \nvermicomposting. Different types of earthworms will produce different types \nof vermicompost. The earthworm used for the paddy straw vermicompost was \na local species, possibly the night crawler species. Meanwhile, other producers \nused Eisenia fetida and this could offer an explanation for the lower pH obtained \nfor the paddy straw vermicompost.\n\n\n\nElton T., A.B. Rosenani, C.I. Fauziah and J. Kadir\n\n\n\nFigure 1: pH of eight types of local vermicompost.\n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\n \nDifferent letters over bars indicate significant differences (P<0.05) in LSD.  \nNote: SMS: Spent mushroom substrate; EFB: empty fruit bunch; SD: Sawdust; PS: Paddy straw; FMW: Fresh \nmarket waste; CM: cow dung manure; GM: goat dung manure; HM: Horse dung manure. \n\n\n\n\n\n\n\nFigure 1: pH of eight types of local vermicompost. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nc a\n\n\n\ng f\nb\n\n\n\ne\n\n\n\nh\n\n\n\nd\n\n\n\n0\n1\n2\n3\n4\n5\n6\n7\n\n\n\nSM\nS \n\n\n\n+ \nG\n\n\n\nM\n +\n\n\n\n C\nM\n\n\n\nSM\nS \n\n\n\n+ \nC\n\n\n\nM\n\n\n\nE\nFB\n\n\n\n +\n C\n\n\n\nM\n\n\n\nSD\n +\n\n\n\n C\nM\n\n\n\n +\n G\n\n\n\nM\n\n\n\nSD\n +\n\n\n\n C\nM\n\n\n\n +\n G\n\n\n\nM\n +\n\n\n\n H\nM\n\n\n\nG\nM\n\n\n\nPS\n +\n\n\n\n C\nM\n\n\n\nFM\nW\n\n\n\n +\n C\n\n\n\nM\n\n\n\npH\n\n\n\nDifferent letters over bars indicate significant differences (P<0.05) in LSD. \nNote:  SMS: Spent mushroom substrate; EFB: empty fruit bunch; SD: Sawdust; PS: Paddy \n\n\n\nstraw; FMW: Fresh market waste; CM: cow dung manure; GM: goat dung manure; HM: \nHorse dung manure.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 107\n\n\n\nTotal N\nTotal N in SPW vermicompost was found to be in the range of  2.57-3.14% \n(Table 1). This range is well above the Australian Standard AS 4454 (Standard \nAustralia, 2012) which requires a minimum total N value of 0.8%. Total N in \nother commercial vermicomposts ranged from 1.50-2.16%. However, the total N \ncontent for SPW vermicompost and other commercial vermicomposts was below \nthe requirement of the Official Organic Fertilizer Standard of Korea (2001) which \nrequires organic fertilizers to contain a minimum total N value of 4.0 %. \n\n\n\nA comparison of the different vermicomposts shows SPW+CM to contain \na significantly high total N value at 3.14% followed by SPW+GM at 2.5%. This \nwas followed by SMS+CM+GM, SMS+CM, GM and PS+CMat 1.94-2.15%.\nEFB+CM, SD+CM+GM, SD+CM+GM+HM and MFW+CM had significantly \nlow values in the range of 1.5-1.7 % total N. The lower total N values of EFB+CM, \nSD+CM+GM, SD+CM+GM+HM and FMW+CM vermicomposts were due to the \nlower animal dung manure composition compared to SMS+CM+GM, SMS+CM, \nGM and PS+CM. In SMS+CM+GM, SMS+CM, GM and PS+CM vermicomposts \nwhere the animal dung manure or animal dung manure mixture (cow and goat \ndung manure) constituted more than 50 % of the total bedding ratio. The highest \ntotal N content was in GM which used 100% goat dung manure. In the case of \nEFB+CM, SD+CM+GM, SD+CM+GM+HM and FMW+CM vermicomposts, \nthe animal dung manure mixture was less than 50% of the mixture. Thus it can be \ndeduced that animal dung manure contributed significantly to the total N content \nof the final products.\n\n\n\nCharacteristics of Vermicomposts of Different Feedstock\n\n\n\nTABLE 1\nPlant macronutrients concentrations of vermicomposts according to different types\n\n\n\nof feedstock\n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \nPlant macronutrients concentrations of vermicomposts according to different types of feedstock \n\n\n\n \nTreatments N  P K Ca Mg \n\n\n\n  (%) \nSPW+CM 3.14a 1.31c 0.85cd 1.10de 0.32c \nSPW+GM 2.57b 1.25c 0.78de 1.04e 0.38c \nEFB+CM 1.60e 1.89a 0.60ef 1.04e 0.63abc \nSD+CM+GM 1.50e 0.62ef 0.39g 2.10bcd 0.95a \nSD+CM+GM+HM 1.50e 0.73de 0.74de 3.57a 0.63abc \nGM 2.14c 0.81d 0.99c 1.96bcde 0.56c \nPS+CM 2.16c 0.54f 1.73a 2.94ab 0.46c \nFMW+CM 1.70de 0.67def 0.65ef 2.69abc 0.91ab \nSMS+CM+GM 2.14c 1.72b 1.40b 2.05bcde 0.45c \nSMS+CM 1.94cd 1.59b 0.50fg 1.82cde 0.58bc \nNote: Different letters indicates statistically significant different values among treatment in LSD (p<0.05. SPW: \nSago pith waste; EFB: Empty fruit bunch; SD: Saw dust; PS: Paddy straw; FMW: Fresh market waste; SMS: Spent \nmushroom substrate; CM: Cow dung manure; GM: Goat dung manure. \n \n\n\n\nTABLE 2 \nPlant micronutrients concentrations of vermicomposts according to different types of feedstocks \n\n\n\n \nTreatments Zn Cu Fe Mn \n\n\n\nConcentration (mg kg-1) \nSPW+CM 36.2c 16.0c 2236.0de 106.5bc \nSPW+GM 28.9cd 12.5de 1620.7ef 100.9c \nEFB+CM 36.9c 21.3a 6611.3a 106.7bc \nSD+CM+GM  26.8d 11.9ef 2354.7cde 78.2d \nSD+CM+GM+HM 36.8c 15.4cd 2932.3bcd 110.5bc \nGM 22.5d 8.9f 1251.7f 65.4d \nPS+CM 58.5a 11.1ef 1703.7ef 71.5d \nFMW+CM  36.2c 10.7ef 2016.0e 74.5d \nSMS+CM+GM  56.5a 20.3ab 3338.0b 126.4ab \nSMS+CM  47.7b 17.5bc 3101.0bc 140.8a \nNote:Different letters indicates statistically significant different values among treatment in LSD  p<0.05. SPW: Sago \npith waste; EFB: Empty fruit bunch; SD: Saw dust; PS: Paddy straw; FMW: Fresh market waste; SMS: Spent \nmushroom substrate; CM: Cow dung manure; GM: Goat dung manure.\n\n\n\nNote: Different letters indicates statistically significant different values among treatment in \nLSD (p<0.05. SPW: Sago pith waste; EFB: Empty fruit bunch; SD: Saw dust; PS: Paddy \nstraw; FMW: Fresh market waste; SMS: Spent mushroom substrate; CM: Cow dung \nmanure; GM: Goat dung manure.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014108\n\n\n\nTotal P\nThe total P measured in SPW vermicompost ranged between 1.25 to 1.31% \nwhile total P in other commercial vermicomposts was between 0.54-1.89% \n(Table 1). Empty fruit bunch vermicompost had the highest total P content at \n1.89%, followed by SMS vermicompost (SMS+CM+GM and SMS+CM) and \nSPW vermicomposts (SPW+CM and SPW+GM). Vermicomposts produced \nfrom this feedstock fulfilled the requirement set by the Official Organic Fertilizer \nStandard of Korea (2001) which stipulates a minimum content of 1.00 % total \nP. The lowest total P content of FMW and PS vermicomposts was 0.67% and \n0.54% respectively. Vermicomposts are rich in soluble inorganic phosphate \nand exchangeable phosphorus (Manshell et al. 1981). The high P content of \nvermicomposts is due to the mineralization of phosphorus as a result of bacterial \nand fecal phosphate activity of earthworm (Edward and Lofty 1972). The very \nhigh P content in EFB could be due to the practice of composting the fibres for \na month to soften them prior to the vermicomposting process; this practice also \nfacilitates the vermicomposting process. Spent mushroom substrate originates \nfrom sawdust. It is easier for ingestion by earthworms compared to sawdust due to \nthe breaking down of the media from mushroom culturing activity. This promotes \ngreater earthworms fecal activities compared to other feedstock.\n\n\n\nTotal K\nSPW vermicompost was found to contain 0.78-0.85% total K while total K for \nthe other commercial vermicomposts contained between 0.39-1.73% (Table 1). \nAmong the commercial vermicomposts, the highest K content was from PS + \nCM (1.73%). This could be due to the high potassium uptake by paddy straw \nduring the maturity period. Different types of feedstock used can affect the K \ncontent of the vermicompost. According to Delgado et al. (1995), sewage sludge \nvermicompost has higher K content compared to the initial substrate. \n\n\n\nMicronutrients (Fe, Zn, Cu and Mn)\nThe micronutrient concentration of the vermicomposts tested is shown in Table \n2. Zn concentration ranged from 22.5\u201356.5 mg kg-1 while Cu concentration was \nbetween 11.1-21.3 mg kg-1. The concentrations were within the permitted level \naccording to the Official Organic Fertilizer Standard of Korea (2001) which \nstipulates maximum permitted levels of below 900 mg kg-1 for Zn and below 500 \nmg kg-1 for Cu. The concentration obtained for Mn was within 78.2 - 140.8 mg \nkg-1 while for Fe, it was within 1251-6611 mg kg-1. These results were within the \nrange obtained for commercial organic fertilizers in Malaysia (Kala et al. 2011), \nthat is, the concentration was between 45 mg kg-1 to 353 mg kg-1 for Zn, 17-88mg \nkg-1 for Cu,  912-24740 mg kg-1 for Fe and 89-827 mg kg-1 for Mn.\n\n\n\nHumic Acid\nThe humic acid content of the vermicompost was between 16-24% (Figure 2). \nSMS+CM+GM, SD+CM+GM+HM, GM and PS+CM had the highest humic acid \n\n\n\nElton T., A.B. Rosenani, C.I. Fauziah and J. Kadir\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 109\n\n\n\ncontent among the vermicomposts analyzed while EFB had the lowest. Although \nSMS+CM+GM had a mixture of spent mushroom substrate with cow and goat \ndung manure, the humic acid content of spent mushroom substrate at 40% was in \nfact lower compared to other feedstock at 60 to 70 %. It can be deduced that the \nhumic acid content was contributed by the animal dung manure. This is strongly \n\n\n\nCharacteristics of Vermicomposts of Different Feedstock\n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \nPlant macronutrients concentrations of vermicomposts according to different types of feedstock \n\n\n\n \nTreatments N  P K Ca Mg \n\n\n\n  (%) \nSPW+CM 3.14a 1.31c 0.85cd 1.10de 0.32c \nSPW+GM 2.57b 1.25c 0.78de 1.04e 0.38c \nEFB+CM 1.60e 1.89a 0.60ef 1.04e 0.63abc \nSD+CM+GM 1.50e 0.62ef 0.39g 2.10bcd 0.95a \nSD+CM+GM+HM 1.50e 0.73de 0.74de 3.57a 0.63abc \nGM 2.14c 0.81d 0.99c 1.96bcde 0.56c \nPS+CM 2.16c 0.54f 1.73a 2.94ab 0.46c \nFMW+CM 1.70de 0.67def 0.65ef 2.69abc 0.91ab \nSMS+CM+GM 2.14c 1.72b 1.40b 2.05bcde 0.45c \nSMS+CM 1.94cd 1.59b 0.50fg 1.82cde 0.58bc \nNote: Different letters indicates statistically significant different values among treatment in LSD (p<0.05. SPW: \nSago pith waste; EFB: Empty fruit bunch; SD: Saw dust; PS: Paddy straw; FMW: Fresh market waste; SMS: Spent \nmushroom substrate; CM: Cow dung manure; GM: Goat dung manure. \n \n\n\n\nTABLE 2 \nPlant micronutrients concentrations of vermicomposts according to different types of feedstocks \n\n\n\n \nTreatments Zn Cu Fe Mn \n\n\n\nConcentration (mg kg-1) \nSPW+CM 36.2c 16.0c 2236.0de 106.5bc \nSPW+GM 28.9cd 12.5de 1620.7ef 100.9c \nEFB+CM 36.9c 21.3a 6611.3a 106.7bc \nSD+CM+GM  26.8d 11.9ef 2354.7cde 78.2d \nSD+CM+GM+HM 36.8c 15.4cd 2932.3bcd 110.5bc \nGM 22.5d 8.9f 1251.7f 65.4d \nPS+CM 58.5a 11.1ef 1703.7ef 71.5d \nFMW+CM  36.2c 10.7ef 2016.0e 74.5d \nSMS+CM+GM  56.5a 20.3ab 3338.0b 126.4ab \nSMS+CM  47.7b 17.5bc 3101.0bc 140.8a \nNote:Different letters indicates statistically significant different values among treatment in LSD  p<0.05. SPW: Sago \npith waste; EFB: Empty fruit bunch; SD: Saw dust; PS: Paddy straw; FMW: Fresh market waste; SMS: Spent \nmushroom substrate; CM: Cow dung manure; GM: Goat dung manure.\n\n\n\nTABLE 2\nPlant micronutrients concentrations of vermicomposts according to different types of \n\n\n\nfeedstocks\n\n\n\nFigure 2: Humic acid contents of eight types of local vermicompost. \n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Different letters over bars indicate significant differences (P<0.05) in LSD . \nNote: SMS: Spent mushroom substrate; EFB: empty fruit bunch; SD: Sawdust; PS: Paddy straw; FMW: Fresh \nmarket waste; CM: cow dung manure; GM: goat dung manure; HM: Horse dung manure. \n\n\n\n \nFigure 2: Humic acid contents of eight types of local vermicompost.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\na\n\n\n\nbc\nc\n\n\n\nbc\n\n\n\na a ab a\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\nS\nM\n\n\n\nS\n +\n\n\n\n G\nM\n\n\n\n +\n C\n\n\n\nM\n\n\n\nS\nM\n\n\n\nS\n +\n\n\n\n C\nM\n\n\n\nE\nFB\n\n\n\n +\n C\n\n\n\nM\n\n\n\nS\nD\n\n\n\n +\n C\n\n\n\nM\n +\n\n\n\n G\nM\n\n\n\nS\nD\n\n\n\n +\n C\n\n\n\nM\n +\n\n\n\n G\nM\n\n\n\n +\n H\n\n\n\nM\n\n\n\nG\nM\n\n\n\nP\nS\n\n\n\n +\n C\n\n\n\nM\n\n\n\nFM\nW\n\n\n\n +\n C\n\n\n\nM\n\n\n\nHu\nm\n\n\n\nic\n a\n\n\n\nci\nd \n\n\n\nco\nnc\n\n\n\nen\ntra\n\n\n\ntio\nn \n\n\n\n(%\n)\n\n\n\nDifferent letters over bars indicate significant differences (P<0.05) in LSD .\nNote: SMS: Spent mushroom substrate; EFB: empty fruit bunch; SD: Sawdust; PS: Paddy \n\n\n\nstraw; FMW: Fresh market waste; CM: cow dung manure; GM: goat dung manure; HM: \nHorse dung manure.\n\n\n\nNote: Different letters indicates statistically significant different values among treatment in \nLSD  p<0.05. SPW: Sago pith waste; EFB: Empty fruit bunch; SD: Saw dust; PS: Paddy \nstraw; FMW: Fresh market waste; SMS: Spent mushroom substrate; CM: Cow dung \nmanure; GM: Goat dung manure.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014110\n\n\n\nElton T., A.B. Rosenani, C.I. Fauziah and J. Kadir\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nTh\ne \n\n\n\nch\nem\n\n\n\nic\nal\n\n\n\n p\nro\n\n\n\npe\nrti\n\n\n\nes\n o\n\n\n\nf t\nhe\n\n\n\n sa\ngo\n\n\n\n p\nith\n\n\n\n w\nas\n\n\n\nte\n v\n\n\n\ner\nm\n\n\n\nic\nom\n\n\n\npo\nst\n\n\n\n, l\noc\n\n\n\nal\nly\n\n\n\n p\nro\n\n\n\ndu\nce\n\n\n\nd \nve\n\n\n\nrm\nic\n\n\n\nom\npo\n\n\n\nst\n a\n\n\n\nnd\n o\n\n\n\nrg\nan\n\n\n\nic\n fe\n\n\n\nrti\nliz\n\n\n\ner\n\n\n\nIS\nSN\n\n\n\n: 1\n39\n\n\n\n4-\n79\n\n\n\n90\n \n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n8:\n x\n\n\n\n \u2013\nx \n\n\n\n(2\n01\n\n\n\n4)\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  M\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n    \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n8,\n\n\n\n 2\n01\n\n\n\n4 \n \n\n\n\n\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\n\n\n\n\nTh\ne \n\n\n\nch\nem\n\n\n\nic\nal\n\n\n\n p\nro\n\n\n\npe\nrti\n\n\n\nes\n o\n\n\n\nf t\nhe\n\n\n\n sa\ngo\n\n\n\n p\nith\n\n\n\n w\nas\n\n\n\nte\n v\n\n\n\ner\nm\n\n\n\nic\nom\n\n\n\npo\nst\n\n\n\n, l\noc\n\n\n\nal\nly\n\n\n\n p\nro\n\n\n\ndu\nce\n\n\n\nd \nve\n\n\n\nrm\nic\n\n\n\nom\npo\n\n\n\nst\n a\n\n\n\nnd\n o\n\n\n\nrg\nan\n\n\n\nic\n fe\n\n\n\nrti\nliz\n\n\n\ner\n \n\n\n\nC\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\ns \n\n\n\nSP\nW\n\n\n\n v\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n \n\n\n\nC\nom\n\n\n\nm\ner\n\n\n\nci\nal\n\n\n\n \nve\n\n\n\nrm\nic\n\n\n\nom\npo\n\n\n\nst\ns \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n fe\nrti\n\n\n\nliz\ner\n\n\n\n \n(K\n\n\n\nal\na \n\n\n\net\n. a\n\n\n\nl. \n20\n\n\n\n11\n) \n\n\n\nO\nff\n\n\n\nic\nia\n\n\n\nl O\nrg\n\n\n\nan\nic\n\n\n\n F\ner\n\n\n\ntil\niz\n\n\n\ner\n S\n\n\n\nta\nnd\n\n\n\nar\nd \n\n\n\nof\n K\n\n\n\nor\nea\n\n\n\n \n(M\n\n\n\nyu\nng\n\n\n\n a\nnd\n\n\n\n Y\nou\n\n\n\nn \n20\n\n\n\n01\n) \n\n\n\npH\n \n\n\n\n5.\n9 \n\n\n\n4.\n5 \n\n\n\n- 6\n.5\n\n\n\n \n 4\n\n\n\n.5\n-9\n\n\n\n.8\n \n\n\n\nna\n \n\n\n\nC\n/N\n\n\n\n ra\ntio\n\n\n\n \n15\n\n\n\n.5\n3 \n\n\n\n  6\n.2\n\n\n\n - \n18\n\n\n\n.3\n \n\n\n\n  3\n.8\n\n\n\n-4\n2.\n\n\n\n7 \nna\n\n\n\n \nTo\n\n\n\nta\nl N\n\n\n\n (%\n) \n\n\n\n2.\n86\n\n\n\n \n1.\n\n\n\n50\n - \n\n\n\n2.\n16\n\n\n\n \n 0\n\n\n\n.7\n0 \n\n\n\n-4\n.4\n\n\n\n0 \n>4\n\n\n\n.0\n0 \n\n\n\nTo\nta\n\n\n\nl P\n (%\n\n\n\n) \n1.\n\n\n\n28\n \n\n\n\n0.\n54\n\n\n\n - \n1.\n\n\n\n89\n \n\n\n\n 0\n.0\n\n\n\n4-\n8.\n\n\n\n85\n \n\n\n\n>1\n.0\n\n\n\n0 \nTo\n\n\n\nta\nl K\n\n\n\n (%\n) \n\n\n\n0.\n82\n\n\n\n \n0.\n\n\n\n39\n - \n\n\n\n1.\n73\n\n\n\n \n 1\n\n\n\n.2\n9-\n\n\n\n6.\n94\n\n\n\n \n>1\n\n\n\n.0\n0 \n\n\n\nTo\nta\n\n\n\nl C\na \n\n\n\n(%\n) \n\n\n\n1.\n07\n\n\n\n \n0.\n\n\n\n52\n - \n\n\n\n1.\n78\n\n\n\n \n  0\n\n\n\n.1\n2-\n\n\n\n12\n.0\n\n\n\n0 \nna\n\n\n\n \nTo\n\n\n\nta\nl M\n\n\n\ng \n(%\n\n\n\n) \n0.\n\n\n\n35\n \n\n\n\n0.\n27\n\n\n\n - \n0.\n\n\n\n50\n \n\n\n\n0.\n3-\n\n\n\n3.\n3 \n\n\n\nna\n \n\n\n\nZn\n \n\n\n\n32\n.6\n\n\n\n \n22\n\n\n\n.5\n-5\n\n\n\n6.\n5 \n\n\n\n45\n-3\n\n\n\n53\n \n\n\n\n<9\n00\n\n\n\n \nC\n\n\n\nu \n14\n\n\n\n.3\n \n\n\n\n 1\n1.\n\n\n\n1-\n21\n\n\n\n.3\n \n\n\n\n17\n-8\n\n\n\n8 \n<5\n\n\n\n00\n \n\n\n\nFe\n \n\n\n\n19\n28\n\n\n\n \n12\n\n\n\n51\n - \n\n\n\n66\n11\n\n\n\n \n91\n\n\n\n2-\n24\n\n\n\n74\n0 \n\n\n\nna\n \n\n\n\nM\nn \n\n\n\n10\n3.\n\n\n\n7 \n  7\n\n\n\n8.\n2-\n\n\n\n14\n0.\n\n\n\n8 \n89\n\n\n\n-8\n27\n\n\n\n \nna\n\n\n\n \nH\n\n\n\num\nic\n\n\n\n a\nci\n\n\n\nd \n(%\n\n\n\n) \n21\n\n\n\n.1\n7 \n\n\n\n16\n.6\n\n\n\n7 \n- 2\n\n\n\n4.\n00\n\n\n\n  \nna\n\n\n\n \nna\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 111\n\n\n\nsupported by GM which is 100 % goat dung manure and PS+CM which comprised \n50 % paddy straw and 50 % cow dung manure. Orlov and Biryukova (1996) \nreported that vermicomposts in general contain 17\u201336% of humic acid. From \nthe results of the vermicomposts tested, the humic acid contents are within the \nrange reported by Orlov and Biryukova (1996). The presence of a high humic acid \ncontent is due to the humification process which occurs naturally in mature animal \ndung manure. Compared to traditional composting, the vermicomposting process \nincreases the rate of humic acid production drastically from 40% to 60%. The \nfragmentation and size reduction of organic matter inside the earthworm intestine \nduring the vermicomposting process enhances the humification process and also \nincreases the microbial activity within the earthworm intestine (Dominguez and \nEdwards 2004).\n\n\n\nCrop Growth Performance of Vermicomposts\nThe effect of different type of fertilizers on crop growth performance (dry weight \nand uptake of N, P, K, Ca uptake and Mg) are shown in Table 4 and soil pH in \nFigure. 3. Maize planted using SPW vermicompost showed significantly higher \ndry weight compared to the other treatments. There were no significant differences \nin term of N uptake among maize planted with vermicompost while maize planted \nwith chemical fertilizers showed significantly lower N uptake in comparison \nto the other three treatments. Phosphorus uptake among the plants showed no \nsignificant differences. SPW+GM showed the best performance in term of K, \nCa and Mg while K, Ca and Mg uptake from plant grown with SPW+CM was \nat par with SMS+GM+CM. However, chemical fertilizers had the lowest K, Ca \nand Mg uptake among all treatments. Treatments with SPW+GM showed the \nhighest soil pH value while chemical fertilizer treatment had the lowest soil pH \n(Figure 3).  As the ideal soil pH for maize growth is between 6.0-7.0; problems \nemerge when the soil pH is lower than 5.5 (Mississippi State University 2008). \nAccording to Mallarino (2011), low soil pH affects plant growth directly and \nindirectly by affecting plant nutrients uptake and by increasing phytotoxic element \nconcentration in soil and influencing microbial activity.\n\n\n\nTABLE 4\nNutrients uptake (g) for maize according to different types of fertilizers\n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nNutrients uptake (g) for maize according to different types of fertilizers \n\n\n\nTreatment Dry weight N P K Ca Mg \n (g pot-1) g pot-1 \n\n\n\nCF 10.2b 0.26b 2.51a 4.62b 3.35b 0.74c \nSMS+GM+CM 15.2ab 0.44a 2.07a 6.17ab 5.23ab 2.60b \nSPW+CM 18.1a 0.48a 2.79a 6.82ab 6.58a 3.33ab \nSPW+GM 20.2a 0.50a 2.80a 8.82a 7.00a 3.83a \n Note: CF: Chemical fertilizer; SMS: Spent mushroom substrate; SPW: Sago pith waste; GM: Goat dung manure; \nCM: Cow dung manure) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Note: CF: Chemical fertilizer; SMS: Spent mushroom substrate; SPW: Sago pith waste; GM: \nGoat dung manure; CM: Cow dung manure \n\n\n\nCharacteristics of Vermicomposts of Different Feedstock\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014112\n\n\n\n  \nCONCLUSION\n\n\n\nSago pith waste is of better quality in term of N and humic acid content compared \nto other vermicomposts. This could be due to sago pith waste vermicompost \nhaving a longer vermicomposting period (30 days) compared to the commercial \nvermicomposts (14 days). Different types of feedstock showed varying results in \nterm of chemical content. Different ratios of co-composting material such as cow, \ngoat and horse dung manure have an effect on the chemical content compared \nto the main feedstock. Beside feedstock, type of earthworms used may affect \nthe quality of the vermicomposts produced; however, it is to be noted that these \nearthworms depend on the composition of the main feedstock. In this study, local \nearthworms were used for the paddy straw vermicompost; this vermicompost had \nthe lowest pH at 4.5.  Vermicompost produced from spent mushroom substrate co-\ncomposted with a mixture of cow and goat dung manure showed the best quality \namong the local vermicomposts. SPW+GM showed better plant growth compared \nto  SMS+GM+CM while SPW+CM was at par with SMS+GM+CM. SPW \nexhibited  good prospects for vermicomposting. However, further studies need to \nbe conducted and results have to be compared with other organic fertilizers such \nas composts of different feedstock.\n\n\n\nElton T., A.B. Rosenani, C.I. Fauziah and J. Kadir\n\n\n\nFigure 3: pH of soil at harvest according to the treatment.\n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: pH of soil at harvest according to the treatment.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nc\nb b\n\n\n\na\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\n7\npH\n\n\n\nT1 T2 T3 T4\n\n\n\nNote: T1: chemical fertilizer; T2: local vermicompost (SMS+GM+CM); T3: \nSPW+CM vermicompost; T4: SPW+GM.\n\n\n\n Different letters over bars indicate significant differences (P<0.05) in LSD.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 113\n\n\n\nCharacteristics of Vermicomposts of Different Feedstock\n\n\n\nREFERENCES\nAhmed, O.H., M.H. Husni, A.R. Anuar and M. M. Hanafi. 2005. Effects of extraction \n\n\n\nand fractionation time on the yield of compost humic acids. New Zealand \nJournal of Crop Horticulture Science. 33: 107-110.\n\n\n\nAwang-Adeni, D.S., S. Abdul-Aziz, K. Bujang and M.A. Hassan. 2010. Bioconversion \nof sago residue into value added products. African Journal of Biotechnology. \n9(14): 2016-2021.\n\n\n\nBremner, J. M. and C. S. Mulvaney. 1982 Total nitrogen. In: Methods of Soil Analysis, \neds. A.L. Page,  R.H. Miller and D.R. Keeny. pp. 1119-1123.American Society \nof Agronomy and Soil Science Society of America, Madison  \n\n\n\nDelgado M, M. Bigeriego, I. Waiter and R. Calbo. 1995. Use of California red worm \nin sewage sludge transformation. Turrialba. 45: 33-41.\n\n\n\nDom\u00ednguez, J. and C.A. Edwards. 2004. Vermicomposting organic wastes: A review. \nIn:S.H Shakir and W.Z.A. Mikha\u00efl (Eds.). pp. 369\u2013396. Soil Zoology for \nSustainable Development in the 21st century, El Cairo. \n\n\n\nEdwards, C.A. and J.R. Lofty. 1972. 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Manual on Soil Sampling and Methods of Analysis. Ottawa: \nSoil Research Institute of Canada.\n\n\n\nMississippi State University. 2008. Corn Fertilization. Retrieved from Mississippi \nState University website: http://msucares.com/pubs/infosheets/is0864.pdf\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014114\n\n\n\nMitra, S. 2003. Sample Preparation Techniques in Analytical Chemistry. New Jersey: \nJohn Wiley & Sons, Inc. \n\n\n\nMoore, D. and S.W. Chiu. 2001. Filamentous fungi as food.   In: Exploitation of \nFimamentous Fungi. ed. S.B. Pointing and D. Hyde.  Hong Kong: Fungal \nDiversity Press. \n\n\n\nMyung, H.U. and L. Youn. 1999. Quality Control for Commercial Compost in Korea. \nFood and Fertilizer Technology Center International Workshop. \n\n\n\nNdegwa, P.M. and S.A. Thompson. 2000. Effect of C-to-N ratio on vermicomposting \nof biosolids. Bioresource Technology. 75 (1): 7\u201312.\n\n\n\nPramanik, P., G.K. Ghosh, P.K. Chosal and P. Banik. 2007. Changes in organic - C, N, \nP and K and enzyme activities in vermicompost of biodegradable organic wastes \nunder liming and microbial inoculants. Bioresource Technology. 98:2485-2494.\n\n\n\nOfficial Fertilizer Standard of Korea. 2001. Ministry of Agriculture and Fishery \n(MAF), Korea.\n\n\n\nOrlov D.S. and O.N. Biryukova, 1996. Humic substances of vermicomposts. \nAgrokhimiya. 12: 60-67.\n\n\n\nSAS Institute Inc. 1999.  Guide to use of PC-SAS Version 6.04 for DOS for Statistical \nAnalysis. SAS Institute Inc., Cary, NC\n\n\n\nShak, K.P.Y., T.Y. Wu, S.L. Lim and C.A. Lee.  2013. Sustainable reuse of rice \nresidues as feedstocks in vermicomposting for organic fertilizer production. \nEnvironmental Science and Pollution Research. 21(2): 1349-1359.\n\n\n\nStandards Australia. 2012. Australian Standard for Composts, Soil Conditioner and \nMulches. AS-4454. Fourth ed. Sydney: Standards Australia\n\n\n\n\n\n\n\nElton T., A.B. Rosenani, C.I. Fauziah and J. Kadir\n\n\n\n\n\n"
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"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n133 \n\n\n\n\n\n\n\n\n\n\n\n Soil Fertility Status of Rehabilitated Forest Soil in Bintulu, Sarawak after \n\n\n\n30 Years of Planting \n \n\n\n\nAiza Shaliha-Jamaluddin1, Zahari Ibrahim2, Daljit Singh Karam3,  \n\n\n\nKeeren Sundara Rajoo4, Shamshuddin Jusop3, Seca Gandaseca1 and Arifin Abdu1* \n \n\n\n\n1Department of Forestry Science & Biodiversity, Faculty of Forestry & Environment, UPM, Malaysia \n2Forestry Department Peninsular Malaysia \n\n\n\n3Department of Land Management, Faculty of Agriculture, UPM, Malaysia \n4Department of Forestry Science, Faculty of Agricultural & Forestry Sciences, UPM Malaysia  \n\n\n\n \n*Corresponding author: arifinabdu@upm.edu.my \n\n\n\n\n\n\n\nABSTRACT \n \n\n\n\nRehabilitation of degraded forestland is critical because it aids in reducing soil nutrient loss, \n\n\n\nimproving vegetation stand and/or composition, and addressing environmental concerns. Thus, the \npurpose of this study was to determine the soil fertility condition in soils in rehabilitated forests after \n\n\n\n30 years of planting. This study was conducted in 16 plots of a 47.5-hectare rehabilitated forest at \n\n\n\nUniversiti Putra Malaysia Bintulu Campus in Sarawak, Malaysia. As of 2010, around 350,000 \n\n\n\nseedlings from 128 Sarawak native species had been planted. Soil samples were taken from different \ndepths at each site (0-15 cm and 15-30 cm). Soil chemical properties were determined using standard \n\n\n\nlaboratory methods while soil compaction analysis was determined using the fall-corn-type soil \n\n\n\npenetrometer (Hasegawa Type H-60). Soil Fertility Index (SFI) and the Soil Evaluation Factor (SEF) \nwere used to estimate soil fertility and site quality. The compaction rate for the soil at rehabilitated \n\n\n\nforest plots was inversely proportional to cumulative depth. The compaction rates for plots in years \n\n\n\n1991 to 2000 showed an increase in compaction rates with the depth of soil. The total cumulative \ndepth for plots 2001 to 2008 had a longer graph trend compared to the previous years. The principal \n\n\n\ncomponent analysis (PCA) showed that pH, OM, exchangeable Mg, CEC, and available P all \n\n\n\ncontributed positively to factor loading in PC1. Our data showed a moderately positive correlation \n\n\n\nbetween CEC and exchangeable (Exch.) Mg, CEC with OM and Exch. Na and Exch. K indicating \nthat negative charges derived from organic matter played an important role in cation retention \n\n\n\ncapacity, nutrient supply, and soil fertility. The SFI analysis (9.26) in rehabilitated forest planted in \n\n\n\nyear 1991 indicated greatest accumulation of organic matter from litter fall. In addition, the SEF \nvalues of the rehabilitated forests in relation to planting years indicated an undulating trend. \n\n\n\nGenerally, SFI and SEF exhibit strong correlations with soil chemical and biological features, \n\n\n\nimplying that these two indices can be used as indicators of soil quality. \n\n\n\n \nKey words: Rehabilitated forest, soil compaction, physico-chemical properties, soil fertility and \n\n\n\nsoil indices (SFI and SEF) \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\nIn most tropical countries, forests are frequently subjected to intensive logging, grazing, and \n\n\n\nshifting cultivation. With biodiversity conservation and management being a major forest \n\n\n\nmanagement priority, and humans living in wooded regions being viewed as a barrier to \n\n\n\nefficient conservation, many measures to keep forest dwellers out of conservation zones have \n\n\n\nbeen tried, but with limited success (Ramakrishnan 2007). It is noted that Southeast Asia saw \n\n\n\n\nmailto:arifinabdu@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n134 \n\n\n\n\n\n\n\nthe greatest drop in forest area, losing about 2.8 million hectares each year. Indonesia, with \n\n\n\nabout 1.9 million hectares lost each year, was the country with the largest forest loss, \n\n\n\nfollowed by Myanmar, Cambodia, Philippines, Malaysia, and the Democratic People's \n\n\n\nRepublic of Korea (FAO 2007).  \n\n\n\n\n\n\n\nDeforestation is a visible manifestation of human activity in the environment. This \n\n\n\nalteration can have several interconnected repercussions, such as a loss in the chemical and \n\n\n\nphysical quality of soil resources (Seeger and Ries 2008). Soil degradation is the total of \n\n\n\ngeological, climatic, biological, and human variables that lead to the degradation of the soil's \n\n\n\nphysical, chemical, and biological potential, putting biodiversity, land usage, and hence \n\n\n\nexistence of human societies at risk. Forest rehabilitation can enhance forest areas while also \n\n\n\nconserve existing primary forests and improve environmental quality. Rehabilitation of \n\n\n\ndegraded forestland has become significantly important as it helps to improve the loss of soil \n\n\n\nnutrients and improve vegetation stand or composition as well as address environmental \n\n\n\nconcerns (Zaidey et al. 2010). In Sarawak, Malaysia, it is noted that rehabilitation of \n\n\n\ndegraded forestlands due to abandoned shifting cultivation has been successfully \n\n\n\nimplemented under the ecosystem rehabilitation program (Kendawang et al. 2004) while in \n\n\n\nPeninsular Malaysia, degraded forestland due to excessive harvesting has also been \n\n\n\nrehabilitated by the enrichment planting technique (Arifin et al. 2008b; Hamzah et al. 2009). \n\n\n\n\n\n\n\nSoil fertility is the capacity to receive, store and transmit energy to support plant \n\n\n\ngrowth. Biological processes have the potential to contribute significantly to chemical and \n\n\n\nphysical processes that influence soil fertility. In short, soil that is rich in nutrients is fertile. \n\n\n\nIn undisturbed rainforests such nutrients are recycled via the litter, slash and burn agriculture \n\n\n\nsystem and the mineralization of organic nutrients from the plant remains or on short-lived \n\n\n\ninputs from ash. The use of soil fertility indicators can assist in determining the influence of \n\n\n\nagricultural operations and forest management on soil attributes. Several challenges defining \n\n\n\nquality and variety of biological, chemical, and physical components that regulate soil \n\n\n\nprocesses complicate the evaluation of soil quality and the identification of critical soil \n\n\n\ncomposition that serve as indicators of the functional qualities of soil (Doran and Parkin \n\n\n\n1994).  \n\n\n\n\n\n\n\nFor a better understanding of effective soil management and conservation, knowledge \n\n\n\nof soil science is required for the rehabilitation of tropical rainforests on highly degraded land \n\n\n\nas well as soil under a rehabilitation program after many years of planting (more than 20 \n\n\n\nyears old). As a result, a multivariate soil quality measurement approach may need to be \n\n\n\nused. Thus this study was conducted to (1) understand the relationship between the  effects of \n\n\n\nsoil compaction on soil chemical properties and soil fertility condition of rehabilitated \n\n\n\nforests; and (2) place emphasis on the significant soil properties identified based on PCA and \n\n\n\nthe applicability of the proposed Soil Fertility Index (SFI) and the Soil Evaluation Factor \n\n\n\n(SEF) as alternative approaches to identify indicators for estimating soil fertility and site \n\n\n\nquality in plantations that are to be rehabilitated.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n135 \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nGeneral Background of Study Area \n\n\n\nThis study was conducted in sixteen plots (planted in years 1991 to 2008, excluding years \n\n\n\n2002 and 2004, where there was no planting) of rehabilitated forest plots at Universiti Putra \n\n\n\nMalaysia Bintulu Campus in Sarawak, Malaysia (Figure 1). This study which included \n\n\n\nPhases I, II and IV covered a total area of about 1.58 acres. \n\n\n\n\n\n\n\n\n\n\n\nFigure 1. Location of Rehabilitated plots in UPM Bintulu Campus, Sarawak \n\n\n\n\n\n\n\nUnder the auspices of Mitsubishi Corporation, Japan, the Joint Research Project on \n\n\n\nTropical Rainforest Ecosystem Rehabilitation began in July 1991. This was a collaborative \n\n\n\nproject between Universiti Putra Malaysia (UPM) and Yokohama National University, Japan \n\n\n\n(YNU). The project's goals were to assess the health of recovered forests by using an \n\n\n\ninterdisciplinary approach to measure indices of forest health quantitatively and qualitatively, \n\n\n\nand hence the long-term viability of forest resources. To determine the health of the \n\n\n\nrecovered forest, the project undertook and integrated research findings in fields such as soil \n\n\n\nscience, plant physiology, water science, biodiversity (terrestrial floral, wildlife, aquatic flora \n\n\n\nand fauna, insect, and microorganisms) and microclimatic variables.  \n\n\n\nThe Miyawaki approach, which is being used in this project, is based on the notion of \n\n\n\nvegetation association and spontaneous regeneration acceleration (Miyawaki 1999). A total \n\n\n\nof 126 indigenous plants from the Dipterocarpaceae and Non-Dipterocarpaceae families were \n\n\n\nplanted at a high density (3 seedlings/m2) using this approach. The project began on a 47.5-\n\n\n\nhectare plot on the UPM campus in Bintulu, Sarawak. As of 2010, around 350,000 seedlings \n\n\n\nfrom 128 native Sarawak species had been planted. In addition, 100 study plots were \n\n\n\nconstructed in the repaired area, with the growth of planted seedlings being observed on a \n\n\n\nregular basis.  \n\n\n\nPreviously, plots 1991 and 1999 had been under shifting cultivation. Ischaemum \n\n\n\nmagnum, Miscanthus floridulus, and Trema orientalis dominated the land before the start of \n\n\n\nthe project. Plot 2008 was a regenerating forest with grassland species such as Macaranga \n\n\n\nNursery \n\n\n\nPhase IV Phase II \n\n\n\nPhase I \n\n\n\nPhase III \n\n\n\nLEGEND: \n\n\n\n\n\n\n\nPHASE I: PLOT \n\n\n\n1991 \n\n\n\n\n\n\n\nPHASE II: PLOT \n\n\n\n1993, 2007 & 2008 \n\n\n\n\n\n\n\nPHASE IV: PLOT \n\n\n\n1995, 1996, 1997, \n\n\n\n1998, 1999, 2000, \n\n\n\n2001, 2002, 2003, \n\n\n\n2005 & 2006 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n136 \n\n\n\n\n\n\n\nspp. and T. orientalis dominating (Yusuf and Abas 1992). The Bintulu, Sarawak-based \n\n\n\nproject is an excellent example of a highly effective forest regeneration effort in a degraded \n\n\n\nenvironment. It should be noted that the recovered forest site is a degraded plantation area. \n\n\n\nThe regeneration began in 1991 and was completed in three phases. The age of the research \n\n\n\nplots/tree stands is estimated to be between 15 and 30 years old as of date. \n\n\n\n\n\n\n\nSoil Taxonomy \n\n\n\nThe underlying soils of the sample plots are from the Berkenu and Nyalau series. Sandstone \n\n\n\nintercalated with ferrogenous shale is the parent material for both soil types. The Berkenu \n\n\n\nSeries is classified as fine loamy, mixed, isohyperthermic Typic Paleudult. The structures are \n\n\n\nweak to moderate, medium subangular blocky, with uniformity improving with depth (Peli et \n\n\n\nal. 1984). The Bekenu Series, according to Paramananthan (2000), is a low-fertility soil. \n\n\n\nAccording to Peli et al. (1984), the Nyalau series is a coarse loamy, kaolinitic, and \n\n\n\nisohyperthermic form of Typic Dystropept. The topsoil is yellowish brown (10YR5/6) while \n\n\n\nthe B horizon is bright yellowish brown (10YR 6/8) to yellowish orange (10YR 7/8). The \n\n\n\nstrength, fineness, and subangularity of the blocky formations vary. The soil has a low \n\n\n\nresponse rate and is well-drained. The amount of organic matter in the soil is rather high, and \n\n\n\nit tends to increase with depth (Peli et al. 1984). \n\n\n\n\n\n\n\nRehabilitated Forest: The Forest Structure and Condition \n\n\n\nA previous study by Kueh et al. (2011) at a similar location indicated that the rehabilitated \n\n\n\nforest had greater structure in comparison to the surrounding natural regenerating secondary \n\n\n\nforest as shown in Table 1. Forest structure evaluation reveals information about the \n\n\n\nbiodiversity and health of the recovered forest. When compared to a naturally regenerating \n\n\n\nsecondary forest, the accelerated natural regeneration approach for rehabilitating damaged \n\n\n\nforest areas shows improved structural metrics like DBH, height, and basal area. This can aid \n\n\n\nin the promotion of reforestation and forest restoration efforts in damaged areas. In general, \n\n\n\nrehabilitated damaged areas do not show full recovery in terms of height and extent of \n\n\n\nnatural vegetation. \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nSelected key forest structural characteristics of the study sites \n\n\n\nBasal Area (m2 0.04 h-1) and Dbh (cm) and height (m) range in the study plots \n Plot 2008 Plot 1999 Plot 1991 Plot NF \n\n\n\nBasal area \n(m2 0.04 h-1) 0.02 0.08 1.56 1.64 \n\n\n\nMean* 0.05-3\u00b10.3x10-5 0.35x10-2\u00b10.2x10-3 0.76x10-2\u00b10.8x10-3 0.30x10-2\u00b10.7x10-3 \n\n\n\n (7.0x10.0-7-0.54x10.0-\n\n\n\n3) \n\n\n\n(0.1x10.3-4-0.02) (0.1x10.0-3-0.09) (0.1x10.0-4-0.28) \n\n\n\nDbh (cm) \nMean* 0.76\u00b10.16 6.00\u00b10.20 8.16\u00b10.38 3.24\u00b10.23 \n\n\n\n (0.04-2.61) (0.82-15.50) (1.31-35.10) (0.41-59.80) \n\n\n\nHeigh (m) \nMean* 0.46\u00b10.15 6.15\u00b10.13 9.30\u00b10.24 4.02\u00b10.14 \n\n\n\n (0.01-1.40) (1.49-10.73) (2.00-20.50) (0.30\u00b126.80) \n*Value are mean\u00b1S.E \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n137 \n\n\n\n\n\n\n\nSoil Sampling and Preparation \n\n\n\nAt selected rehabilitated forest plots, a 20 \u00d7 20 m research plot was established (1991 to 2008 \n\n\n\nplanting sites). For sample replication, the plots were split into two subplots (10 x 10 m). \n\n\n\nUsing an auger, five soil samples were taken at five randomly selected places in each \n\n\n\nsubplot. The soil samples were taken at two distinct depths, ranging from 0 to 15 cm for \n\n\n\ntopsoil and 15 to 30 cm for subsoil. The five soil samples for the subplots were then \n\n\n\ncombined into one composite sample. Each composite sample was replicated three times. All \n\n\n\nsoil samples were air dried at room temperature until completely dried. The soil samples \n\n\n\nwere cleansed of plant debris and coarse stones after drying. The materials were then finely \n\n\n\ncrushed with a mortar and pestle to pass through a 2-mm sieve. The ground samples were \n\n\n\ntagged and maintained in a sealable plastic bag. \n\n\n\n\n\n\n\nSoil Compaction/ Hardness Analysis \n\n\n\nThe assessment of soil compaction at each rehabilitated forest plot was repeated thrice for the \n\n\n\ntop, middle and bottom of the soil portions.  Soil compaction was measured using fall-corn-\n\n\n\ntype soil penetrometer (Hasegawa Type H-60) to a depth of 50 cm (Figure 2).  \n\n\n\nAn one drop penetrability (ODP) figure was plotted to evaluate the soil hardness \n\n\n\nwhere the horizontal axis represents the penetrating depth (cm) per one drop of weight while \n\n\n\nthe vertical axis shows the cumulative depth (cm). In this study, soil compaction was \n\n\n\nclassified using the plotted values of the horizontal axis and are listed as follows: very hard, \n\n\n\nODP < 0.5 cm; hard, ODP between 0.5-1.0 cm; moderate, ODP between 1.0- 2.0 cm; soft, \n\n\n\nODP > 2.0 cm (Ishizuka et al. 1998). In other words, the harder the soil, the smaller the area \n\n\n\nin the Figure. \n\n\n\n\n\n\n\n\n\n\n\nFigure 2. Diagram of a fall-corn-type soil penetrometer/ hardness (Hasegawa Type H-60) \n\n\n\n\n\n\n\nSoil Chemical Analysis \n\n\n\nThe pH of the soil samples (1:2.5 \u2013 soil to water ratio) were measured using a pH meter. The \n\n\n\npH of the soil was measured after 1 h of reciprocal shaking. The measurements were taken in \n\n\n\nsoil to solution ratio (1:2.5) The loss-on-ignition approach was used to estimate organic \n\n\n\nmatter and total organic carbon. The Kjeldahl technique (Bremner and Mulvaney 1982) was \n\n\n\nused to quantify total nitrogen while the LECO-412 machine was used to determine total \n\n\n\ncarbon in soil. The Bray II technique was used to determine the amount of available \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n138 \n\n\n\n\n\n\n\nphosphorus (Peter et al. 2001). The exchangeable cations (K, Ca, Mg, and Na) were \n\n\n\nextracted with 1 M NH4OAc buffered at pH 7 and the Shimadzu AA-6800 AAS was used to \n\n\n\ndetermine the concentrations of K, Ca, Mg, and Na in the solutions.  The cation exchange \n\n\n\ncapacity (CEC) was also measured to access the negatively charged sites on the soil. \n\n\n\n\n\n\n\nData Analysis  \n\n\n\nStatistical Analysis System (SAS) version 9.1 was used for the statistical analysis. Analysis \n\n\n\nof variance (ANOVA) and Tukey\u2019s test were carried out to indicate the differences among \n\n\n\nthe rehabilitated forests plot/years of planting. Pearson correlation analysis was carried out to \n\n\n\nindicate the relationship among the selected soil parameters. The variables that contributed \n\n\n\nsignificantly to the soil physico-chemical properties among the rehabilitated plots were \n\n\n\naccessed using Principal component analysis (PCA).  \n\n\n\n\n\n\n\nSoil Indices \n\n\n\nSoil fertility was assessed using the Soil Fertility Index (SFI) (Moran et al. 2000) and the \n\n\n\nSoil Evaluation Factor (SEF) (Lu et al. 2002) at two different depths in rehabilitated forest \n\n\n\nplots. Based on the following equations, the SFI and SEF indices were derived to measure \n\n\n\nthe intensity of land degradation in the research area: \n\n\n\n\n\n\n\nSFI = pH + Organic matter (% dry soil basis) + Available P (mg kg-1 dry soil) + Exchg. K \n\n\n\n(cmolckg-1) + Exchg.Ca (cmolckg-1) + Exchg. Mg (cmolckg-1) \u2013 Exchg. Al (cmolckg-1) \n\n\n\n\n\n\n\nSEF = [Exchg. K (cmolckg-1) + Exchg. Ca (cmolckg-1) + Exchg Mg (cmolckg-1) \u2013 log (1 + \n\n\n\nExchg. Al (cmolckg-1)] x Organic matter (% dry soil basis) + 5. \n\n\n\n\n\n\n\nBoth SFI and SEF indices were created and utilised to measure soil biomass and \n\n\n\nfertility conditions in the Amazon humid tropical forest of Brazil during secondary forest \n\n\n\nsuccession. In this study, the applicability of the SFI and SEF indices was utilised to compare \n\n\n\nsoil fertility of restored and secondary forests. \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nEffect of Soil Compaction/ Hardness on Chemical Properties of the Soils \n\n\n\n\n\n\n\nIn order to better understand the effects of soil compaction at rehabilitated forest plots/ areas, \n\n\n\nthe distribution of soil compaction from plots 1991 to 2008 are presented in Figure 3 and \n\n\n\nFigure 4.  \n\n\n\nBased on Figure 3, it is noted that the compaction rate is inversely proportional to the \n\n\n\ncumulative depth. This is because compaction and topsoil removal normally result in 70% \n\n\n\nincrease in bulk density and a 23% increase caused by subsoil compaction alone (Woodward, \n\n\n\n1996). The compaction rate or trend showed an increase from 2 cm to 9 cm at plots 1991 to \n\n\n\n2008 with no significant differences beyond this (see red box). It is noted that the compaction \n\n\n\nrate increased from topsoil to subsoils. As we can see from the figure, the compaction rate for \n\n\n\nplots 1991 to 2000 increased with an increasing depth of soil. The compaction values from 0-\n\n\n\n9 cumulative depth were very soft to soft, which indicate a less compact surface soil. This \n\n\n\ncould be due to formation of a litter layer in the forest which contains much decomposition \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n139 \n\n\n\n\n\n\n\nmaterials. According to Hasegawa (2008), soil hardness is assessed as \"softness\" (i.e., S-\n\n\n\nvalue, cm drop1), which characterizes soil penetration resistance based on the depth (cm) to \n\n\n\nwhich a cone (diameter: 20 mm) enters the soil per stroke when a 2-kg mass rammer is \n\n\n\ndropped from a height of 0.5 m. As a result, low \"softness\" suggests that the soil is hard. But \n\n\n\ntowards cumulative depths (15 to 30cm), soil compaction rate increased. The soil was \n\n\n\nmoderately hard as indicated by the total count/ total cumulative depth down to 50 cm which \n\n\n\nwas less than 34 drops. \n\n\n\nThe compaction rate for planting years 2001 to 2008 shows the same compaction \n\n\n\npattern as in years 1991 to 2000 (Figure 4). As we can see from the pattern, the total \n\n\n\ncumulative depth for plots 2001 to 2008 has a longer graph compared to the previous one, \n\n\n\nindicate that the soil within this year was more compacted. This could be attributed to the \n\n\n\ntrees being still young and not being able to accord a wide canopy to cover the soil as well as \n\n\n\nconsiderably less formation of forest litter. The least compact soil was at plot 1991 with a \n\n\n\ntotal cumulative depth 24 and the most compact soil was at plots 2003 and 2008, \n\n\n\nrespectively.  \n\n\n\nHasegawa (2008) employed soil hardness to determine the influence of soil \n\n\n\ncompaction on root development (i.e. soil penetrability). The crucial values of cone \n\n\n\nresistance that caused a slowdown in root growth vary from 1 to 1.7 MPa, whereas values \n\n\n\nthat inhibit root growth range from 3 to 4 MPa, according to Hasegawa (2008). Thus, the \n\n\n\nvertical distribution of soil hardness and the measured depths of root penetration in soil \n\n\n\nprofiles were used to assess the quality of the soil as a growing foundation; for example, if \n\n\n\nthe soil conditions encourage tree development (Hasegawa, 2008).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n140 \n\n\n\n\n\n\n\n \nFigure 3.  Soil compaction pattern at plots 1991 to 2000 \n\n\n\n\n\n\n\n\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\n7\n\n\n\n8\n\n\n\n9\n\n\n\n10\n\n\n\n1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34\n\n\n\nC\no\nm\n\n\n\np\nac\n\n\n\nti\no\nn\n R\n\n\n\nat\ne \n\n\n\n(c\nm\n\n\n\n)\n\n\n\nCumulative Depth (cm)\n\n\n\nDistribution of soil compaction at 1991 to 2000 plots\n\n\n\n1991\n\n\n\n1993\n\n\n\n1995\n\n\n\n1996\n\n\n\n1997\n\n\n\n1998\n\n\n\n1999\n\n\n\n2000\n\n\n\nSubsoilTopsoil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n141 \n\n\n\n\n\n\n\n \nFigure 4. Soil compaction pattern at plots 2001 to 2008 \n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\n7\n\n\n\n8\n\n\n\n9\n\n\n\n1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50\n\n\n\nC\no\nm\n\n\n\np\nac\n\n\n\nti\no\nn\n R\n\n\n\nat\ne \n\n\n\n(c\nm\n\n\n\n)\n\n\n\nCumulative Depth (cm)\n\n\n\nDistribution of soil compaction at 2001 - 2008 plots\n\n\n\n2001\n\n\n\n2002\n\n\n\n2003\n\n\n\n2004\n\n\n\n2005\n\n\n\n2006\n\n\n\n2007\n\n\n\n2008\n\n\n\nTopsoil Subsoil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n142 \n\n\n\n\n\n\n\n\n\n\n\nPrevious studies have found that surface pressure can cause soil particles to move closer \n\n\n\ntogether, thus reducing the volume of air voids (Craig 2004). Although compaction is inevitable \n\n\n\nand purposefully achieved by rollers on the construction sites to increase the bearing capacity of \n\n\n\nsoils and aggregates for road construction, any soil compaction is unwanted in forest stands. \n\n\n\nOther effects of soil compaction are the direct damage caused to roots by the action of machine \n\n\n\nwheels on the forest floor (Craig 2004). Such impacts on soil structure may considerably reduce \n\n\n\nvegetation growth. Physical fertility of soil is determined by the way in which the essential \n\n\n\ngrowth requirements of plants are satisfied. The storage and delivery of water, nutrients, and \n\n\n\noxygen for root absorption are all regulated by the physical quality of the soil (water infiltration \n\n\n\nrate, total plant-available water storage, air-filled porosity at the wettest drained state, structural \n\n\n\nstability to wetting, and salinity and sodicity balancing). These characteristics describe the \n\n\n\nstructure of the soil and its structural stability (Cass et al. 1996). \n\n\n\nNowadays, most large-scale forest operations require the use of heavy machinery to \n\n\n\nincrease safety and to be economically competitive. Such machinery may exert ground pressure \n\n\n\nreaching 300 kPa (Mungoven 1996) and used in harvesting operations such as \u201ccut to length\u201d \n\n\n\n(CTL) method. During mechanized forest operations, the highest degree of soil compaction on \n\n\n\nmachine operating trails typically occurs within the first few traffic passes after which additional \n\n\n\npasses continue to increase soil density but at a lower rate (Aguilera Estaben et al. 2018). \n\n\n\nTrafficking of both harvester and forwarder on top of brush covered machine-operating trails can \n\n\n\nreduce soil compaction and lower soil penetration resistance by dispersing applied loads to a \n\n\n\ngreater area (Suryatmojo 2014). \n\n\n\n\n\n\n\nSoil Chemical Properties for Rehabilitated Forest Plots \n\n\n\nChemical properties of soils between study plots are shown in Table 2. The results show that the \n\n\n\npHw and pHKCl for the plots are generally slightly acidic and range from pH 4.12 (plot 1997) to \n\n\n\npH 4.35 (plot 2005) and pH 3.60 (plot 1991) to pH 3.85 (plot 1995). Because nutrients are less \n\n\n\navailable to plants in acidic soils, serious plant nutritional deficiencies are common. Soil pH \n\n\n\naffects the availability of nutrients to plants. The availability of major plant nutrients such as \n\n\n\nnitrogen, phosphorous, potassium, sulphur, calcium, magnesium, and the trace element \n\n\n\nmolybdenum are reduced and may be insufficient in acidic soils. In acidic soils, nutrients may \n\n\n\nnot only be chemically less available to plants but may also be positionally less available due to \n\n\n\npoor root development. Plants are unable to investigate enough soil volume to compensate for \n\n\n\ndiminished chemical availability when root development is constrained (Chris Gazey 2018). \n\n\n\nOther than that, the results show that the OM in the soils ranges from 2.27 g kg-1 to 5.52 g \n\n\n\nkg-1. This indicates that the OM content in the soil is slightly moderately available. Under acidic \n\n\n\nconditions, H is a component of the humus carboxyl (-COOH). When a soil is limed and the \n\n\n\nacidity is reduced, the H+ from humic acids is extracted more readily and reacts with hydroxyl \n\n\n\n(OH-) to generate water. As the positively charged H is withdrawn, the carboxyl groups on the \n\n\n\nhumus create a negative charge. When the pH of a soil rises, the release of hydrogen from \n\n\n\ncarboxyl groups helps to buffer the rise in pH while also forming the CEC (negative charge) \n\n\n\n(Mielniczuk 1996). Decomposition of organic matter is a significant part of forest nutrient \n\n\n\ncycling because it controls the availability of nutrients to plants and, as a result, determines forest \n\n\n\nproduction (Xu et al. 2013). The pH of the soil has a significant impact on the breakdown of \n\n\n\norganic materials. The slower breakdown rates at high pH were unexpected, given that an earlier \n\n\n\nstudy had shown that decomposition occurs more quickly at lower pH (Chapin et al. 2002).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n143 \n\n\n\n\n\n\n\nTotal organic carbon (TOC) for the soil in all study plots ranged from 2.31 to 2.50 g kg-1. \n\n\n\nFor an Ultisol soil, the average organic carbon content of representative soils in relation to major \n\n\n\nland use for crop land is about 80 tons per hectare, secondary forest is about 180 tons per hectare \n\n\n\nand for primary forest, and it is about 240 tons per hectare, indicating that the TOC content in the \n\n\n\nrehabilitated forest plots is only moderate. Humus affects soil properties. As it slowly \n\n\n\ndecomposes, it colours the soil darker, increases soil aggregation and aggregate stability, \n\n\n\nincreases the CEC (the ability to attract and retain nutrients) and contributes N, P and other \n\n\n\nnutrients (Juma 1998). \n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nChemical properties of soils between rehabilitated plots \n\n\n\nPlot \n\n\n\nParameters \n\n\n\npHw pHKCl \n\n\n\nOM TOC CEC  \nExch. \n\n\n\nAl \nExch. \n\n\n\nK \nExch. \n\n\n\nCa \nExch. \nMg  \n\n\n\nExch. \nNa  \n\n\n\nAv. P  \n\n\n\n(g kg-1) (cmol+ kg-1) \n(mg \nkg-1) \n\n\n\n1991 4.21a 3.60a 5.52a 2.31a 4.71a 2.70abc 0.12ab 0.14a 0.09a 0.02ab 1.88a \n\n\n\n1993 4.18a 3.69a 4.52a 2.43a 3.54a 2.47bcd 0.06ab 0.13a 0.08a 0.02ab 1.32abc \n\n\n\n1995 4.21a 3.85a 3.86a 2.31a 4.68a 2.40bcd 0.07ab 0.16a 0.08a 0.02ab 1.31abc \n\n\n\n1996 4.19a 3.83a 3.25a 2.35a 3.26a 1.95d 0.06b 0.39a 0.07a 0.02ab 1.39abc \n\n\n\n1997 4.12a 3.75a 3.47a 2.34a 3.03a 2.24bcd 0.47a 0.17a 0.06a 0.04a 1.16bc \n\n\n\n1998 4.18a 3.73a 2.72a 2.35a 5.48a 2.32bcd 0.07ab 0.14a 0.10a 0.02ab 1.44abc \n\n\n\n1999 4.19a 3.73a 2.72a 2.32a 3.28a 2.21bcd 0.06b 0.20a 0.09a 0.02ab 1.52abc \n\n\n\n2000 4.24a 3.78a 2.23a 2.32a 4.40a 2.18bcd 0.34ab 0.21a 0.04a 0.02ab 1.31abc \n\n\n\n2001 4.22a 3.81a 2.34a 2.33a 2.52a 2.06cd 0.05b 0.20a 0.05a 0.02ab 1.30abc \n\n\n\n2002 4.23a 3.60a 3.53a 2.34a 3.15a 2.71abc 0.05b 0.37a 0.08a 0.02ab 1.44abc \n\n\n\n2003 4.19a 3.70a 2.97a 2.34a 3.64a 3.26a 0.05b 0.14a 0.05a 0.02ab 1.32abc \n\n\n\n2004 4.19a 3.72a 3.36a 2.36a 4.21a 2.54bcd 0.07ab 0.18a 0.04a 0.02ab 1.60ab \n\n\n\n2005 4.35a 3.75a 2.36a 2.35a 4.39a 2.30bcd 0.06b 0.27a 0.05a 0.03ab 1.32abc \n\n\n\n2006 4.19a 3.81a 2.27a 2.34a 3.90a 2.80ab 0.10ab 0.47a 0.13a 0.02ab 1.05bc \n\n\n\n2007 4.16a 3.82a 2.52a 2.33a 1.98a 2.73ab 0.05b 0.28a 0.04a 0.02ab 1.03bc \n\n\n\n2008 4.22a 3.78a 2.63a 2.50a 1.87a 2.63abc 0.17ab 0.28a 0.05a 0.02ab 0.90c \n\n\n\n  Note: Means with the same letter are not significantly different. \n\n\n\n\n\n\n\nSoil type, soil pH, and soil organic matter level are all factors that affect the cation \n\n\n\nexchange capacity (CEC). Sand, organic matter, silt, and clay particles all contribute to the \n\n\n\ncomposition of soils. Compared to clayey and silty soils, sand-rich soils have a lower cation-\n\n\n\nholding capacity. The CEC values for the study plot range from 1.87 cmol+ kg-1 (plot 2008) to \n\n\n\n5.48 cmol+ kg-1 (plot 1998). CEC is an important predictor of soil fertility since it evaluates a \n\n\n\nsoil's ability to store nutrients. Soils with a high CEC can hold more cations, making them \n\n\n\n(calcium, magnesium, and other cations) sufficient. Low CEC soils, on the other hand, are prone \n\n\n\nto cation deficiency. This low CEC values fits the case for plots 1998 and plot 2008. The CEC of \n\n\n\nvarious clay minerals and soil organic matter varies with pH. The CEC is lowest in soils with a \n\n\n\npH of 3.5 to 4.0 and rises as the pH of an acid soil is increased by liming. It is also worth noting \n\n\n\nthat at low pH, some positive charges may occur in specific soil mineral surfaces. Anions \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n144 \n\n\n\n\n\n\n\n(negatively charged ions) like chloride (Cl-) and sulphate are retained by these positive charges \n\n\n\n(SO4\n2-) (Leticia et al. 2008). \n\n\n\nThe exchangeable cations, calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and \n\n\n\npotassium (K+) are the primary ions linked to CEC in soils and are referred to as the base cations \n\n\n\n(Rayment and Higginson 1992). These cations are replaced by H+, Al3+, and Mn2+ when soils get \n\n\n\nmore acidic, and conventional methods give CEC values that are substantially higher than that in \n\n\n\nthe field (McKenzie et al. 2004). Non-exchangeable bases are unlikely to be useful to plants as \n\n\n\nnutrient sources, but their progressive release helps to restore the supply of exchangeable bases \n\n\n\nin the soil. If overall supply of exchangeable calcium, magnesium, and potassium in soils, \n\n\n\nrepresenti the total supply of these bases, shortages in these bases for plant growth would appear \n\n\n\nin many soils within a few years (Day and Ludeke 1993). \n\n\n\nThe pH of the soil has an impact on phosphorus availability. Phosphorus reacts with iron \n\n\n\nand aluminium in acidic soils. This renders it inaccessible to plants. The availability of P in the \n\n\n\nsoils is shown in Table 1 for each study plot. Organic matter decomposes faster in warm humid \n\n\n\nregions and slower in cool dry climates, releasing P. Phosphorus is released more quickly in \n\n\n\nwell-aerated soils (where oxygen levels are higher) and considerably more slowly in saturated, \n\n\n\nmoist soils. Soils with an intrinsic pH of 6 to 7.5 are excellent for P-availability, but pH values \n\n\n\nbelow 5.5 and between 7.5 and 8.5 limit P-availability to plants due to aluminium, iron, or \n\n\n\ncalcium fixation, all of which are frequently linked with soil parent materials (Ismat et al. 2018). \n\n\n\n\n\n\n\nIdentifying Important Soil Properties in Relation to Soil Fertility in Rehabilitated Forests \n\n\n\nAt different years following planting in rehabilitated forest plots, Principal Component Analysis \n\n\n\n(PCA) revealed four major contributions for selected soil physicochemical parameters (Table 3). \n\n\n\nIn general, four components (PC1, PC2, PC3, and PC4) accounted for 63% of total variability, \n\n\n\nand each component represented a series of variables, making analysis and interpretation easier.\n\n\n\n The results from the topsoils showed that pH, OM, exchangeable Mg, CEC, and available \n\n\n\nP all contributed positively to factor loading in PC1. PC2 and PC4 were dominated by \n\n\n\nexchangeable bases and PC3 contributed to acidity and organic carbon of the soils. The presence \n\n\n\nof exchangeable Al in PC3 suggests that this cation has a substantial impact on soil quality in the \n\n\n\nsoil ecosystem. Ca and Mg uptake can be inhibited by exchangeable Al (de Wit et al. 2010) \n\n\n\nwhich limit fine root development and contribute to soil nutrient imbalances (Angelica et al. \n\n\n\n2012). \n\n\n\nThus, the information derived from the PCA allows the development of simplified \n\n\n\nindicators that can represent more complex variability of soil physico-chemical properties (Aiza-\n\n\n\nShaliha et al. 2013; Arifin et al. 2008b). Organic matter and cation exchange capacity have a \n\n\n\nstrong positive association, according to PCA data, which explains how nutrients in the soil are \n\n\n\nstored in organic matter. The decomposition of organic materials by soil microbes is aided by a \n\n\n\ncertain range of relative humidity or carbon content in the soil (Van Eekeren et al. 2008). As a \n\n\n\nresult, tree roots will be able to absorb nutrients generated by organic debris, allowing them to \n\n\n\ndevelop and mature. The availability of cations is reduced by soil acidity, which is reflected in \n\n\n\nexchangeable Al. CEC and organic matter content have a linear relationship, indicating that \n\n\n\norganic matter and CEC are at their optimal levels for soil nutrient supply and retention. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n145 \n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nSoil parameters used for PCA and results of PCA of the rehabilitated forest  \n\n\n\n PC1 PC2 PC3 PC4 \n\n\n\n+ve relationship pHw, CEC, \n\n\n\nExch. Mg, \nAvailable P and \n\n\n\nOM \n\n\n\nExch. K, Exch. \n\n\n\nNa \n\n\n\npHKCl, Exch. Al, \n\n\n\nTOC \n\n\n\nExch. Ca,  \n\n\n\nContribution pH, CEC and \norganic matter \n\n\n\ncontent \n\n\n\nExchangeable \nbases \n\n\n\nAcidity and \norganic carbon \n\n\n\nExchangeable \nbases \n\n\n\nTotal 3.07 1.41 1.35 1.13 \nVariance (%) 27.89 12.80 12.27 10.27 \n\n\n\nCumulative (%) 27.89 40.691 52.96 63.23 \n\n\n\n\n\n\n\n\n\n\n\nRelationship between Selected Soil Parameters \n\n\n\n\n\n\n\nTable 4 shows the results of correlation analysis among selected soil metrics. The correlation \n\n\n\nanalysis of organic matter (OM) with cation exchange capacity (CEC) and exchangeable Mg for \n\n\n\nsurface soils revealed a moderately positive correlation, indicating that negative charges derived \n\n\n\nfrom organic matter play an important role in cation retention capacity, nutrient supply, and soil \n\n\n\nfertility status of tropical soils like the one studied. The positive correlation indicate that they \n\n\n\nhave an inversely proportional relationship. Jean-Fran and Chevalier (2006) found a favourable \n\n\n\nassociation between OM and selected soil characteristics (soil acidity and organic matter \n\n\n\naccumulation) in a study conducted in Loiret, France. Tilahun (2007) and Getahun et al. (2014) \n\n\n\nobserved that OM is strongly and positively linked with cation exchange capacity, which is \n\n\n\nsimilar to our findings. According to Aubert et al. (2004), old-growth forests demonstrate that \n\n\n\nsoil acidification occurs as a result of tree development, which has an impact on soil properties, \n\n\n\nespecially when the litter is low in nutrients and high in secondary metabolites. \n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nCorrelation between soil parameters of the soil \n\n\n\nNo. Parameters Correlation coefficient (R) \n\n\n\n1 Cation exchange capacity x exchangeable Mg 0.654* \n\n\n\n2 Cation exchange capacity x organic matter 0.691* \n\n\n\n3 Exchangeable Na x exchangeable K 0.554* \n\n\n\nNote: *, Significant difference (p<0.05) level using Pearson correlation \n\n\n\n\n\n\n\nAssessing Soil Fertility Status of Rehabilitated Forests Using Soil Indices \n\n\n\nTo determine the overall impact of a rehabilitation program on soil quality, the SFI and SEF for \n\n\n\nvarious years of planting were determined (Figure 5).  Slightly decreased SFI values were \n\n\n\nobserved towards the planting year. The greater fertility of soil in a rehabilitated forest planted in \n\n\n\nyear 1991 (9.26) is attributed to the greatest accumulation of organic matter from litter fall. \n\n\n\nMeanwhile the least fertile soil was observed in rehabilitated forest planted in year 2007 (5.35). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n146 \n\n\n\n\n\n\n\n1991 1993 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008\n\n\n\nSFI 9.26 7.82 7.29 7.40 7.21 6.33 6.57 6.19 6.10 6.99 5.46 6.90 6.11 5.41 5.35 5.62\n\n\n\nSEF 3.80 3.78 4.15 5.16 5.66 4.43 4.57 5.20 4.57 4.76 3.84 4.13 4.67 5.27 4.49 4.84\n\n\n\n0.00\n\n\n\n1.00\n\n\n\n2.00\n\n\n\n3.00\n\n\n\n4.00\n\n\n\n5.00\n\n\n\n6.00\n\n\n\n7.00\n\n\n\n8.00\n\n\n\n9.00\n\n\n\n10.00\n\n\n\nAs the surface soils were covered with Imperata cylindrica, the SFI value at plot 2007 was the \n\n\n\nlowest among the plots, possibly due to the high nitrogen intake of this species. The SFI value of \n\n\n\nrehabilitated forests declines as the planting year approaches, implying that the nutrient content \n\n\n\nhas been continually absorbed by the tree or bush in an acidic soil with a large supply of \n\n\n\nexchangeable Al restricting the availability of nutrients (Wasli et al. 2011; Arifin et al. 2008a). \n\n\n\nThe SFI results show increasing values when the forest stand gets older (about 30 years \n\n\n\nafter planting), indicating that the soil in plots are reaching optimum fertility.  The index uses \n\n\n\nvalues of pH, organic matter, phosphorus, potassium, calcium, magnesium and aluminium \n\n\n\n(inverse value). Chemical characteristics of the soil appear to be an important factor in \n\n\n\ndistinguishing between rates of restoration succession in various planting years. This is \n\n\n\nespecially true when land use is taken into account as a factor influencing regrowth rates. \n\n\n\nNutrient stock is generally concentrated in the vegetation and the organic horizon of the soil \n\n\n\nprofile in nutrient-poor soils (Ultisol), rather than in the mineral soil itself (Moran et al. 2000). \n\n\n\nMoreover, the SEF values of the rehabilitated forests in the planting years showed an \n\n\n\nundulating trend.  SEF was found to be greater (5.66) for plot 1997, followed by plot 2006 (5.27) \n\n\n\nand plot 2000 (5.20), and least for plot 1993 (3.78). For rehabilitated forests, the SEF value of \n\n\n\nless than five indicates very low soil fertility (Lu et al. 2002). Some of the planting plots (Plots \n\n\n\n1996, 1997, 2000 and 2006) showed a SEF value of more than 5, indicating that these plots had \n\n\n\nreached optimum soil fertility status. With reference to the results obtained for the chemical \n\n\n\nproperties of the soil, the availability of exchangeable calcium, potassium and magnesium, and \n\n\n\norganic matter were high and contributed to the high SEF value. In contrast, the high \n\n\n\nconcentration of Al in other plots might have restricted the availability of exchangeable bases. \n\n\n\nWhen SEF was calculated for soil depth, the SEF values were found to decline in all land \n\n\n\nuses as soil depth increased (Panwar et al. 2011). Based on this finding, 30 years after restoring  \n\n\n\n\n\n\n\nFigure 5. Soil Fertility Index (SFI) and Soil Evaluation Factor SEF) between plots for \n\n\n\nrehabilitated forests at surface soils \n\n\n\n\n\n\n\ndegraded forest land by planting indigenous dipterocarp species, it was still found to be  \n\n\n\ninsufficient to restore soil fertility to natural forest levels. However, in our study, some of the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 133-150 \n\n\n\n\n\n\n\n147 \n\n\n\n\n\n\n\nplanting plots showed that the soil condition was moving towards reaching optimum fertility \n\n\n\nstatus. \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\nSoil compaction has an impact on the physical qualities of the soil, plant growth, root growth, \n\n\n\nand crop output. Compaction damages the physical environment of the soil, which affects not \n\n\n\nonly shoots but also root growth and development. Many plant nutrients (such as exchangeable \n\n\n\ncations and available P) are highly influenced by soil pH and cation exchange capacity. Organic \n\n\n\nmatter is an important component of soils because it has an impact on physical and chemical \n\n\n\ncharacteristics of soils. Levels of exchangeable bases rise with high pH value. If significant \n\n\n\namounts of exchangeable Al are found in soils with pH below 5.2, it can inhibit the availability \n\n\n\nof the nutrients. The availability of organic matter breakdown, exchangeable bases, and cation \n\n\n\nexchange capacity in the soil are all influenced by the state of pH in the soil. Organic matter, pH, \n\n\n\navailable P, exchangeable Mg, and cation exchange capacity all show a significant positive \n\n\n\nassociation, indicating that the majority of nutrients are stored in surface soils. Soil organic \n\n\n\nmatter has a significant connection with CEC and exchangeable Mg. This indicates that soil \n\n\n\norganic matter and cation exchange capacity have a direct proportional connection for the \n\n\n\navailability of the exchangeable bases. Our study shows that rehabilitating forests over arable \n\n\n\nland helped increase OM content, exchangeable cations, accessible minerals, micronutrients, and \n\n\n\nmicrobial activity. SFI and SEF have high substantial relationships with soil chemical and \n\n\n\nbiological characteristics, indicating that these two indices can be employed as soil quality \n\n\n\nindicators. The period of time it takes for rehabilitated forests to revert to natural forest will be \n\n\n\ndetermined by a variety of environmental factors, including climate, disturbance severity and \n\n\n\nsize, and distance from seed sources of natural forest species. \n\n\n\n\n\n\n\n\n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe researchers are grateful to the staff of the Department of Crop Production, Universiti Putra \n\n\n\nMalaysia, Bintulu Sarawak Campus, Department of Land Management, Faculty of Agriculture \n\n\n\nand Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, \n\n\n\nUniversiti Putra Malaysia for their assistance during field sampling and laboratory analysis. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\nAguilera Esteban, D.A., Z.M. de Souza, C.A. Tormena, L.H. 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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 111-134 (2016)                         Malaysian Society of Soil Science\n\n\n\nA Molybdenum-reducing Bacillus sp. Strain Zeid 14 in Soils \nfrom Sudan that Could Grow on Amides and Acetonitrile\n\n\n\nMohd Adnan, A.S.1, Abu Zeid, I.M.2, Ahmad, S.A.3, Halmi, \nM.I.E.4*, Abdullah, S.R.S.4, Masdor, N.A.5, Shukor, M.S.1 and \n\n\n\nShukor, M.Y.3\n\n\n\n1Snoc International Sdn Bhd, Lot 343, Jalan 7/16, Kawasan Perindustrian Nilai 7, \nInland Port, 71800, Negeri Sembilan, Malaysia.\n\n\n\n2Department of Biological Sciences, Faculty of Sciences, King Abdulaziz University, \nP.O. Box 139109, Jeddah 21323, Saudi Arabia\n\n\n\n3Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, \nUniversiti Putra Malaysia, UPM 43400 Serdang, Selangor, Malaysia.\n\n\n\n4Department of Chemical Engineering and Process, Faculty of Engineering and \nBuilt Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi,\n\n\n\nSelangor, Malaysia.\n5Biotechnology Research Centre, Malaysian Agricultural Research and Development \n\n\n\nInstitute, MARDI Headquarters, Serdang, P.O. Box 12301,\n50774 Kuala Lumpur, Malaysia.\n\n\n\nABSTRACT\nAgricultural and industrial activities contribute most to pollutants found globally, \nand bioremediation of these pollutants is being intensely sought. We have isolated \na molybdenum-reducing bacterium from agricultural soil for bioremediation \npurposes. The bacterium was grown in low phosphate medium supplemented with \nmolybdate in a microplate format. The molybdenum-reducing bacterium was then \nfurther screened for amide-degrading properties. The bacterium was able to use \nacrylamide as a source of electron donor for reduction, and was able to grow on \nacrylamide, acetamide and acetonitrile. The growth parameters obtained according \nto the modified Gompertz model were lag periods of 0.468, 0.979 and 1.53 d and \nmaximum specific growth rates of 1.165, 0.932, 0.842 d-1 for acrylamide, acetamide \nand acetonitrile respectively. Optimal conditions for molybdate reduction included \nglucose, pH between 6.0 and 6.8, temperature between 25o C and 34o C, and \nphosphate and molybdate concentrations between 5 and 7.5 mM and 10 and 20 \nmM, respectively. The Mo-blue exhibited a unique absorption spectrum closely \nresembling a reduced phosphomolybdate. Mo-blue production was inhibited by \nthe heavy metals copper, mercury, silver, chromium and cadmium. The bacterium \nwas identified as Bacillus sp. strain Zeid 14. The bacterium will be very useful for \nbioremediation of sites contaminated with molybdenum and amides.\n\n\n\nKeywords: Molybdenum-reducing bacterium, molybdenum blue, Bacillus \nsp., acrylamide, acetonitrile\n\n\n\n___________________\n*Corresponding author : E-mail: zuanfendi88@gmail.com/ zuanfendi@upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016112\n\n\n\nMohd Adnan et al.\n\n\n\nINTRODUCTION\nManufacturing and prospecting exercises are the main contributors of molybdenum \npollution of the natural environment. In Armenia, waste materials, seepages and \neffluents from the molybdenum-copper mine in Alaverdi  have brought on the \npollution of a territory approximately 300 square kilometres in size (Simeonov et \nal., 2011). Metal and molybdenum mining activities in southern Colorado (USA) \ngenerated molybdenum pollution in surrounding areas with molybdenum level \nreaching 2,000 mg/L in the soils. The monitoring authority for e.g. The Water \nQuality Control Commission had stated that portions of about several miles of \nthe Red River are so severely polluted that it is regarded as dead (LeGendre \nand Runnells, 1975). Molybdenum is also a by-product of a uranium mine in \nNorth Cave Hills South Dakota, (USA). At the most polluted site recorded in \n2004, the molybdenum content of 6,550 mg kg-1 was reported (Stone and Stetler, \n2008). Activities of oil shale exploration can also lead to molybdenum pollution. \nIn Jordan, soils from such an exploration exhibited molybdenum concentration \nof 11.7 mg kg-1 (Al Kuisi et al., 2015). In Malaysia, molybdenum is produced \non a small scale from the by-product of copper mining. The Mamut copper \nmine in Sabah, is an example. There are several reports on a number of cases \nof contamination caused by the accidental breakage of the metal-carrying pipe \nleading to episodic contamination of vast agricultural areas, which include the \nRanau River (Kosaka and Wakita, 1978). In Batu Hijau, Sumbawa, Indonesia, \ncopper-gold-molybdenum mining from the copper-gold-molybdenum porphyry \ndeposit is recorded as being  responsible for reduced fish population. As the mine \ndumps approximately several million tonnes of waste tailings into the ocean every \nyear, a slow pollution of the sea is inevitable, and probably contributed for the \nreduced fish population observed (Apte and Kwong, 2003; Angel et al., 2013). \n\n\n\nAside from chemical and physical removal of molybdenum, bacterial \nreductions into non-toxic forms have been suggested as alternatives, and include \nexamples such as the reduction of soluble molybdate into either insoluble \nmolybdenum disulphide (Tucker et al., 1997) or to the colloidal molybdenum \nblue (Ghani et al., 1993). Reduction to molybdenum disulphide requires fully \nanaerobic conditions, and two candidates that are often utilised for this purpose are \nDesulfovibrio and Desulfotomaculum (Biswas et al., 2009).  Columns packed with \npolyacrylamide-immobilised sulphate-reducing bacteria have been attempted, \nand show reasonable success; however, the production of toxic hydrogen sulphide \ngas presents a challenge for such a process to be a commercial success (Tucker \net al., 1997). The reduction of molybdate to molybdenum blue is an alternative \napproach that has been recommended as a bioremediation tool (Ghani et al., \n1993). Several advantages of this approach include observations such as the Mo-\nblue product being formed under low oxygen tension which is colloidal and does \nnot pass through dialysis tubing easily, allowing some means of immobilisation \nand removal (Halmi et al., 2014b). Molybdenum in the form of molybdate \n(sodium or ammonium salts) reduced to molybdenum blue is a phenomenon first \nmentioned in E. coli about more than one hundred years ago in 1896 (Capaldi and \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 113\n\n\n\nPropagation of Molybdenum-reducing Bacillus sp\n\n\n\nProskauer, 1896). This was followed by mention in the last century in 1939 (Jan \n1939). It was reported again in 1985 in E. coli  K12 (Campbell et al., 1985), and \nin 1993 in Enterobacter cloacae strain 48 (Ghani et al., 1993). The potential of \nthis occurrence for use in the bioremediation of molybdenum was first realised \nby Ghani et al. (1993). Several bacterial genera that are potential candidates for \nbioremoval have been reported and include bacteria from the genera of  Bacillus \n(Abo-Shakeer et al., 2013; Othman et al., 2013), Klebsiella  (Lim et al., 2012; \nHalmi et al., 2013; Masdor et al., 2015), Acinetobacter (Shukor et al., 2010b), \nPseudomonas (Shukor et al., 2010a; Ahmad et al., 2013), Enterobacter (Shukor \net al., 2009c) and Serratia (Shukor et al., 2008a; Rahman et al., 2009; Yunus et \nal., 2009; Shukor et al., 2009d).\n\n\n\nAmides and nitriles are toxic xenobiotics. An amide such as acrylamide is \na monomer for polyacrylamide, a polymer. Amongst its many uses include as a \nsewage-flocculating agent, stabilising tunnels and dams, and as industrial adhesives \n(Rahim et al., 2012). Acute cases of acrylamide toxicity has been reported causing \ndeath to fish and cows in Sweden (Svensson et al., 2003). The formulation of the \npesticide glyphosate uses 20-30% polyacrylamide as a dispersing agent (Smith et \nal., 1996), and this could be a substantial source of acrylamide pollution in soils and \nrun-offs. Nitriles are cyanide-substituted carboxylic acids. Acetonitrile (CH3CN) \nis a nitrile compound produced as a by-product of acrylonitrile. It is widely used \nas a precursor for various products. Acetonitrile is being produced to the tune \nof more than 40,000 tons annually (Hakansson et al., 2005). When ingested, \nacetonitrile is converted to toxic cyanide. Its pollution has been documented and \nresearch on its removal via bioremediation is being carried out globally.  The \nacrylonitrile-acrylamide industries are known sources of acrylamide pollution \nwith levels as high as 1 g/L have been reported (Rogacheva and Ignatov, 2001). \nMicrobial degradation of amides and nitriles has been explored as a potential \ntool for their bioremediation. To date, numerous microorganisms have been \nisolated that are capable of degrading these compounds (Shukor et al., 2009a; \nShukor et al., 2009b; Buranasilp and Charoenpanich, 2011; Rahim et al., 2012; \nChandrashekar et al., 2014).\n\n\n\nHerein, a molybdenum-reducing bacterium with the novel capacity to use \nacrylamide as an electron donor for molybdenum reduction as well as able to use \nacrylamide, acetamide and acetonitrile as carbon sources for growth is reported. \nWe also model the growth of the bacterium on these compounds using the modified \nGompertz model and found the model can describe the growth curves adequately. \nThe characteristics of this bacterium with its multiple detoxification capability are \nvery useful for bioremediation.\n\n\n\nMATERIALS AND METHODS\n\n\n\nIsolation, Identification and Maintenance of Molybdenum-Reducing Bacterium\nAn industrially contaminated land in the city of Juba, South Sudan, Africa was \nthe site of soil sampling carried out in 2012. Isolation of molybdenum-reducing \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016114\n\n\n\nbacteria utilised a minimal salts media (MSM) with the phosphate concentration \nset at 5 mM and sodium molybdate at 10 mM. A soil suspension (1 g soil in 10 \nml deionised water) was thoroughly mixed, and 0.1 mL of the soil suspension \nwas then spread onto a petri dish containing agar media (w/v) as follows: \nNa2MoO4\u20222H2O (0.242 % or 10 mM), yeast extract (0.5%), NaCl (0.5%), agar \n(1.5%), MgSO4\u20227H2O (0.05%), (NH4)2\u2022SO4 (0.3%), glucose (1%), and Na2HPO4 \n(0.071% or 5 mM). The pH of the media was adjusted to pH 6.5 (Masdor et \nal., 2015). This media is known as a low phosphate molybdate media or LPM. \nAfter 48 h of incubation at room temperature, several white and ten blue colonies \nappeared on the plate. The ten isolates were repeatedly transferred onto other \nLPM agar plates to purify the bacteria. A quantification of the Mo-blue production \nwas carried out in 100 mL liquid culture (LPM) to select the best isolate. Mo-blue \nproduction was quantified at 865 nm with an extinction coefficient of 16.3 mM-1 \ncm-1 to choose the best isolate. A scanning of the molybdenum blue absorption \nspectrum was carried out between 400 and 900 nm (UV-spectrophotometer, \nShimadzu 1201), and LPM as the baseline correction. Identification of the Mo-\nreducing bacterium was carried out based on phenotypical and biochemical \nmethods as outlined in Bergey\u2019s Manual of Determinative Bacteriology (Holt et \nal., 1994). The results were computed into the ABIS online system (Costin and \nIonut, 2015).\n\n\n\nPreparation of Bacterial Resting Cells\nThe characterisation of molybdenum reduction in a microtiter format including \nthe concentrations of phosphate, molybdate, effects of carbon sources, pH, \ntemperature and heavy metals were carried out at room temperature as done by \nShukor and Shukor (2014). Briefly, bacterial cells were grown aerobically on an \norbital shaker (120 rpm) in several 250 mL shake flasks in a volume of 1 L. The \nmedia utilised was the high phosphate media (HPM) with the only difference  in \nthe LPM being the setting  of the phosphate concentration at 100 mM. This was \ncarried out to prevent bacterial aggregations to molybdenum blue. Bacterial cells \nwere first centrifuged at 10,000 g for 10 min at 4o C. The bacterial pellets formed \nwere rinsed twice with deionised water. A resuspension of the pellet in 20 mL of \nLPM was carried out next, but with glucose omitted. Suitable modifications to the \nLPM media were conducted to accommodate the various characterisation methods \nmentioned above. About 180 \u00b5L of the media was pipetted into the wells of a flat-\nbottom microplate. Sterile glucose (20 \u00b5L) was added to the final concentration of \n1.0 % (w/v). The final volume was 200 \u00b5L. After the microplate was sealed, it was \nincubated at room temperature for two days. Mo-blue production was determined \nat 750 nm on a BioRad Microtiter Plate reader (Model No. 680, Richmond, CA). \nThe extinction coefficient of 11.69 mM-1 cm-1 at 750 nm was utilised to quantify \nMo-blue production (Shukor and Shukor, 2014). Heavy metals were sourced from \nAtomic Absorption Spectrometry standard solutions (Merck KGaA, Darmstadt, \nGermany). \n\n\n\nMohd Adnan et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 115\n\n\n\nTest of Amides and Nitriles as Sources of Electron Donor or Growth\nThe capacity of various amides and nitriles to aid molybdenum reduction was \ncarried out by substituting glucose from the LPM media with nicotinamide, \nacetamide, iodoacetamide, acrylamide, propionamide, acetamide, acetonitrile, \nacrylonitrile 2-chloroacetamide, and benzonitrile to the final concentration of \n1,000 mg/L (Arif et al., 2013). Then 200 \u00b5L of the media was added into the \nmicroplate wells with 50 \u00b5L of resting cells suspension. The microplate was \nincubated at room temperature for three days and Mo-blue production was \ndetermined at 750 nm. The ability of the above compounds to support the growth \nof this bacterium was carried out by using the media below minus molybdate, and \nreplacing glucose with the xenobiotics at 1,000 mg/L in a volume of 50 \u00b5L. The \ningredients of the growth media (LPM) at pH 7.0 were as follows: NaNO3 (0.2%), \nNaCl (0.5%), MgSO4\u20227H2O (0.05%), (NH4)2\u2022SO4 (0.3%), yeast extract (0.01%) \nand Na2HPO4 (0.705% or 50 mM). Then 200 \u00b5L of the media was added into the \nmicroplate wells and mixed with 50 \u00b5L of resting cells suspension. The increase \nin bacterial growth was measured at 600 nm after three days of incubation at room \ntemperature.\n\n\n\nMathematical Modelling of Bacterial Growth on Zenobiotics\nBacterial growth on these xenobiotics was modeled using the modified Gompertz \nmodel (eqn. 1) as this model is frequently used to model microbial growth \n(Zwietering et al., 1990).\n\n\n\n       \n (Equation 1)\n\n\n\nwhere A=bacterial growth at lower asymptote; \u00b5m= maximum specific bacterial \ngrowth rate, \u03bb=lag time, e = exponent (2.718281828) and t = sampling time.\n\n\n\nHPLC Analysis of Acrylamide Degradation\nThe confirmation of acrylamide degradation was carried out by HPLC analysis \n(Agilent,1100 series) with a manual RheodyneTM sample injector of the \ndegradation product, which is an acrylic acid production  or producer?? (Shukor et \nal.,2009b) with slight modifications. Bacterial growth on acrylamide was carried \nout aerobically at room temperature for three days by shaking on an orbital shaker \nat 120 rpm. The media was the high phosphate media supplemented with 1,000 \nmg/L acrylamide in a 100 ml media. A suitable aliquot of the bacterial culture \nwas centrifuged at 10,000 g for 10 min. A 20 \u00b5l sample filtered through a 0.45 \n\u00b5m polytetrafluoroethylene, (PTFE) filter, was injected into the HPLC system \non a column (Supelco Discovery\u00ae HS C18, 4.6 x 150 mm, particle size 5 \u00b5m; \nSigma-Aldrich Co., USA). The mobile phase was ultra pure water with a flow \nrate of 1 mL/min. Acrylic acid, analytical grade from Sigma (St; Louis U.S.A.) \nwas utilised as a standard. Detection was performed at 196 nm using a reference \nwavelength of 360 nm.\n\n\n\nPropagation of Molybdenum-reducing Bacillus sp\n\n\n\n 4 \n\n\n\nBacterial cells were first centrifuged at 10,000 g for 10 min at 4oC. The bacterial pellets \nformed were rinsed twice with deionised water. A resuspension of the pellet in 20 mL of \nLPM was carried out next, but with glucose omitted. Suitable modifications to the LPM \nmedia were conducted to accommodate the various characterisation methods mentioned \nabove. About 180 \u00b5L of the media was pipetted into the wells of a flat-bottom microplate. \nSterile glucose (20 \u00b5L) was added to the final concentration of 1.0 % (w/v). The final volume \nwas 200 \u00b5L. After the microplate was sealed, it was incubated at room temperature for two \ndays. Mo-blue production was determined at 750 nm on a BioRad Microtiter Plate reader \n(Model No. 680, Richmond, CA). The extinction coefficient of 11.69 mM.-1.cm-1 at 750 nm \nwas utilised to quantify Mo-blue production (Shukor and Shukor, 2014). Heavy metals were \nsourced from Atomic Absorption Spectrometry standard solutions (Merck KGaA, Darmstadt, \nGermany).  \n \nTest of Amides and Nitriles as Sources of Electron Donor or Growth \nThe capacity of various amides and nitriles to aid molybdenum reduction was carried out by \nsubstituting glucose from the LPM media with nicotinamide, acetamide, iodoacetamide, \nacrylamide, propionamide, acetamide, acetonitrile, acrylonitrile 2-chloroacetamide, and \nbenzonitrile to the final concentration of 1,000 mg/L (Arif et al., 2013). Then 200 \u00b5L of the \nmedia was added into the microplate wells with 50 \u00b5L of resting cells suspension. The \nmicroplate was incubated at room temperature for three days and Mo-blue production was \ndetermined at 750 nm. The ability of the above compounds to support the growth of this \nbacterium was carried out by using the media below minus molybdate, and replacing glucose \nwith the xenobiotics at 1,000 mg/L in a volume of 50 \u00b5L. The ingredients of the growth \nmedia (LPM) at pH 7.0 were as follows: NaNO3 (0.2%), NaCl (0.5%), MgSO4\u20227H2O \n(0.05%), (NH4)2\u2022SO4 (0.3%), yeast extract (0.01%) and Na2HPO4 (0.705% or 50 mM). Then \n200 \u00b5L of the media was added into the microplate wells and mixed with 50 \u00b5L of resting \ncells suspension. The increase in bacterial growth was measured at 600 nm after three days of \nincubation at room temperature. \n \nMathematical Modelling of Bacterial Growth on Zenobiotics \nBacterial growth on these xenobiotics was modeled using the modified Gompertz model \n(eqn. 1) as this model is frequently used to model microbial growth (Zwietering et al., 1990). \n\n\n\n\uf0fe\n\uf0fd\n\uf0fc\n\n\n\n\uf0ee\n\uf0ed\n\uf0ec\n\n\n\n\uf0fa\n\uf0fb\n\n\n\n\uf0f9\n\uf0ea\n\uf0eb\n\n\n\n\uf0e9\n\uf02b\uf02d\uf02d\uf03d 1)(expexp t\n\n\n\nA\neAy m \uf06c\uf06d\n\n\n\n      (Equation 1) \nwhere A=bacterial growth at lower asymptote; \u00b5m= maximum specific bacterial growth rate, \n\u03bb=lag time, e = exponent (2.718281828) and t = sampling time. \n \nHPLC Analysis of Acrylamide Degradation \nThe confirmation of acrylamide degradation was carried out by HPLC analysis (Agilent,1100 \nseries) with a manual RheodyneTM sample injector of the degradation product, which is an \nacrylic acid production  or producer?? (Shukor et al.,2009b) with slight modifications. \nBacterial growth on acrylamide was carried out aerobically at room temperature for three \ndays by shaking on an orbital shaker at 120 rpm. The media was the high phosphate media \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016116\n\n\n\nRESULTS AND DISCUSSION\nNumerous Mo-reducing bacteria have been reported including two SDS degraders \n(Halmi et al., 2013; Masdor et al., 2015) as well as an Antarctic isolate (Table 1). The \nmicrotiter plate format allows a high throughput characterisation format (Iyamu \net al., 2008; Shukor and Shukor 2014; Masdor et al., 2015). Characterisation the \nbacterium Enterobacter cloacae strain 48 (Ghani et al., 1993)  utilise resting cells \nor whole cells. Other bacterial characterisation works in metal reduction such as \nin chromate (Llovera et al., 1993), selenate (Losi and Frankenberger Jr., 1997) \nand vanadate (Carpentier et al., 2005) also capitalize on the use of resting cells. \nThe use of resting cells bypasses the initial stage of the growth process that is \nnormally affected by toxic xenobiotics. \n\n\n\n 2 \n\n\n\nTable 1. Characteristics of Mo-reducing bacteria isolated to date. \n \n\n\n\nBacteria Optimal \nMolybdate \n\n\n\n(mM) \n\n\n\nOptimal \nPhosphate \n\n\n\n(mM) \n\n\n\nHeavy metals \ninhibition \n\n\n\nOptimal C source Author \n\n\n\nKlebsiella oxytoca \nstrain Aft-7 \n\n\n\n5-20 5-7.5 Cu2+, Ag+, Hg2+ glucose (Masdor et al., \n2015) \n\n\n\nBacillus sp. strain \nA.rzi \n\n\n\n50 4 Cd2+, Cr6+, \nCu2+,Ag+, Pb2+, \nHg2+, Co2+,Zn2+  \n\n\n\nglucose (Othman et al., \n2013) \n\n\n\nBacillus pumilus \nstrain lbna \n\n\n\n40 2.5-5 As3+, Pb2+, Zn2+, \nCd2+, Cr6+, Hg2+, \n\n\n\nCu2+ \n\n\n\nglucose (Abo-Shakeer et \nal., 2013) \n\n\n\nPseudomonas sp. \nstrain DRY1 \n\n\n\n30-50 5 Cd2+, Cr6+, \nCu2+,Ag+, Pb2+, \n\n\n\nHg2+ \n\n\n\nglucose (Ahmad et al., \n2013) \n\n\n\nKlebsiella oxytoca \nstrain hkeem \n\n\n\n80 4.5 Cu2+, Ag+, Hg2+ fructose (Lim et al., 2012) \n\n\n\nAcinetobacter \ncalcoaceticus \nstrain Dr.Y12 \n\n\n\n20 5 Cd2+, Cr6+, Cu2+, \nPb2+, Hg2+ \n\n\n\nglucose (Shukor et al., \n2010b) \n\n\n\nS. marcescens \nstrain Dr.Y9 \n\n\n\n20 5 Cr6+, Cu2+, Ag+, \nHg2+ \n\n\n\nsucrose (Yunus et al., \n2009) \n\n\n\nSerratia sp. strain \nDr.Y8 \n\n\n\n50 5 Cr, Cu, Ag, Hg sucrose (Shukor et al., \n2009d) \n\n\n\nPseudomonas sp. \nstrain DRY2 \n\n\n\n15-20 5 Cr6+, Cu2+, Pb2+, \nHg2+ \n\n\n\nglucose (Shukor et al., \n2010a) \n\n\n\nSerratia sp. strain \nDr.Y5 \n\n\n\n30 5 n.a. glucose (Rahman et al., \n2009) \n\n\n\nEnterobacter sp. \nstrain Dr.Y13 \n\n\n\n25-50 5 Cr6+, Cd2+, Cu2+, \nAg+, Hg2+ \n\n\n\nglucose (Shukor et al., \n2009c) \n\n\n\nSerratia \nmarcescens strain \nDRY6 \n\n\n\n15-25 5 Cr6+, Cu2+, Hg2+ sucrose (Shukor et al., \n2008a) \n\n\n\n\n\n\n\nEnterobacter \ncloacae strain 48 \n\n\n\n20 2.9 Cr6+, Cu2+ sucrose (Ghani et al., 1993) \n\n\n\nEscherichia coli \nK12 \n\n\n\n80 5 Cr6+ glucose (Campbell et al., \n1985) \n\n\n\n \n \nTable 2. Mo-blue production by bacterial isolates as measured at 750 nm (\u00b1standard \ndeviation of triplicate).  \n \n\n\n\nIsolate A 750 nm \na 0.35 \u00b1 0.01 \nb 0.35 \u00b1 0.02 \nc 0.71 \u00b1 0.03 \nd 1.01 \u00b1 0.07 \ne 1.02 \u00b1 0.06 \nf 0.71 \u00b1 0.01 \ng 1.81 \u00b1 0.08 \nh 0.71 \u00b1 0.01 \ni 1.06 \u00b1 0.02 \nj 0.35 \u00b1 0.02 \n\n\n\nMohd Adnan et al.\n\n\n\nTABLE 1\nCharacteristics of Mo-reducing bacteria isolated to date\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 117\n\n\n\nTen Mo-reducing bacterial isolates were determined for their molybdenum \nblue production in LPM media. The best isolate was isolate g (Table 2), and was \nchosen for further studies. Bacillus sp. strain Zeid 14 was incubated at different \ninitial pH ranging from 5.5 to 8.0. The optimum pH for Mo-blue production \noccurred between 6.0 and 6.8 (Figure 1). Optimum temperature for molybdenum \nreduction was between 25\u00b0 C and 34\u00b0 C (Figure 2). Mo-blue production was \ninhibited by the heavy metals of mercury, silver, copper, chromium and cadmium \nat 2 mg/L by 82.9, 68.5, 62.9, 27.3 and 10.2 %, respectively (Figure 3). The target \nfor heavy metals mercury and copper inhibition of bacterial chromate reduction, \nanother similar anionic metal, is the sulfhydryl group as this is a common target \nfor toxic metal ions (Rege et al., 1997; Elangovan et al., 2010), and could \nprobably be the same as in molybdenum reduction. Sites containing these toxic \ncationic metal ions can be modified by adding calcium carbonate, phosphate, \nmanganese oxide, thiosulphate, sulphur, and magnesium hydroxide to reduce \nthe bioavailability and mobility of these toxic cationic metal ions (Hettiarachchi \net al., 2000; Deeb and Altalhi 2009). This action could increase the efficacy of \nmolybdenum bioremediation despite the presence of the above mentioned toxic \nmetal ions.\n\n\n\nTABLE 2\nMo-blue production by bacterial isolates, measured at 750 nm (\u00b1standard deviation\n\n\n\nof triplicate). \n\n\n\nPropagation of Molybdenum-reducing Bacillus sp\n\n\n\n 2 \n\n\n\n\n\n\n\nPseudomonas sp. \nstrain DRY2 \n\n\n\n15-20 5 Cr6+, Cu2+, Pb2+, \nHg2+ \n\n\n\nglucose (Shukor et al., \n2010a) \n\n\n\nSerratia sp. strain \nDr.Y5 \n\n\n\n30 5 n.a. glucose (Rahman et al., \n2009) \n\n\n\nEnterobacter sp. \nstrain Dr.Y13 \n\n\n\n25-50 5 Cr6+, Cd2+, Cu2+, \nAg+, Hg2+ \n\n\n\nglucose (Shukor et al., \n2009c) \n\n\n\nSerratia \nmarcescens strain \nDRY6 \n\n\n\n15-25 5 Cr6+, Cu2+, Hg2+ sucrose (Shukor et al., \n2008a) \n\n\n\n\n\n\n\nEnterobacter \ncloacae strain 48 \n\n\n\n20 2.9 Cr6+, Cu2+ sucrose (Ghani et al., 1993) \n\n\n\nEscherichia coli \nK12 \n\n\n\n80 5 Cr6+ glucose (Campbell et al., \n1985) \n\n\n\n \n \nTable 2. Mo-blue production by bacterial isolates as measured at 750 nm (\u00b1standard \ndeviation of triplicate).  \n \n\n\n\nIsolate A 750 nm \na 0.35 \u00b1 0.01 \nb 0.35 \u00b1 0.02 \nc 0.71 \u00b1 0.03 \nd 1.01 \u00b1 0.07 \ne 1.02 \u00b1 0.06 \nf 0.71 \u00b1 0.01 \ng 1.81 \u00b1 0.08 \nh 0.71 \u00b1 0.01 \ni 1.06 \u00b1 0.02 \nj 0.35 \u00b1 0.02 \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016118\n\n\n\nFigure 1: The effect of pH on molybdenum (molybdate) reduction to molybdenum blue. \nThe error bars signify the standard deviation of the average of triplicate experiments.\n\n\n\nFigure 2: The effect of temperature on molybdenum (molybdate) reduction to \nmolybdenum blue. The error bars signify the standard deviation of the average of \n\n\n\ntriplicate experiments.\n\n\n\nMolybdenum-reducing Bacterium Identification\nIsolate g was a gram-positive, rod-shaped bacterium. The results of morphological \nand various biochemical tests are presented in Table 3. The ABIS online software \nsuggested Bacillus subtilis as the bacterial identity with a homology score of \n91% and accuracy at 92%. Despite this, a more accurate identification method \nemploying 16s rRNA phylogenetic analysis is being conducted to identify this \nspecies further. However, at this moment, the bacterium is identified as Bacillus \nsp. strain Zeid 14.  3 \n\n\n\n\n\n\n\n0.0\n\n\n\n1.0\n\n\n\n2.0\n\n\n\n3.0\n\n\n\n5.5 6.0 6.5 7.0 7.5 8.0\n\n\n\npH\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n \nFigure 1. The effect of pH on molybdenum (molybdate) reduction to molybdenum blue. The \n\n\n\nerror bars signify the standard deviation of the average of triplicate experiments. \n\n\n\n\n\n\n\n0.0\n\n\n\n1.0\n\n\n\n2.0\n\n\n\n3.0\n\n\n\n20 30 40 50 60\n\n\n\nTemperature (oC)\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n \nFigure 2. The effect of temperature on molybdenum (molybdate) reduction to molybdenum \n\n\n\nblue. The error bars signify the standard deviation of the average of triplicate experiments. \n\n\n\n \n 3 \n\n\n\n\n\n\n\n0.0\n\n\n\n1.0\n\n\n\n2.0\n\n\n\n3.0\n\n\n\n5.5 6.0 6.5 7.0 7.5 8.0\n\n\n\npH\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n \nFigure 1. The effect of pH on molybdenum (molybdate) reduction to molybdenum blue. The \n\n\n\nerror bars signify the standard deviation of the average of triplicate experiments. \n\n\n\n\n\n\n\n0.0\n\n\n\n1.0\n\n\n\n2.0\n\n\n\n3.0\n\n\n\n20 30 40 50 60\n\n\n\nTemperature (oC)\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n \nFigure 2. The effect of temperature on molybdenum (molybdate) reduction to molybdenum \n\n\n\nblue. The error bars signify the standard deviation of the average of triplicate experiments. \n\n\n\n\n\n\n\nMohd Adnan et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 119\n\n\n\nFigure 3: The effect of heavy metals on molybdenum (molybdate) reduction to \nmolybdenum blue. The error bars signify the standard deviation of the average of \n\n\n\ntriplicate experiments.\n\n\n\nTABLE 3\nBiochemical tests for Bacillus sp. strain Zeid 14\n\n\n\n 4 \n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\nMerc\nur\n\n\n\ny (\nII)\n\n\n\nSilv\ner\n\n\n\n (I)\n\n\n\nCop\nper \n\n\n\n(II)\n\n\n\nChr\nom\n\n\n\nium\n (V\n\n\n\nI)\n\n\n\nCad\nmium\n\n\n\n (II\n)\n\n\n\nArse\nnic\n\n\n\n (V\n)\n\n\n\nLe\nad\n\n\n\n (II\n)\n\n\n\nCon\ntro\n\n\n\nl\n\n\n\nM\no-\n\n\n\nre\ndu\n\n\n\nci\nng\n\n\n\n A\nct\n\n\n\niv\nity\n\n\n\n (%\n)\n\n\n\n \nFigure 3. The effect of heavy metals on molybdenum (molybdate) reduction to molybdenum \n\n\n\nblue. The error bars signify the standard deviation of the average of triplicate experiments. \n\n\n\nTable 3. Biochemical tests for Bacillus sp. strain Zeid 14. \n \n   Acid production from  \nGram positive staining  +   \nMotility  + N-Acetyl-D-Glucosamine d \nHemolysis  + L-Arabinose + \nGrowth at 45 \u00baC  + Cellobiose + \nGrowth at 65 \u00baC  \u2012 Fructose + \nGrowth at pH 5.7  + D-Glucose + \nGrowth on 7% NaCl media  + Glycerol + \nAnaerobic growth  \u2012 Glycogen + \nCasein hydrolysis  + meso-Inositol + \nEsculin hydrolysis  + Lactose + \nGelatin hydrolysis  + Mannitol + \nStarch hydrolysis  + D-Mannose + \nTyrosine degradation  \u2012 Maltose         + \nBeta-galactosidase (ONPG)  + Melezitose \u2012 \nCatalase  + Melibiose d \nOxidase  d Raffinose + \nUrease  \u2012 Rhamnose \u2012 \nArginine dehydrolase (ADH)  \u2012 Ribose + \nLysine decarboxylase (LDC)  \u2012 Salicin + \nOrnithine decarboxylase (ODC)  \u2012 Sorbitol + \nCitrate utilization  + Sucrose + \nEgg-yolk reaction  \u2012 Starch + \nNitrates reduction  + Trehalose + \nVoges-Proskauer test (VP)  + D-Xylose + \n\n\n\n \nNote: + positive result,\u2212 negative result, d indeterminate result \n \n\n\n\n 4 \n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\nMerc\nur\n\n\n\ny (\nII)\n\n\n\nSilv\ner\n\n\n\n (I)\n\n\n\nCop\nper \n\n\n\n(II)\n\n\n\nChr\nom\n\n\n\nium\n (V\n\n\n\nI)\n\n\n\nCad\nmium\n\n\n\n (II\n)\n\n\n\nArse\nnic\n\n\n\n (V\n)\n\n\n\nLe\nad\n\n\n\n (II\n)\n\n\n\nCon\ntro\n\n\n\nl\n\n\n\nM\no-\n\n\n\nre\ndu\n\n\n\nci\nng\n\n\n\n A\nct\n\n\n\niv\nity\n\n\n\n (%\n)\n\n\n\n \nFigure 3. The effect of heavy metals on molybdenum (molybdate) reduction to molybdenum \n\n\n\nblue. The error bars signify the standard deviation of the average of triplicate experiments. \n\n\n\nTable 3. Biochemical tests for Bacillus sp. strain Zeid 14. \n \n   Acid production from  \nGram positive staining  +   \nMotility  + N-Acetyl-D-Glucosamine d \nHemolysis  + L-Arabinose + \nGrowth at 45 \u00baC  + Cellobiose + \nGrowth at 65 \u00baC  \u2012 Fructose + \nGrowth at pH 5.7  + D-Glucose + \nGrowth on 7% NaCl media  + Glycerol + \nAnaerobic growth  \u2012 Glycogen + \nCasein hydrolysis  + meso-Inositol + \nEsculin hydrolysis  + Lactose + \nGelatin hydrolysis  + Mannitol + \nStarch hydrolysis  + D-Mannose + \nTyrosine degradation  \u2012 Maltose         + \nBeta-galactosidase (ONPG)  + Melezitose \u2012 \nCatalase  + Melibiose d \nOxidase  d Raffinose + \nUrease  \u2012 Rhamnose \u2012 \nArginine dehydrolase (ADH)  \u2012 Ribose + \nLysine decarboxylase (LDC)  \u2012 Salicin + \nOrnithine decarboxylase (ODC)  \u2012 Sorbitol + \nCitrate utilization  + Sucrose + \nEgg-yolk reaction  \u2012 Starch + \nNitrates reduction  + Trehalose + \nVoges-Proskauer test (VP)  + D-Xylose + \n\n\n\n \nNote: + positive result,\u2212 negative result, d indeterminate result \n \n\n\n\nPropagation of Molybdenum-reducing Bacillus sp\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016120\n\n\n\nMolybdenum Blue Absorption Spectrum\nThe Mo-blue produced exhibited a spectrum with a maximum peak at 865 nm, and \na shoulder at 700 nm. This unique spectrum was observed at various incubation \nperiods (Figure 4). Prior to scanning analysis, samples were centrifuged at 10,000 \ng for 10 min. As the incubation period increased, the particular profile was \npreserved. An earlier proposal for the mechanism of molybdenum reduction in \nmicroorganism suggests an initial requirement of an enzymatic reduction from  \nMo6+ to Mo5+ state. This is followed by a second reaction involving phosphate \nions addition that produces Mo-blue (Ghani et al., 1993). Despite this, the \nreaction does not conform to molybdate chemistry as the acidic conditions of \nbacterial fermentation of glucose or sucrose quickly convert molybdate anions \ninto polymolybdates. In the presence of phosphate, a phosphomolybdate is \nformed. This will then be followed by bacterial reduction of phosphomolybdate \ninto Mo-blue, and this is another mechanism that we previously proposed (Shukor \net al., 2007). The involvement of phosphomolybdate during bacterial reduction \nof molybdenum can be inferred from the Mo-blue spectrum obtained. The Mo-\nblue spectra seen in this work, and from nearly all of the isolated Mo-reducing \nbacteria (Shukor et al., 2007; Masdor et al., 2015) showed similar spectra to \nthe phosphate determination method (PDM), which has been confirmed to be \na reduced phosphomolybdate (Clesceri et al., 1989; Chae et al., 1993; Shukor \net al., 2007), supporting the second mechanism. The presence of an unstable \nintermediate species, Cr5+ during bacterial reduction of Cr6+ to Cr3+ has been \nconfirmed using electron paramagnetic and UV-spectroscopic studies (Cervantes \net al., 2001), indicating that the existence of an intermediate species is not unique \nto molybdenum. This was observed in the Shewanella putrefaciens (Myers et \nal., 2000) and Pseudomonas ambigua (Suzuki et al., 1992). Further evidence in \nsupport of the second mechanism is seen in the reduction of phosphomolybdate, \nbut not molybdate, by the enzymes xanthine and aldehyde oxidases to Mo-blue \n(Glenn and Crane, 1956). The process of the formation of phosphomolybdate \nfrom molybdate is chemically complicated. In acidic pHs, and at molybdate \nconcentrations over 1 mM, molybdate ions are instantaneously converted to \npolyions such as H2Mo7O24\n\n\n\n4-, HMo7O24\n5-, Mo7O24\n\n\n\n6-, and Mo12O37\n2-. As the solution \n\n\n\nis acidified to pH of less than 2.0, species such as Mo8O26\n4- and Mo36O112(H2O)16\n\n\n\n8- \nstart to form. All of these forms of molybdenum are called polyoxomolybdates, \nwhich can further incorporate heteroatoms such as silicate or phosphate forming \nthe heteropolyoxomolybdates such as silicomolybdate and phosphomolybdate \n(Krishnan et al., 2008). The species of phosphomolybdate formed in the \nphosphate determination method is PMo12O40\n\n\n\n3\u2212, with \u03b1-Keggin structure. This can \nbe reduced further by a reducing agent, for example, ascorbic acid to form the \n\u03b2-keggin ion, PMo12O40\n\n\n\n7\u2212, which is known as molybdenum blue (Barrows et al., \n1985). Phosphomolybdate can exist in the form of several lacunary species. In \naddition, these lacunary species can interchange, subject to environmental change \nin pH. This makes accurate identification difficult, and requires nuclear magnetic \nresonance and electron spin resonance methods. Fortunately, spectroscopic \n\n\n\nMohd Adnan et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 121\n\n\n\nanalysis has been proposed as a suitable and simple method for distinguishing \nbetween the heteropolymolybdates such as silicomolybdate, phosphomolybdate, \nand sulfomolybdate (Sims, 1961; Chae et al., 1993).\n\n\n\nCarbon Sources as Electron Donor for Molybdenum Reduction\nMo-blue production was optimally supported by glucose in descending efficiency \nby sucrose, fructose, lactose, maltose, glycogen, d-mannose, trehalose, meso-\ninositol and glycerol (Figure 5). The concentration of glucose optimally supporting \nmolybdenum reduction was 1% (w/v) with higher concentrations inhibiting \nreduction (Figure 6). The inhibition seen is probably due to osmotic stress as \ndiscussed previously (Shukor et al., 2009c). Many metal bacterial reductions \nalso utilise glucose effectively. This is seen in selenate (Losi and Frankenberger \nJr., 1997), vanadate (Antipov et al., 2000), chromate (Llovera et al., 1993), and \narsenate (Chang et al., 2012) reductions. Nearly all of the Mo-reducing bacteria \neither prefer glucose or sucrose as the best electron donor (Table 1). Bacterial \nreduction, including molybdate reduction, require NADH and/or NADPH as \nelectron donating substrates. These compounds are efficiently generated with \nassimilable sugars such as glucose or sucrose using metabolic pathways that \ninclude the glycolytic pathway in anaerobic conditions, and the citric acid cycle \nand the electron transport chain under aerobic conditions or in the presence of \nsuitable terminal electron acceptors (Llovera et al., 1993; Losi and Frankenberger \nJr., 1997; Antipov et al., 2000; Shukor et al., 2008b; Chang et al., 2012; Shukor \net al., 2014). Cheaper and more available carbon sources such as molasses that \nare plentiful in Sudan (as  a sugarcane waste product) may be used in the future \n(Medjeber et al., 2015).\n\n\n\nFigure 5: The effect of various carbon sources as electron donor for molybdenum \n(molybdate) reduction to molybdenum blue. The error bars signify the standard deviation \n\n\n\nof the average of triplicate experiments.\n\n\n\nPropagation of Molybdenum-reducing Bacillus sp\n\n\n\n 5 \n\n\n\nEgg-yolk reaction  \u2012 Starch + \nNitrates reduction  + Trehalose + \nVoges-Proskauer test (VP)  + D-Xylose + \n\n\n\n \nNote: + positive result,\u2212 negative result, d indeterminate result \n \n\n\n\n \nFigure 4. Mo-blue scanning absorption spectra produced by Bacillus sp. strain Zeid 14 at \n\n\n\nvarious fermentation time. \n\n\n\n\n\n\n\n\n\n\n\n0.0\n\n\n\n0.5\n\n\n\n1.0\n\n\n\n1.5\n\n\n\n2.0\n\n\n\n2.5\n\n\n\nCell\nob\n\n\n\nios\ne\n\n\n\nFructo\nse\n\n\n\nD-G\nluc\n\n\n\nos\ne\n\n\n\nGlyc\nero\n\n\n\nl\n\n\n\nGlyc\nog\n\n\n\nen\n\n\n\nmes\no-I\n\n\n\nno\nsit\n\n\n\nol\n\n\n\nLa\ncto\n\n\n\nse\n\n\n\nMan\nnito\n\n\n\nl\n\n\n\nD-M\nan\n\n\n\nno\nse\n\n\n\nMalt\nos\n\n\n\ne    \n    \n\n\n\nMele\nzit\n\n\n\nos\ne\n\n\n\nMeli\nbio\n\n\n\nse\n\n\n\nRaff\nino\n\n\n\nse\n\n\n\nRha\nmnos\n\n\n\ne\n\n\n\nRibos\ne\n\n\n\nSali\ncin\n\n\n\nSorb\nito\n\n\n\nl\n\n\n\nSuc\nros\n\n\n\ne\nStarch\n\n\n\nTreha\nlos\n\n\n\ne\n\n\n\nCon\ntro\n\n\n\nl\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n\n\n\n\nFigure 5. The effect of various carbon sources as electron donor for molybdenum \n\n\n\n(molybdate) reduction to molybdenum blue. The error bars signify the standard deviation of \n\n\n\nthe average of triplicate experiments. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016122\n\n\n\nFigure 6: The effect of glucose concentration as electron donor for molybdenum \n(molybdate) reduction to molybdenum blue. The error bars signify the standard deviation \n\n\n\nof the average of triplicate experiments.\n\n\n\nMolybdenum Reduction at Various Phosphate and Molybdate Concentrations \nMo-blue production was supported optimally by phosphate concentration at 5 \nmM. Higher concentrations were detrimental to Mo-blue production (Figure 7) \nwhile the optimal molybdate concentration for reduction was between 10 and 20 \nmM (Figure 8). It is observed generally, that phosphate concentrations beyond 5 \nmM inhibited molybdate reduction in nearly all Mo-reducing bacteria isolated to \ndate (Table 1). The compound or complex phosphomolybdate is highly unstable \nat neutral pH, and is rapidly oxidised (Glenn and Crane 1956). At concentrations \nof phosphate of 20 mM and beyond, the pH is maintained strongly at neutrality \ncausing the rapid destabilisation of phosphomolybdate. In addition, phosphate by \nitself can destabilise the phosphomolybdate complex. This is observed in a study, \nwhere an acidified phosphate solution at 100 mM leads to the destabilisation of \nMo-blue from an ascorbate-reduced phosphomolybdate (Shukor et al., 2002). The \nconcentrations of molybdate supporting optimal Mo-blue production in nearly all \nof the bacteria isolated to date range between 5 and 80 mM (Table 1). In contrast \nto cationic heavy metals, bacteria can tolerate and reduce high concentrations of \nanionic heavy metals. For instance, the most tolerant microorganism can tolerate \nand reduce chromate at 30 mM in Pseudomonas putida (Keyhan et al., 2003), \nselenate at 20 mM in Bacillus sp. (Fujita et al., 1997), arsenate at 60 mM in \nCitrobacter sp. NC-1 (Chang et al., 2012) and vanadate at 50 mM in Pseudomonas \nisachenkovii (Antipov et al., 2000). Thus, these bacteria can be efficient candidates \nfor remediation of molybdenum-polluted areas. In Colorado, contaminated sites \nfrom a discontinued uranium mine displayed molybdenum concentrations as \nmuch as 6,500 mg kg-1 and 900 mg L-1 in soils and water, respectively (Stone and \nStetler 2008). Based on the inhibition of phosphate, remediation of contaminated \nsites that have phosphate above 20 mM will be severely affected. Most type of \n\n\n\nMohd Adnan et al.\n\n\n\n 6 \n\n\n\n0.0\n\n\n\n0.5\n\n\n\n1.0\n\n\n\n1.5\n\n\n\n2.0\n\n\n\n2.5\n\n\n\n0 0.5 1 1.5 2 2.5 3\nPhosphate (mM)\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n\n\n\n\nFigure 6. The effect of glucose concentration as electron donor for molybdenum (molybdate) \n\n\n\nreduction to molybdenum blue. The error bars signify the standard deviation of the average of \n\n\n\ntriplicate experiments. \n\n\n\n\n\n\n\n0.0\n\n\n\n1.0\n\n\n\n2.0\n\n\n\n3.0\n\n\n\n0 10 20 30 40 50\nPhosphate (mM)\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 123\n\n\n\nsoils and water bodies have phosphate concentrations far lower than this value, \nand bioremediation of molybdenum utilising Mo-reducing bacteria would not be \naffected (Jenkins 1973).\n\n\n\nMo-blue Production and Bacterial Growth on Amides and Nitriles \nThe ability of these amides and nitriles to support molybdenum reduction was \nexplored. Of all the xenobiotics tested, acrylamide, propionamide and acetamide \nwere shown to support molybdenum reduction while other xenobiotics tested \ncould not (Figure 9). The amides acrylamide, acetamide and acetonitrile could \nsupport the growth of this bacterium independently of molybdenum reduction \n(Figure 9). The growth of this bacterium on the compounds was modelled \naccording to the modified Gompertz model (Figure 10). The absorbance values at \n600 nm were first converted to natural logarithm. The growth parameters obtained \nwere lag periods of 0.468, 0.979 and 1.53 d and at maximum specific growth rates \nof 1.165, 0.932, 0.842 d-1 for acrylamide, acetamide and acetonitrile respectively. \nThe correlation coefficients obtained for the model at 0.99, 0.98 and 0.98 for \nacrylamide, propionamide and acetamide, respectively, indicated good agreement \nbetween predicted and observed values. HPLC analysis indicated a lowering of \nacrylamide concentration and the presence of the metabolite acrylic acid at the \nend of incubation period (Figure 11). Acrylic acid is also detected as a metabolite \nin previously published results on acrylamide-degrading microorganism (Shukor \net al., 2009a; Shukor et al., 2009b; Rahim et al., 2012).\n\n\n\nThis is the first report on carbon sources other than carbohydrates that could \nsupport Mo- reduction in bacterium. In chromate reduction, xenobiotics such as \n\n\n\n 6 \n\n\n\n0.0\n\n\n\n0.5\n\n\n\n1.0\n\n\n\n1.5\n\n\n\n2.0\n\n\n\n2.5\n\n\n\n0 0.5 1 1.5 2 2.5 3\nPhosphate (mM)\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n\n\n\n\nFigure 6. The effect of glucose concentration as electron donor for molybdenum (molybdate) \n\n\n\nreduction to molybdenum blue. The error bars signify the standard deviation of the average of \n\n\n\ntriplicate experiments. \n\n\n\n\n\n\n\n0.0\n\n\n\n1.0\n\n\n\n2.0\n\n\n\n3.0\n\n\n\n0 10 20 30 40 50\nPhosphate (mM)\n\n\n\nA\nbs\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\n\n\n\n\nPropagation of Molybdenum-reducing Bacillus sp\n\n\n\nFigure 7. Molybdenum blue production at various concentrations of phosphate. The \nconcentration of molybdate was at 10 mM. The error bars signify the standard deviation \n\n\n\nof the average of triplicate experiments.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016124\n\n\n\nphenol is used as electron donors (Anu et al., 2010). Amides such as acrylamide, \nacetamide and propionamide are produced in the order of millions of tonnes per \nyear. Several microbes have been isolated that could use these amides and nitriles \nas carbon or nitrogen sources including Stenotrophomonas acidaminiphila MSU1 \n(Lakshmikandan et al., 2014), Variovorax boronicumulans CGMCC 4969 (Liu \net al., 2013), Pseudomonas azotoformans (Komeda et al., 2004), Pseudomonas \naeruginosa (Chandrashekar et al., 2014), Pseudomonas putida (Nawaz et al., 1989), \nPseudomonas acidovorans (Alt et al., 1975), Pseudomonas sp. (Shukor et al., \n2009b) and Pseudomonas chlororaphis (Ciskanik et al., 1995). Other acrylamide-\ndegrading bacteria that have been reported include Bacillus cereus (Halmi et al., \n2014a),  Pseudonocardia thermophilia, Thermococcus hydrothermalis (Postec et \nal., 2005), Rhodococcus sp. (Nawaz et al., 1998), Burkholderia sp. (Syed et al., \n2012), Enterobacter aerogenes (Buranasilp and Charoenpanich, 2011), Kluyvera \ngeorgiana (Thanyacharoen et al., 2012) and the yeast Rhodotorula sp. (Rahim et \nal., 2012).\n\n\n\nCONCLUSION\nMolybdenum reduction to the colloidal molybdenum blue is a potential \ncandidate for molybdenum bioremediation. We have isolated a Mo-reducing \nbacterium which can utilise acrylamide, an amide, as a source of electron donor. \nIn addition, the amides acrylamide, acetamide and acetonitrile could support \nthe growth of this bacterium. A modified Gompertz model was successfully \nused to model the growth of the bacterium on these compounds, and important \ngrowth parameters were obtained. Reduction of molybdenum required a narrow \nphosphate concentration and glucose as an electron donor. Mo-blue production \nrequires a strict phosphate concentration of between 5.0 and 7.5 mM. The \n\n\n\nMohd Adnan et al.\n\n\n\nFigure 8. Molybdenum blue production at various concentrations of sodium molybdate. \nThe concentration of phosphate was at 5 mM. The error bars signify the standard \n\n\n\ndeviation of the average of triplicate experiments.\n\n\n\n 7 \n\n\n\nFigure 7. Molybdenum blue production at various concentrations of phosphate. The \n\n\n\nconcentration of molybdate was at 10 mM. The error bars signify the standard deviation of \n\n\n\nthe average of triplicate experiments. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0.0\n\n\n\n0.4\n\n\n\n0.8\n\n\n\n1.2\n\n\n\n1.6\n\n\n\n2.0\n\n\n\n2.4\n\n\n\n0 20 40 60 80\n\n\n\nAb\ns \n\n\n\n75\n0 \n\n\n\nnm\n\n\n\nMolybdate (mM)\n \n\n\n\nFigure 8. Molybdenum blue production at various concentrations of sodium molybdate. The \n\n\n\nconcentration of phosphate was at 5 mM. The error bars signify the standard deviation of the \n\n\n\naverage of triplicate experiments. \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 125\n\n\n\nPropagation of Molybdenum-reducing Bacillus sp\n\n\n\n 8 \n\n\n\n0.0\n\n\n\n0.2\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n0.8\n\n\n\nContro\nl\n\n\n\nAcryl\nam\n\n\n\nide\n\n\n\nNico\ntin\n\n\n\nam\nide\n\n\n\nAceta\nmide\n\n\n\nIodoa\nce\n\n\n\ntam\nide\n\n\n\nPro\npionam\n\n\n\nid\n\n\n\n2-C\nhlor\n\n\n\noa\nce\n\n\n\ntam\nide\n\n\n\nAceto\nnitr\n\n\n\nile\n\n\n\nAcryl\nonit\n\n\n\nril\ne\n\n\n\nBenzo\nnitr\n\n\n\nile\n\n\n\nA\nbs\n\n\n\n 6\n00\n\n\n\n n\nm\n\n\n\n0.0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\nM\no-\n\n\n\nB\nlu\n\n\n\ne \n A\n\n\n\n 7\n50\n\n\n\n n\nm\n\n\n\nGrowth\n\n\n\nMo-Blue\n\n\n\n \nFigure 9. Mo-blue production and growth of Bacillus sp. strain Zeid 14 on various \n\n\n\nxenobiotics. The error bars signify the standard deviation of the average of triplicate \n\n\n\nexperiments. \n\n\n\nFigure 9. Mo-blue production and growth of Bacillus sp. strain Zeid 14 on various \nxenobiotics. The error bars signify the standard deviation of the average of triplicate \n\n\n\nexperiments.\n\n\n\nFigure 10. Growth of Bacillus sp. strain Zeid 14 on acrylamide (\u25cf) acetamide (\u25ca) and \nacetonitrile (\u2206) at 1,000 mg/L modelled using the modified Gompertz model (solid \n\n\n\nlines). Growth was carried out aerobically in a 50 mL media at room temperature on an \norbital shaker (120 rpm). The error bars signify the standard deviation of the average of \n\n\n\ntriplicate experiments.\n\n\n\n 9 \n\n\n\n0\n\n\n\n0.2\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n0.8\n\n\n\n0 1 2 3 4 5\nIncubation (day)\n\n\n\nA\nbs\n\n\n\n 6\n00\n\n\n\n n\nm\n\n\n\nAcrylamide\nAcetamide\nAcetonitrile\n\n\n\n \nFigure 10. Growth of Bacillus sp. strain Zeid 14 on acrylamide (\uf098) acetamide (\uf0af) and \n\n\n\nacetonitrile (\uf072) at 1,000 mg/L modelled using the modified Gompertz model (solid lines). \n\n\n\nGrowth was carried out aerobically in a 50 mL media at room temperature on an orbital \n\n\n\nshaker (120 rpm). The error bars signify the standard deviation of the average of triplicate \n\n\n\nexperiments. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016126\n\n\n\nscanning absorption spectrum results indicate that the identity of the Mo-blue is \nlikely a reduced phosphomolybdate. Toxic cationic heavy metals inhibited Mo-\nblue production. The ability of this bacterium to detoxify many toxicants is very \nuseful for bioremediation. Current works include a further characterisation of the \nxenobiotics-degrading properties, purification of the Mo-reducing enzyme and a \nfurther identification of the bacterium via molecular techniques.\n\n\n\nACKNOWLEDGMENT\nSnoc International Sdn Bhd. funded this project.\n\n\n\n 10 \n\n\n\n0\n\n\n\n100\n\n\n\n200\n\n\n\n300\n\n\n\n400\n\n\n\n0 1 2 3 4 5 6 7 8 9\nMinutes\n\n\n\nmAU\n\n\n\n2.\n94\n\n\n\n3\n\n\n\nAcrylamide\n\n\n\n3.\n94\n\n\n\n8\n\n\n\n3.\n24\n\n\n\n2\n\n\n\n4.\n50\n\n\n\n2\n4.\n\n\n\n59\n9\n\n\n\n6.\n27\n\n\n\n1\n\n\n\n2.\n98\n\n\n\n2\n\n\n\n2.\n66\n\n\n\n8\n\n\n\n \nA \n\n\n\n0\n\n\n\n4\n\n\n\n8\n\n\n\n12\n\n\n\n16\n\n\n\n20\n\n\n\n24\n\n\n\n0 1 2 3 4 5 6 7 8 9\n\n\n\nMinutes\n\n\n\nmAU\n\n\n\nAcrylamide Acrylic acid\n\n\n\n2.\n54\n\n\n\n8\n\n\n\n8.\n17\n\n\n\n9\n\n\n\n2.\n64\n\n\n\n2\n\n\n\n7.\n43\n\n\n\n8\n\n\n\n3.\n28\n\n\n\n6\n3.\n\n\n\n68\n8\n\n\n\n4.\n52\n\n\n\n7\n\n\n\n6.\n68\n\n\n\n1\n6.\n\n\n\n81\n1\n\n\n\n7.\n02\n\n\n\n9\n\n\n\n \nB \n \n\n\n\nFigure 11. HPLC chromatogram of acrylamide at the start of incubation (A) and acrylic acid \n\n\n\ndetection during acrylamide degradation (B).  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMohd Adnan et al.\n\n\n\nFigure 11: HPLC chromatogram of acrylamide at the start of incubation (A) and acrylic \nacid detection during acrylamide degradation (B). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 127\n\n\n\nREFERENCES\nAbo-Shakeer, L. K. A., S. A. Ahmad, M. Y. Shukor, N. A. Shamaan, and M. A. \n\n\n\nSyed. 2013. Isolation and characterization of a molybdenum-reducing Bacillus \npumilus strain lbna. J Environ Microbiol Toxicol., 1: 9\u201314.\n\n\n\nAhmad, S. A., M. Y. Shukor, N. A. Shamaan, W. P. Mac Cormack, and M. A. Syed. \n2013. Molybdate reduction to molybdenum blue by an Antarctic bacterium. \nBioMed Res Int., doi: 10.1155/2013/871941\n\n\n\nAl Kuisi, M., M. Al-Hwaiti, K. Mashal and A.M. Abed. 2015. 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World \nJ Microbiol Biotechnol. 25: 1225\u20131234. \n\n\n\nMohd Adnan et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 133\n\n\n\nShukor, M. Y., M. A. Syed, C. H. Lee, M. I. A. Karim, and N. A. Shamaan. 2002. \nA method to distinguish between chemical and enzymatic reduction of \nmolybdenum in Enterobacter cloacae strain 48. Malays. J. Biochem. 7: 71\u201372.\n\n\n\nShukor, Y., H. Adam, K. Ithnin, I. Yunus, N.A. Shamaan and M.A. Syed. 2007. \nMolybdate reduction to molybdenum blue in microbe proceeds via a \nphosphomolybdate intermediate. J. Biol. Sci. 7: 1448\u20131452.\n\n\n\nSimeonov, L.I., M.V. Kochubovski and B.G. Simeonova (eds).  2011. Environmental \nHeavy Metal Pollution and Effects on Child Mental Development. Springer \nNetherlands, Dordrecht.\n\n\n\nSims, R.P.A. 1961. Formation of heteropoly blue by some reduction procedures used \nin the micro-determination of phosphorus. The Analyst. 86: 584\u2013590.\n\n\n\nSmith, E.A., S.L. Prues and F.W. Oehme. 1996. Environmental degradation of \npolyacrylamides. 1. Effects of artificial environmental conditions: Temperature, \nlight, and pH. Ecotoxicol. Environ. Saf. 35: 121\u2013135. \n\n\n\nStone J and L. Stetler. 2008. Environmental Impacts from the North Cave Hills \nAbandoned Uranium Mines, South Dakota. In: Merkel B, Hasche-Berger A \n(eds) Uranium, Mining and Hydrogeology. Springer Berlin Heidelberg, pp \n371\u2013380.\n\n\n\nSuzuki, T. O. H. R. U., N. Miyata, H. Horitsu, K. Kawai, K. Takamizawa, Y. Tai, \nand M. Okazaki. 1992. NAD(P) H-dependent chromium (VI) reductase \nof Pseudomonas ambigua G-1: A Cr(V) intermediate is formed during the \nreduction of Cr(VI) to Cr(III). J. Bacteriol., 174: 5340\u20135345.\n\n\n\nSvensson, K., L. Abramsson, W. Becker, A. Glynn, K-E. Hellen\u00e4s, Y. Lind, and J. \nRosen. 2003. Dietary intake of acrylamide in Sweden. Food Chem Toxicol. 41: \n1581\u20131586.\n\n\n\nSyed, M.A., S.A. Ahmad, N. Kusnin and M.Y.A. Shukor. 2012. Purification and \ncharacterization of amidase from acrylamide-degrading bacterium Burkholderia \nsp. strain DR.Y27. Afr. J. Biotechnol., 11:329\u2013336. \n\n\n\nThanyacharoen, U., A. Tani and J. Charoenpanich. 2012. Isolation and characterization \nof Kluyvera georgiana strain with the potential for acrylamide biodegradation. J \nEnviron Sci Health - Part ToxicHazardous Subst Environ Eng. 47: 1491\u20131499.\n\n\n\nTucker., M.D., L.L. Barton and B.M. Thomson. 1997. Reduction and immobilization \nof molybdenum by Desulfovibrio desulfuricans. J. Environ. Qual., 26: 1146\u2013\n1152.\n\n\n\nPropagation of Molybdenum-reducing Bacillus sp\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016134\n\n\n\nYunus, S., S.M. Yunus, H.M. Hamim, O. M., Anas, S.N. Aripin and S. M. Arif, S. \n2009. Mo (VI) reduction to molybdenum blue by Serratia marcescens strain Dr. \nY9. Pol. J. Microbiol., 58: 141\u2013147.\n\n\n\nZwietering, M.H., I Jongenburger, F.M. Rombouts and K. Van\u2019t Riet. 1990. Modeling \nof the bacterial growth curve. Appl. Environ Microbiol.  56: 1875\u20131881.\n\n\n\nMohd Adnan et al.\n\n\n\n\n\n"
"\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Lime and Fertiliser Application in Combination with Water Management on Rice\n\n\n\n1\n\n\n\nISSN: 1394-7990\nMalaysian Society of Soil ScienceMalaysian Journal of Soil Science Vol.11 : 1-16 (2007)\n\n\n\nEffects of Lime and Fertiliser Application in Combination\nwith Water Management on Rice (Oryza sativa) Cultivated\n\n\n\non an Acid Sulfate Soil\n\n\n\nTotok Suswanto, J. Shamshuddin*, S.R. Syed Omar, Peli Mat\n& C.B.S. Teh\n\n\n\nDepartment of Land Management, Faculty of Agriculture\nUniversiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nAcid sulfate soils are widespread along the coastal plains of the Malay\nPeninsula, with some being cultivated with rice. Following farmers\u2019 prac-\ntice, rice yields are very low due to low pH and prevailing adverse condi-\ntions such as Al and/or Fe toxicity. A study was conducted in a glasshouse\nto determine the effect of lime and fertiliser application in combination\nwith water management on rice cultivated on an acid sulfate soil, using MR\n219 rice variety as the test crop. The soil used was Typic Sulfosaprists. The\nresults showed that soil pH increased from 4.27 to 4.93 by applying 4 t\nGML/ha, thereby reducing Al and/or Fe toxicity. In this treatment, ex-\nchangeable Ca increased from 1.28 to 3.13 cmolc/kg soil, which is above\nthe rice Ca requirement. The increase in exchangeable Ca also reduced Al\ntoxicity. Fertiliser or fertiliser in combination with lime affected rice pro-\nduction significantly. Rice yield was negatively correlated with acid-ex-\ntractable Fe. Additionally, rice yield increased with increasing pH and Ca.\nThe best yield of 14.15 t/ha was obtained for treatment with 4 t/ha lime\ntogether with 120 kg N/ha + 16 kg P/ha + 120 kg K/ha. This shows that\nliming together with prudent fertiliser management improves rice produc-\ntion on an acid sulfate soil.\n\n\n\nKeywords: Rice, ground magnesium limestone, acid sulfate soil, alumi-\nnum, iron\n\n\n\nINTRODUCTION\nThe Kemasin-Semerak Integrated Agriculture Development Project, comprising\na total area of 68,350 ha, was incorporated in 1982.  The project area is located\nin the Kelantan Plain, in the east coast state of Peninsular Malaysia. The plain is\ncharacterised by the presence of a mixture of riverine and marine alluvial soils,\nformed as a result of the rise and fall in sea level in the Quaternary (Djia 1973).\nPeaty materials are sometimes overlain by mixed clayey-sandy sediments and\noccasionally contain pyrite which is scattered over the plains, especially along\nthe coastline. This eventually caused the development of acid sulfate soil condi-\ntions which are harmful to crop cultivation (like rice) on these soils.\n\n\n\n* Corresponding author: Email: Shamshuddin@agri.upm.edu.my\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM1\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 20072\n\n\n\nTotok Suswanto, J. Shamshuddin, S.R. Syed Omar, Peli Mat & C.B.S. Teh\n\n\n\nNormally,  acid  sulfate soils are not suitable for crop production, unless they\nare adequately  ameliorated.  Among the agronomic problems common to acid\nsulfate soils are toxicity due to the presence of Al, decrease in P availability,\nnutrient deficiency, Fe(II) toxicity and plant stress due to the presence of a sul-\nfuric horizon (Dent 1986).\n\n\n\nThe activities of Al3+ in the soil solution are controlled by Al(OH)3 (gibbsite)\nat high pH.  Thus, raising the pH would render the Al inactive, as gibbsite is\ninert. Al in soil solution at 1-2 mg/kg can be toxic to plants (Dobermann and\nFairhurst 2000). Soluble Al accumulates in the root tissues, preventing cell divi-\nsion and elongation (Rorison 1973). Rice roots rapidly absorb Al, causing a\nreduction in root length (Gupta and Toole 1986), and inhibited root growth\nreduces nutrient uptake.\n\n\n\nFe(II) may be released in toxic amounts by a reduction in Fe(III) in flooded\nsoil conditions. In an incubation experiment in Vietnam, Tran and Vo (2004)\nfound soluble Fe exceeding 1000 ppm. But flooding can increase solution pH\nwith a concomitant lowering of soluble Fe (Tran and Vo 2004). According to\nMoore and Patrick (1993), Fe(II) activities are seldom at equilibrium with iron\nsolid phases in acid sulfate soils. Ponnamperuma et al. (1973) reported values of\n5000 mg/kg Fe(II) within two weeks of flooding acid sulfate soils. Iron uptake\nby rice is correlated with Fe2+ activities (Moore and Patrick 1993).  Concentra-\ntions above 500 mg/kg Fe(II) are considered toxic to rice plants growing on acid\nsulfate soils (Nhung and Ponnamperuma 1966).\n\n\n\n According to Rutger (1981), rice plant requires pH of 5.0-7.5 to grow\noptimally, although it can tolerate a pH of 4.3-8.7 (Duke 1973). Coronel (1980)\nfound no adverse effect of pH of 3.5-5.0 on rice root growth in a nutrient culture\nstudy in the Philippines.\n\n\n\nLiming is a normal agronomic practice to manage acid sulfate soils for crop\nproduction.  In Malaysia, some areas of acid sulfate soils have been reclaimed\nfor rice cultivation using lime. In the acid sulfate soils of the Muda Agricultural\nDevelopment Authority (MADA) granary areas in Kedah-Perlis coastal plains\n(northwest coast of Peninsular Malaysia), for instance, rice yield improved sig-\nnificantly after applying 2.5 tonnes of ground magnesium limestone (GML) per\nha (Arulando and Kam 1982). In another area called the Merbok Scheme (in the\nKedah-Perlis coastal plains), rice yield increased from 1.4 t/ha (in 1974) to 4.5 t/\nha (in 1990) after yearly application of 2 t GML/ha (Ting et al. 1993). These\nsuccess stories are frequently reported.\n\n\n\nAcid sulfate soils can also be ameliorated, to some extent, by correct water\nmanagement practices. The acid soil infertility can somewhat be alleviated by\nflooding as  flooding re-introduces an anaerobic condition. In the presence of\neasily-decomposable organic matter, Fe(III) would be reduced, resulting in the\nelimination of soil acidity. Maintaining a high water table level can control the\nrate of pyrite oxidation and thus curtail acid production. Irrigation and leaching\ncan help remove acid water from the area under rice cultivation.\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM2\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Lime and Fertiliser Application in Combination with Water Management on Rice\n\n\n\n3\n\n\n\nThe objective of this study was to determine the effects of lime and fertiliser\napplication in combination with water management on rice cultivated on an acid\nsulfate soil. It is hoped that the study will provide a base to recommend rice\ncultivation on acid sulfate soils of Malaysia.\n\n\n\nMATERIALS AND METHODS\n\n\n\nThe Soils\nThe soils (topsoil) for this study were taken from a rice field trial conducted at\nthe Jelawat Rusa Irrigation Scheme in the Kemasin-Semerak IADP, Kelantan,\nMalaysia (06o 00N, 102o 23E). The soils in the experimental plots belong to the\nNipis-Bakri Associations (organic soils containing sulfidic materials within 100\ncm depth), which is classified as Typic Sulfosaprists. The topsoil contains 25.6\n% organic carbon, while the CEC of the soil in this zone is about 20 cmolc/kg\nsoil. The peaty materials have been somewhat degraded as a result of a long\nhistory of rice cultivation. In the soil profile, the sulfuric layer occurs below a\ndepth of 45 cm. Selected chemical properties of the soils are given in Table 1.\nSoil pH and exchangeable Al at 45-60 cm depth are 3.1 and 13.54 cmolc/kg soil,\nrespectively. These characteristics are typical of an acid sulfate soil.\n\n\n\nThe Rice Variety Tested\nThe rice (Oryza sativa) variety used in the trial was MR 219. This is the most\ncommon rice variety planted by Malaysian rice growers. This is a rice variety\nspecially bred for the conditions prevailing in Malaysia, but not necessarily for\nacid sulfate soils.\n\n\n\nExperimental\nA glasshouse experiment was designed (split-split plot design) to determine the\npotential yield of rice cultivated on an acid sulfate soil under various treatments.\nIt was conducted under a controlled environment at the Glasshouse Unit, Fac-\nulty of Agriculture, Universiti Putra Malaysia. Rice plants were planted in 27-cm\ndiametre pots; with each pot containing 6 kg of air-dried soil. In this study, three\n\n\n\nTABLE 1\nSelected chemical properties of the Nipis-Bakri Associations\n\n\n\nDepth pH Exchangeable cations (cmolc/kg)\ncm water Al Ca Mg K\n\n\n\n  0 -15 4.4 2.72 0.46 0.18 2.20\n15 - 30 3.8 5.94 0.23 0.12 0.41\n30 - 45 3.5 8.82 0.16 0.39 1.32\n45 - 60 3.1 13.54 0.27 0.65 0.99\n60 - 75 2.5 26.73 0.25 0.68 0.75\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM3\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 20074\n\n\n\nTotok Suswanto, J. Shamshuddin, S.R. Syed Omar, Peli Mat & C.B.S. Teh\n\n\n\nfactors were evaluated: (i) lime application; (ii) fertiliser application; and (iii)\nwater management.\n\n\n\nThe experiment was conducted over 120 days. At harvest, the yield in each\npot was recorded, dried out so as to contain 14 % moisture content and weighed.\nStatistical analysis was carried out using SAS, version 8. Water samples were\ntaken at planting (30 days after liming) and 30 days after planting for determina-\ntion of pH, Ca and Mg.\n\n\n\nLime treatment: Lime material used in this study was ground magnesium\nlimestone (GML). The rates were control (no GML), GML at optimum rate and\nGML at the top of soil requirement (lime requirement).  The predicted optimum\nliming rate was 4 t GML/ha, while the lime requirement to increase the soil pH to\n5 for the soil was 13.8 t GML/ha. Lime was applied 30 days before planting.\n\n\n\nFertiliser treatment: In determining the amounts of N, P and K to apply, the\nsimplest way is to use crop nutrient uptake, target yield and initial yield (without\nfertiliser). The gap between target yield and initial yield should come from fertilisers.\nAccording to  IRRI (2002), the  nutrient  uptake per tonne  grain  yield   is\napproximately (i) N \u2013 15-20 kg; (ii) P \u2013 2-3 kg; and (iii) K \u2013 15-20 kg (if the\nstraw remains in the field, the amount is 3-5 kg/ha).\n\n\n\nFor the acid sulfate soil under study, the average rice yield is about 2 t/ha,\nusing typical farmers\u2019 practice. This is far below the national average.  To achieve\nthe optimal target yield of 10 t/ha, the additional yield (provided that soil infertility\nis ameliorated) should be contributed by fertilisers.  From the calculations shown\nin Table 2, the amounts of N, P and K are 120-160, 16-24 and 120-160 kg/ha,\nrespectively.  Producing 8 tonnes of additional grain requires 8 times more nutri-\nent uptake of 15-20 kg N, 2-3 kg P and 15-20 kg K. These rates were calculated\nbased on the assumption that all the rice straw is removed from the field. In the\ncase where the straw remains in the field, the calculation is not the same, espe-\ncially for K.\n\n\n\nThe fertiliser treatments were (i) F0 - no fertilizer; (ii) F1- medium rate of\nfertiliser to achieve 5 t rice/ha (45- 60 N/ha; 6-9 kg P/ha; 45-60); and (iii) F2-\n\n\n\nTABLE 2\nEstimation of fertiliser requirement based on the gap between target and initial\n\n\n\nyield and crop nutrient uptake\n\n\n\nTarget 5 t/ha 10 t/ha\n\n\n\nInitial (t/ha) 2 2\n\n\n\nGap (t/ha) 3 8\n\n\n\nElement Nutrient uptake Fertiliser rate\n(kg/ha) (kg/ha)\n\n\n\nN 15-20 45-60 120-160\nP 2-3 6-9 16-24\nK 15-20 45-60 120-160\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM4\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Lime and Fertiliser Application in Combination with Water Management on Rice\n\n\n\n5\n\n\n\nmaximum rate of fertiliser to achieve 10 t rice/ha (120-160 kg N/ha;16-24 kg P/\nha; 120-160 kg K/ha).\n\n\n\nThe fertilisers were applied at the critical time during the growth period,\nwhich included supplying sufficient N before maximum tillering at 20-35 day\nafter seedling (DAS), providing enough nutrients during panicle initiation (50-55\nDAS) and during grain filling (> 60 DAS).  A growth enhancer (Vitagrow), which\nsupplies micronutrients was applied during the growing period. The details of\nfertiliser application are given in Table 3.\n\n\n\nWater management:  In a continuously submerged rice field, the main limit-\ning factor of yield is reduction in topsoil. In acid sulfate soil conditions, soil\nacidity can be somewhat reduced when the field is flooded, but Fe(II) toxicity\nusually arises. Reduction of topsoil can be prevented, to a certain extent, by\nsurface water management. When surface water is regularly drained from the\nfield and the topsoil is allowed to dry for a few days, it can be oxidised. How-\never, the oxidation process would result in production of protons, leading to\nlowering of pH and an increase in Al ions in the water. This experiment was so\ndesigned to determine the effects of surface water management on the growth\nof rice. The treatments for this study were as follows: (i) W0- continuously\nsubmerged condition (control); and (ii) W1- drying period once (50 DAS) for\nabout 5-12 days.\n\n\n\nTABLE 3\nList of component and rate of fertiliser and schedule of application based on\n\n\n\ncritical times during plant growth\n\n\n\nDAS* Fertiliser application Rate/pot\n\n\n\n0 Seedlings 23 seeds\n(400 seeds/m2)\n\n\n\n20 Subsidised fertiliser F1 0.46 g\n(180 kg/ha) F2 1.19 g\n\n\n\n25 Vitagrow 0.5 mL Vitagrow mixed with\n(Foliar fertiliser) 100 mL water for all treatments\n\n\n\n35 Subsidised fertiliser F1 0.20 g\n(80 kg/ha) F2 0.53 g\n\n\n\n35 Urea F1 0.23 g\n(100 kg/ha) F2  0.57 g\n\n\n\n45 Vitagrow 0.5 mL Vitagrow mixed with\n(Foliar fertiliser) 100 mL water for all treatments\n\n\n\n50 NPK Blue (12:12:17:TE) F1 0.32 g\n(150 kg/ha) F2 0.86 g\n\n\n\n60 NPK Green (15:15:15:TE) F1 0.32 g\n(100 kg/ha) F2 0.86 g\n\n\n\n65 Vitagrow 0.5 mL Vitagrow mixed with\n(Foliar fertiliser) 100 mL water for all treatments\n\n\n\n* Day after seeding\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM5\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 20076\n\n\n\nTotok Suswanto, J. Shamshuddin, S.R. Syed Omar, Peli Mat & C.B.S. Teh\n\n\n\nSoil Analysis\nSoil pH (1:2.5) was determined in water. The cation exchange capacity (CEC)\nwas determined using NH4OAc solution, buffered at pH 7. Exchangeable Ca,\nMg, and K in the NH4OAc extract were determined by atomic absorption spec-\ntrometry (AAS).  Exchangeable Al was extracted by 1 M KCl and determined by\nAAS. The organic carbon was determined by the Walkley-Black method (Nelson\nand Sommers 1996).\n\n\n\n Iron in the soils was determined by double acid method (henceforth re-\nferred to as acid-extractable Fe). It was extracted using 0.05 M HCl in 0.0125 M\nH2SO4.  A five-gram sample of the soil was mixed with 25 mL of the extracting\nsolution and shaken for 15 minutes. The solution was then filtered through\nWhatman filter paper number 42 before determining the Fe by atomic absorption\nspectrometry, Model Perkin Elmer 5100.\n\n\n\nRESULTS\n\n\n\nThe Initial Soil Chemical Properties\nData in Table 1 show the soil chemical characteristics by depth. The topsoil pH\nwas low; the value was even lower at depths below 50 cm. At depths of 45-60\ncm, the pH value was lower than 3.5. This low pH in combination with the\npresence of jarositic mottles in the soil at that depth qualifies the soil to be\nclassified as an acid sulfate soil. The low pH was due to the presence of high\namounts of exchangeable Al, especially at depths below 45 cm, the sulfuric\nlayer.\n\n\n\n The initial topsoil exchangeable Ca was 0.46 cmolc/kg soil, which is lower\nthan the required level for rice growth of 2 cmolc/kg soil (Palhares 2000). The\ninitial exchangeable Mg in the soil was only 0.18, but Mg requirement for rice is\n\n\n\nTABLE 4\nSummary of treatments on glasshouse experiment\n\n\n\nTreatment Symbol Description\n\n\n\nWater Management W0 Continuous submerged (control)\nW1 Once dry period on 50 DAS, 5-12 days.\n\n\n\nLime L0 No lime (control)\nL1 GML at optimum (predicted) rate (4 t/ha)\nL2 GML at top soil requirement (13.8 t/ha)\n\n\n\nFertiliser F0 No fertiliser (control)\nF1 Medium rate fertiliser\n\n\n\n(to achieve 5 t/ha of yield)\nF2 Maximum rate fertiliser\n\n\n\n(to achieve 10 t/ha of yield)\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM6\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Lime and Fertiliser Application in Combination with Water Management on Rice\n\n\n\n7\n\n\n\n1 cmolc/kg soil (Dobermann and Fairhurst 2000). Exchangeable Al was high\nespecially at increasing depths. This means there could be high concentrations of\nAl in the soil solution. According to these researchers, Al concentration of 1-2\nmg/kg in the soil solution would cause toxicity to the growing rice plants. Potas-\nsium seemed to be moderately high and thus would be sufficient for rice growth\nin this soil.\n\n\n\nChanges in Soil Chemical Properties\nIt can be seen that the GML had ameliorated the soil by increasing the soil pH\n(Table 5). Soil pH increased from 4.27 to 4.93 by applying 4 t GML/ha (L1).\nWhen the pH was near 5, the toxic effect of Al toxicity would be minimal. At\nthis pH, Al starts to precipitate, forming inert gibbsite. The pH further increased\nto 5.83 when the liming rate was increased to that of the soil lime requirement\n(L2). At this pH, the Al and Fe are not expected to reduce rice yield.\n\n\n\nExchangeable Ca had increased significantly with GML application (Table 5).\nSo did exchangeable Mg. The exchangeable Ca had increased from 1.28 to 3.13\ncmolc/kg soil by applying 4 t GML/ha. This has passed the critical limit of 2\ncmolc/kg soil (Palhares 2000). At this GML rate, the exchangeable Mg has passed\nthe critical limit of 1 cmolc/kg soil (Dent 1986). The increase on Ca content in the\nGML treated soils had somewhat alleviated Al toxicity (Alva et al. 1986).\n\n\n\nThe Effect of Treatments\nThe analysis of variance showed that (p<0.01) the combination of liming and\nfertiliser application (p<0.01) had affected the yield of rice (MR219) signifi-\ncantly. LSD grouping was applied to determine the effects of lime and fertiliser\napplication. The yield results given in Figure 1 show that lime and fertilisers\nplay a major role in affecting rice yield. It also shows that fertiliser in combina-\ntion with lime improves rice yield significantly.\n\n\n\nEffects of fertiliser application: Fertiliser application had significantly in-\ncreased rice yield even without liming (Fig. 1). This means that in an acid sul-\nfate soil condition, rice yield can be increased simply by heavy fertiliser applica-\n\n\n\nTABLE 5\nThe change in pH, exchangeable Ca, exchangeable Mg and Fe\n\n\n\nwith liming\n\n\n\nTreatment pH Exchangeable Cations (cmolc/kg)\nCa Mg Fe\n\n\n\nL0 4.27c 1.28c 0.50c 0.29a\n\n\n\nL1 4.93b 3.13b 1.02b 0.24b\n\n\n\nL2 5.83a 6.28a 1.28a 0.11c\n\n\n\nLSD0.05 0.12 0.27 0.24 0.03\n\n\n\nMeans with the same letter are not significantly different (p<0.05)by LSD\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM7\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 20078\n\n\n\nTotok Suswanto, J. Shamshuddin, S.R. Syed Omar, Peli Mat & C.B.S. Teh\n\n\n\nFig. 1: Effect of lime in combination with fertilizer on yield\n\n\n\nFig. 2: Effects of water treatment in combination with lime and fertiliser on: (a) tiller number\n(LSD=0.44) and (b) panicle number (LSD=0.40). Means with the same letter are not\n\n\n\nsignificantly different (p=0.05)\n\n\n\n(a)\n\n\n\n(a)\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM8\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Lime and Fertiliser Application in Combination with Water Management on Rice\n\n\n\n9\n\n\n\nFig. 3: Effect of water management (a) and lime (b) on the weight of 1000 seeds\nand   empty spikelet number (LSD=0.40)\n\n\n\ntion. The presence of phosphate resulting from fertiliser application can, to a\ncertain extent, reduce Fe in the solution by forming insoluble FePO4\n(Shamshuddin et al. 2004).\n\n\n\nEffects of lime application: Using GML (L1 and L2), the rice yield can be\nincreased to the level  achieved under intensive fertilisation. It shows that GML\nand fertiliser applied at medium rates are complementary in increasing rice yield.\nTaking cost into consideration, the application of GML is more beneficial than\nfertiliser alone. If this is done, the farmer saves cost by using less fertiliser. In\nthis experiment, the highest yield was achieved in the treatment using GML and\nmaximum fertiliser application (L1F2 and L2F2 with value of 14.15 and13.91 t/\nha, respectively). By application of GML (L1 and L2), the number of empty\nspikelets  decreased significantly resulting in significant grain improvement.\n\n\n\nEffects of water management:  In this study, GML and fertiliser application\nin combination with water management improved rice growth significantly, shown\nby improvement in tiller number, panicle number, panicle length, spikelet num-\nber and grain weight (Fig. 2).\n\n\n\nTiller and panicle number improved significantly (a,b,c p< 0.01 and p<\n0.05) by water management in combination with GML and fertiliser application\n(Fig. 2). The highest tiller number was found in the water management treat-\n\n\n\n(a)\n\n\n\n(b)\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM9\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200710\n\n\n\nTotok Suswanto, J. Shamshuddin, S.R. Syed Omar, Peli Mat & C.B.S. Teh\n\n\n\nment and GML with maximum rate of fertiliser treatment (W1L1F2, W0L2F2),\nwhile the lowest was the treatment without fertiliser (F0). Panicle number was\nfound to be significantly higher than the control for the maximum rate of GML\nand fertiliser application (W0L2F2). Fig. 2 shows that an increase in treatment\nrate could improve the number of tillers and panicles, leading to an increase in\nrice yield.\n\n\n\nGrain weight and empty spikelet numbers were affected by individual factor\nof water management (a, p< 0.01 and p< 0.05) and GML treatment (b, p< 0.01\nand p< 0.05).  Drying the soils produced significantly lower empty spikelet\nnumbers compared to the control treatment.  This is in agreement with the study\nof Hanhart et al. (1997) who concluded that during several weeks of continu-\nously submergence of  field in acid sulfate soils, vegetative growth of rice plants\nwould be retarded and reproductive development would be disturbed, resulting\nin a high percentage of empty grains.  So a dry period and GML application\ncould increase rice yield by reducing empty spikelets. Fig. 3 shows the effects of\nwater management and GML on grain weight and empty spikelet numbers.\n\n\n\nWater management in combination with GML application decreased Al tox-\nicity. Fig. 4 shows that GML application at the lime requirement level elimi-\nnated exchangeable Al from the soil to less than 3 cmolc/kg soil; the effect being\nmore clearly shown for the treatment under one time dry period. Lower ex-\nchangeable Al in a soil is usually associated with a higher pH.  This would\ntranslate into better crop growth, resulting in increased rice yield.\n\n\n\nFig. 4: Effect of water treatment in combination with lime on exchangeable Al (LSD=4.24).\n\n\n\nTABLE 6\nCorrelation (r) between yield and soil parameters\n\n\n\nDependent pH Exch. Ca Exch. Mg Exch. Al Fe\n\n\n\npH ns 0.93** 0.63** -0.64** -0.81**\n\n\n\nYield 0.27* 0.29* ns ns -0.30*\n\n\n\nns: not significant (p=0.05)\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM10\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Lime and Fertiliser Application in Combination with Water Management on Rice\n\n\n\n11\n\n\n\nDISCUSSION\n\n\n\nCa and Mg Deficiency\nThe presence of large amounts of Ca in soils is good in itself. It is by nature that\nCa is, to a certain extent, able to reduce the toxic effect of high Al concentration\n(Alva et al. 1986; Shamshuddin et al. 1991).  This occurred in L1 and L2. The\namelioration of Al toxicity, should there be any, would be shown by an  increase\nin rice yield.  The presence of extra Mg could also contribute to alleviation of Al\ntoxicity as had been shown by Shamshuddin et al. (1991) for maize grown on an\nacid upland soil.\n\n\n\nSoil pH was linearly significantly correlated with exchangeable Ca and Mg,\nwith r values of 0.93 and 0.63, respectively (Table 6).  The presence of these\nmetals would increase pH via their hydrolysis.\n\n\n\nAluminum Toxicity\nLow pH is usually associated with high exchangeable Al, which is clearly shown\nby the data given in Table 1.  The lowest pH coincides with the highest ex-\nchangeable Al. There was a linear negative correlation between pH and exchange-\nable Al, with r value of -0.64 (Table 6).  As seen in Table 1, the initial ex-\nchangeable Al was extremely high in some soil samples, reaching a value of\n26.73 cmolc/kg soil in the subsoil (60-75 cm depth). The lowest value was 2.27\ncmolckg soil, which was in the topsoil.\n\n\n\nIn the water of the experimental plots, Al would probably be in excess of the\ncritical value for rice production of 1-2 mg/kg; this was based on the data avail-\nable from an unpublished study of the area (pers.comm., officer in-charge). This\nAl in the solution can be reduced to an acceptable  level by applying GML at an\nappropriate rate. Our study believes that GML application at 4 t/ha would elimi-\nnate solution Al to the minimal level, making the soil suitable for rice cultiva-\ntion.\n\n\n\nIron Toxicity\nAcid-extractable Fe in the soils was slightly above the critical level, with a value\nof 0.29 cmolc/kg soil in the no lime treatment plot (Table 5). Critical Fe concen-\ntration is known to vary from 0.05 to 5.37 cmolc/kg soil (Dobermann and Fairhurst\n2000), implying that Fe may not be the only source of soil infertility. It is seen\nthat even at the GML rate of 4 t/ha, the acid-extractable Fe concentration is still\nabove the critical value for rice growth.\n\n\n\nRelationship between Rice Yield and Soil Parameters\nIron in the acid sulfate soil is indeed toxic to rice plant as shown by data pre-\nsented in Table 6. The rice yield was significantly correlated with acid-extract-\nable Fe, with r value of -0.30 (p< 0.05).  This means that as the acid-extractable\nFe increased, the rice yield decreased. High amounts of acid-extractable Fe mean\nthat there would also be high amounts of Fe in the soil solution. According to\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM11\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200712\n\n\n\nTotok Suswanto, J. Shamshuddin, S.R. Syed Omar, Peli Mat & C.B.S. Teh\n\n\n\nFig. 5: Changes in solution pH within the first 30 days after liming\n\n\n\n(a)\n\n\n\n Fig. 6: Changes in solution Ca (a) and solution Mg (b) Within the first\n30 days after liming\n\n\n\n(b)\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM12\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Lime and Fertiliser Application in Combination with Water Management on Rice\n\n\n\n13\n\n\n\nPonnamperuma et al. (1973), toxic amounts of Fe(II) would be present in the\nwater after 2 weeks of flooding. This Fe(II) can, to a certain extent, be elimi-\nnated by liming. It is possible that by liming at a GML rate of 4 t/ha, the Fe(II)\nwould have been mostly eliminated from the solution. This can be shown by\nbetter rice growth for this treatment. Relative rice yield (RRY) was correlated\n\n\n\nFig. 8: Relationship between rice yield and solution Ca at planting\n\n\n\nFig. 7: Relationship between rice yield and solution pH at planting (a) and\n30 days after planting (b)\n\n\n\n(a)\n\n\n\n(b)\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM13\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200714\n\n\n\nTotok Suswanto, J. Shamshuddin, S.R. Syed Omar, Peli Mat & C.B.S. Teh\n\n\n\nwith pH, acid-extractable Fe and exchangeable Ca and Al. It is seen that in all\ncases the correlation were poor, with R2 values of 0.08 or less.\n\n\n\nRelationship Between Rice Yield and Solution Parameters\nReasonably high solution pH is essential for rice growth. At pH below 5, there\nwould be high Al in the solution; the level could be toxic to the rice plant. In this\nexperiment, solution pH of the control treatment was 3-4 (Fig. 5). When 4 t\nGML/ha was applied, pH slowly increased. It reached the level of 6 at 30 days\nafter lime application. The rice plant was transplanted 30 days after liming. The\nlime rate of 4 t GML/ha is the recommended rate for rice cultivation on acid\nsulfate soils in Malaysia.\n\n\n\nSolution Ca (Fig. 6a) and solution Mg (Fig. 6b) increased when the GML\nwas dissolved. In the control treatment, solution Ca was low and it did not change\nwith time, within 30 days after liming (Fig. 6a). When GML was applied, solu-\ntion Ca increased with time up to 3 days.  It then started to decrease. It was\nplausible that some of the solution Ca entered the soil and became part of the\nexchangeable Ca, thus no longer existing as free ions in the water. In the ex-\nchange complex, Ca is usually the most abundant element, followed by Mg.\nSolution Mg was higher than solution Ca at 30 days after liming (Fig. 6b). Less\nMg was needed to satisfy the exchange complex of the soil, thus most of the Mg\nfrom the dissolution of the GML remained in the solution. Note that more Ca\nthan Mg is present in the GML.\n\n\n\nRice yield was plotted against pH at planting (Fig. 7a) and at 30 days after\nplanting (Fig. 7b). This figure shows that rice yield increased linearly with in-\ncreasing pH. It means that as the solution pH increased, Al in the solution was\nprecipitated. Thus, the amount of solution Al which caused toxicity to rice plant\ndecreased.\n\n\n\nThe increase in soluble Ca improved the rice growth. This is shown by  an\nincrease in yield with increasing solution Ca (Fig. 8). It means specifically that\nin acid soils it is a good agronomic practice to lime the soil for rice cultivation.\nUnder a normal acid sulfate soil situation, there is insufficient amounts of Ca in\nthis soil for rice production.\n\n\n\nCONCLUSION\nUnder normal unmanaged circumstances, acid sulfate soils in Malaysia are not\nsuitable for rice cultivation. However, application of 4 t GML/ha could amelio-\nrate the soils. Liming acid sulfate soils at this rate has the potential to raise\nexchangeable Ca to above rice requirements, and soil pH would be increased to\nabout 5, thereby eliminating most of the Al and/or Fe toxicity. There should also\nbe sufficient amounts of plant nutrients in the soil. Liming in combination with\nappropriate fertiliser application would make acid sulfate soils suitable for rice\ncultivation.\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM14\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Lime and Fertiliser Application in Combination with Water Management on Rice\n\n\n\n15\n\n\n\nACKNOWLEDGEMENT\nThe authors would like to acknowledge the financial and technical support pro-\nvided by Universiti Putra Malaysia and the Ministry of Science, Technology and\nInnovation, Malaysia.\n\n\n\nREFERENCES\nAlva, A.K., C.J. Asher and D.G. Edwards. 1986. The role of calcium in alleviating\n\n\n\naluminum toxicity. Aust. J. Soil Res. 37: 375-383.\n\n\n\nArulando, X. and S.P. Kam. 1982. Management of acid sulfate soils in the Muda\nIrrigation Scheme, Kedah, Peninsular Malaysia. In International Institute for\nLand Reclamation and Improvement, ed. H. Dosh and N. Breemen, pp: 195-212.\nPubl. 31, Wageningen, The Netherlands.\n\n\n\nCoronel, V.P. 1980. Response of Rice and Wheat at Seedling Stage to Aluminum in\nNutrient Solution and Soil. M.S Thesis, UPLB, The Philippines.\n\n\n\nDent, D.L. 1986. Acid Sulfate Soils: A Baseline for Research and Development.\nWageningen, The Netherlands: International Institute for Land Reclamation\nand Improvement. Publ. 39.\n\n\n\nDjia, H.D. 1973. Geomorphology. In Geology of the Malay Peninsula, ed. D.S. Gobbet\nand C.H. Hutchison, pp: 13-24. New York:  John Wiley & Sons.\n\n\n\nDobermann, A. and T. Fairhurst. 2000. Rice: Nutrient Disorders and Nutrient Man-\nagement. Los Banos, The Philippines: Phosphate Institute of Canada and Inter-\nnational Rice Research Institute.\n\n\n\nDuke, J.A. 1979. Ecosystematic data on economic plants. Quart. J. Crude Drug Res.\n17: 91-110.\n\n\n\nGupta, P.C. and J.C.O. Toole. 1986. Upland Rice: A Global Perspective. Los Banos,\nThe Philippines: International Rice Research Institute.\n\n\n\nHanhart, K., Duong Van Ni, N.F.B. Baker, I. Posma and M.E.F. van Mensvoort. 1997.\nSurface water management under varying drainage conditions for rice on an\nacid sulfate soil in the Mekong Delta, Vietnam. J. Agric. Water Man. 33: 99-\n116.\n\n\n\nMoore, P.A. and W.H. Patrick. 1993. Metal availability and uptake by rice in acid\nsulfate soils. In International Institute for Land Reclamation and Improvement,\ned. D.L. Dent and M.E.F. van Mensvoort, pp.205-224. Publ. 53, Wageningen,\nThe Netherlands.\n\n\n\nNelson, D.W. and L.E. Summers. 1996. Total carbon, organic carbon and organic\nmatter. In Methods of Soil Analysis, ed. D. L. Sparks et al.  pp: 961-1010. SSSA,\nMadison.\n\n\n\nNhung, M.M. and F.N. Ponamperuma. 1966. Effects of calcium carbonate, manga-\nnese dioxide, ferric hydroxide and prolonged flooding and electrochemical\nchanges and growth of rice on a flooded acid sulfate soil. Soil Sci. 102: 29-41.\n\n\n\nPalhares, M. 2000. Recommendation for fertilizer application for soils via qualita-\ntive reasoning. J. Agric. Sys. 67: 21-30.\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM15\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200716\n\n\n\nTotok Suswanto, J. Shamshuddin, S.R. Syed Omar, Peli Mat & C.B.S. Teh\n\n\n\nPonamperuma, F.N., T. Attanandana and G. Beye. 1973. Amelioration of three acid\nsulfate soils for lowland rice. In International Institute for Land Reclamation\nand Improvement, ed. H. Dosh, pp.391-406. Publ.18, Wageningen, The Nether-\nlands.\n\n\n\nRorison, I.H. 1973. The effects of extreme soil acidity on nutrient uptake and physi-\nology of plants. In International Institute for Land Reclamation and Improve-\nment, ed. H. Dosh, pp: 223-254. Publ. 18, Wageningen, The Netherlands.\n\n\n\nRutger, J.N. 1981. Rice: Oryza sativa. In Handbook of Bioresources, ed. T.A. McClure\nand E.S. Lipinsky, pp.199-209.  Boca Raton, Florida:CRS Press Inc.\n\n\n\nShamshuddin, J., S. Muhrizal, I. Fauziah and E. van Ranst. 2004. A laboratory study\nof pyrite oxidation in acid sulfate soils. Commun. Soil. Sci. & Plant Anal. 35:\n117-129.\n\n\n\nShamshuddin, J., I. Che Fauziah and H.A.H. Sharifuddin. 1991. Effects of limestone\nand gypsum application to a Malaysian Ultisol on soil solution and yields of\nmaize and groundnut. Plant and Soil 137: 45-52.\n\n\n\nTing, C.C., S. Rohani, W.S. Diemont and B.Y. Aminuddin. 1993. The development\nof an acid sulfate soil in former mangroves in Merbok, Kedah, Malaysia. In\nInternational Institute for Land Reclamation and Improvement, ed D.L. Dent\nand M.E.F. van Mensvoort, pp: 95-101. Publ. 53, Wageningen, The Nether-\nlands.\n\n\n\nTran, K.T. and T.G. Vo. 2004. Effects of mixed organic and inorganic fertilizers on\nrice yield and soil chemistry of the 8 th crop on heavy acid sulfate soil\n(Hydraquentic Sulfaquepts) in the Mekong Delta of Vietnam. A paper presented\nat the 6th International Symposium on Plant-Soil at Low pH. 1-5 August 2004,\nSendai, Japan.\n\n\n\nMJ of Soil Science 001-016.pmd 08-Apr-08, 10:36 AM16\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : mtmoorthy72@gmail.com \n\n\n\nINTRODUCTION\nThe emission of carbon in the world has been increased due to day by day. Omit S \nSanthropogenic activities, mainly industrialisation. The emission of green house \ngases through anthropogenic activities including deforestation, soil cultivation, \ndraining of wet lands, land use change, forest fires and fossil fuel consumption has \nled to global warming. The destruction and degradation of vegetation by human \nactivities results in the emission of carbon into the ecosystem. Carbon dioxide is \nthe major green house gas that contributes to global warming (Hangarge et al. \n2012). The concentration of carbon dioxide in the atmosphere has been increasing \nevery year. Future atmospheric concentration of carbon dioxide is estimated to \ndouble and push global temperature approximately by 1.5 to 4.0 \u00b0C (Atwell et \nal. 1999). Forests are the major terrestrial sink for carbon in the world. They are \nalso a potential source of biomass. This biomass can provide food, fuel-wood, and \nwood for other domestic and industrial uses (Ravindranath et al.1997). Forests are \nknown to be a major sink and cost-effective option for the mitigation of global \nwarming and climate change. Globally, forests store large amounts of carbon \nsequestered from the atmosphere and retained in living and dead biomass and \nsoil. The forest ecosystem is one of the most important reservoirs of atmospheric \ncarbon (Shrestha and Singh 2007).\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 93-99 (2018)                          Malaysian Society of Soil Science\n\n\n\nCarbon Stock Estimation of Trees in the Virachilai Ayyanar \nSacred Grove in Pudukottai District, Tamil Nadu, India\n\n\n\nThandavamoorthy, M\n\n\n\nVasavi College of Education, Pondicherry- 605 107\n\n\n\nABSTRACT\nWorld carbon emission has been increasing due to the daily anthropogenic advances \nof human beings. Carbon dioxide concentration in the atmosphere has seen an \nincreasing trend every year. Plants in the forests are effective in sequestering and \nstoring carbon below ground and above ground  by their photosynthesis process. \nTrees absorb atmospheric carbon, assimilate and store this carbon as rich organic \ncompounds. Trees in the forests are contributing towards reducing atmospheric \ncarbon. The present study estimated the amount of carbon that is sequestered by \ntrees in Virachilai Ayyanar sacred grove in Pudukottai district of Tamil Nadu. \nThe carbon stock in the various species ranged  from 0.001 tons to 6.41 tons per \nhectare. \n\n\n\nKeywords: Carbon sequestration, above ground biomass, below ground \nbiomass, bio-volume.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201894\n\n\n\nThe carbon stock in forest vegetation is dependant on various factors \nsuch as plant species, geographical location and significantly, land use and \ndeforestation (Shrestha and Singh 2007). Carbon sequestration is a process in \nwhich the atmospheric carbon is captured and stored in the biosphere. Globally \n80% of carbon is stored in above ground biomass and 40% of carbon below \nground biomass (Kirschbaum et al.1996). To quantify the sequestered carbon in \nthe forest ecosystem, carbon pools under various forest types should be assessed. \nThe objective of this paper is to estimate the sequestration of tree species of the \nsacred grove  by calculating the above ground and below ground biomass and \ncarbon storage.\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area\nThe study was conducted in Virachilai Ayyanar sacred grove covers an area of \nabout 7.3 hectares in Pudukkottai district and  about 25 km away from Pudukkottai \ntown. This grove is maintained by the local community of the adjacent village \nwho are known for their strong religious, spiritual, and traditional beliefs and \npractices.\n\n\n\nPudukkottai district covers an area of 4,663 square kilometers and has a \ncoast line of 39 km. It is located between 78.25\u2019 and 79.15\u2019 of the East longitude \nand between 9.50 and 10.40\u2019 of the North latitude. Various types of soils are \nfound in this region. The floristic vegetation of the sacred grove is a typical inland \ntropical dry evergreen forest.\n\n\n\nField Methods\nA 1-hectare plot was identified in the grove and subdivided into 50 x 25 m sub \nplots to study the vegetation. In each plot, all living species equal to or more than \n20 cm girth at breast height (GBH) were identified ((\u226520cm gbh) and their girth \nand height were measured. The plant specimens were collected and identified \ntaxonomically using the publications of Gamble and Fisher (1915-1935), Henry \net al. (1989) and Mathews (1981; 1991; 1993). \n\n\n\nBiomass Estimation\nAbove ground biomass and below ground biomass, litter, dead plant matter and \nsoil organic matter are the major carbon pools of an ecosystem (FAO 2005; IPCC \n2006). The above ground biomass and below ground biomass of the sacred grove \nwas estimated by carbon percentage and by measuring tree height, diameter at \nbreast height (DBH) and wood density. The carbon concentration of different \nparts is rarely measured directly, but generally assumed to be 50% of the dry \nweight(Losi et al.2003; Jana et al. 2009; Chavan and Rasal 2011).\n\n\n\nEstimation of Above Ground Biomass\nThe biomass of a tree is the sum of biomass of its trunk, branches, leaves, flowers, \n\n\n\nThandavamoorthy\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 95\n\n\n\nfruits and roots. For an accurate measurement of biomass, the trees would have to \nbe felled. To avoid destruction, the standing woody biomass has been estimated \nby allometric equation based on diameter and height. The height was measured \nthrough shadow method. The GBH of the trees was calculated by multiplying \nbiomass volume and wood density (Ravindranath and Ostwald 2008). \n\n\n\nThe volume of trees was calculated based on diameter and height. The wood \ndensity value of tree species was obtained from www.worldagroforestry.org.\nThe aboveground biomass was calculated using the following formula\n\n\n\n AGB (kg) =Tree bio volume (TBV) (m3) x wood density (kg/m3)\n TBV = 0.4 x (D)2 x H\n\n\n\nWhere D=GBH/3.14, Diameter (meter) calculated from GBH, and H = Height \n(meter).\nWood density data from Global Wood Density database was used.\n\n\n\nThe standard average density of 0.6 gm/cm is applied whenever the density \nvalue is not available for a  tree species (Potadar Vishnu and Satish Pati 2016).\n\n\n\nEstimation of Below Ground Biomass\nThe below ground biomass includes the biomass of  all live roots c < 2 cm(Chavan \nand Rasal 2011). The below ground biomass is calculated by multiplying above \nground biomass by 0.26 (Chave et al. 200) Ravindranath and Ostwald, (2008).\nBelowground biomass (BGB) kg/tree or ton/tree = above ground biomass (AGB) \nkg/tree or ton/tree x 0.26\n\n\n\nTotal Biomass\nTotal biomass is the sum of the above and below ground biomass \nTotal Biomass (TB) = above ground biomass + below ground biomass\n\n\n\nCarbon Estimation\nGenerally, for any plant species, the total biomass x 50%  is considered as carbon \nstorage (Brown and  Lugo 1982;  Cannell et al.1995;  Dixon 1994; Ravindranath \net al. 1997; Richter et al.1995)\n\n\n\ni.e. Carbon Storage = Biomass x 50% \n      or  Carbon Storage = Biomass/2\n\n\n\nRESULTS AND DISCUSSION\nCarbon sequestration depends upon the size of trees, type of forest and age of trees \n(Terakunpisut et al. 2007).  Thirty-five tree species were recorded from Virachilai \nAyyanar sacred grove in total. The density of woody species (\u226520gbh) was \n398. Based on the density, Albizia amara (82 trees ha-1) is the dominant species \nfollowed by Commiphora caudata (58 trees ha-1) and Euphorbia antiquorrum (47 \ntrees ha-1). \n\n\n\nCarbon Stock Estimation of Trees in the Virachilai Ayyanar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201896\n\n\n\nTable 1 shows the total number of individual species. It also indicates \nthe average GBH in cm and average tree height in m. There were 82 trees of \nthe  dominant species Albizia amara which sequestrated 6.41 tons of carbon \nper hectare. The next dominant species Commiphora caudata sequestrated 2.77 \ntons of carbon. There were only 5 trees of the  species Tamarindus indica but it \nsequestrated more carbon than Commiphora caudata (2.92 tons).\n\n\n\nA study of the data on the biomass accumulation (above ground and below \nground) shows an increasing trend with GBH and height classes. The volume and \nsubsequently, biomass was  found to be related to diameter at breast height (DBH) \n\n\n\nTABLE 1\nCarbon storage in Virachilai Ayyanar sacred grove\n\n\n\n \nSl. \nNo \n\n\n\n\n\n\n\nTree Species \n\n\n\nNo. of \nIndivi\nduals \n(ha-1) \n\n\n\nAver\nage \n\n\n\nGBH \n(cm) \n(ha-1) \n\n\n\nAvera\nge \n\n\n\nheight\n(m) \n\n\n\n(ha-1) \n\n\n\nAverage biomass \n(t/individual) \n\n\n\nCarbon \nstorage = \nBiomass\n\n\n\n/2 (t) \n\n\n\nCarbon \nstorage \n\n\n\n(t/species) Above \nground \nbio \nmass \n\n\n\nBelow \nground \nbio \nmass \n\n\n\nTotal \nBio \nmass \n\n\n\n1 Acacia nilotica 2 47 5 0.03 0.01 0.03 0.02 0.03 \n2 Albizia amara 82 79 7 0.12 0.03 0.16 0.08 6.41 \n3 Atalantia monophylla 8 45 4 0.02 0.01 0.02 0.01 0.10 \n4 Azadirachta indica 1 127 9.5 0.43 0.11 0.54 0.27 0.27 \n5 Bauhinia racemosa 5 46 4.4 0.03 0.01 0.03 0.02 0.08 \n6 Benkara malabarica 12 34 3.5 0.01 0.00 0.01 0.01 0.07 \n7 Carissa spinarum 1 34 3.5 0.01 0.00 0.01 0.01 0.01 \n8 Cissus quadrangularis 1 27 5 0.01 0.00 0.01 0.01 0.01 \n9 Commiphora caudata 58 72 6 0.08 0.02 0.10 0.05 2.77 \n10 Derris ovalifolia 1 71 6 0.07 0.02 0.09 0.05 0.05 \n11 Derris scandens 31 35 8 0.02 0.01 0.03 0.02 0.47 \n12 Dichrostachys cinerea 2 62 5 0.05 0.01 0.06 0.03 0.06 \n13 Diospyros ebenum 1 52 5 0.04 0.01 0.05 0.02 0.02 \n14 Diospyros ferrea 1 40 4 0.02 0.00 0.02 0.01 0.01 \n15 Diospyros montana 5 62 5 0.05 0.01 0.07 0.03 0.17 \n16 Drypetes sepiaria 7 46 4.2 0.02 0.01 0.03 0.01 0.10 \n17 Euphorbia antiquorrum 47 38 3.5 0.01 0.00 0.02 0.01 0.36 \n18 Ficus benghalensis 2 198 12 0.74 0.19 0.94 0.47 0.94 \n19 Gmelina asiatica 3 46 4.3 0.02 0.01 0.03 0.01 0.04 \n20 Grewia rhombifolia 4 42 4.5 0.02 0.01 0.03 0.01 0.06 \n21 Haldina cordifolia 39 39 3.5 0.01 0.00 0.02 0.01 0.32 \n22 Holoptelea integrifolia 4 46 4 0.02 0.01 0.03 0.01 0.05 \n23 Ixora pavetta 3 65 5.5 0.06 0.01 0.07 0.04 0.11 \n24 Manilkara hexandra 35 38 3.5 0.01 0.00 0.02 0.01 0.27 \n25 Mimosa intsia 5 32 4 0.01 0.00 0.01 0.01 0.03 \n26 Morinda pubescens 1 47 4.4 0.02 0.01 0.03 0.01 0.01 \n27 Pamburus  missionis 4 36 4 0.01 0.00 0.02 0.01 0.03 \n28 Pleiospermium alatum 1 35 3.2 0.01 0.00 0.01 0.01 0.01 \n29 Reissantia indica 2 26 3.5 0.01 0.00 0.01 0.00 0.01 \n30 Salvadora persica 1 127 11 0.43 0.11 0.54 0.27 0.27 \n31 Strychnosnux-vomica 7 44 4.2 0.02 0.01 0.02 0.01 0.09 \n32 Tamarindus indica 5 153 13 0.93 0.24 1.17 0.58 2.92 \n33 Tricalyps \n\n\n\naspaherocarpa 2 24 3.5 0.00 0.00 0.01 0.00 0.01 \n34 Ziziphus nimmularia 1 36 3 0.01 0.00 0.02 0.01 0.01 \n35 Ziziphus oenoplia 2 45 4.2 0.03 0.01 0.03 0.02 0.03 \n  386/ \n\n\n\nha.       \n  16.19 \ntons /ha. \n\n\n\n \nA study of the data on the biomass accumulation (above ground and below ground) shows an \n\n\n\nincreasing trend with GBH and height classes. The volume and subsequently, biomass was  found to be \n\n\n\nrelated to diameter at breast height (DBH) and height (H), FSI (1996). The biomass accumulation and \n\n\n\ncarbon sequestration show an increasing trend with age of the  trees. Our  findings are comparable to \n\n\n\nthe results of Mani and Parthasarathy (2007) in five inland sites of tropical dry evergreen forests \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 97\n\n\n\nand height (H), FSI (1996). The biomass accumulation and carbon sequestration \nshow an increasing trend with age of the  trees. Our  findings are comparable to \nthe results of Mani and Parthasarathy (2007) in five inland sites of tropical dry \nevergreen forests located in Pudukottai district of Tamil Nadu State, Forest Survey \nof India (2002-2008), Chavan and Rasal (2011) and Hangarge et al. (2012).\n\n\n\nCONCLUSION\nForests play a significant role in reducing the level of atmospheric carbon dioxide. \nThe present study shows that trees act as a major carbon dioxide sink capturing \ncarbon from the atmosphere and functioning as a sink, with the carbon been stored \nin the form of fixed biomass during the growth process. Virachilai Ayyanar  is \na sacred grove which has a thick vegetation that has high carbon sequestration \npotential.  Therefore it can contribute to reducing the concentration of carbon \ndioxide in the atmosphere. However, this grove is gradually shrinking due to \nanthropogenic activities like agriculture, urbanisation and grazing activities. Thus \nmeasures need to be initiated to preserve the grove area  and to maximise carbon \nsequestration. \n\n\n\nREFERENCES\nAtwell, B.J., P.E.  Kriedemann and  C.G.N. Turnbull (Eds). 1999. Plants in Action.  \n\n\n\nSouth Yarra: ASPPl MacMillan.\n\n\n\nBrown, S. and A.E. Lugo. 1982. 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"\n\nINTRODUCTION\nThe oil palm is a major agricultural crop that requires extensive land for cultivation.  \nThe crop occupies over four million hectares of Malaysia\u2019s land area. Moreover, \nthe oil palm cultivation is expanding rapidly every year due to a high demand for \npalm oil in the market. This has caused new oil palm plantations to move into \nhilly and steep land areas due to limited fertile, lowland areas. Little or no canopy \ncover can cause large losses to soil, nutrients and organic matter on sloping lands \n(Ghulam et al. 1997). Furthermore, without proper conservation methods to \nretain topsoil which is susceptible to soil erosion, reduction on soil productivity \nwill occur (Morgan 2005). In order to reduce soil erosion on sloping lands, empty \nfruit bunches (EFB), Ecomat, oil palm frond heaps, or silt pit have been used by \nmajor oil palm plantations. Ecomat is a biodegradable mat. It is made of natural \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 16: 43-56 (2012)                        Malaysian Society of Soil Science\n\n\n\nEffects of Four Soil Conservation Methods on Soil \nAggregate Stability\n\n\n\nLee Ying Ping1, Christopher Teh Boon Sung1*, Goh Kah Joo2 and  \nAbolfath Moradi1\n\n\n\n1Department of Land management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 UPM Serdang, Selangor, Malaysia\n\n\n\n2Advanced Agriecological Research Sdn. Bhd, 47000 Sg. Buloh, \nSelangor, Malaysia\n\n\n\nABSTRACT\nIn order to reduce soil erosion on sloping lands, empty fruit bunches (EFB), Ecomat, \noil palm frond heaps, or silt pit have been used by major oil palm plantations. \nStudies have shown that at 0-15 cm soil depth, organic matter content in EFB \nis highest among the four treatments. Besides, EFB had the highest humic acid \ncontent compared to the others, about two times higher than control. Our analysis \nshowed that aggregate stability of EFB was the highest among four treatments \nat 54.88%, followed by Ecomat (47.7%), silt pit (44.76%) and finally, control \n(42.12%). We observed that organic matter content inversely correlated with Fe \n(p < 0.05) and Al (p < 0.05) oxides. Finally, yield of humic acids correlated with \nsoil pH (p < 0.05), aggregate stability (p < 0.01) and aggregate size distribution \n(p < 0.01). Among the four treatments, application of EFB as a mulching material \ncommonly practised in oil palm estates was found to be the best practice on sloping \nlands due to its high organic matter and humic substances content that retain soil \nparticles by improving soil aggregate stability and aggregation. \n\n\n\nKeywords: Oil palm fronds, empty fruit bunches, Ecomat, silt pit, humic and \nfulvic acid\n\n\n\n___________________\n*Corresponding author : E-mail: cbsteh@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 201244\n\n\n\nLee Ying Ping, Christopher Teh Boon Sung, Goh Kah Joo and  Abolfath Moradi\n\n\n\noil palm fibres with no chemicals added (Khalid and Tarmizi 2008). As Ecomat \nis light and easy to handle, it is usually used as mulching material to prevent \nerosion on hill slopes. Ecomat also enhances roots growth resulting in increased \nvegetative growth of oil palms (Khalid and Tarmizi 2008). The benefits of using \nEFB as a mulching material in oil palm plantation include improved water holding \ncapacity, soil aeration, soil pH, nutrient status, cation exchange capacity, and as \nwell as reduced leaching and soil loss (Khalid and Tarmizi 2008). Oil palm fronds, \nusually stacked across the slope as mulching material to reduce soil erosion, have \na significant effect on soil pH and nutrients. According to Khalid and Tarmizi \n(1999), pruned fronds release Ca and Mg to the soil where Ca and Mg are essential \nnutrients for oil palm growth development. Besides the use of EFB, palm fronds \nand Ecomat, silt pits, which are long and wide trenches, are dug on to terraces to \nprevent soil erosion as well. Silt pits retain water, leading to significantly higher \nsoil moisture content in comparison to no conservation practices.  \n\n\n\nThe most important soil property in influencing aggregate stability is soil \norganic matter which acts as a binding agent that binds mineral particles into \nstable aggregates (Tisdall and Oades 1982). Soil organic matter is known to \ncorrelate with aggregate stability due to the binding action and other microbial by-\nproducts (Haynes et al. 1997; Hermawan and Bomke 1997; Shepherd et al. 2001). \nThere is also a relationship between soil organic carbon and aggregation. Since \nthe interactions of organic matter with metallic cations and clay particles play a \ncrucial part in soil aggregate stability, the loss of organic matter is closely related \nto decreasing soil aggregate stability (Oades 1988). Humic acids can improve the \nmacrostructural stability of soils by forming a hydrophobic coating around the \naggregates (Mbagwu and Piccolo 1989). Besides that, humic substances form an \norgano-mineral complex with the soil inorganic component and act as a base of \nthe soil aggregates (Cornejo and Hermosin 1996). Piccolo et al. (1997a) suggest \nthat humic substances can be used as soil conditioners as the humic substances can \nimprove aggregate stability significantly as well as it can reduce the disaggregating \neffects of cyclic wetting and drying. Finally, Al and Fe oxides are believed to be \nthe most efficient cations in linking organic matter and clay materials and thus \nreducing soil erosion effectively (Theng and Scharpenseel 1975; Theng 1976). \n\n\n\nHumic substances are important in soils due to their microbial recalcitrant \nproperty which leads to the existence of a stable organic carbon reservoir (Piccolo \n1996). Humic substances are differentiated by solubility in acidic and alkaline \nsolutions. Humic acids are soluble in alkaline solutions, but precipitate in acidic \nsolutions. Fulvic acids are soluble in both acidic and alkaline solutions. Fulvic \nacids have lower molecular weights and higher oxidation states than humic acids \n(Stevenson 1982). Humic acids are heterogeneous components, consisting of \nhydrophilic acidic functional group (made up of carboxylic and phenolic groups) \nand the hydrophilic groups (made up of aliphatic and aromatic carbon groups) \n(Stevenson 1994). Hence, hydrophilic groups in humic acids can increase water \nretention capacity in soils (Stevenson 1982). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 2012 45\n\n\n\nSoil Conservation Methods on Aggregate Stability\n\n\n\nLittle is known about the amount of soil organic constituents in these four \ntypes of soil conservation methods in order to determine which method is best to \npractise in oil palm estates. Hence, the objective of this study was to determine \nthe relative differences in organic constituents and aggregate stability and factors \naffecting these two soil parameters between the four soil conservation methods.\n\n\n\nMATERIALS AND METHODS\nField Experiment\nThe experiment was conducted at Balau Estate, managed by Boustead Plantation \nat Semenyih, Selangor (02\u00ba55\u201957\u201dN 101\u00ba52\u201956\u201dE), with a slope of 6\u00ba. This area \nhas 10-year-old oil palm trees planted with 8 \u00d7 8 m spacing. The soil type is Typic \nPaleudult (Renggam Series) which consists of 37% clay and 56% sand (sandy \nclay texture, USDA Taxonomy classification). The study site has an average pH \nof 4.79, cation exchange capacity of 7.81 cmolc kg-1, organic carbon of 2.2% and \nbulk density of 1.43 Mg m-3 (Moradi et al. 2012). \n\n\n\nThe experimental design was a split-split plot design with 3 replications, \ntreatment (main plot), soil depth (sub plot) and time (sub-sub plot). Thus, total \nexperimental units consisted of 24 plots (4 treatments \u00d7 3 replications \u00d7 2 soil \ndepths). Each plot had four palm trees. Four treatments were randomly arranged \nin each block. The treatments were control (pruned oil palm fronds), EFB, Ecomat \nand silt pit. Application rate of each treatment is shown in Table 1. Duration of the \nexperiment was from January 2009 to December 2009. Mulching materials took \nabout 9 months to decompose and thus data was collected every 3 months starting \nfrom June, September and finally December 2009. The chemical characteristics of \nEFB, Ecomat and palm frond are shown in Table 2.\n\n\n\nMeasurement of Organic Matter\nThe method of Walkley and Black (1934) was used to determine soil organic \ncarbon. The percentage of organic matter was obtained by multiplying the \npercentage of organic carbon by a factor of 1.724 (Nelson and Sommers 1982).\n\n\n\nTABLE 1\nThe four different treatment applications used in this study\n\n\n\nTABLE 1 \nThe four different treatment applications used in this study \n\n\n\n \nTreatment  Application  \n\n\n\nControl  Oil palm frond heaps after pruning  \n(* approx. 4.28 kg m- 2 yr -1 dry matter)   \n \n\n\n\nEFB  1000 kg plot -1 year-1 arrange d in a single layer  \n(* approx. 11.93 kg m - 2 yr-1 dry matter)\n \n\n\n\nEcomat  4 Ecomat carpets, each 1 \u00d7 2 m and 0.02 m thick arranged in a single layer  \n(* approx. 3.23 kg m- 2 yr -1 dry matter)  \n \n\n\n\nSilt pit  1 m wide, 4 m long, 0.5 m deep    \n\n\n\n*Data obtained from Moraidi et al. (2012)  \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 201246\n\n\n\nLee Ying Ping, Christopher Teh Boon Sung, Goh Kah Joo and  Abolfath Moradi\n\n\n\nMeasurement of Al and Fe oxides\nAl and Fe oxides were extracted using dithionite-citrate (DC) method (Soil \nConservation Service 1972). A 0.5 g of air-dried soil was weighed into a 50 mL \ncentrifuge tube. Before the sample was shaken on a mechanical shaker overnight, \n25 mL of 0.68 M sodium citrate and 0.4 g sodium dithionite were added into the \ncentrifuge tube. Subsequently, the sample was centrifuged at 10,000 rpm for 20 \nminutes and filtered to remove suspended material in the extract. Concentrations \nof Al and Fe were measured using Perkin Elmer 5100 atomic absorption \nspectrophotometry (AAS). Conversion factor was applied to obtain the percentage \nof Al and Fe oxides as below:\n   Al oxide (%) = 1.89 \u00d7 Al (%)\n   Fe oxide (%) = 1.43 \u00d7 Fe (%)\n\n\n\nMeasurement of Humic Substances \nExtraction, fractionation and purification of humic substances were determined by \nthe method of Norhayati and Verloo (1984). For extraction of humic substances, \n20 g of air-dried soil were weighed into a 250 mL centrifuge bottle. The sample \nwas shaken with 200 mL of 0.2 N sodium hydroxide for 16 hours. Nylon wool was \nused to remove suspended material and centrifuged at 2600 rpm for 25 minutes. \nThe supernatant was decanted and shaken overnight with 5 g of anhydrous sodium \nsulphate before centrifugation. Secondly, for fractionation of humic substances, \nthe supernatant was filtered and acidified to pH 1 with concentrated sulphuric acid. \nAfter overnight equilibration for 12 hours, the humic acids precipitated out while \nfulvic acid remained in acid solution. To separate humic acids from fulvic acids, \nthe mixture was centrifuged at 7000 rpm for 15 minutes and the acid solution was \ndecanted off.  Finally, fulvic acids were purified in cellulose tubing by dialysis \nfor 1 week with daily change of distilled water whereas humic acids were further \npurified with hydrochloric and hydrofluoric acid mixture by shaking for 48 hours. \nThe humic and fulvic acids were then oven dried at 40\u00b0C for 1-2 weeks. The \ncontent of humic and fulvic acids were expressed as mg kg-1.\n\n\n\nMeasurement of Soil pH in Water\nThe soil pH was measured in the ratio of soil to water 1: 2.5.\n\n\n\nTABLE 2\nPhysico-chemical properties of EFB, Ecomat and palm frond\n\n\n\nTABLE 2 \nPhysico-chemical properties of EFB, Ecomat and palm frond \n\n\n\n \n       \n C/N*  P, %*  K, %*  Ca, %*  Mg, %*  Water \n\n\n\ncontent, w/w, \n%*  \n\n\n\nEFB  56.15  0.05  1.89  0.20  0.12  64.17  \nEcomat  82.09  0.03  1.13  0.17  0.05  12.58  \nPalm \nfrond  \n\n\n\n41.38  0.05  1.51  0.64  0.07  65.57  \n\n\n\n* Data obtained from Moraidi et al . (2012)  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 2012 47\n\n\n\nSoil Conservation Methods on Aggregate Stability\n\n\n\nMeasurement of Aggregate Size Distribution and Aggregate Stability\nNested sieves method was used to determine aggregate size distribution (Kemper \nand Rosenau 1986). Aggregate size distribution was expressed as the mean weight \ndiameter in mm. Aggregate stability (AS) was determined by the wet-sieving \nmethod (Kemper and Rosenau 1986). Aggregate stability was expressed as the \npercentage of aggregates larger than 0.25 mm. \n\n\n\nStatistical Analysis\nAll soil properties were analysed using ANOVA (analysis of variances) to \ndetermine significant differences as well as the correlation coefficients. Significant \ndifferences were analysed using LSD test at 5% significant level. The analyses \nwere carried out using Statistical Analysis System (SAS) version 9.1 (SAS Inst. \n2004).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nOrganic Constituents\nANOVA showed a significant interaction between treatment \u00d7 time \u00d7 soil depth \n(p < 0.05) for soil organic matter. Between the two soil depths, only 0-15 cm soil \ndepth in June and December samplings had significant differences (p < 0.01) (Fig. \n1a) whereas there was no significant difference at 15-30 cm soil depth (Fig. 1b). \nAt 0-15 cm soil depth, organic matter content in EFB was the highest among the \nothers. However, no significant difference was observed at 15-30 cm soil depth \ndue to organic matter being frequently decomposed and concentrated in the top \nlayer of soil. A significant increase in organic C, total N and available P were \nobserved in EFB treatment at 0-15 cm soil depth (Moradi et al. 2012). Studies \nhave shown that high organic matter content in EFB improves chemical and \nphysical properties of soils (Khalid and Tarmizi 2008). Recent studies indicate \nthat EFB is better than Ecomat in improving soil chemical and physical properties \n(Teh et al. 2011). In addition, EFB had the highest amount of dry matter addition \ncompared to Ecomat and palm fronds; thus the amount of nutrients released were \nin proportion to the amount of dry matter added (Table 1). \n\n\n\nANOVA showed significant difference between treatments for humic acids \ncontent (p < 0.05) (Fig. 2). No significant interaction effects were detected in \ncontent of humic acids. EFB had the highest humic acid content compared to the \nothers, about two times higher than control. Furthermore, only the interaction \neffect of treatment \u00d7 time on fulvic acids content was significant (p < 0.05) \n(Fig. 3). In the June sampling, fulvic acids content in EFB and silt pit were the \nhighest, 2980 mg kg-1 and 2960 mg kg-1, respectively. However, in the September \nsampling, there was no significant difference between the treatments. Finally, in \nthe December sampling, the silt pit had the highest fulvic acids content, which \nwas 2420 mg kg-1. The study is also in line with the findings of Soong (1980) and \nTajuddin (1992) who also reported that fulvic acids are greater than humic acids \nin most Malaysian mineral soils. In this study, control, Ecomat and the silt pit \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 201248\n\n\n\nLee Ying Ping, Christopher Teh Boon Sung, Goh Kah Joo and  Abolfath Moradi\n\n\n\nhad higher fulvic acids than humic acids.  However, in the EFB treatment, humic \nacids content was higher than the fulvic acids content. This suggests that EFB as a \nmulching material has a slower rate of humification. As a consequence, losses of \norganic constituents are slower compared to the others treatments. Furthermore, \norganic matter released was the highest in the EFB treatment, thus the humic acid \ncontent would also be high in the EFB treatment.  \n\n\n\nANOVA showed significant interaction only for the effect of treatment \u00d7 time \non Fe oxides (p < 0.05) (Fig. 4). In the June sampling, Fe oxides in control was the \nhighest, at 3.03%. Lowest Fe oxides were observed in EFB and Ecomat treatments \nat 2.11% and 1.65%, respectively. In September and December samplings, there \nwere no significant differences between treatments. ANOVA showed significant \n\n\n\n(b)\n\n\n\nFig. 1: Organic matter content at (a) 0-15 cm and (b) 15-30 cm soil depth. \n\n\n\nFor a given month, means with the same letter are not significantly different at 5% level \naccording to LSD test. (LSD = 1.15%)\n\n\n\n(a)\n\n\n\nSilt pitEcomatEFBControl\n\n\n\nb\n\n\n\na\nSilt pitEcomatEFBControl\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 2012 49\n\n\n\nSoil Conservation Methods on Aggregate Stability\n\n\n\ninteraction effect of treatment \u00d7 time on Al oxides (p < 0.05) (Fig. 5). In June \nsampling, Al oxides in control was the highest at 0.75%. In September sampling,\nthere was no significant difference between treatments. Finally, in December \nsampling, control still ranked the highest in Al oxides, which was 0.91%.  There \nwas significant difference between treatments in soil pH as shown in ANOVA (p \n< 0.01) (Fig. 6). EFB was found to have the highest soil pH compared to others, \nwhich was pH 6.12. The results are consistent with the findings of Zaharah and \nLim (2000) and Lim and Zaharah (2002) where EFB increased the soil pH as well \nas organic matter content in soil. It is also known that during EFB decomposition \nperiod, release of K is much quicker than N and P (Moraidi et al. 2012) (Table 2). \nThis basic cation caused the soil pH to increase gradually.\n\n\n\nMeans with the same letter are not significantly different at 5% level according to LSD test. \n(LSD = 1520 mg kg\u02c9\u00b9)\n\n\n\nFig. 2: Humic acids averaged across soil depths and time.   \n\n\n\nFor a given month, means with the same letter are not significantly different at 5% level \naccording to LSD test. (LSD = 300 mg kg\u02c9\u00b9)\n\n\n\nFig. 3: Fulvic acids averaged across soil depths. \n\n\n\na a\na\n\n\n\nSilt pitEcomatEFBControl\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 201250\n\n\n\nLee Ying Ping, Christopher Teh Boon Sung, Goh Kah Joo and  Abolfath Moradi\n\n\n\nFor a given month, means with the same letter are not significantly different at 5% level \naccording to LSD test. (LSD = 0.23%)\n\n\n\nFig. 5: Al oxides averaged across soil depths. \n\n\n\nFor a given month, means with the same letter are not significantly different at 5% level \naccording to LSD test. (LSD = 0.84%)\n\n\n\nFig. 4: Fe oxides averaged across soil depths.  \n\n\n\nb\n\n\n\nSilt pitEcomatEFBControl\n\n\n\na\n\n\n\nSilt pitEcomatEFBControl\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 2012 51\n\n\n\nSoil Conservation Methods on Aggregate Stability\n\n\n\nMeans with the same letter are not significantly different at 5% level according to LSD test. \n(LSD = 0.51)\n\n\n\nFig. 6: Soil pH averaged across soil depth and time. \n\n\n\nMeans with the same letter are not significantly different at 5% level according to LSD test. \n(LSD = 8.71%)\n\n\n\nFig. 7: Aggregate stability averaged across soil depth and time. \n\n\n\na\n\n\n\na\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 201252\n\n\n\nLee Ying Ping, Christopher Teh Boon Sung, Goh Kah Joo and  Abolfath Moradi\n\n\n\nSoil Aggregate Stability and Aggregate Size Distribution\nANOVA showed treatments to be significantly different in aggregate stability (p < \n0.05) (Fig. 7). Aggregate stability of EFB was the highest among four treatments, \nat 54.88%, followed by Ecomat (47.7%), silt pit (44.76%) and finally, control \n(42.12%). In aggregate size distribution, ANOVA showed significant interaction \neffect of treatment \u00d7 time (p < 0.05) (Fig. 8). There were significant differences \nin September and December samplings. Both samplings indicated that aggregate \nsize distribution of EFB, Ecomat and silt pit were equally higher than control. Not \nmuch change was observed on aggregate size distribution during the six-month \nstudy period in EFB, Ecomat and silt pit. This might be due to the need for a longer \n\n\n\nduration for organic constituents of mulching materials to affect soil aggregate \nstability and aggregation. Moradi et al. (2012) observed no change in the first \nyear of EFB treatment in soil N. However in the second year of experiment, a \nsignificant increase in soil N was observed in EFB treatment. It can be concluded \nthat mulching materials such as EFB require at least 12 months of decomposition \nin order to improve soil chemical and physical properties.\n\n\n\nIron (Fig. 4) and Al oxides (Fig. 5) were low in September in comparison \nwith June and December samplings. In contrast, organic matter (Fig. 1) and fulvic \nacids content (Fig. 3) were high in September sampling. Mulching materials, in \nparticular EFB and Ecomat were estimated to fully decompose after 9 months of \napplication resulting in high amounts of organic matter content in the September \nsampling (Teh et al. 2010). \n\n\n\nFor a given month, means with the same letter are not significantly different at 5% level \naccording to LSD test. (LSD = 0.23 mm)\n\n\n\nFig. 8: Aggregate size distribution averaged across soil depths. \n\n\n\na a a a a a\n\n\n\nSilt pitEcomatEFBControl\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 2012 53\n\n\n\nSoil Conservation Methods on Aggregate Stability\n\n\n\nCorrelation Between Soil Properties\nWe observed that organic matter correlated inversely with Fe (p < 0.05) and Al \n(p < 0.05) oxides (Table 3). This suggests that organic matter is highly correlated \nwith metal-binding agents. Soil pH in tropical soils, in particular in Malaysian \nsoils, is usually low; for example, Fe exists as soluble form at soil pH lower than \n3.5 and Al at pH lower than 5.2. However, due to the application of mulching \nmaterial such as EFB, soil pH and humic substances may have increased, leading \nto Al and Fe chelation to humic substances. Therefore, the effectiveness of Al \nand Fe oxides acting as cementing agents to prevent soil erosion and retain soil \nparticles in this case is not as promising as the effectiveness of organic matter or \nhumic substances. Moreover, both EFB and Ecomat are not good sources for Al \nand Fe.\n\n\n\nResults show that humic acids correlated with soil pH (p < 0.05), aggregate \nstability (p < 0.01) and aggregate size distribution (p < 0.01) (Table 3). Humic \nsubstances persist in soil as they can form stable and strong coordinate bonds \nbetween organic ligands on the humic substances and metals in soil (Syuntaro \net al. 2006). Piccolo et al. (1997b ) stated that a reduction in soil loss of 36% \nand improvement in water retention capacity were observed by adding humic \nsubstances from oxidised coal into two soils with severe structural problems. \nAccording to Soong (1980), humic acids were found to be important in soil \naggregation and the size of the aggregates in the soils when small amounts of \nhumic acids were added to clay-sand mixtures. Humic acids are believed to be \nresponsible for soil aggregation properties rather than organic matter content \ndue to their microbial recalcitrant property (Piccolo 1996). Aggregate stability \nand aggregation is closely dependant on humic acids for retaining soil particles \nbetween the four different soil conservation methods.\n\n\n\nTABLE 3\nCorrelation coefficient (r) between soil properties (n = 12) at 0-15 cm soil depth\n\n\n\nTABLE 3 \nCorrelation coefficient (r) between soil properties (n = 12) at 0-15 cm soil depth \n\n\n\n HA  FA  OM  Al  Fe  AS  AGG  \nFA  0.19  \n\n\n\nOM  0.14 0.48   \nAl  -0.15  -0.56  -0.65**\nFe  -0.25  -0.55  -0.6**  0.83*  \n\n\n\nAS  0.82* 0.19  0.13  -0.04  -0.13    \nAGG  0.74* 0.11  -0.03  -0.02  -0.26  0.75*   \n\n\n\npH  0.88** 0.75  0.68  0.07  -0.1  0.93** 0.85**  \n* indicates p < 0.01 and ** indicates  p < 0.05.  \nHA: Humic acids (mg kg\u02c9\u00b9); \n\n\n\nFe: Fe oxides (%); AS: Aggregate stability (%) and AGG: Aggregate \n size distribution (mm).\n\n\n\nAl: Al oxides (%); \nFA: Fulvic acids (mg kg\u02c9\u00b9); OM: Organic matter content (%); \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 201254\n\n\n\nLee Ying Ping, Christopher Teh Boon Sung, Goh Kah Joo and  Abolfath Moradi\n\n\n\nCONCLUSION\nHumic acids correlated with soil aggregation, aggregate stability and pH instead \nof Al and Fe oxides, fulvic acids and organic matter content. Among the four \ntreatments, application of EFB as a mulching material commonly practised in \noil palm estates was the best practice for sloping lands due to its high amount \nof organic matter and humic acids which assist in retaining soil particles by \nimproving soil aggregate stability and aggregation.\n\n\n\nREFERENCES\nCornejo, J. and Hermos\u00edn, M.C. 1996. Chapter 15 - Interaction of humic substances \n\n\n\nand soil clays, In: Alessandro Piccolo, Editor(s), Humic Substances in Terrestrial \nEcosystems, Elsevier Science B.V., Amsterdam, 1996, Pages 595-624.\n\n\n\nGhulam, M.H., W.A. Yusoff and C. Cyril.  1997. Overland flow and soil erosion in \nsloping agricultural land. Malaysian Journal of Soil Science. 1: 35-49.\n\n\n\nHaynes, R.J., R.S. Swift and K.C. Stephen.  1997. Influence of mixed cropping \nrotations (pasture-arable) on organic matter content, water stable aggregation \nand clod porosity in a group of soils. 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Tillage-\ninduced changes to soil structure and organic carbon fraction in New Zealand \nsoils. Australian Journal of Soil Research. 39: 465-489.\n\n\n\nSoil Conservation Service, U.S. Department of Agriculture. 1972. Soil Survey \nLaboratory Methods and Procedures for Collecting Soil Samples. Soil Survey \nInvestigations Report No. 1 (revised). Washington, D. C.: U.S. Government \nPrinting Office.\n\n\n\nSoong, N.K. 1980. Influence of soil organic matter on soil aggregation of soils in \npeninsular Malaysia. Journal of Rubber Research Institute Malaysia. 28: 32-\n46.\n\n\n\nStevenson, F.J. 1982. Humus Chemistry: Genesis, Composition, Reaction. New York: \nJohn Wiley. \n\n\n\nStevenson, F.J. 1994. Humus Chemistry: Genesis, Composition, Reactions (2nd eds.). \nNew York: John Wiley. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 16, 201256\n\n\n\nSyuntaro, H., Y. Takuya and T. Hiroshi. 2006. Isolation and purification of hydrophilic \nfulvic acids by precipitation. Geoderma. 132: 196-205.\n\n\n\nTajuddin, A. 1992. Influence of Organic Matter on Soil Aggregation and Erodibility of \nSome Malaysian Soils. M.Sc.Agric, Universiti Pertanian Malaysia, Malaysia.\n\n\n\nTeh, C.B.S., K.J. Goh and N.K. Khairun. 2010. Physical changes to oil palm empty \nfruit bunches (EFB) and EFB mat (Ecomat) during their decomposition in the \nfield. Pertanika Journal of Tropical Agricultural Science.33: 39-44.\n\n\n\nTeh, C.B.S., K.J. Goh, C.C Law and T.S Seah. 2011. Short term changes in the soil \nphysical and chemical properties due to different soil and water conservation \npractices in a sloping land oil palm estate. Pertanika Journal of Tropical \nAgricultural Science. 34: 41-62.\n\n\n\nTheng, B.K.G. 1976. Formation and Properties of Clay-Polymer Complexes. New \nYork: Elsevier Science.\n\n\n\nTheng, B.K.G. and H.W. Scharpenseel. 1975. The adsorption of 14C-labeled humic \nacid by montmorillonite. In: Proceedings of Institute Clay Conference, ed. S.W. \nBailey, pp. 643-653. Wilmette, lllinois: Application of Publication.\n\n\n\nTisdall, J.M. and J.M. Oades. 1982. Organic matter and water-stable aggregates in \nsoils. Journal of Soil Science. 33: 141-163.\n\n\n\nWalkley, A. and  C.A. Black. 1934. An examination of degtjareff method for \ndetermining soil organic matter and a proposed modification of the chromic \nacid titration method. Journal of Soil Science 37: 29-37.\n\n\n\nZaharah, A.R. and K.C. Lim. 2000. Oil palm empty fruit bunch as a source of nutrients \nand soil ameliorant in oil palm plantation. Malaysian Journal of Soil Science. \n4: 51-66.\n\n\n\nLee Ying Ping, Christopher Teh Boon Sung, Goh Kah Joo and  Abolfath Moradi\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 135-145 (2016)                         Malaysian Society of Soil Science\n\n\n\nAssessment of Soil Enzyme Activities Based on Soil \nSamples from the Beas River Bed, India Using Multivariate \n\n\n\nTechniques\n\n\n\nKumar, V.a, A. Sharmaa, A.K. Thukrala and R. Bhardwaja*\n\n\n\naDepartment of Botanical and Environmental Sciences, Guru Nanak Dev University, \nAmritsar, Punjab 143005, India\n\n\n\nABSTRACT\nThis study was aimed at assessing soil enzyme activities in the Beas River bed for \nthe  pre-monsoon, post-monsoon and winter seasons. Soil samples were collected \nin triplicates from four sites for each season and analysed for 21 soil characteristics. \nThe soil enzymes assessed were urease, catalase, polyphenol oxidase (PPO) and \ninvertase. The hypothesis tested was that the enzyme activities are determined \nby soil characteristics and other environmental variables. Data were analysed \nusing analysis of variance, multiple comparison test, cluster analysis, principal \ncomponent analysis, stepwise multiple linear regression analysis and artificial \nneural networks. It was concluded from the study that maximum soil urease, PPO \nand invertase activities occurred during the winter season. There were two factors \nunderlying the enzyme activities: factor-1 for urease and catalase, and factor-2 for \nPPO and invertase.  Urease activity was increased to a maximum by the phosphate \ncontent of the soil, an important component of animal excreta. Nickel and Cu are \nthe prosthetic groups of urease and PPO which contributed a maximum to the \nactivities of the respective enzymes.\n\n\n\nKeywords: River Beas, soil enzyme activities, multivariate techniques, \nartificial neural networks\n\n\n\n___________________\n*Corresponding author : E-mail: renubhardwaj82@gmail.com\n\n\n\nINTRODUCTION\nSoil micro flora plays a significant role in the decomposition and mineralisation \nof organic matter by producing enzymes (Burns, 1982). In the soil subsystems, \nbiochemical functions are carried out by soil enzymes (Burns, 1983; Sinsabangh et \nal., 1991). Because of their involvement in the cycling of N, C and P, soil enzymes \nare considered as bio-indicators of soil fertility (Schoenholtz et al., 2000). Soil \nenzymes are formed from plant residues both as extracellular and intracellular \nenzymes (Burns, 1986; Mobley et al., 1989). Soil enzyme activities increase \nwhen there is an increase in content of organic matter. The higher activities of \nenzymes correspond to larger microbial communities and greater stability of \nenzymes adsorbed on the humic materials (Marinari and Antisari, 2010). Enzyme \nactivities act as biomarkers to assess the quality of soil based on their sensitivity \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016136\n\n\n\nKumar et al.\n\n\n\nto soil management practices, nutrient cycling, organic matter decomposition and \nbioremediation activities. Invertase enzyme is important because it releases sugars \nused by microorganisms (Shi et al., 2008; Rahmansyah and Sudiana, 2010). Gu et \nal.   (2009) studied the soil enzymes activities of urease, invertase and polyphenol \noxidase (PPO) from China. Mondal et al., (2015) studied the seasonal variation \nof soil enzyme activities such as urease and invertase in fluoride stressed areas \nof West Bengal, India. Zhang et al., (2015) studied urease, invertase and PPO \nactivities from Gurbantunggut desert, Xinjiang. The present study was aimed \nat assessing soil enzyme activities in the river bed of the River Beas for pre-\nmonsoon, post-monsoon and winter seasons respectively.\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area\nThe River Beas originates in central Himachal Pradesh, India, 32.21\u2032N lat., \n77\u00b005\u2032E at an altitude of 2050 m above sea level, and merges with the river Sutlej \nat Harike, Punjab, after traversing a distance of about 470 km. Soil samples were \ncollected from the river bed of the River Beas between the towns of Beas and \nHarike over a stretch of 63 km at the following sites (Figure 1):\n1. Beas  (31.510\u2019 N and 75.305\u2019 E ) \n2. Kishanpura (31.409\u2019 N and 75.189\u2019 E)\n3. Goindwal Sahib  (31.376\u2019 N and 75.162\u2019 E)\n4. Harike  (31.150\u2019 N and 74.951\u2019 E)\n\n\n\nSoil Sampling and Enzyme Activities\nLandsat (TM) data were obtained from the United States Geological Survey \n(USGS) (http://glovis.usgs.gov/). Map of the study area was prepared by using \nErdas Imagine \u201811\u2019 and Arc GIS 9.3 software. Soil samples were collected on 7th \n\n\n\nJune 2013 (pre-monsoon season), 14th October 2013 (post-monsoon season) and  \n26th February 26, 2014 (winter season)in triplicates at a depth of 0-5 cm and stored \nat 4oC in a refrigerator. All the soil samples were ground and sieved with a 0.6-\nmm sieve in order to remove any effect of particle size before analysis. Standard \nmethods for soil analysis were followed as described earlier (Kumar et al., 2015).\n\n\n\nCatalase activity was measured following the method of Guan et al., (1986). \nTo 2 g of soil, 40 ml of distilled water and 5 ml of 0.3% H2O2 were added. The \nmixture was shaken at 25oC for 20 min. Then, 5 ml of 1.5 M H2SO4 was added and \nthe contents were titrated with 0.1 M KMnO4.\n\n\n\nThe activities of urease and PPO were estimated by following the method of \nGuan (1986). For urease, 5 g of moist soil was incubated at 37oC for 2 h in 20 ml \nof borate buffer. After incubation, 50 ml of 1 M KCl solution was added and the \nmixture was shaken for 30 min. Absorbance was determined using  the  uv-visible \nspectrophotometer at 690 nm.\n\n\n\nFor PPO activity, to 5 g of soil sample, 10 ml of distilled water, 6 ml of \n0.1% ascorbic acid, and 10 ml of 0.02 M catechol were added. Then, the soil \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 137\n\n\n\nAnalysis of Soil Enzyme Activities\n\n\n\nsuspension was incubated in a water bath for 2 min at 30oC. Subsequently 3 ml of \n10% phosphoric acid was added and the filtrate was titrated with 0.005 M iodine.\n\n\n\nInvertase activity was measured by using the method of Guan et al. \n(1986). To 5 g of dried soil, 0.2 ml of methylbenzene was added and allowed to \nstand for 15 min. Then, 15 ml of 18% sucrose solution and 5 ml of phosphate \nbuffer were added, and incubated for 24 h at 37oC. After incubation, 3 ml of 3, \n5-dinitrosalicyclic acid solution and 5 ml of deionized water were added to 0.5 \nml of the filtrate. All tubes were placed in the boiling water bath for 5 min and \nthen cooled to room temperature. Finally the solution was diluted to 50 ml, and \nabsorbance was determined using the uv-visible spectrophotometer at 508 nm. \n\n\n\nStatistical Analysis\nData was statistically analysed by using one way ANOVA, cluster analysis (CA), \nprincipal component analysis (PCA), factor analysis (FA), stepwise multiple \nlinear regression analysis (SMLR) and artificial neural network analysis (ANN). \nMS-Excel-2007, PAST, Minitab-14, Statistica-12 and self-coded software were \nused for the analysis.\n\n\n\nRESULTS AND DISCUSSION\nTable 1 shows the soil characteristics of the sampled soils  from the river bed of \nthe Beas for different seasons. Table 2 summarises the statistics of the enzyme \nactivities in the soil samples collected from the river beds of Beas for different \nseasons. Differences in soil enzyme activities were found to be significant as \ngiven in Table 2. Urease, PPO and invertase activities were found  to be at a \nmaximum during the winter season. Activity of catalase is sensitive to biological \nfactors and is closely related with major soil nutrient elements (Asmar et al., \n\n\n\n6 \n \n\n\n\nMetal analysis \nUrease activity K (-1.01) Ni (0.86) Zn (0.74) Mn(0.54) Fe(-0.53) Na(0.43) \nCatalase activity Ni (0.53) Na (-0.51)     \nPPO activity Ca (-0.84) Fe (-0.59) Cu (0.56)    \nInvertase activity Ni (0.87) Ca (-0.26)     \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 1 Study area \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Study area\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016138\n\n\n\n4 \n \n\n\n\nTable 1 Chemical characteristic of soils from pre-monsoon, post-monsoon and winter seasons \nof Beas river bed. \nCharacteristics Pre-monsoon \n\n\n\nseason \nMean\u00b1SD \n\n\n\nPost-monsoon \nseason \nMean\u00b1SD \n\n\n\nWinter season \nMean\u00b1SD \n\n\n\nF-Ratio \n*(p<0.05) \n\n\n\nHSD \n(p<0.05) \n\n\n\npH 8.26\u00b10.096 8.16\u00b10.074 6.65\u00b10.09 406.50* 0.176 \n\n\n\nConductivity \n\n\n\n(\u00b5S/cm) \n\n\n\n281.00\u00b172.88 357.85\u00b1121.44 156.65\u00b126.63 5.95* 164.34 \n\n\n\nWHC (%) 35.36\u00b12.76 39.30\u00b14.95 38.78\u00b16.82 0.69 ns \n\n\n\nH (%) 0.32\u00b10.07 0.31\u00b10.08 1.05\u00b10.68 4.45* 0.794 \n\n\n\nC (%) 0.16\u00b10.02 0.17\u00b10.02 0.15\u00b10.02 0.25 ns \n\n\n\nN (%) 0.084\u00b10.02 0.094\u00b10.01 0.135\u00b10.04 3.21 ns \n\n\n\nP (mg/g) 0.013\u00b10.010 0.006\u00b10.005 0.093\u00b10.043 13.97* 0.051 \n\n\n\nNa (mg/g) 4.19\u00b11.23 1.91\u00b10.26 4.39\u00b11.12 7.91* 1.93 \n\n\n\nK (mg/g) 2.70\u00b10.42 1.75\u00b10.23 2.13\u00b10.70 3.77 ns \n\n\n\nCa (mg/g ) 15.80\u00b11.11 17.20\u00b13.18 11.85\u00b12.18 5.71* 4.58 \n\n\n\nMg (mg/g) 4.61\u00b11.01 5.73\u00b12.99 3.09\u00b11.01 1.91 ns \n\n\n\nFe (mg/g) 37.48\u00b112.42 28.67\u00b12.94 30.29\u00b16.62 1.273 ns \n\n\n\nZn (mg/g) 0.031\u00b10.006 0.030\u00b10.006 0.031\u00b10.002 0.033 ns \n\n\n\nMn (mg/g ) 1.20\u00b10.39 0.985\u00b10.22 0.385\u00b10.11 9.93* 0.531 \n\n\n\nNi (mg/g) 0.142\u00b10.11 0.34\u00b10.32 0.074\u00b10.056 1.91 ns \n\n\n\nCr (mg/g) 0.023\u00b10.004 0.022\u00b10.003 0.022\u00b10.004 0.088 ns \n\n\n\nCu (mg/g) 0.020\u00b10.007 0.016\u00b10.004 0.016\u00b10.003 0.981 ns \n\n\n\nns = not significant and \u2018*\u2019= significant at p< 0.05. \n\n\n\n\n\n\n\nTable 2 Soil enzyme activities of Beas river bed for pre-monsoon, post-monsoon and winter \n\n\n\nseasons. \n\n\n\nCharacteristics Pre-monsoon \nseason \nMean\u00b1SD \n\n\n\nPost-monsoon \nseason \nMean\u00b1SD \n\n\n\nWinter \nSeason \nMean\u00b1SD \n\n\n\nF-Ratio \n*(p<0.05) \n\n\n\nHSD \n(p<0.05) \n\n\n\nUrease mg N-NH4\n+ \n\n\n\n100 g-1 soil 24 h-1 \n7.34ab\u00b10.94 7.09b\u00b10.96 11.51a\u00b13.32 3.99* 4.31 \n\n\n\nCatalase 0.1 M \nKMnO4 g-1 soil \n\n\n\n0.154b\u00b10.028 0.25a\u00b10.036 0.167b\u00b10.023 18.22* 0.042 \n\n\n\nTABLE 1\nChemical characteristic of soils from pre-monsoon, post-monsoon and winter seasons of \n\n\n\nBeas river bed.\n\n\n\nTABLE 2\nSoil enzyme activities of Beas river bed for pre-monsoon, post-monsoon and\n\n\n\nwinter seasons.\n\n\n\nKumar et al.\n\n\n\n5 \n \n\n\n\nTable 2 Soil enzyme activities of Beas river bed for pre-monsoon, post-monsoon and winter \n\n\n\nseasons. \n\n\n\nCharacteristics Pre-monsoon \nseason \nMean\u00b1SD \n\n\n\nPost-monsoon \nseason \nMean\u00b1SD \n\n\n\nWinter \nSeason \nMean\u00b1SD \n\n\n\nF-Ratio \n*(p<0.05) \n\n\n\nHSD \n(p<0.05) \n\n\n\nUrease mg N-NH4\n+ \n\n\n\n100 g-1 soil 24 h-1 \n7.34ab\u00b10.94 7.09b\u00b10.96 11.51a\u00b13.32 3.99* 4.31 \n\n\n\nCatalase 0.1 M \nKMnO4 g-1 soil \n\n\n\n0.154b\u00b10.028 0.25a\u00b10.036 0.167b\u00b10.023 18.22* 0.042 \n\n\n\nPPO 0.005 M I2 g-1 \n\n\n\nsoil h-1 \n22.25ab\u00b12.18 20.35b\u00b13.52 25.40a\u00b11.97 5.91* 3.44 \n\n\n\nInvertase glucose \nmg g-1 soil 24 h-1 \n\n\n\n2.82c\u00b10.67 3.75a\u00b10.52 4.37a\u00b12.23 8.33* 0.82 \n\n\n\nValues with same superscript or no superscript in the same row imply that the values are not \nsignificantly different from each other at p> 0.05. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 3 Percent variance explained of soil enzyme activities for \ndifferent seasons from Beas river bed. \n\n\n\n\n\n\n\n    Seasons \n\n\n\nPrincipal component \n\n\n\nPC1 PC2 PC1+PC2                  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 139\n\n\n\n1992; Rodriguez-kabana and Truelove, 1982). Mondal et al. (2015) reported the \nrange of invertase (activity to be 0.41 to 3.97 glucose mg g-1 soil 24 h-1 in fluoride \nstressed areas of Birbhum district, West Bengal. Trasar-Cepeda et al., (2008) also \nstudied urease activity (in agricultural and forest soils from Spain and found it to \nrange from1.9 to 17.7 \u00b5mol NH3 g\n\n\n\n-1 h-1.  Our results showed slight variations from \ntheir work.\n\n\n\nCluster analysis was applied to the enzyme activities for different seasons \n(Figure 2). The enzyme activities were similar during the pre-monsoon and post-\nmonsoon seasons, but different during the winter season. The difference in soil \nenzyme activities may be attributed to differences in temperature during the \nseasons, winter being the coldest month. PCA was also applied to the enzyme \nactivities (Table 3). The first two components of PCA explained more than 99% \nof the total variance for the pre-monsoon (86.32% and 12.97%), post-monsoon \n(93.59% and 6.02%) and winter seasons (96.07% and 3.91%), respectively. In \nfactor analysis, two factors were mainly responsible for soil enzyme activities \n(Figure 3). Factor-1 accounted for 39% of the total variance and had negative \nloading on urease, but positive loading on catalase, with communalities 0.551 \nand 0.869. This factor indicates the fertility of the soil. Invertase and PPO had \n\n\n\n7 \n \n\n\n\nFigure 2 Cluster analysis of enzyme activities for different seasons \n\n\n\nF1\n\n\n\nF2\n\n\n\ne\n\n\n\ne\n\n\n\ne\n\n\n\ne\n\n\n\nUrease\n\n\n\nCatalase\n\n\n\nPPO\n\n\n\nInvertase\n\n\n\n-0.727\n\n\n\n0.913\n\n\n\n-0.323\n\n\n\n0.344\n\n\n\n-0.151\n\n\n\n-0.189\n\n\n\n-0.822\n\n\n\n-0.882\n\n\n\n\n\n\n\nFigure 3 Factor analysis of soil enzymes in the different seasons \n\n\n\n\n\n\n\n7 \n \n\n\n\nFigure 2 Cluster analysis of enzyme activities for different seasons \n\n\n\nF1\n\n\n\nF2\n\n\n\ne\n\n\n\ne\n\n\n\ne\n\n\n\ne\n\n\n\nUrease\n\n\n\nCatalase\n\n\n\nPPO\n\n\n\nInvertase\n\n\n\n-0.727\n\n\n\n0.913\n\n\n\n-0.323\n\n\n\n0.344\n\n\n\n-0.151\n\n\n\n-0.189\n\n\n\n-0.822\n\n\n\n-0.882\n\n\n\n\n\n\n\nFigure 3 Factor analysis of soil enzymes in the different seasons \n\n\n\n\n\n\n\nFigure 2: Cluster analysis of enzyme activities for different seasons\n\n\n\nFigure 3: Factor analysis of soil enzymes in the different seasons\n\n\n\nAnalysis of Soil Enzyme Activities\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016140\n\n\n\nnegative loadings on factor-2 which accounted for 37% of the total variance with \ncommunalities 0.896 and 0.780. This factor accounts for decomposition of soil \nmatter.\n\n\n\nIn stepwise MLR analysis (Table 4), 83.6% of urease activity was accounted \nfor by pH and P, and in metal analysis, 99.8% of urease activity was accounted \nfor by Na, K, Ca, Fe, Zn, Mn and Ni. Urease activity is largely dependent on \nP, and P is the most important component of animal and human excreta. Ni is \nthe prosthetic group of urease, and Ni contributes maximum to urease activity. \nDependence of catalase activity on conductivity and C was explained to the extent \nof 38.9% of the variation, and in metal analysis, 74.8% of catalase activity was \naccounted  for by Na and Ni. Conductivity explained maximum variability in \ncatalase activity, and in metal analysis, Ni was found to contribute maximum to \nthe catalase activity. For PPO activity, 48%  was accounted  for by C and N, and \nin metal analysis 79.6% of the PPO activity was explained by Ca, Fe and Cu. N \ncontributed maximum to the PPO activity. Cu is the prosthetic group of PPO, and \ncontributed maximum to the activity of PPO. Dependence of invertase activity on \nconductivity, C and N was explained to the extent of 40.9%. In metal analysis, \n88.5% of invertase activity was accounted for by Ca and Ni. Nitrogen explained \nthe maximum variability in invertase activity, and in metal analysis, maximum \nvariability was explained by Ni. Nayak et al., (2007) reported that the activities of \nsoil enzymes are enhanced to different degrees by organic manure incorporation \nand noted significant and positive relationships of the enzyme activities with C \nand N.  Sucrose, the substrate of soil invertase, is partially responsible for the \nbreakdown of plant litter in the soil (Frankenberger and Johanson,1983). Urease \nenzyme is responsible for the hydrolysis of urea into NH3 and CO2. Urease activity \nindicates the N supply to the plants. Variations in the activity of urease enzyme \nare due to variations in the physcio-chemical characteristics of the soil, organic \nmatter and N accumulation, considered as substrates for soil urease. PPO has \na very important function in the cycling of aromatic compounds. Soil catalase \nis considered to be a potential indicator of aerobic microbial activity and has \nbeen related to the number of micro-organisms and soil fertility (Trasar-Cepeda \net al., 1999). Heavy metals inhibit enzyme reactions by forming complexes \n\n\n\n4 \n \n\n\n\nCr (mg/g) 0.023\u00b10.004 0.022\u00b10.003 0.022\u00b10.004 0.088 ns \n\n\n\nCu (mg/g) 0.020\u00b10.007 0.016\u00b10.004 0.016\u00b10.003 0.981 ns \n\n\n\nns = not significant and \u2018*\u2019= significant at p< 0.05. \n\n\n\n\n\n\n\nTable 2 Soil enzyme activities of Beas river bed for pre-monsoon, post-monsoon and winter \n\n\n\nseasons. \n\n\n\nCharacteristics Pre-monsoon \nseason \nMean\u00b1SD \n\n\n\nPost-monsoon \nseason \nMean\u00b1SD \n\n\n\nWinter \nSeason \nMean\u00b1SD \n\n\n\nF-Ratio \n*(p<0.05) \n\n\n\nHSD \n(p<0.05) \n\n\n\nUrease mg N-NH4\n+ \n\n\n\n100 g-1 soil 24 h-1 \n7.34ab\u00b10.94 7.09b\u00b10.96 11.51a\u00b13.32 3.99* 4.31 \n\n\n\nCatalase 0.1 M \nKMnO4 g-1 soil \n\n\n\n0.154b\u00b10.028 0.25a\u00b10.036 0.167b\u00b10.023 18.22* 0.042 \n\n\n\nPPO 0.005 M I2 g-1 \n\n\n\nsoil h-1 \n22.25ab\u00b12.18 20.35b\u00b13.52 25.40a\u00b11.97 5.91* 3.44 \n\n\n\nInvertase glucose \nmg g-1 soil 24 h-1 \n\n\n\n2.82c\u00b10.67 3.75a\u00b10.52 4.37a\u00b12.23 8.33* 0.82 \n\n\n\nValues with same superscript or no superscript in the same row imply that the values are not \n\n\n\nsignificantly different from each other at p< 0.05. \n\n\n\n\n\n\n\nTable 3 Percent variance explained of soil enzyme activities for \ndifferent seasons from Beas river bed. \n\n\n\n\n\n\n\n    Seasons \n\n\n\nPrincipal component \n\n\n\nPC1 PC2 PC1+PC2                  \n\n\n\nPre-monsoon season 86.32% 12.97% 99.29% \n\n\n\nPost-monsoon season 93.59% 6.02% 99.61% \n\n\n\nWinter season 96.07% 3.91% 99.98% \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 4 Stepwise multiple regression of soil enzyme activities with soil characteristics \n\n\n\nTABLE 3\nPercent variance explained of soil enzyme activities for different seasons from Beas\n\n\n\nriver bed.\n\n\n\nKumar et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 141\n\n\n\nwith their substrates or blocking functional groups of the enzymes (Speir et al., \n1995). A negative relationship of metals, i.e., Fe, Na, K and Ca with enzyme \nactivities, indicates that microbes secreting these enzymes are sensitive to the \nmetal concentration in the soil. Compared to the pre-monsoon and post-monsoon \nseasons, enzyme activities were found to be higher during the winter season. This \nmay be due to high nitrogen and phosphorus contents in the soil, and slow litter \ndecomposition during this season. Boerner et al., (2005) and Mukhopadhyay and \nJoy (2010) studied the variations in soil enzyme activities with respect to seasons. \nHigh soil organic content was responsible for higher enzyme activities in the \nupper layer of soil (Hu et al., 2005). ANN models fitted well with the observed \nand the simulated data (Figures 4 a, b, c and d). The correlations between target \nand output values from ANN for catalase, urease, PPO and invertase were highly \n\n\n\nTABLE 4\nStepwise multiple regression of soil enzyme activities with soil characteristics\n\n\n\n5 \n \n\n\n\nPost-monsoon season 93.59% 6.02% 99.61% \n\n\n\nWinter season 96.07% 3.91% 99.98% \n\n\n\n\n\n\n\nTable 4 Stepwise multiple regression of soil enzyme activities with soil characteristics \n\n\n\nCharacteristics Equation R2 \n Physico-chemical characteristics  \nUrease activity (mg \nN-NH4\n\n\n\n+ 100 g-1 soil \n\n\n\n24 h-1) = \n\n\n\n-29.36 + 4.3 pH + 132 P (mg/g)  0.836 \n(p<0.001) \n\n\n\nCatalase activity \n(0.1 M KMnO4 g-1 \n\n\n\nsoil) = \n\n\n\n0.178 + 0.0003 conductivity (\u00b5S/cm) \u2013 0.42 C (%) 0.389 \n(p<0.05) \n\n\n\nPPO activity (0.005 \nM I2 g-1 soil h-1) = \n\n\n\n32.85 \u2013 100 C (%) + 62 N (%) 0.480 \n(p<0.05) \n\n\n\nInvertase activity \n(glucose mg g-1 soil \n24 h-1) = \n\n\n\n7.39 + 0.019 conductivity (\u00b5S/cm) \u2013 96 C (%) + 68 N (%) 0.409 \n(p<0.05) \n\n\n\n Metal analysis  \nUrease activity (mg \nN-NH4\n\n\n\n+ 100 g-1 soil \n\n\n\n24 h-1) = \n\n\n\n12.93 + 1.17 Na (mg g-1) \u2013 6.71 K (mg g-1) \u2013 27.24 Ca \n(mg g-1)  \u2013 0.25 Fe (mg g-1) + 592 Zn (mg g-1) + 5.03 Mn \n(mg g-1) \u2013 16.30 Ni (mg g-1)  \n \n\n\n\n0.998 \n(p<0.001) \n\n\n\nCatalase activity \n(0.1 M KMnO4 g-1 \n\n\n\nsoil) = \n\n\n\n0.23 \u2013 0.018 Na (mg g-1) + 0.13 Ni (mg g-1) \n \n\n\n\n0.748 \n(p<0.001) \n\n\n\nPPO activity (0.005 \nM I2 g-1 soil h-1) = \n\n\n\n37.71 \u2013 48.50 Ca (mg g-1) \u2013 0.227 Fe + 368 Cu (mg g-1) \n \n\n\n\n0.796 \n(p<0.001) \n\n\n\nInvertase activity \n(glucose mg g-1 soil \n24 h-1) = \n\n\n\n5.54 \u2013 11.1 Ca (mg g-1) + 9.8 Ni (mg g-1) \n \n \n\n\n\n0.885 \n(p<0.001) \n\n\n\nRelevance of dependent variable on the basis of \u03b2-regression coefficients \n \u03b21 \u03b22 \u03b23 \u03b24 \u03b25 \u03b26 \nUrease  activity pH (0.83) P (1.55)     \nCatalase activity Cond. (0.66) C (-0.20)     \nPPO activity C (-0.77) N (0.68)     \nInvertase activity Cond. (0.92) C (-1.02) N (1.04)    \n\n\n\nMetal analysis \nUrease activity K (-1.01) Ni (0.86) Zn (0.74) Mn(0.54) Fe(-0.53) Na(0.43) \nCatalase activity Ni (0.53) Na (-0.51)     \nPPO activity Ca (-0.84) Fe (-0.59) Cu (0.56)    \nInvertase activity Ni (0.87) Ca (-0.26)     \n \n\n\n\nAnalysis of Soil Enzyme Activities\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016142\n\n\n\n8 \n \n\n\n\nCatalase (Target) vs. Catalase (Output)\nTrain(r=0.983), Test(r=0.894), Validation(r=0.823)\n\n\n\n0.06\n0.08\n\n\n\n0.10\n0.12\n\n\n\n0.14\n0.16\n\n\n\n0.18\n0.20\n\n\n\n0.22\n0.24\n\n\n\n0.26\n0.28\n\n\n\n0.30\n0.32\n\n\n\n0.34\n0.36\n\n\n\n0.38\n\n\n\nCatalase (Target)\n\n\n\n0.06\n\n\n\n0.08\n\n\n\n0.10\n\n\n\n0.12\n\n\n\n0.14\n\n\n\n0.16\n\n\n\n0.18\n\n\n\n0.20\n\n\n\n0.22\n\n\n\n0.24\n\n\n\n0.26\n\n\n\n0.28\n\n\n\n0.30\n\n\n\n0.32\n\n\n\n0.34\n\n\n\n0.36\n\n\n\nC\nat\n\n\n\nal\nas\n\n\n\ne \n(O\n\n\n\nut\npu\n\n\n\nt)\n\n\n\n\n\n\n\nUrease (Target) vs. Urease (Output)\n Train(r=0.978), Test(r=0.690), Validation(r=0.946)\n\n\n\n2 4 6 8 10 12 14 16 18 20 22 24 26 28 30\n\n\n\nUrease (Target)\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\n16\n\n\n\n18\n\n\n\n20\n\n\n\n22\n\n\n\n24\n\n\n\n26\n\n\n\n28\n\n\n\nU\nre\n\n\n\nas\ne \n\n\n\n(O\nut\n\n\n\npu\nt)\n\n\n\n\n\n\n\n \nPPO (Target) vs. PPO (Output)\n\n\n\n Train(r=0.989), Test(r=0.908), Validation(r=0.947)\n\n\n\n16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32\n\n\n\nPPO (Target)\n\n\n\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n\n\n\nPP\nO\n\n\n\n (O\nut\n\n\n\npu\nt)\n\n\n\n\n\n\n\nInvertase (Target) vs. Invertase (Output)\n Train(r=0.983), Test(r=0.887), Validation(r=0.820)\n\n\n\n1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0\n\n\n\nInvertase (Target)\n\n\n\n1.0\n\n\n\n1.5\n\n\n\n2.0\n\n\n\n2.5\n\n\n\n3.0\n\n\n\n3.5\n\n\n\n4.0\n\n\n\n4.5\n\n\n\n5.0\n\n\n\n5.5\n\n\n\n6.0\nIn\n\n\n\nve\nrta\n\n\n\nse\n (O\n\n\n\nut\npu\n\n\n\nt)\n\n\n\n\n\n\n\n\n\n\n\n8 \n \n\n\n\nCatalase (Target) vs. Catalase (Output)\nTrain(r=0.983), Test(r=0.894), Validation(r=0.823)\n\n\n\n0.06\n0.08\n\n\n\n0.10\n0.12\n\n\n\n0.14\n0.16\n\n\n\n0.18\n0.20\n\n\n\n0.22\n0.24\n\n\n\n0.26\n0.28\n\n\n\n0.30\n0.32\n\n\n\n0.34\n0.36\n\n\n\n0.38\n\n\n\nCatalase (Target)\n\n\n\n0.06\n\n\n\n0.08\n\n\n\n0.10\n\n\n\n0.12\n\n\n\n0.14\n\n\n\n0.16\n\n\n\n0.18\n\n\n\n0.20\n\n\n\n0.22\n\n\n\n0.24\n\n\n\n0.26\n\n\n\n0.28\n\n\n\n0.30\n\n\n\n0.32\n\n\n\n0.34\n\n\n\n0.36\n\n\n\nC\nat\n\n\n\nal\nas\n\n\n\ne \n(O\n\n\n\nut\npu\n\n\n\nt)\n\n\n\n\n\n\n\nUrease (Target) vs. Urease (Output)\n Train(r=0.978), Test(r=0.690), Validation(r=0.946)\n\n\n\n2 4 6 8 10 12 14 16 18 20 22 24 26 28 30\n\n\n\nUrease (Target)\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\n16\n\n\n\n18\n\n\n\n20\n\n\n\n22\n\n\n\n24\n\n\n\n26\n\n\n\n28\n\n\n\nU\nre\n\n\n\nas\ne \n\n\n\n(O\nut\n\n\n\npu\nt)\n\n\n\n\n\n\n\n \nPPO (Target) vs. PPO (Output)\n\n\n\n Train(r=0.989), Test(r=0.908), Validation(r=0.947)\n\n\n\n16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32\n\n\n\nPPO (Target)\n\n\n\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n\n\n\nPP\nO\n\n\n\n (O\nut\n\n\n\npu\nt)\n\n\n\n\n\n\n\nInvertase (Target) vs. Invertase (Output)\n Train(r=0.983), Test(r=0.887), Validation(r=0.820)\n\n\n\n1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0\n\n\n\nInvertase (Target)\n\n\n\n1.0\n\n\n\n1.5\n\n\n\n2.0\n\n\n\n2.5\n\n\n\n3.0\n\n\n\n3.5\n\n\n\n4.0\n\n\n\n4.5\n\n\n\n5.0\n\n\n\n5.5\n\n\n\n6.0\n\n\n\nIn\nve\n\n\n\nrta\nse\n\n\n\n (O\nut\n\n\n\npu\nt)\n\n\n\n\n\n\n\n\n\n\n\nsignificant, implying that ANN can simulate enzyme activities based on soil \ncharacteristics. \n\n\n\nCONCLUSION\nFrom the present study, it was established that maximum activities of enzymes \noccur in the winter season. Cluster analysis revealed that enzyme activities were \nsimilar during the pre-monsoon and post-monsoon seasons. Factor analysis showed \nthat factor-1 influences the fertility of the soil, whereas factor-2 is responsible \nfor the decomposition of soil organic matter. In SMLR analysis, phosphorus \nexplained maximum variability in urease activity and it to be noted that P is the \nmost important component of human and animal excreta. Nickel is the prosthetic \ngroup of urease and contributes maximum to the activity of urease. Similarly Cu \nis the prosthetic group of polyphenol oxidase enzyme which contributes to the \nactivity of this enzyme. \n\n\n\nFigure 4(b): Correlation between target \nand output urease enzyme using ANN \n\n\n\nmodel\n\n\n\nFigure 4(c): Correlation between target \nand output PPO enzyme using ANN model\n\n\n\nFigure 4(d): Correlation between target \nand output invertase enzyme using ANN \n\n\n\nmodel\n\n\n\nFigure 4(a):  Correlation between target \nand output catalase enzyme using ANN \n\n\n\nmodel\n\n\n\nKumar et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 143\n\n\n\nACKNOWLEDGEMENTS \nThe authors are thankful to the Head, Department of Botanical &    Environmental \nSciences, for providing research facilities. VK is also thankful to the University \nGrants Commission, New Delhi, for providing financial assistance under the \nProgramme on University with Potential for Excellence.\n\n\n\nREFERENCES\nAsmar, F., F. Eiland and N.E. Nielson. 1992. 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"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n1 \n\n\n\n\n\n\n\n\n\n\n\nA Narrative Review of the Impact of Forest Rehabilitation Programs on \n\n\n\nSoil Quality in Peninsular Malaysia \n \n\n\n\nD.S. Karam\n1\n*, Z. Ibrahim\n\n\n\n2\n, R. Rani\n\n\n\n3\n, A.R.A. Wahid\n\n\n\n4\n, Z.A. Kadir\n\n\n\n3\n, M.S.A. Salleh\n\n\n\n3\n, \n\n\n\nH. Abdul-Hamid\n5\n, K.S. Rajoo\n\n\n\n6\n, M.H.M. Ibrahim\n\n\n\n6 \n, M.A.A. Azhar\n\n\n\n1\n, D. Zulperi\n\n\n\n7 \n& A. Abdu\n\n\n\n5\n \n\n\n\n\n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia \n2Forestry Department Peninsular Malaysia \n\n\n\n3Terengganu State Forestry Department, Terengganu, Malaysia \n4Perak State Forestry Department, Perak, Malaysia \n\n\n\n5Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, UPM \n6Department of Forestry Science, Faculty of Agricultural and Forestry Sciences, UPM \n\n\n\n7Department of Plant Protection, Faculty of Agriculture, UPM \n\n\n\n\n\n\n\n*Corresponding author: daljitsingh@upm.edu.my  \n \n\n\n\nABSTRACT \n \n\n\n\nThe rehabilitation of forest areas is not new to Malaysia as forest replanting activities have \n\n\n\nbeen carried out throughout the country for years to meet the demand for woody and non-\n\n\n\nwoody products as well as to nurture degraded forestland. Thus it is important for a soil to be \n\n\n\nevaluated to ascertain the degree to which rehabilitation activities have succeeded in restoring \n\n\n\nforest health, particularly in sustaining soil quality in rehabilitated forests. This review article \n\n\n\naims to provide a corpus of information for forest managers and related agencies who work \n\n\n\nclosely with forestry. The aim is to provide an overview on the importance of soil quality in \n\n\n\nmeasuring the success of forest rehabilitation programs. Research articles on the evaluation \n\n\n\nof soil properties at selected rehabilitated forests in Peninsular Malaysia were included in the \n\n\n\nreview. The impact of forest rehabilitation in relation to soil properties comprising soil \n\n\n\ncompaction, moisture, acidity, macronutrients, cation exchange capacity, microbial count, \n\n\n\nmicrobial enzymatic activity, and microbial biomass is discussed. Natural forest is used as a \n\n\n\nbenchmark to see the effect of forest rehabilitation programs. Our review indicates that \n\n\n\nrehabilitated forests that were established earlier and have gone through a longer period of \n\n\n\ntime have better soil quality compared to the soil of forests established later. This shows that \n\n\n\nrehabilitated forests are able to restore their soil quality and achieve fertility on par with \n\n\n\nnatural forests, if given longer periods of time for recovery. Soil quality analyses should be \n\n\n\ndone regularly to measure the extent of success in rehabilitation programs. \n \n\n\n\nKey words: multi-storied forest management, enrichment planting, soil quality  \n\n\n\n\n\n\n\nINTRODUCTION \n \n\n\n\nThe Food and Agriculture Organization (FAO) (2010) defines a forest as land not classified \n\n\n\nas agricultural or urban, covering more than 0.5 hectares and containing trees of height more \n\n\n\nthan five meters with 10% canopy covering that is able to reach the threshold in situ. Forests \n\n\n\ncan be categorised as natural forest, primary forest, closed forest, permanent forest estate and \n\n\n\nplanted forest (Hertel et al. 2009; Blaser et al. 2011). Dipterocarpus, Dryobalanops, Hopea \n\n\n\nand Parashorea are among the tree genera present in dipterocarp forests which cover \n\n\n\napproximately 17.15 million hectares across Malaysia with 31.9% in Peninsular Malaysia, \n\n\n\n45.66% in Sarawak and 22.39% in Sabah (Blaser et al. 2011). Of the 13.1 million hectares of \n\n\n\npeat swamp forest in Malaysia, Sarawak has the largest area with, 890, 000 hectares. \n\n\n\n\nmailto:daljitsingh@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n2 \n\n\n\n\n\n\n\nMangrove forests cover 709, 700 hectares in Malaysia with 418,723 hectares located in \n\n\n\nSabah (Blaser et al. 2011). They need to be well managed in order to maintain and sustain \n\n\n\nforest health. Forest health can be defined as the capacity of the forest to supply and allocate \n\n\n\nsufficient amounts of water, nutrients and energy needed by the flora and fauna while \n\n\n\nmaintaining the resistance towards biotic and abiotic stress or disturbance (Percy and Ferretti \n\n\n\n2004; Karam et al. 2016). Forest health is usually evaluated through soil property analyses, \n\n\n\necosystem productivity or soil biota (Karam et al. 2016). Table 1 shows tropical forest cover \n\n\n\nin several countries from 2001 till 2020. \n\n\n\n \nTABLE 1 \n\n\n\nTropical forest covers (2001-2020) \n\n\n\nYear Indonesia Laos China Myanmar India Malaysia Thailand Philippines \n\n\n\n Primary forest extent ( million hectares) \n\n\n\n2001 93.8 8.3 1.7 14.0 10.2 15.9 5.9 4.6 \n\n\n\n2010 90.2 8.1 1.7 13.8 10.1 15.0 5.9 4.5 \n\n\n\n2020 84.4 7.5 1.7 13.5 9.9 13.3 5.9 4.4 \n\n\n\n Tree cover extent (million  hectares) \n\n\n\n2001 159.8 8.3 42.8 42.8 35.1 29.1 19.8 18.3 \n\n\n\n2010 157.7 8.1 41.1 40.9 31.4 28.6 18.1 18.1 \n\n\n\n2020 141.7 7.5 38.5 38.2 30.4 23.8 17.4 17.4 \n\n\n\nSource: Butler 2020 \n\n\n\n\n\n\n\n\n\n\n\nNATURAL FORESTS \n \n\n\n\nForests that have attained great age and exhibit a unique biodiversity are known as natural \n\n\n\nforests. Natural forests sometimes also referred to as virgin forests, possess large numbers of \n\n\n\ntrees, shrubs and herbs, multi-layered tree canopies, debris and forest litter (Hackl et al. \n\n\n\n2004). In Malaysia, natural forests were excessively logged in the 1960s, no doubt \n\n\n\ncontributing to the economy of the country (Arifin et al. 2008). Currently concerns about the \n\n\n\nover-exploitation of natural forest resources have increased as methods of logging and \n\n\n\nharvesting have not taken into account the negative environmental impacts caused by heavy \n\n\n\nmachinery and clear-felling on soil quality. As a result of logging activities, natural forest soil \n\n\n\nhas being degraded. Absence of proper silviculture treatment to restore the health of the \n\n\n\nforest has led to soil infertility (Moran et al. 2000).  \n\n\n\nDeforestation of Tropical Forests \n \n\n\n\nDeforestation is the act of forest clearing for agricultural, logging or urban development \n\n\n\npurposes. Deforestation alters climate, vegetation and animal ecology (Yasuoka and Levins \n\n\n\n2007). According to Aquilar-Amuchastequi and Henebry (2007), forests tend to be converted \n\n\n\nor deforested if they do not have any economically valuable resources such as timber or other \n\n\n\nnon-woody products. In Malaysia, most trees species such as Shorea and Dipterocarpus are \n\n\n\nlogged due to their high economic value. The total average annual harvesting production of S. \n\n\n\nparvifolia and S. macroptera is highest followed by Shorea spp, Dipterocarpus and \n\n\n\nKoompasia. Non-sustainable forest opening or development has led to serious environmental \n\n\n\nproblems namely soil erosion, landslides and flooding. In such situations, biodiversity \n\n\n\nvanishes if no initiative is taken to preserve or nurture it. Table 2 shows primary forest loss \n\n\n\nand tree cover changes of selected tropical countries including Malaysia. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n3 \n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nPrimary forest loss and tree cover change in selected tropical countries \n\n\n\nYear Indonesia Laos China Myanmar India Malaysia Thailand Philippines \n\n\n\n Primary forest loss [M ha (%)] \n\n\n\n2002-\n\n\n\n2009 \n\n\n\n-3.63 \n\n\n\n(-3.9%) \n\n\n\n-0.23 \n\n\n\n(-2.7%) \n\n\n\n-0.03 \n\n\n\n(-1.9%) \n\n\n\n-0.19 \n\n\n\n(-1.4%) \n\n\n\n-0.13 \n\n\n\n(-1.2%) \n\n\n\n-0.98 \n\n\n\n(-6.2%) \n\n\n\n-0.07 \n\n\n\n(-1.2%) \n\n\n\n-0.05 \n\n\n\n(-1.1%) \n\n\n\n2010-\n\n\n\n2019 \n\n\n\n-5.83 \n\n\n\n6.5%) \n\n\n\n-0.55 \n\n\n\n(-6.8%) \n\n\n\n-0.04 \n\n\n\n(-2.4% \n\n\n\n-0.38 \n\n\n\n(-2.8%) \n\n\n\n-0.20 \n\n\n\n(-2.0%) \n\n\n\n-1.65 \n\n\n\n(-11.0%) \n\n\n\n0.05 \n\n\n\n(-0.9%) \n\n\n\n0.55 \n\n\n\n(-6.8%) \n\n\n\n Tree cover change [M ha (%)] \n\n\n\n2002-\n\n\n\n2009 \n\n\n\n-2.09 \n\n\n\n(-1.3%) \n\n\n\n-0.23 \n\n\n\n(-2.7%) \n\n\n\n-1.67 \n\n\n\n(-3.9%) \n\n\n\n-1.90 \n\n\n\n(-4.4%) \n\n\n\n-3.67 \n\n\n\n(-10.5%) \n\n\n\n-0.47 \n\n\n\n(-1.6%) \n\n\n\n-0.75 \n\n\n\n(-3.8%) \n\n\n\n-0.18 \n\n\n\n(-1.0%) \n2010-\n\n\n\n2019 \n\n\n\n-15.98 \n\n\n\n(-10.1%) \n\n\n\n-0.55 \n\n\n\n(-6.8%) \n\n\n\n-2.66 \n\n\n\n(-6.5%) \n\n\n\n-2.70 \n\n\n\n(-6.6%) \n\n\n\n-1.18 \n\n\n\n(-3.8%) \n\n\n\n-4.84 \n\n\n\n(-16.9%) \n\n\n\n-1.31 \n\n\n\n(-6.9%) \n\n\n\n-0.80 \n\n\n\n(-4.4%) \n\n\n\nSource: Butler, 2020. Note: \u201c-\u201c shows the rate of forest loss/decrease \n\n\n\n\n\n\n\n\n\n\n\nFOREST REHABILITATION \n \n\n\n\nNatural Regeneration \n\n\n\n\n\n\n\nA secondary forest is a forest that undergoes natural regeneration after severe disturbances \n\n\n\nsuch as fire, pest infestation, shifting cultivation or timber logging for a long period of time. \n\n\n\nAfter logging activities, the forest is left idle to re-grow naturally without any forest \n\n\n\ntreatment. In these forests, pioneer species such as Macaranga spp. will colonize the area due \n\n\n\nto the opening of canopy that allows direct exposure of sunlight. In tropical countries such as \n\n\n\nMalaysia, Thailand, Laos and Indonesia, the opening of forestland for agriculture and timber \n\n\n\nlogging causes extreme soil nutrients loss (Arifin et al. 2008; Hamzah et al. 2009). \n \n\n\n\nEnrichment Planting \n\n\n\n\n\n\n\nMontagnini et al. (1997) stated that the introduction of valuable tree species to degraded \n\n\n\nforest areas without the elimination of valuable individual trees that already exist is known as \n\n\n\nenrichment planting. Enrichment planting is a good technique for restoration of overexploited \n\n\n\nforests because it can increase total tree volume and economical value of the forest (Adjers et \n\n\n\nal. 1995; Karam et al. 2012). Moura-Cousta (1993) states that enrichment planting is more \n\n\n\neffective in lowering negative impacts on soil quality compared to the monoculture technique \n\n\n\nbecause it results in less susceptibility to pests and diseases, higher biodiversity, better water \n\n\n\nconservation and less soil erosion.  \n\n\n\nMulti-storied Forest Plantation \n\n\n\nMulti storied forest management is a technique of forest rehabilitation in which high quality \n\n\n\ntimber trees are employed to create two or more layers of canopies . The upper canopies are \n\n\n\nsecondary forest or planted Acacia mangium while the lower canopy consists of the planted \n\n\n\nor introduced dipterocarps trees species. In Malaysia, the Multi-Storied Forest Management \n\n\n\nProject ran from 1991 to 1999 and was divided into two distinctive phases. Multi-storied \n\n\n\nplanting technique is a method of replanting trees by using fast growing trees to act as a \n\n\n\ncanopy sheltering shade tolerant tree species. The Forest Department Peninsular Malaysia \n\n\n\n(2003) stated that multi-storied forest management has gained significant attention as an ideal \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n4 \n\n\n\n\n\n\n\nforest management technique for conserving biodiversity, preserving the environment and \n\n\n\nproducing timber. Chikus and Bukit Kinta Forest Reserves in Perak have been subjected to \n\n\n\nthis planting technique. Acacia mangium and indigenous high quality timber species \n\n\n\nincluding Shorea and Hopea were planted in the early stage of the project (Karam et al. \n\n\n\n2016).  \n\n\n\nFOREST SOIL QUALITY \n\n\n\nIn the forest, soil is the source of nutrients essential to the successful growth of plant species. \n\n\n\nSoil helps in maintaining forest productivity because it is the site of biological and \n\n\n\nbiochemical processes for nutrient cycling (Karam et al. 2012; Daljit et al. 2013). It is a \n\n\n\ncomplex biodiversity environment because in order for soil to function well, integration \n\n\n\nbetween physical, chemical and biological properties is essential in maintaining soil quality \n\n\n\nand sustaining forest productivity. The opening of natural forests for logging and agriculture \n\n\n\nleads to massive soil degradation. Without a tree canopy to cover the soil, the direct impacts \n\n\n\nof sunlight and rainfall promote soil erosion at a rapid pace (Jennings et al. 2001; Pariona et \n\n\n\nal. 2003). For example, an increase in soil compaction due to machinery decreases air and \n\n\n\nwater pores in the soil and inhibits the activities of soil macro-organisms and other microbes \n\n\n\n(Canillas and Salokhe 2001; Hamza and Anderson 2005). On the floor of undisturbed forests \n\n\n\nsuch as natural forests, the available organic matter layer is a rich source of nutrients from \n\n\n\ndecaying plant and animal parts broken down by microorganisms known as decomposers \n\n\n\n(Banning et al. 2008). In return, these microorganisms obtain the habitat and food resources \n\n\n\nthey require to survive. Deforestation distorts this process and renders the land unproductive \n\n\n\nunless appropriate silviculture treatment approaches are carried out. Karlen et al. (1997) \n\n\n\nsuggested that the evaluation of forest soil quality should not neglect any of the three \n\n\n\nimportant aspects of soil, that is, physical, chemical and biological because all of these \n\n\n\nparameters affect each other when soil is disturbed as a result of human activities. Hence, it is \n\n\n\nimportant to select appropriate parameters to evaluate soil quality so that one gains a clear \n\n\n\npicture of current soil conditions. \n\n\n\nThere are no review articles focusing on soil fertility of forests in Peninsular \n\n\n\nMalaysia. Most of the research work focuses on the physiological growth of trees as a \n\n\n\nmeasurement of success. Furthermore, there are also no review articles compiling studies on \n\n\n\nsoil quality of forest rehabilitation in Peninsular Malaysia. Hence, this review aims to provide \n\n\n\na set of information for forest managers and related agencies who work closely with forestry \n\n\n\nto have an overview of the importance of soil quality in measuring the success of forest \n\n\n\nrehabilitation programs. \n\n\n\nApproach \n\n\n\nSeveral research articles on the soil properties of forest rehabilitation programs in Peninsular \n\n\n\nMalaysia were selected. All research on soil properties of forest rehabilitation was obtained \n\n\n\nfrom Scopus. Table 3 shows studies on the soil properties of rehabilitated forests in Malaysia. \n\n\n\nWe also included natural or primary forests as a base for comparing the impact of forest \n\n\n\nrehabilitation on soil properties. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n5 \n\n\n\n\n\n\n\nMost of the species grown or replanted are Dipterocarpaceae  (Abdu et al. 2008; Zaidey et al. \n\n\n\n2010; Karam et al. 2012; Daljit et al. 2013; Malik et al. 2015) and non-dipterocarpaceae \n\n\n\nspecies  (Zaidey et al. 2010; Heryati et al. 2011; Daljit et al. 2013; Malik et al. 2015; \n\n\n\nRosazlin et al. 2015; Hamad-Sheip et al. (2021). Dipterocarps species planted include Shorea \n\n\n\npauciflora, S. macroptera and Swietenia macrophylla. These dipterocarps species are highly \n\n\n\nvaluable timber trees. However, these trees are shade-tolerant which requires the mother tree \n\n\n\nto protect them from direct sunlight while in the early stages of replanting. Hence, Acacia \n\n\n\nmangium is a favourite species for providing shade for this tree. In multi-storied \n\n\n\nmanagement, Acacia mangium was planted before the sapling of dipterocarps species were \n\n\n\ntransferred and planted on the field. Then, when the dipterocarps tree reaches a certain age, \n\n\n\nthe Acacia mangium will be felled. One of the main advantages of Acacia mangium is that it \n\n\n\nis a fast-growing species. However, due to its fast-growing nature, it also causes competition \n\n\n\nwith other planted tree species for limited nutrients in the soil. \n\n\n\nTABLE 3 \nSelected research work on soil fertility of forest rehabilitation in Peninsular Malaysia  \n\n\n\nSite Forest types Species Rehabilitation Focus References \n\n\n\nPasoh Forest \n\n\n\nReserve \n\n\n\nSecondary forests Dipterocarpaceae Natural regeneration \n\n\n\nfor secondary forest \n\n\n\n\n\n\n\nSoil respiration Adachi et al. 2005 \n\n\n\n\n\n\n\n\n\n\n\nBukit Kinta \n\n\n\nForest Reserve \n\n\n\nHill dipterocarp \n\n\n\nforest \n\n\n\nShorea pauciflora \n\n\n\nShorea macroptera \n\n\n\n\n\n\n\nLine and gap planting \n\n\n\n\n\n\n\nSoil fertility Abdu et al. 2008 \n\n\n\nBidor Forest \n\n\n\nReserve \n\n\n\nLowland \n\n\n\ndipterocarp forest \n\n\n\nAcacia mangium which \n\n\n\nconsequently regenerated \n\n\n\ninto secondary forest \n\n\n\n\n\n\n\nMulti-storied forest \n\n\n\nmanagement system \n\n\n\nSoil \n\n\n\ncharacterization \n\n\n\nZaidey et al. 2010 \n\n\n\nKinta Forest \n\n\n\nReserves \n\n\n\nHill dipterocarp \n\n\n\nforest \n\n\n\nDipterocarp and non-\n\n\n\ndipterocarp species \n\n\n\nMulti-storied forest \n\n\n\nmanagement system \n\n\n\n\n\n\n\nSoil \n\n\n\ncharacterization \n\n\n\nZaidey et al. 2010 \n\n\n\nFRIM Research \n\n\n\nStation, Segamat, \n\n\n\nJohor \n\n\n\n\n\n\n\n- Khaya ivorensis Reforestation Soil fertility Heryati et al.2011 \n\n\n\nTapah Hill Forest \n\n\n\nReserve \n\n\n\nLowland \n\n\n\ndipterocarp forest \n\n\n\n\n\n\n\nSecondary forest and \n\n\n\nShorea leprosula \n\n\n\nEnrichment planting Soil biological \n\n\n\nproperties \n\n\n\nKaram et al. 2012 \n\n\n\nChikus Forest \n\n\n\nReserve \n\n\n\nLowland \n\n\n\ndipterocarp forest \n\n\n\n\n\n\n\nShorea leprosula & Acacia \n\n\n\nmangium \n\n\n\nMulti-storied forest \n\n\n\nmanagement system \n\n\n\nSoil biological \n\n\n\nproperties \n\n\n\nDaljit et al., 2013 \n\n\n\nJengka 18, \n\n\n\nPahang \n\n\n\nTropical lowland \n\n\n\nforest \n\n\n\nNatural forest Natural regeneration Soil carbon \n\n\n\npool and \n\n\n\nselected soil \n\n\n\nproperties \n\n\n\nJeyanny et al. 2014 \n\n\n\nSungai Kial Montane forest Natural forest   Jeyanny et al. 2014 \n\n\n\nKrau Wildlife \n\n\n\nReserve \n\n\n\n Secondary forest Natural regeneration Soil chemical \n\n\n\nproperties \n\n\n\nAmlin et al. 2014 \n\n\n\nSungai Menyala \n\n\n\nforest \n\n\n\nLowland forest Secondary forest Natural regeneration Soil CO2 \n\n\n\nefflux \n\n\n\nMande et al. 2015 \n\n\n\nUPM Lowland forest Pinus caribeae Rehabilitation of \n\n\n\ndegraded land \n\n\n\nSoil fertility Malik et al. 2015 \n\n\n\nAyer Hitam \n\n\n\nForest Reserve \n\n\n\nLowland forest Swietenia macrophylla Reforestation Soil fertility Malik et al. 2015 \n\n\n\nUniversity \n\n\n\nAgricultural Park, \n\n\n\nPuchong, \n\n\n\nSelangor \n\n\n\n- Khaya ivorensis \n\n\n\nOrthosiphon stamineus \n\n\n\nAgroforestry Soil properties Rosazlin et al. 2015 \n\n\n\nAyer Hitam \n\n\n\nForest Reserve \n\n\n\nLowland \n\n\n\ndipterocarp forest \n\n\n\nAcacia mangium Rehabilitation Soil properties Hamad-Sheip et al. \n\n\n\n2021 \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n6 \n\n\n\n\n\n\n\nSoil Compaction \n\n\n\n\n\n\n\nSoil physical properties affect the state and transport all forms of matter and energy in the \n\n\n\nsoil. The degree of compaction in the soil determines the success of plant growth. Lack of air \n\n\n\nand water pores cause plants roots to experience stress because they must expand more \n\n\n\nenergy to penetrate bulky soil to obtain and absorb ground water and nutrients (McQueen and \n\n\n\nShepherd 2002). Soil texture, compaction and moisture content are a few of the important soil \n\n\n\nphysical indicators that need to be included in soil quality evaluation (Sharma and Bhushan \n\n\n\n2001; Reynolds et al. 2002).  \n\n\n\n\n\n\n\nSoil compaction creates conditions of stress for plant roots making it difficult for \n\n\n\nthem to penetrate the soil effectively (Canilass and Salokhe 2001). Furthermore, the activities \n\n\n\nof soil microbes are restricted due to less or no water and air being available in the soil to \n\n\n\ncarry out the nutrient cycling process (Miransari et al. 2009; Beylich et al. 2010). \n\n\n\nPengthamkeerati et al. (2011) found that an increase in soil bulk density had a significant \n\n\n\nimpact on soil microbial distribution and activities. Bulk density and porosity are frequently \n\n\n\nused parameters to evaluate the degree of soil compaction (H\u00e5kansson and Lipiec 2000).   \n\n\n\nIn the forest reserve of Tapah Hill, the bulk density value is higher compared to the \n\n\n\nadjacent forest (Table 4). This shows that after 48 years of enrichment planting, it does \n\n\n\nreduce the soil compaction as compared to the natural regeneration of the secondary forest. \n\n\n\nMalik et al. (2015) found that bulk density is higher in the Pinus caribeae plantation \n\n\n\ncompared to the Shorea macrophylla plantation which could be due to P. caribeae being \n\n\n\nlocated in the city and one of the important attraction sites in UPM. The montane forest of \n\n\n\nSungai Kial Forest Reserve exhibits a lower compaction degree compared to the natural \n\n\n\nforests of Jengka (Jeyanny et al. 2015). This could be due to the elevation of the montane \n\n\n\nforest. \n\n\n\nSoil Moisture \n\n\n\n\n\n\n\nForest trees, shrubs and herbs require an adequate supply of water that is absorbed from the \n\n\n\nsoil through roots. Humidity in the soil also triggers microbial activities because \n\n\n\nmicroorganisms require a certain level of surrounding humidity for physical movements and \n\n\n\nenzyme hydrolysing activation for decomposition of materials belonging to decaying plants \n\n\n\nand animals and for successful nutrient cycling in the soil (Kosmas et al. 1998; Cook and \n\n\n\nOrchard 2008).  Plant transpiration will decrease when there is less moisture available in the \n\n\n\nsoil. Furthermore, plant roots will no longer be able to extract and absorb water from the soil \n\n\n\nonce it reaches the wilting point (Menyailo and Hungate 2003). An adequate amount of \n\n\n\nmoisture in the soil helps to maintain plant cell turgidity and gives certain herbs and shrubs \n\n\n\ntheir physical strength (Wang et al. 2006).   \n\n\n\n\n\n\n\nAs shown in Table 4, the natural forest of Chikus Forest Reserve has the highest \n\n\n\nmoisture content. This is due to the fact that the soil in the natural forest is organic. The range \n\n\n\nof moisture content for all of the forest is between 19.50 to 27.3%. However, the rate of \n\n\n\nmoisture largely depends on rainfall rates. These rates of moisture content are common for \n\n\n\nUltisols and Oxisols in Malaysia. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n7 \n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nSoil moisture and bulk density of selected forests in Peninsular Malaysia \nSites Types Soil moisture \n\n\n\n(%) \n\n\n\nBulk density \n\n\n\n (g cm-3) \n\n\n\nReferences \n\n\n\nPasoh Forest Reserve Primary forest 27.3  Adachi et al. 2005 \n\n\n\n Secondary forest 26.7   \n\n\n\nFRIM Station Segamat, Johor  Khaya ivorensis plantation  1.26 Heryanti et al. 2011 \n\n\n\nTapah Hill Forest Reserve, Perak Enrichment planted forest 26.33 1.16 Karam et al. 2012 \n\n\n\n Secondary forest 20.50 1.24  \n\n\n\nChikus Forest Reserve, Perak Natural forest 52.83  Daljit et al. 2013 \n\n\n\nChikus Forest Reserve Multi-storied forest system 19.50   \n\n\n\nSungai Kial Forest Reserve  Montane forest  0.62 Jeyanny et al. 2014 \n\n\n\nJengka VJR Natural forest  1.17  \n\n\n\nUPM Arboretum Pinus caribeae 21.70 2.27 Malik et al. 2015 \n\n\n\nAyer Hitam Forest Reserve, Selangor Shorea macrophylla 25.90 1.49  \n\n\n\n\n\n\n\nSoil Acidity \n\n\n\n\n\n\n\nSoils are tested to determine whether essential plant nutrients are present and if the pH value \n\n\n\nis correct for raising the desired plants. If the appropriate conditions do not exist, the soil test \n\n\n\naid in determining what must be done to provide the correct balance of nutrients and create \n\n\n\nthe proper soil reaction (Collof et al. 2008). By conducting an analysis on chemical properties \n\n\n\nof soil, the amount of nutrients in the soil and the ability of the soil to supply the nutrients to \n\n\n\nplants can be assessed (Campen and Glahn 1999; Li et al. 2007; Sidari et al. 2008). Chemical \n\n\n\nproperty analyses include soil acidity, electrical conductivity, salinity nutrients and cation \n\n\n\nexchange capacity. \n\n\n\n\n\n\n\nSoils become acidic due to excessive leaching of basic cations such as potassium, \n\n\n\ncalcium, magnesium and sodium down the soil profiles and loss through ground water. Soil \n\n\n\nacidity is an important soil quality evaluation indicator that cannot be neglected in any land \n\n\n\nuse management evaluation (Brunet et al. 1996.). In a tropical forest, soils are at acidic \n\n\n\nconditions in the pH range of 4.5 to 5.5 for most soil series. However, certain tropical soils \n\n\n\nsuch as Ultisols and Oxisols, which are known as highly weathered soils have pH of about \n\n\n\n4.0 (Shamshuddin and Fauziah 2010). Hence, through soil acidity evaluation, we can educate \n\n\n\nland managers on what should be done to decrease soil acidity within acceptable limits that \n\n\n\nwould be suitable for plant growth. \n\n\n\nAll of the forests show an acidic nature as the pH level is below 7 (Table 5; Figure \n\n\n\n1).  This pH level is common for highly weathered soils. The pH can be increased if liming \n\n\n\nprograms are included in forest rehabilitation. However,  liming is usually not done for forest \n\n\n\nsoils as most of the trees planted are  neither fertilized nor the soil amended. The main reason \n\n\n\nfor this is to allow the tree to grow naturally by absorbing available nutrients in the forest \n\n\n\nsoil. Sungai Kial Forest Reserve and Jengka VJR show a pH that is lower than 4. The acidity \n\n\n\nlevel of the forest could be affected by the composition of forest litter which releases humic \n\n\n\nacid. \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n8 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1. pH values of selected forest reserves of Peninsular Malaysia \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\n7\n\n\n\nP1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P11 P12 P13 P14 P14 P15 P16\n\n\n\np\nH\n\n\n\nPlot\n\n\n\nTABLE 5 \npH of selected forest reserves of Peninsualr Malaysia \n\n\n\n\n\n\n\nPlot Sites pH References \n\n\n\nP1 Line planting (multi-storied managed forest) 4.5 Abdu et al. 2008 \n\n\n\nP2 Gap planting  (multi-storied managed forest) 4.40  \n\n\n\nP3 Natural forest  4.10  \n\n\n\nP4 Bidor           4.5 Zaidey et al., 2010 \n\n\n\n P5 Kinta  4.27  \n\n\n\nP6 Planted forest  5.23 Heryanti et al. 2011 \n\n\n\nP7 Secondary forest  4.43  \n\n\n\nP8 Enrichment planting  4.36 Karam et al. 2012 \n\n\n\nP9 Secondary forest  4.19  \n\n\n\nP10 Planted S. leprosula  4.24 Daljit et al. 2013 \n\n\n\nP11 Natural forest  4.21  \n\n\n\nP11 Secondary forest  6.08 Amlin et al. 2014 \n\n\n\nP12 Sungai Kial FR  3.5 Jeyanny et al. 2014 \n\n\n\nP13 Jengka VJR  3.9  \n\n\n\nP14 Secondary forest after 50 years  5.16 Mande et al. 2015 \n\n\n\nP15 P. caribeae  4.12 Malik et al.2015 \n\n\n\nP16 S. macroptera  5.09  \n\n\n\nP17 Khaya ivorensis  4.59 Rosazlin et al. 2015 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n9 \n\n\n\n\n\n\n\nNutrients \n\n\n\n\n\n\n\nMacronutrients include phosphorus, sulphur, nitrogen, potassium, magnesium and calcium \n\n\n\nwhich are needed in larger amounts by plants; it is observed that nutrient deficiency is \n\n\n\nnoticed in a shorter period of planting compared to micronutrient deficiency (Kong et al. \n\n\n\n2006). However, forest plantations normally apply fertilizer at the beginning of planting \n\n\n\nactivities while some rehabilitation activities do not involve the application of any fertilizer \n\n\n\nbecause the land manager wants to prompt the seedlings to adapt to stressed and degraded \n\n\n\nland. Carbon is an important soil constituent as it influences soils physical structure, and \n\n\n\nwater-holding capacity, and also aids in the formation of strong complexes with metal ions \n\n\n\nand the nutrient supply in the soil. Nitrogen plays a vital role in all protein building blocks, \n\n\n\nincluding enzymes, which regulate all biological processes in the soil. Potassium in \n\n\n\nexchangeable form is essential for carbohydrate synthesis, enzyme activation and osmotic \n\n\n\nregulation (Qiu et al. 2007). The maintenance and integrity of cell walls and membranes are \n\n\n\ninfluenced by calcium (Scoones and Toulmin 1998). Exchangeable magnesium plays a major \n\n\n\nrole in the photosynthesis process (Shamshuddin and Fauziah 2010). As for nitrogen, this \n\n\n\nnutrient is the major constituent of amino acids and proteins that play essential roles in \n\n\n\nhealthy plant growth and development (Abanda et al. 2011).  \n\n\n\n\n\n\n\n The carbon to nitrogen ratio (C/N) is an important part of ensuring effective \n\n\n\ncomposting because microorganisms require a balance of carbon and nitrogen in order to \n\n\n\nremain active. The C/N ratio of less than 25 shows that the rate of mineralization is high \n\n\n\nwhile a C/N ratio higher than 30 shows that the immobilization rate is high. The best range of \n\n\n\nC/N ratio is 20-30 which indicates a balance of mineralization and immobilization rates. \n\n\n\nTable 6 shows the rate of total carbon, total nitrogen, and C/N ratio. Based on the table, all \n\n\n\nforests exhibit mineralization of nutrients except for the Khaya ivorensis plantation and \n\n\n\nsecondary forest in FRIM Station Segamat, Johor. Khaya ivorensis plantation exhibits net \n\n\n\nnitrogen mobilization. In contrast, the secondary forest in FRIM Station shows net nitrogen \n\n\n\nimmobilization. This could be due to very low nitrogen content as compared to the carbon \n\n\n\ncontent. \n\n\n\n\n\n\n\nTable 7 shows macronutrients and cation exchange capacity. The hill dipterocarp of Kinta \n\n\n\nForest Reserve, montane forest of Sungai Kial Forest Reserve, and Jengka VJR have  a CEC \n\n\n\nthat is higher than 15 cmolc kg-1. The level of CEC higher than 15 cmolc kg-1 shows that the \n\n\n\n\n\n\n\n\n\n\n\n TABLE 6 \n\n\n\nTotal carbon, total nitrogen and C/N Ratio \nSite Types C (g kg-1) N (g kg-1) C/N ratio References \n\n\n\nBukit Kinta Forest Reserve Line planting 20.50  2.20 9.30 Abdu et al., 2008 \n\n\n\n Gap planting 21.90  2.50  8.80  \n\n\n\n Natural forest 42.90 3.90 11.10  \n\n\n\nBidor Forest Reserve  Lowland dipterocarp 35.8  3.13 12.47 Zaidey et al., 2010 \n\n\n\nKinta forest reserve  Hill dipterocarp 35.87 3.15 11.4  \n\n\n\nFRIM Station, Segamat, Johor Khaya ivorensis 13.78 0.97 22.27 Heryanti et al., 2011 \n\n\n\n Secondary forest 16.03 0.58 49.38  \n\n\n\nSungai Kial Forest Reserve Montane 40.22 0.06  Jeyanny et al., 2014 \n\n\n\nJengka VJR  Natural forest 2.20 0.32   \n\n\n\nKrau Wildlife forest 30 years of Secondary \n\n\n\nforest \n\n\n\n2.76 0.28 10.33 Amlin et al., 2014 \n\n\n\nUPM Arboretum P. caribeae 2.70 2.09 1.29 Malik et al., 2015 \n\n\n\nAyer Hitam Forest Reserve S. macrophylla 0.95 1.52 0.63  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n10 \n\n\n\n\n\n\n\nforest soil is able to hold nutrients well compared to forest soil with CEC less than 15 cmolc \n\n\n\nkg-1. Other forests exhibit nutrient leaching. However, macronutrients are considerably low in \n\n\n\nforest soil due to the absence of fertilizer application. Furthermore, the application of \n\n\n\nfertilizer is not necessary for forest rehabilitation. \n\n\n\nTABLE 7 \n\n\n\nMacronutrients of selected forest reserves in Peninsular Malaysia \n\n\n\nSite Types Ca (cmolc \n\n\n\nkg-1) \n\n\n\nMg (cmolc \n\n\n\nkg-1) \n\n\n\nK (cmolc kg-\n\n\n\n1) \n\n\n\nCEC (cmolc \n\n\n\nkg-1) \n\n\n\nReferences \n\n\n\nKinta Forest Reserve Line planting 0.42 0.17 0.13 5.70 Abdu et al. 2008 \n\n\n\n Gap planting 0.57 0.20 0.14 5.80  \n\n\n\n Natural forest 0.84 0.28 0.20 12.20  \n\n\n\nBidor Forest Reserve  Lowland \n\n\n\ndipterocarp \n\n\n\n0.18 0.16 0.13 10.79 Zaidey et al.2010 \n\n\n\nKinta Forest Reserve  Hill dipterocarp 0.32 0.21 0.16 15.47  \n\n\n\nFRIM Station Khaya ivorensis \n\n\n\nplantation \n\n\n\n0.51 0.21 0.18 7.86 Heryanti et al. \n\n\n\n2011 \n\n\n\n Secondary forest 0.50 0.19 0.17 0.50  \n\n\n\nSungai Kial Forest \n\n\n\nReserve \n\n\n\nMontane forest 161.7 217 439 26.36 Jeyanny et al. \n\n\n\n2014 \n\n\n\nJengka VJR Secondary forest 47.90 98.00 382 15.79  \n\n\n\nKrau Wildlife Reserve  Secondary forest 1.92 0.30 0.08  Amlin et al. 2015 \n\n\n\nUPM Arboretum P. caribeae 2.24 0.23 0.21 12.51 Malik et al. 2015 \n\n\n\nAyer Hitam Forest \n\n\n\nReserve \n\n\n\nS. macrophylla 0.15 0.02 0.10 8.83  \n\n\n\nUniversity Agriculture \n\n\n\nPark UPM \n\n\n\nAgroforestry 0.70 1.54 0.11 11.66 Rosazlin et al. \n\n\n\n2015 \n\n\n\n\n\n\n\n\n\n\n\nMicrobial Population Count and Enzymatic Activity \n\n\n\n\n\n\n\nThe evaluation of forest soil quality in terms of biological properties is essential due to the \n\n\n\nsensitive nature and rapid response of soil microorganisms to environmental changes (Karam \n\n\n\net al. 2012; Daljit et al. 2013). The growth of the microbial population is controlled by \n\n\n\nnutrient supply and moisture content. The growth medium, the energy source, and a variety \n\n\n\nof other chemical and physical factors also define the surrounding environment for the \n\n\n\ngrowth of the microbes.  \n\n\n\n\n\n\n\nEnzymes are an integral part of nutrient cycling in the soil and are usually associated \n\n\n\nwith viable proliferating cells and specific enzyme activities such as dehydrogenase, \n\n\n\nphosphatise, \u03b2-glucosidae and many others that can be measured as an estimation of soil \n\n\n\nmicrobial activity (Trasar-Cepeda et al. 1998; Bandick and Dick 1999). Changes in soil \n\n\n\nmicrobial activities reflect the degree of disturbance in the affected soil, whereby opening of \n\n\n\nforest due to logging and deforestation activities causes rapid turnover and changes in \n\n\n\nmicrobial activities (Priess and F\u00f6lster 2001). Much nutrient cycling in the soil takes place on \n\n\n\nthe root surface; in forests, this biochemical process is part of microbial activities on tree \n\n\n\nroots (Gaspar et al. 2001). When trees and ground vegetation are cleared in the forest, \n\n\n\nbiochemical processes are disrupted; hence, microorganisms cannot synthesize food \n\n\n\nresources and nutrients effectively. \n\n\n\n\n\n\n\nNatural forests have a higher bacterial and fungal count (Table 8). However, the rates \n\n\n\nfor rehabilitated forests including multi-storied forests, and secondary and enrichment planted \n\n\n\nforests show improvement in the bacterial and fungal count. As for microbial enzymatic \n\n\n\nactivity, enrichment planted forests show a higher level. This could be due to the effect of \n\n\n\nenrichment planting practices which enhance the forest macro and microorganism activities \n\n\n\nin the soil. The enrichment planting technique enriches the forest canopy through replanting \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n11 \n\n\n\n\n\n\n\nof trees at the spot which lacks trees due to deforestation. Furthermore, most of the \n\n\n\nsuccession species grown wild in the secondary forest are Macaranga spp. As for multi-\n\n\n\nstoried managed forests, the microbial enzymatic activity is lower than in natural forests and \n\n\n\nthis is expected as the period after planting for the multi-storied forests is only 18 (as of 2010 \n\n\n\nat the time of sampling was done) compared to 48 years of enrichment planted forests. \n\n\n\nHence, this shows that a longer period of rehabilitation will show an increase in soil quality \n\n\n\nover time. \n\n\n\n \nTABLE 8 \n\n\n\nBacterial count and enzymatic activity of selected forest reserves in Peninsular Malaysia \n\n\n\n Bacterial count Fungal count Enzymatic \n\n\n\nNatural forest 5.37 4.37 58.54 \n\n\n\nMulti-storied forest 4.91 4.06 52.34 \n\n\n\nSecondary forest 4.04 3.99 54.92 \n\n\n\nEnrichment planted forest 4.21 4.16 64.34 \n\n\n\nSource: Karam et al. 2012; Daljit et al. 2013 \n\n\n\n\n\n\n\nMicrobial Biomass \n\n\n\n\n\n\n\nMicrobial biomass is the mass of living microorganisms in a particular ecosystem at a given \n\n\n\ntime. Microbial biomass responds rapidly to different land use management techniques under \n\n\n\ndifferent climatic conditions (Debosz et al. 1999; Raubuch and Joergensen 2002). Kwabiah et \n\n\n\nal. (2003) explained that microbial biomass enhances the process of transforming organic \n\n\n\ncompounds into an inorganic form that will be made available for uptake by plants. Groffman \n\n\n\net al. (2001) and Salamanca et al. (2006), in a study on soil microbial biomass and activity in \n\n\n\ntropical forests, found that in disturbed forests left for natural regeneration growth, microbial \n\n\n\nactivity was low, but there was a faster growth rate for vegetation that supplies soil microbial \n\n\n\nbiomass with a considerable and adequate amount of organic matter. Jia et al. (2004), in a \n\n\n\nstudy on the distribution of soil microbial biomass and nutrients at different stages of natural \n\n\n\nregeneration forests, found that organic matter quality has a greater effect on the quality of \n\n\n\nsoil microbial biomass than quantity. This finding indicates that microbial biomass is an \n\n\n\nimportant constituent of soil organic matter, and the availability of soil microbes in organic \n\n\n\nmatter shows the recovery process of rehabilitated and natural regenerated forests.   \n \n\n\n\n Microbial biomass C (MBC) levels in all of the forests was higher than microbial \n\n\n\nbiomass N (MBN) (Table 9). The reason for levels of MBC and MBN in rehabilitated forests \n\n\n\nbeing relatively low compared to natural forests could be due to compaction that decreases \n\n\n\nsoil microbial biomass due to lower availability of organic matter and soil moisture content \n\n\n\n(Hakansson and Lipiec 2000; Xiao et al. 2005). The abundance of forest litter in undisturbed \n\n\n\nnatural forest promotes the growth of microbial C and N.  \n\n\n\nTABLE 9 \n\n\n\nMicrobial biomass C and N of selected forest reserves in Peninsular Malaysia \n\n\n\n MBC MBN MBC/MBN Ratio \n\n\n\nNatural forest 824 149 6.35 \n\n\n\nMulti-storied forest 542 37 16.79 \n\n\n\nSecondary forest 325 162 1.03 \nEnrichment planted forest 465 239 1.91 \n\n\n\nSource: Karam et al. 2012 \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 1-16 \n\n\n\n\n\n\n\n12 \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nSoil is undeniably the most important earth constituent for plants to grow and humans to live. \n\n\n\nIn the forest, the soil is the source of nutrients essential to the successful growth of plant \n\n\n\nspecies. It is a complex biodiverse environment because in order for soil to function well, \n\n\n\nintegration between physical, chemical, and biological properties is essential in maintaining \n\n\n\nsoil quality and sustaining forest productivity. The opening of natural forests for logging and \n\n\n\nagriculture leads to massive soil degradation. Without a tree canopy to cover the soil, the \n\n\n\ndirect impacts of sunlight and rainfall promote soil erosion at a rapid pace. The objective of \n\n\n\nforest rehabilitation is not only to provide a continuous supply of economically important \n\n\n\nwoody and non-woody products but also to restore the deforested area to its initial or original \n\n\n\nstate. Evaluation of forest soil quality should not neglect any of the three important aspects of \n\n\n\nsoil physical, chemical and biological properties because all of these parameters affect each \n\n\n\nother when soil experiences disturbance as a result of human activities. Findings in different \n\n\n\nforest management systems as discussed in this article shows the impact of forest treatment \n\n\n\non soil quality. In short, it is important to select appropriate parameters to evaluate soil \n\n\n\nquality so that one can gain a clear picture of current soil conditions to ensure proper actions \n\n\n\ncan then be taken to restore the condition of problematic soils to its optimum level. \n\n\n\n\n\n\n\nACKNOWLEDGEMENT \n\n\n\n\n\n\n\nAll of the authors would like to acknowledge and thank every researcher whose works were \n\n\n\nused in this review article. This review article is part of research work supported by the \n\n\n\nNational Conservation Trust Fund by the Ministry of Energy and Natural Resources (KeTSA) \n\n\n\nMalaysia. [Ref: KeTSA(S) 600-2/1/48/6 Jld. 2 (14)]. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\n\n\n\n\nAbanda, P.A., J.S.Compton, and R.E. Hannigan. 2011. 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"\n\n___________________\n\n\n\n* Corresponding author: e-mail: radziah@agri.upm.edu.my \n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science  Vol. 13: 43-57                                   Malaysian Society of Soil Science\n\n\n\nProduction of Hydrolytic Enzymes in Rice (Oryza sativa L.) \n\n\n\nRoots Inoculated with N\n2\n-Fixing Bacteria\n\n\n\nA.M. Asilah1, O. Radziah1* & M. Radzali2\n\n\n\n1Department of Land Management, Faculty of Agriculture,\n\n\n\nUniversiti Putra Malaysia, 43400  UPM, Serdang, Selangor, Malaysia\n\n\n\n2Department of Biochemistry, Faculty of Biotechnology and Biomolecular \n\n\n\nSciences,Universiti Putra Malaysia, 43400  UPM, Serdang, Selangor, Malaysia\n\n\n\nINTRODUCTION\n\n\n\nNitrogen is the most important input required for rice production. In sustainable \n\n\n\nrice cultivation, the strategies are to plant crops which are less dependent on \n\n\n\nfertilizer and to emphasize on the use of plant growth-promoting rhizobacteria \n\n\n\n(PGPR) which can fix nitrogen for the plant, or that can change root morphology \n\n\n\nfor more nutrients absorption (Biswas et al. 2000a). The importance of biological \n\n\n\nnitrogen fixation (BNF) technology can play a role in substituting commercially \n\n\n\navailable N fertilizer use in rice culture. BNF by some PGPR like Rhizobium sp. \n\n\n\nABSTRACT\nAn experiment was conducted to determine the production of hydrolytic enzymes \n\n\n\nendoglucanase (EG) and endopolymethylgalacturonase (EPMG) in rice (Oryza \n\n\n\nsativa L.) roots inoculated with N\n2\n-Fixing bacteria. Screening for hydrolytic \n\n\n\nenzymes by N\n2\n-Fixing bacteria, using the plate method showed that nine out of \n\n\n\n12 bacterial strains were positive for carboxymethylcellulose (CMC) and pectin \n\n\n\nreactions. Three of the isolates, Sb34, Sb41 and Sb42 were inoculated to MR219 \n\n\n\nrice seedling. The bacterial population and the production of hydrolytic enzymes \n\n\n\nwere monitored for 45 days of plant growth. The scanning (SEM) and transmission \n\n\n\nelectron microscopy (TEM) were used to observe bacterial colonization on plant \n\n\n\nroots.  In general, the populations of inoculated diazotrophs were higher in the \n\n\n\nrhizosphere than the endosphere. There were significant effects of different \n\n\n\ndiazotrophs inoculations on the rice rhizosphere and endosphere populations. \n\n\n\nPlants inoculated with diazotrophs showed significantly higher specific enzyme \n\n\n\nactivities and soluble proteins compared to the non-inoculated control. SEM and \n\n\n\nTEM observations revealed the abilities of the diazotrophs to colonize the surfaces \n\n\n\nand interior of the roots.  Inoculation significantly increased root growth of rice \n\n\n\nwith substantial increase in root length, volume and surface area in the inoculated \n\n\n\nplants.\n\n\n\n\n\n\n\nKeywords: Rice (Oryza sativa L.); Endoglucanase (EG);\n\n\n\n      Endopolymethylgalacturonase (EPMG); N\n2\n-Fixing Bacteria\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200944\n\n\n\nhas been shown to improve the root surface area, root volume and nitrogen uptake \n\n\n\nof rice seedlings (Biswas et al., 2000b). \n\n\n\n Colonization by the bacteria on the surface and inside of the roots is \n\n\n\nimportant for plant growth improvement. N\n2\n-fixing bacteria (Diazotrophs) \n\n\n\nimprove plant growth by several mechanisms such as, increasing availability of \n\n\n\nthe nutrients, improving soil structure, inducing plant defense systems, producing \n\n\n\nantibiotics, outcompeting pathogens and providing growth-stimulating substances \n\n\n\nor enzymes (Reinhold-Hurek et al., 1993). The ability to produce hydrolytic \n\n\n\nenzymes is an important mechanism in the entry of bacteria into plant roots. \n\n\n\nPGPR like Rhizobium sp. produce hydrolytic enzymes which cause thinning \n\n\n\nand solubilization of plant fibrillar wall, leading to bacterial transition from \n\n\n\nintracellular to intercellular of rice roots (Mateos et al., 1992). Plant cell walls \n\n\n\nconsist mainly of cellulose, whereas the middle lamella, which connects the cells, \n\n\n\nconsists mainly of pectin (Verma et al., 2001). Endoglucanase (EG) classified \n\n\n\nas cellulases enzymes can hydrolyze the internal bonds of the cellulose chain \n\n\n\nwhile the endopolymethylgalacturonase (EPMG) classified as pectic enzymes are \n\n\n\ninvolved in pectin degradation. These two enzymes are called cellulose and pectin \n\n\n\ndegrading enzymes. Combination of these enzymes can degrade the plant cell \n\n\n\nwalls (Pilnik, 1982).  The enzymes EG and EPMG have been found in maize and \n\n\n\nsorghum roots inoculated with Gluconacetobacter diazotrophicus and AM fungi \n\n\n\n(Adriano-Anaya et al. 2006). These enzymes could be important in assisting the \n\n\n\nbeneficial microbes to enter the plant roots. The objective of the present study was \n\n\n\nto determine the effect of rhizobacterial inoculation on production of hydrolytic \n\n\n\nenzymes and its relationship with root colonization and plant growth. \n\n\n\nMATERIALS AND METHODS\n\n\n\nScreening for Hydrolytic Enzymes Production in N\n2\n-Fixing Bacteria  \n\n\n\nTwelve N\n2\n-Fixing bacterial strains were screened for hydrolytic enzyme \n\n\n\nactivities. Eleven strains (Sb2, Sb3, Sb6, Sb13, Rhizobium sp. (Sb 16), Sb20, \n\n\n\nCorynebacterium sp. (Sb26), Sb34, Sb35, Sb41 and Sb42) were isolated from \n\n\n\nrice rhizosphere and one strain (Bacillus sphaericus-UPM B10) was isolated from \n\n\n\noil palm. The enzymes were assayed by dropping 100 \u00b5L of bacterial cultures \n\n\n\non Jensen\u2019s agar medium containing substrates carboxymethylcellulose (CMC) \n\n\n\nfor EG, and citrus pectin for EPMG and incubated for 24 hours at 30oC. The \n\n\n\nplates were then stained with Congo red (0.1%). After two to three washes with \n\n\n\n1M NaCl, the appearance of a clear halo zone around the colonies indicated the \n\n\n\nenzyme activities.\n\n\n\nSeedlings Germination and Inoculation\n\n\n\nRice seeds (Oryza sativa L. variety MR 219) were surface-sterilized with 70% \n\n\n\nethanol (AR Grade) for 5 min, and then for five seconds with 3% of CloroxTM. \n\n\n\nSeeds were germinated on moist filter paper in petri dishes and after seven days, 25 \n\n\n\nA.M. Asilah, O. Radziah & M. Radzali\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 45\n\n\n\nuniform seedlings were selected and transplanted to pots (17cm x 11cm) containing \n\n\n\nsterilized sand. Three bacterial strains (Sb34, Sb41 and Sb42) previously isolated \n\n\n\nfrom soils collected from Tanjong Karang paddy field in Malaysia were selected \n\n\n\nfor this study. Each strain was grown in Erlenmeyer flasks (200 mL) containing \n\n\n\nJensen\u2019s N-free broth medium. The cultures were shaken (100 rpm) at 37 \u00b1 1 0C \n\n\n\non the orbital shaker, centrifuged at 4000 rpm for 40 min (Sigma:N 316) and then \n\n\n\nwashed with phosphate buffer solution (PBS). Approximately 2 x 107 cfu/mL of \n\n\n\nlive bacterial cells were applied to each seedling.\n\n\n\nBacterial Population  \n\n\n\nBacterial population in growth culture was determined by transferring 10 g of \n\n\n\nsand into a conical flask containing 95 mL sterilized distilled water. A series of \n\n\n\n10-fold dilutions of the suspension up to 10-7 was prepared and population was \n\n\n\ndetermined using the drop plate method (Somasegaran et al. 1985) on nutrient \n\n\n\nagar (NA) plate. Rhizosphere population was determined by transferring about \n\n\n\n1 g of fresh plant roots into a conical flask containing 99 mL sterilized distilled \n\n\n\nwater and the population was determined as described earlier. For endophytes \n\n\n\npopulation, fresh roots were surface sterilized with 70% ethanol for 5 minutes, \n\n\n\nfollowed by 3% CloroxTM for 5 seconds. Roots were homogenized using sterilized \n\n\n\nmortar and pestle, and the bacterial population determined. \n\n\n\nEnzymes Extraction \n\n\n\nThe EG and EPMG activity of rice roots were measured as described by Adriano-\n\n\n\nAnaya et al. (2006) with some modifications. Roots were pulverized in a chilled \n\n\n\nmortar. The resulting powder was homogenized in 100 mM Tris-HCl buffer (pH \n\n\n\n7.0) containing 0.02 g polyvinylpyrrolidone, 10 mM MgCl\n2\n, 10 mM NaHCO\n\n\n\n3\n, 10 \n\n\n\nmM \u00df-mercaptoethanol, 0.15 mM phenylmethyl sulfonyl fluoride (PMSF) and \n\n\n\n0.3% (w/v) X-100 Triton. Sodium azide (0.03%) was added to all solutions to \n\n\n\ninhibit microbial growth. The liquid was filtered through filter paper (Whatman  \n\n\n\nNo.1) and centrifuged at 20, 000 g for 20 min at 40C. The ratio of root weight to \n\n\n\nvolume of solution was 1:5.\n\n\n\n The supernatant fraction was dialyzed using dialysis tubing (Snakeskin \n\n\n\nPleated Dialysis Tubing: Pierce, MWCO: 7000) against several hundred volumes \n\n\n\nof the same diluted extracting solutions (1:9, v/v) for 18 h at 40C. The samples \n\n\n\nwere frozen until used. \n\n\n\nEnzymes Activities \n\n\n\nThe activities of EG and EPMG were assayed by the viscosity method (Adriano-\n\n\n\nAnaya et al., 2006) using CMC and citrus pectin as substrates with some \n\n\n\nmodifications. The reduction in viscosity was determined at 0-30 min intervals. \n\n\n\nApproximately 0.5 mL of the reaction mixture was sucked from a 2 mL tube into \n\n\n\na 1 mL syringe, then allowed to flow down to the 2 mL tube and the time taken \n\n\n\nfor the meniscus to flow down from the 0.60 to 0.10 mL mark (between 1 and 3 \n\n\n\nmin) was recorded. The reaction mixture in the 2 mL tube contained 1 mL of 0.5% \n\n\n\nHydrolytic Enzymes in Rice Roots\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200946\n\n\n\nsubstrate in 50 mM citrate-phosphate buffer (pH 5.0) and 0.2 mL supernatant. \n\n\n\nViscosity reduction was determined at 370C. \n\n\n\n One unit of enzyme activity was expressed at the specific activity, U (where \n\n\n\nU=RA mg-1 protein), and RA is the relative activity calculated by applying the \n\n\n\nformula:- \n\n\n\n                  %V = (T\n0\n \u2013 T\n\n\n\nA\n) x 100 x T\n\n\n\n0\n ; \n\n\n\n          T\n50\n\n\n\n  = 50T\nA\n %V-1 ;\n\n\n\n            RA = T\n50\n\n\n\n x103 \n\n\n\n      (Bateman, 1963)\n\n\n\nwhere :                    \n\n\n\n RA = the reciprocal of time in hour for 50% viscosity loss,\n\n\n\n  T\n0\n  = the viscosity of the reaction mixture at 0 time,\n\n\n\n  T\nA\n  = the viscosity of the reaction mixture at 30 min,\n\n\n\n  V   = the viscosity loss of the reaction mixture at 30 min, and,\n\n\n\n  T\n50\n\n\n\n = the time necessary to reach a 50% of viscosity loss of the reaction\n\n\n\n          mixture at 30 min. \n\n\n\nControls for the enzyme assays consist of autoclaved extracts of the enzymes.\n\n\n\nSoluble Protein Determination \n\n\n\nTotal soluble proteins for the supernatants were quantitatively determined using \n\n\n\nBradford assay with BSA as the standard (Bradford, 1976). Standard curve was \n\n\n\nconstructed to determine total soluble proteins in the samples. The  soluble protein \n\n\n\nreagent was prepared by dissolving Coomassie Brilliant Blue G-250 (100 mg) in \n\n\n\n50 mL 95% ethanol; 100 mL 85% (w/v) phosphoric acid.\n\n\n\nRoot Growth \n\n\n\nRoot morphology was observed by using the root scanner, Win Rhizo STD1600 \n\n\n\nWIA - Epson Expression 1680.\n\n\n\nRoot Colonization\n\n\n\nThe bacterial colonization on roots was observed by using scanning electron \n\n\n\nmicroscope (SEM) and transmission electron microscopy (TEM). The seedlings \n\n\n\n(5 days old) inoculated with diazotrophs were cut into 1cm3 for SEM and 1mm3 for \n\n\n\nTEM, pre-fixed with 4% glutaraldehyde, and washed with 0.1M sodium cacodylate \n\n\n\nbuffer. For post-fixation, the samples were fixed in 1% osmium tetroxide. Samples \n\n\n\nfor SEM were dehydrated in series of acetone (30, 60 and 90%) and then dried in \n\n\n\na critical point dryer, mounted onto the stub, coated with gold and observed under \n\n\n\nthe SEM (Philips XL 30 ESEM). Samples for TEM were dehydrated in series of \n\n\n\nacetone (30, 60 and 90%) and then infiltrated with resin, embedded into beam \n\n\n\ncapsules, polymerized in oven, cut into thick and ultra- thin sections and stained. \n\n\n\nThey were then observed under the TEM (Leo 912AB EFTEM).\n\n\n\nA.M. Asilah, O. Radziah & M. Radzali\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 47\n\n\n\nStatistical analysis \n\n\n\nThe factorial laboratory experiment (4 bacterial strains x 4 time samplings) with 4 \n\n\n\nreplications was laid out in a completely randomized design and data were analyzed \n\n\n\nusing SAS (9.1 version). Treatments means were compared using Tukey test  \n\n\n\n(P \u2264 0.05).\n\n\n\nRESULTS\n\n\n\nHydrolytic Enzymes Production\n\n\n\nThe ability of these N\n2\n-Fixing bacteria to produce hydrolytic enzymes to degrade \n\n\n\ncell wall is crucial in their penetration into plant roots. Nine out of the 12 bacterial \n\n\n\nstrains were positive with CMC and pectin reactions (Table 1). The positive \n\n\n\nhydrolytic enzyme activities were indicated by the appearance of halo zones \n\n\n\naround the colonies (Fig. 1). Two bacterial strains (Sb41 and Sb42) produced the \n\n\n\nclearest halo zones indicating high enzyme activities. \n\n\n\nTABLE 1\n\n\n\nHydrolytic reaction of bacterial strains with CMC and citrus pectin substrates\n\n\n\nDiazotrophs Population in Root \n\n\n\nThere was significant effect of diazotrophs inoculation on the rice rhizosphere \n\n\n\nand endosphere populations. In general, the populations of inoculated diazotrophs \n\n\n\nwere higher in the rhizosphere followed by the endosphere. In the rhizosphere, \n\n\n\nSb41 population was significantly lower than the other two isolates at the 6th \n\n\n\nday of inoculation. The bacterial strain Sb42 population remained stable after 30 \n\n\n\ndays of inoculation (Fig. 2a). For endosphere, Sb41 population showed a gradual \n\n\n\nincrease from day 6 up to the maximum at day 30 and then decreased slightly at \n\n\n\nday 45. Sb42 remained constant throughout the plant growth (Fig. 2b). \n\n\n\nHydrolytic Enzymes in Rice Roots\n\n\n\n\n\n\n\n\n\n\n\n          Strains  \n\n\n\n\n\n\n\nSubstrates  \n\n\n\nSb2 Sb3 Sb6 Sb13 Sb16 Sb20 Sb26 Sb34 Sb35 Sb41 Sb42 \nUPM \n\n\n\nB10 \n\n\n\nReaction in  \n\n\n\nCMC  \n\n\n\n- + - + + - + + + + + + \n\n\n\nReaction in \n\n\n\npectin  \n\n\n\n- + - + + - + + + + + + \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200948\n\n\n\nFig. 1: Hydrolytic reactions of bacterial strains with CMC and pectin substrates. \n\n\n\n(a&b) Positive and negative reaction with CMC, (c&d) Positive and negative reaction \n\n\n\nwith pectin. White arrows show the halo zones around the colonies\n\n\n\nHydrolytic Enzyme Activities \n\n\n\nThe differences in hydrolytic enzyme activities (EG and EPMG) and soluble \n\n\n\nprotein content of the three isolates are shown in Fig. 3. Inoculation of diazotrophs \n\n\n\nincreased the production of EG and EPMG.  Significant differences (p\u22640.05) were \n\n\n\nobserved for EG found in three isolates over non-inoculated control at days 6 and \n\n\n\n45 after inoculation (Fig. 3a). At the 45th day, the activities of EG for Sb42, Sb34 \n\n\n\nand Sb41 increased by 292.33%, 133.30% and 47.09%, respectively, over the \n\n\n\nnon-inoculated control. The increase in EG enzymes paralleled the increased plant \n\n\n\ngrowth. Similarly, significantly higher specific enzyme activities of EPMG was \n\n\n\nobserved in inoculated plants at 30th day of inoculation. The enzyme activities \n\n\n\nof Sb42, Sb41 and Sb34 increased by 251.81, 221.44 and 170.35%, respectively, \n\n\n\ncompared to the non-inoculated control. At the 45th day of inoculation, Sb42 \n\n\n\ninoculated plants produced significantly highest (176.90%) EPMG compared to \n\n\n\nother treatments (Fig. 3b).\n\n\n\nA.M. Asilah, O. Radziah & M. Radzali\n\n\n\na) Positive reaction with CMC b) Negative reaction with CMC         \n\n\n\nc) Positive reaction with pectin d) Negative reaction with pectin\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 49\n\n\n\nFig. 2: Changes in growth of different strains of diazotrophs at different growth  stages\n\n\n\n of MR219 rice. (a) Rhizosphere, (b) Endosphere. Bars in the line indicate standard\n\n\n\n error (n = 4)\n\n\n\nTotal Soluble Protein \n\n\n\nTotal soluble protein produced by three isolates is as shown in Fig. 3c. Production \n\n\n\nof total soluble protein increased with increased plant growth. The three isolates \n\n\n\nproduced significantly higher total soluble protein over the non-inoculated control \n\n\n\nat the 30th and 45th day of inoculation. Percentage increase of total soluble protein \n\n\n\nover the control produced by Sb42, Sb41 and Sb34 at 45 days of inoculation were \n\n\n\n601.62, 509.39 and 437.22%, respectively. Non-inoculated plants showed a slight \n\n\n\nincrease in soluble protein at the 30th day of inoculation but decreased at the 45th day.\n\n\n\nHydrolytic Enzymes in Rice Roots\n\n\n\n\n\n\n\nRhizosphere  population\n\n\n\n0.0\n\n\n\n2.0\n\n\n\n4.0\n\n\n\n6.0\n\n\n\n8.0\n\n\n\n0 5 10 15 20 25 30 35 40 45 50\n\n\n\nDays after inoculation\n\n\n\nB\na\nc\nte\n\n\n\nri\na\nl \n\n\n\np\no\n\n\n\np\nu\n\n\n\nla\nti\n\n\n\no\nn\n\n\n\n\n\n\n\n(l\no\n\n\n\ng\n1\n0\n c\n\n\n\nfu\n/g\n\n\n\n)\n\n\n\nSb 34\n\n\n\nSb 41\n\n\n\nSb 42\n\n\n\na)\n\n\n\n\n\n\n\nEndosphere population\n\n\n\n0.0\n\n\n\n2.0\n\n\n\n4.0\n\n\n\n6.0\n\n\n\n8.0\n\n\n\n0 5 10 15 20 25 30 35 40 45 50\n\n\n\nDays after inoculation\n\n\n\nB\na\nc\nte\n\n\n\nri\na\nl \n\n\n\np\no\n\n\n\np\nu\n\n\n\nla\nti\n\n\n\no\nn\n\n\n\n\n\n\n\n(l\no\n\n\n\ng\n1\n0\n c\n\n\n\nfu\n/g\n\n\n\n)\n\n\n\nSb 34\n\n\n\nSb 41\n\n\n\nSb 42\n\n\n\nb)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200950\n\n\n\nFig. 3: Effect of diazotrophs inoculation on specific activity of hydrolytic \n\n\n\nenzymes and protein content produced in rice roots. (a) Endoglucanase, (b) \n\n\n\nEndopolymethylgalacturonase, (c) Soluble protein content. Bars in the line indicate \n\n\n\nstandard error (n = 4)\n\n\n\nA.M. Asilah, O. Radziah & M. Radzali\n\n\n\n\n\n\n\nEndoglucanase\n\n\n\n0.00\n\n\n\n500.00\n\n\n\n1000.00\n\n\n\n1500.00\n\n\n\n2000.00\n\n\n\n2500.00\n\n\n\n3000.00\n\n\n\n3500.00\n\n\n\n0 5 10 15 20 25 30 35 40 45 50\n\n\n\nDays after inoculation\n\n\n\nS\np\n\n\n\ne\nc\nif\n\n\n\nic\n a\n\n\n\nc\nti\n\n\n\nv\nit\n\n\n\ny\n\n\n\n(u\nn\n\n\n\nit\ns\n m\n\n\n\ng\n-1\n p\n\n\n\nro\nte\n\n\n\nin\n)\n\n\n\ncontrol\n\n\n\nSb 34\n\n\n\nSb 41\n\n\n\nSb 42\n\n\n\na)\n\n\n\n \nEndopolymethylgalacturonase\n\n\n\n0.00\n\n\n\n100.00\n\n\n\n200.00\n\n\n\n300.00\n\n\n\n400.00\n\n\n\n500.00\n\n\n\n600.00\n\n\n\n0 5 10 15 20 25 30 35 40 45 50\n\n\n\nDays after inoculation\n\n\n\nS\np\n\n\n\ne\nc\nif\n\n\n\nic\n a\n\n\n\nc\nti\n\n\n\nv\nit\n\n\n\ny\n\n\n\n(u\nn\n\n\n\nit\ns\n m\n\n\n\ng\n-\n\n\n\n1\n p\n\n\n\nro\nte\n\n\n\nin\n)\n\n\n\ncontrol\n\n\n\nSb 34\n\n\n\nSb 41\n\n\n\nSb 42\n\n\n\nb)\n\n\n\n\n\n\n\nSoluble protein content\n\n\n\n0.00\n\n\n\n1.00\n\n\n\n2.00\n\n\n\n3.00\n\n\n\n4.00\n\n\n\n5.00\n\n\n\n6.00\n\n\n\n0 5 10 15 20 25 30 35 40 45 50\n\n\n\nDays after inoculation\n\n\n\nS\no\n\n\n\nlu\nb\n\n\n\nle\n p\n\n\n\nro\nte\n\n\n\nin\n c\n\n\n\no\nn\n\n\n\nte\nn\n\n\n\nt \n\n\n\n(m\ng\n\n\n\n)\n\n\n\ncontrol\n\n\n\nSb 34\n\n\n\nSb 41\n\n\n\nSb 42\n\n\n\nc)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 51\n\n\n\nFig. 4: Effect of diazotrophs inoculation on root length, root volume and root surface \n\n\n\narea. (a) Root length, (b) Root volume, (c) Root surface area. Bars in the line indicate \n\n\n\nstandard error (n = 4)\n\n\n\nHydrolytic Enzymes in Rice Roots\n\n\n\n \nRoot length\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n0 5 10 15 20 25 30 35 40 45 50\n\n\n\nDays after inoculation\n\n\n\nR\no\n\n\n\no\nt \n\n\n\nle\nn\n\n\n\ng\nth\n\n\n\n (\nc\nm\n\n\n\n)\n\n\n\ncontrol\n\n\n\nSb 34\n\n\n\nSb 41\n\n\n\nSb 42\n\n\n\na)\n\n\n\n \nRoot volume\n\n\n\n0.0\n\n\n\n0.2\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n0.8\n\n\n\n1.0\n\n\n\n1.2\n\n\n\n0 5 10 15 20 25 30 35 40 45 50\n\n\n\nDays after inoculation\n\n\n\nR\no\n\n\n\no\nt \n\n\n\nv\no\n\n\n\nlu\nm\n\n\n\ne\n (\n\n\n\nc\nm\n\n\n\n3\n)\n\n\n\ncontrol\n\n\n\nSb 34\n\n\n\nSb 41\n\n\n\nSb 42\n\n\n\nb)\n\n\n\n \nRoot surface area\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n0 5 10 15 20 25 30 35 40 45 50\n\n\n\nDays after inoculation\n\n\n\nS\nu\n\n\n\nrf\na\nc\ne\n a\n\n\n\nre\na\n (\n\n\n\nc\nm\n\n\n\n2\n)\n\n\n\ncontrol\n\n\n\nSb 34\n\n\n\nSb 41\n\n\n\nSb 42\n\n\n\nc)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200952\n\n\n\nFig. 5: SEM micrographs showing the location of diazotrophs on rice roots of inoculated \n\n\n\nplants. (a) in disrupted zones of the mucigel (M), (b) through the fissures (F) at lateral \n\n\n\nroot emergence, (c) in the emerging zones (EZ) of the secondary roots\n\n\n\nA.M. Asilah, O. Radziah & M. Radzali\n\n\n\nAA\n\n\n\nB\n\n\n\nC\n\n\n\n\n\n\n\n\n\n\n\nM\n\n\n\nF\n\n\n\nEZ\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 53\n\n\n\nFig. 6: TEM micrographs showing the diazotrophs colonization of interior roots a & b. \n\n\n\nBacterial cells shown by the white arrow in intercellular spaces, (c & d) Bacterial cells \n\n\n\nin intracellular spaces\n\n\n\nRoot Growth \n\n\n\nDiazotrophs inoculation significantly increased root length, volume and surface \n\n\n\narea compared to non-inoculated plants (Fig. 4). At 45 days of growth Sb41 \n\n\n\ninoculated plants produced the highest root length, followed by plants treated with \n\n\n\nSb42 and Sb34 (Fig. 4a). Substantial increase in percentage of root volume over \n\n\n\nthe control was found for Sb41, Sb42 and Sb34 inoculated plants (107.10, 93.99 \n\n\n\nand 61.20%, respectively) (Fig. 4b). The percentage increment of root surface \n\n\n\narea over the control in plants inoculated with Sb41, Sb34, and Sb42 were 39.72, \n\n\n\n30.71 and 27.70%, respectively (Fig. 4c).\n\n\n\nHydrolytic Enzymes in Rice Roots\n\n\n\nA B\n\n\n\nC D\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200954\n\n\n\nRoot Colonization\n\n\n\nRoots from five days inoculated plants observed through SEM and TEM showed \n\n\n\nthat the diazotrophs were able to colonize the surface and interior of the roots. \n\n\n\nSEM micrographs showed that the diazotrophs were localized in the disrupted \n\n\n\nzones of the mucigel on root surface (Fig. 5a). The bacteria also entered the rice \n\n\n\nroots through the fissures at the emergent point of lateral roots (Fig. 5b). There \n\n\n\nwere clusters of diazotrophs colonizing the emergent point of the secondary \n\n\n\nroots (Fig. 5c). The TEM micrograph indicated the intercellular and intracellular \n\n\n\ninvasion of rice roots by the diazotrophs (Fig. 6a-d).\n\n\n\nDISCUSSION\n\n\n\nNine of the isolates showed the ability to produce EG and EPMG hydrolytic \n\n\n\nenzymes. The evidence of halo zones around the colonies in CMC and pectinase \n\n\n\nmedia plates, proved that the isolates were able to produce these enzymes (Table \n\n\n\n1). Naher et al. (2009a) also found that some diazotrophs isolated from rice can \n\n\n\nproduce hydrolytic enzymes. There were significant differences in the production \n\n\n\nof protein and specific enzymes activities among three isolates. At several stages \n\n\n\nof plant development, the N\n2\n-Fixing bacteria penetrated the walls of rice plant cell, \n\n\n\nand it has been hypothesized that enzymes of plant or bacterial origin could be \n\n\n\ninvolved in the degradation of cell wall polymers to facilitate invasion (Callaham \n\n\n\net al., 1981). Other studies showed that N\n2\n-Fixing bacteria produced hydrolytic \n\n\n\nenzymes to assist their penetration into plant (Jimenez-Zurdo et al., 1996; Mateos \n\n\n\net al., 2001; Mostajeran et al., 2007). \n\n\n\n The ability of these three bacterial isolates to grow in N-free Jensen\u2019s \n\n\n\nmedium showed that they are able to fix nitrogen. These N\n2\n-fixing bacteria with \n\n\n\nthe ability to produce EG and EPMG hydrolytic enzymes can enter the plant \n\n\n\nroots as endophytes. This is an agreement with other studies which proposed that \n\n\n\nhydrolytic enzymes pectinases and cellulases may play a role in the mechanisms \n\n\n\nby which endophytic bacteria penetrate into and persist in the host plant (Verma \n\n\n\net al., 2001; Reinhold-Hurek and Hurek, 1998). Diazotrophic plant-associated \n\n\n\nbacteria is beneficial to the plant via nitrogen fixation. Colonization of diazotrophs \n\n\n\non surface and inside of roots is important for growth improvement. Earlier \n\n\n\nresearch also proved that production and activities of plant cell wall degrading \n\n\n\nenzymes are involved in plant growth promotion (Fry, 2004). The increased root \n\n\n\ngrowth of this plant might indicate the increased of hydrolytic enzyme activities \n\n\n\nas observed for sorghum by Adriano-Anaya et al. (2006).\n\n\n\n The study showed that diazotrophs inoculation in rice can improve root \n\n\n\ncolonization, stimulate root and plant growth. The ability to colonize plant roots \n\n\n\ncan lead to increase biological nitrogen fixing activity for the plant. Previous \n\n\n\nstudies also showed that inoculation with PGPR such as Rhizobium sp. improved \n\n\n\nroot surface area, root volume and nitrogen uptake of rice seedlings (Biswas et al., \n\n\n\n2000b; Naher et al., 2009b). Inoculation of plant with Azospirillum sp. has been \n\n\n\nshown to positively affect the root biomass and surface area (Bashan et al., 2004). \n\n\n\nA.M. Asilah, O. Radziah & M. Radzali\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 55\n\n\n\nSEM and TEM observations showed that diazotrophs were able to colonize on the \n\n\n\nroots surfaces and the interior of the roots after five days of inoculation. Similarly, \n\n\n\na number of studies have shown the ability at diazotrophs to colonize the root \n\n\n\nsurfaces and survive as endophytes, and subsequently improve plant growth \n\n\n\n(Whipps, 2001; Chanway et al., 2000). This study indicated that production of \n\n\n\nhydrolytic enzymes by the indigenous bacterial isolates and their association with \n\n\n\nrice plant may increase the growth and N\n2\n-fixing activity of the plant.\n\n\n\nCONCLUSION\n\n\n\nResults of this study proved that colonization of N\n2\n-fixing bacteria on the surface \n\n\n\nand interior of rice roots is important for nitrogen fixation and plant growth \n\n\n\nenhancement. The association of these diazotrophs with the plants significantly \n\n\n\nincreased the production of hydrolytic enzymes in the root system which is an \n\n\n\nimportant mechanism for endophytic colonization and subsequently nitrogen \n\n\n\nfixation.\n\n\n\nACKNOWLEDGEMENT\n\n\n\nThe authors are grateful to the Ministry of Science, Technology and Innovation \n\n\n\n(MOSTI) for funding this project under the Science fund grant: 02-01-04-\n\n\n\nSF0169. \n\n\n\nREFERENCES\nAdriano-Anaya, M. L., M. Salvador-Figueroa, J. A. Ocampo and I. Garcia Romero. \n\n\n\n 2006. Hydrolytic enzyme activities in maize (Zea mays) and sorghum \n\n\n\n (Sorghum bicolor) roots inoculated with Gluconacetobacter \n\n\n\n diazotrophicus and Glomus intraradices. Soil Biol&Biochem. 38:879-886.\n\n\n\nBashan, Y., H. Gina and L. E. de-Bashan. 2004. Azospirillum-plant relationships: \n\n\n\n physiological, molecular, agricultural and environmental advances (1997-\n\n\n\n 2003). Can. J. Microbiol. 50:521-577.\n\n\n\nBateman, D. F. 1963. Pectolytic activities of culture filtrates of Rhizoctonia solani    \n\n\n\n and extract of Rhizoctonia-infected tissues of bean. Phytopathology \n\n\n\n 87:677-685.\n\n\n\nBiswas, J. C., J. K. Ladha and F. B. Dazzo. 2000a. Rhizobia inoculation improves \n\n\n\n nutrient uptake and growth of lowland rice. Soil Sci. Soc. Am. J. 64:1644-\n\n\n\n 1650.\n\n\n\nBiswas, J. C., J. K. Ladha, F. B. Dazzo, Y. G. Yanni and B. G. Rolfe. 2000b. \n\n\n\n Rhizobial inoculation influences seedling vigor and yield of rice. 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B. Dazzo and E. Martinez-Molina. 1996. \n\n\n\n Cell-bound cellulose and polygalacturoenase production by Rhizobium \n\n\n\n and Bradyrhizobium species. Soil Biol. Biochem. 28:917-921. \n\n\n\nMateos, P., Jiminez-Zurdo, J. Chen, A. Squartini, S. Haack, E. Martinez-Molina, \n\n\n\n D. Hubbell and F. B. Dazzo. 1992. Cell-associated pectinolytic and \n\n\n\n cellulolytic enzymes in Rhizobium leguminosarum bv. trifolii. Appl. \n\n\n\n Environ. Microbiol. 58: 1816-1822.\n\n\n\nMateos, P. F., D. L. Baker, M. Petersen, E. Velazquez, J. I. Jimenez-Zuardo, E. Martinez- \n\n\n\n Molina, A. Squartini, G. Orgambide, D. H. Hubbell and F. B. Dazzo. 2001. \n\n\n\n Erosion of root epidermal cell walls by Rhizobium polysaccharide-degrading \n\n\n\n enzymes as related to primary host infection in the Rhizobium-legume symbiosis. \n\n\n\n Can. J. Microbiol. 47: 475-487.\n\n\n\nMostajeran, A., R. Amooaghaie and G. Emtiazi. 2007. The participation of the cell \n\n\n\n wall hydrolytic enzymes in the initial colonization of Azospirillum \n\n\n\n brasilense on wheat roots. Plant Soil 291: 239-248.\n\n\n\nNaher, U. A., O. Radziah, Z. H. Shamsuddin, M. S. Halimi and I. M. Razi. 2009a. \n\n\n\n Isolation of diazotrophs from different soils of tanjong karang rice \n\n\n\n growing area in Malaysia. Int. J. Agric. Biol. 11: 547-552.\n\n\n\nNaher, U. A., R. Othman, Z. H. J. Shamsuddin, H. M. Saud and M. R. Ismail. \n\n\n\n 2009b. Growth enhancement and root colonization of rice seedlings by \n\n\n\n Rhizobium and Corynebacterium spp. Int. J. Agric. Biol. 11: 586-590.\n\n\n\nPilnik, W. 1982. Enzymes in the beverage industry. Dupuy, D. ed. Use of Enzymes \n\n\n\n in Food Technology. Technique et Documentation. Paris: Lavoisier.\n\n\n\nReinhold-Hurek, B., T. Hureck, M. Claeyssens and M. Van-Montasu. 1993. \n\n\n\n Cloning expression in Escherchia Coli and characterization of cellulolytic \n\n\n\n enzymes of Azoarcus sp. J. Bacteriol. 175: 7056-7065.\n\n\n\nA.M. Asilah, O. Radziah & M. Radzali\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 57\n\n\n\nReinhold-Hurek, B. and T. Hurek. 1998. Life in grasses: diazotrophic endophytes. \n\n\n\n Trend Microbiol. 6: 139-144.\n\n\n\nSomasegaran, P. and H. J. Hoben. 1985. General microbiology of Rhizobium. \n\n\n\n Methods in legume-Rhizobium technology.\n\n\n\nVerma, S. C., J. K. Ladha and A. K. Tripathi. 2001. Evaluation of plant growth \n\n\n\n promoting and colonization ability of endophytic diazotrophs from deep \n\n\n\n water rice. Journal of Biotechnology 91: 127-141.\n\n\n\nWhipps, J. M. 2001. Microbial interactions and biocontrol in the rhizosphere. J. \n\n\n\n Exp. Bot. 52: 487-511.\n\n\n\n\n\n\n\n\n\n\n\nHydrolytic Enzymes in Rice Roots\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : abdhafiz@usm.my \n\n\n\nINTRODUCTION\nSubterranean termite infestation can cause great damage to a wide variety of \nhuman structures such as houses and wood-based furniture. Moreover, the \ninfestation of this insect can also adversely affect agricultural sectors such as \noil palm and rubber tree plantations. The infestation is practically controlled \nby creating a chemical barrier around the building structures (Horwood 2007; \nKamble and Saran 2005; Spomer and Kamble 2010). Nevertheless, excessive \napplication of termiticide to control subterranean termite can lead to environmental \ncontamination. Termiticide contamination occurs in several ways: precipitation, \nleaching and dust (Arias-Est\u00e9vez et al. 2008; Delcour et al. 2015; Gustafson 1989; \nHuseth and Groves 2014). Among these, leaching appears to contribute the most \nto massive environmental contamination that further leads to underground water \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 77-92 (2018)                          Malaysian Society of Soil Science\n\n\n\nLeaching of Termiticides Containing Bifenthrin, Fipronil \nand Imidacloprid in Different Types of Soils under \n\n\n\nLaboratory Conditions\n\n\n\nMohd Fawwaz Mohd Rashid1, Shahrem Md Ramli2, and\nAbdul Hafiz Ab Majid1,*\n\n\n\n1Household and Structural Urban Entomology Laboratoty, Vector Control Research \nUnit, School of Biological Sciences, Universiti Sains Malaysia, \n\n\n\n11800 Minden, Penang, Malaysia\n2Ensystex (Malaysia) Sdn Bhd, No. 5, Jalan Bukit Permai Utama 1, Taman\n\n\n\nIndustri Bukit Permai, Cheras, 56100 Kuala Lumpur\n\n\n\nABSTRACT\nTermiticides need to persist in the soil to give continuous protection to the building \nstructures or plantations. Leaching contributes to massive contamination that can \nfurther lead to underground water contamination. The leaching activity of three \ndifferent termiticides (bifenthrin, fipronil and imidacloprid) in different types of \nsoils (sandy loam and loamy sand) under laboratory conditions was evaluated. \nLeaching activity of the termiticides using a soil column method revealed that \nbifenthrin had a good adsorption characteristic as its concentration at the top of \nthe column was higher (Sandy loam = 892.77mg/L; Loamy sand= 1060.93 mg/L) \ncompared to fipronil and imidacloprid. There was no significant difference between \nsoil types (p= 0.131) but there was a significant difference between termiticides \nused (p= 0.00). The concentration of bifenthrin was higher in the treated area (0-5 \ncm \u2013 top layer) due to the higher Koc value and lower water solubility compared to \nimidacloprid and fipronil. Thus, bifenthrin is recommended during a rainy season \nfor soil treatment. \n\n\n\nKeywords: Termiticides, leaching, imidacloprid, fipronil, bifenthrin.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201878\n\n\n\ncontamination (Bajeer 2012). In general, termiticides need to be persistent in the \nsoil to give  continuous protection to human structures or plantations.\n\n\n\nThe environmental fate of termiticide is mainly controlled by its behaviour \nin the soil, where several physio-chemical and biological processes control the \nmovement and dissipation in other environmental components such as air, water \nand biota (Tang et al. 2012). It is crucial to recognise the fate of termiticide in \nthe field to prevent a maximum effect on non-target insects as well as to avoid \nany environmental contamination. Uncontrolled termiticide treatment may also \nlead to other problems. For example, leaching of termiticide is toxic to non-target \norganisms, causing indirect accumulation, which simultaneously affects the \nhuman population through food chains. Polluted soil, surface and ground water \nare harmful to the environment and human health (Bajeer 2012).\n\n\n\nMany countries in Southeast Asia rely heavily on the use of soil termiticide \nagainst subterranean termites in the urban environment including Malaysia (Lee \n2002). Imidacloprid (chloronicotinyl class), bifenthrin (pyrethroid class) and \nfipronil (phenyl pyrazole class) with a novel mode of action are among the most \ncommonly used termiticides for controlling subterranean termites. Imidacloprid \nhas a higher water solubility (510 mg/L at 20 \u00b0C) (Fern\u00e1ndez-Bayo et al. 2007; \nPeterson 2007) compared to fipronil (1.9 to 2.4 mg/L at 20 \u00b0C) (Husen et al. 2009) \nand bifenthrin (0.1 mg/L at 20 \u00b0C) (Fecko 1999). Furthermore, bifenthrin has \na greater Koc value (1.31-3.02 x 105) (Fecko 1999) followed by fipronil (803) \n(Connelly 2001) and imidacloprid (132-310) (Fossen 2006).\n\n\n\nSoil texture has an important impact on termiticide performance, but the \neffects differ within termiticides.  According to Wiltz (2010), the clay content in \nsoils is significantly related to termite mortality across all termiticides, application \nrates, and exposure times. In assays conducted with bifenthrin, fipronil and \nchlorfenapyr, C. formosanus mortality was the highest when clay content was \nlow (Wiltz 2010). According to Osbrink and Lax (2002), the mortality of C. \nformosanus workers was higher in the fipronil-treated sand than in treated mixture \nof soil and clay. Sorption is greatly influenced by the amount of clay and organic \nmatter. Fipronil indicates a significant decrease in adsorption coefficient as the \nsoil clay content decreases, thereby, increasing its bioavailability (Bob\u00e9 et al. \n1997; Cox et al. 1998; 2001). \n\n\n\nBased on a previous study, the most economic important termite pest \nCoptotermes sp.was found in two types of soil- sandy loam and loamy sand (Ab \nMajid & Ahmad 2013a). These two types of soil are also commonly treated with \nimidacloprid, fipronil and bifenthrin base termiticides known as soil corrective \ntreatment to prevent termite infestation of the building structure (Ab Majid \n&Ahmad 2013a; Ab Majid & Ahmad 2013b; Ab Majid & Hassan 2008; Ab Majid \n& Ahmad 2011). Therefore, the objective of this study was to evaluate the leaching \nactivity of three different termiticides (bifenthrin, fipronil and imidacloprid) in \ntwo types of soils (sandy loam and loamy sand) under laboratory conditions by \nevaluating the termiticides residues left in different parts (top, middle, bottom) of \nthe soil column and the concentration in the leachates.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 79\n\n\n\nMATERIALS AND METHODS\n\n\n\nSoil Samples\nSoil samples were collected from two sites i.e. Durian valley, Universiti Sains \nMalaysia (USM) (5\u00b021.35\u2019N; 100\u00b018.16\u2019E) for sandy loam and Teluk Bahang, \nPenang (5\u00b026.47\u2019N; 100\u00b013.04\u2019E) for loamy sand. The soils were taken \napproximately 10 cm from the top layer (A-horizon). The remaining debris such \nas stones, vegetation and macro faunas were removed. The soils were air-dried at \nroom temperature (20-25 \u00b0C), sieved through a 2-mm sieve, air dried and stored \nat ambient temperature. The soils were subsequently analysed for particle size, pH \nand organic matter content. Soil pH was determined using a pH meter (HANNA \nHI 8424, Romania). The soils were mixed with distilled water at a ratio of 1:2 and \nleft overnight to obtain the pH value.\n\n\n\nSoil Texture\nSoil texture was analysed following the method of Bouyoucus (1962). Soil \nequivalent to 50 g  was mixed with 100 mL of 6% hydrogen peroxide (H2O2) in \na 500 mL beaker. The mixture was left overnight at room temperature. Then, the \nbeaker was placed on a hot plate at 90 \u00b0C for 10 min. Then, 50 mL 1M NaOH \nwere added into the beaker, followed by the addition of distilled water to achieve \na final volume of 400mL. The mixture was left to rest for 20 min. \n\n\n\nThe mixture was then stirred for 10 min, transferred into a 1 L measuring \ncylinder  to which was  added distilled water to achieve a final volume of \n1 L. The mixture was allowed to rest to achieve a thermal equilibrate and the \ntemperature was recorded. The measuring cylinder was inverted several times to \nensure the contents were thoroughly mixed. Then, a hydrometer was immediately \nlowered into the suspension and readings were recorded after 40 sec. After 2 h, \nthe hydrometer was placed into the measuring cylinder. The buoyancy of soil \nparticles and temperature were then recorded using hydrometer and thermometer) \nrespectively. The percentage of sand, silt and clay was obtained from readings \ncollected from the hydrometer and thermometer. Soil texture was determined by \nusing USDA textural triangle based on the proportion of sand, silt and clay. The \ninterception percentage of sand, silt and clay indicates the soil texture.\nOrganic Matter Content\n\n\n\nOrganic matter content was obtained using a loss on ignition technique \n(Ball 1964; Pallasser et al. 2013). Three replicates of 1-g soil and an empty can \nwere weighed and recorded, respectively. Each can was placed into a muffle \nfurnace (Lab-Heat BLUEM) for 16 h at a temperature of 440 \u00b0C for the ignition \nprocess and cooled overnight. The cans with ash were weighed and recorded. The \ncalculation for organic matter content is shown below:\n\n\n\n6 \n \n\n\n\nadded into the beaker, followed by the addition of distilled water to achieve a final 112 \n\n\n\nvolume of 400mL. The mixture was left to rest for 20 min.  113 \n\n\n\nThe mixture was then stirred for 10 min, transferred into a 1 L measuring 114 \n\n\n\ncylinder  to which was  added distilled water to achieve a final volume of 1 L. The 115 \n\n\n\nmixture was allowed to rest to achieve a thermal equilibrate and the temperature was 116 \n\n\n\nrecorded. The measuring cylinder was inverted several times to ensure the contents 117 \n\n\n\nwere thoroughly mixed. Then, a hydrometer was immediately lowered into the 118 \n\n\n\nsuspension and readings were recorded after 40 sec. After 2 h, the hydrometer was 119 \n\n\n\nplaced into the measuring cylinder. The buoyancy of soil particles and temperature 120 \n\n\n\nwere then recorded using hydrometer and thermometer) respectively. The percentage 121 \n\n\n\nof sand, silt and clay was obtained from readings collected from the hydrometer and 122 \n\n\n\nthermometer. Soil texture was determined by using USDA textural triangle based on 123 \n\n\n\nthe proportion of sand, silt and clay. The interception percentage of sand, silt and 124 \n\n\n\nclay indicates the soil texture. 125 \n\n\n\nOrganic Matter Content 126 \n\n\n\nOrganic matter content was obtained using a loss on ignition technique (Ball 1964; 127 \n\n\n\nPallasser et al. 2013). Three replicates of 1-g soil and an empty can were weighed 128 \n\n\n\nand recorded, respectively. Each can was placed into a muffle furnace (Lab-Heat 129 \n\n\n\nBLUEM) for 16 h at a temperature of 440\u00b0C for the ignition process and cooled 130 \n\n\n\novernight. The cans with ash were weighed and recorded. The calculation for organic 131 \n\n\n\nmatter content is shown below: 132 \n\n\n\n     \n(            )  (           )\n\n\n\n\n\n\n\n 133 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201880\n\n\n\nTermiticides\nThree soil treatments were carried out using termiticides containing different \nactive ingredients: (1) 20% Imidacloprid, (Prothor); (2) 5% Fipronil, (Chalcid \n5.0); and (3) 10% Bifenthrin (Maxxthor). Formulated products of Prothor 200 SC \n(Ensystex (Malaysia) Sdn. Bhd., Kuala Lumpur), Maxxthor 100 SC (Ensystex \n(Malaysia) Sdn. Bhd., Kuala Lumpur) and Chalcid 5.0 SC (Hextar Chemicals \nSdn. Bhd., Selangor, Malaysia) were purchased from a local distributor.  Table 1 \nshows the sorption parameters for the termiticides used in this study.\n\n\n\nTABLE 1\nSoil sorption parameters of termiticides tested\n\n\n\nPreparation of Columns\nSoil column apparatus was prepared as shown in Figure 1. Untreated soils were \nair-dried and sieved. Then, the soils were packed in the columns up to the height \nof approximately 20 cm. The soils were added by using a spoon to create an \noptimum packing. The total weight of the soils packed in the columns was \ndetermined and the weight of all duplicate columns was set at 500 g. Then, the \nsoil columns were pre-wetted with artificial rain from bottom to top to remove \nair pockets within the soils. Five mL (1000 mg/L) of each termiticide was poured \non the top of the soil columns. The top surface of the column was covered with a \nfilter paper, to allow the soils to evenly distribute when artificial rain was poured \ninto the columns. Then, artificial rainfall was poured into the soil columns at 100 \nmL/L with the aid of a peristaltic pump (Bajeer 2012). After 5 h, the soils were left \nto drain, the column was divided into three sections (top= 0-5 cm, middle= 10-15 \ncm and bottom= 25-30 cm) for further analysis (Figure 2).\n\n\n\nTermiticide Extraction from Soils\nThe collected samples were placed in a black plastic and immediately stored in a \nfreezer (below -14 \u00b0C) prior to analysis to prevent termiticide degradation. Then, \nthe soil samples were removed from the freezer and air-dried for 24 h. Ten grams \nof the soil samples for each replicate was weighed into a 200 mL conical flask. \nAbout 40 mL of acetonitrile (ACN) (HPLC grade) were then added to each soil \nsample. The flasks were covered with aluminium foil and were agitated overnight \nat 200 rpm using a shaker at 20 \u00baC. \n\n\n\n7 \n \n\n\n\nTermiticides 134 \n\n\n\nThree soil treatments were carried out using termiticides containing different active 135 \n\n\n\ningredients: (1) 20% Imidacloprid, (Prothor); (2) 5% Fipronil, (Chalcid 5.0); and (3) 136 \n\n\n\n10% Bifenthrin (Maxxthor). Formulated products of Prothor 200 SC (Ensystex 137 \n\n\n\n(Malaysia) Sdn. Bhd., Kuala Lumpur), Maxxthor 100 SC (Ensystex (Malaysia) Sdn. 138 \n\n\n\nBhd., Kuala Lumpur) and Chalcid 5.0 SC (Hextar Chemicals Sdn. Bhd., Selangor, 139 \n\n\n\nMalaysia) were purchased from a local distributor.  Table 1 shows the sorption 140 \n\n\n\nparameters for the termiticides used in this study. 141 \n\n\n\nTABLE 1 142 \n\n\n\nSoil sorption parameters of termiticides tested 143 \n\n\n\nTermiticide Koc(L/kg) Log \nKow \n\n\n\nH2O Solubility \n(mg/L) \n\n\n\nHenry\u2019s law \nconstant \n(atmm3 \n/mol) \n\n\n\nReference \n\n\n\nBifenthrin 1.31 x 105 - \n3.02 x 105 \n\n\n\n6.0 0.1 (25\u02daC) 7.2 x 10-3 Fecko \n(1999) \n\n\n\nFipronil 3.8 x 103 \n\u2013 1.2 x 104 \n\n\n\n4.01 2.4 (pH 5) \n2.2 (pH 9) \n\n\n\n3.7 x 10-5 Connelly \n(2001) \n\n\n\nImidacloprid 1.3 x 102 - \n3.1 x 102 \n\n\n\n0.57 514 (20\u02daC, pH \n7) \n\n\n\n6.5 x 10-11 Fossen \n(2006) \n\n\n\n 144 \n\n\n\nPreparation of Columns 145 \n\n\n\nSoil column apparatus was prepared as shown in Figure 1. Untreated soils were air-146 \n\n\n\ndried and sieved. Then, the soils were packed in the columns up to the height of 147 \n\n\n\napproximately 20 cm. The soils were added by using a spoon to create an optimum 148 \n\n\n\npacking. The total weight of the soils packed in the columns was determined and the 149 \n\n\n\nweight of all duplicate columns was set at 500 g. Then, the soil columns were pre-150 \n\n\n\nwetted with artificial rain from bottom to top to remove air pockets within the soils. 151 \n\n\n\nFive mL (1000 mg/L) of each termiticide was poured on the top of the soil columns. 152 \n\n\n\nThe top surface of the column was covered with a filter paper, to allow the soils to 153 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 81\n\n\n\nThe samples were left to stand for 1 h to allow soil particles to settle. A total \nof 1.5 mL of clear supernatant was filtered through 0.22 \u00b5m Choice Syringe Filters \nwith nylon Membrane (Thermo Scientific, China) into a 2.0 mL microcentrifuge \ntube. Aliquots were centrifuged (Eppendorf Centrifuge 5424) at 12 000 rpm x \ng for 20 min. One mL of supernatants was transferred into 2 mL auto-injector \nvial and was sealed with a PTFE lined screw cap after passing through a 0.22 \n\u00b5m Choice Syringe Filters with nylon membrane (Thermo Scientific, China). \nSamples were analysed after the extraction procedure.\n\n\n\nFigure 1: Setup of apparatus for the leaching soil column experiment\n\n\n\nFigure 2: Schematic diagram of the soil column showing different layers.\n\n\n\n8 \n \n\n\n\nevenly distribute when artificial rain was poured into the columns. Then, artificial 154 \n\n\n\nrainfall was poured into the soil columns at 100 mL/L with the aid of a peristaltic 155 \n\n\n\npump (Bajeer 2012). After 5 h, the soils were left to drain, the column was divided 156 \n\n\n\ninto three sections (top= 0-5 cm, middle= 10-15 cm and bottom= 25-30 cm) for 157 \n\n\n\nfurther analysis (Figure 2). 158 \n\n\n\n 159 \n\n\n\n 160 \n\n\n\nFigure 1: Setup of apparatus for the leaching soil column experiment 161 \n\n\n\n 162 \n\n\n\n 163 \n\n\n\n  164 \n\n\n\n9 \n \n\n\n\n 165 \n\n\n\n 166 \n\n\n\n 167 \n\n\n\n 168 \n\n\n\n 169 \n\n\n\n 170 \n\n\n\n 171 \n\n\n\n 172 \n\n\n\nFigure 2: Schematic diagram of the soil column showing different layers. 173 \n\n\n\n 174 \n\n\n\nTermiticide Extraction from Soils 175 \n\n\n\nThe collected samples were placed in a black plastic and immediately stored in a 176 \n\n\n\nfreezer (below -14\u00b0C) prior to analysis to prevent termiticide degradation. Then, the 177 \n\n\n\nsoil samples were removed from the freezer and air-dried for 24 h. Ten grams of the 178 \n\n\n\nsoil samples for each replicate was weighed into a 200 mL conical flask. About 40 179 \n\n\n\nmL of acetonitrile (ACN) (HPLC grade) were then added to each soil sample. The 180 \n\n\n\nflasks were covered with aluminium foil and were agitated overnight at 200 rpm 181 \n\n\n\nusing a shaker at 20\u00baC.  182 \n\n\n\nThe samples were left to stand for 1 h to allow soil particles to settle. A total 183 \n\n\n\nof 1.5 mL of clear supernatant was filtered through 0.22 \u00b5m Choice Syringe Filters 184 \n\n\n\nwith nylon Membrane (Thermo Scientific, China) into a 2.0 mL microcentrifuge 185 \n\n\n\ntube. Aliquots were centrifuged (Eppendorf Centrifuge 5424) at 12 000 rpm x g for 186 \n\n\n\n20 min. One mL of supernatants was transferred into 2 mL auto-injector vial and was 187 \n\n\n\n0-5 cm (top \n\n\n\nlayer) \n\n\n\n25-30 cm \n\n\n\n(bottom layer) \n\n\n\n10-15 cm \n\n\n\n(middle layer) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201882\n\n\n\nTermiticide Extraction from Leachate\nA liquid-liquid extraction was done according to the modified version from EPA \nmethod 3350 (USEPA, 2007)(not in ref list). The collected leachate was stored in \n-14\u00b0C to prevent degradation. About 10 mL of the leachate was mixed with 10 \nmL of acetonitrile in a universal bottle. Then, the mixture was vortexed on Grant-\nbio (Type: PV-1) for 10 sec. The mixture in the universal bottle was allowed \nto settle down for 30 min following which 10 mL of supernatant was obtained.  \nThe supernatant was transferred to a new universal bottle to which was added \nanother 10 mL of ACN. The new mixture was sonicated with an ultrasonic cleaner \n(Model: CE-7200A). One mL of the new supernatant was filtered through 0.22 \n\u00b5m Choice Syringe Filters with nylon Membrane (Thermo Scientific, China) and \ntransferred into a 1.5mL vial for UPLC analysis.  \n        \nResidual Analysis\nTechnical grade of bifenthrin (98.8%), fipronil (97.9%) (Sigma-Aldrich, Malaysia) \nand imidacloprid (99.5%) (Chem Service) were used as standards. Acetonitrile \n(Fisher Chemical, Malaysia) was used as a universal solvent to dissolve the \ntermiticides. Extracted termiticides were separately analysed using an ACQUITY \nUPLC systems (Waters) coupled with PDA detector. A BEH C18 stainless steel \ncolumn, (2.1 mm x 100 mm) of 1.7 \u00b5m particle size was used. The injection \nvolume was set at 5.0 uL and the flow rate at 1.0mL/min. \n\n\n\nA mobile phase, UV wavelength and retention times for all termiticides are \nsummarised in Table 2 (Baskaran et al. 1999; Saran and Kamble 2008).\n\n\n\nData analysis\nThe termiticide residue was measured following a standard formula by CEPA, \n(1993)(not in ref list) and Ong et al. (2016):\n\n\n\n       (Spl H)(Std C)(Std V)(V)\n R= ------------------------------ ,\n       (Std H)(Spl V)(Spl W)\n\n\n\nTABLE 2\nUPLC setting for termiticides used in this study\n\n\n\n11 \n \n\n\n\nA mobile phase, UV wavelength and retention times for all termiticides are 211 \n\n\n\nsummarised in Table 2 (Baskaran et al. 1999; Saran and Kamble 2008). 212 \n\n\n\nTABLE 2 213 \n\n\n\nUPLC setting for termiticides used in this study 214 \n\n\n\nTermiticide Mobile phase \n(Water: CAN) \n\n\n\nWavelength \n(nm) \n\n\n\nRetention time \n(minutes) \n\n\n\nBifenthrin 60:40 204 1.082 \n\n\n\nFipronil 20:80 280 1.756 \n\n\n\nImidacloprid 30:70 270 1.190 \n\n\n\n 215 \n\n\n\nData analysis 216 \n\n\n\nThe termiticide residue was measured following a standard formula by CEPA, 217 \n\n\n\n(1993)(not in ref list) and Ong et al. (2016): 218 \n\n\n\n 219 \n(Spl H)(Std C)(Std V)(V) 220 \n\n\n\nR= ------------------------------ , 221 \n(Std H)(Spl V)(Spl W) 222 \n\n\n\n 223 \n\n\n\nwhere R is termiticide residue of part per million (mg/L), Spl H is the sample peak 224 \n\n\n\nheight area, Std C is the standard concentration in mg/L, Std V is the standard 225 \n\n\n\nvolume injected in \u00b5l, V is the volume after the injection in mL, Std H is the standard 226 \n\n\n\npeak height area, Spl V is the sample volume injected in \u00b5l, and Spl W is the sample 227 \n\n\n\nweight in g. 228 \n\n\n\nA two-way ANOVA (SPSS version 21) was conducted to compare 229 \n\n\n\nsignificance of differences between the factors examined in this study. Data 230 \n\n\n\nnormality was evaluated using IBM SPSS Statistics version 21. Data transformation 231 \n\n\n\nwas applied as the results were abnormal. Analysis of variance (ANOVA) was 232 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 83\n\n\n\nwhere R is termiticide residue of part per million (mg/L), Spl H is the sample peak \nheight area, Std C is the standard concentration in mg/L, Std V is the standard \nvolume injected in \u00b5l, V is the volume after the injection in mL, Std H is the \nstandard peak height area, Spl V is the sample volume injected in \u00b5l, and Spl W is \nthe sample weight in g.\n\n\n\nA two-way ANOVA (SPSS version 21) was conducted to compare significance \nof differences between the factors examined in this study. Data normality was \nevaluated using IBM SPSS Statistics version 21. Data transformation was applied \nas the results were abnormal. Analysis of variance (ANOVA) was performed to \ndetermine the significance of differences between types of soils, termiticides and \ndifferent parts/layers (top, middle and bottom).\n\n\n\nRESULTS\nTable 3 summarises the characteristics of two soil samples used in this study. The \ntested soil was sandy loam, containing 80% of sand, 16.4% clay and 3.6% silt, \nwhile, the other soil was loamy sand, containing a slightly higher percentage of \nsand (84.6%) with lower clay (11%) and silt (4.4%) than sandy loam. Sandy loam \nwas more acidic (pH 4.4) compared to loamy sand (pH 4.8). The organic matter \ncontent for sandy loam and loamy sand were 11.4% and 8.5%, respectively. \n\n\n\nThe concentration of each termiticide in the soil column is shown in Figure \n3. Bifenthrin noted the highest residual termiticide in the top layer of the soil \ncolumn (sandy loam=892.77mg/L; loamy sand = 1060.93 mg/L). Meanwhile, \nfipronil presented a slightly lower level than bifenthrin with a concentrations of \n585.67 mg/L in sandy loam and 66.59 mg/L in loamy sand. Imidacloprid, on the \nother hand, showed the lowest level of termiticide residual (sandy loam= 16.37 \nmg/L; loamy sand= 0.97 mg/L) compared to bifenthrin and fipronil.\n\n\n\nAll factors significantly affected the termiticide concentrations, except \n\n\n\nTABLE 3\nCharacteristics of soil samples used in this study\n\n\n\n12 \n \n\n\n\nperformed to determine the significance of differences between types of soils, 233 \n\n\n\ntermiticides and different parts/layers (top, middle and bottom). 234 \n\n\n\n 235 \n\n\n\nRESULTS 236 \n\n\n\nTable 3 summarises the characteristics of two soil samples used in this study. The 237 \n\n\n\ntested soil was sandy loam, containing 80% of sand, 16.4% clay and 3.6% silt, while, 238 \n\n\n\nthe other soil was loamy sand, containing a slightly higher percentage of sand 239 \n\n\n\n(84.6%) with lower clay (11%) and silt (4.4%) than sandy loam. Sandy loam was 240 \n\n\n\nmore acidic (pH 4.4) compared to loamy sand (pH 4.8). The organic matter content 241 \n\n\n\nfor sandy loam and loamy sand were 11.4% and 8.5%, respectively.  242 \n\n\n\n 243 \n\n\n\nTABLE 3 244 \nCharacteristics of soil samples used in this study 245 \n\n\n\n 246 \nCharacteristics Loamy sand Sandy loam \n\n\n\npH 4.4\u00b10.05 4.8\u00b10.04 \n\n\n\nOrganic matter content \n(%) \n\n\n\n11.4\u00b10.14 8.5\u00b10.45 \n\n\n\nSand (%) 80.0 84.6 \n\n\n\nClay (%) 16.4 11.0 \n\n\n\nSilt (%) 3.6 4.4 \n\n\n\n 247 \n\n\n\n 248 \n\n\n\nThe concentration of each termiticide in the soil column is shown in Figure 249 \n\n\n\n3. Bifenthrin noted the highest residual termiticide in the top layer of the soil column 250 \n\n\n\n(sandy loam=892.77mg/L; loamy sand = 1060.93 mg/L). Meanwhile, fipronil 251 \n\n\n\npresented a slightly lower level than bifenthrin with a concentrations of 585.67 mg/L 252 \n\n\n\nin sandy loam and 66.59 mg/L in loamy sand. Imidacloprid, on the other hand, 253 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201884\n\n\n\nfor soil types (p= 0.131) (Table 4). Termiticides used had a significant effect \non termiticide concentration (p= 0.001). Bifenthrin significantly showed a \nhigher concentration in the soil column and leachate compared to fipronil and \nimidacloprid. The parts of soil column also showed a significant difference (p= \n0.001), where termiticide concentrations were significantly higher at the top than \nat the middle and bottom parts of the soil columns. The interaction of termiticide, \nsoil and parts affected the termiticide concentration (p=0.001). The termiticide \nconcentration reported higher levels at the upper part of soil in the loamy sand soil \ntreated with bifenthrin compared to fipronil and imidacloprid. (Please note that in \nTable 4 sig values are not given in bold though you say they are given in bold in \nyour notes at the bottom of the table)   \n\n\n\nFigure 4 shows the leaching patterns of bifenthrin in this study. Based on \n2-way ANOVA, no significant difference (F= 0.674, df= 1, P= 0.424) was observed \nbetween sandy loam and loamy sand. Bifenthrin concentrations remained high in \nthe top of the column for both soil types, while the concentrations degraded at \nthe middle and bottom parts of the columns at an average of 97.07 mg/L. The \nconcentrations of leachate, however, remained high in both soils (SL= 756.53; \nLS= 517.49 mg/L).\n\n\n\nTABLE 4\nEffects of termiticide, soil type and part of the soil columns on termiticide concentration\n\n\n\n13 \n \n\n\n\nshowed the lowest level of termiticide residual (sandy loam= 16.37 mg/L; loamy 254 \n\n\n\nsand= 0.97 mg/L) compared to bifenthrin and fipronil. 255 \n\n\n\n 256 \n\n\n\nAll factors significantly affected the termiticide concentrations, except for soil types 257 \n\n\n\n(p= 0.131) (Table 4). Termiticides used had a significant effect on termiticide 258 \n\n\n\nconcentration (p= 0.001). Bifenthrin significantly showed a higher concentration in 259 \n\n\n\nthe soil column and leachate compared to fipronil and imidacloprid. The parts of soil 260 \n\n\n\ncolumn also showed a significant difference (p= 0.001), where termiticide 261 \n\n\n\nconcentrations were significantly higher at the top than at the middle and bottom 262 \n\n\n\nparts of the soil columns. The interaction of termiticide, soil and parts affected the 263 \n\n\n\ntermiticide concentration (p=0.001). The termiticide concentration reported higher 264 \n\n\n\nlevels at the upper part of soil in the loamy sand soil treated with bifenthrin 265 \n\n\n\ncompared to fipronil and imidacloprid. (Please note that in Table 4 sig values are not 266 \n\n\n\ngiven in bold though you say they are given in bold in your notes at the bottom of 267 \n\n\n\nthe table)    268 \n\n\n\n 269 \nTABLE 4 270 \n\n\n\nEffects of termiticide, soil type and part of the soil columns on termiticide 271 \nconcentration 272 \n\n\n\n 273 \n\n\n\nVariable df Mean Square F-value P-value \nTermiticides 2 1813.222 265.94 0 \nSoils 1 16.055 2.355 0.131 \nParts 3 672.424 98.622 0 \nTermiticide * soil 2 45.602 6.688 0.003 \nTermiticide * Parts 6 239.171 35.079 0 \nSoil * Parts 3 52.971 7.769 0 \nTermiticide * soil * Parts 6 39.056 5.728 0 \nNotes: df = degree of freedom; significant values are given in bold274 \n\n\n\nFipronil also showed the highest concentration at the top of the soil column \nin both soils (SL= 585.67 mg/L; LS= 66.59 mg/L) compared to the middle and \nlow parts as well as in the leachate. This finding indicates that most of the fipronil \nremained in the termiticide-treated area (Figure 5). \n\n\n\nA similar pattern was also shown by imidacloprid, except that the top of the \nsoil column of sandy loam showed a higher concentration (16.37 mg/L) compared \nto loamy sand (0.97 mg/L). Both soils lost almost 98.36% to 99.90% at the top of \nthe soil columns. Imidacloprid indicated a low concentration (1.55 mg/L) at the \nmiddle, bottom and leachate of the soil column in both soils (Figure 6).\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 85\n\n\n\nDISCUSSION\nBifenthrin showed a higher concentration in the top part of the soil column \ncompared to fipronil and imidacloprid, indicating that bifenthrin is the most \npersistent among termiticides tested. A similar finding was reported by Horwood \n(2007) who found that bifenthrin was higher than chlorfenapyr, chlorpyrifos, \nfipronil and imidacloprid. According to Kamble and Saran (2005), as bifenthrin \n\n\n\n14 \n \n\n\n\n\n\n\n\n \nFigure 3: The concentrations of bifenthrin, fipronil and imidacloprid in soil columns (SL= Sandy loam; LS= Loamy sand) \n\n\n\n0\n\n\n\n200\n\n\n\n400\n\n\n\n600\n\n\n\n800\n\n\n\n1000\n\n\n\n1200\n\n\n\n1400\n\n\n\nup m\nid dn\n\n\n\nle\nac\n\n\n\nha\nte up m\nid dn\n\n\n\nle\nac\n\n\n\nha\nte up m\nid dn\n\n\n\nle\nac\n\n\n\nha\nte up m\nid dn\n\n\n\nle\nac\n\n\n\nha\nte up m\nid dn\n\n\n\nle\nac\n\n\n\nha\nte up m\nid dn\n\n\n\nle\nac\n\n\n\nha\nte\n\n\n\nSL LS SL LS SL LS\n\n\n\nBifenthrin Fipronil Imidacloprid\n\n\n\nCo\nnc\n\n\n\nen\ntr\n\n\n\nat\nio\n\n\n\nn \n(m\n\n\n\ng/\nL)\n\n\n\n \nLeaching descriptive data of termiticide tested \n\n\n\nFigure 3: The concentrations of bifenthrin, fipronil and imidacloprid in soil columns \n(SL= Sandy loam; LS= Loamy sand)\n\n\n\nFigure 4:Leaching patterns of bifenthrin in sandy loam (SL) and loamy sand (LS) in \nupper layer (up), middle layer (mid), down layer (dn) and leachate\n\n\n\n15 \n \n\n\n\nFigure 4 shows the leaching patterns of bifenthrin in this study. Based on 2-\n\n\n\nway ANOVA, no significant difference (F= 0.674, df= 1, P= 0.424) was observed \n\n\n\nbetween sandy loam and loamy sand. Bifenthrin concentrations remained high in the \n\n\n\ntop of the column for both soil types, while the concentrations degraded at the \n\n\n\nmiddle and bottom parts of the columns at an average of 97.07 mg/L. The \n\n\n\nconcentrations of leachate, however, remained high in both soils (SL= 756.53; LS= \n\n\n\n517.49 mg/L). \n\n\n\n\n\n\n\n\n\n\n\nFigure 4:Leaching patterns of bifenthrin in sandy loam (SL) and loamy sand (LS) in \nupper layer (up), middle layer (mid), down layer (dn) and leachate \n\n\n\nFipronil also showed the highest concentration at the top of the soil column \n\n\n\nin both soils (SL= 585.67 mg/L; LS= 66.59 mg/L) compared to the middle and low \n\n\n\nparts as well as in the leachate. This finding indicates that most of the fipronil \n\n\n\nremained in the termiticide-treated area (Figure 5). \n\n\n\nup mid dn leachate \n0 \n\n\n\n200 \n\n\n\n400 \n\n\n\n600 \n\n\n\n800 \n\n\n\n1000 \n\n\n\n1200 \n\n\n\nCo\nnc\n\n\n\nen\ntr\n\n\n\nat\nio\n\n\n\nn \n(m\n\n\n\ng/\nL)\n\n\n\n\n\n\n\nBifenthrin residual in soil column \n\n\n\nBifenthrin (SL)\n\n\n\nBifenthrin (LS)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201886\n\n\n\nhas a higher Koc value, it has a high capability to remain in the soil particles, \nresulting in low leaching potential. A study by Meyer et al. (2013) also found \nthat bifenthrin bonded to soils and sediment due to the strong binding capability \ntowards soil particles. \n\n\n\nAs artificial rain was applied on the top of the column, termiticide with low \nwater solubility probably remained on the treated site. Bifenthrin has the lowest \nwater solubility (0.1mg/L) (Kamble and Saran 2005) compared to fipronil (1.9 to \n2.4 mg/waL) (Zhu et al. 2004) and imidacloprid (510 mg/L) (Ping et al. 2010) and \nis also hydrophobic (Lee et al. 2002; Liu et al. 2004). Due to its hydrophobicity, \nbifenthrin repelled the water molecules from the artificial rain used in this study, \n\n\n\nFigure 5: Leaching patterns of fipronil in sandy loam (SL) and loamy sand (LS) soil in \nupper layer (up), middle layer (mid), down layer (dn) and leachate\n\n\n\nFigure 6: Leaching patterns of imidacloprid in sandy loam (SL) and loamy sand (LS) soil \nin upper layer (up), middle layer (mid), down layer (dn) and leachate\n\n\n\n16 \n \n\n\n\n\n\n\n\nFigure 5: Leaching patterns of fipronil in sandy loam (SL) and loamy sand (LS) soil \nin upper layer (up), middle layer (mid), down layer (dn) and leachate \n\n\n\n\n\n\n\nA similar pattern was also shown by imidacloprid, except that the top of the \n\n\n\nsoil column of sandy loam showed a higher concentration (16.37 mg/L) compared to \n\n\n\nloamy sand (0.97 mg/L). Both soils lost almost 98.36% to 99.90% at the top of the \n\n\n\nsoil columns. Imidacloprid indicated a low concentration (1.55 mg/L) at the middle, \n\n\n\nbottom and leachate of the soil column in both soils (Figure 6). \n\n\n\n \nFigure 6: Leaching patterns of imidacloprid in sandy loam (SL) and loamy sand \n(LS) soil in upper layer (up), middle layer (mid), down layer (dn) and leachate \n\n\n\nup mid dn leachate \n0 \n\n\n\n100 \n\n\n\n200 \n\n\n\n300 \n\n\n\n400 \n\n\n\n500 \n\n\n\n600 \n\n\n\n700 \n\n\n\nCo\nnc\n\n\n\nen\ntr\n\n\n\nat\nio\n\n\n\nn \n(m\n\n\n\ng/\nL)\n\n\n\n\n\n\n\nFipronil residual in soil column \n\n\n\nFipronil (SL)\n\n\n\nFipronil (LS)\n\n\n\nup mid dn leachate \n0 \n\n\n\n2 \n\n\n\n4 \n\n\n\n6 \n\n\n\n8 \n\n\n\n10 \n\n\n\n12 \n\n\n\n14 \n\n\n\n16 \n\n\n\n18 \n\n\n\nCo\nnc\n\n\n\nen\ntr\n\n\n\nat\nio\n\n\n\nn \n(m\n\n\n\ng/\nL)\n\n\n\n\n\n\n\nImidacloprid residual in soil column \n\n\n\nImidacloprid (SL) Imidacloprid (LS)\n\n\n\n16 \n \n\n\n\n\n\n\n\nFigure 5: Leaching patterns of fipronil in sandy loam (SL) and loamy sand (LS) soil \nin upper layer (up), middle layer (mid), down layer (dn) and leachate \n\n\n\n\n\n\n\nA similar pattern was also shown by imidacloprid, except that the top of the \n\n\n\nsoil column of sandy loam showed a higher concentration (16.37 mg/L) compared to \n\n\n\nloamy sand (0.97 mg/L). Both soils lost almost 98.36% to 99.90% at the top of the \n\n\n\nsoil columns. Imidacloprid indicated a low concentration (1.55 mg/L) at the middle, \n\n\n\nbottom and leachate of the soil column in both soils (Figure 6). \n\n\n\n \nFigure 6: Leaching patterns of imidacloprid in sandy loam (SL) and loamy sand \n(LS) soil in upper layer (up), middle layer (mid), down layer (dn) and leachate \n\n\n\nup mid dn leachate \n0 \n\n\n\n100 \n\n\n\n200 \n\n\n\n300 \n\n\n\n400 \n\n\n\n500 \n\n\n\n600 \n\n\n\n700 \n\n\n\nCo\nnc\n\n\n\nen\ntr\n\n\n\nat\nio\n\n\n\nn \n(m\n\n\n\ng/\nL)\n\n\n\n\n\n\n\nFipronil residual in soil column \n\n\n\nFipronil (SL)\n\n\n\nFipronil (LS)\n\n\n\nup mid dn leachate \n0 \n\n\n\n2 \n\n\n\n4 \n\n\n\n6 \n\n\n\n8 \n\n\n\n10 \n\n\n\n12 \n\n\n\n14 \n\n\n\n16 \n\n\n\n18 \n\n\n\nCo\nnc\n\n\n\nen\ntr\n\n\n\nat\nio\n\n\n\nn \n(m\n\n\n\ng/\nL)\n\n\n\n\n\n\n\nImidacloprid residual in soil column \n\n\n\nImidacloprid (SL) Imidacloprid (LS)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 87\n\n\n\ncausing it to remain at the top of the column. The efficacy of termiticides is also \ninfluenced by volatilisation capability (Chai et al. 2013; Delcour et al. 2015; \nHusen et al. 2009; Joshi et al. 2016; Richman et al. 2006). In field treatment, \nthe actual concentration of termiticides fails to reach the target species if the \ntermiticides rapidly volatise after the application on the soil. In this study, the \ntop column was uncovered, thus, bifenthrin might volatise during the treatment \nperiod. The concentration of bifenthrin remained high in the top part of the treated \nsoils in the column, which due to low ability, has the potential to volatise in the \nenvironment (Fecko 1999; Gan et al. 2005)\n\n\n\nInterestingly, the concentration of fipronil in the top part of the column \ncontaining sandy loam was higher than in the top part of the column containing \nloamy sand, while, the middle part, bottom part and leachate showed no difference \nin its concentrations. Generally, fipronil has a moderate Koc value, ranging from \n396 to 825 L kg-1 (Spomer and Kamble 2010; Ying and Kookana 2001). In this \nstudy, fipronil had good capability to hold onto soil particles after the artificial rain \nwas applied into the soil column. The adsorption of fipronil is also influenced by \nthe soil organic matter (Shuai et al. 2012). Previous studies have indicated that \npesticide sorption increases when soil organic matter (SOM) increases (Bekb and  \nYenig 1999; Gonzalez and Ukrainczyk 1996; Mallawatantri and Mulla 1992).  \nSandy loam showed a higher percentage of organic matter than loamy sand \nwith 11.4% and 8.5%, respectively. Thus, fipronil was able to retain its higher \nconcentration in sandy loam compared to loamy sand.\n\n\n\nMoreover, fipronil has a moderate water solubility, ranging from 1.9 to 2.4 \nmg/L compared to bifenthrin and imidacloprid (Kamble and Saran 2005; Zhu et \nal. 2004). Thus, fipronil might moderately leach out from the treated soil. A study \nby Shuai et al. (2012) indicated that fipronil was desorbed from the treated soil \nthrough a leaching process or runoff due to artificial rain simulation. Another \nstudy indicated that fipronil lost 96% of its concentration during the experiment \n(Horwood 2007). \n\n\n\nImidacloprid showed the highest dissipation process either by leaching or \ndegradation. Imidacloprid has the lowest Koc value compared to other termiticides \ntested in this study. The Koc values of imidacloprid are in the range of 156 to \n960, depending on multiple factors such as soil types, water application and \nconcentration (Cox et al. 1998; Kamble and Saran 2005; Leiva et al 2015; Oliver \net al. 2005). Low Koc values indicate low sorption between imidacloprid and \nsoil surface, which further suggests high leaching potential (Fern\u00e1ndez-Bayo et \nal. 2007). The above statement clearly explains the small amount of imidacloprid \nretained in the treated soils. Imidacloprid applied on a well-drained sandy soil \nregion was reported to be easily accessible in the groundwater (Huseth and \nGroves 2014). In addition, a survey conducted from 1999 to 2005 by the U.S. \nGeological Survey (USGS) reported that 13% of imidacloprid was detected in \ngroundwater due to the leaching process, in an area that was dominated by sandy \nand excessively drained soil (Leiva et al. 2015).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201888\n\n\n\nFurthermore, imidacloprid has higher water solubility compared to \nbifenthrin and fipronil (Kamble and Saran 2005; Ping et al. 2010; Kurwadkar et \nal.2014). Therefore, considering its high-water solubility and a weak sorption at \nthe soil surface, the leaching of imidacloprid from the treated soils used in this \nstudy was easier. The massive loss of imidacloprid might also be affected by soil \nproperties such as clay content, organic matter and characteristics of pesticides \nsuch as chemical characteristics, water solubility, charge distribution on pesticide \nmolecules and molecular charge (Das et al. 2015; Murphy et al. 1992). Clay is \nimportant to avoid the occurrence of leaching, as it provides a large surface area. \nIn this study, the clay content for sandy loam was higher (16.4%) than loamy \nsand (11%). Thus, the concentration of imidacloprid at the top of the column \ncontaining sandy loam was higher than the top column containing loamy sand. \n\n\n\nOrganic matter is an important element attributed to absorption and leaching \nprocess (Bajeer 2012). Liu et al. (2006) indicated that organic matter was the \nprimary sorptive medium for imidacloprid. This statement was also supported by \nKurwadkar et al. (2014) who mentioned that low sorption of imidacloprid could \nbe affected by the presence of low organic matter content. Soil organic matter \nis closely related to cation exchange capacity (Bajeer 2012; Kurwadkar et al. \n2014). High organic matter content leads to high exchange capacity.  Therefore, \nthe higher amount of cation exchange capacity and organic matter in soil could \nincrease the adsorption of termiticide, and simultaneously reduce the leaching \nprobability.\n\n\n\nCONCLUSION\nBifenthrin had a higher concentration in the treated area (0-5 cm; top layer) than \nfipronil and imidacloprid due to its higher Koc value and lower water solubility \ncompared to imidacloprid and fipronil. There was no significant difference for \nsoil tested (sandy loam and loamy sand). Thus, soil type used in this study did not \naffect the leaching process in the soil column.\n \n\n\n\nREFERENCES\nAb Majid,A.H. and A.H. Ahmad. 2013a. 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"\n\nINTRODUCTION\nIt is well known that the availability of essential nutrients in soil affect yield and \nyield components of crop (Ye et al. 2011). The availability of nutrients in soils \ndepends on soil characteristics especially soil pH (Chien et al. 2011; Lindsay \n1979; Shenker and Chen 2005; Wang et al. 2006). Fertilization and addition of \nacidifying amendments are common practices in high pH soils to enhance plant \nnutrient availability and improve plant performance. Elemental sulphur, as a \nsoil amendment, is of special interest to increase plant nutrient availability in \nthe soil system since it possesses a slow release acidifying characteristic and is \nreadily available (Chien et al. 2011). The acidifying function of S originates from \nits microbial oxidation to sulphuric acid over time (Vidyalakshmi et al. 2009). \nHowever, according to some authors, application of elemental sulphur as a soil \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 75-86 (2014)                         Malaysian Society of Soil Science\n\n\n\nElemental Sulphur Application Effects on Nutrient \nAvailability and Sweet Maize (Zea mays L.) Response in a \n\n\n\nHigh pH Soil of Malaysia\n\n\n\nKarimizarchi, M.1, 2, H. Aminuddin1*, M.Y. Khanif1 and \nO. Radziah1\n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti \nPutra Malaysia, 43400 Serdang, Selangor, Malaysia.\n\n\n\n2National Salinity Research Centre, Yazd, Iran.\n\n\n\nABSTRACT\nAs plants grown in high pH soils usually suffer from nutrient deficiency, the \npresent study was carried out to determine the influence of elemental sulphur \nas a soil acidulate on soil chemical properties and maize performance in a high \npH soil of Malaysia. After 0, 20 and 40 days of soil incubation with different \namounts of elemental sulphur (0, 0.5, 1 and 2 g S kg-1 of soil), maize plants were \ngrown for 45 days under glasshouse conditions. Application of elemental sulphur \nat a rate of 0.5 g S kg-1 soil decreased soil pH value from the background level \nof 7.03 to 6.29  but significantly increased availability of Mn and Zn by 0.38% \nand 0.91%, respectively. This resulted in a 45.06% increase in total dry weight \nof maize. Further pH reduction due to the acidifying character of elemental \nsulphur at addition rates of 1 and 2 g kg-1 soil increased Mn and Zn availability, \nbut significantly decreased maize performance. Overall, it can be concluded that \nwhen used in appropriate amounts, elemental sulphur can efficiently enhance \nsoil fertility and maize performance by providing micronutrients for balanced \nfertilization.\n\n\n\nKeywords:   Mn and Zn, nutrient release, soil acidification.\n\n\n\n___________________\n*Corresponding author : E-mail: karimi_nsrc@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201476\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and O. Radziah\n\n\n\namendment does not  have a significant effect on soil chemical properties such as \nacidity and plant nutrients availability (Sameni and Kasraian 2004; Shenker and \nChen 2005; Skwierawska et al. 2012). Regardless of S rate, this can be due to \nboth unsuccessful oxidation of applied sulphur as well as high carbonate content \nof the soil under investigation. At the same time, the successful oxidation of \nelemental sulphur and significant change in soil chemical properties and nutrient \navailability is well documented for some soils (Cui et al. 2004; Wang et al. \n2006). Nonetheless, it is difficult to predict the response of plant nutrients to soil \nacidification; moreover, their interactions affect availability to crops as an over-\nabundance of one may result in deficiency of another. \n\n\n\nApart from low yield per unit area as a consequence of low nutrient \navailability in soils, the low nutrient content of agricultural products is the \nmain reason for malnutrition in human beings (Mayer et al. 2008). Therefore \nany attempt to increase the nutrient concentration of agricultural products would \nhelp to achieve the goals of the General Assembly of the United Nations toward \nmitigating world health and poverty issues \n\n\n\nAcidification of soil through elemental sulphur application may increase \nplant micronutrient availability and could serve as another option to improve \nplant production potential (Cui et al. 2004). This seems to be true especially for \nnutrients that are found in large amounts. The ability of Bintang Series soil to be \noxidized by elemental S has been studied by Karimizarchi et al. (2013; 2014). \nThey showed that Bintang Series soil can successfully oxidize elemental sulphur \nup to 1 g S kg-1 soil. While the oxidation of sulphur and its effect on Bintang \nSeries soil has been documented, the release of plant micronutrients into the soil \nand its significance on plant performance needs to be quantified. Therefore, the \npresent study was carried out to evaluate the effect of elemental S applied to \nBintang Series soil on maize performance and soil chemical properties including \npH and availability of selected plant nutrients.\n\n\n\nMATERIALS AND METHODS\nA pot experiment was conducted from 24th of March to 15th of June 2013 at \nUniversiti Putra Malaysia to elucidate the effect of elemental sulphur application \ntiming and application rates on plant nutrient availability and maize growth. A \ncompletely randomized block design with factorial treatment combination was \nused with the following factors: (i) application of elemental sulphur at four levels \n(0, 0.5, 1 and 2 g S per kg of soil) and (ii) application of elemental sulphur three \ntimes at 20-day intervals (0, 20 and 40 days before planting of maize). Each \ntreatment was replicated four times and randomized in four rows.\n\n\n\nSite Description and Soil Characterization \nSoil samples were collected from the A horizon (0 - 20 cm) of Bintang Series soil \nlocated in Perlis, Malaysia (6\u00b0 31\u02b9 01.61\u02b9\u02b9 N and 100\u00b0 10\u02b9 12.43\u02b9\u02b9 E). The area, \nBukit Bintang, is developed from limestone parent materials and is under natural \nvegetation (forest). Soil samples were air dried and ground to pass through 2.0 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 77\n\n\n\nNutrients Availability from S Application to High pH Soil\n\n\n\nmm mesh size before use. Soil electrical conductivity and pH was measured in a \nsoil water suspension (10 g soil to 25 ml deionized water) 24 h after shaking for \n30 min on a reciprocal shaker. Total carbon, nitrogen and sulphur were determined \nby CHNS LECO analyzer. Soil mechanical analysis was done using the pipette \nmethod (Gee et al. 1986) and texture class was determined using the United \nStates Department of Agriculture (USDA) soil textural triangle. Total micro and \nmacronutrients were extracted by aqua regia digestion method using microwave \noven (Chen and Ma 2001). Briefly, 0.50 g of soil sample was weighed into a \n120 mL Teflon-PFA microwave digestion vessel. The samples were digested at \n0.69 *106 Pa for 5.5 min, then filtered through Whatman no. 42 filter. Finally, \nsolutions were made up to 100 mL with distilled water inside volumetric flasks. \nThe concentration of all nutrients was determined by an inductively coupled \nplasma optical emission spectrometer (PerkinElmer Optima 8300). Titrimetric \nmethod was used for determination of total calcium carbonate (Bashour and \nSayegh 2007). In this method, a given weight of soil is reacted with an excess of \nacid. The acid that is not used in the dissolution of carbonates is back titrated with \nsodium hydroxide solution. \n\n\n\nSweet maize (Zea mays L.) seeds, Masmadu, were provided by the \nMalaysian Agricultural and Development Research Institute (MARDI 2008). \nSeeds were germinated under laboratory conditions and transplanted into 30 cm \n(diameter) by 50 cm (height) plastic pots after 24 h. Each pot contained 10 kg \nsoil and received three plants which were thinned to one within one week. Plants \nwere grown for 45 days in the greenhouse located in Universiti Putra Malaysia \n(UPM). By weighing each pot, plants were irrigated daily to maintain 90% of soil \nfield capacity moisture content. All plants were supplied with fertilizers based on \nMARDI\u2019s recommendation; 120 kg N ha-1 in the form of urea, 80 kg P2O5 in the \nform of triple superphosphate and 100 kg K2O in the form of muriate of potash. \n\n\n\nPlant Available Soil Nutrient Extraction and Determination\nPlant micronutrients in the soil such as Fe, Mn, Zn and Cu were extracted by CaCl2 \nas the un-buffered and neutral extracting solution (Jones 2001; Ye et al. 2011). The \nconcentration of all nutrients was determined by an inductively coupled plasma \noptical emission spectrometer (ICP-OES), PerkinElmer, Optima 8300.\n\n\n\nStatistical Analysis\nThe relationship between plant and soil properties was subjected to different \nregression models at a probability level of 0.05 with the help of Sigmaplot \nsoftware. Using SAS 9.1, Anova analysis and Tukey\u2019s test at \u03b1 = 0.05 was \nemployed to determine the significant differences among the treatments.\n\n\n\nRESULTS AND DISCUSSION \n\n\n\nFertility Evaluation of Bintang Series Soil \nThe Bintang Series soil with a pH value of 7.5 (Table 1), developed on limestone \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201478\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and O. Radziah\n\n\n\nmaterials, in Peninsular Malaysia, is low in organic matter and all extractable \nmicronutrients including Fe, Mn, Zn and Cu. For instance, the available Cu \nextracted by Mehlich No.1 in Bintang Series soil (0.08 mg kg-1 of soil) is far \nbelow the recommended adequate range of 0.1 to 10 mg kg-1 of soil (Jones 2001). \nAdditionally, as zinc concentration extracted by Mehlich No.1 is less than the \noptimum Zn level by a factor of 200, it is likely that plants grown in this soil \nwould suffer from intensive zinc deficiency. Regarding the adequate range of Fe \n(2.5 to 5 mg kg-1) reported by Jones (2001), our expectation is that plant growth \nwould not be negatively affected due to the limited availability of Fe in Bintang \nSeries soil. At the same time, available soil Mn (9 mg kg-1 of soil) is below the \ncritical deficiency level of 10 mg kg-1, and this would restrict plant production \nin this soil but not as much as copper and zinc would. As the Bintang Series soil \ncomprises large amounts of Fe, Mn and Zn (Table 1), it appears that acidification \nof soil through elemental sulphur application may increase plant micronutrient \navailability and improve plant production potential. However, as total sulphur and \nCu is very low in this soil, it appears that addition of soil amendments or fertilizers \nwith high amounts of these nutrients is essential for successful plant production.\n\n\n\nSoil pH \nOur results found soil pH to be significantly affected by S rate, while application \ntiming and its interaction with S rate was not significant (data not shown). With \nincreasing S rate, soil pH decreased from the initial value of around 7.03 to 6.29, \n5.26 and 3.94 at sulphur application rates of 0.5, 1 and 2 g kg-1, respectively.  Figure \n\n\n\nTABLE 1\nSoil physico-chemical properties of Bintang series soil\n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \nSoil physico-chemical properties of Bintang series soil \n\n\n\n \nSoil property Unit Value or \n\n\n\nconcentration \nSoil property Unit Value or \n\n\n\nconcentration \npH - 7.30 Silt % 66.40 \n\n\n\nCaCO3 % Trace Clay % 24.60 \n\n\n\nC % 1.75 Available P mg kg-1 1.6 \n\n\n\nN % 0.12 Available Fe mg kg-1 3.20 \n\n\n\nS % 0.004 Available Mn mg kg-1 9.00 \n\n\n\nC/N - 14.58 Available Zn mg kg-1 0.16 \n\n\n\nC/S - 437.50 Available Cu mg kg-1 0.08 \n\n\n\nCEC cmol+ kg-1 soil 11.50 Sulfate mg kg-1 Trace \n\n\n\nBS % 56.00 Total Fe % 2.81 \n\n\n\nFC % 20.00 Total Mn % 0.19 \n\n\n\nTexture - Silt loam Total Zn mg kg-1 22 \n\n\n\nSand % 9 Total Cu mg kg-1 8.4 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 79\n\n\n\n1 shows the scatter diagram and regression line relating sulphur application rate to \nsoil pH for 3 sulphur incubation times. The regression line slopes downward at a \ninclination of -1.52, which is consistent with the negative relationship anticipated \nbetween S rate and soil pH (Cui et al. 2004; Shenker and Chen 2005; Vidyalakshmi \net al. 2009). While it reflected successful oxidation of elemental sulphur, this wide \nrange of soil pH provided a good opportunity to assess the effects of soil pH, from \nalkaline to acidic, on plant nutrient release in the soil system and plant growth \nperformance.\n\n\n\nSoil Nutrient Composition and Release\nOur results showed that application of elemental S up to 1 g kg-1 did not have \nsignificant effect on the availability of some micronutrients such as Fe and Cu \n(Table 2). Addition of elemental S at a rate of 2 g kg-1 increased Fe availability \nmore than five fold, while Cu appeared in a detectable amount (0.132 mg kg-1) only \nat maximum S rate. The significant and positive effect of S addition on availability \nof Mn started at S application rate of 0.5 g kg-1. For instance, S application at 0.5 \nand 1 g kg-1 resulted in 0.38 and 1.40 % increase in Mn availability, respectively. \nIt should be noted that the highest plant nutrients release was recorded at highest \nS rate with 3.86 % increase. The highest availability of Zn was 4.94 mg kg-1 at \nthe highest S application rate, that is, 164-fold higher than without application of \nS (0.03 mg kg-1). \n\n\n\nNutrients Availability from S Application to High pH Soil\n\n\n\nFigure 1: Soil pH changes in response to elemental sulphur application rate.\n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\npH = 6.94 - 1.52 S\nR 2 = 0.98 \n\n\n\nS application rate (g/kg)\n\n\n\n0.0 0.5 1.0 1.5 2.0 2.5\n\n\n\nS\noi\n\n\n\nl p\nH\n\n\n\n3.5\n\n\n\n4.0\n\n\n\n4.5\n\n\n\n5.0\n\n\n\n5.5\n\n\n\n6.0\n\n\n\n6.5\n\n\n\n7.0\n\n\n\n7.5\n\n\n\n\n\n\n\nFigure 1: Soil pH changes in response to elemental sulphur application rate. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201480\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and O. Radziah\n\n\n\nWhile our results showed plant nutrient availability from the soil to be  \nsignificantly affected by the addition of sulphur (Table 2), there are also contrasting \nreports on the effect of elemental S on nutrient availability (Klikocka 2011; Safaa \net al. 2013; Skwierawska et al. 2012). The effectiveness of elemental sulphur \napplication on plant nutrient availability was not observed in some soils (Sameni \nand Kasraian 2004; Shenker and Chen 2005; Skwierawska et al. 2012). At the \nsame time, the positive effect of elemental sulphur on plant nutrient availability \nthat is in line with our results was reported by Cui et al. (2004). They reported \n1.8 times increase in CaCl2 extractable Zn with a 0.3 unit reduction in soil pH \ndue to addition of 200 mmol S kg-1 of soil. As different soils may show different \nresponses to soil acidification as an effective strategy for plant nutrient availability \nenhancement (Wang et al. 2006), it is necessary to find the optimum sulphur rate to \nobtain optimum pH for each specific soil in which nutrient availability increases, \nand concurrently, extreme soil acidification and its consequences such as nutrient \ntoxicity for plants can be avoided.\n\n\n\nAs was anticipated, solubility and availability of nutrients in Bintang Series \nsoil were differently affected by elemental sulphur rates (as soil amendment) \nunder our investigation conditions. This is in line with the findings of Modaihsh et \nal. (1989) who reported that Cu responded least to S addition while Mn responded \nthe most. \n\n\n\nThe considerable increase in release of soil nutrients with a big difference \nbetween rates of S (Table 2) can be attributed to the effect of elemental S to amend \nsoil pH (Figure 1) and the release of plant nutrients from unavailable pools to soil \nsolution. In line with our findings, it is well accepted that high concentrations of \nhydrogen ions may increase plant nutrient availability in soils by displacement \nof cations from exchangeable sites changing the oxidation state of nutrients, and \nincreasing soil mineral weathering rate; high concentrations of hydrogen  may \nalso enhance nutrient uptake by plants (Lambers et al. 2008; Viani et al. 2014).\n\n\n\nTABLE 2\nAvailability of soil nutrients (mg kg-1) in response to elemental sulphur application rate \n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \nAvailability of soil nutrients (mg kg-1) in response to elemental sulphur application rate  \n\n\n\n\n\n\n\nSulphur rate \n(g kg-1 soil) \n\n\n\nNutrient concentration (mg kg-1) \n\n\n\nFe Mn Zn Cu \n\n\n\n0 0.146 b\u2020 1.61 d 0.030 c Tr\u2021 \n\n\n\n0.5 0.163 b 7.26 c 0.20 c Tr \n\n\n\n1 0.214 b 26.67 b 1.47 b Tr \n\n\n\n2 0.887 a 73.41 a 4.94 a 0.132  \n        Notes: \u2020Means within column followed by the same letter are not significant at the 0.05 level,  \n        according to Tukey test. Values denote the means across incubation time.  \n\n\n\n          \u2021Tr-traces \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNotes: \u2020Means within column followed by the same letter are not significant at the 0.05 \nlevel, according to Tukey test. Values denote the means across incubation time. \n\n\n\n \u2021Tr-traces\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 81\n\n\n\nMaize Response to Sulphur Application Rate\nApplication of elemental sulphur had a significant effect on maize performance \nin Bintang Series soil; however, it showed insignificant response to S application \ntiming. Additionally, the interaction of S application timing and S levels had no \nsignificant effect on maize biomass production (data not shown). In relation to \nthe remedial effect of sulphur, the application of elemental sulphur at 0.5 and 1 g \nkg-1 significantly improved plant performance by 45.06 and 36.67 %, respectively. \nHowever, addition of 2 g S kg-1 significantly decreased total dry weight of maize \nby 38.34 % compared to the plants in control pots (Figure 2). \n\n\n\nThe increase in maize performance can be attributed to an increase in soil \nnutrient availability (Table 2) due to a moderate reduction in soil pH (Figure 1). \nThis is further supported by plant analysis results (Table 3) where plant Zn and \n\n\n\nMn in untreated leaves were below the sufficiency range while they were in the \nsufficiency range in plants treated with 0.5 and 1 g S kg-1 soil (Table 3). Maize \nbiomass reduction at the highest rate of S, 2 g kg-1, may be attributed to both direct \nand indirect consequences of elemental S oxidation resulting in an increase in soil \nacidity and nutrient toxicity in plants under our study conditions. As can be seen \nfrom Table 3, the concentration of Mn and Zn at the highest sulphur application \nrate falls beyond the adequate range recommended by (Barker and  Pilbeam 2007) \nwhile the concentration of Cu and P is in the less adequate range. The decreasing \ntrend in P and Cu concentration in maize leaves (Table 3) can be attributed to the \ninteraction of Cu with Zn and Mn and interaction of P with Ca in soil solution \n(Barker and Pilbeam 2007).\n\n\n\nNutrients Availability from S Application to High pH Soil\n\n\n\nFigure 2: Maize response to elemental sulphur application rate.\n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\nY=32.46+31.56X-19.1X2\n\n\n\nR2 =0.86**\n\n\n\nSulphur Application Rate (gS/kg soil )\n\n\n\n0.0 0.5 1.0 1.5 2.0 2.5\n\n\n\nM\nai\n\n\n\nze\n B\n\n\n\nio\nm\n\n\n\nas\ns \n\n\n\n(g\npo\n\n\n\nt-1\n)\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n\n\n\n\nFigure 2: Maize response to elemental sulphur application rate. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201482\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and O. Radziah\n\n\n\nThe relationship between elemental S rate and maize biomass followed a \nnon- linear quadratic regression model with a R2 value of 0.86 (Figure 2). Based \non the regression model, the rate of S for maximum biomass of maize was 0.83 g \nkg-1. Considering a 10% decrease in yield as a critical level, the sufficiency range \nfor S rate would be from 0.34 to 1.29 mg S kg-1. The ability of elemental sulphur \nto enhance plant performance is of special interest and its diverse effects have \nbeen intensively studied (Motior et al. 2011; Ye et al. 2011; Zhao et al. 2008). \nFor instance, Zhao et al. (2008) reported the positive effect of elemental sulphur \napplication at a rate of 30 mg kg-1 on soybean performance in a fluvo-aquic soil \nwith a pH of 7.5. However, application of sulphur did not improve sugarcane \nyield in histosols of the Everglades area with low micronutrient but high organic \nmatter content (Ye et al. 2011).\n\n\n\nMaize Response to Soil pH \nOur data showed that the maximum maize performance (45.81 g pot-1) occurred \nat soil pH of 6.29 where 0.5 g S kg-1 soil was added. As maize performance at a \npH of 5.26 was equal to 94.2 % of maize performance at a pH of 6.29, it appears \nthat maximum production can be achieved at pH range of 6.29 to 5.26. This is \nbecause of the increase in soil nutrient availability as depicted in Table 2. As can \nbe seen from Table 2, availability of all micro nutrients (Fe, Mn, and Zn) was \nsignificantly increased due to S addition. This is in agreement with the general \nopinion of improving plant performance with soil pH decrease from alkaline to \n\n\n\nTABLE 3\nConcentration of nutrients in maize leaves in response to elemental sulphur\n\n\n\napplication rate \n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nConcentration of nutrients in maize leaves in response to elemental sulphur application rate  \n\n\n\nNutrient  \nin leave Elemental sulphur application rate (g S kg-1 soil) \n\n\n\n0 0.5 1 2 Sufficiency \nrange\u2021 \n\n\n\nN (%) 2.43 bc 2.28 b 2.5 b 2.8 a 2.5-3.2 \n\n\n\nP (%) 0.13 a 0.1 b 0.09 b 0.07 c 0.3-0.5 \n\n\n\nK (%) 2.1 b 2.00 b 2.1 b 2.7 a 2-3.5 \n\n\n\nFe (mg kg-1) 61.24 a 69.95 a 64.56 a 63.35 a 50-300 \n\n\n\nMn (mg kg-1) 35.86 d 81.69 c 199.68 b 691.72 a 50-160 \n\n\n\nZn (mg kg-1) 63.43 c 103.63b 121.13 b 166.73 a 20-100 \n\n\n\nCu (mg kg-1) 9.83 a 8.11 a 4.92 b 3.4 b 7-20 \n\n\n\n         Notes: \u2020Means within column followed by the same letter are not significant at the 0.05 level, according to \n         Tukey test.  \n       \u2021 Barker et al. (2007) \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNotes: \u2020Means within column followed by the same letter are not significant at the 0.05 \nlevel, according to Tukey test. \n\n\n\n \u2021 Barker et al. (2007)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 83\n\n\n\nNutrients Availability from S Application to High pH Soil\n\n\n\nslightly acidic conditions. While the decreasing trend of soil pH continued and \nreached the minimum of 3.93, a minimum biomass production of 19.47 g pot-1 was \nobserved. At this soil pH, a significant reduction of 57.48 % in total dry weight of \nmaize was observed. This negative effect of soil pH on plant performance can be \ndue to the toxic level of plant nutrients as a result of an increase in solubility of \nsoil minerals (Lambers et al. 2008; Viani et al. 2014). Maize response to soil pH \nfollowed the non-linear quadratic regression model (Figure 3), with R2 = 0.79**. \nThe predicted maximum yield, 45.6 g pot-1, was obtained by equating the first \nderivatives of the response equation to zero, solving for S, substituting the value \nof S into the response equation, and solving for Y.\n\n\n\nCONCLUSION\nThe concentrations of Fe, Cu, Zinc and Mn in non-amended Bintang Series soil \nwere far below the recommended adequate range and accounted for poor plant \nperformance. The improved maize biomass for plants receiving elemental sulphur \nat 0.5 and 1 g S kg-1 soil was due to an increase in Fe, Zn and Mn concentrations at \na sufficient level. This is attributed to the significant decrease in soil pH resulting \nfrom the acidifying effect of elemental sulphur. Additionally, the limited growth \nof maize treated with 2 g S kg-1 soil is attributed to the toxic level of Mn and Zn \nelements. Therefore, the utilization of elemental sulphur as a cost effective and \nreadily available source of soil amendment at sufficient rates is recommended \n\n\n\nISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 18: x \u2013x (2014)                                              Malaysian Society of Soil Science \n \n \n \n\n\n\n \nMalaysian Journal of Soil Science Vol. 18, 2014 \n\n\n\n\n\n\n\n\n\n\n\nY = -225.91 + 95.07X - 8.31X\n2\n\n\n\nR\n2\n\n\n\n=0.79\n**\n\n\n\nSoil pH\n\n\n\n4 5 6 7\n\n\n\nTo\nta\n\n\n\nl D\nry\n\n\n\n W\nei\n\n\n\ngh\nt (\n\n\n\ng/\npo\n\n\n\nt)\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n\n\n\n\nFigure 3: Maize response (total dry weight per pot) to soil pH. \nFigure 3: Maize response (total dry weight per pot) to soil pH.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201484\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and O. Radziah\n\n\n\nto address soil fertility problems of the Bintang Series soil. Overall, soil pH \nreduction is a suitable strategy to alleviate nutrient deficiency in high pH soils. As \nvarious soils have different responses to acidification and a specific optimum pH \nmay exist, this pH value should be determined to avoid unnecessarily high soil \nacidification.\n\n\n\nREFERENCES\nBarker, A. V. and D. J. Pilbeam. 2007. Handbook of plant nutrition. New York: CRC \n\n\n\npress. \n\n\n\nBashour, I. I. and A.H. Sayegh. 2007. Methods of Analysis for Soils of Arid and Semi-\narid Regions. Food and Agriculture Organization of the United Nations.\n\n\n\nChen, M. and L.Q. Ma. 2001. Comparison of three aqua regia digestion methods for \ntwenty Florida soils. Soil Science Society of America Journal. 65(2): 491-499. \n\n\n\nChien, S. H,  M.M. Gearhart and S. Villagarc\u00eda. 2011. Comparison of ammonium \nsulfate with other nitrogen and sulfur fertilizers in increasing crop production \nand minimizing environmental impact: a review. Soil Science. 176(7): 327-335. \n\n\n\nCui, Yanshan, Y.T. Dong, H.F. Li and Q.G. Wang. 2004. Effect of elemental sulphur \non solubility of soil heavy metals and their uptake by maize. Environment \nInternational. 30(3): 323-328. \n\n\n\nGee, G. W., J.W. Bauder and A. Klute. 1986. Particle-size Analysis. Methods of Soil \nAnalysis. Part 1. Physical and Mineralogical Methods. Madison, Wisconsin: \nAmerican Society of Agronomy Inc.\n\n\n\nJones, J.B. 2001. Laboratory Guide for Conducting Soil Tests and Plant Analysis. \nWashington, D.C.: CRC Press.\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and O. Radziah. 2013. Elemental \nsulfur oxidation rate in a Malaysian high pH soil. In: Soil Science Conference \nof Malaysia. K. Wan Rasidah (eds). pp. 39-43. Pahang: Malaysian Society of \nSoil Science.\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and O. Radziah.  2014. Incorporation \nand oxidation of elemental sulphur in high pH soils of Malaysia. 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Chen. 2005. Increasing iron availability to crops: Fertilizers, \norgano-fertilizers, and biological approaches. Soil Science and Plant Nutrition. \n51(1):1-17. \n\n\n\nSkwierawska, M., L. Zawartka, A. Skwierawski and A. Nogalska. 2012. The effect of \ndifferent sulfur doses and forms on changes of soil heavy metals. Plant, Soil and \nEnvironment-UZEI. 58:135-140. \n\n\n\nViani, R.A.G., R.R. Rodriguesb, T.E.  Dawsonc, H. Lambersd and  R.S. Oliveira. \n2014. Soil pH accounts for differences in species distribution and leaf nutrient \nconcentrations of Brazilian woodland savannah and seasonally dry forest \nspecies. Perspectives in Plant Ecology, Evolution and Systematics. http://dx.doi.\norg/10.1016/j.ppees.2014.02.001.\n\n\n\nVidyalakshmi, R., R. Paranthaman and R. Bhakyaraj.  2009. Sulphur oxidizing \nbacteria and pulse nutrition - A review. 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"\n\nINTRODUCTION\nNatural disasters such as earthquakes, erosion, landslides, and floods that often \noccur in Indonesia not only result in human casualties but also destroy ecosystems. \nAttempts should be made to overcome the aftermath of these incidents with \nstrategies to restore ecosystems and reduce such events in the future. West Sumatra \nis one of the areas prone to natural disasters, especially floods and landslides. \nSaidi et al. (2011) demonstrated the danger of landslides in areas with steep slopes \nand high rainfall and where the soils are derived from pumice tuff. Zuidam and \nZuidam (1979) reported that landslides are caused by the interaction of several \nfactors such as precipitation, land use, slope, water table, permeability, soil texture \nand structure. Soils at landslide sites are of expansive soft-clay, collapsible and \ndispersive and have c slope stability (Mugagga et al. 2011). The occurence of \nlandslides is also due to an increase in water pressure in the pore space. On the other \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 11-31 (2020)                         Malaysian Society of Soil Science\n\n\n\nReducing Runoff and Erosion to Improve the Strength of \nSoil Derived from Pumice Tuff in Patamuan, West Sumatra, \n\n\n\nIndonesia\n\n\n\nSaidi, A.*,  Adrinal, Setia Loannisa, S. and Fiantis, D.\n\n\n\nDepartment of Soil Science, Faculty of Agriculture, Andalas University,\nKampus Unand Limau Manis, Padang 25163, Indonesia\n\n\n\nABSTRACT\nErosion and landslides that occur in volcanic areas of  Indonesia are crucial \nproblems that cannot be overcome completely. This study was designed to \ndetermine the ability of different plant types to reduce water runoff and soil erosion \nand  improve soil physical properties. Soil samples were taken from landslide-\nprone areas in Patamuan, West Sumatra.  Plots created from steel plates of 1.5 x \n0.5 x 0.3 m  in dimension and set at 22\u00b0 angle, were placed in the greenhouse and \na rain simulator was used to irrigate the plots. The experiment was a completely \nrandomised design with three replications. The plant types were Tithonia shrub, \nvetiver, king grass and Napier grass.  Results showed that runoff and soil erosion \nwere reduced from 93 to 74 liter/m2, and from 1.03 to 0.17 kg/m2, respectively. \nPlanting Tithonia decreased runoff while Napier grass reduced soil erosion.   Soil \nmoisture content at field capacity increased from 14% to 20%, and macropores \nfrom 31% to 41%; however there was a decrease in micropores from 17.5% to \n14%.  King grass increased root density (RD) and root area ratio (RAR), but \nreduced the relative soil particle detachment rate (RSD).  King grass and vetiver \nhad a positive impact on reducing both runoff and soil erosion.\n\n\n\nKeywords: Landslides, Napier-king grasses, vetiver, Tithonia, root system\n\n\n\n___________________\n*Corresponding author : E-mail: saidiamrizal@gmail.com \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202012\n\n\n\nhand, landslides do occur on grounds that are not saturated. It is necessary to pay \nattention to lowering ground water pressure (negative pore pressure) associated \nwith elevated levels of ground water due to rain water infiltration (Chowdhury \nand Flentje 2014). Soil properties have a major role in landslides. Properties such \nas texture, structure, solum depth and permeability greatly affect water infiltration \nand surface runoff.  Implementing a system of conservation farming contributes \ntowards controlling soil erosion, landslides and soil degradation due to erosion.  \nOne of the factors that influence soil erosion and landslides is the type of vegetation. \nVegetation is a factor that can be managed either through the choice of plants or \nplant density settings. According to Truong (2011), plants effectively decrease soil \nerosion and landslides. Vegetative conservation has several advantages compared \nto mechanical and chemical conservation techniques. It is relatively easy to apply, \ncheap, and is able to provide nutrients for plant growth. \n Plants can indirectly protect the land from physical property destruction, \nparticularly the damage caused by water runoff and soil erosion. This protective \nability of plants depends on several factors: growth rate of the plants, plant height, \nplant canopy cover and the state of plant shoot and root systems. According to \nPhillips and Marden (2006), plants control soil erosion via the canopy cover, and \ninteraction between plant stems and roots with the soil erosion processes. Canopy \ncover reduces soil detachment and transportation.  Balangcod et al. (2015) \nreported that vetiver grass is planted extensively in several countries to prevent \nsoil erosion. This plant has a rapid growth rate and can hold soil through its ability \nto grow on steep slopes. Troung and Loch (2004) reported that using grass as \na hedgerow crop improves slope stability and reduces erosion by 80%. Vetiver \ngrass, known in Indonesia as \u2018fragrant root\u2019, is a tropical plant that has many \nadvantages. This grass has the ability to reduce slope erosion. Vetiver leaves and \nstems slowly wash out sediment from run-off while its roots bind the soil under \nthe plant to a depth of 3 m thus preventing soil erosion and landslides. Hakim and \nAgustian (2003) and Hakim et al. (2007) state that the planting of king grass and \nTithonia as hedgerows provide double benefits because these plants not only serve \nas fertiliser but help in reducing erosion.\n The objective of the research was to determine the effects of planting \ndifferent plant types as a conservation measure in controlling runoff, erosion and \nlandslides. \n\n\n\nMATERIAL AND METHODS \nThe soils in the study area are derived from pumice tuff  (QPT) and  pumice tuff \nwith  hyperstine mineral (Qhpt) (Kastowo et al. 1996). This area is one of the sites \nin West Sumatra Province that is prone to natural disasters such as landslides and \nfloods. The soils have shallow solum depth, are sandy loam to clay loam on the \ntop layer, sandy on the bottom layer and have a single-grained soil structure. They \nhave low organic matter content and a specific gravity of less than one which \ncauses it to float and be easily carried away by water flow. It can easily cause \nerosion and promote landslides. The dominant land characteristic that affects the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 13\n\n\n\nvulnerability of soils leading to landslides is very high rainfall with no dry months \n(Saidi et al.  2011).  \n The study was conducted in a greenhouse at the Agriculture Faculty, \nAndalas University Padang. Soil samples derived  from pumice tuff parent \nmatterial were taken from landslide prone areas in Patamuan district, Padang \nPariaman Regency. The soils were analysed at the Laboratory of the Soil Science \nDepartment, Agriculture Faculty, Andalas University, Padang and The Laboratory \nof Soil Research Institute and Agroclimate, Bogor. \n Plots made of steel plates, 1.5 m x 0.5 m x 0.3 m (0.225 m3) in size \n(Figure 1) were placed in the greenhouse at 60 cm height and 40 cm distance \nbetween each other. Plot slope was set at 22\u00b0 to simulate a sloping field and placed \nin a South-North direction.   The plots were planted with a variety of plants at \na distance of 10 cm x 40 cm. The experiment with one factor (plant type) was \nsubjected to five treatments as follows: A = Control (Plot without treatment); B \n= Plot planted with vetiver (Vetiveria zizanioides); C = Plot planted with king \ngrass  (Pennisetum tydoides); D = Plot planted with Tithonia shrubs (Tithonia \ndiversivolia); and E = Plot planted with Napier grass (Penisetum purpureum).  \nThe study used a completely randomised design with three replications. ANOVA \nwas used to process the data with the statistical tests being the F test and HSD test \nat 5% confidence level. \n The rain simulator was made of a PVC pipe, 0.5 in diameter and arranged \nexactly over the research plots. These pipes had rectangular holes which provided \nwater to all the plots. According to Grismer (2012), a rain simulator fitted with \na water droplet of approximately 2.2 mm diameter matches simulated rainfall at \nan intensity that often occurs in the field. Subsequently Darvishan et al. (2016) \nfound that the incidence of high intensity simulated rainfall is 60 mm per hour.  \nHowever, as the study area had a high rainfall of  > 4000 mm per year, rainfall \nintensity designed for this research was  96 mm per 30 min r 182 mm per hour \n(Figure 1). \n\n\n\nFigure 1. Research plots in the greenhouse with the rain simulator\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202014\n\n\n\n The plants were planted as an alley crop at a planting distance of 40 \ncm x 10 cm. These plants were allowed to grow for 3 months. Water runoff, \nsoil erosion and suspension nutrients were measured by the extent of the water \nrunoff contained in the plastic buckets (Figure 2). Suspended sediments can be \ncalculated by weighing the soil without water, that is, by separating the soil from \nwater. \n  \n\n\n\nFigure 2. Plot and water sediment bucket \n\n\n\n Soil sampling was conducted for all experiment plots at the end of \nthe research. Undisturbed soil samples were taken by ring samples while  the \nBelgian auger was used for soil composites. Observations on physical properties \nof soil were as follows: (i) bulk density with gravimetric method, (ii) total pore \nspace, (iii) organic C content by the method of Walkley and Black (1934),  (iv) \nsoil permeability by the  method of Dariah et al.  (2006), (v) water content at \nvarious pF by pressure plate membrane apparatus method, (vi) soil compaction \nby penetrometer, and (vii) shear strength by shear strength equipment. \n Parameters of plant roots were noted to express the genetically determined \nsoil-binding capacity of a specific plant root system. The ratio between the \nshoot weight of a specific plant and root weight require constant weight (60\u00b0 C). \nCharacteristics of the plant roots were observed by determining the following \nparameters (Mwango et al. 2014): (i) root diameter (mm) (D), measured with \nmicrometer NSK Japan; (ii) density of the root (RD) (kg/m3) = root dry weight / \nvolume of soil (m3); (iii) root length density (RLD) (km/m3) = total length of the \nroot / soil volume to penetrate the roots; (iv) root area ratio  (RAR) = (RLD x \nmean cross-sectional area of a single root (RCSA); and (v) relative  soil particles \ndetachment  rate (RSD)= e -1.45 RD (1 <RD <5 mm e -0.47 RD (RD> 5mm)\n This study used a digital camera  to capture images of plant roots,  plant \ncanopy, and the bond between soil particles and plant roots.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 15\n\n\n\nTABLE 1 \nInitial soil physical properties of the parent material of pumice tuff in\n\n\n\nPartamuan district, Padang Pariaman Regency\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Physical Properties of Pumice Tuff  Parent Material\nObservations on the physical properties of soil in the laboratory, based on the \ncriteria for physical properties of the Soil Research Institute Bogor (2011), are \npresented in Table 1.\n\n\n\n\n\n\n\n\n\n\n\n8 \n\n\n\nThis study used a digital camera  to capture images of plant roots,  plant canopy, and the 1 \n\n\n\nbond between soil particles and plant roots.  2 \n\n\n\nRESULTS AND DISCUSSION 3 \n\n\n\nSoil Physical Properties of Pumice Tuff  Parent Material 4 \n\n\n\nObservations on the physical properties of soil in the laboratory, based on the criteria for 5 \n\n\n\nphysical properties of the Soil Research Institute Bogor (2011), are presented in Table 1. 6 \n\n\n\n 7 \n\n\n\nTABLE 1 8 \n\n\n\nInitial soil physical properties of the parent material of pumice tuff in Partamuan district, 9 \n\n\n\nPadang Pariaman Regency 10 \n\n\n\nNo Parameters of soil physical properties  Soil depth (cm) Notes \n0 -20 40-60  \n\n\n\n1 Sand (%) 46 52  \n2 Silt (%) 27 10  \n3 Clay (%) 27 38  \n4 Organic material  ( %) 4.2 3.44  \n5 Bulk density (g/cm3) 0.77 0.79  \n6 Total pore space (%) 70.94 69.94  \n7 Field water content (% ) 18.8 20.0  \n8 QDP(% volume) 30.6 30.2 High \n9 SDP(% volume) 5.2 7.3 Low \n10 AWC (% volume) 17.4 19.1 High \n11 Macropore space (% volume)  41.94 40.24 High \n12 Micropore space (% volume)  29.0 29.7 High \n13 Permeability (Cm/jam) 9.92 8.45 Quick \n\n\n\n 11 \n\n\n\nTable 1 shows that the soil has a clay loam texture at the top layer and a sandy loam texture 12 \n\n\n\nat the bottom layer. Clay content in the top layer is lower than at the bottom layers. 13 \n\n\n\nAccording to Geist and Cochran (1990), the clay content of the pumice tuff material is 14 \n\n\n\nlower than other volcanic soil parent material such as basalt and volcanic ash. According to 15 \n\n\n\n Table 1 shows that the soil has a clay loam texture at the top layer and a \nsandy loam texture at the bottom layer. Clay content in the top layer is lower than \nat the bottom layers. According to Geist and Cochran (1990), the clay content of \nthe pumice tuff material is lower than other volcanic soil parent material such \nas basalt and volcanic ash. According to Mugagga et al. (2011), an area that has \nmore than 32% clay content will have extremely high expansive potential while \n10% clay content is an indicator of expansion potential. The soil structure in the \ntopsoil and subsoil is unstructured single grain with organic matter content in \nthe top and bottom layers being low. Bulk density and total pore space in both \ntop and bottom layers are medium. The low water binding of the soil samples is \nreflected in the available water content (AWC) which is only about 17.0 - 19.0% \nby volume, while total pore space is almost 71%, which indicates that macropores \nare filled with air of around 40 to 42% by volume. This is because the pumice \ntuff has plenty of air pore spaces with its dominant material containing sand. Soil \npermeability is classified as moderate for both top and bottom layers.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202016\n\n\n\nWater Runoff and Soil Erosion and Nutrient Losses\nThe results of the ANOVA and HSD test  show that plant types can decrease \nsignificantly the amount of water runoff and soil erosion, and Nitrogen and \nPotassium loss, except for phosphorus loss  (Tables 2 and 3 and Figures 3 and 4).\n\n\n\nTABLE 2 \nF Ratio of water runoff, soil erosion, and nutrient loss \n\n\n\n\n\n\n\n\n\n\n\n9 \n\n\n\nMugagga et al. (2011), an area that has more than 32% clay content will have extremely 1 \n\n\n\nhigh expansive potential while 10% clay content is an indicator of expansion potential. The 2 \n\n\n\nsoil structure in the topsoil and subsoil is unstructured single grain with organic matter 3 \n\n\n\ncontent in the top and bottom layers being low. Bulk density and total pore space in both 4 \n\n\n\ntop and bottom layers are medium. The low water binding of the soil samples is reflected in 5 \n\n\n\nthe available water content (AWC) which is only about 17.0 - 19.0% by volume, while total 6 \n\n\n\npore space is almost 71%, which indicates that macropores are filled with air of around 40 7 \n\n\n\nto 42% by volume. This is because the pumice tuff has plenty of air pore spaces with its 8 \n\n\n\ndominant material containing sand. Soil permeability is classified as moderate for both top 9 \n\n\n\nand bottom layers.   10 \n\n\n\n Water Runoff and Soil Erosion and Nutrient Losses 11 \n\n\n\nThe results of the ANOVA and HSD test  show that plant types can decrease significantly 12 \n\n\n\nthe amount of water runoff and soil erosion, and Nitrogen and Potassium loss, except for 13 \n\n\n\nphosphorus loss  (Tables 2 and 3 and Figures 3 and 4). 14 \n\n\n\nTABLE 2  15 \n\n\n\nF Ratio of water runoff, soil erosion, and nutrient loss  16 \n\n\n\nNo Parameters  F Ratio  Prob > F  \n\n\n\n1 Water runoff (L/m2)  8.2916 0.0032* \n\n\n\n2 Soil erosion  (Kg/m2) 27.8425 <.0001* \n\n\n\n3 Nitrogen loss (g/L/m2) 14.4361 0.0004* \n\n\n\n4 Phosphorus loss(g/L/m2) 2.6720 0.0946 ns \n\n\n\n5 Potassium loss (g/L/m2) 13.1534 0.0005* \n\n\n\n 17 \n\n\n\nTABLE 3\nInfluence of plant type on  water runoff and soil erosion\n\n\n\n\n\n\n\n\n\n\n\n10 \n\n\n\n 1 \n\n\n\nTABLE 3 2 \n\n\n\nInfluence of plant type on  water runoff and soil erosion 3 \n\n\n\nNo Treatments  Average runoff (L/m2)  Average soil loss (Kg/m2) \n\n\n\n1 Control 93.778 A  1.032 A \n\n\n\n2 Vetiver grass  89.333 AB 0.409   B \n\n\n\n3 King grass 73.778  BC 0.284   BC \n\n\n\n4 Tithonia shrub  70.667   C 0.471   B \n\n\n\n5 Napier grass 75.556  BC 0.169    C \n\n\n\n 4 \n\n\n\n 5 \n\n\n\nFigure 3. Effect of plant type on runoff 6 \nFigure 3. Effect of plant type on runoff\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 17\n\n\n\nFigure 4. Effect  of plant type on soil erosion\n\n\n\n The decreasing order of soil erosion is  Napier grass > king grass > vetiver \ngrass> Tithonia plant > control. The extent to which these plants can  reduce soil \nerosion are as follows: Napier grass from 1,032 to 0.17 g/m2 (83.5%),  king grass \nfrom 1.032 to 0.284 g/m2 (72.48%),  vetiver from 1.032 to 0.409 g/cm2 (60.37%), \nand Tithonia shrub from 1.032 to 0.471 g/cm2 (54.36%). However, Matano et al. \n(2015) recommends the use of Tithonia to prevent erosion as the cultivation of \nthese plants can reduce the impact of raindrops on soil aggregates, thus reducing \nwater runoff and soil erosion. Our data that corresponds to the role of canopy cover \nin the prevention of soil erosion is as follows: 57.00% for Napier grass, 45.33% \nfor king grass, 44.67% for Tithonia plant, and 40.67% for vetiver. Canopy cover \ndescribes the proportion of soil surface that is covered by the vertical projection of \nplants (Jennings et al. 1999; Davidova et al 2015). Kusminingrum (2011), in his \nstudy, discusses the role of vetiver grass and bahia in minimising the occurrence \nof slope erosion. According to him, a minimum canopy cover of 60% will give \nan average reduction of more than 96% in erosion rates. Yacob et al. (2015) state \nthat vetiver grass, desho grass, and Napier grass can reduce soil loss, stabilise \nthe bund, and resist drought compared to the control. According to Xu Liyu et \nal. (2004), vetiver can reduce water runoff as much as 59.7% and soil erosion by \n92.7%. Therefore, all plants were found to have a significant influence on runoff \nand soil erosion and did not differ in their influence except for Napier grass. There \nwas no significant difference in the influence of king grass, Tithonia shrub and \nvetiver grass in runoff and soil erosion.\n Effect of  plant type on loss of nutrients though soil erosion is shown in \nTable 4 and Figures 5  and 6. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202018\n\n\n\nFigure 5. Effect of plant type on nitrogen loss\n\n\n\nTABLE 4\nEffect of  plant type  on nutrient loss through eroded soil, i.e. nitrogen and potassium\n\n\n\n\n\n\n\n\n\n\n\n12 \n\n\n\n59.7% and soil erosion by 92.7%. Therefore, all plants were found to have a significant 1 \n\n\n\ninfluence on runoff and soil erosion and did not differ in their influence except for Napier 2 \n\n\n\ngrass. There was no significant difference in the influence of king grass, Tithonia shrub and 3 \n\n\n\nvetiver grass in runoff and soil erosion. 4 \n\n\n\nEffect of  plant type on loss of nutrients though soil erosion is shown in Table 4 and Figures 5 \n\n\n\n5  and 6.  6 \n\n\n\nTABLE 4 7 \n\n\n\nEffect of  plant type  on nutrient loss through eroded soil, i.e.  nitrogen and potassium 8 \n\n\n\nNo Treatments  Nitrogen  (g/L/m2) Potasium (g/L/m2) \n1 Control 0.03550874  A 0.00429017  A \n2 Vetiver grass 0.00958954   B 0.00131819  B \n3 King grass 0.00958954   B 0.00146947   B \n4 Tithonia shrub 0.01703360   B 0.00215289   B \n5 Napier grass  0.00454205   B 0.00061543   B \n\n\n\n 9 \n\n\n\n 10 \n\n\n\nFigure 5. Effect of plant type on nitrogen loss 11 \n\n\n\n 12 \n\n\n\n Figure 6. Effect of plant type on potassium loss\n\n\n\n The influence of plant type on nutrient loss though erosion is significantly \ndifferent in control. But each plant type did not differ significantly in its effects on \nnutrients loss. It can be concluded that the combination of plants had an effect on \nreducing nutrients loss by runoff and erosion but each plant did not demonstrate a \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 19\n\n\n\nreal impact. The sequence of impact on nutrient loss for nitrogen, and potassium \nis as follows: control > Tithonia shrub > King grass > vetiver >  Napier grass. \n\n\n\nSoil Physical Properties  \nEffect of plant type on soil physical properties was not significantly different \nexcept for soil compaction and slow drainage pores (Tables 5 and 6). \n\n\n\nTABLE 5\n F-ratio from parameters of soil physical properties\n\n\n\n\n\n\n\n\n\n\n\n14 \n\n\n\nTABLE 5 1 \n\n\n\n F-ratio from parameters of soil physical properties 2 \n\n\n\nNo Parameters of soil physical properties F Ratio Prob > F \n\n\n\n1 Organic matter (%) 2.3707 0.1223 \n\n\n\n2 Bulk density (g/cm3) 0.8920 0.5034 \n\n\n\n3 Porosity (%) 0.9538 0.4731 \n\n\n\n4 Water content of pF 1.00 ( % volume) 1.2642 0.3462 \n\n\n\n5 Water content of pF 2.00  ( % volume) 1.8495 0.1961 \n\n\n\n6 Water content of pF 2.54 ( % volume) 0.7540 0.5778 \n\n\n\n7 Water content of pF 4.2  ( % volume) 1.8011 0.2053 \n\n\n\n8 Availability water content ( % volume) 0.3022 0.8700 \n\n\n\n9 Slow drainage pores ( % volume) 3.3364 0.0556* \n\n\n\n10 Quick drainage pores ( % volume) 1.0426 0.4326 \n\n\n\n11 Soil compaction (g/cm3) 3.7306 0.0415* \n\n\n\n12 Shear strength (kg/cm2 ) 3.1108 0.0662 \n\n\n\n13 Macropores (% volume ) 1.2621 0.3469 \n\n\n\n14 Micropores (% volume) 0.7540 0.5778 \n\n\n\n 3 \n\n\n\n 4 \n\n\n\nTABLE 6 5 \n\n\n\nEffect of plant type on soil compaction and slow  drainage pores  6 \n\n\n\nNo Treatments  Soil compaction (g/cm3)  Slow drainage pore (% \n\n\n\nvolume) \n\n\n\n1 Control 24.500 AB 5.43  B \n\n\n\n2 Vetiver grass 16.667  B 6.87  B \n\n\n\n3 King grass 18.833 AB 7.60 AB \n\n\n\n4 Tithonia shrub 29.333 A 8.83 A \n\n\n\n5 Napier grass  19.583 AB 5.27  B   \n\n\n\n 7 \n\n\n\n\n\n\n\n\n\n\n\n14 \n\n\n\nTABLE 5 1 \n\n\n\n F-ratio from parameters of soil physical properties 2 \n\n\n\nNo Parameters of soil physical properties F Ratio Prob > F \n\n\n\n1 Organic matter (%) 2.3707 0.1223 \n\n\n\n2 Bulk density (g/cm3) 0.8920 0.5034 \n\n\n\n3 Porosity (%) 0.9538 0.4731 \n\n\n\n4 Water content of pF 1.00 ( % volume) 1.2642 0.3462 \n\n\n\n5 Water content of pF 2.00  ( % volume) 1.8495 0.1961 \n\n\n\n6 Water content of pF 2.54 ( % volume) 0.7540 0.5778 \n\n\n\n7 Water content of pF 4.2  ( % volume) 1.8011 0.2053 \n\n\n\n8 Availability water content ( % volume) 0.3022 0.8700 \n\n\n\n9 Slow drainage pores ( % volume) 3.3364 0.0556* \n\n\n\n10 Quick drainage pores ( % volume) 1.0426 0.4326 \n\n\n\n11 Soil compaction (g/cm3) 3.7306 0.0415* \n\n\n\n12 Shear strength (kg/cm2 ) 3.1108 0.0662 \n\n\n\n13 Macropores (% volume ) 1.2621 0.3469 \n\n\n\n14 Micropores (% volume) 0.7540 0.5778 \n\n\n\n 3 \n\n\n\n 4 \n\n\n\nTABLE 6 5 \n\n\n\nEffect of plant type on soil compaction and slow  drainage pores  6 \n\n\n\nNo Treatments  Soil compaction (g/cm3)  Slow drainage pore (% \n\n\n\nvolume) \n\n\n\n1 Control 24.500 AB 5.43  B \n\n\n\n2 Vetiver grass 16.667  B 6.87  B \n\n\n\n3 King grass 18.833 AB 7.60 AB \n\n\n\n4 Tithonia shrub 29.333 A 8.83 A \n\n\n\n5 Napier grass  19.583 AB 5.27  B   \n\n\n\n 7 \n\n\n\nTABLE 6\nEffect of plant type on soil compaction and slow  drainage pores\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202020\n\n\n\nFigure 7. Effect of plant type on soil compaction\n\n\n\nFigure 8. Effect of plant type on slow drainage pores\n\n\n\n Table 5 and 6 and Figures 7 and 8 show that plant type has a significant \neffect on soil compaction and  slow drainage pore compared to control.\n The compaction decreased in this order:  Tithonia shrub > control > Napier \ngrass > king grass  > vetiver grass. The slow drainage pore increased in this order: \nNapier > control > vetiver grass > king grass >  Tithonia shrubs.  This allowed for \nthe reorganisation process between the soil particles and organic matter to form \na soil structure where the slow drainage pores increased while the quick drainage \npore decreased. Slow drainage  increased in this order: Tithonia shrub > king grass \n> vetiver>  control > Napier.  According to Xu Liyu et al. (2004), vetiver grass can \nincrease soil moisture  as much as 42.1%. This is because the canopy of king grass \nis larger than that of other plants, so it requires greater amounts of water.  Phillips \nand Marden (2006) state that plant roots tend to change the physical properties of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 21\n\n\n\nthe surface soil, promoting infiltration and thus reducing the incidence of overland \nflow. Several types of plants have roots to support organic matter in the soil which \nhelps to increase the development of soil structure processes. But type of plant \ndoes not have a significant effect on macropores and micropores.  The effect of \nplant species on soil physical properties can be seen on slow drainage pores.  This \nis because the initial soil texture was sandy loam with low organic matter content \nsuch that the  macropores were much larger than the micropores. The  capacity to \nhold groundwater and capillary action is low. For soil to have a better structure, it \nneeds the assistance of organic materials.\n\n\n\nPlant Height and Canopy Cover\nThe influence of plant height and plant canopy cover can be seen in Tables 7 and  \n8  and Figures 9 and 10.\n\n\n\nTABLE 7\nF-ratio of plant and parameters of root characteristics\n\n\n\n\n\n\n\n\n\n\n\n17 \n\n\n\nTABLE 7 1 \n\n\n\nF-ratio of plant and parameters of root characteristics 2 \n\n\n\nNo Plant and root characteristic parameters F-ratio  Prob > F  \n\n\n\n1 Plant height (cm) 103.7498 <.0001* \n\n\n\n2 Canopy cover area (%)  3.6619 0.0631 \n\n\n\n3 Dry weight of root (gram) 4.3679 0.0424* \n\n\n\n4 Dry weight of shoot (gram) 1.4549 0.2978 \n\n\n\n5 Root shoot ratio  11.6040 0.0028* \n\n\n\n6 Root density (RD) 4.5899 0.0377* \n\n\n\n7 Root length density (RLD) 6.2576 0.0171* \n\n\n\n8 Root area ratio (RAR) 5.0516 0.0298* \n\n\n\n9 Relative soil particle detachment rate (RSD)  6.6330 0.0146* \n\n\n\n 3 \n\n\n\nTABLE 8 4 \n\n\n\nEffect of plant types on plant height  and canopy cover  5 \n\n\n\nNo Treatments Plant height (cm) Canopy cover (%) \n\n\n\n1 King grass 220.333  A 44.667  A \n\n\n\n2 Napier grass 178.667  B 57.000  A \n\n\n\n3 Vetiver grass 160.667  B 40.667  A \n\n\n\n4 Tithonia shrub 83.667   C 45.333  A \n\n\n\n 6 \n\n\n\n\n\n\n\n\n\n\n\n17 \n\n\n\nTABLE 7 1 \n\n\n\nF-ratio of plant and parameters of root characteristics 2 \n\n\n\nNo Plant and root characteristic parameters F-ratio  Prob > F  \n\n\n\n1 Plant height (cm) 103.7498 <.0001* \n\n\n\n2 Canopy cover area (%)  3.6619 0.0631 \n\n\n\n3 Dry weight of root (gram) 4.3679 0.0424* \n\n\n\n4 Dry weight of shoot (gram) 1.4549 0.2978 \n\n\n\n5 Root shoot ratio  11.6040 0.0028* \n\n\n\n6 Root density (RD) 4.5899 0.0377* \n\n\n\n7 Root length density (RLD) 6.2576 0.0171* \n\n\n\n8 Root area ratio (RAR) 5.0516 0.0298* \n\n\n\n9 Relative soil particle detachment rate (RSD)  6.6330 0.0146* \n\n\n\n 3 \n\n\n\nTABLE 8 4 \n\n\n\nEffect of plant types on plant height  and canopy cover  5 \n\n\n\nNo Treatments Plant height (cm) Canopy cover (%) \n\n\n\n1 King grass 220.333  A 44.667  A \n\n\n\n2 Napier grass 178.667  B 57.000  A \n\n\n\n3 Vetiver grass 160.667  B 40.667  A \n\n\n\n4 Tithonia shrub 83.667   C 45.333  A \n\n\n\n 6 \n\n\n\nTABLE 8\nEffect of plant types on plant height  and canopy cover \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202022\n\n\n\n The bioengineering plants have a significant influence on canopy cover \n(Table 7). Napier grass had the largest canopy cover,  followed by king grass, \nTithonia plant and vetiver grass. Vetiver grass  had the lowest canopy cover \nbecause its leaves were small and long, offering little cover crop canopy (Table \n8 and Figure  10).  Furthermore, the effect of  plant  type on plant height and  \ncanopy cover was significant in this order:  king grass > Napier grass > vetiver \ngrass > Tithonia.  Hairiah et al. (2006) recommend that king grass  be combined \nwith vetiver or Napier as it  has a better growth rate compared to the other plants.  \nWhile the influence of vigorous Tithonia is lower than  that of the  other plants, \nsurface runoff and erosion of soils is high compared to control. According to \n\n\n\nFigure 9. Effect  of plant type on plant height\n\n\n\nFigure 10. Effect  of plant type on canopy cover\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 23\n\n\n\nPhilips and Marden (2006), the canopy, stem and roots of plants interact with the \nerosion processes of soil detachment and transport. The plant canopy changes \nraindrop size distribution and reduces the fall velocity, thus reducing the kinetic \nenergy available for erosion via rain drop impact.\n \nDry Weight of Root and Root-Shoot  Ratio\nType of plant has a significant influence  on dry weight of plant shoot  and root and \nratio of shoot-root as can be seen in Tables 7 and 9. \n\n\n\n\n\n\n\n\n\n\n\n19 \n\n\n\ntype on plant height and  canopy cover was significant in this order:  king grass > Napier 1 \n\n\n\ngrass > vetiver grass > Tithonia.  Hairiah et al. (2006) recommend that king grass  be 2 \n\n\n\ncombined with vetiver or Napier as it  has a better growth rate compared to the other plants.  3 \n\n\n\nWhile the influence of vigorous Tithonia is lower than  that of the  other plants, surface 4 \n\n\n\nrunoff and erosion of soils is high compared to control. According to Philips and Marden 5 \n\n\n\n(2006), the canopy, stem and roots of plants interact with the erosion processes of soil 6 \n\n\n\ndetachment and transport. The plant canopy changes raindrop size distribution and reduces 7 \n\n\n\nthe fall velocity, thus reducing the kinetic energy available for erosion via rain drop impact.  8 \n\n\n\nDry Weight of Root and Root-Shoot  Ratio 9 \n\n\n\nType of plant has a significant influence  on dry weight of plant shoot  and root and ratio of 10 \n\n\n\nshoot-root as can be seen in Tables 7 and 9.  11 \n\n\n\nTABLE 9 12 \n\n\n\nEffect of plant type on dry  weight of roots  and root-shoot ratio 13 \n\n\n\nNo Treatments Dry weight of root (g) Root-shoot  ratio \n\n\n\n1 Vetiver grass 5.517  A 0.257  A \n2 King grass 5.010 AB 0.110  B \n3 Tithonia shrub 1.283 B 0.0067 B \n\n\n\n4 Napier grass 3.400 AB 0.108  B \n\n\n\n 14 \n\n\n\nTABLE 9\nEffect of plant type on dry  weight of roots  and root-shoot ratio\n\n\n\nTable 9 and Figures 11 and 12 show that the dry weight and root shoot ratio of \nvetiver is higher than king grass and Napier grass. Root dry weight is lowest for \nthe Tithonia shrub. Shoot growth is highest in vetiver grass followed by king \ngrass, Napier grass and   Tithonia shrub.  However, root-shoot ratio is the lowest \nin Tithonia shrub because root growth of tithonia shrub is lowest, followed by \nNapier grass, king grass, and vetiver grass. Yakob et al. (2015) evaluated four \ngrasses on their ability to stabilise and conserve soil. These were vetiver grass, \nNapier grass, Desho  grass (Pennisetum pedicelluatum),  Rodes grass   (Chloris \ngayana) and guinea grass (Panicum coloratum). Their  results showed that Desho \ngrass provided more biomass than any other grass and gave the most steady \nranking followed by Napier grass and vetiver grass. \n\n\n\nParameters of Root Characteristics \nFrom Tables 7 and 10 and  Figures 13,14,15, and 16,  it can be seen that \nbioengineering plants and grasses have a significant influence on relative soil \nparticle detachment rate  and all parameters of  root characteristics, that is   root \ndensity,  root length density and root area ratio. \n\n\n\nTable 10  shows that root density of vetiver   is higher than that of  king grass, \nNapier grass and Tithonia shrub. Root growth of plants is determined by root \ndensity, root length density and root area ratio.  Root density in this research  is \nin  this order:  king grass  -4.24 kg m-3;  vetiver grass-3.623 kg m-3;  Napier grass \n2.390 kg m-3;  and the lowest is Tithonia shrub at 9.93 kg m-3 (Figures 13 and 14). \nParameters of root density (RD)  can be affected by the ability of various plants to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202024\n\n\n\nFigure 11. Effect of plant type on dry weight root\n\n\n\n\n\n\n\n\n\n\n\n21 \n\n\n\nal. (2015) evaluated four grasses on their ability to stabilise and conserve soil. These were 1 \n\n\n\nvetiver grass, Napier grass, Desho  grass (Pennisetum pedicelluatum),  Rodes grass   2 \n\n\n\n(Chloris gayana) and guinea grass (Panicum coloratum). Their  results showed that Desho 3 \n\n\n\ngrass provided more biomass than any other grass and gave the most steady ranking 4 \n\n\n\nfollowed by Napier grass and vetiver grass.  5 \n\n\n\n 6 \n\n\n\nParameters of Root Characteristics  7 \n\n\n\n From Tables 7 and 10 and  Figures 13,14,15, and 16,  it can be seen that bioengineering 8 \n\n\n\nplants and grasses have a significant influence on relative soil particle detachment rate  and 9 \n\n\n\nall parameters of  root characteristics, that is   root density,  root length density and root 10 \n\n\n\narea ratio.  11 \n\n\n\nTABLE 10 12 \n\n\n\n Effect of plant type on root density (RD), root length density (RLD), root area ratio (RAR) 13 \n\n\n\nand relative soil particle detachment rate (RSD) 14 \n\n\n\nNo Treatments  Root density \n(RD) \n\n\n\nRoot length \ndensity (RLD)  \n\n\n\nRoot area \nratio (RAR) \n\n\n\nRelative soil particle \ndetachment rate \n\n\n\n ( RSD) \n\n\n\n1 Vetiver grass 3.623 AB 5.280   B 0.082 AB 0.022  B \n\n\n\n2 King grass 4.243 A 26.267 A 0.323 A 0.006  B \n\n\n\n3 Tithonia shrub 0.993  B 2.450   B 0.033  B 0.291 A \n\n\n\n4 Napier grass  2.390 AB 14.783 AB 0.189 AB 0.033  B \n\n\n\n 15 \n\n\n\nFigure 12. Effect of plant type on root shoot ratio\n\n\n\nTABLE 10\nEffect of plant type on root density (RD), root length density (RLD), root area\n\n\n\nratio (RAR) and relative soil particle detachment rate (RSD)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 25\n\n\n\npenetrate soil and also by  soil physical properties. As vetiver  grass had the highest  \nroot density, its growth was greater than the king and Napier grasses and Tithonia \nshrub, but canopy cover of Napier grass was better than king grass,  vetiver,and \nTithonia. Mwango et al. (2014), in  their study, reported that Tithonia shrub  had \na higher percentage  of total root mass than Guatemala grass  and Napier grass at \ntwo locations, namely Majulai  and Migambo in Tanzania but Guatemala grass \nhad a higher root density (kg/m3)   than Napier grass and Tithonia  shrub.\n The density of root length (RLD) is total length of root divided by volume \nof roots that permeate the soil sample (De Baets et al. cited in  Ola  et al. 2015). \nHigher RLDs enhance soil aggregation in crop species. According to Rilling et \nal. (2002),  the ability of roots to reinforce a soil is determined not only by root \ncharacteristics such as  as RLD but also by their distribution within the soil. RLD \nand RAR of king grass was higher than Napier grass, vetiver grass and Tithonia \nshrub. Thus, the ability of king grass to reinforce a soil is better than the other \ngrasses.\n Effect of plant type on root area ratio (RAR) and the relative particle \ndetachment rate (RSD) is in this order: king grass > vetiver  > Napier grass  > \nTithonia shrub (Table 10 and Figure 15 and 16). In this study, type of plant had \na significant effect on root area ratio. King grass had a  higher index of root area \nratio compared to Napier grass, vetiver grass and Tithonia. Root area ratio is used \nas an indicator of root density, and its role in reinforcement of the plant on soil \nerosion in landslide prone areas is mentioned in a study by  Lateh et al.( 2013).  \n A comparison of root growth development showed king grass gave better \nvalue compared to Napier grass, vetiver and Tithonia shrub.   The values of  root \ndensity, root length density, and  root area ratio of all the grasses is compared in \nFigure 17.         \n\n\n\nFigure 13. Effect of plant type on root density\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202026\n\n\n\nFigure 14. Effect of plant type on root length density(RLD)\n\n\n\nFigure 15. Effect of plant type on root area ratio\n\n\n\nFigure 16. Effect of plant type on relative soil particle detachment rate (RSD) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 27\n\n\n\nFigure 17. Comparison of root growth of  vetiver, king grass, Tithonia and Napier grass.\n\n\n\n Table 10 shows that RSD value   follows this  order: king grass >Napier  \ngrass >  vetiver grass> Tithonia scrubs.  Mwango et al. (2014) state that the RSD \nrate can be  used to determine the most effective plant for erosion control. Length \nof plant roots is in the following order: King Grass> Napier> vetiver> Tithonia. \nThe smaller the RSD value, the better the root function in stabilising soil and \nreducing erosion and landslides.   The RSD rate is directly related to the ability \nof plant roots to lessen the blow of raindrops on the soil  and in turn decrease \nsoil erosion. King grass is more effective in reducing erosion compared to other \ngrasses in this order: vetiver grass > Napier grass > Tithonia. Mwango et al. \n(2014) obtained similar results with their research in Tanzania. They state that \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202028\n\n\n\nRSD rates are generally lowest in the top soil and increase with soil depth (and \nwith decreasing RD). The lower RSD rate can be atributed to the presence of a \nmore favorable environmental condition which allows for greater plant growth. \n\n\n\nCONCLUSIONS \nPlant type has an effect  on reducing runoff and erosion compared to control, but \nthere is a difference in influence among the plants: (1) The effect of plant type on \nincreasing slow drainage pores is as follows: vetiver grass> king grass >Tithonia \nshrub >Napier grass.  (2) The effect of plant type on  reducing surface runoff is \nas follows:  vetiver grass >king grass >Tithonia shrub >Napier grass. (3) The role \nof these plants in reducing soil erosion is as follows: vetiver grass > king grass \n> Tithonia shrub > Napier grass.  King grass has a higher root density (RD) than \nvetiver and Napier grasses while Tithonia  shrub has the lowest root density. King \ngrass has the lowest RSD rate while vetiver grass and  Napier  grass have higher \nrates; Tithonia has the highest RSD rate.  \n It is recommended that a combination of vetiver, Napier, and king grass \nbe cultivated   to reduce soil erosion and landslides. Our study results show that \nTithonia shrub cannot be recommended to reduce soil erosion and landslides.\n\n\n\nACKNOWNLEDGEMENT\nWe would like to thank the  Rector of Andalas University for financial support  for \nthis study (Research grant from Universitas Andalas no. 524/XIV/A/Unand-2016)\n\n\n\nREFERENCES\nBalangcod, K.D., F.M. 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"\n\n___________________\n*Corresponding author : Shakeri@pnu.ac.ir\n\n\n\nINTRODUCTION\nPotassium in soil exists in four different forms including structural potassium \n(5000-25000 mg/kg), fixed or non-exchangeable potassium (50-750 mg/kg), \nexchangeable potassium (40-600 mg/kg) and soluble potassium (1-10 mg/kg) \n(Sparks 2000). The amount of potassium in each one of these forms is controlled \nby the quantity of clay, potassium uptake by plants, application of potassium \nfertilisers, losses through leaching, and the relative effect of K+ fixation and \nrelease processes that occur in the soil (Kirkman et al. 1994). Long-term non-\napplication of K would cause a large amount of soil K to be depleted (Tan et al. \n2017). Soils with different properties have different amounts of non-exchangeable \npotassium (Jalali, 2007). Non-exchangeable potassium is the main portion \nof the reserve of available potassium in soil as well as is an important factor \nto determine soil potassium fertility (Huoyan et al. 2016). Questions such as \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22:59-75 (2018)                          Malaysian Society of Soil Science\n\n\n\nEffect of Soil Buffering Capacity and Clay Minerals on the \nRate Coefficient of Non-Exchangeable Potassium Release\n\n\n\nSirous Shakeri\n\n\n\nDepartment of Agriculture, Payame Noor University (PNU), Tehran, Iran\n\n\n\nABSTRACT\nPotassium (K) is an essential element for plant growth. The difference in K release \nfrom the non-exchangeable K sources can be a result of soil properties. This \nresearch was carried out to assess the effect of soil buffering capacity and clay \nminerals on the rate coefficient of non-exchangeable potassium release as well \nas evaluation of kinetic equations in describing and predicting non-exchangeable \npotassium release in calcareous soils in the southwest of Iran. Extraction of non-\nexchangeable potassium was performed with 0.01 M CaCl2 and 0.01 M oxalic acid \nconsecutively  15 times within 15-min intervals, in duplicate. The results showed \nthat due to the high buffering capacity of the soils resulting from a high carbonate \nlevel and neutralising oxalic acid, no significant difference was observed between \nthe amount of cumulative non-exchangeable potassium released by oxalic acid and \nCaCl2. The results also showed that the coefficient of potassium release rate (b) in \nElovich equation is significantly correlated with non-exchangeable potassium and \nsome physical and chemical characteristics, as samples containing more clay and \norganic carbon with more cation exchange capacity had the maximum potassium \nrelease rates. Therefore the coefficient of potassium release rate (b) can be a more \naccurate indicator for plant available potassium. \n\n\n\nKeywords: Non-exchangeable potassium, calcareous soils, potassium \nrelease rate.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201860\n\n\n\nhow potassium is released in the soil solution and which part of that has more \nimportance for plant utilisation and production, are very important and several \nstudies have been conducted about them (Havlin and Westfall 1985). Availability \nof non-exchangeable potassium does not depend on its amount, but on the rate of \nrelease and amount of potassium, which could be changed into its exchangeable \nform (Jalali 2005). The difference in K release from the non-exchangeable K \nsources can result from the differences in soil texture and organic matter content \nof soils (Akbas et al. 2017). In calcareous soils, Ca2+ is known as a common \ncation that replaces interlayer potassium. Presence of Ca2+ in irrigation water \nand soil minerals, which are able to release Ca2+, has a great importance in the \ndetermination of leaching K+ in soils of arid and semi-arid regions. Further, \nsequential extractions of K+ by Ca2+ is one of the suitable methods to evaluate \nthe release kinetics of non-exchangeable potassium in calcareous soils (Jalali and \nRowell 2003). Also, K is weakly maintained compared to Ca2+, therefore K is \nmore readily exchangeable than Ca2+ (Zhang et al. 2015).\n\n\n\nRoots of plants exude large amounts of organic acids in their rhizosphere. \nAmong the different roles of rhizosphere secretions, the capacity of organic \nacids in dissolving the cations which have been adsorbed or have precipitated \nare important (Jones and Edwards1993; Rajawat et al. 2016). Increasing mineral \nweathering by creating a complex of metal-organic compounds and promoting the \nexchange of H+ by K+, are two main mechanisms, which increase availability of \nnon-exchangeable potassium by using organic acids (Wang et al. 2011). Oxalic \nacid is an organic acid that has an important role in improving availability of \nelements in the soil. This acid is one of the simplest acids with two pKa values, \n1.23 and 4.19, which occur in sediments, forest and agriculture soils especially in \nthe rhizosphere of plants (Fox and Comerford 1990). \n\n\n\nDifferent equations have been used by researchers to describe and predict the \nmechanism of non-exchangeable potassium release as well as its rate of release. \nSome of these equations, most used by researchers, are first order, parabolic \ndiffusion, power function and Elovich equation (Martin and Sparks 1983; Jalali \n2005 ;Srinivasarao et al. 2006; Rajashekhar 2015). Sequential extractions by \ndifferent extractants such as 0.01 M calcium chloride (Srinivasarao et al. 2006; \nHosseinpur and Motaghian 2013; Rajashekhar 2015; Ghiri et al. 2011) and low-\nmolecular-weight organic acids (Jalali 2005; Jalali and Zarabi 2006; Srinivasarao \net al. 2006) are some of the methods used to release non-exchangeable potassium. \nThe purpose of conducting this research was (i) to assess the effect of soil \nbuffering capacity and clay minerals on the rate coefficient of non-exchangeable \npotassium release, and (ii) to evaluate kinetic equations that best describe and \npredict non-exchangeable potassium release in calcareous soils of Bahmaei plain \nin Kohgiluyeh and Boyer-Ahmad Province in the southwest of Iran.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 61\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area Characteristics\nThe study area is located between longitudes 30\u00b039\u2019 10\u201d E to 31\u00b011\u2019 23\u201d E and \nlatitudes 49\u00b051\u2019 44\u201d N to 50\u00b026\u2019 43\u201dN, in the southwest of Iran. The average \naltitude is 1470 m. The climate of the region is arid and semi-arid. The mean \nannual temperature and precipitation are 20.5 \u00b0C and 480 mm, respectively. In \ngeneral, the mountains surrounding the study area are a small part of the great \nZagros sync. The studied pedons are mostly located on calcareous deposits. \nPredominant formations in the area are alluvial belonging to the lower cretaceous \nof Darian formation.\n\n\n\nSoil Sampling and Soil Analyses\nBased on aerial photos and topographic maps, physiographic units were \ndistinguished and separated, and on each unit, a profile was dug and four \nrepresentative pedons were selected. Soil samples were prepared in each \ndiagnostic horizon to determine physico-chemical properties, mineralogical \nanalyses, different K+ forms, and desorption kinetic experiments. Particle-size \ndistribution was determined by the hydrometer method (Buoyoucos 1962). \nCalcium carbonate equivalent (CCE) was measured by back neutralisation \ntitration with HCl (Loeppert and Suarez 1996). Measurement of organic carbon \n(OC) was  by wet oxidation in the presence of potassium dichromate and sulfuric \nacid followed by back titration using ferrous ammonium sulfate to determine the \nunreacted dichromate based on the procedures of  Nelson and Sommers (1982), \nsoil pH from saturated paste and electrical conductivity (EC) in soil saturated \nextract using a conductometer, cation exchange capacity (CEC) using sodium \nacetate (CEC at pH 7 with Ammonium Acetate) (Chapman 1965) and gypsum \nby precipitation with acetone (Richards 1954). Soluble K+ was measured by \nflame photometer in the saturated extract. Exchangeable K+ was extracted by 1.0 \nM NH4OAc at pH 7.0 (McLean and Watson 1985). Non-exchangeable K+ was \nextracted by boiling 1.0 M HNO3 (Pratt 1965). Digestion by aqua-regia and HF \nmethod was carried out to determine total K+ (Buckley and Cranston 1971). \n\n\n\nAfter performing physico-chemical tests and estimating various potassium \nforms on all samples, four surface samples were selected to conduct kinetics of \nnon-exchangeable potassium release. First, exchangeable potassium was removed \nthrough saturation by equilibrating 10 g soil with 1 M CaCl2, three times for 48 \nh. Then the extra CaCl2 was washed out of soil by suspending with alcohol and \ndeionised water followed by centrifugation. Samples were dried in an oven at \n65\u00b0C and ground to resolve the agglomeration. In the next step, 20 mLof calcium \nchloride solution (0.01 M) was added to a centrifuge tube containing Ca-saturated \nsoil (2 g). Similarly, in another centrifuge tube, 2 g of soil and 20 mLof oxalic acid \nsolution (0.01 M) were added respectively. The extraction pairs, as prepared for \ndifferent samples, were stirred on a shaker for 15 min at 25 \u00b0C, centrifuged and \nthe supernatant solution was stored for subsequent determination. The remaining \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201862\n\n\n\nsoil in the tube was re-extracted  a further 14 times and the released potassium in \neach of  the 15 stages was determined for K+ concentration by flame photometry \n(Elico Model CL-360) (Jalali 2006).\n\n\n\nThen the released non-exchangeable potassium was fitted with different kinetics \nequations versus time. These equations are as follows:\n\n\n\nln (Y\u00ba \u2013 Y ) = a \u2013 b t First order\nY = a + b ln t  Elovich\nY = a + b t1/2  Parabolic diffusions\nln Y = ln a + b ln t Power function\n\n\n\nY: is  the amount of cumulative potassium (mg/kg), released within the time t (h), \nY\u00ba: is  the maximum released cumulative potassium (mg/kg) and a and b are the \nconstants of the equations. The equations were compared based on coefficients of \ndetermination (r2) and standard errors of estimation (SE). The standard errors of \nestimation were calculated using the following relation:  \n\n\n\n SE = {(q-q*)2/(n-2)}1/2\n\n\n\nIn this equation q and q* represent the amount of the measured and predicted non-\nexchangeable potassium, respectively, and n is the number of the evaluated data.\n\n\n\nFor the preparation of samples for clay minerals analyses, cementation \nagents including carbonates, organic matter and iron oxides were removed by \n1 N HCl, 30% H2O2 and dithionite citrate bicarbonate, respectively (Mehra and \nJackson 1960; Kittrick and Hope 1963; Jackson 1975). Samples containing \ngypsum, depending on the quantity of gypsum, were washed by distilled water. \nAfter separation of the clay fraction, samples were saturated with Mg2+ and K+, \nusing MgCl2 and 1 N KCl, respectively. The Mg2+ and K+ saturated samples were \nsaturated using ethylene glycol and heated at 550 \u00b0C, respectively. In addition, to \ndiscriminate kaolinite and Fe chlorite, samples were treated with 1 N HCl. These \nfive treatments were analysed by XRD diffraction. Relative abundance of clay \nminerals based on peak\u2019s intensity was measured semi-quantitatively according \nto the method of Johns et al. (1954).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Characteristics \nBased on our results, calcic, gypsic and argillic subsurface horizons were observed \nin the studied area. Due to the arid and semi-arid climate as well as dry and wet \nperiods of the region during the year, in addition to the  calcareous and gypsum \nparent materials, calcic and gypsic subsurface horizons are predictable in the soils \nof the region. However, the argillic horizon observed in pedon 2 is most probably \nrelated to the past humid climate. This is because at the present time, conditions \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 63\n\n\n\nare not suitable for dispersion and transfer of clay to lower horizons and formation \nof argillic horizon. In addition, due to a high temperature, lack of rainfall and \nlow vegetation cover, the ochric epipedon was the only epipedon in the area. \nAccording to U.S. Soil Taxonomy (Soil Survey Staff 2014), as a characteristic of \nthe surface and subsurface horizons and also a consequence of the soil moisture \nand soil temperature regime, the soils of the region are  included in the  orders \nof Inceptisols and Alfisols (Table 1). The results of the physicochemical analysis \n(Table 1) show that the amount of clay in the samples is variable, between 19.5% \nin C horizon of pedon 3, which is an Inceptisols soil, and 60.7% in Bt2k horizon \nwhich is an Alfisols soil, with an  average of approximately 39% in the study area. \nCCE is high with an average of approximately 55.5%, with the  maximum value \nbeing 81.7%  observed in the C horizon of pedon 1, while its minimum value \nwas observed in surface horizon of pedon 2 which is  an Alfisols. The average \nOC is approximately 0.55%, with A horizon in pedon 4 which is under forest \ncover having the maximum amount (1.94 %) and C horizons of pedons 1 and 3 \nhaving the minimum amounts of approximately 0.1% of the organic carbon. CEC \nis variable, between 8.6 and 39.1 cmol (+) kg-1. The maximum amount of CEC \nwas related to the surface horizon of pedon 4, while its minimum amount was \nobserved in C horizon of pedon 3. Some amount of gypsum was also observed \nin the studied pedons with the maximum being found in Bk2y horizon of pedon \n1. Given the calcareous nature of the soils in the region, the pH of the soils was \nexpectedly variable, ranging between 7.1 and 7.8. \n\n\n\nSoluble potassium was variable, ranging  between 0.5 mg/kg and 9.1mg/kg \nwith the  average being 2.7 mg/kg. The average of the exchangeable potassium \nwas 136 mg/kg, with the  maximum amount being observed in A horizon of pedon \n4 (325 mg/kg) and the  minimum amount in C horizon of pedon 4. The average \nnon-exchangeable potassium was 245 mg/kg, with the  maximum and minimum \namounts being observed, like the exchangeable potassium, in A horizon of pedon \n4 and C horizon of pedon 1, respectively. The average of the structural potassium \n\n\n\nTABLE 1\nPhysico-chemical analysis, different forms of potassium and cumulative non-\n\n\n\nexchangeable K released in different media of extraction (0.01 M CaCl2 and 0.01 M \noxalic acid) in representative pedons (mg/kg)\n\n\n\n5 \n \n\n\n\nTABLE 1 \nPhysico-chemical analysis, different forms of potassium and cumulative non-exchangeable K released in different media of \n\n\n\nextraction (0.01 M CaCl2 and 0.01 M oxalic acid) in representative pedons (mg/kg) \n \n\n\n\nPed. Soil \nno. Horizon Sol* Exch* Nonex* Struc* K1 K2 Clay \n\n\n\n(%) pH CCE \n(%) \n\n\n\nGypsum \n(%) \n\n\n\nOC \n(%) \n\n\n\nCEC \n(cmol \nkg-1) \n\n\n\nTaxonomy \n\n\n\n1 \n\n\n\n1 Ap 3.8 67 155 1328 110 128 24.2 7.6 70.1 0.4 0.58 15.6 \nTypic \n\n\n\nCalcixerepts \n2 Bk1 1 44 91 779 - - 42.3 7.7 77.9 0.3 0.27 22.0 \n3 Bk2y 0.7 39 56 639 - - 40.8 7.8 81.2 5.3 0.1 20.1 \n4 C 0.7 33 44 597 - - 50.3 7.8 81.7 0.4 0.1 26.2 \n\n\n\n2 \n\n\n\n5 Ap 0.5 129 223 2451 137 145 26.1 7.5 35.4 0.4 1.22 20.1 \nAquic \n\n\n\nHaploxeralfs \n6 Bt1 0.9 142 310 3594 - - 35.1 7.9 36.2 0.4 0.29 27.2 \n7 Bt2k 0.8 181 372 3990 - - 60.7 7.7 41.6 0.4 0.34 34.3 \n8 Cky 1.5 168 341 3057 - - 54.7 7.9 43.9 2.7 0.24 31.3 \n\n\n\n3 \n9 Ap 9.1 155 308 4158 170 176 36.1 7.7 36.2 0.3 1.05 18.1 \n\n\n\nTypic \nHaploxerepts 10 Bk 5 116 325 2510 - - 25.2 7.9 57.4 0.5 0.51 25.8 \n\n\n\n11 C 4.2 44 104 1268 - - 19.5 7.1 76.3 0.5 0.1 8.6 \n\n\n\n4 \n12 A 3.7 325 488 4244 295 274 46.2 7.6 44.2 0.3 1.94 39.1 Typic \n\n\n\nHaploxerepts 13 Bw 3.9 280 388 2976 - - 31.7 7.8 47.9 0.4 0.85 28.4 \n14 C 2 181 223 2399 - - 52.7 7.8 46.7 0.4 0.15 34.4 \n\n\n\nMean   2.7 136 245 2428 178 181 39.0 7.7 55.5 0.9 0.55 25.1  \n\n\n\nNotes: Sol*: Soluble K; Exch: Exchangeable K; Nonex*: Non-exchangeable K; Struc*: Structural K; K1 and K2: cumulative non-\nexchangeable K released in 0.01 M CaCl2 and 0.01 M oxalic acid respectively. \n \nThe results show that the clay minerals in this region are smectite, chlorite, illite, palygorskite \nand kaolinite respectively with smectite being the predominant mineral (Table 2). \nConsidering the climate of the region, the smectite in the soils is most likely inherited from \nparent materials and parent rock. Further, with increasing depth, the amount of smectite \nshowed an ascending trend, suggesting the possible hereditary nature of this mineral in this \nregion. On the other hand, the conversion of other minerals such as illite and palygorskite to \nsmectite in the surface of the soil  and its transfer to the lower horizons, due to the smaller \nsize of smectite, can be another reason for the increase in smectite with increasing depth in \nthis region. The source of the minerals of chlorite, kaolinite and illite in the region is \nhereditary, as in other soils of the regions in the South of Iran. Palygorskite, which \nmorphologically has a fibrous form, is often found in soils of the arid and semi-arid regions.. \nExistence of high levels of Si and Mg, high pH, and low Al and Fe, are ideal conditions for \npalygorskite formation. \n \n \n \n\n\n\nTABLE 2 \nSemi-quantitative analysis of the soil clays \n\n\n\nPedon Soil no. Horizon Smectite Illite Chlorite Vermiculite Kaolinite  Palygorskite \n1 1 Ap +++ ++ ++ - +  + \n1 2 Bk1 ++ +++ + - +  + \n2 5 Ap ++++ ++ ++ - +  + \n2 6 Bt1 +++ +++ + - +  + \n3 9 Ap ++++ ++ ++ - -  - \n3 10 Bk +++ +++ + - +  + \n4 12 A ++++ ++ + - +  + \n4 13 Bw +++ ++ + - +  + \n\n\n\nNotes : trace or not detected; +: 10-20%; ++: 20-30%; +++: 30-45%;  ++++ : 45-55% \n  \n \nPotassium Release by 0.01 M Cacl2 and 0.01 M Oxalic Acid \n \nCumulative non-exchangeable K released in different media of extraction (0.01M CaCl2 and \n0.01M oxalic acid) in representative pedons (mg/kg) is shown in Table 1 while Figure 1 \nindicates the cumulative amount of K+ released by sequential extractions with 0.01 M CaCl2 \nand 0.01 M oxalic acid versus time. As can be seen, there is no significant difference between \npotassium extracted with CaCl2, and oxalic acid; the mean potassium extracted by CaCl2 was \n178 mg/kg (from 110 to 295 mg/kg), while the amount of potassium released by sequential \nextractions using oxalic acid varied between 128 and 274 mg/kg and had a mean of 181 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201864\n\n\n\nof the studied pedons was 2428 mg/kg. The average amounts of cumulative non-\nexchangeable K+ released by 0.01M CaCl2 and 0.01M oxalic acid were 178 mg/\nkg and 181 mg/kg respectively, with the amount of potassium released by the two \nextractions being maximum in A horizon of pedon 4 and minimum in Ap horizon \nof pedon 1. \n\n\n\nThe results show that the clay minerals in this region are smectite, chlorite, \nillite, palygorskite and kaolinite respectively with smectite being the predominant \nmineral (Table 2). Considering the climate of the region, the smectite in the soils \nis most likely inherited from parent materials and parent rock. Further, with \nincreasing depth, the amount of smectite showed an ascending trend, suggesting \nthe possible hereditary nature of this mineral in this region. On the other hand, \nthe conversion of other minerals such as illite and palygorskite to smectite in the \nsurface of the soil  and its transfer to the lower horizons, due to the smaller size \nof smectite, can be another reason for the increase in smectite with increasing \ndepth in this region. The source of the minerals of chlorite, kaolinite and illite \nin the region is hereditary, as in other soils of the regions in the South of Iran. \nPalygorskite, which morphologically has a fibrous form, is often found in soils of \nthe arid and semi-arid regions.. Existence of high levels of Si and Mg, high pH, \nand low Al and Fe, are ideal conditions for palygorskite formation.\n\n\n\nTABLE 2\nSemi-quantitative analysis of the soil clays\n\n\n\n5 \n \n\n\n\nTABLE 1 \nPhysico-chemical analysis, different forms of potassium and cumulative non-exchangeable K released in different media of \n\n\n\nextraction (0.01 M CaCl2 and 0.01 M oxalic acid) in representative pedons (mg/kg) \n \n\n\n\nPed. Soil \nno. Horizon Sol* Exch* Nonex* Struc* K1 K2 Clay \n\n\n\n(%) pH CCE \n(%) \n\n\n\nGypsum \n(%) \n\n\n\nOC \n(%) \n\n\n\nCEC \n(cmol \nkg-1) \n\n\n\nTaxonomy \n\n\n\n1 \n\n\n\n1 Ap 3.8 67 155 1328 110 128 24.2 7.6 70.1 0.4 0.58 15.6 \nTypic \n\n\n\nCalcixerepts \n2 Bk1 1 44 91 779 - - 42.3 7.7 77.9 0.3 0.27 22.0 \n3 Bk2y 0.7 39 56 639 - - 40.8 7.8 81.2 5.3 0.1 20.1 \n4 C 0.7 33 44 597 - - 50.3 7.8 81.7 0.4 0.1 26.2 \n\n\n\n2 \n\n\n\n5 Ap 0.5 129 223 2451 137 145 26.1 7.5 35.4 0.4 1.22 20.1 \nAquic \n\n\n\nHaploxeralfs \n6 Bt1 0.9 142 310 3594 - - 35.1 7.9 36.2 0.4 0.29 27.2 \n7 Bt2k 0.8 181 372 3990 - - 60.7 7.7 41.6 0.4 0.34 34.3 \n8 Cky 1.5 168 341 3057 - - 54.7 7.9 43.9 2.7 0.24 31.3 \n\n\n\n3 \n9 Ap 9.1 155 308 4158 170 176 36.1 7.7 36.2 0.3 1.05 18.1 \n\n\n\nTypic \nHaploxerepts 10 Bk 5 116 325 2510 - - 25.2 7.9 57.4 0.5 0.51 25.8 \n\n\n\n11 C 4.2 44 104 1268 - - 19.5 7.1 76.3 0.5 0.1 8.6 \n\n\n\n4 \n12 A 3.7 325 488 4244 295 274 46.2 7.6 44.2 0.3 1.94 39.1 Typic \n\n\n\nHaploxerepts 13 Bw 3.9 280 388 2976 - - 31.7 7.8 47.9 0.4 0.85 28.4 \n14 C 2 181 223 2399 - - 52.7 7.8 46.7 0.4 0.15 34.4 \n\n\n\nMean   2.7 136 245 2428 178 181 39.0 7.7 55.5 0.9 0.55 25.1  \n\n\n\nNotes: Sol*: Soluble K; Exch: Exchangeable K; Nonex*: Non-exchangeable K; Struc*: Structural K; K1 and K2: cumulative non-\nexchangeable K released in 0.01 M CaCl2 and 0.01 M oxalic acid respectively. \n \nThe results show that the clay minerals in this region are smectite, chlorite, illite, palygorskite \nand kaolinite respectively with smectite being the predominant mineral (Table 2). \nConsidering the climate of the region, the smectite in the soils is most likely inherited from \nparent materials and parent rock. Further, with increasing depth, the amount of smectite \nshowed an ascending trend, suggesting the possible hereditary nature of this mineral in this \nregion. On the other hand, the conversion of other minerals such as illite and palygorskite to \nsmectite in the surface of the soil  and its transfer to the lower horizons, due to the smaller \nsize of smectite, can be another reason for the increase in smectite with increasing depth in \nthis region. The source of the minerals of chlorite, kaolinite and illite in the region is \nhereditary, as in other soils of the regions in the South of Iran. Palygorskite, which \nmorphologically has a fibrous form, is often found in soils of the arid and semi-arid regions.. \nExistence of high levels of Si and Mg, high pH, and low Al and Fe, are ideal conditions for \npalygorskite formation. \n \n \n \n\n\n\nTABLE 2 \nSemi-quantitative analysis of the soil clays \n\n\n\nPedon Soil no. Horizon Smectite Illite Chlorite Vermiculite Kaolinite  Palygorskite \n1 1 Ap +++ ++ ++ - +  + \n1 2 Bk1 ++ +++ + - +  + \n2 5 Ap ++++ ++ ++ - +  + \n2 6 Bt1 +++ +++ + - +  + \n3 9 Ap ++++ ++ ++ - -  - \n3 10 Bk +++ +++ + - +  + \n4 12 A ++++ ++ + - +  + \n4 13 Bw +++ ++ + - +  + \n\n\n\nNotes : trace or not detected; +: 10-20%; ++: 20-30%; +++: 30-45%;  ++++ : 45-55% \n  \n \nPotassium Release by 0.01 M Cacl2 and 0.01 M Oxalic Acid \n \nCumulative non-exchangeable K released in different media of extraction (0.01M CaCl2 and \n0.01M oxalic acid) in representative pedons (mg/kg) is shown in Table 1 while Figure 1 \nindicates the cumulative amount of K+ released by sequential extractions with 0.01 M CaCl2 \nand 0.01 M oxalic acid versus time. As can be seen, there is no significant difference between \npotassium extracted with CaCl2, and oxalic acid; the mean potassium extracted by CaCl2 was \n178 mg/kg (from 110 to 295 mg/kg), while the amount of potassium released by sequential \nextractions using oxalic acid varied between 128 and 274 mg/kg and had a mean of 181 \n\n\n\nPotassium Release by 0.01 M Cacl2 and 0.01 M Oxalic Acid\nCumulative non-exchangeable K released in different media of extraction (0.01M \nCaCl2 and 0.01M oxalic acid) in representative pedons (mg/kg) is shown in Table \n1 while Figure 1 indicates the cumulative amount of K+ released by sequential \nextractions with 0.01 M CaCl2 and 0.01 M oxalic acid versus time. As can be \nseen, there is no significant difference between potassium extracted with CaCl2, \nand oxalic acid; the mean potassium extracted by CaCl2 was 178 mg/kg (from 110 \nto 295 mg/kg), while the amount of potassium released by sequential extractions \nusing oxalic acid varied between 128 and 274 mg/kg and had a mean of 181 \nmg/kg. In his study, Jalali (2007) extracted about 340 mg/kg in calcareous soils \nof Hamadan province in the West of Iran. In another experiment in calcareous \nsoils of Iran, Ghiri et al. (2011) extracted about 152 mg/kg of non-exchangeable \npotassium using 0.01M calcium chloride. Non-exchangeable potassium release is \nnot necessarily the result of dissolution of minerals containing potassium, rather \nit may be a slow exchangeable reaction. During slow exchange between clay \nminerals such as mica, the ion replacing potassium in anhydrate form should first \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 65\n\n\n\nenter into unexpanded inner layers and then simultaneously these inner layers \nare expanded under the influence of hydration of such ions. These ions are then \nallowed to stabilise or are captured and the released potassium in its hydrated form \nspreads slowly to exchange sites in the external parts of clay particles (Sparks and \nHuang 1985). \n\n\n\nIn all soil samples and in both extractions, the process of potassium release \nwas almost the same with the release rate being initially high and decreasing \ngradually. Since the non-exchangeable potassium is located between the layers \nand in the sites at the edge, in the early stages the potassium located on the edge of \nthe minerals which are more available is released. With increasing time, potassium \nlocated in the wedge-shaped locations is released, with the  extraction becoming \nincreasingly difficult. Ca2+, due to the larger hydrating radius compared to K+, \ncannot be easily located between the layers of minerals and releases the potassium, \nand over time, the distance of potassium from the edges increases and the release \nbecomes slower (Jalali 2005). Therefore, the first part of the cumulative release \n\n\n\nFigure 1: Cumulative amount of K+ released with time by successive extractions \nwith 0.01 M CaCl2 (1) and 0.01 M oxalic acid (2)\n\n\n\n6 \n \n\n\n\n  \n \nPotassium Rrelease by 0.01 M Cacl2 and 0.01 M Ooxalic Aacid \n \nCumulative non-exchangeable K released in different media of extraction (0.01M CaCl2 and \n0.01M Oxalicoxalic acid) in representative pedons (mg/kg) is shown in Ttable 1 while . \nFurther, Figure. 1 indicates the cumulative amount of K+ released by sequential extractions \nwith 0.01 M CaCl2 and 0.01 M Oxalicoxalic acid versus time. As can be seen, there is no \nsignificant difference between potassium extracted with CaCl2, and oxalic acid;, so that the \nmean potassium extracted by CaCl2 was 178 mg/kg (from 110 to 295 mg/kg), while the \namount of potassium released by sequential extractions using oxalic acid varied between 128 \nand 274 mg/kg and had a mean of 181 mg/kg. In an expermenthis study,, Jalali (2007) \nextracted of about 340 mg/kg in calcareous soils of Hamadan province in the Wwest of Iran. \nIn another experiment in calcareous soils of Iran, Ghiri et al.(2011) extracted about 152 \nmg/kg of Nonnon-exchangeable potassium using 0.01M calcium chloride. Non-exchangeable \npotassium release is not necessarily the result of dissolution of minerals containing \npotassium, rather it may be a slow exchangeable reaction. During slow exchange between \nclay minerals such as mica, the ion replacing potassium in anhydrate form should first enter \ninto unexpanded inner layers and then simultaneously these inner layers are expanded under \nthe influence of hydration of such ions. These described ions are then allowed to stabilisze or \nbeing are captured and the released potassium in its hydrated form spreads slowly to \nexchange sites in the external parts of clay particles (Sparks and Huang, 1985). \n  \nIn all soil samples and with in both extractions, the process of potassium release is was \nalmost the same and with the releasing release rate is being high initially high and reduces \ndecreasing gradually. Since the Nonnon-exchangeable potassium is located between the \nlayers and in the edge sites at the edge, in the early stages first the potassium located on the \nedge of the minerals which are more available is released. With increasing time, potassium \nlocated in the wedge-shaped locations is released, whose with the  extraction is becoming \nincreasingly more difficult. Ca2+, due to the larger hydrating radius compared to K+, cannot \nbe easily located between the layers of minerals and releases the potassium, and over time, \nthe distance of potassium from the edges increases and the release becomes slower (Jalali , \n2005). Therefore, the first part of the cumulative release curve is related to edge potassium \nand the second part is associated with the wedge and interlayer potassium (Benipal et al. \n2006). By investigation investigating the release of soil potassium releasing,, Hosseinpur et \nal. (2014) announced stated that potassium releasing release rate is fast initially, but renders \nrateslowed slows down in the next stage. Further, the amount of potassium released in the \nfirst part of the curve is a perfect indicator of available potassium to plant. They also \nannounced reported the release of potassium follows followed the diffusion process. \n \n\n\n\n6 \n \n\n\n\n  \n \nPotassium Rrelease by 0.01 M Cacl2 and 0.01 M Ooxalic Aacid \n \nCumulative non-exchangeable K released in different media of extraction (0.01M CaCl2 and \n0.01M Oxalicoxalic acid) in representative pedons (mg/kg) is shown in Ttable 1 while . \nFurther, Figure. 1 indicates the cumulative amount of K+ released by sequential extractions \nwith 0.01 M CaCl2 and 0.01 M Oxalicoxalic acid versus time. As can be seen, there is no \nsignificant difference between potassium extracted with CaCl2, and oxalic acid;, so that the \nmean potassium extracted by CaCl2 was 178 mg/kg (from 110 to 295 mg/kg), while the \namount of potassium released by sequential extractions using oxalic acid varied between 128 \nand 274 mg/kg and had a mean of 181 mg/kg. In an expermenthis study,, Jalali (2007) \nextracted of about 340 mg/kg in calcareous soils of Hamadan province in the Wwest of Iran. \nIn another experiment in calcareous soils of Iran, Ghiri et al.(2011) extracted about 152 \nmg/kg of Nonnon-exchangeable potassium using 0.01M calcium chloride. Non-exchangeable \npotassium release is not necessarily the result of dissolution of minerals containing \npotassium, rather it may be a slow exchangeable reaction. During slow exchange between \nclay minerals such as mica, the ion replacing potassium in anhydrate form should first enter \ninto unexpanded inner layers and then simultaneously these inner layers are expanded under \nthe influence of hydration of such ions. These described ions are then allowed to stabilisze or \nbeing are captured and the released potassium in its hydrated form spreads slowly to \nexchange sites in the external parts of clay particles (Sparks and Huang, 1985). \n  \nIn all soil samples and with in both extractions, the process of potassium release is was \nalmost the same and with the releasing release rate is being high initially high and reduces \ndecreasing gradually. Since the Nonnon-exchangeable potassium is located between the \nlayers and in the edge sites at the edge, in the early stages first the potassium located on the \nedge of the minerals which are more available is released. With increasing time, potassium \nlocated in the wedge-shaped locations is released, whose with the  extraction is becoming \nincreasingly more difficult. Ca2+, due to the larger hydrating radius compared to K+, cannot \nbe easily located between the layers of minerals and releases the potassium, and over time, \nthe distance of potassium from the edges increases and the release becomes slower (Jalali , \n2005). Therefore, the first part of the cumulative release curve is related to edge potassium \nand the second part is associated with the wedge and interlayer potassium (Benipal et al. \n2006). By investigation investigating the release of soil potassium releasing,, Hosseinpur et \nal. (2014) announced stated that potassium releasing release rate is fast initially, but renders \nrateslowed slows down in the next stage. Further, the amount of potassium released in the \nfirst part of the curve is a perfect indicator of available potassium to plant. They also \nannounced reported the release of potassium follows followed the diffusion process. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201866\n\n\n\ncurve is related to edge potassium and the second part is associated with the \nwedge and interlayer potassium (Benipal et al. 2006). By investigating the release \nof soil potassium, Hosseinpur et al. (2014) stated that potassium release rate is \nfast initially, but slows down in the next stage. Further, the amount of potassium \nreleased in the first part of the curve is a perfect indicator of available potassium to \nplant. They also reported the release of potassium followed the diffusion process.\n\n\n\nOn average, CaCl2 and oxalic acid released about 73% and 74% of non-\nexchangeable potassium. The results also showed no significant difference \nbetween the amounts of cumulative non-exchangeable potassium released by \noxalic acid and the amount released with CaCl2. At pH values below 3.5, oxalic \nacid is in the form of a non-ionised molecule. At pH values between 3.5 and 4.5, \none of the acidic protons is ionised and both protons are released at pH values \ngreater than 4.5 (Wani 2012; Shu-Xin et al. 2007). Therefore, with pH>4.5, there \nare two negative free charges that can be exchanged with cations such as K, Ca \nand Na. The non-exchangeable potassium release mechanism by oxalic acid \nin different soils can be either an exchange or degradation of minerals. In the \nstudied area, calcium carbonate of the soil is high. Since the solubility constant \nof calcium carbonate (4.5\u00d710-9 M) is larger than that of calcium oxalate (1.7\u00d710-9 \nM), there are large amounts of Ca2+ and equal amounts of  hydroxyl ions (OH)- \nin the soil. As a result, the hydrogen released from oxalic acid, which probably \ncauses degradation of minerals in acidic soils, expands to neutralise hydroxyl ions \nin calcareous soils. Moreover, Ca2+ of the soil solution in the presence of oxalate \nions is precipitated as calcium oxalate, preventing  the exchange of potassium \nwith oxalate. Since calcareous soils are rich in calcium and have a high buffering \ncapacity, they prevent the impact of this acid on minerals and release less \npotassium compared with soils possessing lower pH or less calcium carbonate. \nShu-Xin et al. (2007) and Wani, (2012), reported that soils with mica and smectite \nas dominant minerals, and calcareous soils which have a  high buffering capacity \nrelease less potassium than other soils. Some researchers obtained the opposite \nresults. Jalali and Zarabi (2006) reported that oxalic acid could release more non-\nexchangeable potassium than CaCl2 in some calcareous soils. It appears  that a  \nlesser amount of calcium carbonate in their soil samples is responsible for higher \npotassium release by oxalic acid. \n\n\n\nTo investigate the release rate of non-exchangeable potassium in the soils of \nthe area, released non-exchangeable potassium data were fitted with first order, \nElovich, parabolic diffusion and power function equations. These results are \nshown in Table 3. Figures 2 and 3 reveal various kinetic models to describe and \npredict the non-exchangeable potassium release  Coefficient of determination and \nstandard errors were used to determine the best equation. As can be seen, the \nequations of power function, parabolic diffusion and Elovich for the 0.01M CaCl2 \nextraction have a coefficient of determination close to each other, but the lowest \nstandard errors were obtained by power function equation. For the extraction of \n0.01M oxalic acid, power function and parabolic diffusion equations have the \nhighest coefficient of determination. Power function equation for CaCl2 and oxalic \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 67\n\n\n\nacid have a coefficient of determination of 0.982- 0.987 (average of 0.985) and \n0.990 - 0.998 (average  of 0.995) and standard errors between 0.05 - 0.08 (average \n0.06) and 0.02 - 0.05 (average of 0.04), respectively. Due to very low standard \nerrors and a larger coefficient of determination for both extractions, this equation \nis acknowledged as the best equation to describe non-exchangeable potassium \nrelease for the soils of area. Havlin and Westfall (1985) indicate that if potassium \nrelease follows the power function equation, it suggests that the diffusion process \ncould be the most probable mechanism that controls the potassium release. \nFollowing the power function equation, parabolic diffusion and Elovich equations \nare recognised as the best equations based on the coefficient of determination \n(with means of 4.8 and 5.6, respectively) and standard errors (with means of 0.989 \nand 0.986, respectively) for CaCl2 extraction. For the oxalic acid extraction, the \n\n\n\nTABLE 3\nCoefficient of determination (r2) and standard errors of the estimate (SE) of various \n\n\n\nkinetic models for K release in soils\n\n\n\nFigure 2: Relationship between observed and predicted K+ release kinetics by \nrepresentative soils for successive extractions with 0.01 M CaCl2 as described by four \n\n\n\nmathematical models.\n\n\n\n8 \n \n\n\n\nTABLE 3 \nCoefficient of determination (r2) and standard errors of the estimate (SE) of various kinetic models for K release in soils \n\n\n\n \nSoil no. Elovich First  order Parabolic diffusion Power function \n\n\n\nr2 SE r2 SE r2 SE r2 SE \n\n\n\n2lCCa \n1 0.987 3.2 0.958 0.219 0.989 3.0 0.985 0.06 \n5 0.992 2.8 0.953 0.22 0.988 3.5 0.987 0.05 \n9 0.990 4.3 0.958 0.22 0.984 5.5 0.982 0.06 \n12 0.987 9.0 0.964 0.203 0.983 10.5 0.985 0.08 \n\n\n\nMean 0.989 4.8 0.958 0.216 0.986 5.6 0.985 0.06 \nOxalic acid \n\n\n\n1 0.931 9.7 0.915 0.286 0.993 3.2 0.998 0.02 \n5 0.979 5.6 0.914 0.330 0.996 2.4 0.990 0.05 \n9 0.943 12.1 0.929 0.249 0.997 2.9 0.998 0.03 \n12 0.950 18.5 0.923 0.299 0.997 4.8 0.994 0.05 \n\n\n\nMean 0.951 11.5 0.920 0.291 0.995 3.4 0.995 0.04 \n\n\n\n\n\n\n\n \n \nFigure 2: Relationship between observed and predicted K+ release kinetics by representative soils for successive extractions \nwith 0.01 M CaCl2 as described by four mathematical models. \n\n\n\n8 \n \n\n\n\nTABLE 3 \nCoefficient of determination (r2) and standard errors of the estimate (SE) of various kinetic models for K release in soils \n\n\n\n \nSoil no. Elovich First  order Parabolic diffusion Power function \n\n\n\nr2 SE r2 SE r2 SE r2 SE \n\n\n\n2lCCa \n1 0.987 3.2 0.958 0.219 0.989 3.0 0.985 0.06 \n5 0.992 2.8 0.953 0.22 0.988 3.5 0.987 0.05 \n9 0.990 4.3 0.958 0.22 0.984 5.5 0.982 0.06 \n12 0.987 9.0 0.964 0.203 0.983 10.5 0.985 0.08 \n\n\n\nMean 0.989 4.8 0.958 0.216 0.986 5.6 0.985 0.06 \nOxalic acid \n\n\n\n1 0.931 9.7 0.915 0.286 0.993 3.2 0.998 0.02 \n5 0.979 5.6 0.914 0.330 0.996 2.4 0.990 0.05 \n9 0.943 12.1 0.929 0.249 0.997 2.9 0.998 0.03 \n12 0.950 18.5 0.923 0.299 0.997 4.8 0.994 0.05 \n\n\n\nMean 0.951 11.5 0.920 0.291 0.995 3.4 0.995 0.04 \n\n\n\n\n\n\n\n \n \nFigure 2: Relationship between observed and predicted K+ release kinetics by representative soils for successive extractions \nwith 0.01 M CaCl2 as described by four mathematical models. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201868\n\n\n\nparabolic diffusion and Elovich equations with coefficients of determination of \n0.995 and 0.991 respectively and standard errors of 3.4 and 11.5 respectively \nwere identified as the best equations to describe and predict the non-exchangeable \npotassium release of soil samples, after the power function equation. Srinivasarao \net al. (2006), and Rajashekhar, (2015) concluded that power function equation, \nparabolic equation, and the first order equations are the best kinetic equations \nto describe potassium release in their studies For calcareous soils Jalali (2006) \nintroduced Elovich equations, power function and diffusion parabolic function, \nHosseinpur and Motaghian (2013), the power function and parabolic diffusion \nequations, Jalali and Khanlari (2014) the parabolic diffusion and Elovich equations, \nand power function and Jalali and Zarabi (2006) reported power function as the \nbest equations to describe K release for the two extractants of calcium chloride \nand oxalic acid.\n\n\n\nRelease Rate Coefficient of the Equations\nThe parameters of kinetic equations of potassium release are found in Table 4 \nwhile the correlation between some physic-chemical properties and the constant \nb (slope of all equations) of the kinetic equations of non-exchangeable potassium \nrelease (the released cumulative potassium with extractions by 0.01M CaCl2 and \n0.01M oxalic acid) are shown in Table 5. The release rate coefficient of non-\nexchangeable potassium release (b) with extraction by 0.01M CaCl2 in the Elovich \nequations showed a positive and significant relationship with clay (0.964*), CEC \n(0.962*) and the extracted potassium with CaCl2 (0.977**) parameter b of parabolic \n\n\n\n9 \n \n\n\n\n \n \n \nFigure 3: Relationship between observed and predicted K+ release kinetics by representative soils for successive \nextractions with 0.01 M oxalic acid as described by four mathematical models \n \n \nRelease Rate Coefficient of the Equations \nThe parameters of kinetic equations of potassium release are found in Table 4 while the \ncorrelation between some physic-chemical properties and the constant b (slope of all \nequations) of the kinetic equations of non-exchangeable potassium release (the released \ncumulative potassium with extractions by 0.01M CaCl2 and 0.01M oxalic acid) are shown in \nTable 5. The release rate coefficient of non-exchangeable potassium release (b) with \nextraction by 0.01M CaCl2 in the Elovich equations showed a positive and significant \nrelationship with clay (0.964*), CEC (0.962*) and the extracted potassium with CaCl2 \n(0.977**) parameter b of parabolic equation (1.00**). It also indicated a positive relationship \nwith OC (0.904), non-exchangeable potassium (0.842) and coefficient b of power function \nequation (0.842). The constant b with the extraction of 0.01M oxalic acid in Elovich equation \nalso showed a positive and significant relationship with clay (0.971*), CEC (0.953*), the \npotassium extracted with calcium chloride (0.998**) and parameter b of parabolic equation \n(1.00**) along with a positive relationship with OC (0.898), non-exchangeable potassium \n(0.856) and coefficient b of power function equation (0.778). The slope of Elovich equation \n(b) indicates interlayer potassium release rate, and its intercept (a) shows the initial and \nimmediate rate of potassium release (Mengel et al. 1998). The average release rate \ncoefficients for CaCl2 and oxalic acid in the Elovich equation were 56 and 63 mg/kg  min-1 \nrespectively with the  minimum and maximum amounts for both extractions being observed \nin sample 1 (A horizon of pedon 1) and sample 12 (A horizon of pedon 4) respectively. As \nobserved,  soil 1, which has the minimum amount of coefficient of release, has the minimum \namount of clay, CEC and OC, while soil 12 has the maximum amount of clay, OC and CEC. \nThe positive correlation of the amount of clay, OC and CEC with the rate coefficient of the \nElovich equation also confirms it. Singh et al. (2002) attributed the increase in non-\nexchangeable potassium release due to the presence of organic material and expressed the \nview that organic material has an important role in the distribution of different forms of \npotassium in soils. They attributed the increase in plant growth with the addition of organic \nmaterial to soil to the corresponding increase in non-exchangeable potassium release which \nfurnishes the plant with the required potassium. They rationalised the increase in the release \n\n\n\nFigure 3: Relationship between observed and predicted K+ release kinetics by \nrepresentative soils for successive extractions with 0.01 M oxalic acid as described by \n\n\n\nfour mathematical models\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 69\n\n\n\nequation (1.00**). It also indicated a positive relationship with OC (0.904), non-\nexchangeable potassium (0.842) and coefficient b of power function equation \n(0.842). The constant b with the extraction of 0.01M oxalic acid in Elovich equation \nalso showed a positive and significant relationship with clay (0.971*), CEC \n(0.953*), the potassium extracted with calcium chloride (0.998**) and parameter b \nof parabolic equation (1.00**) along with a positive relationship with OC (0.898), \nnon-exchangeable potassium (0.856) and coefficient b of power function equation \n(0.778). The slope of Elovich equation (b) indicates interlayer potassium release \nrate, and its intercept (a) shows the initial and immediate rate of potassium release \n(Mengel et al. 1998). The average release rate coefficients for CaCl2 and oxalic \nacid in the Elovich equation were 56 and 63 mg/kg  min-1 respectively with the  \nminimum and maximum amounts for both extractions being observed in sample \n1 (A horizon of pedon 1) and sample 12 (A horizon of pedon 4) respectively. As \nobserved,  soil 1, which has the minimum amount of coefficient of release, has the \n\n\n\nTABLE 4\nParameters of models used to describe release kinetics of non-exchangeable K+ into \n\n\n\n0.01M CaCl2 and 0.01M oxalic acid in soils\n\n\n\nTABLE 5\nLinear correlation (Pearson) coefficients between some physico-chemical properties and \nthe constant b into 0.01 M CaCl2 (Below the diagonal, white background) and into 0.01 \n\n\n\nM oxalic acid (Above the diagonal, gray background)\n\n\n\n11 \n \n\n\n\nTABLE 4 \nParameters of models used to describe release kinetics of non-exchangeable K+ into 0.01M CaCl2 and 0.01M oxalic acid in \n\n\n\nsoils \n \n\n\n\nSoil \nno. \n\n\n\nElovich First  order Parabolic diffusion Power function \na \n\n\n\nmg/kg  \nb \n\n\n\nmg/kg  min-1 \na \n\n\n\nmg/kg  min \nb \n\n\n\n(mg/kg )-1 \na \n\n\n\nmg/kg \nb \n\n\n\nmg/kg  min-1/2 \na \n\n\n\nmg/kg \nb \n\n\n\nmg/kg  min-1 \n2Cacl \n\n\n\n1 -79 35 4.9 -0.016 -4.4 7.9 1.6 0.58 \n5 -79 39 5.0 -0.015 6.4 9.0 2.3 0.50 \n9 -118 53 5.3 -0.016 -3.6 12.1 2.1 0.57 \n12 -239 98 5.9 -0.016 -26.3 22.4 2.2 0.65 \n\n\n\nMean -129 56 5.3 -0.016 -7.0 12.8 2.1 0.58 \nOxalic acid \n\n\n\n1 -121 44 5.2 -0.014 -29.0 10.4 0.9 0.74 \n5 -114 47 5.3 -0.016 -13.2 10.9 1.7 0.63 \n9 -169 61 5.5 -0.014 -40.6 14.4 1.1 0.77 \n12 -281 99 6.1 -0.016 -70.9 23.4 1.2 0.83 \n\n\n\nMean -171 63 5.5 -0.015 -38.4 14.8 1.2 0.74 \n\n\n\n\n\n\n\nTABLE 5 \nLinear correlation (Pearson) coefficients between some physico-chemical properties and the constant b into 0.01 M CaCl2 \n\n\n\n(Below the diagonal, white background) and into 0.01 M oxalic acid (Above the diagonal, gray background) \n \n\n\n\n Clay CCE OC CEC Nonexch. K1 K2 Elovichb First   b\norder \n\n\n\nParabolic  b\ndiffusion \n\n\n\nPower  b\nfunction \n\n\n\nClay 1 .143 .852 .863 .954* - .970* .971* -.341 .971* .799 \n\n\n\nCCE .143 1 .019 .343 -.113 - .261 .305 .089 .314 .547 \n\n\n\nOC .852 .019 1 .945 .756 - .921 .898 -.783 .891 .422 \n\n\n\nCEC .863 .343 .945 1 .694 - .960* .953* -.685 .950* .599 \n\n\n\nNonexch. .954* -.113 .756 .694 1 - .860 .856 -.210 .856 .719 \n\n\n\nK1 .965* .247 .933 .966* .854 1 - - - - - \n\n\n\nK2 - - - - - - 1 .998** -.509 .997** .742 \n\n\n\nElovichb .964* .315 .904 .962* .842 .997** - 1 -.468 1.000** .778 \n\n\n\nFirst  order b -.463 -.588 .027 -.194 -.424 -.334 - -.398 1 -.456 .172 \nParabolic  b\n\n\n\ndiffusion .964* .306 .909 .963* .843 .998** - 1.000** -.388 1 .787 \nPower  b\n\n\n\nfunction .775 .706 .497 .714 .619 .767 - .815 -.815 .808 1 \n\n\n\n **. Correlation is significant at the 0.01 level,*. Correlation is significant at the 0.05 level.  \n\n\n\n \nThe correlation of the soil properties and the potassium release rate coefficient in the \nparabolic equation for both extractions of CaCl2 and oxalic acid was the same as the Elovich \nequation. The main reason for the high correlation of the release rate coefficient of parabolic \nequation with soil properties can be the presence of illite in soils which slowly releases \npotassium, in addition to organic matter and clay content and size. In the studied region, illite \nwas a hereditary mineral. Further, because of the rich resources of this mineral in the \nsurrounding formations and its transfer to the pedons, and despite its decomposability and \nalteration of illite to other minerals such as smectite, the amount of this mineral is high in \nthese soils. As a result, in the pedons though the dominant mineral is smectite, the level of \nillite is also high. Srinivasarao et al. (2006) reported that the highest release rate coefficients \nof parabolic diffusion equation were in soils with dominant mineral of illite and explained \nthat this mineral has fixed potassium with a great power and releases it at a slower rate. Their \nresults showed that the release of non-exchangeable potassium in illite dominant mineral soils \nfollowed the diffusion law. The parameter b in the equation of power function for the 11 \n\n\n\n\n\n\n\nTABLE 4 \nParameters of models used to describe release kinetics of non-exchangeable K+ into 0.01M CaCl2 and 0.01M oxalic acid in \n\n\n\nsoils \n \n\n\n\nSoil \nno. \n\n\n\nElovich First  order Parabolic diffusion Power function \na \n\n\n\nmg/kg  \nb \n\n\n\nmg/kg  min-1 \na \n\n\n\nmg/kg  min \nb \n\n\n\n(mg/kg )-1 \na \n\n\n\nmg/kg \nb \n\n\n\nmg/kg  min-1/2 \na \n\n\n\nmg/kg \nb \n\n\n\nmg/kg  min-1 \n2Cacl \n\n\n\n1 -79 35 4.9 -0.016 -4.4 7.9 1.6 0.58 \n5 -79 39 5.0 -0.015 6.4 9.0 2.3 0.50 \n9 -118 53 5.3 -0.016 -3.6 12.1 2.1 0.57 \n12 -239 98 5.9 -0.016 -26.3 22.4 2.2 0.65 \n\n\n\nMean -129 56 5.3 -0.016 -7.0 12.8 2.1 0.58 \nOxalic acid \n\n\n\n1 -121 44 5.2 -0.014 -29.0 10.4 0.9 0.74 \n5 -114 47 5.3 -0.016 -13.2 10.9 1.7 0.63 \n9 -169 61 5.5 -0.014 -40.6 14.4 1.1 0.77 \n12 -281 99 6.1 -0.016 -70.9 23.4 1.2 0.83 \n\n\n\nMean -171 63 5.5 -0.015 -38.4 14.8 1.2 0.74 \n\n\n\n\n\n\n\nTABLE 5 \nLinear correlation (Pearson) coefficients between some physico-chemical properties and the constant b into 0.01 M CaCl2 \n\n\n\n(Below the diagonal, white background) and into 0.01 M oxalic acid (Above the diagonal, gray background) \n \n\n\n\n Clay CCE OC CEC Nonexch. K1 K2 Elovichb First   b\norder \n\n\n\nParabolic  b\ndiffusion \n\n\n\nPower  b\nfunction \n\n\n\nClay 1 .143 .852 .863 .954* - .970* .971* -.341 .971* .799 \n\n\n\nCCE .143 1 .019 .343 -.113 - .261 .305 .089 .314 .547 \n\n\n\nOC .852 .019 1 .945 .756 - .921 .898 -.783 .891 .422 \n\n\n\nCEC .863 .343 .945 1 .694 - .960* .953* -.685 .950* .599 \n\n\n\nNonexch. .954* -.113 .756 .694 1 - .860 .856 -.210 .856 .719 \n\n\n\nK1 .965* .247 .933 .966* .854 1 - - - - - \n\n\n\nK2 - - - - - - 1 .998** -.509 .997** .742 \n\n\n\nElovichb .964* .315 .904 .962* .842 .997** - 1 -.468 1.000** .778 \n\n\n\nFirst  order b -.463 -.588 .027 -.194 -.424 -.334 - -.398 1 -.456 .172 \nParabolic  b\n\n\n\ndiffusion .964* .306 .909 .963* .843 .998** - 1.000** -.388 1 .787 \nPower  b\n\n\n\nfunction .775 .706 .497 .714 .619 .767 - .815 -.815 .808 1 \n\n\n\n **. Correlation is significant at the 0.01 level,*. Correlation is significant at the 0.05 level.  \n\n\n\n \nThe correlation of the soil properties and the potassium release rate coefficient in the \nparabolic equation for both extractions of CaCl2 and oxalic acid was the same as the Elovich \nequation. The main reason for the high correlation of the release rate coefficient of parabolic \nequation with soil properties can be the presence of illite in soils which slowly releases \npotassium, in addition to organic matter and clay content and size. In the studied region, illite \nwas a hereditary mineral. Further, because of the rich resources of this mineral in the \nsurrounding formations and its transfer to the pedons, and despite its decomposability and \nalteration of illite to other minerals such as smectite, the amount of this mineral is high in \nthese soils. As a result, in the pedons though the dominant mineral is smectite, the level of \nillite is also high. Srinivasarao et al. (2006) reported that the highest release rate coefficients \nof parabolic diffusion equation were in soils with dominant mineral of illite and explained \nthat this mineral has fixed potassium with a great power and releases it at a slower rate. Their \nresults showed that the release of non-exchangeable potassium in illite dominant mineral soils \nfollowed the diffusion law. The parameter b in the equation of power function for the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201870\n\n\n\nminimum amount of clay, CEC and OC, while soil 12 has the maximum amount \nof clay, OC and CEC. The positive correlation of the amount of clay, OC and \nCEC with the rate coefficient of the Elovich equation also confirms it. Singh et al. \n(2002) attributed the increase in non-exchangeable potassium release due to the \npresence of organic material and expressed the view that organic material has an \nimportant role in the distribution of different forms of potassium in soils. They \nattributed the increase in plant growth with the addition of organic material to \nsoil to the corresponding increase in non-exchangeable potassium release which \nfurnishes the plant with the required potassium. They rationalised the increase \nin the release process  to the acidification of the root environment by the organic \nmaterial. The amount, type and size of clays have important effects on the release \nrate coefficient. The results indicate that samples with the maximum amount of \nrelease contain more clay and also smectite mineral than  other samples. Smectites \nare 2:1 clay types with high CEC and variable interlayer distance. Homogenous \nsubstitution causes the development of permanent negative charge in these clays \nwhich is neutralised by cations such as potassium. Due to greater expandability \nand lower layer charge, smectites easily release their interlayer potassium. \nSrinivasarao et al. (2006) reported that the greater rate of non-exchangeable \npotassium release in the soils with dominant smectite mineral suggests that the \nexpansion properties of smectite results in potassium release in sites on the edges \nand wedge zones of minerals. Also the positive correlation of the organic material \nwith the release coefficient of Elovich equation(b) can be due to development of \ndecomposition and degradation conditions of minerals and as a result, conversion \nof minerals such as illite to smectite by the organic material. Boyle et al. (1974) \nstate that an increase in organic material results in increasing non-exchangeable \npotassium release from minerals and consequently leads to greater  concentration \nof soluble and exchangeable potassium. Kaolinite was observed in hereditary form \nand in trace amounts in the studied region. Kaolinite mineral can easily release \nthe potassium from its external surfaces as only its external surface is available.  \nFurther due to the low amount of cation exchange capacity, kaolinite has a low \namount of exchangeable potassium which is negligible. Vermiculite and illite \nminerals have low expandability due to a high layer charge and as a result, the \nions are not exchanged easily. Despite the high amount of interlayer and structural \npotassium, the above minerals have a lower release capacity than the smectites.  \nSong and Huang (1988) attribute the difference in the rate and amount of non-\nexchangeable potassium release in the soil to factors such as a varying percentage \nof potassium in the minerals, the tininess and largeness and degree of weathering \nof minerals. Similarly, in the micaceous minerals, they attribute the difference \nto the extent of structural hydroxyl navigation, degree of tetrahedral rotation, \nthe layer charge site, amount of potassium depletion, the degree of tetrahedral \ndistortion, difference in the chemical composition and structural defects. Lopez \nand Navarro, (1997) observed  that  the coefficient b in the Elovich equation had \na high correlation with potassium removed by forage plants. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 71\n\n\n\nThe correlation of the soil properties and the potassium release rate \ncoefficient in the parabolic equation for both extractions of CaCl2 and oxalic acid \nwas the same as the Elovich equation. The main reason for the high correlation \nof the release rate coefficient of parabolic equation with soil properties can be the \npresence of illite in soils which slowly releases potassium, in addition to organic \nmatter and clay content and size. In the studied region, illite was a hereditary \nmineral. Further, because of the rich resources of this mineral in the surrounding \nformations and its transfer to the pedons, and despite its decomposability and \nalteration of illite to other minerals such as smectite, the amount of this mineral \nis high in these soils. As a result, in the pedons though the dominant mineral is \nsmectite, the level of illite is also high. Srinivasarao et al. (2006) reported that the \nhighest release rate coefficients of parabolic diffusion equation were in soils with \ndominant mineral of illite and explained that this mineral has fixed potassium \nwith a great power and releases it at a slower rate. Their results showed that the \nrelease of non-exchangeable potassium in illite dominant mineral soils followed \nthe diffusion law. The parameter b in the equation of power function for the \nextractions of CaCl2  and oxalic acid showed a positive correlation with clay, \nOC, CEC, non-exchangeable potassium, potassium extracted by CaCl2, potassium \nextracted by oxalic acid and also coefficient b of Elovich and parabolic equations. \nThe average potassium release rate coefficient (b) of the power function equation \nfor CaCl2 was 0.58 mg/kg  min-1and between 0.50-0.65 mg/kg  min-1, while in \noxalic acid it was 0.74 mg/kg  min-1 and between 0.83-0.63 mg/kg  min-1, which \nis less than 1 in both extractions. When the coefficient of the potassium release \nrate of power function equation is less than 1, it implies that the non-exchangeable \npotassium release rate decreases over time (Feigenbaum et al. 1981). Mengel and \nUhlenbecker (1993) state that there is a relationship between the b parameter in \nparabolic diffusion, the power function and Elovich equations with potassium \nextracted by the ryegrass plant. According to them, parameter b can be a more \nrealistic indicator for plant available potassium.\n\n\n\nCONCLUSION\nThe results showed that due to the high buffering capacity of the soils because of \na high carbonate level and neutralising oxalic acid, no significant difference was \nobserved between the amount of cumulative non-exchangeable potassium released \nby oxalic acid and CaCl2. Considering the two coefficients of determination and \nstandard errors of estimation, for the extraction of CaCl2, equations of power \nfunction, Elovich and parabolic diffusion, respectively are the best equations to \npredict the released non-exchangeable potassium in soils of the region. Similarly \nequations of power function, parabolic diffusion and Elovich are the best to \ndescribe non-exchangeable potassium for the extraction of oxalic acid. The results \nalso demonstrate that the rate coefficient of non-exchangeable potassium release \n(b) of Elovich equation has a positive and significant relationship with some of \nthe physical and chemical properties of the soils. Therefore the coefficient of \npotassium release rate (b) can be a more accurate indicator for plant available \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201872\n\n\n\npotassium. Further, the results indicate that the soil samples which have more \nsmectite minerals, have a larger coefficient of release, as smectite releases \ninterlayer potassium easily due to more expandability and lower layer charge.\n\n\n\nACKNOWLEDGEMENT\nThis research is a part of a research project fellowship whose costs were borne \nby Payame Noor University (Grant number: D/96/245/23671/1).Their support is \nhighly appreciated.  \n\n\n\nREFERENCES\nAkbas, F. H. Gunal and N. Acir. 2017. 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Pedosphere 17: 457-\n466.\n\n\n\nSingh, M.V.P. Singh and D.D. Reddy. 2002. Potassium balance and release kinetics \nunder continuous rice-wheat cropping system in Vertisol. Field Crops Research \n77: 81-91.\n\n\n\nSoil Survey Staff. 2014. Keys to Soil Taxonomy. USDA: NRCS, Washington. \n\n\n\nSong, S.K and P.M. Huang. 1988. Dynamics of potassium release from potassium-\nbearing minerals as influenced by oxalic and citric acids. Soil Science Society of \nAmerica Journal 52: 383-390.\n\n\n\nSparks, D.L and P.M. Huang. 1985. Physical chemistry of soil potassium. Potassium \nin Agriculture 16: 238-249.\n\n\n\nSparks, D.L. 2000. Bioavailability of soil potassium. In: Handbook of Soil Science, \npp 38-52. Town ??? Publisher????\n\n\n\nSrinivasarao, C.T., R. Rupa, A. Subba Rao, G. Ramesh and S.K. Bansal. 2006. \nRelease kinetics of non-exchangeable potassium by different extractants from \nsoils of varying mineralogy and depth. Communications in Soil Science and \nPlant Analysis 37: 473-491.\n\n\n\nTan, D., Z. Liu, L. Jiang, J. Luo and J. Li. 2017. Long-term potash application and \nwheat straw return reduced soil potassium fixation and affected crop yields in \nNorth China. Nutrient Cycling in Agroecosystems 108(2): 121-133.\n\n\n\nWang, H.Y., Q.H. Shen, J.M. Zhou, J. Wang, C.W. Du and X.Q. Chen. 2011. Plants \nuse alternative strategies to utilise non-exchangeable potassium in minerals. \nPlant and Soil 343: 209-220.\n\n\n\nWani, M.A. 2012. Oxalic Acid effect on potassium release from typical rice soils of \nKashmir. Communications in Soil Science and Plant Analysis 43: 1136-1148.\n\n\n\nZhang, Y.,  S. Yang, M. Fu, J. Cai, Y. Zhang, R. Wang, Z. Xu, Y. Bai and Y. Jiang. \n2015. Sheep manure application increases soil exchangeable base cations in a \nsemi-arid steppe of Inner Mongolia. Journal of Arid Land 7(3) : 361-369.\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : Email: rosenani@agri.upm.edu.my\n\n\n\nISSN: 1394-7900\n\n\n\nMalaysian Journal of Soil Science Vol. 13: 77-91                                   Malaysian Society of Soil Science\n\n\n\nComposting Oil Palm Wastes and Sewage Sludge For Use In \n\n\n\nPotting Media Of Ornamental Plants\n\n\n\nD.R. Kala 1, A.B. Rosenani1*,  C.I. Fauziah 1 & L.A. Thohirah2\n\n\n\n1Department of Land Management, Universiti Putra Malaysia, 43400 Serdang, \n\n\n\nSelangor Darul Ehsan, Malaysia.\n\n\n\n2Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, \n\n\n\n43400 Serdang, Selangor Darul Ehsan, Malaysia.\n\n\n\nINTRODUCTION\n\n\n\nCurrently, very limited choices of potting media are available in the market, i.e. \n\n\n\nmainly peat, coconut coir dust (CCD) and red clay soils. Therefore suitable substitutes \n\n\n\nor alternatives need to be sought before shortage occurs. Moreover every time a \n\n\n\ncontainer-grown plant is sold, the rooting substrate is sold with it, necessitating the \n\n\n\nneed for more substrate. Peat is one of the traditional organic materials that have \n\n\n\nABSTRACT\nThe use of oil palm wastes, particularly the empty fruit bunch (EFB), frond and \n\n\n\ntrunk as compost are now receiving greater attention by researchers. Currently, \n\n\n\nthese organic waste materials have not been fully utilized on a large scale, either \n\n\n\nagriculturally or industrially, for manufacture of useful by-products. Another \n\n\n\norganic waste that needs to be appropriately disposed of in Malaysia is the sewage \n\n\n\nsludge. Co-composting these waste materials could potentially convert these \n\n\n\nwastes into value added product. The objective of this study was to determine \n\n\n\nthe best formulation using oil palm wastes and sewage sludge in producing a \n\n\n\ncomposted material to be used as a potting media in horticulture. Composting \n\n\n\ndifferent oil palm wastes with sewage sludge was carried out in the glasshouse \n\n\n\nusing a polystyrene box. Shredded oil palm wastes (EFB, frond and trunk) were \n\n\n\nmixed with sewage sludge in 3 different ratios (1:0, 3:1 and 4:1 ratio) and adjusted \n\n\n\nto 60% moisture content. Based on the temperature, C/N, NH\n4\n\n\n\n+-N and NO\n3\n\n\n\n- - N + \n\n\n\nNO\n2\n\n\n\n- -N patterns of the oil palm wastes added with sludge during composting, the \n\n\n\nEFB, frond and trunk added with sludge composts seemed to perform similarly. \n\n\n\nHowever, due to the small volume of compost, the temperature did not sustain > \n\n\n\n45oC because of dissipation of the heat. Oil palm trunk with sewage sludge at 4:1 \n\n\n\nratio was found to be the most optimum compost as potting media for ornamental \n\n\n\nplants because of its texture suitable for potting media, not stringent or stiff, had \n\n\n\nhigh nutrient contents (2.05 % N, 0.640 % P, 1.39 % K, 0.705 % Ca, 0.229% Mg), \n\n\n\npH 6.2 and low C/N ratio, 19.\n\n\n\nKeywords: potting substrate, co-composts, oil palm empty fruit bunch, oil  \n\n\n\n     palm fronds, oil palm trunk\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200978\n\n\n\nbeen used extensively in ornamental horticultural industries to prepare potting/\n\n\n\nplanting media. However, peat is a finite resource and large scale of peat extraction \n\n\n\ncauses environmental damage (Barber 1993; Barkham 1993; Buckland 1993).\n\n\n\nRising prices and decreased availability of fertilizer have also caused growers \n\n\n\nto look for   renewable and organic sources of nutrients (Roe 1998).  Various \n\n\n\nagricultural and municipal waste materials including municipal solid waste, \n\n\n\nbiosolids, animal manures, yard trimmings, agricultural residues, waste paper, food \n\n\n\nprocessing wastes are composted as potting media without any negative effects on \n\n\n\na variety of crops raised in these substrates (Inbar et al. 1986; Bugbee and Frink, \n\n\n\n1989; Beeson 1996; Eklind et al. 2001; Hashemimajd et al. 2004). Composting is \n\n\n\na very popular process in the management of organic solid wastes because (a) it \n\n\n\nreduces the volume (b) microorganisms are destroyed during composting and (c) \n\n\n\nthe end product is rich in nutrients content (Kulhman et. al. 1989).\n\n\n\n In Malaysia, agricultural and municipal waste materials including oil palm \n\n\n\nwastes, yard trimmings, agricultural residues, waste paper, paddy straw, sewage \n\n\n\nsludge and animal manures are increasing each year leading to disposal problems. \n\n\n\nMalaysia as a major exporter of oil palm with a wide planted area of more than \n\n\n\n3.9 million hectares in year 2005, created more than 51 million tonnes of oil palm \n\n\n\nwastes particularly the empty fruit bunch (EFB), frond and trunk (MPOB, 2006). \n\n\n\nOil palm wastes, particularly the empty fruit bunches (EFB), fronds and trunks \n\n\n\ncomposts were reported to have many characteristics that are equal or superior to \n\n\n\npeat  in growing media (Lin and Ratnalingam 1980). Another organic waste that \n\n\n\nneeds to be disposed off is the sewage sludge. Malaysia produces 5 million cubic \n\n\n\nmeters of domestic sludge. By the year 2022, the amount will be increased to 7 \n\n\n\nmillion cubic meters per year (Indah Water 1997). Recently in Malaysia, there \n\n\n\nhas been a great interest in converting oil palm wastes into composts with other \n\n\n\norganic materials. Sewage sludge has been reported to have significant amounts \n\n\n\nof primary nutrient, N, and other macro and micronutrients and is suitable for \n\n\n\ncomposting, for agricultural purposes (Akhtar and Malik 2000; Lazzari et \n\n\n\nal.,2000; Barrena et al. 2005), due to high organic matter content (50% to 70%) of \n\n\n\nthe   total solids content and as potting media for horticulture plants (Smith 1992 \n\n\n\nand Ingelmo 1998; Zubillaga 2001 and Perez et. al. 2006). Due to high moisture \n\n\n\ncontent, sewage sludges need to be mixed with dry materials (such as sawdust, \n\n\n\nvegetal remains, straw), which act as bulking agents, absorbing the moisture \n\n\n\nand providing the composting mass with an appropriate degree of sponginess \n\n\n\nand aeration (Sanchez Monedero et al. 2001; Iranzo et al. 2004; Tremier et al. \n\n\n\n2005). Therefore there is a potential for composting oil palm wastes with sludge \n\n\n\nto produce composts, physically similar to peat.\n\n\n\n Currently, these organic waste materials have not been fully utilized on \n\n\n\na large scale either agriculturally or industrially, for manufacture of useful \n\n\n\nby-products. Research in recycling of oil palm wastes and sewage sludge is \n\n\n\nimportant in reducing waste management problems and conserve plant nutrients. \n\n\n\nHowever, the amount of sludge added need to be restricted due to its content \n\n\n\nof heavy metals. The resulting compost would be suitable in potting media for \n\n\n\nD.R. Kala, A.B. Rosenani,  C. I Fauziah & L.A. Thohirah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 79\n\n\n\ngrowth of ornamental plants in Malaysia due to prohibited use of sewage sludge \n\n\n\nfor food crops. Presently, no work has yet been done on composting of oil palm \n\n\n\nwastes with sewage sludge. Therefore, this study was conducted to determine the \n\n\n\noptimum mixture of oil palm wastes (EFB, frond and trunk) and sewage sludge \n\n\n\nthat will produce compost suitable for use in potting media of ornamental plants. \n\n\n\nThe resulting compost should be fine textured, odourless, rich in macro- and \n\n\n\nmicronutrients and with acceptable levels of heavy metals. It may partially or \n\n\n\nfully substitute peat in the normally used potting media for ornamental plants.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSeveral whole EFB\u2019s trunk chips and fronds were collected from an oil palm \n\n\n\nplantation, Durian Estate, Golden Hope Sdn Bhd, Selangor. They were first \n\n\n\nmanually chopped into small pieces and then shredded with a mechanical shredder \n\n\n\ninto smaller pieces of 6-10 cm to hasten composting process. Dewatered sewage \n\n\n\nsludge was collected from Indah Water Konsortium (IWK) wastewater treatment \n\n\n\nplant. The chemical compositions of the oil palm wastes and sewage sludge used \n\n\n\nin this experiment were as given in Table 1. The pH of oil palm wastes was almost \n\n\n\nneutral, whilst the sludge had a pH of 5.24. Total nitrogen content in oil palm \n\n\n\nwastes ranged from 0.75 to 1.10 % N and C/N ratio of 46.25 to 69.71, whereas \n\n\n\nthe sludge had 2.82 % N and C/N ratio of 13.26. The Ca and P contents in the \n\n\n\nsewage sludge were higher than the oil palm wastes. However P content in the \n\n\n\ntrunk was lower than the EFB and frond. Magnesium content in sludge was lower \n\n\n\nthan the oil palm wastes. The micronutrient contents (Pb, Cd, Mn, Zn, Fe and \n\n\n\nCu) in sludge was higher than in the oil palm wastes, however the concentrations \n\n\n\ndid not exceed the maximum permitted concentration in sludges according to \n\n\n\nthe CEC (1986). The treatments for this experiment consisted of types of oil \n\n\n\npalm wastes (EFB, frond and trunk) which were mixed with sewage sludge in 3 \n\n\n\nratios (v:v), i.e. 1:0 (control), 4:1 and 3:1 and 5 replicates. The experiment was \n\n\n\nconducted in a glasshouse (28 - 31oC) and laid-out in a randomized complete \n\n\n\nblock design using a white polystyrene box measuring 0.6 m (length), 0.5 m \n\n\n\n(width) and 0.4 m (height). The shredded oil palm wastes were mixed manually \n\n\n\nwith sewage sludge according to the treatments and then placed in a polystyrene \n\n\n\nbox up to 75% volume of the box. Water was sprinkled onto the compost to keep \n\n\n\nthe mixture moist to about 60 % moisture content and composted for 12 weeks. \n\n\n\nDuring composting, the mixture was turned every three days and water was \n\n\n\nsprayed when there was lost in moisture. The composting process was completed \n\n\n\nwhen the temperature at the center of the compost heap had cooled down to the \n\n\n\nambient air temperature, 29oC. The composting materials were monitored for \n\n\n\nchanges in physical (colour, odour, temperature) and chemical properties (total C, \n\n\n\ntotal N, mineral N) during the composting process. A sample of the compost from \n\n\n\neach box was taken weekly during turning, and divided into two portions. One \n\n\n\nof which was immediately frozen for NH\n4\n\n\n\n+-N and (NO\n3\n\n\n\n- - N + NO\n2\n\n\n\n- -N) analysis, \n\n\n\nwhile the other was air-dried and ground for other chemical analysis. At the end \n\n\n\nof 12 weeks of composting, volume reduction were calculated and samples were \n\n\n\nOil Palm Wastes and Sewage Sludge Co-Compost\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200980\n\n\n\nanalysed for chemical properties (pH, total N, total C and macronutrients and \n\n\n\nheavy metals concentration)\n\n\n\n The presences of obnoxious odour in the compost were recorded. Compost \n\n\n\ncolour (white or gray colour), due to the growth actinomycetes was also monitored \n\n\n\nthrough visual observation during weekly sampling of the compost. Percentage \n\n\n\nof volume reduction was calculated by taking the difference between the volume \n\n\n\nof the composting material at the beginning and volume of the compost after the \n\n\n\ncomposting process of 12 weeks. The pH was determined in the suspension of \n\n\n\n1:5 (w/v) compost: deionized water using pH-meter (Mettler MP 225). Compost \n\n\n\nmoisture content was determined by gravimetric method whereby 10 g of air-\n\n\n\ndried compost was placed in the oven at 105oC for 24 hours. The samples were \n\n\n\nthen removed from the oven and placed at room temperature to cool off. The \n\n\n\nweight of the oven-dried compost was recorded.\n\n\n\nTable 1\n\n\n\nChemical characteristics of the raw materials used for the composting experiment (n=3).\n\n\n\n  Organic C was determined according to the combustion method (McKeague, \n\n\n\n1976). One gram of compost was placed in a crucible and put into a furnace at 350 \noC for an hour. The temperature was then raised to 550oC and left for 4 hours. The \n\n\n\nremaining ash was weighed and organic C was calculated from the loss in weight \n\n\n\nduring ashing. Total N was determined using the Kjeldhal method (Bremner and \n\n\n\nMulvaney, 1982). Total analysis of the heavy metals and macronutrients were \n\n\n\ndetermined using the aqua-regia method. The extraction solution was made using \n\n\n\nHCl and HNO\n3\n solution (3:1). Heavy metals (Pb, Cd, Cu, Ni, Mn, Zn, and Fe) \n\n\n\nand macronutrients (Mg, Ca and K) in the solution were determined using atomic \n\n\n\nabsorption spectrophotometer, Model PE 5100. Mineral N (NH\n4\n\n\n\n+-N and NO\n3\n\n\n\n- -N \n\n\n\n+ NO\n2\n\n\n\n- -N) was determined according to Bremner (1965). A sub-sample of 10 \n\n\n\ng was removed for extraction of mineral N with 40 ml of 2 M KCl. The sample \n\n\n\n\n\n\n\nParameter     EFB   Frond  Trunk  Sludge \n\n\n\npH        7.30 a     6.70 a    6.80 a    5.24 b  \n\n\n\nC %   50.87 a  52.28 a  52.18 a  37.41 b \n\n\n\nN %      1.10 b    0.75 c                  0.77 c    2.82 a \n\n\n\nC/N   46.25 b              69.71 a   67.77 a  13.26 c \n\n\n\nCa %     0.17 b    0.17 b     0.15 b    0.83 a \n\n\n\nMg %     0.13 a    0.12 a     0.13 a    0.09 b \n\n\n\nK %      2.06 a    1.63 b     1.46 c     0.08 d \n\n\n\nP %     0.11 b    0.08 b          0.05 c       0.63 a \n\n\n\nPb  (mg.kg\n-1\n\n\n\n)     7.67 b    7.37 b     5.33 b               68.00 a \n\n\n\nCd  (mg.kg\n-1\n\n\n\n)     1.30 b    1.33 b     0.56 c     3.50 a \n\n\n\nMn (mg.kg\n-1\n\n\n\n)              42      b   47     b   39      b              257     a \n\n\n\nZn  (mg.kg\n-1\n\n\n\n)  37      b   38     b   94      b             1322    a \n\n\n\nFe  (mg.kg\n-1\n\n\n\n)     1076  b              1090     b  951     b            19000   a \n\n\n\nCu  (mg.kg\n-1\n\n\n\n)     8     b      9    b    13     b              178     a \n\n\n\nMeans with different letters within the row indicate significant differences (p<0.05) using \n\n\n\nDuncan\u2019s Multiple Range Test. \n\n\n\nEFB- Empty fruit bunch \n\n\n\nD.R. Kala, A.B. Rosenani,  C. I Fauziah & L.A. Thohirah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 81\n\n\n\nwas shakened for an hour, then 10 ml of the filtered extract was distilled with \n\n\n\nMgO for NH+\n\n\n\n4\n-N and Devarda\u2019s alloy for NO-\n\n\n\n3\n-N and NO-\n\n\n\n2\n-N, and collected in \n\n\n\nboric acid. Titration was done with 0.0025 M HC1. All experimental data were \n\n\n\nanalysed statistically using analysis of variance (ANOVA). Duncan\u2019s multiple \n\n\n\nrange test (DMRT) was used for comparison of treatment means when F values \n\n\n\nwere significant at p<0.05.\n\n\n\n\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nCompost Characteristics during Composting\n\n\n\nTemperature Patterns: The temperature pattern shows the microbial activity and \n\n\n\nthe occurrence of the composting process (Bernal et al. 2009). The optimum \n\n\n\ntemperature range for composting is 40\u201365oC (de Bertoldi et al. 1983), temperatures \n\n\n\nabove 55oC are required to kill pathogenic microorganisms.  In this study, it was \n\n\n\nobserved that the compost did not reach thermophilic stage (> 45 oC) (Fig. 1). This \n\n\n\ncould probably be due to the dissipating of heat due to small volume. Moreover, \n\n\n\nthe materials, especially the EFB, were light and spongy and easily compacted. \n\n\n\nHowever the addition of sewage sludge increased the temperature compared to \n\n\n\nthe control EFB, frond and trunk represented as (E\n1:0\n\n\n\n, F\n1:0\n\n\n\n and T\n1:0\n\n\n\n). Generally, \n\n\n\ntemperatures in all the compost mixture were in the range of   28.1 oC to 43.3 \noC. There were similar patterns of temperature variations during composting in \n\n\n\nall the treatments. In this study, it was found that the EFB mixtures resulted in a \n\n\n\nmore rapid rise in temperature within 48 hours compared to the trunk and frond \n\n\n\nmixtures and reached a maximum temperature of 41.5oC after 1 week similar to \n\n\n\nthose reported by Thambirajah (1988). This rapid increase indicates an intensive \n\n\n\nmicrobial activity reflecting a higher degradation rates occurring during the first \n\n\n\nstage. Finally, temperature decreased and stabilized at 28.0 oC to 30.2 oC after \n\n\n\n60 days, during the maturation phase. At this stage, the bio-oxidation phase of \n\n\n\ncomposting was considered completed (Hachicha et al. 2008).\n\n\n\nFig. 1. Changes in temperature of the composting blends of oil palm wastes (E=EFB, \n\n\n\nF=frond and T=trunk) and sewage sludge at different ratios (1:0, 3:1 and 4:1) during 12 \n\n\n\nweeks of composting.\n\n\n\nOil Palm Wastes and Sewage Sludge Co-Compost\n\n\n\n25\n\n\n\n27\n\n\n\n29\n\n\n\n31\n\n\n\n33\n\n\n\n35\n\n\n\n37\n\n\n\n39\n\n\n\n41\n\n\n\n43\n\n\n\n45\n\n\n\n0 10 20 30 40 50 60 70 80 90\n\n\n\nd ays o f co m p o stin g\n\n\n\no\nC\n\n\n\nE  1 :0 E  3 :1\nE  4 :1 F  1 :0\nF3 :1 F 4:1\nT 1 :0 T  3:1\nT 4 :1 A m b ie n t\n\n\n\nT\ne\n\n\n\nm\np\n\n\n\ne\nra\n\n\n\ntu\nre\n\n\n\n (\n  \n\n\n\n C\n)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200982\n\n\n\nC/N Dynamics: Initial C/N ratios of the compost mixture were higher than 30 (Fig. \n\n\n\n2). However C/N ratio decreased in each treatment as time progressed and this is \n\n\n\ndue to the mineralization of organic matter by microorganisms (Bernal et al. 1998; \n\n\n\nBrewer and Sullivan, 2003; Grigatti et al. 2004; Tognetti, et al. 2007).  Initial \n\n\n\nvalues (C/N of 34.0 to 69.7) dropped to 18.96 to 41.50 at the end of biological \n\n\n\nprocess (12 weeks). Addition of sewage sludge showed significant differences \n\n\n\n(p<0.05) in C/N ratios among the treatments during composting period. The frond \n\n\n\nand trunk composts (F\n1:0\n\n\n\n and T\n1:0\n\n\n\n) still had high C/N ratio even after 12 weeks of \n\n\n\ncomposting. The possible reason for the C/N ratio to go up in the intermediate \n\n\n\nstage of the composting period could be due to greater nitrogen loss than \n\n\n\ncarbon. According to Hutchings (1985), the evolution of nitrogen content during \n\n\n\ncomposting would be conditioned not only by the quantity of total nitrogen and \n\n\n\nits mineralization rate, but also by the loss of this element through volatilization, \n\n\n\ndenitrification and immobilization process that may occur in the compost. The \n\n\n\nEFB and trunk + sludge composts had C/N ratio ranging from 18.96 to 22.16. This \n\n\n\nis within the recommended value by the Council of European Communities (CEC \n\n\n\n1986) for compost (C/N< 22).\n\n\n\nMineral N (NH\n4\n+-N and NO3- - N + NO\n\n\n\n2\n- -N)\n\n\n\nFig. 3 shows changes in NH\n4\n + - N concentration in the different compost mixture \n\n\n\nover a period of 12 weeks. In general, NH\n4\n\n\n\n+-N concentration in the various \n\n\n\nmixtures increased with time. The compost in E\n3:1\n\n\n\n and E\n4:1\n\n\n\n exhibited a distinct \n\n\n\nincrease in the concentration of  NH\n4\n\n\n\n+-N from 2 to 6 weeks and drop in the \n\n\n\nconcentration of NH\n4\n\n\n\n+-N after week 6 in treatments E\n1:0\n\n\n\n and E\n3:1\n\n\n\n.  In treatment \n\n\n\nE\n4:1\n\n\n\n the drop in ammonium concentration was after week 8. Whereas, treatment \n\n\n\nF\n4:1\n\n\n\n and F\n3:1\n\n\n\n showed an increase in NH\n4\n\n\n\n+-N up to week 8 and 10, respectively, and \n\n\n\nthen decreased. Decreased NH\n4\n\n\n\n+-N concentrations towards stable values at the \n\n\n\nend of the thermophilic phase have also been reported by other authors (Laos et \n\n\n\nal. 2002; Levanon and Pluda, 2002; Banegas et al. 2007). However, the rate of \n\n\n\nammonification  in compost mixtures T\n1:0\n\n\n\n,  T\n3:1, \n\n\n\nand  T\n4:1\n\n\n\n showed a slow increase \n\n\n\nin the initial 6 weeks period and a distinct increase there after, without a drop in  \n\n\n\nNH\n4\n\n\n\n+-N up to the 12th week . \n\n\n\n Fig. 4 shows the changes in NO\n3\n\n\n\n--N + NO\n2\n\n\n\n--N concentration in the various \n\n\n\ntreatments. The compost mixtures initially contained low  nitrates (i.e 0.6 to 3.6 \n\n\n\nmg/kg), but at the maturation phase, where temperature decreases to mesophilic \n\n\n\nand subsequently, ambient levels, nitrification reactions, in which ammonia (a \n\n\n\nby-product from waste stabilization) is biologically oxidized to become nitrite \n\n\n\n(NO\n2\n\n\n\n--N) and finally nitrate (NO\n3\n\n\n\n- -N) take place.  Generally, all the treatments \n\n\n\nin this study exhibited an increasing trend in NO\n3\n\n\n\n-- N + NO\n2\n\n\n\n--N concentration \n\n\n\nthroughout the 12 weeks. This was also observed by other authors (Parkinson et \n\n\n\nal. 2004; Huang et al. 2004; Banegas et al. 2007). \n\n\n\nD.R. Kala, A.B. Rosenani,  C. I Fauziah & L.A. Thohirah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 83\n\n\n\nFig. 2. Changes in C/N ratio of the composting blends of oil palm wastes (E=EFB, \n\n\n\nF=frond and T=trunk) and sewage sludge at different ratios (1:0, 3:1 and 4:1) during 12 \n\n\n\nweeks of composting.\n\n\n\nFig. 3. Changes in NH\n4\n + - N concentration of the composting blends of oil palm wastes \n\n\n\n(E=EFB, F=frond and T=trunk) and sewage sludges at different ratios (1:0, 3:1 and \n\n\n\n4:1) during 12 weeks of composting.\n\n\n\nOil Palm Wastes and Sewage Sludge Co-Compost\n\n\n\n15\n\n\n\n25\n\n\n\n35\n\n\n\n45\n\n\n\n55\n\n\n\n65\n\n\n\n75\n\n\n\n0 2 4 6 8 10 12\nweeks\n\n\n\nC\n/N\n\n\n\n r\na\n\n\n\nti\no\n\n\n\nE 1:0 E 3:1 E 4:1\n\n\n\nF 1:0 F3:1 F4:1\n\n\n\nT 1:0 T3:1 T4:1\n\n\n\n\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n0 2 4 6 8 10 12\nweeks\n\n\n\nN\nH\n\n\n\n4\n+\n- \n\n\n\nN\n m\n\n\n\ng\n.k\n\n\n\ng\n-1\n\n\n\nE 1:0 E 3:1 E 4:1\nF 1:0 F 3:1 F 4:1\nT 1:0 T 3:1 T 4:1\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200984\n\n\n\nFig. 4. Changes in  NO\n3\n\n\n\n- -N and NO\n2\n\n\n\n- -N of the composting blends of oil palm wastes \n\n\n\n(E=EFB, F=frond and T=trunk) and sewage sludges at different ratios (1:0, 3:1 and \n\n\n\n4:1) during 12 weeks of composting.\n\n\n\nChanges in Composts Characteristics\n\n\n\nPhysical Changes: At the beginning of the composting process, putrefaction \n\n\n\nodours occurred in the oil palm wastes with sewage sludge mixtures. Nitrogen \n\n\n\nand sulfur compounds (amines and H\n2\nS) are the cause of malodour. However, this \n\n\n\nodour decreased and produced humus-like odour as the composts matured with \n\n\n\ntime. After 12 weeks, composts with added sludge had darker colour compared to \n\n\n\nthe controls. Mathur et al. (1993), reported that the C=O group of quinones and \n\n\n\nketones in conjugation caused the dark colour of humic subtances. The trunk + \n\n\n\nsludge composts had finer particle size similar to peat (< 20 mm). This is within \n\n\n\nthe recommended level of CEC (1986). The EFB and frond + sludge composts \n\n\n\nwere still fibrous with long strands (> 40 mm) and did not look matured. Therefore, \n\n\n\nthese composts would need to be composted more than 12 weeks to achieve finer \n\n\n\nparticle size.  \n\n\n\nVolume Reduction: The percentage of volume reduction of compost mixtures after \n\n\n\n12 weeks of composting process varies widely from 10.6 to 47.0 % (Table 2), \n\n\n\ndepending upon the initial moisture content of the particular compost volume \n\n\n\nand starting materials used. According to Bernal et al. (2009), during the active \n\n\n\nphase of the composting process the organic-C decreases in the material due to \n\n\n\ndecomposition of the OM by the microorganisms. This loss of OM reduces the \n\n\n\nweight of the pile and decreases the C/N ratio. There were significant differences \n\n\n\n(p<0.05) in volume reduction between the treatments compared to the control. \n\n\n\nThe higher percentage of volume reduction was seen in the EFB compost E\n4:1\n\n\n\n\n\n\n\nand E\n3:1 \n\n\n\n(44.9 and 47.0%, respectively) compared to trunk and frond compost. \n\n\n\nAccording to Hashim et al. (1993) decomposition of EFB during 12 months \n\n\n\nresulted in loss of physical structure and showed an inferior growth of plants due \n\n\n\nD.R. Kala, A.B. Rosenani,  C. I Fauziah & L.A. Thohirah\n\n\n\n\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n0 2 4 6 8 10 12\nweeks\n\n\n\nN\n0\n\n\n\n3- \n+\n\n\n\n N\n0\n\n\n\n2-  m\ng\n k\n\n\n\ng\n-1\n\n\n\nE 1:0 E 3:1 E 4:1\nF 1:0 F 3:1 F 4:1\nT 1:0 T 3:1 T 4:1\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 85\n\n\n\nto poor water holding capacity and poor anchorage in plants as a potting media. \n\n\n\nThe lower percentage of volume reduction for frond and trunk were characterised \n\n\n\nby a more compacted and bulky initial material than the EFB which was spongy.\n\n\n\nTable 2.\n\n\n\nChemical characteristics of the oil palm wastes (E=EFB, F=frond and T=trunk) and sewage \n\n\n\nsludge at different ratios (1:0, 3:1 and 4:1) after 12 weeks of composting (n=5).\n\n\n\npH: According to Zucconi et al. (1987), organic substrates having a wide range of \n\n\n\npH levels (pH 3 to 11) can be composted. Availability of plant nutrients is affected \n\n\n\nto a great extent by the pH of the growth medium. Although the composting process \n\n\n\nis relatively insensitive to pH, because of the wide range of organisms involved \n\n\n\n(Esptein et al. 1977), the optimum pH range for compost appears to be 6.5 to 8.5 \n\n\n\n(Jeris and Regan 1973: Willson 1993). Compost mixtures with high pH however \n\n\n\ncan lead to loss of N as NH\n3\n and odour problems (Miller et al. 1991). In this \n\n\n\nstudy, initially the compost mixture had pH ranging from 6.2 to 7.3. As bacteria \n\n\n\nand fungi digest organic material, they release organic acids. In the early stages of \n\n\n\ncomposting, these acids often accumulate. According to Hachicha et al. (2008), \n\n\n\nthe resulting drop in pH encourages the growth of fungi and breakdown of lignin \n\n\n\nand cellulose. The pH of the compost mixtures after 12 weeks of composting \n\n\n\nranged from 5.8 to 6.9 were within the recommended level by CEC (1986) for \n\n\n\ncompost (5.5 to 8.0). \n\n\n\nC/N ratio: There were significant (p<0.05) differences in the C/N ratio between \n\n\n\nthe compost mixtures (Table 2). The frond and trunk composts in control, F\n1:0\n\n\n\n\n\n\n\nand T\n1:0\n\n\n\n still had high C/N ratio even after 12 weeks of composting. The EFB \n\n\n\nand trunk + sludge composts had C/N ratio ranging from 18.96 to 22.16. This is \n\n\n\nwithin the recommended value by CEC (1986) for compost. Addition of sludge to \n\n\n\nthe oil palm wastes resulted in significantly (p<0.05) lower C/N ratio in the final \n\n\n\ncompost than the control. However, there were no differences in C/N ratio in the \n\n\n\nfinal compost of EFB and trunk added with sludge (3:1 and 4:1). \n\n\n\n\n\n\n\nParameter E1:0 E3:1 E4:1 F1:0 F3:1 F4:1 T1:0 T3:1 T4:1 \n\n\n\npH 6.9 a 6.7 a 6.9 a 6.1 b 5.8 c 6.0 bc 6.3 b 6.1 b 6.2 b \n\n\n\nVol. rdn. 19.7 b 44.9 a 47.0 a 12.6 c 18.2 b 18.8 b 10.6 d 15.6 b 14.8 bc \n\n\n\nN % 1.48 c 1.78 c 1.93 b 1.22 f 1.63 d 1.45 e 1.18 f 2.04 a 2.05 a \nC/N 32.6 b 21.83 cd 22.16 \n\n\n\ncd \n\n\n\n41.5 a 29.67 b 24.6 c 41.24 a 19.0 d 18.98 d \n\n\n\nCa % 0.320 g 0.440 e 0.420 e 0.350 f 0. 520 c 0.489d 0.287 h 0.645 b 0.702 a \nMg % 0.260 c 0.40 a 0.330 b 0.180 f 0.220 e 0.180 f 0.160 f 0.250 d 0.230 d \n\n\n\nK % 2.11 bc 4.03 a 2.46b 1.89 cd 2.21 bc 2.05 bc 1.32 f 1.66 de 1.39 ef \n\n\n\nP % 0.428 c 0.585 d 0.469 e 0.444 e 1.025 a 0.808 c 0.330 f 0.885 b 0.64 d \nPb  (mg.kg-1) 9.35 h 62 a 34.73 b 9.97 h 29.0 d 26.35 f 15.33 g 33.19 c 26.35 e \n\n\n\nCd  (mg.kg-1) 1.53 e 3.9 a 3.43 b 2.0 d 3.32 b 2.97 c 0.96 f 2.78 c 1.63 e \nMn (mg.kg-1) 46.1 c 108 a 99.43 \n\n\n\nab \n\n\n\n52.8 c 98.46 \n\n\n\nab \n\n\n\n84.85 b 46.88 c 87.97b 92.64 \n\n\n\nbb \n\n\n\nZn  (mg.kg-1) 112 g 881 a 723 c 66 h 675 d 495 e 188 f 829 b 671 d \nFe  (mg.kg-1) 3163 cd 7335 a 5322 ab 1201 d 5239 ab 4692 bc 1205 d 6794 ab 6310 ab \n\n\n\nCu  (mg.kg-1) 17.88 d 68.31 b 67.63 b 9.5 f 53.33 b 52.33 b 14.67 e 78.6 a 68.83 b \n\n\n\nVol. rdn - volume reduction \n\n\n\nMeans with different letters within the row indicate significant differences (p<0.05) using \n\n\n\nDuncan\u2019s multiple range test \n\n\n\n\n\n\n\nOil Palm Wastes and Sewage Sludge Co-Compost\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200986\n\n\n\nTotal Macronutrients Content \n\n\n\nTotal N content was significantly (p<0.05) higher in the trunk + sludge composts, \n\n\n\nT\n3:1\n\n\n\n and T\n4:1\n\n\n\n followed by the EFB + sludge composts, E\n4:1\n\n\n\n and E\n3:1\n\n\n\n and frond + \n\n\n\nsludge composts, (F\n3:1\n\n\n\n and F\n4:1\n\n\n\n). Addition of sludge to the oil palm wastes resulted \n\n\n\nin significantly higher (p<0.05) total N in the final compost than the control. \n\n\n\nKulhman, (1989) reported that total N content of composts is from 0.5 to 2.7 %. \n\n\n\nIn this study the final compost of oil palm wastes + sludge had total N content \n\n\n\nranging from 1.18 to 2.05 % (Table 2) which was more than the recommended \n\n\n\nlevel by CEC (1986) for compost (0.6 % N). Phosphorus concentration was highest \n\n\n\nin the frond composts, F\n3:1 \n\n\n\n(1.025 %) followed by trunk compost, T\n3:1\n\n\n\n (0.885 %). \n\n\n\nAll the composts had more than the recommended P content (0.5 %) by CEC \n\n\n\n(1986), compared to E\n1:0\n\n\n\n, F\n1:0\n\n\n\n, T\n1:0\n\n\n\n and E\n4:1\n\n\n\n. Compost + sludge ratio 3:1 had higher \n\n\n\nP concentration than 4:1 ratio, obviously due to the higher P content of sludge \n\n\n\ncompared to the oil palm wastes. According to Taiz and Zeiger (1991) P plays \n\n\n\nan important role in ATP buildup in plants. Phosphorus is the main component \n\n\n\nin the production of nucleic acids, nucleotides, co-enzymes, phospholipids and \n\n\n\nphytic acids. According to Morgan (1998), insufficient P concentration in plants \n\n\n\ncould lead to stunted growth and stem and leaves turning to purple. Generally, K \n\n\n\nconcentration in this study was more than the recommended level by CEC (1986) \n\n\n\nin compost (0.3 % K). The percentage of K was highest in the EFB compost \n\n\n\nE\n3:1\n\n\n\n (4.026 %). This could be due to the initial higher K in EFB than the frond \n\n\n\nand trunk. The frond compost, F\n3:1\n\n\n\n and F\n4:1\n\n\n\n had higher K concentrations than \n\n\n\nthe trunk compost, T\n3:1\n\n\n\n and T\n4:1\n\n\n\n. Calcium concentration was higher in composts \n\n\n\nwith sewage sludge than the controls. This could be due to higher Ca content \n\n\n\nof sludge compared to the oil palm wastes. The Ca content was significantly \n\n\n\nhigher (p<0.05) in the trunk + sludge composts, T\n3:1\n\n\n\n and T\n4:1\n\n\n\n (0.705 and 0.635%, \n\n\n\nrespectively) followed by the frond + sludge compost, F\n3:1 \n\n\n\nand F\n4:1\n\n\n\n (0.523 and \n\n\n\n0.477%, respectively). However, the Ca content in this study was less than 2.0%, \n\n\n\nwhich is the recommended level by CEC (1986). According to Eysinga et al., \n\n\n\n(1980), Ca deficiency in plants could lead to smaller flower, stunted growth and \n\n\n\ndeformation of petals. The concentration of Mg was highest in the EFB compost, \n\n\n\nE\n3:1\n\n\n\n followed by E\n4:1\n\n\n\n. The Mg content in this study was less 0.3% which is the \n\n\n\nrecommended level in compost by CEC (1986), except for treatments in E\n3:1\n\n\n\n\n\n\n\nand E4:1. The trunk composts, T\n3:1\n\n\n\n and T\n4:1\n\n\n\n had Mg concentration of 0.237 and \n\n\n\n0.229 %, respectively. The frond compost, F\n3:1\n\n\n\n and F\n4:1\n\n\n\n had 0.207 and 0.182 % \n\n\n\nMg concentration, respectively. The composts, F\n1:0\n\n\n\n, F\n4:1\n\n\n\n and T\n1:0 \n\n\n\nhad lower Mg \n\n\n\ncontent than the other composts. According to Bernal et al. (2009), as a result \n\n\n\nof the dry weight loss of the material during composting, the concentration of \n\n\n\nmineral elements increases. Generally addition of sludge to oil palm wastes had \n\n\n\nsignificantly increased the total macronutrient content of the oil palm wastes + \n\n\n\nsludge composts compared to the control. \n\n\n\nTotal Heavy Metals Content \n\n\n\nComposts added with sludge contained 26.35 to 62.0 mg kg-1 Pb, 495.0 to 881.0 \n\n\n\nmg kg-1 Zn, 4692 to 7335 mg kg-1 Fe, 87.97 to 107.99 mg kg-1 Mn, 1.63 to 3.90 \n\n\n\nD.R. Kala, A.B. Rosenani,  C. I Fauziah & L.A. Thohirah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 87\n\n\n\nmg kg-1 Cd and 52.33 to 76.45 mg kg-1 Cu (Table 2). Generally, addition of sludge \n\n\n\nto oil palm wastes had significantly increased the heavy metal contents of the oil \n\n\n\npalm wastes + sludge composts compared to the control due to the higher heavy \n\n\n\nmetal contents in sewage sludge. Compost mixtures with 3:1 ratio had higher Pb, \n\n\n\nZn and Cd concentrations than 4:1 ratio but were within the recommended level \n\n\n\nby CEC (1986), for compost. \n\n\n\n\n\n\n\nCONCLUSION\n\n\n\nAfter 12 weeks of composting oil palm wastes with sewage sludge, most of the \n\n\n\nbiodegradable fraction of EFBs, fronds and trunks had decomposed and the most \n\n\n\nresistant fraction remained. Compared to EFBs and trunks, the fronds had higher \n\n\n\npercentage of C/N ratio after 12 weeks, which ranged from 18.96 to 41.40. Supply \n\n\n\nof microorganism from the addition of sewage sludge and higher N availability had \n\n\n\nhastened the composting process. The high amounts of available micronutrients \n\n\n\nreleased in the oil palm wastes with sewage sludge compost could be good source \n\n\n\nof nutrients for plants and could be used as slow release nutrient source for a \n\n\n\nlong duration, throughout the whole vegetative and flowering stages in plants. At \n\n\n\nweek 12, the trunk composts had finer particle size similar to peat. However the \n\n\n\nEFB and frond + sludge composts were still fibrous with long strands and did not \n\n\n\nlook matured and may need to be composted more than 12 weeks to achieve finer \n\n\n\nparticle size. \n\n\n\n Generally, the trunk + sludge composts seemed to be better than the EFB + \n\n\n\nsludge and frond + sludge composts. Total N and Ca contents were highest in the \n\n\n\ntrunk + sludge compost at week 12. 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Biosolids compost as component of \n\n\n\n potting media for bedding plants. Gartenbauwissenschaft 66: 304\u2013309.\n\n\n\nZucconi, F. and M.D. Bertoldi. 1987. Compost specifications for the production \n\n\n\n and characterization of compost from municipal solid waste. In: Bertoldi \n\n\n\n M.D., Ferranti, M.P., Hermite, P.L. and Zucconi, F. (eds). Compost: \n\n\n\n production, quality and use. Elsevier Applied Science, London. pp 30-50.\n\n\n\nOil Palm Wastes and Sewage Sludge C0-Compost\n\n\n\n\n\n"
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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 179-185  \n \n\n\n\n179 \n \n\n\n\nMethane Emission Under Alternative Irrigation Regimes in \nMalaysian Rice Cultivation   \n\n\n\n  \nNur Fitriah Pauzai1, Muhammad Firdaus Sulaiman1*, Adibah Mohd Amin1, \n\n\n\nNur Azleen Jamal Jaganathan1, Amalia Mohd Hashim2, Mohd Fairuz Md Suptian3 \n  \n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia,  \n43400 Serdang, Selangor, Malaysia  \n\n\n\n2Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, UPM, \n43400 Serdang, Selangor, Malaysia  \n\n\n\n3Agrobiodiversity & Environment Research Centre, MARDI, Serdang, Selangor 43400, Malaysia  \n  \n\n\n\n*Correspondence: muhdfirdaus@upm.edu.my   \n  \n\n\n\nABSTRACT  \nRice cultivation under continuous flooding (CF) is a major anthropogenic emitter of methane gas (CH4) \ndue to the oxygen-deprived state of the submerged soil. The potentials of alternative irrigation regimes \ni.e., mid-season drainage (MD) and alternate wetting and drying (AWD) to reduce CH4 emissions from \nMalaysian rice cultivation were investigated in the present study. Rice (Oryza sativa var. MR297) was \ntransplanted into 15 tanks and randomly assigned to each of the three treatments: CF, MD and AWD \nin a randomized complete block design (RCBD).  Emissions of CH4 were measured weekly by collecting \nair samples using static chambers and analyzing the air samples for CH4 concentration using gas \nchromatography (GC). The present study found that cumulative CH4 emissions per planting cycle were \n70.24, 30.75, and 15.93 g CH4 m\u22122 from treatments CF, AWD and MD, respectively. Methane emissions \nof the MD and AWD treatments were 77.07% and 57.81% lower, respectively, compared to CH4 \nemissions of CF. The present study indicated that AWD and MD had the potential to reduce CH4 \nemission in rice cultivation.  \n  \nKey words: Methane, paddy rice, continuous flooding (CF), mid-season drainage (MD), \nalternate wetting and drying (AWD)   \n  \n\n\n\nINTRODUCTION  \nPaddy rice (Oryza sativa) is one of the most important crops in the world. More than half of \nthe world's population consume rice on a daily basis, resulting in a global consumption of \naround 486.62 Mt between 2018 and 2019 (FAO, 2018). To meet the enormous demand for \nrice, there are approximately 167.25 million ha of rice paddy fields worldwide, the majority of \nwhich are in the Asia Pacific region (FAO, 2008). Malaysia's rice production in 2021 was \n2,428,893 Mt from 647,859 ha of planted area, which is an increment of 3.1% compared to \n2020.   \n  \nConventional rice cultivation practices involve flooding the rice fields for irrigation and to \nsuppress weed growth. Flooded rice fields prevent oxygen from penetrating into the soil, \ncreating an anaerobic condition in the paddy soil. This condition fosters the growth of \nmethanogenic bacteria that produce CH4. The longer the flooding lasts, the more methanogens \naccumulate, the more CH4 are produced (WRI, 2014). Methane is a potent greenhouse gas that \nhas a higher global warming potential (GWP) compared to carbon dioxide (CO2), 28 to 34 \ntimes more on a weight basis (IPCC, 2013).   \n  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 179-185  \n \n\n\n\n180 \n \n\n\n\nAlternate wetting and drying (AWD) is one of the water management techniques that can \npotentially reduce CH4 emissions in paddy fields (Allen and Sander, 2019). AWD controls \nwater usage and supplies it intermittently to paddy fields. Another water management technique \nthat may potentially reduce CH4 emission is mid-season drainage (MD) (Liu et al., 2019). MD \nsupplies water to crops throughout the planting except for about seven days at the end of the \ntillering stage. Both these irrigation practices reduce the duration of soil submergence, therefore \npotentially reducing CH4 emission from rice cultivation. Despite its potentials, AWD and MD \nare not commonly practiced in Malaysia and its effectiveness in Malaysian rice paddy has never \nbeen studied. The present study was conducted to investigate the effectiveness of the two \nirrigation methods in reducing CH4 emissions compared to the conventional continuous \nflooding (CF) on Malaysian rice soil planted with Malaysian rice cultivar.  \n\n\n\n  \nMATERIALS AND METHODS  \n\n\n\n  \nField setup   \nThe study was conducted at the Faculty of Agriculture, Universiti Putra Malaysia, Serdang, \nSelangor, Malaysia (2\u00b0 59' 05.1\"N 101\u00b0 43' 59.4\"E).  Rice (Oryza sativa var. MR 297) was \nplanted under a rain shelter where germinated seedlings were transplanted into polyethylene \ntanks 14 days after sowing. This variety was chosen as it has a relatively short maturity period \nof 110 days and was developed to resist diseases such as bacterial panicle blight. The tanks \nwere filled with soil obtained from a paddy field in Pendang, Kedah. The soil used is of the \nBriah series (fine, mixed, isohyperthermic, typic endoaquepts). The soil particle size \ndistribution was 54.20% clay, 39.14% silt, and 6.65% sand. The mean total carbon, nitrogen, \nand sulphur of the soil was 1.95%, 0.13%, and 0.08% respectively, while the soil pH ranged \nbetween 5.04 to 5.61.   \n  \nExperimental Unit Setup  \nFifteen units of polyethylene tanks each measuring 40 cm \u00d7 50 cm (d \u00d7 h) were used in the \nstudy. Three treatments with five replications for each treatment were assigned to the \nexperiment. The treatments assigned were continuous flooding (CF), alternate wetting and \ndrying (AWD) and mid-season drainage (MD). The details of each treatment is described in \nsection 2.3.  A 25 cm \u00d7 4 cm (l \u00d7 d) polyvinyl chloride (PVC) pipe was installed in each tank, \nwith 15 cm of the pipe buried in the soil to measure the soil water level. The PVC pipes were \ndrilled with 0.5 cm diameter holes to allow water movement into and out of the pipes. A 30 cm \nplastic ruler was attached to the inside wall of each tank to monitor the water levels in the tanks.  \n  \nWater Management Treatments  \nThe tanks in CF treatment were flooded throughout the planting period. From the 1st to the 39th \nday after transplant (DAT), all experimental units were flooded up to 5 cm above the soil \nsurface. From the 40th DAT until the 96th DAT, the tanks were flooded up to 10 cm above the \nsoil surface. Before harvesting the grain, starting from the 97th DAT, all experimental units \nwere not irrigated and were left to dry. Meanwhile, water management for the MD treatment \nwas identical to CF, except from the 50th to 57th DAT, where irrigation was withheld for seven \ndays, after which it resumed as usual. Whereas in the AWD treatment, soil was flooded during \nthe first 20 DAT, where the water level was maintained at 5 cm above the soil. From the 21st \nDAT onward, AWD surface water was left to dry until the water level reached 15 cm below \nthe soil surface. The soil was then irrigated until the water level reached 10 cm above the soil. \nThe treatment was implemented for 75 days and stopped for two weeks during the flowering \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 179-185  \n \n\n\n\n181 \n \n\n\n\nstage. After that, the irrigation resumed until the 96th DAT.  Table 1 provides a brief description \nof the aforementioned treatments.  \n  \nTable 1: Description of the irrigation treatments employed in the study  \n\n\n\nTreatment  Description  \nContinuous flooding (CF)  Water is maintained at 5-10 cm above soil surface at all times until \n\n\n\nharvest.  \nAlternate  wetting-drying \n(AWD)  \n\n\n\nWater is allowed to dry out until soil water level reaches 15 cm \nbelow surface, after which it will be irrigated to reach soil water \nlevel of 10 cm above surface.   \n\n\n\nMid-season drainage (MD)  Water is allowed to dry out from DAT 50 to 57. Apart from that \nperiod, water is maintained at 5-10 cm above soil surface at all \ntimes until harvest.  \n\n\n\n  \nStatic Chamber Setup  \nThe static chamber method was used in this study to measure CH4 emissions (Yu et al., 2013). \nIt is composed of three components; chamber head, collar, and extension. The chambers were \nconstructed from a 6 mm thick acrylic sheet. The dimensions of the collar, chamber, and short \nextension were 23 \u00d7 23 \u00d7 23 cm (l \u00d7 w \u00d7 h), while the size of the long extension was 40 \u00d7 23 \n\u00d7 23 cm (l \u00d7 w \u00d7 h). The top of the chamber head consisted of a sampling port constructed \nusing heparin caps from which air samples were drawn, a 120 mm battery-powered 9V fan, \nand a digital thermometer. The base of the chamber head was affixed with closed-cell foam, \nwhich provided an impermeable seal when placed over the collar. After transplanting the rice \nseedlings, the collars were inserted into the soil with 15 cm of the collar height protruding \nabove the soil surface. The collars served as the permanent base to support the chambers, which \nwere placed on top of the collars. Only the chamber is placed directly on top of the collar during \nthe growth stages where the plant height is <20 cm whereas the extension will be placed \nbetween the collar and the chamber as the plant height increases. A long extension was added \non the 20th to the 34th DAT, then a short extension was added on the 41st to the 110th DAT.  \n  \nMethane Emissions Measurement  \nAir samples were collected weekly throughout the study between 0900 and 1000. Once the \nstatic chamber was placed on the top of the collar, the fan on the chamber was switched on to \nhomogenize the air in the chamber headspace. Air samples from each experimental unit were \ncollected at 10-minute intervals for 30 minutes and transferred to evacuated 12 ml borosilicate \nvials with rubber septum (Labco Exetainer\u00ae, Labco Ltd., Lampeter, UK). Collected air \nsamples were analysed using gas chromatography (GC) (HP6890N Network Gas \nChromatograph, Agilent Technologies, CA, USA). Soil CH4 fluxes (Fcham) expressed in units \nof \u00b5mol m\u22122 s \u22121 were calculated using the following equations (Hutchinson and Livingston, \n1993):  \n \n \n\n\n\nFcham = !\"\n!#\n\u00d7 $%\n\n\n\n&\n\u00d7 '\n\n\n\n$(\n \n\n\n\nWhere \ud835\udf15C/\ud835\udf15t is the rate of change of mixing ratio of the gas of interest (nmol mol-1), Vc (m3) is \nthe chamber headspace volume, A (m2) is the area covered by the chamber, M (g mol-1) is the \nmolecular mass of CH4 and Vm (m3 mol-1) is the molecular volume at chamber temperature and \nbarometric pressure calculated from the ideal gas law.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 179-185  \n \n\n\n\n182 \n \n\n\n\n  \nRESULTS AND DISCUSSION  \n\n\n\nFigure 1 shows the recorded daily water depth fluctuations in CF, MD, and AWD throughout \nthe planting period. CF was continuously flooded from the first DAT until the 98th DAT. The \nrange of water loss from the CF tank was around 3.5-4.5 cm during the vegetative phase, 2-9 \ncm during the reproductive phase, and 5.5-9.4 cm during the ripening phase. MD was flooded \nthroughout the planting period until the 98th DAT but withheld irrigating from the 50th to the \n56th DAT. The range of water loss from the MD tank was around 2.8-4.8 cm during the \nvegetative phase, 3.9-9 cm during the reproductive phase, and 2-9.5 cm during the ripening \nphase. Treatment AWD commenced on the 21st DAT until the 98th DAT, where tanks are left \nirrigated until the water depth reaches 15 cm below the soil surface, after which they were \nirrigated again until 10 cm above the soil surface. On average, it took around three to five days \nfor the water depth to reduce from 10 cm above the soil surface to 15 cm below the soil surface.  \n\n\n\n\n\n\n\n \nFigure 1: Water depth above and below the soil surface of CF, MD, and AWD.  \n\n\n\n  \nFigure 2 shows the mean weekly CH4 fluxes measured from all experimental units. Significant \ndifferences existed between treatments from the third to the sixth week of air sampling. There \nwere no significant differences in CH4 flux rates between treatments on weeks one, seven, and \neight of air sampling. CF showed significantly higher CH4 fluxes in weeks three to six, with \nmean flux rates ranging from 0.003 to 1.476 \u03bcmol m-2 s-1.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 179-185  \n \n\n\n\n183 \n \n\n\n\n \nFigure 2: Mean weekly soil CH4 fluxes measured from the CF, MD and AWD treatments. \nMeans with different letters within each sampling date are significantly different at P<0.05  \n\n\n\n  \nCF and AWD had the highest CH4 flux between weeks and within treatments on week six, with \nmean rates of 1.48 \u00b5mol m-2 and 0.70 \u00b5mol m-2 s-1, respectively. For MD, however, the highest \nbetween weeks and within treatment mean CH4 flux was recorded on week two with a mean \nflux rate of 0.35 \u00b5mol m-2 s-1. Overall, the CH4 flux rates in CF showed increasing trends for \nrice growth, which was low during the vegetative stage and peaked at the end of the \nreproductive stage, then declined after the flowering stage. When the mean weekly fluxes were \ninterpolated and extrapolated to account for daily fluxes, the present study observed a reduction \nof between 57.81% to 77.07% from AWD and MD, respectively, when compared to CH4 \nemissions from CF. The cumulative CH4 fluxes for the growing season were 70.24, 30.75, and \n15.93 g CH4 m-2 for CF, AWD, and MD, respectively.  \n\n\n\n  \nThe constant submerged soil under CF conditions emitted high CH4 emissions compared to \nMD and AWD, especially peaking on the 75th DAT during the flooded flowering stage due to \nthe full development of the aerenchyma system. For MD treatment, the drying period from the \n50th to the 56th DAT reduced CH4 emission from the 50th day to the 70th day. The CH4 emission \nincreased after the 70th until the 95th DAT due to the re-irrigation process. While a series of wet \nand dry cycles under AWD emitted intermediate methane emission, they are still lower when \ncompared to CF. Emissions from AWD peaked at 75th DAT due to flooding during the \nflowering stage.  \n\n\n\n  \nMethane emission trends from CF and AWD coincide with the different stages of rice growth. \nThe high fluxes observed during the flowering stage were similar to other studies (Ma et al., \n2010; Gaihre et al., 2011). Such observation is potentially due to the well-developed \naerenchyma tissue during the flowering stage, which is responsible for plant-mediated CH4 \ntransport (Adhya et al., 1994). This is supported by Yao et al. (2000), who stated that CH4 \nconductance is positively correlated with plant size, especially with the root volume at the \nreproduction stage.  \n\n\n\n  \nLow methane emissions during the vegetative phase may be due to the low root volume and \nincomplete aerenchyma system. Chandrasekaran et al. (2022) found that rice emitted high \nmethane emissions during the reproductive stage and was lower during the vegetative and \nmaturity phases. A study by Gaihre et al. (2011) also found that CH4 emission increased with \nincreasing rice growth and peaks during the flowering stage, and is reduced toward maturity. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 179-185  \n \n\n\n\n184 \n \n\n\n\nAccording to Zhan et al. (2010), CH4 emission decreases during the maturity stage due to \ndecreasing dissolved organic carbon (DOC) and root exudates that reduce the transport capacity \nof the aerenchyma tissues. Besides, the population of methanogens also plays an important role, \nwhich is less populated during the vegetative stage compared to the reproductive stage, where \nthey have abundantly established.  In essence, alternate wetting-drying (AWD) and mid-season \ndrainage (MD) irrigation techniques demonstrated that the two strategies have the potential to \nminimise CH4 emissions from Malaysian rice cultivation. \n\n\n\n  \nCONCLUSION  \n\n\n\nThe alternative irrigation regimes, i.e., alternate wetting and drying (AWD) and mid-season \ndrainage (MD), have shown a 57.81% to 77.07% reduction of CH4 emissions compared to \ncontinuous flooding (CF). Findings of the present study had demonstrated that alternative \nirrigation could reduce CH4 emission from Malaysian rice cultivation. However, implementing \nalternative irrigation regimes in Malaysia requires more concentrated effort and participation \nfrom all stakeholders (i.e., rice farmers, researchers and extension officers, and the \ngovernment). Further research, especially implementation of the alternative irrigation regimes \non commercial rice fields, is required to observe whether the present study's findings can be \nreplicated. Advocation and education to farmers by extension officers are also important as \nchanges to the farmers' long-held conventional rice cultivating method are challenging to \nimplement. Finally, the government must have a clear policy on implementing the use of \nalternative irrigation regimes in rice cultivation in Malaysia if it is serious about ensuring that \nthe country's greenhouse gas reduction target is achieved while still being able to produce rice.  \n \n\n\n\nACKNOWLEDGEMENTS  \nThis study was made possible through funding from the Malaysian Ministry of Higher \nEducation under the Fundamental Research Grant Scheme (Grant No.: \nFRGS/1/2019/STG03/UPM/02/8). \n \n\n\n\nREFERENCES  \nAdhya, T., Rath, A. K., Gupta, P., Rao, V., Das, S., Parida, K., Parashar, D. and Sethunathan, N. 1994. \n\n\n\nMethane emission from flooded rice fields under irrigated conditions. Biology and Fertility of \nSoils 18 (3): 245\u2013248. Springer.  \n\n\n\nAllen JM, and Sander BO. 2019. The Diverse Benefits of Alternate Wetting and Drying (AWD). Los \nBa\u00f1os, Philippines: International Rice Research Institute. Available online at: \nwww.ccafs.cgiar.org.  \n\n\n\nChandrasekaran, D. Tabassum-Abbasi, Tasneem Abbasi, and Shahid Abbas Abbasi. 2022. Assessment \nof Methane Emission and the Factors That Influence It, from Three Rice Varieties Commonly \nCultivated in the State of Puducherry. Atmosphere 2022, 13, 1811. https://doi.org/10.3390/ \natmos13111811.  \n\n\n\nFood and Agriculture Organization (FAO). 2018, Rice Market Monitor. Available online at \nhttps://www.fao.org/markets-and-trade/commodities/rice/rmm/en/  \n\n\n\nFAO. 2008, Rice Market Monitor. https://www.fao.org/markets-and-\ntrade/commodities/rice/rmm/en/  \n\n\n\nGaihre, Y.K, Tirol-Padre, A., Wassmann, R., Aquino, E., Pangga, G. and StaCruz, P. 2011. Spatial and \ntemporal variations in methane fluxes from irrigated lowland rice fields. Philipp. Agric. Sci 94 \n(4): 335\u2013342.  \n\n\n\nHutchinson, G. and Livingston, G. 1993, In Agricultural Ecosystem Effects on Trace Gases and \nGlobal Climate Change, 63\u201378, American Society of Agronomy, Inc. Crop Science Society of \nAmerica, Inc. Soil Science Society of America.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 179-185  \n \n\n\n\n185 \n \n\n\n\nIntergovernmental Panel on Climate Change (IPCC), 2013. Climate Change 2013: The Physical Science \nBasis. Contribution of Working Group l to the Fifth Assessment Report of the Intergovernmental \nPanel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New \nYork, 1535 pp.  \n\n\n\nLiu, X., Zhou, T., Liu, Y., Zhang, X., Li, L. and Pan, G. 2019. Effect of mid-season drainage on CH4 \nand N2O emission and grain yield in rice ecosystem: A meta-analysis. Agricultural Water \nManagement 213: 1028\u20131035.  \n\n\n\nMa, K., Qiu, Q. and Lu, Y. 2010. Microbial mechanism for rice variety control on methane emission \nfrom rice field soil. Global Change Biology 16 (11): 3085\u20133095. Publisher: Wiley Online \nLibrary.  \n\n\n\nSearchinger, T., & Adhya, T. K. (2015). Wetting and drying: reducing greenhouse gas emissions and \nsaving water from rice production. World Resources Institute, Washington DC.  \n\n\n\nWorld Resources Institute, December 16, 2014. Wetting and Drying: Reducing Greenhouse \nGas Emissions and Saving Water from Rice Production. \n\n\n\nYao, H., Yagi, K. and Nouchi, I. 2000. Importance of physical plant properties on methane transport \nthrough several rice cultivars. Plant and Soil 222 (1): 83\u201393. Publisher: Springer.  \n\n\n\nYu, K., Hiscox, A., DeLaune, R. D., DeLaune, R. D., Reddy, K. R., Richardson, C. J., and Megonigal, \nJ. P. (2013). Greenhouse Gas Emission by Static Chamber and Eddy Flux Methods. SSSA Book \nSeries. doi:10.2136/sssabookser10.c22.  \n\n\n\nZhan M, Cao C, Wang J, Jiang Y, Cai M, Yue L, and Shahrear A. 2010. Dynamics of methane emission, \nactive soil organic carbon and their relationship in wetland integrated rice-duck systems in \nSouthern China. Nutr Cycl Agroecosyst. 89, 1\u201313  https://doi.org/10.1007/s10705-010-9371-7 \n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n97 \n\n\n\n\n\n\n\nSoil Cadmium Contamination and Ecological Implications in a Tropical \n\n\n\nUrban Ecosystem: A Case Study of Air Hitam Sanitary Landfill in \n\n\n\nPuchong, Malaysia \n \n\n\n\nRajoo, S. Keeren1,2, Ismail, Ahmad3, Arifin, Abdu4*, Karam, S. Daljit5, Rosli, Zamri1, \n\n\n\nZulperi, Dzarifah4, Abdullah, Rosazlin6, and Zheng, L.T. Alvin7 \n\n\n\n \n1Department of Forestry Science, Faculty of Agriculture Science and Forestry,  \n\n\n\nUniversiti Putra Malaysia Bintulu Campus, Nyabau Road, 97008 Bintulu, Sarawak, Malaysia \n2Institute of Ecosystem Science Borneo, UPM Bintulu Campus, \n\n\n\n Nyabau Road, 97008 Bintulu, Sarawak, Malaysia \n3Academy of Sciences Malaysia, 50480, Kuala Lumpur, Malaysia \n\n\n\n4Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment  \nUniversiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia \n\n\n\n5Department of Land Management, Faculty of Agriculture, UPM, 43400 Selangor, Malaysia \n6Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603, KL, Malaysia \n\n\n\n7Faculty of Humanities, Management and Science, Universiti Putra Malaysia Bintulu Campus  \n97008 Bintulu, Sarawak, Malaysia \n\n\n\n \nCorrespondence: *arifinabdu@upm.edu.my  \n\n\n\n\n\n\n\nABSTRACT \nCadmium (Cd) is a mobile heavy metal that is highly toxic to almost all lifeforms. Urban eco-systems \n\n\n\nare susceptible to Cd contamination due to certain anthropogenic activities. Despite being recognised \n\n\n\nas an acutely toxic element, its biogeochemical behaviour is still poorly studied and understood, \nespecially in urban ecosystems of tropical countries. Therefore, this study was undertaken to address \n\n\n\nthis knowledge gap. This study was conducted at Air Hitam Sanitary Landfill (AHSL) in Puchong, \n\n\n\nMalaysia. Samples were collected from various abiotic and biotic factors representing the \nbiogeochemical cycle, including soil, flora, arthropods, atmospheric deposition, leachates, and river \n\n\n\nwater samples. Acid digestion using aqua regia was conducted to determine the total Cd concentration \n\n\n\nin all samples collected. Cd concentrations at AHSL were relatively high in all biotic and abiotic factors \nwith the concentrations showing a range of 0.019 ppm to 1.568 ppm. The bulk of Cd contamination in \n\n\n\nthe ecosystem was found to eventually end up in rivers. The average Cd concentration in the river \n\n\n\nsamples exceeded several environmental guidelines. There was also evidence of Cd entering food \n\n\n\nchains via soil arthropods and plants. Thus, Cd poses a credible threat to inhabitants of tropical urban \necosystems. \n\n\n\n\n\n\n\nKey words: Heavy metals, Malaysia, landfill, municipal solid waste, toxic elements \n\n\n\n\n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\nCadmium (Cd) has long been documented as a relatively mobile and heavy metal that is acutely \n\n\n\ntoxic to almost all life forms (Cullen and Maldonado 2012). Natural mobilisation of Cd \n\n\n\ncontained in the crust and mantle occurs primarily due to volcanic eruptions but can also be \n\n\n\ncaused by weathering of parent materials and burning of vegetation. However, the natural \n\n\n\nbiogeochemical cycle of Cd has been significantly altered by anthropogenic sources. This \n\n\n\nchange begun during the industrial revolution, due to an increase in fossil fuel burning and \n\n\n\nmetal extraction. Unfortunately, this phenomenon has only grown worse due to rapid \n\n\n\nindustrialization across the globe.  \n\n\n\n\n\n\n\nSoil Cd, like almost all other heavy metals, is mainly from anthropogenically mobilised sources \n\n\n\n(Rajoo et al. 2013). This includes atmospheric deposition due to Cd attaching itself to particles \n\n\n\n\nmailto:arifinabdu@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n98 \n\n\n\n\n\n\n\noriginating from fossil fuel combustion, non-ferrous mining, smelting and manufacturing \n\n\n\n(Feng et al. 2019). Globally, atmospheric deposition of Cd is a major soil contaminant. In \n\n\n\nChina, almost 20% of the total arable lands are believed to be contaminated by atmospheric Cd \n\n\n\n(Xue et al. 2014). In Wales and England, almost 53% of Cd contamination in agricultural soils \n\n\n\nis from atmospheric deposition (Nicholson et al. 2003). To make matters worse, atmospheric \n\n\n\nCd often has high bioavailability since up to 84% of the dry deposition of Cd is water soluble \n\n\n\nwith a cation exchangeable fraction (Feng et al. 2019). Besides atmospheric deposition, soil \n\n\n\nCd contamination can be due to fertilizers and pesticides that are directly applied to the soil \n\n\n\n(Cullen and Maldonado 2012). In urban ecosystems, a major source of soil Cd contamination \n\n\n\nis from municipal waste disposal, mainly nickel-cadmium batteries (Kubier et al. 2019). Thus, \n\n\n\nlandfills are common locations for soil Cd and groundwater contamination. \n\n\n\n\n\n\n\nDespite being recognised as a highly toxic element, its biogeochemical behaviour is still poorly \n\n\n\nstudied and understood in most ecosystems (Hernandez et al. 2022). This is especially true for \n\n\n\nurban ecosystems, where soil Cd pollution is most prevalent. Even worse, there is relatively no \n\n\n\nstudies in relation to the biogeochemical behaviour of Cd in tropical countries like Malaysia, \n\n\n\nwhere the climatic conditions significantly differ from countries where there is such literature \n\n\n\n(Cullen and Maldonado 2012; Xue et al. 2014; Feng et al. 2019; Hernandez et al. 2022; Rajoo \n\n\n\net al., 2023). This knowledge gap has resulted in a poor understanding of how Cd behaves in \n\n\n\necosystems and its potential of affecting human health, either directly or by infiltrating our \n\n\n\nfood chains. Therefore, this study was undertaken to address this knowledge gap. The \n\n\n\nobjectives of this study are: (1) to determine the soil biogeochemical behaviour of Cd in \n\n\n\nMalaysian urban ecosystems, and (2) to determine the pollution potential of Cd in Malaysian \n\n\n\nurban ecosystems. \n\n\n\n\n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nStudy Site: Air Hitam Sanitary Landfill (AHSL) \n\n\n\nThe AHSL site is located near the Air Hitam Forest Reserve in Mukim Petaling, Daerah \n\n\n\nPetaling, Puchong (longitude 101\u00b0 39\u2019 55\u2019\u2019 E and latitude 03\u00b0 0\u2019 10\u2019\u2019 N) (Figure 1). The \n\n\n\nSelangor State Government Council approved Worldwide Sita Environmental Management \n\n\n\nSdn. Bhd. to develop this sanitary landfill on 22nd March 1995. ASHL was built in 1995 and \n\n\n\nwas the first engineered sanitary landfill site in Malaysia, covering a total of 42 hectares. \n\n\n\nDuring the 11 years ASHL operated, it received approximately 6.2 million tons of domestic \n\n\n\nwaste. ASHL is surrounded by residential housing, highways and manufacturing industries. \n\n\n\nAHSL was officially closed on 31 December 2006 and the 5-year Landfill Closure and Post-\n\n\n\nClosure Maintenance Plan (LCPCMP) was put in place (2007-2011). \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n99 \n\n\n\n\n\n\n\n \nFigure 1. Location of Air Hitam Sanitary Landfill \n\n\n\n\n\n\n\nAHSL was selected for this study since it is a prime location for soil Cd contamination that \n\n\n\nwill be able to accurately represent its biogeochemical behaviour in an urban ecosystem. As a \n\n\n\nwaste disposal site, there is a high likelihood of Cd accumulation in the soil (Kubier et al. \n\n\n\n2019). Moreover, since it is surrounded by manufacturing industries and heavy vehicle usage, \n\n\n\natmospheric deposition of Cd might be prevalent in this location. AHSL is also equipped with \n\n\n\nseveral amenities, including a ground water drainage system, a leachate collection system and \n\n\n\ntreatment plant. Thus, groundwater and leachate samples could be efficiently collected at this \n\n\n\nsite. As the leachate was released into a nearby river, river samples could also be collected at \n\n\n\nthis site. Moreover, AHSL had planted vegetation and soil arthropods, thus the biotic \n\n\n\ncomponents of the biogeochemical cycle could also be evaluated.  \n\n\n\n\n\n\n\nSample Collection \n\n\n\nSamples were collected from various abiotic and biotic factors representing the biogeochemical \n\n\n\ncycle (Figure 2). Soil sampling was conducted at three locations of AHSL: Phases 1-5, Phase \n\n\n\n6 and Phase 7. Six subplots (20 m x 20 m) were randomly established (completely randomised \n\n\n\ndesign) at each sampling location. Composite samples were obtained using an auger from each \n\n\n\nsubplot at 0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm and 80-100 cm. All the samples were kept \n\n\n\nin a standard plastic container and air-dried before being analysed. Samples of the soil was air \n\n\n\ndried for three days, pounded with a mortar and pestle, and then sieved through a 2-mm mesh. \n\n\n\nThis was done to produce a homogenous mixture for analyses. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n100 \n\n\n\n\n\n\n\n \nFigure 2. Air Hitam Sanitary Landfill layout and sampling locations \n\n\n\n\n\n\n\nAtmospheric deposition sampling was conducted at the six soil sampling subplots of Phase 1 \n\n\n\nof AHSL. A wide plastic sheet was placed on the ground and secured with spikes. Sediment \n\n\n\n(soil) deposited on the plastic sheet was carefully collected using plastic vials twice a day for \n\n\n\na month. The process was conducted until a sufficient number of samples were collected for \n\n\n\nanalyses. \n\n\n\n\n\n\n\nSoil arthropod samples were collected using the same soil sampling plots, that is six subplots \n\n\n\n(20 m x 20 m) were randomly established (completely randomised design). The soil arthropods \n\n\n\nwere collected from these sampling plots using appropriate pit fall methods. Plants were \n\n\n\nselected within the vicinity of the soil sampling locations. The fresh sample of plants was \n\n\n\nseparated into three parts: roots, stem and leaves. These plant parts were then dried in an oven \n\n\n\nat 60\u2070 C for 24 h and shredded into small pieces before further analysis. Leachate and sludge \n\n\n\nsampling was conducted at the leachate collection ponds, that is Pond 1, Pond 2 and Pond 3 \n\n\n\n(Figure 3), using PVC pipes of 10 cm. As shown in Figure 3, the leachate from AHSL is \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n101 \n\n\n\n\n\n\n\ngenerated to Pond 1, where no treatment is applied. The raw, untreated leachate is then \n\n\n\nchannelled to Pond 2 where aeration is applied and transferred to Pond 3 for further aeration. \n\n\n\nLeachate that has undergone treatment is discharged into a nearby river. Samples were also \n\n\n\ncollected from the discharged water and from the local river at 25-m intervals, for a total \n\n\n\ndistance of 250 m. \n\n\n\n\n\n\n\n \nFigure 3. Schematic diagram of leachate flow \n\n\n\n\n\n\n\n\n\n\n\nLaboratory Analysis \n\n\n\nAcid digestion was used to determine the concentration of Cd in all the samples collected \n\n\n\n(Figure 4). After digestion, the total concentrations of Cd were determined using Atomic \n\n\n\nAbsorption Spectrometer (AAS). Basic physico-chemical analyses were conducted to \n\n\n\ncharacterise the soil characteristics (Gupta 2007).  \n\n\n\n\n\n\n\n \nFigure 4. Acid digestion of samples \n\n\n\n\n\n\n\n\n\n\n\nStatistical Analysis  \n\n\n\nThe data was statistically analysed using the SPSS program (Version 23). Appropriate \n\n\n\nstatistical analyses were conducted to analyse the research data, such as analysis of variance \n\n\n\n(ANOVA), t-test and regression. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n102 \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nSoil Characteristics \n\n\n\nThe surface soil of AHSL was added soil to cover the buried municipal waste. The soil was an \n\n\n\nOxisol, heavily weathered from being exposed to weathering (Karam et al. 2022). The soil pH \n\n\n\nranged from 5.47 to 6.21 with the average pH being 5.94. Soil pH influences the availability \n\n\n\nof heavy metals which are usually more available in a lower pH as most metals are cationic \n\n\n\n(USDA NRCS 2000). The soil EC at AHSL ranged from 115.1 to 293.00 \u00b5S cm-1 with the \n\n\n\naverage being 219.4 \u00b5S cm-1. In general, soils with EC below 300 \u00b5S cm-1 are considered to \n\n\n\nbe sterile soils with little microbial activity, making it \u2018unhealthy\u2019 for plant growth as essential \n\n\n\nenzymes might not be present for the synthesis of soil nutrients (Karam et al. 2013; Masri et \n\n\n\nal. 2022; Shaliha-Jamaluddin et al. 2022). This indicates that the plants growing at AHSL are \n\n\n\nprimarily pioneer species, that is plants that grow in disturbed ecosystems. The soil bulk density \n\n\n\nranged from 1.40 to1.73 g cm-3; 11.56 to 12.50% moisture content and 34.91 to 47.16% \n\n\n\nporosity. Generally, soils having a bulk density of between 1.0 g cm-3 and 2.0 g cm-3 are \n\n\n\nconsidered to have a low organic content, which is likely, as AHSL has a highly weathered and \n\n\n\nexposed soil. The low porosity was due to the soil being compacted to cover the municipal \n\n\n\nwaste buried beneath. This also explains the low moisture content of the soil. The concentration \n\n\n\nof extractable phosphorus was low in all AHSL phases. Low phosphorus is often associated \n\n\n\nwith high concentration of Fe in the soils, which is characteristic of an Oxisol (Karam et al., \n\n\n\n2013). Low organic matter is also often associated with lower NPK levels in soils, which is \n\n\n\nevident at AHSL (Cavanagh and O\u2019Halloran 2003). \n\n\n\n\n\n\n\nSoil Cd Concentrations \n\n\n\nThe average soil Cd concentrations ranged from 0.018ppm to 0.029pmm, with the lowest being \n\n\n\nat Phase 1 and the highest at Phase 7. However, a one-way ANOVA between subjects found \n\n\n\nno significant different between the phases in Cd concentrations [F (2, 87) = 1.442, p = 0.242]. \n\n\n\nSimilarly, there was no significant difference in Cd concentrations when it came to soil depth. \n\n\n\nA linear regression established that soil depth could not predict Cd concentration with statistical \n\n\n\nsignificance [F (1, 99) = 0.287, p = >0.05]. As shown in Figure 5 Cd concentration decreased \n\n\n\nwith soil depth but was not statistically significant. Soil Cd concentration at the study site was \n\n\n\nnot from the buried municipal waste since the waste was buried deep underground and had \n\n\n\nliners surrounding it. Thus, the Cd was largely from atmospheric deposition, as further \n\n\n\nexplained in the next section. \n\n\n\n\n\n\n\n \nFigure 5. Cd concentrations according to sampling phase (left) and soil depth (right). \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n103 \n\n\n\n\n\n\n\nEffects of Atmospheric Deposition on Topsoil Cd Concentration \n\n\n\nA t-test failed to reveal a statistically reliable difference between the mean concentration of Cd \n\n\n\nin the top soil (M = 0.021, s = 0.003) and in the dry atmospheric deposition (M = 0.025, s = \n\n\n\n0.005), t (16.85) = 1.995, p = 0.062, \u03b1 = 0.05 (Figure 6). This meant that dry atmospheric \n\n\n\ndeposition was the likely primary contributor to Cd concentration in the topsoil of AHSL \n\n\n\n(Cullen and Maldonado 2012). At lower pH levels, Cd was found to be highly concentrated in \n\n\n\nmobile states, indicating that it is very likely to be an environmental hazard through dry \n\n\n\natmospheric deposition (Lee et al. 2014). As the dissolution of Fe hydroxides can lower pH \n\n\n\nlevels, it is likely that the high Fe concentration found in the dry atmospheric deposition \n\n\n\nsamples was responsible for the mobile state of the Cd, thus contributing to high Cd \n\n\n\nconcentration in the dry atmospheric deposition at AHSL (Lee et al. 2014). A high \n\n\n\nconcentration of Cd is common in the dust of urban and industrial areas (Qiu et al. 2016). Cd \n\n\n\ncan cause widespread ecological damage that could have severe repercussions on human \n\n\n\nhealth, usually affecting kidney, bone and lungs (Qiu et al. 2016). \n\n\n\n\n\n\n\n \nFigure 6: Mean Cd concentrations of topsoil and dry atmospheric deposition samples \n\n\n\n\n\n\n\n\n\n\n\nCd Concentration in Leachate Collection Ponds \n\n\n\nAs seen in Figure 7, there was a significant difference in Cd concentration in the leachate ponds \n\n\n\n[F (2, 33) = 7200.72, p = 0.0]. A Tukey post-hoc test revealed that the last leachate collection \n\n\n\npond (Pond 3) had a statistically significant higher concentration of Cd (0.393 \u00b1 0.015 ppm) \n\n\n\ncompared to Pond 1 (0.005 \u00b1 0.002 ppm, p = 0.0) and Pond 2 (0.013 \u00b1 0.001 ppm, p = 0.0). \n\n\n\nThere was no statistical difference in Cd concentration between Pond 1 and Pond 2 (p = 0.061). \n\n\n\nThis shows that Cd concentration did not decrease after aeration treatment as expected, but \n\n\n\nactually increased.  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n104 \n\n\n\n\n\n\n\n \nFigure 7. Mean concentration of Cd from leachate collection ponds \n\n\n\n\n\n\n\n\n\n\n\nAs seen in Figure 8, there was a significant difference in Cd concentration in the leachate \n\n\n\nsediments [F (2, 33) = 124.938, p = 0.0]. A Tukey post-hoc test revealed that the sediment in \n\n\n\nthe last leachate collection pond (Pond 3) had a statistically significant higher concentration of \n\n\n\nCd (1.57 \u00b1 0.12 ppm) than the leachate collection ponds of Pond 1 (0.905 \u00b1 0.08 ppm, p = 0.0) \n\n\n\nand Pond 2 (1.22 \u00b1 0.09 ppm, p = 0.0). Pond 1 had statistically higher Cd concentration in its \n\n\n\nsediments compared to Pond 2 (p = 0.0). This shows that Cd concentration increased even after \n\n\n\naeration treatment, as in the case of leachate Cd concentration levels (Figure 7). \n\n\n\n\n\n\n\n \nFigure 8. Mean concentration of Cd in sediments from leachate collection ponds \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n105 \n\n\n\n\n\n\n\nThe Cd concentration in the sediments of AHSL leachate ponds was found to be several times \n\n\n\nhigher than in the topsoils of Malaysia, indicating that the aeration treatment was able to \n\n\n\nsolidify these elements. According to Rahman and Zaim (2015), Cd concentration in the \n\n\n\nsediments was almost 75 times higher than in our topsoils.  Aeration treatment can be compared \n\n\n\nto a common wastewater treatment in the 1990s known as flotation, whereby heavy metals are \n\n\n\nseparated from the liquid phase using bubble attachment (Lundh et al. 2000). The heavy metals \n\n\n\nrise towards the surface of leachate ponds and need to be removed as sludge. Hence, this \n\n\n\ntreatment can only be effective if the sludge is removed, otherwise the sludge sinks to the \n\n\n\nbottom of the leachate treatment ponds along with the accumulated heavy metals (Lundh et al. \n\n\n\n2000). The heavy metals then re-enter the leachate, which explains why the treated leachate of \n\n\n\nAHSL still contains high concentrations of Cd. Hence, for aeration treatment to be efficient in \n\n\n\nremoving Cd, sludge removal also needs to be conducted. \n\n\n\n\n\n\n\nRiver Cd Concentration  \n\n\n\nAs river samples were collected from 25-m intervals, it can be determined whether river \n\n\n\ndistance affected concentration of Cd. Linear regression analysis was conducted to determine \n\n\n\nriver distance relationship with Cd concentration. Cd concentrations were generally lower with \n\n\n\nincreasing river distance; however, the linear regression established that river distance could \n\n\n\nnot predict cadmium concentration with statistical significance, F (1, 8) = 14.065, p = >0.05 \n\n\n\n(Figure 9). \n\n\n\n\n\n\n\n \nFigure 9. Cd concentration based on river sampling distance (meters) \n\n\n\n\n\n\n\nCd Content in Soil Arthropods  \n\n\n\nTwo arthropod species were selected for this study, Orthomorpha coarctata and Trigoniulus \n\n\n\ncorallines (Figure 10). The average density of Orthomorpha coarctata was 35.17\u00b17.7 /400m2, \n\n\n\nwhile the average density for Trigoniulus corallines was 14.33\u00b15.6 /400m2. The arthropod \n\n\n\nsamples were divided into washed and unwashed samples. A one-way ANOVA between \n\n\n\nsubjects was conducted to compare the heavy metal concentration in the washed arthropod \n\n\n\nsamples, unwashed arthropod samples and the topsoil. If the results were significantly different, \n\n\n\nTukey HSD test was conducted to determine the phases that were significantly different. \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n106 \n\n\n\n\n\n\n\n\n\n\n\nFigure 10. Orthomorpha coarctata (A) and Trigoniulus corallines (B) samples \n\n\n\n\n\n\n\nThere was a significant difference in Cd concentration [F (2, 33) = 13.081, p = 0.000] between \n\n\n\ntop soil and Orthomorpha coarctata (washed and unwashed). A Tukey post-hoc test revealed \n\n\n\nthat the topsoil had a statistically significant lower concentration of Cd (0.021 \u00b1 0.003 ppm) \n\n\n\ncompared to both the Orthomorpha coarctata samples, that is the unwashed samples (0.025 \u00b1 \n\n\n\n0.001 ppm, p = 0.0) and washed samples (0.025 \u00b1 0.001 ppm, p = 0.0). There were no statistical \n\n\n\ndifference in Cd concentration between the unwashed and washed Orthomorpha coarctata \n\n\n\nsamples. This indicates that Orthomorpha coarctata is an arthropod species that accumulates \n\n\n\nCd (Figure 11). \n\n\n\n\n\n\n\n\n\n\n\n \nFigure 11. Mean concentration of Cd based on topsoil and Orthomorpha coarctata (washed \n\n\n\nand unwashed) samples \n\n\n\n\n\n\n\nSimilarly, there was a significant difference in Cd concentration [F (2, 33) = 4.149, p = 0.000] \n\n\n\nbetween top soil and Trigoniulus corallinus (washed and unwashed). A Tukey post-hoc test \n\n\n\nrevealed that the topsoil had statistically significant lower concentration of Cd (0.021 \u00b1 0.003 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n107 \n\n\n\n\n\n\n\nppm) than in both the Trigoniulus corallinus samples, that is, the unwashed (0.023 \u00b1 0.0008 \n\n\n\nppm, p = 0.0) and washed samples (0.023 \u00b1 0.0009 ppm, p = 0.0). No statistical differences \n\n\n\nwere found in Cd concentration between the unwashed and washed Trigoniulus corallinus \n\n\n\nsamples. This indicates that Trigoniulus corallinus is an arthropod species that accumulates Cd \n\n\n\n(Figure 12).   \n\n\n\n\n\n\n\n\n\n\n\nFigure 12. Mean concentration of Cd according to topsoil and Trigoniulus corallinus \n\n\n\n(washed and unwashed) \n\n\n\n\n\n\n\nThe results found that both Orthomorpha coarctata and Trigoniulus corallines are potential \n\n\n\nhyperaccumulators of Cd, which is quite a prevalent characteristic in arthropods (Boyd 2009). \n\n\n\nThese two species could be used as bioindicators for Cd, making contamination control easier \n\n\n\n(Nummelin et al. 2007). However, this is also a cause for concern since there is potential for \n\n\n\nCd to infiltrate human food chains via these soil arthropods.  \n\n\n\n\n\n\n\nPollution Potential of Cd at ASHL \n\n\n\nLand and groundwater contamination in Malaysia is addressed in the Environmental Quality \n\n\n\nAct 1974 (EQA), a legislation that focuses on the prevention, abatement and control of \n\n\n\npollution in the environment of Malaysia (Rajoo et al. 2013). Further, there is also a set of \n\n\n\nguidelines titled \u2018Contaminated Land Management and Control Guidelines\u2019, produced by the \n\n\n\nMalaysian Department of Environment (DOE). Compliance to the guidelines is purely \n\n\n\nvoluntary; however, DOE is seeking to make it mandatory. Despite these legislations and \n\n\n\nguidelines, Malaysia still has no specific definition of \u2018contaminated land and/or groundwater\u2019, \n\n\n\nno soil or groundwater quality standards and almost no government initiative to identify \n\n\n\ncontaminated sites (Ripin et al. 2014). However, the Malaysian government did conduct a \n\n\n\nnational study to create a set of standards for water quality in 1985 (DOE 1985). The study was \n\n\n\ncalled the \u2018Development of Water Quality Criteria and Standards for Malaysia\u2019 and was headed \n\n\n\nby a multidisciplinary team of experts from universities throughout the country (DOE 1985). \n\n\n\nHence, when it comes to water quality in rivers or lakes, there is a Malaysian standard to refer \n\n\n\nto, unlike for soils and groundwater. Due to the lack of a standardised environmental quality \n\n\n\nstandard in Malaysia, it is common for soil quality assessment in Malaysia to be conducted by \n\n\n\ncomparing it to the standards of a foreign country; the most common being the Dutch Standard \n\n\n\n(Ripin et al. 2014).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n108 \n\n\n\n\n\n\n\n\n\n\n\nThe average concentration of Cd in the topsoil of AHSL was 0.021ppm and 0.025ppm for \n\n\n\natmospheric deposition, which was significantly lower than the permissible limits of the Dutch \n\n\n\nStandard (0.8ppm). However, the average Cd concentration in the river (0.402ppm) exceeded \n\n\n\nthe Dutch Standards target value (0.4ppm). It also significantly exceeded the World Health \n\n\n\nOrganization\u2019s Standard for drinking water (0.003ppm) and the Canadian Environmental \n\n\n\nQuality Guidelines (0.09ppm). Even based on DOE\u2019s guidelines for Malaysia, the Cd levels \n\n\n\nwas in the lowest category at Class V (higher than 0.01ppm). \n\n\n\n\n\n\n\nTransference of Cd at Air Hitam Sanitary Landfill  \n\n\n\nBased on Cd concentrations in various biotic and abiotic factors at AHSL, the nature of Cd \n\n\n\ncould be determined, that is, how it is transferred from one aspect to another and whether Cd \n\n\n\naccumulates in any of these biotic or abiotic factors. Cd concentration at AHSL is relatively \n\n\n\nhigh in all biotic and abiotic factors, with the concentration being in the range of 0.019 ppm to \n\n\n\n1.568 ppm (Figure 13). The lowest Cd concentration was in the deep soil while the highest was \n\n\n\nin the leachate pond sludge. The plant\u2019s concentration of Cd was twice higher than in the topsoil \n\n\n\nand arthropod, meaning the distribution of Cd in the food web was primarily in the plants \n\n\n\n(Achary et al. 2017). This means that Cd contamination of our food chain could be possible \n\n\n\nand hence attention needs to be given to this aspect. Cd atmospheric deposition was \n\n\n\nsignificantly higher compared to topsoil concentration, meaning that Cd contamination via \n\n\n\natmospheric deposition was prevalent at AHSL. This is likely due to the various industries and \n\n\n\nvehicles at Seri Kembangan that release Cd into the atmosphere (Qiu et al. 2016). Hence, more \n\n\n\nenvironmental measures need to be in place in order to reduce atmospheric release of Cd to \n\n\n\nprevent further environmental contamination. \n\n\n\n\n\n\n\nThe Cd concentration in the leachate pond\u2019s sediment was more than 70 times higher than the \n\n\n\nCd concentration in most natural Malaysian soils, including the topsoil of AHSL, meaning that \n\n\n\nleachate aeration treatment was able to solidify Cd (Ripin et al. 2014). However, as the leachate \n\n\n\nsediment was not removed, it will be released into the river along with the treated leachate \n\n\n\n(Lundh et al. 2000). This is evident as reflected by the Cd concentration in the river being \n\n\n\nbeyond the allowable limits of several international environmental guidelines and being in the \n\n\n\nlowest quality class for Malaysia\u2019s environmental guideline (He et al. 2015). All this indicates \n\n\n\nthat Cd contamination is a serious concern at AHSL and could possibly be a threat to both the \n\n\n\necosystem and human health, namely to those residing near AHSL. \n\n\n\n\n\n\n\n \nFigure 13. Concentration of Cd in various biotic and abiotic factors of AHSL \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\nThe major contributor of soil Cd in this urban ecosystem was atmospheric deposition. \n\n\n\nHowever, the bulk of Cd pollution came from the municipal waste buried at AHSL, as evident \n\n\n\nby the significantly high concentration of Cd in the leachate and sediments. Since the leachate \n\n\n\nand sedimentation are released into a nearby river after aeration treatment, Cd is then \n\n\n\ntransferred into the river ecosystem. The Cd concentration level in this river is of major \n\n\n\nconcern, as demonstrated by its values exceeding several environmental guidelines. Moreover, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 97-110 \n\n\n\n\n\n\n\n109 \n\n\n\n\n\n\n\ndue to Cd being a highly mobile element, it is very likely that the heavy metal is seeping into \n\n\n\nour food chain via biotic factors. 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"\n\nINTRODUCTION\nSoil health is a key factor for increasing agricultural production. This calls for \nlong-term studies at fixed sites for monitoring changes in soil fertility status. \nThe rapidly increasing human populations and the need of for land for various \nagricultural activities has brought about extensive land use changes and soil \nmanagement practices throughout the world (Cunningham et al., 2005). Long-\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 31-41  (2019)                         Malaysian Society of Soil Science\n\n\n\nChanges in Soil Physico-Chemical Properties and Fertility \nStatus of Long-Term Cultivated Soils in Southwestern \n\n\n\nBangladesh\n\n\n\nMehjabin Hossain, Md. Tareq Bin Salam\n\n\n\nSoil, Water and Environment Discipline, Khulna University, Bangladesh-9208\n\n\n\nABSTRACT\nSustainable soil management is essential for maintaining proper soil health for \nfuture production of crops. A comparative study was carried out at Dumuria soil \nseries in Khulna district to observe the current fertility and physical changes of \nsoils over a period of time due to different land use. Soil physical (soil texture, \nwater holding capacity, bulk density and total porosity) and chemical properties \n(Nitrogen (N), Phosphorous (P), Potassium (K), Sulphur (S), soil organic matter \n(SOM), soil organic carbon (SOC) along with cation exchange capacity (CEC), \nsodium absorption ratio (SAR), exchangeable sodium percentage (ESP), base \nsaturation percentage (BSP) and % salt properties were determined. Except for \nthe control, all the soils had silt loam texture. Water holding capacity varied from \n(33.57 \u00b1 3.3 to 55.57 \u00b1 5.2)% and all soil indicated good porosity (average 47 \u00b1 \n5.59)%. Soil pH (5.96 to 7.4) indicated that the soils were neutral to alkaline in \nnature and had an average salt percentage (0.11% \u00b1 0.05). The SOM was higher \nin natural vegetative soil (2.45 \u00b1 0.46)% and decreased over the period of land use \nfor cultivation. In terms of ESP and SAR, 50 to 10 years cultivated soil showed \nthe highest value and a significant difference was observed among the treatments \n(p \u2264 0.05). For BSP, 100 to 50 years cultivated soil showed the highest value and \nuncultivated soil showed the lowest value and was statistically insignificant among \ntreatment (p \u2264 0.05). Overall observation showed that long term land use resulted \nin a significant decline in soil quality. So, sustainable soil management should be \nincorporated in the development of suitable agricultural management such as the \nuse of organic matter or incorporate organic mixed with inorganic fertilizer and \nadaptation of soil conservation farming. Proper strategies should be adopted to \nseek a sustainable solution that better addresses of soil fertility management. \n\n\n\nKeywords: Soil physico-chemical properties, bulk density, porosity, CEC, \nSAR \n\n\n\n___________________\n*Corresponding author : E-mail: tareqss_ku@rocketmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201932\n\n\n\nterm cultivation has found to result in a statistically significant decrease in the \ntotal amount of soil organic matter (SOM) along with concentrations of all carbon \ncompounds (Sommerfeldt et al., 1988). Agricultural sustainability requires \nperiodic evaluation of soil fertility status. This is important to understand the \nfactors which impose serious constraints on increased crop production under \ndifferent land use types and for adoption of suitable land management practices \n(Chimdi et al., 2012). However, information about the effects of land use changes \non soil physico-chemical properties is essential in order to draw up appropriate \nrecommendations for optimal and sustainable utilizations of land resources. \nAs soil physical and chemical properties play a central role in transport and \nreaction of water, solutes and gases in soils, their knowledge is very important in \nunderstanding soil behavior to applied stresses and transport phenomena in soils, \nhence for soil conservation and planning of appropriate agricultural practices. \nThe anthropogenic changes in land use have altered the characteristics of the \nEarth\u2019s surface, leading to changes in soil physico-chemical properties such \nas soil fertility, soil erosion sensitivity and soil moisture content (Abad et al., \n2014). These changes may be caused by soil compaction that reduces soil volume \nand consequently lowers soil productivity and environmental quality (Abad et \nal., 2014). Soil physical and chemical properties have been proposed as suitable \nindicators for assessing the effect of land-use changes and management (Janzen \net al., 1992; Alvarez and Alvarez, 2000). This approach has been used extensively \nby several authors to monitor land-cover and land-use change patterns (Schroth \net al., 2002; Walker and Desanker, 2004; Yao et al., 2010). Therefore, this study \nwas carried out to in order to evaluate the influence of different land use on soil \nphysicochemical properties in soils of Dumuria Upazilla, Khulna, Bangladesh.\n\n\n\nMATERIALS AND METHODS\n\n\n\nExperimental Site\nThe experimental site was at Chechuria village in Dumuria Upazilla under Khulna \ndistrict (22\u00b039\u201900\u2019\u2019N 89\u00b015\u201900\u2019\u2019E and 22\u00b056\u201900\u2019\u2019N 89\u00b032\u201900\u2019\u2019E). Five sites have \nbeen chosen where 4 were from different ages of agricultural field and one was \nfrom uncultivated soil. All soils are in Dumuria soil series. In Table 1, details of \nsampling code are presented.\n\n\n\nCollecting, Processing and Storing Soil from Sites\nComposite soil sites from 0-15 cm of the were collected then air dried, ground \nand passed through a 2 mm sieve for nutrient analysis. This process was replicated \nthrice and then the soils were sent to the laboratory of Soil, Water & Environment \nDiscipline, Khulna University for physico-chemical analyzes.\n\n\n\nAnalytical Procedure of Soil Physicochemical Properties\nChemical characterization of the sited soils included the analysis of organic matter \n(SOM), organic carbon (SOC), cation exchange capacity (CEC) at pH 7.0, base \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 33\n\n\n\nsaturation, soil EC, soil pH, nitrogen, phosphorous, potassium and sulfur; whereas \nthe physical characterization consists of particle size analysis, soil structure, water \nholding capacity (WHC), bulk density (BD) and total porosity (PT) determination. \nThe sites were allowed to dry in the open air until friable. Organic carbon was \ndetermined using the Walkley - Black wet oxidation procedure and the soil organic \nmatter content was determined from the organic carbon (Nelson and Sommers, \n1996). Soil pH was determined in distilled water using the pH meter with a water \nratio of 1:2. Available phosphorus (P) and exchangeable cations were determined. \nAvailable P was determined by Bray-1 extraction followed by molybdenum blue \ncolorimetry (Frank et al., 1998). Exchangeable potassium (K+) and sodium (Na+) \nwere determined by flame photometry (Vogelmann et al., 2010; Olorunfemi \net al., 2016) while exchangeable magnesium (Mg2+) and calcium (Ca2+) were \ndetermined by atomic absorption spectrophotometer after extraction with 1M KCl \n1.0 mol l-1 (Senjobi and Ogunkunle, 2010). The cation exchange capacity (CEC) \nat pH 7.0 was determined following the procedure described by Reeuwijk (2002). \nSoil particle sizes were determined using the hydrometer method described in \nAgbede and Ojeniyi (2009) and classification was carried out using the USDA \nclassification system (Soil Survey Staff, 1999). Soil water holding capacity \n(WHC) was determined following the method described by Ibitoye (2006). The \nbulk density (BD) was obtained by the gravimetric soil core method described by \n(Blake and Hartage, 1986) and particle density (PD) was assumed to be 2.65 g \ncm-3 (Osunbitan et al., 2005; Li and Shao, 2006; Zhang et al., 2006; Price et al., \n2010). Total porosity (PT) was obtained from BD and PD using the equation and \nrelationship developed by Danielson and Sutherland (1986).\n\n\n\n % Porosity = (1-(BD/PD) x100)\n\n\n\n3 \n \n\n\n\nwhere 4 were from different ages of agricultural field and one was from uncultivated soil. All \n\n\n\nsoils are in Dumuria soil series. In Table 1, details of sampling code are presented. \n\n\n\nTable1: Site coding at different stages of soil and its cropping pattern in Dumuria Upazilla \n\n\n\nFarming status Stages of soil Sampling code Land use pattern \n\n\n\nUnder Cultivation \n\n\n\n>100 years A \nRice-Rice-Rice \n\n\n\nWheat-Rice-Rice \n\n\n\n100-50 years B \nWheat-Rice-Rice \n\n\n\nVegetable-Rice-Rice \n\n\n\n50-10 years C \nRice-Fallow-Rice \n\n\n\nWheat-Fallow-Rice \n\n\n\n<10 years D \nRice-Rice-Rice \n\n\n\nRice-Rice-Fallow \n\n\n\nNon-cultivated soil Control E Natural vegetation \n\n\n\n\n\n\n\nCollecting, Processing and Storing Soil from Sites \n\n\n\nComposite soil sites from 0-15 cm of the were collected then air dried, ground and passed \n\n\n\nthrough a 2 mm sieve for nutrient analysis. This process was replicated thrice and then the \n\n\n\nsoils were sent to the laboratory of Soil, Water & Environment Discipline, Khulna University \n\n\n\nfor physico-chemical analyzes. \n\n\n\nAnalytical Procedure of Soil Physicochemical Properties \n\n\n\nChemical characterization of the sited soils included the analysis of organic matter (SOM), \n\n\n\norganic carbon (SOC), cation exchange capacity (CEC) at pH 7.0, base saturation, soil EC, \n\n\n\nsoil pH, nitrogen, phosphorous, potassium and sulfur; whereas the physical characterization \n\n\n\nconsists of particle size analysis, soil structure, water holding capacity (WHC), bulk density \n\n\n\n(BD) and total porosity (PT) determination. The sites were allowed to dry in the open air until \n\n\n\nfriable. Organic carbon was determined using the Walkley - Black wet oxidation procedure \n\n\n\nand the soil organic matter content was determined from the organic carbon (Nelson and \n\n\n\nSommers, 1996). Soil pH was determined in distilled water using the pH meter with a water \n\n\n\nratio of 1:2. Available phosphorus (P) and exchangeable cations were determined. Available \n\n\n\nP was determined by Bray-1 extraction followed by molybdenum blue colorimetry (Frank et \n\n\n\nTABLE 1 \nSite coding at different stages of soil and its cropping pattern in Dumuria Upazilla\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201934\n\n\n\nwhere: BD = bulk density and PD = particle density (= 2.65 Mg/m3). The default \nvalue of 2.65 Mg/m3 is used as a \u2018rule of thumb\u2019 based on the average bulk density \nof rock with no pore space (Fasinmirin and Olorunfemi, 2013). \n\n\n\nBSP (%) = [(Summation of Ca2+, Mg2+, K+ and Na+ content in cmolc /kg soil) \u00f7 \nCEC (cmolc /kg soil)] x 100\n\n\n\n SAR = [Na+] \u00f7 (\u221a\u00bd {[Ca2+] + [Mg2+]})\n ESP = ([Na+] \u00f7 CEC) x 100\n\n\n\nwhere, BSP is base saturation percentage SAR is sodium absorption ratio and \nESP is exchangeable sodium percentage. Percent of salt presented in the soil was \ndetermined by the following equation:\n\n\n\nSalt (%) = 0.064 x EC\n\n\n\nStatistical Analyses\nThe statistical analyses of the results obtained from soil sites were performed as \ndescribed by Zaman et al., (1982). One way ANOVA (SPSS version 16.0) was \nused to test for significance among the treatment means and post hoc comparison \nwas used to compare the soil chemical properties from the different land uses.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Physical properties\nSoil physical properties are presented in Table 2. All cultivated soils had a silt loam \ntexture. This is due to long term soil tillage effects on soil particle size. G\u00fclser et \nal. (2016) reported that heterogeneity and variation of soil physical parameters \nin a field due to soil plowing should be taken into consideration for successful \nagricultural management. Table 1 shows that site C had the highest percentage of \nmoisture. This is due to charged particles of more clay enhancing the absorption \nsite of water molecules for site C.  Bulk density of the sites ranged from 1.63 to \n1.26 mg/m3 indicating that, all sites except site C are porous soils. This is due to \nsite C contains less sand and more clay which indicates less chance of forming \nsoil pores. It has been stated that bulk density is primarily affected by soil texture \n(Canarache, 1991) since well graded soils containing both fine and coarse particles \nresults in a higher number of contact points than in a poorly graded soil (Kohnke \nand Franzmeier, 1995). The WHC of all soil sites ranged widely from 33.57% to \n55.57%. The average WHC value was found to be significantly affected by land \nuses. The highest WHC value (55.57%) was recorded in uncultivated soil which \nalso had the highest organic matter content (2.45%), This was probably due to \nthe ability of SOM to act as a sponge in the soil, thereby retaining soil moisture. \nOrganic matter intimately mixed with mineral soil materials has a considerable \ninfluence in increasing moisture holding capacity (FAO, 2005). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 35\n\n\n\nTABLE 2 \nSummary statistics for surface soil physical parameters under different land uses\n\n\n\n5 \n \n\n\n\nResults and Discussion \n\n\n\nSoil Physical properties \n\n\n\nSoil physical properties are presented in Table 2. All cultivated soils had a silt loam texture. \n\n\n\nThis is due to long term soil tillage effects on soil particle size. G\u00fclser et al. (2016) reported \n\n\n\nthat heterogeneity and variation of soil physical parameters in a field due to soil plowing \n\n\n\nshould be taken into consideration for successful agricultural management. Table 1 shows \n\n\n\nthat site C had the highest percentage of moisture. This is due to charged particles of more \n\n\n\nclay enhancing the absorption site of water molecules for site C.  Bulk density of the sites \n\n\n\nranged from 1.63 to 1.26 mg/m3 indicating that, all sites except site C are porous soils. This is \n\n\n\ndue to site C contains less sand and more clay which indicates less chance of forming soil \n\n\n\npores. It has been stated that bulk density is primarily affected by soil texture (Canarache, \n\n\n\n1991) since well graded soils containing both fine and coarse particles results in a higher \n\n\n\nnumber of contact points than in a poorly graded soil (Kohnke and Franzmeier, 1995). The \n\n\n\nWHC of all soil sites ranged widely from 33.57% to 55.57%. The average WHC value was \n\n\n\nfound to be significantly affected by land uses. The highest WHC value (55.57%) was \n\n\n\nrecorded in uncultivated soil which also had the highest organic matter content (2.45%), This \n\n\n\nwas probably due to the ability of SOM to act as a sponge in the soil, thereby retaining soil \n\n\n\nmoisture. Organic matter intimately mixed with mineral soil materials has a considerable \n\n\n\ninfluence in increasing moisture holding capacity (FAO, 2005).  \n\n\n\nTable 2: Summary statistics for surface soil physical parameters under different land \nuses \n\n\n\n Sand (%) Silt (%) Clay (%) Textural \nClass \n\n\n\nWHC(%) BD \n(g/cm3) \n\n\n\nPorosity \n(%) \n\n\n\nSite \ncode \n\n\n\n        Mean \u00b1 SD \n\n\n\nA 23\u00b11.27bc 60\u00b12.75a 17\u00b10.9c Silt loam 33.57\u00b13.3c 1.34\u00b1 \n0.05ab \n\n\n\n49\u00b11.33ab \n\n\n\nB 25\u00b11.4b 48\u00b12.9ab 27\u00b11.2b Silt loam 39.57\u00b14.3bc 1.26\u00b1 \n0.07c \n\n\n\n52\u00b11.87a \n\n\n\nC 12\u00b11.8c 51\u00b13.4ab 37\u00b12.3a Silt loam 41.57\u00b13.4b 1.63\u00b1 \n0.02a \n\n\n\n38\u00b10.53b \n\n\n\nD 23\u00b11.6bc 54\u00b14.5ab 23\u00b11.76bc Silt loam 46.43\u00b14.4ab 01.39\u00b1 \n0.04b \n\n\n\n48\u00b11.07ab \n\n\n\nE 40\u00b11.95a 43\u00b13.2b 17\u00b11.65c loam 55.57\u00b15.2a 1.31\u00b1 \n0.04ab \n\n\n\n51\u00b11.07a \n\n\n\nSoil Chemical Properties\n\n\n\nSoil pH, EC and %Salt\nThe results of the chemical properties of the site soils are presented in Table 3. \nThe average pH value of all sites ranged from 5.96 to 7.4 that indicating medium \nacidic to mildly alkaline pH values. The pH level of the soil directly affects soil \nlife and the availability of essential soil nutrients for plant growth. Factors such as \nparent material, rainfall, and type of vegetation are dominant in determining the \npH of soils. Analysis of variance of the soil properties between land uses showed \nthat the pH distribution is homogenous (p \u2265 0.05) among the different treatments. \nIn terms of soil EC, the average value ranged from 0.52 ds/m to 2.7 ds/m that \nindicate non saline to slightly saline characteristics. Percent salt varies from 0.03 \nto 0.14 that indicates that all site soil carries minimal amount of salt that shows \ngood soil behavior. \n\n\n\nTotal Nitrogen and Available Phosphorus\nTotal nitrogen of all sites ranged from 0.83 to 2.9%. The nitrogen value significantly \ndiffered among the land uses. Site E showed the highest value. This may due \nto higher nitrogen mineralization of nitrogen in the natural vegetative soil due \nto continuous addition of organic matter. Sommerfeldt, (1988) found that long-\nterm annual manure applications increase soil organic matter and nitrogen that is \nvery similar to our result. The available phosphorus of all sites ranged from 3.73 \nmg/kg to 12.16 mg/kg. The available phosphorus was not significantly different \namong the land uses. Site D slightly have lower values than soils where as site B \nshowed the highest value. This may be due to soil organic matter being the main \nsource of available Phosphorus (Mamo and Haque, 1987). The availability of \nphosphorus under most soils declined due to the impact of fixation, abundant crop \nharvest and erosion (Yeshaneh, 2015). Soils with inherent pH values between 6 to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201936\n\n\n\n7.5 with moist and warm conditions are ideal for P-availability, while pH values \nbelow 5.5 and between 7.5 and 8.5 limits P-availability to plants due to fixation \nby aluminum, iron, or calcium, often associated with soil parent materials (Soil \nSurvey Staff, 2009). \n  \nPotassium and Sulfur\nThe potassium of all sites ranged from 120.91 mg/kg to 26.05 mg/kg. The \npotassium value significantly differs among the land uses with site C showed \n\n\n\n7 \n \n\n\n\nTable 3: Summary statistics for surface soil chemical parameters under different land \n\n\n\nuses  \n\n\n\nSites \n\n\n\n A B C D E \nSoil \n\n\n\nParameters \nMean \u00b1 SD \n\n\n\npH 5.96\u00b10.30c 6.6\u00b10.39b 7.4\u00b10.45a 6.16\u00b10.6b 6.25\u00b10.5b \nEC (ds/m) 1.18\u00b10.03ab 1.83\u00b10.04ab 2.7\u00b10.06a 0.52\u00b10.024b 2.25\u00b10.035a \nOC (%) 0.72\u00b10.07ab 0.27\u00b10.03b 1.09\u00b10.06a 0.31\u00b10.04b 1.42\u00b10.12a \n\n\n\nSOM (%) 1.24\u00b10.11bc 0.47\u00b10.12c 1.88\u00b10.18\nb 0.54\u00b10.15c 2.45\u00b10.46a \n\n\n\nTotal \nNitrogen \n\n\n\n(%) \n2.30\u00b10.87b 0.83\u00b10.14c 2.37\u00b10.89\n\n\n\nb 1.33\u00b10.72bc 2.9\u00b10.94a \n\n\n\nAvailable \nPhosphorus \n\n\n\n(mg/kg) \n9.92\u00b11.13b 12.16\u00b12.2a 11.01\u00b11.7a 3.73\u00b10.9c 3.89\u00b10.6c \n\n\n\nPotassium \n(mg/kg) 120.91\u00b17.7a 44.08\u00b13.32b 26.05\u00b12.2c 45.34\u00b13.87b 76.83\u00b16.45ab \n\n\n\nSulfur \n(mg/kg) \n\n\n\n23.58\u00b13.34b\nc 45.05\u00b16.65b 75.79\u00b13.5a 57.26\u00b15.58a\n\n\n\nb 14.74\u00b12.89c \n\n\n\nCEC \n(cmolc/kg) 4.78\u00b11.23b 5.18\u00b1 1.03a 5.52\u00b1 \n\n\n\n1.01a 4.65\u00b1 1.03b 4.75\u00b1 1.05b \n\n\n\nSAR 0.074\u00b10.02b 0.095 \u00b1 0.039a 0.096\u00b1 \n0.023a \n\n\n\n0.074 \u00b1 \n0.04b \n\n\n\n0.089\u00b10.031a\nb \n\n\n\nESP (%) 2.71\u00b1 \n0.75ab 2.65\u00b1 0.68ab 2.95\u00b1 \n\n\n\n0.98a 2.14\u00b1 0.64b 2.48\u00b10.88ab \n\n\n\nBSP (%) 78.68\u00b1 \n11.94bc 84.83\u00b1 11.94a 80.79\u00b1 \n\n\n\n10.94b \n79.80\u00b1 \n14.94b 76.95\u00b1 13.94c \n\n\n\n%Salt 0.08\u00b10.02ab 0.12\u00b10.06ab 0.17\u00b10.04a 0.03\u00b10.001b 0.14\u00b10.01a \n\n\n\n\n\n\n\nTotal Nitrogen and Available Phosphorus \n\n\n\nTotal nitrogen of all sites ranged from 0.83 to 2.9%. The nitrogen value significantly differed \n\n\n\namong the land uses. Site E showed the highest value. This may due to higher nitrogen \n\n\n\nTABLE 3\nSummary statistics for surface soil chemical parameters under different land uses \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 37\n\n\n\nthe lowest value. This may due to the decrease in clay content creating a barrier \nto build water-stable aggregates thus decreasing K content. This result supports \nthe study of Zhang and He, (2014).  The sulfur concentration of all sites ranged \nfrom 75.79 mg/kg to 14.74 mg/kg and was significantly different with land use \nSite E had slightly lower values because it was in uncultivated soil where sulfur \ncontaining fertilizer had not been applied. \n\n\n\nSoil Organic Carbon and Organic Matter\nSoil organic carbon (SOC) of the site soils varied from 0.27 % to 1.42% and soil \norganic matter (SOM) of site soils varied from 0.47 % to 2.45%. Organic carbon \nwhich is an index of soil organic matter differed among the different land uses. The \nSOC was found to be higher in the control soil as the site had been uncultivated \nwith natural addition of organic residue. A great number of studies have reported \nsimilar observations. Yimer et al. (2007) in Ethiopia also compared croplands, \nforestlands and grazing lands and found that soil organic C and total N decreased \nin croplands compared to forestlands. Soils underlying native vegetation (e.g., \nundisturbed) generally feature high SOM as a result of ample litter cover, organic \ninputs, root growth and decay, and abundant burrowing fauna (Price et al. 2010).\n\n\n\nSodium Adsorption Ratio (SAR) and Exchangeable Sodium Percentage (ESP)\nSAR values ranged from 0.074% to 0.096% and ESP values from 2.14% to \n2.95%. Soils that have more than 6% ESP are considered to have structural \nstability problems related to potential dispersion (van de Graaff and Patterson \n2001). Though there is a difference among the values of ESP, all sites had good \nstructural stability.\n\n\n\nBase Saturation Percentage (BSP)\nIn all sites, the base saturation percentage ranged from 76.95% to 84.83%. Soils \nwith 70% or greater BS are unlikely to limit agronomic crop growth due to acidity. \nBase saturation was higher in all sites indicating future possibilities of limiting \ncrop production. \n\n\n\nCorrelation between soil properties\nThere was a considerable degree of correlation among the various chemical \nproperties measured (Table 4). The linear correlation analysis of the five soil \nchemical properties for the study area showed significant correlation soil attribute \npairs (P \u2264 0.01; P \u2264 0.05) (Table 4). \n\n\n\nCONCLUSION\nThis research evaluated and characterized physiochemical properties of soils \nof similar geological and climatic conditions but under different land use in \nSouthwestern Bangladesh. All soils had good porosity and almost similar texture. \nUncultivated soil had the highest organic matter content and is an indication of \nthe affinity of organic matter for water. Soils with high organic matter content \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201938\n\n\n\nand clay particles demonstrated high CEC values. Uncultivated soil naturally had \nthe highest organic carbon value compared to cultivated soil. Long term land \nuse significantly declines soil organic carbon thus impacting negatively climate \nin global C-sequestration. In terms of ESP and SAR, site C showed the highest \nvalue and a significant difference was observed among the treatments (p \u2264 0.05). \nIn BSP, site B showed the highest value and site E showed the lowest value and \nwas statistically insignificant among treatments (p \u2264 0.05). Overall observation \nshowed that long term land use exhibited a significant decline in soil quality. \nLand uses and soil management appear to be good predictor of soil fertility \nstatus. Success in soil management depends on the understanding of how the soil \nresponds to agricultural practices over time. So, sustainable soil management \nshould be incorporated in the development of suitable agricultural management \nsuch as use of organic matter or incorporation of organic mix inorganic fertilizer \nor so on.\n\n\n\nTABLE 4\nCorrelations: N, P, K, S, Ca, Mg, % OC, % OM, %S with %OC and %OM\n\n\n\n9 \n \n\n\n\nSAR values ranged from 0.074% to 0.096% and ESP values from 2.14% to 2.95%. Soils that \n\n\n\nhave more than 6% ESP are considered to have structural stability problems related to \n\n\n\npotential dispersion (van de Graaff and Patterson 2001). Though there is a difference among \n\n\n\nthe values of ESP, all sites had good structural stability. \n\n\n\nBase Saturation Percentage (BSP) \n\n\n\nIn all sites, the base saturation percentage ranged from 76.95% to 84.83%. Soils with 70% or \n\n\n\ngreater BS are unlikely to limit agronomic crop growth due to acidity. Base saturation was \n\n\n\nhigher in all sites indicating future possibilities of limiting crop production.  \n\n\n\nCorrelation between soil properties \n\n\n\nThere was a considerable degree of correlation among the various chemical properties \n\n\n\nmeasured (Table 4). The linear correlation analysis of the five soil chemical properties for the \n\n\n\nstudy area showed significant correlation soil attribute pairs (P \u2264 0.01; P \u2264 0.05) (Table 4).  \n\n\n\n \n \n \n \n \nTable 4: Correlations: N, P, K, S, Ca, Mg, % OC, % OM, %S with %OC and %OM \n \n\n\n\n N P K S Ca Mg %OC %OM \n\n\n\n% OC \n0.566 \n\n\n\n0.055 \n\n\n\n0.204* \n\n\n\n0.524 \n\n\n\n0.656 \n\n\n\n0.020 \n\n\n\n-0.007 \n\n\n\n0.983** \n\n\n\n0.710 \n\n\n\n0.010 \n\n\n\n0.657* \n\n\n\n0.020 \n\n\n\n1 \n\n\n\n1 \n \n\n\n\n% OM \n0.563 \n\n\n\n0.057 \n0.206     \n0.522** \n\n\n\n0.655* \n\n\n\n0.021 \n\n\n\n-0.005 \n\n\n\n0.989** \n\n\n\n0.708 \n\n\n\n0.010 \n\n\n\n0.659 \n\n\n\n0.020 \n\n\n\n0.732* \n\n\n\n0.576 \n\n\n\n1 \n\n\n\n1 \n\n\n\nCell Contents: Pearson correlation,  P-Value (*p\u22640.01 & **p\u22640.05)  \n \n\n\n\nConclusion \n\n\n\nThis research evaluated and characterized physiochemical properties of soils of similar \n\n\n\ngeological and climatic conditions but under different land use in Southwestern Bangladesh. \n\n\n\nAll soils had good porosity and almost similar texture. 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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 196-203 \n\n\n\n196 \n \n\n\n\nGlyphosate Leaching Through a Sandy Loam Soil Amended with Cattle \nDung or Rice Husk Ash: A Laboratory Column Study \n\n\n\nJamilu Garba1, Abd Wahid Samsuri 2*, Muhammad Saiful Ahmad Hamdani3,  \nTariq Faruq Sadiq4 and Abba Nabayi5 \n\n\n\n\n\n\n\n1Department of Soil Science, Faculty of Agriculture, Ahmadu Bello University,  \n1044 Zaria Nigeria \n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia,  \n43400 UPM Serdang, Selangor, Malaysia \n\n\n\n3Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, \n 43400 UPM Serdang, Selangor, Malaysia \n\n\n\n4Department of Soil and Water, College of Agriculture, Salahaddin University, Erbil, Iraq \n5Department of Soil Science, Faculty of Agriculture, Federal University, 7156 Dutse, Nigeria \n\n\n\n \n*Correspondence:  samsuriaw@upm.edu.my   \n\n\n\n \nABSTRACT \n\n\n\nThis study investigated the influence of added organic materials on glyphosate mobility through sandy \nloam soil. An equivalent rate of 10 t ha-1 of either cattle dung or rice husk ash were mixed with the soil \nand inserted into leaching tubes Both soil mixture and controls were spiked with 20 mL of 200 mg L-1 \nglyphosate. This was followed by an addition 100 mL of stimulated rainfall and the leachate collected \nat time intervals (0,3,10,22,32,40,50 and 65 days) for glyphosate analysis.  After 65 days, soils were cut \ninto three, dried and determined for its glyphosate residual concentration. The data obtained was \nanalysed and results revealed no significance difference (p > 0.05) in glyphosate concentration between \nthe treatments, at time intervals (0,3,10,22,32,40,50 and 65 days) and from residual concentration in \nsoil after leaching. Nonetheless, an increased concentration was obtained from both cattle dung (10%) \nand rice husk (9%) compared with control, indicating potential influence of these wastes on glyphosate \nmobility. The order of cumulative glyphosate concentration from post-leaching soils was as follows: \ncontrol with 17.798 \u00b5g g-1; > soil + rice husk ash with 15.484 \u00b5g g-1; and > soil + cattle dung with \n14.918 \u00b5g g-1. Meanwhile, irrespective of the treatments applied, the concentration of glyphosate in the \nsoil layers were of the following order: top layer with 17.020 \u00b5g g-1; > middle with 16.745 \u00b5g g-1; and \n> lower layer with 14.436 \u00b5g g-1. The length of each layer was about 3.3 cm, suggesting low glyphosate \nmobility. \n \nKey words: sorption, soil organic matter, mobility, sandy loam, herbicide \n\n\n\n \nINTRODUCTION \n\n\n\nGlyphosate [N-(phosphonomethyl)glycine] is often applied to agricultural land for weed \ncontrol. Though it is a foliage applied herbicide, glyphosate enters the soil through spray drift \nor direct surface application. Even though, glyphosate is largely regarded as relatively immobile \nin weathered tropical soils, previous studies indicate that its transport from application sites to \nthe surrounding environment is significant (Battaglin et al. 2014; Ronco et al. 2016; Poiger et \nal. 2017; Masiol et al.  2018; Padilla and Selim 2018). These results coupled with concern about \nthe environmental and health effects of glyphosate (Torretta et al. 2018; Van Bruggen et al. \n2018) necessitate continuous investigation into its mobility in soils and thorough risk \nassessment concerning the food chain contamination impact and adverse health effects of \nglyphosate. \n \nSoil types and properties generally affect glyphosate profile mobility. Al-Rajab et al.  (2008) \nreported high glyphosate leaching in clay loam compared to silty clay loam and sandy loam \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 196-203 \n\n\n\n197 \n \n\n\n\nsoils of the Lorraine region in eastern France. Similarly, glyphosate was not detected in the \nsandy soil of Lilla site but was detected in the leachate collected from the clay soil of Lanna \nsite, both in southwest Sweden (Aronsson et al. 2011). Glyphosate leaching is therefore, \ngoverned by macro porous soil structure which supports preferential flow of water and solute. \nMeanwhile, in non-structured soils with no or less macro pores, glyphosate leaching is minimal. \nGlyphosate leaching is largely controlled by the sorption capacity of soil (Berzins et al. 2019). \nThus, soil with amorphous oxides, high content of clay and organic matter, and high CEC \nstrongly adsorb glyphosate and render it immobile.  \n\n\n\nApplication of organic waste as manure is a common practice among our farmers and its \nbenefits includes improving soil structure, porosity and water holding capacity of the soils, \nimproving soil aggregation and water retention and reducing leaching of contaminants (Aldana \net al. 2020). Cattle dung and rice husk ash are abundantly found in Malaysia and largely \nincorporated into the soil as a supplementary source of nutrient but their potential as \namendments in soil remediation still remains largely undocumented. It was hypothesised that \napplication of these wastes will increase the content of soil organic matter to a significant level \n(20 to 50%) which in turns will increase the adsorption capacity of soil, hence decreasing \nglyphosate leaching. The present study therefore aims to investigate the influence of adding \ncattle dung or rice husk ash on leaching of glyphosate through a Malaysian sandy loam soil in \na laboratory column study.  \n\n\n\nMATERIALS AND METHODS \n\n\n\nSoil Sampling, Collection of Cattle Dung and Rice Husk Ash \nBenta series is a sandy loam that was collected from Sementa Hulu (Latitude. 38041\u201966.3\u201dN, \nLongitude. 1010 94\u2019 72.5\u201dE), Raub district, Pahang Malaysia. Surface soils (0-20 cm) were \nsampled from different locations in the sample area and later bulked to one composite sample. \nCattle dung was collected from the animal section of the experimental farm, Faculty of \nAgriculture, Universiti Putra Malaysia. (Latitude. 290 86\u2019 46.0\u201dN, Longitude. 1010 73\u201931.3\u201dE). \nRice husk ash was obtained from BERNAS rice mill Selangor, Malaysia. (Latitude. \n3\u00b040\u201932.4\u201dN, Longitude. 100\u00b059\u201942.5\u201dE). \n \nAll the samples were dried at the drying room, Department of Land Management, Faculty of \nAgriculture, Universiti Putra Malaysia. The soils and cattle dung were ground using laboratory \npestle and mortar followed by sieving of the soils with 2-mm sieve while a 1-mm sieve was \nused for cattle dung and rice husk ash. The sieved soils, cattle dung and rice husk were all stored \nin a clean container for analysis and further studies. \n\n\n\nColumn Leaching Experiment \nThis study was performed according to Ismail et al. (2002) with some modification. One \nhundred grams of sampled soil in triplicate was weighed into a plastic container, and added \nwith 0.5 g of either cattle dung or rice husk ash to obtain an equivalent rate of 10 tons ha-1.  The \nsoils and added residues were weighed into three different containers as replicates and controls \nwere also included. Then, the soils containing either cattle dung or rice husk ash were \nthoroughly mixed and homogenised by repeated stirring with a stainless steel rod.  Meanwhile, \nleaching tubes, 20 cm in length by 2.5 cm in diameter, were placed on a rake in the laboratory \nwith the bottom sealed with cotton and covered by filter paper to support bedding. The controls \nand soil mixture were placed into the column tubes with the top 10 cm space for addition of \nwater. The water was later added to settle the soils into the column. Thereafter, 20 mL of 200 \nmg L-1 glyphosate solution was evenly spiked into each column by using a needle syringe. This \nwas followed by the addition of 100 mL of stimulated rainfall (pH 4.5) and leachate was \ncollected at 0, 3, 10, 22, 32, 40, 50 and 65 days  for glyphosate analysis. Though  20-cm leaching \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 196-203 \n\n\n\n198 \n \n\n\n\ntubes were chosen to mimic rooting depth, only 10 cm  of its length was filled with soil due to \nrapid inactivation of glyphosate in soil (Sprankle et al. 1975), Therefore, it was assumed that \nthe spiked glyphosate  will as usual remain on the first 10 cm of  the soil surface. The pH of the \nwater used was adjusted to 4.5 to mimic the pH of natural rainwater. The leachate was collected \nat day zero to  mimic an immediate rain event after glyphosate application which is reported to \ncause mobility of the herbicide (Vereecken 2005), and then collected at the remaining days \nearlier mentioned (3, 10, 22, 32, 40, 50 and 65 days). It is to be noted that at each day of leachate \ncollection, 100 mL of water was freshly added and flow rate adjusted such that the water eluted \nwas collected after 24 h.   All the samples were arranged in a completely randomized design \n(CRD) and maintained at 230C on a bench in the laboratory. At the end of this experiment, the \nsoil in each column was divided into three layers, dried and analysed for glyphosate residue.  \n \nGlyphosate Residues Analysis \nGlyphosate residue in both leachate and divided soil layers was analysed by high performance \nliquid chromatography (HPLC) using the method described in Garba et al. ( 2018). Briefly, 1 \nmL of leachate or extracted solution containing glyphosate from the divided soil layers was \nmixed with 2 mL 0.05 M borate buffer (pH 9) solution and 1 mL 0.02 M 9-\nflourenylmethlylchloroformate chloride (FMOC-Cl) and poured into a 25- mL centrifuge tube \nThe mixture was shaken with end-to-end shaker at 180 rpm for 1 h. This was followed by a \nwashing with the addition of 2 mL diethyl ether and vortex mixed for 2 min. The organic layer \nfrom the mixture was removed and the aqueous solution containing glyphosate-FMOC was \ntransferred to HPLC vials for analysis. The instrument used was HPLC Agilent 1100 series \n(Agilent Santa Clara, USA) equipped with a G 1315B ultraviolet detector. The analysis of \nglyphosate  was carried out using a stationary phase of Agilent\u00ae Zorbax Eclipse plus C18 (4.6 x \n150 mm, 5 \u00b5m) column. The mobile phase solvent was acetonitrile and 0.05 M KH2PO4 mixture \n(30:70 v/v) using isocratic mode. The flow rate was 0.7 mL min-1 with a column temperature \nof 40oC and 20 \u00b5l injection volume with the wave lengths being 206 and 210 nm. This method \nhad the lowest limit of detection and quantification of 0.024 mg L-1 and 0.076 mg L-1 \nrespectively. \n \nStatistical Analysis \nAll the data obtained was subjected to analysis of variance (ANOVA) using SAS 9.4 (Cary, \nNorth Caroline) statistical software to determine the significant difference between the \ntreatment means at 95% confidence level with the significant means separated using student\u2019s \nTukey range test.  \n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\nAnalysis of variance revealed no significant difference (p > 0.05) between glyphosate residual \nconcentration in the leachate of control and amended soils. The difference in glyphosate \nresidual content between control and amended soils across the leaching period was also not \nsignificant (p > 0.05). Similarly, post-leaching glyphosate residue contents between the soil \nlayers of all treatments was not statistically different (p > 0.05). Table 1 shows the cumulative \nvalues of glyphosate residual concentration from different treatments in the studied soils. \nThough the residual contents were statistically similar, application of cattle dung or rice husk \nash showed an increase in glyphosate residue compared to control. The soil applied with cattle \ndung had a glyphosate residue of 36.064\u00b5g mL-1 followed by soil with rice husk ash with 34.571 \n\u00b5g mL-1 and control had 26.125 \u00b5g mL-1, representing 37%, 36% and 27% of the total residue, \nrespectively. The increase in glyphosate mobility from cattle dung or rice husk ash applied soil \ncompared to control can be attributed to their effect on soil properties and glyphosate molecule \nitself. Results of the chemical analyses (Table 2) showed cattle dung and rice husk ash to have \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 196-203 \n\n\n\n199 \n \n\n\n\nan alkaline condition with their P contents being 0.216 and 0.173% respectively. Our previous \nstudy (Garba et al. 2019) revealed that both cattle dung and rice husk ash contain oxygen acidic \nfunctional groups with the former containing more carboxylic and lactones than the latter with  \ncattle dung having  an additional amines group. Therefore, incorporation of either of these into \nthe soil might result in ionization or protonation of these functional groups and increasing soil \nnegative charges (Novak et al. 2010; Song and Guo 2012; Zhang et al.  2014) which lead to \nincreasing glyphosate repulsion and mobility. Similarly, decomposition of these added residues \ncan increase the soil pH, which increases soil negative charges resulting in greater repulsion \nand mobility of glyphosate. Application of cattle dung or rice husk ash improves soil structure \nthereby increasing preferential flow and glyphosate mobility (Landry et al. 2005).  \n\n\n\nTABLE 1  \nCumulative values of glyphosate concentration in the leachate of the treated soils \n\n\n\nTreatments Concentration in the leachate (\u00b5g mL-1) \n\n\n\nControl  26.125\u00b10.59 \n\n\n\nSoil + cattle dung 36.064\u00b11.45 \n\n\n\nSoil + rice husk ash 34.571\u00b11.06 \n\n\n\nNote:  p>0.05 (n= 24, \u00b1 SE)   \n\n\n\nMobility of glyphosate in soil occurs in solution or in suspension through preferential pathways \nsuch as macro pores and cracks between the soil aggregates ( De Jonge et al. 2000; Borggaard \nand Gimsing 2008), and depends on the hydraulic conductivity of soils. The amount of water \n(100 mL) applied at each day of leachate collection mimic heavy rainfall and the 24 h flow rate \nadjustments stimulate normal water flow within the soils.  This flow increases soil solution and \nmoves along with colloidal particles. In either case, glyphosate in solution or bounded to soil \ncolloids will be transported by this water flow. As glyphosate strongly adsorbs soil mineral and \norganic colloids (Sprankle et al. 1975; Piccolo et al. 1994), it is therefore less mobile in soil. \nHowever, increasing pH and soil negative charges results in greater desorption leading to its \nmobility. The present study is in agreements with Cheah et al. (1997) who reported low \nglyphosate mobility in Malaysian muck and sandy loam soils. In a laboratory soil column study \ninvolving sandy and sandy loam soils of Malaysia, Ismail et al. (2002) reported glyphosate \nmobility from the soils with higher amounts of glyphosate detected at 0-10 cm suggesting its \nlow mobility. \n\n\n\nTABLE 2 \n\n\n\nSelected properties of soil, cattle dung and rice husk ash \n\n\n\nParameter Soil Cattle dung Rice husk ash \npH 6.7 8.14 9.95 \nEc (\u00b5s/cm) 24 2183 3320 \nC (%) 1.67 31 1.80 \nN (%) 0.16 2.53 Nd \nP (g/kg) 0.007 2.16 1.73 \nK (g/kg) 0.13 9.16 1.93 \nCEC (cmol(+)/kg) 12.67 34.50 10.20 \n\n\n\nNote: nd, not detected \n\n\n\nThe interaction of treatments and time on glyphosate mobility during the study period is shown \nin Figure 1. The control column had low residual glyphosate concentration compared to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 196-203 \n\n\n\n200 \n \n\n\n\namended column and out of the total leached from this column, 20% was at day 50 followed by \n19%, 17%, 16% and 11% for days 40, 10, 65 and 3, respectively. The amount leached at day 0 \nwas 7% while 5% and 4% were for days 32 and 22. The cattle dung-applied soil column had \nmore glyphosate leachate than that with rice husk ash.  Out of the total glyphosate leached from \nthis column, 37% was at day 10 while 19% 13% 12% and 7% leached at days 65, 40, 50 and 3, \nrespectively. Moreover, the percent glyphosate leached at day 0 was 6% while 3% and 4% \nleached at days 32 and 22, respectively. Similarly, out of the total glyphosate leached from rice \nhusk ash applied column, 30% was at day 10. This was followed by 17% at day 50, 16% at day \n65 and 14% at day 40. The remaining day intervals of 3, 0, 33 and 22 had glyphosate leached \nof 9%, 6%, 4% and 3%, respectively.  \n\n\n\n\n\n\n\nFigure 1. Residual glyphosate concentration among the treatments in the leachate of the studied \nsoil over time (p > 0.05). \n\n\n\nThe length of each soil column was 10 cm which is within the rooting zone and region of high \nmicrobial function. Therefore, available glyphosate in soil solution can rapidly be broken down, \nresulting in no or less leaching. A strong binding of glyphosate to soil colloids and its immediate \nand complete breakdown by soil microorganisms makes  for unlikely contamination of \nunderground water (Solomon et al. 2007), unless in the event of a substantial spill or an \nabnormal amount used. Results of P mineralisation (Garba et al. 2017) showed that cattle dung \nand rice husk ash significantly (p <0.05) increased extractable P from this soils. Result of \nchemical analysis (Table 2) indicates that cattle dung contains more P than rice husk ash thus, \nsuggesting an increase in the amount of extractable P in soils applied with cattle dung compared \nto rice husk ash. Application of these residues therefore, resulted in increasing soil inorganic P \nwhich competes with glyphosate for adsorption (Borggaard and Gimsing 2008). Furthermore, \ninorganic P had a higher affinity (De Jonge et al. 2000;  Gimsing et al. 2007; Kj\u00e6r et al. 2011) \nfor adsorption surface than glyphosate, resulting in a greater amount of glyphosate in soil \nsolution and consequently its mobility.  \n\n\n\nResults of the post-leaching glyphosate residue analysis (Table 3) showed more glyphosate \ncontent in control (17.798 \u00b5g g-1) followed by the column with rice husk ash (15.484 \u00b5g g-1) \nand cattle dung (14.918 \u00b5g g-1). This difference in readings obtained is attributed to increased \nleaching of glyphosate from the soils due to cattle dung or rice husk ash application. Therefore, \nthe residue left was inversely related to the rate of glyphosate mobility from each treatment. \nFrom the glyphosate residual concentration in the soil layers, it is found that middle layers of \ncontrol and column amended with cattle dung had more glyphosate residue compared to their \n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\n16\n\n\n\n0 3 10 22 32 40 50 65\n\n\n\nC\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \n(\u00b5\n\n\n\ng \nm\n\n\n\nl-1\n)\n\n\n\ndays\n\n\n\ncontrol soil + cattle dung soil + rice husk ask\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 196-203 \n\n\n\n201 \n \n\n\n\nrespective top and lower layers. Meanwhile, top layer of the column amended with rice husk \nash had more glyphosate residue than its middle and lower layers. The length of each soil layer \nwas about 3.3 cm. The present study showed a high glyphosate residue in the top 5cm of both \nsoils suggesting its low mobility due to adsorption by soil mineral and organic surfaces. This is \nin agreement with Okada et al. (2016) who reported a higher glyphosate residue(67.53% of \ninitial applied doses) on the top layer (0-5 cm) of undisturbed columns of Mollisols. \n\n\n\nTABLE 3 \n\n\n\nPost-leaching glyphosate residual concentration (\u00b5g g-1) among the treatments at different \nlayers of the studied soils \n\n\n\nTreatments Top layer Middle layer Lower layer Total \nControl 6.324\u00b11.30 6.770\u00b11.00 4.704 \u00b10.35 17.798 \nSoil + cattle dung 4.891\u00b10.36 5.225\u00b10.09 4.802\u00b10.45 14.918 \nSoil + rice husk ash 5.804\u00b10.15 4.750\u00b10.40 4.930\u00b10.60 15.484 \n\n\n\nNote: p>0.05 (n=3, \u00b1 SE) \n\n\n\nCONCLUSION \nThe present study investigated glyphosate leaching in soil columns amended with cattle dung \nor rice husk ash with the aim of predicting its mobility in soils. Results revealed no significant \ndifference (p > 0.05) on glyphosate mobility between the control and column with cattle dung \nor rice husk ash. However, there was an increase of 10% and 9% of glyphosate residue in soils \namended with cattle dung and rice husk ash respectively. This indicates the potential of these \norganic amendments in enhancing glyphosate leaching. There was increasing glyphosate \nleaching over the period of incubation, reaching a maximum at the 10th day interval (30% of \nthe initially applied doses). Then the leaching declined but increased steadily over time. Thus, \nat the end of the incubation period (65 days), the leaching recorded was only 16% of the initially \napplied glyphosate. The result of post-leaching residue analysis showed more glyphosate \ncontent at the top soils (17.020 \u00b5g g-1) compared to middle (16.745 \u00b5g g-1) and lower layer \n(14.436 \u00b5g g-1) hence suggesting its low mobility. This is attributed to its strong adsorption by \nmineral and organic surfaces. Therefore, based on the present result, it can be concluded that \neven with the soil application of these residues which may likely increases glyphosate \ndesorption still had low mobility of this herbicide.  \n \n\n\n\nACKNOWLEDGMENTS \nThe authors would like to thank Universiti Putra Malaysia for providing a financial support to complete \nthis study through UPM/GP/IPS/2016-9471900 research grant. The PhD scholarship to the first author \nprovided by tertiary education trust fund (Tetfund) Nigeria through Zamfara State College of Education, \nMaru, Nigeria is also highly acknowledged.  \n \n\n\n\nREFERENCES \n\n\n\nAl-Rajab, A. J., S. Amellal and M. Schiavon. 2008. Sorption and leaching of 14C-glyphosate in \nagricultural soils. Agronomy for Sustainable Development 28(3): 419\u2013428. \nhttps://doi.org/10.1051/agro:2008014 \n\n\n\nAldana, G. O., C. Hazlerigg, E. Lopez-capel and  D. Werner. 2020. Agrochemical leaching reduction in \nbiochar-amended tropical soils of Belize. 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"\n\n___________________\n*Corresponding author : zewdusiraw@yahoo.com\n\n\n\nINTRODUCTION\nIn Ethiopia, large-scale public investment in soil and water conservation (SWC) \nhas its roots in the severe droughts of the early 1970s (Kr\u00fcger et al. 1997). This \ndrought and subsequent famines forced the Ethiopian government to engage in \nsoil and water conservation measures, initially through food aid programs (Amede \net al. 2007). Gradually, the focus shifted from food relief to land conservation \nand then to livelihood improvements (Haregeweyn et al. 2015). Some of the \nmajor SWC and land management programs the government implemented in \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 19-33 (2018)                          Malaysian Society of Soil Science\n\n\n\nEffects of Community-Based Watershed Development on Soil \nProperties in the Northwestern Highlands of Ethiopia\n\n\n\nZewdu Siraw1*, Woldeamlak Bewket1 and Mekonnen Adnew2\n\n\n\n1*Department of Geography and Environmental Studies, Addis Ababa University, \nAddis Ababa, Ethiopia, P. O. Box: 1176\n\n\n\n2Department of Geography & Environmental Studies, Debre Markos University \n\n\n\nABSTRACT\nThis study assessed the effects of community-based watershed development \n(CBWD) on the physical and biochemical properties of soils in the northwestern \nhighlands of Ethiopia by using a comparative approach. Two adjacent micro-\nwatersheds, namely Tija Baji (with conservation since 2000) and Tata (without \nconservation) were compared for selected properties of soils. Twenty-four \ncomposite and 24 undisturbed soil core samples were collected from the two micro-\nwatersheds, and analysed following standard soil laboratory analysis procedures. \nThe results showed that CBWD had brought about significant improvements in \nsome of the soil properties considered. The physical soil properties that showed \nsignificant improvement were bulk density, total porosity, field capacity and \npermanent wilting point. Similarly, total nitrogen, organic carbon, soil organic \nmatter and exchangeable calcium were the biochemical properties that showed \nsignificant (P <0.1) improvement at the conserved watershed. In contrast, cation \nexchange capacity, exchangeable potassium, magnesium and sodium, and \npercentage base saturation of the chemical parameters and soil texture of the \nphysical parameters did not show statistically significant change. The results \nindicate the potential of conservation measures implemented through a CBWD \napproach to improve key soil properties, and restore soil degradation and land \nproductivity. We recommend further research on cost-benefit analysis to evaluate \nbenefits against investment costs as well as on success factors to draw lessons for \nscaling up to larger area. \n\n\n\nKeywords: Soil degradation, watershed development, soil properties, \nEthiopia.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201820\n\n\n\ncollaboration with international organizations include Food-for-Work (FFW \n1973-2002), Managing Environmental Resources to Enable Transition to more \nsustainable Livelihoods (MERET 2003-2015), Sustainable Land Management \nProgram I (SLMP-I 2008-2013), the Productive Safety Nets Program (PSNP \n2005-present), and Sustainable Land Management Program II (SLMP-II 2014-\n2018). In addition to these, large-scale soil and water conservation activities \nhave been implemented annually since 2010 through what is called community \nmobilisation, which is a free labour contribution campaign program (Haregeweyn \net al. 2015; MoANR 2016).\n\n\n\nMany studies have evaluated ecological and socio-economic effects of \nSWC interventions in Ethiopia. The results are mixed. Some examples are as \nfollows: Dagnew et al. (2015) reported a significant decrease in runoff volume at \nDebre Mawi watershed in northwestern Ethiopia, after SWC works; Wolancho et \nal. (2011) at Bokole watershed and Tiki et al. (2015) at Hawassa Zuria (both in \nSouth Ethiopia) and Hailu et al. (2012) at Goromti watershed (western Ethiopia) \nfound significant improvements in soil pH at conserved sites compared to non-\nconserved sites. Other studies covering different parts of Ethiopia reported \nsignificant improvements in some of the soil properties they studied, such as in \ntotal nitrogen content (Demelash and  Stahr 2010; Amare et al. 2013; Tiki et al. \n2015; Yaebiyo et al. 2015; Challa et al., 2016), available phosphorous (Demelash \nand Stahr  2010; Wolancho et al. 2011; Yaebiyo et al. 2015), available magnesium \nand sodium (Mengistu et al. 2016), soil organic carbon (Hailu et al. 2012; Amare \net al. 2013; Yaebiyo et al. 2015; Challa et al. 2016), cation exchange capacity \n(Amare et al. 2013; Mengistu et al. 2016), and soil texture (Demelash and Stahr \n2010). \n\n\n\nIn contrast, there are also studies covering different parts of the country \nthat reported absence of significant positive changes in soil properties following \nSWC measures. Mengistu et al. (2016) found non-significant improvement in soil \nhydrology (total moisture content, field capacity and available water capacity) at \nAnjeni watershed after 25 years of conservation work. Demelash and Stahr (2010), \nWolancho et al. (2011) and Hailu et al. (2012) reported absence of significant \nimprovements in cation exchange capacity and total nitrogen. Wolancho et \nal. (2011) found significantly lower organic carbon, available phosphorous, \navailable potassium and pH in conserved sites than in non-conserved sites. \nSimilarly, Damene et al. (2012) compared soil nutrient conditions before and after \nintervention at Lake Maybar Watershed in Wello and found that organic carbon \ndeclined from 1.98% to 1.58% and available phosphorous dropped by 5.16 ppm. \nThis same study indicated that exchangeable K+, Ca2+ and Mg2+ decreased by 0.15 \ncmol (+)/kg, 10.35 cmol (+)/kg, and 6.01 cmol (+)/kg, respectively, after terracing.\n\n\n\nA major conclusion that can be drawn from a  review of previous studies, \nas shown above, is that soil response to SWC interventions is site-specific, which \napparently depends on complex and interacting site specific factors such as the \nlocal geology, geomorphology, topography, climate and land use history. This \nsuggests the need for site-specific studies to assess benefits of SWC measures \n\n\n\nZewdu Siraw\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 21\n\n\n\nin terms of positive changes in soil properties. The aim of this study was to \nassess effects of conservation measures implemented through a community-\nbased watershed development (CBWD) program on selected physical and \nbiochemical properties of soils. The study was conducted in a micro-watershed \n(Tija Baji watershed) in the northwestern highlands of Ethiopia. At this watershed, \ncommunity-based conservation measures have been implemented for the past \n15 years. Before the conservation intervention, Tija Baji watershed was one of \nthe most degraded landscapes of the Woreda (district) and in consequence land \nproductivity was very low and most households in the area were food insecure. \nThe watershed was selected as one of the 600 sites of MERET intervention in \n2000 (Technical Assistant to NGOs [TANGO] 2012). MERET is a project \nsupported by the UN World Food Program (UNWFP) that aims at enhancing land \nproductivity and diversifying livelihoods through conservation of biophysical \nresources and encompasses dealing with both environmental and socio-economic \ndimensions. The major CBWD activities carried out in the study site are farmland \nand hillside terracing, gully rehabilitation, area closure, re-vegetation and nursery \nmanagement. The ecological and livelihood benefits of the CBWD intervention \nare yet to be researched.\n\n\n\nMATERIALS AND METHODS\n\n\n\nApproach\nThere are two approaches for evaluating conservation effects on soil properties, \nas is commonly known in evaluation studies: (i) before- and after-intervention \ncomparison, and (ii) paired-sites, i.e., comparison of conserved and non-conserved \nsites. According to Kumar et al. (2014), the \u2018before and after\u2019 comparison suffers \nfrom some major limitations. These are: (i) it is unable to account for changes in \nsoil properties that are not due to conservation interventions, and (ii) baseline data \nare mostly absent, particularly in the developing countries. In this study, we used \nthe paired-sites comparison approach, and compared soil samples taken from \nconserved and non-conserved (control) sites. The two sites are located adjacent \nto each other and are similar in terms of landform, soil, climate and land use \nconditions, but the control is without external conservation intervention (Figure 1). \n\n\n\nStudy Area Description\nThe study was conducted in two micro-watersheds, namely Tija Baji (conserved \nsite) and Tata (non-conserved or control site), in the northwestern part of Ethiopia, \nwhich extends over 670.66 ha and 1,079.5 ha, respectively. The two micro-\nwatersheds have the same mountainous and steep slope topography. Altitude \nvaries between 2,437 and 3,101m asl at Tata and 2,451and 2,807m asl at Tija Baji \nwatershed (Ethiopian Mapping Agency [EMA] 1998). Tija Baji and Tata are the \ntwo streams that drain the areas into Feres Mada River, which empties into the \nmain Abay (Blue Nile) River (EMA 1998).\n\n\n\nEffects of Community-Based Watershed Development\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201822\n\n\n\n Figure 1: Location of the study micro-watersheds\n\n\n\nThe climate is a sub-tropical type (locally called Weyena Dega). \nMeteorological records from a nearby station (Merto Lemaryam, 2, 531m asl; \nat 8km from the sites) indicates that the mean annual rainfall is 1053 mm, and \nranges from 941 mm to 1203 mm. The rainfall pattern is mono-modal largely \nconcentrated between June and September, and is generally unreliable to support \nlong-cycle crops. The mean annual temperature is 23.6 oC, and varies between \n22.5 oC and 25.0 oC. The dominant soil types in the area are Vertisols. Because of \nthe rugged topography, climate and cultivation of steep slopes, the landscape is \nexposed to a high level of soil erosion risk by surface runoff.\n\n\n\nSmall scale mixed agriculture is the main livelihood system in the micro-\nwatersheds. The main crops cultivated in the area are teff (Eragro stistef), wheat \n(Tiriticum vulgare), barley (Hordeum vulgare), maize (Zea mays), chickpeas \n(Cicera ritinum) and horse bean (Pisum sativum). Vegetables are also important \ncrops grown using irrigation around homesteads. Cattle, goat, donkey and sheep \nare the common livestock in the watersheds. There are some households engaged \nin micro scale businesses (local drink preparation, cattle and grain trading) and \nin handcrafts (carpentry, waving and tannery). However, these constitute only a \nvery small proportion of the total number of households in the micro-watersheds \n(RKAO, 2016). \n\n\n\nThe two micro-watersheds have similar biophysical and socio-economic \nconditions. Their soils have developed from the same parent material and under \n\n\n\n5 \n\n\n\n\n\n\n\nbaseline data are mostly absent, particularly in the developing countries. In this study, we used \n\n\n\nthe paired-sites comparison approach, and compared soil samples taken from conserved and non-\n\n\n\nconserved (control) sites. The two sites are located adjacent to each other and are similar in terms \n\n\n\nof landform, soil, climate and land use conditions, but the control is without external \n\n\n\nconservation intervention (Figure 1).  \n\n\n\n\n\n\n\nStudy Area Description \n\n\n\nThe study was conducted in two micro-watersheds, namely Tija Baji (conserved site) and Tata \n\n\n\n(non-conserved or control site), in the northwestern part of Ethiopia, which extends over 670.66 \n\n\n\nha and 1,079.5 ha, respectively. The two micro-watersheds have the same mountainous and steep \n\n\n\nslope topography. Altitude varies between 2,437 and 3,101m asl at Tata and 2,451and 2,807m \n\n\n\nasl at Tija Baji watershed (Ethiopian Mapping Agency [EMA] 1998). Tija Baji and Tata are the \n\n\n\ntwo streams that drain the areas into Feres Mada River, which empties into the main Abay (Blue \n\n\n\nNile) River (EMA 1998). \n\n\n\n \nFigure 1: Location of the study micro-watersheds \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 23\n\n\n\nsimilar climatic conditions. The livelihood systems and economic activities are \nthe same; both sites are under intensive cultivation (RKAO 2016). The major \ndifference between the two micro-watersheds is the presence (in Tija Baji) and \nabsence (in Tata) of land conservation measures. The Tija Baji watershed has \nbeen under intensive CBWD work over the past 15 years (2000-2015), and \ndifferent types of physical and biological SWC measures, water harvesting \nstructures, soil fertility management and livelihood improvement activities have \nbeen implemented. The participants worked for 40 days per year, and in return \nreceived 3kg wheat per day for 20 days and the other 20 days were free labour \ncontributions. \n\n\n\nSoil Sampling and Analysis \nThe data input to this study were soil samples collected from the conserved \nand non-conserved micro-watersheds in January and February of 2016. Eight \ntransect walks were established (four for each micro-watershed) at the upper and \nlower parts. Then six sample plots were selected from each of the upstream and \ndownstream parts of the micro-watersheds. The sample plots were taken from \nsimilar soil types (i.e., Vertisols). Soil samples from the selected plots were \ncollected by delineating 10m x 10m size plots and sub-samples were collected \nfrom five small pits of 20 cm depth (which is the plow depth and where most \nchanges are expected to occur as a result of conservation activities), at four \ncorners and at the centre of a plot giving five sub-samples using the auger. Then a \ncomposite sample for each particular sample plot was produced by hand mixing \nthe five sub-samples in a clean plastic bag. Core samples were also taken at the \ncentre of each plot using a sharp-edged steel cylinder (core sampler) for bulk \ndensity analysis. Thus, 24 composite and 24 undisturbed core samples (2 micro-\nwatersheds * 2 watershed positions * 6 locations) were collected for laboratory \nanalysis.\n\n\n\nThe samples were air dried, crushed, and passed through a 2-mm sieve for \nlaboratory analysis. Soil pH was determined in 1:2.5 soil water suspensions as \ndescribed by Van-Reeuwijk (1993). Particle size distribution was determined by \nthe hydrometer method (Bouyoucus 1951) and bulk density was determined from \nthe undisturbed core samples as outlined by Carter and Gregorich (2008). Total \nporosity was determined using the formula: \n\n\n\nWhere, P is total porosity (%), BD is the bulk density (g/cm3) and d is the \nparticle density equal to 2.65 g/cm3 (Landon 1991).\n\n\n\nAvailable phosphorous was measured by Olsen method (Olsen and Sommers \n1982), flame photometer (Black et al. 1965) for exchangeable potassium and \nsodium, and atomic absorption spectrophotometer for determining exchangeable \n\n\n\n7 \n\n\n\n\n\n\n\nSoil Sampling and Analysis  \n\n\n\nThe data input to this study were soil samples collected from the conserved and non-conserved \n\n\n\nmicro-watersheds in January and February of 2016. Eight transect walks were established (four \n\n\n\nfor each micro-watershed) at the upper and lower parts. Then six sample plots were selected \n\n\n\nfrom each of the upstream and downstream parts of the micro-watersheds. The sample plots were \n\n\n\ntaken from similar soil types (i.e., Vertisols). Soil samples from the selected plots were collected \n\n\n\nby delineating 10m x 10m size plots and sub-samples were collected from five small pits of 20 \n\n\n\ncm depth (which is the plow depth and where most changes are expected to occur as a result of \n\n\n\nconservation activities), at four corners and at the centre of a plot giving five sub-samples using \n\n\n\nthe auger. Then a composite sample for each particular sample plot was produced by hand \n\n\n\nmixing the five sub-samples in a clean plastic bag. Core samples were also taken at the centre of \n\n\n\neach plot using a sharp-edged steel cylinder (core sampler) for bulk density analysis. Thus, 24 \n\n\n\ncomposite and 24 undisturbed core samples (2 micro-watersheds * 2 watershed positions * 6 \n\n\n\nlocations) were collected for laboratory analysis. \n\n\n\n\n\n\n\nThe samples were air dried, crushed, and passed through a 2-mm sieve for laboratory analysis. \n\n\n\nSoil pH was determined in 1:2.5 soil water suspensions as described by Van-Reeuwijk (1993). \n\n\n\nParticle size distribution was determined by the hydrometer method (Bouyoucus 1951) and bulk \n\n\n\ndensity was determined from the undisturbed core samples as outlined by Carter and Gregorich \n\n\n\n(2008). Total porosity was determined using the formula:  \n\n\n\n  (    \n )    \n\n\n\n\n\n\n\nWhere, P is total porosity (%), BD is the bulk density (g/cm3) and d is the particle density equal \n\n\n\nto 2.65 g/cm3 (Landon 1991). \n\n\n\n\n\n\n\nAvailable phosphorous was measured by Olsen method (Olsen and Sommers 1982), flame \n\n\n\nphotometer (Black et al. 1965) for exchangeable potassium and sodium, and atomic absorption \n\n\n\nspectrophotometer for determining exchangeable magnesium and calcium. The Kjeldahl method \n\n\n\nwas used for total Nitrogen (Bremner and Mulvaney 1982). Cation exchange capacity (CEC) was \n\n\n\ndetermined by the ammonium acetate method (Chapman 1965). The percentage base saturation \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201824\n\n\n\nmagnesium and calcium. The Kjeldahl method was used for total Nitrogen \n(Bremner and Mulvaney 1982). Cation exchange capacity (CEC) was determined \nby the ammonium acetate method (Chapman 1965). The percentage base saturation \nwas calculated by dividing the sum of the base forming cations (K+, Mg2+, Ca2+ \nand Na+) by CEC of the soil and multiplying by 100 (Landon 1991). Soil organic \ncarbon content was measured by the Walkley-Black method, and the amount of \nsoil organic matter was calculated by multiplying the percent of organic carbon \nby a factor of 1.724 (Landon 1991). The pressure plate membrane at 0.33 and 15 \nbars were used to determine soil moisture content at field capacity and permanent \nwilting point, respectively. Available Water Holding Capacity was estimated from \nthe difference between the water content at field capacity and permanent wilting \npoint. Soil analysis was conducted at Debre Markos Soil Laboratory Center and \nAdet Agricultural Research Center.\n\n\n\nData Analysis\nThe t-test was used to test mean difference in soil properties between soils of the \nconserved and non-conserved micro-watersheds and one way ANOVA was used \nto test the difference among the relative locations within the micro-watersheds. \nTukey\u2019s test was used to determine the significance of the variation among relative \nlocations within the micro-watersheds at P < 0.05 level.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPhysical Soil Properties\nTexture\nTable 1 presents soil particle size distribution at the conserved and non-conserved \nmicro-watersheds. Clay soil fraction dominated in both micro-watersheds. \nThe proportions of silt and sand particles were slightly higher at the conserved \nmicro-watershed than the non-conserved. On the other hand, the proportion of \nclay particle size was relatively higher at the non-conserved micro-watershed \nthan the conserved. However, the difference in clay, silt and sand soil contents \nbetween the conserved and non-conserved micro-watersheds was not statistically \nsignificant. A study conducted at Sheka watershed (south Ethiopia) by Yaebiyo \net al. (2015)  found similar statistically non-significant difference in soil particle \n\n\n\n9 \n\n\n\n\n\n\n\nsimilarity of parent materials. Another study by Tiki et al. (2015) indicated that such non-\n\n\n\nsignificant differences in soil texture between conserved and non-conserved sites could be \n\n\n\nattributed to the time factor, when watershed conservation is young and it cannot affect \n\n\n\nweathering process to create a significant effect on soil texture. According to Yubin et al. (2014), \n\n\n\ndegraded soil with topsoil might take 20 years to recover. However, over a very long period, \n\n\n\npedogenesis processes such as erosion, deposition, eluviation, and weathering can change soil \n\n\n\ntexture (Brady and  Weil 2002). \n\n\n\n \nTABLE 1 \n\n\n\nMean values (\u00b1 standard error) of physical properties of soils in 0- to 20cm depth in the Tija Baji \nand Tata watersheds \n\n\n\nRelative \n\n\n\nposition  \n\n\n\n     Physical soil properties   \n\n\n\nBD(g cm-3) \n\n\n\n\n\n\n\nTP (%) Clay (%) Silt (%) Sand (%) Texture class \n\n\n\n CONS CONT CONS  CONT  CONS  CONT  CONS  CONT  CONS CONT   CONS  CONT  \n\n\n\nUpper 1.25\u00b1.03 1.33\u00b1.07 53.02\u00b11.0 49.75\u00b12.6 54.33\u00b12.7 53.33\u00b13.7 23.33\u00b1.0.9 24.00\u00b12.1 22.33\u00b11.9 20.7\u00b11.8 Clay                Clay \n\n\n\nLower 1.24\u00b1.03 1.30\u00b1.03 53.08\u00b11.1 50.82\u00b11.1 54.67\u00b15.9 60.33\u00b12.2 23.67\u00b13.0 19.67\u00b11.7 21.67\u00b13.3 20.0\u00b11.1 Clay Clay \n\n\n\nMean 1.24\u00b1.02 1.32\u00b1.03 53.05\u00b12.5 50.28\u00b14.8 54.50\u00b13.1 57.83\u00b12.1 23.50\u00b11.5 21.83\u00b11.5 22.00\u00b11.8 20.3\u00b11.0 Clay Clay \n\n\n\nNotes: BD- bulk density; TP-total porosity; SD - standard deviation; CONT- control; CONS- conserved \n\n\n\n\n\n\n\nWithin micro-watersheds, the clay fraction was slightly higher at the downstream part of both \n\n\n\nTija Baji and Tata watersheds compared to their upstream parts. In contrast, the sand fraction \n\n\n\nwas slightly higher at the upstream parts. Silt fraction was higher at the lower part of the \n\n\n\nconserved watershed and at the upper part of the non-conserved. However, this variation was not \n\n\n\nstatistically significant (Table 4). \n\n\n\n\n\n\n\nBulk Density and Total Porosity \n\n\n\nThe mean bulk density of soils at the non-conserved micro-watershed (1.32 g.cm-3) was \n\n\n\nsignificantly (P<0.1) higher than the conserved micro-watershed (1.24 g.cm-3) (Table 1). This is \n\n\n\nassociated with the higher soil organic matter in the conserved micro-watershed (Table 3). \n\n\n\nDemelash and Stahr (2010) and Challa et al. (2016) also found statistically significant higher \n\n\n\nbulk density in non-conserved sites in the northwestern and central highlands of Ethiopia, \n\n\n\nrespectively. Gebreselassie et al. (2015) also indicated that non-conserved plots had significantly \n\n\n\nhigher bulk density than conserved plots due to high organic matter accumulation at the \n\n\n\nconserved plots in the Zikre watershed in  northwestern Ethiopia. The variations among the \n\n\n\nTABLE 1\nMean values (\u00b1 standard error) of physical properties of soils in 0- to 20cm depth in\n\n\n\nthe Tija Baji and Tata watersheds\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 25\n\n\n\nsize distribution between conserved and non-conserved sites. According to Foth \n(1990), the absence of significant difference in soil texture between adjacent sites \ncould be due to homogeneity of soil forming processes and similarity of parent \nmaterials. Another study by Tiki et al. (2015) indicated that such non-significant \ndifferences in soil texture between conserved and non-conserved sites could be \nattributed to the time factor, when watershed conservation is young and it cannot \naffect weathering process to create a significant effect on soil texture. According \nto Yubin et al. (2014), degraded soil with topsoil might take 20 years to recover. \nHowever, over a very long period, pedogenesis processes such as erosion, \ndeposition, eluviation, and weathering can change soil texture (Brady and  Weil \n2002).\n\n\n\nWithin micro-watersheds, the clay fraction was slightly higher at the \ndownstream part of both Tija Baji and Tata watersheds compared to their upstream \nparts. In contrast, the sand fraction was slightly higher at the upstream parts. Silt \nfraction was higher at the lower part of the conserved watershed and at the upper \npart of the non-conserved. However, this variation was not statistically significant \n(Table 4).\n\n\n\nBulk Density and Total Porosity\nThe mean bulk density of soils at the non-conserved micro-watershed (1.32 g.cm-3) \nwas significantly (P<0.1) higher than the conserved micro-watershed (1.24 g.cm-\n\n\n\n3) (Table 1). This is associated with the higher soil organic matter in the conserved \nmicro-watershed (Table 3). Demelash and Stahr (2010) and Challa et al. (2016) \nalso found statistically significant higher bulk density in non-conserved sites in \nthe northwestern and central highlands of Ethiopia, respectively. Gebreselassie \net al. (2015) also indicated that non-conserved plots had significantly higher \nbulk density than conserved plots due to high organic matter accumulation at the \nconserved plots in the Zikre watershed in  northwestern Ethiopia. The variations \namong the relative locations within micro-watersheds were not statistically \nsignificant (Table 4). Total soil porosity was higher (53.0%) at the conserved \nmicro-watershed than the non-conserved (50.1%), and the difference was \nstatistically significant at P<0.1 level (Table 1). Variation in soil porosity between \nthe upstream and downstream parts within micro-watersheds was statistically \nnon-significant. \n\n\n\nField Capacity, Permanent Wilting Point and Water Holding Capacity\nThe hydrological soil properties of the conserved and non-conserved micro-\nwatersheds are presented in Table 2. The field capacity, permanent wilting point \nand water holding capacity of soil at the conserved watershed were slightly \nhigher than the soil at the non-conserved watershed. This might be attributed \nto the relatively high soil organic matter and cation exchange capacity at Tija \nBaji watershed. However, the observed difference between conserved and non-\nconserved watersheds was statistically significant at P<0.1. On the other hand, there \nwas statistically significant (P<0.05 level) difference in field capacity between the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201826\n\n\n\nupper and lower parts of both conserved and non-conserved watersheds (Table \n5). At another watershed (Anjeni), Mengistu et al. (2016) have found statistically \nsignificant higher field capacity and available water holding capacity at the \nconserved watershed. This watershed is located in the northwestern highlands of \nEthiopia, and experienced soil and water management practices for more than 25 \nyears. They also found high but statistically insignificant permanent wilting point \nat this conserved watershed.\n\n\n\nTABLE 2\nMean values (\u00b1 standard error) of FC, AWC and PWP of conserved and non-conserved \n\n\n\nmicro-watersheds\n\n\n\n10 \n\n\n\n\n\n\n\nrelative locations within micro-watersheds were not statistically significant (Table 4). Total soil \n\n\n\nporosity was higher (53.0%) at the conserved micro-watershed than the non-conserved (50.1%), \n\n\n\nand the difference was statistically significant at P<0.1 level (Table 1). Variation in soil porosity \n\n\n\nbetween the upstream and downstream parts within micro-watersheds was statistically non-\n\n\n\nsignificant.  \n\n\n\n\n\n\n\nField Capacity, Permanent Wilting Point and Water Holding Capacity \n\n\n\nThe hydrological soil properties of the conserved and non-conserved micro-watersheds are \n\n\n\npresented in Table 2. The field capacity, permanent wilting point and water holding capacity of \n\n\n\nsoil at the conserved watershed were slightly higher than the soil at the non-conserved \n\n\n\nwatershed. This might be attributed to the relatively high soil organic matter and cation exchange \n\n\n\ncapacity at Tija Baji watershed. However, the observed difference between conserved and non-\n\n\n\nconserved watersheds was statistically significant at P<0.1. On the other hand, there was \n\n\n\nstatistically significant (P<0.05 level) difference in field capacity between the upper and lower \n\n\n\nparts of both conserved and non-conserved watersheds (Table 5). At another watershed (Anjeni), \n\n\n\nMengistu et al. (2016) have found statistically significant higher field capacity and available \n\n\n\nwater holding capacity at the conserved watershed. This watershed is located in the northwestern \n\n\n\nhighlands of Ethiopia, and experienced soil and water management practices for more than 25 \n\n\n\nyears. They also found high but statistically insignificant permanent wilting point at this \n\n\n\nconserved watershed. \n\n\n\n\n\n\n\nTABLE 2 \nMean values (\u00b1 standard error) of FC, AWC and PWP of conserved and non-conserved micro-\n\n\n\nwatersheds \nRelative \n\n\n\nlocations \n\n\n\nHydrological properties \n\n\n\nFC (%) PWP (%) AWC (%) \n\n\n\nConserved Control Conserved Control Conserved Control \n\n\n\nUpper 34.54\u00b1.76 33.86\u00b1.90 21.80\u00b1.92 21.28\u00b11.3 12.74\u00b11.29 12.58\u00b1.59 \n\n\n\nLower 39.43\u00b1.96 36.01\u00b1.90 26.58\u00b12.9 23.87\u00b11.2 12.85\u00b12.46 12.14\u00b1.39 \n\n\n\nAverage 36.98\u00b1.67 34.94\u00b1.95 24.19\u00b11.4 22.58\u00b11.1 12.79\u00b1.43 12.36\u00b1.33 \n\n\n\nNotes: FC- field capacity; PWP- permanent wilting point; AWC- available water holding capacity; SD- standard \n\n\n\ndeviation \n\n\n\n\n\n\n\nBiochemical Soil Properties\nSoil pH\nThe pH of soils at the conserved micro-watershed was lower than the non-\nconserved (Table 3), and the difference was statistically significant at P<0.05. The \nhigher organic matter content likely increased H+ and hence reduced pH. Soil pH \nwas significantly (at P<0.1) higher at the upper parts of both watersheds, where \norganic matter contents were lower. \n\n\n\nTABLE 3\nMean value (\u00b1 standard error) of chemical properties of soils in the conserved (Tija Baji) \n\n\n\nand non-conserved (Tata) micro-watersheds\n\n\n\n11 \n\n\n\n\n\n\n\nBiochemical Soil Properties \n\n\n\nSoil pH \n\n\n\nThe pH of soils at the conserved micro-watershed was lower than the non-conserved (Table 3), \n\n\n\nand the difference was statistically significant at P<0.05. The higher organic matter content \n\n\n\nlikely increased H+ and hence reduced pH. Soil pH was significantly (at P<0.1) higher at the \n\n\n\nupper parts of both watersheds, where organic matter contents were lower.  \n\n\n\n\n\n\n\nTABLE 3 \nMean value (\u00b1 standard error) of chemical properties of soils in the conserved (Tija Baji) and \n\n\n\nnon-conserved (Tata) micro-watersheds \n\n\n\nWS  RLW \n\n\n\nPH \n\n\n\n(H2O) \n\n\n\nCEC \n\n\n\n[cmol \n\n\n\n(+) kg-1] \n\n\n\nNa \n\n\n\n[cmol \n\n\n\n(+) kg-1] \n\n\n\nK \n\n\n\n[cmol \n\n\n\n(+) kg-1] \n\n\n\nCa \n\n\n\n[cmol \n\n\n\n(+) kg-1] \n\n\n\nMg \n\n\n\n[cmol \n\n\n\n(+) kg-1] \n\n\n\nPBS \n\n\n\n(%) \n\n\n\nTN \n\n\n\n(%) \n\n\n\nAv. P \n\n\n\n(ppm) \n\n\n\n\n\n\n\nOC \n\n\n\n(%) \nOM \n\n\n\n(%) \n\n\n\nCS  UP 6.31\u00b12.1 30.12\u00b13.12 0.43\u00b1.19 0.54\u00b1.03 15.73\u00b11.35 4.63\u00b1.37 72.64\u00b15.28 0.12\u00b1.01 10.34\u00b12.7 1.02\u00b1.4 1.76.07 \n\n\n\nCT UP 6.97\u00b1.3 23.63\u00b12.48 0.29\u00b1.08 0.53\u00b1.04 13.54\u00b11.17 4.20\u00b1.38 80.36\u00b14.23 0.09\u00b1.01 12.14\u00b13.1 0.69\u00b1.11 1.19\u00b1.19 \n\n\n\nDF  -0.66 +6.49 +0.14 +0.01 +2.19 +0.43 -7.72 +0.03 -1.8 +0.33 +0.57 \n\n\n\nCS LR 6.39\u00b1.1 27.78\u00b11.63 0.42\u00b1.31 0.61\u00b1.04 18.37\u00b11.50 5.02\u00b1.31 88.33\u00b14.28 0.14\u00b1.03 16.55\u00b11.8 1.06\u00b1.20 1.82\u00b1.35 \n\n\n\nCT LR 7.01\u00b1.2 31.58\u00b13.32 0.25\u00b1.05 0.56\u00b1.06 14.36\u00b11.13 5.12\u00b1.72 66.75\u00b16.12 0.08\u00b1.02 8.88\u00b13.2 0.81\u00b1.11 1.39\u00b1.2 \n\n\n\nDF  -0.62 -3.80 +0.17 +0.05 +4.01 -0.10 +11.58 +0.06 +7.67 +0.25 +0.81 \n\n\n\nCS mean 6.35\u00b1.13 28.95\u00b11.71 0.43\u00b1.17 0.57\u00b1.03 17.25\u00b1.78 4.83\u00b1.23 80.48\u00b14.01 0.12\u00b1.01 13.45\u00b11.8 1.04\u00b1.09 1.79\u00b1.16 \n\n\n\nCT mean 6.99\u00b1.17 27.61\u00b12.23 0.27\u00b1.04 0.55\u00b1.03 13.95\u00b11.04 4.66\u00b141 73.55\u00b16.12 0.09\u00b1.01 10.47\u00b12.2 0.75\u00b1.07 1.29\u00b1.13 \n\n\n\nDF  -0.64 +1.34 +0.16 +0.02 +3.30 +0.17 +6.93 +0.03 +2.98 +0.29 +0.50 \n\n\n\nNotes: CEC- cation exchange capacity; PBS- percentage base saturation; SD- standard deviation; WS-watersheds; \nRLW- relative location in the watersheds; UP- upper; LR- lower; DF- difference \n \n\n\n\nOrganic Carbon \n\n\n\nAverage organic carbon at the conserved micro-watershed (1.04%) was higher than in the non-\n\n\n\nconserved (0.75%), with the difference being statistically significant at P<0.05 level. This \n\n\n\ndifference was related to increased biomass cover in the conserved watershed (Figure 2). Lower \n\n\n\norganic carbon was observed in the upper parts of both watersheds, but the variations among \n\n\n\nrelative locations within micro-watersheds were statistically non-significant. Significant \n\n\n\ndifference in soil organic carbon between control and treated sites in different localities in \n\n\n\nEthiopia have been reported by previous studies (Hailu et al. 2012; Amare et al. 2013; Yaebiyo \n\n\n\net al. 2015; Chala et al. 2016).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 27\n\n\n\nOrganic Carbon\nAverage organic carbon at the conserved micro-watershed (1.04%) was higher \nthan in the non-conserved (0.75%), with the difference being statistically \nsignificant at P<0.05 level. This difference was related to increased biomass cover \nin the conserved watershed (Figure 2). Lower organic carbon was observed in the \nupper parts of both watersheds, but the variations among relative locations within \nmicro-watersheds were statistically non-significant. Significant difference in soil \norganic carbon between control and treated sites in different localities in Ethiopia \nhave been reported by previous studies (Hailu et al. 2012; Amare et al. 2013; \nYaebiyo et al. 2015; Chala et al. 2016). \n\n\n\nFigure 2. Conservation practices at Tija Baji watershed\n\n\n\nThe average soil organic matter content at the conserved micro-watershed \n(1.79%) was higher than at the non-conserved (1.29%) with the difference  being \nstatistically significant at P<0.05 level. The higher soil organic matter content in \nthe conserved watershed apparently contributed to the observed changes in the \nother soil properties such as total porosity.\n\n\n\nTotal Nitrogen\nTotal nitrogen at the conserved micro-watershed (0.12%) was higher than at the \nnon-conserved (0.09%) with the difference being statistically significant at P<0.05 \nlevel (Table 3). Reduced soil erosion and increased soil organic matter partly \nexplain the higher total nitrogen in the conserved watershed. Our finding is in \nagreement with many previous studies (e.g., Demelash and  Stahr 2010; Damene \net al. 2012; Amare et al., 2013; Tiki et al. 2015; Yaebiyo et al. 2015; Challa \net al. 2016; Ademe et al. 2017) that reported higher total nitrogen at conserved \nsites, compared to non-conserved, in different parts of Ethiopia. Variations in \ntotal nitrogen content between upper and lower parts of both watersheds were \nstatistically significant at P< 0.1 level (Table 4).\n\n\n\nAvailable Phosphorous\nAvailable phosphorous content was slightly higher (13.45ppm) at the conserved \nwatershed than at the non-conserved (10.47ppm), but the difference was not \n\n\n\n12 \n\n\n\n\n\n\n\n  \nFigure 2. Conservation practices at Tija Baji watershed \n\n\n\n\n\n\n\nThe average soil organic matter content at the conserved micro-watershed (1.79%) was higher \n\n\n\nthan at the non-conserved (1.29%) with the difference  being statistically significant at P<0.05 \n\n\n\nlevel. The higher soil organic matter content in the conserved watershed apparently contributed \n\n\n\nto the observed changes in the other soil properties such as total porosity. \n \n\n\n\nTotal Nitrogen \n\n\n\nTotal nitrogen at the conserved micro-watershed (0.12%) was higher than at the non-conserved \n\n\n\n(0.09%) with the difference being statistically significant at P<0.05 level (Table 3). Reduced soil \n\n\n\nerosion and increased soil organic matter partly explain the higher total nitrogen in the conserved \n\n\n\nwatershed. Our finding is in agreement with many previous studies (e.g., Demelash and  Stahr \n\n\n\n2010; Damene et al. 2012; Amare et al., 2013; Tiki et al. 2015; Yaebiyo et al. 2015; Challa et al. \n\n\n\n2016; Ademe et al. 2017) that reported higher total nitrogen at conserved sites, compared to non-\n\n\n\nconserved, in different parts of Ethiopia. Variations in total nitrogen content between upper and \n\n\n\nlower parts of both watersheds were statistically significant at P< 0.1 level (Table 4). \n\n\n\n\n\n\n\nAvailable Phosphorous \n\n\n\nAvailable phosphorous content was slightly higher (13.45ppm) at the conserved watershed than \n\n\n\nat the non-conserved (10.47ppm), but the difference was not statistically significant (Table 4). \n\n\n\nVariations in available phosphorus content between upper and lower parts of the watersheds \n\n\n\nwere not statistically significant (Table 4). \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201828\n\n\n\nstatistically significant (Table 4). Variations in available phosphorus content \nbetween upper and lower parts of the watersheds were not statistically significant \n(Table 4).\n\n\n\nExchangeable Bases and Cation Exchange Capacity\nThe exchangeable potassium, magnesium and sodium content of soils in the \nconserved micro-watershed were slightly higher than that in the non-conserved, but \nthe differences were statistically non-significant (Tables 3 & 4).The exchangeable \ncalcium content of the conserved watershed (17.25cmol(+) kg-1) was significantly \nhigher (P<0.05) than the control watershed (13.95cmol(+)kg-1). The exchangeable \npotassium, magnesium and sodium contents did not show significant variations \nacross the upper and lower parts of both micro-watersheds. Exchangeable calcium \nshowed a significant difference among the relative locations within the micro-\nwatersheds (P<0.05; Table 4). \n\n\n\n14 \n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nStatistical results of t-test (between conserved and non-conserved micro-watersheds) and one-\n\n\n\nway ANOVA (among relative locations within the micro-watersheds) \n\n\n\nSoil  \n\n\n\nproperties \n\n\n\nBetween watersheds Among relative locations within \n\n\n\nwatersheds \n\n\n\nT-value P-value F P-value \n\n\n\npH (H2O) 2.971 0.007** 2.732 0.071*** \n\n\n\nCEC[cmol(+) kg-1] 0.465 0.647 1.624 0.215 \n\n\n\nNa[cmol(+) kg-1] 0.859 0.406 0.235 0.871 \n\n\n\nK[cmol(+) kg-1] 0.594 0.559 0.504 0.684 \n\n\n\nCa[cmol(+) kg-1] 2.368 0.028** 2.648 0.047** \n\n\n\nMg[cmol(+) kg-1] 0.347 0.733 0.768 0.525 \n\n\n\nPBS (%) 1.208 0.240 3.453 0.036** \n\n\n\nTN (%) 2.753 0.012** 2.535 0.086*** \n\n\n\nAva.P (ppm) 1.034 0.312 1.428 0.264 \n\n\n\nOC (%) 2.280 0.033** 1.082 0.188 \n\n\n\nOM (%) 2.280 0.033** 1.757 0.188 \n\n\n\nBD (g cm-3) 1.749 0.099*** 1.007 0.410 \n\n\n\nTP (%) 1.749 0.099*** 1.007 0.410 \n\n\n\nClay (%) 0.882 0.387 0.523 0.671 \n\n\n\nSilt (%) 0.789 0.438 0.919 0.450 \n\n\n\nSand (%) 0.793 0.436 0.222 0.880 \n\n\n\nFC (%) 1.753 0.094*** 7.830 0.001* \n\n\n\nPWP (%) 1.954 0.064*** 2.700 0.073*** \n\n\n\nAWC (%) 1.066 0.307 1.428 0.264 \n\n\n\nNotes: *Significant at P<0.01; **Significant at P<0.05; ***Significant at P<0.1; PBS- percentage base saturation; \n\n\n\nFC- field capacity; PWP- permanent wilting point; AWC- available water holding capacity; CEC- cation exchange \n\n\n\ncapacity; BD- bulk density \n\n\n\n\n\n\n\nPercentage Base Saturation \n\n\n\nThe percentage base saturation at the conserved micro-watershed was slightly higher (80.5%) \n\n\n\nthan in the non-conserved watershed (73.5%) (Table 3), but the difference was not statistically \n\n\n\nsignificant (Table 4). Within watersheds, percentage base saturation was higher in the \n\n\n\nTABLE 4\nStatistical results of t-test (between conserved and non-conserved micro-watersheds) and \n\n\n\none-way ANOVA (among relative locations within the micro-watersheds)\n\n\n\nNotes: CEC- cation exchange capacity; PBS- percentage base saturation; SD- standard \ndeviation; WS-watersheds; RLW- relative location in the watersheds; UP- upper; LR- \nlower; DF- difference\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 29\n\n\n\nThe CEC of soils at the conserved micro-watershed was higher (28.95 cmol \n(+) kg-1) than at the non-conserved (27.61cmol (+) kg-1), but the difference was not \nstatistically significant. Similarly, the CEC of soils did not show statistically \nsignificant variations among the relative locations considered in both micro-\nwatersheds (Table 4). \n\n\n\nPercentage Base Saturation\nThe percentage base saturation at the conserved micro-watershed was slightly \nhigher (80.5%) than in the non-conserved watershed (73.5%) (Table 3), but \nthe difference was not statistically significant (Table 4). Within watersheds, \npercentage base saturation was higher in the downstream part of the conserved \nwatershed and upstream part of the non-conserved watershed with the variations \nbeing statistically significant at P<0.05 (Table 4).\n\n\n\nTABLE 5\nMultiple comparisons test of soil properties across the upper and lower parts of both \n\n\n\nmicro-watersheds\n\n\n\n14 \n\n\n\n\n\n\n\nBD (g cm-3) 1.749 0.099*** 1.007 0.410 \n\n\n\nTP (%) 1.749 0.099*** 1.007 0.410 \n\n\n\nClay (%) 0.882 0.387 0.523 0.671 \n\n\n\nSilt (%) 0.789 0.438 0.919 0.450 \n\n\n\nSand (%) 0.793 0.436 0.222 0.880 \n\n\n\nFC (%) 1.753 0.094*** 7.830 0.001* \n\n\n\nPWP (%) 1.954 0.064*** 2.700 0.073*** \n\n\n\nAWC (%) 1.066 0.307 1.428 0.264 \n\n\n\nNotes: *Significant at P<0.01; **Significant at P<0.05; ***Significant at P<0.1; PBS- percentage base saturation; \n\n\n\nFC- field capacity; PWP- permanent wilting point; AWC- available water holding capacity; CEC- cation exchange \n\n\n\ncapacity; BD- bulk density \n\n\n\n\n\n\n\nPercentage Base Saturation \n\n\n\nThe percentage base saturation at the conserved micro-watershed was slightly higher (80.5%) \n\n\n\nthan in the non-conserved watershed (73.5%) (Table 3), but the difference was not statistically \n\n\n\nsignificant (Table 4). Within watersheds, percentage base saturation was higher in the \n\n\n\ndownstream part of the conserved watershed and upstream part of the non-conserved watershed \n\n\n\nwith the variations being statistically significant at P<0.05(Table 4). \n\n\n\n\n\n\n\nTABLE 5 \n\n\n\nMultiple comparisons test of soil properties across the upper and lower parts of both micro-\n\n\n\nwatersheds \n\n\n\nSoil  \n\n\n\nproperties \n\n\n\nSignificant difference among relative \n\n\n\nlocations \n\n\n\nP \n\n\n\nCa[cmol(+) kg-1] Tija Baji downstream Vs Tata upstream 0.071*** \n\n\n\n Tija Baji downstream Vs Tata downstream 0.064*** \n\n\n\nPBS (%) Tija Baji downstream Vs Tata downstream 0.031** \n\n\n\nFC (%) Tija Baji downstream Vs Tata  upstream 0.001* \n\n\n\n Tija Baji downstream Vs Tija Baji upstream 0.005* \n\n\n\nPWP (%) Tija Baji downstream Vs Tata upstream     0.064*** \n\n\n\nNotes: *Significant at P<0.01;**Significant at P<0.05; ***Significant at P<0.1; PBS- percentage base saturation; \n\n\n\nFC- field capacity; PWP-0 permanent wilting point \n\n\n\n \nCONCLUSIONS\n\n\n\nThe objective of this study was to assess the effects of conservation measures \nimplemented through a CBWD approach on selected physical and biochemical \nproperties of soils by comparing conserved and non-conserved sites in the \nnorthwestern highlands of Ethiopia. The findings show that CBWD had brought \nabout significant improvement in some of the soil properties considered. Soil \nbulk density, total porosity, field capacity and permanent wilting point were \nsignificantly improved soil physical properties at the conserved micro-watershed, \ncompared with the non-conserved. Similarly, exchangeable calcium, total \nnitrogen, organic carbon and soil organic matter showed significant improvement \nat the conserved micro-watershed. The observed differences between conserved \nand non-conserved micro-watersheds indicate the potential of CBWD to improve \nkey soil properties, and restore soil degradation and land productivity. There is \nclearly a need to sustain the conservation investments so that the full and long-\nterm benefits in land productivity will be achieved. We suggest further research \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201830\n\n\n\non cost-benefit analysis to evaluate benefits against investment costs as well as on \nsuccess factors to draw lessons for implementation of CBWD programs at scale. \n\n\n\nACKNOWLEDGMENTS\nWe are grateful to Addis Ababa University and Association of African Universities \nfor their financial support to the first author. We are also very grateful to the \nlocal communities, Development Agents, Community Members and Watershed \nPlanning Team in the study watersheds for providing us with information about \nthe study sites and for the overall support during fieldwork.\n\n\n\nREFERENCES\nAdeme, Y., T. Kebede, A. Mullatu and T. Shafi. 2017. Evaluation of the effectiveness \n\n\n\nof soil and water conservation practices on improving selected soil properties in \nWonago district, Southern Ethiopia. Journal of Soil Science and Environmental \nManagement 8(3): 70-79.\n\n\n\nAmare, T. A. 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"\n\nINTRODUCTION\nIt is known that intensive agricultural practices have significant effects on soil \ndegradation through loss of soil organic matter and  a decline in soil structure \nresulting in soil compaction and root growth (Usowics and Lipiec 2009; Busscher \nand Bauer 2003). Nitrogen is one of the most important plant nutrients in arable \nagricultural fields. The nitrogen cycle in soil is largely microbiologically mediated \nand the main components involve the transformation of organic N into plant-\navailable mineral forms, primarily nitrate (NO3-N) and ammonium (NH4-N) \nnitrogen. This process depends on several agronomic practices, mainly cropping \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 55-68 (2019)                         Malaysian Society of Soil Science\n\n\n\nShort Term Effects of Different Tillage Methods on Nitrate \nContent in Soil and Corn Yield \n\n\n\nKemal Cagatay Selvi1, Coskun G\u00fclser2* and Mehmet Arif Beyhan1\n\n\n\n1Ondokuz May\u0131s University, Faculty of Agriculture, Agricultural Machinery and \nTechnologies Engineering Department, 55139 Samsun, Turkey\n\n\n\n2Ondokuz May\u0131s University, Faculty of Agriculture, Soil Science and Plant Nutrition \nDepartment 55139 Samsun, Turkey\n\n\n\nABSTRACT\nResidual nitrate nitrogen in a soil profile is influenced by agricultural practices \nsuch as fertilisation, irrigation and cultivation.  In this study, the effects of different \ntillage methods and timing on corn yield were investigated in relation to soil \npenetration resistance and variation of nitrate nitrogen (NO3-N) through a vertisol \nsoil depth. A field experiment was carried out with three different tillage times \n(Fall-at the end of October, Early-at the middle of May; and Late-at the end of \nMay) and tillage methods (mouldboard, chisel and direct drilling) in the Black \nSea Agricultural Research Institute in 2011. Nitrate nitrogen values in 0-20, 20-40 \nand 40-60 cm soil layers were measured for the six different soil sampling times \nusing the potentiometric method. Corn yield values generally decreased when the \nfirst soil tillage time when mould board and chisel applications were delayed or \nwere made at the end of May. The fall tillage treatment with mouldboard had the \nhighest corn yield (61.1 Mg/ha) while the lowest yield value (30.9 Mg/ha) was \nfound with the direct drilling treatment. Generally, late tillage timing at the end of \nMay reduced corn yield due to changing soil structure with reducing penetration \nresistance, increasing macroporosity and nitrate leaching in the soil profile. In \nconclusion, fall season soil tillage using mouldboard in clay soils is suggested to \nachieve optimum plant growth soil conditions resulting in high corn yield, and \nbecause of nitrate leaching, it also results in  beneficial effects on conservation of \nwater pollution.\n\n\n\nKeywords:\t Tillage\tmethods,\ttillage\ttiming,\tnitrate,\tsoil\tprofile,\tcorn.\n\n\n\n___________________\n*Corresponding author : cgulser@omu.edu.tr\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201956\n\n\n\nsystems, soil tillage, and N fertilisation (source, amount, and time of application) \n(Montemurro 2009). Different nitrogen forms (e.g., urea and ammonium) in most \nfertilisers can be transformed into nitrate N when applied into soil in crop rotation \nsystems (Ju et al. 2003; Li et al. 2011). A considerable amount of inorganic \nnitrogen, especially in residual nitrate form, accumulates in the soil profile after \nharvesting crops in the intensive agricultural production systems (Li et al. 2011). \nThe residual soil nitrate is a key factor for optimising crop N management, \nimproving N use efficiency, and reducing the impact of farmland N losses on the \nenvironment (Zhang et al. 2017).\n Soil tillage has a large impact on governing plant nutrient dynamics \n(Pekrun et al. 2003). Tillage techniques affect the root absorption of macronutrients \nand trace elements. The distribution pattern of macronutrients and micronutrients \nin topsoil is usually modified by tillage systems (Ozpinar and Cay 2009). Regular \nsoil disturbance causes a decline in soil N due to the mineralisation of organic \nmatter (McCarty et al., 1995). Cultivation exposes organic matter previously not \naccessible to microbial attack. Therefore most N losses occur during the first few \nyears after cultivation (Stevenson 1965). Due to the decrease in soil disturbance \nin no-till, the N mineralisation rate is much slower. Lower mineralisation, higher \nleaching and higher denitrification (due to higher surface water content) in no-\ntill tend to lower available N, particularly in spring (Thomas and Frye 1984). \nG\u00fclser and Candemir (2012) reported that nitrate has an important role in plant \nnutrition as it is leached easily in a soil profile. Nitrate leaching in soils is affected \nby several factors such as soil texture, soil management systems, fertiliser types, \nirrigation process and climate conditions.\n Penetration resistance provides an indicator of when soil strength becomes \ntoo great for effective penetration by crop roots. In several studies, soil penetration \nresistance has been used to determine tillage effect on soil physical properties and \nto estimate soil trafficability and soil resistance to plowing, seedling emergence \nand root growth (Hakansson et al. 1988; Bengough and Young 1993; G\u00fclser et \nal. 2011). Root growth of most agricultural crops is reduced dramatically when \npenetration resistance exceeds about 1.7 or 2 MPa (Bengough and Mullins 1990; \nCanarache 1990; Arshad et al. 1996). \n Also, the effects of tillage on net mineralisation are very much affected \nby the time of tillage and environmental conditions prevailing during and after \nthe tillage operations. As a consequence, the timing of tillage can be used as a \nmanagement tool to control the seasonal pattern of mineralisation (Pekrun et \nal. 2003). Salem et al. (2015) determined the short term effects of four tillage \ntreatments on soil physical properties and maize productivity in a field experiment \nin the spring of 2013 on a loamy soil. They found a clear difference in corn yield \nbetween zero tillage and conventional tillage treatments due to an increase in \nsoil compaction in conventional tillage and a decrease in soil temperature in zero \ntillage.\n Alternative tillage systems to reduce of NO3-N leaching is an important \nresearch subject in farming practices that reduce excess fertiliser use and improve \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 57\n\n\n\nwater quality. The effects of tillage and nitrogen management systems on N use \nby plants and nitrate movement through the soil profile have been studied (Ju \net al. 2003; Al-Kaisi and Licth 2004; Li et al. 2011). Tillage is one of the most \nimportant factors influencing NO3-N transport to groundwater in agricultural \npractices. It has a direct effect on both surface and subsurface soil water properties \nand leaching characteristics. Kitur et al. (1984) determined that 28 to 42% of \nfertiliser N remained in the soil depending on tillage and the N rate applied to \ncorn. We hypothesised that soil tillage methods and timing can produce beneficial \neffects on soil structural properties and nitrate variation in a clay soil depth and \ncan provide an opportunity to increase corn yield. Therefore, the objective of this \nresearch was to evaluate the effects of tillage methods and timing on penetration \nresistance, nitrate contents through a vertisol soil depth, and corn yield.\n\n\n\nMATERIALS AND METHODS\nThis study was carried out in a field experiment at the Black Sea Agricultural \nResearch Institute, Samsun-Turkey in 2011where the  mean annual rainfall was \n844 mm. The chemical and physical characteristics of the Vertisol soil determined \nin soil samples for each sampling depth were as follows: particle size distribution \nby hydrometer method, bulk density (BD) by undisturbed soil core method \n(Demiralay 1993), soil pH, 1:1 (w:v) soil:water suspension by pH meter, electrical \nconductivity (EC 25\u00baC) in the same suspension by EC meter, and organic C \ncontent by Walkley-Black method (Kacar 1994). According to the soil properties \ngiven in Table 1, the results can be summarised as follows: (i) soils in each depth \nhad a clay textural class; (ii) none were  saline, (iii)  pH was neutral, and (iv)low \nin organic matter content (Soil Survey Staff 1993). Predominant clay type in the \nsoil is was montmorillonite with a high shrink-swell potential.\n The field experiment was carried out with three different tillage timings \n(fall (F) at the end of  October, 2010; early (E) at the middle of May, 2011 and late \n(L) at the end of May, 2011) and three different tillage methods (Mouldboard (M), \nchisel (C) and direct drilling (DD)) in a factorial experimental design with three \nreplicates. Plot dimension of each treatment was 4.2 m wide (six crop rows) and \n7 m long. Except for the DD tillage method, secondary tillage applications with \ndisc harrow and rotary tiller were applied to all plots for seedbed preparation at \nthe same time on 6 June 2011. A corn variety known as \u201cKaradeniz Yildizi\u201d was \nused as a plant material. Sowing corn seeds with 70 cm spacing in rows using \ndirect seeding machine was completed on 15 June 2011. \n In the Black Sea Region of Turkey, corn needs around 200kg N ha-1 \nof fertiliser during the growing period. Fertilisation of 390kg ha-1 calcium \nammonium nitrate (26%N) was made with sowing on 15 June 2011, while the \nsecond fertilisation of 300 kg/ha ammonium nitrate (33% N) was made on 26 July \n2011. Nitrate (NO3-N) values of soil samples taken from 0-20 cm, 20-40 cm and \n40-60 cm depths from the plots were measured potentiometrically using Consort \nP903 analyser for six different soil sampling dates; 4th and 19th July, 8th  and 24th  \n\n\n\nAugust, 15th  September and 28th  October, 2011.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201958\n\n\n\n Corn yield was also determined at harvest on 22nd October 2011. Two \nrows of plants in the middle of each plot were harvested at 3 m distance and total \nbiomass yield was determined.\n Soil penetration values were measured from soil surface to 0.45 m depth \nat 0.05 m intervals using a hand-held penetrometer, 16.60 mm in diameter and \n30\u00b0 in angle cone, at the time of soil sampling. Penetration resistance (PR) in MPa \nwas calculated by using the following equation (Korucu 2002; Selvi 2001);\n\n\n\n PR= 0.0981 F/A   (1)\n\n\n\nwhere PR is the penetration resistance (MPa), F is recorded force value (kgf) and \nA is the base area of cone in cm2.\n\n\n\nSPSS was used for the variance analyses of the experimental data.\n\n\n\nRESULTS\n\n\n\nPenetration Resistance\nSoil penetration resistance (PR) and gravimetric soil moisture content (W) values \nmeasured at different soil depths for each tillage treatment are given in Table 2.\n Based on the results of variance analysis, effects of different tillage \nmethods on PR values were found to be statistically significant (P<0.05), but \nthe triple interactions among treatment x depth x timing were insignificant. \nGravimetric soil moisture contents (W) increased from soil surface to 60 cm \nsoil depth for all treatments. Soil moisture content in the first sampling time was \nhigher than in the last soil sampling time. PR values generally decreased with \n\n\n\nTABLE 1\nSome soil properties of the experimental field\n\n\n\n\n\n\n\n4 \n \n\n\n\nMATERIALS AND METHODS \n\n\n\nThis study was carried out in a field experiment at the Black Sea Agricultural Research Institute, \n\n\n\nSamsun-Turkey in 2011where the  mean annual rainfall was 844 mm. The chemical and physical \n\n\n\ncharacteristics of the Vertisol soil determined in soil samples for each sampling depth were as \n\n\n\nfollows: particle size distribution by hydrometer method, bulk density (BD) by undisturbed soil \n\n\n\ncore method (Demiralay 1993), soil pH, 1:1 (w:v) soil:water suspension by pH meter, electrical \n\n\n\nconductivity (EC25\u00baC) in the same suspension by EC meter, and organic C content by Walkley-\n\n\n\nBlack method (Kacar 1994). According to the soil properties given in Table 1, the results can be \n\n\n\nsummarised as follows: (i)soils in each depth had a clay textural class; (ii) none were  saline, (iii)  \n\n\n\npH was neutral, and (iv)low in organic matter content (Soil Survey Staff 1993). Predominant clay \n\n\n\ntype in the soil is was montmorillonite with a high shrink-swell potential. \n\n\n\nTABLE 1 \nSome soil properties of the experimental field \n\n\n\n \nMean values of soil \nproperties \n\n\n\nSoil depth, cm \n\n\n\n0-20 20-40 40-60 \n\n\n\nClay, % 74.17 75.40 75.81 \n\n\n\nSilt, % 16.40 15.22 15,45 \n\n\n\nSand, % 9.43 9.38 8.74 \n\n\n\nSoil texture class clay clay clay \n\n\n\nOrganic matter, % 2.05 1.72 1.39 \n\n\n\nEC, dS m-1 0.93 0.69 0.46 \n\n\n\npH(1:1) 6.95 7.25 7.14 \n\n\n\n \nThe field experiment was carried out with three different tillage timings (fall (F) at the end of \n\n\n\nOctober, 2010; early (E) at the middle of May, 2011 and late (L) at the end of May, 2011) and \n\n\n\nthree different tillage methods (Mouldboard (M), chisel (C) and direct drilling (DD)) in a factorial \n\n\n\nexperimental design with three replicates. Plot dimension of each treatment was 4.2 m wide (six \n\n\n\ncrop rows) and 7 m long. Except for the DD tillage method, secondary tillage applications with \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 59\n\n\n\nincreasing W content. PR values in chisel treatments were always lower than that \nin mouldboard (M) and direct drilling (DD) treatments. In each tillage method, \nPR value increased from surface (0-20 cm) to 20-40 cm soil depth. When the soil \ndepth increased from surface to 40-60 cm soil layer, increments in the PR values \ndecreased in mould board fall (MF), mouldboard early (ME), chisel early (CE) \nand DD treatments at the last soil sampling time. PR values in DD treatment were \ngenerally higher than in the other treatments. The highest PR value (1.53 MPa) \nwas observed in the 0-20 cm soil depth of DD treatment (P <0.001). The lowest \nPR value (0.62 MPa) was observed in 0-20 cm soil depth of mouldboard late \n(ML) treatment in the first sampling time (P <0.001).\n \nNO3-N Through Soil Depth Influenced Tillage Methods and Timing\nDescriptive statistics for the effects of soil tillage methods on soil NO3-N contents \nmeasured in 0-20, 20-40 and 40-60 cm depths at the six different soil sampling \ntimes are given in Table 3. While the highest mean NO3-N content value (296.3 \nmg kg-1) was obtained from 0-20 cm depth of MF application, the lowest mean \nNO3-N content value (134.8 mg kg-1) was obtained from  20-40 cm depth of ME \napplication. Soil NO3-N contents showed variation during the corn growth period \n\n\n\nTABLE 2\nEffects of different tillage methods on penetration resistance (PR) and gravimetric \n\n\n\nmoisture content (W)at different soil depths at the first and last sampling dates\n\n\n\nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; \nCE: chisel early; CL: chisel late; DD: direct drilling.\n\n\n\n\n\n\n\n6 \n \n\n\n\nTABLE 2 \nEffects of different tillage methods on penetration resistance (PR) and gravimetric moisture \n\n\n\ncontent (W)at different soil depths at the first and last sampling dates \n \n\n\n\nTillage \nmethod \n\n\n\nSoil \nsampling \ndate \n\n\n\nSoil depth \n\n\n\n0-20 cm 20-40 cm 40-60 cm  \n\n\n\nPR, MPa W, % PR, MPa W, % PR, MPa W, % \n\n\n\nMF \n04.07.2011 0.81 56 1.07 53 1.07 60 \n\n\n\n28.10.2011 1.28 26 1.40 36 1.33 43 \n\n\n\nME \n04.07.2011 0.64 53 0.78 57 0.80 66 \n\n\n\n28.10.2011 1.23 30 1.54 36 1.40 49 \n\n\n\nML \n04.07.2011 0.62 62 0.73 56 0.73 61 \n\n\n\n28.10.2011 1.13 27 1.23 37 1.23 52 \n\n\n\nCF \n04.07.2011 0.78 49 1.00 56 1.00 62 \n\n\n\n28.10.2011 0.98 30 1.20 41 1.20 48 \n\n\n\nCE \n04.07.2011 0.72 50 0.95 55 0.97 57 \n\n\n\n28.10.2011 0.98 29 1.23 39 1.20 45 \n\n\n\nCL \n04.07.2011 0.69 54 0.93 55 0.93 53 \n\n\n\n28.10.2011 0.94 27 1.17 34 1.17 47 \n\n\n\nDD \n04.07.2011 0.81 49 1.19 49 1.20 51 \n\n\n\n28.10.2011 1.53 32 1.56 41 1.50 53 \nPooled Standard Deviation 0.16 7.64 0.15 5.23 0.13 4 \nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \n\n\n\nearly; CL: chisel late; DD: direct drilling. \n\n\n\n \nBased on the results of variance analysis, effects of different tillage methods on PR values \n\n\n\nwere found to be statistically significant (P<0.05), but the triple interactions among treatment x \n\n\n\ndepth x timing were insignificant. Gravimetric soil moisture contents (W) increased from soil \n\n\n\nsurface to 60 cm soil depth for all treatments. Soil moisture content in the first sampling time was \n\n\n\nhigher than in the last soil sampling time. PR values generally decreased with increasing W \n\n\n\ncontent. PR values in chisel treatments were always lower than that in mouldboard (M) and direct \n\n\n\ndrilling (DD) treatments. In each tillage method, PR value increased from surface (0-20 cm) to 20-\n\n\n\n40 cm soil depth. When the soil depth increased from surface to 40-60 cm soil layer, increments in \n\n\n\nthe PR values decreased in mould board fall (MF), mouldboard early (ME), chisel early (CE) and \n\n\n\nDD treatments at the last soil sampling time. PR values in DD treatment were generally higher than \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201960\n\n\n\nand generally decreased from the first to last sampling date. Mean NO3-N contents \nin surface soil layer (0-20 cm) were generally higher than that in subsurface soil \nlayers (20-40 cm and 40-60 cm).\n Effects of different tillage methods on mean NO3-N values in different soil \ndepths are given in Figure 2. The highest total mean NO3-N contents along the soil \nprofile (0-60 cm depth) for mouldboard (706 mg kg-1) and chisel (588 mg kg-1) \ntreatments were determined when the first tillage application was done in the fall \nseason. \n\n\n\nCorn Yield\nThe effects of different tillage methods and timing on corn yield, given in Figure \n3, were found to be statistically significant (P<0.01). While the highest corn yield \n(61.11 Mg ha-1) was obtained from  MF treatment, DD treatment had the lowest \ncorn yield (30.95 Mg ha-1). In both tillage treatments (mouldboard and chisel), \ncorn yields were higher in fall tillage timing than late tillage timing. The corn \n\n\n\nTABLE 3\nDescriptive statistics of NO3-N (mg kg-1) values in soil depths influenced by tillage \n\n\n\nmethods\n\n\n\n Notes:  MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall;\nCE: chisel early; CL: chisel late; DD: direct drilling. \n\n\n\n\n\n\n\n8 \n \n\n\n\nTABLE 3 \nDescriptive statistics of NO3-N (mg/kg) values in soil depths influenced by tillage methods \n\n\n\nTillage \nmethod \nand \ntiming \n\n\n\nDepth, \ncm Minimum Maximum Mean Standard \n\n\n\ndeviation CV, % Skewness Kurtosis \n\n\n\nMF \n0-20 71.5 829.2 296.3 283.8 95.8 1.67 2.91 \n20-40 63.5 485.0 218.0 173.2 79.5 0.73 -1.10 \n40-60 49.7 323.1 192.1 106.0 55.2 -0.19 -1.40 \n\n\n\nME \n0-20 91.9 492.4 266.5 160.7 60.3 0.25 -1.68 \n20-40 63.3 196.6 134.8 55.6 41.2 -0.42 -2.00 \n40-60 64.2 217.2 150.0 59.4 39.6 -0.42 -1.42 \n\n\n\nML \n0-20 66.9 331.1 195.1 95.8 49.1 0.04 -0.73 \n20-40 54.7 315.9 180.4 97.5 54.0 0.12 -1.33 \n40-60 49.9 236.6 158.3 69.0 43.6 -0.54 -0.21 \n\n\n\nCF \n0-20 82.4 330.7 211.3 98.3 46.5 -0.04 -1.64 \n20-40 99.9 271.2 185.8 71.9 38.7 0.02 -2.19 \n40-60 70.1 327.1 191.2 97.9 51.2 -0.03 -1.10 \n\n\n\nCE \n0-20 81.1 299.0 198.2 80.8 40.8 -0.28 -1.07 \n20-40 88.3 241.5 152.0 56.6 37.2 0.51 -0.10 \n40-60 61.3 238.9 152.8 63.0 41.3 -0.18 -0.35 \n\n\n\nCL \n0-20 124.8 364.3 216.7 86.7 40.0 0.92 0.99 \n20-40 68.8 322.6 197.1 104.5 53.0 -0.41 -1.67 \n40-60 65.9 237.4 171.3 66.2 38.6 -0.70 -0.23 \n\n\n\nDD \n0-20 67.6 406.5 184.2 127.8 69.4 1.21 1.01 \n20-40 72.1 212.9 137.3 57.3 41.7 0.08 -2.04 \n40-60 56.0 221.1 141.2 70.2 49.8 -0.07 -2.17 \n\n\n\n Notes:  MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \nearly; CL: chisel late; DD: direct drilling. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 61\n\n\n\nFig. 1: Changes in mean penetration resistance along the soil profile with different \ntillage methods and timing. \n\n\n\nNotes: MF: mould board fall; ME: mouldboard early; mouldboard late; CF: chisel fall; \nCE: chisel early; CL: chisel late; DD: direct drilling.\n\n\n\n\n\n\n\n7 \n \n\n\n\nin the other treatments. The highest PR value (1.53 MPa) was observed in the 0-20 cm soil depth \n\n\n\nof DD treatment (P <0.001). The lowest PR value (0.62 MPa) was observed in 0-20 cm soil depth \n\n\n\nof mouldboard late (ML) treatment in the first sampling time (P <0.001). \n\n\n\n \n \nFigure 1. Changes in mean penetration resistance along the soil profile with different tillage \nmethods and timing.  \nNotes: MF: mould board fall; ME: mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \nearly; CL: Chisel late; DD: direct drilling. \n \n\n\n\nNO3-N Through Soil Depth Influenced Tillage Methods and Timing \n\n\n\nDescriptive statistics for the effects of soil tillage methods on soil NO3-N contents measured in 0-\n\n\n\n20, 20-40 and 40-60 cm depths at the six different soil sampling times are given in Table 3. While \n\n\n\nthe highest mean NO3-N content value (296.3 mg/kg) was obtained from 0-20 cm depth of MF \n\n\n\napplication, the lowest mean NO3-N content value (134.8 mg/kg) was obtained from  20-40 cm \n\n\n\ndepth of ME application. Soil NO3-N contents showed variation during the corn growth period and \n\n\n\ngenerally decreased from the first to last sampling date. Mean NO3-N contents in surface soil layer \n\n\n\n(0-20 cm) were generally higher than that in subsurface soil layers (20-40 cm and 40-60 cm). \n\n\n\nEffects of different tillage methods on mean NO3-N values in different soil depths are given \n\n\n\nin Figure 2. The highest total mean NO3-N contents along the soil profile (0-60 cm depth) for \n\n\n\nmouldboard (706 mg/kg) and chisel (588 mg/kg) treatments were determined when the first tillage \n\n\n\napplication was done in the fall season.  \n\n\n\nFig. 2. Effects of tillage methods and the first tillage times on mean NO3-N (mg kg-1) \ncontents  along soil profile\n\n\n\nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; \nCE: chisel early; CL: chisel late; DD: direct drilling. \n\n\n\n\n\n\n\n9 \n \n\n\n\n\n\n\n\nFigure 2. Effects of tillage methods and the first tillage times on mean NO3-N (mg/kg) contents  \nalong soil profile  \nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \nearly; CL: chisel late; DD: direct drilling.  \n \n\n\n\nCorn Yield \n\n\n\nThe effects of different tillage methods and timing on corn yield, given in Figure 3, were found to \n\n\n\nbe statistically significant (P<0.01). While the highest corn yield (61.11 Mg/ha) was obtained from  \n\n\n\nMF treatment, DD treatment had the lowest corn yield (30.95 Mg/ha). In both tillage treatments \n\n\n\n(mouldboard and chisel), corn yields were higher in fall tillage timing than late tillage timing. The \n\n\n\ncorn yields in all early tillage treatments were relatively higher than in the late tillage applications. \n\n\n\n \nFigure 3. Effects of tillage methods and timing on corn yield  \nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \nearly; CL: chisel late; DD: direct drilling. \n\n\n\nyields in all early tillage treatments were relatively higher than in the late tillage \napplications.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201962\n\n\n\nDISCUSSION\nSoil penetration resistance is more sensitive than bulk density in detecting the \neffects of tillage management (Hammel 1989). Effects of different tillage methods \non PR were more evident in the first 20 cm soil layer (Table 2). Mean PR values \nalong the soil profile decreased when the first soil tillage was delayed from fall \nto late tillage time. Soil moisture contents through soil depth generally increased \nin all tillage treatments and moisture content in subsurface soil layers of DD \ntreatment were generally higher than in other treatments. Soil water content is \naffected by tillage due to changes in soil structure and soil physical properties \nsuch as infiltration, surface runoff and evaporation (Zhai et al. 1990). Sarauskis et \nal. (2009) reported that the increase in soil water content in conservation tillage \ncan be attributed to reduced evaporation, greater infiltration, and soil protection \nfrom rainfall impact. There were significant negative correlations between PR \nand W for 0-20 cm (-0.796**), 20-40 cm (-0.814**) and 40-60 cm (-0.714**) \nsoil layers statistically (P<0.01). It is known that soil tillage has a loosening effect \non soil structure and reduces soil bulk density and PR (Kirisci 2001). G\u00fclser and \nCandemir (2012) found that increasing total porosity and soil moisture content by \nthe application of organic wastes decreased penetration resistance of a clay soil. \nIn DD or no tillage treatment, only natural soil loosening factors such as drying-\nwetting, freezing-thawing cycles or fauna activity, can reduce soil bulk density \nand soil strength (Hakansson and Lipiec 2000; Moraru and Rusu 2010; G\u00fclser et \nal. 2011).\n Total NO3-N content decreased with decreasing mean PR along the clay \nsoil profile. Except for DD and CL methods, generally fall tillage for mouldboard \nand chisel treatments had higher PR values and NO3-N contents than early \nand late tillage applications (Figure 2). Nitrate N content in soil profile for DD \n\n\n\nFig. 3: Effects of tillage methods and timing on corn yield \n\n\n\nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel \nfall; CE: chisel early; CL: chisel late; DD: direct drilling.\n\n\n\n\n\n\n\n9 \n \n\n\n\n\n\n\n\nFigure 2. Effects of tillage methods and the first tillage times on mean NO3-N (mg/kg) contents  \nalong soil profile  \nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \nearly; CL: chisel late; DD: direct drilling.  \n \n\n\n\nCorn Yield \n\n\n\nThe effects of different tillage methods and timing on corn yield, given in Figure 3, were found to \n\n\n\nbe statistically significant (P<0.01). While the highest corn yield (61.11 Mg/ha) was obtained from  \n\n\n\nMF treatment, DD treatment had the lowest corn yield (30.95 Mg/ha). In both tillage treatments \n\n\n\n(mouldboard and chisel), corn yields were higher in fall tillage timing than late tillage timing. The \n\n\n\ncorn yields in all early tillage treatments were relatively higher than in the late tillage applications. \n\n\n\n \nFigure 3. Effects of tillage methods and timing on corn yield  \nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \nearly; CL: chisel late; DD: direct drilling. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 63\n\n\n\ntreatment was lower than in other treatments. Similarly, Sainju and Singh (2001) \nreported that NO3\u2013N accumulation in the soil profile was greater in intensive \ntillage systems than no-tillage using corn and a cover crop. Chisel tillage had \nhigher total NO3-N accumulation in the 0 to 60 cm soil depth than in  other tillage \nsystems, except for MF (Figure 2). Al-Kaisi and Licth (2004) reported that chisel \nplow resulted in higher residual soil nitrate buildup than strip tillage and no tillage \nin the 0 to 120 cm soil profile. It is known that soil hydrological properties such \nas water flow in soil, especially in clay soils, and leaching process are related \nto macropores and continuity of pores between aggregates which are mostly \ninfluenced by soil tillage applications (Addiscott and Dexter 1994; Armstrong and \nHarris 1996). Hillel (2004) reported that swelling of clays with wetting process \nimposes pressure on macropores and decreases infiltration rate with increasing \nmicropores in soil. Dexter (2004) reported that soil compaction occurs usually \nbecause of loss or reduced size of the largest pores, increases in soil bulk density \nand soil strength, and decreases in macro porosity, infiltration and water-holding \ncapacity. There was a significant positive correlation (0.793*) between PR values \nand sum of mean NO3-N contents in 0-60 cm soil layer (P<0.05) (Figure 4). \nTherefore, it can be explained that leaching of NO3-N in soil profile was less in \nfall tillage application than in early and late tillage applications due to decreasing \nmacropores and permeability in soil profile over time. \n Although the PR values in soil profile were higher in DD application \ncompared to other applications (Figure 1), sum of mean NO3-N contents in DD \nwas lower than in other tillage methods (Figure 2). It can be explained that NO3-N \nmight be consumed by uncontrolled and excessive amount of weed population in \nDD plots.   \n\n\n\nFig. 4: Effects of penetration resistance on sum of total nitrate content through\nsoil depth\n\n\n\nNotes: MF: mouldboard fall; ME:mouldboard early; mouldboard late; CF: chisel \nfall; CE: chisel early; CL: chisel late; DD: direct drilling.\n\n\n\n\n\n\n\n11 \n \n\n\n\nmacropores and continuity of pores between aggregates which are mostly influenced by soil tillage \n\n\n\napplications (Addiscott and Dexter 1994; Armstrong and Harris 1996). Hillel (2004) reported that \n\n\n\nswelling of clays with wetting process imposes pressure on macropores and decreases infiltration \n\n\n\nrate with increasing micropores in soil. Dexter (2004) reported that soil compaction occurs usually \n\n\n\nbecause of loss or reduced size of the largest pores, increases in soil bulk density and soil strength, \n\n\n\nand decreases in macro porosity, infiltration and water-holding capacity. There was a significant \n\n\n\npositive correlation (0.793*) between PR values and sum of mean NO3-N contents in 0-60 cm soil \n\n\n\nlayer (P<0.05) (Figure 4). Therefore, it can be explained that leaching of NO3-N in soil profile was \n\n\n\nless in fall tillage application than in early and late tillage applications due to decreasing \n\n\n\nmacropores and permeability in soil profile over time. \n\n\n\n \nFigure 4. Effects of penetration resistance on sum of total nitrate content through soil depth  \nNotes: MF: mouldboard fall; ME:mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \nearly; CL: chisel late; DD: direct drilling. \n \n\n\n\nAlthough the PR values in soil profile were higher in DD application compared to other \n\n\n\napplications (Figure 1), sum of mean NO3-N contents in DD was lower than in other tillage \n\n\n\nmethods (Figure 2). It can be explained that NO3-N might be consumed by uncontrolled and \n\n\n\nexcessive amount of weed population in DD plots.    \n\n\n\nIn this study, corn yield decreased with increasing soil penetration resistance. The lowest corn \n\n\n\nyield (30.95 Mg/ha) and the highest mean soil PR (1.3 MPa) were obtained in DD treatment. \n\n\n\nSimilarly, other researchers reported that zero tillage in a short-term study decreased corn yield \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201964\n\n\n\n In this study, corn yield decreased with increasing soil penetration \nresistance. The lowest corn yield (30.95 Mg ha-1) and the highest mean soil \nPR (1.3 MPa) were obtained in DD treatment. Similarly, other researchers \nreported that zero tillage in a short-term study decreased corn yield compared to \nconventional tillage due to higher soil compaction under zero tillage (Afzalinia \nand Zabihi 2014; Salem et al. 2015). Basamba et al. (2004) also indicated that the \nhighest yield was obtained from mouldboard application compared with direct \ndrilling application. As the soil tillage treatments were delayed from fall to late \nspring season, the corn yields also decreased. Similarly, Myrbeck et al. (2012) \nreported that high yields were realised with early autumn tillage practices. The \nlowest corn yields in CL treatment indicated that uptake of NO3-N as well as other \nnutrients by corn decreased. Therefore, total NO3-N content through soil depth of \nCL was higher than in CE application. There was a significant positive correlation \n(0.712*) between corn yield and sum of mean soil nitrate content in 0-60 cm soil \nlayer (P<0.05) (Figure 5). Increasing NO3-N contents in rhizosphere increased \nthe yield of corn plant. Similarly, Varshney et al. (1993) reported that mouldboard \ntillage treatment had higher corn yield and more residual NO3-N in the 60 cm soil \nprofile than no-till treatment.\n \n\n\n\nCONCLUSIONS\nTotal NO3-N contents in fall soil tillage treatments were higher than that in the \nother treatments, except for CL application. While mouldboard application in fall \nseason had the highest corn yield, DD tillage treatment had the lowest yield and \ntotal NO3-N content through soil depth. Corn yield generally decreased when the \nfirst soil tillage was delayed for mouldboard and chisel tillage. Lower corn yield \n\n\n\nFig. 5: Effects of sum of total nitrate content through soil depth on corn yield \n\n\n\nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; \nCE: chisel early; CL: chisel late; DD: direct drilling.).\n\n\n\n\n\n\n\n12 \n \n\n\n\ncompared to conventional tillage due to higher soil compaction under zero tillage (Afzalinia and \n\n\n\nZabihi 2014; Salem et al. 2015). Basamba et al. (2004) also indicated that the highest yield was \n\n\n\nobtained from mouldboard application compared with direct drilling application. As the soil tillage \n\n\n\ntreatments were delayed from fall to late spring season, the corn yields also decreased. Similarly, \n\n\n\nMyrbeck et al. (2012) reported that high yields were realised with early autumn tillage practices. \n\n\n\nThe lowest corn yields in CL treatment indicated that uptake of NO3-N as well as other nutrients \n\n\n\nby corn decreased. Therefore, total NO3-N content through soil depth of CL was higher than in CE \n\n\n\napplication. There was a significant positive correlation (0.712*) between corn yield and sum of \n\n\n\nmean soil nitrate content in 0-60 cm soil layer (P<0.05) (Figure 5). Increasing NO3-N contents in \n\n\n\nrhizosphere increased the yield of corn plant. Similarly, Varshney et al. (1993) reported that \n\n\n\nmouldboard tillage treatment had higher corn yield and more residual NO3-N in the 60 cm soil \n\n\n\nprofile than no-till treatment. \n\n\n\n \nFigure 5. Effects of sum of total nitrate content through soil depth on corn yield  \nNotes: MF: mouldboard fall; ME: mouldboard early; mouldboard late; CF: chisel fall; CE: chisel \nearly; CL: chisel late; DD: direct drilling.). \n \n\n\n\nConclusions \n\n\n\nTotal NO3-N contents in fall soil tillage treatments were higher than that in the other treatments, \n\n\n\nexcept for CL application. While mouldboard application in fall season had the highest corn yield, \n\n\n\nDD tillage treatment had the lowest yield and total NO3-N content through soil depth. Corn yield \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 65\n\n\n\nin CL treatment probably decreased nitrate uptake by plants and increased total \nNO3-N content through soil depth. Delaying soil tillage from fall to spring caused \nlower PR, higher macroporosity and leaching of NO3-N along the soil profile. Fall \nseason soil tillage using mould- board in clay soils can be suggested to lead to \noptimum plant growth conditions in soil and high corn yield. Fall season tillage \ntiming can also produce beneficial effects on conservation of water pollution \nby nitrate leaching in the Black Sea Region of Turkey. This study indicated that \ntillage time and methods need to be evaluated for the availability of N for crop \ngrowth and to amount of NO3-N remaining in soil profile for different crops and \nsoil types in sustainable agricultural practices.  \n\n\n\nACKNOWLEDGEMENTS\nWe are very grateful to the Black Sea Agricultural Research Institute, Samsun-\nTurkey for hosting and facilitating the fieldwork with supporting soil tillage \nmachines and research plots.\n\n\n\nREFERENCES\nAddiscott, T.M. and A.R. Dexter. 1994. Tillage and crop residue management effects \n\n\n\non losses of chemicals from soils. Soil and Tillage Research 30:125-131.\n\n\n\nAfzalinia, S. and J. Zabihi. 2014. 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"\n\n___________________\n*Corresponding author : E-mail: ferdous28@yahoo.com\n\n\n\nINTRODUCTION\nRadioactive elements are generally classified into two categories, naturally \noccurring and artificially produced. Radioactivity due to natural radionuclides in \nrocks, soil and water generates a significant component of background radiation \nexposure to the population in the area. The terrestrial component of the natural \nbackground radiation is dependent on the composition of the rocks, soil and water \nin which the natural radionuclides are contained (Karahan and Bayulken, 2000). \nAmong the radioactive elements in the environment, the most abundant are 40K, \nand the radioisotopes of the natural decay series of 238U and 232Th, which are \npresent in the earth\u2019s crust. Therefore materials from the earth\u2019s crust such as soil, \nand building materials become a major source of external radiation exposure to \nhumans in the environment (United Nations Scientific Committee on the Effect \nof Scientific Radiation (UNSCAER), 1993; Lopez et al., 2004). In general, \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 59-71 (2015)                        Malaysian Society of Soil Science\n\n\n\nRadioactivity Distributions in Soils from Habiganj District, \nBangladesh and their Radiological Implications\n\n\n\nFerdous, J.1*, M.M. Rahman1,  Rubina Rahman2, S. Hasan3\n\n\n\nand N. Ferdous3\n\n\n\n1Health Physics Division, Atomic Energy Centre, Ramna, Dhaka-1000, Bangladesh\n2Department of Physics, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh\n\n\n\n3Department of Physics, Dhaka University, Dhaka-1000, Bangladesh\n\n\n\nABSTRACT\nA high purity germanium detector (HPGe) which is a low background gamma-\nray spectrometry system, was used for radioactivity measurement of soils from \nHabiganj District of Bangladesh to establish a radiation map within this area as \na baseline record for future studies. The radioactivity concentration levels of 238U \n(Raeq), \n\n\n\n232Th and 40K were measured in soil samples. From the measured specific \nactivities of the above three natural radionuclides, the radium equivalent activity \n(Raeq), the external hazard index (Hex), the external gamma absorbed dose rate and \nthe annual effective dose were calculated in this study. The activity concentration \nlevels were found to be in the range of 5 to 19 Bq kg-1 for 238U (Raeq), 7 to 38 \nBq kg-1 for 232Th, and 93 to 392 Bq kg-1 for 40K with mean values of 11, 22 and \n227 Bq kg-1, respectively. No 137Cs was found in this study. Radium equivalent \nactivity (Raeq), gamma absorbed dose rate, external hazard index (Hex) and annual \neffective dose values were found to be 59 Bq kg-1, 28 nGy h-1, 0.162 and 33 \u00b5Sv, \nrespectively, which indicates that the study area is radiologically safe for human \nbeings. \n\n\n\nKeywords: Annual effective dose rate, HPGe detector, radioactivity, \nradium\tequivalent\tactivity,\texternal\thazard\tindex.\t\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201560\n\n\n\nJ. Ferdous, M.M. Rahman,  Rubina Rahman, S. Hasan and N. Ferdous\n\n\n\nthe release of low levels of artificial radionuclides occurs during the normal \noperations of nuclear facilities (e.g., nuclear ore processing, uranium enrichment, \nfuel fabrication, operation of reactors, operation of particle accelerators, etc.), and \nthe production and application of radioisotopes in the fields of nuclear medicine, \nresearch, and industrial and agricultural processes (UNSCEAR,1977; Pearson \nand de Fraja Frangipane, 1975). The fission product 137Cs is strongly absorbed \nand retained by soil as natural radionuclides which are randomly distributed \nat different depths of soil. Humans are exposed to natural terrestrial radiation \nthat originates predominantly from the upper 30 cm of the soil. Humans are \nalso exposed to radiation via contamination of the food chain, which occurs as \na result of direct deposition of radionuclides on plant leaves, root uptake from \ncontaminated soil or water and from direct ingestion of contaminated water. To \nassess these exposures, radioactivity studies have been previously carried out on \nsoil samples in other parts of the world. (Santos Junior et al., 2010; El-Arabi et \nal., 2000; Ahmed et al., 2006; Veiga et al., 2006; El-Mageed et al., 2010; Al-Jundi \net al., 2003; Al-Sulaiti et al., 2008; El-Shershaby et al., 2002; Arafa et al., 2004; \nBadhan et al., 2009; Alaamer et al., 2008). \n\n\n\nHabiganj District is a border district between Bangladesh and India. \nAlthough it is important from both geological and natural resources perspectives, \ninformation about radioactivity have not been measured This study deals with \nnatural radioactivity for soil samples in this area to establish a baseline record in \nthis area and to assess any radiological hazards emanating from such radioactivity. \nHence, the radium equivalent activity (Raeq), gamma absorbed dose rate, the \nexternal hazard index (Hex), and annual effective dose rate were evaluated and \ncompared to the limits proposed by United Nations (UNSCEAR, 2008).\n\n\n\nMATERIALS AND METHODS\n\n\n\nGeological Outline\nHabiganj District is in the north-eastern part of Bangladesh (Figure 1). It is \nlocated at 91\u00b010\u2032 E - 91\u00b0 40\u2032 E longitude and 23\u00b057\u2032 N - 24\u00b042\u2032 N latitude. It \nwas established as a district only 27 years ago, but is developing rapidly due \nto its natural resources. It has three gas fields, namely Habiganj, Bibiyana and \nRashidpur. Mineral sand is also found in Habiganj. The Indian state of Tripura \nis to the south of Habiganj. The geographical position and natural resources of \nHabiganj indicate the likelihood of radioactivity but information on radioactivity  \nis scarce  as  systematic measurements have not being undertaken in this area so \nfar. This study aimed to study the concentration of natural radionuclides in soils of \nHabiganj District to establish baseline radioactivity data for the area.\n\n\n\nSample Collection and Preparation \nTwenty-two soil samples were collected at a depth of 0-5 cm from twenty \ndifferent locations in Habiganj District by employing a conventional method. At \nthe laboratory, samples dried at room temperature, were crushed, cleaned, and \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 61\n\n\n\nRadioactivity Distributions in Soils of Habiganj District\n\n\n\nground in a large sized mortar and their respective weights were determined. All \nthe samples were then dried in an oven at 105\u00baC for 24 hours, homogeneously \nground, and 250 g of each sample was then transferred into uncontaminated \nempty plastic containers measuring 7 cm in diameter and 8.3 cm in height and \nlabeled. The containers were then sealed tightly with screw-on caps, with the gap \nbetween the cap and cotainer wrapped with thick vinyl tape. The filled containers \nwere stored for a period of about 4 weeks. This was done  to enable 226Ra and 228Ra \nreach their secular equilibrium with its decay products in the Uranium series and \nThorium series, respectively. The sealing of the  containers was also necessary to \nensure that radon gas and its progeny were measured.\n\n\n\nExperimental Procedure\nEach sample was measured with a gamma-ray counting system, a high resolution \nEG & G Orte high purity germanium (HPGe) co-axial detector coupled with a \nSilena Emca Plus multichannel analyser (MCA), and associate microprocessors. \nThe effective volume of the detector was 83.47 cm3.  The energy resolution of the \ndetector was found to be 1.69 keV at 1332 keV energy peak of 60Co (Figure 2) \nwith a relative efficiency of 19.6%. The detector was shielded with copper ring (2 \nmm) at the side and lead (76.2 mm) at the side and the top to reduce background \nradiation level. To minimise the effect of scattered radiation from the shield, the \ndetector was located at the center of the chamber. The samples were then placed \n\n\n\nFigure 1: Map of sample locations within Habiganj District, Bangladesh\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201562\n\n\n\nJ. Ferdous, M.M. Rahman,  Rubina Rahman, S. Hasan and N. Ferdous\n\n\n\nover the detector for at least 5000 seconds. The spectra were either evaluated with \nthe computer software program Maestro (EG & G ORTEC), or manually with the \nuse of a spread sheet (Microsoft Excel) to calculate the natural radioactivity. The \n\u03b3-ray energies of 228Ac (911 keV) and 228Ac (969 keV) were used to determine \nthe concentration of 232Th, and the \u03b3-transition of 214Bi (609 and 1120 keV) and \n214Pb (295 and 351 keV) were used to determine the activity of 238U. The 40K \nand 137Cs radionuclides were measured from their respective \u03b3-ray energies of \n1460 keV and 662 keV. In order to determine the background distribution in the \nenvironment around the detector, an empty sealed plastic container was measured \nin the same manner as the rest of the samples. The background spectra were used \nto correct the net peak area of gamma rays of the measured isotopes. \n\n\n\nThe energy calibration of the MCA was obtained using standard point sources \nsuch as 22Na, 57Co, 60Co, 133Ba, 137Cs, etc.  The efficiency of the detector for different \nradionuclides of interest of different energies were determined by mixing standard \nsources of known activities and different energies such as 122, 245, 344, 411, 444, \n779, 963, 1086, 1112 and 1408 keV supplied by Health Physics Division, Atomic \nEnergy Centre, Dhaka (AECD) and following the standard method. The unknown \nefficiencies of different radionuclides were then calculated using Eq. (1) to draw a \nstandard efficiency curve (Figure 2). The efficiency calibration curve was drawn \nup using different energy peaks covering arange of up to 2000 keV to obtain the \nefficiency of the detector for the particular gamma ray energy of interest.\n\n\n\n  (1)\n\n\n\nFigure 2:  Efficiency calibration curve\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 63\n\n\n\nRadioactivity Distributions in Soils of Habiganj District\n\n\n\nwhere \nP\u03b3 = fraction of number of gamma-rays emitted from a particular radionuclide\nA = activity of the radionuclide present in the samples\n\n\n\nThe radioactivity concentrations of different radionuclides were based on the \nmeasured detector efficiencies (for corresponding radionuclides) as a function of \nthe energy curve for  the same counting geometry and time. Thus, using their \nmeasured efficiencies, the activities of the measured radionuclides were calculated \nwith Eq. (2).\n\n\n\n     \n  (2)\n\n\n\nwhere\nA= activity of the radionuclide in Bq kg-1 present in the sample \nP\u03b3 = the fraction of number of gamma-rays emitted from a particular radionuclide \n\u03b5 = absolute efficiency of the detector for a particular gamma-ray energy emitted \nfrom the specific radionuclide of interest \n\u03c9 =  weight of the sample\n\n\n\nCalculation of the Radiological Effects\nThe most widely used radiation hazard index is called the radium equivalent \nactivity (Raeq). The Raeq is a weighted sum of activities of the 238U, 232Th and 40K \nradionuclides based on the assumption that 370 Bq kg-1 of 238U, 259 Bq kg-1 of \n232Th and 4810 Bq kg-1 of 40K produce the same gamma ray dose rate (Kohshi, \n2001). Radium equivalent activity can be calculated from the relation in Eq. (3) \nsuggested by Beretka and Mathew (1985). \n\n\n\nRaeq = ( ATh \u00d7 1.43)+ AU + (Ak \u00d7 0.077)    (3)\n\n\n\nwhere\nATh = activity concentration of 232Th in Bq kg-1 \nAU = activity concentration of 238U in Bq kg-1 \nAK = activity concentration of 40K in Bq kg-1 \n\n\n\nThe external hazard index (Hex) due to the emitted gamma-rays of the samples \nwas calculated using Eq.(4).\n\n\n\n        \n   (4)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201564\n\n\n\nJ. Ferdous, M.M. Rahman,  Rubina Rahman, S. Hasan and N. Ferdous\n\n\n\nwhere\nAU, ATh, and AK are the activity concentrations of  238U, 232Th and 40K, respectively \n\n\n\nThe calculated average Hex was found to be less than unity. \n\n\n\nThe total air absorbed dose rate (nGy h-1) due to the mean activity concentrations \nof  238U, 232Th and 40K in Bq kg-1 was calculated using  Eq. (5) (UNSCEAR, 1993; \nBeck, 1972):\n\n\n\n       D = 0.462AU + 0.604ATh + 0.042Ak    (5)\n\n\n\nwhere\nAU, ATh and AK are the mean activity concentrations of 238U, 232Th and 40K \nrespectively in Bq kg-1. El-Shershaby (2002) derived this equation for calculating \nthe absorbed dose rate in air at a height of 1.0 m above the ground from the \nmeasured radionuclides concentrations in environmental materials. \n\n\n\nTo estimate the annual effective dose, the following was taken into account: \n(a) the conversion coefficient from absorbed dose in air to effective dose; and (b) \nthe indoor occupancy factor. Applying the conversion factor of 0.7 Sv Gy-1 to \nconvert absorbed dose in air to human effective dose, and an outdoor occupancy \nfactor of 0.2 as recommended by UNSCEAR (2000), the average annual effective \ndose due to gamma radiation from terrestrial sources of all soil samples in this \nstudy was assessed using Eq. (6) (UNSCAER, 2000).\n\n\n\nDeff (Sv)  =   D(nGy h-1) \u00d7 (24  \u00d7 365)(h)  \u00d7 0.7 \u00d7 0.2   (6)\n\n\n\nwhere\nDeff = effective dose equivalent in Sv \nD = total absorbed dose rate in air in (nGy h-1)\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nActivity Concentrations in Soil Samples\nThe highest activity concentration for 238U and 232Th was observed in Nobigonj \nand Azmirigonj soils (18.83 \u00b1 0.016 and 38.14 \u00b1 0.034 Bq kg-1), respectively, \nwhilst the lowest activity concentrations were observed in Chunarughat (5.28 \u00b1 \n0.015 and 7.13 \u00b1 0.025 Bq kg-1, respectively) (Table 1). The average concentration \nof 238U and 232Th was measured as 11.09 \u00b1 0.01 Bq kg-1 and 21.98 \u00b1 0.029 Bq kg-1, \nrespectively.  The highest activity concentration for 40K was observed in Lakhai \n(392 \u00b1 0.74 Bq kg-1) whilst the lowest activity concentration was observed in \nBaniachong soil ( 93.14 \u00b1 0.62 Bq kg-1). The average concentration of 40K was \nmeasured as 227 \u00b1 0.68 Bq kg-1. It was also observed that the measured activity \nconcentration of 40K exceeded markedly the values of both uranium and thorium \nas it was the most abundant radioactive element under consideration (Table 1). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 65\n\n\n\nRadioactivity Distributions in Soils of Habiganj District\n\n\n\nMoreover, the average values obtained fell within the range of corresponding \nworld values and other published results mentioned in Table 2. The world average \nactivity concentration and ranges of 238U,  232Th , and 40K are 35 Bq kg-1 with a \nrange of 17 - 60 Bq kg-1, 30 Bq kg-1 with a range of 11 - 64 Bq kg-1 and  400 Bq kg-1 \nwith a  range of 140 - 850 Bq kg-1, respectively (UNSCEAR, 2000). The average \nactivity concentrations for 238U, 232Th and 40K in this study were lower than world \naverages for these radionuclides in the soils. The anthropogenic radionuclide 137Cs \nwas also analysed in this study that has been assumed to be deposited in soil of \nBangladesh as a result of atmospheric fallout following the Chernobyl disaster on \n26 April 1986 and other previous atmospheric tests of nuclear devices around the \nworld (Mollah et al., 1986; Miah et al., 1985). No 137Cs was detected in any of \nthe samples. The errors quoted were the standard deviations from the means and \nrepresent the spread in the concentrations of the natural radionuclides in the soil. \nIt can be seen that the activity concentration of 238U, 232Th and 40K in this study \nwere comparable with other published results in Bangladesh  (Kabir et al., 2009).\n\n\n\n\n\n\n\n 10 \n\n\n\n \nTABLE 1 \n\n\n\nRadioactivities of 238U, 40K and 232Th in soil samples at different locations  of  \nHabigonj district. \n\n\n\n\n\n\n\nSample location Sample no 238U (Bq kg-1) 232Th(Bq kg-1) \n \n\n\n\n40K(Bq kg-1) \n \n\n\n\nHabiganj Sadar 1 \n2 \n3 \n\n\n\n9.83 \u00b1 0.016 \n8.91 \u00b1 0.012 \n6.96 \u00b1 0.015 \n\n\n\n22.51 \u00b1 0.029 \n24.62 \u00b1 0.029 \n17.93 \u00b1 0.028 \n\n\n\n169.64\u00b10.66 \n263.52\u00b10.69 \n263.35\u00b10.69 \n\n\n\nChunarughat \n \n\n\n\n4 \n5 \n6 \n\n\n\n14.16 \u00b1 0.015 \n7.54 \u00b1 0.015 \n5.28 \u00b1 0.015 \n\n\n\n16.99 \u00b1 0.031 \n7.13 \u00b1 0.025 \n13.30 \u00b1 0.027 \n\n\n\n321.57\u00b10.72 \n212.22\u00b10.67 \n151.44\u00b10.65 \n\n\n\nBahubal \n \n\n\n\n7 \n8 \n9 \n\n\n\n6.45  \u00b1 0.027 \n8.70 \u00b1 0.015 \n11.74\u00b1 0.015 \n\n\n\n13.76 \u00b1 0.026 \n17.22 \u00b1 0.028 \n14.19 \u00b1 0.027 \n\n\n\n241.56\u00b10.67 \n165.52\u00b10.65 \n161.25\u00b10.66 \n\n\n\nLakhai \n \n\n\n\n10 \n11 \n12 \n\n\n\n14.71 \u00b1 0.012 \n14.69 \u00b1 0.011 \n11.08 \u00b1 0.012 \n\n\n\n34.82 \u00b1 0.033 \n34.33 \u00b1 0.032 \n21.81 \u00b1 0.029 \n\n\n\n231.85\u00b10.68 \n350.29\u00b10.72 \n392.63\u00b10.74 \n\n\n\nAzmirigonj \n \n\n\n\n13 \n14 \n\n\n\n12.65 \u00b1 0.012 \n11.24 \u00b1 0.012 \n\n\n\n38.14 \u00b1 0.034 \n27.88 \u00b1 0.031 \n\n\n\n233.19\u00b10.68 \n191.65\u00b10.66 \n\n\n\nBaniachong \n \n\n\n\n15 \n16 \n\n\n\n12.36 \u00b1 0.012 \n12.61 \u00b1 0.012 \n\n\n\n32.86 \u00b1 0.033 \n30.37 \u00b1 0.032 \n\n\n\n218.61\u00b10.68 \n93.14\u00b10.62 \n\n\n\nNobigonj \n \n\n\n\n17 \n18 \n19 \n\n\n\n18.83 \u00b1 0.015 \n11.25 \u00b1 0.016 \n11.55 \u00b1 0.012 \n\n\n\n19.25 \u00b1 0.028 \n21.28 \u00b1 0.029 \n16.47 \u00b1 0.028 \n\n\n\n284.78\u00b10.69 \n356.54\u00b10.73 \n176.91\u00b10.66 \n\n\n\nMadhabpur 20 \n21 \n\n\n\n14.71 \u00b1 0.016 \n10.52 \u00b1 0.012 \n\n\n\n17.97 \u00b1 0.029 \n20.23 \u00b1 0.028 \n\n\n\n250.71\u00b10.69 \n154.43\u00b10.64 \n\n\n\nAverage                       11.09 \u00b1 0.01 21.98 \u00b1 0.03 227.9\u00b1  0.67 \n \n\n\n\n\n\n\n\nTABLE 1\nRadioactivities of 238U, 40K and 232Th in soil samples at different locations  of \n\n\n\nHabigonj district.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201566\n\n\n\nJ. Ferdous, M.M. Rahman,  Rubina Rahman, S. Hasan and N. Ferdous\n\n\n\nRadiation Hazard Indices \nThe average Raeq was 59 Bq kg-1 (Table 4). This was below the recommended \nvalue 370 Bq kg-1 which the OECD deems safe (1979) (OECD,1979). The average \nabsorbed gamma dose measured in air was 28 nGy h-1 (Table 4). According to the \nrecent UNSCEAR reports, the corresponding world average value is 58 nGy h-1. \nThis indicates that the average absorbed dose rate in air outdoors from the soil \nwas lower in Habiganj District than  the world average. \n\n\n\nThe calculated value of Hex obtained was 0.16 (Table 4). The calculated \nvalues of Hex obtained in this study ranged from 0.135 \u2013 0.346, which were found \n\n\n\nTABLE 2\nComparison of radioactivity levels of the soil samples of different countries with that of \n\n\n\nthis study\n\n\n\n\n\n\n\n 11 \n\n\n\nTABLE 2 \nComparison of radioactivity levels of the soil samples of different countries with that of this \n\n\n\nstudy \n \n\n\n\nCountries Average \nSpecific \nradioactivity \nof  238U  \n(Bq kg-1) \n \n\n\n\nAverage \nSpecific \nradioactivity \nof  232Th  \n(Bq kg-1) \n\n\n\nAverage \nSpecific \nradioactivity of  \n40K(Bq kg-1) \n\n\n\nReferences \n\n\n\nEgypt  17 18 320 (UNSCEAR, 2000)  \nUSA  40 35 370 (UNSCEAR, 2000) \nChina  32 41 440 (UNSCEAR, 2000) \nJapan 33 28 310 (UNSCEAR, 2000) \nMalaysia 67 82 310 (UNSCEAR, 2000) \nIndia 29 64 400 (UNSCEAR, 2000) \nIran 28 22 640 (UNSCEAR, 2000) \nDenmark 17 19 460 (UNSCEAR, 2000) \nPoland 26 21 410 (UNSCEAR, 2000) \nGreece 25 21 360 (UNSCEAR, 2000) \nRomania 32 38 490 (UNSCEAR, 2000) \nSpain 32 33 470 (UNSCEAR, 2000) \nLuxembourg 35 50 620 (UNSCEAR, 2000) \nBangladesh  48 53 481 (Kabir, K. A., 2009) \nSaudi Arabia 15 11 225 (Alaamer, A. S., \n\n\n\n(2008)   \nNigeria  14 19 896 (Okeyode, I., \n\n\n\nOluseye, A. 2010) \nTurkey  21 25 298 (Bozkurt A., 2007) \n\n\n\nPakistan  30 56 642 (Akhtar, N.,  2004) \nWest Bank-\nPalestine  \n \n\n\n\n69 48 630 (Dabayneh K M.,  \n2008) \n\n\n\nWorldwide \naverage \n \n\n\n\n35 30 400 (UNSCEAR, 2000) \n\n\n\nHabigonj \n(Bangladesh \nPresent \nstudy) \n\n\n\n11.09  21.98  227  \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 67\n\n\n\nRadioactivity Distributions in Soils of Habiganj District\n\n\n\nto be lower than the recommended value of 1 implying that the radiation hazard \nis insignificant for the population living in the investigated areas of Habiganj \nDistrict (UNSCEAR, 2008). The annual effective dose was calculated as 9.07 \n\u00b5Sv, which was below annual maximum permissible dose level (1mSv or 1000 \n\u03bcSv) to the general public (ICRP, 1990).\n\n\n\nCONCLUSION\nThe measurements made showed that the levels of radioactivity from the decay \nchain of 238U and 232Th, as well as the primordial radionuclide 40K were present in \nall soil samples. The measured activity concentrations of 238U, 232Th and 40K across \nthe soil samples varied from 5.28 \u00b1 0.02 Bq kg-1 to 18.83 \u00b1 0.12 Bq kg-1, 7.13 \u00b1 0.04 \nBq kg-1 to 38.14 \u00b1 0.13 Bq kg-1, and 93.14 \u00b10.06 to 392 \u00b10.74 Bq kg-1,respectively \nand with mean values of 11.09 \u00b1 0.01 Bqkg-1; 21.98 \u00b1 0.03 Bq kg-1, and 227 \u00b1 \n0.70 Bq kg-1,respectively. The average activity concentrations for 238U, 232Th and \n40K in this study were lower than the worldwide average for these radionuclides in \nsoils. The artificial radionuclide 137Cs was not observed in statistically significant \namounts above background level in this study. This radioactivity monitoring study \nmay be continued to establish a complete database of environmental radioactivity \nin Bangladesh. \n\n\n\nRaeq, gamma absorbed dose rate, the Hex, and annual effective dose rate of \nthe soil samples collected were 59 Bq kg-1, 28 nGy h-1, 0.162, 33 \u00b5Sv, respectively. \nThe results show that the Hex values for all soil samples were below the limit of \nunity, meaning that the radiation dose is below the permissible limit of 1 mSv y-1 \nrecommended by ICRP 60 for general public (ICRP, 1990).\n\n\n\nThe values of the radiation hazard parameters from this study were not \nextremely high compared to either the world averages or the recommended limits, \n\n\n\nTABLE 3\nAverage activity concentration of 238U, 232Th & 40K in soil samples for different regions \n\n\n\nwithin Bangladesh.\n\n\n\n\n\n\n\n 12 \n\n\n\n \nTABLE 3 \n\n\n\nAverage activity concentration of 238U, 232Th & 40K in soil samples for different regions \nwithin Bangladesh. \n\n\n\n \nSample location Average activity concentration in Bq kg-1 Reference \n\n\n\n238U 232Th 40K \nChittagong 35 60 438 (Chowdhury et al., 1999) \nPabna 33 47 449 (Royet al., 2001) \nDhaka 33 55 574 (Miah et al., 1998) \nNine southern \ndistricts \n\n\n\n42 81 833 (Chowdhury et al., 2006) \n\n\n\nJessore 48 53 481 (Miah et al., 1985) \nSitakunda 31 62 467 (Rahman et al., 2012) \nKuakata Sea \nBeach \n\n\n\n29 91 875 (Islam et al., 2012) \n\n\n\nSylhet 55 125 491 (Chowdhury et al.,  1999) \nBangladesh  \n(average) \n\n\n\n48 53 481 (Miah et al., 1985) \n\n\n\nHabiganj \n(Bangladesh, \nPresent study) \n\n\n\n11.09 21.98 227  \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201568\n\n\n\nJ. Ferdous, M.M. Rahman,  Rubina Rahman, S. Hasan and N. Ferdous\n\n\n\nand therefore unlikely to cause additional radiological health risks to the people \nliving in the areas studied. The average concentrations of the radionuclides in soil \nsamples collected from Habiganj District were at the normal environmental levels \nand were similar to the concentrations obtained in the surrounding countries.\n\n\n\nACKNOWLEDGEMENTS\nThe authors wish to express their deepest sense of gratitude to  the Director, Atomic \nEnergy Centre, Dhaka (AECD) and Dr Aleya Begum, Head of Department, Health \nPhysics Division, Atomic Energy Centre, Dhaka (AECD) for making available \ntheir research and laboratory facilities.    \n\n\n\nTABLE 4\nRadium equivalent activity, dose rate, annual effective dose and External Hazard Index \n\n\n\nfor soil samples at different locations in Habiganj\n\n\n\n\n\n\n\n 13 \n\n\n\nTABLE 4 \nRadium equivalent activity, dose rate, annual effective dose and External Hazard Index for \n\n\n\nsoil samples at different locations in Habiganj \n \n\n\n\nSample \nlocation \n\n\n\nSample \nno \n\n\n\nRadium equivalent \nactivity,  \nRaeq (Bqkg-1)   \n \n\n\n\nDose Rate,  \nD (nGy/h) \n\n\n\nExternal \nhazard \nindex Hex \n\n\n\nAnnual \neffective dose, \nDeff (10-6 Sv) \n\n\n\nHabiganj Sadar 1 \n2 \n3 \n\n\n\n53.90 \n62.57 \n51.04 \n \n\n\n\n25.26 \n30.06 \n25.11 \n \n\n\n\n0.155 \n0.174 \n0.143 \n \n\n\n\n30.98 \n36.86 \n30.80 \n \n\n\n\nChunarughat \n \n\n\n\n4 \n5 \n6 \n \n\n\n\n60.97 \n32.60 \n34.90 \n\n\n\n30.31 \n16.71 \n16.83 \n\n\n\n0.171 \n0.092 \n0.097 \n\n\n\n37.17 \n20.49 \n20.64 \n\n\n\nBahubal \n \n\n\n\n7 \n8 \n9 \n \n\n\n\n43.04 \n44.91 \n43.33 \n\n\n\n21.44 \n21.37 \n20.77 \n\n\n\n0.121 \n0.124 \n0.120 \n\n\n\n26.29 \n26.21 \n25.48 \n\n\n\nLakhai \n \n\n\n\n10 \n11 \n12 \n\n\n\n80.74 \n88.31 \n69.76 \n\n\n\n37.57 \n42.24 \n34.79 \n \n\n\n\n0.222 \n0.245 \n0.196 \n \n\n\n\n46.07 \n51.80 \n42.66 \n \n\n\n\nAzmirigonj \n \n\n\n\n13 \n14 \n\n\n\n83.52 \n64.53 \n\n\n\n38.68 \n30.08 \n \n\n\n\n0.230 \n0.178 \n \n\n\n\n47.43 \n37.17 \n \n\n\n\nBaniachong \n \n\n\n\n15 \n16 \n\n\n\n74.67 \n62.56 \n\n\n\n34.75 \n28.08 \n \n\n\n\n0.206 \n0.171 \n \n\n\n\n20.49 \n30.98 \n \n\n\n\nNobigonj \n \n\n\n\n17 \n18 \n19 \n \n\n\n\n66.28 \n66.65 \n47.48 \n\n\n\n32.28 \n33.03 \n22.71 \n\n\n\n0.184 \n0.187 \n0.132 \n\n\n\n39.59 \n40.51 \n27.86 \n\n\n\nMadhabpur 20 \n21 \n22 \n\n\n\n57.96 \n50.26 \n47.33 \n\n\n\n28.18 \n23.56 \n21.96 \n \n\n\n\n0.161 \n0.139 \n0.130 \n \n\n\n\n34.56 \n28.90 \n26.94 \n \n\n\n\nAverage 58.51 27.99 0.160 33.18             \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 69\n\n\n\nRadioactivity Distributions in Soils of Habiganj District\n\n\n\nREFERENCES\nAhmed, N. 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United \nNations Sales Publications No. E.10.Xi.3 Volume I. United Nations, New York, \npp.. 1220. \n\n\n\nVeiga, R., N. Sanches, R.M. Anjos, K. Macario, J. Bastos, M. Iguatemy, J.G. Aguiar, \nA.M.A. Santos, B. Mosquera, C. Carvalho, M. Baptista Filho and N.K. Umisedo. \n2006. Measurement of natural radioactivity in Brazilian beach sands. Radiation \nMeasurements 41: 189-196. \n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: sudharmaidevi@gmail.com \n\n\n\nINTRODUCTION\nCoconut (Cocos nucifera L.), considered to be the most important and useful \nof the tropical palms, is grown in more than 80 countries in the tropics. It has \nbeen cultivated in India from time immemorial, and India ranks third in the \nworld in coconut cultivation area and first in coconut production (www.bgci.org/\neducation/1685/). Though the palms do not require special care, they respond \nwell to plant protection and nutrient management practices. Tissue nutrient \ncomposition has a significant effect on growth, development and yielding ability \nof plants. The nutrient potassium (K) has a special role in coconut nutrition as it \nimproves the quality of nuts.  The yield of the coconut palm is also increased with \naddition of K, hence a good share (52%) of the cultivation expenses account for \nthe cost of this nutrient alone. India expends INR. 21270 crores (Kinekar, 2011) \nannually to import K fertilisers, thus efforts are required to increase its efficient \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 107-114 (2015)                     Malaysian Society of Soil Science\n\n\n\nEffect of Potassium-Sodium Interaction on Foliar Nutrient \nConcentration and Nut Quality of Coconut (Cocos nucifera)\n\n\n\nSudharmaidevi C.R.*, V. Vinith, and G.V. Kavitha \n\n\n\nDepartment  of Soil Science and Agricultural Chemistry,\nCollege of Agriculture, Vellayani, \u2013 695 522, Trivandrum \n\n\n\nABSTRACT\nField experiments were carried out for three consecutive years to evaluate the \neffect of the interaction of potassium and sodium on foliar nutrient concentration, \nquality and quantity of coconut, grown in an acidic Ultisols in Kerala, India. The \ntreatments comprised of different levels of potassium (K) as muriate of potash and \nsodium (Na) (common salt), either alone or in combination. The results showed \nthat interaction between K and Na did not exert a significant effect on the foliar \nconcentrations of nitrogen (N), phosphorous (P) and magnesium (Mg) as against \nK, Na and calcium (Ca). Full or partial omission of K for >2 years had a negative \nimpact on nut yield, but this could be corrected by application of equal proportions \nof K and Na, which resulted in an increase in nut yield. The interaction between \nK and Na did not exert a significant effect on the quality of the kernel as indicated \nby biochemical characteristics. The treatments receiving K with Na registered a \nhigher content of K and sugar in coconut water, but Na content was highest in full \nK treatment. Significant differences were not observed between treatments for pH, \ntotal mineral, and vitamin C content of the coconut water. The treatments studied \ncould be beneficial to farmers cultivating coconut in acidic soils the world over.\n\n\n\nKeywords:\t Foliar\t nutrients\t concentrations,\t nut\t quality,\t potassium-\nsodium interaction, acidic Ultisol\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015108\n\n\n\nSudharmaidevi C.R., V. Vinith, V.  and G.V. Kavitha \n\n\n\nuse. In a study conducted with several tropical crops, Sudharmaidevi et al. (2006) \nfound a beneficial interaction between K and sodium (Na) on crop yield in acidic \nsoils. They reported that application of common salt increases yields and reduces \nthe potash requirement of the crops tested. This study aimed to evaluate the effect \nof the application of Na (in the form of common salt) with K (as a muriate of \npotash) on foliar nutrients concentrations and quality of the nut in coconut. \n\n\n\nMATERIALS AND METHODS\n\n\n\nSite Description\nThe experiment was carried out in a 29-year old coconut plantation of the \nInstructional Farm of College of Agriculture, Kerala Agricultural University, \nTrivandrum, India (8\u00b030 \u2019E latitude, 76o54 \u2019E longitude, and 29 m above sea \nlevel). The experiments were run for three consecutive years starting from 2004 \n- 2005. The variety of coconut was West Coast Tall, planted at a spacing of 7 m2. \nThe mean monthly rainfall of the study location during the cropping season ranged \nfrom 0 to 201 mm. The mean maximum and minimum temperature ranges were \n29.6oC to 33.3oC, and 21.6oC to 24.9oC, respectively. The soil of the experimental \nsite was loamy skeletal kaolinitic isohyperthermic Rhodic Haplustult with a \nlow cation exchange capacity (CEC) (3.2 cmol (p+) kg-1), acidic (pH <6.0), and \nwith an electrical conductivity of < 0.01 dS m-1.  The soil had a medium level \nof available nitrogen (N) (301 kg ha-1) and K (241.1 kg ha-1), and high level of \navailable P (36.2 kg ha-1). \n\n\n\nTreatments and Experimental Design\nThe experiment was laid out in randomised block design with seven treatments \nand ten replications (Figure 1). One tree was taken as one treatment.   \n\n\n\nFigure 1: Lay out plan of the experimental field\n\n\n\n 10 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 1: Lay out plan of the experimental field \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 109\n\n\n\nPotassium-Sodium Interaction on Coconut Quality\n\n\n\nThe treatments comprised different levels of K and Na, either alone or \nin combination (Table 1). For the combination treatments, a partial dose of K \nwas supplied as muriate of potash with the balance supplied as equivalent Na \nof common salt. Cattle manure at 25 kg palm-1 year-1, N at 0.5 kg palm-1 year-1 \nand phosphorus (P) at 0.32 kg palm-1 year-1 were applied uniformly to all palms. \nPotassium and Na were applied as per treatment requirements (Table 1). The \nrecommended dose of K for coconut is 1.2 kg K palm-1 year-1.   Potassium was \nsupplied as muriate of potash (60% K) and Na, as common salt (39.3% Na) \n(KAU, 2011).\n\n\n\nChemical Analyses \nKernel and Coconut Water\nMoisture and carbohydrate contents of the kernel were determined by the \ngravimetric method and titrimetry, respectively. Protein was estimated by the \ncolorimetric method described by Spies (1957) and total minerals content by \ngravimetry.  Coconut water was analysed for pH, total sugars, total minerals \n(determined by measuring total ash content), and vitamin C content (Thimmaiah, \n1999).  \n\n\n\nFoliar Analysis\nFor the determination of foliar nutrient concentration, five whole leaflets from \nthe middle portion of the 14th leaf in each palm were collected. The leaflet \nsamples were air dried for two days and then oven dried at 65oC for 48 hours and \n\n\n\nTABLE 1\nTreatments and dose of K and Na fertilisers applied\n\n\n\n 6 \n\n\n\n \nTABLE 1 \n\n\n\nTreatments and dose of K and Na fertilisers applied \n \n\n\n\nSl.no Treatments \n \nDose of K or/and Na (palm-1 year-1) \n\n\n\n1 100%  RD of K as MOP  MOP \u2013 2 kg \n\n\n\n2 50% RD of K as MOP  MOP \u2013 1 kg \n\n\n\n3 100% RD of K replaced by Na (of  CS) Common salt \u2013 3 kg \n\n\n\n4 50% RD of K replaced by Na (of  CS) Common salt \u2013 1.5 kg \n\n\n\n5 \n25% RD of K replaced by Na (of  CS) + 75% \nRD of K as MOP  Common salt \u2013 0.75 kg  +  MOP -1.5 kg \n\n\n\n6 \n50% RD of K replaced by Na (of  CS) + 50% \nRD of K as MOP  Common salt \u2013 1.5 kg  +  MOP -1 kg \n\n\n\n7 \n75% RD of K replaced by Na \n (of  CS ) + 25% RD of K as MOP  Common salt \u2013 2.25 kg  +  MOP -0.5 kg \n\n\n\n*Cattle manure @ 25 kg palm-1 year-1, nitrogen @ 0.5 kg N palm-1 year-1 and phosphorus @ 0.32 kg P palm-1 \nyear-1 were applied uniformly to all palms  \nRD \u2013 Recommended dose ; MOP - Muriate of potash; CS \u2013 Common salt\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015110\n\n\n\nSudharmaidevi C.R., V. Vinith, V.  and G.V. Kavitha \n\n\n\npowdered. The powdered samples were passed through a 1-mm sieve and mixed \nthoroughly. Weighed 10 g samples were drawn from this powder and stored in \nplastic bottles for wet acid digestion. From the stored sample, 0.5 g was digested \nwith 10 ml concentrated H2SO4 and 1 g of catalyst mixture for determination of \nN by the modified Kjeldahl method. For determination of P, K, Na, Ca and Mg, \n0.5 g samples were digested with 7 ml of a mixture of nitric and perchloric acid in \nthe ratio of 9:4. Phosphorous was estimated by vanadomolybdate yellow colour \nmethod using a spectrophotometer (Systronics Model 169). Sodium and K were \nestimated using flame photometer (Elico Model CL 22 D). Calcium and Mg were \nestimated by Versenate titration (Cheng and Bray, 1951). \n\n\n\nNut Yield\nThe number of nuts per palm was counted in each harvest and recorded. \n\n\n\nStatistical Analyses\nStatistical analyses of the data were carried out using two-way analysis of variance \n(ANOVA).  The F values for treatments were compared with tabled values. If the \nvalues were found to be significant, critical difference values at 5% significance \nlevel were calculated to compare means and interpret results. \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nEffect of the Interaction of K and Na on Foliar Concentration of Plant Nutrients\nNo significant difference could be observed between treatments in the foliar \nconcentrations of N and P during the final year of the experiment (Table 2). \nBut significant differences were noticed in the foliar concentrations of K and \nNa. The concentrations of N, P and K were found to decrease in the treatments \nreceiving half the recommended dose of K alone, or half or full Na alone. But in \ntreatments where Na was also applied with K, this reduction was not observed. It \nwas interesting to note that the foliar K concentrations in treatments receiving Na \nwith K were higher than in the treatment of full K. In contrast, foliar content of \nNa was the highest in plots treated with 100 % K. The results of this study agree \nwith Rubio et al., (1995) who found that a high affinity for K uptake was activated \nby micromolar Na concentrations and vice versa. Evidence for an increase in K \nuptake in the presence of Na has also been seen in wheat (Box and Schachtman, \n2000). Thus, these findings suggest that the efficiency of K use could be increased \nby adopting combined applications of K and Na. \n\n\n\nAs far as secondary nutrients were concerned, there was a significant \ndifference in the foliar Ca content of palms. In all the Na treated palms, there \nwas a considerable increase in the Ca content compared to palms given the full K \ntreatment. No significant difference could be observed in the Mg content. The low \nCa content in the full K treated palms might be due to the antagonistic effects of \nK on the absorption of Ca at the absorption sites as reported on a study on onion \n(Singh and Verma, 2001).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 111\n\n\n\nPotassium-Sodium Interaction on Coconut Quality\n\n\n\nEffect of the Interaction of K and Na on Quality  of Nuts \nEffect of the Interaction of K and Na on Quality of Coconut Kernel \nThe interaction of K and Na did not exert significant effect on the quality of \nkernel as indicated by the biochemical characteristics (Table 3). The conversion \npercentage of kernel to copra was high in all the treatments which received K and \nNa together. However, K and Na interaction did not exert significant effect on \nthe quality of the kernel as indicated by the biochemical characteristics. Further, \nthough K is known as the \u2018quality nutrient\u2019, a combined application of K and Na \ndid not enhance the quality of the kernel in this study. \n\n\n\nEffect of the Interaction of K and Na on Quality of Coconut Water\nThe sugar content of coconut water was found to increase with the application \nof Na alone or in combination with K. The treatments which received Na and K \ntogether registered higher values of total sugars in the third year. A significant \ndifference was not observed between the treatments for pH, total ash content (as \na measure of total minerals), and vitamin C content (Table 4). \n\n\n\nThe highest K content was noticed in the treatment where K and Na were \napplied at 50:50 proportions, and the highest Na content was from the treatment \nwhere K alone was applied at the full recommended dose.  Coconut water is \nconsumed worldwide for its nutrition and health benefits. The health benefits of \ndrinking coconut water are mainly attributed to its potassium and sugar content. \nThe findings of this study thus indicate that the application of K and Na in equal \n\n\n\nTABLE 2\nEffect of interaction between K and Na on foliar nutrient concentration (%) during the \n\n\n\nfinal year of the experiment\n\n\n\n 7 \n\n\n\n \nTABLE 2 \n\n\n\nEffect of interaction between K and Na on foliar nutrient concentration (%) during the \nfinal year of the experiment \n\n\n\n\n\n\n\n Treatments N P K Na Ca Mg \n\n\n\n100% RD of K as MOP  1.67 0.24 1.41 0.42 1.65 0.55 \n50% RD of K as MOP  1.62 0.17 1.15 0.29 1.52 0.61 \n100% RD of K replaced by Na (of  CS ) 1.51 0.13 1.34 0.31 1.88 0.69 \n50% RD of K replaced by Na (of  CS ) 1.53 0.24 1.26 0.26 1.74 0.70 \n25% RD of K replaced by Na (of  CS )+ \n75% RD of K as MOP  1.63 0.26 1.44 0.34 1.75 0.77 \n50% RD of K replaced by Na (of  CS ) \n+ 50% RD of K as MOP  1.64 0.25 1.48 0.36 2.01 0.74 \n75% RD of K replaced by Na (of  CS )+ \n25 % RD of K as MOP  1.65 0.25 1.49 0.37 2.01 0.77 \nCD (0.05) NS NS 0.13 0.01 0.14 NS \n\n\n\n \nMOP \u2013 Muriate of potash; CS \u2013 common salt; RD- Recommended dose; \nCD- Critical difference at 5 % significance level; NS \u2013 Not significant \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015112\n\n\n\nSudharmaidevi C.R., V. Vinith, V.  and G.V. Kavitha \n\n\n\nproportions enhances the health benefits of coconut water. The effect of Na in \nincreasing the sugar content in crops has been widely reported (Guerrier,1996; \nAbdulla and Ahmamad,1990;  Isroismail, 2007). \n\n\n\nEffect of the Interaction of K and Na on Nut Yield\nNut yield varied significantly, with the highest yield being recorded in the \ntreatment that  received 50%  of the recommended dose of K as a muriate of \npotash and 50% Na (Table 3). The three treatments where half the dose of K, \nor the full or half dose of Na alone was applied resulted in decreased nut yield \nduring the third year. No significant difference was observed between treatments \nreceiving the full recommended dose of K and treatments where the balance of \nthe dose was supplied as Na in the form of common salt. Thus, the results indicate \nthat full or partial omission of K for a long period, without replacement by Na, \ncould negatively impact nut yield. The effects of insufficient nutrient application \nwill be reflected in nut yields for a longer period given that the coconut is a tree \ncrop. However, it should be noted that in the treatments where 50% or 75% of the \nrecommended dose of K was replaced with equivalent Na, the yield was found to \nincrease. An increase in crop yield with combined a application of K and Na has \nbeen reported by Ivahupa et al., (2006). \n\n\n\nTABLE 3\nEffect of interaction between K and Na on the biochemical characters of coconut kernel \n\n\n\nduring the final year of the experiment\n\n\n\n 8 \n\n\n\n \nTABLE 3 \n\n\n\nEffect of interaction between K and Na on the biochemical characters of coconut kernel \nduring the final year of the experiment \n\n\n\n\n\n\n\n Treatments \nMoisture CHO \n\n\n\nTotal \nminerals \nestimated \nfrom \ntotal ash Protein \n\n\n\nKernel \nto copra \nconversi\non (%) \n\n\n\nYield \n(nuts palm-1 \nyear-1) \n\n\n\n(%) (%) (%) (%) \n100 % RD of K as MOP  46.2 7.1 0.79 3.52 57.8 138 \n\n\n\n50 % RD of K as MOP  42.3 7.3 0.71 3.02 35.0 125 \n100 % RD of K \nreplaced by Na (of  CS) 48.2 7.0 0.78 3.08 50.4 117 \n50 % RD of K replaced \nby Na (of  CS) 49.1 7.5 0.86 3.18 51.4 126 \n25 % RD of K replaced \nby Na (of  CS)+ 75 % \nRD of K as MOP  46.3 7.1 0.83 2.98 61.9 132 \n50 % RD of K replaced \nby Na (of  CS) + 50 % \nRD of K as MOP  40.8 7.3 0.80 2.92 62.0 147 \n75 % RD of K replaced \nby Na (of  CS)+ 25 % \nRD of K as MOP  44.5 6.9 0.78 2.92 58.2 144 \nCD (0.05) NS NS NS NS 3.55 11.04 \n\n\n\n \nMOP \u2013 Muriate of potash; CS \u2013 common salt; RD - Recommended dose; \nCD - Critical difference at 5 % significance level; NS \u2013 Not significant \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 113\n\n\n\nPotassium-Sodium Interaction on Coconut Quality\n\n\n\nTABLE 4\nEffect of interaction between K  and Na on quality of coconut water during the final year \n\n\n\nof the experiment\n\n\n\nCONCLUSION\nThe results of this study showed the beneficial interactions between applied K and \nNa in coconut palms when treated in equal proportions. This interaction resulted in \nan increase in foliar concentrations of K and Ca, which boosted nut yield without \ncompromising nut quality. The treatments studied could be beneficial to farmers \ncultivating coconut trees in acidic soils the world over.\n\n\n\nACKNOWLEDGEMENT\nThe authors gratefully acknowledge the financial support provided by the Kerala \nState Council for Science, Technology, and Environment for this study.\n\n\n\nREFERENCES\nAbdullah, Z and Ahmad, R. 1990. Effect of pre and post kinetin trestments on salt \n\n\n\ntolerance of different potato cultivars growing on saline soils. J. Agron. Crop \nSci. 165 (2-3): 94-102.\n\n\n\nBox, S. and D.P. Schachtman. 2000. The effect of low concentration of Na on K \nuptake and growth of wheat. Aust.J. Pl. Physiol. 27(2): 175-182.\n\n\n\nCheng, K.L. and R.H. Bray. 1951. Determination of Ca and Mg in soil and plant \nmaterial. Soil Sci. 72: 449-458.\n\n\n\n 9 \n\n\n\nTABLE 4 \nEffect of interaction between K  and Na on quality of coconut water during the final year \n\n\n\nof the experiment \n \n\n\n\n Treatments \n\n\n\nTotal \nminerals \n(%) \n\n\n\nTotal \nsugars \n(%) pH K (%) \n\n\n\nNa \n(%) \n\n\n\nVit.C  \nmg per \n100 ml \n\n\n\n100 %  RD of K as MOP  0.3 0.70 5.73 0.34 0.08    2.6 \n\n\n\n50 % RD of K as MOP  0.3 0.67 5.70 0.26 0.01    2.3 \n100 % RD of K replaced by Na (of  \nCS) 0.3 0.69 5.71 0.29 0.02    2.4 \n50 % RD of K replaced by Na (of  \nCS) 0.3 0.84 5.90 0.36 0.01    2.6 \n\n\n\n25 % RD of K replaced by Na (of  \nCS)+ 75 % RD of K as MOP  0.3 0.77 5.82 0.36 0.06    2.8 \n50 % RD of K replaced by Na (of  \nCS) + 50 % RD of K as MOP  0.3 0.86 5.87 0.41 0.07    2.4 \n75 % RD of K replaced by Na (of  \nCS)+ 25 % RD of K as MOP  0.3 0.81 5.63 0.38 0.05    2.7 \n\n\n\nCD (0.05) NS 0.09 NS 0.03 0.001     NS \n \nMOP \u2013 Muriate of potash; CS \u2013 common salt; RD \u2013 Recommended dose; \nCD - Critical difference at 5 % significance level; NS \u2013 Not significant \n  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015114\n\n\n\nSudharmaidevi C.R., V. Vinith, V.  and G.V. Kavitha \n\n\n\nGuerrier, G. 1996. Fluxes of Na, K and Cl and osmotic adjustment in Lycopersicon \npimpinellifolium and L. esculentum during short and long term exposure to \nNaCl. Physiol. Pl. 97 (3):583 \u2013 591.\n\n\n\nIsroismail. 2007. Application of Na and partial substitution of K-Na in different \nvarieties of sugarcane planted on Inceptisol soil. Sugar Tech. 9(4): 256-262.\n\n\n\n \nIvahupa, S.R., C.J. Asher, F.P.C. Blamey and J.N. O\u2019Sullivan. 2006. Effects of sodium \n\n\n\non potassium nutrition in three tropical root crop species. J. Plant Nutrition. \n29(6): 1095-1108. \n\n\n\nKAU. 2011. Package of Practices Recommendations (Crops), Kerala Agricultural \nUniversity, Vellanikkara, Thrissur.\n\n\n\nKinekar, B.K. 2011. Potassium fertilizer situation in India: Current use and perspectives \nKarnataka. J. Agric. Sci., 24 (1) : (1-6) 2011.\n\n\n\nRubio, F.,W. Gassman and J.I. Schroeder. 1995. Sodium-driven potassium uptake by \nthe plant potassium transporter HKT1 and mutations conferring salt tolerance. \nScience 270(5242): 1660-1663. \n\n\n\nSingh, S.P. and A.B. Verma. 2001. Response of onion to potassium application. Indian \nJ. Agron. 46(1): 182-185. \n\n\n\nSpies, J.R. 1957. Colorimetric procedures for amino acids. In: Methods in Enzymology, \nVol. III, ed. S.P. Colowick and N.O. Kaplan (pp. 467-471). New York: Academic \nPress.\n\n\n\nSudharmaidevi, C.R., S. Sunu and S. Neenu.  2006. Interactive effect of applied K and \nNa on plant nutrient concentration, uptake efficiency and yield of some tropical \ncrops. Proc. 18th World Congress of Soil Science, July 9-15,  Philadelphia, \nPennsylvania, USA.\n\n\n\n  \nThimmaiah, S.R. 1999. Standard Methods of Chemical Analysis. Ludhiana, New \n\n\n\nDelhi: Kalyani Publishers, pp.276-278. \n www.bgci.org/education/1685.\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nINTRODUCTION\n\n\n\nin efforts to increase rice production in Malaysia. While Malaysian farmers apply \nN, P and K fertilizers widely,  it is found that the applications of micronutrients \n\n\n\nEffects of Micronutrient Fertilizers on the Production of \nMR 219 Rice (Oryza sativa L.) \n\n\n\nY.A. Liew 1*, S. R. Syed Omar 1, M.H.A Husni 1, \nM.A. Zainal Abidin2  & N.A.P. Abdullah3\n\n\n\n1 2\n\n\n\n 3\n\n\n\nABSTRACT\n\n\n\nMinimum for plant nutrients requirement is an obstacle in efforts to increase rice \nproduction in Malaysia. Ignorance of the importance of micronutrients application \nby Malaysian farmers hinders the achievement of high yields in rice production. \n\n\n\nrice production in Sawah Sempadan, Tanjong Karang, Malaysia. Soil and foliar \nsamples were collected and analyzed to determine the micronutrients content and \nthen compared with the critical nutrients levels needed by rice. Special fertilizers \ncomprising formulated mixture of K, Mg, Zn, Cu, Mn and B were then distributed \nto farmers along with a recommended manuring programme. The timing and \nquantity of fertilizer applied were closely supervised. The investigations were \ncarried out over two continuous seasons where rice yield data were collected and \nsubjected to statistical analysis. The results showed that the application of special \n\n\n\nKeywords:  Fertilizer, Law of Minimum, micronutrients, rice\n\n\n\n___________________\n*Corresponding author : E-mail: \n\n\n\n\n\n\n\n\nthe globe. Low levels of trace elements are expected in the oldest landscapes in \nzones of high rainfall and temperature, and where trace element concentrations \n\n\n\nessential even though needed in minute amounts. Plants cannot complete their life \n\n\n\nIn Malaysia, most of the granary areas are well established with irrigation \nsystems with the  farmers practising double cropping with high yielding varieties \n\n\n\net al.\npiece of land with high yielding varieties requires better fertilizer management as \n\n\n\n(Wei et al.\npractical due to the continuous removal of micronutrients after harvesting, as \nwell as losses due to leaching or surface runoffs. The objective of this study is to \n\n\n\nMATERIALS AND METHODS\nField experiments were conducted at Sawah Sempadan, Kampung Seri Tiram \n\n\n\nSulfaquent by USDA Soil Taxonomy System. This area had been cultivated with \n\n\n\nthe subsidized fertilizer given by the Malaysian government. This area has been \nexperiencing low rice yields with the average production being 4.5 tonnes per \nhectare, with severe infections of fungal diseases namely Brown Spot (\n\n\n\n) and Sheath Blight ( ).\nSoil samples were randomly collected from 3 different spots in each of \n\n\n\nthe experimental plot. Flag leaves of the rice plant at maximum tillering were \nrandomly sampled from the experiment plot. Soil and leaves samples collected \nwere analyzed for nutrient content according to the methods listed in Table 1. Plant \n\n\n\nwith the critical nutrients range required by rice as reported by Dobermann and \n\n\n\n , M.A. Zainal Abidin  & N.A.P. Abdullah\n\n\n\n\n\n\n\n\nMicronutrient Fertilizers for Rice\n\n\n\n\n\n\n\n\n\n\n\n \nTa\n\n\n\nbl\ne \n\n\n\n1\nM\n\n\n\net\nho\n\n\n\nds\n o\n\n\n\nf S\noi\n\n\n\nl a\nnd\n\n\n\n P\nla\n\n\n\nnt\n A\n\n\n\nna\nly\n\n\n\nsi\ns\n\n\n\n\n\n\n\n Sa\nm\n\n\n\npl\nes\n\n\n\n \nC\n\n\n\nhe\nm\n\n\n\nic\nal\n\n\n\n Pr\nop\n\n\n\ner\ntie\n\n\n\ns\nM\n\n\n\net\nho\n\n\n\nd \nof\n\n\n\n A\nna\n\n\n\nly\nsi\n\n\n\ns\n\n\n\nSo\nil\n\n\n\n \nTo\n\n\n\nta\nl C\n\n\n\nar\nbo\n\n\n\nn \n \n\n\n\nD\nry\n\n\n\n c\nom\n\n\n\nbu\nst\n\n\n\nio\nn \n\n\n\nw\nith\n\n\n\n to\nta\n\n\n\nl c\nar\n\n\n\nbo\nn \n\n\n\nan\nal\n\n\n\nyz\ner\n\n\n\n\n\n\n\nEx\ntra\n\n\n\nct\nab\n\n\n\nle\n P\n\n\n\n \nB\n\n\n\nra\ny \n\n\n\n2 \nm\n\n\n\net\nho\n\n\n\nd \n(0\n\n\n\n.1\nN\n\n\n\n H\nC\n\n\n\nl +\n 0\n\n\n\n.0\n3N\n\n\n\n N\nH\n\n\n\n4F\n) \n\n\n\nEx\ntra\n\n\n\nct\nab\n\n\n\nle\n K\n\n\n\n, C\na,\n\n\n\n M\ng\n\n\n\n \nSh\n\n\n\nak\nin\n\n\n\ng \nw\n\n\n\nith\n 1\n\n\n\n N\n a\n\n\n\nm\nm\n\n\n\non\niu\n\n\n\nm\n a\n\n\n\nce\nta\n\n\n\nte\n, p\n\n\n\nH\n 7\n\n\n\n (1\n:5\n\n\n\n v\n/w\n\n\n\n)\n\n\n\nEx\ntra\n\n\n\nct\nab\n\n\n\nle\n Z\n\n\n\nn,\n M\n\n\n\nn,\n C\n\n\n\nu,\n F\n\n\n\ne\nM\n\n\n\neh\nlic\n\n\n\nh \nN\n\n\n\no.\n 1\n\n\n\n (0\n.0\n\n\n\n5 \nN\n\n\n\n H\nC\n\n\n\nl +\n 0\n\n\n\n.0\n25\n\n\n\n N\n H\n\n\n\n2S\nO\n\n\n\n4)\n\n\n\nEx\ntra\n\n\n\nct\nab\n\n\n\nle\n B\n\n\n\n \nH\n\n\n\not\n w\n\n\n\nat\ner\n\n\n\n (1\n:2\n\n\n\n v\n/w\n\n\n\n)\n \n\n\n\npH\n \n\n\n\npH\n w\n\n\n\nat\ner\n\n\n\n (1\n:1\n\n\n\n v\n/w\n\n\n\n)\n \n\n\n\nLe\naf\n\n\n\n \nTo\n\n\n\nta\nl N\n\n\n\nitr\nog\n\n\n\nen\nK\n\n\n\nje\nda\n\n\n\nhl\n M\n\n\n\net\nho\n\n\n\nd\n\n\n\n \nP,\n\n\n\n K\n, C\n\n\n\na,\n M\n\n\n\ng,\n \n\n\n\nD\nry\n\n\n\n a\nsh\n\n\n\nin\ng\n\n\n\n\n\n\n\n  \nZn\n\n\n\n, M\nn,\n\n\n\n C\nu,\n\n\n\n F\ne,\n\n\n\n B\n  \n\n\n\nSo\nur\n\n\n\nce\n: J\n\n\n\non\nes\n\n\n\n (2\n00\n\n\n\n1)\n\n\n\n\n\n\n\n\n , M.A. Zainal Abidin  & N.A.P. Abdullah\n\n\n\n\n\n\n\n\n\n\n\nTa\nbl\n\n\n\ne \n2\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\ns \nco\n\n\n\nnt\nen\n\n\n\nt i\nn \n\n\n\nso\nil \n\n\n\nsa\nm\n\n\n\npl\nes\n\n\n\n\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\n \nTo\n\n\n\nta\nl C\n\n\n\n\n\n\n\n(%\n)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nC\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nin\n\n\n\n so\nil \n\n\n\nsa\nm\n\n\n\npl\nes\n\n\n\n \n12\n\n\n\n.0\n1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSt\nan\n\n\n\nda\nrd\n\n\n\n\n\n\n\ner\nro\n\n\n\nr\n \n\n\n\n0.\n92\n\n\n\n\n\n\n\nP \n   \n\n\n\n\n\n\n\n37\n.2\n\n\n\n3\n\n\n\n3.\n67\n\n\n\n\n\n\n\nK\n   \n\n\n\n\n\n\n\n72\n.3\n\n\n\n6\n\n\n\n3.\n99\n\n\n\n\n\n\n\nC\na \n\n\n\n29\n3.\n\n\n\n36\n\n\n\n28\n.9\n\n\n\n7\n \n\n\n\nM\ng \n\n\n\n11\n9.\n\n\n\n64\n\n\n\n8.\n72\n\n\n\n\n\n\n\nZn 1.\n11\n\n\n\n0.\n07\n\n\n\n\n\n\n\nM\nn \n\n\n\n5.\n27\n\n\n\n0.\n16\n\n\n\n C\nu \n\n\n\n0.\n12\n\n\n\n0.\n01\n\n\n\n04\n \n\n\n\nFe\n \n\n\n\n91\n.5\n\n\n\n3.\n81\n\n\n\n B\n\n\n\n0.\n15\n\n\n\n0.\n07\n\n\n\n\n\n\n\npH 4.\n61\n\n\n\n0.\n03\n\n\n\n\n\n\n\n\n\n\n\nm\ng \n\n\n\nkg\n -1\n\n\n\n\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\ns c\non\n\n\n\nte\nnt\n\n\n\n in\n le\n\n\n\naf\n sa\n\n\n\nm\npl\n\n\n\nes\n \n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\n \nN\n\n\n\n \nP\n\n\n\n \nK\n\n\n\n \nC\n\n\n\na\n \n\n\n\nM\ng\n\n\n\n \nZn\n\n\n\n \nM\n\n\n\nn\n \n\n\n\nC\nu\n\n\n\n \nFe\n\n\n\n \nB\n\n\n\n\n\n\n\nC\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nin\n\n\n\n\n\n\n\nle\naf\n\n\n\n sa\nm\n\n\n\npl\nes\n\n\n\n \n2.\n\n\n\n38\n \n\n\n\n0.\n18\n\n\n\n \n1.\n\n\n\n01\n \n\n\n\n1.\n88\n\n\n\n \n0.\n\n\n\n38\n \n\n\n\n39\n.4\n\n\n\n5\n \n\n\n\n24\n7.\n\n\n\n53\n \n\n\n\n8.\n29\n\n\n\n \n32\n\n\n\n4.\n45\n\n\n\n \n0.\n\n\n\n32\n \n\n\n\nSt\nan\n\n\n\nda\nrd\n\n\n\n e\nrr\n\n\n\nor\n \n\n\n\n0.\n06\n\n\n\n9\n \n\n\n\n0.\n00\n\n\n\n6\n \n\n\n\n0.\n04\n\n\n\n5\n \n\n\n\n0.\n02\n\n\n\n7\n \n\n\n\n0.\n00\n\n\n\n8\n \n\n\n\n3.\n45\n\n\n\n \n38\n\n\n\n.2\n7\n\n\n\n \n1.\n\n\n\n31\n \n\n\n\n51\n.0\n\n\n\n5\n \n\n\n\n0.\n25\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n(%\n)\n\n\n\n\n\n\n\n\n\n\n\n \nm\n\n\n\ng \nkg\n\n\n\n-1\n \n\n\n\n\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\ns c\non\n\n\n\nte\nnt\n\n\n\n in\n so\n\n\n\nil \nsa\n\n\n\nm\npl\n\n\n\nes\n \n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\ns c\non\n\n\n\nte\nnt\n\n\n\n in\n le\n\n\n\naf\n sa\n\n\n\nm\npl\n\n\n\nes\n \n\n\n\n\n\n\n\n\nMicronutrient Fertilizers for Rice\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nC\nrit\n\n\n\nic\nal\n\n\n\n n\nut\n\n\n\nrie\nnt\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nin\n\n\n\n so\nil \n\n\n\npl\nan\n\n\n\nte\nd \n\n\n\nw\nith\n\n\n\n ri\nce\n\n\n\nTA\nB\n\n\n\nLE\n 5\n\n\n\nC\nrit\n\n\n\nic\nal\n\n\n\n n\nut\n\n\n\nrie\nnt\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nin\n\n\n\n le\naf\n\n\n\n fo\nr r\n\n\n\nic\ne\n\n\n\n\n\n\n\n \nTA\n\n\n\nB\nLE\n\n\n\n 4\nC\n\n\n\nrit\nic\n\n\n\nal\n n\n\n\n\nut\nrie\n\n\n\nnt\ns c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n so\n\n\n\nil \npl\n\n\n\nan\nte\n\n\n\nd \nw\n\n\n\nith\n r\n\n\n\nic\ne\n\n\n\n\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\ns\n \n\n\n\nP \n   \n\n\n\n\n\n\n\nK\n   \n\n\n\n\n\n\n\nC\na \n\n\n\n \nM\n\n\n\ng \n \n\n\n\nZn\n  \n\n\n\nm\ng \n\n\n\nkg\n-1\n\n\n\n\n\n\n\nM\nn \n\n\n\n \nC\n\n\n\nu)\n \n\n\n\nFe\n  \n\n\n\nB\n  \n\n\n\nC\nrit\n\n\n\nic\nal\n\n\n\n n\nut\n\n\n\nrie\nnt\n\n\n\ns \nco\n\n\n\nnc\nen\n\n\n\ntra\ntio\n\n\n\nn \nin\n\n\n\n so\nil\n\n\n\n \n12\n\n\n\n-2\n0\n\n\n\n \n15\n\n\n\n \n20\n\n\n\n \n36\n\n\n\n \n1 \n\n\n\n3 \n\u2013 \n\n\n\n30\n \n\n\n\n0.\n1\n\n\n\n \n4 \n\n\n\n- \n5 \n\n\n\n0.\n5\n\n\n\n\n\n\n\nSo\nur\n\n\n\nce\n: D\n\n\n\nob\ner\n\n\n\nm\nan\n\n\n\nn \nan\n\n\n\nd \nFa\n\n\n\nirh\nur\n\n\n\nst\n (2\n\n\n\n00\n0)\n\n\n\n\n\n\n\n \nTA\n\n\n\nB\nLE\n\n\n\n 5\nC\n\n\n\nrit\nic\n\n\n\nal\n nu\n\n\n\ntri\nen\n\n\n\nts\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n le\n\n\n\naf\n fo\n\n\n\nr r\nic\n\n\n\ne\n \n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\ns\n \n\n\n\nN\n \n\n\n\nP\n \n\n\n\nK\n \n\n\n\nC\na\n\n\n\n \nM\n\n\n\ng\n \n\n\n\nZn\n \n\n\n\nM\nn\n\n\n\n \nC\n\n\n\nu\n \n\n\n\nFe\n \n\n\n\n\n\n\n\n\n\n\n\n \n(%\n\n\n\n)\n \n\n\n\n\n\n\n\n\n\n\n\nm\ng \n\n\n\nkg\n-1\n\n\n\n\n\n\n\nC\nrit\n\n\n\nic\nal\n\n\n\n n\nut\n\n\n\nrie\nnt\n\n\n\ns \nco\n\n\n\nnc\nen\n\n\n\ntra\ntio\n\n\n\nn \nin\n\n\n\n le\nav\n\n\n\nes\n >\n 2\n\n\n\n.0\n \n\n\n\n> \n0.\n\n\n\n18\n \n\n\n\n>1\n.5\n\n\n\n \n> \n\n\n\n0.\n15\n\n\n\n \n> \n\n\n\n0.\n15\n\n\n\n \n25\n\n\n\n \u2013\n 5\n\n\n\n0\n \n\n\n\n40\n -\n\n\n\n 7\n00\n\n\n\n \n7 \n\n\n\n- \n15\n\n\n\n \n75\n\n\n\n -\n 1\n\n\n\n50\n \n\n\n\n6 \n\u2013 \n\n\n\n15\n \n\n\n\nSo\nur\n\n\n\nce\n: D\n\n\n\nob\ner\n\n\n\nm\nan\n\n\n\nn \nan\n\n\n\nd \nFa\n\n\n\nirh\nur\n\n\n\nst\n (2\n\n\n\n00\n0)\n\n\n\nB\n\n\n\n\n\n\n\n\n\n\n\n\n \nTA\n\n\n\nB\nLE\n\n\n\n 6\n\n\n\n\u2018S\npe\n\n\n\nci\nal\n\n\n\n F\nor\n\n\n\nm\nul\n\n\n\nat\ned\n\n\n\n F\ner\n\n\n\ntil\nis\n\n\n\ner\ns\u2019\n\n\n\n fo\nr 1\n\n\n\n ha\n o\n\n\n\nf r\nic\n\n\n\ne \nfi\n\n\n\nel\nd\n\n\n\n\n\n\n\nFe\nrti\n\n\n\nlis\ner\n\n\n\n \nC\n\n\n\nuS\nO\n\n\n\n4\nZn\n\n\n\nSO\n4\n\n\n\n \nM\n\n\n\nnS\nO\n\n\n\n4\nB\n\n\n\nor\non\n\n\n\n E\ntib\n\n\n\nor\n 4\n\n\n\n8\n \n\n\n\nC\nal\n\n\n\n. B\nor\n\n\n\nat\ne\n\n\n\n \nK\n\n\n\nie\nse\n\n\n\nrit\ne\n\n\n\n\n\n\n\nW\nei\n\n\n\ngh\nt o\n\n\n\nf f\ner\n\n\n\ntil\nis\n\n\n\ner \n10\n\n\n\n k\ng\n\n\n\n \n10\n\n\n\n k\ng\n\n\n\n \n5 \n\n\n\nkg\n \n\n\n\n15\n k\n\n\n\ng\n \n\n\n\n10\n k\n\n\n\ng\n \n\n\n\n15\n k\n\n\n\ng\n \n\n\n\n\n\n\n\n(4\n8%\n\n\n\n B\n2O\n\n\n\n3)\n \n\n\n\n(9\n%\n\n\n\n B\n2O\n\n\n\n3)\n \n\n\n\nEq\nui\n\n\n\nva\nle\n\n\n\nnt\n w\n\n\n\nei\ngh\n\n\n\nt \nof\n\n\n\n n\nut\n\n\n\nrie\nnt\n\n\n\n \n4 \n\n\n\nkg\n o\n\n\n\nf C\nu\n\n\n\n \n4 \n\n\n\nkg\n o\n\n\n\nf Z\nn\n\n\n\n \n3.\n\n\n\n6 \nkg\n\n\n\n o\nf M\n\n\n\nn\n \n\n\n\n2 \nkg\n\n\n\n o\nf B\n\n\n\n \n0.\n\n\n\n25\n k\n\n\n\ng \nof\n\n\n\n B\n \n\n\n\n2.\n43\n\n\n\n k\ng \n\n\n\nM\ng\n\n\n\n\n\n\n\nM\nO\n\n\n\nP\n\n\n\n60\n k\n\n\n\ng\n\n\n\n29\n.9\n\n\n\n k\ng \n\n\n\nK\n\n\n\n , M.A. Zainal Abidin  & N.A.P. Abdullah\n\n\n\n\n\n\n\n\nThese fertilizers were supplied along with a special manuring programme \n\n\n\nFarmers involved in the experiments were required to follow precisely the given \npractices in their fertilizer management programme. Field maintenance and use of \npesticides were based on normal practices. Data were collected during harvesting \nfor two   subsequent seasons.The total yields harvested by a combined harvester at \nthe experimental plots were recorded at the weighing bridge. Data collected were \nsubjected to analyses of variance (ANOVA) performed using the SAS program \n\n\n\nManuring programme schedule for participating farmers\n\n\n\nRESULTS AND DISCUSSION\nNutrients contents in the soil and leaves samples analyzed were compared with the \ncritical nutrients range required by rice as reported by Dobermann and Fairhurst \n\n\n\nfoliar analysis showed that the K, Cu and B contents in leaf samples were far from \noptimum for  rice production. \n\n\n\nMicronutrient Fertilizers for Rice\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 7\n\n\n\nManuring programme schedule  for  participating  farmers\n\n\n\nTime of fertilizer\napplication\n\n\n\n Fertilizer application for one farmer\u2019s lot  (1.2  hectares)  \n\n\n\n15 DAS 120 kg of subsidized NPK  (17.5 : 15.5 : 10)  \n\n\n\n25 DAS Foliar spray of 480 mL Robust\u00ae + 900 mL Vita-Grow Plus\u00ae  \n\n\n\n30 DAS 125 kg \u2018Special Formulated Fertilizer \u2019 + 120 kg of subsidized Urea (46% N)\n\n\n\n45 DAS Foliar spray of 900 mL of Robust \u00ae + 900 mL of Vita -Grow Plus\u00ae\n\n\n\n55 DAS 180 kg of subsidized NPK Fertilizer (17.5 : 15.5 : 10)  \n\n\n\n65 DAS Foliar Spray of 900 mL of Robust \u00ae + 900 mL of Vita-Grow Plus\n\n\n\n85 DAS Foliar spray of 1,200 mL of Robust\u00ae\n\n\n\n* DAS = Days after seeding  \n\n\n\n\n\n\n\n\n\n\n\nfertilizer\n\n\n\n\n\n\n\n\nTABLE 8\nEffects of special formulated on rice yield for two\n\n\n\nexperimental seasons\n\n\n\nComparison of rice yields harvested by six farmers\n\n\n\nand lime applications that promote nutrient imbalances, increasing micronutrients \ndemand, altering micronutrients availability, and hastening the depletion of \n\n\n\n\n\n\n\nTABLE 8\n\n\n\nEffects of special formulated formulated on rice yield for two \nexperimental seasons  \n\n\n\n\n\n\n\nExperimental season  Rice yield\n(tonnes per hectare)\n\n\n\nUntreated Season (main season  2007) 4.62 b  \n\n\n\nExperiment Season 1 ( off -season 2007)  5.87 a  \n\n\n\nExperiment Season 2 (main s eason 2008) 4.97 b  \n\n\n\n* Means  followed with the same letter are not significantly different at\n 5% level by LSD\n\n\n\n\n\n\n\n , M.A. Zainal Abidin  & N.A.P. Abdullah\n\n\n\n\n\n\n\n\n\n\n\nTABLE 9\n\n\n\nComparison of rice yields harvested by six farmers.\n\n\n\n\n\n\n\n\n\n\n\nFarmers  Rice y ield  \n\n\n\n  (tonnes per hectare)\n\n\n\nE  6.65  a  \n\n\n\nB  5.84  ab  \n\n\n\nF  5.83ab  \n\n\n\nA  5.16ab  \n\n\n\nC  4.84  ab  \n\n\n\nD  4.21  b\n* Means followed with the same letter are not significantly different at  \n\n\n\n5% level by LSD  \n\n\n\n\n\n\n\n\nrice farming of two seasons a year further depletes the exhausted micronutrients \npool in the soil.\n\n\n\nof the special fertilizer. The special fertilizer provided was formulated according \nto the requirements of the selected experimental site. The nutrients added were \n\n\n\nyields, even on heavy-textured lowland soils with high inherent fertility. Thus, \n\n\n\nin nutrients management may cause severe disease infestation. It has also  been \nreported that K removal in rice produced in rain-fed lowland light-textured soils \nexceeded K additions via fertilizer application and the soil K balance was negative \n\n\n\net al.\n\n\n\ntonnes per ha. The experimental site was heavily infested by diseases particularly \nbrown spot (Drechslera oryzae) and sheath blight (Rhizoctonia solani). Results \nfrom the soil and foliar analysis indicated that the plants in the experimental area \nwere experiencing low inherent soil micronutrients from the soil especially Zn, \nCu and B. Application of the special fertilizer to the depleted paddy soils that \n\n\n\nmicronutrients in the crop production system could reduce nutrients imbalance, \nproducing healthier plants and increased crop yields.\n\n\n\n(Yang et al\nbe corrected by fertilizer addition and/or recycling of organic matter to provide \na balanced nutrient environment that favours rice production (Buri et al.\nZinc has been long associated with the tillering of the rice plant. Under severe \n\n\n\n et al.\n\n\n\nMicronutrient Fertilizers for Rice\n\n\n\n\n\n\n\n\nserious decline in rapeseed yield.  Kalayci et al\n\n\n\nCopper plays an important role in activating the enzymatic activities and is \n\n\n\nwith high organic matter where Cu is complexed to the organic substances. The \n\n\n\n(Table 1) and this may contribute to the formation of organic-Cu complexes \n\n\n\net al. \n\n\n\nin pollen germination and pollen tube growth where it is responsible for seed and \n\n\n\nmale sterility when the wheat anthers were impaired by limited B concentration \nin tissue.  \n\n\n\nThe addition of a special fertilizer consisting  of six nutrients inclusive of \n\n\n\nthe added nutrients especially in relation to micronutrients that had been depleted \nin the soil. The conventional approach of nutrients management in rice planting \nwhich emphasizes N, P and K application has resulted in nutrients imbalance in \nthe soil. Micronutrients are exhausted over a long period of intensive bi-season \n\n\n\nof nutrients to support their production. Nutrients mining due to an imbalance \n\n\n\nproduction. An imbalance in nutrients management, especially N, favours insect \n\n\n\nresearch should be carried out to study plant nutrients and disease interaction in \norder to increase rice production in Malaysia.\n\n\n\n , M.A. Zainal Abidin  & N.A.P. Abdullah\n\n\n\n\n\n\n\n\n81\n\n\n\nCONCLUSION\n\n\n\nincreased rice production is due to the combined effect of the applied nutrients \nwhich appear to  alter the nutrients imbalance in a rice planting area which has \nundergone a long, continuous period of bi-season intensive rice farming. \n\n\n\nACKNOWLEDGEMENTS\n\n\n\nproviding the micronutrients fertilizer to be used in this study, and also the staff \nof Pertubuhan Peladang Kawasan (PPK) Tanjong Karang, Selangor, for their \n\n\n\nREFERENCES\n\n\n\nConceptual Plant Pathology Volume 3: Defence. Singh et al. (eds). Gordon and \nBreach Science Publishers. \n\n\n\nB\nfactors to rice production in West  Africa  lowlands.  94\n\n\n\nphosphorous, potassium, and sulfur in intensive, irrigated lowland rice. Field \n 56:\n\n\n\nDo\nbalance, and soil nutrient-supplying power in intensive, irrigated rice system. I. \n\n\n\n 46\n\n\n\nDobe\nManagement. 1st Edition. Potash & Phosphate Institute (PPI), Potash & \nPhosphate Institute of Canada (PPIC) and International Rice Research Institute \n\n\n\nEva\nMineral Nutrition and Plant Disease. Datnoff  et al. (eds.). The American \nPhytopathological Society. \n\n\n\n \nho\n\n\n\nfertilization effects on growth of soybean on a calcareous soil. \n 25 (8)\n\n\n\n\n\n\n\nMicronutrient Fertilizers for Rice\n\n\n\n\n\n\n\n\nInter\nInstitute. Las Banas, Phillipines. \n\n\n\nJon\n\n\n\nKala\n\n\n\n63\n\n\n\nMars\nPress, London. \n\n\n\n 89:\n\n\n\ninternational bread wheat, durum wheat, triticale and barley germplasm will \nboost production on soil low in boron. 86:\n\n\n\nRi\nand Plant Disease. Datnoff et al. (eds.). The American Phytopathological \nSociety. \n\n\n\nSom\nPublishing Academy. \n\n\n\nWei\navailability of soil micronutrients after 18 years of cropping and fertilization. \n\n\n\n 91\n\n\n\nWhit\n 60\n\n\n\nWihard\nbalances in rainfed lowland rice on a light-textured soil.  \n64:\n\n\n\nYang,\ntheir interactions on seed yield of rapeseed (  L.).  19 \n(11)\n\n\n\n , M.A. Zainal Abidin  & N.A.P. Abdullah\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 195-209 (2016)                         Malaysian Society of Soil Science\n\n\n\nThe effect of oil palm frond-based compost as growing media \namendment for rubber (Hevea brasiliensis, M\u00fcll. Arg.) \n\n\n\nplanting material \n\n\n\nCharlos Togi Stevanus1, Ari Fina Bintarti2, Nico Setiawan2\n\n\n\n   1 Sembawa Research Center \u2013 Indonesian Rubber Research Institute (IRRI), Jln. \nRaya Palembang-Betung Km. 29, P.O, Box 1127, Palembang 30001, \n\n\n\nSouth Sumatera, Indonesia\n2 Faculty of Agriculture, Tridinanti University, Jl. Kapten Marzuki No. 2446, \n\n\n\nKamboja Palembang 30129, South Sumatera,  Indonesia\n\n\n\nABSTRACT\nOil palm frond (OPF) disposal is poorly managed which leads to environmental \nproblem. With an appropriate composting management, OPF have a potential as \norganic compost that can be applied as soil amendment to promote rubber plant \ngrowth. This study was aimed to observe the characteristic of OPF composting \nprocess and to investigate the optimum dose of OPF-based compost to promote \nrubber plant growth. The study was carried out by analysis of OPF composting \nprocess and application test of the compost as media amendment of rubber planting \nmaterial. Composting temperature of the chopped OPF (3 to 5 cm),  reached \nambient temperature on day 75 with C/N ratio of 15.32. The highest percentage of \ncompost particles-size distribution was > 4.75 mm in size. Generally, the quality \nof OPF-based compost has met the minimum standard of Indonesian National \nStandard for compost quality, except for K2O content. The application test revealed \nthat treatment of 20 % compost + 80 % subsoil was the optimum dose to increase \nrubber plant growth compared to other treatments. The final cation exchange \ncapacity (CEC) value of control decreased compared to the initial value, on the \ncontrary, the CEC value of compost added media increased along with increased \ndoses of compost. Media amended with compost also increase P, K, Ca, and Mg \ncontents compared to control.\n\n\n\nKeywords: Oil palm frond, compost, rubber plant, growing media.\n\n\n\n___________________\n*Corresponding author : E-mail: togie-stevanus@yahoo.id\n\n\n\nINTRODUCTION\nOil palm agro-industries generate by-products such  as organic wastes and oil \npalm fronds (OPF) besides empty fruit bunches (EFB), oil palm trunks (OPT), \npalm pressed fibres (PPF), palm shells and palm oil mill effluent (POME) whose \nmanagement  is of greater  concern because until now disposing of this waste \ncontinues to be a major problem. Based on pruning data from the management \nof  the Sembawa Research Center,  40 to 48 fronds of 8-14 years old plants are \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016196\n\n\n\nmaintained  after pruning, in other words, about 16 to 24 pruned fronds per tree \nper year are produced by the plantations. Assuming that  the frond dry weight is \nbetween  4 to 5 kg (Simanihuruk  et al., 2007), the potential for pruned fronds \nproduced by the plantation reaches 64-120 kg/tree/year or approximately 8.3-15.6 \nton/ha/year (population of 130 trees/ha). Because of its high content of lignin (20-\n21 %) and cellulose (40-50 %) (Abdul Khalil et al., 2012), rapid decomposition \nof the OPF  is  difficult to  achieve, so the palm oil fronds are just piled on the \nground or burnt. \n\n\n\nDisposal  of OPF waste is poorly managed and this leads to environmental \nproblems. Based on the Roundtable on Sustainable Palm Oil (RSPO)  guidelines, \nthe use of fire for waste disposal should  be avoided  except in specific situations; \nthis advice is also identified in the ASEAN guidelines or other regional best practice \n(Lord and Clay, 2006). Besides, to leave the OPF for natural decomposition not \nonly obstructs the re-plantation process but also encourages the spread of diseases \nsuch as the Ganoderma as well as  harbours insects like the  rhinoceros beetles \nwhich are harmful to the trees in the plantation (Abdullah and Sulaiman, 2013). \nAlternatively, OPF can be composted in a controlled way and returned to the \nplantation as soil or growing media amendment. Several authors have studied the \npotential of OPF as organic source of compost (Ahmad, 2012; Erwan et al., 2012; \nVakili et al., 2014; Yuniati, 2014).\n\n\n\nGrowing media play an important role in providing a suitable growing \nenvironment for root formation and initial growth of the rubber plant (Wibawa et \nal., 1993). The recent problem of a limited supply of rubber planting materials in \nSouth Sumatera was largely attributed to limitations in soil with suitable physical, \nchemical, and biological characteristics for promoting rubber plant growth. The \naddition of compost into media is an alternative to improve the quality of growing \nmedia. Several studies have reported on the composting of OPF to produce organic \nmaterial as potting media in ornamental plants (Kala et al., 2009), cauliflower \n(Erwan et al., 2013), etc. \n\n\n\nStudies have also reported that the addition of compost into growing media \nincreased nutrient uptake of N, P, and K in the leaves (El-Naggar and El-Nasharty, \n2009), increased plant height and root dry weight (Mikhail et al., 2005), and \nimproved soil physical properties (density, total porosity, and water holding \ncapacity) (Mastouri et al., 2005) compared to media without compost. Based on \nthose studies, the addition of compost as soil amendment is seen  to have positive \neffects on soil quality and plant health. However, there are no studies reporting on \nthe potential of OPF-based compost as media amendment to improve the growth \nof rubber planting materials in the nursery. Therefore, the objectives of this study \nwas to observe the characteristic of OPF-based compost and to determine the \neffective and optimum composition and dose of OPF-based compost in promoting \nrubber plant growth in polybags.\n\n\n\nStevanus et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 197\n\n\n\nMATERIALS AND METHODS\nThe study was carried out from July 2014 to February 2015 in Sembawa Research \nCenter rubber plantation, Indonesian Rubber Research Institute (IRRI), South \nSumatera. The study was divided into two sub-experiments: the first experiment \nwas OPF-based compost processing while the second experiment was the \napplication of OPF-based compost as media amendment of rubber plants in \npolybags. \n\n\n\nSampling and Preparation of Composting Materials\nIn this study, OPF, cow manure, and cover crop of Mucuna bracteata as green \nmaterial were used as composting materials. The OPF and M. bracteate were \ncollected from the oil palm and rubber plantations of  the Sembawa Research \nCenter, Indonesian Rubber Research Institute (IRRI).  The cow manure was \ncollected from Sembawa breeding center (BPTU). Organic C and total N contents \nof OPF were analysed before  the composting process. Sampling for C/N ratio \nwas randomly selected from the mix of chopped leaflets and rachis of the fronds. \nBased on the analysis, the percentage of organic C of OPF was very high which \nindicates  that the wastes require a long time to completely decompose. Therefore, \nit is necessary to add other organic materials like cow manure and green material \nin order to reach the optimum C/N ratio for the composting process. The analysis \nof organic C and total N for each material was conducted to evaluate the best ratio \nof composting materials (Table 1). \n\n\n\nThe best composition of composting materials for the composting process \nbased on previous experiment was OPF: cow manure: M. bracteata of 40:40:20 \n(data not shown). The initial C/N ratio of the mixture was determined from \nliterature values of C/N ratios of OPF, M. bracteata, and cow manure using \nequation of mixed materials:\n\n\n\n\n\n\n\nwhere OPFm = mass of OPF in kg, OPF C/N = C/N ratio of OPF, Mbm = mass of M. bracteata \nin kg, Mb C/N = C/N ratio of M. bracteata, CMm = mass of cow manure in kg,  and CM C/N \n= C/N ratio of cow manure.\n\n\n\nBased on  best composition and the equation, the initial C/N ratio of the mixture \nwas 41.84. \n\n\n\nCompost Growing Media for Rubber Planting Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016198\n\n\n\n           Note: *C/N ratio of  the mix of chopped leaflets and rachis\n\n\n\nComposting Process\nThe OPF were chopped into small pieces of 3-5 cm to speed up the composting \nprocess. All the materials were mixed together according to the ratio and \nmoistened with water until the moisture level of the mixture reached 60%. The \ncomposting process was conducted by aerobic composting using the  pile system. \nEach pile was of 1 m width and height, respectively, and 500 kg of total weight. \nMoisture content was maintained at around 40-60% throughout the composting \nperiod by watering. The composts were turned over every five days to ensure good \naeration. The composting process was  for a duration of 75 days. The compost \nwas considered mature when ambient temperature was achieved and its structure \nbecame friable and crumbly.\n\n\n\nAnalysis of Physical and Chemical Characteristics of Compost\nObserved parameters in this study included temperature, pH, particle-size \ndistribution, and chemical content of C/N ratio, P, K, Ca, Mg, and CEC. The \ntemperature was taken every two days during the composting period and  was \nmeasured at three different points in the compost pile using a thermometer. The \npH value was determined in the suspension of 1:5 (w/v) compost : deionised water \nusing pH-meter (Mettler Toledo 320). The total Kjeldahl nitrogen (TKN) in the \ncompost was determined using the Kjeldahl method (Mlangeni et al., 2013). The \ntotal organic carbon (TOC) was determined by using Walkley and Black method \nfor carbon analysis (Sato et al., 2014). The content analysis of chemicals P, K, Ca, \nMg was determined by wet oxidation with HNO3+HClO4, phosphorus content \nmeasured colorimetrically (UV-Vis Merck Pharo 300), K, Ca, Mg by atomic \nabsorption spectrophotometry (VARIAN SPECTRAA 55B) (Meller et al., 2015). \nCation exchange capacity of the sample was determined at pH 7 with ammonium \nacetate (Saidi, 2012). The particles-size distribution of compost was measured by \nusing a series of sieves of 4.75 mm, 2 mm, 0.85 mm, and 0.45 mm.\n\n\n\nApplication of OPF-based Compost as Growing Media \nThe next experiment was the  test on the application of OPF-based compost as \ngrowing media amendment of rubber plants using subsoil as the main media. This \nis because subsoil generally has a lower fertility level than topsoil, especially the \n\n\n\nStevanus et al.\n\n\n\n4 \n \n\n\n\ncompletely decompose. Therefore, it is necessary to add another organic materials like cow \n\n\n\nmanure and green material in order to reach the optimum C/N ratio for composting process. The \n\n\n\nanalysis of organic C and total N for each material was conducted to evaluate the best ratio of \n\n\n\ncomposting materials (Table 1).  \n\n\n\nThe best composition of composting materials for the composting process based on \n\n\n\nprevious experiment was OPF: cow manure: M. bracteata of 40:40:20 (data not shown). The \n\n\n\ninitial C/N ratio of the mixture was determined from literature values of C/N ratios of OPF, M. \n\n\n\nbracteata, and cow manure using equation of mixed materials: \n\n\n\n\n\n\n\n*annotation:  OPFm = mass of OPF in kg, OPF C/N = C/N ratio of OPF, Mbm = mass of M. \n\n\n\nbracteata in kg, Mb C/N = C/N ratio of M. bracteata, CMm = mass of cow manure \n\n\n\nin kg, CM C/N = C/N ratio of cow manure. \n\n\n\nBased on these best composition and the equation, the initial C/N ratio of the mixture was 41,84. \n\n\n\nTable 1. Organic C and total N contents of composting materials \n\n\n\nMaterials Organic C Total N C/N Ratio  \n\n\n\nOil palm frond 73.25 0.75 97.43* \n\n\n\nMucuna bracteata 66.24 3.55 18.66 \n\n\n\nCow manure 13.71 0.36 38.15 \n\n\n\nannotation : *C/N ratio of  the mix of chopped leaflets and rachis. \n\n\n\nComposting process \n\n\n\nThe OPF were chopped into small pieces of 3-5 cm to speed up the composting process. \n\n\n\nAll the materials were mixed together according to the ratio and moistened with water until the \n\n\n\nmoisture of the mixture reached 60 %. The composting process were conducted by aerobic \n\n\n\ncomposting with pile system. Each pile had 1 m of width and height, respectively and 500 kg of \n\n\n\ntotal weight. Moisture content was maintained around 40-60% throughout the composting period \n\n\n\nby watering. The composts were turned over every five days to ensure the composts have a good \n\n\n\nTABLE 1\nOrganic C and total N contents of composting materials\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 199\n\n\n\nchemical characteristics which are  not suitable for  the growing media of rubber \nplanting material. This study was conducted with the objectives of determining \nthe effectiveness of OPF-based compost in improving the quality of the growing \nmedia and promoting rubber plant growth. Rubber budded stumps clone PB 260 \nwere the  planting material in this experiment. Polybag media were prepared by \nmixing compost with subsoil at the ratio of 0 % compost + 100 % subsoil as \ncontrol (A), 20  % compost + 80 % subsoil (B), 40 % compost + 60 % subsoil \n(C), 60 % compost + 40 % subsoil (D), 80 % compost + 20 % subsoil (E), 100 \n% compost (F). This experiment was carried out in a randomised block design \n(RBD) with four replications. \n\n\n\nOne budded stump was planted in each polybag of 15 cm x 35 cm with an \nequal volume of media. The seedlings were watered every day and weeding was \nconducted manually. A combination of urea : SP-36 : KCl : kieserite at the ratio of \n4:3:2:2 (gram per polybag) was used for the first application and 10:9:4:4 (gram \nper polybag) was applied monthly for 5 months. Diameter of stem and plant \nheight were recorded every 15 days (from day 30 until day 150). Root dry weight \nand total dry weight were measured on the last day of the experiment (day 150). \nThe seedlings were harvested for shoot weight, while the roots were carefully \nremoved from the polybag to record root weight. Fresh weight was recorded and \nthe plant samples were oven-dried at 65oC until constant weight was achieved (\u00b1 \n3 days).\n\n\n\nEach medium treatment was analysed for initial and final pH ( ratio of 1 \n:5, soil  to water) and macro- and micronutrients contents as well. Total N was \ndetermined using the  Kjeldahl method (Mlangeni et al., 2013) while total organic \ncarbon (TOC) was determined by Walkey and Black method (Sato et al., 2014). \nThe chemical contents of P, K, Ca, Mg were analysed using wet oxidation with \nHNO3+HClO4; phosphorus content was measured colorimetrically (UV-Vis \nMerck Pharo 300) while K, Ca,  and  Mg by atomic absorption spectrophotometry \n(VARIAN SPECTRAA 55B) (Meller et al., 2015). Cation excahange capacity \nof the sample was determined at pH 7 with ammonium acetate (Saidi, 2012). \nStatistical data analysis was performed with SPSS version 16.0. to determine the \ndifferences between treatments and the relationship between treatments and the \ngrowth parameters. \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nCharacteristics of OPF-based Compost\nTemperature\nTemperature is a simple physical parameter to indicate the composting rate and \nthe maturity of compost (Hanifarianty et al., 2014). The maximum temperature \nof the composting process of OPF ranged between 55-61oC from day 4 to day 8. \nThe  composting temperature  was observed to fluctuate from day 12 to day 21 \nand  then remain relatively constant from day 30 till day 75 (Figure 1).\n\n\n\nCompost Growing Media for Rubber Planting Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016200\n\n\n\nFigure 1. Temperature fluctuation of OPF composting process\n\n\n\nThe fluctuation in  temperature during  the composting process will \nautomatically change the microbial community in the compost pile (Faatih \net al., 2008). There are three phases of temperature change associated with \ndecomposing activity of microbes: mesophilic, thermophilic, and cooling phase \nor compost maturation. The first phase of temperature starts with  the mesophilic \nat the initial stage of the composting process. Faatih et al. (2008) reported that \ngenerally, mesophilic microbes actively decompose at the temperature range < \n450C. Then, the temperature increases rapidly from day 2 till day 4 and reaches  \na maximum temperature on day 6 which lasts till day 8. This indicates that the \ncomposting process has reached the thermophilic phase. Insam et al. (2011) said \nthat composting process reaches the  thermophilic phase at a temperature of 65oC. \nThis is a very important phase as most of  the pathogenic microbes  as well as insect \nlarvae and weed seeds in the compost will be killed. The thermophilic microbes \nplay a crucial role in degrading carbohydrate and protein including cellulose and \nlignin; thus, they can speed up the decompostion process of organic material and \nthe maximum temperature can be reached (Faatih et al., 2008). \n\n\n\nThe temperature of the  compost decreased continuously, approaching \nambient temperature on the third week, indicating that the cooling phase or \nmaturation of compost had occurred. In this phase, the microbial metabolism \nactivity decreased because all of the organic materials had been decomposed \n(Dewi et al., 2007). The  thermophilic microbial population began to decline and \nwas replaced by mesophilic microbes that decomposed the cellulose remains. \nOil palm frond composting process reached ambient temperature on day 75.  A \nprevious study conducted by Tiqua et al., (1997) showed that compost reached \nambient temperature on day 74. Their study also concluded that compost is \nconsidered mature when the temperature inside the compost pile has reached the \nambient temperature.\n\n\n\nStevanus et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 201\n\n\n\nParticle Size Distribution\nThe result of particle size distribution analysis of OPF-based compost was \ngrouped into five size fractions i.e. < 0.42 mm, 0.42-0.85 mm, 0.85-2 mm, 2-4.75 \nmm, and > 4.75 mm. In general, the largest percentage of particle size distribution \nwas > 4.75 mm, while the remaining was 2-4.75 mm and < 2 mm (Figure 2). \nFaatih et al. (2008) stated that microbes play an important role in reducing the \nparticle size of organic material at the mesophilic phase. In addition, the particle \nsize is also determined by raw materials for composting. The smaller the particle, \nthe bigger the surface area for microbes to degrade the material and the faster the \ncomposting process.\n\n\n\nToo small particle size can cause anaerobic condition, meanwhile too large \nmaterial particle will cause a large pore space so the optimum temperature for the \ncomposting is not reached (Olds College Composting Technology Center, 1999). \nTherefore, it is necessary to chop the OPF before composting process. The size \nof chopped OPF in this study ranged between 3-5 cm. This resulted the largest \ndistribution of compost particles was in the fraction > 4.75 mm. The addition of \nmanure as a raw material in this study may have contributed to the particle size \nof compost, because according to G\u00f3mez-Mu\u00f1oz et al. (2012) in the presence of \nmanure can improve particle fineness.\n\n\n\nFigure 2. Particle size distribution of OPF-based compost\n\n\n\nTotal weight loss and characteristic of compost\nThe initial weight of compost pile was 500 kg, meanwhile the final weight of \nmature compost was 389.6 kg. Hence, the total weight loss of OPF-based compost \nafter composting process was 110.4 kg or 22.08 %. Final C/N ratio was15.32 \nand final pH of compost was above neutral (pH>7).  Indonesian National \nStandard (Standar Nasional Indonesia or SNI) of compost quality is a regulation \nof the quality of certain organic product for consumer safety and environmental \npollution prevention in Indonesia. Most of all macronutriens contents in OPF-\n\n\n\nCompost Growing Media for Rubber Planting Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016202\n\n\n\nbased compost after maturity at day 75 has met the minimum SNI compost quality \nexcept K2O content (Table 2).\n\n\n\nRubber plant growth\nDiameter, height, root dry weight, and total dry weight of rubber planting material\nThe observation data of plant growth for 120 days show that stem diameter of \ntreatment B (20 % compost + 80 % subsoil) is not significantly different from \ncontrol (no added compost) but significantly different from other treatments. \nTreatment B resulted in the highest stem diameter, meanwhile treatment F \nresulted in the lowest stem diameter (100 % compost) (Table 3). The observation \ndata of plant height parameter show that treatment B is not significantly different \nfrom control, yet significantly different from treatment E (80 % compost + 20 % \nsubsoil) and treatment F (100 % compost). Treatment B resulted in the highest \nplant height compared to other treatments, meanwhile treatment F showed the \nlowest plant height.\n\n\n\n9 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2. Particle size distribution of OPF-based compost \n\n\n\nTotal weight loss and characteristic of compost \n\n\n\nTable 2  Final weight and nutrient content of OPF-based compost \n\n\n\nAnalysis Value \n\n\n\nIndonesian minimum standard of compost \n\n\n\nquality (National Standardization Agency \n\n\n\nof Indonesia, 2004)   \n\n\n\nUnit \n\n\n\nTotal weight loss 22.08  % (w/w) \n\n\n\npH H2O 7.45 6.80  \n\n\n\nOrganic matter 61.83* 27 % \n\n\n\nTotal N 2.34 0.40 % \n\n\n\nC/N ratio 15.32 10 % \n\n\n\nP2O5 0.62 0.10 % \n\n\n\nK2O 0.13 0.20 % \n\n\n\nCa 0.18 n.a % \n\n\n\nMg 0.06 ** % \n\n\n\nannotation : n.a = not available \n\n\n\n  *  the value obtained by multiplying the conversion factor of 1.724 with C organic \n\n\n\n **  the value can not exceeds the maximum standard of 0.6 % \n\n\n\nStevanus et al.\n\n\n\nTABLE 2\nFinal weight and nutrient content of OPF-based compost\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 203\n\n\n\nannotation: values followed by the same letter within a column are not\nsignificantly different at the 0.05 level\n\n\n\nTreatment B resulted in the highest root dry weight and significantly different \nfrom control and other treatments. Meanwhile, the highest total dry weight was \nobtained in treatment B, however there is no significant difference compared to \ncontrol. The use of OPF-based compost with application doses of 75 - 100 % has \na decreasing pattern in plant growth compared to control. This is consistent with \nanother study conducted by Do and Scherer (2013) and L\u00f3pez et al. (2008) stated \nthat the application of compost with the maximum dose of 50 % can promote \nthe plant growth. This is because the higher the percentage of compost as media \namendment the higher electrical conductivity and nutrient excess level that lead to \nplant growth inhibition (Arancon and Edwards, 2005; L\u00f3pez et al., 2008).\n\n\n\nFinal nutrient content of growing media\nNutrients release of organic materials that play an important role for plant growth \nis very slow (Seran et al., 2010), therefore, there is possibility that nutrients of \ncompost are not optimally used by plant. The nutrient content analysis of growing \nmedia at the end of experiment was conducted to determine the nutrient residues \nof compost that are still available in the soil.\n\n\n\nEach of soil nutrients analyzed (Table 4) was compared to the soil nutrient \nclassification for rubber (Table 5). Comparison of pH values among initial soil, \ncontrol, and other treatments shows that the addition of compost increased the \npH value of acid (4.5-5.5) to slightly acid (5.6-6.5). The CEC values of initial \nsoil and control are low but the CEC values of the compost treatments tend to \nincrease slowly with the increasing dose of compost. The addition of compost \nincreased the organic C, especially for the 80 and 100 % compost treatment.  \nMoreover, growth media supplemented with compost also have an increased \nP, K, Ca, and Mg contents compared to control. In addition, the N contents of \ntreatments with compost application dose of 50 % - 100 % were also slightly \n\n\n\n10 \n \n\n\n\nAnalysis Value \n\n\n\nIndonesian minimum standard of compost \n\n\n\nquality (National Standardization Agency \n\n\n\nof Indonesia, 2004)   \n\n\n\nUnit \n\n\n\nC/N ratio 15.32 10 % \n\n\n\nP2O5 0.62 0.10 % \n\n\n\nK2O 0.13 0.20 % \n\n\n\nCa 0.18 n.a % \n\n\n\nMg 0.06 ** % \n\n\n\nannotation : n.a = not available \n\n\n\n  *  the value obtained by multiplying the conversion factor of 1.724 with C organic \n\n\n\n **  the value can not exceeds the maximum standard of 0.6 % \n\n\n\nRubber plant growth \n\n\n\n\n\n\n\nTable 3. Plant growth parameters (height and diameter) and plant dry weight  \n\n\n\nannotation: values followed by the same letter within a column are not significantly different at the \n\n\n\n0.05 level \n\n\n\n Treatment B resulted in the highest root dry weight and significantly different from \n\n\n\ncontrol and other treatments. Meanwhile, the highest total dry weight was obtained in treatment B, \n\n\n\nhowever there is no significant difference compared to control. The use of OPF-based compost \n\n\n\nwith application doses of 75 \uf02d 100 % has a decreasing pattern in plant growth compared to \n\n\n\nTreatment \nPlant height \n\n\n\n(cm) \n\n\n\nPlant diameter \n\n\n\n(mm) \n\n\n\nRoot dry weight \n\n\n\n(gram) \n\n\n\nTotal dry weight \n\n\n\n(gram) \n\n\n\nA (control) 60.50abc 7.61ab 3.98a 52.97ab \n\n\n\nB 71.08a 8.07a 6.30b 65.54b \n\n\n\nC 59.27abc 6.95c 3.07a 49.23ab \n\n\n\nD 64.56ab 7.23bc 2.76a 62.09b \n\n\n\nE 53.43bc 6.64c 3.30a 47.33ab \n\n\n\nF 46.42c 6.66c 3.00a 39.30a \n\n\n\nTABLE 3\nPlant growth parameters (height and diameter) and plant dry weight\n\n\n\nCompost Growing Media for Rubber Planting Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016204\n\n\n\nincreased compared to control. Mikkelsen and Hartz (2008) stated that compost \ngenerally behave as a slow-release source of N over many months or years since \nthe rapidly decomposable compounds have been previously degraded during the \ncomposting process. The OPF-based compost residue can be used as a \u201cnutrient \nbank\u201d for the growth of rubber plants in the field. \n\n\n\nTABLE 4\nInitial nutrients content of soil and final nutrients content of media of each treatment\n\n\n\nTABLE 5\nSoil nutrient classification for rubber\n\n\n\nSource: Adiwiganda et al. (1994)\n  \n\n\n\nNutrients content of Leaf\nEach of leaf nutrient analyzed was compared with nutrient sufficiency ranges \n(NSR) from references (Table 6). Generally, the addition of compost was able \nto increase N nutrient of plants. N nutrient of treatment C and D were deficient, \nB was sufficient, while E and F were high. In this research, treatment B or 20 % \n\n\n\n12 \n \n\n\n\nalso slightly increase compared to control. Mikkelsen and Hartz (2008) stated that compost \n\n\n\ngenerally behave as a slow-release source of N over many months or years since the rapidly \n\n\n\ndecomposable compounds have been previously degraded during the composting process. The \n\n\n\nOPF-based compost residue can be used as a \"nutrient bank\" for the growth of rubber plants in the \n\n\n\nfield.  \n\n\n\nTable 4. Initial nutrients content of soil and final nutrients content of media of each treatment \n\n\n\nTreatment pH \nCEC \n\n\n\ncmol(+) /kg \n\n\n\nOrganic C \n\n\n\n(%) \n\n\n\nN \n\n\n\n(%) \n\n\n\nP \n\n\n\n(mg/kg) \n\n\n\nK \n\n\n\ncmol(+) /kg \n\n\n\nCa \n\n\n\ncmol (+) /kg \n\n\n\nMg \n\n\n\ncmol (+) /kg \n\n\n\nInitial soil 4.59 9.86 3.02 0.19 43.21 0.03 0.12 0.02 \n\n\n\nA (control) 4.8 9.7 1.33 0.06 199.1 0.23 2.59 9.7 \n\n\n\nB 6.3 11 2.41 0.09 427.3 0.44 2.42 11 \n\n\n\nC 6.4 10.8 1.9 0.07 412.7 0.4 2.52 10.8 \n\n\n\nD 5.8 13.8 2.79 0.12 482.8 0.4 2.84 13.8 \n\n\n\nE 6.3 24.9 6.09 0.33 370.6 0.58 3.11 24.9 \n\n\n\nF 6 37.3 6.3 0.7 413.1 0.45 3.05 37.3 \n\n\n\n \nTable 5. Soil nutrient classification for rubber \n\n\n\nParameters \nVery \n\n\n\nLow \nLow Intermediate High Very High \n\n\n\nC (%) < 1.00 1.00-2.00 2.01-3.00 3.01-4.00 > 4.00 \n\n\n\nN (%) < 0.10 0.10-0.20 0.21-0.50 0.51-0.80 > 0.80 \n\n\n\nP2O5 (ppm) < 5 5 - 15 16 - 25 26 - 35 > 35 \n\n\n\nK (me/100 g) < 0.10 0.10-0.30 0.31-0.50 0.51-0.70 > 0.70 \n\n\n\nCa (me/100 g) < 0.25 0.25-1.00 1.01-1.75 1.76-2.50 > 2.50 \n\n\n\nMg (me/100 g) < 0.20 0.20-0.50 0.51-0.80 0.81-1.10 > 1.10 \n\n\n\nCEC (me/100 g) < 5 5 - 16 17 - 28 29 - 40 > 40 \n\n\n\npH Acid \n\n\n\n4.5-5.5 \n\n\n\nSlightly Acid \n\n\n\n5.6-6.5 \n\n\n\nNeutral \n\n\n\n6.6-7.5 \n\n\n\nslightly Alkaline \n\n\n\n7.6-8.5 \n\n\n\nAlkaline \n\n\n\n> 8.5 \n\n\n\n12 \n \n\n\n\nalso slightly increase compared to control. Mikkelsen and Hartz (2008) stated that compost \n\n\n\ngenerally behave as a slow-release source of N over many months or years since the rapidly \n\n\n\ndecomposable compounds have been previously degraded during the composting process. The \n\n\n\nOPF-based compost residue can be used as a \"nutrient bank\" for the growth of rubber plants in the \n\n\n\nfield.  \n\n\n\nTable 4. Initial nutrients content of soil and final nutrients content of media of each treatment \n\n\n\nTreatment pH \nCEC \n\n\n\ncmol(+) /kg \n\n\n\nOrganic C \n\n\n\n(%) \n\n\n\nN \n\n\n\n(%) \n\n\n\nP \n\n\n\n(mg/kg) \n\n\n\nK \n\n\n\ncmol(+) /kg \n\n\n\nCa \n\n\n\ncmol (+) /kg \n\n\n\nMg \n\n\n\ncmol (+) /kg \n\n\n\nInitial soil 4.59 9.86 3.02 0.19 43.21 0.03 0.12 0.02 \n\n\n\nA (control) 4.8 9.7 1.33 0.06 199.1 0.23 2.59 9.7 \n\n\n\nB 6.3 11 2.41 0.09 427.3 0.44 2.42 11 \n\n\n\nC 6.4 10.8 1.9 0.07 412.7 0.4 2.52 10.8 \n\n\n\nD 5.8 13.8 2.79 0.12 482.8 0.4 2.84 13.8 \n\n\n\nE 6.3 24.9 6.09 0.33 370.6 0.58 3.11 24.9 \n\n\n\nF 6 37.3 6.3 0.7 413.1 0.45 3.05 37.3 \n\n\n\n \nTable 5. Soil nutrient classification for rubber \n\n\n\nParameters \nVery \n\n\n\nLow \nLow Intermediate High Very High \n\n\n\nC (%) < 1.00 1.00-2.00 2.01-3.00 3.01-4.00 > 4.00 \n\n\n\nN (%) < 0.10 0.10-0.20 0.21-0.50 0.51-0.80 > 0.80 \n\n\n\nP2O5 (ppm) < 5 5 - 15 16 - 25 26 - 35 > 35 \n\n\n\nK (me/100 g) < 0.10 0.10-0.30 0.31-0.50 0.51-0.70 > 0.70 \n\n\n\nCa (me/100 g) < 0.25 0.25-1.00 1.01-1.75 1.76-2.50 > 2.50 \n\n\n\nMg (me/100 g) < 0.20 0.20-0.50 0.51-0.80 0.81-1.10 > 1.10 \n\n\n\nCEC (me/100 g) < 5 5 - 16 17 - 28 29 - 40 > 40 \n\n\n\npH Acid \n\n\n\n4.5-5.5 \n\n\n\nSlightly Acid \n\n\n\n5.6-6.5 \n\n\n\nNeutral \n\n\n\n6.6-7.5 \n\n\n\nslightly Alkaline \n\n\n\n7.6-8.5 \n\n\n\nAlkaline \n\n\n\n> 8.5 \n\n\n\nStevanus et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 205\n\n\n\n13 \n \n\n\n\nNutrient \n\n\n\nElement \n\n\n\nTreatment Plant nutrient \n\n\n\nvalue \n\n\n\nLiterature optimum \n\n\n\nrange \n\n\n\nNutrient \n\n\n\nStatus \n\n\n\nReference \n\n\n\nN (%) A (control) 2.90 3.30 \u2013 3.50 Deficient Adiwiganda et al. \n\n\n\n(1994)  B 3.31  Sufficient \n\n\n\n C 2.96  Deficient  \n\n\n\n D 2.91  Deficient  \n\n\n\n E 3.63  High  \n\n\n\n F 3.62  High  \n\n\n\nP (%) A (control) 0.14 0.23 \u2013 0.24 Deficient Adiwiganda et al. \n\n\n\n(1994)  B 0.25  High \n\n\n\n C 0.19  Deficient  \n\n\n\n D 0.23  Deficient  \n\n\n\n E 0.18  Deficient  \n\n\n\n F 0.23  Deficient  \n\n\n\nK (%) A (control) 1.06 1.31 \u2013 1.40 Deficient Adiwiganda et al. \n\n\n\n(1994)  B 1.12  Deficient \n\n\n\n C 1.15  Deficient  \n\n\n\n D 1.16  Deficient  \n\n\n\n E 1.15  Deficient  \n\n\n\n F 1.1  Deficient  \n\n\n\nMg (%) A (control) 0.24 0.21 \u2013 0.22 High Adiwiganda et al. \n\n\n\n(1994)  B 0.38  High \n\n\n\n C 0.38  High  \n\n\n\n D 0.33  High  \n\n\n\n E 0.62  High  \n\n\n\n F 0.41  High \n\n\n\n\n\n\n\n\n\n\n\nTABLE 6\nA comparison of obtained optimum plant nutrient range for rubber in each treatment\n\n\n\n14 \n \n\n\n\nNutrient \n\n\n\nElement \n\n\n\nTreatment Plant nutrient \n\n\n\nvalue \n\n\n\nLiterature optimum \n\n\n\nrange \n\n\n\nNutrient \n\n\n\nStatus \n\n\n\nReference \n\n\n\nCa (%) A (control) 0.86 0.60 \u2013 1.00 Sufficient Thiagalingam, et al. \n\n\n\n(2000)  B 0.93  Sufficient \n\n\n\n C 1.14  High  \n\n\n\n D 0.89  Sufficient  \n\n\n\n E 1.11  High  \n\n\n\n F 0.83  Sufficient  \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nOil palm frond-based compost reached maturity on day 75 that can be indicated from the \n\n\n\ndecline of compost pile temperature until it reached ambient temperature and its C/N ratio < 20. \n\n\n\nGenerally, the OPF-based compost characteristic has met the minimum standard of SNI for \n\n\n\ncompost quality with the exception of K2O content. The compost application with treatment B (20 \n\n\n\n% OPF-based compost + 80 % subsoil) was the optimum mixed media to promote the growth of \n\n\n\nrubber planting material. Media supplemented with OPF-based compost has an increased pH, \n\n\n\nCEC, C organic, P, K, Ca, and Mg content in soil compared to control. These OPF-based compost \n\n\n\nresidues are beneficial as \u201cnutrient bank\u201d for further development and growth of rubber plant in \n\n\n\nthe field. Furthermore, the comparison of leaves nutrients showed that treatment B also increased \n\n\n\nN and P absorption.  \n\n\n\n\n\n\n\nREFERENCES \n\n\n\n\n\n\n\nAbdul Khalil, H.P.S., M. Jawaid, A. Hassan, M.T. Paridah and A. Zaidon. 2012. Oil palm biomass \n\n\n\nfibres and recent advancement in oil palm biomass fibres based hybrid biocomposites. In \n\n\n\nComposites and their applications, ed. N. Hu, p. 187\uf02d220. InTech, Rijeka. doi: 10.5772/ \n\n\n\n48235.  \n\n\n\nCompost Growing Media for Rubber Planting Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016206\n\n\n\ncompost application was able to increase N absorption compared to treatment C \nand D. Meanwhile, high levels of N nutrient in treatment D and E due to high level \nof N soil (Table 4) as a result of high application dose of compost. In addition, \nthe B treatment also increased the absorption of P compared to other treatments. \nMeanwhile, the compost addition did not affect the level of K, Ca and Mg nutrient \nabsorption in plants. This can be seen from the same nutrient status of K, Ca and \nMg in all treatments and control.\n\n\n\nCONCLUSION\nOil palm frond-based compost reached maturity on day 75 which was indicated \nfrom the decline of compost pile temperature until it reached ambient temperature \nand its C/N ratio < 20. 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Effects of turning frequency on \ncomposting of spent pig-manure sawdust litter. Biores. Tech. 62: 37-42. doi: \n10.1016/S0960-8524(97)00080-1.\n\n\n\nThiagalingam, K. 2000. Soil and Plant Sample Collection, Preparation and \nInterpretation on Chemical Analysis. A training manual and guide. AACM \nInternational. 46 p.\n\n\n\nVakili, M., H.M. Zwain, M. Rafatullah, Z. Gholami and R. Mohammadpour. 2014. \nPotentiallity of palm oil biomass with cow dung for compost production. KSCE \nJ. Civ. Eng. 19(7): 1994-1999. doi: 10.1007/s12205-014-0420-7.\n\n\n\nWibawa, A., S. Soemarsono, Hendarsono and R. Soedradjad. 1993. Effect of liming \nand NPK fertilizer on the growth of cocoa seedling in peat soil medium. Pelita \nPlantation. 8(4): 85-90.\n\n\n\nYuniati, S. 2014. Composting of Palm Oil Midrib-leaf with Different Biodecomposter \nand Used as Ameliorant. Thesis, Bogor Agricultural University.\n\n\n\nCompost Growing Media for Rubber Planting Material\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n44 \n \n\n\n\nGlyphosate Degradation in Malaysian Sandy Loam Soil Amended with \n\n\n\nCow Dung or Rice Husk Ash as Influenced by Soil Moisture Content \n\n\n\n\n\n\n\nJamilu Garba1, Abd Wahid Samsuri 2*, Muhammad Saiful Ahmad Hamdani3  \n\n\n\nTariq Faruq Sadiq4 and Abba Nabayi5 \n \n\n\n\n1Department of Soil Science, Faculty of Agriculture, Ahmadu Bello University, 1044 Zaria Nigeria \n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400   UPM \nSerdang, Selangor, Malaysia \n\n\n\n3Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400 UPM \n\n\n\nSerdang, Selangor, Malaysia \n4Department of Soil and Water, College of Agriculture, Salahaddin University, Erbil, Iraq \n\n\n\n5Department of Soil Science, Faculty of Agriculture, Federal University, 7156 Dutse, Nigeria \n\n\n\n\n\n\n\n*Corresponding author: samsuriaw@upm.edu.my  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\nThis study investigated the effect of cow dung or rice husk ash amendment on glyphosate degradation \n\n\n\nin a Malaysian sandy loam soil under three moisture regimes. The control and soils amended with 10 \n\n\n\ntons ha-1 of either cow dung or rice husk ash were spiked with 5000 \u00b5g g-1 of glyphosate. Water was \n\n\n\nadded accordingly to obtain soil moisture content equivalent to either a submerged condition, field \n\n\n\ncapacity or permanent wilting point. Glyphosate degradation was monitored for 65 days and the data \n\n\n\nobtained was fitted to first order-double exponential decay model (FODED). The results revealed that \n\n\n\nglyphosate degradation occurred in two-phases; an initial high degradation rate followed by a slow \n\n\n\nrate representing degradation of labile and non-labile phases, respectively. The rate constants of the \n\n\n\nlabile phases ranged from 0.0063 to 0.0604 \u00b5g day-1 while those of non-labile phases were from \n\n\n\n0.0077 to 0.0732 \u00b5g day-1.  The degradation rate was generally higher in the labile phase. Irrespective \n\n\n\nof moisture content, the degradation data from the cow dung-amended soil fitted the FODED model \n\n\n\nbest (0.042 \u2265 r2 \u2264 0.909) followed by the rice husk ash-amended soil (0.023 \u2265 r2 \u2264 0.914) and the \n\n\n\ncontrol (0.030 \u2265 r2 \u2264 0.756). Meanwhile, irrespective of the amendments, soils maintained at field \n\n\n\ncapacity had the highest degradation rate in labile phases (k1 = 0.0371 \u2013 0.0604 \u00b5g day-1) while at non-\n\n\n\nlabile phases, soils maintained at permanent wilting point recorded the highest rate (k2 = 0.0077 \u2013 \n\n\n\n0.0732 \u00b5g day-1). The soils maintained at field capacity generally had the lowest glyphosate half-life \n\n\n\n(11- 42 days) followed by soils at permanent wilting point (9 \u2013 110 days), with the longest half-life \n\n\n\nbeing shown by the submerged soils (13-178 days). It can be concluded that the application of cow \n\n\n\ndung or rice husk ash increased glyphosate degradation in the soil especially when the soil moisture \n\n\n\ncontent was maintained at field capacity.     \n\n\n\nKey words: glyphosate, sandy loam, degradation, agricultural waste, soil moisture \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\nGenerally organic manure still remains the most affordable fertilizer supplement for our soils. \n\n\n\nMeanwhile, drudgery in hand weeding necessitates the application of herbicides for weed \n\n\n\ncontrol even among the smallholder farmers. The benefits of applying organic manure \n\n\n\nincludes enhancement of soil biological activities and an increase in soil organic matter \n\n\n\nwhich  result in improved nutrient mobilization and availability,  decomposition of toxic \n\n\n\nsubstances,    increased soil aggregate stability and  water retention (Kala et al. 2011). \n\n\n\n\nmailto:samsuriaw@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n45 \n \n\n\n\nGlyphosate is a  common weed killer applied to agricultural land. It is absorbed across the \n\n\n\nfoliage and is trans-located throughout the plant via curticular wax. It inhibits the shikimic \n\n\n\npathway occuring in plants which is responsible for synthesis of aromatic amino acid and \n\n\n\nconsequent plant death (Kr\u00fcger et al.  2013). Glyphosate gets into the soil usually through \n\n\n\nspray drift and then undergoes adsorption, degradation and leaching processes. Degradation \n\n\n\nof glyphosate in soil is achieved by different native strains of bacteria and fungi and this  \n\n\n\noccurs through sarcosine and aminomethylphosphonic (AMPA) pathways (Arfarita et al. \n\n\n\n2013; Sviridov et al. 2015). Soil organic matter and moisture are some of the factors affecting \n\n\n\npesticide degradation in soil (Shahgholi and Ahangar 2014). An increase in glyphosate \n\n\n\nmineralisation has been reported  in soils as a result of incorporating soybean and corn \n\n\n\nresidues (Rampoldi et al. 2008), with a higher mineralisation rate detected in soil applied \n\n\n\nwith fresh residues of soybean. Glyphosate degradation was  also  found to significantly \n\n\n\nincrease with increasing moisture content in loess soil (Bento et al. 2016). \n\n\n\nPeninsula Malaysia has a high mean annual rainfall of 2440 mm (Tan 2018) which  is \n\n\n\nspatially distributed across the land resulting in the  delineation of the country into eight \n\n\n\ndifferent rainfall regions (Wong et al. 2016). This variation of rainfall is associated with \n\n\n\ntopography and seasonal changes of monsoon winds passing over the South China Sea \n\n\n\n(Camerlengo and Somchit 2000; Wong 2016). It is evident that this variation of rainfall \n\n\n\npattern brings about a fluctuation in soil moisture content with consequent effects on soil \n\n\n\nbiochemical functions. Changes in soil moisture affects microbial colony development and  \n\n\n\ntheir eco-physiological activities and enzyme action (Borowik and Wyszkowska 2016). The \n\n\n\npresent study therefore mimics these fluctuations in soil moisture and their effect on \n\n\n\nglyphosate degradation in soil subsequent to application of two different agricultural residues. \n\n\n\nThus, the objective of this study was to evaluate the influence of added organic residues and \n\n\n\nvariations in soil moisture content on glyphosate degradation. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\nSampling of Soil, Cow Dung and Rice Husk Ash \n\n\n\nThe soil sampling site was Sementa Hulu (Lat. 3.841663 0N, Long. 101.947251 0E), Raub \n\n\n\ndistrict, Pahang Malaysia. Surface soils (0-20 cm) were collected from different locations in \n\n\n\nthe sampling site and later bulked to one composite sample. Cow dung was collected from the \n\n\n\nanimal section of the experimental farm, Faculty of Agriculture, Universiti Putra Malaysia, \n\n\n\nwhile rice husk ash was obtained from BERNAS Rice Mill Selangor, Malaysia (Lat. \n\n\n\n3\u00b040'32.4\"N, Long. 100\u00b059'42.5\"E). All the samples were properly dried at the drying room, \n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra Malaysia. The \n\n\n\nsoils and cow dung were ground using laboratory pestle and mortar followed by sieving of \n\n\n\nthe soils with a 2-mm sieve while cow dung and rice husk ash were sieved at 1 mm. The \n\n\n\nsieved soils and organic materials were stored in a clean container before the degradation \n\n\n\nstudy. As the soil had been previously characterized (Garba et al. 2019b) and shown to \n\n\n\ncontain74.17 % sand, 5.83 % silt and 20 % clay, it was classified as sandy loam. Further, the \n\n\n\nsoil had pH of 6.73, EC of 0.024 ds m-1, CEC of 12.67 cmol(+) kg-1 and N, P and K to be \n\n\n\n0.16%, 0.70% and 0.33%, respectively. Our previous article (Garba et al. 2019a) had reported \n\n\n\nboth the physical and chemical properties of cow dung and rice husk ash. Cow dung was \n\n\n\nreported to have a pH of 8.14, EC of 0.22 ds m-1, CEC of 34.50 cmol(+) kg-1 and N, P and K \n\n\n\nof 2.53%, 0.22% and 0.92%, respectively. Meanwhile rice husk ash was shown to have a pH \n\n\n\nof 9.95, EC of 0.33 ds m-1, CEC of 10.20 cmol(+) kg-1 with P and K of  0.17% and 0.19% \n\n\n\nrespectively with  N not being detected in rice husk  ash. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n46 \n \n\n\n\nGlyphosate Degradation Study \n\n\n\nThree were carried out in triplicate. The mixture of soils and organic materials was \n\n\n\nhomogenised through sets of 250 g of the 2-mm sieved soil were weighed into plastic \n\n\n\ncontainers (11 cm x 9 cm) to which was added 1.15 g of either cow dung or rice husk ash to \n\n\n\ngive an equivalent rate of 10 tons ha-1. Equal amounts of soils without cow dung or rice husk \n\n\n\nash served as control. All treatments repeated stirring with a stainless steel rod. Both control \n\n\n\nand amended soils were added with a millipore\u00ae of water appropriate for obtaining moisture \n\n\n\ncontent of submerged condition, field capacity and permanent wilting point, representing \n\n\n\nvery high, optimal and very low moisture levels. All the treatments were spiked with 25 mL \n\n\n\nof 200 mg L-1 of standard glyphosate solution and arranged in completely randomized design \n\n\n\non a laboratory bench at 230C. The soils were weighed regularly to adjust the moisture \n\n\n\ncontent. The residual concentration of glyphosate was measured at days 1, 3, 13, 23, 34, 44, \n\n\n\n55 and 65. On each of these days, 5g of soil from each treatment was weighed into centrifuge \n\n\n\ntubes to which was added 20 mL 0.01 M KH2PO4 solution and shaken for 2 h on a rotary \n\n\n\nshaker at 100 rpm. This was followed by centrifugation at 10,000 rpm for 10 min and \n\n\n\nfiltering of the supernatant with 0.45\u00b5m syringe filter. Later, 1 mL of the filtrate was \n\n\n\nderivatized and analysed for glyphosate using HPLC-UV as described in Garba et al. (2018).   \n\n\n\n\n\n\n\nData Analysis \n\n\n\nIn order to investigate the kinetics of glyphosate degradation form the studied soils, the \n\n\n\nresidual concentration data obtained from the day intervals were fitted to first-order double \n\n\n\nexponential decay model (FODED) as reported by Sarmah and Close :  \n\n\n\n     X(t) = X1(t) + X2(t)                                                                                                                                             (1) \n\n\n\n       = Xsol exp(-k\n1\n\n\n\nt) + Xsorb exp(-k\n2\n\n\n\nt)                                                                         (2) \n\n\n\nwhere Xsol and Xsorb are concentrations in solution and sorbed phases, t is the time (days), k1 \n\n\n\nand k2 are degradation rate constants at solution and sorbed phase, respectively. The half-life \n\n\n\n(t\u00bd) of glyphosate from the studied soil was calculated using the equation below; \n\n\n\n           t\u00bd = \n\ud835\udc59\ud835\udc5b2\n\n\n\n\ud835\udc58\n                                                                                       (3) \n\n\n\nThe mean values of glyphosate residual concentration from both control and amended soils at \n\n\n\ndifferent moisture regimes were subjected to analysis of variance at 0.05 confidence level \n\n\n\nusing SAS 9.4. The significant means were compared using Tukey\u2019s HSD test. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n47 \n \n\n\n\nRESULTS AND DISCUSSION \n\n\n\nGlyphosate degradation rate from control and treatment soils during the incubation period \n\n\n\n(Figure 1) shows two phases: initial and final phase with a high rate of glyphosate \n\n\n\ndegradation in the final phase. This could be attributed to glyphosate adsorption by this soil. \n\n\n\nHowever, as weaker bonds exist between glyphosate and adsorption sites of the soil, it may \n\n\n\nlikely desorb into soil solution and undergo degradation. Glyphosate adsorbed to soil colloids \n\n\n\nthrough H bonding (Piccolo and Celano 1994) and porous diffusion (Herath et al. 2016), all \n\n\n\nof which are weaker bonds and could result in its  desorption and bioavailability. The initial \n\n\n\nphase is ascribed to microbial degradation of free glyphosate available in soil solutions while \n\n\n\nthe final phase is due to microbial attacks on adsorbed glyphosate.  \n\n\n\n Degradation and adsorption are interrelated processes that determine the fate of \n\n\n\nglyphosate in soils. Our  adsorption study found the studied soils to have 86 % glyphosate \n\n\n\nadsorption efficiency, and  a higher adsorption efficiency of 87 % and 90 % with the \n\n\n\napplication of cow dung and rice husk ash, respectively.  \n\n\n\nSimilarly results from the  desorption study found that glyphosate desorbed from both \n\n\n\nthe control and amended soils, with the desorption efficiency for control, soil + cow dung and \n\n\n\nsoil + rice husk ash being, 14 %, 10 % and 8 %,  respectively. Therefore, the initial phase of \n\n\n\nglyphosate degradation observed in the present study can be said to be responsible for the \n\n\n\ndecay of the small amount of the instant desorbed glyphosate molecules. However, the larger \n\n\n\nmolecules adsorbed by the soil tend to be released over time into the soil solution, hence \n\n\n\nserving as the compounds of the final phase of degradation. It can therefore be postulated that \n\n\n\nas the pool of glyphosate in the labile phase depletes due to degradation, changes in the soil \n\n\n\ncondition will lead to its replenishment from the sorbed phase.  Microbial mineralization is \n\n\n\nthe predominant mechanism of glyphosate transformation in soils which is non-specific and \n\n\n\noccurs through co-metabolic process (Accinelli et al. 2005; Sprankle et al. 1975a) mediated \n\n\n\nby both native and introduced strains of bacteria and fungi (Sprankle et al. 1975b) with the \n\n\n\nresultant products  being inorganic phosphorus, CO2 and NH4\n+. The AMPA pathway is more \n\n\n\ncommonly reported for glyphosate degradation compared to that of sarcosine (Franz et al. \n\n\n\n1997; Giesy et al. 2000).  This pathway involves  glyphosate molecules being attacked by \n\n\n\noxidoreductase (Figure 2) leading to the cleavage of glyphosate C-N bond yielding to AMPA \n\n\n\nand glyoxylate. Being an energy substrate, glyoxylate will be mineralized in the Krebs cycle \n\n\n\nyielding CO2 as end products while AMPA will be exported to extracellular spaces which \n\n\n\nwill further be transformed to methylamine and inorganic P through cleaving of its C-P bonds \n\n\n\nby C-P lyase enzymes ( Sviridov et al. 2015; Giesy et al. 2000; Franz et al. 1997)   \n\n\n\nThis result is in conformity of with the study of  Eberbach(1998) who studied \n\n\n\nglyphosate degradation in four Victorian soils: clay, loamy sand and two silt clay loam soils. \n\n\n\nThis study reported two sources of glyphosate in these soils: labile and non-labile phases. \n\n\n\nOver the first 40 days of the study, degradation of glyphosate was from labile and non-labile \n\n\n\nphases while after the first 40 days, glyphosate was only from the non-labile phase.   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n48 \n \n\n\n\n\n\n\n\n\n\n\n\n \nFigure 1. Rate of glyphosate degradation in (a.) Control, (b.) Soil amended with cow dung \n\n\n\nand (c.) Soil amended with rice husk ash under different moisture regimes \n\n\n\n\n\n\n\n0.0\n\n\n\n2.0\n\n\n\n4.0\n\n\n\n6.0\n\n\n\n8.0\n\n\n\n10.0\n\n\n\n12.0\n\n\n\n14.0\n\n\n\n16.0\n\n\n\n18.0\n\n\n\n0 10 20 30 40 50 60 70\n\n\n\nco\nn\n\n\n\nce\nn\n\n\n\ntr\nat\n\n\n\nio\nn\n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n)\ncontrol soil\n\n\n\nsubmerged field capacity permanent wilting point\n\n\n\na\n\n\n\n0.0\n\n\n\n2.0\n\n\n\n4.0\n\n\n\n6.0\n\n\n\n8.0\n\n\n\n10.0\n\n\n\n12.0\n\n\n\n14.0\n\n\n\n16.0\n\n\n\n18.0\n\n\n\n0 10 20 30 40 50 60 70\n\n\n\nco\nn\n\n\n\nce\nn\n\n\n\ntr\nat\n\n\n\nio\nn\n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n)\n\n\n\nsoil + cow dungb\n\n\n\n0.0\n\n\n\n2.0\n\n\n\n4.0\n\n\n\n6.0\n\n\n\n8.0\n\n\n\n10.0\n\n\n\n12.0\n\n\n\n14.0\n\n\n\n16.0\n\n\n\n18.0\n\n\n\n0 10 20 30 40 50 60 70\n\n\n\nco\nn\n\n\n\nce\nn\n\n\n\ntr\nat\n\n\n\nio\nn\n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n)\n\n\n\ndays\n\n\n\nsoil + rice husk ashc\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n49 \n \n\n\n\nO\n\n\n\nglyoxylic acid\n\n\n\nNP\nHO\n\n\n\nHO\n\n\n\nO\n\n\n\nP\n\n\n\nO\n\n\n\nHO\n\n\n\nHO\nNH2\n\n\n\nCO2H\n\n\n\nH\n\n\n\nAMPA\n\n\n\nH CO2H\n\n\n\nPi CH3NH4\n+\n\n\n\nCO2 NH4\n+\n\n\n\nC-P lyase\n\n\n\nGlyphosate\n\n\n\n Figure 2. The AMPA pathway of glyphosate degradation in soil.\n\n\n\nCO2\n\n\n\nglyphosate oxidoreductase\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n50 \n \n\n\n\n A study of glyphosate degradation for 60 days using 14C-glyphosate on Malaysian \n\n\n\nsoils showed slow and rapid glyphosate degradation from muck and sandy loam soil, \n\n\n\nrespectively (Cheah et al. 1998). The authors reported 14CO2 evolution of 14.61% and 9.4% \n\n\n\nfor aerobic and anaerobic muck soils 60 days after treatment, and attributed this to high \n\n\n\nadsorption capacity of the muck soil. Meanwhile a rapid degradation in the non-sterilized \n\n\n\naerobic sandy loam (89.97%) was attributed to microbial processes. Also, the initial rapid \n\n\n\ndegradation rate observed was followed by a slower rate which commenced approximately 30 \n\n\n\ndays after treatment indicating microbial action on the free glyphosate and subsequent attack \n\n\n\non the adsorbed glyphosate, respectively. \n\n\n\n Table 1 shows the rate constant, R2 and calculated half-lives of the degradation of \n\n\n\nglyphosate from different moisture regimes in the studied soils. The best fit data to FODED \n\n\n\nmodel (R2 = 0.914) was the non-labile glyphosate degradation at moisture condition of \n\n\n\npermanent wilting point in rice husk ash-amended soil while the poorly fit data was that of \n\n\n\nthe non-labile phase of rice husk ash-amended soil (R2 = 0.023). The calculated half-live  \n\n\n\nfrom the different moisture levels at both labile and non-labile phases show that at condition \n\n\n\nof field capacity, glyphosate had a half-life range of 11-42 days in both control and amended \n\n\n\nsoils. The submerged moisture regime in control, cow dung or rice husk ash amended soils \n\n\n\nhad glyphosate half-life of 25,13 and 14 days, respectively, at labile phase while the half-life \n\n\n\nat the non-labile phase was 161, 178 and 20 days respectively. Meanwhile, at permanent \n\n\n\nwilting point, the half-life for glyphosate degradation in the labile phase for control and soil \n\n\n\namended with cow dung and rice husk ash was 17, 28 and 110 days, respectively  while at  \n\n\n\nthe non-labile phase, it was 90, 9 and 16 days, respectively.  \n\n\n\nTABLE 1 \n\n\n\nRate constant describing glyphosate degradation, R2 and calculated half-lives in control and \n\n\n\namended soils at different moisture regimes \nSoil Moisture regimes Phase k (\u00b5g day-1) R2 Half-life (day) \n\n\n\nControl Submerged Labile 0.0282 0.557 25 \n\n\n\n  Non-labile 0.0043 0.026 161 \n\n\n\n Field capacity Labile 0.0526 0.756 13 \n\n\n\n  Non-labile 0.0167 0.099 42 \n\n\n\n Permanent wilting point Labile 0.0404 0.698 17 \n\n\n\n  Non-labile 0.0077 0.030 90 \n\n\n\nSoil + cow dung Submerged Labile 0.0547 0.586 13 \n\n\n\n  Non-labile 0.0039 0.042 178 \n\n\n\n Field capacity Labile 0.0371 0.371 19 \n\n\n\n  Non-labile 0.0368 0.498 19 \n\n\n\n Permanent wilting point Labile 0.0252 0.303 28 \n\n\n\n  Non-labile 0.0732 0.909 9 \n\n\n\nSoil + rice husk ash Submerged Labile 0.0501 0.660 14 \n\n\n\n  Non-labile 0.0349 0.614 20 \n\n\n\n Field capacity Labile 0.0604 0.671 11 \n\n\n\n  Non-labile 0.0295 0.338 23 \n\n\n\n Permanent wilting point Labile 0.0063 0.023 110 \n\n\n\n  Non-labile l 0.0438 0.914 16 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n51 \n \n\n\n\n The overall results from both soils show that the condition of field capacity generally \n\n\n\nhad low half-life for glyphosate degradation with narrow variation between labile and non-\n\n\n\nlabile phases but the half-life varied widely between labile and non-labile phases in \n\n\n\nsubmerged and permanent wilting points. This suggests strong binding forces retain \n\n\n\nglyphosate molecules at permanent wilting point but with increasing moisture, the water \n\n\n\nmolecules weaken the forces and make available the glyphosate in soil solution, hence its \n\n\n\nrapid degradation. However, as anaerobic conditions prevail at submerged conditions, it leads \n\n\n\nto a low rate of oxygen diffusion and consequently  retards degradation (Topp et al. 1997). \n\n\n\nMoisture greatly affects soil microbial community and activity as well as enzyme function. \n\n\n\nDry soils have a higher water potential which decreases its soil microbial function (Geisseler \n\n\n\net al. 2011) but increasing  moisture causes a decrease in soil water potential and increasing \n\n\n\nmicrobial activity. Meanwhile, at higher moisture levels as in flooded rice paddies, the \n\n\n\ndecrease in  soil oxygen diffusion rate will also lower the soil redox potential and \n\n\n\nconsequently lead to low microbial activity (Woli\u0144ska and  Bennicelli 2010) .  \n\n\n\nOur study results therefore demonstrated that when glyphosate is applied to these soils \n\n\n\nin flooded conditions or in a very low moisture regimes, it will be retained for a longer period \n\n\n\nbefore degradation. However, in a condition of optimum soil moisture, timely glyphosate \n\n\n\ndegradation will be achieved thereby remediating its toxicity effect. Our study results concur \n\n\n\nwith a study by Kanissery et al. (2015) where three different types of glyphosate-spiked \n\n\n\nMollisols of Illinois were incubated under aerobic and anaerobic conditions. The authors \n\n\n\nreported lower half-life values of glyphosate for aerobic conditions compared to anaerobic \n\n\n\nconditions. Similarly, a more rapid glyphosate degradation was reported under optimum soil \n\n\n\nwater conditions by Aslam et al. (2015). The results of an earlier study by Rueppel et al. \n\n\n\n(1977) also found increased glyphosate degradation in soil under aerobic conditions \n\n\n\ncompared to anaerobic conditions.  \n\n\n\nTable 2 shows an increase in glyphosate residual concentration compared to control \n\n\n\nunder the moisture regimes in the order of permanent wilting point > submerged > field \n\n\n\ncapacity but the trends in soils with cow dung and rice husk ash was in the order of \n\n\n\nsubmerged > field capacity > permanent wilting point. Compared to control, application of \n\n\n\ncow dung or rice husk ash increased glyphosate residue at field capacity and in submerged \n\n\n\nconditions but at permanent wilting point, there was a decrease in glyphosate residue. This \n\n\n\nindicates that application of cow dung or rice husk ash limit glyphosate degradation with \n\n\n\nincreasing moisture in the studied soils which might be due to changes in mineralogical and \n\n\n\nsurface properties of the soils affecting the bioavailability and degradation of this herbicide. \n\n\n\nSchroll et al. (2006) observed an increase in glyphosate degradation when comparing air dry \n\n\n\ncondition to very low moisture content on loamy soils; however, mineralization was found to \n\n\n\nbe considerably reduced as the moisture approached water holding capacity of the soils.  \n\n\n\nMoisture controls redox condition and solubility of organic matter in the soil. Increasing \n\n\n\nmoisture will lead to reduction of Fe from the oxide mineral (Kanissery et al. 2015) and this \n\n\n\nresults in glyphosate bioavailability through its release into the soil solution. Likewise, a \n\n\n\nfluctuation in soil moisture alters microbial activity and solubility of soil organic matter \n\n\n\nwhich controls the release of glyphosate adsorbed to soil organic colloids. Glyphosate is \n\n\n\ngenerally considered to be easily degraded in soil, but its persistence varies widely, with its \n\n\n\nhalf-life ranging between 1 and 197 (Bento et al. 2016). Soil microbes and adsorption greatly \n\n\n\naffect glyphosate persistence in soils (Erban et al. 2018). As glyphosate serves as C, N and P \n\n\n\nsource to soil bacteria and fungi, it is easily degraded by different strains of these organisms. \n\n\n\nHowever, due to its effect on the shikimic pathway in both plants and in these microbes, the \n\n\n\npresence of glyphosate in soil can affect growth and function of the microbes and its \n\n\n\nconsequent persistence. Of equal importance, glyphosate complexes divalent cations in soil, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 44-54 \n\n\n\n\n\n\n\n52 \n \n\n\n\nbinds soil organic matter and strongly adsorbs to oxides of Fe and Al.  Therefore application \n\n\n\nof this residue increases the content of organic matter and enhances glyphosate persistence. \n\n\n\nThe mode of action of glyphosate is inhibition of 5-enolpyruvylshikimate-3-phosphate \n\n\n\nsynthase, resulting in the depletion of essential aromatic amino acids needed for plant and \n\n\n\nmicrobial growth (Haney et al. 2000). Consequently, glyphosate inhibits nitrogen fixation, \n\n\n\ngrowth of mycorrhizal fungi and is  toxic to earthworms (Giesy et al. 2000).  \n\n\n\nTable 2 \nGlyphosate residual concentration among treatments in studied soils at different moisture regimes \n\n\n\n Soil (mg kg-1) \n\n\n\nMoisture regime Control Soil + cow dung Soil + rice husk ash \n\n\n\nPermanent wilting point 6.434abc\u00b11.03 4.335c\u00b10.67 4.890bc\u00b10.79 \n\n\n\nField capacity 4.115c\u00b10.70 5.040bc\u00b10.81 5.394abc\u00b10.98 \n\n\n\nSubmerged 5.018bc\u00b10.82 7.516a\u00b11.15 7.092ab\u00b11.05 \n\n\n\n Means with the same letter are not statistically different at p>0.05. (n= 3, \u00b1 SE) \n\n\n\nCONCLUSION \n\n\n\nOur study results confirmed the role of moisture and added residue on glyphosate solubility, \n\n\n\nbioavailability and subsequent degradation. Water acts as a solvent for herbicide dilution, \n\n\n\nmovements and diffusion besides being essential for microbial functioning. On the other \n\n\n\nhand, application of cow dung and rice husk ash increased C, nutrient and organic matter \n\n\n\ncontent of the studied soils, thereby, stimulating microbial activity and subsequent glyphosate \n\n\n\ndegradation. Therefore, it can be suggested that rate of glyphosate degradation is generally \n\n\n\ncontrolled by fluctuating soil moisture level. Depending on soil organic matter content, \n\n\n\nglyphosate degradation tends to increase with increasing soil moisture content. However, in \n\n\n\nconditions of very high moisture as in rice paddies, the   low rate of oxygen diffusion results \n\n\n\nin an anaerobic condition which can retard glyphosate degradation. \n\n\n\nFUNDING \n\n\n\nThe authors would like to thank Universiti Putra Malaysia for providing a financial support to \n\n\n\ncomplete this study through UPM/GP/IPS/2016-9471900 research grant. 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Dehydrogenase activity response to soil reoxidation \n\n\n\nprocess described as varied conditions of water potential, air porosity and oxygen \n\n\n\navailability. Polish Journal of Environmental Studies 19(3): 651\u2013657. \n\n\n\nWong, C. L., J. Liew, Z. Yusop and T. Ismail. 2016. Rainfall characteristics and \n\n\n\nregionalization in Peninsular Malaysia based on a high resolution. Water 8(500): 1\u2013\n\n\n\n16. https://doi.org/10.3390/w8110500 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
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"\n\nINTRODUCTION\n\n\n\nIn Malaysia, glufosinate ammonium (GLUF), which is an organophosphate \n\n\n\nbroad-spectrum contact herbicide, is widely used in the oil palm plantations \n\n\n\nfor the control of a wide range of broad-leaved weeds and grasses, to make it \n\n\n\neasier for the collection of palm oil fruitlets and to ensure the safety of workers \n\n\n\nagainst snakes. Glufosinate ammonium is now registered for use in 50 countries \n\n\n\nand has been marketed through several trade names including Basta, Rely, Finale \n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 14: 41-52 (2010)                         Malaysian Society of Soil Science\n\n\n\nAdsorption and Desorption of Glufosinate Ammonium\n\n\n\nin Soils Cultivated with Oil Palm in Malaysia\n\n\n\nS.M.J. Jariani1*, A.B. Rosenani 1, A.W. Samsuri 1, A.J. Shukor 2 \n\n\n\n& H.K. Ainie3\n\n\n\n1Department of Land Management and 2Department of Crop Science, \n\n\n\nFaculty of Agriculture, University Putra Malaysia, \n\n\n\n43400 Serdang, Selangor, Malaysia\n\n\n\n3 Product Development and Advisory Service Division, \n\n\n\nMalaysian Palm Oil Board, Bangi, Selangor, Malaysia\n\n\n\nABSTRACT\nIn Malaysia, glufosinate ammonium (GLUF) is a commonly used herbicide in \n\n\n\noil palm plantations to control broad-leaved weeds and grasses. Adsorption and \n\n\n\ndesorption of (GLUF) were studied using the batch equilibrium technique in four \n\n\n\nmineral soils, Inceptisols (Selangor), Oxisols (Munchong) and Ultisols (Serdang \n\n\n\nand Rengam) series and peat  (Histosols) collected under oil palm cultivation \n\n\n\nfrom 0-15 cm and 15-30 cm depths. Adsorption coefficients of the herbicide were \n\n\n\ncorrelated with soil properties i.e. organic matter content, clay content, cation \n\n\n\nexchange capacity (CEC) and pH. The concentrations of GLUF used were (0, \n\n\n\n0.25, 0.5, 1, 1.5, 3, 5 and 10 \u00b5g/mL). The adsorption and desorption isotherms \n\n\n\nwere fitted using linear and Freundlich equations. Adsorption of GLUF was \n\n\n\nin the following order: Selangor > Rengam> Munchong> peat > Serdang. The \n\n\n\nresults indicate that the adsorption of GLUF is positively correlated only with clay \n\n\n\ncontent. The high sorption of the Selangor soil could be explained by the high \n\n\n\nclay content in Selangor series soil compared to the other soil series. However, the \n\n\n\norder of GLUF desorption was in the following order: Serdang> peat> Munchong> \n\n\n\nRengam> Selangor. Results indicate that adsorption of GLUF was mainly on the \n\n\n\nclay fraction of the soil and the binding strength of adsorbed GLUF was high as \n\n\n\nindicated by the order of GLUF desorption from the soils.\n\n\n\n\n\n\n\nKeywords: Batch equilibrium test, linear equation, Freundlich equation,\n\n\n\n      Glufosinate ammonium, sorption isotherm \n\n\n\n___________________\n\n\n\n*Corresponding author : E-mail: jar0101@gmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201042\n\n\n\nand Challenge. This herbicide was introduced to the Malaysian market in 1980s \n\n\n\n(Ismail and Ahmad 1994). Basta is one of the most commonly used GLUF in \n\n\n\nMalaysia.  Duke and Lydon (1987) reported that GLUF was developed with two \n\n\n\nattributes common to many other new agricultural chemicals: (1) it is formulated \n\n\n\nin water rather than solvents and (2) it was originally developed from a natural \n\n\n\nmicrobial product. Glufosinate ammonium is a natural compound isolated from \n\n\n\ntwo species of streptomycetes (Tachibana et al. 1986; Krieg et al. 1990). This \n\n\n\ncauses photosynthesis to stop and the herbicides reaction results in death of \n\n\n\nthe plant cells. This herbicide is also being incorporated into the field cropping \n\n\n\nsystems with the use of transgenic, glufosinate tolerant, crops. The introduction \n\n\n\nof genetically modified crops, resistant to GLUF has lead  to a significant increase \n\n\n\nin the use of this herbicide. (Jewell and Buffin, 2001).\n\n\n\nKnowledge of a pesticide\u2019s structure and some of its physico-chemical \n\n\n\nproperties often permits an estimation of its behaviour and adsorption mechanism. \n\n\n\nIts properties, such as acidity or basicity (denoted by pKa or pKb), water solubility \n\n\n\nand molecular size, affect the adsorption-desorption by soil colloids (Bailey \n\n\n\nand White 1970). Glufosinate ammonium, which  has a molecular structure of \n\n\n\nC\n5\nH\n\n\n\n15\nN\n\n\n\n2\nO\n\n\n\n4\nP, has a very high water solubility of 1370 g/L (Behrendt et al. 1990). \n\n\n\nIts half-life has been determined in numerous laboratory studies and it varies from \n\n\n\n3 to 42 days in some studies and up to 70 days in others (Behrendt et al. 1990; \n\n\n\nFaber et al. 1997; Tomlin 2000; Devos et al. 2008). The shortest half life tends to \n\n\n\nbe in soils with a high clay and organic matter content (Ismail and Ahmad 1994). \n\n\n\nThe nature of the functional groups is one of the structural factors determining the \n\n\n\nchemical characteristic of a pesticide molecule thus influencing its adsorption on \n\n\n\nsoil colloids and the adsorption mechanism. Both amino and carbonyl groups that \n\n\n\nexist in GLUF chemical structure may participate in hydrogen bonding. Hydrogen \n\n\n\nbonding appears to be the most important mechanism for adsorption of polar non-\n\n\n\nionic organic molecules such as GLUF on clay minerals (Khan 1980).\n\n\n\nFollowing its application, a large proportion of GLUF will settle on the soil.  \n\n\n\nAdsorption is one of the most important factors that affect the fates of pesticides \n\n\n\nin the soil and consequently determine their distribution in the soil and water \n\n\n\nsystem (Giles et al. 1960). Glufosinate ammonium has been shown to be strongly \n\n\n\nadsorbed by soil high in clay content and low in soil with low clay content \n\n\n\n(Behrendt et al. 1990, Jewell and Buffin 2001; Autio et al. 2004). \n\n\n\nWhile much is known about the physiological activity, efficacy, and mode of \n\n\n\naction of GLUF, little data or information is available concerning their adsorption \n\n\n\nand desorption in soils cultivated under oil palm in Malaysia (Ismail and \n\n\n\nAhmad 1994) where life cycle assessment of oil palm is important. Nowadays, \n\n\n\nenvironmental safety is an important aspect to be considered in the oil palm \n\n\n\nindustry to ensure minimum environmental impact of agronomic practices and \n\n\n\nfor the product to be labeled as environmentally friendly, especially in exporting \n\n\n\npalm oil to other countries with high environmental safety standards. Therefore, \n\n\n\nthe objectives of this study are to determine the adsorption and desorption of \n\n\n\nGLUF in different soil types on which oil palms are grown and the correlations \n\n\n\nS.M.J. Jariani, A.B. Rosenani,  A.W. Samsuri  A.J. Shukor  & H.K. Ainie\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 43\n\n\n\nbetween the properties of these soils (clay content, organic matter, pH, CEC) with \n\n\n\nadsorption and desorption of GLUF.\n\n\n\nMATERIALS AND METHODS\n\n\n\nChemicals Used in the Study\n\n\n\nGlufosinate ammonium was obtained from Riedel-de Haen (Seelze, Germany). \n\n\n\nThe chemical structure and basic properties of GLUF are shown in Fig. 1 and \n\n\n\nTable 1, respectively. Acetonitrile, acetone and diethyl ether, all of HPLC grade, \n\n\n\nwere purchased from Scharlau Science (Barcelona, Spain). Analytical-reagent \n\n\n\ngrade potassium dihydrogenphosphate, disodium tetraborate decahydrate, \n\n\n\nand hydrochloric acid (37%), potassium hydroxide and 9-Fluorenylmethyl \n\n\n\nChloroformate (FMOC-Cl) were purchased from Merck.\n\n\n\nFig. 1: Chemical structure of GLUF\n\n\n\nTABLE 1\n\n\n\nBasic properties of glufosinate ammonium\n\n\n\nSource: Pesticide Action Network, North America. http://www.pesticide.info.org\n\n\n\nStock standard solutions (400 ug/mL) of GLUF as well as mixed diluted \n\n\n\nstandards were prepared with HPLC-grade water. Acetone, 0.125 M borate buffer \n\n\n\nsolution and 0.01M FMOC-Cl in acetone were used to perform derivatization \n\n\n\nprior to HPLC analysis.\n\n\n\n\n\n\n\nCollection of Soil Samples and Characterization\n\n\n\nThe soils in this study represent the soil types under oil palm cultivation in \n\n\n\nMalaysia. The samples were collected from several locations that were not \n\n\n\nGLUF in Soils Cultivated with Oil Palm\n\n\n\nMolecular formula  Molecular mass  Solubility in H 2 O  pKa\n\n\n\nC 5 H15 N2 O4 P  198.19  >500g/L at 20\u00baC  9.15 + 0.07  \n\n\n\nN\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201044\n\n\n\nexposed to GLUF application. Five types of soil with different soil texture were \n\n\n\nchosen for this study. Soil samples chosen were identified as Inceptisol (Selangor \n\n\n\nSeries), Oxisol (Munchong Series), Ultisols (Rengam and Serdang Series) and \n\n\n\nHistosol (peat) according to USDA Soil Taxonomy. Soil samples were collected \n\n\n\nat two different depths: topsoil (0-15 cm) and subsoil (15-30 cm). Collected soils \n\n\n\nwere air dried and passed through a 2-mm sieve.\n\n\n\nSoil texture of each soil was determined using the pipette method of Day \n\n\n\n(1965) using Calgon as the dispersing agent; the textural class was determined \n\n\n\nusing the USDA soil textural triangle. The cation exchange capacity (CEC) \n\n\n\nwas determined by the leaching method, using 100 mL ammonium acetate (1M \n\n\n\nNH\n4\nOAc) (Thomas, 1982). Soil organic carbon was determined by the Walkley \n\n\n\nand Black method, (1984). One gram of soil was weighed into an Erlenmayer \n\n\n\nflask to measure organic carbon (OC). The pH of the soils was recorded based \n\n\n\non the soil-water suspension 1:1(v/v) using Beckman Digital pH meter. The clay \n\n\n\nmineral contents were analysed by X-ray diffraction technique (XRD). Important \n\n\n\nsoil properties are listed in Table 2 and the results of XRD analysis are shown in \n\n\n\nTable 3.\n\n\n\nTABLE 2\n\n\n\nParticle size distribution and chemicals characterization of soils used in this study\n\n\n\nSorption and Desorption Studies\n\n\n\nSorption isotherms were carried out using the batch equilibrium technique which \n\n\n\npermits convenient evaluation of parameters that influence the adsorption process \n\n\n\n(OECD 2000). The analyses were performed with 7 different concentrations of the \n\n\n\nactive substances (GLUF) in triplicates. Two g of each soil were weighed into 50 \n\n\n\nmL propylene centrifuge tubes. Then, 20 mL of deionized water containing 0.25, \n\n\n\n0.5, 1, 1.5, 3, 5 and 10 ug/mL of GLUF were added. The tubes were equilibrated \n\n\n\nat room temperature for 24 hours after which they were centrifuged for 30 min at \n\n\n\n3500 rpm. Then, the supernatant was removed and the equilibrium concentration \n\n\n\n(Ce) of GLUF was determined in the supernatant by HPLC. \n\n\n\nAdsorption isotherms were fitted to linear and Freundlich equations \n\n\n\n(logarithmic form). In linear form: Cs = Kd Ce, where Kd is a constant. For \n\n\n\nFreundlich equation: log Cs = log Kf +1/nf log Ce. The amount of herbicide \n\n\n\nadsorbed (Cs, \u00b5g/g) was calculated from the difference between the initial (Cini, \n\n\n\n\u00b5g/L) and the equilibrium concentrations of pesticide in solution (Ce, \u00b5g/L). \n\n\n\nS.M.J. Jariani, A.B. Rosenani,  A.W. Samsuri  A.J. Shukor  & H.K. Ainie\n\n\n\nSoils \n\n\n\nseries \n\n\n\n\n\n\n\n\n\n\n\n Clay  Silt  \n\n\n\n(%) \n\n\n\nSand\n\n\n\nSelangor   55.64  42.14 1.88\n\n\n\nDepth\n\n\n\n(cm)\n\n\n\n0 -15\n\n\n\n15 -30\n\n\n\n\n\n\n\nTexture\n\n\n\nSilty clay\n\n\n\nClay  \n\n\n\n(\n\n\n\nOC\n\n\n\n% )\n\n\n\n2.75\n\n\n\n1.94\n\n\n\nOM\n\n\n\n(%) \n\n\n\n4.79\n\n\n\n3.38\n\n\n\n pH  \n\n\n\n(H2O)\n\n\n\n4.31\n\n\n\n4.11\n\n\n\n\n\n\n\nCEC \n\n\n\n(cmol(+)kg\n-1\n\n\n\n) \n\n\n\n15 .76\n\n\n\n12 .66  64.93  36.54 0.51\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 45\n\n\n\nThe constants Kf and 1/n are the empirical Freundlich constants. Desorption \n\n\n\ndetermination was conducted immediately after the adsorption experiment. First, \n\n\n\nthe supernatant were replaced with 20 ml of distilled water and the mixture was \n\n\n\nthen shaken for 24 hours at room temperature. The suspension was subsequently \n\n\n\ncentrifuged and analyzed similarly as the adsorption experiment. The correlations \n\n\n\nbetween the adsorption coefficients and the soil properties were then determined.\n\n\n\nAnalysis of GLUF\n\n\n\nThe clear supernatants were analyzed for GLUF. Samples were first derivatised \n\n\n\nby adding 0.8 ml of borate buffer (0.025 M) and 0.8 ml of acetone together \n\n\n\nwith 0.2 ml of FMOC-CL (0.01 M) solutions into 1 mL of sample. The mixture \n\n\n\nwas swirled and left at room temperature for 30 minutes. After the reaction, the \n\n\n\nsamples were washed with 1 ml of diethyl ether and ready for determination using \n\n\n\nhigh performance liquid chromatography (HPLC) equipped with a fluorescence \n\n\n\ndetector (Sancho et al. 1994). The HPLC used was a Hewlett-Packard series 1100 \n\n\n\nconsisting of Model 1046A Programmable Flourescence detector (Hewlewtt-\n\n\n\nPackard) set at 270 nm (excitation) and 315 nm (emission). The analytical column \n\n\n\nwas NH2 Bondapak (3.9 x 300 mm). Acetonitrile: 0.05 M phosphate (pH 6) in \n\n\n\nwater (25:75, v/v) was used as the mobile phase. The pH of the aqueous buffer \n\n\n\nsolution was adjusted with 2M KOH and 1M HCl.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Physico-chemical Properties\n\n\n\nTable 2 shows the physico-chemical properties of the soils used in this study. In \n\n\n\ngeneral, soil pH in the surface was higher than subsurface in all soils and all the \n\n\n\nsoils were acidic with pH ranging from 4.11 to 5.52. Selangor topsoil (0-15 cm) \n\n\n\nwas silty-clay while its subsoil (15-30 cm) was clayey in texture. Selangor topsoil \n\n\n\nhad a higher percentage of organic carbon (OC), organic matter (OM), pH and \n\n\n\ncation exchange capacity (CEC) compared to the subsoil. Both the topsoil and \n\n\n\nsubsoil  of Rengam series was sandy clay in texture. Its topsoil also showed a \n\n\n\nhigher percentage of OC, OM and pH compared to the subsoil. The CEC value of \n\n\n\nthe Rengam soil was higher in the subsoil. Serdang topsoil was sandy loam while \n\n\n\nits subsoil was sandy clay loam. The OC, OM, pH and CEC were slightly higher \n\n\n\nin Serdang topsoil compared to its subsoil. Peat showed the highest value of \n\n\n\nCEC compared to the other 4 soil types due to the higher organic matter content. \n\n\n\nSelangor soil had the highest CEC content compared to the other mineral soils. \n\n\n\nwhile its subsoil had a higher content of clay (65%) compared to the other soils. \n\n\n\nThe subsoils showed a higher percentage of clay content than the topsoils of all \n\n\n\nthe soil types. \n\n\n\nGLUF in Soils Cultivated with Oil Palm\n\n\n\n\n\n\n\n\nM\nalay\n\n\n\nsian\n Jo\n\n\n\nu\nrn\n\n\n\nal o\nf S\n\n\n\no\nil S\n\n\n\ncien\nce V\n\n\n\no\nl. 1\n\n\n\n4\n, 2\n\n\n\n0\n1\n0\n\n\n\n4\n6\n\n\n\nS\n.M\n\n\n\n.J. Jarian\ni, A\n\n\n\n.B\n. R\n\n\n\no\nsen\n\n\n\nan\ni,  A\n\n\n\n.W\n. S\n\n\n\nam\nsu\n\n\n\nri\n  A\n\n\n\n.J. S\nh\nu\nk\no\nr  &\n\n\n\n H\n.K\n\n\n\n. A\nin\n\n\n\nie\n\n\n\nTABLE 3\n\n\n\nClay mineral contents in soils used for this study\n\n\n\nSoil series  Depth (cm ) Minerals  \n\n\n\nKaolinite  Quartz  Illite  Montmorillonite  Vermiculite  Gibsite  \n\n\n\nSelangor  0 -15  +++  +++  ++  +  +   \n\n\n\n 15 -30  +++  ++  ++  +++  +++   \n\n\n\nMunchong  0 -15  ++  ++     +  \n\n\n\n 15 -30  ++  +  +    +  \n\n\n\nRengam  0 -15  +++  +++      \n\n\n\n 15 -30  +++  +++      \n\n\n\nSerdang  0 -15  ++  +++     +  \n\n\n\n 15 -30  ++  +++  +    +  \n\n\n\n*    +++ = abundant\n   \n\n\n\n++ = present\n   \n\n\n\n+   = traces\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 47\n\n\n\nSoil Mineralogy\n\n\n\nIn general, kaolinite mineral and quartz were the most abundant minerals in the \n\n\n\nsoil samples (Table 3). Montmorillonite and vermiculite were only present in the \n\n\n\nSelangor subsoil. The higher CEC content in Selangor soil can be explained by the \n\n\n\nhigher content of 2:1 layer silicates, montmorillonite and vermiculite. Selangor \n\n\n\nseries soil has high % clay as well as % silt. \n\n\n\nTABLE 4\n\n\n\nThe adsorption and desorption constant of Kd, Kf, n, for all soils.\n\n\n\nAdsorption of GLUF\n\n\n\nAdsorption isotherm data were fitted to the linear and Freundlich sorption equations \n\n\n\nto compare the adsorption capacity of GLUF on the soil studied. The adsorption \n\n\n\nisotherm parameters of GLUF by the soils are shown in Table 4. In general, both \n\n\n\nthe linear and Freundlich sorption isotherms fitted the GLUF adsorption data well \n\n\n\nin all soils as indicated by the R2 values which are close to 1. Sorption data and \n\n\n\nvalues for Kd, Kf, n and R2 for all soils are shown in Table 4. High Kd and \n\n\n\nKf values indicate strong adsorption capacity of the pesticide to the soils. The \n\n\n\nvalues for Kd are larger than Kf in Munchong and Serdang Series and peat. In \n\n\n\ntopsoil, Selangor Series soil showed the highest Kd and Kf followed by Rengam, \n\n\n\nMunchong, peat and Serdang as shown in Figs. 2 and 3. High adsorption capacity \n\n\n\nof Selangor series soil could be explained by its high percentage of clay content \n\n\n\ncompared to the other soils. Effects of clay content on the adsorption of GLUF \n\n\n\nhave been previously reported (Behrendt et al. 1990; Autio et al. 2004). The high \n\n\n\nadsorption capacity found on the Selangor series compared to the Serdang series \n\n\n\ncould be explained by its mineral composition. Selangor series soil contained an \n\n\n\nGLUF in Soils Cultivated with Oil Palm\n\n\n\nSorption  Desorption\n\n\n\nSoil series  Depth \n\n\n\n(cm)\n\n\n\nLinear  Freundlich Average % \n\n\n\nK d R\n2\n K f R\n\n\n\n2\n n  desorbed  \n\n\n\nSelangor  0 -15  13.537 0.9848 18.433 0.9553 0.6430  38.280  \n\n\n\n15 -30 34.190 0.9950  39.692 0.9955 0.7431  26.753  \n\n\n\nMunchong 0 -15  4.040  0.8321  3.192 0.907  0.9005  78.841  \n\n\n\n15 -30 6.046  0.9600  5.445 0.9463 0.8505  45.990  \n\n\n\nRenggam  0 -15  4.801  0.9011  6.563 0.9169 0.7875  74.798  \n\n\n\n15 -30 12.640 0.9990  13.237 0.9910 0.8466  41.642  \n\n\n\nSerdang  0 -15  1.764  0.7908  0.412 0.8363 0.7504  98.003  \n\n\n\n15 -30 4.991  0. 9801  2.940 0.9996 0.7712  56.094  \n\n\n\nPeat  0 -15  3.841  0.8126  2.370 0.8613 0.3769  83.090  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201048\n\n\n\nS.M.J. Jariani, A.B. Rosenani,  A.W. Samsuri  A.J. Shukor  & H.K. Ainie\n\n\n\nCe(ug)/mL)\n\n\n\nC\ns(\nu\ng\n)/\nm\nL\n)\n\n\n\nCe(ug)/mL)\n\n\n\nC\ns(\nu\ng\n)/\nm\nL\n)\n\n\n\n(a)                                                                 (b)\n\n\n\nFig. 2: Linear sorption isotherms of GLUF in (a) topsoil (0-15cm) and  (b) subsoil \n\n\n\n(15-30cm) for the four soils.\n\n\n\n(a)                                                             (b)\n\n\n\nFig. 3: Log-transformed Freundlich sorption isotherms of GLUF in (a) topsoil (0-15cm) \n\n\n\nand (b) subsoil (15-30cm) for the four soils\n\n\n\n(a)                                                                (b)\n\n\n\nlog Ce(ug/ml)\n\n\n\nlo\ng\n\n\n\n C\ns(\n\n\n\nu\ng\n\n\n\n/m\nl)\n\n\n\nlog Ce(ug/ml)\n\n\n\nlo\ng\n\n\n\n C\ns(\n\n\n\nu\ng\n\n\n\n/m\nl)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 49\n\n\n\nGLUF in Soils Cultivated with Oil Palm\n\n\n\n(c)                                                                    (d)\n\n\n\n(e)                                                                 (f)\n\n\n\n(g)                                                                 (h)\n\n\n\nFig. 4: Sorption and desorption isotherm of GULF in topsoil (0-15cm) and subsoil (15-\n\n\n\n30cm) of (a) Selangor-topsoil, (b) Selangor-subsoil, (c) Rengam-topsoil, (d) Rengam-\n\n\n\nsubsoil, (e) Munchong-topsoil, (f) Munchong-subsoil, (g) Serdang-topsoil, (h) Serdang-\n\n\n\nsubsoil series soil and (i) peat\n\n\n\n(i)\n\n\n\nSorption (\u2666)  \n\n\n\nDesorption (\u25a1)\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201050\n\n\n\nabundant amount of kaolinite while Serdang series soil was dominated by quartz \n\n\n\nand was low in clay content (Table 3). Adsorption of organophosphate pesticides \n\n\n\nhas been reported to be related to organic matter as well as clay content of soils \n\n\n\n(Khan 1980). However, in this study, peat which is high in organic carbon content, \n\n\n\nexhibited low GLUF adsorption capacity.  This is probably because organic carbon \n\n\n\nreduces GLUF adsorption by competing with the pesticide molecules for sorption \n\n\n\nsites on the soil surface. This result is supported by a study done by Autio et al. \n\n\n\n(2004).\n\n\n\nAs clay content has been reported to be the main factor affecting GLUF \n\n\n\nadsorption in mineral soils, GLUF adsorption should be higher in Munchong \n\n\n\nseries soil than in Rengam series soil since Munchong series soil has a higher \n\n\n\nclay content than Rengam series soil. However, the results showed otherwise, \n\n\n\nwhich suggest that other factors such as CEC could have also influenced the soils \n\n\n\nadsorption capacity for GLUF, as Rengam series soil  was found to have a higher \n\n\n\nCEC value than Munchong soil. \n\n\n\nEffect of clay content on the adsorption capacity of GLUF is demonstrated \n\n\n\nby the higher adsorption capacity of subsoils for GLUF than the topsoils in all \n\n\n\nsoils studied (Table 4 and Fig. 4). In all soils studied, the subsoil had higher clay \n\n\n\ncontent than the topsoil. The correlation between the Kd and Kf values of GLUF \n\n\n\nand the clay content of the soil was highly significant (Table 5). Table 5 also \n\n\n\nshows no significant correlation between adsorption capacities of GLUF with \n\n\n\nCEC, probably, because of the low CEC in highly weathered soil used in the \n\n\n\nstudy (Table 2).\n\n\n\nTABLE 5\n\n\n\nCorrelations between soil properties of soils and adsorption isotherm \n\n\n\nof GLUF (n=3)\n\n\n\nDesorption of GLUF\n\n\n\nThe average desorption of GLUF was generally higher in the topsoil of all soils \n\n\n\nstudied which was low in clay content compared to the subsoil (Table 4). The \n\n\n\nhighest average desorption was on Serdang series soil followed by peat, Munchong \n\n\n\nand Rengam series soil. Selangor series soil showed the lowest average desorption \n\n\n\npercentage among the soils with only 26.75% and 38.28% in the subsoil and \n\n\n\ntopsoil, respectively. Low desorption percentage of Selangor series soil indicates \n\n\n\nthat once GLUF is adsorbed onto the soil colloids, it is strongly bound and its \n\n\n\npotential to be released into the environment is low. Serdang topsoil which has the \n\n\n\nS.M.J. Jariani, A.B. Rosenani,  A.W. Samsuri  A.J. Shukor  & H.K. Ainie\n\n\n\nHerbicide % Clay Organic matter pH  CEC\n\n\n\nGLUF  K d 0.888*  ns  ns  ns\n\n\n\nK f 0.903*  ns  ns  ns\n\n\n\n* indicates that correlation is significant at P = 0.05 ;  ns = not significant  \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 51\n\n\n\nlowest adsorption capacity shows the highest average desorption percentage with \n\n\n\nalmost 100% of the herbicide being recovered. \n\n\n\nCONCLUSION\n\n\n\nAs expected, adsorption of GLUF was generally related with clay content. In \n\n\n\nmineral soils, CEC also plays an important role in the adsorption of GLUF. \n\n\n\nHowever, further studies should be conducted to confirm the effects of CEC on \n\n\n\nsoil sorption capacity for GLUF using a wider range of CEC values. The highest \n\n\n\nadsorption of GLUF was found in Selangor series soil followed by Rengam. \n\n\n\nMunchong series soil, peat and Serdang series soil. Generally, mineral soils have a \n\n\n\nhigher adsorption capacity of GLUF than peat. Desorption was lowest in soil with \n\n\n\na high clay content. Higher desorption was seen in Serdang series soil followed \n\n\n\nby peat, Munchong, Rengam and Selangor series soil. High desorption recovery \n\n\n\nof GLUF from the highly weathered soils (Serdang and Munchong) suggests that \n\n\n\nthis compound has potential  to be leached into the groundwater.\n\n\n\nACKNOWLEDGEMENT\n\n\n\nWe thank the Malaysian Palm Oil Board (MPOB) for fellowship support under \n\n\n\nGraduate Student Assistantship Scheme (GSAS). Our special thanks also go to \n\n\n\nGolden Hope Sdn. Bhd for allowing us use the of their plantation site for soil \n\n\n\nsampling.\n\n\n\nREFERENCES\nAutio, S., K. Siimes, P. Laitinen, S. Ramo, S. Oinonen and  L. Eronen 2004. Adsorption \n\n\n\nof sugarbeet herbicides to Finnish soils. Chemosphere 55: 215-226.\n\n\n\nBailey, G.W. and J.L. White. 1970. Factors influencing adsorption, desorption and \n\n\n\nmovement of pesticides in soils. Residue Review 32: 29-32.\n\n\n\nBehrendt, H., M. Matthies, H. Gildemeister and G. Gorlitz. 1990. Leaching and \n\n\n\ntransformation of glufosinate-ammonium and its main metabolite in a layered \n\n\n\nsoil column. Environ. Toxicol. Chem. 9: 541\u2013549.\n\n\n\nDevos, Y., M. Cougnon, S. Vergucht, R. Bulcke, G. Haesaert, W. Steurbaut and D. \n\n\n\nReheul 2008. Environmental impact of herbicide regimes used with genetically \n\n\n\nmodified herbicide-resistant maize. Transgenic Res. 17:1059-1077\n\n\n\nDuke, S.O. and J. Lydon. 1987. Herbicides from natural compounds. Weed  Technol. \n\n\n\n1: 122-128.\n\n\n\nFaber, M.J., R.S. Gerald and G.T. Dean 1997. Persistence and leachability of \n\n\n\nglufosinate-ammonium in a Northern Ontario terrestrial environment. Journal \n\n\n\nof Agriculture and Food Chemistry 45 : 3672-3676.\n\n\n\nGiles, C.H., T.H. MacEwan, S.N. Nakhwa and D. Smith, 1960. Studies in adsorption. \n\n\n\nPart XI. A system of classification of solution adsorption isotherms, and its use \n\n\n\nin diagnosis of adsorption mechanism and in measurement of specific surface \n\n\n\nareas of solids. Journal of Chemical Society 14: 3973\u20133993.\n\n\n\nGLUF in Soils Cultivated with Oil Palm\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201052\n\n\n\nIsmail, B.S. and A.R. Ahmad. 1994. Attenuation of the herbicidal activities of \n\n\n\nglufosinate ammonium and imazapyr in two soils. Agriculture, Ecosystems and \n\n\n\nEnvironment 47: 279-285.\n\n\n\nJewell, T. and D. Buffin. 2001. Health and Environmental Impacts of Glufosinate \n\n\n\nAmmonium. Pesticides Action Network UK.\n\n\n\nKhan, S.U. (ed.) 1980. Pesticides in the Soil Environment. Elsevier Scientific \n\n\n\nPublishing Company. Amsterdam, Netherlands.\n\n\n\nKrieg, L.C., M.A. Walker, T. Senaratna and B.D. Mckersie. 1990. Growth, ammonia \n\n\n\naccumulation and glutamine synthetase activity in alfalfa (Medicago sativa L.) \n\n\n\nshoots and cell cultures treated with phosphinothricin. Plant Cell Rep. 9:80-83. \n\n\n\nPesticide Action Network, North America. http://www.pesticide.info.org\n\n\n\nSancho, J.V., F.J Lopez, F.Hernandez, E.A.Hogendoorn, P. van Zoonen. 1994. \n\n\n\nRapid Determination of glufosinate in environmental water sample using \n\n\n\n9-flourenylmethoxycarbonyl precolumn derivatization, Large-volume injection \n\n\n\nand coupled-column liquid chromatography. Journal of Chromatography A \n\n\n\n678:59-67.\n\n\n\nTachibana, K., T. Watanabe, Y. Seikizawa and T. Takematan. 1986. Accumulation of \n\n\n\nammonia in plants treated with bialaphos. Journal of  Pecticide Science 11: \n\n\n\n33-37.\n\n\n\nThomas, G.W. 1982. Exchangeable cations. In: Page, A.L., Miller, R.H. and Keeney, \n\n\n\nD.R. (Editors). Methods of Soil Analysis. Part 2. Chemical and Microbiological \n\n\n\nProperties-Agronomy Monograph No. 9 (2nd edition). Madison, WI: American \n\n\n\nSociety of Agronomy  pp 159-165. \n\n\n\nTomlin, C. (ed.) 2000. The Pesticide Manual 12th ed. The British Crop Protection, \n\n\n\nFarnham, Surrey, UK.\n\n\n\nS.M.J. Jariani, A.B. Rosenani,  A.W. Samsuri  A.J. Shukor  & H.K. Ainie\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nINTRODUCTION\nThe continuous demand for timber supply in the global market has prompted \ntropical countries including Malaysia to provide timber for world market \nconsumption. Various measures were undertaken to establish plantation forests \n\n\n\nAcacia mangium, Gmelina arborea and Paraserianthes falcataria for producing \nAcacia mangium is one of the popular fast \n\n\n\ngrowing species found in South-East Asian forest plantations. Belonging to the \nLeguminosae\n\n\n\nAcacia may be sold for about \nUSD 73 m-3 and is currently utilised as raw materials for the sawn timber, veneer, \n\n\n\nImpact of Thinning of Acacia Mangium Plantation on\n Soil Chemical Properties\n\n\n\nJeyanny Vijayanathan1*, Ahmad Zuhaidi Yahya1 , Adzmi Yaacob2 \nAmir Saaiffuddin Kassim1 and  Suhaimi Wan Chik1\n\n\n\n1\n\n\n\n \n2School of Applied Science, Universiti Teknologi Mara (UiTM),\n\n\n\n40450 Shah Alam, Selangor\n\n\n\nABSTRACT\nThis study was conducted to determine soil organic carbon, nitrogen, available \nphosphorus and pH at 3 soil depths under different ages and cycle of Acacia \nmangium after thinning of planted forest as well as to determine their variability in \n\n\n\nP and pH. Composite surface litters were analyzed for total C, N, P, Ca, Mg and \nnd cycle \n\n\n\nof A. mangium\nphosphorus decreased in the 1st rotation of Acacia mangium\nold stands and in plots which underwent thinning. Soil C, N, P decreased with \n\n\n\nKeywords: Soil properties, tropical, planted forest, rotation, selective \n                      harvesting \n\n\n\n___________________\n*Corresponding author : Email: jeyanny@frim.gov.my\n\n\n\n\n\n\n\n\n76\n\n\n\nJeyanny Vijayanathan, Ahmad Zuhaidi Yahya , Adzmi Yaacob Amir Saaiffuddin Kassim \nand  Suhaimi Wan Chik\n\n\n\nspecies is known to have a symbiotic relationship with soil microbes (Lee et al. \n\n\n\nThe current surge in climate change research, and the depleting supply of \nrenewable energy such as wood, has pushed the sustainable forest management \nconcept into the limelight of the present decade. This has persuaded stakeholders \n\n\n\nwell developed forest management plan which includes selective thinning (the \nremoval of trees selectively to improve the growth rate or health of the remaining \n\n\n\net al.\npurpose.  Nevertheless, human-induced changes such as felling, thinning, shifting \ncultivation and replanting will have impacts on soil nutrients pools, thus altering \n\n\n\nCarbon, N and P are essential nutrients important for the establishment of \nfavorable timber plantation stands. Unfortunately, most of the tropical soils have \nlow levels of these nutrient reserves, low nutrient retention ability and are prone \nto drought (Tiarks et al.\nwood biomass of A. mangium\nis critical for the suitable growth and height of A.mangium (Nik Muhammad and \n\n\n\nare available in soil after thinning activities have been carried out arises. Better \ninformation on the soil chemical nutrient status will allow forest plantation owners \n\n\n\nsoil pH under different ages and rotation of Acacia mangium after thinning as \nwell as to determine their distribution according to soil depths. Information on the \nsoil chemical status after thinning will assist forest managers to strategize future \nmanagement practices. \n\n\n\nMATERIALS AND METHODS\n\n\n\nSite Description\nThe research plot was established in Kemasul Forest Reserve, Mentakab, Pahang, \n\n\n\nThe area was planted with A. mangium with 3.7 x 3 m spacing by the Forestry \n\n\n\nwas carried out and the soil chemical properties are presented in Table 1.  During \n\n\n\nThe plantation consisted of 4 major blocks with various sizes with a total area of \n\n\n\n\n\n\n\n\n77\n\n\n\nSoil Chemical Properties of Thinned Forest\n\n\n\nBungor (Typic Paleudult Plinthaquic Paleudult\n\n\n\nPlot Designation and Sampling \nThe plots were as follows: \n\n\n\nA.mangium nd rotation of 4-year-old stands, \nA.mangium nd rotation of 3-6 month-old seedlings\nA.mangium nd\n\n\n\nEndospermum \nmalaccences, Shorea leprosula, Sapium baccatum  and Trema orientalis\n\n\n\nst rotation of A. mangium\nUsually, each rotation constitutes a full life cycle of a tree which can be harvested \nfor trading purposes. The other sites had undergone standard silvicultural practices \nof thinning at harvesting and the A. mangium were allowed to regenerate naturally \n\n\n\nand were heterogeneous due to their topography. Three subsamples of the soil \nwere taken in each replicate plot after thinning activities to prepare a composite \n\n\n\nof 9 composite soil samples were collected for each plot representing each soil \n\n\n\ncollected manually and brought to the laboratory. \n\n\n\nTABLE 1\nInitial soil chemical properties of Durian and Bungor series soil at site\n\n\n\n \n  Durian soil series Bungor soil series \nDepth (cm) 0-10 10-30 0-10 10-30 \nSoil Properties          \nOrganic C (%) 1.38 0.58 1.5 0.43 \nTotal N (%) 0.12 0.06 0.12 trace \nAvailable P (mg kg-1) 2.90 2.00 2.60 1.50 \n K (cmolc  kg-1) 0.16 0.08 0.10 0.06 \nCa (cmolc kg-1) 0.39 0.35 0.30 0.15 \nMg (cmolc kg-1) 0.23 0.09 0.27 0.08 \npH (water)  4.72 4.61 4.62 4.91 \n\n\n\n     Source: Amir Husni (1983)  \n\n\n\n\n\n\n\n\n78\n\n\n\nJeyanny Vijayanathan, Ahmad Zuhaidi Yahya , Adzmi Yaacob Amir Saaiffuddin Kassim \nand  Suhaimi Wan Chik\n\n\n\nAnalysis\nPrior to analysis, soil samples were air dried until constant weight before crushing \n\n\n\nsubjected to organic carbon analysis according to Walkey and Black method and \nsoil nitrogen was determined using Kjedahl digestion (Malaysian Standard 678 \n\n\n\nFRIM soil chemistry laboratory practice before grinding using a mechanical \ngrinder and passed through 1-mm sieve. Subsamples were put into a furnace \n\n\n\nand K, using an Inductively Coupled Plasma Optical Emission Spectrometer (ICP \n\n\n\ntest was used for means comparison using Statistical Analysis System version \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Chemical Properties \n\n\n\nA.mangium, \n-1.  Generally, pH ranged between 3.8 and 4.5 \n\n\n\ncarbon, nitrogen, available phosphorus and pH decreased with increasing soil \ndepth for most of the plots.\n\n\n\nnd\n\n\n\nproperties is due to many factors. The formation of new roots of the nitrogen \nnd rotation may have boosted the soil C and N content.  \n\n\n\nRoots play an important role in preserving carbon stocks below ground levels.  \n\n\n\nin situ after thinning, provided \nample feed for soil and plant microbial activities, leading to soil carbon stocks (\n\n\n\n\n\n\n\n\n79\n\n\n\nSoil Chemical Properties of Thinned Forest\n\n\n\nSo\nil \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nC\n, t\n\n\n\not\nal\n\n\n\n N\n, A\n\n\n\nva\nila\n\n\n\nbl\ne \n\n\n\nP,\n a\n\n\n\nnd\n p\n\n\n\nH\n o\n\n\n\nf n\nat\n\n\n\nur\nal\n\n\n\n fo\nre\n\n\n\nst\n a\n\n\n\nnd\n d\n\n\n\niff\ner\n\n\n\nen\nt a\n\n\n\nge\n c\n\n\n\nla\nss\n\n\n\nes\n o\n\n\n\nf A\nca\n\n\n\nci\na \n\n\n\nm\nan\n\n\n\ngi\num\n\n\n\n p\nla\n\n\n\nnt\nat\n\n\n\nio\nn \n\n\n\nat\n re\n\n\n\nse\nar\n\n\n\nch\n si\n\n\n\nte\n\n\n\nC\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn\nSo\n\n\n\nil \nde\n\n\n\npt\nh \n\n\n\n(c\nm\n\n\n\n)\nO\n\n\n\nrg\nan\n\n\n\nic\n C\n\n\n\n (%\n) \n\n\n\nTo\nta\n\n\n\nl N\n (%\n\n\n\n) \nA\n\n\n\nv.\n P\n\n\n\n (m\ng \n\n\n\nkg\n  ) \n\n\n\npH\n (w\n\n\n\nat\ner\n\n\n\n) \n0-\n\n\n\n5 \n \n\n\n\n5-\n10\n\n\n\n \n10\n\n\n\n-3\n0 \n\n\n\n \n0-\n\n\n\n5 \n \n\n\n\n5-\n10\n\n\n\n \n10\n\n\n\n-3\n0 \n\n\n\n  \n0-\n\n\n\n5 \n5-\n\n\n\n10\n \n\n\n\n10\n-3\n\n\n\n0 \n0-\n\n\n\n5 \n5-\n\n\n\n10\n \n\n\n\n10\n-3\n\n\n\n0 \nSi\n\n\n\nte\n \n\n\n\nN\nat\n\n\n\nur\nal\n\n\n\n F\nor\n\n\n\nes\nt (\n\n\n\nP1\n) \n\n\n\n0.\n74\n\n\n\n b\n \n\n\n\n0.\n79\n\n\n\n \n0.\n\n\n\n65\n \n\n\n\n \n0.\n\n\n\n08\n b\n\n\n\n \n0.\n\n\n\n06\n b\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n \n3.\n\n\n\n21\n \n\n\n\n1.\n46\n\n\n\n \n1.\n\n\n\n25\n a\n\n\n\n \n4.\n\n\n\n07\n \n\n\n\n4.\n21\n\n\n\n \n4.\n\n\n\n54\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n1s\n\n\n\nt r\not\n\n\n\nat\nio\n\n\n\nn \n(P\n\n\n\n2)\n \n\n\n\n2.\n41\n\n\n\n a\nb \n\n\n\n1.\n14\n\n\n\n \n0.\n\n\n\n92\n \n\n\n\n \n0.\n\n\n\n13\n a\n\n\n\nb \n0.\n\n\n\n08\n a\n\n\n\nb \n0.\n\n\n\n08\n \n\n\n\n \n2.\n\n\n\n17\n \n\n\n\n0.\n98\n\n\n\n \n0.\n\n\n\n37\n b\n\n\n\n \n4.\n\n\n\n36\n \n\n\n\n3.\n98\n\n\n\n \n4.\n\n\n\n18\n \n\n\n\n> \n20\n\n\n\n y\nea\n\n\n\nrs\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n2n\nd \n\n\n\nro\nta\n\n\n\ntio\nn \n\n\n\n(P\n3)\n\n\n\n \n1.\n\n\n\n83\n a\n\n\n\nb \n1.\n\n\n\n41\n \n\n\n\n1.\n08\n\n\n\n\n\n\n\n0.\n10\n\n\n\n a\nb \n\n\n\n 0.\n09\n\n\n\n a\nb \n\n\n\n0.\n07\n\n\n\n\n\n\n\n2.\n01\n\n\n\n \n1 .\n\n\n\n32\n \n\n\n\n1.\n03\n\n\n\n a\nb \n\n\n\n4.\n49\n\n\n\n \n4.\n\n\n\n32\n \n\n\n\n4.\n23\n\n\n\n \n4 \n\n\n\nye\nar\n\n\n\ns  \n 2n\n\n\n\nd \nro\n\n\n\nta\ntio\n\n\n\nn \n(P\n\n\n\n4)\n \n\n\n\n1.\n88\n\n\n\n a\nb \n\n\n\n0.\n74\n\n\n\n \n0.\n\n\n\n65\n \n\n\n\n \n0.\n\n\n\n13\n a\n\n\n\n 0\n.0\n\n\n\n8 \nab\n\n\n\n 0\n.0\n\n\n\n6 \n \n\n\n\n2.\n46\n\n\n\n \n1.\n\n\n\n31\n \n\n\n\n0.\n81\n\n\n\n a\nb \n\n\n\n3.\n82\n\n\n\n \n4.\n\n\n\n09\n \n\n\n\n4.\n19\n\n\n\n \n3-\n\n\n\n6 \nm\n\n\n\non\nth\n\n\n\ns \n 2n\n\n\n\nd \nro\n\n\n\nta\ntio\n\n\n\nn \n(P\n\n\n\n5)\n \n\n\n\n3.\n16\n\n\n\n a\n \n\n\n\n1.\n72\n\n\n\n \n0.\n\n\n\n92\n \n\n\n\n \n0.\n\n\n\n14\n a\n\n\n\n \n0.\n\n\n\n10\n a\n\n\n\n \n0.\n\n\n\n08\n \n\n\n\n \n1.\n\n\n\n77\n \n\n\n\n1.\n07\n\n\n\n \n0.\n\n\n\n38\n b\n\n\n\n \n3.\n\n\n\n98\n \n\n\n\n3.\n95\n\n\n\n \n4.\n\n\n\n16\n \n\n\n\n0-\n3 \n\n\n\nm\non\n\n\n\nth\ns \n\n\n\n SE\n \n\n\n\n0.\n28\n\n\n\n \n0.\n\n\n\n13\n \n\n\n\n0.\n06\n\n\n\n\n\n\n\n0.\n07\n\n\n\n \n0.\n\n\n\n00\n \n\n\n\n0.\n00\n\n\n\n\n\n\n\n0.\n25\n\n\n\n \n0.\n\n\n\n13\n \n\n\n\n0.\n10\n\n\n\n \n0.\n\n\n\n14\n \n\n\n\n0.\n05\n\n\n\n \n0.\n\n\n\n05\n \n\n\n\n F \nte\n\n\n\nst\n  \n\n\n\n* \nns\n\n\n\n \nns\n\n\n\n\n\n\n\n**\n \n\n\n\n**\n \n\n\n\nns\n \n\n\n\n  \nns\n\n\n\n \nns\n\n\n\n \n**\n\n\n\n \nns\n\n\n\n \nns\n\n\n\n \nns\n\n\n\n\n\n\n\nSE\n: \n\n\n\nst\nan\n\n\n\nda\nrd\n\n\n\n e\nrr\n\n\n\nor\n  *\n\n\n\n - s\nig\n\n\n\nni\nfic\n\n\n\nan\nt \n\n\n\nat\n 0\n\n\n\n.0\n5 \n\n\n\nle\nve\n\n\n\nl \n *\n\n\n\n* -\n s\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\nt \nat\n\n\n\n 0\n.0\n\n\n\n1 \nle\n\n\n\nve\nl  \n\n\n\n  \nns\n\n\n\n \u2013\n n\n\n\n\not\n s\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\nt\n \n\n\n\nIn\ndi\n\n\n\nvi\ndu\n\n\n\nal\n v\n\n\n\nal\nue\n\n\n\ns \nin\n\n\n\n c\nol\n\n\n\num\nns\n\n\n\n w\nith\n\n\n\n t\nhe\n\n\n\n s\nam\n\n\n\ne \nle\n\n\n\ntte\nrs\n\n\n\n a\nre\n\n\n\n n\not\n\n\n\n s\nig\n\n\n\nni\nfic\n\n\n\nan\ntly\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nt \nat\n\n\n\n p\n \n\n\n\n 0\n.0\n\n\n\n5 \nac\n\n\n\nco\nrd\n\n\n\nin\ng \n\n\n\nto\n T\n\n\n\nuk\ney\n\n\n\n te\nst\n\n\n\n1\n\n\n\n\n\n\n\n\n(Binkley et al.\nlevels of C in natural forest and treated plots would be pH, microbial population \nand organo-mineral interactions (Motavalli et al\n\n\n\nThe increased soil N in P4 and P5 compared to P1 may be due to the nature \nAcacia (Power et al\n\n\n\nthe soil as well as residues from previous fertilizer applications. Elevated organic \nnd rotation is also due to the leaching effect after thinning \n\n\n\nactivities. Carbon and N which constitutes an integral part of organic matter \nnaturally undergoes leaching due to microbial activity, temperature and soil \nrespiration (Andersson et al\n\n\n\nof water and the increased solute levels in the percolating water (Brouwer and \n\n\n\nThinning also caused space widening, allowing ample penetration of sunlight to \nassist decomposition processes that increase C and N stocks in soil. Herdiyanti \n\n\n\nA. mangium \nplantation which underwent thinning. \n\n\n\net al\n\n\n\ntropical soils.  All the plots showed very low available P content in our study, \n\n\n\n3 mg kg -1 in Kemasul Forest Reserve. The low P availability correlates with the \nacidic pH found in Kemasul Forest Reserve, which is fairly common for tropical \n\n\n\ndirect transfer of P from the soil to A. mangium trees via mycorrhizal relationships \n\n\n\nthe possibilities of P residual from previous fertilization. \n\n\n\nLitter Chemical Composition\n\n\n\nP3 and P5. The values of leaf litter for all plots varied from 31-34 % for organic \nC and approximately 1 % for N, K and Ca.  The values were less than 1% for P \nand Mg.  \n\n\n\nElevated levels of N, P, Ca and K for plots which underwent thinning (P3 - \n\n\n\nJeyanny Vijayanathan, Ahmad Zuhaidi Yahya , Adzmi Yaacob Amir Saaiffuddin Kassim \nand  Suhaimi Wan Chik\n\n\n\n\n\n\n\n\n81\n\n\n\nsoil microbes (Roig et al.\n\n\n\ntree stands over the years. Magnesium levels were unusually high in P1 and P4 \nand this could be due the inherent properties of leaf litter in the respective plots. \nHowever, our results concur with Wan Rasidah et al.\nyear old A. mangium stands.  \n\n\n\nThinning practices in Acacia mangium plantation increases carbon and \n\n\n\nreduce possible competition between tree roots and saprophytic microorganisms \nfor water and nutrients and simultaneously enhance mineralisation (Vesterdal et \nal\nthe availability of P. In order to maintain good and healthy stands, P fertilizer \napplications may be carried out to replenish the soil nutrient status of the \nplantation.\n\n\n\nthe thinned plots and the 1st rotation plot serve as a nutrient reservoir that can \nslowly incorporate into the mineral soil by decomposition process to enhance \n\n\n\net al.\n\n\n\nTABLE 3\nChemical composition of litter from natural forest and different age classes of \n\n\n\nAcacia mangium plantation at research site\n\n\n\nSoil Chemical Properties of Thinned Forest\n\n\n\nConcentration Org. C Total N Total P Total Ca Total Mg  Total K  \nSite % \n\n\n\nNatural Forest (P1) 33.12 ab 1.09 d 0.02 b 0.99 b 0.28 a 0.21 a \n  \u2020 (0.50) (0.01) (0.01) (0.01) (0.05) (0.02) \n       \n1st rotation  32.40 ab 1.59 b 0.02 b 0.40 d 0.23 b 0.32 a \n> 20 years (P2) (0.08) (0.02) (0.01) (0.02) (0.01) (0.04) \n       \n2nd rotation  34.24 a 1.29 c 0.01 c 1.25 a 0.21 c 0.52 a \n4 years (P3) (0.06) (0.00) (0.01) (0.01) (0.01) (0.01) \n \n2nd rotation  32.38 ab 1.73 a 0.03 a 0.71 c 0.26 a 0.26 a  \n3-6 months (P4) (0.44) (0.02) (0.01) (0.01) (0.01) (0.02) \n \n2nd rotation  31.38 b 1.31 c 0.02 b 0.72 c 0.23 b 1.17 b \n0-3 months (P5) (0.92) (0.01) (0.01) (0.02) (0.01) (0.57) \nIndividual values in columns with the same letters are not significantly different at p  0.05 according to \nTukey test \n\n\n\n \u2020 Values in parentheses represent standard error \n\n\n\n\n\n\n\n\nCONCLUSION\nThe impact of thinning on soil chemical properties, especially C and N, can only \n\n\n\nnd rotation A. mangium\nseedlings compared to the other plots.  Generally, low levels of P were found \nin the 1st rotation Acacia mangium\nwhich underwent thinning. Soil C, N, P decreased with increasing soil depths. \n\n\n\nlower compared to other plots and the concentration of N, P and K were higher in \n\n\n\nforest. Plots which underwent thinning showed elevated levels of N, P, K and Ca \ndue to the larger amount of organic debris in these plots after thinning activities. \n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to thank the Director General of FRIM for the research \n\n\n\nto the staff of Soil Survey Unit and Forest Plantation Programme, FRIM for data \n\n\n\nREFERENCES\nAcacia mangium/Acacia hybrid. In A Manual for Forest \n\n\n\nPlantation Establishment in Malaysia\nResearch Institute Malaysia, Kepong. \n\n\n\nAmir Husni, M.S. 1983. 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Schulte and D.Ruhiyat, pp.133-136 New York: \nSpringer.  \n\n\n\nZ\navailability in Malaysian soils. In Proceedings of the Seminar on Chemistry \n& Fertility of Malaysian Soils,\nMalaysian Society of Soil Science & Universiti Pertanian Malaysia. Serdang, \nMalaysia.\n\n\n\nSoil Chemical Properties of Thinned Forest\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: -astaraei@um.ac.ir \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 67-85 (2021)                          Malaysian Society of Soil Science\n\n\n\nImpact of Pseudomonas putida Inoculation on Alleviating \nMercury Stress in Turnip Planted on a Saline Soil \n\n\n\nAlsaleh A.E., A.R. Astaraei A.R.*, Emami H., Lakzian A.\n\n\n\nDepartment of Soil Science, Faculty of Agriculture, Ferdowsi University of \nMashhad, Iran\n\n\n\nABSTRACT\nIncreasing mercury (Hg) accumulation in soil deteriorates cultivated soils, \ndecreases growth and yield of plants, and contaminates the food chain. Therefore, \nthe main objective of this study was to evaluate the effects of three Hg levels (0, \n75 and 150 mg. l-1) in a saline soil with and without Pseudomonas putida (PTCC \n1696) on the growth parameters of the turnip plant. Pots were filled with 3 kg of \nsaline soil (EC: 8.65 dS.m-1) and inoculated with Pseudomonas putida. Turnip \n(Brassica rapa L.) seeds were sown in all the pots. After 10 days, the soils were \ntreated with either 0, 75 or 150 mg. l-1 of Hg until the final Hg concentrations \nin soil were either 0, 75 or 150 mg/kg. The pots were arranged in a completely \nrandomised design in a greenhouse. After 60 days of planting, soluble sugars, \nchlorophyll a (Chla), chlorophyll b (Chlb) and catalase enzymes (CAT) in leaf \nsamples were determined while fresh and dry weights of roots and shoots and Hg \nconcentrations in turnip were determined 70 days after sowing. The results showed \nthat inoculated soils produced plants with higher soluble sugars, Chla, Chlb; (are \nChla and Chlb the same as Chla and Chlb) fresh and dry weights of roots and shoots \nwere also significantly higher perhaps due to improved nutrients uptake from the \nstressed soil. At the same time, CAT and total Hg concentrations in the roots and \nshoots were reduced probably due to efficient nutrient uptake even when Hg was \npresent. The addition of Pseudomonas putida to saline soil contaminated with \nHg alleviated salinity and Hg toxicity stress of the turnip plants. In conclusion, \nHg polluted saline soil inoculated with Pseudomonas putida (PTCC 1696) was \nefficient in increasing the quality and quantity of turnip plants and improving soil \nhealth compared to the non-inoculated soil. \n\n\n\nKeyword:    Mercury, salinity, plant photosynthetic pigments, bacterium \n       inoculation.\n\n\n\nINTRODUCTION\nHeavy metals, namely mercury (Hg), copper (Cu), chromium (Cr), zinc (Zn), \ncadmium (Cd), and lead (Pb) are mutagenic, toxic to cells, and induce carcinogenic \nchanges in human beings and other organisms (Naif et al. 2019). Mercury (Hg) \noccurs naturally in the environment in different chemical species with different \nsolubility, reactivity and toxicity, causing differing impacts on ecosystems and \nhuman health (UNEP 2002). Hg is a pollutant that has been found to have serious \neffects on both environmental and human health, mainly in developing countries \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202168\n\n\n\n(Nagajyoti et al. 2010). Unlike many other pollutants that are simply removed \nfrom the environment (Xu et al. 2012), Hg metal is considered to be a global \npollutant as it accumulates in natural ecosystems and has harmful impacts on \nhumans and wildlife (Driscoll et al. 2013; Richardson et al. 2013). Driscoll et al. \n(2013) reported that approximately 45% of the atmospheric Hg is deposited on \nthe earth\u2019s ecosystems with soils being the greatest reservoir accounting for up to \n75% of the Hg stocked in the biosphere (Mason and Sheu 2002). Hg particularly \naccumulates in the uppermost soil layers due to its high affinity for the thiol groups \nto soil organic matter (OM) (Skyllberg et al. 2006). Therefore, Hg predestination \nin soils is mainly a function of organic carbon (OC) dynamics (Smith-Downey et \nal. 2010). \n Elimination of contamination by heavy metals such as Hg is difficult \nin a region because the metals cannot be transformed into harmless forms. \nMany methods have been applied to eliminate heavy metals from the aquatic \nenvironment. The common methods include chemical oxidation, chemical \nprecipitation, reduction, filtration, electrochemical treatment, and extraction \nusing solvents (Mortaheb et al. 2009). These traditional methods have various \ndrawbacks including the unpredictable removal of heavy metals and the huge \namount of sludge generated which is highly toxic. \n Heavy metal removal by means of bioremediation is an alternate way, \nparticularly by applying recombinant and naturally available indigenous \nmicroorganisms for the effective removal of toxic substances (Mahajan and \nKaushal 2018). There is a need for eco-friendly remedial technologies to address \nglobal environmental degradation resulting mainly from anthropogenic activities \n(De-Bashan et al. 2012). Bioremediation is environment friendly and is cheaper \nthan chemical methods. Also, the dead biomass of bacteria or living microbes \ncan remove heavy metals through the bioaccumulation and biosorption process \n(Joutey et al. 2015). Biological systems have been adapted for removal of toxic \nheavy metals from petrochemical wastewaters (Zeroul et al. 2001). Bioremoval, \na biological system for elimination of metal ions from polluted environments, has \nthe possibility of achieving greater performance with lower cost than treatments \nthat are not biological (Kondoh et al. 1998). \n Developments in biotechnology suggest that bacteria and fungi can remove \nheavy metals from aquatic solutions by adsorption (Saglam et al. 1999). Many \nspecies of bacteria are adapted to resist toxic effects of metals, so microbial \ndiversity remains high (Iwasaki et al., 2009; Gough and Stahl 2011; Berg et al. \n2012; Stanaway et al., 2012). \n Pseudomonas are ubiquitous soil and water microorganisms dwelling \nin different environments and have diverse lifestyles. Strains of Pseudomonas \nputida are common inhabitants of soil and are crucial in recycling of organic \nmatter in nature; they possess bioremediation potential as they carry genes to \ncope with natural and xenobiotic chemicals (Segura et al. 2009). Green-Ruiz \n(2006) observed high levels of passive biosorption of heavy metal ions for \nnonviable cells of Pseudomonas putida, Brevibacterium sp. and Bacillus sp. The \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 69\n\n\n\nmicrobes synthesise various metabolites to degrade the complex wastes and also \ndevelop the ability to survive in the presence of various toxic heavy metals in \ntheir environment (Tang et al. 2018). The important advantage of this process is \nhigh sorption ability, very low operating cost, potent biosorbent revival, and the \npossibility of metal recovery (Goksungur et al. 2005). Therefore, the aims of this \nresearch were to study the impact of Pseudomonas putida in alleviating Hg and \nsalinity stress in turnip plants planted in Hg contaminated saline soils.\n\n\n\nMATERIALS AND METHODS\nSoil Sampling and Chemical Analyses\nIran\u2019s land area of 65 million hectares covers both arid and semi-arid regions.  Of \nthis, about 16 - 23 million hectares of land have saline and sodic/alkaline soils, \nmostly calcareous in nature. These soils are mostly high in salts, pH, and alkaline \ncarbonates and are low in organic matter (OM) and microorganism population \nand activity. Hence they do not have the essential nutrient elements needed for \nagricultural crop production. As most of the heavy metal pollution studies at \npresent are conducted on ordinary soils (without any stresses), we decided to study \nthe harmful effects of Hg levels in a highly stressed saline soil (high salinity, with \ncalcareous nature, low OM, low microbial population and activity, and mostly \ndeficient in plant nutrient elements), which was a novel approach to studying a \nsoil, in this respect. \n Soil from a surface layer (0-15 cm depth) was collected from the Chenaran \nregion (mostly having natural different saline soils) in Khrasan Razavi state. In \nthe laboratory, the soil was air-dried in the shade and sieved (\u2264 2 mm). The visible \nplant materials in the sieved soil were then removed. The physico-chemical \ncharacteristics of the soil were determined based on international standard \nmethods as listed in Table 1.\n Pots containing 3 kg saline soil (EC: 8.65 dS.m-1) were inoculated with \nPseudomonas putida. and turnip (Brassica rapa L.) seeds were sown in the pots. \nAfter 10 days, the soil was treated with three levels of Hg (0, 75 and 150 mg. l-1) \n\n\n\nTABLE 1\n Chemical properties of soil at the start of experiment\n\n\n\n4 \n \n\n\n\nlow OM, low microbial population and activity, and mostly deficient in plant nutrient elements), \n\n\n\nwhich was a novel approach to studying a soil, in this respect.  \n\n\n\n\n\n\n\nSoil from a surface layer (0\u201315 cm depth) was collected from the Chenaran region (mostly \n\n\n\nhaving natural different saline soils) in Khrasan Razavi state. In the laboratory, the soil was air-\n\n\n\ndried in the shade and sieved (\u2264 2 mm). The visible plant materials in the sieved soil were then \n\n\n\nremoved. The physico-chemical characteristics of the soil were determined based on \n\n\n\ninternational standard methods as listed in Table 1. \n\n\n\n \nTABLE 1 \n\n\n\n Chemical properties of soil at the start of experiment \n\n\n\nParameters Unit value \nTexture - clay loam \nsand % 26 \nsilt % 46 \nclay % 28 \npH - 8.96 \nEC dS m-1 8.65 \nNa+ meq.l-1 34.8 \nCa+2 meq.l-1 29 \nMg+2 meq.l-1 22 \nHCO3\n\n\n\n-2 meq.l-1 2.5 \nCl- meq.l-1 37 \nSO4\n\n\n\n-2 meq.l-1 45.9 \n \n\n\n\nPots containing 3 kg saline soil (EC: 8.65 dS.m-1) were inoculated with Pseudomonas putida. \n\n\n\nand turnip (Brassica rapa L.) seeds were sown in the pots. After 10 days, the soil was treated \n\n\n\nwith three levels of Hg (0, 75 and 150 mg. l-1) until the final concentrations of Hg were equal \n\n\n\nto 0, 75 and 150 mg/kg of soil. This experiment was conducted as a completely randomised \n\n\n\ndesign (factorial) with three replications (3 Hg levels* 2 bacteria inoculations* 3 replications) \n\n\n\nunder greenhouse conditions. \n\n\n\n\n\n\n\nAgar Preparation for Increasing Microbial Growth and Population \n\n\n\nPreparations for nutrient agar to the stock culture in test tubes were similar to the preparation of \n\n\n\nthe growth media. The nutrient agar consisted of 5% of pepton from 3% of meat extract and 12% \n\n\n\nof agar. The nutrient agar was purchased from Merck Sdn. Bhd. The nutrient for the stock \n\n\n\nculture was prepared by suspending 20 g in 1 litre of demineralised water and heating in a \n\n\n\nboiling water bath or in a current of steam and autoclaving about 15 min at 121\u02daC (Azoddein et \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202170\n\n\n\nuntil the final concentrations of Hg were equal to 0, 75 and 150 mg/kg of soil. \nThis experiment was conducted as a completely randomised design (factorial) \nwith three replications (3 Hg levels* 2 bacteria inoculations* 3 replications) under \ngreenhouse conditions.\n\n\n\nAgar Preparation for Increasing Microbial Growth and Population\nPreparations for nutrient agar to the stock culture in test tubes were similar to the \npreparation of the growth media. The nutrient agar consisted of 5% of pepton \nfrom 3% of meat extract and 12% of agar. The nutrient agar was purchased from \nMerck Sdn. Bhd. The nutrient for the stock culture was prepared by suspending \n20 g in 1 litre of demineralised water and heating in a boiling water bath or \nin a current of steam and autoclaving about 15 min at 121oC (Azoddein et al. \n2015). Pseudomonas putida (PTCC) 1696) was obtained from Iranian Microbial \nResearch Center in Tehran (due to its highly effective remediation of many heavy \nmetals).\n\n\n\nSoil Treatment and Planting\nIn the greenhouse experiment, turnip seeds (Brassica rapa L.) were sown in pots \nfilled with 3 kg of saline soil (diameter 23 cm, height 21.5 cm), and irrigated \nwith tap water. Ten days after germination, the soil in the pots was treated with \n200 ml of mercury chloride solutions of Hg treatments (0, 75 and 150 Hg mg. \nl-1). Simultaneously, the soil in the pots was treated with 50 ml of Pseudomonas \nputida 10 days after planting (plants at three leaf stage). Sixty days after planting, \nleaf samples were collected and investigated for photosynthetic pigments, soluble \nsugars, catalase enzymes and chlorophylls a and b. Plants were harvested 70 \ndays after sowing and fresh and dry weights of roots and shoots and mercury \nconcentration in roots and shoots of turnip were determined. Soil in each pot \nafter plant harvest was collected and determined for total and available mercury \nconcentration.\n\n\n\nMeasurement of Plant Growth and Physiological Parameters\nSixty days after planting, fresh leaf samples were taken and used for photosynthetic \npigments. Leaf chlorophyll a, b was determined (Lichtenthaler 1987). Soluble \nsugars were determined by the method of Omokolo et al. (1996). Catalase \nenzymes (CAT) were determined by the method of Chance and Maehly (1955). \nMercury concentrations in root and shoot of turnip were also determined (Greger \net al. 2005).\n\n\n\nDetermination of Mercury in Soil and Plant Materials\nSoil samples (0.5g) were treated with 12 ml of Aqua Regia (HNO3: HCl, 1:3) \nsolution. The mixture was heated at a low temperature initially for 1 h, to \nwhich was added 20 ml of 2 % HNO3. The mixture was then digested at a high \ntemperature for 30 min and subsequently diluted with 25 ml of 2 % HNO3 and \nfiltered with Whatman No.42 filter paper. To measure Hg, the filtrated solution \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 71\n\n\n\nwas analysed by ICP-OES (SPECTRO ARCOS Model76004SSS Germany) \n(UNEP/IAEA, UNEP 1985). Dried plant samples (0.5 g) were digested with 5 \nml of diacid (HNO3:HCIO4 1:3). The mixture was heated at a low temperature \nfor 1 h, and 3 ml of diacid was added. The sample was diluted with 50 ml of 2 \n% HNO3 and filtered with Whatman No.42 filter paper. The concentration of Hg \nwas measured by ICP-OES. (SPECTRO ARCOS Model 76004SSS Germany) \n(Greger et al. 2005).\n\n\n\nStatistical Analysis\nThe experiment was performed as a completely randomised design (factorial) \nwith three replications, and included 18 pots for treatment at three levels of Hg \n(0, 75 and 150 Hg mg. l-1) in which the effect of bacterium inoculation and no \ninoculation was investigated under greenhouse conditions. Data were subjected to \nstatistical analysis using JMP8 software. The significant differences (P<0.05, and \nP<0.01) between treatments and control were statistically evaluated by Student\u2019s \nt-test methods.\n\n\n\nRESULTS AND DISCUSSION\nEffect of Applied Hg on Turnip Growth under Soil Saline Stress\nThe effects of single Hg treatment on turnip growth (Table 2), showed that the \nshoot fresh weight, shoot dry weight, root fresh weight and root dry weight of \nturnip significantly decreased at Hg levels of 75 and 150 mg. l-1 in comparison \nwith the control (P<0.01). Earlier studies have indicated that Hg at higher \nconcentrations is highly phytotoxic to cells and induced visible injuries and \nphysiological alterations (Zhou et al. 2007). The reduction of plant growth caused \nby mercury was observed for Triticum aestivum and other plant species such as \ntomato (Cho and Park 2000) and tobacco (Suszcynsky and Shann 1995). Reduced \ngrowth was also indicated by a high dry weight to fresh weight ratio in plants \ngrown with 10 and 25 \u03bcM Hg. An increased dry weight to fresh weight ratio may \nbe a sign of reduced water uptake, which in turn causes inhibited elongation and \nenlargement of cells leading to reduced growth (Mukherji and Mukherji 1979).\n\n\n\nEffect of Applied P. putida on Turnip Growth under Soil Saline Stress\nSingle P. putida treatment on turnip growth (Table 3) resulted in a significant \nincrease in the shoot and root fresh weights and in the shoot and root dry \nweights of turnip compared to the control (P<0.01). Plant growth-promoting \n\n\n\nTABLE 2\nEffect of Hg levels on turnip growth\n\n\n\n6 \n \n\n\n\nStatistical Analysis \n \n\n\n\nThe experiment was performed as a completely randomised design (factorial) with three \n\n\n\nreplications, and included 18 pots for treatment at three levels of Hg (0, 75 and 150 Hg mg. l-1) \n\n\n\nin which the effect of bacterium inoculation and no inoculation was investigated under \n\n\n\ngreenhouse conditions. Data were subjected to statistical analysis using JMP8 software. The \n\n\n\nsignificant differences (P<0.05, and P<0.01) between treatments and control were statistically \n\n\n\nevaluated by Student\u2019s t-test methods. \n\n\n\nRESULTS AND DISCUSSION \n\n\n\nEffect of Applied Hg on Turnip Growth under Soil Saline Stress \n\n\n\nThe effects of single Hg treatment on turnip growth (Table 2), showed that the shoot fresh \n\n\n\nweight, shoot dry weight, root fresh weight and root dry weight of turnip significantly \n\n\n\ndecreased at Hg levels of 75 and 150 mg. l-1 in comparison with the control (P<0.01). Earlier \n\n\n\nstudies have indicated that Hg at higher concentrations is highly phytotoxic to cells and \n\n\n\ninduced visible injuries and physiological alterations (Zhou et al. 2007). The reduction of \n\n\n\nplant growth caused by mercury was observed for Triticum aestivum and other plant species \n\n\n\nsuch as tomato (Cho and Park 2000) and tobacco (Suszcynsky and Shann 1995). Reduced \n\n\n\ngrowth was also indicated by a high dry weight to fresh weight ratio in plants grown with 10 \n\n\n\nand 25 \u03bcM Hg. An increased dry weight to fresh weight ratio may be a sign of reduced water \n\n\n\nuptake, which in turn causes inhibited elongation and enlargement of cells leading to reduced \n\n\n\ngrowth (Mukherji and Mukherji 1979). \n \n\n\n\nTABLE 2 \nEffect of Hg levels on turnip growth \n\n\n\nValues are the mean of three replicates and different letters within columns indicate significant differences P<0.01 by Student\u2019s t-test compared to control. \n       \n \n\n\n\nEffect of Applied P. putida on Turnip Growth under Soil Saline Stress \n\n\n\nSingle P. putida treatment on turnip growth (Table 3) resulted in a significant increase in the \n\n\n\nshoot and root fresh weights and in the shoot and root dry weights of turnip compared to the \n\n\n\ncontrol (P<0.01). Plant growth-promoting rhizobacteria (PGPR) within genera that are known \n\n\n\nto stimulate growth of plants are Azospirillum, Azotobacter, Bacillus, Enterobacter, and \n\n\n\nPseudomonas. The latter has been studied in recent years for its beneficial effects when used \n\n\n\nHg levels \n(mg .l-1) \n\n\n\nShoot fresh weight \n(g pot-1)  \n\n\n\nShoot dry weight \n(g pot-1) \n\n\n\nRoot fresh weight \n(g pot-1) \n\n\n\nRoot dry weight \n(g pot-1) \n\n\n\n0 5.65a 2.26a 18.76a 4.47c \n75 3.28b 1.97b 16.65b 3.56b \n\n\n\n150 1.58c 1.55c 13.1c 2.96a \n\n\n\nValues are the mean of three replicates and different letters within columns indicate \nsignificant differences P<0.01 by Student\u2019s t-test compared to control.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202172\n\n\n\nrhizobacteria (PGPR) within genera that are known to stimulate growth of plants \nare Azospirillum, Azotobacter, Bacillus, Enterobacter, and Pseudomonas. The \nlatter has been studied in recent years for its beneficial effects when used as \norganic fertiliser or as an agent for biological control of pathogens (Lugtenberg \nand Kamilova 2009). Furthermore, the positive response of the plants to the \ninoculation of the different strains of P. putida for both cultivars and in particular \nPrestige may be due to a hormonal effect of the rhizobacteria, whether produced \ndirectly via an auxin and/or gibberellin (Yao et al. 2010). Azotobacter spp. and \nPseudomonas spp. are the most important bacteria that increase soil mineral \nelements with the production of matters increased the dry matter accumulation, \nnumber of nodules, seed yield and grain protein by 71%, 86%, 36% and 16%, \nrespectively, compared to noninoculated plants. Nitrogen in roots and shoots \nincreased by 46% and 40%, respectively, at 136 mg Cr/kg that regulate growth \nand development and yield of plants (Hayat et al. 2010). Pseudomonas bacteria \nare able to produce the hormones, auxin and gibberellic, as well as vitamins. The \nbacteria lead to increased nutrients uptake resulting in increased plant weight and \nyield. This increase in plant growth and yield by growth-promoting rhizobacteria \nis due to its ability to produce siderophore and increase the level of iron in the \nplant (Bhattacharyya and Jha 2012).\n\n\n\nTABLE 3\nEffect of P. putida levels on turnip growth\n\n\n\n7 \n \n\n\n\nas organic fertiliser or as an agent for biological control of pathogens (Lugtenberg and \n\n\n\nKamilova 2009). Furthermore, the positive response of the plants to the inoculation of the \n\n\n\ndifferent strains of P. putida for both cultivars and in particular Prestige may be due to a \n\n\n\nhormonal effect of the rhizobacteria, whether produced directly via an auxin and/or \n\n\n\ngibberellin (Yao et al. 2010). Azotobacter spp. and Pseudomonas spp. are the most important \n\n\n\nbacteria that increase soil mineral elements with the production of matters increased the dry \n\n\n\nmatter accumulation, number of nodules, seed yield and grain protein by 71%, 86%, 36% and \n\n\n\n16%, respectively, compared to noninoculated plants. Nitrogen in roots and shoots increased \n\n\n\nby 46% and 40%, respectively, at 136 mg Cr/kg that regulate growth and development and \n\n\n\nyield of plants (Hayat et al. 2010). Pseudomonas bacteria are able to produce the hormones, \n\n\n\nauxin and gibberellic, as well as vitamins. The bacteria lead to increased nutrients uptake \n\n\n\nresulting in increased plant weight and yield. This increase in plant growth and yield by \n\n\n\ngrowth-promoting rhizobacteria is due to its ability to produce siderophore and increase the \n\n\n\nlevel of iron in the plant (Bhattacharyya and Jha 2012). \n \n\n\n\nTABLE 3 \nEffect of P. putida levels on turnip growth \n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate significant differences P < 0.05 and P<0.01 by Student\u2019s t-test when \ncompared to control. \n \n\n\n\nInteraction Effects of Hg and P. putida Treatments on Turnip Growth under Soil Saline \n\n\n\nStress \n\n\n\nInteraction effects of Hg and P. putida treatments on turnip growth (Table 4), showed that the \n\n\n\nshoot and root fresh weights, shoot and root dry weights of turnip significantly increased at Hg \n\n\n\nlevels of 75 and 150 mg. l-1 with P. putida in comparison with no P. putida (control) (P<0.01). \n\n\n\nEnhanced plant biomass yield in treatments with plant growth-promoting bacteria (PGPB) is \n\n\n\nsupported by previous studies on PGPB inoculation in the presence of organic toxicants and \n\n\n\nmetals (Gurska et al. 2009). In the presence of copper, Pseudomonas spp. enhanced the growth \n\n\n\nof canola and common reed (Gurska et al. 2009). Pseudomonas putida UW4 inoculation \n\n\n\nimproved growth of cucumber (Cucumis sativus) (Gamalero et al. 2010) and canola under \n\n\n\nsalinity stress (Cheng et al. 2012). \n\n\n\nTABLE 4 \nInteraction effect of P. putida levels on turnip growth under Hg levels stress \n\n\n\nP. putida \nlevels \n\n\n\nShoot fresh weight \n(g pot-1)  \n\n\n\nShoot dry weight \n(g pot-1) \n\n\n\nRoot fresh weight \n(g pot-1) \n\n\n\nRoot dry weight \n(g pot-1) \n\n\n\nwithout  \n P. putida   2.83 b 1. 6 b 15.98 b 3.59 b \n\n\n\nwith  \nP.putida 4.17a 2.25a 16.36a 3.74a \n\n\n\nP. putida Hg levels Shoot fresh Shoot dry weight Root fresh weight   Root dry \n\n\n\nNotes: Values are the mean of three replicates and different letters within columns \nindicate significant differences P < 0.05 and P<0.01 by Student\u2019s t-test when compared to \ncontrol.\n\n\n\nInteraction Effects of Hg and P. putida Treatments on Turnip Growth under Soil \nSaline Stress\nInteraction effects of Hg and P. putida treatments on turnip growth (Table 4), \nshowed that the shoot and root fresh weights, shoot and root dry weights of \nturnip significantly increased at Hg levels of 75 and 150 mg. l-1 with P. putida in \ncomparison with no P. putida (control) (P<0.01). Enhanced plant biomass yield in \ntreatments with plant growth-promoting bacteria (PGPB) is supported by previous \nstudies on PGPB inoculation in the presence of organic toxicants and metals \n(Gurska et al. 2009). In the presence of copper, Pseudomonas spp. enhanced the \ngrowth of canola and common reed (Gurska et al. 2009). Pseudomonas putida \nUW4 inoculation improved growth of cucumber (Cucumis sativus) (Gamalero et \nal. 2010) and canola under salinity stress (Cheng et al. 2012).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 73\n\n\n\n8 \n \n\n\n\n\n\n\n\nTABLE 4 \nInteraction effect of P. putida levels on turnip growth under Hg levels stress \n\n\n\n\n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate significant differences P<0.05 and P<0.01 by Student\u2019s t-test \ncompared to control. \n \n\n\n\n\n\n\n\nIn general, mercury phytotoxicity exerted some effects on the biomass of the plant, while \n\n\n\ninhibiting plant growth and showing long-terms impacts on the fertility of the soil (Sahi et al. \n\n\n\n2006). This study noted that  P. putida had a positive impact on turnip growth parameters. In \n\n\n\ngeneral, the analysis of variance (Table 8) with respect to the shoot and root fresh and dry \n\n\n\nweights results obtained under the P. putida effect showed that both the shoot and root fresh \n\n\n\nand dry weights were significant (P<0.01) respectively. In this experiment, with p. putida \n\n\n\ninoculation, there was a  significant increase in the shoot (47.35%) and root fresh (2.37%) and \n\n\n\nshoot (40.62%) and root (4.17%) dry weights. In general, inoculation of bacteria under Hg \n\n\n\nstress promoted plant growth and biomass. The increased biomass can be attributed to \n\n\n\nabsorption of nutrients such as nitrogen and phosphorus due to the increase in root \n\n\n\ndevelopment (Goenadi et al. 2000). As indicated by Azoddein et al. (2015), the mercury was \n\n\n\nremoved and the percentage of reduction was about 89% for two days. This result indicates \n\n\n\nthat mercury Hg2+ is volatile to Hg0. The final value of mercury concentration was about \n\n\n\n0.001 mg l-1. The safety limit in wastewater is about 0.005 mg l-1. So, the results of our study \n\n\n\nproved that mercury can be removed with high efficiency using P. putida, thus improving the \n\n\n\ngrowth of the plant in a saline soil. \n\n\n\n\n\n\n\nEffect of Applied Hg levels on Mercury Concentration of Turnip under Soil Saline Stress \n\n\n\nHg treatment (Figures 1 and 2) showed that mercury concentration in shoot and root \n\n\n\nsignificantly increased at Hg levels of 75 and 150 mg. l-1 respectively in comparison with the \n\n\n\ncontrol (P<0.01). Hg accumulation, both in leaves and roots of the treated turnip plants \n\n\n\nincreased with increased concentration of HgCl2. There was a comparatively higher amount of \n\n\n\nHg in roots than in leaves. The level of Hg found in 7-day old leaves with 2.5, 5, 10 and 25 \u03bc M \n\n\n\nHgCl2 treated plants were 2.32, 5.39, 14.72 and 41.32 mg Kg\u22121 DW respectively. In roots of \n\n\n\nP. putida \nlevels \n\n\n\nHg levels \n(mg .l-1) \n\n\n\nShoot fresh \nweight \n\n\n\n(g pot-1) \n\n\n\nShoot dry weight \n(g pot-1) \n\n\n\nRoot fresh weight \n(g pot-1) \n\n\n\n  Root dry \nweight \n\n\n\n(g pot-1) \n 0 4.4 b 1.92 d 16.74 a 4.12  b \n\n\n\nWithout P. putida 75 2.9c 1.8e 15.41  b 3.74 c \n 150 1.2d 1.76f 13.00c 2.91e \n 0 6.9a 2.6a 18.98 a 4.83 a \n\n\n\nWith P. putida 75 3.66 bc 2.14 b 16.9  b 3.39d \n 150 1.37d 2.01c 13.2c 3.01e \n\n\n\nTABLE 4\nInteraction effect of P. putida levels on turnip growth under Hg levels stress\n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate \nsignificant differences P<0.05 and P<0.01 by Student\u2019s t-test compared to control.\n\n\n\nIn general, mercury phytotoxicity exerted some effects on the biomass of the plant, \nwhile inhibiting plant growth and showing long-terms impacts on the fertility of \nthe soil (Sahi et al. 2006). This study noted that  P. putida had a positive impact \non turnip growth parameters. In general, the analysis of variance (Table 8) with \nrespect to the shoot and root fresh and dry weights results obtained under the P. \nputida effect showed that both the shoot and root fresh and dry weights were \nsignificant (P<0.01) respectively. In this experiment, with p. putida inoculation, \nthere was a  significant increase in the shoot (47.35%) and root fresh (2.37%) and \nshoot (40.62%) and root (4.17%) dry weights. In general, inoculation of bacteria \nunder Hg stress promoted plant growth and biomass. The increased biomass can \nbe attributed to absorption of nutrients such as nitrogen and phosphorus due to the \nincrease in root development (Goenadi et al. 2000). As indicated by Azoddein et \nal. (2015), the mercury was removed and the percentage of reduction was about \n89% for two days. This result indicates that mercury Hg2+ is volatile to Hg0. The \nfinal value of mercury concentration was about 0.001 mg l-1. The safety limit in \nwastewater is about 0.005 mg l-1. So, the results of our study proved that mercury \ncan be removed with high efficiency using P. putida, thus improving the growth \nof the plant in a saline soil.\n\n\n\nEffect of Applied Hg levels on Mercury Concentration of Turnip under Soil Saline \nStress\nHg treatment (Figures 1 and 2) showed that mercury concentration in shoot and \nroot significantly increased at Hg levels of 75 and 150 mg. l-1 respectively in \ncomparison with the control (P<0.01). Hg accumulation, both in leaves and roots \nof the treated turnip plants increased with increased concentration of HgCl2. There \nwas a comparatively higher amount of Hg in roots than in leaves. The level of Hg \nfound in 7-day old leaves with 2.5, 5, 10 and 25 \u03bc M HgCl2 treated plants were \n2.32, 5.39, 14.72 and 41.32 mg Kg-1 DW respectively. In roots of these treated \nplants, the concentrations of Hg were 11.26, 47.25, 187.18 and 721.54 mg Kg-1 \nDW respectively (Gopal et al. 2012).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202174\n\n\n\nEffect of P. putida Inoculation on Mercury Concentration of Turnip under Soil \nSaline Stress\nThe effects of P. putida treatment on mercury concentration in turnip (Figures 3 \nand 4), showed that mercury levels in both shoot and root decreased significantly \nwith P. putida inoculation in comparison with no inoculation (control) (P < 0.01). \nAzoddein et al. (2015) conducted a study to remove mercury using P. putida  pure \nculture ATTC 49128 at optimum growth parameters such as techniques of culture, \nacclimatisation time and speed of incubator shaker. The removal of two different \nmercury concentrations, 1 and 4 mg. l-1 showed that the overall levels of mercury \nremoval in this study were between 80 and 89.   \n\n\n\n9 \n \n\n\n\nthese treated plants, the concentrations of Hg were 11.26, 47.25, 187.18 and 721.54 mg Kg\u22121 \n\n\n\nDW respectively (Gopal et al. 2012). \n\n\n\n\n\n\n\n\n\n\n\n Figure 1. Effect of Hg levels in shoot Hg concentration                                                               Figure 2.Effect of Hg levels in root Hg concentration \n\n\n\n\n\n\n\n Effect of P. putida Inoculation on Mercury Concentration of Turnip under Soil Saline \n\n\n\nStress \n\n\n\nThe effects of P. putida treatment on mercury concentration in turnip (Figures 3 and 4), showed \n\n\n\nthat mercury levels in both shoot and root decreased significantly with P. putida inoculation in \n\n\n\ncomparison with no inoculation (control) (P < 0.01). Azoddein et al. (2015) conducted a study \n\n\n\nto remove mercury using P. putida  pure culture ATTC 49128 at optimum growth parameters \n\n\n\nsuch as techniques of culture, acclimatisation time and speed of incubator shaker. The removal \n\n\n\nof two different mercury concentrations, 1 and 4 mg. l-1 showed that the overall levels of \n\n\n\nmercury removal in this study were between 80 and 89.    \n\n\n\n\n\n\n\n\n\n\n\nFigure 3.Effect of P. putida levels in shoot Hg concentration                                                 Figure. 4. Effect of P. putida levels in root Hg concentration \n\n\n\nc \n\n\n\nb \n\n\n\na \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\nHg 0 Hg75 Hg150Hg\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n sh\n\n\n\noo\nt \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nMercury levels (mg. l-1) \n\n\n\nc \n\n\n\nb \n\n\n\na \n\n\n\n0\n10\n20\n30\n40\n50\n60\n70\n\n\n\n Hg0 Hg75 Hg150Hg\n co\n\n\n\nnc\nen\n\n\n\ntr\nat\n\n\n\nio\nn \n\n\n\nin\n ro\n\n\n\not\n \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nMercury levels (mg. l-1) \n\n\n\na \n\n\n\nb \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\nControl P. putida\n\n\n\nHg\n co\n\n\n\nnc\nen\n\n\n\ntr\nat\n\n\n\nio\nn \n\n\n\nin\n sh\n\n\n\noo\nt \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nP.putida levels \n\n\n\na \n\n\n\nb \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\nControl P.putida\n\n\n\nHg\n co\n\n\n\nnc\nen\n\n\n\ntr\nat\n\n\n\nio\nn \n\n\n\nin\n ro\n\n\n\not\n \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nP.putida levels \n\n\n\n Figure 1. Effect of Hg levels in shoot Hg \nconcentration                                                              \n\n\n\nFigure 3.Effect of P. putida levels in shoot \nHg concentration                                                                                                              \n\n\n\nFigure 2.Effect of Hg levels in root Hg \nconcentration\n\n\n\nFigure. 4. Effect of P. putida levels in root \nHg concentration\n\n\n\n9 \n \n\n\n\nthese treated plants, the concentrations of Hg were 11.26, 47.25, 187.18 and 721.54 mg Kg\u22121 \n\n\n\nDW respectively (Gopal et al. 2012). \n\n\n\n\n\n\n\n\n\n\n\n Figure 1. Effect of Hg levels in shoot Hg concentration                                                               Figure 2.Effect of Hg levels in root Hg concentration \n\n\n\n\n\n\n\n Effect of P. putida Inoculation on Mercury Concentration of Turnip under Soil Saline \n\n\n\nStress \n\n\n\nThe effects of P. putida treatment on mercury concentration in turnip (Figures 3 and 4), showed \n\n\n\nthat mercury levels in both shoot and root decreased significantly with P. putida inoculation in \n\n\n\ncomparison with no inoculation (control) (P < 0.01). Azoddein et al. (2015) conducted a study \n\n\n\nto remove mercury using P. putida  pure culture ATTC 49128 at optimum growth parameters \n\n\n\nsuch as techniques of culture, acclimatisation time and speed of incubator shaker. The removal \n\n\n\nof two different mercury concentrations, 1 and 4 mg. l-1 showed that the overall levels of \n\n\n\nmercury removal in this study were between 80 and 89.    \n\n\n\n\n\n\n\n\n\n\n\nFigure 3.Effect of P. putida levels in shoot Hg concentration                                                 Figure. 4. Effect of P. putida levels in root Hg concentration \n\n\n\nc \n\n\n\nb \n\n\n\na \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\nHg 0 Hg75 Hg150Hg\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n sh\n\n\n\noo\nt \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nMercury levels (mg. l-1) \n\n\n\nc \n\n\n\nb \n\n\n\na \n\n\n\n0\n10\n20\n30\n40\n50\n60\n70\n\n\n\n Hg0 Hg75 Hg150Hg\n co\n\n\n\nnc\nen\n\n\n\ntr\nat\n\n\n\nio\nn \n\n\n\nin\n ro\n\n\n\not\n \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nMercury levels (mg. l-1) \n\n\n\na \n\n\n\nb \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\nControl P. putida\n\n\n\nHg\n co\n\n\n\nnc\nen\n\n\n\ntr\nat\n\n\n\nio\nn \n\n\n\nin\n sh\n\n\n\noo\nt \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nP.putida levels \n\n\n\na \n\n\n\nb \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\nControl P.putida\n\n\n\nHg\n co\n\n\n\nnc\nen\n\n\n\ntr\nat\n\n\n\nio\nn \n\n\n\nin\n ro\n\n\n\not\n \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nP.putida levels \n\n\n\nInteraction Effects of Hg and P. putida Treatments on Mercury Concentration of \nTurnip Plant under Soil Saline Stress\nInteraction effects of Hg levels and P. putida treatments on mercury concentration \nof root and shoot turnip plant (Figures 5 and 6) showed that mercury concentration \nin shoot and root were significantly decreased at Hg levels of 75 and 150 mg. \nl-1 respectively compared to P. putida treatment with no inoculation (control) \n(P<0.01). Efficient removal of mercury using P. putida was successfully achieved \nby mercury-resistant bacteria, P. putida, in the laboratory study. The overall levels \nof mercury removal in this study were between 80% and 89% indicating that \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 75\n\n\n\n10 \n \n\n\n\nInteraction Effects of Hg and P. putida Treatments on Mercury Concentration of Turnip \nPlant under Soil Saline Stress \n \n\n\n\nInteraction effects of Hg levels and P. putida treatments on mercury concentration of root and \n\n\n\nshoot turnip plant (Figures 5 and 6) showed that mercury concentration in shoot and root were \n\n\n\nsignificantly decreased at Hg levels of 75 and 150 mg. l-1 respectively compared to P. putida \n\n\n\ntreatment with no inoculation (control) (P<0.01). Efficient removal of mercury using P. \n\n\n\nputida was successfully achieved by mercury-resistant bacteria, P. putida, in the laboratory \n\n\n\nstudy. The overall levels of mercury removal in this study were between 80% and 89% \n\n\n\nindicating that the microbial detoxification system for mercury was highly effective under \n\n\n\nthese conditions (Azoddein et al. 2015). \n\n\n\n\n\n\n\nFigure 5. Interaction effect of Hg and P. putida treatments in shoot Hg concentration.  Figure. 6. Interaction effect of Hg and P. putida treatments in root Hg \nconcentration \n\n\n\nIn general, our study results agreed with many previous studies (Ren et al. 2014; Cheng et al. \n\n\n\n2012), which found that Hg was mainly accumulated in roots. The high Hg concentration in \n\n\n\nturnip roots is attributed the direct exposure of roots to Hg; moreover, a large amount of Hg \n\n\n\nwas absorbed by roots along with other essential micronutrients (Greger et al. 2005; Chen and \n\n\n\nYang 2012), and thus immobilised in root cells. However, a significant inhibition of Hg \n\n\n\nuptake by turnip was observed when plants were exposed to Hg and application of P. putida \n\n\n\n(P<0.01) (Figures 5 and 6). This finding could be explained by the transport of Hg+2 to Hg0, \n\n\n\nbacteria that possess the mer operon and are able to enzymatically reduce Hg+2 to the volatile \n\n\n\nand less toxic form of mercury Hg0 (Barkay and Wagner-Dobbler 2005). The gene mer A is \n\n\n\npart of an operon which comprises regulatory genes encoding transport proteins (Narita et al. \n\n\n\n2003). In general, many mercury resistant isolates possess the mer R, mer P and mer genes \n\n\n\nencoding proteins for regulatory function, transport and extracellular binding, and mercuric \n\n\n\n(II) reductase, respectively (Silver and Hobman 2007). Ghosh et al. (1996) found the ability \n\n\n\nof microorganisms to remove mercury from the culture medium to be affected by high \n\n\n\ne \n\n\n\nc \n\n\n\na \n\n\n\ne \n\n\n\nd \n\n\n\nb \n\n\n\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n45\n50\n\n\n\nHg 0 Hg 75 Hg 150\n\n\n\nHg\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n sh\n\n\n\noo\nt \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nMercury levels (mg. l-1) \n\n\n\nControl P.putida\n\n\n\ne \n\n\n\nc \n\n\n\na \n\n\n\ne \n\n\n\nd \n\n\n\nb \n\n\n\n0\n10\n20\n30\n40\n50\n60\n70\n\n\n\nHg 0 Hg 75 Hg 150\n\n\n\nHg\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n ro\n\n\n\not\n \n\n\n\n (m\ng \n\n\n\nkg\n-1\n\n\n\n) \n\n\n\nMercury levels (mg. l-1) \n\n\n\nControl P. putida\n\n\n\nFigure 5. Interaction effect of Hg and P. putida treatments in shoot Hg concentration.  \nFigure. 6. Interaction effect of Hg and P. putida treatments in root Hg concentration\n\n\n\nthe microbial detoxification system for mercury was highly effective under these \nconditions (Azoddein et al. 2015).\n In general, our study results agreed with many previous studies (Ren et \nal. 2014; Cheng et al. 2012), which found that Hg was mainly accumulated in \nroots. The high Hg concentration in turnip roots is attributed the direct exposure \nof roots to Hg; moreover, a large amount of Hg was absorbed by roots along with \nother essential micronutrients (Greger et al. 2005; Chen and Yang 2012), and \nthus immobilised in root cells. However, a significant inhibition of Hg uptake by \nturnip was observed when plants were exposed to Hg and application of P. putida \n(P<0.01) (Figures 5 and 6). This finding could be explained by the transport of \nHg+2 to Hg0, bacteria that possess the mer operon and are able to enzymatically \nreduce Hg+2 to the volatile and less toxic form of mercury Hg0 (Barkay and \nWagner-Dobbler 2005). The gene mer A is part of an operon which comprises \nregulatory genes encoding transport proteins (Narita et al. 2003). In general, \nmany mercury resistant isolates possess the mer R, mer P and mer genes encoding \nproteins for regulatory function, transport and extracellular binding, and mercuric \n(II) reductase, respectively (Silver and Hobman 2007). Ghosh et al. (1996) found \nthe ability of microorganisms to remove mercury from the culture medium to \nbe affected by high concentrations of HgCl2, suggesting that it may be due to \nsequestering of intracellular Hg by cell components that bind to the metal.\n\n\n\nEffect of Applied Hg Levels on Photosynthetic Pigments and Some Enzymatic \nAntioxidant Activity\nThe effects of Hg levels on chlorophylls a, b, soluble sugars and catalase enzymes \n(CAT) (Table 5), showed that the chlorophylls a, b and soluble sugars of turnip \nsignificantly decreased at Hg levels of 75 and 150 mg. l-1 respectively compared \nto the control (P<0.01). Gopal et al. (2012) reported that for roots and leaves of \nwheat plants with increased concentration of Hg up to 10 \u03bcM, low activities of \nthese enzymes were observed at 25 \u03bcM Hg. In contrast, results of this research \nshowed that with increasing levels of Hg treatments, there was a significant \nincrease in CAT content compared to the control (P<0.01). Skrebsky et al. (2008) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202176\n\n\n\nreported an increase in both root and shoot biomass of Pfaffia glomerata plantlets \nat low Cd levels. On the other hand, the reduction in root growth, chlorophyll \ncontent and total soluble protein was caused by the increased concentration of Hg \nto 10 and 25 \u03bcM in their study. Many organic compounds including chlorophyll \nhave been used as biomarkers for the early detection of metal toxicity in plants \n(Prasad 2003). The biosynthesis of chlorophyll is known to be inhibited by Hg that \ninteracts with \u03b4- aminolevulenic acid dehydratase (Prasad and Prasad 1987). CAT \nactivity increased in roots of wheat plants grown with 2.5 and 5 \u03bcM HgCl2. Also, \nthe enhancement of enzyme activity in leaves of plants grown with 2.5, 5 and 10 \n\u03bcM HgCl2 was recorded compared to control plants. In the leaves of wheat plants \ntreated with 2.5, 5 and 10 \u03bcM HgCl2, CAT activity increased by 18, 27 and 36 % \nrespectively. However, CAT activity decreased by 23 % in the leaves of plants \ngrown with 25 \u03bcM HgCl2. In roots, the activity was elevated by 36% and 74 % with \n2.5 and 5 \u03bcM HgCl2, while it decreased by 24% and 36 % respectively with 10 and \n25 \u03bcM HgCl2 (Gopal et al. 2012). Furthermore, sugar depletion normally occurs \nduring ontogeny of plants. For instance, variations in environmental factors such \nas light, water or temperature and attacks by pathogens or herbivores may lead \nto a significant decrease in the efficiency of photosynthesis in source tissues and \nthus reduce the supply of soluble sugars to sink tissues. Under conditions of sugar \ndeprivation, substantial physiological and biochemical changes occur to sustain \nrespiration and other metabolic processes (Mariana et al. 2009). Drought, salinity, \nlow temperature and flooding, in general, increase soluble sugar concentrations, \nwhereas high light irradiance (PAR, UVBR), heavy metals, nutrient shortage and \nozone decrease sugar concentrations (Gill et al. 2001).\n\n\n\n12 \n \n\n\n\n\n\n\n\nTABLE 5 \nEffect of Hg levels on chlorophylls, soluble sugars and CAT of turnip \n\n\n\n\n\n\n\n \nNotes: Values are the mean of three replicates and different letters within columns indicate significant differences P<0.05 and \nP<0.01 by Student\u2019s t-test compared to control.; Chl a : Chlorophyll a;  Chl b: chlorophyll b; CAT: catalase enzymes  \n \n\n\n\nEffect of P. putida Inoculation on Photosynthetic Pigments and Some Enzymatic \nAntioxidants Activity under Soil Salinity Stress \n\n\n\nThe effects of P. putida treatment on chlorophylls a, b, soluble sugars and CAT (Table 6), \n\n\n\nshowed that the chlorophylls a, and b of turnip significantly increased with P. putida \n\n\n\ninoculation compared to no P. putida (control) (P<0.01). However, soluble sugars of turnip \n\n\n\nsignificantly increased with P. putida compared to the control (P<0.05). In contrast, our results \n\n\n\nshowed a significant reduction in CAT content compared to no P. putida (control) (P<0.01). \n\n\n\nDelshad et al. (2017), who worked on a comparison of different treatments of bio-fertilisers, \n\n\n\nreported that the highest increases in chlorophyll a, chlorophyll b and total chlorophyll were \n\n\n\nrelated to treatment of P. putida compared to the control treatment (without P. putida). \n\n\n\nTABLE 6 \nEffect of P. putida levels on chlorophylls, soluble sugars and CAT of turnip \n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate significant differences P<0.05 and \nP<0.01 by Student\u2019s t-test when compared to control.  Note: Chla : Chlorophyll a;  Chlb: chlorophyll b; CAT: catalase \nenzymes  \n \n \n\n\n\n \n Interaction Effects of Hg and P. putida Treatments on Photosynthetic Pigments and some \nEnzymatic Antioxidants Activity under Soil Salinity Stress \n\n\n\nInteraction effects of Hg and P. putida treatments on chlorophylls a, b, soluble sugars and \n\n\n\nCAT (Table 7) showed that chlorophylls a, b and soluble sugars of turnip significantly \n\n\n\nincreased at Hg levels of both 75 and 150 mg. l-1 with P. putida inoculation compared to no P. \n\n\n\nputida (P<0.01). With respect to CAT content, results showed that with increasing levels of \n\n\n\nHg with and without P. putida inoculation, a significant (P<0.01) increase in CAT content \n\n\n\nwas noted with P. putida inoculation compared to control (without P. putida inoculation) \n\n\n\nHg levels \n(mg .l-1) \n\n\n\nChla \n(\u03bc g/g FW ) \n\n\n\nChlb \n (\u03bc g/g FW ) \n\n\n\nSoluble sugars \n(mg/g FW) \n\n\n\n CAT \n(mg/g FW) \n\n\n\n0 1.55a 0.3a 0.29a 0.52c \n75 1.38b 0.16b 0.24b 0.73b \n\n\n\n150 1.22c 0.11c 0.10c 1.29a \n\n\n\nP. putida \nlevels \n\n\n\nChla \n(\u03bc g/g FW ) \n\n\n\nChlb \n (\u03bc g/g FW ) \n\n\n\nSoluble sugars \n(mg/g FW) \n\n\n\n CAT \n(mg/g FW) \n\n\n\nwithout   \nP.putida 0.83a 0.1a 0.21 b 1.53 a \nwith \n\n\n\nP. putida 1.53b 0.16 b 0.22 a                        1.16  b                \n\n\n\nTABLE 5\nEffect of Hg levels on chlorophylls, soluble sugars and CAT of turnip\n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate \nsignificant differences P<0.05 and P<0.01 by Student\u2019s t-test compared to control.; Chl a : \nChlorophyll a;  Chl b: chlorophyll b; CAT: catalase enzymes\n\n\n\nEffect of P. putida Inoculation on Photosynthetic Pigments and Some Enzymatic \nAntioxidants Activity under Soil Salinity Stress\nThe effects of P. putida treatment on chlorophylls a, b, soluble sugars and CAT \n(Table 6), showed that the chlorophylls a, and b of turnip significantly increased \nwith P. putida inoculation compared to no P. putida (control) (P<0.01). However, \nsoluble sugars of turnip significantly increased with P. putida compared to the \ncontrol (P<0.05). In contrast, our results showed a significant reduction in CAT \ncontent compared to no P. putida (control) (P<0.01). Delshad et al. (2017), who \nworked on a comparison of different treatments of bio-fertilisers, reported that \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 77\n\n\n\nthe highest increases in chlorophyll a, chlorophyll b and total chlorophyll were \nrelated to treatment of P. putida compared to the control treatment (without P. \nputida).\n\n\n\nTABLE 6\nEffect of P. putida levels on chlorophylls, soluble sugars and CAT of turnip\n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate \nsignificant differences P<0.05 and P<0.01 by Student\u2019s t-test when compared to control.  Note: \nChla : Chlorophyll a;  Chlb: chlorophyll b; CAT: catalase enzymes \n\n\n\n12 \n \n\n\n\n\n\n\n\nTABLE 5 \nEffect of Hg levels on chlorophylls, soluble sugars and CAT of turnip \n\n\n\n\n\n\n\n \nNotes: Values are the mean of three replicates and different letters within columns indicate significant differences P<0.05 and \nP<0.01 by Student\u2019s t-test compared to control.; Chl a : Chlorophyll a;  Chl b: chlorophyll b; CAT: catalase enzymes  \n \n\n\n\nEffect of P. putida Inoculation on Photosynthetic Pigments and Some Enzymatic \nAntioxidants Activity under Soil Salinity Stress \n\n\n\nThe effects of P. putida treatment on chlorophylls a, b, soluble sugars and CAT (Table 6), \n\n\n\nshowed that the chlorophylls a, and b of turnip significantly increased with P. putida \n\n\n\ninoculation compared to no P. putida (control) (P<0.01). However, soluble sugars of turnip \n\n\n\nsignificantly increased with P. putida compared to the control (P<0.05). In contrast, our results \n\n\n\nshowed a significant reduction in CAT content compared to no P. putida (control) (P<0.01). \n\n\n\nDelshad et al. (2017), who worked on a comparison of different treatments of bio-fertilisers, \n\n\n\nreported that the highest increases in chlorophyll a, chlorophyll b and total chlorophyll were \n\n\n\nrelated to treatment of P. putida compared to the control treatment (without P. putida). \n\n\n\nTABLE 6 \nEffect of P. putida levels on chlorophylls, soluble sugars and CAT of turnip \n\n\n\n\n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate significant differences P<0.05 and \nP<0.01 by Student\u2019s t-test when compared to control.  Note: Chla : Chlorophyll a;  Chlb: chlorophyll b; CAT: catalase \nenzymes  \n \n \n\n\n\n \n Interaction Effects of Hg and P. putida Treatments on Photosynthetic Pigments and some \nEnzymatic Antioxidants Activity under Soil Salinity Stress \n\n\n\nInteraction effects of Hg and P. putida treatments on chlorophylls a, b, soluble sugars and \n\n\n\nCAT (Table 7) showed that chlorophylls a, b and soluble sugars of turnip significantly \n\n\n\nincreased at Hg levels of both 75 and 150 mg. l-1 with P. putida inoculation compared to no P. \n\n\n\nputida (P<0.01). With respect to CAT content, results showed that with increasing levels of \n\n\n\nHg with and without P. putida inoculation, a significant (P<0.01) increase in CAT content \n\n\n\nwas noted with P. putida inoculation compared to control (without P. putida inoculation) \n\n\n\nHg levels \n(mg .l-1) \n\n\n\nChla \n(\u03bc g/g FW ) \n\n\n\nChlb \n (\u03bc g/g FW ) \n\n\n\nSoluble sugars \n(mg/g FW) \n\n\n\n CAT \n(mg/g FW) \n\n\n\n0 1.55a 0.3a 0.29a 0.52c \n75 1.38b 0.16b 0.24b 0.73b \n\n\n\n150 1.22c 0.11c 0.10c 1.29a \n\n\n\nP. putida \nlevels \n\n\n\nChla \n(\u03bc g/g FW ) \n\n\n\nChlb \n (\u03bc g/g FW ) \n\n\n\nSoluble sugars \n(mg/g FW) \n\n\n\n CAT \n(mg/g FW) \n\n\n\nwithout   \nP.putida 0.83a 0.1a 0.21 b 1.53 a \nwith \n\n\n\nP. putida 1.53b 0.16 b 0.22 a                        1.16  b                \n\n\n\nInteraction Effects of Hg and P. putida Treatments on Photosynthetic Pigments \nand some Enzymatic Antioxidants Activity under Soil Salinity Stress\nInteraction effects of Hg and P. putida treatments on chlorophylls a, b, soluble \nsugars and CAT (Table 7) showed that chlorophylls a, b and soluble sugars \nof turnip significantly increased at Hg levels of both 75 and 150 mg. l-1 with \nP. putida inoculation compared to no P. putida (P<0.01). With respect to CAT \ncontent, results showed that with increasing levels of Hg with and without P. \nputida inoculation, a significant (P<0.01) increase in CAT content was noted \nwith P. putida inoculation compared to control (without P. putida inoculation) \nwhich showed a lesser increasing trend (Table 7). The induction of enzymatic \nantioxidants catalase (CAT), ascorbate peroxidase(APX), peroxidase(POX) and \nsuperoxide dismutase (SOD) was found in the roots and leaves of wheat plants \nwith increasing concentrations of Hg up to 10 \u03bcM but low activities of these \nenzymes were observed at 25 \u03bcM Hg (Gopal et al. 2012).\n\n\n\nTABLE 7\nEffect of P. putida levels on chlorophylls, soluble sugars and CAT of turnip under Hg \n\n\n\nstress\n\n\n\n13 \n \n\n\n\nwhich showed a lesser increasing trend (Table 7). The induction of enzymatic antioxidants \n\n\n\ncatalase (CAT), ascorbate peroxidase(APX), peroxidase(POX) and superoxide dismutase \n\n\n\n(SOD) was found in the roots and leaves of wheat plants with increasing concentrations of Hg \n\n\n\nup to 10 \u03bcM but low activities of these enzymes were observed at 25 \u03bcM Hg (Gopal et al. \n\n\n\n2012). \n\n\n\n\n\n\n\n \nTABLE 7 \n\n\n\nEffect of P. putida levels on chlorophylls, soluble sugars and CAT of turnip under Hg stress \n \n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate significant differences P<0.05 and \nP<0.01 by Student\u2019s t-test compared to control; Note: Chl a : Chlorophyll a;  Chl b: chlorophyll b; CAT: catalase enzymes  \n \n \n\n\n\nIn this research, we noticed a reduction in the photosynthetic pigments under Hg stress. In \n\n\n\ngeneral, the high concentrations of this heavy metal are extremely phytotoxic to the plant cells \n\n\n\nand could cause perceptible damage, as well as physiological disorders (Zhou et al. 2007). In \n\n\n\nplants, the metal ions in the photosynthetic pigments may be replaced by mercury ions, thereby \n\n\n\nreducing photosynthesis rates (Kupper et al. 1998). Our findings showed that high \n\n\n\nconcentrations of mercury ion had significant phytotoxic effects on the plant cells, and also \n\n\n\ndecreased chlorophyll a and b levels especially in the presence of soil salinity. Furthermore, the \n\n\n\nresults demonstrated that the mercury ion could easily accumulate in higher plants (Israr et al. \n\n\n\n2006). In addition, bacterial inoculation also improved plant physiological parameters such \n\n\n\nas chlorophyll a, soluble sugar, malondialdehyde, and proline content. A salt-tolerant strain K. \n\n\n\noxytoca Rs-5 was also able to produce Indole-3-acetic acid (IAA) (Yue et al. 2007). The \n\n\n\nefficiency of plant enhanced phytoremediation (PEP) is dependent on different factors such as \n\n\n\nPGPB inoculum biomass, plant species, plant-microbe specificity and type of contaminants \n\n\n\n(Seniyat and Lesley 2019). \n\n\n\nP. putida \nlevels \n\n\n\nHg levels \n(mg .l-1) \n\n\n\nChla \n(\u03bc g/g FW ) \n\n\n\nChlb \n(\u03bc g/g FW ) \n\n\n\nSoluble sugars \n(mg/g FW) \n\n\n\n CAT \n(mg/g FW) \n\n\n\n 0 1.19c 0.21 c 0.29 b 0.12 e \nwithout   \nP.putida 75 0.9e 0.16d 0.24 d 0.29 c \n\n\n\n 150 0.71f 0.09f 0.101e 0.66a \n 0 1.92a 0.4a 0.3 a 0.10 f \n\n\n\nwith   \n  P. putida 75 1.41 b 0.23 b 0.25 c 0.21d \n\n\n\n 150 1.02d 0.11e 0.109f 0.42b \n\n\n\nNotes: Values are the mean of three replicates and different letters within columns indicate \nsignificant differences P<0.05 and P<0.01 by Student\u2019s t-test compared to control; Note: Chl a \n: Chlorophyll a;  Chl b: chlorophyll b; CAT: catalase enzymes \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202178\n\n\n\n In this research, we noticed a reduction in the photosynthetic pigments \nunder Hg stress. In general, the high concentrations of this heavy metal are \nextremely phytotoxic to the plant cells and could cause perceptible damage, as \nwell as physiological disorders (Zhou et al. 2007). In plants, the metal ions in \nthe photosynthetic pigments may be replaced by mercury ions, thereby reducing \nphotosynthesis rates (Kupper et al. 1998). Our findings showed that high \nconcentrations of mercury ion had significant phytotoxic effects on the plant cells, \nand also decreased chlorophyll a and b levels especially in the presence of soil \nsalinity. Furthermore, the results demonstrated that the mercury ion could easily \naccumulate in higher plants (Israr et al. 2006). In addition, bacterial inoculation \nalso improved plant physiological parameters such as chlorophyll a, soluble sugar, \nmalondialdehyde, and proline content. A salt-tolerant strain K. oxytoca Rs-5 was \nalso able to produce Indole-3-acetic acid (IAA) (Yue et al. 2007). The efficiency \nof plant enhanced phytoremediation (PEP) is dependent on different factors such \nas PGPB inoculum biomass, plant species, plant-microbe specificity and type of \ncontaminants (Seniyat and Lesley 2019).\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 79\n\n\n\n14\n \n\n\n\n\n\n\n\n \nTA\n\n\n\nB\nLE\n\n\n\n 8\n \n\n\n\nA\nna\n\n\n\nly\nsis\n\n\n\n o\nf v\n\n\n\nar\nia\n\n\n\nnc\ne o\n\n\n\nf H\ng \n\n\n\nan\nd \n\n\n\nP.\n p\n\n\n\nut\nid\n\n\n\na \nle\n\n\n\nve\nls \n\n\n\non\n p\n\n\n\nho\nto\n\n\n\nsy\nnt\n\n\n\nhe\ntic\n\n\n\n p\nig\n\n\n\nm\nen\n\n\n\nts,\n so\n\n\n\nm\ne a\n\n\n\nnt\nio\n\n\n\nxi\nda\n\n\n\nnt\ns e\n\n\n\nnz\nym\n\n\n\nat\nic\n\n\n\n ac\ntiv\n\n\n\nity\n, H\n\n\n\ng \nco\n\n\n\nnc\nen\n\n\n\ntra\ntio\n\n\n\nns\n in\n\n\n\n sh\noo\n\n\n\nt a\nnd\n\n\n\n ro\not\n\n\n\n o\nf t\n\n\n\nur\nni\n\n\n\np \npl\n\n\n\nan\nt\n\n\n\n\n\n\n\nS.\n V\n\n\n\n. \nSh\n\n\n\noo\nt f\n\n\n\nre\nsh\n\n\n\n \nw\n\n\n\nei\ngh\n\n\n\nt \nSh\n\n\n\noo\nt d\n\n\n\nry\n \n\n\n\nw\nei\n\n\n\ngh\nt \n\n\n\nR\noo\n\n\n\nt f\nre\n\n\n\nsh\n \n\n\n\nw\nei\n\n\n\ngh\nt \n\n\n\nSh\noo\n\n\n\nt d\nry\n\n\n\n \nw\n\n\n\nei\ngh\n\n\n\nt \nH\n\n\n\ng \nco\n\n\n\nnc\nen\n\n\n\ntra\ntio\n\n\n\nn \nin\n\n\n\n \nsh\n\n\n\noo\nt \n\n\n\nH\ng \n\n\n\nco\nnc\n\n\n\nen\ntra\n\n\n\ntio\nn \n\n\n\nin\n \n\n\n\nro\not\n\n\n\n \nC\n\n\n\nhl\na \n\n\n\nC\nhl\n\n\n\nb \nSo\n\n\n\nlu\nbl\n\n\n\ne \nsu\n\n\n\nga\nrs\n\n\n\n \nC\n\n\n\nA\nT \n\n\n\nM\ner\n\n\n\ncu\nry\n\n\n\n (H\ng)\n\n\n\n \n24\n\n\n\n.9\n9*\n\n\n\n* \n0.\n\n\n\n76\n**\n\n\n\n \n49\n\n\n\n.1\n**\n\n\n\n \n3.\n\n\n\n48\n**\n\n\n\n \n26\n\n\n\n45\n.6\n\n\n\n**\n \n\n\n\n57\n49\n\n\n\n.1\n2*\n\n\n\n* \n0.\n\n\n\n72\n9*\n\n\n\n* \n0.\n\n\n\n09\n0*\n\n\n\n* \n0.\n\n\n\n06\n**\n\n\n\n \n0.\n\n\n\n94\n5*\n\n\n\n* \nP.\n\n\n\n p\nut\n\n\n\nid\na \n\n\n\n8.\n13\n\n\n\n3*\n* \n\n\n\n1.\n88\n\n\n\n**\n \n\n\n\n0.\n63\n\n\n\n**\n \n\n\n\n0.\n10\n\n\n\n**\n \n\n\n\n11\n4.\n\n\n\n45\n**\n\n\n\n \n10\n\n\n\n5.\n27\n\n\n\n**\n \n\n\n\n3.\n54\n\n\n\n1*\n* \n\n\n\n0.\n09\n\n\n\n2*\n* \n\n\n\n0.\n00\n\n\n\n03\n* \n\n\n\n8.\n40\n\n\n\n**\n \n\n\n\nH\ng \n\n\n\n\u00d7 \nP.\n\n\n\n p\nut\n\n\n\nid\na \n\n\n\n1.\n5*\n\n\n\n \n0.\n\n\n\n12\n5*\n\n\n\n* \n0.\n\n\n\n03\n5*\n\n\n\n* \n0.\n\n\n\n42\n**\n\n\n\n \n28\n\n\n\n.6\n1*\n\n\n\n* \n33\n\n\n\n.6\n7*\n\n\n\n* \n0.\n\n\n\n02\n5*\n\n\n\n* \n0.\n\n\n\n00\n2*\n\n\n\n* \n0.\n\n\n\n00\n00\n\n\n\n00\n5ns\n\n\n\n \n0.\n\n\n\n70\n5*\n\n\n\n* \nEr\n\n\n\nro\nr \n\n\n\n0.\n25\n\n\n\n \n0.\n\n\n\n00\n1 \n\n\n\n0.\n09\n\n\n\n \n0.\n\n\n\n00\n4 \n\n\n\n0.\n41\n\n\n\n \n1.\n\n\n\n38\n \n\n\n\n0.\n00\n\n\n\n04\n \n\n\n\n0.\n00\n\n\n\n00\n3 \n\n\n\n0.\n00\n\n\n\n00\n45\n\n\n\n \n0.\n\n\n\n00\n45\n\n\n\n \nN\n\n\n\not\nes\n\n\n\n: ns\n n\n\n\n\non\n -s\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\nt; \n* \n\n\n\nan\nd \n\n\n\n**\n S\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\nt a\nt 5\n\n\n\n%\n a\n\n\n\nnd\n 1\n\n\n\n%\n p\n\n\n\nro\nba\n\n\n\nbi\nlit\n\n\n\ny \nle\n\n\n\nve\nls\n\n\n\n re\nsp\n\n\n\nec\ntiv\n\n\n\nel\ny;\n\n\n\n C\nhl\n\n\n\n a \n: C\n\n\n\nhl\nor\n\n\n\nop\nhy\n\n\n\nll \na;\n\n\n\n  C\nhl\n\n\n\n b:\n c\n\n\n\nhl\nor\n\n\n\nop\nhy\n\n\n\nll \nb;\n\n\n\n C\nA\n\n\n\nT:\n c\n\n\n\nat\nal\n\n\n\nas\ne \n\n\n\nen\nzy\n\n\n\nm\nes\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 8\n\n\n\nA\nna\n\n\n\nly\nsi\n\n\n\ns o\nf v\n\n\n\nar\nia\n\n\n\nnc\ne \n\n\n\nof\n H\n\n\n\ng \nan\n\n\n\nd \nP.\n\n\n\n p\nut\n\n\n\nid\na \n\n\n\nle\nve\n\n\n\nls\n o\n\n\n\nn \nph\n\n\n\not\nos\n\n\n\nyn\nth\n\n\n\net\nic\n\n\n\n p\nig\n\n\n\nm\nen\n\n\n\nts\n, s\n\n\n\nom\ne \n\n\n\nan\ntio\n\n\n\nxi\nda\n\n\n\nnt\ns e\n\n\n\nnz\nym\n\n\n\nat\nic\n\n\n\n a\nct\n\n\n\niv\nity\n\n\n\n, H\ng \n\n\n\nco\nnc\n\n\n\nen\ntra\n\n\n\ntio\nns\n\n\n\n in\n sh\n\n\n\noo\nt a\n\n\n\nnd\n \n\n\n\nro\not\n\n\n\n o\nf t\n\n\n\nur\nni\n\n\n\np \npl\n\n\n\nan\nt\n\n\n\nN\not\n\n\n\nes\n: n\n\n\n\ns \nno\n\n\n\nn \n-s\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\nt; \n* \n\n\n\nan\nd \n\n\n\n**\n S\n\n\n\nig\nni\n\n\n\nfic\nan\n\n\n\nt a\nt 5\n\n\n\n%\n a\n\n\n\nnd\n 1\n\n\n\n%\n p\n\n\n\nro\nba\n\n\n\nbi\nlit\n\n\n\ny \nle\n\n\n\nve\nls\n\n\n\n re\nsp\n\n\n\nec\ntiv\n\n\n\nel\ny;\n\n\n\n C\nhl\n\n\n\n a :\n C\n\n\n\nhl\nor\n\n\n\nop\nhy\n\n\n\nll \na;\n\n\n\n  C\nhl\n\n\n\n b: \nch\n\n\n\nlo\nro\n\n\n\nph\nyl\n\n\n\nl b\n; C\n\n\n\nAT\n: c\n\n\n\nat\nal\n\n\n\nas\ne \n\n\n\nen\nzy\n\n\n\nm\nes\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202180\n\n\n\nCONCLUSION\nAccording to our results, exposure to mercury levels of 75, and 150 mg l-1 \n\n\n\nsignificantly increased the mercury uptake by turnip (P<0.01), while mercury \nlevels of 0, 75, and 150 mg. l-1 significantly reduced growth of turnip through \ndiminishing the chlorophylls and soluble sugars (P<0.01), but in contrast, \nincreasing the catalase enzyme. Also, results showed that inoculated plants with \nP. putida had a better visual appearance, minimising Hg toxicity by increasing \nphotosynthetic pigments, soluble sugars, chlorophylls a and b. However, with \nrespect to CAT content, with increasing levels of Hg with and without P. putida \ninoculation, a significant (P<0.01) increase in CAT content was noted; P. putida \ninoculation, compared to control (without P. putida inoculation) showed a lesser \nincreasing trend. This bacterium inoculation led to a significant decrease in Hg \nconcentration in the root and aerial parts of the turnip plant (P<0.01). Inoculation \nwith selected soil microbial complex improves plant physiological behaviour, \nwhich is important for plant establishment and soil protection. Also, the efficiency \nof PEP is dependent on different factors such as PGPB inoculum biomass, plant \nspecies, plant-microbe specificity and type of contaminants.\n  \n\n\n\nACKNOWLEDGEMENTS\nWe extend our appreciation to Ferdowsi University of Mashhad for providing \nfunds to support this study.\n\n\n\nREFERENCES\nAzoddein, A.A.M., R.M. Yunus, N.M. Sulaiman, A.B. Bustary and K. Sabar. \n\n\n\n2015. Mercury removal using Pseudomonas putida (attc 49128): effect of \nacclimatization time, speed and temperature of incubator. World Academy of \nScience, Engineering and Technology. International Journal of Biotechnology \nand Bioengineering 9(2): 204-209.\n\n\n\nBarkay, T and I. Wagner-Dobler. 2005. Microbial transformations of mercury: \npotentials, challenges, and achievements in controlling mercury toxicity in the \nenvironment. Advances in Applied Microbiology 57:1-52.\n\n\n\nBerg, J.K., K. Brandt, W.A. Al-Soud, P.E. Holm, L.H. Hansen, S.J. Srensen and \nO. Nybroe. 2012. 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"\n\n___________________\n*Corresponding author : E-mail: vladamir.simansky@uniag.sk\n\n\n\nINTRODUCTION\nThe European Union currently assigns to Slovakia 22,200 hectares of vineyard. \nThis allocation is not final, and it can change depending on market demand.  \nThe mentioned area is not used at present, although viticulture is an important \ncomponent of agricultural production in Slovakia. \n\n\n\nVineyards are grown as a monoculture for many years in the same place \nand this causes changes to the soil environment. In general, the vine does not \nmake demands on the soil. At present time, farmers are directing more attention to \nphysical properties of the soil than its chemical composition. A key factor of soil \nquality is the structural state (Jackson et al. 2003; Karlen 2004). Agro-technical \noperations and environmental changes modify the soil structure (Bronick and Lal \n2005) and organic matter content. For example, added farmyard manure (organic \ncomposts) (Tisdall and Oades 1982; \u0160imansk\u00fd 2011b), fertilizers (\u0160imansk\u00fd et al. \n2006), and crop residues (Triberti et al. 2008) to soils can positively affect soil \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 17: 39-48 (2013)                          Malaysian Society of Soil Science\n\n\n\nThe Effect of Different Soil Management Practices on the \nStructure of Vineyard Soil\n\n\n\nVladim\u00edr \u0160imansk\u00fd1*, Nora Poll\u00e1kov\u00e11, M\u00e1ria Horv\u00e1tov\u00e11 and L\u00fddia Jedlovsk\u00e1 2\n\n\n\n1 Department of Soil Science, Faculty of Agrobiology and Food Resources, Slovak \nUniversity of Agriculture, Tr. A. Hlinku 2, 949 76 Nitra, Slovakia. \n\n\n\n2 Department of Environmentalism and Zoology, Faculty of Agrobiology and Food \nResources, Slovak University of Agriculture\n\n\n\nABSTRACT\nThe effect of different soil management practices on soil structure was studied \nin a vineyard. In 2006, an experiment of the different management practices in a \nproductive vineyard (Leptosol) was established in Nitra-Dra\u017eovce (Slovakia). The \nfollowing treatments were established: 1. control (grass without fertilization), 2. \nT (tillage), 3. T+FM (tillage+farmyard manure), 4. G+NPK3 (grass+NPK 120-\n55-195 kg ha-1), 5. G+NPK1 (grass+NPK 80-35-135 kg ha-1). The results showed \nthat the highest value of the critical level of soil organic matter was seen in the \nG+NPK3 treatment. In the tilled treatment (T), the highest vulnerability of the soil \nstructure was observed. The application of nutrients in G+NPK1 had a negative \ninfluence on the content of water-stable micro-aggregates. However, higher doses \nof fertilization (NPK 120-55-195 kg ha-1) had a positive effect on the decrease in \nwater-stable micro-aggregates. Overall, G+NPK3 (NPK 120-55-195 kg ha-1) gave \nthe best improvement in the structure of the soil. \n\n\n\nKeywords: Crusting index, Leptosol, soil management, soil structure, \nvineyard.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201340\n\n\n\nstructure stability and decrease the erosion processes. In productive vineyards, soil \nstructure can be influenced by mulching (Glab and Kulig 2008) or grass sown in \nrows or between rows of vine (Cellete et al. 2008; White 2009). The relationship \nbetween soil organic matter and soil structure has been studied in different \nclimatic conditions, soil types, and soil managements (Elliot 1986; Oades and \nWaters 1991; \u0160imansk\u00fd et al. 2008), but its relationships in Rendzic Leptosols, \nwhich are used for vineyards, is not well understood as yet. Leptosols are very \nshallow soils over continuous rock and soils that are extremely gravelly and/or \nstony. Leptosols are azonal soils which are particularly common in mountainous \nregions. Leptosols are the most extensive soils on earth, extending over about \n1.7 billion ha (WRB 2006). In Slovakia, Leptosols cover 3.5% of the agricultural \nland. The total area of Slovak Republic is 4.9 mil. ha, of which agricultural land \nconstitutes 2.4 mil. ha. \n\n\n\nThe aim of this work is to evaluate effects of different soil management \npractices on the soil structure of a vineyard as well as to assess the effects of soil \nchemical properties and soil organic matter on soil structure in Rendzic Leptosols.  \n\n\n\nMETHODOLOGY\nIn 2006, an experiment on the different management practices in a vineyard \nwas carried out in Nitra-Dra\u017eovce (48\u00b021\u20196.16\u201dN; 18\u00b03\u201937.33\u201dE), Slovakia. It is \nlocated in the Nitra wine-growing area. The area has a temperate climate with an \nannual average rainfall of 550 mm and an annual mean temperature of \u2265 10 oC. \nThe soil was developed on limestone and magnesian limestone. The soil type was \nclassified according to WRB classification as a Rendzic Leptosol (WRB 2006). \nThe soil samples (depth 0-0.3 m) contained 17.0\u00b11.6 g kg-1 of  organic carbon, \n1867\u00b1103 mg kg-1 of total N, 99\u00b18 mg kg-1 of total P, 262\u00b115 mg kg-1 of total K, \nand base saturation percentage was 99.3\u00b10.01 % with an initial pH of 7.18\u00b10.08 \n(in 2000). Rock fragments were observed in the soil profile to a depth of 0.3 m = \n8%.\n\n\n\nIn 2000, the vines (Vitis vinifera L. cv. Chardonnay) were planted in rows \n(3 m x 1 m; 3300 plants ha-1) and were trained using a rheinish-hessian system. \nA variety of grasses were used in the inter-rows of the vines, which were sown \nin 2003. The vines were protected against the detrimental effects of diseases and \npests. The experiment was conducted on a randomized complete block design \nwith four replicates. It included the following treatments: \n\n\n\n1. Co \u2013 control - grass sown in the rows and between vine rows (minimal \nhuman impact to the soil in comparison to other treatments); \n\n\n\n2. T \u2013 tillage - yearly medium tilth to a  depth of 0.25 m and intensive \ncultivation between vine rows during the growing season with hoes \n(an average of 3 times per vegetation season of vine - depending on \nclimatic conditions); \n\n\n\n3. T+FYM \u2013 tillage+farmyard manure - medium tilth to a depth of 0.25 \nm with ploughed farmyard manure at a  rate of 40 t ha-1 (organic carbon \ncontent 10.2%, total nitrogen content 0.45%) and intensive cultivation \n\n\n\nVladim\u00edr \u0160imansk\u00fd, Nora Poll\u00e1kov\u00e1, M\u00e1ria Horv\u00e1tov\u00e1 and L\u00fddia Jedlovsk\u00e1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 41\n\n\n\nbetween vine rows during the growing season. First application of \nFYM was released in autumn 2005 and next in autumn 2009; \n\n\n\n4. G+NPK3 \u2013 doses of NPK fertilizers in 3rd intensity for vineyards, that \nis, 120 N kg ha-1, 55 P kg ha-1 and 195 K kg ha-1. The dose of nutrients \nwas divided: 2/3 applied into the soil in the spring (bud burst - on \nMarch) and 1/3 during flowering (in May). The grass was sown in and \nbetween the vine rows; \n\n\n\n5.  G+NPK1 \u2013 doses of NPK fertilizers in 1st intensity for vineyards, that \nis, 80 N kg   ha-1, 35 P kg ha-1 and 135 K kg ha-1. The dose of nutrients \nwas divided: 1/2 applied into the soil in the spring (bud burst - on \nMarch) and 1/2 during flowering (on May). The grass was sown in and \nbetween the vine rows. \n\n\n\nSoil samples were collected from all treatments from a depth of 0\u20130.3 m, \nduring the spring of 2008-2011. In each location, soil samples were collected \nand mixed to homogenise the sample. Soil samples were dried at laboratory \ntemperature and standard soil analyses were used for determination: soil pH (1:2.5 \n- soil: water), sorptive parameters (Fiala et al. 1999), total organic carbon content \n(Dziadowiec and Gonet 1999) and optical parameters of humus substances and \nhumic acids in soil samples, labile carbon content (CL) (Loginov et al. 1987) \nand hot water soluble carbon (CHWD) (K\u0151rschens 2002). Soil samples for the \ndetermination of the structure parameters were taken with the aid of a spade to \nmaintain the soil aggregates. Soil samples were dried at laboratory temperature \nand divided by sieve (dry and wet sieve) to 7 size fractions. We calculated the \nvulnerability coefficient (Kv) according to Valla et al. (2000), as well as the \nstability index of water-stable aggregates (Sw) and values of sum of mean weight \ndiameters (MWD) in fractions of aggregates. The index of crusting (Ic) (Lal and \nShukla 2004) and critical soil organic matter content (St) according to Pieri (1991) \nas one of the most important parameters of soil structure stability were calculated \nas well.\n\n\n\nThe obtained results were statistically evaluated. Analysis of variance \n(ANOVA) was performed by using the Statgraphics Centurion XV.I (Statpoint  \nTechnologies, Inc., USA). Treatment differences were considered significant at P \nvalues < 0.05 by the LSD multiple-range test. Correlations between soil organic \nmatter and soil structure stability were determined. \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nStability, Water Resistance and Vulnerability Parameters of Soil Structure\nThe results of the effects of different soil management practices in a vineyard \non stability, water resistance and vulnerability parameters of soil structure are \nshown in Table 1. In comparison to all soil management practices in a vineyard, \na higher stability index value of water-stable aggregates (1.64\u00b10.17), but without \nstatistical significance was seen during the treatment with ploughed farmyard \nmanure. Added organic manures can positively affect physical properties and \n\n\n\nEffect of Management Practices on Soil Structure\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201342\n\n\n\nespecially the structure of soil (Pagliai et al. 1987; Mbagwu 1992; Obi and Ebo, \n1995). In G+NPK3 (6.99\u00b10.33), the most favourable values of the critical level \nof soil organic matter (statistically significant in comparison to all treatments) \nwere observed during the period (2008-2011). Similarly, the values of crust index \nwere the most favourable in G+NPK3. Index of crusting (Ic) is a very important \nparameter of soil structure based on textural composition and soil organic matter \ncontent (Lal and Shukla 2004). Soil crust formation is dependent on soil tillage \nand fertilization as presented by \u0160imansk\u00fd et al. (2008). In our case, the effect of \nsoil texture was eliminated because the experiment was based on one soil type \nwith a defined particle size distribution (569 g kg-1 of sand, 330 g kg-1 of silt and \n101 g kg-1 of clay). This means the values of Ic have been affected by soil organic \nmatter content (SOM). SOM is an important agent responsible for binding soil \nmineral particles together (Oades and Waters 1991) and it decreases the amount \nof soil crust formed (\u0160imansk\u00fd et al. 2008). The application of NPK fertilizers \n(in 1st and 3rd intensities) as well as application of farmyard manure increased the \ncontent of SOM (\u0160imansk\u00fd 2011a), which led to a decrease (positive effect) in  \nIc, but without statistical significance (Table 1). Agbede (2010) states that adding \na combination of organic manures together with NPK fertilizers to the soil can \nimprove the physical properties of soils. For control, between values of mean \nweight diameters of structure aggregates (MWDs - dry pre-sieved) and mean \nweight diameters of water-stable aggregates (MWDm), the lowest differences \nwere determined. In this case, this means that the vulnerability of soil structure \n(Kv) was the lowest. In comparison to all soil management practices in a vineyard, \na higher value of Kv was seen in the T treatment. Tillage disrupts soil aggregates \nand decreases SOM (Elliot 1986; Plante and McGill 2002). \n\n\n\nTABLE 1\nStatistical evaluation of soil structure parameters\n\n\n\n                                                                                                      ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n\n\n\n\n\n\n\n \nTABLE 1 \n\n\n\nStatistical evaluation of soil structure parameters \n \n\n\n\nParameters \nSoil management \n\n\n\nCo T T+FM G+NPK3 G+NPK1 \nSw 1.51\u00b10.24a 1.63\u00b10.15a 1.64\u00b10.17a 1.58\u00b10.11a 1.37\u00b10.09a \nSt 6.67\u00b10.58b 5.69\u00b10.72a 6.68\u00b10.85b 6.99\u00b10.33b 6.64\u00b10.45b \nIc 0.88\u00b10.14a 0.99\u00b10.11b 0.87\u00b10.19a 0.84\u00b10.04a 0.88\u00b10.06a \n\n\n\nMWDs 2.17\u00b10.11a 2.57\u00b10.16b 2.36\u00b10.21ab 2.38\u00b10.19ab 2.30\u00b10.14ab \nMWDm 1.20\u00b10.05a 0.98\u00b10.05a 1.11\u00b10.08a 1.28\u00b10.02a 1.04\u00b10.04a \n\n\n\nKv 1.87\u00b10.18a 3.14\u00b10.38b 2.27\u00b10.45a 2.05\u00b10.28a 2.30\u00b10.16a \nCo \u2013 control, T \u2013 tillage, T+FM \u2013 tillage+farmyard manure, G+NPK3 \u2013 grass+NPK 120-55-195 kg ha-1, \nG+NPK1 \u2013 grass+NPK 80-35-135 kg ha-1. \nDifferent letters in the same column indicate that treatment means are significantly different at P<0.05 \naccording to LSD. \nSt \u2013 critical level of soil organic matter, Ic \u2013 index of crusting, Kv \u2013 vulnerability coefficient, Sw \u2013 index of \nstability, MWDs \u2013 mean weight diameter \u2013dried sieve, MWDm - mean weight diameter of water-stable \naggregates \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nCo \u2013 control, T \u2013 tillage, T+FM \u2013 tillage+farmyard manure, G+NPK3 \u2013 grass+NPK 120-55-195 kg ha-1, \nG+NPK1 \u2013 grass+NPK 80-35-135 kg ha-1.\nDifferent letters in the same column indicate that treatment means are significantly different at P<0.05 \naccording to LSD.\nSt \u2013 critical level of soil organic matter, Ic \u2013 index of crusting, Kv \u2013 vulnerability coefficient, Sw \u2013 index of \nstability, MWDs \u2013 mean weight diameter \u2013dried sieve, MWDm - mean weight diameter of water-stable \naggregates\n\n\n\nVladim\u00edr \u0160imansk\u00fd, Nora Poll\u00e1kov\u00e1, M\u00e1ria Horv\u00e1tov\u00e1 and L\u00fddia Jedlovsk\u00e1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 43\n\n\n\nSoil management practices had a statistically significant influence on the \ncontent of water-stable micro-aggregates (WSAmi) during the 2008-2011 period. \nIn T (25.15\u00b16.24), the lowest content of WSAmi was determined; however, in \nG+NPK1 (35.81\u00b111.9) the highest content of WSAmi was detected (Table 2). In T, \nthe content of WSAmi was lowered by 18 % in comparison to the control. The main \ncause of decreased WSAmi could be the lower content of the SOM in WSAmi, just \nas in the study of Scott (1998), as well as the disruption of soil macro-aggregates \n(Six et al. 2002). However, in G+NPK3 (almost twice the dose of nutrients), \nWSAmi  content was lower by 25% in comparison to G+NPK1 (Table 2). \n\n\n\nCorrelations between Soil Chemical Properties, Soil Organic Matter and Soil \nStructure Parameters\nIn the soil solution, the concentration of hydrogen cations had a significant effect \non the soil crust. The higher values of pH as well as the lower values of hydrolytic \nacidity where the lower vulnerability of soil was used to create the soil crust were \nobserved in all soil management practices in the vineyard (Table 3). Stability of \nthe soil structure is connected with acid soils (Scott 2000; Huffman et al. 2001). \nRoberts and Carbon (1971) recorded that water resistance does not develop under \nalkaline conditions due to the higher solubility of humic substances. There are \nvery important factors for the formation of a favourable structure of soil for the \nright quantity and quality of SOM (Fortun et al. 1989; \u0160imansk\u00fd et al. 2007) \nwhich confirmed our results (Table 3). The higher content of total organic carbon \nin soil positively affected Kv, St, Sw and Ic. The quality and stability of SOM also \nhad a positive influence on Sw.   \n\n\n\nA very important factor for forming of individual size fractions of aggregates \nis soil organic matter (\u0160imansk\u00fd et al. 2007); on the other hand, it is not important \nfor all size fractions of aggregates because some aggregates are formed by \nchemical factors (Six et al. 2004). Correlations between chemical properties, \n\n\n\nTABLE 2\nStatistical evaluation of percentage of size fractions of water-stable aggregates\n\n\n\n                                                                                                      ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \nStatistical evaluation of percentage of size fractions of water-stable aggregates \n\n\n\n\n\n\n\nSize fractions  \nof water-stable  \n\n\n\naggregates  \nin mm \n\n\n\nSoil management \n\n\n\nCo T T+FM G+NPK3 G+NPK1 \n<0.25 29.94\u00b110.1b 24.63\u00b17.25a 25.15\u00b16.24a 26.74\u00b19.27a 35.81\u00b111.9c \n\n\n\n0.25-0.5 7.67\u00b11.18ab 12.96\u00b15.25b 13.01\u00b13.64b 8.46\u00b12.52ab 6.46\u00b12.24b \n0.5-1 10.32\u00b13.64a 16.47\u00b14.98b 15.70\u00b16.35ab 13.06\u00b14.48ab 10.45\u00b13.48a \n1-2 17.79\u00b15.48a 20.25\u00b18.01a 17.88\u00b16.24a 15.91\u00b13.22a 17.25\u00b12.56a \n2-3 13.94\u00b12.45a 13.84\u00b13.99a 14.15\u00b15.22a 14.84\u00b13.61a 13.47\u00b12.41a \n3-5 17.22\u00b15.26b 10.29\u00b12.17a 11.21\u00b14.20ab 15.48\u00b13.68ab 15.04\u00b12.51ab \n>5 3.08\u00b11.56a 1.57\u00b10.62a 2.94\u00b10.94a 5.72\u00b11.44a 1.54\u00b10.25a \n\n\n\nCo \u2013 control, T \u2013 tillage, T+FM \u2013 tillage+farmyard manure, G+NPK3 \u2013 G+NPK3 \u2013 grass+NPK 120-55-195 kg \nha-1, G+NPK1 \u2013 grass+NPK 80-35-135 kg ha-1. \nDifferent letters in the same column (a, b, c) indicate that treatment means are significantly different at P<0.05 \naccording to LSD. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nCo \u2013 control, T \u2013 tillage, T+FM \u2013 tillage+farmyard manure, G+NPK3 \u2013 G+NPK3 \u2013 grass+NPK 120-55-\n195 kg ha-1, G+NPK1 \u2013 grass+NPK 80-35-135 kg ha-1.\nDifferent letters in the same column (a, b, c) indicate that treatment means are significantly different at \nP<0.05 according to LSD.\n\n\n\nEffect of Management Practices on Soil Structure\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201344\n\n\n\norganic matter and size fractions of water-stable aggregates are shown in Table \n4. An important negative correlation was detected between pH and WSA 0.25-1 \nmm content. A 0.25-1 mm size fraction of WSA was formed in acid pH with a \nhigher portion of basic cations in sorptive complex.  Highly negative correlations \nbetween WSAmi (r=-0.509, P<0.05), WSA 2-3 mm (r=-0.465, P<0.05) and \nexchangeable Na+ were found. These facts have been confirmed by several \nstudies (Levy and Torrento 1995; Am\u00e9zketa 1999; Bronick and Lal 2005). At the \nsame time, we found a positive correlation between WSA 2-3 mm and CHWD. A \npositive correlation between WSAmi and QHS was also found. \n\n\n\nCONCLUSION\nThe highest value of the critical level of soil organic matter was seen in the \ntreatment with higher doses of fertilization (3rd intensity). In the tilled treatment, \n\n\n\nTABLE 3\nCorrelation coefficients between some chemical properties and soil structure parameters\n\n\n\nTABLE 4\nCorrelation coefficients between chemical properties and size fractions content of water-\n\n\n\nstable aggregates\n\n\n\n                                                                                                      ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \nCorrelation coefficients between some chemical properties and soil structure parameters \n\n\n\n \nParameters Kv MWDs St Sw Ic \npH n.s. n.s. n.s. n.s. 0.455* \nH n.s. n.s. n.s. n.s. -0.459* \nTOC -0.546* n.s. 0.952*** 0.525* -0.459* \nCL n.s. n.s. 0.638** n.s. -0.447* \nCHWD n.s. n.s. n.s. n.s. -0.634** \nCHA:CFA n.s. n.s. n.s. 0.580* -0.568** \nQHS n.s. -0.475* n.s. -0.519* n.s. \nQHA n.s. -0.469* n.s. n.s. -0.493* \n\n\n\nn.s. \u2013 non-significant; *P<0.05; ** P<0.01; *** P<0.001; n = 20 \nSt \u2013 critical level of soil organic matter, Ic \u2013 index of crusting, Kv \u2013 vulnerability coefficient, Sw \u2013 index of \nstability, MWDs \u2013 mean weight diameter \u2013dried sieve, H \u2013 hydrolytic acidity, TOC \u2013 total organic carbon, CL \u2013 \nlabile carbon, CHWD \u2013 hot water soluble carbon, CHA:CFA \u2013 carbon of humic acids to carbon of fulvic acids ratio, \nQHS \u2013 colour quotient of humic substances, QHA \u2013 colour quotient of humic acids  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nn.s. \u2013 non-significant; *P<0.05; ** P<0.01; *** P<0.001; n = 20\nSt \u2013 critical level of soil organic matter, Ic \u2013 index of crusting, Kv \u2013 vulnerability coefficient, Sw \u2013 index of \nstability, MWDs \u2013 mean weight diameter \u2013dried sieve, H \u2013 hydrolytic acidity, TOC \u2013 total organic carbon, \nCL \u2013 labile carbon, CHWD \u2013 hot water soluble carbon, CHA:CFA \u2013 carbon of humic acids to carbon of fulvic \nacids ratio, QHS \u2013 colour quotient of humic substances, QHA \u2013 colour quotient of humic acids \n\n\n\n                                                                                                      ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 4 \nCorrelation coefficients between chemical properties and size fractions content of water-\n\n\n\nstable aggregates \n \n\n\n\nParameters Size fractions of water-stable aggregates in mm \n>5 3-5 2-3 1-2 0.5-1 0.25-0.5 <0.25 \n\n\n\npH n.s. n.s. n.s. n.s. -0.552* -0.503* n.s. \nS n.s. n.s. n.s. n.s. n.s. 0.449* n.s. \nT n.s. n.s. n.s. n.s. n.s. 0.446* n.s. \nNa+ n.s. n.s. -0.465* n.s. n.s. n.s. -0.509* \nTOC n.s. n.s. n.s. n.s. n.s. n.s. n.s. \nCL n.s. n.s. n.s. n.s. n.s. n.s. n.s. \nCHWD n.s. n.s. 0.512* n.s. n.s. n.s. n.s. \nQHS n.s. n.s. n.s. n.s. n.s. n.s. 0.519* \nQHA n.s. n.s. n.s. n.s. n.s. n.s. n.s. \n\n\n\nn.s. \u2013 non-significant; *P<0.05; ** P<0.01; *** P<0.001; n = 20 \nS \u2013 sum of basic cations, T \u2013 sorption capacity, Na+ - exchangeable Na, TOC \u2013 total organic carbon, CL \u2013 labile \ncarbon, CHWD \u2013 hot water soluble carbon, QHS \u2013 colour quotient of humic substances, QHA \u2013 colour quotient of \nhumic acids  \n\n\n\nVladim\u00edr \u0160imansk\u00fd, Nora Poll\u00e1kov\u00e1, M\u00e1ria Horv\u00e1tov\u00e1 and L\u00fddia Jedlovsk\u00e1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 45\n\n\n\nthe highest vulnerability of the soil structure was observed. The application of \nnutrients in 1st intensity of fertilisation of the vineyard had a negative influence \non the content of water-stable micro-aggregates; however, higher doses of \nfertilization (3-rd intensity) had a positive effect on the decrease of water-stable \nmicro-aggregates. Overall, in the treatment of the application of nutrients in \n3rd intensity of fertilization of the vineyard, the best structure state of soil was \nobserved.\n\n\n\nThe quantity and quality of the soil organic matter as well as its stability \nare very important factors of stability, water resistance and vulnerability of soil \nstructure in all soil management practices in a vineyard.\n\n\n\nSoil structure cannot be evaluated only on the base of a one parameter, but it \nmust always be assessed comprehensively using multiple indicators.\n\n\n\nACKNOWLEDGEMENT\nThis project was supported by the Scientific Grant Agency of the Ministry of \nEducation, Science, Research and Sport of the Slovak Republic and the Slovak \nAcademy of Sciences (No. 1/0300/11 and No. 1/0084/13).\n\n\n\nREFERENCES\nAgbede, T.M. 2010. Tillage and fertilizer effects on some soil properties, leaf nutrient \n\n\n\nconcentrations, growth and sweet potato yield on an Alfisol in Southwestern \nNigeria. 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Influence of tillage system and \nfertilisation on soil structure stability and chemical properties of Haplic Luvisol. \nActa Phytotechnica et zootechnica 9: 75-80. \n\n\n\n\u0160imansk\u00fd, V., E. Tobia\u0161ov\u00e1 and J. Chlp\u00edk. 2008. Soil tillage and fertilization of Orthic \nLuvisol and their influence on chemical properties, soil structure stability and \ncarbon distribution in water-stable macro-aggregates. Soil and Tillage Research. \n100: 125-132.\n\n\n\n\u0160imansk\u00fd, V., E. Tobia\u0161ov\u00e1 and A. Zaujec. 2007. Influence of soil organic matter on \naggregate stability of chernozems. Phytopedon (Bratislava). 6: 25-29. \n\n\n\nTisdall, J.M. and J.M. Oades. 1982. Organic matter and water-stable aggregates in \nsoils. Journal of Soil Science. 33: 141-163.\n\n\n\nEffect of Management Practices on Soil Structure\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201348\n\n\n\nTriberti, L., A. Nastri, G. Giordani, F. Comellini, G. Baldoni and G. Toderi. 2008. Can \nmineral and organic fertilisation help sequestrate carbon dioxide in cropland?  \nEuropean Journal of Agronomy. 29: 13-20.\n\n\n\nValla, M., J. Koz\u00e1k and V. Ondr\u00e1\u010dek. 2000. Vulnerability of aggregates separated from \nselected anthrosols developed on reclaimed dumpsites. Rost. V\u00fdr. 46: 563-568.\n\n\n\nWhite,R.E. 2009. Understanding Vineyard Soils. New York: Oxford University Press.\n\n\n\nWRB, 2006. World Reference Base for Soil Resources 2006. World Soil Resources \nReports No. 103. FAO, Rome, 103 pp.\n\n\n\nVladim\u00edr \u0160imansk\u00fd, Nora Poll\u00e1kov\u00e1, M\u00e1ria Horv\u00e1tov\u00e1 and L\u00fddia Jedlovsk\u00e1 \n\n\n\n\n\n"
"\n\nINTRODUCTION\nIn order to produce enough food for the growing world population, there is a \nneed for increasing agricultural and innovative measures to enhance productivity. \n\n\n\nnormally apply excessive N fertilizers (Fei et al.\nby NO3\n\n\n\n-  from nitrogenous fertilizers and over-irrigation is a worldwide concern \n(Sharmasarkar et al. 3\n\n\n\n- into the \nshallow ground water systems has been a matter of concern for environmental \nprofessionals and the public at large. Excessive application of N fertilizers relative \n\n\n\nAssessment on Residual Soil Nitrate of Intensively Fertilized \nBanana Farms of Jalgaon Region\n\n\n\nV.A. Khatik*, D.B. Sarode, R.N. Jadhav, S.T. Ingle and \nS.B. Attarde\n\n\n\nSchool of Environmental and Earth Sciences \nNorth Maharashtra University, Jalgaon, Maharashtra, India\n\n\n\nABSTRACT\nAccumulation of NO3\n\n\n\n-\n\n\n\nground water is an important issue regarding ground water pollution especially \nin intensively fertilized agricultural areas. The study was undertaken to study the \nlevels of residual soil NO3\n\n\n\n- -N in intensively fertilized banana producing farms at \nJalgaon district of Maharashtra, India. A total of 144 composite soil samples was \ncollected from post-harvested banana farms at various soil depths and analysed \nfor NO3\n\n\n\n- -N content. The results obtained showed higher NO3\n- -N content in the \n\n\n\nsoil and the mean values of NO3\n- -1 in Chopda, \n\n\n\nNO3\n- -1 which is in the high NO3\n\n\n\n--N category, 74 % in \nthe medium category with NO3\n\n\n\n- -1 and 16 % in \nthe low NO3\n\n\n\n--N category which contained NO3\n- -1. Analysis \n\n\n\nof NO3\n-\n\n\n\nshowed accumulation of NO3\n-\n\n\n\nin NO3\n-\n\n\n\nThe residual soil NO3\n--N may leach down to the ground water of the region and \n\n\n\n3\n--N levels in ground water and to \n\n\n\navoid accumulation in the soil, application of organic manure is recommended \nearly in the season. In addition, it is suggested that chemical fertilizers be applied \nlate in the season, which will reduce the leaching losses and higher amounts of \nNO3\n\n\n\n-\n\n\n\nKeywords: Volcanic black soil, residual NO3\n- -N, N fertilizers, leaching losses\n\n\n\n___________________\n*Corresponding author : Email: wak675@gmail.com\n\n\n\n\n\n\n\n\n88\n\n\n\nto crop needs can lead to accumulation of residual soil NO3\n- that could eventually \n\n\n\nleach to ground water systems (Kanwar et al.\n\n\n\nmore immediate, but the health issue has attracted much public concern.\nIn a recent survey, higher concentrations of NO3 \n\n\n\n- -N in 36 % of 161 collected \nsamples from ground water resources of Jalgaon district have been reported; \n\n\n\net al.\nlimits of NO3\n\n\n\n- -1, intended to prevent infant \n\n\n\ninvestigation has also shown increased risk of human gastric cancer with food \nintake polluted by nitrosamine compounds (Cai et al. et al.\nNitrate and NO -\n\n\n\net al.\nwater needs of the area is met from ground water resources.\n\n\n\nmore bananas than most countries of the world. According to Mahabanana \n\n\n\nJalgaon is one of the highest fertilizer-consuming districts of the Maharashtra \nstate (Sargaonkar et al.\n\n\n\net al.\n-1 for banana farms. \n\n\n\nNumerous studies have been carried out on effects of agricultural practices \non soil and environmental pollution (Mathuthu et al. 1993, Millburp et al.\nespecially in developed countries.  However, in many tropical countries, little \nor no validated and standardized information is available on the magnitude of \nfertilizer application on NO3\n\n\n\n- and phosphorus pollution of streams and adjoining \nagricultural lands. The negative impact of this lack of information is perhaps \ncounteracted by the belief that fertilizer use is still below optimum in many of these \ncountries and therefore does not pollute the environment (Olarewaju et al\nHowever, many agronomic experiments have suggested that crops typically use \n\n\n\net \nal.\nnitrate leaching (Boumans et al.\n\n\n\nV.A. Khatik, D.B. Sarode, R.N. Jadhav, S.T. Ingle and S.B. Attarde\n\n\n\n\n\n\n\n\n89\n\n\n\nIn order to avoid N losses, Pilbeam et al.\net al\n\n\n\n3\n- and found \n\n\n\nthat with increasing rates of urea and water, there was an increase in NO3\n- \n\n\n\nconcentration and its downward movement in the soil.\nAlthough an intensively cultivated and agriculture-dominating region of \n\n\n\nIndia, no work has been reported on effects of agricultural practices on soil and \nenvironmental pollution on  Jalgaon district. A critical evaluation of the existing \nstatus suggests the need for a systematic investigation of the occurrence of NO3\n\n\n\n- \n\n\n\n-N in soil and water to design effective management and mitigation measures for \nsustainable agricultural practices. In this context, the objectives of the present \nwork were to determine the residual NO3\n\n\n\n- -N content in soil and study its horizontal \nand vertical distribution in the post-harvested banana farms of Jalgaon region. \n\n\n\nPost harvest NO3\n- -N test data obtained in the present work would be of use \n\n\n\nto design sustainable manure and nitrogenous fertilizer application programmes \nfor banana farms. Thus, results obtained in the present work can be utilized in \ndrawing up a nutrient management programme for the banana farms. \n\n\n\nTABLE 1\n\n\n\nResidual Soil Nitrate in Banana Farm\n\n\n\nYear Sr. \nNo. \n\n\n\nChemical fertilizer \ntype 2005-06 2006-07 2007-08 2008-09 2009-10 \n\n\n\n1 Urea 117906 153386 187607 198166 206452 \n2 Ammonium sulphate 4673 3542 3350 3624 4904 \n\n\n\n3 Diammonium \nphosphate 26984 32274 41165 76154 75594 \n\n\n\n4 Single super \nphosphate 29240 53338 54458 46106 50349 \n\n\n\n5 Muriate of potash 40365 42098 59553 82069 90864 \n\n\n\n6 Calcium ammonium \nnitrate 102 216 293 123 518 \n\n\n\n7 Monoammonium \nphosphate 0 0 5050 2401 152 \n\n\n\n9 Single super \nphosphate 0 0 0 7848 0 \n\n\n\n8 Other mix 6593 7825 5253 3495 362 \n N:  P:  K \n8 20:20:00 20200 25602 15243 7275 14885 \n9 15:15:15 6889 12752 13237 17962 17362 \n\n\n\n10 19:19:19 3741 1743 939 0 0 \n11 10:26:26 23596 43608 40961 33329 27493 \n12 23:23:00 1304 3359 762 3027 5531 \n13 12:32:16 8088 14759 10650 5500 8126 \n14 14:35:14 2057 4468 0 1677 4164 \n15 18:18:10 11122 16905 18854 19983 20678 \n\n\n\n Total 302860 415875 457375 508739 527434 \nAll values are in metric tonnes (Source: Department of Agriculture, \n\n\n\n     Zillah Parishad, Jalgaon)\n\n\n\n\n\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area\nJalgaon district is located in the north-west region of the Maharashtra state of \nIndia. It is bounded by the Satpuda mountain ranges in the north, Ajanta mountain \nranges in the south, Dhule district in the west and Buldhana district in the east; it \n\n\n\nin volcanic black soil that is well suited for cotton and banana production. The \n\n\n\nby dug wells and bore wells is common practice for the banana crops in Jalgaon \ndistrict (Figs. 1 and 2).\nSoil\nThe soils of this district are wholly derived from basalt rock. They differ from \nthe rest of the Deccan trap soil area, which are mostly alluvial in origin, as they \n\n\n\nhave been transported from the mountain ranges. The black soils are mostly clays \nthat form deep cracks during the dry season and usually respond to application \n\n\n\nsoil types of Jalgaon region and chemical and physical properties of soil is given \n\n\n\nFig. 1: Location of the Jalgaon district in Maharashtra state of India.\n\n\n\nV.A. Khatik, D.B. Sarode, R.N. Jadhav, S.T. Ingle and S.B. Attarde\n\n\n\n\n\n\n\n\n91\n\n\n\nFig. 2: Map of Jalgaon district.\n\n\n\nResidual Soil Nitrate in Banana Farm\n\n\n\nBlack Soil \nParameter \n\n\n\nMedium \nblack Deep black \n\n\n\n \nForest Soil \n \n\n\n\nLoamy \nsoil \n\n\n\nSandy \nsoil \n\n\n\nColour brownish \nblack Black dark brown \n\n\n\nto black gray reddish or \nyellowish \n\n\n\nDepth (feet) 3 to 6 6 3 to 6 6 0.5 to 2 \nDrainage good Impeded good good excessive \nSlope flat flat undulating flat sloppy \nErosion slightly Nil nil nil heavy \nSand (%) 45-50 30-40 40-50 24-30 65-75 \nSilt (%) 15-20 25-35 20-25 35-45 10-15 \nClay (%) 25-35 30-45 25-30 25-30 10-15 \nCaCO3 (%)  0-5 0-5 5-8 1-3 1-5 \npH  7.5-8.5 8.0-8.5 6.0-7.0 6.5-7.5 6.5-7.5 \nNitrogen (%) 0.05-0.08 0.06-0.09 0.01-0.15 .08-0.09 0.03-0.05 \n\n\n\nP2O5 \n15.00-\n20.00 15.00-25.00 15.00-20.00 20.0-\n\n\n\n25.0 5.00-10.00 \n\n\n\nK2O  15.00-\n20.00 20.00-25.00 20.00-25.00 20.0-\n\n\n\n25.0 5.00-15.00 \n\n\n\n(Source: http://www.maharashtra.gov) \n\n\n\nPhysico-chemical properties of soils in the study area\n\n\n\n\n\n\n\n\nsampling grid area was established for the spatial and comprehensive collection \n\n\n\nm grid length in each row (Sharmasarkar et al.\ntaken at each sampling location and composited for each depth at each sampling \nlocation. Sampling was done by using soil-sampling devices, which included 5.1 \ncm diameter hand auger and 1.9 cm diameter soil probe. These probes and augers \nwere selected for their standardized size. Immediately after collection, samples \n\n\n\nNo rain was observed during the sampling period of 4 months. Approximately, \nafter 18 hours of collection, the samples were brought to the laboratory for analysis \nof the NO3\n\n\n\n--N content.\n\n\n\ndiameter screen. The samples were then analysed for residual NO3\n- -N content \n\n\n\nusing chromotrophic acid method. The details of sampling procedures and \n3\n- -N extraction are \n\n\n\nRESULTS AND DISCUSSION\nThe distribution of residual NO3\n\n\n\n- -N for post-harvested banana farms of Jalgaon \n\n\n\nFigures 3 to 5.\n\n\n\nFig. 3: Residual soil NO3\n-\n\n\n\nV.A. Khatik, D.B. Sarode, R.N. Jadhav, S.T. Ingle and S.B. Attarde\n\n\n\nComposites Samples\n\n\n\n\n\n\n\n\n93\n\n\n\nResults obtained in the present study indicate that NO3\n--N content in the soil \n\n\n\n-1 \n\n\n\nA comparative assessment of results shows statistically considerable differences \nin the residual soil NO3\n\n\n\n--N levels of three tahsil areas under study (Figs. 3 to 5). \nThe considerable variability in the distribution of the residual soil NO3\n\n\n\n- -N levels \nin the soils of the different tahsils of the Jalgaon region may be explained by the \nuneven rates of application of N fertilizers and different soil capacities towards \nthe volatilization of NH3.\n\n\n\nFig. 4: Residual soil NO3\n-\n\n\n\nResidual Soil Nitrate in Banana Farm\n\n\n\nFig. 5: Residual soil NO3\n-\n\n\n\nComposites Samples\n\n\n\nComposites Samples\n\n\n\n\n\n\n\n\n94\n\n\n\nResidual traces of NO3\n- -N in the majority of collected samples were higher \n\n\n\nin surface samples. Moreover, the processes of retention, sorption and slow \n3\n - -N \n\n\n\nNO3\n- -N, which is due to the utilisation of NO3\n\n\n\n- -N by crop roots. However, the \npresence of relatively excess unutilized NO3\n\n\n\n- -N in the post-harvested farms \n\n\n\nregion. This relatively excess NO3\n- -N can leach down to the deeper soil layers \n\n\n\nhealth of the people who consume untreated water from the underground water \nresources of Jalgaon region.\n\n\n\nStatistical Analysis\n\n\n\nmade at different depths and at different sampling locations. Probability level \n\n\n\nwell.\nTables 3, 4 and 5 incorporate the statistical analysis of results for Chopda, \n\n\n\nRaver and Yawal region, respectively. These include statistical parameters such as \nmean, median, mode, standard deviation, minimum and maximum. The average, \nminimum and maximum values of residual soil NO3\n\n\n\n- -N vary between 53.7 to 84, \n-1, respectively, for three tahsils of Jalgaon \n\n\n\ndistrict under study.\n\n\n\nV.A. Khatik, D.B. Sarode, R.N. Jadhav, S.T. Ingle and S.B. Attarde\n\n\n\nTABLE 3\nDescriptive of NO3\n\n\n\n--N leachates\n\n\n\n\n\n\n\n\n\n\n\nChopda \nDepth (cm) 10 20 30 40 \nMean 73.43       66.39       59.37       53.75       \nMedian 77.50       71.40       64.85       56.00       \nSt. dev. 29.86    27.26    24.58     24.25 \nMinimum 20.70      18.80      19.80       15.90       \nMaximum 117.80       112.70       98.60       96.60       \n\n\n\n\n\n\n\n\n95\n\n\n\nTable 6 incorporates the survey conducted by Khatik et al.\nof NO3\n\n\n\n- -N in the water resources of Chopda, Raver and Yawal regions. Of the \n\n\n\nconcentrations above the permissible limit. This is evidence of NO3\n- -N leaching \n\n\n\nin the study area. \n\n\n\nResidual Soil Nitrate in Banana Farm\n\n\n\n\n\n\n\nRaver \nDepth (cm) 10 20 30 40 \nMean 82.14       76.47       69.52       62.18       \nMedian 83.70      78.60       71.70      56.95      \nSt. dev. 25.47 25.02        23.13        23.12        \nMinimum 40.70      32.80      27.30      19.10      \nMaximum 128.90       117.50       104.90       101.00       \n\n\n\nTABLE 6\nNO3\n\n\n\n--N in ground water resources of the study area\n\n\n\nTABLE 4\nDescriptive statistics of NO3\n\n\n\n--N leachates\n\n\n\nTABLE 5\n Descriptive statistics of NO3\n\n\n\n--N leachates\n\n\n\n \u00a0\n\n\n\nYawal \nDepth (cm) 10 20 30 40 \nMean 89.32       84.43       77.63       68.91       \nMedian 90.55     85.50     77.00     69.80     \nSt. dev. 13.50        15.76        15.74        16.89        \nMinimum 65.70      60.00      58.00      43.80      \nMaximum 114.00       120.70       118.70       110.20       \n\n\n\nTahsil  Chopda Raver Yawal \nTotal samples 15 15 15 \nMinimum NO3\n\n\n\n- level (mg L  ) 16.8 22.1 27.4 \nMaximum NO3\n\n\n\n- level (mg L  ) 79.7 70.8 60.2 \nAverage NO3\n\n\n\n- level (mg L   ) 39.8 43.7 40.8 \nStd. Dev. 19.2 16.0 10.7 \nNo. of samples above permissible limit 6 5 4 \n\n\n\n-l\n\n\n\n-l\n\n\n\n-l\n\n\n\n(Source: Khatik et al. 2010). \n\n\n\n\n\n\n\n\n96\n\n\n\n3\n- -N content of \n\n\n\n-1\n\n\n\n-1 -1\n\n\n\nsamples are in the low NO3\n- -N category. The percentage of samples in the medium \n\n\n\nclass is about 74 % and the percentage of the samples falling in the high category \nis about 9 %. Thus, soils in the majority of banana farms in the study area contain \nmedium amounts of residual NO3\n\n\n\n- -N. \n\n\n\nFigure 7 presents the percent average distribution of NO3\n- -N in soil samples at \n\n\n\n% of NO3\n- -N accumulation whereas accumulation NO3\n\n\n\n- \n\n\n\n3\n- -N \n\n\n\nNO3\n- -N in the soil indicates relatively higher concentrations of NO3\n\n\n\n- -N in the \n\n\n\nV.A. Khatik, D.B. Sarode, R.N. Jadhav, S.T. Ingle and S.B. Attarde\n\n\n\nFig. 7: Percent mean average value of soil NO3\n\n\n\nFig. 6: Percent frequency distribution of soil NO3-N levels\n\n\n\nNitrate concentration\n\n\n\n\n\n\n\n\n97\n\n\n\nSUMMARY AND CONCLUSIONS\nIt is evident from the results that the residual soil NO3\n\n\n\n- -N levels in the study area \nare high. About 83 % of the samples had NO3\n\n\n\n- \n\n\n\nkg-1\n3\n- \n\n\n\nis indicative of NO3\n -\n\n\n\npractices are not effectively managed, then NO3\n- -N leaching may continue and \n\n\n\nnearby water bodies can occur. \nIt may be concluded from the results of the present study and the recently \n\n\n\nconducted survey of water resources of Jalgaon region for NO3\n- -N that there can \n\n\n\nbe unprecedented higher levels of NO3\n- -N in some underground water resources \n\n\n\nand Yawal tahsils of this district may not be suitable for drinking purpose. It is \nadvisable to treat the water before drinking to remove excess NO3\n\n\n\n- -N content. \nTo minimize N losses from such a  highly chemical fertilizer consuming \n\n\n\nsystem, it is suggested  that organic manure be applied early in the season, \n\n\n\nthe season when leaching losses are less and crop uptake is more vigorous. \nTo obtain detailed status of NO3\n\n\n\n--N leaching in the Jalgaon region, further \nstudies can be conducted by sampling the soils at greater depths using hydraulic \nsoil sampling machines.\n\n\n\nACKNOWLEDGEMENTS\nThe authors are thankful to the Department of Soil Conservation, Jalgaon, for \nhelp rendered in conducting the soil sampling for the present work. We are also \nthankful to Department of Agriculture of Zillah Parishad, Jalgaon for providing \nannual data on fertilizer use in the study region. \n\n\n\nREFERENCES\n\n\n\nJalgaon.\n\n\n\nA\nirrigation amounts and urea fertilization rate in Jordan valley. Dirasat. 13: 35-\n36.\n\n\n\nA\n66-67.\n\n\n\nB\n\n\n\n1995 period.  Environmental Monitoring and Assessment\n\n\n\nCa\ncase-control study in Fujian province. World Journal of Gastroenterology\n\n\n\nResidual Soil Nitrate in Banana Farm\n\n\n\n\n\n\n\n\n98\n\n\n\nCr Journal of Preventive \nMedicine\n\n\n\nJournal of Soil \nand Water Conservation\n\n\n\nDo\nPhilosophical Transactions of the Royal Society of \n\n\n\nLondon\n\n\n\nFei\nrates on nitrate nitrogen distribution in saline soil in the Hai river basin, China. \nJournal of Soils and Sediments\n\n\n\nHi\nfarm De Marke. Netherlands Journal of Agricultural Science\n\n\n\nhttp://en.wikipedia.org/wiki/Jalgaon.\n\n\n\nhttp://www.maharashtra.gov.in/english/gazetteer/jalgaon/genecosoils.html\n\n\n\nhttp://www.maharashtra.gov.in/english/gazetteer/jalgaon/jalgaon.html\n\n\n\nK\nestimation of residual soil nitrate nitrogen: a risk based analysis. Water, Air and \nSoil Pollution\n\n\n\nKhatik, V.A., S.D. Narkhede, R.N. Jadhav, D.B. Sarode, S.T. Ingle and S.B. Attarde. \n\n\n\nJalgaon district, Maharashtra. International Journal of Agricultural Science. \n\n\n\nMathuthu, A.S., F.M. Zaranyika and S.B. Jonnalgadda. 1993. Monitoring of \nEnvironment \n\n\n\nInternational.\n\n\n\nM\n\n\n\nCanada. Journal of Environmental Quality. 19: 448-454.\n\n\n\nO\nphosphorus loss from agricultural land: implications for non-point pollution. \nNutrient Cycling in Agroecosystems. 85: 79-85.\n\n\n\nPi\nnitrate from cropped rainfed terraces in the mid-hills of nepal. Nutrient Cycling \nin Agroecosystems\n\n\n\nRogers, M. A, T.L. Vaughan, S. Davis and D.B. Thomas. 1995.  Consumption of \nnitrate, nitrite, and nitrosodimethylamine and the risk of upper aerodigestive \ntract cancer. Cancer Epidemiology, Biomarkers and Prevention\n\n\n\nV.A. Khatik, D.B. Sarode, R.N. Jadhav, S.T. Ingle and S.B. Attarde\n\n\n\n\n\n\n\n\n99\n\n\n\nS\nfor Tapi river basin in India.  Environmental Monitoring and Assessment. 117: \n\n\n\nSharmasarkar, F.C., S. Sharmasarkar, R. Zhang, G.F. Vance and S. Miller. 1999. \nMicrospatial variability of soil nitrate nitrogen following nitrogen fertilization \nand drip irrigation. Water Air and Soil Pollution\n\n\n\nM\n\n\n\nCargo Unit in Maharashtra. Submitted to Principal Secretary to Agriculture \n\n\n\nWei-Min, Y., Y. Ying-Nan, L. Ren-Xia, Z. Tian-Shu, L. Ru-Tao and C. Gui-Dong. \n1998. Diet and gastric cancer: a case-control study in Fujian province, China. \nWorld Journal of Gastroenterology\n\n\n\nResidual Soil Nitrate in Banana Farm\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: siva@putra.upm.edu.my\n\n\n\nINTRODUCTION\nSoil loss, and its associated impacts, can pose critical threats to the environment \n(Bhuyan et al. et al\nmanage soil loss, it is important to understand soil loss processes. GIS-based \nspatial modeling has emerged as an important tool in soil erosion studies and \n\n\n\nespecially at the watershed scale (Memarian et al.\n\n\n\nallows the capture of terrain indices such as stream power, wetness index, sediment \ntransport capacity index and erosion hazard index in three-dimension. WEPP \nmodel is used to predict soil erosion and sedimentation at the watershed scale. \n\n\n\nAccuracy of GeoWEPP in Estimating Sediment Load and \nRunoff from a Tropical Watershed \n\n\n\nM. Ebrahimpour1, S. K. Balasundram2*, J. Talib1, \nA. R. Anuar1 and H. Memarian1\n\n\n\n1Department of Land Management, Universiti Putra Malaysia, 43400 UPM \nSerdang, Selangor, Malaysia\n\n\n\n2Department of Agriculture Technology, Universiti Putra Malaysia, 43400 UPM \nSerdang, Selangor, Malaysia\n\n\n\nABSTRACT\nGeoWEPP, an integration of WEPP and TOPAZ within a GIS interface, was used \nto predict sediment load and runoff at the Lui Watershed, Selangor, Malaysia. Input \n\n\n\nwas used to estimate stochastic climatic parameters. Soil properties such as \n\n\n\nalgorithm for different land use types and for all hillslopes during the simulation \n\n\n\nan underestimation of runoff compared to measured data. This work shows that \nGeoWEPP is able to predict runoff more accurately than sediment load. \n\n\n\nKeywords:  GeoWEPP, sediment load, runoff, watershed, GIS\n\n\n\n\n\n\n\n\nM. Ebrahimpour, S. K. Balasundram, J. Talib, A. R. Anuar and H. Memarian\n\n\n\nbecause it facilitates and improves model application. GeoWEPP is a geo-spatial \nerosion prediction model interfaced in ArcGIS. The limitations of WEPP, that is, \nmanual generation of input data and its application in small watersheds, can be \novercome by GeoWEPP. \n\n\n\nThis work was carried out with the aim of investigating the performance of \nGeoWEPP for sediment load and runoff prediction at the Lui Watershed.\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area\n\n\n\n and basin length \nof 11.5 km (Fig. 1). Minimum and maximum altitudes of the basin are 61 and \n\n\n\nThe Lui Watershed is steep with an average slope of 35 % (Fig. 2)\n\n\n\n6 m3 and an average annual \n3 tonnes. The average annual precipitation at Kg. Lui \n\n\n\n1\n\n\n\nThe remaining portion of this watershed consists of mixed horticulture and crops, \nurban areas, and mining land .\n\n\n\nGeoWEPP\nGeoWEPP was developed as a combined project conducted by the Agriculture \nResearch Service, Purdue University, and the USDA National Soil Erosion \n\n\n\nscale. This interface integrates WEPP model and TOPAZ (TOpography \n\n\n\nbased and daily continuous model that simulates soil erosion and deposition using \na spatially- and temporally-distributed method (Amore et al. \n\n\n\nIn WEPP, the runoff between rill and inter-rill regions is separated and this \nallows for soil erosion in the rills and inter-rill regions to be calculated separately. \n\n\n\nregions is delivered to rills. WEPP computes normalised sediment load using non-\n\n\n\n1Nearest to Sg. Lui hydrometer station\nBa\n\n\n\n\n\n\n\n\nAccuracy of GeoWEPP \n\n\n\nFig. 2: Slope map of the Lui Watershed\n\n\n\nFig. 1: Geographic location of the Lui Watershed\n\n\n\n\n\n\n\n\nM. Ebrahimpour, S. K. Balasundram, J. Talib, A. R. Anuar and H. Memarian\n\n\n\nModelling\n\n\n\nLui Watershed were generated within GeoWEPP and WEPP interfaces while \n\n\n\nalgorithm, which is based on the digital elevation model. \nTOPAZ determines channel network based on the steepest downward path, \n\n\n\nconsidering eight adjacent cells of each raster pixel. Channel network was adjusted \n\n\n\nwas utilised as the base map to extract the above parameters (Fig. 3).\n\n\n\nDaily values of precipitation, temperature, solar radiation, relative humidity \nand wind speed, obtained from Kuala Lumpur International Airport and Petaling \n\n\n\nThe Lui Watershed consists of four dominant soil types, which are Steepland, \nRengam-Jerangau association, Munchong-Seremban association and Telemong-\nAkob-Local alluvium association (Fig. 4). The important physico-chemical soil \n\n\n\nlevel, hydraulic conductivity, rill erodibility and inter-rill erodibility, and critical \nshear stress are given in Table 1.\n\n\n\nFig. 3: Flow accumulation map of the Lui Watershed\n\n\n\n\n\n\n\n\nAccuracy of GeoWEPP \n\n\n\ngenerated inter-rill cover data for each year using growth parameters, soil and \nclimatic data. \n\n\n\nFig. 4: Soil type map of the Lui Watershed\n\n\n\nTABLE 1\n\n\n\n \nParticles (%) \n\n\n\nSoil type Depth  \n(cm) Clay Silt Fine \n\n\n\nsand \nCoarse \nsand \n\n\n\nCEC1   \n(m.e./100g \n\n\n\nsoil) \n\n\n\nOC2 \n(%) \n\n\n\nSteepland 0-15 32 8 30 30 6.64 0.9 \nSteepland 15-30 35 8 20 37 5.91 0.5 \nSteepland 30-60 38 7 20 35 5.66 0.4 \nRengam-Jerangau 0-15 36 6 36 22 7.41 1.2 \nRengam-Jerangau 15-40 45 6 29 20 5.66 0.5 \nRengam-Jerangau 40-\n\n\n\n120 \n47 6 29 22 5.52 0.4 \n\n\n\nTelemong-Akob-Local \nalluvium \n\n\n\n0-15 59 31 4 6 22.76 4.0 \nTelemong-Akob-Local \nalluvium \n\n\n\n15-30 64 29 4 Trace 20.52 1.3 \nTelemong-Akob-Local \nalluvium \n\n\n\n30-45 62 32 2 Trace 21.20 0.6 \nTelemong-Akob-Local \nalluvium \n\n\n\n45-90 60 34 6 - 24.48 0.4 \nMunchong-Seremban 0-15 45 10 35 10 12.83 1.6 \nMunchong-Seremban 15-20 57 9 32 Trace 10.22 0.6 \nMunchong-Seremban 20-60 60 13 20 7 8.90 0.5 \n1Cation Exchange Capacity, 2Organic Carbon  \n\n\n\n\n\n\n\n\nM. Ebrahimpour, S. K. Balasundram, J. Talib, A. R. Anuar and H. Memarian\n\n\n\nRESULTS AND DISCUSSION\nMonthly simulated sediment load from GeoWEPP was compared to monthly \nmeasured sediment load at Sg. Lui hydrometer station (Fig. 6). Student t-test at \n\n\n\n-1 yr-1\n\n\n\nwas almost half of the predicted annual value (1.1 t ha-1 yr-1\n\n\n\noverestimates sediment load. Similar results were reported for WEPP application \nin forest lands (Page-Dumroese et al.\npredictive ability of WEPP has been shown in other studies. For example, Zhange \net al.\n\n\n\nThe Lui Watershed is subjected to anthropogenic manipulations in \nhydrological status. Some landforms resulting from urban development and \nagricultural activities were not captured in the land use and topography maps. \nThis omission included most of the ponds, which can affect sedimentation \nprocess via an increase in deposition rate (Memarian et al\nmost of the empirical research built into the WEPP model is based on gentle \n\n\n\nslopes, and is exposed to high intensity rainfall. These WEPP limitations coupled \nwith limited reliable range of calibration parameters possibly led to sediment load \noverestimation.       \n\n\n\nFig. 5: Land use map of the Lui Watershed\n\n\n\n\n\n\n\n\n31\n\n\n\nAccuracy of GeoWEPP \n\n\n\nThis study also examined the robustness of GeoWEPP in run-off prediction. \nMeasured and predicted average monthly runoff were strongly correlated (r = \n\n\n\n(Fig. 7). However, several of the WEPP-predicted values were less than \nthe measured values. The measured daily runoff value, averaged on a monthly \n\n\n\nthe watershed is naturally prone to high spatial variation in rainfall, which is a \ncommon meteorological characteristic in tropical watersheds (Memarian et al. \n\n\n\nFig. 7: Measured runoff height versus predicted runoff height\n\n\n\n1:1\n\n\n\nr = 0.48\n\n\n\nFig. 6: Measured sediment load versus predicted sediment load\n\n\n\n1:1\n\n\n\n\n\n\n\n\nM. Ebrahimpour, S. K. Balasundram, J. Talib, A. R. Anuar and H. Memarian\n\n\n\nCONCLUSION\nGeoWEPP, a geo-spatial erosion prediction model was applied to predict sediment \nload and runoff at the Lui Watershed, upstream of the Langat River, Selangor, \nMalaysia. Results showed that GeoWEPP overestimated sediment load and \nunderestimated runoff. Sediment trapping features like ponds were not captured in \nthe land use and topography maps, the base data source in this work, due to scale \nlimitations. In addition, GeoWEPP was developed based on gentle slopes, while \nthe Lui Watershed comprises steep slopes. These limitations are believed to have \ncaused model over-prediction in sediment load at the watershed outlet.  Model \nunder-estimation of run-off was primarily due to isolated rainfall data, recorded \nfrom only one rain gauge. GeoWEPP robustness for sediment load prediction at \nLui Watershed was poor and seriously limited by land cover data scale and basin \ngeomorphologic/land cover properties. Based on correlation analysis and model \nbias, GeoWEPP was able to predict runoff more accurately than sediment load. \n\n\n\nACKNOWLEDGEMENTS\nThe authors acknowledge Universiti Putra Malaysia for procuring land use, \ntopography and soil maps, and the Department of Irrigation and Drainage, \nMalaysia for supplying hydrological data.\n\n\n\nREFERENCES\n\n\n\nforest road design. Western Journal of Applied Forestry\n\n\n\nAk\n\n\n\nBuilding and Environment.\n\n\n\nAk\nand sediment transport models. Catena.\n\n\n\nAm\nWEPP application for soil erosion computation from three Sicilian basins. \nJournal of Hydrology.\n\n\n\nBh\nthree erosion simulation models. Environmental Modelling and Software. 17 \n\n\n\nFlangan, D. and M. Nearing. 1995. USDA Water erosion prediction project: hillslope \nIndiana: US Department of \n\n\n\nAgriculture, Agriculture Research Service\n\n\n\nGa\nAccessed from http://grl.ars.usda.gov/topaz/TOPAZ1.htm\n\n\n\n\n\n\n\n\n33\n\n\n\nAccuracy of GeoWEPP \n\n\n\nMemarian, H., S.K. Balasundram, J. Talib, C.B.S. Teh, M.S. Alias, K.C. Abbaspour \n\n\n\nEnvironment Asia\n\n\n\nMi http://www.geog.\nbuffalo.edu/~rensch/geowepp/arcgeowepp\n\n\n\nPag\n\n\n\nForest Ecology and Management\n\n\n\nRiv\n\n\n\nSorooshian, S. and V.K. Gupta. 1995. Model calibration. In:\nComputer Models of Watershed Hydrology. Water Resources Publications. pp. \n\n\n\nW Ministry of Agriculture \nand Lands, Malaysia. pp. 54-69.\n\n\n\nZhan\nsoil erosion in catchments. Environmental Software.\n\n\n\n\n\n"
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\uf0f3\uf0ef\n\n\n\n\uf0ac\uf0b8\uf0bb\uf020\uf0bf\uf0b3\uf0b1\uf0ab\uf0b2\uf0ac\uf020 \uf0b7\uf0b2\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf0ad\uf020\uf0bf\uf0ae\uf0bb\uf020\uf0b1\uf0ba\uf020\uf0bd\uf0b1\uf0b2\uf0ad\uf0b7\uf0bc\uf0bb\uf0ae\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf020\uf0bf\uf0ad\uf020\uf0bf\uf020 \uf0ba\uf0bb\uf0ae\uf0ac\uf0b7\uf0b4\uf0b7\uf0a6\uf0bb\uf0ae\uf020 \uf0ae\uf0bb\uf0b0\uf0b4\uf0bf\uf0bd\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf0f2\uf020\n\n\n\n\uf0cc\uf0b8\uf0bb\uf020\uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0b3\uf0b7\uf0bd\uf0ae\uf0b1\uf0b2\uf0ab\uf0ac\uf0ae\uf0b7\uf0bb\uf0b2\uf0ac\uf0ad\uf020\uf0ae\uf0bb\uf0b0\uf0b1\uf0ae\uf0ac\uf0bb\uf0bc\uf020\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0b7\uf0ad\uf020\uf0ad\uf0ac\uf0ab\uf0bc\uf0a7\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0ad\uf0b7\uf0b3\uf0b7\uf0b4\uf0bf\uf0ae\uf020\uf0ac\uf0b1\uf020\uf0ac\uf0b8\uf0b1\uf0ad\uf0bb\uf020\n\n\n\n\uf0f3\uf0ef \uf0f3\uf0ef\n\n\n\n\uf0f3\uf0ef\n\n\n\n\uf0f3\uf0ef \uf0f3\uf0ef\n\n\n\n\uf0f3\uf0ef\n\n\n\n\uf0ac\uf0b8\uf0bb\uf020\uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0bb\uf0ad\uf020\uf0bc\uf0b7\uf0bc\uf020\uf0b2\uf0b1\uf0ac\uf020\uf0bb\uf0a8\uf0bd\uf0bb\uf0bb\uf0bc\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b3\uf0bf\uf0a8\uf0b7\uf0b3\uf0ab\uf0b3\uf020\uf0b0\uf0bb\uf0ae\uf0b3\uf0b7\uf0ac\uf0ac\uf0bb\uf0bc\uf020\uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0b8\uf0bb\uf0bf\uf0aa\uf0a7\uf020\n\n\n\n\uf0d2\uf0b7\uf0ac\uf0ae\uf0b1\uf0b9\uf0bb\uf0b2\uf020\uf0b3\uf0b7\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf0b7\uf0a6\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b0\uf0bf\uf0ac\uf0ac\uf0bb\uf0ae\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0ae\uf0bb\uf0bb\uf020\uf0bf\uf0b0\uf0b0\uf0b4\uf0b7\uf0bd\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ae\uf0bf\uf0ac\uf0bb\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0ad\uf0bb\uf0a9\uf0bf\uf0b9\uf0bb\uf020\uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0b7\uf0b2\uf020\n\n\n\n\uf0ac\uf0b8\uf0bb\uf020\uf0ac\uf0b8\uf0ae\uf0bb\uf0bb\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf020\uf0bf\uf0ae\uf0bb\uf020\uf0ad\uf0b8\uf0b1\uf0a9\uf0b2\uf020\uf0b7\uf0b2\uf020\uf0da\uf0b7\uf0b9\uf0ad\uf0f2\uf020\uf0ef\uf0f4\uf020\uf0ee\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ed\uf0f2\uf020\uf0df\uf0bd\uf0bd\uf0ab\uf0b3\uf0ab\uf0b4\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0d2\uf0d8\n\uf0ec\n\uf0f5\uf0f3\uf0d2\uf020\uf0b7\uf0b2\uf020\uf0de\uf0ab\uf0b2\uf0b9\uf0b1\uf0ae\uf020\n\n\n\n\uf0ac\uf0b8\n\n\n\n\uf0ed\n\uf0f3\uf0f3\uf0d2\uf020\uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b7\uf0b2\uf0bd\uf0ae\uf0bb\uf0bf\uf0ad\uf0bb\uf0bc\uf020\uf0b3\uf0b1\uf0ae\uf0bb\uf020\uf0ae\uf0bf\uf0b0\uf0b7\uf0bc\uf0b4\uf0a7\uf0f2\uf020\uf0cc\uf0b8\uf0b7\uf0ad\uf020\uf0b7\uf0b2\uf0bc\uf0b7\uf0bd\uf0bf\uf0ac\uf0bb\uf0ad\uf020\uf0bf\uf020\uf0ae\uf0bf\uf0b0\uf0b7\uf0bc\uf020\n\n\n\n\uf0ec\n\uf0f5\n\n\n\n\uf0b7\uf0b2\uf020\uf0d2\uf0d8\n\uf0ec\n\uf0f5\n\n\n\n\uf0ed\n\uf0f3\uf0f3\uf0d2\uf0f2\uf020\uf0cc\uf0b8\uf0b7\uf0ad\uf020\uf0ad\uf0ab\uf0b9\uf0b9\uf0bb\uf0ad\uf0ac\uf0ad\uf020\uf0d2\uf020\n\n\n\n\uf0ec\n\uf0f5\uf0f3\uf0d2\uf020 \uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020 \uf0b7\uf0b2\uf0bd\uf0ae\uf0bb\uf0bf\uf0ad\uf0bb\uf0bc\uf020 \uf0ab\uf0b0\uf020 \uf0ac\uf0b1\uf020 \uf0ef\uf0ee\uf0ac\uf0b8\n\n\n\n\n\n\n\n\n\uf0ef\uf0f0\uf0e9\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0d2\uf0b7\uf0ac\uf0ae\uf0b1\uf0b9\uf0bb\uf0b2\uf020\uf0d3\uf0b7\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf0b7\uf0a6\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0bb\uf0a9\uf0bf\uf0b9\uf0bb\uf020\uf0cd\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0cc\uf0ae\uf0bb\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf0ad\n\n\n\n\n\n\n\n\n\uf0ef\uf0f0\uf0e8 \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf020\n\uf020\n\n\n\n\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ef\uf0ee\uf0f0\n\n\n\n\uf0ef\uf0ec\uf0f0\n\n\n\n\uf0ef\uf0ea\uf0f0\n\n\n\n\uf0f0 \uf0ef \uf0ee \uf0ec \uf0ea \uf0e8 \uf0ef\uf0ee\n\uf0a9\uf0bb\uf0bb\uf0b5\n\n\n\n\uf020\n\n\n\nFig. 2: Nitrogen mineralization of different application rates of sewage sludge  \nin Jawa soil series (Bars indicate standard deviation of means). \n\n\n\n\uf020 \uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf0f0\uf020\uf0b5\uf0b9\uf020\uf0d2\uf0f1\uf0b8\uf0bf\uf020\n\n\n\n\uf020 \uf020\uf020\uf020\uf020\uf020\uf0ef\uf0ec\uf0f0\uf020\uf0b5\uf0b9\uf020\uf0d2\uf0f1\uf0b8\uf0bf\uf020\n\uf020\uf020\uf020\uf020\uf020\uf0ec\uf0ee\uf0f0\uf020\uf0b5\uf0b9\uf020\uf0d2\uf0f1\uf0b8\uf0bf\uf020\n\n\n\n\uf09b\uf0f2\uf0f2\uf0f2\uf0f2\uf0f2\uf020\uf020\uf020\uf020\uf0d2\uf0d1\uf0ed\n\uf0f3\uf020\uf0f3\uf020\uf0d2\uf020\n\n\n\n\uf0c1\uf0c1\uf020\uf0c1\uf0c1\uf020\uf020\uf020\uf020\uf0d2\uf0d8\uf0ec\n\uf0f3\uf020\uf0f3\uf020\uf0d2\uf020\n\n\n\n\uf0c1\uf0c1\uf0c1\uf0c1\uf0c1\uf020\uf020\uf020\uf0cc\uf0b1\uf0ac\uf0bf\uf0b4\uf020\uf0b7\uf0b2\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0d2\uf020\n\n\n\n\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0ef\uf0f0\uf0f0\n\n\n\n\uf0ef\uf0ee\uf0f0\n\n\n\n\uf0ef\uf0ec\uf0f0\n\n\n\n\uf0ef\uf0ea\uf0f0\n\n\n\n\uf0f0 \uf0ef \uf0ee \uf0ec \uf0ea \uf0e8 \uf0ef\uf0ee\n\n\n\n\uf0a9\uf0bb\uf0bb\uf0b5\n\uf020\n\n\n\nFig. 1: Nitrogen mineralization of different application rates of sewage sludge \uf020\nin Bungor soil series (Bars indicate standard deviation of means).\uf020\n\n\n\n\uf020 \uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf0f0\uf020\uf0b5\uf0b9\uf020\uf0d2\uf0f1\uf0b8\uf0bf\uf020\n\uf020 \uf020\uf020\uf020\uf020\uf020\uf0ef\uf0ec\uf0f0\uf020\uf0b5\uf0b9\uf020\uf0d2\uf0f1\uf0b8\uf0bf\uf020\n\n\n\n\uf020\uf020\uf020\uf020\uf020\uf0ec\uf0ee\uf0f0\uf020\uf0b5\uf0b9\uf020\uf0d2\uf0f1\uf0b8\uf0bf\uf020\n\uf09b\uf0f2\uf0f2\uf0f2\uf0f2\uf0f2\uf020\uf020\uf020\uf020\uf0d2\uf0d1\uf0ed\n\n\n\n\uf0f3\uf020\uf0f3\uf020\uf0d2\uf020\n\n\n\n\uf0c1\uf0c1\uf020\uf0c1\uf0c1\uf020\uf020\uf020\uf020\uf0d2\uf0d8\uf0ec\n\uf0f3\n\uf020\uf0f3\uf020\uf0d2\uf020\n\n\n\n\uf0c1\uf0c1\uf0c1\uf0c1\uf0c1\uf020\uf020\uf020\uf0cc\uf0b1\uf0ac\uf0bf\uf0b4\uf020\uf0b7\uf0b2\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0d2\uf020\n\n\n\n\n\n\n\n\n\uf0ef\uf0f0\uf0e7\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0ac\uf0bb\uf0a8\uf0ac\uf0ab\uf0ae\uf0bb\uf020\uf0bf\uf0ad\uf020\uf0de\uf0ab\uf0b2\uf0b9\uf0b1\uf0ae\uf0f2\uf020\uf0d7\uf0b2\uf020\uf0b9\uf0bb\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0b3\uf0b7\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf020\uf0d2\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0b8\uf0b7\uf0b9\uf0b8\uf0bb\uf0ae\uf020\uf0b7\uf0b2\uf020\n\n\n\n\uf0ba\uf0b1\uf0ae\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b1\uf0a8\uf0b7\uf0bc\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0bf\uf0b3\uf0b3\uf0b1\uf0b2\uf0b7\uf0ab\uf0b3\uf020\uf0ac\uf0b1\uf020\uf0b2\uf0b7\uf0ac\uf0ae\uf0bf\uf0ac\uf0bb\uf020\uf0b2\uf0ab\uf0b3\uf0be\uf0bb\uf0ae\uf020\uf0b1\uf0b2\uf0b4\uf0a7\uf020\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0b1\uf0ab\uf0ad\uf0bf\uf0b2\uf0bc\uf0ad\uf020\uf0b0\uf0bb\uf0ae\uf020\uf0b9\uf0ae\uf0bf\uf0b3\uf020\uf0b1\uf0ba\uf020\n\n\n\n\uf0b7\uf0b2\uf020\uf0cd\uf0bb\uf0ae\uf0bc\uf0bf\uf0b2\uf0b9\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0b3\uf0bf\uf0a7\uf020\uf0b8\uf0bf\uf0aa\uf0bb\uf020\uf0be\uf0bb\uf0bb\uf0b2\uf020\uf0aa\uf0bb\uf0ae\uf0a7\uf020\uf0b4\uf0b1\uf0a9\uf020\uf0bf\uf0ac\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0be\uf0bb\uf0b9\uf0b7\uf0b2\uf0b2\uf0b7\uf0b2\uf0b9\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0bb\uf0a8\uf0b0\uf0bb\uf0ae\uf0b7\uf0b3\uf0bb\uf0b2\uf0ac\uf0f2\uf020\n\uf0f3\uf0ef\uf020 \uf0b9\uf0bf\uf0aa\uf0bb\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0b8\uf0b7\uf0b9\uf0b8\uf0bb\uf0ad\uf0ac\uf020 \uf0b0\uf0ae\uf0bb\uf0bc\uf0b7\uf0bd\uf0ac\uf0bb\uf0bc\uf020 \uf0b1\uf0ae\uf020 \uf0b0\uf0b1\uf0ac\uf0bb\uf0b2\uf0ac\uf0b7\uf0bf\uf0b4\uf0b4\uf0a7\uf020\n\n\n\n\uf0b3\uf0b7\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf0b7\uf0a6\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0d2\uf020\uf0f8\uf0d2\n\uf0f0\n\uf0f7\uf020\uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf020\uf0b7\uf0b2\uf020\uf0bf\uf0b4\uf0b4\uf020\uf0ac\uf0b8\uf0ae\uf0bb\uf0bb\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf020\uf0ac\uf0ae\uf0bb\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0f8\uf0cc\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0ec\uf0f7\uf0f2\uf020\uf0cc\uf0b8\uf0b7\uf0ad\uf020\uf0b7\uf0ad\uf020\n\n\n\n\uf0cc\uf0bb\uf0ae\uf0ae\uf0a7\uf020\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\n\n\n\n\uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0ae\uf0bf\uf0ac\uf0bb\uf0f2\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0d2\n\uf0b1\n\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b5\n\n\n\n\uf0d2\uf020\uf0b3\uf0b7\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf0b7\uf0a6\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020 \uf0b7\uf0b2\uf020 \uf0ad\uf0b1\uf0b7\uf0b4\uf020 \uf0ac\uf0ae\uf0bb\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\n\n\n\n\uf0ad\uf0bb\uf0a9\uf0bf\uf0b9\uf0bb\uf020 \uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0aa\uf0bf\uf0ae\uf0b7\uf0bb\uf0bc\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0ae\uf0bf\uf0ac\uf0bb\uf020\uf0b1\uf0ba\uf020 \uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0bf\uf0b2\uf0bc\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0ac\uf0a7\uf0b0\uf0bb\uf020\uf0b1\uf0ba\uf020 \uf0ad\uf0b1\uf0b7\uf0b4\uf020 \uf0ae\uf0bb\uf0bd\uf0bb\uf0b7\uf0aa\uf0b7\uf0b2\uf0b9\uf020 \uf0ac\uf0b8\uf0bb\uf020\n\n\n\n\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0a9\uf0bf\uf0ad\uf0ac\uf0bb\uf0f2\uf020\uf0d3\uf0bb\uf0bf\uf0b2\uf020\uf0b5\uf020\uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0b4\uf0b1\uf0a9\uf0bb\uf0ae\uf020\uf0ac\uf0b8\uf0bf\uf0b2\uf020\uf0ae\uf0bb\uf0b0\uf0b1\uf0ae\uf0ac\uf0bb\uf0bc\uf020\uf0be\uf0a7\uf020\uf0b3\uf0b1\uf0ad\uf0ac\uf020\uf0ae\uf0bb\uf0ad\uf0bb\uf0bf\uf0ae\uf0bd\uf0b8\uf0f2\uf020\uf0d7\uf0b2\uf020\uf0ac\uf0b8\uf0b7\uf0ad\uf020\n\n\n\n\uf0b5\uf020\uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020\uf0bc\uf0b7\uf0bc\uf020\uf0b2\uf0b1\uf0ac\uf020\uf0b7\uf0b2\uf0bd\uf0ae\uf0bb\uf0bf\uf0ad\uf0bb\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0ae\uf0bf\uf0ac\uf0bb\uf020\uf0bf\uf0ac\uf020\uf0b3\uf0b1\uf0ad\uf0ac\uf020\uf0ac\uf0ae\uf0bb\uf0bf\uf0ac\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b4\uf0bb\uf0aa\uf0bb\uf0b4\uf0ad\uf0f2\uf020\n\n\n\n\uf0cc\uf0b8\uf0bb\uf020\uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0d2\n\uf0b1\n\n\n\n\uf0f3\uf0ef \uf0f3\uf0ef\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0f7\uf0f2\uf020\n\n\n\n\uf0cc\uf0b8\uf0bb\uf020\uf0b5 \uf0f3\uf0ef\uf0f2\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0d2\uf020\uf0bf\uf0aa\uf0bf\uf0b7\uf0b4\uf0bf\uf0be\uf0b7\uf0b4\uf0b7\uf0ac\uf0a7\uf020\n\n\n\n\uf0b7\uf0b2\uf0bc\uf0bb\uf0a8\uf020\uf0f8\uf0d2\n\uf0b1\n\uf0b5 \uf0f3\uf0ef\n\n\n\n\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0d2\n\n\n\n\uf0bd\uf0b1\uf0b3\uf0b0\uf0bf\uf0ae\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0de\uf0ab\uf0b2\uf0b9\uf0b1\uf0ae\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0d6\uf0bf\uf0a9\uf0bf\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf020\uf0bd\uf0b1\uf0ab\uf0b4\uf0bc\uf020\uf0be\uf0bb\uf020\uf0bc\uf0ab\uf0bb\uf020\uf0ac\uf0b1\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b4\uf0b1\uf0a9\uf020\uf0bf\uf0b3\uf0b1\uf0ab\uf0b2\uf0ac\uf020\uf0b1\uf0ba\uf020\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\n\n\n\n\uf0b3\uf0b7\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf0b7\uf0a6\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020 \uf0b0\uf0b1\uf0ac\uf0bb\uf0b2\uf0ac\uf0b7\uf0bf\uf0b4\uf0f2\uf020 \uf0d2\uf0b7\uf0ac\uf0ae\uf0b1\uf0b9\uf0bb\uf0b2\uf020 \uf0b7\uf0b3\uf0b3\uf0b1\uf0be\uf0b7\uf0b4\uf0b7\uf0a6\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020 \uf0be\uf0a7\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0b8\uf0bb\uf0ac\uf0bb\uf0ae\uf0b1\uf0ac\uf0ae\uf0b1\uf0b0\uf0b8\uf0b7\uf0bd\uf020\n\n\n\n\uf0f0\n\n\n\n\uf0ef\uf0f0\n\n\n\n\uf0ee\uf0f0\n\n\n\n\uf0ed\uf0f0\n\n\n\n\uf0ec\uf0f0\n\n\n\n\uf0eb\uf0f0\n\n\n\n\uf0ea\uf0f0\n\n\n\n\uf0e9\uf0f0\n\n\n\n\uf0e8\uf0f0\n\n\n\n\uf0f0 \uf0ef \uf0ee \uf0ec \uf0ea \uf0e8 \uf0ef\uf0ee\n\n\n\n\uf0a9\uf0bb\uf0bb\uf0b5\n\n\n\n\uf020\n\n\n\nFig. 3: Nitrogen mineralization of different application rates of sewage sludge  \nin Serdang soil series (Bars indicate standard deviation of means). \n\n\n\n\uf020 \uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf020\uf0f0\uf020\uf0b5\uf0b9\uf020\uf0d2\uf0f1\uf0b8\uf0bf\uf020\n\n\n\n\uf020 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\uf0cd\uf0ab\uf0be\uf0f3\uf0cd\uf0bf\uf0b8\uf0bf\uf0ae\uf0bf\uf0b2\uf020\uf0df\uf0ba\uf0ae\uf0b7\uf0bd\uf0bf\n\n\n\n\uf0dd\uf0bf\uf0b2\uf0f2\uf020\uf0d6\uf0f2\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0f2\uf020\uf0e9\uf0e8\n\n\n\n\uf0bd\uf0b1\uf0b3\uf0b0\uf0b1\uf0ad\uf0ac\uf020\uf0b1\uf0b2\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0b0\uf0b1\uf0ae\uf0b1\uf0ad\uf0b7\uf0ac\uf0a7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0bf\uf0b9\uf0b9\uf0ae\uf0bb\uf0b9\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0f2\uf020\uf0d6\uf0f2\uf020\uf0b1\uf0ba\uf020\uf0db\uf0b2\uf0aa\uf0b7\uf0ae\uf0b1\uf0b2\uf0f2\uf020\uf0cf\uf0ab\uf0bf\uf0b4\uf0f2\uf020\uf0ef\uf0f0\n\n\n\n\uf0d6\uf0f2\uf020\n\n\n\n\uf0db\uf0b2\uf0aa\uf0b7\uf0ae\uf0b1\uf0b2\uf0f2\uf020\uf0cf\uf0ab\uf0bf\uf0b4\uf0f2\uf020\uf0ef\uf0ee\n\n\n\n\uf0d2\uf0b7\uf0ac\uf0ae\uf0b1\uf0b9\uf0bb\uf0b2\uf020\uf0d3\uf0b7\uf0b2\uf0bb\uf0ae\uf0bf\uf0b4\uf0b7\uf0a6\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0bb\uf0a9\uf0bf\uf0b9\uf0bb\uf020\uf0cd\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0cc\uf0ae\uf0bb\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf0ad\n\n\n\n\n\n\n\n\n\uf0ef\uf0ef\uf0ee \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0ac\uf0b1\uf020\uf0ac\uf0b8\uf0bb\uf0b7\uf0ae\uf020\uf0ad\uf0ab\uf0b7\uf0ac\uf0bf\uf0be\uf0b7\uf0b4\uf0b7\uf0ac\uf0a7\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0ab\uf0ad\uf0bb\uf020\uf0bf\uf0ad\uf020 \uf0ba\uf0bb\uf0ae\uf0ac\uf0b7\uf0b4\uf0b7\uf0a6\uf0bb\uf0ae\uf020\uf0b3\uf0bf\uf0ac\uf0bb\uf0ae\uf0b7\uf0bf\uf0b4\uf0ad\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0aa\uf0bb\uf0b9\uf0bb\uf0ac\uf0bf\uf0be\uf0b4\uf0bb\uf020\uf0bd\uf0ae\uf0b1\uf0b0\uf020\uf0b0\uf0ae\uf0b1\uf0bc\uf0ab\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf0f2\uf020\uf0df\uf0bd\uf0ac\uf0bf\uf020\n\n\n\n\uf0d8\uf0b1\uf0ae\uf0ac\uf0b7\uf0bd\uf0ab\uf0b4\uf0ac\uf0ab\uf0ae\uf0bf\uf0bb\uf020\uf0ed\uf0f0\uf0ee\n\n\n\n\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0f2\uf020\uf0cd\uf0b1\uf0bd\uf0f2\uf020\n\n\n\n\uf0df\uf0f2\uf020\uf0d6\uf0f2\uf020\uf0ec\uf0ec\n\n\n\n\uf0ad\uf0b4\uf0ab\uf0bc\uf0b9\uf0bb\uf020\uf0bf\uf0b3\uf0bb\uf0b2\uf0bc\uf0bb\uf0bc\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf020\uf0bf\uf0ad\uf020\uf0bf\uf0ba\uf0ba\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020\uf0be\uf0a7\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0bb\uf0b2\uf0aa\uf0b7\uf0ae\uf0b1\uf0b2\uf0b3\uf0bb\uf0b2\uf0ac\uf0bf\uf0b4\uf020\uf0ba\uf0bf\uf0bd\uf0ac\uf0b1\uf0ae\uf0ad\uf0f2\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0f2\uf020\uf0cd\uf0b1\uf0bd\uf0f2\uf020\uf0df\uf0b3\uf0f2\uf020\uf0d6\uf0f2\uf020\n\n\n\n\uf0ec\uf0eb\n\n\n\n\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b0\uf0b4\uf0bf\uf0b2\uf0ac\uf0ad\uf0f2\uf020\uf0db\uf0af\uf0ab\uf0b7\uf0b0\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b3\uf0bf\uf0b2\uf0bf\uf0b9\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b1\uf0ba\uf020\uf0bd\uf0b1\uf0b2\uf0ad\uf0ab\uf0b3\uf0bf\uf0be\uf0b4\uf0bb\uf0ad\uf0f2\uf020\uf0cb\uf0b2\uf0b7\uf0aa\uf0f2\uf020\uf0d9\uf0bb\uf0b2\uf0ac\uf020\uf0b0\uf0b0\uf020\uf0ee\uf0ec\uf0ed\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: abdo.soil@yahoo.com \n\n\n\nINTRODUCTION\nSugar beet (Beta vulgaris L.) is the second largest crop for sugar production in \nEgypt after sugar cane. Sugar beet has been an important crop in Egyptian crop \nrotation as a winter crop both in poor and fertile soils.\n\n\n\nSalinity of soil is a major abiotic stress that has adverse effects on \nphysiological and metabolic processes of plants leading to diminished growth \nand yield of plants (David, 2007; Yokoi et al., 2002; Azizpour et al., 2010). Plant \ngrowth is suppressed severely at high salinity stress due to factors such as osmotic \nstress, mineral nutrition absorption imbalance, and specific ion toxicity, all \ncombining to reduce nutrient uptake consquentially causing physiological drought \nto plants (Yusuf et al., 2007; David, 2007). Fertilisation plays an important role in \npromoting plants to tolerate salt stress (Ghoulam et al., 2002).\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 95-105 (2015)                     Malaysian Society of Soil Science\n\n\n\nEffect of Potassium Fertilisation and Salicylic Acid on Yield, \nQuality and Nutrient Uptake of Sugar Beet (Beta vulgaris L.) \n\n\n\nGrown in Saline Soil\n\n\n\nMerwad,  A.M.A.*\n\n\n\nZagazig University, Faculty of Agriculture, Soil Science Department, \n44511 Zagazig, Egypt\n\n\n\n\n\n\n\nABSTRACT\nA field experiment on sugar beet (Beta Vulgaris L.) grown in saline soil was \ncarried out during the 2014 growing season to study the effect of potassium (K) \nfertilisation rates of 0, 100, 150 and 200 kg ha-1 and foliar spray of salicylic acid \n(SA) solution of 1000 mg L-1 (sprayed  twice at the rate of 1200 L per ha each time) \non yield, quality, nutrient contents, and uptake. The application of 200 kg ha-1 of \nK in combination with salicylic acid foliar spray gave the highest root length, \nroot diameter, shoot and root yield, sucrose, juice purity percentage, gross sugar \nyield and possible extractable white sugar, nitrogen (N), phosphorous (P) and K \ncontent and uptake. The highest increase in sucrose (20%) as well as possible \nextractable white sugar (184%) were obtained by the addition of 200 kg ha-1 of K \nin combination with salicylic acid foliar spray.  The K, sodium (Na) and \u03b1-amino \nN contents in sugar beet decreased with the application of K with SA foliar spray. \nThe highest values of K, Na and \u03b1-amino N contents were observed in the non-\ntreated plants. Potassium, N and \u03b1-amino N contents decreased by 48.5%, 68% \nand 76.6%, respectively, when treated with 200 kg ha-1 of K with the addition of \nSA spray. \n\n\n\nKeywords: Nutrient uptake, potassium fertilisation, salicylic acid, sugar \nbeet.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201596\n\n\n\nSalicylic acid (SA) is used for raising plants\u2019 resistance to the undesirable \neffects of biotic and abiotic stresses and participates in regulating their \nphysiological processes. Salicylic acid has a significant effect on different aspects \nof plant growth and development, photosynthesis, evaporation, ion transmission \nand absorption, and also causes changes in leaf anatomy and chloroplast structure \n(Sakhabutdinova et al., 2003 ). Salicylic acid is  recognised as a plant hormone \n(Hayat and Ahmed, 2007). It plays diverse physiological roles in plants including \nplant growth, photosynthesis, and nutrient uptake. (Janda et al., 2007). Among \nabiotic stresses, SA alleviates water stress (Singh and Usha, 2003), heat stress \n(Tasgin et al., 2003) and salinity stress (Khodary, 2004; El-Tayeb, 2005).\n\n\n\nPotassium (K) is an essential element for plant growth with respect to its \nphysiological and biochemical functions. It is necessary for activating starch \nsynthetase enzyme (Fathy et al., 2009; Ibrahim et al., 2002). Wang et al., (2013) \nreport that K plays an essential role in enzyme activation, protein synthesis, \nphotosynthesis, osmoregulation, stomatal movement, energy transfer, phloem \ntransport, cation-anion balance, and stress resistance. Mehrandish et al., (2012) \nshowed that the application of K increased root yield, shoot yield, sugar yield, \nsugar content and other qualitative characteristics of sugar beet crops. Salami and \nSaadat (2013), and Shahidi and Khalafi (2010) found that K increased shoot yield, \nroot yield, sucrose, juice purity percentage, gross sugar yield, and nitrogen (N), \nphosphorous (P) and K content and uptake.\n\n\n\nNeseim et al., (2014) found that K in combination with yeast foliar spray \nincreased root and white sugar yield and gave a decrease in sodium and \u03b1- amino \nN content of sugar beet crop. The objective of this study was to test the effect of \nK and foliar spray of SA in sugar beet grown in saline soil.\n\n\n\nMATERIALS AND METHODS\nA field experiment was carried out during the 2014 growing season in Port Said \nGovernorate, Egypt to study the effect of K fertilisation and foliar spray of SA \non yield, quality, and nutrient uptake by sugar beet (Beta vulgaris L.) grown in \na saline soil. The physical and chemical properties of the soil were determined \naccording to Piper (1951), Black et al., (1965) and Jackson (1973) are shown in \nTable 1.\n\n\n\nThe experimental design was a randomised complete block design in three \nreplications. The plot area was 21 m2 (3 m \u00d7 7 m). Each plot had five rows 60 \ncm apart and 7 m long. Before planting, all plots were were fertilised with 31 kg \nP ha-1 as ordinary super phosphate (65 g P kg-1). An amount of 60 kg N ha-1  as \nammonium sulphate (205 g N kg-1) were then applied in two equal doses. The \nfirst dose was after thinning and the second dose was before the second irrigation. \nThe first factor concerned K fertilisation. Treatments were applied before the first \nirrigation in 0, 100, 150, 200  kg ha-1 dose of K in the form of potassium sulphate \n(410 g K kg-1). The second factor concerned SA foliar spray. Sprayings of SA at \n1000 mg L-1 (1200 L ha-1) were done 40 days and 80 days after seeding. No K \nfertilisation was given to the plants concerned as the second factor.\n\n\n\nMerwad, A.M.A.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 97\n\n\n\nAt harvest (195 days after seeding), ten plants were taken at random from \neach plot. The shoot and roots were separated and dried at 70oC in an oven. Dry \nplant samples of shoot were ground and analysed for nutrient contents (N, P and \nK). Total N was determined using the micro-Kjeldahl method by Chapman and \nPratt (1961). Phosphorous and K were determined by digesting in concentrated  \nH2SO4/HClO4 (Chapman and Pratt, 1961). Measurements of  P was done  \ncolourimetrically using ascorbic acid (Watanabe and Olsen, 1965), whilst K was \nmeasured by flame photometer (Chapman and Pratt, 1961). The \u03b1- amino N, Na \nand K concentrations were determined according to the procedure by Bhador et \nal., (2010). Sucrose was estimated in fresh samples of sugar beet root by using a \nsaccharometer according to the method by Ahadi and Sobhani (2005). \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nEffect of K Fertilisation and Foliar Spraying of SA on Root Length, Root \nDiameter and Yield of Sugar Beet Crop Grown on Saline Soil\nPotassium fertilisation as well as foliar spray with SA caused positive and \nsignificant effects on root length, root diameter, shoot and root yield of sugar \n\n\n\nPotassium-Salicylic Acid Interaction on Sugar Beet Grown in Saline Soil\n\n\n\nTABLE 1\nSeleceted physical and chemical properties of the investigated soil\n\n\n\n7 \n \n\n\n\nTABLE 1 \nSeleceted physical and chemical properties of the investigated soil \n\n\n\n\n\n\n\n* Soil paste                       ** Soil paste extract       \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nSoil characteristics Values \n\n\n\n Soil particles distribution  \nSand ,% 82.92 \nSilt,% 13.64 \nClay,% 3.44 \nTextural class Sandy  \n\n\n\n Field capacity (FC),% 10.85 \n CaCO3, (g kg-1) 17.6 \n Organic matter,(g kg-1) 5.62 \n\n\n\npH* 8.01 \nEC,( dS m-1) ** 7.62 \n\n\n\n Soluble cations and anions, (mmolc L-1) **  \nCa++ 35.56 \nMg++ 12.71 \nNa+ 19.23 \nK+ 8.50 \nCO3\n\n\n\n= - \nHCO3\n\n\n\n- 21.66 \nCl\u2013  28.54 \nSO4\n\n\n\n=  25.80 \n Available nutrient , (mg kg-1soil) \n\n\n\nN \n \n\n\n\n95.62 \n            P 7.25 \n            K 78.59 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201598\n\n\n\nbeet crop grown in saline soil (Table 2) . The highest values of root length and \nroot diameter (31.63 and 15.30 cm, respectively) were obtained by K fertilization \nat  200 kg K ha-1 and sprayed with SA, while the lowest values were obtained in \nplants not receiving K as well as SA. Application of K at 200 kg ha-1 with foliar \nspray of SA gave the highest fresh shoot and root weights (15.87 and 80.32 Mg \nha-1, respectively). The lowest fresh shoot and root weights (8.65 and 39.19 Mg \nha-1, respectively) were obtained from  the untreated plants. Increased fertilisation \nwith K gave increased weights of fresh shoot and root weights, increasing by up \nto 58% and 89.6 %, respectively, at the highest K dosing rate. Foliar spray with \nSA increased the fresh shoot and root weights by 12% and 14%, respectively. \nThese results are in agreement with those obtained by Attia (2004) and Ahadi \nand Sobhani (2005). Potassium helps in maintaining a normal balance between \ncarbohydrates and proteins (Moneral et al., 2007). Salami and Saadat (2013) \napplied up to 95 kg ha-1 of K to sugar beet and increased shoot and root fresh and \ndry weight. \n\n\n\nRoot length, root diameter, shoot and root yield increased when treated with \nSA. Gutrierrez-Coronado et al. (1998) reported increases in the growth of shoots \nand roots of soybean plant in response to SA treatment.  SA enhanced growth  in \nwheat (Singh and Usha, 2003) and maize (Khodary, 2004) under water stress, and  \nbarley under salt stress (El-Tayeb, 2005).\n\n\n\nMerwad, A.M.A.\n\n\n\nTABLE 2\nEffect of potassium fertilisation and foliar spray of salicylic acid on root length, root \n\n\n\ndiameter and yield of sugar beet crop grown on saline soil\n\n\n\n8 \n \n\n\n\n\n\n\n\nTABLE 2 \nEffect of potassium fertilisation and foliar spray of salicylic acid on root length, root diameter \n\n\n\nand yield of sugar beet crop grown on saline soil \n \n\n\n\nPotassium \nfertilization \n\n\n\n(A) \n\n\n\nSalicylic \n acid spray \n\n\n\n(B) \n\n\n\nRoot \nlength \n(cm) \n\n\n\nRoot \ndiameter \n\n\n\n(cm) \n\n\n\nShoot yield \n(Mg ha-1) \n\n\n\n\n\n\n\nRoot yield \n(Mg ha-1) \n\n\n\nFresh \nweight \n\n\n\nDry \n Weight \n\n\n\nFresh \nweight \n\n\n\nDry \n weight \n\n\n\nK0 \n  \n\n\n\nWithout 20.47 10.17 8.65 2.88 39.19 8.16 \nWith 24.20 10.83 10.32 3.44 41.43 8.63 \n\n\n\nMean   22.33 10.50 9.48 3.16 40.31 8.39 \nK1 \n  \n\n\n\nWithout 25.43 11.20 11.19 3.73 43.73 9.11 \nWith 27.13 11.57 11.91 3.97 53.02 11.05 \n\n\n\nMean   26.28 11.38 11.55 3.85 48.37 10.08 \nK2 \n  \n\n\n\nWithout 27.50 12.10 12.70 4.23 56.35 11.74 \nWith 28.83 13.43 13.81 4.60 67.30 14.02 \n\n\n\nMean   28.17 12.77 13.25 4.41 61.83 12.88 \nK3 \n  \n\n\n\nWithout 29.57 13.73 14.13 4.71 72.62 15.13 \nWith 31.63 15.30 15.87 5.29 80.32 16.73 \n\n\n\nMean   30.60 14.52 15.00 5.00 76.47 15.93 \n                                                     Mean of salicylic acid spray \n\n\n\n  Without 25.74 11.80 11.67 3.89 52.97 11.04 \n  With 27.95 12.78 12.98 4.33 60.52 12.61 \n\n\n\nLSD A 0.215  0.464 0.155 1.58 0.328 \n B 0.236  0.328 0.109 1.11 0.232 \n AB 0.325  NS NS 2.23 0.464 \n\n\n\nK0: Without potassium fertilisation, K1: 100 kg K ha-1, K2: 150 kg K ha-1\n , K3: 200 kg K ha-1 , \n\n\n\nMg = mega gram= 106 g \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 99\n\n\n\nSalicylic acid is a strong ameliorator for stress conditions (Hayat and Ahmed, \n2007). It plays diverse physiological roles in plant growth, photosynthesis and the \nuptake of nutrients (Janda et al., 2007; Merwad, and Abdel-Fattah, 2015).\n\n\n\nEffect of K Fertilisation and Foliar spraying of SA on Quality of Sugar Beet Crop \nGrown on Saline Soil.\nThe effects of K fertilisation and foliar spray of salicylic acid on sucrose content, \njuice purity percentage, gross sugar yield and possible extractable white sugar are \nshown in Table 3. All parameters increased significantly with K fertilisation as \nwell as foliar spray with SA. The highest values of sucrose content, juice purity, \ngross sugar yield and possible extractable white sugar (19.68%, 85.82%, 15.8 and \n13.56 Mg ha-1, respectively) were obtained under the highest rate of K fertilisation \ntreatment (200 kg K ha-1) in combination with SA acid spray. \n\n\n\nThe highest increase in sucrose content was 20% whilst the highest possible \nextractable white sugar was 184.3% and both were achieved with K fertilisation \nof 200 kg ha-1. These results agree with those obtained by Salami and Saadat \n(2013) who obtained a 17% increase in sucrose content in sugar beet upon \napplying K at a rate of 95 kg ha-1. The results obtained by this study were also \nsimilar to those obtained by Shahidi and Khalafi (2010) and Neseim et al. (2014). \nThe white sugar yield is an important parameter of sugar beet. Most of the quality \nparameters such as sucrose content, juice purity and gross sugar yield have been \nreported to increase upon K fertilisation (Zaifi zaden and  Amjadi, 2001). Ibrahim \n\n\n\nPotassium-Salicylic Acid Interaction on Sugar Beet Grown in Saline Soil\n\n\n\nTABLE 3\nEffect of potassium fertilisation and foliar spray of salicylic acid on some quality of \n\n\n\nsugar beet crop grown on saline soil\n\n\n\n9 \n \n\n\n\n \nTABLE 3 \n\n\n\nEffect of potassium fertilisation and foliar spray of salicylic acid on some quality of sugar beet \ncrop grown on saline soil \n\n\n\n\n\n\n\nPotassium \nfertilization \n\n\n\n(A) \n\n\n\n Salicylic \n Acid spray  \n\n\n\n(B) \n\n\n\nSucrose \n (%) \n\n\n\nJuice purity \n(%) \n\n\n\nGross sugar \nyield (Mg ha-1) \n\n\n\nPossible \nextractable \nwhite sugar \n(Mg ha-1) \n\n\n\nK0 \n  \n\n\n\nWithout 15.56 78.30 6.10 4.77 \nWith 16.80 80.00 6.96 5.57 \n\n\n\nMean   16.18 79.15 6.53 5.17 \nK1 \n  \n\n\n\nWithout 18.14 81.65 7.93 6.48 \nWith 18.53 82.30 9.83 8.09 \n\n\n\nMean   18.340 81.98 8.88 7.28 \nK2 \n  \n\n\n\nWithout 18.91 82.60 10.66 8.80 \nWith 18.98 83.31 12.78 10.64 \n\n\n\nMean   19.95 82.96 11.72 9.72 \nK3 \n  \n\n\n\nWithout 19.13 84.16 13.89 11.69 \nWith 19.68 85.82 15.80 13.56 \n\n\n\nMean   19.40 84.99 14.85 12.63 \n                                                                                       Mean of salicylic acid spray  \n\n\n\n  Without 17.93 81.68 9.65 7.94 \nWith 18.50 82.86 11.34 9.49 \n\n\n\nLSD 0.05% A 0.184 0.492 0.328 0.297 \n B 0.143 0.348 0.232 0.195 \n AB 0.286 0.464 0.464 0.389 \n\n\n\nK0: Without potassium fertilisation, K1: 100 kg K ha-1, K2: 150 kg K ha-1, K3: 200 kg K ha-1 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015100\n\n\n\net al., (2002) found that application of K at 95 kg ha-1 increased of the contents \nof sugar, sucrose, juice purity, gross sugar yield and possible extractable white \nsugar of sugar beet. Moussa and Khodary (2003) reported that foliar spraying \nSA to salinity-stressed maize stimulated plant growth rate via accelerating their \nphotosynthesis and carbohydrate metabolism.\n\n\n\nEffect of K Fertilisation and Foliar Spraying of SA on Impurity Components in a \nPaste of Sugar Beet Crop Grown in Saline Soil.\nContents of K, Na and \u03b1-amino N in beet paste (Table 4) decreased with K \napplication as well as with foliar spray with SA. The average decreases in K \ncontent due to K application were 11.1%, 18.6% and 25.2% caused by K1, K2 and \nK3, respectively. Comparable respective decreases in Na contents were 13.0%, \n27.9% and 34.0%. Comparable respective decreases in \u03b1-amino N contents were \n4.5%, 19.6% and 35.8%. The non-K fertilized, non-SA sprayed plants showed \nthe highest contents of K, Na and \u03b1-amino N (5.60, 2.57 and 1.89 mmol 100 \ng-1 beet paste, respectively). Plants that received the highest K in combination \nwith foliar SA spray showed decreases of 32.6%, 40.5% and 43.4% in K, Na \nand \u03b1-amino N, respectively. These results agreed with those obtained by Salami \nand Saadat (2013) who applied 95 kg K ha-1 and obtained decreases in K, Na \nand \u03b1-amino N contents in sugar beet.  The current results were similar to those \nreported by  Eskandar Zadeh (1999),  El-Yamani (1999), Hellal et al. (2009), \nNeseim et al.,(2014) and Merwad and Abdel-Fattah (2015). \n\n\n\nThe average decrease in K content due to foliar spray with SA were 10.7%. \nComparable decreases in Na and \u03b1-amino N contents were 13.0% and 13.1 %, \nrespectively. Plants sprayed with SA but not fertilized with K contained K, Na \n\n\n\nMerwad, A.M.A.\n\n\n\nTABLE 4\nEffect of potassium fertilisation and foliar spray of salicylic acid on impurity components \n\n\n\nin paste of sugar beet crop grown on saline soil\n\n\n\n10 \n \n\n\n\n\n\n\n\nTABLE 4 \nEffect of potassium fertilisation and foliar spray of salicylic acid on impurity components in \n\n\n\npaste of sugar beet crop grown on saline soil \n \n\n\n\nPotassium \nfertilisation \n\n\n\n(A) \n\n\n\nSalicylic \n acid spray  \n\n\n\n(B) \n\n\n\nK content  \n(mmol  kg-1 beet paste) \n\n\n\nNa content  \n(mmol kg-1 beet paste) \n\n\n\n\u03b1-amino N  \n(mmol kg-1  beet paste) \n\n\n\nK0 \n  \n\n\n\nWithout 56.0 25.7 18.9 \nWith 50.3 23.7 16.3 \n\n\n\nMean   53.2 24.7 17.6 \nK1 \n  \n\n\n\nWithout 49.7 23.7 17.7 \nWith 45.0 19.3 15.8 \n\n\n\nMean   47.3 21.5 16.8 \nK2 \n  \n\n\n\nWithout 46.3 19.3 15.5 \nWith 40.3 16.3 12.7 \n\n\n\nMean   43.3 17.8 14.1 \nK3 \n  \n\n\n\nWithout 42.0 17.2 11.9 \nWith 37.7 15.3 10.7 \n\n\n\nMean   39.8 16.3 11.3 \n                           Mean of salicylic acid spray \n  Without 48.5 21.5 16.0 \n With 43.3 18.7 13.9 \n\n\n\n LSD 0.05% A 0.129 0.104 0.090 \n B 0.091 0.074 0.064 \n AB NS NS NS \n\n\n\n K0: Without potassium fertilisation, K1: 100 kg K ha-1, K2: 150 kg K ha-1\n , K3: 200 kg K ha-1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 101\n\n\n\nand \u03b1-amino N lower by 10.2%, 7.8% and 13.8%, respectively, in comparison \nwith those not sprayed with SA and not fertilised with K. Armin and Asgharipour \n(2012) found that the application of boric acid decreases K, Na, \u03b1-amino N content \nin sugar beet. This study\u2019s results are similar to those reported by  Javaheripour et \nal. (2005) and Kristek et al. (2006).\n        \nEffect of K Fertilisation and Foliar Spraying of SA on Contents and Uptake of N, \nP and K in Shoots of Sugar Beet Crop Grown on Saline Soil.\nPotassium fertilization rates as well as foliar spraying of SA caused a positive \nand significant effect on the uptake of N, P and K nutrients and contents in shoots \n(Table 5). The highest values of N, P and K contents of sugar beet (3.67%, 0.48% \nand 4.47%, respectively) and N, P and K uptake (194.2, 25.6 and 236.1 kg ha-1, \nrespectively) were obtained with a K-fertilisation rate of 200 kg K ha-1 combined \nwith the spraying of SA. However, untreated plants showed the lowest contents \n(2.7%, 0.16% and 1.8%)  and uptake   (59.6, 4.54 and 51.8 kg ha-1) of N, P and \nK, respectively.\n\n\n\nIncreased application of K increases the N, P and K contents by up to 47%, \n150% and 120%, respectively, and was comparable to increases in uptake of up to \n132%, 277% and 248%, respectively compared to without K application. These \nresults agreed with those obtained by Fathy et al. (2009) and Salami and Saadat \n(2013) who reported that the application of K at a rate of 95 kg ha-1 increase \ncontents and uptake of N, P and K of shoot sugar beet crop. These results are \nsimilar to findings reported by Horn and Furstenfeld (2001), Etemadi (2000) and \nMack et al., (2007). Positive response to its application is a manifestation of K \n\n\n\nPotassium-Salicylic Acid Interaction on Sugar Beet Grown in Saline Soil\n\n\n\nTABLE 5\nEffect of potassium fertilisation and foliar spray of salicylic acid on contents and uptake \n\n\n\nof N, P and K in shoots of sugar beet crop grown on saline soil.\n\n\n\n11 \n \n\n\n\nTABLE 5 \nEffect of potassium fertilisation and foliar spray of salicylic acid on contents and uptake of N, P \n\n\n\nand K in shoots of sugar beet crop grown on saline soil. \n \n\n\n\n  K0: Without potassium fertilisation, K1: 100 kg K ha-1, K2: 150 kg K ha-1\n , K3: 200 kg K ha-1 \n\n\n\n\n\n\n\nPotassium \nfertilization \n\n\n\n(A) \n\n\n\nSalicylic \nacid spray \n\n\n\n(B) \n\n\n\nContencentration (g kg-1) Uptake ( kg ha-1) \n\n\n\nN P K N P K \n\n\n\nK0 \n  \n\n\n\nWithout 20.7 1.6 18.0 59.66 4.54 51.80 \nWith 25.7 2.1 20.7 88.23 7.33 71.03 \n\n\n\nMean  23.2 1.8 19.4 73.95 5.94 61.41 \nK1 \n  \n\n\n\nWithout 24.0 2.7 29.3 89.63 9.96 109.42 \nWith 28.3 3.0 30.7 112.33 11.91 121.72 \n\n\n\nMean  26.2 2.85 30.0 100.9 10.94 115.57 \nK2 \n  \n\n\n\nWithout 28.0 3.4 34.7 118.65 14.41 146.75 \nWith 31.0 3.8 37.3 142.73 17.50 171.80 \n\n\n\nMean  29.5 3.6 36.0 130.69 15.95 159.27 \nK3 \n  \n\n\n\nWithout 31.7 4.1 40.7 149.18 19.31 191.56 \nWith 36.7 4.8 44.7 194.18 25.57 236.14 \n\n\n\nMean   34.2 4.5 42.7 171.68 22.44 213.85 \n  Mean of salicylic acid spray \n  Without 26.1 2.9 30.7 104.28 12.06 124.88 \n With 30.4 3.4 33.3 134.37 15.58 150.17 \n\n\n\n LSD0.05% A 0.166 0.023 0.113 11.22 1.302 6.537 \n B 0.117 0.016 0.080 7.936 0.920 4.016 \n AB NS NS NS NS 1.841 9.245 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015102\n\n\n\nmaintaining a balance between carbohydrate and proteins (Moustafa and Darwish, \n2001; Moneral et al., 2007).\n\n\n\nSpraying SA increased the contents of N, P and K by an average of 16.5%, \n17.2% and 8.5%, respectively, and N, P and K uptake by an average of 28.9%, \n29.2% and 20.3%, respectively, compared to without SA. These results agree with \nthose obtained by Zahra et al., (2010) and  Merwad and Abdel-Fattah (2015).\n\n\n\nCONCLUSION\nPotassium application and SA spray had significant positive effects on shoot and \nroot yields, quality, and N, P and K contents and uptake by sugar beets. The highest \nvalues were obtained at the highest rate of potassium fertilisation applied together \nwith the spraying of SA. The highest yield and quality (sucrose, juice purity, gross \nsugar yield and possible extractable white sugar) were obtained with a potassium \ndose of 200 kg ha-1 in conjunction with spraying with SA. This study recommends \nthe application of potassium combined with foliar spray with with SA to obtain a \nhigh quality of sugar from the sugar beet crop grown on saline soils.\n\n\n\nREFERENCES\nAhadi, S.A. and M.M. Sobhani. 2005. Effect of different irrigation amounts and \n\n\n\npotassium fertilizer rates on yield and quality of sugar beet and water efficiencies. \nJ. Agric. Sci. 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\uf0ed\uf0eb\uf020\n\n\n\n\uf0d9\uf0e6\uf020\uf0d9\uf0bb\uf0a6\uf0b7\uf0ae\uf0bf\uf020\uf020\uf020\uf0d3\uf0e6\uf020\uf0d3\uf0bf\uf0b2\uf0bf\uf0b9\uf0b7\uf0b4\uf020\uf020\uf020\uf0d8\uf0e6\uf020\uf0ce\uf0bf\uf0b8\uf0bf\uf0bc\uf020\uf020\n\n\n\n\n\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf020\n\uf020\n\uf020\n\n\n\n\uf020 Fig. 1: Location of the study site (Adapted from Wickens 1991). 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\uf0bd\uf0bf\uf0ac\uf0bd\uf0b8\uf0b3\uf0bb\uf0b2\uf0ac\uf020\n\n\n\n\uf0b3\uf0bf\uf0b2\uf0bf\uf0b9\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b7\uf0b2\uf020\uf0bd\uf0b1\uf0b3\uf0be\uf0b7\uf0b2\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\uf0bf\uf0b0\uf0b0\uf0ae\uf0b1\uf0b0\uf0ae\uf0b7\uf0bf\uf0ac\uf0bb\uf020\uf0b0\uf0ae\uf0bf\uf0bd\uf0ac\uf0b7\uf0bd\uf0bb\uf0ad\uf020\uf0bd\uf0bf\uf0b2\uf020\uf0b7\uf0b3\uf0b0\uf0ae\uf0b1\uf0aa\uf0bb\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0af\uf0ab\uf0bf\uf0b4\uf0b7\uf0ac\uf0a7\uf020\n\n\n\n\uf0f8\uf0c9\uf0bf\uf0b2\uf0b7\uf020\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\n\n\n\n\n\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0ee\n\uf0f3\uf0ef\n\n\n\n\uf0ee\n\uf020\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b4\uf0b1\uf0b2\uf0b9\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0b3\uf0bb\uf0bc\uf0b7\uf0ab\uf0b3\uf0f3\n\n\n\n\uf0f3\uf0ef\uf0a7\uf0ae\uf0f3\uf0ef\n\n\n\n\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2 \uf0f3\uf0ef\uf0a7\uf0ae\uf0f3\uf0ef\uf020\n\n\n\n\uf0f3\uf0ef\uf020\uf0a7\uf0ae\uf0f3\uf0ef\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0ae\uf0bb\uf0af\uf0ab\uf0b7\uf0ae\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\uf0b3\uf0bf\uf0b7\uf0b2\uf0ac\uf0bf\uf0b7\uf0b2\uf020\n\n\n\n\uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0b3\uf0bf\uf0ac\uf0ac\uf0bb\uf0ae\uf020\uf0bd\uf0b1\uf0b2\uf0ac\uf0bb\uf0b2\uf0ac\uf020\uf0f8\uf0ce\uf0bf\uf0ad\uf0b3\uf0ab\uf0ad\uf0ad\uf0bb\uf0b2\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0df\uf0b4\uf0be\uf0ae\uf0bb\uf0bd\uf0b8\uf0ac\uf020\uf0ef\uf0e7\uf0e7\uf0e8\uf0f7\uf0f2\uf020\uf0ce\uf0bb\uf0b4\uf0bf\uf0ac\uf0b7\uf0aa\uf0bb\uf0b4\uf0a7\uf020\uf0b4\uf0b1\uf0a9\uf020\uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020\n\n\n\n\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf0ad\uf0bb\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf020\uf0bd\uf0b1\uf0ab\uf0b4\uf0bc\uf020\uf0be\uf0bb\uf020\uf0bf\uf0ac\uf0ac\uf0ae\uf0b7\uf0be\uf0ab\uf0ac\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\uf0b8\uf0b7\uf0b9\uf0b8\uf020\uf0bd\uf0b4\uf0bf\uf0a7\uf020\uf0bd\uf0b1\uf0b2\uf0ac\uf0bb\uf0b2\uf0ac\uf020\uf0ac\uf0b8\uf0bf\uf0ac\uf020\uf0ad\uf0b4\uf0b1\uf0a9\uf0ad\uf020\uf0bc\uf0bb\uf0bd\uf0b1\uf0b3\uf0b0\uf0b1\uf0ad\uf0b7\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\n\n\n\n\uf0cc\uf0b1\uf0ac\uf0bf\uf0b4\uf020\uf0d2\n\n\n\n\uf0f3\uf0ef\uf020\n\n\n\n\uf0f3\uf0ef \uf0f3\uf0ef\n\n\n\n\uf0ac\uf0b8\uf0bb\uf020\uf0ad\uf0b8\uf0b1\uf0ae\uf0ac\uf020\uf0f8\uf0e9\uf0f2\uf0e9\uf0e7\uf020\uf0ac\uf020\uf0b8\uf0bf\uf0f3\uf0ef \uf0f3\uf0ef\uf0f7\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ca\uf0bb\uf0ae\uf0ac\uf0b7\uf0ad\uf0b1\uf0b4\uf0ad\uf020\n\uf0f3\uf0ef\uf0f7\uf0f2\uf020\n\n\n\n\uf0f3\uf0ef\uf0f7\uf020\uf0bf\uf0ae\uf0bb\uf020\uf0b4\uf0b1\uf0a9\uf0bb\uf0ae\uf020\uf0ac\uf0b8\uf0bf\uf0b2\uf020\uf0bb\uf0ad\uf0ac\uf0b7\uf0b3\uf0bf\uf0ac\uf0bb\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0a9\uf0b1\uf0ae\uf0b4\uf0bc\uf020\uf0ca\uf0bb\uf0ae\uf0ac\uf0b7\uf0ad\uf0b1\uf0b4\uf0ad\uf0f3\uf0d2\uf020\uf0b0\uf0b1\uf0b1\uf0b4\uf020\uf0a9\uf0b8\uf0bb\uf0ae\uf0bb\uf020\uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020\n\uf0f3\uf0ef\n\n\n\n\uf0f3\uf0ef\uf020\uf020\uf020\uf020\n\n\n\n\n\n\n\n\n\uf0e9\uf0f0 \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0f0\uf0fb\n\n\n\n\uf0ee\uf0f0\uf0fb\n\n\n\n\uf0ec\uf0f0\uf0fb\n\n\n\n\uf0ea\uf0f0\uf0fb\n\n\n\n\uf0e8\uf0f0\uf0fb\n\n\n\n\uf0ef\uf0f0\uf0f0\uf0fb\n\n\n\n\uf0d0\uf0da \uf0d9 \uf0d8 \uf0d3\n\n\n\n\uf0f0\uf0f2\uf0e9\uf0f3\uf0f0\uf0f2\uf0e7\uf020\uf0b3\n\n\n\n\uf0f0\uf0f2\uf0eb\uf0f3\uf0f0\uf0f2\uf0e9\uf020\uf0b3\n\n\n\n\uf0f0\uf0f2\uf0ed\uf0f3\uf0f0\uf0f2\uf0eb\uf020\uf0b3\n\n\n\n\uf0f0\uf0f2\uf0ee\uf0f3\uf0f0\uf0f2\uf0ed\uf020\uf0b3\n\n\n\n\uf0f0\uf0f2\uf0ef\uf0f3\uf0f0\uf0f2\uf0ee\uf020\uf0b3\n\n\n\n\uf0f0\uf0f3\uf0f0\uf0f2\uf0ef\uf020\uf0b3\n\n\n\n\uf020\n\uf020\n\n\n\nFig. 2:  Contribution of soil organic carbon in the different to total soil carbon in the profile \n(%) in the permanent fallow (PF), Gezira rotation (G), Rahad rotation (H) and Managil \n\n\n\nrotation in the irrigated Vertisols \n\uf020\n\n\n\n\uf020\nFig. 3:  Contribution of soil organic carbon in the different depths to total soil carbon in the \n\n\n\nprofile (%) in the natural forest (NF), sesame-sorghum rotation (S-S) and continuous sorghum \n(CS) in the rain-fed Vertisols  \n\n\n\n\n\n\n\n\n\uf0e9\uf0ef\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf020\n\n\n\nFig. 4: Contribution of soil inorganic carbon in the different soil depths to total soil carbon in \nthe profile (%) in the permanent  fallow (PF), Gezira rotation (G), Rahad rotation (H) and \n\n\n\nManagil rotation in the irrigated Vertisols \n\n\n\n\uf020\n\n\n\n\uf0f0\uf0fb\n\n\n\n\uf0ee\uf0f0\uf0fb\n\n\n\n\uf0ec\uf0f0\uf0fb\n\n\n\n\uf0ea\uf0f0\uf0fb\n\n\n\n\uf0e8\uf0f0\uf0fb\n\n\n\n\uf0ef\uf0f0\uf0f0\uf0fb\n\n\n\n\uf0d2\uf0da \uf0cd\uf0f3\uf0cd \uf0dd\uf0cd\n\n\n\n\uf0f0\uf0f2\uf0eb\uf0f3\uf0f0\uf0f2\uf0e7\uf020\uf0b3\n\n\n\n\uf0f0\uf0f2\uf0eb\uf0f3\uf0f0\uf0f2\uf0e9\uf020\uf0b3\n\n\n\n\uf0f0\uf0f2\uf0eb\uf0f3\uf0f0\uf0f2\uf0e9\uf020\uf0b3\n\n\n\n\uf0f0\uf0f2\uf0ed\uf0f3\uf0f0\uf0f2\uf0eb\uf020\uf0b3\n\n\n\n\uf0f0\uf0f2\uf0ef\uf0f3\uf0f0\uf0f2\uf0ee\uf020\uf0b3\n\n\n\n\uf0f0\uf0f3\uf0f0\uf0f2\uf0ef\uf020\uf0b3\n\n\n\n\uf020\nFig. 5: Contribution of soil inorganic carbon in the different soil depths to total soil carbon in \n\n\n\nthe profile (%) in the natural forest (NF) sesame-sorghum rotation (S-S) and continuous \nsorghum (CS) in the rain-fed Vertisols \uf020\n\n\n\n\n\n\n\n\n\uf0e9\uf0ee \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0cc\uf0df\uf0de\uf0d4\uf0db\uf020\uf0eb\uf020\n\n\n\n\uf0dd\uf0ae\uf0b1\uf0b0\uf020\uf0ac\uf0b7\uf0b4\uf0b4\uf0bf\uf0b9\uf0bb\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ba\uf0bb\uf0ae\uf0ac\uf0b7\uf0b4\uf0b7\uf0ad\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ab\uf0b2\uf0bc\uf0bb\uf0ae\uf020\uf0b7\uf0ae\uf0ae\uf0b7\uf0b9\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0ca\uf0bb\uf0ae\uf0ac\uf0b7\uf0ad\uf0b1\uf0b4\uf0ad\uf020\n\uf020\n\n\n\n\uf0dd\uf0ae\uf0b1\uf0b0\uf020 \uf0cc\uf0b7\uf0b4\uf0b4\uf0bf\uf0b9\uf0bb\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ba\uf0bb\uf0ae\uf0ac\uf0b7\uf0b4\uf0b7\uf0ad\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\n\n\n\n\uf0dd\uf020 \uf0dc\uf0b7\uf0ad\uf0bd\uf020\uf0b0\uf0b4\uf0b1\uf0ab\uf0b9\uf0b8\uf0b7\uf0b2\uf0b9\uf020\uf0f8\uf0f0\uf0f2\uf0ef\uf0e8\uf020\uf0f3\uf020\uf0f0\uf0f2\uf0ee\uf0f0\uf020\uf0b3\uf0f7\uf0f4\uf020\uf0b8\uf0bf\uf0ae\uf0ae\uf0b1\uf0a9\uf0b7\uf0b2\uf0b9\uf020\uf0f8\uf0f0\uf0f2\uf0ef\uf0f0\uf020\uf0f3\uf020\uf0f0\uf0f2\uf0ef\uf0eb\uf020\uf0b3\uf0f7\uf0f4\uf020\uf0ae\uf0b7\uf0bc\uf0b9\uf0b7\uf0b2\uf0b9\uf0f4\uf020\uf0b9\uf0ae\uf0bb\uf0bb\uf0b2\uf020\uf0ae\uf0b7\uf0bc\uf0b9\uf0b7\uf0b2\uf0b9\uf0f4\uf020\uf0ef\uf0ee\uf0f0\uf020\uf0b5\uf0b9\uf020\n\n\n\n\uf0d2\uf020\uf0b8\uf0bf\uf0f3\uf0ef\uf020\n\uf0c9\uf020 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\uf0b7\uf0b2\uf020 \uf0ac\uf0b8\uf0bb\uf020\uf0ad\uf0bb\uf0b3\uf0b7\uf0bf\uf0ae\uf0b7\uf0bc\uf020 \uf0ac\uf0ae\uf0b1\uf0b0\uf0b7\uf0bd\uf0ad\uf020 \uf0ba\uf0b1\uf0ae\uf020 \uf0b7\uf0b2\uf0bd\uf0ae\uf0bb\uf0bf\uf0ad\uf0bb\uf0bc\uf020\uf0b0\uf0ae\uf0b1\uf0bc\uf0ab\uf0bd\uf0ac\uf0b7\uf0aa\uf0b7\uf0ac\uf0a7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf020\n\n\n\n\uf0bd\uf0bf\uf0ae\uf0be\uf0b1\uf0b2\uf020\uf0ad\uf0bb\uf0af\uf0ab\uf0bb\uf0ad\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0f2\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cb\uf0ad\uf0bb\uf020\uf0d3\uf0bf\uf0b2\uf0bf\uf0b9\uf0f2\uf020\uf0ef\uf0e7\uf0e6\uf020\uf0ee\uf0ef\uf0e9\uf0f3\uf0ee\uf0ee\uf0ee\uf0f2\n\n\n\n\uf0cd\uf0bb\uf0bd\uf0ac\uf0b1\uf0ae\uf020\uf0ce\uf0bb\uf0b0\uf0b1\uf0ae\uf0ac\uf020\uf0d2\uf0b1\uf0f2\uf020\uf0ee\uf0f0\uf0ed\uf0e7\uf0e8\uf0f3\uf0cd\uf0ab\uf0f2\n\n\n\n\n\n"
"\n\nINTRODUCTION\n\n\n\nChromobacterium violaceum is a Gram-negative facultative anaerobic bacterium \n\n\n\npathogenic to mammals and human. It inhabits soil and water and is widely \n\n\n\nfound in the tropical and subtropical regions of the world (McGowan and \n\n\n\nSteinberg 1995). It produces an antibiotic - violacein, a purple pigment that \n\n\n\ngives C. violaceum its characteristic violet colour (Forbes 2002). It also produces \n\n\n\nhydrogen cyanide (HCN) (Sneath 1956) and both substances are controlled by \n\n\n\nquorum sensing. Quorum sensing is a cell-to-cell communication system used by \n\n\n\nbacteria to control gene expression by signal molecules (Miller and Bassler 2001). \n\n\n\nThe signal use by C. violaceum to control the violacein and HCN is homoserine \n\n\n\nlactone (C6-HSL) (Srivastava and Gera 2006). Hydrogen cyanide was reported \n\n\n\nto have a negative effect on root metabolism and inhibit plant growth (Lambers \n\n\n\n1980; Schippers et al. 1990). Other cyanogenic bacteria such as Pseudomonas \n\n\n\nfluorescens are also reported to inhibit the growth of beans by cyanide production \n\n\n\n(Alstrom et al. 1989). \n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 14: 95-99 (2010)                        Malaysian Society of Soil Science\n\n\n\nGreen Bean (Vigna radiata) Seedling Growth Inhibition by \n\n\n\nChromobacterium violaceum under In-vitro Condition\n\n\n\nWai Keong Loke1 & Halimi Mohd Saud2\n\n\n\n1Institute of Tropical Agriculture, \n2Department of Agriculture Technology, Faculty of Agriculture,\n\n\n\nUniversiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan\n\n\n\nABSTRACT\nChromobacterium violaceum is a pathogenic soil bacterium producing violacein \n\n\n\nand hydrogen cyanide both of which is controlled by quorum sensing with the \n\n\n\nsame signal molecule homoserine lactone (C6-HSL). A study was carried out \n\n\n\nto determine if quorum sensing was a factor that was required for inhibiting \n\n\n\nthe growth of green bean (Vigna radiata) seedling. The results showed that C. \n\n\n\nviolaceum which reached quorum level inhibited the growth of green bean seedling \n\n\n\nas much as 86.5% for the shoot length and 92.1% for the root length. However \n\n\n\ninhibition was reduced to 37.5% for the shoot and 17.5% for root if the quorum \n\n\n\nlevel of C. violaceum was not reached under an aseptic environment. Furthermore \n\n\n\nsterilised inoculant (killed) which had not reached quorum level would not affect \n\n\n\nthe growth of green bean seedlings. These results indicate that quorum sensing in \n\n\n\nC. violaceum is a factor that determines its inhibitory effect on seedling growth.\n\n\n\nKeywords: Chromobacterium violaceum, quorum sensing, seedling growth, \n\n\n\nVigna radiata inhibition\n\n\n\n___________________\n\n\n\n*Corresponding author : Email: halimi@agri.upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201096\n\n\n\nThe tropical climate in Malaysia offers a very conductive environment for \n\n\n\nthe growth of C. violaceum and it is believed to be widely distributed locally \n\n\n\nin agriculture and non-agriculture soils. It was reported that Malaysia has the \n\n\n\nhighest human infection of C. violaceum in Southeast Asia (Anupop  2008). No \n\n\n\nstudy has been reported on the effect of C. violaceum on crop growth in Malaysia \n\n\n\nalthough cyanide production by rhizobacteria is considered a possible mechanism \n\n\n\nof plant growth inhibition. This study was carried out to determine if quorum \n\n\n\nsensing in indigenous isolates of C. violaceum is a determining factor in plant \n\n\n\ngrowth inhibition.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSeeds of green bean were surface sterilized by shaking in 1% sodium hypochlorite \n\n\n\nfor 4 minutes followed by 70% ethanol for 2 minutes and rinsed in sterilized \n\n\n\ndistilled water for one minute. The seeds were pre-germinated for 24 hours on wet \n\n\n\ncotton in a Petri dish prior to its transfer into a 50 mL test-tube. The tubes were \n\n\n\nfilled with sterilized vermiculite to the 15 mL level of the tube. A single colony \n\n\n\nof C. violaceum from the stock culture was transferred into LB broth (Luria and \n\n\n\nBurrous 1955) media tubes A and B and grown to log phase by 16 hours. Tube A \n\n\n\nwas C. violaceum inoculant which did not reach quorum level whereas Tube B \n\n\n\nwas incubated for three days at room temperature until quorum level was reached. \n\n\n\nQuorum level is the condition where the signal molecule C6-HSL reaches a certain \n\n\n\nlevel to activate the genes expression for production of violacein and cyanide \n\n\n\n(Srivastava and Gera 2006). The quorum level was reached as indicated by the \n\n\n\npresence of violacein that turns the media into purple colour (Fig.1). In Tube A, \n\n\n\nno purple colour was observed indicating that quorum level was not reached. A \n\n\n\n0.5 ml inoculant from Tube A and Tube B was used to inoculate the seedlings. As \n\n\n\na non-inoculated control, the inoculant was replaced with distilled water and the \n\n\n\nsame volume of Azospirillum brasilense Sp7 inoculant was used as a comparison. \n\n\n\nFive tubes were used for each treatment as replicates. The average shoot and \n\n\n\nroot length was measured after seven days of growth and percentage increase or \n\n\n\ndecrease in growth was compared against treatment using distilled water only.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nResults in Table 1 show the average shoot and root length obtained after seven \n\n\n\ndays of growth and the percent increase (+) or decrease (-) in terms of shoot \n\n\n\nand root length. The results clearly show that C. violaceum is inhibitory to the \n\n\n\ngrowth of green bean seedling. The greatest reduction in shoot and root length \n\n\n\n(86.5% and 92.1%, respectively) was obtained from seedlings treated with C. \n\n\n\nviolaceum that had reached quorum level indicating that cyanide is produced \n\n\n\nand inhibits the growth of shoot and root. Even if C. violaceum that has reached \n\n\n\nquorum was killed by sterilisation, the cyanide that has been produced during \n\n\n\nits growth is still active and inhibits the green bean seedlings and reduces shoot \n\n\n\ngrowth by 87.5% for shoot and 92.1% for root. Treatment with C. violaceum \n\n\n\nthat did not reach quorum level still inhibits the growth of both shoot and root. \n\n\n\nWai Keong Loke & Halimi Mohd Saud\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 97\n\n\n\nThe reduction was recorded at 37.5% for shoot and 17.5% for root. However for \n\n\n\nsterilized non-quorum C. violaceum,  the effect was negligible compared to the \n\n\n\ncontrol. This was because the dead bacteria were not able to reach quorum level \n\n\n\nto produce cyanide while no other inhibitory compounds were produced during \n\n\n\nthe treatment. Seedlings treated with A. brasilense Sp7, a plant-growth promoting \n\n\n\nrhizobacteria, showed promotion of growth by increasing shoot length by 13.5% \n\n\n\nand root length by 20.6%.\n\n\n\nTABLE 1\n\n\n\nEffect of C.violaceum on root and shoot length of green bean (V. radiata) after seven \n\n\n\ndays of growth\n\n\n\nThe results showed clearly the inhibitory effect of C. violaceum on green bean \n\n\n\nseedlings. The effects were most pronounced when the plants were inoculated with \n\n\n\nC. violaceum that has reached quorum level whereas the effect is less pronounced \n\n\n\nif the inoculants has not reached quorum level. One of the characteristic of C. \n\n\n\nviolaceum is production of an antibiotic, violacein and also cyanide when it \n\n\n\nreaches quorum level (Forbes 2002). Cyanide has been implicated as a possible \n\n\n\nagent of plant growth inhibition (Alstrom and Burns 1989) and has been shown to \n\n\n\nhave a negative effect on the growth of velvet leaf (Abutilon theoprasii) and corn \n\n\n\n(Zea mays) (Adam and Zdor 2001). In 2001, Kremer and Souissi confirmed that \n\n\n\nHCN produced by cyanogenic rhizobacteria is the major inhibitory compound on \n\n\n\ngrass. These results confirm the possible inhibitory effect of C. violaceum similar \n\n\n\non other HCN-producing pseudomonads as reported by Schippers et al. (1990). \n\n\n\nThe results also indicate that inhibitory effect is negligible if the quorum level \n\n\n\nhas not been reached supporting the idea that quorum level is needed for the \n\n\n\nproduction of cyanide which will inhibit the growth of green bean. As long as \n\n\n\nquorum level is not breached, cyanide will not be produced and plant growth will \n\n\n\nnot be inhibited.\n\n\n\nIn-vitro Inhibition of Green Bean Growth\n\n\n\nTreatment s \nAverage shoot \n\n\n\nlength (cm)  \n\n\n\nReduction / \n\n\n\nincrease  (%)  \n\n\n\nAverage root \n\n\n\nlength (cm)  \n\n\n\nReduction/  \n\n\n\nincrease  \n\n\n\n(%) \n\n\n\nSterilised distilled water \n\n\n\n(Control)\n10.4 cm 0 % 6.3 cm 0 % \n\n\n\nA. brasilense  Sp7 11.8 cm +13.5 % 7.6 cm +20.6 %\n\n\n\nC. violaceum  \n\n\n\nnon- quorum level  (Tube A)  \n6.5 cm -37.5 % 5.2 cm -17.5 %\n\n\n\nC. violaceum\n\n\n\nquorum level  (Tube B)  \n1.4 cm -86.5 % 0.5 cm -92.1 %\n\n\n\nC. violaceum\n\n\n\nNon quorum level  (Tube A) - \n\n\n\nSterilis ed\n\n\n\n10.8 cm +3.8 % 6.2 cm -1.6 %\n\n\n\nC. violaceum\n\n\n\nquorum level  (Tube B) - \n\n\n\nSterilis ed \n\n\n\n1.3 cm -87.5 % 0.5 cm -92.1 %\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201098\n\n\n\nFig. 1: C.violaceum culture reaching quorum level (B) and non quorum level (A). \n\n\n\nCulture that reached quorum level can be differentiated based on the purple colour \n\n\n\nof the media\n\n\n\nFig. 2: Effect of non-quorum level C.violaceum (A) and quorum level C.violaceum (B) \n\n\n\ninoculations on seedling growth of green bean (V. radiata)\n\n\n\nAlthough the results demonstrate the inhibitory effect of C. violaceum when \n\n\n\nquorum level is reached, it is believed that such a quorum level will be difficult \n\n\n\nto reach under a natural environment. Thus it is expected that the effect of C. \n\n\n\nviolaceum in agriculture soils would be minimal. However, a detailed study needs \n\n\n\nto be carried out to confirm this hypothesis.\n\n\n\nWai Keong Loke & Halimi Mohd Saud\n\n\n\nFig.1:  C. violaceum culture reaching quorum level (B) and non quorum level (A). Culture\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 99\n\n\n\nCONCLUSION\n\n\n\nThe quorum level of C. violaceum is the determining factor that inhibits shoot and \n\n\n\nroot growth under in-vitro conditions.\n\n\n\n\n\n\n\nREFERENCES\nAdam, O. and R. Zdor. 2001. Effect of cyanogenic rhizobacteria on the growth of \n\n\n\nvelvetleaf (Abutilon theophrasii) and corn (Zea mays) in autoclaved soil and the \n\n\n\ninfluence of supplemented glycine. Soil Biology Biochemistry 33: 801-809.\n\n\n\nAlstrom, S. and R.G. Burns. 1989. Cyanide production by rhizobacteria as a possible \n\n\n\nmechanism of plant growth inhibition. Biology Fertility of Soils 7: 232-238.\n\n\n\nAnupop, J. 2008. Human Chromobacterium violaceum infection in Southeast Asia: \n\n\n\ncase reports and literature review. Southeast Asian Journal of Tropical Medicine \n\n\n\nPublic Health  39: 452-460.\n\n\n\nForbes, B.A., F.S. Daniel and S.W. Alice. 2002. Diagnostic Microbiology: 11th Ed. \n\n\n\nMosby, pp 423-434.\n\n\n\nKremer, R.J. and T. Souissi. 2001. Cyanide production by rhizobacteria and potential \n\n\n\nfor suppression of weed seedling growth. Current Microbiology 43: 182-186.\n\n\n\nLambers, H. 1980. The physiological significance of cyanide-resistant respiration in \n\n\n\nhigher plants. Plant, Cell and Environment  3: 293-302.\n\n\n\nLuria and Burrous. 1955. Journal of Bacteriology 74: 461.\n\n\n\nMcGowan, Jr, J.E. and J.P. Steinberg. 1995. Other gram negative bacilli, in Mandell \n\n\n\nG. L., Bennett J.E. Dolin R. (Ed). Principles and Practice of Infectious Diseases, \n\n\n\nNew York, NY, Churchill Livingstone, pp 2106-2115.\n\n\n\nMiller, M.B. and B.L. Bassler. 2001. Quorum sensing in bacteria. Annual Review of \n\n\n\nMicrobiology  55: 165-199.\n\n\n\nSchippers, B., A. Bakker. P. Bakker and R. van Peer. 1990. Beneficial and deleterious \n\n\n\neffects of HCN-producing pseudomonads on rhizosphere interactions. Plant \n\n\n\nand Soil 129: 75-83.\n\n\n\nSneath, P. H. A. 1956. Cultural and biochemical characteristics of the genus \n\n\n\nChromobacterium. Journal of General Microbiology 15: 70\u201398.\n\n\n\nSrivastava, S. and C. Gera. 2006. Quorum-sensing: The phenomenon of microbial \n\n\n\ncommunication. Current Science  90: 10-15.\n\n\n\nIn-vitro Inhibition of Green Bean Growth\n\n\n\n\n\n"
"\n\nINTRODUCTION\nHuman activity has put great stress on the Sub-Watershed of Krueng Jreue of \n\n\n\nthe Krueng Aceh Watershed. The high rate of population growth and the increase \nof land-based activities has led to changes in land use in the Sub-Watershed of \nKrueng Jreue. According to Ministerial Decree No. 328/2009, Krueng Aceh was \na critical watershed and designated as a priority watershed.\n\n\n\n The result of analysis of land cover data developed by Citra Spot during the \n2009-2013, indicates that there is a change of land use in the Sub-Watershed of \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 89- 104 (2017)                         Malaysian Society of Soil Science\n\n\n\nAnalysis of Soil Quality for Hydrological Disaster Mitigation \nin Sub-Watershed of Krueng Jreue, Aceh Besar Regency-\n\n\n\nIndonesia\n\n\n\nHelmi1, Hairul Basri2, Sufardi2, Helmi2\n\n\n\n1Doctoral Study Program of Agricultural Sciences Unsyiah, Tgk. Chik Pante Kulu \nStreet, Darussalam, Banda Aceh 23111 (Indonesia)\n\n\n\n1College of Forestry Tengku Chik Pante Kulu, 23111 Darussalam Banda Aceh \n(Indonesia)\n\n\n\n2Faculty of Agriculture of Syiah Kuala University, Tgk. Hasan Krueng Kalee Street \nNo. 3 Darussalam, Banda Aceh 23111 (Indonesia)\n\n\n\nABSTRACT\nLand conversion of forest to non-forest has led to a reduction in soil fertility, \nexhibited by a lower soil quality index value (SQI) in the Sub-Watershed of \nKrueng Jreue. This research aimed to determine soil quality associated with \nvarious land uses using the SQI value approach. SQI calculated based on the \ncriteria proposed by Mausbach & Seybold (1998), which is suitable for field \nconditions using analysis of Minimum Data Set (MDS). The parameters analyzed \ninclude rooting depth, soil texture, bulk density, total porosity, pH of the soil, \nC-organic, N-total, P-available, K-exchangeable and soil respiration.  The result \nshowed that the quality criteria of the soil consist of three classes: low, medium \nand high, which have soil quality index values of 0.27, 0.52, and 0.64 respectively. \nThe area of research was mapped accordingly by class: (1) high, covering an area \nof 14016.98  ha (60.38%); (2) medium, covering an area of 8542,90 ha (36.79%); \nand (3) low, covering an area of 658,18 ha (2.83%). Types of land use categorized \nas having high quality are  primary forest, secondary forest and residential area \nwith respective values of 0.66; 0.64, and 0.63. Open land, grassland, moor, rice \nfield and shrubs were of medium quality, with respective values of 0.47; 0.48; \n0.52; 0.51 and 0.55.\n\n\n\nKeywords: Soil quality, Soil quality index, Land use, Hydrological disaster \nmitigation, Sub-Watershed of Krueng Jreue \n\n\n\n___________________\n*Corresponding author : E-mail: helmiusi@gmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201790\n\n\n\nKrueng Jreue. That change caused a reduction in the primary forest from 1584.81 \nha (6.82%) to 1576.51 ha (6.79%), a decrease of 8.30 ha (Regional Development \nPlanning Agency Aceh, 2013). The reduction of forest land affects to watershed \nflow by diminishing the amount of existing water. Water supply in the Sub-\nWatershed of Krueng Jreue ranges from 0.24 to 3.22 m sec-1, while the total \namount of water demand for agriculture and households ranges from 0.18 to 6.44 \nm sec-1 (Isnin et al., 2012). \n\n\n\n The resources of the Sub-Watershed of Krueng Jreue should be managed \nsustainably by identifying the links between land issues and hydrology and the \nrelationship between the upstream-downstream regions which interconnected and \naffect the unit watershed ecosystem. One approach to improve the management \nand land use systems in the region is through the evaluation of soil quality \n(Rahmanipour et al., 2014). Soil quality can determine the productivity of plants, \nanimals and the quality of the environment over a long period (Wander et al., \n2002). The evaluation of soil quality is important to optimize the production and \nconservation of natural resources (Shahab et al., 2013) and can serve as a tool for \nagricultural managers and policymakers to gain a better understanding of how the \nagricultural system can affect soil resources (Dong et al., 2013). \n\n\n\n Land and water resources relate to the hydrologic cycle. Climate change has \ninfluenced changing in the hydrologic cycle (Tallaksen et al., 2009), such as floods \nand drought as a hydrological disaster.  Hydrologic disasters cannot be avoided, \nbut with the development of science and technologically supported, accurate data, \nthey can be anticipated to minimize associated losses and environmental damage. \nEarly warning, a non-structural tool implemented in developing countries \n(Jayawardena, 2015), is a major factor in disaster risk reduction and it is necessary \nin order to anticipate the occurrence of a hydrological disaster and thus minimize \nlosses with disaster preparedness (Bokal et al., 2014).\n\n\n\n In order to prepare for potential disaster, it is important to understand the \ncharacteristics of the region and its response to changes in the hydrological cycle \ndue to climate change (Van Huijgevoort et al., 2014). Including Sub-Watershed of \nKrueng Jreue, it is an important information in planning, territory management and \nearly anticipation of the negative effects and the risk of damage in a hydrological \ndisaster, both in the short and long term.\n\n\n\n According to the above problems, it is necessary to carry out research to \nanalyze the causes of the disaster that may occur in the sub-watershed based on \nbiophysical aspects. This study aimed to analyze the quality of the soil with an soil \nquality index (SQI) approach based on the criteria of  Mausbach and Seybold, 1998  \n(Sanchez-Navarro et al., 2015), which reveal the changes in land characteristics \naffecting the SQI Krueng Jreue Sub-Watershed. This research suggests mitigation \nefforts of hydrological disasters in the Sub-Watershed of Krueng Jreue, so that the \nnegative impacts and risks of flood and drought damage can be minimized.\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 91\n\n\n\nMATERIALS AND METHODS\nThe research was conducted in Watershed of Krueng Aceh, in the Sub-\n\n\n\nWatershed of Krueng Jreue. Administratively, it is part of the Aceh Besar Regency, \nwith coordinates of 5o 12\u2019-5o 28\u2019 N and 95o 20\u2019- 95o32\u2019 E and an area of 23218.06 \nha. This research was conducted from November 2015 to January 2016.\n\n\n\n The materials used include administrative maps, maps of soil type, slope \nmaps, land use maps, soil map units with a scale of 1:50.000, and chemical \ncompounds for analysis of physical and chemical properties. The tools used were \na GPS unit, altimeter, ground drill, Munsell Soil Color Chart, meter, pH meter, \ndigital camera, bag and ring samples, hoes, spades, knives, and tools for analysis \nof physical and chemical properties.  \n\n\n\n The research was conducted using a descriptive method, field survey, and \nanalysis in the laboratory. The results were obtained by performing several \nanalysis stages, including: (1) the analysis of soil properties as indicators of soil; \n(2) analysis of soil quality index based on criteria from Mausbach and Seybold \n(1998); and (3) the analysis of soil quality based on land quality index values of \neach unit of land mapping unit (LMU) and land use.\n\n\n\n Soil analysis includes: (1) physical properties of the soil:   soil texture, bulk \ndensity, and total porosity; (2) chemical properties of the soil: pH, C-organic, \nN-total, P-available and K-exchangeable; and (3) biological properties of soil: \nsoil respiration. Components, parameters, and methods of soil analysis are shown \nin Table 1.\n\n\n\nTABLE 1 \nComponents, parameters, and methods of soil analysis\n\n\n\n3 \n \n\n\n\nuse. \n      Soil analysis included: (1) physical properties of the soil:  soil texture, bulk density, \nand total porosity; (2) chemical properties of the soil: pH, C-organic, N-total, P-available \nand K-exchangeable; and (3) biological properties of the soil: soil respiration. \nComponents, parameters, and methods of soil analysis are shown in Table 1. \n \nTABLE 1  \nComponents, parameters, and methods of soil analysis \n \nNo. Component Parameter Unit Analysis Method \n\n\n\n1 Physical \nproperties of soil  \n\n\n\nTexture of soil  \nBulk density \nTotal porosity \n\n\n\n% \ng cm-3 \n% \n\n\n\nPipette (Stokes Law) \n    Ring sample (Core Method) \nGravimetry \n\n\n\n2 Chemical \nproperties of soil \n\n\n\npH  \nC-organic \nN-total \nP-available \nK-exchangeable \n\n\n\n- \n% \n% \nmg kg-1 \ncmol kg-1 \n\n\n\n    Electrometric 1 : 2,5 \nWalkley& Black \nKjeldahl \n    Bray II \n    1 N NH4OAc pH 7 \n\n\n\n3 Biological \nproperties of soil \n\n\n\nSoil respiration mg.C CO2kg-1 Verstraete, 1981(not in ref list) \n\n\n\nSource: Laboratory of Soil and Plant and Laboratory of Biological Soil, Faculty of Agriculture \n(2016) \n \n The results of the soil analysis were arranged in a matrix and calculated based on \nthe criteria of Mausbach and Seybold (1998) which was modified in accordance with the \nconditions of the land. Modifications were carried out on some aspects, namely: \n(1)  The indicator of C-total was replaced by C-organic, taking into consideration that \n\n\n\nlevels of C-organic soil are not significantly different from levels of C-total, because \nthe soil does not contain CaCO3 as a source of C-inorganic. In addition, it is much \neasier to measure C-organic levels. \n\n\n\n(2)  The indicator of aggregate stability was approximated by the percentage of \ndust+clay. The percentage of dust + clay determines the stability of the aggregate that \nplays a role in the regulatory function of humidity and also as a filter and land buffer. \n\n\n\n(3) The weight of some soil indicators were adjusted to the proficiency of soil indicators \nin order to improve the quality of soil where the research took place. \n\n\n\n(4) The upper and lower limits of several soil indicators were raised or lowered, \naccording to land conditions and assessment criteria of the physical, chemical and \nbiological soils.  \n\n\n\n \nThe modification of the soil indicators, the weight index, and the functioning \n\n\n\nscale assessment are presented in Table 2. \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n The results of the soil analysis were arranged in a matrix and calculated \nbased on the criteria of Mausbach and Seybold (1998), which was modified in \naccordance with the conditions of the land. Modifications were carried out on \nsome aspects, namely:\n\n\n\n(1)  The indicator of C-total is replaced by C-organic, with consideration \nof levels of C-organic soil that were not significantly different than \nlevels of C-total, because the soil does not contain CaCO3 as a source \nof C-inorganic. In addition, the measurement of C-organic levels is \nmuch easier to do.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201792\n\n\n\n(2) The indicator of aggregate stability was approximated by the \npercentage of dust + clay. The percentage of dust + clay will \ndetermine the stability of the aggregate that plays a role in the \nregulatory function of humidity and as a filter and land buffer.\n\n\n\n(3) The weight of some soil indicators were adjusted to the proficiency \nof soil indicators in order to improve the quality of soil in which the \nresearch took place.\n\n\n\n(4)  The upper and lower limits of several soil indicators were raised \nor lowered, according to land conditions and assessment criteria of \nphysical, chemical and biological soil. \n\n\n\nThe modification of the soil indicators, the weight index, and the functioning \nscale assessment are presented in Table 2.\n\n\n\nTABLE 2\nModification of land indicator, the weight index, and functioning\n\n\n\nassessment scale\n\n\n\n4 \n \n\n\n\nTABLE 2 \nModification of land indicator, the weight index, and functioning assessment scale \n \n\n\n\nSoil function \n\n\n\n \nWeight \n\n\n\nI \n \n\n\n\nSoil indicators Weight \nII \n\n\n\nWeight \nIII \n\n\n\nWeight \n Index \n\n\n\nAssessment function \n\n\n\nLower \nlimit \n\n\n\nUpper \nlimit \n\n\n\nX1 Y1 X2 Y2 \n\n\n\n1 2 3 4 5 6 7 8 9 10 \n\n\n\nPreservation of \nbiological \nactivity \n\n\n\n\n\n\n\n         \n0.4 Rooting medium 0.33       \n\n\n\n Rooting depth (cm)  0.6 0.079 5 0 180 1 \n\n\n\n  Bulk density (g cm-3)  0.4 0.053 2.1 0 1.3 1 \n\n\n\n  Humidity 0.33       \n\n\n\n  Total porosity (%)  0.2 0.026 20 0 80 1 \n\n\n\n  C-organic (%)  0.4 0.053 0.2 0 3 1 \n\n\n\n  Dust+ Clay (%)  0.4 0.053 0 0 100 1 \n\n\n\n  Nutrients 0.33       \n\n\n\n  pH  0.1 0.013 4 0 8 1 \n  P-available (mg kg-1)  0.2 0.026 2.5 0 50 1 \n\n\n\n  \nK-exchangeable  \n(cmol kg-1)  0.2 0.026 \n\n\n\n \n0.2 \n\n\n\n \n0 \n\n\n\n \n100 \n\n\n\n \n1 \n\n\n\n C-organic (%)  0.3 0.040 0.2 0 3 1 \n\n\n\n N-total  (%)  0.2 0.026 0.2 0 5.2 1 \nArrangement \nand distribution \nof water \n\n\n\n Dust + Clay (%) 0.60  0.18 0 0 100 1 \n\n\n\n   0.3 Total porosity (%) 0.20  0.06 \n \n\n\n\n20 \n \n0 \n\n\n\n \n80 \n\n\n\n \n1 \n\n\n\n    Bulk density (g cm-3) 0.20  0.06 2.1 0 1.3 1 \n\n\n\nFilter and buffer  \nDust+Clay (%) \n \n\n\n\n0.60 \n  \n\n\n\n0.18 \n \n\n\n\n0 0 100 1 \n\n\n\n 0.3 Total porosity (%) 0.10  0.03 20 0 80 1 \n  Microbiological process 0.30       \n\n\n\n\n\n\n\n\n\n\n\nC-organic (%)  0.33 0.030 0.2 0 3 1 \n\n\n\nN-total (%)  0.33 0.030 0.2 0 5.2 1 \n\n\n\nSoil respiration(mgC-CO2kg-1)  0.33 0.030 0 0 20 1 \nTotal 1.0         \n\n\n\nSource:  Modification of  Mausbach and Seybold (1998) \n \n Measurement steps in the calculation of soil quality index were as follows:  \n(1) The weighted index was calculated by multiplying the weight of soil used which was \n\n\n\nweight I (No. 2) with weight II (No. 4) with weight III (No. 5). For example, the \nweighted index of rooting depth was obtained by multiplying 0.4 (weight I) to 0.33 \n(weight II) to 0.60 (weight III), and the result was equal to 0.079. \n\n\n\n(2) Scores were calculated by comparing observational data of soil indicators and \nfunction of assessment (7-10). Scores ranged from 0 (low condition) to 1(high \ncondition). Scoring was done through interpolation in accordance with the range \nspecified by the data obtained. \n\n\n\n(3) The soil quality index was calculated by multiplying the weighted index (number 6) \nand soil indicator score. \n\n\n\n \nThe determination of soil quality index (SQI) was based on the formula by Liu et al. \n(2014)::SQI  \u2211         \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 93\n\n\n\n Measurement steps in the calculation of soil quality index are: \n(1) The weighted index is calculated by multiplying the weight of soil \n\n\n\nuse which is the weight I (No. 2) with a weight II (No. 4) with a \nweight III (No. 5). For example, the weighted index of rooting depth \nis obtained by multiplying 0.4 (weight I) to 0.33 (weight II) to 0.60 \n(weight III), and the result is equal to 0.079.\n\n\n\n(2)  Scores are calculated by comparing observational data of soil \nindicators and function of assessment (7-10). Scores are range from \n0 (low condition) to 1 (high condition). Scoring through interpolation \nin accordance with the range specified by the data obtained.\n\n\n\n(3)  The soil quality index is calculated by multiplying the weighted \nindex (number 6) and soil indicator score.\n\n\n\nThe determination of soil quality index (SQI) was based on the formula by \nLiu et al. (2014)::\n\n\n\n SQI =\u2211n\ni=1Wi x Si\n\n\n\nwhere\n  \nSQI = Soil quality index Si = Determined indicator score\nWi =  Weight index  n = Total soil indicators\n\n\n\nSQI data were compared to the criteria of soil quality based on the value of \nSQI to distinguish five classes: (1) very low, (2) low, (3) medium, (4) high, and \n(5) very high, as shown in Table 3.\n\n\n\nTABLE 3\nSoil quality criteria based on the SQI value\n\n\n\n5 \n \n\n\n\nwhere \nSQI = Soil quality index Si = Determined indicator score \n\n\n\n             Wi =  Weight index n = Total soil indicators \n \n \n SQI data were compared to the criteria of soil quality based on the value of SQI \nto distinguish five classes: (1) very low, (2) low, (3) medium, (4) high, and (5) very high, \nas shown in Table 3. \n \nTABLE 3 \nSoil quality criteria based on the SQI value \n \n\n\n\nNo. SQI value                  Soil quality criteria \n1 \n2 \n3 \n4 \n5 \n\n\n\n0.00 \u2013 0.19 \n0.20 \u2013 0.39 \n0.40 \u2013 0.59 \n0.60 \u2013 0.79 \n0.80 \u2013 1.00 \n\n\n\nVery low \n                          Low \n                          Medium \nHigh \nVery high \n\n\n\nSource: Partoyo (2005) \n \n\n\n\nRESULTS AND DISCUSSION \n \n\n\n\nAssessment of Quality Index for Soil Functions \n \nPreservation of Biological Activity \nThere are several soil indicators that support the functions of biological activity including \nrooting medium, humidity, and nutrients. The results of indicator analysis across the \nparameters of rooting medium, humidity, and nutrients show significant fluctuations. The \ncalculation of SQI data based on the function of soil for the preservation of biological \nactivity is listed in Table 4.  \n \nTABLE  4 \nResults of SQI calculation based on the function of land for the preservation of biological activity \n \nAssessment  \nindicator  \n\n\n\nUnit Soil Quality Index \nOL SH GL RD RF MR SF PF \n\n\n\nRooting medium \nRooting depth \nBulk density \n\n\n\ncm \ng.cm-3 \n\n\n\n0.001 \n0.051 \n\n\n\n0.035 \n0.053 \n\n\n\n0.030 \n0.053 \n\n\n\n0.011 \n0.055 \n\n\n\n0.033 \n0.055 \n\n\n\n0.037 \n0.055 \n\n\n\n0.083 \n0.055 \n\n\n\n0.104 \n0.057 \n\n\n\nHumidity \nTotal porosity % 0.010 0.010 0.011 0.012 0.011 0.012 0.012 0.014 \nC-organic % 0.018 0.025 0.020 0.024 0.007 0.024 0.031 0.021 \nDust + Clay % 0.033 0.035 0.029 0.048 0.034 0.035 0.037 0.038 \nNutrient \nPh \nP-available \nK-exchangeable \nC-organic \nN-total \n\n\n\n- \nmg kg-1 \ncmol kg-1 \n\n\n\n% \n% \n\n\n\n0.006  \n0.004 \n0.002 \n0.014 \n0.002 \n\n\n\n0.006 \n0.003 \n0.005 \n0.019 \n0.002 \n\n\n\n0.008 \n0.003 \n0.007 \n0.015 \n0.001 \n\n\n\n0.008 \n0.0001 \n\n\n\n0.004 \n0.018 \n0.002 \n\n\n\n0.008 \n0.002 \n0.009 \n0.005 \n\n\n\n0.0001 \n\n\n\n0.008 \n0.006 \n0.006 \n0.008 \n0.002 \n\n\n\n0.008 \n0.002 \n0.005 \n0.018 \n0.002 \n\n\n\n0.008 \n0.002 \n0.013 \n0.016 \n0.001 \n\n\n\nTotal  0.141 0.192 0.176 0.182 0.166 0.185 0.255 0.272 \nNote: OL= Open Land; SH= Shrubs; GL= Grassland; RD=Residential;RF= Rice Field; MR= \nMoor; SF= Secondary Forest; PF= Primary Forest  \n \n As shown in Table 4, the highest SQI based on preservation of biological activity \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201794\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nAssessment of Quality Index for Soil Functions\n\n\n\nPreservation of Biological Activity\nThere are several soil indicators that support the functions of biological activity \nincluding rooting medium, humidity, and nutrients. The results of indicator \nanalysis across the parameters of rooting medium, humidity, and nutrients show \nsignificant fluctuations. The calculation of SQI data based on the function of soil \nfor the preservation of biological activity is listed in Table 4. \n\n\n\nTABLE  4\nResults of SQI calculation based on the function of land for the preservation \n\n\n\nof biological activity\n\n\n\n5 \n \n\n\n\nwhere \nSQI = Soil quality index Si = Determined indicator score \n\n\n\n             Wi =  Weight index n = Total soil indicators \n \n \n SQI data were compared to the criteria of soil quality based on the value of SQI \nto distinguish five classes: (1) very low, (2) low, (3) medium, (4) high, and (5) very high, \nas shown in Table 3. \n \nTABLE 3 \nSoil quality criteria based on the SQI value \n \n\n\n\nNo. SQI value                  Soil quality criteria \n1 \n2 \n3 \n4 \n5 \n\n\n\n0.00 \u2013 0.19 \n0.20 \u2013 0.39 \n0.40 \u2013 0.59 \n0.60 \u2013 0.79 \n0.80 \u2013 1.00 \n\n\n\nVery low \n                          Low \n                          Medium \nHigh \nVery high \n\n\n\nSource: Partoyo (2005) \n \n\n\n\nRESULTS AND DISCUSSION \n \n\n\n\nAssessment of Quality Index for Soil Functions \n \nPreservation of Biological Activity \nThere are several soil indicators that support the functions of biological activity including \nrooting medium, humidity, and nutrients. The results of indicator analysis across the \nparameters of rooting medium, humidity, and nutrients show significant fluctuations. The \ncalculation of SQI data based on the function of soil for the preservation of biological \nactivity is listed in Table 4.  \n \nTABLE  4 \nResults of SQI calculation based on the function of land for the preservation of biological activity \n \nAssessment  \nindicator  \n\n\n\nUnit Soil Quality Index \nOL SH GL RD RF MR SF PF \n\n\n\nRooting medium \nRooting depth \nBulk density \n\n\n\ncm \ng.cm-3 \n\n\n\n0.001 \n0.051 \n\n\n\n0.035 \n0.053 \n\n\n\n0.030 \n0.053 \n\n\n\n0.011 \n0.055 \n\n\n\n0.033 \n0.055 \n\n\n\n0.037 \n0.055 \n\n\n\n0.083 \n0.055 \n\n\n\n0.104 \n0.057 \n\n\n\nHumidity \nTotal porosity % 0.010 0.010 0.011 0.012 0.011 0.012 0.012 0.014 \nC-organic % 0.018 0.025 0.020 0.024 0.007 0.024 0.031 0.021 \nDust + Clay % 0.033 0.035 0.029 0.048 0.034 0.035 0.037 0.038 \nNutrient \nPh \nP-available \nK-exchangeable \nC-organic \nN-total \n\n\n\n- \nmg kg-1 \ncmol kg-1 \n\n\n\n% \n% \n\n\n\n0.006  \n0.004 \n0.002 \n0.014 \n0.002 \n\n\n\n0.006 \n0.003 \n0.005 \n0.019 \n0.002 \n\n\n\n0.008 \n0.003 \n0.007 \n0.015 \n0.001 \n\n\n\n0.008 \n0.0001 \n\n\n\n0.004 \n0.018 \n0.002 \n\n\n\n0.008 \n0.002 \n0.009 \n0.005 \n\n\n\n0.0001 \n\n\n\n0.008 \n0.006 \n0.006 \n0.008 \n0.002 \n\n\n\n0.008 \n0.002 \n0.005 \n0.018 \n0.002 \n\n\n\n0.008 \n0.002 \n0.013 \n0.016 \n0.001 \n\n\n\nTotal  0.141 0.192 0.176 0.182 0.166 0.185 0.255 0.272 \nNote: OL= Open Land; SH= Shrubs; GL= Grassland; RD=Residential;RF= Rice Field; MR= \nMoor; SF= Secondary Forest; PF= Primary Forest  \n \n As shown in Table 4, the highest SQI based on preservation of biological activity Table 4 shows that the highest SQI based on preservation of biological \n\n\n\nactivity is contained in the primary forest (0.272) while the lowest is found in \nthe rice fields and in open land, with values of 0.166 and 0.141 respectively. The \nopen land in Sub-Watershed of Krueng Jreue is dominated by Oxisol soil type by \nrelatively infertile, topography 15-40%  and elevations 55-61 meters above sea \nlevel (m asl). Rice field is dominated by Inceptisol soil type, topography 0-8% \nand elevations 47 meters above sea level (m asl). Primary forests have high soil \nquality and SQI due to the content of organic matter and higher water retention \nwhen compared to other land uses (Karam et al., 2013), such as plantations and \nrice fields.\n\n\n\nArrangement and Distribution of Water\nSoil as the setting for the distribution of water was assessed using the parameters \nclay + dust percentage, total porosity and bulk density. Indicators on some land \nuse assessments show that the value of the difference was not significant, with \na difference in clay + dust of 0.085; total porosity of 0.028; and bulk density of \n0.011. The results of SQI calculations based on soil functions as regulation and \ndistribution of water are shown in Table 5.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 95\n\n\n\nThe results of total assessment indicators and water distribution arrangements in \nseveral land use categories show no significant fluctuations. Table 5 shows that the \nhighest SQI values are in the residential (0.253) and primary forest (0.225), while \nthe lowest are in the grassland (0.183) and open land (0.192). Grassland in Sub-\nWatershed of Krueng Jreue are dominated Imperata cylindrica plant by relatively \ninfertile,  Inceptisol and Entisol soil type, topography 8->40% and elevations \nbetween 32-108 meters above sea level (m asl). According to Cambardella and \nElliott (1992), the dust + clay percentage refers to soil composition in terms of the \nproportion of particles in a soil mass. It can also be defined as soil texture. Soil \nporosity refers to the existence of spaces (connected or not) between soil particles, \nconsisting of either water or air. Texture and bulk density have significant impacts \non the rate of infiltration. The physical properties determine how much water can \nflow into the pore spaces in the soil surface so as to increase the rate of water \ninfiltration. Coarse soil texture has a higher degree of infiltration, so that the \ncapacity of sand-soil infiltration is much larger than the clay (Foth, 1990).\n\n\n\nFilter and Buffer\nThe filter and buffer capacity of a soil can be defined by the percentage of dust + \nclay, total porosity, C-organic, N-total and soil respiration. Assessment indicators \nin some land use categories show no significant variation in the values, with a dust \n+ clay differential of 0.063; total porosity of 0.005; C-organic of 0.014; N-total \nof 0.003 and soil respiration of 0.003. Results of the SQI calculation based on the \nfunction of soil as a filter and buffer are shown in Table 6.\n\n\n\nTABLE 6\nResults of SQI calculation based soil functions as a filter and buffer\n\n\n\nTABLE 5\nResults of SQI calculations based on soil functions as arrangement and distribution \n\n\n\nof water\n\n\n\n6 \n \n\n\n\nwas found in the primary forest (0.272) while the lowest was found in the rice fields and \nin open land, with values of 0.166 and 0.141 respectively. Open land in Sub-Watershed of \nKrueng Jreue is dominated by Oxisol soil type and is relatively infertile with a \ntopography of 15-40% and an elevation of  55-61 m above sea level (m asl). Rice fieldis \nare dominated by Inceptisol soil type, with a topography of 0-8% and an elevation of 47 \nm above sea level (m asl). Primary forests have high soil quality and SQI due to the \ncontent of organic matter and higher water retention when compared to other land uses \nsuch as plantations and rice fields (Karam et al. 2013). \n \nArrangement and Distribution of Water \nSoil, as the setting for the distribution of water, was assessed using the parameters of clay \n+ dust percentage, total porosity and bulk density. Indicators on some land use \nassessments showed that the value of the difference was not significant, with the  \ndifference in clay+dust being 0.085, total porosity being 0.028, and bulk density being \n0.011. The results of the SQI calculations based on soil functions as regulation and \ndistribution of water are shown in Table 5. \n \n \n \n \n \n \n \nTABLE 5 \nResults of SQI calculations based on soil functions as arrangement and distribution of water \n \nAssessment \nindicators \n\n\n\nUnit Soil Quality Index \n      OL      SH      GL     RD      RF       MR      SF PF \n\n\n\nDust+Clay \nTotal porosity \nBulk pensity \n\n\n\n% \n% \ng.cm-3 \n\n\n\n0.113 \n0.023 \n0.058 \n\n\n\n0.119 \n0.023 \n0.060 \n\n\n\n0.099 \n0.024 \n0.060 \n\n\n\n0.162 \n0.028 \n0.063 \n\n\n\n0.117 \n0.026 \n0.063 \n\n\n\n0.115 \n0.025 \n0.060 \n\n\n\n0.127 \n0.027 \n0.063 \n\n\n\n0.128 \n0.032 \n0.065 \n\n\n\nTotal 0.192 0.202 0.183 0.253 0.206 0.200 0.21 0.225 \nNote: OL= Open Land; SH= Shrubs;GL= Grassland; RD=Residential; RF= Rice Field; MR= \nMoor; SF= Secondary Forest; PF= Primary Forest  \n \n\n\n\nThe results of total assessment indicators and water distribution arrangements in \nseveral land use categories showed no significant fluctuations. Table 5 shows that the \nhighest SQI values are in the residential (0.253) and primary forest (0.225), while the \nlowest are in the grassland (0.183) and open land (0.192). Grassland in Sub-Watershed of \nKrueng Jreue was dominated by Imperata cylindrica plants due to the  relatively infertile \nInceptisol and Entisol soil types, topography of  8->40% and an elevation between 32-\n108 m above sea level (m asl). According to Cambardella and Elliott (1992), the dust + \nclay percentage refers to soil composition in terms of the proportion of particles in a soil \nmass. It can also be defined as soil texture. Soil porosity refers to the existence of spaces \n(connected or not) between soil particles, consisting of either water or air. Texture and \nbulk density have significant impacts on the rate of infiltration. The physical properties \ndetermine how much water can flow into the pore spaces in the soil surface so as to \nincrease the rate of water infiltration. Coarse soil texture has a higher degree of \ninfiltration, with the capacity of sand-soil infiltration being much larger than in clay (Foth \n1990). \n \nFilter and Buffer \nThe filter and buffer capacity of a soil can be defined by the percentage of dust+clay, total \n\n\n\n7 \n \n\n\n\nporosity, C-organic, N-total and soil respiration. Assessment indicators in some land use \ncategories show no significant variation in the values, with a dust+clay differential of \n0.063, total porosity of 0.005, C-organic of 0.014, N-total of 0.003 and soil respiration of \n0.003. Results of the SQI calculation based on the function of soil as a filter and buffer \nare shown in Table 6. \n \nTABLE 6 \nResults of SQI calculation based soil functions as a filter and buffer \n \n\n\n\nAssessment \nIndicator \n\n\n\nUnit Soil Quality Index  \n      OL      SH      GL     RD      RF       MR      SF PF \n\n\n\nDust + Clay \nTotal porosity \n\n\n\n% \n% \n\n\n\n0.113 \n0.011 \n\n\n\n0.119 \n0.011 \n\n\n\n0.099 \n0.012 \n\n\n\n0.162 \n0.014 \n\n\n\n0.140 \n0.011 \n\n\n\n0.115 \n0.012 \n\n\n\n0.127 \n0.013 \n\n\n\n0.128 \n0.016 \n\n\n\nMicrobiological process \nC-organic \nN-total \nSoil respiration \n\n\n\n% \n% \n\n\n\nmg.C-CO2kg-1 \n\n\n\n0.010 \n0.002 \n0.003 \n\n\n\n0.014 \n0.002 \n0.006 \n\n\n\n0.011 \n0.001 \n0.003 \n\n\n\n0.013 \n0.002 \n0.003 \n\n\n\n0.009 \n0.001 \n0.003 \n\n\n\n0.007 \n0.003 \n0.003 \n\n\n\n0.018 \n0.003 \n0.004 \n\n\n\n0.011 \n0.001 \n0.003 \n\n\n\nTotal 0.139 0.152 0.125 0.194 0.140 0.140 0.164 0.160 \nNote: OL= Open Land; SH= Shrubs; GL= Grassland; RD=Residential; RF= Rice Field; MR= \nMoor; SF= Secondary Forest; PF= Primary Forest  \n \n Table 6 shows that there are no significant changes in SQI for the clay+dust \nindicator across the land use categories of open land, shrubs, rice field, moor, secondary \nforest and primary forest. However, the SQI value is greater for grassland. The total \nporosity assessment indicator does not show any significant differences among each land \nuse category. The only SQI values that exhibit change are the C-organic and soil \nrespiration parameters. Porosity plays a role  as a filter in the management of soil quality. \nSoil porosity is controlled by soil texture, structure, and content of organic matter. \nAccording to Buckman and Brady (1990), the movement of water and air in the soil is \ndetermined by the parameters of soil porosity. The size of the soil porosity is determined \nand described by the state of the soil structure. Granular soil structure can provide \nadequate porosity for infiltration. C-organic and N-total play roles as a nutrient buffers. \nApplication  of organic matter into the soil makes the soil microbes breakdown into \nenergy to produce nutrients that plants need. The process of decomposition of organic \nmaterial by microbes is measured by C-organic and N-total levels. Organic matter is \nnecessary to provide nutrients and improve the cation exchange capacity (CEC) (Jien and \nWang 2013). Increased soil CEC via fertilisers can reduce the loss of nutrients and \nimprove the efficiency of fertilisation (Aprile 2012). \n \nSoil Quality Index (SQI) \nThe SQI calculation is obtained by multiplying the weight index value (Wi) with the \ndetermined indicator value (Si). Soil quality index is further based on SQI. The SQI value \nranges from 0 to 1. As the value approaches 1, it indicates better soil quality. Soil criteria \nof several land use categories at Sub-watershed KruengJreue are found in Table 7.   \n \n \n \n \n \nTABLE 7 \nSoil quality criteria in several lands uses \n\n\n\n Soil Quality Index value Soil quality \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201796\n\n\n\n Table 6 shows that there are no significant changes in SQI for the clay + \ndust indicator across the land use categories of open land, shrubs, rice field, moor, \nsecondary forest and primary forest. However, there is a greater SQI value on \ngrassland. The total porosity assessment indicator does not show any significant \ndifferences among each land use category. The only SQI values that exhibit \nchange are in the C-organic and soil respiration parameters. The role of porosity \nis a filter in the management of soil quality. Soil porosity is controlled by soil \ntexture, structure, and content of organic matter. Buckman and Brady (1990), the \nmovement of water and air in the soil is determined by the parameters of soil \nporosity. The size of the soil porosity is determined and described by the state \nof the soil structure. Granular soil structure can provide adequate porosity for \ninfiltration. C-organic and N-total play roles as a nutrient buffers. Application  \nof organic matter into the soil affects soil microbes work down into energy to \nproduce nutrients that plants need. The process of decomposition of organic \nmaterial by microbes is measured by C-organic and N-total levels. Organic matter \nis necessary to provide nutrients and improve the cation exchange capacity (CEC) \n(Jien and Wang, 2013). Increased soil CEC via fertilizers can reduce the loss of \nnutrients, so as to improve the efficiency of fertilization (Aprile, 2012).\n\n\n\nSoil Quality Index (SQI)\nThe SQI calculation is obtained by multiplying the weight index value (Wi) with \nthe determained indicator value (Si). Soil quality index is further based on SQI. \nThe SQI value ranges from 0 to 1. As the value approaches 1, it indicates better \nsoil quality. Soil criteria of several land use categories at Sub-watershed Krueng \nJreue are illustrated in Table 7 \n\n\n\nTABLE 7\nSoil quality criteria in several lands uses\n\n\n\n8 \n \n\n\n\n \nLand use \n\n\n\n\n\n\n\nSoil Quality Index value Soil quality \ncriteria \n\n\n\nPreservation of \nbiological \nactivity \n\n\n\nArrangement\n& distribution \n\n\n\nof water \n\n\n\nFilter & \nbuffer \n\n\n\nTotal \n\n\n\nOpen land (OL) \nShrubs (SH) \nGrassland (GL) \nResidential (RD) \nRice Field (RF) \nMoor (MR) \nSecondary forest (SF) \nPrimary forest (PF) \n\n\n\n0.141 \n0.192 \n0.176 \n0.184 \n0.166 \n0.185 \n0.255 \n0.272 \n\n\n\n0.192 \n0.202 \n0.183 \n0.253 \n0.206 \n0.200 \n0.216 \n0.225 \n\n\n\n0.139 \n0.152 \n0.126 \n0.194 \n0.140 \n0.140 \n0.164 \n0.160 \n\n\n\n0.47 \n0.55 \n0.48 \n0.63 \n0.51 \n0.52 \n0.64 \n0.66 \n\n\n\nMedium \nMedium \nMedium \n\n\n\nHigh \nMedium \nMedium \n\n\n\nHigh \nHigh \n\n\n\nTotal 1.574 1.676 1.236   \naverage 0.197 0.209 0.155   \n \n Table 7 shows that agricultural, residential, and open land can be classified \nspecifically into eight categories. Entisol and Inceptisol soil orders are found at five land \nuse categories including rice field, shrubs, grassland and moor, with the exception being \nopen land where the Oxisol shows medium criteria soil. Inceptisol soil order is found in \nthe secondary and primary forest while the Entisol is found at residential areas and \nexhibits high soil category. This is because  the Entisol and Inceptisol orders are soils \nwhich are not developed. The main characteristic is determined by the dominant material \nand is usually found in soils with a variety of main material sources (Wu and Chen 2005). \n The results of SQI analysis of several lands use areas show significant \nfluctuation. The highest SQI value was found in the secondary forest (0.64) and primary \nforest (0.66) while the lowest was in the grassland and open land with values of 0.48 and \n0.47 respectively. The openness in open land and grassland areas causes a higher rate of \nrunoff, erosion, and sedimentation, as well as a decrease in soil fertility. The change in \nthe rate of runoff is caused by a decrease in soil infiltration capacity and poor vegetation \ncovering the land (Dunne et al. 1991; Bergkamp 1998). Changes in vegetation cover \ndensity cause surface runoff and erosion to increase by decreasing the soil\u2019s resistance \ntowards surface runoff and infiltration (Abraham et al. 1995). It is understood that the use \nand misuse of land determines the significance of surface runoff and soil erosion (Dunjo  \net al. 2004). The most significant impact is the degradation indicated by the decrease in \nsoil quality characteristics indicating that  cannot  be used for agriculture.     \n  The soil quality of fores areas is high and needs to be conserved. Such a \ncondition is a result of dense forest with large amounts of leaf litter. The surface of forest \nmineral soil is rich in organic material (Kaiser et al. 2016) and has better soil quality \ncompared to grassland and vegetation (Nuria et al. 2003). Even though forest land \nconsists of much organic material, only a limited amount is absorbed into the soil due to \nthe slow speed of the decaying progress of the material. However, the organic material of \nsoil is the main controlling factor for soil fertility for agriculture with low external inputs \n(Sitompul et al., 2000). The organic material accumulates at the soil surface and then i \nforms a tight layer consisting of leaf litter. This process is influenced by the \nmicroenvironment in the forest, especially at the level of the soil surface. The tree canopy \ncan limit the amount of sunlight, which in turn can discourage microbial activity in the \norganic recycling process. Microbial activity is an indicator of soil quality. Plants depend \non organisms living beneath the soil surface to produce organic mineral nutrition for its \ngrowth and development (Chen et al. 2003). Forest areas are regarded as land withhighest \nsoil quality due to minimum anthropogenic activity contact.  \n \n\n\n\n Table 7 shows that agricultural, residential, and open land are divided more \nspecifically into eight categories. Entisol and Inceptisol soil orders at five land \nuse categories including rice field, shrubs, grassland, moor, except Oxisol order \nat open land shows medium criteria soil. Inceptisol soil order is found in the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 97\n\n\n\nsecondary and primary forest, as well as Entisol at residential area, has high soil \ncategory. It occurs because Entisol and Inceptisol orders are soils which are not \ndeveloped. The main characteristic is determined by the dominant material and \nit is usually found in soil with variety sources of main material (Wu and Chen, \n2005).\n\n\n\nThe results of SQI analysis of several lands use areas shows significant \nfluctuation. The highest SQI value is located in the secondary forest (0.64) and \nprimary forest (0.66) while the lowest is in the grassland and open land with \nvalues of 0.48 and 0.47. Land openness in open land and grassland areas causes \nthe rate of runoff, erosion, and sedimentation, as well as a decrease in soil fertility. \nThe change in the rate of runoff is caused by the decrease of soil infiltration \ncapacity and poor vegetation to cover the land (Dunne et al., 1991; Bergkamp, \n1998). Changes in cover vegetation density causes surface runoff and erosion to \nincrease by decreasing the soil\u2019s resistance towards surface runoff and infiltration \n(Abraham et al., 1995). It is understood that the use and misuse of land determines \nthe significance of surface runoff and soil erosion (Dunjo et al., 2004). The most \nsignificant impact is the degradation indicated by the decrease of soil quality \ncharacteristic so that it cannot be used for agriculture.    \n\n\n\n  The soil quality of forestry areas is high and needs to be conserved. Such \ncondition is the result of dense forest with large amount of leaf litter. The surface \nof forest mineral soil is rich in organic material (Kaiser et al., 2016) and has \nbetter soil quality compared to grassland and vegetation (Nuria et al., 2003). \nEven though forest land consists of much organic material, only a limited amount \nis absorbed into the soil due to the slow speed of the decaying progress of \nmaterial. However, the organic material of soil is the main controlling factor of \nsoil fertility for agriculture with low external inputs (Sitompul et al., 2000). The \norganic material accumulates at the soil surface and then it forms a tight layer \nconsisting of leaf litter. This process is influenced by the microenvironment in \nthe forest, especially at the level of the soil surface. The tree canopy can limit \nthe amount of sunlight, which in turn can discourage microbial activity in the \norganic recycling process. Microbial activity is an indicator of soil quality. Plants \ndepend on organisms living beneath the soil surface in order to produce organic \nmineral nutrition for its growth and development (Chen et al., 2003). The forestry \narea is regarded as the land which has the highest soil quality due to minimum \nanthropogenic activity contact. \n\n\n\nSoil Quality Zonation Map of Research Territory\nThe criteria of soil quality based on SQI in the research territory is divided into \nthree classes i.e. low, medium, and high. Table 8 displays soil quality based on \naverage value of SQI as follows. \n\n\n\n Table 8 shows that soil quality in the research area is divided into three \nclasses namely low, medium, and high and each of these classes has an SQI \naverage value of 0.27, 0.52, and 0.64. The average value of SQI value 0.52 \nand 0.64  increase from the average value of SQI 0.27 by 48,07% and 57.81% \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201798\n\n\n\nrespectively. The SQI average values range from 0.27 (low) to 0.52 (medium) to \n0.64 (high). Meanwhile, the very low criteria (SQI value = 0.00-0.19) and very \nhigh criteria (SQI value = 0.80-1.00) are also found in the research spot. Detailed \nillustration of the three soil quality zones in the research territory is displayed in \nFigure 1.  \n\n\n\nTABLE 8\nSoil quality criteria based on average value of SQI\n\n\n\nFigure 1 describes the dominant soil quality criteria in the research territory \nincluding high, medium, and low zone classes. The territory which has high soil \nquality is found in the western and southern area of study which is dominated by \nresidential, secondary and primary forest area. The northern and eastern part of \nthe research territory, the soil quality is in medium category with the majority of \nuse was for rice field, shrubs, and moor. Meanwhile, the low soil quality zones are \ndominated by open land and grassland. Soil quality criteria based on the territorial \nsize of research location is presented in Table 9. \n\n\n\n9 \n \n\n\n\nSoil Quality Zonation Map of Research Territory \nSoil quality criteria based on SQI in the research territory is divided into three classes i.e. \nlow, medium, and high. Table 8 shows soil quality criteria based on average values of \nSQI.  \n \nTABLE 8 \nSoil quality criteria based on average value of SQI \n \n\n\n\nLand mapping unit Average values of SQI  Criteria \n  8 \n\n\n\n1, 2, 3, 4,  6, 9, 11, 12, 13, 14, 15, 19, 20, 21 \n5, 7, 10, 16, 17, 18 \n\n\n\n\n\n\n\n    0.27 \n    0.52 \n    0.64 \n\n\n\n  Low \n  Medium \n  High \n\n\n\n \n Table 8 shows that soil quality in the research area is divided into three classes of  \nlow, medium, and high and each of these classes has an average SQI value of 0.27, 0.52, \nand 0.64 respectively. The average SQI values of 0.52 and 0.64 increase from the average \nSQI value of 0.27 by 48,07% and 57.81% respectively. The SQI average values range \nfrom 0.27 (low) to 0.52 (medium) to 0.64 (high). Meanwhile, the very low criteria (SQI \nvalue = 0.00-0.19) and very high criteria (SQI value = 0.80-1.00) are also found in the \nresearch territory studied. Detailed illustration of the three soil quality zones in the \nresearch territory is displayed in Figure1.   \n \n\n\n\n \n \nFigure1. Soil quality zonation map of Krueng Jreue Sub-Watershed  \n \n\n\n\nFigure 1 describes the dominant soil quality criteria of high, medium, and low \nzone classes in the research territory.. The territory which has high soil quality is found in \nthe western and southern areas of the study which is dominated by residential, secondary \n\n\n\nFig.1: Soil quality zonation map of Krueng Jreue Sub-Watershed \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 99\n\n\n\nTABLE 9\nSoil quality criteria found in the  research territory\n\n\n\n10 \n \n\n\n\nand primary forest areas. In the northern and eastern parts of the research territory, the \nsoil quality is in the  medium category and mainly used for rice cultivation while the \nremaining area constitutes shrubs and moor. Meanwhile, the low soil quality zones are \npredominantly open land and grassland. Soil quality criteria based on the territorial size \nof research location is presented in Table 9.  \n\n\n\n \nTABLE 9 \nSoil quality criteria found in the  research territory \n \n\n\n\nCriteria               SQI Value                           Territorial area \n                    ( ha)  (%) \nLow \nMedium \nHigh \n\n\n\n0.27 \n0.52 \n0.64 \n\n\n\n658.18 \n8542.90 \n14016.98 \n\n\n\n2.83 \n36.79 \n60.38 \n\n\n\nTotal 1.43 23218.06 100 \nAverage  0.48   \n\n\n\n \n Table 9 shows that the average SQI value in the research territory is 0.48. The \nlow criteria zone has an area of 658.18 ha, the medium zone 8542.90 ha, and the high \nzone 14016.98 ha, that is,  2.83%, 36.79% and 60.38% of the entire research territory \nrespectively. High soil quality is determined by several characteristics such as low soil \nbulk density, balanced ratio of clay and sandy soil, clay-textured, good porosity, neutral \npH, C-organic, relatively high N, P, and K, and containing beneficial microorganisms. \nLima et al. (2011) emphasise that high soil quality is indicated by deep root development, \nan abundance of organic materials, crumb soil friability, density of vegetation, and  good \nnumber of worms in the soil.  \n \nComponents and Hydrological Disaster Mitigation Measures \nBesides the main components of soil quality, the hydrological disaster vulnerability in \nKruengJreue Sub-Watershed is influenced by land use change, and soil properties. Land \nuse, as a biophysical element of the watershed, has a biological element in the form of \nvegetation and a physical element in the form of soil type and land slope. The biology \nelement of the watershed is dynamic and changes easily:  changes in climate and \nagricultural cultivation may result in a mismatch in land capability causing watershed \ndegradation. Impacts of watershed degradation, among others, include floods and \ndroughts of  increasing magnitude and  frequency that can threaten the sustainability of \nagricultural development. \n              Changes in land use from forest to non-forest can cause completion (is it \ndepletion?) of the soil surface (soil permeability) to decrease infiltration rate and increase \nrun-off. Open land has the lowest soil quality (average SQI = 0.47) compared to all other \nland uses. This oxidised soil has a poor land cover, shallow root depth (14-15 cm), low \ndust + clay (62%), low total porosity (42%), and high bulk density (1.33 g cm-3) . \nGrassland, rice field, moor and shrub have medium grade criterion (average SQI = 0.52), \nmedium root (58-75 cm), low dust + clay (63%), low total porosity (43%), and high bulk \ndensity (1.29 g cm3). Generally, it is known that the higher the percentage of dust + clay, \nthe lower the soil permeability.  \n             Usually, total porosity will be followed by soil permeability. Bulk density can be \nused to demonstrate soil boundary values in limiting root capability for soil penetration, \nand root growth (Pearson et al., 1995)(Not in ref list). Bulk density is a soil characteristic \nthat describes the level of soil completion?. High-density soils may complicate the \ndevelopment of plant roots through limited macropores and inhibited water penetration \n\n\n\n Table 9 shows that the average value of SQI in the research territory is 0.48. \nThe low criteria zone has an area of 658.18 ha, the medium zone has an area \nof 8542.90 ha, and the high zone has an area of 14016.98 ha with percentages \nof 2.83%, 36.79% and 60.38% of the entire research territory. High soil quality \nis determined by several characteristics such as low of soil bulk density, \ncontaining balanced clay and sandy soil, clay textured, good porosity, neutral pH, \nC-organic, relative high of N, P, and K, and containing beneficial microorganisms. \nFurthermore, Lima et al. (2011) emphasize that high soil quality is indicated \nby several indicators i.e. development of deep root, an abundance of organic \nmaterials, crumb soil friability, density of vegetation, and the numbers of worms. \n\n\n\nComponents and Hydrological Disaster Mitigation Measures\nThe main components and additional soil quality against hydrological \n\n\n\ndisaster vulnerability in Krueng Jreue Sub-Watershed, are influenced by land \nuse change, and soil properties. Land use as biophysical element of watershed, \nconsist of biological element in the form of vegetation and physical element of \nland in the form of soil type and land slope. The watershed biology element is \ndynamic so it is easy to change, including climate and agricultural cultivation, \nresulting in mismatch and land capability causing watershed degradation. Impacts \nof watershed degradation, among others, floods and droughts that are increasingly \nlarger and increasing frequency can threaten the sustainability of agricultural \ndevelopment.\n\n\n\nChanges in land use from forest to non-forest can cause completion of the \nsoil surface (soil permeability) to decrease infiltration rate and increase run-off. \nOpen land has the lowest soil quality (average of SQI = 0.47) compared to all \nother land uses. This oxidized soil has a poor land cover, has shallow root depth \n(14-15 cm), low dust + clay (62%), total porosity is less good (42%), and high \nbulk density (1.33 g cm-3) . While grassland, rice field, moor and shrub have \nmedium grade criterion (average of SQI = 0.52), have medium root (58-75 cm), \ndust + clay rather low (63%), total porosity is less good (43%), and bulk density \nis rather high (1.29 g cm-3). The higher the percentage of dust + clay, the lower the \npermeability of the soil.\n\n\n\nGenerally the addition of total porosity will be followed by the addition of \nsoil permeability. Bulk density can be used to demonstrate soil boundary values \nin limiting root capability for soil penetration, and for root growth (Pearson et \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017100\n\n\n\nal., 1995). Bulk density is a soil characteristic that describes the level of soil \ncompletion. High-density soils may complicate the development of plant roots, \nlimited macropores and inhibited water penetration (Sudaryanto, 2010). Open \nland is not protected from raindrops and easily transported by run-off when \nrainfall intensity is high enough. Therefore, this open condition causes the land \nvulnerable to flood and drought hazards.\n\n\n\n While residential in Krueng Jreue Sub-Watersheds that have farm yard and \nrare population, secondary forest and primary forest have good quality (average \nof SQI = 0.64). These three fields have deep root depths (150-156 cm), high dust \n+ clay (77%), good total porosity (51%), and low bulk density (1.25 g cm-3). \nThe hydrological disaster vulnerability rate is relatively low, if rooting deepens, \nthe percentage of dust + clay and porosity is higher and the bulk density is low. \nTexture and total porosity are one unity. The smaller the texture size of a soil the \nlarger the porosity. The soil contains a lot of clay, its porosity is bigger than the \nsandy soil. Thus, the smoother the soil, the greater the porosity.\n\n\n\nFurthermore, the slope of the land is also a major component of the \nvulnerability of hydrological disasters, especially floods and landslides. The \ngreater the slope of the land, the greater the occurrence of floods and landslides, \nespecially the slopes of 15- <25% and 25- <40% in open land, grasslands and \nshrubs. The slope of the land is closely related to land management factors, flood \nand landslide hazards. In line with land-use change from forest to non-forest, it \ncan decrease soil function, causing ecosystem damage and degradation of soil \nquality, thus increasing critical land area in Krueng Jreue Sub-Watershed. If the \nanalysis of soil properties is good, the degree of hydrological disaster is not so \nhigh. Thus, the higher the quality of the soil, the less likely the occurrence of \nhydrological disasters. \n\n\n\nTo minimize the negative impacts and low quality of soil quality in Krueng \nJreue Sub-Watershed, so on low land quality criteria (open land) and medium \n(grassland, rice field, moor and shrub), hydrological disaster mitigation should \nbe done in a structural and non-structural way. Structurally way is by maintaining \nconservation areas as natural reservoirs, increasing infiltration and soil percolation \nby making absorption wells or combining with biopores (infiltration well). \nConservation land processing, minimizing soil treatment, contour soil treatment, \ncontour and contour drilling. Conducting soil conservation mechanically, \nespecially on land that has a rather steep topography (15- <25%) and steep (25- \n<40%) with terracing system. Non-structural hydrological disaster reduction can \nbe done by maintaining the area of primary and secondary forest at minimum \n30% of the watershed area. Forests have a very important role in holding off \nrun-off, which significantly reduces the occurrence of floods and droughts. In \nforest development in watershed areas, the socio-economic conditions of forest \ncommunities are a very important factor to consider, so that agroforestry is the \nright choice (Masto et al., 2008).\n\n\n\n Conditions of population growth has made if difficult to maintain the \ncondition of the forest as before. Farmers own national land ownership of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 101\n\n\n\nfarmers is only 0.25 ha of farmers-1, so farmers cannot plant their land. One of the \nalternatives that must be done is by applying agroforestry system. Agroforestry is \nvery appropriate to be developed in watershed management, because: (1) is able \nto cover the soil surface perfectly, effectively suppress run-off, flood and drought, \nand can increase infiltration and groundwater reserves, (2) strong both at the top \nand bottom soil layer, will improve the stability of the cliff, thereby reducing the \nvulnerability to landslides, and (3) soil structure and water content, increase in \norganic matter and nutrient availability, increasing activity and diversity, solum \nformation (Atmojo, 2008).\n\n\n\nIn addition to agroforestry, the application of conservation farming models to \nsuit soil characteristics, or with local or site-specific agro-ecosystem conditions. \nCrop rotation, increase diversification of vegetation and create cultivation pattern \nby combining high strata (annual crops) with middle strata plant (plantation) \nand low strata (horticulture/food). Planting plant species that can withstand soil \nstability and high canopy density. Using cover crops of the legume family and \nimmersing mulch residue crops and crop residues.\n\n\n\nThe use of soil fertilizer, fertilization, calcification, addition of organic material \nby returning litter of crops and increasing the amount of organic residue in the \nform of litter of vegetation with a variety of types. Integrated management system \nby combining artificial fertilizer (Urea, SP-36) and organic fertilizer (manure, \ngreen manure, straw, compost). Organic farming programs can be prioritized on \nlands with high P, K content and very low to low organic content. Implementation \nof integrated conservation farming on slopes, planting by adjusting contour and \nplanting in strips according to contour\n\n\n\n.\nCONCLUSIONS\n\n\n\nThe soil criteria in the research territory consists of three classes i.e. low, medium, \nand high, with soil quality index values of 0.27; 0.52; and 0.64. Soil quality based \non the territorial size of the research area is dominated by (1) high class, 14016.98 \nha (60.38%); (2) medium, 8542.90 ha (36.79%); and (3) low, 658.18 ha (2.83%).   \nTypes of land use with high quality soil include a primary forest, secondary forest \nand residential area each with a value of 0.66, 0.64 and 0.63. In contrast, open \nland, grassland, rice field, shrubs and moor were dominated by medium quality \nsoil, each with a value of 0.47; 0.48; 0.52; 0.51 and 0.55.  Main components and \nadditional soil quality against hydrological disaster  vulnerability, affected by land \nuse change, and soil properties. Hydrological disaster mitigation measures in land \nquality zones with low criteria are carried out in a structural and non-structural \nway.\n\n\n\nREFERENCES\nAbrahams, A. D.,   A.J. Parsons and    J. Wainwright. 1995.  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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 218-225 \n \n\n\n\n\n\n\n\n218 \n\n\n\nASSESSING SOIL QUALITY STATUS IN GHANA'S OIL PALM \nSMALLHOLDER PLANTATIONS \n\n\n\n \nKannesan, Joseph1, Rajoo, S. Keeren1,2*, Abdu, Arifin3,  \n\n\n\nKaram, S. Daljit4, Rosli, Zamri2, Izani, Norul2 & Zulperi, Dzarifah5 \n \n\n\n\n1Institute of Ecosystem Science Borneo, Universiti Putra Malaysia Bintulu Sarawak Campus,  \nNyabau Road, 97008 Bintulu, Sarawak, Malaysia \n\n\n\n2Department of Forestry Science, Faculty of Agricultural & Forestry Sciences,  \nUPM Bintulu Sarawak Campus, Nyabau Road, 97008 Bintulu, Sarawak, Malaysia \n\n\n\n3Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment,  \nUniversiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia \n\n\n\n4Department of Land Management, Faculty of Agriculture, UPM Malaysia \n5Department of Plant Protection, Faculty of Agriculture, UPM Malaysia \n\n\n\n \nCorrespondence: * keeren.rajoo@upm.edu.my \n\n\n\n \nABSTRACT \n\n\n\nDespite continuous governmental initiatives, oil palm yields in Ghana is significantly lower than \nSoutheast Asian nations, being unable to even satisfy domestic demand. It hypothesized that one of the \nmain limiting factors to oil palm production in Ghana is due to poor soil suitability. However, there is \nlimited soil studies in Ghana oil palm plantations. Thus, this study was conducted to evaluate the soil \nfertility status of smallholder oil palm plantations in the Brong Ahafo and Ashanti Regions of Ghana. \nBy understanding the soil condition, appropriate land management strategies can be conducted to \nincrease the productivity of Ghana\u2019s smallholder oil palm plantations. Soil sampling was conducted in \nfour plantations, two each in the Ashanti Region and Brong Ahafo Region, with 25 samples collected \nfrom each plantation. Samples were taken from between palm trunks, at a depth of 0-20 cm, using a \nsoil auger. Appropriate soil analyses were conducted to determine the chemical soil properties of the \nsamples. Additionally, the Nutrient Index was calculated to evaluate the soil\u2019s sufficiency for crop \nnutrition. The soil analysis revealed differences between the regions, with Brong Ahafo exhibiting lower \nsoil quality, particularly in organic matter and available phosphorus. Both regions displayed low \nNutrient Index values, indicating inadequate soil nutrient supply for optimal crop growth. To enhance \nsmallholder productivity and profitability, addressing knowledge gaps and providing support in land \npreparation, field maintenance, and fertilizer access is crucial. Government intervention and subsidies \nfor smallholders can boost yields, meet domestic palm oil demand, and improve farmers' livelihoods in \nGhana. \n \nKey words: Brong Ahafo, Ashanti, Soil Nutrient Index, oil palm. \n \n\n\n\nINTRODUCTION \nOil palm is a major oil crop that meets nearly 30% of the world's demand for edible vegetable \noil (Leslie, 2010). The high demand for palm oil has led to its rapid expansion across the globe. \nIn West Africa, the consumption of palm oil and its derivatives is expected to increase as the \npopulation grows. This includes Ghana. \n\n\n\nIn Ghana, oil palm is the second most important perennial crop after cocoa. However, \nthe yield of fruit bunches in Ghana is low, compared to the major producing countries in \nSoutheast Asia and Latin America, where the climate is more favorable. To meet the growing \nlocal demand for palm oil and reduce imports, the Ghanaian government is encouraging both \nnational and foreign investors to plant more oil palm. Area expansion is therefore proposed to \nincrease local production and reduce crude palm oil imports. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 218-225 \n \n\n\n\n\n\n\n\n219 \n\n\n\nAn analysis of the availability of suitable land for oil palm plantations and the obtainable yields \nis essential information for government policy makers and investors. In the late 1960s, a mass \nland survey was conducted to determine areas in Ghana with suitable environmental conditions \nfor oil palm cultivation. However, environmental conditions in the oil palm belt in Ghana have \nchanged rapidly since the 1960s, due to climate change and the land use changes. Moreover, \nthe 1960s survey primarily identified suitable oil palm cultivation sites based on 400 and 250 \nmm mean annual water deficit isolines. Soil suitability, which is a major limiting factor in oil \npalm cultivation, has rarely been studied in Ghana (Ruml and Qaim, 2020). \n\n\n\nSoil suitability is a critical factor in oil palm cultivation. Oil palms grow best in well-\ndrained, deep soils with high organic matter content and a pH of 5.5-7.0 (Rhebergen et al., \n2016). Soils that are too acidic, alkaline, or poorly drained can reduce oil palm yields and \nincrease susceptibility to pests and diseases (Rhebergen et al., 2016). By understanding the soil \ncondition, appropriate land management strategies can be conducted to increase the suitability \nof the soil for oil palm cultivation.  \n\n\n\nAs mentioned previously, oil palm yield in Ghana is low despite continuous efforts by \nthe Ghanian government to increase production. It hypothesized that one of the main limiting \nfactors to oil palm production in Ghana is due to poor soil suitability (Brady and Weil, 1999). \nMoreover, there is limited studies on the soil condition in Ghana oil palm plantations, therefore \nland management strategies are inefficiently conducted in these plantations. This is especially \ntrue for smallholders since they lack the resources to monitor and manage these plantations as \nefficiently as multinational plantation companies (Khatun et al., 2020). Therefore, in this paper, \nwe aim to address this knowledge gap. The main objective of this study is to evaluate the soil \nfertility status of smallholder oil palm plantations in the Brong Ahafo and Ashanti Regions of \nGhana. The findings of this study can be utilized by both oil palm farmers and the government \nto improve oil palm production in Ghana. \n \n\n\n\nMATERIALS AND METHODS \nStudy site  \nThis study was conducted in smallholder oil palm plantations in two districts: Brong Ahafo \nand Ashanti Regions of Ghana (Figure 1). Ghana is a country located in West Africa, bordered \nby Cote d'Ivoire, Burkina Faso, Togo, and the Gulf of Guinea.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Location of study sites \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 218-225 \n \n\n\n\n\n\n\n\n220 \n\n\n\nThe smallholder plantations studied in the Ashanti Region were located close to \nAmansie Oil Mills (N 5\u00b0 33' 11.958'', W 1\u00b0 34' 4.027'') in Amansie district, whereas for the \nBrong Ahafo Region, the smallholder farmers\u2019 were located close to Paa Joe Oilpalm \nPlantation (N 7\u00b0 3' 45.828'', W 1\u00b0 24' 0.359''). The smallholder plantations ranged from 0.5 to \n1 ha, which is common area sizes for Ghanaian oil palm smallholder plantations. \n \nSoil sampling and analyses \nPrior to conducting sampling, oil palm smallholders were informed of the purpose of this study. \nSampling was only conducted after the farmers provided consent to partake in the study. A \ntotal of 65 smallholders were approached, with 40 smallholders in Ashanti Region, and 25 \nsmallholders in Brong Ahafo Region (Figure 2). Moreover, the general oil palm management \nstrategies implemented by the smallholders were also documented via interviews and field \nobservations. \n \n\n\n\n\n\n\n\nFigure 2: Meeting with oil palm smallholders at Derma district of Ashanti Region \n \n\n\n\nA total of 25 soil samples were collected from each study site. Samples were taken from \nbetween palm trunks, at a depth of 0-20 cm, using a soil auger in a diagonal pattern. The \nsamples were placed in labeled plastic bags and transported to the Oil Palm Research Institute \n(OPRI) for analysis. All samples were air-dried to constant weight to prevent microbial \ndegradation. The soil samples were then pounded using a mortar and pestle, and then sieved \nusing a 2mm sieve before soil analyses.  \n\n\n\nSoil pH was assessed through the measurement of a 1:2.5 soil-to-water suspension \n(w/v). Total nitrogen levels were determined using the modified Macro-Kjeldahl method. To \nassess available phosphorus, an extraction was performed with Bray 1 solution, followed by \nquantification using a spectrophotometer (Bray, 1945). Organic carbon content was determined \nusing the method described by Nelson & Sommers (1982). The extraction of exchangeable \nbases was carried out using a 1.0M ammonium acetate solution. Subsequently, sodium and \npotassium concentrations in the extract were determined utilizing Atomic Absorption \nSpectrophotometer (AAS), while calcium and magnesium were quantified through titration. \nEffective cation exchange capacity (ECEC) was then calculated as the cumulative total of \nexchangeable cations (K, Ca, Mg, and Na) and exchangeable acidity (Al + H) (Thomas, 1982). \nParticle size analysis was conducted using the pipette method. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 218-225 \n \n\n\n\n\n\n\n\n221 \n\n\n\nData analyses \nThe SPSS program (Version 23) was utilized to conduct appropriate statistical analyses, \nincluding t-test, as well as the calculation of means and standard deviations.  \n\n\n\nFurthermore, to assess the nutrient condition of the soil samples, the Nutrient Index in \nsoils was computed following the method introduced by Ravikumar (2013). The Nutrient Index \n(N.I.) is determined using the formula:  \n \nN.I. = (L \u00d7 1 + NM \u00d7 2 + NH \u00d7 3) / TNS \n \nWhere: \nL = Number of samples in the low category, \nM = Number of samples in the medium category, \nH = Number of samples in the high category, and \nTNS = Total number of samples. \n \nThis Nutrient Index serves as a predictive tool to evaluate the sufficiency of each soil quality \nindicator in fertile soil based on soil test results obtained from the laboratory. \n\n\n\nThe soil properties were also evaluated using the Tropical Soil Quality Index (Arifin et \nal., 2012). The index was developed based on the Soil Quality Index developed by Amacher et \nal. (2007), to access tropical soils more accurately. Under the index, each soil parameter is \ngiven a value, and an overall percentage value is obtained for the combined soil properties.  \n \n\n\n\nRESULTS AND DISCUSSION \n \nGeneral condition of smallholder plantations \nSmallholders in oil palm cultivation grapple with various challenges due to their limited \nknowledge (Khatun et al., 2020). These encompass land preparation, seedling selection, \nplanting density, and field maintenance. Timely upkeep is vital for oil palm growth and \nproductivity. Moreover, government support is lacking, with minimal assistance from \nextension departments. The promised central palm oil mill was never realized due to shifting \npolicies favoring foreign investors through a \"Private Public Partnership\" model and a \"one \ndistrict one industry\" policy. Capital constraints hindered mill establishment. \n\n\n\nThe absence of logistical support left smallholders unable to sell their Fresh Fruit \nBunches (FFB), leading to disillusionment and plantation neglect. An entrepreneurial effort to \naddress this included a British-backed one-ton-per-hour (1TPH) mill, which failed due to \nfunding shortages and high transport costs (Khatun et al., 2020). Government intervention \nremained inadequate, and a lack of skills and capital rendered the project economically \nunviable. This situation demoralized smallholders who couldn't afford fertilizers and \nagrochemicals, resulting in reduced yields and frustration. \n \nGeneral soil and environmental characteristics \nThe environmental characteristics of the two regions under consideration exhibit notable \ndisparities. Brong Ahafo is characterized by undulating topography with a relatively steeper \ngradient, ranging between 250 to 400 meters in elevation. The region is also associated with \npoorer soil quality, specifically Savannah Oxisols, and experiences comparatively lower levels \nof precipitation, ranging from approximately 1,190 millimeters to 1,310 millimeters annually. \nFor this region, the soil also had relatively high sand particles (69.3% \u00b1 11.3). The higher \npercentage of sand in soil samples from Brong Ahafo will have a direct impact on soil fertility \nand crop productivity in the study area because sandy soils have a low capacity to retain \nnutrients and water. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 218-225 \n \n\n\n\n\n\n\n\n222 \n\n\n\n \nConversely, the Ashanti region features gently undulated to flat terrain, with elevations \n\n\n\nranging from 150 to 250 meters above sea level. The soil quality in Ashanti is notably superior, \ncharacterized by Forest Ochrosols, and the region receives higher levels of annual rainfall, \nranging from approximately 1,700 millimeters to 1,870 millimeters. This region also recorded \nlower sand particles (59.8% \u00b1 12.8), and higher clay content (24.1% \u00b1 7.5). This characteristic \nis generally more favorable due to clay particles playing a bigger role in holding nutrients and \nwater. As oil palm crops require high amounts of irrigation, water retention potential of soil is \nvital. \n \nSoil chemical properties \nTable 1 shows the chemical properties of the studied regions. In the present study, Brong Ahafo \nregion exhibited a pH of 5.07 \u00b1 0.28, whereas Ashanti region had soil pH of 5.82 \u00b1 0.41. The \nrelatively lower acidity observed in Brong Ahafo region can be attributed to its soil type, as \nOxisols are generally acidic (Rajoo et al., 2013a). Soil pH constitutes a vital soil property, as \nit directly impacts nutrient availability (Rhebergen et al., 2016). Like most flora tolerant of \nacidic soils, oil palm trees grow well in pH ranges of between 4.3 to 6.5 (Rajoo et al., 2013b), \nthe soil pH of both regions are suitable for oil palm cultivation (Rhebergen et al., 2016). \n \nTable 1: The soil chemical properties of Brong Ahafo and Ashanti region \nSoil properties / Location Mean \u00b1 SE \n\n\n\nBrong Ahafo region Ashanti region \npH 5.07 \u00b1 0.28a 5.82 \u00b1 0.41b \nCEC (cmolc kg-1) 2.16 \u00b1 0.49a 3.28 \u00b1 0.81b \nOrganic carbon (%) 0.72 \u00b1 0.36a 1.65 \u00b1 0.44b \nOrganic matter (%) 1.16 \u00b1 0.25a 2.93 \u00b1 0.72b \nTotal nitrogen (%) 0.07 \u00b1 0.02a 0.16 \u00b1 0.01b \nAvailable phosphorus (mg kg-1) 4.34 \u00b1 2.95a 7.81 \u00b1 3.63b \nPotassium (cmolc kg-1) 0.25 \u00b1 0.08a 0.39 \u00b1 0.18b \nMagnesium (cmolc kg-1) 0.32 \u00b1 0.16a 0.51 \u00b1 0.2b \nCalcium (cmolc kg-1) 0.66 \u00b1 0.35a 1.35 \u00b1 0.57b \nSodium (cmolc kg-1) 0.65 \u00b1 0.54a 0.68 \u00b1 0.32a \n\n\n\nNotes: Mean \u00b1 SE = Mean \u00b1 standard error, CEC = cation exchange capacity. Means within a \ncolumn followed by different letters differ significantly (p < 0.05). \n \n\n\n\nSoil samples from the Brong Ahafo region recorded low organic matter (1.16 \u00b1 0.25%) \nand organic carbon (0.72 \u00b1 0.36%), likely due to poor soil management practices. In contrast, \nthe Ashanti region recorded higher organic matter (2.93 \u00b1 0.72%) and organic carbon (1.65 \u00b1 \n0.44%), likely due to factors such as the use of manure and pruned palm fronds as mulch. The \norganic carbon recorded for soil samples from the Brong Ahafo region falls below the critical \nthreshold of 1.2% for oil palm trees. Organic matter is an essential component of soil, providing \nnutrients, improving structure, and sequestering carbon. Higher organic matter results in higher \norganic carbon, which is an important source of plant nutrients. Organic matter also enhances \nsoil physicochemical properties, boosting productivity. Studies have also found that organic \nmatter feeds soil microflora and fauna and retains and improves nutrient cycles (Karam et al., \n2013). Smallholders in Brong Ahafo region should practice mulching by cutting pruned palms \ninto smaller pieces for mulch, which has been found to improve soil organic matter. \n\n\n\nNitrogen is essential for plant growth and development, and its deficiency is more \nlimiting than any other nutrient (Ravikumar, 2013). This is especially true for oil palm \ncultivation. Soil samples from two locations in Ghana, the Brong Ahafo region and the Ashanti \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 218-225 \n \n\n\n\n\n\n\n\n223 \n\n\n\nregion, had organic nitrogen mean values of 0.07 \u00b1 0.02% and 0.16 \u00b1 0.01%, respectively. The \nnitrogen content in soil samples from the Ashanti region exceeded the critical threshold of \n0.15% required for optimal growth of oil palm trees. This is likely due to the higher organic \nmatter content in the soil, as there is a direct correlation between organic matter and nitrogen \nlevels. However, the nitrogen content in soil samples from the Brong Ahafo region fell below \nthe critical threshold, indicating a need for improvement to meet the nitrogen requirements of \noil palm trees. To address this deficiency, the application of nitrogen-based fertilizers and the \nincorporation of organic resources such as compost and manure are essential strategies to boost \nthe nitrogen content in the soil in the Brong Ahafo region (Ravikumar, 2013). However, since \nthis region has already acidic soils (5.07 \u00b1 0.28), it is essential that fertilizer regimes are \nconducted efficiently to avoid overly increasing the acidity of the soil. \n\n\n\nThe available phosphorus content in soil samples from the Brong Ahafo region ranged \nfrom 0.32 to 19.29 mg kg-1, with an average value of 4.34 \u00b1 2.95 mg kg-1. In contrast, soil \nsamples from the Ashanti region had a mean phosphorus value of 7.81 \u00b1 3.63 mg kg-1. \nAvailable phosphorus content below 25 mg kg-1 is insufficient for optimal growth of oil palm \ntrees. However, it is important to note that the ideal phosphorus level may vary depending on \nseveral factors, such as soil type, climate, and specific topographic conditions. The soil from \nthe Ashanti region had a significantly higher level of available phosphorus than the soil from \nthe Brong Ahafo region. The lower available phosphorus content in the soil from the Brong \nAhafo region is likely due to its high acidity (Ravikumar, 2013). To address this deficiency and \nincrease the available phosphorus content in the soil, phosphorus-based fertilizers, liming, and \nphosphorus-solubilizing microorganisms should be used. \n \nNutrient Index and Tropical Soil Quality Index values  \nTable 2 shows the Nutrient Index values of the soil properties of the evaluated regions \n(Ravikumar, 2013). The Nutrient Index serves as a valuable metric to gauge the adequacy of \nsoil quality indicators in delivering the necessary nutrients for crop growth and development. \nSoil in Brong Ahafo region had 8 soil properties in the low category, whereas Ashanti region \nhad eight. Both regions had high classification for potassium values. Overall, both regions had \na Nutrient Index that is low, that is 1.22 for Brong Ahafo region and 1.33 for Ashanti region. \nThe low Nutrient Index values in Brong Ahafo and Ashanti regions indicate that the soils in \nthese regions are not able to supply all of the nutrients that crops need to grow and produce \nwell. This could be due to several factors, such as naturally low fertility soils, environmental \nfactors, and also poor agricultural practices. However, it also has to be noted that the index \nproposed by Ravikumar (2013) is a rough guideline since different crops have different \nnutritional requirements. \n \nTable 3 shows the Tropical Soil Quality Index (TSQI) values of the study sites. Similar to the \nNutrient Index values, Ashanti region had better scores than the Brong Ahafo Region. \nHowever, the value for Ashanti region (61.54%) was significantly better than the Brong Ahafo \nRegion (23.08%). The values for the Ashanti region was in the range of tropical forests (Aiza \net al., 2022; Karam et al., 2022), indicating that the soil quality in this region was of relatively \nhigh quality. However, the Brong Ahafo Region exhibited soil quality issues in several soil \nproperties, including pH level, total nitrogen, available phosphorus and potassium content.  \n \n \n \n \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 218-225 \n \n\n\n\n\n\n\n\n224 \n\n\n\nTable 2: The Nutrient Index of soil in the Brong Ahafo and Ashanti region \nSoil properties / Location Brong Ahafo region Ashanti region \npH Low Low \nCEC (cmolc kg-1) Low Low \nOrganic matter (%) Low Low \nTotal nitrogen (%) Low Medium \nAvailable phosphorus (mg kg-1) Low Low \nPotassium (cmolc kg-1) High High \nMagnesium (cmolc kg-1) Low Low \nCalcium (cmolc kg-1) Low Low \nSodium (cmolc kg-1) Low Low \n\n\n\n \nTable 3: Tropical Soil Quality Index values of soil samples  \nSoil properties / Location Full score Brong Ahafo region Ashanti region \npH 2 1 2 \nOrganic carbon (%) 2 0 1 \nTotal nitrogen (%) 2 0 1 \nAvailable phosphorus (mg kg-1) 1 0 0 \nPotassium (cmolc kg-1) 2 0 1 \nMagnesium (cmolc kg-1) 2 0 1 \nCalcium (cmolc kg-1) 1 1 1 \nSodium (cmolc kg-1) 1 1 1 \nTotal Score 13 3 8 \nPercentage (%) 100 23.08 61.54 \n\n\n\n \nAs mentioned previously, smallholders had limited knowledge mainly in land preparation and \nfield maintenance, which is evident by the poor soil condition of both regions. Although \nAshanti region is better in terms of soil suitability for oil palm cultivation, nevertheless, both \nregions require precise and efficient land management strategies.  \n \n\n\n\nCONCLUSION \nIn summary, smallholders' oil palm plantations languished due to limited knowledge, \ngovernment policies, logistical challenges, and a failed entrepreneurial venture. Their inability \nto access support exacerbated the situation, leading to declining yields and dissatisfaction. This \nis evident by the poor soil condition of both studied regions. Nevertheless, Ashanti region \nexhibited better overall soil quality, having soil conditions similar to that of tropical forests. It \nis also important to note that the general soil and environmental conditions of both regions, \nnamely in terms of soil pH and annual rainfall, is suitable for oil palm cultivation. To improve \nthe productivity and profitability of smallholder plantations, it is important to address the \nunderlying challenges that smallholders face. The government needs to provide the \nsmallholders with access to training and support especially in terms of land preparation and \nfield maintenance. Governmental subsidies of fertilizers would also be highly beneficial to \nthese farmers. By addressing these challenges, it is possible to for Ghana to satisfy the domestic \ndemand for oil palm while improving the livelihood of its farming communities. \n \n\n\n\nACKNOWLEDGEMENTS \nThe authors would like to thank oil palm farmers of Brong Ahafo and Ashanti regions, the Oil \nPalm Research Institute (OPRI) and all other parties that made this study possible.  \n \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 218-225 \n \n\n\n\n\n\n\n\n225 \n\n\n\nREFERENCES  \nAiza, S.J., Zahari, I., Karam, D.S., Rajoo, K.S., Shamshuddin, J., Seca, G. and Arifin, A. 2022. 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"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n88 \n\n\n\n\n\n\n\nImpact of Biochar Treatment on Chemical Properties of a Sandy Spodosol \n\n\n\nDeveloped from Marine Sediments \n\n\n\n\n\n\n\nSyuhada, A.B.1, Shamshuddin, J.1*, Fauziah, C.I.1, Arifin, A.2 and Panhwar, Q.A.3 \n\n\n\n\n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400 \n\n\n\nSerdang, Selangor, Malaysia \n2Department of Forest Production, Faculty of Forestry, Universiti Putra Malaysia, 43400 Serdang, \n\n\n\nSelangor, Malaysia \n3Soil & Environmental Sciences Division, Nuclear Institute of Agriculture, 70060 Tando Jam, \n\n\n\nSindh, Pakistan \n\n\n\n*Corresponding author: shamshud@upm.edu.my  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nAn experiment using leaching columns was conducted for 72 weeks to determine the impact of \n\n\n\nbiochar treatment on a sandy Spodosol. Biochar treatment was found to have a positive impact on \nsoil chemical properties. The treated topsoil had higher exchangeable K, Ca and Mg compared to the \n\n\n\ncontrol. Due to low clay and organic matter content, much of the cations released by biochar could \n\n\n\nnot be retained in that zone. This is evidenced by the increased concentration of elements in leachates \ncollected from the leaching columns. It means that a significant portion of the nutrients, initially in \n\n\n\ninsoluble form, was susceptible to leaching or podzolization. Some of the nutrients were transported \n\n\n\ndownwards and subsequently retained in the spodic horizon. The biochar treatment increased soil \n\n\n\npH, total C and the CEC of the spodic layer with the CEC increase being positively correlated with \nC content. Notwithstanding, the ameliorative impact of biochar treatment is at best short-term. The \n\n\n\napplication of high amounts of biochar is necessary to raise soil fertility to the level suitable for crop \n\n\n\nproduction. Hence, it is recommended that biochar be applied in combination with some NPK \nfertilizers to sustain crop growth/production in the long term. \n\n\n\n\n\n\n\nKey words: Biochar, leaching column experiment, marine sediments, podsolization, \n\n\n\nSpodosol \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nSpodosols, developed from marine sediments of Holocene age, are widespread in the coastal \n\n\n\nplains of Peninsular Malaysia (Roslan et al. 2010). The most important soil attribute for their \n\n\n\nlow fertility is the sandy texture of such soils (have >95% sand). This pedological feature \n\n\n\nsignificantly affects the physico-chemical properties of the Spodosols. It can be assumed that \n\n\n\nsuch soils have a high proportion of drainage pores, leading to a high leaching rate. Water and \n\n\n\ndissolved substances in the soils are rapidly transported downwards and subsequently \n\n\n\naccumulate in the Spodic layer or just disappear into the groundwater.  \n\n\n\n\n\n\n\nThe soils cannot sustain crop production due to their low inherent nutrient availability. \n\n\n\nIt is made worse by the lack of organic matter in the topsoil that results in a low cation exchange \n\n\n\ncapacity; hence, its water retention is insufficient for crop growth. Consequently, only a small \n\n\n\narea of Spodosols in the  coastal plains is utilized for the production of crops (Roslan et al. \n\n\n\n\nmailto:shamshud@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n89 \n\n\n\n\n\n\n\n2011). Nevertheless Syuhada et al. (2016) showed that the low fertility Spodosol can, to a \n\n\n\ncertain extent, be ameliorated for corn cultivation using biochar made from oil palm empty fruit \n\n\n\nbunches (EFB). \n\n\n\n\n\n\n\nThe abundant occurrence of sandy Spodosols in the coastal regions of Peninsular \n\n\n\nMalaysia raises concern among the agronomists working to formulate sustainable management \n\n\n\npractices to restore or improve their fertility. It is believed that EFB-biochar application can \n\n\n\nsomewhat increase fertility of these soils. Under a high temperature environment coupled with \n\n\n\nhigh rainfall, the ameliorative impact is at best short-term. This is because organic matter \n\n\n\nexposed to the tropical environment is easily decomposed with the nutrients released being \n\n\n\nrapidly leached out of the soils.  \n\n\n\n\n\n\n\nSeveral agronomists have found biochar to provide a more enduring improvement to \n\n\n\nsoil fertility (Manickam et al. 2015). Application of biochar is one of the ways to minimize \n\n\n\nleaching risk of nutrients as biochar is found to have a significant impact on soil quality via \n\n\n\nincreases in surface area, porosity, cation exchange capacity, soil pH and water retention (Abel \n\n\n\net al. 2013; Kameyama et al. 2012; Major et al. 2009). The above-mentioned effects of biochar \n\n\n\napplication help curtail leaching loss of mobile nutrients, enhance microbial activities \n\n\n\n(Lehmann et al. 2011) and increase nutrient availability for crop uptake (Major et al. 2010). \n\n\n\nThus, adding biochar into the sandy Spodosol under investigation is a feasible option to improve \n\n\n\nfertility and sustain crop growth/production.  \n\n\n\n\n\n\n\nSoil columns studies are often used to evaluate the impact of biochar application on soil \n\n\n\nchemical properties and leaching losses of nutrients (Laird et al. 2010; Novak et al. 2009). This \n\n\n\nresearch was conducted on loamy or clayey soils, using various types of biochar produced from \n\n\n\ndifferent feedstock. No similar study has yet been conducted on sandy Spodosols of the humid \n\n\n\ntropics. It is plausible that some C from biochar applied into the top soil of a sandy Spodosol \n\n\n\ncan be sequestrated in the spodic horizon of Spodosols under a tropical environment. Hence, \n\n\n\nthe current study addressed the chemical changes that take place in a sandy Spodosol found in \n\n\n\nPeninsular Malaysia as affected by application of EFB-biochar and the ameliorative impact of \n\n\n\napplying the biochar on the infertile Spodosol. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\nSoil and Biochar Used in the Study \n\n\n\nThe soil for the experiment was taken from Forest Research Institute of Malaysia (FRIM) \n\n\n\nExperimental Station in Setiu, Terengganu, Malaysia. The area is located approximately at \n\n\n\nlatitude 5.54\u00b0 N and longitude 102.87\u00b0 E. The experimental site was planted with a forest \n\n\n\nspecies (Shoreapalem banica). The soil had a spodic horizon below 100 cm depth, fitting well \n\n\n\ninto the definition of Spodosol (Soil Survey Staff 2014). Identified as Jambu Series, the soil is \n\n\n\ntaxonomically classified as sandy, siliceous, isohyperthermic family of Typic Haplorthods.  \n\n\n\nAccording to (Roslan et al. 2010), quartz is the dominant mineral not only in the sand, but also \n\n\n\nin the silt and/or clay fraction of the soil. Soil samples used for the current experiment were \n\n\n\ntaken from the topsoil, eluvial and spodic horizon (the dark colored bottom of the soil profile \n\n\n\nshown in Figure 1). They were air-dried under glasshouse conditions at Universiti Putra \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n90 \n\n\n\n\n\n\n\nMalaysia (Serdang, Malaysia), sieved (<2 mm) and kept for the leaching column experiment. \n\n\n\nThe chemical characteristics of the soil are shown in Table 1. \n\n\n\n\n\n\n\nBiochar was obtained from a local producer in Malaysia, with feedstock from oil palm \n\n\n\nempty fruit bunches (EFB). The biochar was prepared via slow pyrolysis using medium thermal \n\n\n\nprocess (300-350\u00b0 C). With a pH of 9.73, the biochar contained 18.3 % ash, 53.3% C, 11 g kg-\n\n\n\n1 N, 69 g kg-1 K, 6.3 g kg-1Ca and 5.3 g kg-1 Mg. The particle size of the biochar was 2 to 5 mm; \n\n\n\nbut was ground to pass through 2-mm sieve and thoroughly mixed with the soil for the \n\n\n\nexperiment to ensure homogeneity. A large portion of the biochar used in this study was from \n\n\n\nthe < 1 mm size fraction. \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nThe chemical properties of Jambu Series \n\n\n\n\n\n\n\nSoil horizon \n\n\n\n\n\n\n\n\n\n\n\npH \n\n\n\n(water) \n\n\n\n\n\n\n\nTotal C \n\n\n\n\n\n\n\n\n\n\n\nTotal N \n\n\n\n\n\n\n\n\n\n\n\nCEC \n\n\n\n\n\n\n\nExchangeable cation \n\n\n\nK Ca Mg \n\n\n\n  ----------------- % ---------------- ------- (cmolc kg-1) ------ \n\n\n\nTopsoil (0 - 20 cm) 4.71 0.56 0.02 1.05 0.04 0.40 0.01 \n\n\n\nEluvial horizon (20 - 87 cm) 4.13 0.03 0.00 0.22 0.02 0.08 0.01 \n\n\n\nSpodic horizon (>100 cm) 3.67 0.71 0.01 3.32 0.02 0.35 0.02 \n\n\n\n\n\n\n\n\n\n\n\nSoil Column Setup \n\n\n\nSoil columns were prepared using PVC pipes of 15 cm inner diameter and 100 cm length. They \n\n\n\nwere fitted with perforated PVC end-cap at the bottom to allow for the outflow of leachate to \n\n\n\nensure the soil remained free-draining (Figure 1). The last 2 cm of each column was filled with \n\n\n\na small amount of glass wool, overlain by a layer of coarse sand to minimize soil loss during \n\n\n\nleaching events. Air-dried surface soil (0-20 cm) from the eluvial (E) horizon (20-87 cm) and \n\n\n\nspodic horizon (>100 cm depth) were packed into the columns to simulate the 0-90 cm of the \n\n\n\nSpodosol profile occurring in the field (Figure 1). The top 0-10 cm (topsoil) and 10-40 cm of \n\n\n\nthe soil profile in the columns were packed with soil from surface and E horizon, respectively.  \n\n\n\n\n\n\n\nA thin spodic layer was created at 40-45 cm depth (hereby referred to as spodic layer) \n\n\n\nwith soil taken from the spodic horizon, underlain by soil from the E horizon at 45-90 cm depth. \n\n\n\nNote that the subsoil at 45-90 cm of the created soil profile was supposed to be packed with the \n\n\n\nsoil taken from below the spodic horizon in the field; however, we were unable to collect it \n\n\n\nbecause the spodic layer was too deep below the surface (> below100 cm depth). Therefore, the \n\n\n\nsoil packed below the spodic horizon of the created profile was re-constructed using soil \n\n\n\nmaterial from the E horizon.  \n\n\n\n\n\n\n\nEach soil layer was packed in the column at an initial bulk density of 1.41, 1.69 and 1.43 \n\n\n\ng cm-3(three levels of bulk density) and four depth levels at 0-10, 10-40, 40-45 and 45-90 cm \n\n\n\ndepths, respectively. When all layers of the soil were packed/stabilized, there was 8 cm head \n\n\n\nspace above the soil columns. The soil in the 0-10 cm depth of the leaching column (hereby \n\n\n\ncalled topsoil) was subjected to one of the following treatments: (1) 0 g kg-1; (2) 5 g kg-1; (3) 10 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n91 \n\n\n\n\n\n\n\ng kg-1); and (4) 15 g kg-1 of biochar, designated as B0 (control), B5, B10 and B15, respectively. \n\n\n\nThese rates corresponded to 0, 10, 20 and 30 t ha-1 with each treatment being replicated three \n\n\n\ntimes. A total of nine replicates for each treatment were set up so as to allow for destructive soil \n\n\n\nsampling at weeks 24, 48 and 72 consecutively.  \n\n\n\n\n\n\n\nFigure 1 shows the diagrammatic illustration of the experimental setup. The soil \n\n\n\ncolumns were placed upright in a glasshouse where they received water once a week over a \n\n\n\nstudy duration of 72 weeks, at an amount equivalent to the mean annual rainfall in Terengganu \n\n\n\nfrom year 2000 to 2009 of 3340 mm. The water was gently poured onto the top of each column \n\n\n\nmanually. Filter paper was placed on the soil surface to help dissipate the water drops as they \n\n\n\nimpacted on the upper surface of the column.  \n\n\n\n\n\n\n\n \nFigure 1. Diagrammatic illustration of the created Spodosol profile in a soil column \n\n\n\n\n\n\n\nSample Collections and Chemical Analyses \n\n\n\n\n\n\n\nThe columns were freely drained during the duration of the experiment and leachates were \n\n\n\ncollected 48 hours after each leaching event. The leachates were analyzed once every 4 weeks. \n\n\n\nThe concentrations of K, Ca and Mg in the leachates were determined using Optima 8300 ICP-\n\n\n\nOES spectrometer (PerkinElmer, Massachusetts, USA). Columns (n= 3) from each treatment \n\n\n\nwere destructively sampled at time increments of 24, 48 and 72 weeks. The soil columns were \n\n\n\nsectioned vertically and analyzed at seven depth intervals of 0-10, 10-25, 25-40, 40-45, 45-67.5, \n\n\n\nand 67.5-90 cm below the soil surface.  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n92 \n\n\n\n\n\n\n\nThe soil from the various depths was air-dried and passed through a 2-mm sieve. Soil \n\n\n\npH was taken using  a ratio of 1:2.5 soil to water; exchangeable Al was extracted with 1 M KCl \n\n\n\nat 1:10 ratio by shaking for 30 min; total C and total N were determined using TruSpec \n\n\n\nCHNanalyser (Leco, Michigan, USA); and CEC and exchangeable cations (Ca, Mg and K) were \n\n\n\ndetermined using ammonium acetate buffered at pH 7 (Schollenberger  and Simon, 1945). The \n\n\n\nNH4\n+ ions for determination of CEC was determined by an Auto-analyser (Quik Chem 8000 \n\n\n\nSeries FIA+ System, Lachat Instruments, Loveland, USA), while exchangeable cations were \n\n\n\nmeasured using Optima 8300 ICP-OES spectrometer (PerkinElmer, Massachusetts, USA). \n\n\n\n\n\n\n\nStatistical Analysis \n\n\n\nThe experimental layout for every sampling time (week 24, week 48 and week 72) was a \n\n\n\nfactorial combination of four (4) different rates (0, 5, 10 and 15 g kg-1) of biochar and 7 sampling \n\n\n\ndepths with three replications. A completely randomized design (CRD) was used for all \n\n\n\nstatistical analyses. At every sampling time, a two-way analysis of variance (ANOVA) was \n\n\n\ncarried out for each variable measured with biochar rate, sampling depth and their interactions \n\n\n\nas fixed effects. Tukey's Studentized Range test was used to calculate the differences among \n\n\n\ntreatment means. The functional relationship (trend comparison) between response of variables \n\n\n\nand biochar rate for variables with significant biochar rate and/or biochar rate x sampling depth \n\n\n\ninteraction was performed by polynomial contrast and regression analysis.  \n\n\n\n\n\n\n\nIn all cases (except for soil pH), significant regression were only detected for soil \n\n\n\nsampled in the topsoil (0-10 cm depth) and spodic layer (40-45 cm depth); thus, only regression \n\n\n\nequations for both sampling depths are reported in this paper. Thereafter, the relationship \n\n\n\nbetween variables for every sampling time was determined by correlation and regression \n\n\n\nanalysis. Statistical analysis was performed using SAS, software version 9.1 with differences, \n\n\n\nunless otherwise stated, significant at p \u22640.05.  \n\n\n\n\n\n\n\n  RESULTS AND DISCUSSION \n\n\n\nImpact of EFB-Biochar Treatment on Soil Acidity \n\n\n\n\n\n\n\nIn comparing biochar rates and soil depths, our study results detected a significant difference at \n\n\n\nevery sampling time ; however, this was not the case for their interaction (Table 2). At week \n\n\n\n24, biochar addition increased soil pH, but at week 48, only treatment B15 had a higher pH than \n\n\n\nthat of the control treatment. At week 72, there was no further increase in soil pH at any rate of \n\n\n\nbiochar treatment (Figure 2a). Linear polynomial contrast for the responses of soil pH to \n\n\n\nbiochar rate was significant for soil sampled at weeks 24 and 48, while quadratic and cubic \n\n\n\npolynomial contrast was only significant for soil sampled at week 72 (Table 2). The linear \n\n\n\nregression equation so obtained demonstrated that soil pH increased by 0.06 and 0.04 unit per \n\n\n\ng kg-1 of biochar applied for weeks 24 and 48, respectively (Figure 2a). According to Joseph et \n\n\n\nal. (2010), the observed increase in soil pH was likely due to the dissolution of alkaline \n\n\n\ncarbonates or oxides/hydroxides present in the ash fraction of the biochar. On the other hand, \n\n\n\nCa originating from the EFB-biochar may have displaced some Al on the exchange sites which \n\n\n\ncould result in a slightly lower soil pH (Egiarte et al. 2006). \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n93 \n\n\n\n\n\n\n\nAt week 24, the topsoil had the highest soil pH (Figure 2b). However, at week 48, the \n\n\n\npH of the topsoil showed a decreasing trend with values lower than that of the subsoil (below \n\n\n\n40 cm depth). This was probably because of the oxidation of non-aromatic C fractions to form \n\n\n\nacidic carboxyl groups resulting from biochar addition (Cheng et al. 2006; Nguyen et al. 2010), \n\n\n\nleading to the subsequent decline in soil pH (Joseph et al. 2010). At week 72, the pH of the soil \n\n\n\nat every depth had decreased but with the topsoil still having the highest value.  \n\n\n\n\n\n\n\nThe results seemed to indicate that the biochar-treated Spodosol became acidic again \n\n\n\ntowards the end of week 72, suggesting that the ameliorative impact of EFB-biochar treatment \n\n\n\nwas only short-term. The efficacy of the biochar as a soil ameliorant decreased when it had \n\n\n\nundergone ageing due to loss of K, Mg and Ca via leaching that partly contributed to the slight \n\n\n\nincrease in soil acidity. Thus, EFB-biochar can only be an effective \u2018liming agent\u2019 for sandy \n\n\n\nSpodosol provided that high amounts are applied on the soil (Rabileh et al. 2015).  \n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nProbability (p) values derived from ANOVA for chemical properties of soil  \n\n\n\n\n\n\n\n(a) Week 24 \n\n\n\nSource \n\n\n\nDepth \n\n\n\npH \n\n\n\n<0.0001 \n\n\n\nTC \n\n\n\n<0.0001 \n\n\n\nCEC \n\n\n\n<0.0001 \n\n\n\nK \n\n\n\n<0.0001 \n\n\n\nCa \n\n\n\n<0.0001 \n\n\n\nMg \n\n\n\n<0.0001 \n\n\n\nRate <0.0001 <0.0001 0.04 0.0002 0.18 <0.0001 \n\n\n\nDepth*rate 0.38 <0.0001 0.02 <0.0001 <0.0001 <0.0001 \n\n\n\nRate linear <0.0001 <0.0001 0.01 <0.0001 0.09 <0.0001 \n\n\n\nRate Quadratic  0.001 0.72 0.29 0.35 0.20 0.07 \n\n\n\nRate cubic 0.51 0.37 0.84 0.87 0.58 0.01 \n\n\n\n\n\n\n\n(b) Week 48 \n\n\n\n      Source pH TC CEC K Ca Mg \n\n\n\nDepth <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 \n\n\n\nRate <0.0001 0.003 0.09 <0.0001 0.12 <0.0001 \n\n\n\nDepth*rate 0.32 <0.0001 0.54 <0.0001 0.01 <0.0001 \n\n\n\nRate linear <0.001 0.0002 0.02 <0.0001 0.02 <0.0001 \n\n\n\nRate Quadratic  0.19 0.51 0.71 0.15 0.65 0.08 \n\n\n\nRate cubic 0.17 0.97 0.45 0.36 0.92 0.42 \n\n\n\n\n\n\n\n(c) Week 72 \n\n\n\n      Source pH TC CEC K Ca Mg \n\n\n\nDepth <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 \n\n\n\nRate 0.010 0.02 <0.0001 0.01 0.08 <0.0001 \n\n\n\nDepth*rate 0.80 0.0001 0.0001 0.001 0.14 <0.0001 \n\n\n\nRate linear 0.26 0.003 <0.0001 0.01 0.02 <0.0001 \n\n\n\nRate Quadratic  0.02 0.37 0.12 0.17 0.27 0.57 \n\n\n\nRate cubic 0.010 0.63 0.99 0.03 0.93 0.60 \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n94 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2. Interaction effects of biochar rate \u00d7 sampling time (a) and depth \u00d7 sampling time (b) \n\n\n\non soil pH. Error bars represent \u00b1 standard error of the means; (a) n = 21, (b) n = 12. Points \n\n\n\nin (a) and (b) are placed at the center of the depth increment they represent. Significant at p\u2264 \n\n\n\n0.05. \n\n\n\n\n\n\n\nImpact of EFB-biochar treatment on soil C content \n\n\n\nFigure 3 shows the impact of biochar treatment on soil C content of the treated samples. \n\n\n\nPolynomial contrast and regression analyses indicated that the response of total C in the topsoil \n\n\n\nincreased linearly with every g kg-1 increase in EFB-biochar rate applied at 0.36, 0.50 and 0.29 \n\n\n\n% for weeks 24, 48 and 72, respectively (Table 3). Total C in the biochar treated top soil was \n\n\n\nhigher than that of the control treatment for every sampling time, indicating that a large amount \n\n\n\nof the applied biochar remained in place even after being subjected to the continuous wet-dry \n\n\n\n4\n\n\n\n4.5\n\n\n\n5\n\n\n\n5.5\n\n\n\n6\n\n\n\n6.5\n\n\n\n0 5 10 15\n\n\n\nS\no\nil\n\n\n\n p\nH\n\n\n\nBiochar rate (t ha-1)\n\n\n\nWeek 24\n\n\n\nWeek 48\n\n\n\nWeek 72\n\n\n\n(a)\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n3 4 5 6 7\n\n\n\nS\no\nil\n\n\n\n d\ne\np\n\n\n\nth\n (\n\n\n\nc\nm\n\n\n\n)\n\n\n\nSoil pH\n\n\n\nWeek 24\n\n\n\nWeek 48\n\n\n\nWeek 72\n\n\n\n(b\n\n\n\n) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n95 \n\n\n\n\n\n\n\ncycles for 72 weeks. However, only soils in the columns treated with B15 were significantly \n\n\n\nhigher compared to that of the control treatment (Figure 3a). \n\n\n\nThe biochar-treated soil in the spodic layer appears to have a significantly higher C \n\n\n\ncontent compared to that of the control treatment for week 24 only. This result suggests that \n\n\n\nsome C from the topsoil had moved down to the subsoil via leaching or perhaps by the process \n\n\n\nof podzolization. Total C in the spodic layer increased linearly by 0.18% (R2=0.71, P<0.001) \n\n\n\nwith every g kg-1 of EFB-biochar added (Table 2). At every sampling time, soil without EFB-\n\n\n\nbiochar treatment showed significantly higher C in the topsoil and spodic layer compared to \n\n\n\nthat of the other soil depths. A similar trend was observed for the biochar-treated soils, but their \n\n\n\ntotal C was higher. This finding is consistent with that of Lehmann et al. (2007), which means \n\n\n\nthat in real situations in the field, some C from EFB-biochar is transported downwards over \n\n\n\ntime. \n\n\n\n  Total C of the treated soil columns in the topsoil and spodic layer showed a small \n\n\n\ndecrease with time (Figure 3b). The recalcitrant nature of the biochar does not mean that it \n\n\n\nremains unchanged forever (Atkinson et al. 2010). Schmidt and Noack (2000) noted that besides \n\n\n\nhaving recalcitrant aromatic ring structures, biochar contains some easily degradable aliphatic \n\n\n\nand oxidized C structures. Hence, some of the unstable C in the EFB-biochar might have \n\n\n\nundergone change through oxidation and mineralization. However, this is not consistent with \n\n\n\nthe finding of Laird et al. (2010) who reported no detectable loss of biochar C during the 72 \n\n\n\nweeks of their soil column study. The soil tested by Laird et al. (2010) was a Mollisol dominated \n\n\n\nby 2:1 phyllosilicates (smectite and vermiculite) with high CEC that can curtail C loss via \n\n\n\nleaching. Note that the clay fraction of the sandy Spodosol under study was dominated by \n\n\n\nkaolinite which has low CEC (Roslan et al. 2010). \n\n\n\n\n\n\n\n  Addition of EFB-biochar to the sandy Spodosol containing some kaolinite increased C \n\n\n\nin the topsoil, but its level decreased as the biochar aged with time. This is due to the effects of \n\n\n\nexposure to the high soil temperature and leaching environment, resulting in rapid \n\n\n\nmineralization of C in the EFB-biochar. The biochar-C could have been leached as dissolved \n\n\n\norganic materials, which was eventually transported down the soil column. It was possible that \n\n\n\nsome of the dissolved C accumulated or was temporarily trapped in the spodic layer via the \n\n\n\nprocess of podzolization. Because of the loose soil structures in the re-packed spodic layer or \n\n\n\npossibly due to the presence of low amount of 2:1 phyllosilicates,the dissolved C could have \n\n\n\ncontinued to move further below the said layer. \n\n\n\n\n\n\n\nThe assumed podzolization process occurring in the soil columns during the period of \n\n\n\nthe experiment could have been promoted by special microbes existing in the Spodosol, which \n\n\n\nwas identified by Zainuri (1981) as fungi species. According to Lundstr\u00f6m et al. (2000) and \n\n\n\nVan Sch\u00f6ll et al. (2008), fungi could play a crucial role in the podzolization of Spodosols. \n\n\n\nHydroxyl, aliphatic and quinone are the dominant functional groups present in biochar (Liu et \n\n\n\nal. 2015). Accordingly, the organic C released into the soil by the decomposition of the EFB-\n\n\n\nbiochar could form stable Al-Fe-complexes, which is the key mechanism of the podzolization \n\n\n\nprocess. \n\n\n\n\n\n\n\nThe biochar-treated soil had significantly higher C in the spodic layer compared to that \n\n\n\nof the control treatment. This means that some C in the topsoil had been transported down the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n96 \n\n\n\n\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n0 0.2 0.4 0.6 0.8 1 1.2\n\n\n\nS\no\nil\n\n\n\n d\ne\np\n\n\n\nth\n (\n\n\n\nc\nm\n\n\n\n)\n\n\n\nTotal C (%)\n\n\n\nWeek 24\n\n\n\nWeek 48\n\n\n\nWeek 72\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6\n\n\n\nS\no\nil\n\n\n\n d\ne\np\n\n\n\nth\n (\n\n\n\nc\nm\n\n\n\n)\n\n\n\nTotal C (%)\n\n\n\nB0\nB5\nB10\nB15\n\n\n\nRate\n\n\n\n(a)\n\n\n\nsoil profile via leaching and/or the process of podzolization. If the latter could be proven to \n\n\n\noccur in the studied soil, it is plausible that some C from the EFB-biochar can be sequestered \n\n\n\nin the spodic layer of sandy Spodosols in the tropics.   \n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nRegression equation for the response of total C and CEC in the topsoil  \n\n\n\n\n\n\n\nSoil properties and depth \n\n\n\nEquation R2 \n\n\n\nWeek 24 Week 48 Week 72 \n\n\n\nTotal C (%)    \n\n\n\n0-10cm \ny=0.363x-0.108 \n\n\n\nR2=0.81*** \n\n\n\ny=0.504x-0.455 \n\n\n\nR2=0.59** \n\n\n\ny=0.29x-0.115 \n\n\n\nR2=0.61** \n\n\n\n40-45cm \ny=0.182x+0.557 \n\n\n\nR2=0.71*** \n\n\n\ny=0.068x+0.632 \n\n\n\nR2=0.43* nsa \n\n\n\nCEC (cmolc g kg-1) \n\n\n\n  \n0-10cm \n\n\n\ny=0.364x+0.032 \n\n\n\nR2=0.65** ndb \n\n\n\ny= 0.473x+0.037 \n\n\n\nR2=0.8*** \n\n\n\n40-45cm ns nd ns \nTopsoil and spodic layer to biochar rate for the soil sampled at weeks 24, 48 and 72.  \naNot significant at p>0.05 \nbNot determined because there was no significant effect of biochar rate or biochar rate x depth interaction for CEC \n\n\n\nof soil sampled at week 48  \n\n\n\n* Significant at p\u22640.05, ** significant at p\u22640.01, *** significant at p\u22640.001 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n97 \n\n\n\n\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7\n\n\n\nS\no\nil\n\n\n\n d\ne\np\n\n\n\nth\n (\n\n\n\nc\nm\n\n\n\n)\n\n\n\nCEC (cmolc kg-1)\n\n\n\nB0\nB5\nB10\nB15\n\n\n\nRate\n\n\n\n(c)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Interaction effect of biochar rate \u00d7 depth (a), depth \u00d7 sampling time (b) on soil total \n\n\n\nC %, and (c)biochar rate \u00d7 depth on the CEC. Error bars represent \u00b1 standard error of the \n\n\n\nmeans (n = 9). Points are placed at the center of the depth increment they represent. Significant \n\n\n\nlevel: p\u2264 0.05. \n\n\n\n\n\n\n\nImpact of EFB-Biochar Treatment on Soil CEC \n\n\n\n\n\n\n\nA significant effect of biochar rate x depth interaction on CEC of the soil was observed at weeks \n\n\n\n24 and 72 (Table 2). At week 24, only soil treated with B15 significantly increased the topsoil \n\n\n\nCEC compared to that of the control (Figure 3c), while at week 72, the CEC of the soil at the \n\n\n\nsame depth increased significantly in the soil treated with B10 and B15. There was a significant \n\n\n\nlinear polynomial contrast for the response of CEC to the biochar treatment at weeks 24 and 72 \n\n\n\n(Table 2). Regression analysis (R2=0.65, P<0.01; R2=0.8, P<0.001, for weeks 24 and 72, \n\n\n\nrespectively) showed that the CEC increased by 0.36 and 0.47 unit for every g biochar kg-1soil \n\n\n\napplied at weeks 24 and 72 (Table 3). No significant difference was found in the spodic layer \n\n\n\ndue to EFB-biochar treatment.  \n\n\n\n\n\n\n\nSoil in the spodic layer was initially quite high in CEC, with values higher than that of \n\n\n\nthe other layers in the soil profile. This is expected as the spodic horizon of a Spodosol contains \n\n\n\nhigh amounts of organic matter. Due to EFB-biochar addition, the CEC of the soil in the spodic \n\n\n\nlayer was also significantly higher than that of the control treatment. The CEC of the topsoil \n\n\n\nappears to show an increasing trend with the rate of biochar addition but not significantly \n\n\n\ndifferent. This is consistent with the finding of other researchers who reported the ability of \n\n\n\nbiochar to only slightly increase the CEC of a soil (Novak et al. 2009; Steiner et al. 2007). \n\n\n\n\n\n\n\nIt is believed that the small increase in the topsoil CEC was due to the addition of C \n\n\n\nreleased by the EFB-biochar (Chan et al. 2008). This is shown by the linear relationship between \n\n\n\nCEC and C at every sampling time (R2=0.62, P<0.001; R2=0.41, P<0.001; R2= 0.66; P< 0.001, \n\n\n\nat weeks 24, 48 and 72, respectively) (Figure 4). The CEC of the topsoil was respectively \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n98 \n\n\n\n\n\n\n\ny = 3.57x + 0.227\n\n\n\nR\u00b2 = 0.56***\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n0 0.5 1 1.5 2 2.5 3\n\n\n\nC\nE\n\n\n\nC\n (\n\n\n\nc\nm\n\n\n\no\nlc\n\n\n\n k\ng\n\n\n\n-1\n)\n\n\n\nTotal C (%)\n\n\n\nincreased by 3.44, 2.93 and 4.1 cmolc kg-1 for every 1 % increase in C content, at weeks 24, 48 \n\n\n\nand 72. Liang et al. (2006) and Atkinson et al. (2010) explained the occurrence of a \n\n\n\nphenomenon by the oxidation of C present in the biochar that oxygenates functional groups, \n\n\n\nand producing negative charge. The CEC of fresh biochar is relatively low; however, it \n\n\n\nincreases during the period of incubation in soil due to the oxidation of biochar surfaces and/or \n\n\n\nadsorption of organic acids (Cheng et al. 2006). This notion is supported by the findings of \n\n\n\nanother study (Laird et al. 2010). \n\n\n\n\n\n\n\nA slight increase in the topsoil CEC is indicative of oxidation of the easily degradable \n\n\n\nC present in the EFB-biochar. Note that soil sampled from the E horizon was not significantly \n\n\n\naffected by addition of the EFB-biochar, which was probably due to the very low clay mineral \n\n\n\nand/or C content in that zone. By and large, the CEC of the biochar-treated soil in the leaching \n\n\n\ncolumns except for the spodic layer remained very low (< 3 cmolc kg-1). This suggests that a \n\n\n\nlarge amount of EFB-biochar (at least 15 g kg-1) is needed to increase the CEC of the sandy soil \n\n\n\nsignificantly. In spite of ameliorative impact on the soil, application of EFB-biochar at the \n\n\n\nproposed rate is considered not a feasible agronomic practice due to the high cost of producing \n\n\n\nEFB-biochar (Manickam et al. 2015). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4. Impact of EFB-biochar treatment on relationship between CEC and total C in the soil \n\n\n\nEffects of EFB-Biochar Treatment on Leachate Composition \n\n\n\nLeachate K \n\n\n\n\n\n\n\nPotassium concentration in the leachates increased with increasing biochar rates, but decreased \n\n\n\nwith time (Figure 5a). This is due to the continued leaching of K from the soil, evidenced by \n\n\n\nthe higher concentration of K in the leachate at the beginning of the leaching event compared \n\n\n\nthat of the latter. It is known that K is not tightly bound to the biochar (Milligan et al. 2008); \n\n\n\ntherefore, it is subjected to immediate leaching after the EFB-biochar was applied. Furthermore, \n\n\n\nleaching of K is dependent on the concentration of other cations, especially Ca. Note that Ca is \n\n\n\nthe dominant cation in soil and thus it competes with K for the exchange sites. If a limited \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n99 \n\n\n\n\n\n\n\nnumber of cation exchange sites is available, divalent cation is preferentially exchanged. This \n\n\n\nis due to the greater force of attraction to the exchange sites for a divalent ion compared to that \n\n\n\nof a monovalent ion (Hodson and Langan 1999). Therefore, K is more available for movement \n\n\n\nwithin the leachate as explained by Novak et al. (2009). \n\n\n\n\n\n\n\nLeachate Ca \n\n\n\nCalcium concentration initially increased with biochar rate, but it dramatically dropped up to \n\n\n\nweek 12 (Figure 5b). This result reflects the increasing amounts of Ca added into the soil \n\n\n\ncolumn with higher rates of biochar application and demonstrates that some of the Ca in EFB-\n\n\n\nbiochar is easily soluble and partly mobile (Lehmann et al. 2011). At week 72, Ca concentration \n\n\n\nin the leachates of the control treatment was higher compared to that of the biochar treatments, \n\n\n\nindicative of the high retention ability of the EFB-biochar for Ca. We hypothesized that the \n\n\n\npresence of EFB-biochar in the soil would slow down the eventual loss of Ca from the soil. This \n\n\n\nhypothesis is supported by the increasing availability of Ca in the treated topsoil. \n\n\n\nLeachate Mg \n\n\n\nMagnesium concentration in the leachates for all treatments showed a declining trend for the \n\n\n\nfirst 24 weeks of the experiment (Figure 5c). Thereafter, Mg concentration due to B5 treatment \n\n\n\nincreased to the maximal level at week 48. This can be attributed to the mobilization of the \n\n\n\nexchangeable Mg that was accumulated in the spodic layer for the first 24 weeks. During this \n\n\n\ntime, Mg present in the spodic layer could have been  released due to proton exchange at the \n\n\n\nexchange sites, and as leaching continued, Mg became soluble and was leached out of the soil \n\n\n\ncolumns. Nutrients such as Ca and Mg are below the level required for healthy plant growth \n\n\n\n(Palhares et al. 2000). \n\n\n\nContribution of EFB-biochar to soil fertility \n\n\n\nThe topsoil of the current study contained high amounts of sand; consequently, its CEC was \n\n\n\nvery low. Hence, the less strongly-held K present on the exchange sites of the topsoil treated \n\n\n\nwith the EFB-biochar could have been easily leached out. The translocation of K through the \n\n\n\nsoil column may be also be related to the dissolved C because the EFB-biochar contained \n\n\n\norganic ligands with affinity to form soluble complexes with minerals (McBride et al. 1997). \n\n\n\nThus, the formation of organo-mineral complexes by organic ligands from EFB-biochar is an \n\n\n\nimportant mechanism to retain some nutrients in the topsoil.  \n\n\n\n  EFB-biochar applied into the topsoil could have undergone degradation and ageing with \n\n\n\nthe time. It is possible that microbial breakdown is one of the degradation mechanisms. It is \n\n\n\nalso possible that part of the applied EFB-biochar was degraded due to long-term exposure to \n\n\n\nhigh soil temperature and a strong leaching environment. Be that as it may, we believe that \n\n\n\ndegradation and ageing of biochar led to greater surface oxidation, resulting in a slight CEC \n\n\n\nincrease, which enhanced cation retention in soil under treatment. It appears that the availability \n\n\n\nof K, Mg and Ca in the zone of EFB-biochar application (topsoil) was still low for crop \n\n\n\nrequirement (Egiarte et al. 2006). This means that application of EFB-biochar alone is unable \n\n\n\nto sustain crop production on the nutrient-deficient Spodosols in Malaysia or even Southeast \n\n\n\nAsia.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n100 \n\n\n\n\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n3\n\n\n\n3.5\n\n\n\n4\n\n\n\n0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72C\no\n\n\n\nn\nc\ne\nn\n\n\n\ntr\na\nti\n\n\n\no\nn\n\n\n\n o\nf \n\n\n\nM\ng\n\n\n\n i\nn\n\n\n\n l\ne\na\nc\nh\n\n\n\na\nte\n\n\n\n (\nm\n\n\n\ng\n L\n\n\n\n-1\n)\n\n\n\nWeek\n\n\n\nB0\n\n\n\nB5\n\n\n\nB10\n\n\n\nB15\n\n\n\nRate(c)\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\n16\n\n\n\n0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72C\no\n\n\n\nn\nc\ne\nn\n\n\n\ntr\na\nti\n\n\n\no\nn\n\n\n\n o\nf \n\n\n\nC\na\n i\n\n\n\nn\n l\n\n\n\ne\na\nc\nh\n\n\n\na\nte\n\n\n\n (\nm\n\n\n\ng\n L\n\n\n\n-1\n)\n\n\n\nB0\n\n\n\nB5\n\n\n\nB10\n\n\n\nB15\n\n\n\n(b)\nRate\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72\n\n\n\nC\no\n\n\n\nn\nc\ne\nn\n\n\n\ntr\na\nti\n\n\n\no\nn\n\n\n\n o\nf \n\n\n\nK\n i\n\n\n\nn\n l\n\n\n\ne\na\nc\nh\n\n\n\na\nte\n\n\n\n (\nm\n\n\n\ng\n L\n\n\n\n-1\n)\n\n\n\nB0\n\n\n\nB5\n\n\n\nB10\n\n\n\nB15\n\n\n\n(a) Rate\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5. Effects of biochar application on the concentration of K in the leachates (a), K in the \n\n\n\nleachates (b), Ca in the leachates (c) Mg collected from soil columns during the 72 weeks of the \n\n\n\nstudy. The first point on the graph represents the concentration of Mg in the leachate collected \n\n\n\nat week 1 of the leaching event. Error bars represent \u00b1 standard error of the means (n = 3).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 88-103 \n\n\n\n\n\n\n\n101 \n\n\n\n\n\n\n\nCONCLUSIONS \n\n\n\nTreating a sandy Spodosol with EFB-biochar had a positive impact on soil chemical properties. \n\n\n\nSoils treated with the biochar had higher exchangeable K, Ca and Mg compared to those of the \n\n\n\ncontrol treatment. The higher K, Ca and Mg concentration in the leachates collected from the \n\n\n\nbiochar-treated soils compared to that of the control during the first week means that a \n\n\n\nsignificant portion of the nutrients initially present in the biochar was in soluble form, \n\n\n\nsusceptible to leaching losses. To reduce leaching rates, the CEC of the soil needs to be \n\n\n\nsignificantly increased via biochar treatment. The C and CEC of the treated topsoil and those \n\n\n\nof the spodic layer were increased with treatment; the CEC was positively correlated with the \n\n\n\nsoil C content. The ameliorative impact of EFB-biochar application failed to create a favorable \n\n\n\ncondition for crop growth because it was only a short-term event. Application of high amounts \n\n\n\nof biochar is necessary to enhance soil fertility to sustain crop growth. At the rate applied during \n\n\n\nthe experiment, the EFB-biochar was unable to provide sufficient nutrients for crop requirement \n\n\n\nto sustain it growth. Hence, its application at that rate is not a feasible agronomic practice \n\n\n\nwithout undergoing innovation in the method of application. Organic acids released by the EFB-\n\n\n\nbiochar could have played a role in transporting the organo-metal-complexes down the soil \n\n\n\ncolumns via the process of podzolization. This suggests that some C present in the biochar could \n\n\n\nhave been sequestered in the spodic layer of the biochar-treated Spodosol within the period of \n\n\n\n72 weeks.  \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n \n\n\n\nWe wish to express our utmost appreciation to Universiti Putra Malaysia (UPM) for the \n\n\n\nfinancial and technical supports given during the conduct of the research. The study was \n\n\n\nfinanced by UPM under Research University Grant Scheme (RUGS), number 01-03-10-0983-\n\n\n\nRU (Vote: 91988).  \n\n\n\n\n\n\n\nREFERENCES \n\n\n\nAbel, S., A. Peters, s. Trinks, H. Schonsky, M. Facklam and G. Wessolek. 2013. Impact of \n\n\n\nbiochar and hydrochar addition on water retention and water repellency of sandy soil. \n\n\n\nGeoderma 202: 183-191. \n\n\n\nAtkinson, C.J., J.D. Fitzgerald and N.A. Hipps. 2010. Potential mechanisms for achieving \n\n\n\nagricultural benefits from biochar application to temperate soils: a review. Plant Soil. \n\n\n\n337: 1-18. \n\n\n\nChan, K.Y., L.A. van Zwitten, I. Meszaros, A. Downie and S. Joseph. 2008. Using poultry litter \n\n\n\nbiochars as soil amendments. Aust. J. Soil Res. 46: 437-444. \n\n\n\nCheng, C., J. Lehmann, J.E Thies, S.D. Burton and M.H. Engelhard. 2006. Oxidation of black \n\n\n\ncarbon by biotic and abiotic processes. Organic Geochem. 37: 1477-1488. \n\n\n\nEgiarte, G. M., A. Camps, E. 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"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n120 \n\n\n\n\n\n\n\n\n\n\n\nSoil Nitrogen Content and Storage in Age Sequence Acacia Mangium \n\n\n\nPlantations in the Southeastern Region of Vietnam \n \n\n\n\nCuong, L.V.1*, Sang, T.V.2, Bolanle-Ojo, O.T.3, Bao, T.Q.1, Ngoan, T.T.1, Tuan, N.T.1, \n\n\n\nHung, N.X.1, Long L.V.1, Duong D.T.T.1 and Phu, N.V.1 \n\n\n\n \n1 Faculty of Forestry, Vietnam National University of Forestry, Hanoi 100000, Vietnam \n\n\n\n 2 Institute of Ecology and Works Protection, Vietnam Academy for Water Resources \n\n\n\nHanoi 100000, Vietnam \n3 Forestry Research Institute of Nigeria, P.M.B. 5054, Ibadan, Nigeria \n\n\n\n\n\n\n\n*Corresponding author: cuongvfu.90@gmail.com  \n\n\n\n\n\n\n\nABSTRACT \n\n\n\n \nTo better understand soil nitrogen (N) sequestration by Acacia mangium Willd. in the plantations of the \n\n\n\nSoutheastern region of Vietnam, a study was conducted to examine soil N content and storage in three \ndifferent-aged A. mangium stands (4, 7 and 11 years old). Soil samples were collected at different depths \n\n\n\nfrom 0\u201350 cm. Field measurements were taken based on established national standard methods. We \n\n\n\nused the modified Kjeldahl method to determine soil total N concentration. Soil total N concentration \nat the various soil depths for each plantation decreased significantly with increasing depth, but increased \n\n\n\nsignificantly with plantation age. Soil total N stocks at the topsoil (0\u201350 cm) increased from 6.13 Mg. \n\n\n\nN. ha-1 in 4-year-old stands to 9.71 Mg. N. ha-1 in 11-year-old stands. Soil total N storage showed obvious \n\n\n\ntopsoil aggregation with more than 60% of soil total N storage in the 0\u201330 cm depth for each stand. \nHence, protection of total N stocks present in the topsoil of planted forests is very critical in the context \n\n\n\nof N sequestration. Furthermore, stand characteristic parameters (i.e., stand age, plant biomass, stand \n\n\n\ndensity, tree height and diameter at breast height, and canopy closure) significantly affected soil total \nN storage. The findings from this study indicate that taking stand age into consideration is greatly \n\n\n\nbeneficial for forest soil N storage assessment and highlights the potential of A. mangium for N \n\n\n\nsequestration in plantation ecosystems. \n \n\n\n\nKey words: Acacia mangium plantations, nitrogen sequestration, age-sequence, plantation \n\n\n\nforestry, Vietnam \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nSoils constitute the main terrestrial reservoir for organic carbon (C) and approximately 1500 \n\n\n\nPgC are accumulated in the first meter of the soil (Batjes 1996; Scharlemann et al. 2014). \n\n\n\nParticularly in forest soil, C sequestration plays an important role in the global C cycle, and \n\n\n\ncomprises about 73% of global soil organic C storage (Sedjo 1993). Soil organic C \n\n\n\naccumulation rate is greatly reliant on the net primary productivity of plants (Jobb\u00e1gy and \n\n\n\nJackson 2000; Reich et al. 2006), which is predominantly limited by nitrogen in most terrestrial \n\n\n\necosystems (Vitousek and Howarth 1991; Knops and Tilman 2000). Thus, soil nitrogen \n\n\n\nreservoir has been considered as an index of C sequestration potential (Luo et al. 2004; \n\n\n\nVesterdal et al. 2008). Accurate assessment of soil total nitrogen (TN) storage in different stand \n\n\n\nages is crucial to understanding the relationship between C cycles in terrestrial ecosystems and \n\n\n\nglobal climate change.  \n\n\n\n\n\n\n\n Acacia mangium Willd. is a principal tree species for afforestation in the Southeastern \n\n\n\nregion of Vietnam and plays an important role in the global C cycle because its plantation area \n\n\n\n\nmailto:cuongvfu.90@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n121 \n\n\n\n\n\n\n\ncovers over 800,000 ha (MARD 2018), accounting for 19% of the total plantations in Vietnam \n\n\n\n(MARD 2020) and about 0.27% of all plantation forests in the world (FAO 2020). In addition \n\n\n\nto producing wood for industries, A. mangium planted forests also have a role in providing \n\n\n\nenvironmental service such as reducing negative impacts on C and N cycles through the uptake \n\n\n\nand storage of C and N. \n\n\n\n\n\n\n\n Therefore a better knowledge of changes in soil and total nitrogen (TN) storage of A. \n\n\n\nmangium plantations is essential to improve the C storage of soils and reduce the emission of \n\n\n\ngreenhouse gases. Several studies have been carried out on growth and wood properties, \n\n\n\nbiomass and productivity, C sequestration, soil physico-chemical properties, biological \n\n\n\nnitrogen fixation and nutrient cycling in A. mangium plantations (Ribet and Drevon 1996; Xu \n\n\n\net al. 1998; Dhamodaran and Chacko 1999; Saharjo and Watanabe 2000; Hai et al. 2009; \n\n\n\nMatali and Metali 2015; Paula et al. 2015; Cuong et al. 2020). However, soil nitrogen \n\n\n\nsequestration by A. mangium plantations in the Southeastern region of Vietnam is still \n\n\n\nunknown. Hence, the present study was designed to assess soil nitrogen of an A. mangium \n\n\n\nforest ecosystem in an age-sequence. The primary objective of this research was to explore soil \n\n\n\nTN content and storage in A. mangium plantations over three different ages along various soil \n\n\n\ndepths. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nDescription of Study Area \n\n\n\n\n\n\n\nThe study site is located in the Changriec Historical-Cultural Forest, Tayninh Province, \n\n\n\nVietnam (11\u00b000\u203230\u2033 to 11\u00b035\u203213\u2033N and 106\u00b000\u203200\u2033 to 106\u00b007\u203210\u2033E) (Figure 1). The region has \n\n\n\na tropical monsoon climate, including a rainy (May to November) and a dry season (December \n\n\n\nto April). The mean annual temperature is 26.9\u00b0C with the lowest temperature of 21\u00b0C in the \n\n\n\nmonth of December and the highest temperature of 35.2 \u00b0C in the month of April. Annual mean \n\n\n\nhumidity is 78.3%, and the monthly sunshine hours average about 181-277 h (Tuan and Dinh \n\n\n\n2020). The terrain of the research area is relatively flat, with an elevation of 28-53 m a.s.l. with \n\n\n\nslopes of 3-5\u00b0. The soil type is grey brown, developed on ancient alluvium, with a soil depth \n\n\n\nof above 100 cm. The soil is loamy in texture with a pH (H2O) ranging from 5.10-5.54. The \n\n\n\nconcentrations of soil sand, silt and clay are 41.19, 46.06, and 12.76%, respectively (Cuong et \n\n\n\nal. 2020). There are a large number of A. mangium plantation stands with different ages and \n\n\n\ndensities. Other woody plants in this region include Acacia hybrid (Acacia auriculiformis A. \n\n\n\nCunn. ex Benth. \u00d7 A. mangium Willd.), Hopea sp., Dipterocarpus obtusifolius Teijsm. ex Miq. \n\n\n\nand Tectona grandis L.f. The plantation forests take up about 40% of the total forest area. The \n\n\n\nmain shrub and herb plants include Mallotus apelta (Lour.) M\u00fcll. Arg., Tetracera scandens \n\n\n\n(L.) Merr., Chromolaena odorata (L.) R.M. King & H. Rob., Saccharum arundinaceum \n\n\n\n(Retz.), Mimosa pudica var. tetrandra (Willd.) D.C., Chrysopogon aciculatus (Retz.) Trin., \n\n\n\nMaesa perlarius (Lour.) Merr., Lygodium microphyllum (Cav.) R. Br., Dryopteris parasitica \n\n\n\n(L.) Kuntze, Helicteres angustifolia var. obtusa (Wall. ex Kurz) Pierre and Cynodon dactylon \n\n\n\n(L.) Pers. In this study, we selected three different-aged (4, 7, 11 years) A. mangium plantation \n\n\n\nforests, which were all covered by cassava under agriculture before the afforestation. \n\n\n\nSubsequently pure A. mangium forests were planted in similar slope, direction and elevation. \n\n\n\nNo treatments such as fertilization and irrigation were carried out after afforestation. From \n\n\n\nFebruary to April 2019, four plots (40 m \u00d7 25 m in size) were selected in each stand. All the \n\n\n\nsampling plots were less than 1.0 km apart (Figure 1). Within each plot, tree height (H) and \n\n\n\ndiameter at breast height (DBH) were recorded for every tree. Other descriptive details of the \n\n\n\nsites are given by Cuong et al. (2020) and are reproduced in Table 1.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n122 \n\n\n\n\n\n\n\nFigure 1. Map of experimental plots in Changriec Historical-Cultural Forest (Tayninh \n\n\n\nProvince, Southeastern region, Vietnam) \n\n\n\n\n\n\n\nTABLE 1 \nCharacteristics of the sampled stands of Acacia mangium plantations in Changriec Historical - \n\n\n\nCultural Forest (Southeastern region, Vietnam) \n\n\n\nMeasured variables \nStand age (years) \n\n\n\n4 7 11 \n\n\n\nPlants \n\n\n\nStand area (ha) 2.6 2.2 3.6 \n\n\n\nMean DBH (cm) 13.78 \u00b10.38a 17.94 \u00b1 0.86b 21.78 \u00b1 0.85c \nMean Hvn (m) 14.72 \u00b1 0.17a 17.29 \u00b1 0.56b 18.60 \u00b1  0.21c \n\n\n\nStand density (tree. ha\u20131) 888 \u00b1 30a 728 \u00b1 22b 610 \u00b1 29c \n\n\n\nCanopy closure 0.83 \u00b1 0.01a 0.81    \u00b1 0.01b 0.79 \u00b1 0.03b \n\n\n\nElevation (m a.s.l.) 38 40 40 \n\n\n\nSoil depth (cm) >100 >100 >100 \n\n\n\nAbove\u2013\n\n\n\nground \n\n\n\nbiomass \n\n\n\nTrees (Mg. ha\u20131) 55.08 \u00b1 3.98a 109.18 \u00b1 4.44b 175.17 \u00b1 5.11c \n\n\n\nUnderstory (Mg. ha\u20131) 4.05 \u00b1 0.05a 4.31 \u00b1 0.05b 4.80 \u00b1 0.11c \n\n\n\nTAB (Mg. ha\u20131) 59.1 \u00b1 3.98a 113.4 \u00b1 4.46b 179.96 \u00b1 5.07c \n\n\n\nBelow\u2013\n\n\n\nground \n\n\n\nbiomass \n\n\n\nTrees (Mg. ha\u20131) 17.64 \u00b1 0.68a 34.38 \u00b1 1.43b 35.40 \u00b1 1.87b \n\n\n\nUnderstory (Mg. ha\u20131) 0.82 \u00b1 0.02a 0.92 \u00b1 0.02b 1.19 \u00b1 0.02c \n\n\n\nTBB (Mg. ha\u20131) 18.46 \u00b1 0.67a 35.30 \u00b1 1.41b 36.59 \u00b1 1.87c \n\n\n\nLitter biomass (Mg. ha\u20131) 11.43 \u00b1 0.91a 11.90 \u00b1 0.55ab 13.29 \u00b1 1.16b \n\n\n\nNote: Data represent the mean \u00b1 standard deviation (SD). Different capital letters indicate a significant \ndifference between different stands (p < 0.05). DBH, diameter at breast height (1.3 m); H, tree height; \n\n\n\nTAB, total above-ground biomass; TBB total below-ground biomass. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n123 \n\n\n\n\n\n\n\nSoil Sampling and Laboratory Analyses \n\n\n\n\n\n\n\nA soil corer (5 cm inner diameter) was used to dig to a depth of 50 cm from the four corners \n\n\n\nand the center of each sample plot. Following the removal of understory vegetation and litter, \n\n\n\nsamples were taken from four depths (0-10, 10-20, 20-30 and 30-50 cm).  Soil samples from \n\n\n\nthe same depth layer in the same plot were mixed in equal volume proportions, air-dried \n\n\n\nnaturally and stored at room temperature. Soil samples were analysed at the Centre of Forestry \n\n\n\nResearch and Climate Change Laboratory at the Vietnam National University of Forestry \n\n\n\n(VNUF). Soil bulk density (BD) of different soil layers (0-10, 10-20, 20-30 and 30-50 cm) was \n\n\n\ndetermined by collecting samples from a stainless steel cylinder (100 cm3) and oven drying the \n\n\n\ncored soil at 105\u25e6C to a constant weight. Soil BD was calculated by dividing the mass of oven-\n\n\n\ndried soil by the volume of the core (Blake and Hartge 1986). The other soil samples were \n\n\n\nsieved through a 2-mm screen to remove plant roots and other debris before soil TN analysis. \n\n\n\nThe content of soil TN was measured according to the Vietnam National Standard method \n\n\n\n(TCVN 6498:1999 - ISO 11261:1995) adopted by Thanh and Cuong (2016; 2017) and Cuong \n\n\n\net al. (2017). TN concentration in the soil was determined by the modified Kjeldahl method \n\n\n\nafter digestion with a mixture of C7H6O3 and H2SO4.  \n\n\n\n\n\n\n\nCalculation of Soil Total Nitrogen Storage  \n\n\n\n\n\n\n\nTotal nitrogen storage (NS) in each soil layer was computed according to the TN content of the \n\n\n\nsoil layer, its soil BD and sampling depth. Coarse fractions (>2 mm) were very rare in the soil \n\n\n\nsamples. Thus, Eqn. 1 was used to compute soil TN storage (Deng et al. 2013; Thanh and \n\n\n\nCuong 2016; Wang et al. 2019; Xu et al. 2019):  \n\n\n\n\n\n\n\n                         \ud835\udc41\ud835\udc46\ud835\udc56 = \ud835\udc47\ud835\udc41\ud835\udc56 \u00d7 \ud835\udc35\ud835\udc37\ud835\udc56 \u00d7 \ud835\udc51\ud835\udc56 \u00d7 10\u22121   [1] \n\n\n\n\n\n\n\nwhere \ud835\udc41\ud835\udc46\ud835\udc56, total nitrogen storage in the soil layer i (Mg. N. ha-1); i represents the 0\u201310 cm, 10\u2013\n\n\n\n20 cm, 20\u201330 cm, and 30\u201350 cm soil layers; \ud835\udc47\ud835\udc41\ud835\udc56, the total nitrogen concentration of the soil \n\n\n\nlayer i (g. kg-1); \ud835\udc35\ud835\udc37\ud835\udc56, the bulk density of the soil layer i (g. cm-3); and \ud835\udc51\ud835\udc56, the thickness of the \n\n\n\nsoil layer i (cm). \n\n\n\n\n\n\n\nStatistical Analyses \n\n\n\n\n\n\n\nThe data were checked for normality and homogeneity of variances using the Kolmogorov-\n\n\n\nSmirnov test and the Levene\u2019s test, respectively. The difference between the stand means and \n\n\n\nwithin-stand variations was examined by one-way ANOVA (analysis of variance) followed by \n\n\n\nFisher\u2019s Least Significant Difference (LSD) test at p<0.05. Pearson's correlation coefficients \n\n\n\nwere calculated to characterise the relationships between soil total nitrogen storage and \n\n\n\nenvironmental variables (e.g., TAB, TBB, litter biomass, stand density, DBH, Hvn, canopy \n\n\n\nclosure, and stand age). Statistical analysis, including mean value, standard deviation, Pearson's \n\n\n\ncorrelation, and ANOVA were carried out using SPSS 25.0 software package (IBM Corp \n\n\n\n2017).  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n124 \n\n\n\n\n\n\n\nRESULTS \n\n\n\n\n\n\n\nSoil Nitrogen Concentration in Three Differently Aged A. mangium Stands \n\n\n\nFigure 2 demonstrates the soil TN concentration at layers of 0-10, 10-20, 20-30 and 30-50 cm \n\n\n\nin the 4-, 7-, and 11-year-old stands. Statistically significant differences were observed among \n\n\n\ndifferent aged stands of different soil layers for soil TN values (p<0.05). Irrespective of stand \n\n\n\nage, soil TN content significantly decreased with increasing soil depth, being greater in the top \n\n\n\nlayer (0\u201310 cm) than in deeper layers (10-20, 20-30 and 30-50 cm) (p<0.05). Mineral soil TN \n\n\n\nconcentration at all soil depths increased significantly with increasing stand age (p<0.05). At \n\n\n\ndepths of 0-10, 10-20, 20-30 and 30-50 cm, soil TN concentrations of the 4-year-old stand were \n\n\n\n1.10, 0.97, 0.82, and 0.51, respectively; soil TN contents of the 7-year-old stand were 1.33, \n\n\n\n1.24, 1.15, and 0.91, respectively; and soil TN concentrations of the 11-year-old stand were \n\n\n\n1.54, 1.45, 1.37, and 1.25, respectively.  \n\n\n\n\n\n\n\nFigure 2.  Total nitrogen content in different depths in Acacia mangium plantations of three \n\n\n\ndifferent ages.  \nNote: Different uppercase letters indicate a significant difference between stand ages at the same \nhorizon (p < 0.05); different lowercase letters indicate a significant difference between different soil \n\n\n\ndepths in the same stand (p < 0.05). Error bars represent standard deviation (SD). \n\n\n\n\n\n\n\nSoil Bulk Density in A. mangium Stands \n\n\n\n\n\n\n\nAs described in Figure 3, the soil BD value in a 4-year-old stand was the highest among all \n\n\n\nstand ages at four soil depths (0\u20130.1, 0.1\u20130.2, 0.2\u20130.3 and 0.3\u20130.5 m) (p < 0.05). Nevertheless, \n\n\n\nthere was no distinguishable difference in soil BD between 11- and 7-year-old stands at 0\u201310 \n\n\n\nand 10\u201320 cm soil layers (p > 0.05). The average soil BD value in 4-, 7-, and 11-year-old stands \n\n\n\nat the depth of 0\u201310 cm was 1.46, 1.33 and 1.31 g. cm3, respectively (Figure 3). The mean soil \n\n\n\nBD value in 4-, 7- and 11-year-old stands at the depth of 10\u201320 cm was 1.50, 1.38 and 1.35 g. \n\n\n\ncm3, respectively (Figure 3). The average soil BD value in 4-, 7- and 11-year-old stands at the \n\n\n\ndepth of 20\u201330 cm was 1.64, 1.44 and 1.39 g. cm3, respectively (Figure 3). Soil BD increased \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n125 \n\n\n\n\n\n\n\nsignificantly as soil depth increased across all stand ages (Figure 3, p < 0.05). Soil BD value \n\n\n\nin the upper 0\u201310 cm soil layer was 1.7 \u2013 1.9 times significantly lower than that present in the \n\n\n\n30\u201350 cm soil layer.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Soil bulk density at various depths in Acacia mangium plantations of three different \n\n\n\nages  \n\n\n\nNote: Different uppercase letters indicate a significant difference between stand ages at the same \nhorizon (p < 0.05); different lowercase letters indicate a significant difference between different soil \n\n\n\ndepths in the same stand (p < 0.05). Error bars represent standard deviation (SD). \n \n\n\n\n\n\n\n\nSoil Nitrogen Storage in A. mangium Stands \n\n\n\n\n\n\n\nFigure 4 summarizes the soil layer nitrogen stocks over an age-sequence of three A. mangium \n\n\n\nstands. The NS in the four soil layers (0\u201310 cm, 10-20, 10\u201330, and 30\u201350 cm) followed a \n\n\n\nsignificant increasing trend with stand age (p < 0.05). The soil nitrogen stocks (all Mg. N. ha-1) \n\n\n\nwere 1.61, 1.77, and 2.01 in 0\u20130.1 m soil depth; 1.45, 1.71, and 1.95 in 0.1\u20130.2 m soil depth; \n\n\n\n1.33, 1.65, and 1.90 in 0.2\u20130.3 m soil depth; and 1.74, 2.88, and 3.84 in 0.3\u20130.5 m soil depth \n\n\n\nin the 4-, 7-, and 11-year-old stands, respectively. The NS observed in the 0\u201350 cm soil layer \n\n\n\nat different stand ages was 6.13, 8.01, and 9.71 Mg. N. ha-1 for the 4-, 7-, and 11-year-old stands, \n\n\n\nrespectively. The uppermost 30 cm of soil stocked a large proportion of nitrogen with the NS \n\n\n\nin the 0\u201330 cm soil layer accounting for 60.43%, 64.01%, and 71.65% of the total nitrogen \n\n\n\nstorage in the 0\u201350 cm soil layer for the three stands (Figure 5). \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n126 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4. Soil total nitrogen storage (Mg. N. ha-1) in different depths in 4-, 7- and 11-year-old \n\n\n\nAcacia mangium plantations.  \nNote: Data represent the mean \u00b1 standard deviation (SD). Different capital letters indicate a significant \n\n\n\ndifference between different stands at the same horizon (p < 0.05). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5. Nitrogen distributions (%) at different soil depths in the 4-, 7- and 11-year-old Acacia \n\n\n\nmangium stands \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n127 \n\n\n\n\n\n\n\nRelationships between Soil Total Nitrogen Storage and Environmental Variables \n\n\n\nTable 2 presents the correlation analyses between the NS and the various influential \n\n\n\nenvironmental factors. It can be seen that NS strongly and positively correlates with DBH (r = \n\n\n\n0.509, p < 0.01), H (r = 0.505, p < 0.01), and stand age (r = 0.534, p < 0.01) but is significantly \n\n\n\nnegatively associated with stand density (r = -0.511, p < 0.01), and canopy closure (r = -0.363, \n\n\n\np < 0.05). Plant biomass variables including TAB (r = 0.527, p < 0.01), TBB (r = 0.477, p < \n\n\n\n0.01), and litter biomass (r = 0.316, p < 0.05) are also significantly positively correlated with \n\n\n\nNS. \n\n\n\nTABLE 2 \n\n\n\nPearson Correlation Coefficient values (r) between soil total nitrogen storage and \n\n\n\nenvironmental variables at different stand ages of Acacia mangium plantations \n\n\n\nEnvironmental factors NS (Mg. N. ha-1) \n\n\n\nTAB (Mg. ha\u20131) 0.527** \n\n\n\nTBB (Mg. ha\u20131) 0.477** \n\n\n\nLitter biomass (Mg. ha\u20131) 0.316* \n\n\n\nStand density (tree. ha\u20131) -0.511** \n\n\n\nDBH (cm) 0.509** \n\n\n\nHvn (m) 0.505** \n\n\n\nCanopy closure -0.363* \n\n\n\nStand age 0.534** \n\n\n\nNote. NS- soil total nitrogen storage; TAB- total above-ground biomass; TBB - total below-\n\n\n\nground biomass; DBH - diameter at breast height; Hvn, tree height. *, ** show significant \n\n\n\neffects at p < 0.05 and p < 0.01, respectively.  \n\n\n\n\n\n\n\nDISCUSSION \n\n\n\n\n\n\n\nChanges in NS following afforestation and stand age have been widely reported in several \n\n\n\nstudies.  Interestingly, the results on changes in NS after afforestation have been varied in these \n\n\n\nstudies. Some studies did not show any significant increase in NS with increasing forest age \n\n\n\n(Markewitz et al. 2002; Sartori et al. 2007) while other studies have reported an initial decline \n\n\n\nin NS during the early stage after afforestation, followed by a gradual increase with stand \n\n\n\ndevelopment (Noh et al. 2010; Wang et al. 2019). This phenomenon may be ascribed to \n\n\n\nnumerous factors such as climate, soil properties, tree species, management operations (such \n\n\n\nas thinning and felling), N2 fixation by free-living organisms, atmospheric nitrogen deposition, \n\n\n\nand previous land use, all of which may independently or jointly overshadow the effect of stand \n\n\n\nage on NS (Berthrong et al. 2009; Mao et al. 2010). In our study, we found a very significant \n\n\n\npositive correlation between soil total nitrogen storage and stand age (Table 2) as in the case \n\n\n\nof studies carried out by Mao et al. (2010), Miao et al. (2014) and Ngaba et al. (2020). The \n\n\n\nremarkable increase in NS with age of A. mangium plantation may be related to the higher \n\n\n\ninputs of plant biomass in the older forests. There is a significantly positive correlation between \n\n\n\nplant biomass and NS (Table 2) indicating the accumulation of NS with increasing plant \n\n\n\nbiomass due to the crucial control of plant litter and roots on NS (Li et al.2019). Liu et al. \n\n\n\n(2020) also found that plant litter and roots were the primary factors for the accumulation of \n\n\n\nsoil N because variations in the quality and quantity of plant litter and roots can impact soil \n\n\n\norganic matter decomposition mechanisms, regulate the decomposition of soil organic matter, \n\n\n\nand finally affect the storage of soil N (Manuel et al. 2015). Additionally, other stand \n\n\n\nparameters such as stand density, canopy closure, mean DBH, and H may also impact the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n128 \n\n\n\n\n\n\n\nchange in NS in forests at different ages (Table 1). Our study found that stand density, DBH, \n\n\n\nHvn and canopy closure had a significant effect on the NS (Table 2). In our previous study, \n\n\n\nstand features such as stand density, DBH, Hvn and canopy closure were found to have \n\n\n\nsignificant effects on plant biomass in this study area (Cuong et al. 2020), which could have \n\n\n\ncreated variations in NS along with stand development. Finzi et al. (1998) reported that the \n\n\n\nforest canopy structure can alter the NS because it may influence the soil temperature, soil \n\n\n\nmoisture content and growth of understory vegetation. Besides, other N sources and \n\n\n\nmechanisms may also impact NS. For instance, atmospheric N deposition, biological N fixation \n\n\n\ncapacity, and release of nitrogen from bedrock are often cited to explain NS increase during \n\n\n\nforest development (Morford et al. 2011; Yang et al. 2011; Hou et al. 2016). \n\n\n\n Soil TN storage is mainly measured by TN content, BD and soil depth. In this study, \n\n\n\nsoil depth was fixed at 0\u201310 cm, 10-20, 20\u201330, and 30\u201350 cm. Therefore, TN content and BD \n\n\n\ndetermine soil TN storage. Our study found an inverse relationship between soil BD and soil \n\n\n\nT (Figures 2, 3 and 4). Lower soil BD is closely connected with greater soil porosity and tends \n\n\n\nto enhance soil microbial activity, tree root growth, and other underground biological activities, \n\n\n\nthereby increasing soil organic matter formation and soil nitrogen content (Anh et al. 2014; \n\n\n\nMiao et al. 2014; Duan et al. 2020). Our findings demonstrate that the change in soil BD \n\n\n\npartially reflected soil TN trend.  \n\n\n\n In the three age-sequence A. mangium stands of the current study, soil TN content was \n\n\n\nhighest in the 0\u201310 cm surface soil layer and demonstrated a decreasing trend with increasing \n\n\n\ndepth (Figure 2). This result is congruent with most existing studies (Jobb\u00e1gy and Jackson \n\n\n\n2001; Zhang et al. 2018; Mahdavi et al. 2019). The is because surface soil is impacted strongly \n\n\n\nby external environmental factors, soil microorganisms, and nutrient return from the surface \n\n\n\nlitter which leads to a high concentration of N in the surface soil (Xu et al. 2019). With \n\n\n\nincreasing soil depth, the input of organic matter is restricted by soil permeability, microbial \n\n\n\ndecomposition activity and root absorption (Berger et al. 2002) which reduces the N content \n\n\n\nof deep soil. For the 4-, 7-, and 11-year-old stands, the soil TN concentration of the 0-10, 10-\n\n\n\n20, 20-30 and 30-50 cm soil layers increased with increasing stand age (Figure 2), showing an \n\n\n\nobvious accumulation process of nitrogen in the mineral soil layers after afforestation. This \n\n\n\nmight be due to slow decomposition and litter productivity in high older stands (Noh et al. \n\n\n\n2010). Additionally, since most litter and fine roots are distributed in the surface soil, the NS \n\n\n\naccumulates in the surface soil. In our current research, approximately 60.43\u201371.65% of NS \n\n\n\nwas observed in the top 30 cm soil in all three A. mangium stands (Figure 5). This means that, \n\n\n\nalthough it is vulnerable to human disturbance and soil erosion, the top soil in the study area is \n\n\n\nthe main nitrogen pool. Thus, these findings suggest that protection of nitrogen in the topsoil \n\n\n\nfrom human disturbances and soil erosion is necessary to promote nitrogen sequestration. \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nThe results obtained in this study show changes in soil TN concentration and stock for the \n\n\n\nplantation ecosystem across an age sequence of A. mangium stands (from 4- to 11-years old) \n\n\n\non Changriec Historical - Cultural Forest, Southeastern region, Vietnam. Soil TN content in \n\n\n\nthe mineral soil at different depths for each plantation decreased significantly with increasing \n\n\n\nsoil depth and and this decrease was increasingly significant with plantation age.  Soil TN \n\n\n\nstorage at the top soil (0\u201350 cm) increased significantly with stand age, from 6.13 Mg. N. ha-1 \n\n\n\nin 4-year-old stand to 9.71 Mg. N. ha-1 in a 11-year-old stand. Soil TN stocks indicated an \n\n\n\nobvious topsoil aggregation trend, with more than 60% NS being in 0\u201330 cm depth for each \n\n\n\nstand. The study results suggest that protection of the NS present in the topsoil of plantations \n\n\n\nis very crucial in the context of nitrogen sequestration. Moreover, stand features like stand age, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 120-132 \n\n\n\n\n\n\n\n129 \n\n\n\n\n\n\n\nplant biomass, stand density, tree height and diameter at breast height, and canopy closure \n\n\n\nsignificantly influence NS. Our findings provide new insights that will significantly improve \n\n\n\nour knowledge of soil nitrogen stocks in A. mangium forests and can be used in forest \n\n\n\nmanagement activities to enhance nitrogen sequestration function. Nevertheless, long-term \n\n\n\nmonitoring of changes in NS is necessary to improve the assessment of NS across the A. \n\n\n\nmangium lands of the Changriec Historical - Cultural Forest in Southeastern region, Vietnam. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nThis study was funded by the Chinese Government Doctoral Scholarship Foundation \n\n\n\n(2017GXZ025473) and the Doctoral Scholarship Foundation of Vietnam National University \n\n\n\nof Forestry (No.1872/QD/DHLN-TCCB). We would like to thank the Editor and anonymous \n\n\n\nReviewers for their constructive comments and suggestions, which have significantly \n\n\n\nimproved the quality of this manuscript. We gratefully acknowledge the help given by Prof. \n\n\n\nXu Xiaoniu (Anhui Agricultural University, China) and Assoc. Prof. Nguyen Minh Thanh \n\n\n\n(Vietnam National University of Forestry, Vietnam), in the preparation and writing of the \n\n\n\nmanuscript. 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"\n\nINTRODUCTION\n\n\n\nCultivation of upland rice depends on resultant ash from burning as the nutrient \n\n\n\nsource.  Burning supplies a considerable amount of potassium (K). Nitrogen (N) \n\n\n\nfrom ash may fulfill only a fraction of the requirement as it is mostly lost in \n\n\n\nburning. As a result, the amount of these elements may be insufficient for better \n\n\n\nplant growth. Therefore, application of chemical fertilizers can be a good practice \n\n\n\nto fulfill the upland rice nutrient requirements. However, nutrient requirement and \n\n\n\nthe efficiency of fertilizer utilisation by the upland rice varieties vary markedly. \n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 14: 15-25 (2010)                        Malaysian Society of Soil Science\n\n\n\nModel Comparisons for Assessment of NPK Requirement of \n\n\n\nUpland Rice for Maximum Yield \n\n\n\nA. Hartinee1, 2, M.M. Hanafi3*, J. Shukor4 & T.M.M. Mahmud4\n\n\n\n1Department of Land Management , 3Institute of Tropical Agriculture,\n4Department of Crop Science, Universiti Putra Malaysia, \n\n\n\n43400 UPM Serdang, Selangor, Malaysia\n\n\n\n2Horticulture Research Centre, Malaysian Agricultural Research and \n\n\n\nDevelopment Institute, MARDI, P.O Box 12301, \n\n\n\nGeneral Post Office, 50774, Kuala Lumpur, Malaysia\n\n\n\nABSTRACT\nUpland rice farmers in Malaysia still depend on resultant ash from burning for K \n\n\n\nand N sources. Efficient use of chemical fertilizers in upland rice needs accurate \n\n\n\nassessment of required nutrient elements. The present study was performed to \n\n\n\ndetermine the N, P, and K requirements of three upland rice varieties grown on \n\n\n\nidle land (Bukit Tuku soil, AQUIC KANDIUDULT) using four response models. \n\n\n\nA glasshouse experiment was conducted using 0-200 kg N ha-1 (urea, 46%N), \n\n\n\n0-120 kg P\n2\nO\n\n\n\n5\n ha-1 (TSP, 45% P\n\n\n\n2\nO\n\n\n\n5\n), and 0-150 kg K\n\n\n\n2\nO ha-1 (MOP, 60% K\n\n\n\n2\nO), \n\n\n\neach at five levels.  Three upland rice varieties used in the experiment were Ageh, \n\n\n\nKendinga and Strao. The grain yield (14% moisture content) was measured at \n\n\n\nharvest and fitted using linear (L), linear with plateau (LP), quadratic (Q), and \n\n\n\nquadratic with plateau (QP) response models. The QP proved itself as the best \n\n\n\nfitted response model for the determination of fertilizer recommendation rates for \n\n\n\nmaximum yield of upland rice cultivars used. The fertilizer rates were 112 kg N \n\n\n\nha-1, 78 kg P\n2\nO\n\n\n\n5\n ha-1 and 158 kg K\n\n\n\n2\nO ha-1 for Ageh (QP); 138 kg N ha-1 (LP), 87 kg \n\n\n\nP\n2\nO\n\n\n\n5\n ha-1 (QR), 119 kg K\n\n\n\n2\nO ha-1 (QP) for Kendinga; and 125 kg N ha-1 (Q), 85 kg \n\n\n\nP\n2\nO\n\n\n\n5\n ha-1 (LP) and 127 kg K\n\n\n\n2\nO ha-1 (L) for Strao.\n\n\n\nKeywords:  Fertilizer recommendation rates, linear, linear plateau, nutrient\n\n\n\n        requirement, quadratic plateau, upland rice \n\n\n\n___________________\n\n\n\n*Corresponding author : E-mail: mmhanafi@agri.upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201016\n\n\n\nThis is due to the inherent plant physiological process (germination, respiration, \n\n\n\nand dormancy), soil fertility, biological factors and climatic conditions. A better \n\n\n\nunderstanding of the various aspects of nutrient use can lead to improved crop \n\n\n\nyields and reduce cost of production. Apart from adequate supplies of nutrients, \n\n\n\nother factors, such as water availability, use of high yielding cultivars and the \n\n\n\ncontrol of diseases, insects and weeds are important factors that contribute to \n\n\n\nhigher crop yields. Due to the increasing cost of chemical fertilizers, fertilizer \n\n\n\napplication should be efficient and sufficient in amounts for nutrients uptake \n\n\n\nby plants. The most economic fertilizer recommendation is only possible when \n\n\n\ninformation on the optimum fertilizer rate has been collected and then made \n\n\n\navailable.\n\n\n\nDetermination of an optimum amount of fertilizer requires experience which \n\n\n\nserves as a basis for predicting how the crop will respond to fertilizer application. \n\n\n\nA simple definition of an optimal rate is that rate which produces maximum \n\n\n\neconomic return (Colwell 1994). Curve fitting techniques are often used to estimate \n\n\n\noptimal fertilizer rates, but a major problem exists in selecting the best model for \n\n\n\na particular soil-cropping situation (Alivelu et al. 2003). Yield responses are often \n\n\n\ndescribed with a quadratic equation. For a quadratic function, yields increase to a \n\n\n\nmaximum with increasing soil test nutrient concentration, then decline in a mirror \n\n\n\nimage of the increments. Since toxicities are not usually encountered, the decline \n\n\n\nis not real.  Furthermore, the maximum is not reached until soil test values are \n\n\n\nwell beyond the expected point of diminishing returns (Cox 1992).\n\n\n\nA more recent expression of yield response to soil test nutrient concentration \n\n\n\nis the linear plateau (LP), or continuation at zero slope, at a maximum yield with \n\n\n\nincreasing nutrient concentration. This approach is extremely direct, leaving no \n\n\n\ndoubt as to the exact predicted critical level. Use of the LP function is becoming \n\n\n\nmore common as routine statistical procedures are now available for calculation.  \n\n\n\nAccording to Cerrato and Blackmer (1990), the choice of the model will affect \n\n\n\nthe predicted fertilizer rate. When compared to other nutrients, the optimal rate \n\n\n\nof nitrogenous fertilizer application is important to reduce the environmental \n\n\n\nimpact of excessive N and to increase profitability in crop production (Bilbao \n\n\n\net al. 2004). However, choosing the fertilizer rate can be complicated because \n\n\n\nthe farmer rationally decides as to whether to choose the minimum or maximum \n\n\n\nfertilizer rate or possibly some rate between these limits due to financial constrains \n\n\n\nand cost of fertilizer. The maximum rate gives the largest profit per hectare of land \n\n\n\nand is the most profitable rate if no other land is available and enough capital is \n\n\n\navailable to buy the fertilizer, whereas the minimum rate gives the highest return \n\n\n\nof every single fertilizer investment and is the best choice when each investment \n\n\n\nfor fertilizer is limited. To our best knowledge, the upland rice farmers never use \n\n\n\nany chemical fertilizers.  Hence, there is a great potential for yield increment of \n\n\n\nupland rice by application of chemical fertilizers.  However, no information is \n\n\n\navailable on the rates of chemical fertilizers used by the farmer for any upland rice \n\n\n\ncultivars.  Therefore, selection of a response model for the fertilizer rate prediction \n\n\n\nfor maximum yield which directly fulfils the crop\u2019s requirement and provide \n\n\n\nA. Hartinee, M.M. Hanafi, J. Shukor & T.M.M. Mahmud\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 17\n\n\n\nreasonable income to the farmer is crucial to provide the best recommendation to \n\n\n\nfarmers. The objectives of the present study were (i) to determine the requirements \n\n\n\nof  N, P and K for upland rice and (ii) to explore a response model for prediction \n\n\n\nof nutrient requirements for maximum yield in Bukit Tuku soil.\n\n\n\nMATERIALS AND METHODS\n\n\n\nPlanting of Upland Rice\n\n\n\nA glasshouse study was conducted for a duration of five months (from September \n\n\n\n2004 to January 2005) at the Faculty of Agriculture glasshouse complex, \n\n\n\nUniversiti Putra Malaysia. The soil (Bukit Tuku series) was ground and sieved to \n\n\n\npass through a 2.0 mm sieve size. The soil physico-chemical characteristics are \n\n\n\ngiven in Table 1. Approximately 15 kg of the soil was weighed and packed into\n\n\n\n\n\n\n\nTABLE 1\n\n\n\nPhysical and chemical properties of Bukit Tuku idle soil at two soil depths\n\n\n\nModel to Assess NPK Requirement of Aerobic Rice\n\n\n\nParameters  Soil depth (cm)  \n\n\n\n0 \u2212 20  20 \u2212 40  \n\n\n\npH W  4.82  4.69  \n\n\n\npH KCl  3.6  3.4  \n\n\n\nNitrogen (%)  1.89  1.3  \n\n\n\nPhosphorus (mg kg 19.92  18.3  \n\n\n\nPotassium (mg kg 58.53  32.8  \n\n\n\nCalcium (mg kg 488.3  306.8  \n\n\n\n65.83  54.17  \n\n\n\nIron (mg kg 175.4  112.9  \n\n\n\nAluminium (mg kg 668  629  \n\n\n\nCEC (cmolc  kg 5.71  5.32  \n\n\n\nSoil pF (%):             46.75  47.69  \n\n\n\n1  36.79  35.95  \n\n\n\n    2  29.95  27.55  \n\n\n\n2.54  25.41  20.43  \n\n\n\n4.19  17.98  9.77  \n\n\n\nAWC  7.43  10.65  \n\n\n\n\n\n\n\n\u22121\n)\n\n\n\n\u22121\n)\n\n\n\n\u22121\n)\n\n\n\nMagnesium (mg kg) \u2212 1\n\n\n\n\u22121\n)\n\n\n\n\u22121\n)\n\n\n\n\u22121\n)\n\n\n\n0\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201018\n\n\n\neach polybag. Seeds of three upland rice varieties (Ageh, Kendinga and Strao) \n\n\n\nwere treated with fungicide (Benlate @ 3 g a.i. per kg seed) before sowing. Ten \n\n\n\nseeds were dibbled at 4 cm depth in each of the polybags. After emergence, the \n\n\n\nseedlings were thinned to 8 plants per polybag.\n\n\n\nExperimental Design and Treatments\n\n\n\nFive levels of nitrogenous fertilizer were 0, 50, 100, 150, and 200 kg N ha-1.  With \n\n\n\neach dose of nitrogen, a blanket dose of 90 kg P\n2\nO\n\n\n\n5\n ha-1 (1.02 g TSP per polybag) \n\n\n\nand 120 kg K\n2\nO ha-1 (1.02 g MOP per polybag) was used.  The five levels of \n\n\n\nphosphorus fertilizer were 0, 30, 60, 90, and 120 kg P\n2\nO\n\n\n\n5\n ha-1 , accompanied \n\n\n\nby a common dose of 150 kg N ha-1 (1.67 g urea per polybag) and 120 kg K\n2\nO \n\n\n\nha-1 (1.02 g MOP per polybag).  The five rates of potassium fertilizer were 0, \n\n\n\n60, 90, 120, and 150 kg K\n2\nO ha-1 accompanied by similar blanket doses of NP \n\n\n\nmentioned above.  A total of 15 treatments were assigned for each of the three \n\n\n\nupland rice (Ageh, Kendinga and Strao) varieties resulting in 45 individual units. \n\n\n\nThe experimental units were arranged in a complete randomized design with 3 \n\n\n\nreplications (135 polybags). The fertilizer rates were selected based on nutrient \n\n\n\nuptake of upland rice in a pre-trial experiment. The N fertilizer was applied in 3 \n\n\n\nequal splits, first at 3 weeks after germination then at early tillering stage, and \n\n\n\nlastly at panicle initiation (PI). Phosphorus fertilizer was applied at early tillering \n\n\n\nstage, and K fertilizer application was in two equal splits, first at early tillering \n\n\n\nand the other at flowering stage. Insecticide (Mapa Malathion 57\u00ae) and fungicide \n\n\n\n(Benlate\u00ae) were applied at 2.57 kg a.i. Malathion ha-1 and 2.25 kg a.i. Benomyl \n\n\n\nha-1, respectively, using a knapsack sprayer when necessary during the experiment. \n\n\n\nWater was applied to field capacity once daily for each polybag. The grain yield \n\n\n\nwas measured at 14% moisture content.\n\n\n\nStatistical Analysis\n\n\n\nThe non-linear procedure (PROC NLIN) of SAS (SAS, 2001) was used for \n\n\n\ncomparison of response curves. The response curves were linear (L), quadratic \n\n\n\n(Q), and linear with plateau (LP), and quadratic with plateau (QP) functions. The \n\n\n\nyield data was fitted using PROC REG and PROC NLIN methods. The L function \n\n\n\nmodel is defined by the following equation.\n\n\n\n Y = a + bX             [1]\n\n\n\nwhere Y is grain yield (g hill-1), X is fertilizer application rate (kg ha-1), and a \n\n\n\n(intercept), b (linear coefficient), are constants obtained by fitting data to the \n\n\n\nmodel function. \n\n\n\nThe LP function model is defined by the following equations:\n\n\n\n Y = a + bX  if X < C                     [2]\n\n\n\n Y = P  if X \u2265 C                         [3]\n\n\n\nwhere Y is grain yield (g hill-1), X is fertilizer application rate (kg ha-1), and a \n\n\n\n(intercept), b (linear coefficient), C (critical fertilizer rate, which occurs at the \n\n\n\nintersection of the linear response and the plateau lines), and P (plateau yield) is \n\n\n\nthe constant obtained by fitting data to the model function.\n\n\n\nA. Hartinee, M.M. Hanafi, J. Shukor & T.M.M. Mahmud\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 19\n\n\n\nThe Q model is defined by the equation\n\n\n\n Y = a + bX + cX2       [4]\n\n\n\nwhere Y is grain yield (g hill-1), X is fertilizer application rate (kg ha-1), and a \n\n\n\n(intercept), b (linear coefficient), and c (quadratic coefficient) are constants \n\n\n\nobtained by fitting data to the model function.\n\n\n\nThe QP model is defined by following equations:\n\n\n\n Y = a + bX + cX2 if X < C                   [5]\n\n\n\n Y = P   if X \u2265 C                   [6]\n\n\n\nwhere Y is grain yield (g hill-1), X is fertilizer application rate (kg ha-1), and a \n\n\n\n(intercept), b (linear coefficient), and c (quadratic coefficient), C (critical fertilizer \n\n\n\nrate, which occurs at the intersection of the quadratic response and the plateau \n\n\n\nlines), and P (plateau yield)  is the constant obtained by fitting data to the model \n\n\n\nfunction.\n\n\n\nFor the Q model (Eq. 4), predicted maximum yield was obtained by equating \n\n\n\nthe first derivatives of the response equation to zero, solving for X, substituting \n\n\n\nthe values of X into the response equation, and solving for Y. For the L and Q with \n\n\n\nplateau models (Eq. 2, Eq. 3,  Eq. 5, and  Eq. 6), the plateau yields represented the \n\n\n\nmaximum yields. The analysis of variance (ANOVA) was performed using PROC \n\n\n\nANOVA of the Statistical Analysis System (SAS 2001) and the protected Least \n\n\n\nSignificant Difference Test (LSD) was used for means comparison.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nGrain yields of Ageh, Kendinga and Strao rice varieties showed significant \n\n\n\nvariation with the differing fertilizer rates used. All varieties produced the lowest \n\n\n\ngrain yield at zero N, P and K fertilizer rates (Table 2). The increase in the grain \n\n\n\nyield following the addition of N as compared to control was the highest (194%) \n\n\n\nfor Kendinga variety followed by Ageh (168%), and Strao (77%).  Ageh produced \n\n\n\nthe highest yield at and above 100 kg N ha-1.  Strao gave maximum grain yield at \n\n\n\n100 kg N ha-1 but yield declined for additional N.  However, Kendinga showed \n\n\n\nbest performance at 150 kg N ha-1. For P fertilization, Ageh gained maximum \n\n\n\nincrease (103%) in comparison to control. The minimum yield increase (57%) \n\n\n\nwas observed for Kendinga which was achieved at an application rate of 60-90 \n\n\n\nkg P\n2\nO\n\n\n\n5\n ha-1. However, Kendinga variety showed the highest increase in grain \n\n\n\nyield (168%) followed with an almost similar yield increase for Strao and Ageh \n\n\n\nrice varieties in the case of K fertilizer application. This suggests that application \n\n\n\nof fertilizers contributed to the increase in grain yields significantly for Ageh, \n\n\n\nKendinga, and Strao rice varieties.  \n\n\n\nRates of Nitrogen, Phosphorus and Potassium Fertilization for Maximum Yield\n\n\n\nWith the exception of some treatments, the majority of the data fitted the models \n\n\n\nfairly well as indicated by regression (R2) values (Table 3).  Based on that, the \n\n\n\nN, P, and K rates for maximum yield of the three upland rice varieties derived \n\n\n\nfrom the 4 response models are shown in Table 4.  The amount of fertilizers \n\n\n\nobtained for maximum yield of the three upland rice varieties differed between the \n\n\n\nModel to Assess NPK Requirement of Aerobic Rice\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201020\n\n\n\nresponse models used.  Several response models did not produce any significant \n\n\n\nresults when the data was evaluated by PROC REG and PROC NLIN methods.  \n\n\n\nTherefore, the fertilizer rates for maximum yield could not be ascertained for the \n\n\n\nnon-significant response models. \n\n\n\nTABLE 2\n\n\n\nEffect of N, P and K fertilizers rates on yield of upland rice\n\n\n\nA. Hartinee, M.M. Hanafi, J. Shukor & T.M.M. Mahmud\n\n\n\nMeans  within a column followed by the same letters are not significantly different at 5% \n\n\n\nlevel by LSD.\n\n\n\n\n\n\n\n \nObserved yield\n\n\n\n\n\n\n\nRates\n\n\n\n Ageh  Kedinga  Strao  \n\n\n\nkg ha -1\n \u2014\u2014\u2014\u2014 g hill\n\n\n\n-1\n\u2014\u2014\u2014\u2014 \n\n\n\nN  \n\n\n\n0  7.67 \nc\n 6.33 \n\n\n\nc\n 11.83 \n\n\n\nc\n \n\n\n\n50  16.46 \nb\n\n\n\n 7.17 \nc\n\n\n\n 17.89 \nb\n\n\n\n\n\n\n\n100  19.84 \na\n\n\n\n 13.99 \nb\n\n\n\n 20.96 \na\n\n\n\n\n\n\n\n150  20.04 \na\n\n\n\n 18.60 \na\n\n\n\n 20.55 \nab\n\n\n\n\n\n\n\n200  20.59 \na\n\n\n\n 17.57 \na\n\n\n\n 17.74 \nb\n\n\n\n\n\n\n\nP2 O5   \n\n\n\n0  10.32 \nb\n\n\n\n 10.84 \nc\n\n\n\n 9.97 \nb\n\n\n\n\n\n\n\n30  16.11 \nab\n\n\n\n 13.95 \nb\n\n\n\n 11.01 \nb\n\n\n\n\n\n\n\n60  20.45 \na\n\n\n\n 16.34 \na\n\n\n\n 14.95 \nab\n\n\n\n\n\n\n\n90  20.98 \na\n\n\n\n 17.04 \na\n\n\n\n 19.64 \na\n\n\n\n\n\n\n\n120  19.66 \na\n\n\n\n 15.94 \na\n\n\n\n 19.74 \na\n\n\n\n\n\n\n\nK 2 O    \n\n\n\n0  12.27 \nc\n\n\n\n 6.62 \nc\n 13.35 \n\n\n\nc\n \n\n\n\n60  17.69 \nb\n\n\n\n 14.49 \nb\n\n\n\n 15.52 \nb\n\n\n\n\n\n\n\n90  17.05 \nb\n\n\n\n 15.85 \nab\n\n\n\n 16.49 \nb\n\n\n\n\n\n\n\n120  22.55 \na\n\n\n\n 15.90 \nab\n\n\n\n 22.16 \na\n\n\n\n\n\n\n\n150  19.67 \na\n\n\n\n 17.76 \na\n\n\n\n 20.53 \na\n\n\n\n\n\n\n\nFertilizer\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 21\n\n\n\nTABLE 3\n\n\n\nRelationships between the yield of selected upland rice varieties and the levels \n\n\n\nof nutrient using several response models\n\n\n\nThe yield responded significantly in accordance with the LP response model \n\n\n\nwith increasing N and P rates for all the three upland rice varieties.  In the case of \n\n\n\nK fertilization, Kendinga and Strao showed significant response in LP, whereas \n\n\n\nthe response of Ageh was significant only in the QP model (Table 4). The N, P \n\n\n\nand K rates for maximum yield of the three upland rice varieties using all the \n\n\n\nmodels ranged between 60 and 142 kg N ha-1, 51 to 125 kg P\n2\nO\n\n\n\n5\n ha-1, and 81 to \n\n\n\n158 kg K\n2\nO ha-1, respectively (Table 4). The maximum N prediction rates by the \n\n\n\nresponse models were in the order: Q > QP> LP for Ageh rice variety, LP > L for \n\n\n\nKendinga rice variety, and Q > LP for Strao rice variety. This shows that for Ageh \n\n\n\nand Strao rice varieties, the Q model tended to give higher maximum fertilizer \n\n\n\nrates  compared to those of LP and QP response models. However, contrasting \n\n\n\nresults were observed for the Kendinga rice variety.   \n\n\n\nThe Q response model resulted in the highest maximum P rates for Ageh \n\n\n\n(125 kg P\n2\nO\n\n\n\n5\n ha-1) and Kendinga (87.50 kg P\n\n\n\n2\nO\n\n\n\n5\n ha-1) rice varieties and the L \n\n\n\nresponse model suggests maximum for Strao (104.67 kg P\n2\nO\n\n\n\n5\n ha-1) rice variety. \n\n\n\nModel to Assess NPK Requirement of Aerobic Rice\n\n\n\nNutrient Variety Linear R\n2 Plataeu R\n\n\n\n2\n\n\n\nNitrogen Ageh y = 11.04 + 0.06N 0.73\nns\n\n\n\ny = 8.58 + 0.14N 20.24 0.99\n**\n\n\n\nKendinga y = 5.95 + 0.07N 0.87\n*\n\n\n\ny = 3.72 + 0.10N 17.94 0.99\n**\n\n\n\nStrao y = 14.90 + 0.03N 0.39\nns\n\n\n\ny = 11.46 + 0.14N 19.69 0.99\n**\n\n\n\nPhosphorus Ageh y = 12.79 + 0.08P 0.70\nns\n\n\n\ny = 11.27 + 0.14N 20.51 0.99\n**\n\n\n\nKendinga y = 12.17 + 0.04P 0.70\nns\n\n\n\ny = 10.76 + 0.11N 16.43 0.99\n**\n\n\n\nStrao y = 9.42 + 0.09P 0.93\n**\n\n\n\ny = 7.85 + 0.13N 19.44 0.99\n**\n\n\n\nPotassium Ageh y = 13.07 + 0.06K 0.75\nns\n\n\n\ny =12.27 + 0.13N 19.24 0.99\nns\n\n\n\nKendinga y = 8.22 + 0.07K 0.87\n*\n\n\n\ny = 7.10 + 0.12N 16.56 0.99\n**\n\n\n\nStrao y = 10.16 + 0.08K 0.89\n*\n\n\n\ny = 8.42 + 0.14N 19.59 0.99\n**\n\n\n\nQuadratic R\n2 Plataeu R\n\n\n\n2\n\n\n\nNitrogen Ageh y = 8.23 + 0.17N - 0.0006N\n2\n\n\n\n0.97\n**\n\n\n\ny = 7.70+0.22N-0.00099N\n2\n\n\n\n20.23 1.00\n**\n\n\n\nKendinga y = 5.10 + 0.10N - 0.0002N\n2\n\n\n\n0.89\nns\n\n\n\ny =5.09+0.10N-0.00017N\n2\n\n\n\n20.37 0.99\nns\n\n\n\nStrao y = 11.86 + 0.15N - 0.0006N\n2\n\n\n\n0.99\n**\n\n\n\ny =11.83+0.16N-0.0009N\n2\n\n\n\n19.75 0.99\nns\n\n\n\nPhosphorus Ageh y = 10.22 + 0.25P - 0.001P\n2\n\n\n\n0.99\n**\n\n\n\ny = 10.19+0.26P-0.002P\n2\n\n\n\n20.46 0.99\n*\n\n\n\nKendinga y = 10.73 + 0.14P - 0.0008P\n2\n\n\n\n0.99\n**\n\n\n\ny =10.78+0.14P-0.0008P\n2\n\n\n\n16.54 0.99\n*\n\n\n\nStrao y = 9.27 + 0.10P - 0.00009P\n2\n\n\n\n0.93\nns\n\n\n\ny = 9.27+0.10P-0.00009P\n2\n\n\n\n39.3 0.99\nns\n\n\n\nPotassium Ageh y = 12.18 + 0.11K - 0.0003K\n2\n\n\n\n0.81\nns\n\n\n\ny = 12.18+0.11K-0.0003K\n2\n\n\n\n20.6 0.99\nns\n\n\n\nKendinga y = 6.83 + 0.15K - 0.00052K\n2\n\n\n\n0.97\n**\n\n\n\ny = 6.66+0.17K-0.0007K\n2\n\n\n\n16.78 0.99\n*\n\n\n\nStrao y = 9.53 + 0.11K - 0.0002 K\n2\n\n\n\n0.91\nns\n\n\n\ny = 9.53+0.11K-0.0002K\n2\n\n\n\n23.48 0.99\nns\n\n\n\nNote:\n**\n\n\n\n, \n*\n =  significant at 1 and 5% levels, respectively.\n\n\n\nns\n = non-significant\n\n\n\nLinear- Plataeu\n\n\n\nQuadratic-Plataeu\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201022\n\n\n\nThe fertilizer rates differed greatly and depended on the response model used. The \n\n\n\nLP response model suggests the lowest amount of P fertilizer as the maximum \n\n\n\ndose with the rates for Ageh, Kendinga and Strao rice varieties being 63.96, 51.33 \n\n\n\nand 85.91 kg P\n2\nO\n\n\n\n5\n ha-1, respectively (Table 4). \n\n\n\nTABLE 4\n\n\n\nRates of N, P and K maximum yield for three upland rice varieties\n\n\n\nThe highest K rates for Ageh, Kendinga and Strao rice varieties were also \n\n\n\nobtained from different response models. The maximum K rates of Kendinga rice \n\n\n\nvariety showed a significant response to each model used. The highest K rates \n\n\n\npredicted was in the order: Q > QP > L > LP. These results indicate that the Q \n\n\n\nresponse model tended to predict as much as 50% more compared to maximum \n\n\n\nrates from the LP models. Therefore, selecting the best response model to predict \n\n\n\nthe maximum fertilizer rate is important for any meaningful recommendation to \n\n\n\nbe made. The linear response and plateau functions are now used extensively to \n\n\n\nA. Hartinee, M.M. Hanafi, J. Shukor & T.M.M. Mahmud\n\n\n\nFertiliser  Variety  \nFertiliser rate  \n\n\n\nLinear  LP  Quadratic  QP  \n\n\n\n \n\u2014\u2014\u2014\u2014\u2014\u2014\u2014  kg N ha\n\n\n\n-1\n \u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n\n\n\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014 \u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n\n\n\n\n\n\n\nN  Ageh  *ns  82.44  141.67  112.23\n\n\n\n Kendinga  85.00  138.15  ns  ns  \n\n\n\n Strao  ns  59.95  125.00  ns  \n\n\n\n kg P2\nO\n\n\n\n5  h a\n-1\n\n\n\n\u2014\u2014\u2014\u2014\u2014\u2014\u2014 \u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014 kg P2\nO\n\n\n\n5  h a\n-1\n\n\n\n\n\n\n\nP  Ageh  ns  63.96  125.00  78.48  \n\n\n\n Kendinga  ns  51.35  87.50  83.35  \n\n\n\n Strao  104.67  85.91  ns  ns  \n\n\n\n\n\n\n\nK  Ageh  ns  ns  ns  ns  \n\n\n\n Kendinga  117.43  81.59  144.23  119.55  \n\n\n\n Strao  127.00  81.36  ns  ns  \n\n\n\n* ns= response not significant at 5% level.\n\n\n\n\n\n\n\nLP= Linear with plateau\n\n\n\n QP= Quadratic with plateau\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 23\n\n\n\nrelate crop yields to soil test levels and it has been confirmed by many agronomists \n\n\n\nthat this model not only fits most data but also gives an immediate estimate of the \n\n\n\ncritical yield level at the intersection of the two lines (Cox 1996). In the case of \n\n\n\nthe quadratic model, predicted maximum yield and its associated soil test level \n\n\n\nwere almost always excessive, as this model does not often fit the data well in \n\n\n\na sufficient range.  Cerrato and Blackmer (1990) reported this to be true when \n\n\n\ncomparing models describing corn yield response to N fertilizer.  Therefore, it can \n\n\n\nbe concluded that the LP and QP models are the best to describe the reasonable \n\n\n\nmaximum fertilizer rates in this study. However, the different maximum fertilizer \n\n\n\nrates would be able to produce the maximum yield prediction when solving the \n\n\n\nequation with several response models.\n\n\n\nPredicted Yields of Three Upland Rice Varieties\n\n\n\nThe predicted yield of three upland rice varieties was calculated using the maximum \n\n\n\nrates obtained from the statistically significant response models in Table 3.  The \n\n\n\npredicted yields of the three upland rice varieties showed mirror image trends as in \n\n\n\nthe maximum fertilizers rates (Table 5). The results showed that the QP response \n\n\n\nmodel tends to predict a similar maximum yield value as obtained by Q model \n\n\n\nbut with a lower amount than the maximum N, P and K rates.  The maximum \n\n\n\npredicted yield of Ageh rice variety using maximum N fertilizer rates obtained \n\n\n\nfrom the QP (112 kg N ha-1) and LP (82 kg N ha-1) models were 20.22 and 20.23 \n\n\n\ng hill-1, respectively.  This value was obtained by a lower amount of fertilizer than \n\n\n\nthat of Q (141 kg N ha-1) with maximum yields being 20.27 g hill-1 (Table 5).  \n\n\n\nSimilar results were also observed for the maximum predicted yield of Ageh and \n\n\n\nKendinga rice varieties using maximum P rates, and Kendinga using maximum K \n\n\n\nrates (Table 5). However, L and LP predicted higher maximum predicted yields \n\n\n\nfor Ageh, Kendinga and Strao rice varieties, which were congruent with higher \n\n\n\nfertilizers rates. In this study, maximum predicted yields of Ageh, Kendinga and \n\n\n\nStrao rice varieties obtained from the LP response model were almost similar to \n\n\n\nthe other response models (Table 5).\n\n\n\nYield estimation using maximum fertilizer rate equations derived from LP \n\n\n\nand QP response models were lower than those for L and Q response models. \n\n\n\nAlthough, Q and QP estimated similar maximum yields, the predicted maximum \n\n\n\nfertilizers values by Q differed greatly. Cerrato and Blackmer (1990) stated that \n\n\n\nthe Q model tended to overestimate the maximum yields because of the sharpness \n\n\n\nof the quadratic response curve near economic optimum and maximum and \n\n\n\nthe model often identifies unattainable yields as being the optimum. Therefore, \n\n\n\nmaximum N, P and K rates obtained either by QP or LP can be recommended for \n\n\n\nAgeh, Kendinga and Strao rice varieties for achieving maximum predicted yield. \n\n\n\nModel to Assess NPK Requirement of Aerobic Rice\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201024\n\n\n\nTABLE 5\n\n\n\nPredicted yield of upland rice using fertilizers rates obtained by response models\n\n\n\nCONCLUSION\n\n\n\nThe grain yield of Ageh, Kendinga and Strao rice varieties were significantly \n\n\n\naffected by the N, P and K application rates. Addition of N, P and K fertilizers \n\n\n\nincreased the grain yield of all the upland rice varieties with the value ranging \n\n\n\nfrom 7 to 22 g hills-1 for N, 6 to 18 g hills-1 for P\n2\nO\n\n\n\n5\n, and 9 to 22 g hills-1 for K\n\n\n\n2\nO, \n\n\n\nrespectively.  The best response model for the upland rice yield data obtained from \n\n\n\nthis experiment was using QP, corresponding to the best fertilizer recommendation \n\n\n\nrates for Ageh, Kendinga and Strao rice varieties for maximum yield. \n\n\n\nACKNOWLEDGEMENTS\n\n\n\nWe wish to thank the Ministry of Science, Technology, and Innovation (MOSTI) \n\n\n\nfor providing a research grant (54038100) through the Intensification of Research \n\n\n\nin Priority Areas (IRPA); Department of Agriculture Sarawak, Department \n\n\n\nof Agriculture Sabah and Malaysian Palm Oil Board (MPOB) for their field \n\n\n\nassistance; and the National Science Fellowship (NSF) to Miss Hartinee Abbas.\n\n\n\nA. Hartinee, M.M. Hanafi, J. Shukor & T.M.M. Mahmud\n\n\n\nFertiliser  Variety  \nPredicted Yield  \n\n\n\nLinear LP Quadratic QP  \n\n\n\n   \u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014  g hill\n-1\n\n\n\n \u2014\u2014\u2014\u2014\u2014\u2014\u2014 \n\n\n\nN  Ageh  \n*\nns  20.23  20.27  20.22  \n\n\n\n Kendinga  11.90  17.93  ns  ns  \n\n\n\n Strao  ns  19.69  21.24  ns  \n\n\n\nP  Ageh  ns  20.50  25.85  20.45  \n\n\n\n Kendinga  ns  16.42  16.86  16.54  \n\n\n\n Strao  18.84  19.43  ns  ns  \n\n\n\nK  Ageh  ns  ns  ns  ns  \n\n\n\nKendinga  16.44  16.56  17.65  16.77  \n\n\n\nStrao  20.32  19.58  ns  ns  \n\n\n\n* ns= response not significant at 5% level  \n\n\n\n \nLP= Linear with plateau\n\n\n\n QP= Quadratic with plateau\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 25\n\n\n\nREFERENCES\nAlivelu, K., S. Srivastava, A. Subba Rao, K.N. Singh, G. Selvakumari and N.S. Raju.  \n\n\n\n2003.  Comparison of modified Mitscherlich and response plateau models for \n\n\n\ncalibrating soil test based nitrogen recommendations for rice on Typic Ustropept.  \n\n\n\nCommun. Soil Sci. Plant Anal. 34 (17 & 18): 2633-2643.\n\n\n\nBilbao, M., J.J. Martinez and A. Delgado. 2004. Evaluation of soil nitrate as a predictor \n\n\n\nof nitrogen requirement for sugar beet grown in a Mediterranean climate.  \n\n\n\nJournal of Agronomy 96: 18-25.\n\n\n\nCerrato, M.E. and A.M. Blackmer. 1990. Comparison of models for describing corn \n\n\n\nyield response to nitrogen fertilizer.  Journal of Agronomy 82: 138-143.\n\n\n\nColwell, J.D. 1994. Estimating fertilizer Requirements: A Qualitative Approach.  \n\n\n\nCAB Int., Wallingford, UK.\n\n\n\nCox, F.R. 1992.  Range in soil phosphorus critical levels with time.  Soil Science \n\n\n\nSociety of American Journal 56: 1504-1509.\n\n\n\nCox, F.R. 1996. Economic phosphorus fertilization using a linear response and plateau \n\n\n\nfunction. Commun. Soil Sci. Plant Anal. 27(3 & 4): 531-543.\n\n\n\nSAS. 2001. Statistical Analysis System. Version 8.02, SAS Institute Inc., Cary, NC, \n\n\n\nUSA.\n\n\n\nModel to Assess NPK Requirement of Aerobic Rice\n\n\n\n\n\n"
"\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Nitrohumic Acids on Loamy Sand and Clay Loam\n\n\n\n17\n\n\n\nISSN: 1394-7990\nMalaysian Society of Soil ScienceMalaysian Journal of Soil Science Vol.11 : 17-27 (2007)\n\n\n\nEffects of Nitrohumic Acids Derived from Low Grade Coal\nof Sarawak on Aggregate Stability of Loamy Sand and\n\n\n\nClay Loam\n\n\n\nS.F. Sim*,  S. Lau,  N. Omar & D.F. Abang\n\n\n\nFaculty of Resource Science & Technology\nUniversiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia\n\n\n\nABSTRACT\nHumic acids are commonly used to improve the aggregate stability of soils;\nhowever, it is not popular in this region. In this paper, low grade coal from\nMukah was used as the source of nitrohumic acids and the effects of the\nnitrohumic acids on the aggregate stability of loamy sand (83.5% sand, 16.4%\nclay) and clay loam (31% sand, 37% clay) were investigated. Nitrohumic acids\nwere prepared with nitric acid pretreatment and extracted with acid base frac-\ntionation. On loamy sand and clay loam, six application rates of nitrohumic\nacids (0.00, 0.05, 0.10, 0.50, 1.00 and 10.00 g/kg) and 5 wetting and drying\ncycles were used to assess the changes in the aggregate stability. Results showed\nthat the aggregate stability of loamy sand was improved with nitrohumic acids\namendment. For a clay loam sample, the aggregate stability was instead re-\nduced. Nevertheless, there was an upper limit (0.10 g/kg) where the aggregate\nstability was improved or alleviated. In conclusion, the effect of nitrohumic\nacids on aggregate stability is dependent on soil type.\n\n\n\nKeywords: Nitrohumic acids, low grade coal, aggregate stability, loamy\nsand, clay loam\n\n\n\nINTRODUCTION\nSoil  structure is an imperative factor in determining soil productivity. Under\nintensive  cropping, soils usually experience progressive depletion in organic\nmatter content  resulting in reduced infiltration rates, increased slaking and crust-\ning, accelerated runoff erosion and consequently poor crop productivity (Pic-\ncolo et al. 1997). Conventionally, organic residues are incorporated as soil con-\nditioners to improve organic matter status.  However, for undecomposed resi-\ndues, a large quantity is needed to obtain significant improvements (Piccolo et\nal. 1997). Some synthetic polymers such as polyacrylamides and polyvinyl\nalcohols are found to function similarly (Gabriels 1990; Bryan 1992; Sojka and\nLentz 1994). Nevertheless, these synthetic conditioners are found to be easily\ndegraded by microorganisms, thus frequent applications are required imposing\nextra cost; they are therefore less favorable (Grula et al. 1994).  Humic sub-\n\n\n\n* Corresponding author: Email: sfsim@frst.unimas.my\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM17\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200718\n\n\n\nS.F. Sim,  S. Lau,  N. Omar  and D.F. Abang\n\n\n\nstances have been well established as a potential soil conditioner, and are\nregarded as better than the manure and synthetic conditioners.  Fortun et al.\n(1989) reported  that humic substances improved and prolonged aggregate sta-\nbility at low application rate, performing better than the bulk farmyard manure.\nIn addition, Rausa et al. (1989) reported that they are free from pollutants and\nare highly reactive towards soil components due to the presence of acidic func-\ntional groups; the polycondensed aromatic structure renders them more resistant\nto microbial attack.\n\n\n\nToday, humic acids have become commercially available in the form of in-\nexpensive soluble salts, referred to as sodium or potassium humates. These hu-\nmic acid products have been well accepted by the agriculture community else-\nwhere. In Malaysia, they are less commonly used as these products are imported;\ntherefore,  they are relatively higher in selling price than other soil conditioners.\nHowever, the possibility of producing humic acids from indigenous sources will\nmake the products better known. In Sarawak, abundance of low grade coal was\ndiscovered in Mukah, serving as a potential source of humic acids.  A prelimi-\nnary study revealed that the regeneration process with nitric acid prior to extrac-\ntion produced humic acids with high yields, low ash content and high acidic\nfunctional groups (Sim et al. 2006). Research on the effect of the locally pro-\nduced humic acids to improve the aggregate stability is limited. This paper re-\nports on the potential of the nitrohumic acids derived from the low grade coal of\nMukah in improving the aggregate stability of two soil textures: loamy sand and\nclay loam.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSoil Samples\nTopsoil was collected from the construction site located at 10th mile of Kuching,\nlying between latitudes 24\u00b0 59\u2019 N and 25\u00b0 11\u2019 N, and longitudes 19\u00b0 27 E and 19\u00b0\n44\u2019 E. The construction site covers approximately 22 hectares of land and was at\nthe clearing stage of preparation for construction of a hostel for the Malaysian\nAirforce.  The soil samples were predetermined with field textural analysis and\nthe  classifications were confirmed with soil textural classification system devel-\noped by the U.S. Department of Agriculture (Soil Survey Staff 1999). The soil\nsamples  were subjected to analyses that included pH, particle size, moisture\ncontent and organic matter content. The particle size analysis was performed\naccording to the pipette method (Dewis and Freitae 1970). The soil organic mat-\nter was expressed on an oven-dry weight basis and determined by the combus-\ntion  of  samples  at 800oC for 2 hours (Allen 1989).  Soil pH was measured with\na pH metre on a suspension of soil in water (1:1).\n\n\n\nPreparation of Nitrohumic Acids\nNitrohumic acids  are  referred  to as humic acids prepared with nitric acid pre-\ntreatment prior to acid base fractionation. They were prepared from low grade\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM18\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Nitrohumic Acids on Loamy Sand and Clay Loam\n\n\n\n19\n\n\n\ncoal of Mukah. The coal sample was ground and sieved through a 50 m sieve\nprior  to  chemical  treatment and extraction procedure. The coal sample was pre-\ntreated with 10 % nitric acid prior to extraction. One gram of coal sample was\nadded to 5 mL of nitric acid at 70oC for 2 hours with continuous stirring. The\nnitrated coal was filtered, washed with distilled water and oven-dried at 60oC.\nThe extraction method described by Swift (1996) was adopted with modifica-\ntions.  Ten gram of coal sample was added to 100 mL of KOH (0.5M) and heated\nat 70oC for 2 hours. The supernatant was filtered through a 50 m sieve and the\ncoal residue was washed with distilled water until the wash water was clear. The\ncoal residue was dried at 105oC and weighed. The supernatant was acidified with\nconcentrated H2SO4 to pH 1-2 and allowed to stand overnight. The precipitated\nnitrohumic acids were separated by centrifugation at 6,000 rpm for 15 minutes.\nThe nitrohumic acids were washed with distilled water and centrifuged.  The gel-\nliked nitrohumic acids were oven-dried at 60oC and stored in desiccators (Inter-\nnational Humic Substances Society 1983; Dick et al. 2002).\n\n\n\nCharacterisation of Nitrohumic Acids\nThe nitrohumic acids were subjected to analyses that included moisture content,\nash content, acidic functional groups content, FTIR and UV/Vis spectroscopy.\nMoisture content was determined by drying the samples at 105\u00b0C overnight while\nash content was determined by combustion of the samples at 800\u00b0C for 2 hours.\nTotal acidity was measured using the barium hydroxide method and the carboxyl\ngroups were determined by using calcium acetate method (Stevenson 1982).\nPhenolic content was calculated as the difference between total acidity and the\ncarboxyl groups. FTIR spectra of the samples were recorded on KBr pellets (2\nmg nitrohumic acids and 100 mg KBr) using FTIR spectrophotometer. For UV-\nVis analysis, approximately 5.0 mg of nitrohumic acids were dissolved in 25 mL\nof 0.05 mol L-1 NaHCO3 solutions with pH adjusted to 8-9 with 0.1 M NaOH to\nassist solubility.  Absorbance at 465 nm and 665 nm were recorded. The E4/E6\nvalue was calculated based on the ratio of absorbance at 465 nm and 665 nm.\n\n\n\nPreparation of Humic Acids Solution\nFive nitrohumic acid stock solutions were prepared separately by dissolving 0.005\ng, 0.010 g, 0.050 g, 0.100 g and 1.000 g of nitrohumic acids in 30 mL of distilled\nwater. Sodium hydroxide at 0.1M was added dropwise until the pH stabilised at\n7.0. The final volumes were made up to 50 mL.  A control of distilled water was\nalso prepared simultaneously.\n\n\n\nAggregate Stability Determination\nThe nitrohumate solutions were mixed with 100 g of 2-5 mm air-dry aggregates\nto yield the following treatment rates: 0.00, 0.05, 0.10, 0.50, 1.00 and 10.00 g/kg.\nLanyon (2001) reports that soil aggregates of 2-5 mm are the most stable fraction\nto withstand the wetting and drying cycles. The soil samples amended with\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM19\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200720\n\n\n\nS.F. Sim,  S. Lau,  N. Omar  and D.F. Abang\n\n\n\nnitrohumic acids were homogenised and incubated for 14 days at 25oC prior to\ncontinuous wetting and drying cycles.\n\n\n\nAggregate stability of the soil samples was  measured by wetting and drying\nprocedure (Kemper and Rosenau 1986). The incubated soil samples were air-\ndried and sieved to obtain 1-2 mm aggregates. Three grams of the 1-2 mm aggre-\ngates were weighed and distributed in a 0.5 mm mesh sieve and placed into a 10\nL beaker containing water until the level of 20 mm height above the base of\nscreen. The aggregates were allowed to sit overnight and agitated 20 times on\nthe following day. The 0.5 mm sieve was removed and oven-dried at 105oC for 2\nhours. The aggregate stability was calculated according to the following equa-\ntion:\n\n\n\nAggregate stability (%) = WR \u2013 SW    x 100\n3.00 - SW\n\n\n\nwhere WR = total weight of aggregates retained on 0.5 mm sieve.\nSW = weight of 2.0 to 0.5 mm sand.\n\n\n\nThe procedure was repeated for 2, 3, 4 and 5 cycles in duplicates.\n\n\n\nStatistical Analysis\nAnalysis of variance with repeated measurements at 95% confidence level was\nemployed to analyse the significant differences between the aggregate stability\nof soils (loamy sand and clay loam) with and without application of nitrohumic\nacids. The analysis was performed as a split plot design for repeated measure-\nments in a completely randomised design with Statistica 6.0.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nProperties of Soils and Nitrohumic Acids\nTable 1 summarises the general properties of the loamy sand and clay loam\nsamples.  The loamy sand was dominated by sand fraction, while the clay loam\nhad a rather balanced fraction of sand, silt and clay.  According to Brown (2006),\nclay loam is the soil material with the most evenly distributed sand, silt, and clay\nfractions, with 27 to 40% clay and 20 to 45% sand (Schut 1997).  Soil erodibility\nis a measure of erosion index, EI; lower EI value indicates better stability (Pic-\ncolo et al. 1997).  The experimental results indicate that loamy sand is poten-\ntially more susceptible to erosion than clay loam.  In addition to the mineralogi-\ncal properties, soil physical and chemical properties such as organic matter con-\ntent, moisture content, and pH are other important parameters influencing soil\nerodibility (Brady and Weil 1996).  Chaney and Swift (1984) found a highly\nsignificant correlation between the aggregate stability and the organic matter\ncontent, moisture content and pH. Greater stability was reported with increased\nsoil moisture and decreased pH.  Soil moisture functions bind the soil particles,\nhence improving the stability, while lower pH reflects the presence of Al and Fe\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM20\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Nitrohumic Acids on Loamy Sand and Clay Loam\n\n\n\n21\n\n\n\nin the soil solution that contributes to the formation of organic matter complexes.\nDespite the relationships, the organic matter content, moisture content and pH of\nboth soils were statistically similar (p > 0.05).\n\n\n\nTable 2 summarises the characteristics of the nitrohumic acids. The acidic\nfunctional groups of the nitrohumic acids were comparatively higher than the\nacidic functional groups content of the humic acids prepared without nitration\nprocess (Sim et al. 2006). Typically, nitration process results in enrichment of\ncarboxyl groups, leading to the formation of humic acids with a variety of ali-\nphatic dicarboxylic acids, benzene carboxylic acids, hydroxybenzoic acids and\nnitro compounds (Chambury et al. 1945). The acidic functional groups upon\ncontact with soil aggregates react quickly with polyvalent cations (Ca2+, Mg2+,\nAl3+) found on surfaces of clay particles to form well described humus polyva-\nlent metal-clay complexes  (Greenland  1977; Theng  1982), thus  demonstrating\nan improved aggregate stability.\n\n\n\nEffects of Nitrohumic Acids Pre-treatment on Response to Wetting and Drying\nCycles\nTable 3 summarises the aggregate stability of the loamy sand and clay loam\nupon 5 wetting-drying cycles, with the superscript indicating the percentage of\naggregate stability relative to the controls (referred to as the aggregate stability\nof soils without application of humic acids for loamy sand and clay loam respec-\n\n\n\nTABLE 1\nSoil properties\n\n\n\nProperties Loamy sand Clay loam\n\n\n\nSand, % 83.5 31.0\nSilt, % 0.0 31.0\nClay, % 16.4 37.0\nAggregate stability, % 43 18\nSoil moisture, % 0.50 \u00b1 0.04 0.56  0.02\nOrganic matter, % 0.38 \u00b1 0.05 0.10  0.03\nEI (sand + silt/ OM + clay) 4.97 1.67\npH 4.59 4.76\n\n\n\nTABLE 2\nCharacterisation of nitrohumic acids\n\n\n\nProperties\n\n\n\nMoisture content, % 14.92  0.13\nAsh content, % 2.99  0.02\nTotal acidity, meq g-1 19.25  1.58\n-COOH, meq g-1 7.70  1.75\nPhenolic OH, meq g-1 11.55  2.37\nE4/E6 4.92  0.09\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM21\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200722\n\n\n\nS.F. Sim,  S. Lau,  N. Omar  and D.F. Abang\n\n\n\ntively).  Successive wetting and drying cycles consistently reduced the aggre-\ngate stability of both soils.  According to Utomo and Dexter (1982), wetting and\ndrying cycles create planes of weakness which allow the soils to break up into\nsmaller aggregates upon the impact of mechanical stress by the wetting up and\nfurther agitation by the wet sieving procedure.\n\n\n\nApplication of humic acids on the aggregate stability of loamy sand and\nclay loam demonstrated different observations. Loamy sand showed an improved\naggregate stability with increasing amounts of nitrohumic acids up to 0.10 g/kg.\nAt 1.00 g/kg, aggregate  stability was disrupted before improvement was re-\nsumed with the  treatment of 10.00 g/kg of nitrohumic acids. Statistical analysis\nimplied that cycles and application rate had significant effects on the aggregate\nstability with p-value 0.043 and 0.000, respectively. However, no apparent inter-\naction effect (p-value 0.575) was found between both factors on the aggregate\nstability of the samples.  Inconsistent improvements in aggregate stability with\nhumic acids was similarly reported by other studies.  Piccolo et al. (1997) re-\nported that there was an upper limit beyond which beneficial effects of humic\nsubstances failed.  The aggregate stability decreased at application rates > 0.10\ng/kg suggesting that higher rates of humic substances can penetrate the clay do-\nmain and form complex chelates with the polyvalent cations inside the\nintercrystalline domains, resulting in displacement of the less strongly bound\nclay particles (Theng 1982).  Durgin and Chaney (1984) further investigated the\n\n\n\nTABLE 3\nAggregate stability of loamy sand and clay loam upon 5 successive\n\n\n\nwetting-drying cycles (superscript values are the percentages of aggregate\nstability relative to the controls)\n\n\n\nCycle\n\n\n\nApplication rates 1 2 3 4 5\n(mg/kg)\n\n\n\nLoamy sand\n\n\n\n0.00 56.5(100) 56.2(99.5) 55.7(98.6) 54.5(96.5) 54.0(95.6)\n\n\n\n0.05 58.7(103.8) 58.3(103.2) 57.9(102.5) 57.7(102.1) 57.0(100.9)\n\n\n\n0.10 63.2(111.7) 62.4(110.4) 61.5(108.8) 61.2(108.3) 60.5(107.1)\n\n\n\n0.50 57.4(101.6) 56.8(100.5) 55.9(98.9) 55.0(97.3) 54.7(96.8)\n\n\n\n1.00 52.3(92.6) 51.7(91.5) 51.3(90.8) 51.0(90.3) 50.7(89.7)\n\n\n\n10.00 69.0(122.1) 68.0(120.4) 67.2(118.9) 62.8(115.2) 62.0(109.7)\n\n\n\nClay Loam\n\n\n\n0.00 16.6(100) 16.4(98.8) 16.0(96.4) 15.7(94.6) 15.3(92.2)\n\n\n\n0.05 11.8(71.1) 11.4(68.7) 10.8(65.1) 10.4(62.7) 10.2(61.4)\n\n\n\n0.10 9.9(59.6) 8.5(51.2) 8.2(49.4) 7.9(47.6) 7.4(44.6)\n\n\n\n0.50 10.5(63.3) 10.0(60.2) 9.7(58.4) 9.2(55.4) 9.0(54.2)\n\n\n\n1.00 12.0(72.3) 11.3(68.1) 11.0(66.3) 10.7(64.5) 10.2(61.4)\n\n\n\n10.00 16.5(99.4) 15.4(92.8) 15.0(90.4) 9.9(59.6) 8.2(49.4)\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM22\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Nitrohumic Acids on Loamy Sand and Clay Loam\n\n\n\n23\n\n\n\ndispersive effect of humic substances, indicating that high molecular weight aro-\nmatic and aliphatic polycarboxylic acids were able to disperse kaolinite by off-\nsetting the positive charge on the edges of clay particles and promoting clay\ndispersion. The improved aggregate stability at 10.00 g/kg after the reduction in\nthe  aggregate  stability  at  1.00 g/kg  could be explained with the reduced\ndispersive effect  as Jastrow and Miller (1998) showed that the amounts of dis-\npersed clays decreased with the increase in aggregate stability.\n       For clay loam samples, application of nitrohumic acids failed to improve the\naggregate  stability. The aggregate  stability  was  gradually reduced  with in-\ncreasing  amounts  of nitrohumic acids to 0.10 g/kg. Further increases in applica-\ntion rate demonstrated an improved aggregate stability, however, it was not bet-\nter than the control.   Statistically, the cycles and application rate both demon-\nstrated  significant  effects  on  the  aggregate  stability with p-values calculated\nat 0.036 and 0.00, respectively. Clay loam consists of a considerable amount  of\nclay fraction, and is thus anticipated to be more susceptible to dispersive effect.\nVisser and Caillier (1988) suggest that humic acids are 140 times more effective\nin dispersing fine clay fraction (< 0.6 mm) and 1.2 times for coarse clay (0.6-20\nmm). The apparent dispersion effect of humic acids on clay soil was evident in\nanother study; Fortun et al. (1989) concluded that humic fraction increased the\naggregate stability of sandy loam more efficiently than clay soil.\n       The  overall  results indicate that the aggregate stability of the loamy sand is\nimproved with nitrohumic acids amendment, but not for clay loam. Indirectly, it\nsuggests that aggregate stability of soils that are more susceptible to erosion could\npossibly be better improved with nitrohumic acids. Mbagwu and Piccolo (1989)\nreported similar observations that natural fragile soils benefited more from the\namendment with humic acids than the more stable one. Nevertheless, it did not\nimply that the humic acids could not function to improve the aggregate stability\nof clay loam. Fig. 1 illustrates the average aggregate stability of both soils after 5\nconsecutive wetting and drying cycles. For loamy sand, as revealed by Piccolo et\nal. (1997), there was an upper limit for the beneficial effect of humic substances\n\n\n\nFig. 1: Average aggregate stability of loamy sand and clay loam after 5 consecutive\nwetting and drying cycles.\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM23\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200724\n\n\n\nS.F. Sim,  S. Lau,  N. Omar  and D.F. Abang\n\n\n\nto be observed. For clay loam, on the other hand, the upper limit  appeared to be\nthe state  where dispersive effect took place most significantly; beyond which\naggregate  stability  indicated  progressive improvement. The  aggregate  stabil-\nity  is predicted to improve further with application rates at > 10.00 g/kg as\nevidenced by Jastrow and Miller (1998) who reported an increase in aggregate\nstability with decreased amounts of dispersed clays.\n\n\n\nSoil Organic Matter and Aggregate Stability\nThe relationship between organic matter content and aggregate stability has been\ndiscussed extensively. Contradictory results are often reported by researchers.\nKay and Angers (1999) reported that a minimum of 2 % organic matter content is\nnecessary to maintain structural stability; Boix-Fayos et al. (2003) showed that a\nthreshold of 3-3.5 % organic matter content had to be attained to achieve in-\ncreases in aggregate stability. However, Macrae and Mehuys (1987) and Perfect\nand Kay (1990) failed to find a significant correlation between aggregate stabil-\nity and soil organic matter content. A regression study was performed to investi-\ngate the relationship between soil organic matter content and aggregate stability;\ninsignificant correlation with R2 value at 0.172 and 0.099 were attained for loamy\nsand and clay loam, respectively (Fig. 2).\n\n\n\nFig. 2: Regression analysis of organic matter content and aggregate stability for loamy\nsand and clay loam\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM24\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Nitrohumic Acids on Loamy Sand and Clay Loam\n\n\n\n25\n\n\n\n       FTIR was employed to investigate the association between nitrohumic acids\nand soils.  Practically, no observable difference was noted from the spectra for\nsoils amended with and without nitrohumic acids (spectra not shown). This does\nnot indicate failure of the incorporation of nitrohumic acids onto soil particles\nbut inappropriateness of the technique.  Relative to the soil samples, a very low\namount of humic acids was added ranging from 0.005 to 1% in aggregate stabil-\nity study.  Debosz et al. (2002) support the postulation that the effects of organic\nmatter on soil properties are more likely to be attributed to quality  and not quan-\ntity.\n\n\n\nCONCLUSION\nNitrohumic acids sourced from the low grade coal of Sarawak could be em-\nployed to improve the aggregate stability of soils, particularly erosion suscep-\ntible soils. Nevertheless, there was an upper limit where the beneficial effects\ncould be observed; beyond which the dispersive effect took place resulting in\ndecreased aggregate stability.  The more stable soils, clay loam samples in this\nstudy, failed to demonstrate improvements in the aggregate stability upon\nnitrohumic acids amendment.  This is likely because the soils consisted of sig-\nnificant amounts of clay fractions which are more susceptible to clay dispersion\neffect.  As a whole, the effects of nitrohumic acids on the aggregate stability are\ndependent on soil type.\n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to thank Universiti Malaysia Sarawak for financial sup-\nport. Thanks are also due to Ir. Liew Siaw Kiat for assisting in site sampling.\n\n\n\nREFERENCES\nAllen, S.E. 1989. Chemical Analysis of Ecological Materials, pp. 15-16. London:\n\n\n\nBlackwell Scientific.\n\n\n\nBoix-Fayos, C., A. Calvo-Cases, A.C. Imeson and M.D. Soriano-Soto. 2003. Influence\nof soil properties on the aggregation of some Mediterranean soils and the use of\naggregate size and stability as land degradation indicators. Catena 44: 47-67.\n\n\n\nBrady, N.C. and R.R. Weil. 1996. The Nature and Properties of Soils. New Jersey: Prentice\nHall.\n\n\n\nBrown, R.B. 2006. Soil Texture. Florida: Institute of Food and Agricultural Science,\nUniversity of Florida.\n\n\n\nBryan, R. 1992. The influence of some soil conditioners on soil properties: laboratory\ntests on Kenyan soil samples. Soil Technol. 5: 225-247.\n\n\n\nChambury, H.B., J.W. Eckerd, J.S. Latorre And C.R. Kinney. 1945.  Chemistry of N in\nhumic acids from HNO3-treaated coal. J. Am. Chem. Soc. 67: 625-628.\n\n\n\nChaney, K. And R.S. Swift. 1984. The influence of organic matter on aggregate stability\nin some British soils. J.  Soil Sci. 35: 223-230.\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM25\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200726\n\n\n\nS.F. Sim,  S. Lau,  N. Omar  and D.F. Abang\n\n\n\nDebosz, K., S.O. Petersen, L.K. Kure and P. Ambus. 2002.  Evaluating effects of sewage\nsludge  and  household compost on soil physical, chemical and microbiological\nproperties. Appl. Soil Ecol. 19: 237-248.\n\n\n\nDewis, J. and F. Freitae. 1970. Physical and Chemical Methods of Soil and Water Analy-\nsis, pp. 39-57. 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PhD Dissertation, Department of\nCivil and Environmental Engineering, The University of Melbourne. pp. 33-48.\n\n\n\nMbagwu, J.S.C. and A. Piccolo. 1989.  Changes in soil aggregate stability induced by\namendment with humic substances. Soil Technol. 2: 49-57.\n\n\n\nMacrae, R. J. and G.R. Mehuys. 1987. Effects of green manuring in rotation with corn on\nthe physical properties of two Quebec soils. Biol. Agric. Hort. 4: 257-270.\n\n\n\nPerfect, E. and B.D. Kay. 1990.  Relations between aggregate stability and organic com-\nponents for a silt loam soil. Can. J. Soil Res. 70: 731-735.\n\n\n\nPiccolo, A., G. Pietramellara, and J.S.C. Mbagwu. 1997.  Use of humic substances as soil\nconditioners to increase aggregate stability. Geoderma 75: 567-277.\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM26\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nEffects of Nitrohumic Acids on Loamy Sand and Clay Loam\n\n\n\n27\n\n\n\nRausa, R., V. Calemma and E. Girardi. 1989. Humic acids by dry oxidation of coal with\nair under pressure. Analytical and spectroscopical characteristics. In Proc. Int. Conf.\nCoal Sci., Tokyo, Vol. I, pp. 237-240. Tokyo: IAES\n\n\n\nSchut, P. 1997. Soil Texture. Canada: Agriculture and Agrifood Canada,\n\n\n\nSim, S.F., S. Lau, N.C. Wong, J. Asing, M.F. Md Nor and A.S. Mohd Pauzan. 2006.\nCharacterization of the coal derived humic acids from Mukah, Sarawak as soil con-\nditioner. J. Braz. Chem. Soc. 17: 582-587.\n\n\n\nSoil Survey Staff. 1999.  Soil Taxonomy: A Basic System of Soil Classification for Mak-\ning and Interpreting Soil Surveys (2nd ed.). Washington, D.C.: US Department of\nAgriculture Soil Conservation Service.\n\n\n\nSojka, R.E. and R.D. Lentz. 1994. Time for yet another look at soil conditioners. Soil Sci.\n158: 233-234.\n\n\n\nStevenson, F.J. 1982. Humus Chemistry Genesis, Composition, Reactions. New York:\nJohn Wiley & Sons.\n\n\n\nSwift, R.S. 1996. Organic matter characterization. In Methods of Soil Analysis Part 3:\nChemical Methods, ed. D.L. Spark. SSSA and ASA. Madison: Wiley International.\n\n\n\nTheng, B.K.G. 1982. Clay-polymer interaction: summary and perspectives. Clays Clay\nMiner. 30: 1-10.\n\n\n\nUtomo, W.H. and A.R. Dexter. 1982. Changes in soil aggregate water stability induced\nby wetting and drying cycles in non-saturated soil. J. Soil Sci. 33: 623-637.\n\n\n\nVisser, S. A. and M., Caillier. 1988. Observations on the dispersion and aggregation of\nclays by humic substances, I. dispersive effects of humic acids. Geoderma 42: 331-\n337.\n\n\n\nMJ of Soil Science 017-027.pmd 08-Apr-08, 10:43 AM27\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 147-159  (2020)                         Malaysian Society of Soil Science\n\n\n\nArbuscular Mycorrhizal Fungi (AMF) and NPK Fertilisation \nRate on the Growth of Soursop (Annona muricata L.) \n\n\n\nSeedlings\n\n\n\nNadiah, N.S.H.1,  Nursyahidah, R.1, Jaafar, N.M.1,*, \nZaharah, S.S.2 and Muharam, F.M.3 \n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 UPM Serdang, Selangor, Malaysia\n\n\n\n2Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia\n43400 UPM Serdang, Selangor, Malaysia\n\n\n\n3Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 UPM Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nSoursop (Annona muricata L.) has been increasingly cultivated in Malaysia. In \nview of the importance of the crop, there is a need to understand the effects of \nagronomic management such as NPK fertiliser application and the inoculation \nof arbuscular mycorrhizal fungi (AMF) on soursop growth and nutrient uptake. \nTherefore, this study aimed to determine the effects of AMF and fertiliser on the \ngrowth and nutrient uptake of soursop seedlings. The experiment was conducted \nunder glasshouse condition in UPM, Serdang, Selangor, Malaysia using \ncompletely randomised design (CRD) with five treatments which comprised AMF \ninoculations with full and half dose of NPK 15:15:15 fertilisation. The treatments \nwere: T1- Control (without AMF and NPK fertiliser); T2- AMF only; T3- AMF \nwith 50% NPK fertiliser; T4- AMF with full amount (100%) NPK fertiliser; \nand T5- full amount (100%) NPK fertiliser only (without AMF). Plant growth, \nsoil microbial population AMF development, \u2018nutrient\u2019 status of the plants and \nsoils were determined after the 8th week of planting. Soursop seedlings grown in \nsoils treated with 100% NPK 15:15:15 fertiliser (T5) had the highest chlorophyll \ncontent, root volume, N uptake and soil N and K. Surprisingly, inoculation of \nAMF (T2) had similar effects to that of NPK 15:15:15 fertiliser (T5) on plant P \nuptake. Mycorrhizal spore production even at low numbers (66 spores/10 g soil) \nindicated probable symbiotic interaction with soursop seedling roots at the nursery \nstage.\n \nKey words:    Arbuscular mycorrhizal fungi, fertiliser rate, soursop, \n         seedlings, symbiosis.\n\n\n\n___________________\n*Corresponding author : E-mail: j_noraini@upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020148\n\n\n\nINTRODUCTION\nMalaysia\u2019s tropical climate allows various fruits to be grown throughout the \nyear. Soursop (Annona muricata L.), a native fruit tree to South America, has \nbeen increasingly planted in Malaysia owing to its nutritional value and great \neconomic potential and high demand, either for medicinal or food products. \nHowever, soursop cultivation in Malaysia is still new and information on soursop \ncultivation on Malaysian soils  is limited. \n Soursop can grow in soils ranging from sandy to clay loam, with well-\ndrained deep soil favouring their growth as good aeration enables development \nof roots (Hashim and Khalid 1993; Pinto and Silva 1994; Pinto et al. 2005). Pinto \nand Silva (1994) and Pinto et al. 2005 suggest that suitable soil pH for soursop is \nbetween 6.0 and 6.5, meanwhile Mohd Khalid et al. (1993) suggest that soil pH \nof between 5.0 to 6.5 is favourable for soursop growth. Soursop seedlings require \n1-2 g of mixed fertiliser (15N:15P:15K) once a month (Hashim and Khalid 1993) \nand they are normally left to grow in polybags containing potting mix (growth \nmedia) until the age of 6 months before being transferred to the field.\n Inoculation of beneficial microorganisms such as mycorrhiza can be \nexplored for soursop cultivation at the nursery stage in Malaysia. Arbuscular \nmycorrhizal fungi (AMF) has been generally known for its symbiotic relationship \nwith over 90% of plant species (Bonfante and Genre 2010). Through this \nsymbiosis, AMF improves phosphorus uptake, increases drought tolerance and \ndisease resistance of the host plants. The AMF can increase the solubilisation of \nphosphorus (P), which is normally fixed in most tropical soils, and can increase \nthe uptake of nutrients and water by plants thus improving plant vigour, yield and \nbioactive compound contents (Beltrano and Ronco 2008; Solaiman et al. 2014; \nCaser et al. 2019).\n Previous studies on soursop gave positive results when AMF was applied \non soil. Application of AMF to soursop seedlings has been reported to increase \ngrowth (Silva et al. 2008; Chu et al. 2001), and increase plant tolerance to lesions \nby nematode, Pratylenchus coffeae (Ang\u00e9lica 2003). Silva et al. (2008) found \nthat soursop seedling growth was improved with 10% organic manure with AMF \n(Acaulospora longula Spain & Schenck and Gigaspora albida Schenck & Smith). \nHowever, G. albida did not experience symbiosis with soursop roots when the \nsoil was fertilised. The potential for mycorrhiza to be used as a management tool \nfor soursop cultivation with NPK fertiliser is not known.\n Since the agronomic aspects of soursop cultivation in Malaysia using \nsoil beneficial microorganisms are not well understood, the current study was \nundertaken with the objective of determining the effects of AMF combined with \nNPK 15:15:15 fertiliser application on soursop growth, soil nutrient availability \nand plant uptake as well as AMF development.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 149\n\n\n\nMATERIALS AND METHODS\n\n\n\nExperiment Setup\nThe experiment was carried out under glasshouse conditions located at the \nComplex 11A, Faculty of Agriculture, UPM Serdang, Malaysia using 4-month \nold soursop seedlings. The plants were grown in a growth media containing two \namounts of fertiliser (50% and 100%) and inoculated with and without AMF \n(Glomus mossea). The experimental design was a completely randomised design \n(CRD), replicated four times. The treatments were T1- control; T2- AMF only; \nT3- AMF +50% NPK (15:15:15); T4- AMF +100% NPK (15:15:15); T5- 100% \nNPK (15:15:15) only. Topsoil, sand and organic matter used for planting media \nwere air dried and sieved to 2-mm size and autoclaved to remove all spores and \ninoculants before planting. Topsoil, sand and organic matter were mixed and 1.6 \nkg were placed in each of the 20 polybags (polibag size of 8 in x 8 in) with the \nratio of  2:1:1. The topsoil, sand and organic matter were analysed for soil pH \n(H2O) and C, N, P and  K were determined before planting. The soil mixtures were \nsterilised at a temperature of 121\u02daC in an autoclavefor 1 h.\n The roots of  the 4-month-old soursop seedlings (purchased from \nnursery) were washed and cleaned to remove the soil from previous planting and \ntransferred into the polybag according to the treatment with one plant per polybag. \nFor the control, no AMF and fertiliser were applied. Approximately 20g of AMF \n(60 spores/10g) were added into the AMF treatments while 20g of autoclaved soil \nwere added into the treatments without AMF in order to provide the same soil \nconditions. The treated seedlings  were evaluated after 60 days of transplanting.\n\n\n\nPlant Growth and Nutrient Analysis\nSoursop plant height was measured by using a measuring tape. Plant chlorophyll \nwas measured using a Minolta SPAD 502 Chlorophyll Meter by selecting fully \nopened leaves from the top of th shoot. Fresh leaves, stem and root were weighed \nto obtain their fresh weight. The fresh roots were rinsed with tap water to clean \noff the soil particles and analysed using WinRHIZO Root Scanner Analyser for \nroot analysis. Leaves, stem and root were dried in the oven using a separate brown \npaper at a temperature of 50\u02daC for about 3-4 days. Dry weight of leaves, stem \nand root were determined and recorded. The nutrient content in plant tissue was \nanalysed by using Kjeldal procedure (Horneck and Miller 1998) for nitrogen \nconcentration while dry ashing method (Jones and Warner, 1969) was used to \nextract phosphorus and potassium in plant tissue.\n\n\n\nDetermination of Soil Nutient\nTotal N was determined by using the Kjeldahl method (Bremner 1960) and the soil \nfiltrates were determined using Auto-Analyzer. Soil available P was determined \nby using Bray 2 method (Bray and Kurtz 1945) while exchangeable K for every \nsample was determined by using the modified shaking method (Schollenbereger \nand Simon 1945).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020150\n\n\n\nDetermination of AMF Spore and Soil Microbial Population\nThe number of AMF spores in soil was determined by using the wet sieving \nand decanting technique (Schenck 1982). About 10 g of fresh soil sample were \nsuspended in 100 mL of water. The suspension was decanted through a 250 \u00b5m at \nthe top, 106 \u00b5m at the middle and 45 \u00b5m at the bottom. Microbial population such \nas bacteria, fungi and actinomycetes were determined by using total plate count \ntechnique (Parkinson et al. 1971). After incubation at 28\u02daC in the incubator for \n2-4 days, colonies formed were counted and population was calculated as colony \nforming units (cfu) per dry soil. Only petri dishes containing 30-300 colonies \nwere counted. The microbial population per 10 g of soil was determined using the \nfollowing formula:\n\n\n\nStatistical Analysis\nAll data were subjected to analysis of variance (ANOVA) using Statistical \nAnalysis System (Ver 9.4; SAS Institute Inc., Cary, NC, USA, 2013) and mean \nseparations using Tukey\u2019s honestly significant difference (HSD) test at \u03b1=0.05.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nProperties of Growth Medium\nThe mixtures of soil,  sand, topsoil and organic soil were analysed separately \nfor pH and  nitrogen (N), phosphorus (P)  and potassium (K) contents prior to \nplanting. The results are shown in Table 1.\n\n\n\nTABLE 1\nProperties of growth medium (N, P, K concentration and pH)\n\n\n\nconcentration while dry ashing method (Kalra, 1997) was used to extract phosphorus and \n\n\n\npotassium in plant tissue. \n\n\n\n\n\n\n\nDetermination of Soil Nutient \n\n\n\nTotal N was determined by using th Kjeldahl method (Bremmer 1960) and the filtrates were \n\n\n\ndetermined using an auto-analyser machine ((XYZ Auto Sampler ASX S 20 series). Soil \n\n\n\navailable P was determined by using Bray 2 method (Bray and Kurtz 1945) while \n\n\n\nexchangeable K for every sample was determined by using the modified shaking method \n\n\n\n(Schollenbereger and Simon 1945). \n\n\n\n\n\n\n\nDetermination of AMF Spore and Soil Microbial Population \n\n\n\nThe number of AMF spores in soil was determined by using the wet sieving and decanting \n\n\n\ntechnique (Schenck 1982). About 10 g of fresh soil sample were suspended in 100 mL of \n\n\n\nwater. The suspension was decanted through a 250 \u00b5m at the top, 106 \u00b5m at the middle and \n\n\n\n45 \u00b5m at the bottom. Microbial population such as bacteria, fungi and actinomycetes were \n\n\n\ndetermined by using total plate count technique (Parkinson et al. 1971). After incubation at \n\n\n\n28\u2103in the incubator for 2-4 days, colonies formed were counted and population was \n\n\n\ncalculated as colony forming units (cfu) per dry soil. Only petri dishes containing 30-300 \n\n\n\ncolonies were counted. The microbial population per 10 g of soil was determined using the \n\n\n\nfollowing formula: \n\n\n\n\n\n\n\n                                          \n(                                                                         )\n\n\n\n(                        )\n \n\n\n\n\n\n\n\nStatistical Analysis \n\n\n\nAll data were subjected to analysis of variance (ANOVA) using Statistical Analysis System \n\n\n\n(Ver 9.4; SAS Institute Inc., Cary, NC, USA, 2013) and mean separations using Tukey\u2019s \n\n\n\nhonestly significant difference (HSD) test at P\u22640.05. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\nProperties of Growth Medium \n\n\n\nThe mixtures of soil,  sand, topsoil and organic soil were analysed separately for pH and  \n\n\n\nnitrogen (N), phosphorus (P)  and potassium (K) contents prior to planting. The results are \n\n\n\nshown in Table 1. \n\n\n\n\n\n\n\nTABLE 1 \nProperties of growth medium (N, P, K concentration and pH) \n\n\n\n \nTreatment Nutrient concentration  pH \n\n\n\nN (%) P (mg/kg) K (mg/kg) \nTopsoil  0.058 24.3 0.06 6.98 \nSand 0.016 141.3 NA 6.71 \nOrganic matter (OM) 0.496 1044 1.67  7.29 \nGrowth media \nmixture(2:1:1) \n\n\n\n0.192 499.5 0.45 7.46 \n\n\n\nNA=Not available \n\n\n\n\n\n\n\nData presented in Table 2 show that AMF treatments with and without fertiliser \n\n\n\napplication had significant effects (P\u22640.05) on plant height, chlorophyll content and root \n\n\n\nvolume. Inoculation of AMF with 50% NPK 15:15:15 fertiliser (T3) showed the highest plant \n\n\n\nheight compared to 100% NPK fertiliser (T4) and control (T1). This implies that AMF is able \n\n\n\nto function symbiotically in soil and stimulate plant growth when lesser amounts of fertiliser \n\n\n\n(half dosage) were applied. Plant chlorophyll content was highest in treatment with 100% \n\n\n\nfertiliser (40.69 SPAD unit), followed by plants in soil inoculated with AMF combined with \n\n\n\n50% fertiliser (39.37 SPAD unit). However, no significant effects (P \u2265 0.05) were noted for \n\n\n\nshoot dry weight and root dry weight. \n\n\n\nTreatment with 100% fertiliser produced a higher root volume (4.51 cm3) compared \n\n\n\nto control. Similar results were obtained by Hodge et al. (2000) who also showed that the \n\n\n\napplication of AMF enhanced root development and that AMF was capable of  promoting \n\n\n\nroot growth. In general, nutrient uptake by plant roots play the most important role in nutrient \n\n\n\nefficiency which also depends on root size and morphology (Gutshick1993). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 151\n\n\n\n Data presented in Table 2 show that AMF treatments with and without \nfertiliser application had significant effects (P\u22640.05) on plant height, chlorophyll \ncontent and root volume. Inoculation of AMF with 50% NPK 15:15:15 fertiliser \n(T3) showed the highest plant height compared to 100% NPK fertiliser (T4) \nand control (T1). This implies that AMF is able to function symbiotically in soil \nand stimulate plant growth when lesser amounts of fertiliser (half dosage) were \napplied. Plant chlorophyll content was highest in treatment with 100% fertiliser \n(40.69 SPAD unit), followed by plants in soil inoculated with AMF combined \nwith 50% fertiliser (39.37 SPAD unit). However, no significant effects (P \u2265 0.05) \nwere noted for shoot dry weight and root dry weight.\n Treatment with 100% fertiliser produced a higher root volume (4.51 \ncm3) compared to control. Similar results were obtained by Hodge et al. (2000) \nwho also showed that the application of AMF enhanced root development and \nthat AMF was capable of  promoting root growth. In general, nutrient uptake by \nplant roots play the most important role in nutrient efficiency which also depends \non root size and morphology (Gutshick 1993).\n\n\n\nTABLE 2 \nPlant and root growth measurements in response to AMF and fertilisation\n\n\n\nTABLE 2  \nPlant and root growth measurements in response to AMF and fertilisation \n\n\n\n \n \n \nTreatment \n\n\n\nPlant \nheight \n(cm) \n\n\n\nPlant biomass (g) Chlorophyll \ncontent  \n\n\n\n(SPAD units) \n\n\n\n\n\n\n\nRoot \nvolume \n(cm3) \n\n\n\nShoot Root  \n\n\n\nT1 \n(Control) \n\n\n\n25.13 bc 1.16a 0.35 a 36.80 bc 2.61 b \n\n\n\nT2 \n(AMF) \n\n\n\n27.80 ab 1.34a 0.46 a 35.18 c 3.53 ab \n\n\n\nT3 \n(AMF + 50% \nfertiliser) \n\n\n\n28.83 a 1.15a 0.31 a 39.38 ab 2.18 ab \n\n\n\nT4 \n(AMF + 100% \nfertiliser) \n\n\n\n24.13 c 1.20a 0.30 a 35.43 c 2.21 b \n\n\n\nT5 \n(100% fertiliser) \n\n\n\n28.13ab 1.43a 0.45 a 40.69 a 4.51 a \n\n\n\nTrt  (Pr > F) 0.0394* 0.4086ns 0.0723ns 0.5289ns 0.0052** \n     Means with the same letter in a column are not significantly different at P>0.05*to \n     Tukey\u2019s HSD test. \n     * Significant at P\u22640.05(ANOVA); **Significant at P\u22640.01;  ns \u2013 not significant at  \n     P>0.05  (ANOVA) \n \n\n\n\nThe AMF and fertilisation treatments had a significant effect (P\u22640.05) on N \n\n\n\nconcentration in plant as shown in Table 3. Treatments T3 and T5 showed higher N \n\n\n\nconcentrations in plant at 3.11% and 2.78%, respectively, compared to control (1.67%). This \n\n\n\ncould be related to the high chlorophyll content of both treatments. The high uptake of N \n\n\n\nfrom the soil led to accumulation of N in plant tissue is a result of higher plant photosynthetic \n\n\n\nactivities as reflected by the higher chlorophyll content. However, no significant effects \n\n\n\n(P>0.05) were noted for AMF or fertiliser  on P concentration and K concentration in soursop \n\n\n\nplants.  \n\n\n\n\n\n\n\nTABLE 3  \nPlant nutrient concentration and uptake in response to AMF and fertilisation \n\n\n\n \nTreatment Nutrient concentration (%) Nutrient uptake (g/plant) \n\n\n\nN P K N  P K \nT1  \n(Control) \n\n\n\n1.67 c 0.24 a 3.50 a 3.03 b 0.31 c 4.82 a \n\n\n\n The AMF and fertilisation treatments had a significant effect (P\u22640.05) \non N concentration in plant as shown in Table 3. Treatments T3 and T5 showed \nhigher N concentrations in plant at 3.11% and 2.78%, respectively, compared to \n\n\n\nMeans with the same letter in a column are not significantly different at P>0.05* by \nTukey\u2019s HSD test.\n*Significant (P\u22640.05); **Very Significant (P\u22640.01);  ns \u2013 not significant (P>0.05)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020152\n\n\n\ncontrol (1.67%). This could be related to the high chlorophyll content of both \ntreatments. The high uptake of N from the soil led to accumulation of N in plant \ntissue is a result of higher plant photosynthetic activities as reflected by the higher \nchlorophyll content. However, no significant effects (P>0.05) were noted for \nAMF or fertiliser  on P concentration and K concentration in soursop plants. \n\n\n\nTABLE 3 \nPlant nutrient concentration and uptake in response to AMF and fertilisation\n\n\n\nTABLE 3  \nPlant nutrient concentration and uptake in response to AMF and fertilisation \n\n\n\n \nTreatment Nutrient concentration (%) Nutrient uptake (g/plant) \n\n\n\nN P K N  P K \nT1  \n(Control) \n\n\n\n1.67 c 0.24 a 3.50 a 3.03 b 0.31 c 4.82 a \n\n\n\nT2  \n(AMF) \n\n\n\n1.91 b 0.25 a 3.13 a 3.18 b 0.44 a 5.61 a \n\n\n\nT3 \n(AMF + 50% \nfertiliser) \n\n\n\n2.78 a 0.27 a 3.27 a 3.71b 0.39 ab 5.23 a \n\n\n\nT4 \n(AMF + 100% \nfertiliser) \n\n\n\n2.20 b 0.22 a 3.29 a 3.32 b 0.36 ab 5.00 a \n\n\n\nT5  \n(100% fertiliser) \n\n\n\n3.11 a 0.23 a 3.45 a 5.85 a 0.44 a 6.49 a \n\n\n\nTrt  (Pr > F) <0.0001**** 0.2951ns 0.3353ns 0.0098* 0.0481* 0.3511ns \n\n\n\nMeans with the same letter in column are not significantly different at P>0.05* to  Tukey\u2019s) \nHSD test. \n*- Significant at P\u22640.05 (ANOVA), **  Very significant at P\u22640.01(ANOVA); \n**** Extremely significant at P\u22640.0001 (ANOVA); ns \u2013 not significant at P>0.05  (ANOVA) \n \n \n\n\n\nThe uptake of N by soursop plant in the treatment with 100% fertiliser (T5) was \n\n\n\nsignificant (P\u2264 0.05) with highest uptake (5.85g/plant) compared to other treatments. In \n\n\n\ncomparison to N uptake, 100% fertiliser (T5) and AMF (T2) or combination treatments (T4, \n\n\n\nT5) gave significantly (P \u2264 0.05) higher P uptake compared to control. This indicates that \n\n\n\nnutrient uptake by soursop plant is more affected by NPK fertiliser than AMF symbiosis. In \n\n\n\ncontrast, K uptake of plant, however, was to be insignificantly affected (P \u2265 0.05) by the \n\n\n\ntreatments. Indirectly, AMF could have improved the plant growth processes via root area \n\n\n\nexploration for greater water and nutrient uptake (Azizah 1999). \n\n\n\nSignificant effects (P \u2264 0.05) of  100% NPK fertilisation treatments were exhibited in \n\n\n\nsoil N, P and K (Table 4). Soil supplied with 100% fertiliser gave higher N concentration in \n\n\n\nsoil (0.15%) compared to AMF treatments and control. Treatment with 100% fertiliser gave \n\n\n\nthe highest exchangeable K in soil (0.48mg/kg) compared to control (0.44mg/kg) and the \n\n\n\nlowest in treatment with AMF and 50% fertiliser (0.41mg/kg). In contrast, P in soil was \n\n\n\nfound to be the highest in treatment inoculated with AMF and combined with 100% fertiliser \n\n\n\n(400.17mg/kg) compared to others. This indicates that AMF is able to increase P availability \n\n\n\nin soil which is in line with previous findings (Sadhana 2014). However, there were no \n\n\n\nsignificant effects on soil pH between the treatments. \n\n\n\nTABLE 4  \nSoil properties in response to application of  AMF and fertiliser \n\n\n\n \nTreatment  Soil N  \n\n\n\n(%) \nSoil P \n\n\n\n(mg/kg) \nSoil K  \n\n\n\n(mg/kg) \npH \n\n\n\nT1  \n(Control) \n\n\n\n0.07b 343.00 b 0.44 bc 7.42 a \n\n\n\nT2  \n(AMF) \n\n\n\n0.08 b 358.88 b 0.45 abc 7.56 a \n\n\n\nT3 \n(AMF + 50% fertiliser) \n\n\n\n0.07 b 371.70 ab 0.41 c 7.58 a \n\n\n\nT4  \n(AMF + 100% fertiliser) \n\n\n\n0.08 b 400.17 a 0.47 ab 7.59 a \n\n\n\nT5  \n(100% fertiliser) \n\n\n\n0.15 a 336.93 b 0.48 a 7.41 a \n\n\n\n Trt  (Pr > F) 0.0144* 0.0191* 0.6872ns 0.0783ns \n\n\n\n     Means with the same letter in column are not significantly different at P>0.05 * to  \n     Tukey\u2019s HSD test \n     * Significant at P\u22640.05 (ANOVA); ns \u2013 not significant at P>0.05 (ANOVA) \n \n \n\n\n\nThe soil inoculated with AMF showed the highest AMF spores (66 spores/10g soil) \n\n\n\nfollowed by the treatment with 50% fertiliser (51 spores/10g soil) and AMF with 100% \n\n\n\nfertiliser (27 spores/10g soil). The AMF sporulation was better in soil without fertiliser \n\n\n\napplication. This observation was similar to the finding by Ortas (2003). Although the \n\n\n\npercentage of root infection could not be observed, the high number of spores in soil indicates \n\n\n\npositive AMF develoment resulting in a better symbiotic association with soursop plants \n\n\n\nwhich subsequently benefited the host plant. The AMF alone or in combination with 50% \n\n\n\nfertiliser flourished the fungal population compared to AMF with 100% fertiliser treatment \n\n\n\nand fertiliser alone.  \n\n\n\nSoil treated with 100% fertiliser had the least total bacterial population compared to \n\n\n\ncontrol or other AMF inoculated treatments. This finding is similar to the study of Medina et \n\n\n\nal. (2003) where the application of mycorrhiza increased the population density of bacteria in \n\n\n\nthe rhizosphere. Mycorrhizosphere effects that could occur include the stimulation of growth \n\n\n\nand activities of AMF related microorganisms like mycorrhizal helper bacteria (MHB) and \n\n\n\nphosphate solubilising bacteria (PSB). Mycorrhizal treatment had significant effects on the \n\n\n\nfungal and bacterial population but not on the actinomycetes population. The data are \n\n\n\npresented in Table 5. \n\n\n\n\n\n\n\n\n\n\n\nTABLE 4 \nSoil properties in response to application of  AMF and fertiliser\n\n\n\nMeans with the same letter in a column are not significantly different at P>0.05* by \nTukey\u2019s HSD test.\n*Significant (P\u22640.05); **Very Significant (P\u22640.01);  ns \u2013 not significant (P>0.05)\n\n\n\nMeans with the same letter in a column are not significantly different at P>0.05* by \nTukey\u2019s HSD test.\n*Significant (P\u22640.05); **Very Significant (P\u22640.01);  ns \u2013 not significant (P>0.05)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 153\n\n\n\n The uptake of N by soursop plant in the treatment with 100% fertiliser \n(T5) was significant (P\u2264 0.05) with highest uptake (5.85g/plant) compared to \nother treatments. In comparison to N uptake, 100% fertiliser (T5) and AMF (T2) \nor combination treatments (T4, T5) gave significantly (P \u2264 0.05) higher P uptake \ncompared to control. This indicates that nutrient uptake by soursop plant is more \naffected by NPK fertiliser than AMF symbiosis. In contrast, K uptake of plant, \nhowever, was to be insignificantly affected (P \u2265 0.05) by the treatments. Indirectly, \nAMF could have improved the plant growth processes via root area exploration \nfor greater water and nutrient uptake (Azizah 1999).\n Significant effects (P \u2264 0.05) of  100% NPK fertilisation treatments were \nexhibited in soil N, P and K (Table 4). Soil supplied with 100% fertiliser gave \nhigher N concentration in soil (0.15%) compared to AMF treatments and control. \nTreatment with 100% fertiliser gave the highest exchangeable K in soil (0.48mg/\nkg) compared to control (0.44mg/kg) and the lowest in treatment with AMF and \n50% fertiliser (0.41mg/kg). In contrast, P in soil was found to be the highest in \ntreatment inoculated with AMF and combined with 100% fertiliser (400.17mg/\nkg) compared to others. This indicates that AMF is able to increase P availability \nin soil which is in line with previous findings (Sadhana 2014). However, there \nwere no significant effects on soil pH between the treatments.\n The soil inoculated with AMF showed the highest AMF spores (66 \nspores/10g soil) followed by the treatment with 50% fertiliser (51 spores/10g soil) \nand AMF with 100% fertiliser (27 spores/10g soil). The AMF sporulation was \nbetter in soil without fertiliser application. This observation was similar to the \nfinding by Ortas (2003). Although the percentage of root infection could not be \nobserved, the high number of spores in soil indicates positive AMF develoment \nresulting in a better symbiotic association with soursop plants which subsequently \nbenefited the host plant. The AMF alone or in combination with 50% fertiliser \nflourished the fungal population compared to AMF with 100% fertiliser treatment \nand fertiliser alone. \n Soil treated with 100% fertiliser had the least total bacterial population \ncompared to control or other AMF inoculated treatments. This finding is similar \nto the study of Medina et al. (2003) where the application of mycorrhiza increased \nthe population density of bacteria in the rhizosphere. Mycorrhizosphere effects \nthat could occur include the stimulation of growth and activities of AMF \nrelated microorganisms like mycorrhizal helper bacteria (MHB) and phosphate \nsolubilising bacteria (PSB). Mycorrhizal treatment had significant effects on the \nfungal and bacterial population but not on the actinomycetes population. The data \nare presented in Table 5.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020154\n\n\n\nDISCUSSION\nApplication of 100% NPK 15:15:15 fertiliser (T5) had significant effects on \nsoursop growth in terms of plant height, chlorophyll content and root volume. \nPlant chlorophyll content, significantly improved in 100% NPK 15:15:15 \nfertiliser treatment. Mycorrhizal alone (T2) had similarly lower root volume and \nwas similar in effect to that in soils treated with AMF + 50% fertiliser (T3). This \nresult shows that AMF is able to function symbiotically in soil and stimulate plant \nphotosynthesis when lesser amounts of fertiliser (50% fertiliser) are applied.\n The application of AMF alone has been previously shown to enhance \nroot development (Hodge et al. 2000). In general, the uptake of nutrients by \nthe roots played the most important role in nutrient efficiency (Gutshick 1993) \nwhich also depended on root size and morphology. The application of AMF alone \nand full rate fertiliser only gave a significantly higher root volume compared to \nother treatments. The higher root volume indicates a wider area of soils for the \nroots to explore that help with the uptake of nutrients into the roots (Grant et al. \n2005). The underlying mechanisms and factors resposible for AMF inability to \nfunction well in this study could be due to the amount of available nutrients in \n\n\n\nphosphate solubilising bacteria (PSB). Mycorrhizal treatment had significant effects on the \n\n\n\nfungal and bacterial population but not on the actinomycetes population. The data are \n\n\n\npresented in Table 5. \n\n\n\n\n\n\n\n\n\n\n\nTABLE 5   \nMicrobial population including mycorhizal development in response to application of AMF \n\n\n\nand fertiliser \n \n\n\n\nTreatment  Number of \nAMF  spore \n\n\n\ncounts / 10 g of \nsoil \n\n\n\nFungal   \npopulation \n\n\n\nBacterial \npopulation \n\n\n\n\n\n\n\nActinomycetes \npopulation \n\n\n\n\n\n\n\n (Log 10 cfu/g) \n\n\n\nT1  \n(Control) \n\n\n\n5 d 2.73 a 5.73 a 4.02 a \n\n\n\nT2  \n(AMF) \n\n\n\n66 a 2.65 a 5.67 a 3.97 a \n\n\n\nT3 \n(AMF + 50% \nfertiliser) \n\n\n\n51 b 2.50 a 5.62 a 3.73 a \n\n\n\nT4  \n(AMF + 100% \nfertiliser) \n\n\n\n27 c 2.10 b 5.74 a 3.66 a \n\n\n\nT5  \n(100% fertiliser) \n\n\n\n0 d 1.80 b 4.62 b 3.78 a \n\n\n\n Trt  (Pr > F) <0.0001**** 0.0005*** 0.0002*** 0.1035ns \n  Means with the same letter in the same column are not significantly different at P>0.05* \n  according to Tukey\u2019s HSD test. \n  ****  Extremely significant at P\u22640.0001 (ANOVA); *** Extremely significant at  \n  P\u22640.001  (ANOVA); ns \u2013 not significant at P>0.05  (ANOVA) \n \n \n\n\n\n \nDISCUSSION \n\n\n\nApplication of 100% NPK 15:15:15 fertiliser (T5) had significant effects on soursop growth \n\n\n\nin terms of plant height, chlorophyll content and root volume. Plant chlorophyll content, \n\n\n\nsignificantly improved in 100% NPK 15:15:15 fertiliser treatment. Mycorrhizal alone (T2) \n\n\n\nhad similarly lower root volume and was similar in effect to that in soils treated with AMF + \n\n\n\nTABLE 5  \nMicrobial population including mycorhizal development in response to application of \n\n\n\nAMF and fertiliser\n\n\n\nMeans with the same letter in a column are not significantly different at P>0.05* by \nTukey\u2019s HSD test.\n*Significant (P\u22640.05); **Very Significant (P\u22640.01);  ns \u2013 not significant (P>0.05)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 155\n\n\n\nthe applied fertilisers. This might also be due to late inoculation of AMF as the \nsoursop seedlings were only inoculated at 16 weeks, thereby reducing optimal \nAMF colonisation of plant roots. \n Nutrients such as N, P and K composition in soil were greatly influenced \nby fertilisation as found in the highest concentrations when using 100% fertiliser \ntreatment. N and K concentrations were highest in soils treated with full rate \nfertiliser. Meanwhile, the highest P concentration in soil was found in AMF \nwith full rate fertiliser treatment compared to treatments with AMF alone and  \nAMF with half rate fertiliser. This result indicates that AMF is able to increase \nP availability in soil which is in agreement with previous findings by Sadhana \n(2014). In addition, the increased amounts of soil nutrients might be due to the \nincrease in microbial population (Rachel and Randy 2011).\n Application of AMF species Glomus mosseae is envisaged to improve \nthe uptake of poorly mobile nutrients such as N and immobile nutrients like P \n(Barea et al. 2005). However, in this study, the highest significant N and P nutrient \nuptake by soursop plants was observed in the treatment with full rate (100%) \nfertiliser. This result indicates that  nutrient uptake by soursop plants responded \nmore to NPK fertiliser than to AMF symbiosis. In terms of nutrient concentration \nin plant tissues, the application of AMF did not affect P and K but it did for N \nuptake. Indirectly, AMF could have improved the plant growth processes via root \narea exploration and root volume for more water and nutrients uptake (Azizah \n1999). The concentration of N in plant tissue was highest when half rate fertiliser \ncombined with AMF was applied to soils. This could be related to the high \nchlorophyll content of both treatments and the uptake of N by plants in full rate \nfertiliser treatment. The high uptake of N from the soil accumulated N in plant \ntissue, a result of higher plant photosynthetic activities due to high chlorophyll \ncontent in plant leaves.\n Inoculation of AMF significantly affected the soil microbial population \nand the number of AMF spores in soil. Normally, AMF hyphal glomalin and plant \nroot exudates are able to stimulate the microbial population in soil (Johannson \net al. 2004). These AMF sporulated better in soil without fertiliser application. \nThis is in line with Ortas (2003). Although the percentage of root infection could \nnot be observed, the high spore numbers in soil as shown in Table 5 indicate \npositive AMF development that resulted in a better symbiotic association with \nsoursop plants which benefited the host plants. Inoculation of AMF was able \nto stimulate the population of bacteria in soil compared to application of 100% \nfertiliser which is in line with the study of Medina et al. (2003) which found \nthat the application of mycorrhiza increased the population density of bacteria in \nthe rhizophere. The densities of AMF spores and hyphae have been observed to \ndecrease under different soil and climatic conditions by the addition of mineral \nP fertiliser (Douds and Millner  1999; Kahiluoto et al. 2001). The presence of \nmycorrhizophere effects could be due the stimulation of growth and activities \nof AMF-related microorganisms such as mycorrhizal helper bacteria (MHB) \nand phosphate solubilising bacteria (PSB). These bacteria could have modified \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020156\n\n\n\nthe AMF mycelium environment  and benefited AMF in terms of  enhancing its \ndevelopment and formation especially mycelium thus promoting root growth and \nsoil conditions such as pH and P availability (Barea et al. 2005).\n Fertilisers applied to soil at the highest amount (100%) either alone or \nwith AMF did not stimulate bacterial and AMF spores. This could be due to less \nfavourable soil conditions for AMF mycelium development that created non \nconducive mycorrhizospheric effects on other soil microbes. The inconsistency \nin shaping AMF communities could be affected by AMF species identity (Wang \net al. 1993), plant community composition (Smith and Read 2008) and soil pH \n(Rousk et al. 2010). However, treatments with AMF with full rate fertiliser did not \nstimulate the fungal population. The only mycorrhizal treatments that stimulated \nfungi population were treatment with AMF with 50% fertiliser and AMF. This \nmay have been a result of the  high rate of fertiliser application which may have \nled to changes in soil microbial population over the short period which in turn \ndisrupted the relationship between AMF and plants (Bradley et al. 2006).\n Most of the previous studies on soursop and its symbiosis with AMF \ninvolved Acaulospora sp. and Gigaspora sp. To date, there has  been  no Glomus \nsp. study  reported on soursop in the  tropics (Silva et al. 2008). Therefore, the \nresults found in this study especially on non-availability of the effects of AMF \nroot infection might  be due to the plant not establishing a successful symbiotic \nassociation with AMF species tested; this warrants further research. Further, the \napplication of only 20 g of AMF inoculation (60 spores/10g) of the  Glomus \nmosseae species to soursop seedlings might not have been sufficient,and that \ntoo it was applied at the late stage of  4 months.  Therefore, the effect of the \ninfection might  have been delayed and the extent of AMF colonisation may have \nbeen insufficient to prove the effectiveness of AMF application on soursop plant  \ngrowth. \n\n\n\nCONCLUSIONS\nSoursop seedlings grown in soils treated with 100% NPK 15:15:15 fertiliser \n(T5) had the highest chlorophyll content, root volume, N uptake and soil N \nand K. Mycorrhizal alone (T2) had similar root volume compared to that in \nAMF + 50% fertiliser (T3). Mycorrhizal spores and soil microbial population \nwere not stimulated in soils with 100% fertiliser. Interestingly, inoculation of \nAMF (T2) had similar effects to that of NPK 15:15:15 fertiliser (T5) on plant \nP uptake. Mycorrhizal development even at low spore production (66 spores/10 \ng soil), indicated probable symbiotic interaction with soursop seedlings roots \nat the nursery stage. It is recommended that more trials on inoculation of AMF \nspecies on soursop be carried out and that they should be inoculated in the earlier \nstage of seed germination rather than older plant seedlings to ensure succsesful \ncolonisation and symbiosis.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 157\n\n\n\nACKNOWLEDGEMENT\nThis study was conducted in collaboration with Ms. Nursyahidah Romli and \nfunded by UPM (Putra Grant GP-IPM/2015/9462400: Arbuscular Mycorrhizal \nFungi Inoculation for Improved Soursop (Annona muricata) Growth and \nBioactive Compounds). The author thanks Prof. Dr. Radziah Othman for generous \nassistance provided during this study.\n\n\n\nREFERENCES\nAng\u00e9lica, C. B. J. 2003. 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Walker. 1993. Effects of pH on \narbuscular mycorrhiza I. Field observations on the long-term liming \nexperiments at Rothamsted and Woburn. New Phytologist 124(3): 465-472.\n\n\n\n \n\n\n\n\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 119-134 (2019)                         Malaysian Society of Soil Science\n\n\n\nNutrient Quality and Phytotoxicity Assessment of \nPhospho-Compost Prepared Under Two Bone Meal \n\n\n\nFortification Methods\n\n\n\nOyeyiola Y.B.1* and Omueti J.A.I.2\n\n\n\n1Department of Crop Production and Soil Science, Ladoke Akintola University of \nTechnology Ogbomoso, Oyo State, Nigeria\n\n\n\n2Department of Agronomy, University of Ibadan, Oyo State, Nigeria\n\n\n\nABSTRACT\nThe advent of the green revolution, characterised by intensive use of chemical \nfertilisers, has contributed significantly to degradation processes in the low \nactivity clay mineral tropical soils. This has led to recent campaigns to use organic \nfertilisers. The use of phospho-compost, which is a phosphorus (P) fortified \ncompost as an amendment on P-deficient tropical soils is relatively new in Nigeria \nand information on its production, especially on the P fortification method that \nassures nutrient quality and environmental safety, is scanty. This study evaluated \nthe effects of P fortification and methods on phospho-compost nutrient quality \nand phytotoxicity effects on maize seedling performance. Two carbon sources \n(sawdust (SD) and rice bran (RB)), poultry manure (PM), Gliricidia sepium (GL) \nleaves and bone meal (BM) (phosphorus fortifier) were mixed in 1:3:0.125:0.125 \n(60:180:7.5:7.5 kg feedstock mix) ratio under two bone meal (phosphorus) \nfortification methods: Co-composting (BMC) and post-stability fortification \n(BMP) methods were used to prepare four phospho-composts, SD+PM+GL+BMC, \nSD+PM+GL+BMP, RB+PM+GL+BMC, and RB+PM+BMP,  following a \nstandard procedure. Two bone meal unfortified sawdust (SD+PM+GL) and rice \nbran (RB+PM+GL) based composts were included for comparison. Data were \ntaken on pH, electrical conductivity, P, calcium (Ca), magnesium (Mg), manganese \n(Mn) and iron (Fe) contents of the cured compost. Data on maize seed germination \nand root elongation percentages were used for estimation of germination index \nof maize in each compost extract. Data were analysed by descriptive statistics \nand multidimensional analysis (MDA) was computed to rank the properties of \nthe composts for use on tropical acid soils. Phosphorus fortification improved pH, \nelectrical conductivity and P content of composts. The post-stability phosphorus \nfortification method, however, was superior in increasing pH (8%), P (223%), Ca \n(139%), Mg (15%) and germination index of maize (64%) and reducing Mn (23%) \nand Fe contents (68%) compared to the co-composting phosphorus fortification \nmethod. The MDA results showed a decreasing ranking order of the composts \nfor use on tropical acid soils as SD+PM+GL+BMP > SD+PM+GL+BMP > \nRB+PM+GL > SD+PM+GL > SD+PM+GL+BMC > RB+PM+GL+BMC. Post-\nstability bone meal fortification method is thereby recommended for use in the \nproduction of high quality and environmentally safe phospho-compost.. \n\n\n\n___________________\n*Corresponding author :yboyeyiola@lautech.edu.ng   \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019120\n\n\n\nINTRODUCTION\nThe advent of the green revolution, characterised by intensive use of chemical \nfertilisers, has contributed significantly to degradation processes in the low cation \nexchange capacity (nutrient reserve) and organic matter contents of tropical soils. \nThis has led to recent campaigns for the use of organic fertilisers on these soils. \nThe use of compost (an important class of organic fertiliser) is bedeviled by \nits presupposed slow nutrient release. In an attempt to alleviate this limitation, \nfortification of compost with extra nutrient sources has been proposed (Ogazi \nand Omueti 2000).  Compost fortified with nitrogen sources such as Gliricidia \nsepium, phosphorus sources such as rock phosphate and  bone meal and with \nmicroorganisms efficient in solubilising nutrients in composted feedstock for \nimproved nutrient release and uptake by plants have been reported (Chen et al. \n2001; Ajay et al. 2012; Hellal et al. 2013; Oyeyiola et al. 2015).\n Phospho-compost is a P fortified compost (Chen et al. 2001) that is useful \nin ameliorating soils characterised by high P-fixing intensity, acidification, low \navailable P and organic matter contents (Ajay et al. 2012; Oyeyiola et al. 2015). \nThe fortification of compost with P sources such as rock phosphate and bone meal \ncould be achieved during the composting process (i.e. co-composting fortification \nmethod) along with other carbon and nitrogen feedstock. It can also be done after \ncompost stability and curing (i.e. post-stability fortification method). However, \nthe co-composting fortification method is widely reported (Singh and Amberger \n1991; Mahimairaja et al. 1995; Akande et al. 2005; Hellal et al. 2013).\n Phytotoxicity assessment in compost is a measure of compost maturity \nand safety for use in the environment (Zucconi et al. 1981; Gariglio et al. 2002). \nIt shows the presence or absence of toxic compounds that could adversely \naffect plant growth. Immature compost induces unnecessarily higher microbial \nactivities in the field resulting in the reduction of concentrations of oxygen in \nthe soil and soil nutrient immobilisation especially nitrogen and phosphorus. It \nalso introduces phytotoxic compounds such as heavy metals, ammonia, phenolic \ncompounds and excess soluble salt into the soils. Different methods have been \nused in the evaluation of phytotoxicity in compost including determination of \nthe concentrations of volatile organic acids in the water extract from compost, \ntemperature in the composting pile, concentration of heavy metals in the resultant \ncompost and the use of germination index values of seeds of crops sown in \ncompost tea/extract (Zucconi et al. 1981; Tiquia et al. 1996; Selim et al. 2012)\n This work therefore aimed at assessing the nutrient quality of phospho-\ncompost prepared under two fortification methods, co-composting and post-\nstability fortification methods, and to compare the germination index values of \nmaize in the phospho-compost tea/extract under the two fortification methods \nalong with an unfortified compost. The study also utilised multidimensional \n\n\n\nKeywords: Phospho-compost, bone meal compost fortification, co-\ncomposting fortification, post-stability fortification, phytotoxicity \nin compost.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 121\n\n\n\nanalysis (MDA) to rank the resultant phospho-composts using their individual \nchemical characteristics and germination index values for use as an amendment \non typical acid soils.\n\n\n\nMATERIALS AND METHODS\nThe composting was carried out at the composting shed of the Department of \nCrop Production and Soil Science, Ladoke Akintola University of Technology, \nOgbomoso, Oyo State, Nigeria between April and June, 2015.\n\n\n\nFeedstock Collection and Nutrient Content Determination \nThe feedstock utilised for the composting were sawdust (SD), rice bran (RB), \npoultry manure (PM), Gliricidia sepium leaves (GL) and bone meal (for the \nphosphorus fortification). The sawdust was sourced from Aanu-Oluwapo sawmill \ncentre and rice bran from the rice mill centre at Orita Naira. The poultry manure \nand Gliricidia sepium leaves were obtained from the Teaching and Research \nFarm, Ladoke Akintola University of Technology while the bone meal was from \nGlory-Vet feed mill, Takie, all in Ogbomoso, Oyo State, Nigeria.\n Composite samples of each of the feedstock were subjected to proximate \nanalysis prior to composting following procedures for plant sample analyses. The \npH was determined on 1:4 (feedstock: water) ratio after 15 min of equilibration \nusing a glass electrode calibrated in buffer solutions 4 and 7. Total nitrogen was \ndetermined by Micro-Kjeldahl method as described by IITA (1978). Oven dried, \nground feedstock were subjected to wet digestion using perchloric acid, HNO3 and \nH2SO4 acid mixture and the digest read on the atomic absorption spectrophotometer \nfor the determination of Ca, Mg, Fe, Mn and Zn while concentrations of  K and Na  \nconcentrations were read on the flame photometer (IITA 1978). The phosphorus \nin the digest was determined by Vanado-Molybdate yellow method (IITA 1978) \nwhile the moisture content was by oven-dried method (105oC for 24 h). The \nprocedure for ash free organic carbon described by Anderson and Ingram (1996) \nwas followed for the determination of the carbon content.\n\n\n\nPreparation of Unfortified-and Phosphorus-Fortified Composts \n(Phospho-composts)\nThe windrow composting method described by Omueti et al. (2000) and Oyeyiola \n(2016) was followed. The poultry manure was mixed with each carbon source \nat a ratio of 3:1. Each windrow received 180 kg poultry manure, 60 kg carbon \nsource (sawdust or rice bran) and 7.5 kg chopped Gliricidia sepium leaves. Bone \nmeal applied at 7.5 kg per feedstock mix was included for the co-composting \nphosphorus fortification method treatment while the same quantity of bone meal \nwas applied to a second set of unfortified compost after compost stability to \nachieve the post-stability phosphorus fortification method treatment. Appropriate \nfeedstock were thoroughly mixed in each windrow, moistened to 60% moisture \ncontent using water and piled up to achieve a height of 1 m in the windrow for \nefficient heat conservation.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019122\n\n\n\n The composting process was monitored daily. Temperature in each pile \nwas measured daily at 11:00 a.m. using a mercury thermometer inserted to a depth \nof 35 cm at three portions of each compost and the average value was taken as \nrepresentative temperature per pile. Turning was done every three days for the first \ntwo weeks and every five days thereafter till compost stability was achieved. The \npH and electrical conductivity were assessed fortnightly following procedures \nearlier mentioned.\n The composts were adjudged stable when the temperature in each pile \nbecame similar with the ambient and kept relatively constant for two weeks. \nAll the composts were thereafter allowed to air dry and cure for two weeks, \nbefore samples were taken from each cured compost and subjected to nutrient \ncontent determination where nitrogen, carbon, phosphorus, calcium, magnesium, \npotassium, iron, manganese, copper and zinc contents were assessed following \nprocedures earlier described.\n Altogether, six compost types were prepared:\n 1. Sawdust based compost without bone meal fortification tagged \n\n\n\n(SD+PM+GL)\n 2. Rice bran based compost without bone meal fortification tagged \n\n\n\n(RB+PM+GL)\n 3. Sawdust based compost co-composted with bone meal tagged \n\n\n\n(SD+PM+GL+BMC)\n 4. Rice bran based compost co-composted with bone meal tagged \n\n\n\n(RB+PM+GL+BMC)\n 5. Sawdust based compost fortified with bone meal after compost \n\n\n\nstability tagged (SD+PM+GL+BMP) \n 6. Rice bran based compost fortified with bone meal after compost \n\n\n\nstability tagged (RB+PM+GL+BMP).\n\n\n\nData Collection\nDuring the composting process, data were taken on daily temperature in the \ncomposting pile, and ambient temperature, pH and electrical conductivity in \nthe decomposing feedstock fortnightly. At maturity, the resultant composts were \ntested for nitrogen, carbon, phosphorus, calcium, magnesium, potassium, iron, \nmanganese, copper and zinc contents. \n\n\n\nPhytotoxicity Assessment of the Phospho-composts Prepared\nThe procedure for the germination test in compost extract described in a number \nof studies (Zucconi et al. 1981; Tiquia et al. 1996) and modified by Oyeyiola \n(2016) was adopted. The extract from each compost was obtained by shaking 5 \ng of each stabilised compost in 50 ml distilled water in 120 ml plastic bottles on \na mechanical shaker for 1 h. Each solution was filtered through Whatmann filter \npaper to get an individual compost extract. Ten milliliter of each compost extract \nrepresented 100 % concentration treatment while 5 ml compost extract mixed \nwith 5 ml distilled water represented 50% concentration treatment. Experimental \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 123\n\n\n\nunit that received only 10 ml distilled water was included for comparison. The \nexperiment was replicated three times and laid out in a completely randomised \ndesign (CRD) to bring about 39 experimental units.\n Appropriate volume of compost extract and distilled water were applied \nunto 1 g oven sterilised cotton wool placed on petri dishes. Ten equal sized maize \nseeds (with surface earlier sterilised by immersion in 75 % ethanol for 2 min \nfollowed by rinsing in distilled water) were evenly placed on the soaked cotton \nwool. Each petri dish was thereafter taped with foil paper to prevent water loss \nwhile encouraging inflow of oxygen. The whole set-up was placed in the dark and \nmaintained at room temperature for a period of 72 h.\n\n\n\nData Computations\nData were collected on:\n(1) Number of germinated seeds for the estimation of germination percentage \n\n\n\nusing the formula:\nGermination percentage = Number of seeds germinated in compost extract x   100\n                                            Number of seeds germinated in distilled water\n\n\n\n(2) Mean root length of the germinated seedlings from each petri dish for the \nestimation of root elongation percentage using the formula:\n\n\n\nRoot elongation percentage = Mean root length of seedlings in compost extract  x 100\n                                                 Mean root length of seedlings in distilled water\n\n\n\nThe germination and root elongation percentages per treatment for estimating \ngermination index values as proposed by Selim et al. (2012) using the formula:\n\n\n\nGermination index = Seed germination percentage  x  Root elongation percentage\n                                                                                100\n\n\n\nData Analysis\nData collected were analysed by descriptive statistics using Genstat statistical \npackage (8th edition) and Excel software at 5% probability level. Multidimensional \nanalysis (MDA) was computed to rank the properties of the six composts for use \non tropical acid soils using:\n\n\n\nC.I. = \u03a3K1W1, \u03a3K2W2, ----, \u03a3KnWn\n\n\n\nwhere C.I. is choice index, The D of D as indicated in Table 4 is the direction a \nparticular component of the compost is desired to follow for higher performance \nin the soil the compost is added to. It has been subsumed in the KnWn value. It tells \nwhich of the values in each row will be the denominator for dividing the other \nvalues. It could be a positive or negative value. It is positive when higher value \nof the parameter is desired and negative when lower value is desired for higher \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019124\n\n\n\nperformance. It can be deleted from this section while it would be retained in \nTable 4, where K is homogenised constant, W is variable weight.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nThe Nutrient Characteristics of Feedstock Used for Compost Preparation\nAll the feedstocks used for the compost preparation were in the alkaline pH range \n(except rice bran (RB) that was slightly acidic (Table 1). Poultry manure (PM) and \nsawdust (SD) had higher calcium content. Micronutrients were variable in all the \nfeedstock with PM having the highest concentrations and SD, the least. Gliricidia \nsepium leaf was high in N and P contents. Bone meal was characterised by high \nP and Ca.\n\n\n\nTemperature Changes in the Composting Pile\nTemperature in all the composting piles rose from the initial 32oC to 55oC and \nabove between 7 to 15 days for the six compost types (Figure.1). This high \ntemperature is considered suitable for the destruction of weed seeds and pathogens \nin the composting feedstock (Ogazi and Omueti 2000). Beyond this day however, \nthe temperature decreased gradually until the temperatures stabilised with \nthe ambient at a range of 31 to 33oC between 72 to 81 days of the composting \nexercise. This depicts complete decomposition of all decomposable constituents \nin the feedstock used. \n\n\n\nEffects of Bone Meal Fortification on Number of Days to Compost Stability\nRice bran based compost with or without bone meal stabilised earlier (47 - 63 \ndays of composting) than the sawdust based (73 - 79 days of composting) ones \n(Figure 2). The lower organic carbon (803.5 g kg-1), higher nitrogen content (6.2 \ng kg-1) and light textured nature of the rice bran depicted presence of more easily \ndecomposable organic matter by the decomposing microorganisms and larger \nsurface area for wider microbial activities compared to the sawdust containing \ncellulotic compounds which encouraged a faster rate of stability. Bone meal \nfortification encouraged reduction in the number of days to compost stability \nin sawdust based compost by six days compared to a similar compost without \nbone meal. This, however, was not the case in the rice bran-based compost where \nstability was delayed by sixteen days under bone meal fortification.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 125\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n o\nf t\n\n\n\nhe\n c\n\n\n\nom\npo\n\n\n\nst\nin\n\n\n\ng \nfe\n\n\n\ned\nst\n\n\n\noc\nks\n\n\n\n8 \n \n\n\n\nT\nA\n\n\n\nB\nL\n\n\n\nE\n 1\n\n\n\n\n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\n c\non\n\n\n\nte\nnt\n\n\n\n o\nf t\n\n\n\nhe\n c\n\n\n\nom\npo\n\n\n\nst\nin\n\n\n\ng \nfe\n\n\n\ned\nsto\n\n\n\nck\ns \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npH\n \n\n\n\nN\n \n\n\n\nO\nrg\n\n\n\n. C\n \n\n\n\nP \nC\n\n\n\na \nM\n\n\n\ng \nK\n\n\n\n\n\n\n\nM\nn \n\n\n\nFe\n \n\n\n\nC\nu \n\n\n\nZn\n \n\n\n\nFe\ned\n\n\n\nst\noc\n\n\n\nk \n  \n\n\n\ng/\nkg\n\n\n\n\n\n\n\nm\ng/\n\n\n\nkg\n   \n\n\n\nSa\nw\n\n\n\ndu\nst\n\n\n\n \n7.\n\n\n\n4 \n1.\n\n\n\n3 \n98\n\n\n\n3.\n5 \n\n\n\n0.\n5 \n\n\n\n26\n.7\n\n\n\n \n0.\n\n\n\n8 \n2.\n\n\n\n8 \n \n\n\n\n15\n.2\n\n\n\n \n15\n\n\n\n.3\n \n\n\n\n0.\n5 \n\n\n\n5.\n4 \n\n\n\nR\nic\n\n\n\ne \nbr\n\n\n\nan\n \n\n\n\n5.\n9 \n\n\n\n6.\n2 \n\n\n\n80\n3.\n\n\n\n5 \n5.\n\n\n\n1 \n8.\n\n\n\n6 \n2.\n\n\n\n5 \n2.\n\n\n\n7 \n \n\n\n\n25\n9.\n\n\n\n6 \n44\n\n\n\n \n1.\n\n\n\n5 \n14\n\n\n\n.5\n \n\n\n\nPo\nul\n\n\n\ntry\n m\n\n\n\nan\nur\n\n\n\ne \n7.\n\n\n\n6 \n26\n\n\n\n \n62\n\n\n\n7.\n7 \n\n\n\n19\n.4\n\n\n\n \n27\n\n\n\n.1\n \n\n\n\n6.\n2 \n\n\n\n20\n.6\n\n\n\n\n\n\n\n38\n5.\n\n\n\n4 \n31\n\n\n\n8.\n1 \n\n\n\n23\n \n\n\n\n70\n \n\n\n\nBo\nne\n\n\n\n m\nea\n\n\n\nl \n8.\n\n\n\n1 \n2.\n\n\n\n1 \n31\n\n\n\n3 \n12\n\n\n\n.8\n \n\n\n\n10\n5.\n\n\n\n3 \n4 \n\n\n\n0.\n5 \n\n\n\n \n41\n\n\n\n.7\n \n\n\n\n33\n4.\n\n\n\n6 \n6.\n\n\n\n2 \n92\n\n\n\n.6\n \n\n\n\nG\nlir\n\n\n\nic\nid\n\n\n\nia\n \n\n\n\nse\npi\n\n\n\num\n \n\n\n\n6.\n5 \n\n\n\n32\n \n\n\n\n91\n0 \n\n\n\n27\n \n\n\n\n11\n.4\n\n\n\n \n4.\n\n\n\n3 \n27\n\n\n\n.4\n \n\n\n\n  \n24\n\n\n\n.3\n \n\n\n\n54\n.8\n\n\n\n \n5.\n\n\n\n3 \n18\n\n\n\n.1\n \n\n\n\nM\nea\n\n\n\nn \n7.\n\n\n\n4 \n13\n\n\n\n.5\n \n\n\n\n72\n7.\n\n\n\n5 \n13\n\n\n\n.0\n \n\n\n\n35\n.8\n\n\n\n \n3.\n\n\n\n6 \n10\n\n\n\n.8\n \n\n\n\n \n14\n\n\n\n5.\n2 \n\n\n\n15\n3.\n\n\n\n4 \n7.\n\n\n\n3 \n40\n\n\n\n.1\n \n\n\n\nSD\n \n\n\n\n0.\n9 \n\n\n\n14\n.4\n\n\n\n \n26\n\n\n\n7.\n6 \n\n\n\n10\n.7\n\n\n\n \n35\n\n\n\n.8\n \n\n\n\n2.\n0 \n\n\n\n12\n.3\n\n\n\n\n\n\n\n16\n8.\n\n\n\n1 \n15\n\n\n\n8.\n7 \n\n\n\n9.\n1 \n\n\n\n38\n.7\n\n\n\n\n\n\n\nSD\n =\n\n\n\n S\nta\n\n\n\nnd\nar\n\n\n\nd \nde\n\n\n\nvi\nat\n\n\n\nio\nn \n\n\n\n(P\n <\n\n\n\n 0\n.0\n\n\n\n5)\n. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019126\n\n\n\nFig. 1: Temperature changes in the composting pile*\n\n\n\nNotes:\nRB+PM+GL is rice bran based compost without bone meal fortification   \nSD+PM+GL is sawdust based compost without bone meal fortification \nRB+PM+GL+BMC is rice bran based compost co-composted with bone meal \nSD+PM+GL+BMC is sawdust based compost co-composted with bone meal\nRB+PM+GL+BMP is rice bran based compost fortified with bone meal after compost stability \n(yet to be fortified at this stage)\nSD+PM+GL+BMP is sawdust based compost fortified with bone meal after compost stability \n(yet to be fortified at this stage)\n*Mean of temperatures \u00b1 Standard deviation (P< 0.05): \n              RB+PM+GL+BMC: 38.4 \u00b1 5.8\n              SD+PM+GL+BMC: 41.9 \u00b1 6.3\n              RB+PM+GL          : 37.2 \u00b1 7.2\n              RB+PM+GL+BMP: 37.5 \u00b1 7.1\n              SD+PM+GL : 42.3 \u00b1 6.9\n              SD+PM+GL+BMP: 42.1 \u00b1 6.8\n\n\n\n9 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1. Temperature changes in the composting pile* \n\n\n\nNotes: \nRB+PM+GL is rice bran based compost without bone meal fortification    \nSD+PM+GL is sawdust based compost without bone meal fortification  \nRB+PM+GL+BMC is rice bran based compost co-composted with bone meal  \nSD+PM+GL+BMC is sawdust based compost co-composted with bone meal \nRB+PM+GL+BMP is rice bran based compost fortified with bone meal after compost stability (yet to be \nfortified at this stage) \nSD+PM+GL+BMP is sawdust based compost fortified with bone meal after compost stability (yet to be \nfortified at this stage) \n*Mean of temperatures \u00b1 Standard deviation (P< 0.05):  \n\n\n\n              RB+PM+GL+BMC: 38.4 \u00b1 5.8 \n\n\n\n              SD+PM+GL+BMC: 41.9 \u00b1 6.3 \n\n\n\n              RB+PM+GL          : 37.2 \u00b1 7.2 \n\n\n\n              RB+PM+GL+BMP: 37.5 \u00b1 7.1 \n\n\n\n              SD+PM+GL : 42.3 \u00b1 6.9 \n\n\n\n              SD+PM+GL+BMP: 42.1 \u00b1 6.8 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 127\n\n\n\nFig. 2: Effects of bone meal fortification on number of days to compost stability\n\n\n\nNotes:\nRB+PM+GL is rice bran based compost without bone meal fortification   \nSD+PM+GL is sawdust based compost without bone meal fortification \nRB+PM+GL+BMC is rice bran based compost co-composted with bone meal \nSD+PM+GL+BMC is sawdust based compost co-composted with bone meal\nRB+PM+GL+BMP is rice bran based compost fortified with bone meal after compost stability\nSD+PM+GL+BMP is sawdust based compost fortified with bone meal after compost stability\nSD is standard deviation (P < 0.05).\n\n\n\nEffects of Bone Meal Fortification and Fortification Method on pH, Electrical \nConductivity and Nutrient Content of Composts Prepared\nBone meal fortification under co-composting or post-stability fortification method \nincreased the alkalinity status of the resultant composts compared to the unfortified \ncomposts (Table 2). This is attributed to the dissolution of the calcium in the bone \nmeal into the active sites of the composts. Post-stability fortification method and \nuse of sawdust, however, produced more alkaline composts which are beneficial \nfor use on low activity clay acid soils of the tropics. The electrical conductivity \nof the resultant composts was within the acceptable range for good composts. \nNonetheless, post-stability bone meal fortification method produced compost \nwith lower dissolved salts content. This is well explained by the higher calcium, \nmagnesium, phosphorus and lower sodium concentrations of post-stability bone \nmeal fortified composts. The higher calcium contents masked the dissolution \n\n\n\n\n\n\n\nFig. 2: Effects of bone meal fortification on number of days to compost stability \n\n\n\nWhere: \nRB+PM+GL is Rice bran based compost without bone meal fortification    \nSD+PM+GL is Sawdust based compost without bone meal fortification  \nRB+PM+GL+BMC is Rice bran based compost co-composted with bone meal  \nSD+PM+GL+BMC is Sawdust based compost co-composted with bone meal \nRB+PM+GL+BMP is Rice bran based compost fortified with bone meal after compost stability \nSD+PM+GL+BMP is Sawdust based compost fortified with bone meal after compost stability \nSD is Standard deviation (P < 0.05). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\nDa\n\n\n\nys\n to\n\n\n\n st\nab\n\n\n\nili\nty\n\n\n\n\n\n\n\nCompost types \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019128\n\n\n\neffects of sodium salts in these composts. It is to be noted that lower calcium \nand magnesium contents were observed in co-composting P fortification method \ncompared to the unfortified composts. The possibility of immobilisation of most \nof the dissolved calcium ions by the increasing microbial population during active \ndecomposition phases of the feedstock seem to exist in the compost prepared \nthrough co-composting phosphorus fortification method over the post-stability \nphosphorus fortification method, highlighting the possibility of temporary nutrient \nlosses under the co-composting phosphorus fortification method.\n\n\n\nTABLE 2\nEffects of bone meal fortification and fortification method on nutrient composition\n\n\n\n of compost\n\n\n\n11 \n \n\n\n\nand lower sodium concentrations of post-stability bone meal fortified composts. The higher \ncalcium contents masked the dissolution effects of sodium salts in these composts. It is to be \nnoted that lower calcium and magnesium contents were observed in co-composting P \nfortification method compared to the unfortified composts. The possibility of immobilisation of \nmost of the dissolved calcium ions by the increasing microbial population during active \ndecomposition phases of the feedstock seem to exist in the compost prepared through co-\ncomposting phosphorus fortification method over the post-stability phosphorus fortification \nmethod, highlighting the possibility of temporary nutrient losses under the co-composting \nphosphorus fortification method. \n\n\n\nEffects of bone meal fortification on carbon and nitrogen contents were more pronounced on the \nrice bran based composts. Post-stability bone meal fortification produced compost with least \nnitrogen and carbon contents. Generally, bone meal fortification increased C/N ratio in both \ncarbon source based composts with an implication for further nitrogen fortification in such \ncomposts especially if such composts are to be used for raising high nitrogen feeder crops like \nmaize on nitrogen deficient soils. \n\n\n\nTABLE 2 \n\n\n\nEffects of bone meal fortification and fortification method on nutrient composition of compost \n\n\n\n      N C P Ca Mg K \n\n\n\n Compost types pH \nEC \n\n\n\n(dS/cm)     \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.g/kg\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. \nRB+PM+GL 7.8 0.3 5.9 251.8 10.7 83.3 3.7 13.8 \nSD+PM+GL 7.8 0.3 6.8 243.2 12.2 93.6 3.6 14.2 \nRB+PM+GL+BMC 7.9 0.2 5.5 326.2 13.8 55.2 3.6 14.2 \nSD+PM+GL+BMC 8.1 0.3 6.8 247.1 11.9 83.6 3.3 14.7 \nRB+PM+GL+BMP 8.4 0.1 5.2 225.2 25.6 131.7 4.0 12.8 \nSD+PM+GL+BMP 8.5 0.1 5.4 257.4 39.0 144.9 3.8 12.8 \nMean 8.1 0.2 5.9 258.5 18.9 98.7 3.7 13.8 \nSD 0.3 0.1 0.7 34.9 11.3 33.5 0.2 0.8 \n\n\n\nNotes: \nRB+PM+GL is rice bran based compost without bone meal fortification    \nSD+PM+GL is sawdust based compost without bone meal fortification  \nRB+PM+GL+BMC is rice bran based compost co-composted with bone meal  \nSD+PM+GL+BMC is sawdust based compost co-composted with bone meal \nRB+PM+GL+BMP is rice bran based compost fortified with bone meal after compost stability \nSD+PM+GL+BMP is sawdust based compost fortified with bone meal after compost stability  \nSD is standard deviation(P < 0.05). \n\n\n\n\n\n\n\n\n\n\n\n Effects of bone meal fortification on carbon and nitrogen contents were \nmore pronounced on the rice bran based composts. Post-stability bone meal \nfortification produced compost with least nitrogen and carbon contents. Generally, \nbone meal fortification increased C/N ratio in both carbon source based composts \nwith an implication for further nitrogen fortification in such composts especially \nif such composts are to be used for raising high nitrogen feeder crops like maize \non nitrogen deficient soils.\n\n\n\nNotes:\nRB+PM+GL is rice bran based compost without bone meal fortification   \nSD+PM+GL is sawdust based compost without bone meal fortification \nRB+PM+GL+BMC is rice bran based compost co-composted with bone meal \nSD+PM+GL+BMC is sawdust based compost co-composted with bone meal\nRB+PM+GL+BMP is rice bran based compost fortified with bone meal after compost stability\nSD+PM+GL+BMP is sawdust based compost fortified with bone meal after compost \nstability \nSD is standard deviation (P < 0.05).\n\n\n\nEffects of Bone Meal Fortification and Fortification Method on Micronutrient \nConcentrations in Compost\nPost-stability bone meal fortification method reduced the micronutrient contents \nof the resultant phospho-composts compared to the phospho-composts prepared \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 129\n\n\n\nthrough co-composting fortification method and unfortified composts (Table 3). \nIron content reduced by 68 and 53% in rice bran and sawdust based phospho-\ncompost, respectively. Manganese  concentration was reduced by 20 and 23% \nin rice bran and sawdust based phospho-compost, respectively. This property is \ndesired for matured compost as it alleviates the fear of possible toxicity effects \nin plants from fields amended with such phospho-composts (Tiquia et al. \n1996). Copper concentration was however least in compost without bone meal \nfortification while co-composting fortification had the least Zn concentrations.\nNotes\nRB+PM+GL is rice bran based compost without bone meal fortification   \nSD+PM+GL is sawdust based compost without bone meal fortification \nRB+PM+GL+BMC is rice bran based compost co-composted with bone meal \nSD+PM+GL+BMC is sawdust based compost co-composted with bone meal\nRB+PM+GL+BMP is rice bran based compost fortified with bone meal after compost stability\nSD+PM+GL+BMP is sawdust based compost fortified with bone meal after compost \nstability \nSD is standard deviation (P < 0.05).\n\n\n\nEffects of Bone Meal Fortification and Fortification Method on Germination \nIndex of Maize\nAll the twelve compost tea/extract treatments considered during the phytotoxicity \ntrial gave germination index values of above 60% (Figure 3) which was earlier \nreported critical for matured composts (Zucconi and De Bertoldi 1987) and that \nabove 80% is considered free of phytotoxic substances that ensure optimum \nseedling performance and environmental safety (Tiquia et al. 1996).  Seven of \nthe twelve compost extracts, however, enhanced maize germination index values \n(range of 107 - 136%) above the control (100 % germination index value) which \nreceived only distilled water with highest values obtained from the unfortified \ncomposts. This shows the presence of growth stimulating substances in the compost \nextracts (Delgado et al. 2010; Ancuta et al. 2013; Oyeyiola 2016). Regardless of \nthe carbon source used, co-composting phosphorus fortification method applied at \n\n\n\nTABLE 3 \nEffects of bone meal fortification and fortification method on micronutrient \n\n\n\nconcentrations of compost\n\n\n\n12 \n \n\n\n\nEffects of Bone Meal Fortification and Fortification Method on Micronutrient \nConcentrations in Compost \n\n\n\nPost-stability bone meal fortification method reduced the micronutrient contents of the resultant \n\n\n\nphospho-composts compared to the phospho-composts prepared through co-composting \n\n\n\nfortification method and unfortified composts (Table 3). Iron content reduced by 68 and 53% in \n\n\n\nrice bran and sawdust based phospho-compost, respectively. Manganese  concentration was \n\n\n\nreduced by 20 and 23% in rice bran and sawdust based phospho-compost, respectively. This \n\n\n\nproperty is desired for matured compost as it alleviates the fear of possible toxicity effects in \n\n\n\nplants from fields amended with such phospho-composts (Tiquia et al. 1996). Copper \n\n\n\nconcentration was however least in compost without bone meal fortification while co-\n\n\n\ncomposting fortification had the least Zn concentrations. \n\n\n\nTABLE 3  \n\n\n\nEffects of bone meal fortification and fortification method on micronutrient concentrations of \ncompost \n\n\n\n  Mn Fe Cu Zn \nCompost types    \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026mg/kg\u2026\u2026\u2026\u2026\u2026\u2026\u2026 \nRB+PM+GL 297.6 102.3 6.4 93.5 \nSD+PM+GL 269.9 210.7 6.4 90.2 \nRB+PM+GL+BMC 331.8 233.7 7.8 89.0 \nSD+PM+GL+BMC 310.5 277.3 8.1 88.0 \nRB+PM+GL+BMP 265.1 73.8 7.2 93.5 \nSD+PM+GL+BMP 239.0 131.4 6.9 96.0 \nMean 285.7 171.5 7.1 91.7 \nSD 33.9 80.7 0.7 3.1 \n\n\n\nNotes \nRB+PM+GL is rice bran based compost without bone meal fortification    \nSD+PM+GL is sawdust based compost without bone meal fortification  \nRB+PM+GL+BMC is rice bran based compost co-composted with bone meal  \nSD+PM+GL+BMC is sawdust based compost co-composted with bone meal \nRB+PM+GL+BMP is rice bran based compost fortified with bone meal after compost stability \nSD+PM+GL+BMP is sawdust based compost fortified with bone meal after compost stability  \nSD is standard deviation(P < 0.05). \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019130\n\n\n\n100 and 50 % concentration under the post-stability fortification method supported \nhigher germination index values. The 50% concentration mimics a lower field \napplication rate and /or delaying seed sowing after compost application while \nthe opposite holds for the 100% concentration (Oyeyiola 2016). Present findings \n\n\n\nFig. 3:  Effects of bone meal fortification and fortification method on germination index \nof maize\n\n\n\nshowed that lower application rate and/or delaying seeding after application \nof phospho-compost (50 % concentration) prepared through co-composting \nphosphorus fortification method reduced maize seedling performance by 28 and \n40% in rice bran and sawdust based phospho-composts while similar application \nconditions (50% concentration) improved maize seedling performance by 35 \nand 23% in rice bran and sawdust based phospho-composts prepared under post-\nstability phosphorus fortification method.\n \nNotes:\nDW is distilled water \nRB+PM+GL is rice bran based compost without bone meal fortification   \nSD+PM+GL is sawdust based compost without bone meal fortification \nRB+PM+GL+BMC is rice bran based compost co-composted with bone meal \nSD+PM+GL+BMC is sawdust based compost co-composted with bone meal\nRB+PM+GL+BMP is rice bran based compost fortified with bone meal after compost stability\nSD+PM+GL+BMP is sawdust based compost fortified with bone meal after compost stability\nSD is standard deviation (P < 0.05).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 3:  Effects of bone meal fortification and fortification method on germination index of \nmaize. \n\n\n\nWhere: \nDW is Distilled water  \nRB+PM+GL is Rice bran based compost without bone meal fortification    \nSD+PM+GL is Sawdust based compost without bone meal fortification  \nRB+PM+GL+BMC is Rice bran based compost co-composted with bone meal  \nSD+PM+GL+BMC is Sawdust based compost co-composted with bone meal \nRB+PM+GL+BMP is Rice bran based compost fortified with bone meal after compost stability \nSD+PM+GL+BMP is Sawdust based compost fortified with bone meal after compost stability \nSD is Standard deviation (P < 0.05). \n \n\n\n\n\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n140\n\n\n\n160\n\n\n\nGe\nrm\n\n\n\nin\nat\n\n\n\nio\nn \n\n\n\nin\nde\n\n\n\nx \n(%\n\n\n\n) \n\n\n\nTreatments \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 131\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nR\nan\n\n\n\nki\nng\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\non\ne \n\n\n\nm\nea\n\n\n\nl f\nor\n\n\n\ntifi\ned\n\n\n\n a\nnd\n\n\n\n u\nnf\n\n\n\nor\ntifi\n\n\n\ned\n c\n\n\n\nom\npo\n\n\n\nst\n p\n\n\n\nre\npa\n\n\n\nre\nd \n\n\n\nus\nin\n\n\n\ng \nm\n\n\n\nul\ntid\n\n\n\nim\nen\n\n\n\nsi\non\n\n\n\nal\n a\n\n\n\nna\nly\n\n\n\nsi\ns\n\n\n\n15\n \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\n\n\n\n\nR\nan\n\n\n\nki\nng\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\non\ne \n\n\n\nm\nea\n\n\n\nl f\nor\n\n\n\ntif\nie\n\n\n\nd \nan\n\n\n\nd \nun\n\n\n\nfo\nrti\n\n\n\nfie\nd \n\n\n\nco\nm\n\n\n\npo\nst\n\n\n\n p\nre\n\n\n\npa\nre\n\n\n\nd \nus\n\n\n\nin\ng \n\n\n\nm\nul\n\n\n\ntid\nim\n\n\n\nen\nsi\n\n\n\non\nal\n\n\n\n a\nna\n\n\n\nly\nsi\n\n\n\ns \n\n\n\nV\nar\n\n\n\nia\nbl\n\n\n\nes\n \n\n\n\nD\n o\n\n\n\nf D\n \n\n\n\nV\nar\n\n\n\nia\nbl\n\n\n\ne \nw\n\n\n\nei\ngh\n\n\n\nt \nSD\n\n\n\n+P\nM\n\n\n\n+G\nL+\n\n\n\nB\nM\n\n\n\nC\n \n\n\n\nR\nB\n\n\n\n+P\nM\n\n\n\n+G\nL+\n\n\n\nB\nM\n\n\n\nC\n \n\n\n\nSD\n+P\n\n\n\nM\n+G\n\n\n\nL+\nB\n\n\n\nM\nP \n\n\n\nR\nB\n\n\n\n+P\nM\n\n\n\n+G\nL+\n\n\n\nB\nM\n\n\n\nP \nSD\n\n\n\n+P\nM\n\n\n\n+G\nL \n\n\n\nR\nB\n\n\n\n+P\nM\n\n\n\n+G\nL \n\n\n\nC\n/N\n\n\n\n \n- \n\n\n\n3 \n2.\n\n\n\n96\n \n\n\n\n1.\n58\n\n\n\n \n2.\n\n\n\n29\n \n\n\n\n2.\n48\n\n\n\n \n3.\n\n\n\n00\n \n\n\n\n2.\n52\n\n\n\n \nP \n\n\n\n+ \n4 \n\n\n\n1.\n22\n\n\n\n \n1.\n\n\n\n42\n \n\n\n\n4.\n00\n\n\n\n \n2.\n\n\n\n63\n \n\n\n\n1.\n25\n\n\n\n \n1.\n\n\n\n10\n \n\n\n\nC\na \n\n\n\n+ \n4 \n\n\n\n2.\n31\n\n\n\n \n1.\n\n\n\n52\n \n\n\n\n4.\n00\n\n\n\n \n3.\n\n\n\n64\n \n\n\n\n2.\n58\n\n\n\n \n2.\n\n\n\n30\n \n\n\n\nM\ng \n\n\n\n+ \n2 \n\n\n\n1.\n65\n\n\n\n \n1.\n\n\n\n80\n \n\n\n\n1.\n90\n\n\n\n \n2.\n\n\n\n00\n \n\n\n\n1.\n80\n\n\n\n \n1.\n\n\n\n85\n \n\n\n\nK\n \n\n\n\n+ \n1 \n\n\n\n1.\n00\n\n\n\n \n0.\n\n\n\n97\n \n\n\n\n0.\n87\n\n\n\n \n0.\n\n\n\n87\n \n\n\n\n0.\n97\n\n\n\n \n0.\n\n\n\n94\n \n\n\n\nM\nn \n\n\n\n- \n3 \n\n\n\n2.\n31\n\n\n\n \n2.\n\n\n\n16\n \n\n\n\n3.\n00\n\n\n\n \n2.\n\n\n\n70\n \n\n\n\n2.\n66\n\n\n\n \n2.\n\n\n\n41\n \n\n\n\nFe\n \n\n\n\n- \n3 \n\n\n\n0.\n80\n\n\n\n \n0.\n\n\n\n95\n \n\n\n\n1.\n68\n\n\n\n \n3.\n\n\n\n00\n \n\n\n\n1.\n05\n\n\n\n \n2.\n\n\n\n16\n \n\n\n\nC\nu \n\n\n\n- \n2 \n\n\n\n1.\n58\n\n\n\n \n1.\n\n\n\n64\n \n\n\n\n1.\n83\n\n\n\n \n1.\n\n\n\n78\n \n\n\n\n2.\n00\n\n\n\n \n2.\n\n\n\n00\n \n\n\n\nZn\n \n\n\n\n- \n2 \n\n\n\n2.\n00\n\n\n\n \n1.\n\n\n\n98\n \n\n\n\n1.\n83\n\n\n\n \n1.\n\n\n\n88\n \n\n\n\n1.\n95\n\n\n\n \n1.\n\n\n\n88\n \n\n\n\npH\n \n\n\n\n+ \n4 \n\n\n\n3.\n81\n\n\n\n \n3.\n\n\n\n72\n \n\n\n\n4.\n00\n\n\n\n \n3.\n\n\n\n95\n \n\n\n\n3.\n67\n\n\n\n \n3.\n\n\n\n67\n \n\n\n\nEC\n \n\n\n\n- \n3 \n\n\n\n1.\n00\n\n\n\n \n1.\n\n\n\n50\n \n\n\n\n3.\n00\n\n\n\n \n3.\n\n\n\n00\n \n\n\n\n1.\n00\n\n\n\n \n1.\n\n\n\n00\n \n\n\n\nG\nI \n\n\n\n+ \n4 \n\n\n\n3.\n15\n\n\n\n \n2.\n\n\n\n43\n \n\n\n\n2.\n88\n\n\n\n \n2.\n\n\n\n74\n \n\n\n\n3.\n50\n\n\n\n \n4.\n\n\n\n00\n \n\n\n\nTo\nta\n\n\n\nl \n \n\n\n\n \n23\n\n\n\n.7\n8 \n\n\n\n21\n.6\n\n\n\n6 \n31\n\n\n\n.2\n9 \n\n\n\n30\n.6\n\n\n\n7 \n25\n\n\n\n.4\n3 \n\n\n\n25\n.8\n\n\n\n3 \nR\n\n\n\nan\nk \n\n\n\n\n\n\n\n5 \n6 \n\n\n\n1 \n2 \n\n\n\n4 \n3 \n\n\n\nNo\nte\n\n\n\ns:\n \n\n\n\nD\n o\n\n\n\nf D\n is\n\n\n\n d\nire\n\n\n\nct\nio\n\n\n\nn \nof\n\n\n\n d\nes\n\n\n\nire\n, G\n\n\n\nI i\ns \n\n\n\nge\nrm\n\n\n\nin\nat\n\n\n\nio\nn \n\n\n\nin\nde\n\n\n\nx \nD\n\n\n\nW\n is\n\n\n\n d\nist\n\n\n\nill\ned\n\n\n\n w\nat\n\n\n\ner\n  \n\n\n\nR\nB\n\n\n\n+P\nM\n\n\n\n+G\nL \n\n\n\nis \nric\n\n\n\ne \nbr\n\n\n\nan\n b\n\n\n\nas\ned\n\n\n\n c\nom\n\n\n\npo\nst\n\n\n\n w\nith\n\n\n\nou\nt b\n\n\n\non\ne \n\n\n\nm\nea\n\n\n\nl f\nor\n\n\n\ntif\nic\n\n\n\nat\nio\n\n\n\nn \n   \n\n\n\nSD\n+P\n\n\n\nM\n+G\n\n\n\nL \nis \n\n\n\nsa\nw\n\n\n\ndu\nst\n\n\n\n b\nas\n\n\n\ned\n c\n\n\n\nom\npo\n\n\n\nst\n w\n\n\n\nith\nou\n\n\n\nt b\non\n\n\n\ne \nm\n\n\n\nea\nl f\n\n\n\nor\ntif\n\n\n\nic\nat\n\n\n\nio\nn \n\n\n\n \nR\n\n\n\nB\n+P\n\n\n\nM\n+G\n\n\n\nL+\nB\n\n\n\nM\nC\n\n\n\n is\n ri\n\n\n\nce\n b\n\n\n\nra\nn \n\n\n\nba\nse\n\n\n\nd \nco\n\n\n\nm\npo\n\n\n\nst\n c\n\n\n\no-\nco\n\n\n\nm\npo\n\n\n\nst\ned\n\n\n\n w\nith\n\n\n\n b\non\n\n\n\ne \nm\n\n\n\nea\nl  \n\n\n\nSD\n+P\n\n\n\nM\n+G\n\n\n\nL+\nB\n\n\n\nM\nC\n\n\n\n is\n s\n\n\n\naw\ndu\n\n\n\nst\n b\n\n\n\nas\ned\n\n\n\n c\nom\n\n\n\npo\nst\n\n\n\n c\no-\n\n\n\nco\nm\n\n\n\npo\nst\n\n\n\ned\n w\n\n\n\nith\n b\n\n\n\non\ne \n\n\n\nm\nea\n\n\n\nl \nR\n\n\n\nB\n+P\n\n\n\nM\n+G\n\n\n\nL+\nB\n\n\n\nM\nP \n\n\n\nis \nric\n\n\n\ne \nbr\n\n\n\nan\n b\n\n\n\nas\ned\n\n\n\n c\nom\n\n\n\npo\nst\n\n\n\n fo\nrti\n\n\n\nfie\nd \n\n\n\nw\nith\n\n\n\n b\non\n\n\n\ne \nm\n\n\n\nea\nl a\n\n\n\nfte\nr c\n\n\n\nom\npo\n\n\n\nst\n st\n\n\n\nab\nili\n\n\n\nty\n \n\n\n\nSD\n+P\n\n\n\nM\n+G\n\n\n\nL+\nB\n\n\n\nM\nP \n\n\n\nis \nsa\n\n\n\nw\ndu\n\n\n\nst\n b\n\n\n\nas\ned\n\n\n\n c\nom\n\n\n\npo\nst\n\n\n\n fo\nrti\n\n\n\nfie\nd \n\n\n\nw\nith\n\n\n\n b\non\n\n\n\ne \nm\n\n\n\nea\nl a\n\n\n\nfte\nr c\n\n\n\nom\npo\n\n\n\nst\n st\n\n\n\nab\nili\n\n\n\nty\n  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019132\n\n\n\nRanking of the Bone Meal Fortified and Unfortified Composts Prepared Using \nMultidimensional Analysis\nThe results of the multidimensional analysis which sum up the product of \nhomogenised constant and variable weights of individual parameters considered \nimportant for selection of composts suitable for use as an amendment on highly \nweathered soils of the tropics are presented in Table 4. The decreasing ranking \norders of the six composts are SD+PM+GL+BMP > SD+PM+GL+BMP > \nRB+PM+GL > SD+PM+GL > SD+PM+GL+BMC > RB+PM+GL+BMC with \ntotal homogenised variable weights of: 31.3, 30.7, 25.8, 25.4, 23.8 and 21.7 \nrespectively. \n \n\n\n\nCONCLUSIONS\nPost-stability phosphorus fortification method improved phosphorus, calcium and \nmagnesium by 86, 139 and 11% respectively in rice bran based phospho-compost, \n228, 73 and 15% respectively in sawdust based phospho-compost. However, it \nreduced micronutrient concentrations, especially iron and manganese, by as high \nas 68% in rice bran based phospho-compost and 23% in sawdust based phospho-\ncompost respectively compared to co-composting phosphorus fortification \nmethod and unfortified composts. But it is to be noted that there was a decrease in \nnitrogen content by 6 and 21% in rice bran and sawdust based phospho-compost \nrespectively.\n The two phosphorus fortification methods studied in this work gave \ngermination index values greater than 60% which is considered suitable for \ncompost to be used as a soil amendment. However, co-composting phosphorus \nfortification method encouraged production of phospho-compost that could \nforestall the occurrence of seedling scotch when seed sowing is immediately \nfollowed by compost application; however a  few days of delay in seeding after \ncompost application is required for the post-stability phosphorus fortification \nmethod to encourage proper equilibration of the freshly added bone meal \n(phosphorus source) with the soil. Seven of the twelve treatments assessed during \nthe phytotoxicity test had germination index values of above 100% showing the \npresence of growth stimulating substances in the resultant phospho-composts. \n The results of the MDA showed a decreasing ranking order of the six \ncomposts for use on tropical acid soil as SD+PM+GL+BMP > SD+PM+GL+BMP \n> RB+PM+GL > SD+PM+GL > SD+PM+GL+BMC > RB+PM+GL+BMC. \nPost-stability bone meal fortification method is thereby recommended for use \nin the production of high quality and environmentally safe phospho-compost. \nSummarily, enhanced maize seedling performance was achieved by a higher \napplication rate of 100% concentration and/or immediate seeding after application \nof phospho-compost prepared through co-composting bone meal fortification \nmethod  or a lower application rate of 50% concentration and/or delaying seeding \ntime after application of phospho-compost prepared through post-stability bone \nmeal fortification method. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 133\n\n\n\nREFERENCES\nAjay, K.K., M.M. Verma  and N. De. 2012. Phospho-compost influences nutrient use \n\n\n\nefficiency, yield and quality of rice (Oryza sativa). Asian Journal of Soil Science \n7(2): 172-177.\n\n\n\nAkande, M.O., J.A. Adediran and F.I. Oluwatoyinbo. 2005. Effect of rock phosphate \namended with poultry manure on soil available P and yield of maize and cowpea. \nAfrican Journal of Biotechnology 4: 444-448\n\n\n\nAncuta, D, S. Renate, B. Carmen and R. Sumalan. 2013. Seed germination and \nseedling growth of tomato as affected by different types of compost extract. \nJournal of Horticulture, Forestry and Biotechnology 17 (1): 155 - 160 \n\n\n\nAnderson, J.M. and J.S.I. Ingram.1996. Tropical Soil Biology and Fertility: A \nHandbook of Methods (2nd ed.) Wallingford: CAB International, 56-57 pp.\n\n\n\nChen, Jen-Hshuan., Wu, Jeng-Tzung and Huang, Wei-Tin. 2001. Effects of compost \non the availability of nitrogen and phosphorus in strongly acidic soils. Technical \nBulletin, FFTC, 10pp\n\n\n\nDelgado, M., J.V. Martin, R.M. De Imperial, C. Leon-Cofreces and M.C. Garcia. \n2010. Phytotoxicity of uncomposted and composted poultry manure. African \nJournal of Plant Science 4(5): 154-162\n\n\n\nGariglio, N.F., M.A. Buyatti, R.A. Pilatti, R.  Gonzalez and M.R. Acosta. 2002. Use \nof germination bioassay to test compost maturity of willow (Salix spp) sawdust. \nNew Zealand Journal of Crop and Horticultural Science 30: 135 \u2013 139.\n\n\n\nHellal F.A.A., F. Nagumo and  R.M. Zewainy. 2013. Influence of phospho-compost \napplication on phosphorus availability and uptake by maize grown in red soil of \nIshigaki island, Japan. Journal of Agricultural Sciences 4(2):102-109\n\n\n\nInternational Institute of Tropical Agriculture(IITA). 1978. Selected Methods for Soil \nand Plant Analysis. Manual series No. 1 (2nd ed.), 1-24 pp.\n\n\n\nMahimairaja, S., N.S. Bolan and M.J. Hedley. 1995. 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Effects of phosphor compost \non acidity indices in an acidic soil in Ilesha, Southwestern Nigeria. In: Scientific \nTrack Proceedings of the 3rd African Organic Conference. Achieving Social \nand Economic Development through Ecological and Organic Agriculture \nAlternatives, ed. G. Rahmann, T.I. Olabiyi and V.I.O. Olowe,Lagos, Nigeria.\n\n\n\nSelim S.M., M.S. Zayed and H.M. Atta. 2012. Evaluation of phytotoxicity of compost \nduring composting process. Nature and Science 10 (2): 6-9.\n\n\n\nSingh, C.P. and A. Amberger. 1991. Solubilization and availability of phosphorus \nduring decomposition of phosphate rock enriched straw and urine. Biological \nAgriculture and Horticulture 7:261-269.\n\n\n\nTiquia, S.M., N.F.Y. Tam and I.J. Hodgkiss.1996. Effects of composting on \nphytotoxicity of spent pig manure sawdust litter. Environmental Pollution 93: \n249 \u2013 256.\n\n\n\nZucconi, F, M. Forte, A. Monaco and M. De Bertoldi. 1981. Biological evaluation of \ncompost maturity. 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"\n\nINTRODUCTION\nSoil-plant-man is recognized as a major pathway for the transfer of radionuclides \nto human beings [(Safety Series, No. 57 (IAEA 1982)]. The radioactivity of \nenvironmental samples from sites and products suspected of contamination must \nbe investigated before free access to them is given to the public (Owono 2010). \nRadionuclides in soils are frequently transferred to different plant tissues by direct \ntransfer via the root system, or by fallout of radionuclides and resuspension of \ncontaminated soil followed by deposition on plant leaves (Noordijk et al. 1992). \nThe uptake of radionuclides from soil to plant is characterized by the transfer \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 61-74 (2014)                         Malaysian Society of Soil Science\n\n\n\nTransfer of Natural Radionuclides from Soil to Plants in \nNorth Western Parts of Dhaka \n\n\n\nGaffar, S.1, M. J. Ferdous2*, A. Begum2 and S.M. Ullah1\n\n\n\n1Department of Soil Water and Environment, University of Dhaka, \nDhaka-1000, Bangladesh.\n\n\n\n2Health Physics Division, Atomic Energy  Centre, Shahbagh, \nDhaka-1000. Bangladesh.\n\n\n\nABSTRACT\nThe radioactivity of environmental samples from sites and suspected of \ncontamination must be analyzed before free access is given to the public.  Towards \nthis end, plant and corresponding soil samples were collected from two different \nlocations of North- western parts of Dhaka (Savar and Manikganj) and the activity \nconcentrations of natural radionuclides 226Ra (238U-chain), 228Ra (232Th-chain) and \nnon-chained 40K were measured using gamma ray spectrometry. Soils of Savar \ncontained more radioactive 40K than Manikganj whereas soils of Manikganj \ncontained more 226Ra and 228Ra than Savar. The influence of certain soil properties \non the activity concentrations and transfer factors (TF) of natural radionuclides \nwere investigated by correlating the observed data with those of soil properties. \nThe activity concentrations of 40K were much higher than those of 226Ra and 228Ra \nin plants for both locations due to higher uptake from soils. The transfer factors \nfor 226Ra, 228Ra and 40K were found to range from 0.082 to 0.926, 0.153 to 0.563 \nand 1.274 to 3.741 at Savar and 0.087 to 0.455, 0.061 to 0.806 and 0.738 to 1.949 \nat Manikganj, respectively. The soil to plant transfer factors for 40K was found to \nbe much higher in plants, which might be due to the essentiality of this element \nin plants. Our study showed that activity concentrations of these radionuclides in \nplants and their plant transfer factors seem to depend on the activity concentrations \nof the same radionuclides in soil.\n\n\n\nKeywords:    Activity concentrations, plants, soil, soil parameters, transfer  \n         factors  \n\n\n\n___________________\n*Corresponding author : E-mail: ferdous28@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201462\n\n\n\nGaffar, S., M. J. Ferdous, A. Begum and S.M. Ullah\n\n\n\nfactor (TF): the ratio of radionuclide concentration in plant to soil per unit mass \n(Staven et al. 2003; Yassine et al. 2003). The TF is usually used for assessing the \nimpact of radionuclide releases into the environment. Due to a predicted long-\nterm transfer of radionuclides in the environment, knowledge of the geochemical \nand ecological cycles is also needed as they relate to the behaviour of not only \nradionuclides but also associated elements. In general, transfer factors show a \nwide range of variations depending upon several factors including soil properties \nsuch as pH, clay mineral, Ca, K and organic matter content, species of plants and \nother environmental conditions [(TRS No. 310 (IAEA, 1990)].\n\n\n\nAs countries in the South Asian region like Bangladesh expand applications \nof nuclear technology, the TFs of the radionuclides in soil to the crops are viewed \nas one of the most significant parameters in environmental safety estimation of \nnuclear facilities (Chibowski 2000). Over the years, some work on the transfer \nor pathway mechanism of naturally occurring radionuclides to plants and human \npopulation have been reported but data are still very sparse in this area especially \nin Bangladesh. Not all soils have the same amount of natural radionuclides \npresent. Therefore, the uptake in plants also varies which in turn results in different \npublic dose rates. The aim of the present investigation was to measure activity \nconcentrations of naturally occurring radionuclides deposited in the soil and plant \nand to determine soil-to-plant TF of 226Ra, 228Ra and 40K of some plants consumed \nas staple by the populations in the North-western parts of Dhaka, mainly Savar \nand Manikganj. An investigation of this nature is useful for both the assessment \nof public dose rates and the performance of epidemiological studies. Also, \nmaintaining reference-data records will assist in ascertaining possible changes in \nenvironmental radioactivity due to nuclear, industrial, and other human activities.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSample Collection\nTwo sampling sites were selected in the North-western part of Dhaka for the \ncollection of plant and soil samples. Figure 1 shows the sampling sites of Savar \n(latitude 23\u00b058\u2019N and longitude 90\u00b020\u2019 E) and Manikganj (latitude 23\u00b052\u2019 N and \nlongitude 90\u00b006\u2019E). Plants commonly grown and consumed were collected in the \nharvesting season. To ensure sufficient representation of each area, five plants \nfrom the different sampling sites of both Savar and Manikganj were collected. \nCrops from Savar included red amaranthus (Amaranthus tricolor; S1), elephant\u2019s \near (Colocasia esculenta; S2), bitter ground (Momordica charantia; S3), bind \nweed (Ipomoea aquatic; S4) and snake gourd (Trichosanthes anguina; S5). Crops \nfrom Manikganj included jute leaf (Corchorus capsulrais; M1), green amaranthus \n(Amaranthus lividus; M2), pumpkin (Cucurbita maxima; M3), pumpkin leaf \n(Cucurbita maxima, M4) and wax gourd (Benincasa hispida, M5). About 5-7 kg \n(on fresh weight basis) of edible parts of the plants was collected. Approximately \n1 kg of soil surrounding the roots of corresponding plants was collected at a depth \nof 0 to 15 centimeters (cm) from each sampling site in both Savar and Manikganj. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 63\n\n\n\nSample Preparation \nPlant samples were cut into small pieces and primarily dried in air by spreading \non separate sheets of brown paper. The samples were then dried in an electric \noven at 70 \u00baC until friable stage. Then the samples were ground to powder by a \ngrinder. Each of the collected soil samples were dried in the air by spreading on \nseparate sheets of paper after it was transported to the laboratory. After air drying, \nthe larger aggregates were broken by gentle crushing with a hammer. The soil \nsamples were then dried in an electric oven at 105 \u00baC and sieved through a 2 mm \nsieve. The properties of soil were determined by standard methods. Some selected \nphysicochemical properties of soils of Savar and Manikganj are presented in \nTable 1. Each of the plant and soil samples was transferred to cylindrical plastic-\ncontainers of approximately equal size and shape. In order to maximize counting \nefficiency and precision and to minimize self-absorption for that specific geometry, \ncontainers of similar size and shape were used. The containers were then sealed \ntightly, wrapped with thick vinyl tapes around their screw necks, and stored for \nat least four weeks to reach equilibrium between the 238U (226Ra) and 232Th (228Ra) \ntheir respective progenies prior to measurement (Kabir et al. 2009).\n\n\n\nRadioactivity Measurements \nRadioactivity in soil and plant samples was measured by gamma ray spectrometry \nsystem. The gamma ray spectrometry system consists of a High Purity \nGermanium (HPGe) detector, a detector shield (lead and steel), a preamplifier, \na linear amplifier, high voltage power supply, a multichannel analyzer system \nand a printer. The mass of the samples varied because of the varying density of \nthe sample material but the counting time was 5000 s for each sample. Direct \ndetermination of 226Ra and 228Ra in the samples without any chemical treatment \n\n\n\nTransfer of Natural Radionuclides from Soil to Plant\n\n\n\nFigure 1: Map showing sampling sites at Savar and Manikganj\n\n\n\n\n\n\n\n 13 \n\n\n\n\n\n\n\nFigure 1: Map showing sampling sites at Savar and Manikganj \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201464\n\n\n\nusing semiconductor \u03b3-ray spectrometer is difficult because radionuclides do not \nemit any intensive \u03b3-rays (lines) of their own. But they have several progenies \nwhich have more intensive lines and activities equal to their parents in the state of \nequilibrium (Bunzl and Trautmannsheimer 1999). As a result, the measurements \nof the radionuclides relied on detecting emissions from their progenies. The \nradioactivity concentration of 226Ra was determined from \u03b3-ray energies of its \ndaughter 214Bi (609.31 keV) while the 228Ra was determined from \u03b3-ray energies \nof its daughter 228Ac (911.07 keV). The radioactivity concentration of 40K was \ndetermined from the \u03b3-ray energy of 1460.80 keV. The efficiency calibration of the \ndetector was performed by using standard sources and the geometry of the counting \nsamples was the same as that of the standard samples. Having established the \nefficiency curve, the measurements of radioactivity in plant and soil samples were \ncarried out. Prior to sample counting, two background counts (owing to naturally \noccurring radionuclides in the environment around the detector) were taken twice \nduring weekends for 5000 s each, and the average of this background was then \nsubtracted from the samples counted during that week. Having determined the \nintegral counts under the interested gamma-energy peaks, the gamma activity was \ncalculated based on the measured efficiency of the detector from the following \nequation (Sheppard and Evenden 1988):  \n        \n \n\n\n\n      where,  A is the activity in Bq/kg; C is the net gamma counting rate in count \nper second (cps); \u03b5(E) is the efficiency of the detector at energy E (keV); P\u03b3 is the \nphoton emission probability at energy E (keV) intensity of the radionuclide and \nW is the dry mass of the sample.\n\n\n\nGaffar, S., M. J. Ferdous, A. Begum and S.M. Ullah\n\n\n\nTABLE 1\nSelected physicochemical properties of soils of Savar and Manikganj\n\n\n\n\n\n\n\n 8 \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nSelected physicochemical properties of soils of Savar and Manikganj \n\n\n\nParameter \n \n\n\n\n \nSample \n\n\n\npH \n(H2O) \n\n\n\nCEC \n(meq/100g) \n\n\n\nOrganic \nmatter \n\n\n\n(%) \n \n\n\n\nParticle size distribution \n(%) \n\n\n\nExchangeable \nnutrients (meq/100g) \n\n\n\nSand Silt Clay Ca K \nSavar \n\n\n\nS1 7.04 2.24 1.93 30.02 55.58 14.4 2.32 0.1598 \nS2 7.07 2.49 1.37 14.71 65.81 19.49 2.8 0.1023 \nS3 6.78 2.09 2.33 16.69 61.14 22.17 1.28 0.0691 \nS4 7.81 1.36 1.13 14.41 66.03 19.56 2.24 0.0639 \nS5 5.21 1.98 1.61 14.66 68.38 16.96 1.2 0.0614 \n\n\n\nAverage 6.78 2.03 1.67 18.09 63.39 18.52 1.97 0.0913 \nManikganj \n\n\n\nM1 4.95 5.18 2.89 7.97 56.72 35.32 2.32 0.0754 \nM2 6.17 3.01 2.74 4.1 58.53 37.39 1.36 0.0665 \nM3 5.53 3.01 2.82 2.76 56.75 40.49 1.6 0.0818 \nM4 5.53 3.01 2.82 2.76 56.75 40.49 1.6 0.0818 \nM5 5.99 4.78 2.66 5.78 59.05 35.17 1.76 0.0754 \n\n\n\nAverage 5.63 3.79 2.79 4.67 57.56 37.77 1.73 0.076 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 65\n\n\n\nTransfer Factors \nThe soil-to-plant transfer factor (TF) of radionuclides was calculated as the ratio \nof the activity concentration in the edible part of the plant (in Bq/kg dry weight) \nto the activity concentration in the soil (in Bq/kg dry weight) according to the \nequation (Noordijk et al. 1992):\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nRadioactivity Concentration of 226Ra, 228Ra and 40K\nThe activity concentrations of 226Ra (238U Chain) for soils of Savar were  found to \nbe within the range of 21\u00b17 to 26\u00b16 Bq/kg (Table 2).The average value for 226Ra \nof soils of Savar was found 24\u00b12 Bq/kg which is within the range of the world \naverage of 35 Bq/kg (UNSCEAR 2000). The average activity concentration of \n228Ra (232Th Chain) in soils from Savar was 41\u00b14 Bq/kg with a range of 37\u00b12 \nto 46\u00b16 Bq/kg. Few soils contained relatively high levels of 228Ra compared \nto the world average value of 40 Bq/kg (UNSCEAR 2000). The radioactivity \nconcentration of 40K (non-chained) ranged from 369\u00b138 to 483\u00b142 Bq/kg with an \naverage value of 408\u00b140 Bq/kg which is close to the world average of 400 Bq/\nkg (UNSCEAR 2000). Radioactivity of 226Ra, 228Ra and 40K was also measured \nin edible parts of plants corresponding to the soils collected from Savar, Dhaka. \nResults revealed that the concentration of 226Ra in the vegetable samples varied \nbetween 2\u00b11 and 19\u00b114 Bq/kg with an average value of 9\u00b17 Bq/kg. The \nmaximum activity was found in Colocasia esculenta (19\u00b114 Bq/kg, S2) and \nminimum 226Ra was found in Momordica charantia (2\u00b11 Bq/kg, S3). The activity \nof 228Ra ranged from 7\u00b14 to 22\u00b116 Bq/kg with an average value of 16\u00b16 Bq/\nkg. The activity concentrations of 228Ra in vegetables were comparatively higher \nthan those of 226Ra. Ipomoea aquatic (22\u00b116 Bq/kg, S4) and Momordica charantia \n(7\u00b14 Bq/kg, S3) were the highest and lowest accumulator of 228Ra, respectively \n(Table 2). Activity concentrations of 40K varied widely depending on plant type. \nConcentration of 40K in plants ranged from 503\u00b195 to 1528\u00b1145 Bq/kg with an \naverage value of 917\u00b1445 Bq/kg. The activity concentrations of 40K were found \nto be high in all plants. Colocasia esculenta was the highest (1528\u00b1145 Bq/kg, \nS2) accumulator of 40K. Among the investigated radionuclides, 40K was found to \naccumulate in large amounts in different vegetables. This higher activity of 40K \nmight be attributed to the higher biological requirement of plants for potassium as \nit is a major essential nutrient element. Minimum 40K concentration was found in \nIpomoea aquatic (503\u00b195 Bq/kg, S4). \n\n\n\nThe activity concentrations of 226Ra (238U Chain) for soils of Manikganj \nwere 22\u00b15 to 30\u00b15 Bq/kg (Table 3). The average value for 226Ra in soils was \nfound to be 26\u00b13 Bq/kg which is within the range of the world average value \nof 35 Bq/kg (UNSCEAR 2000). The average activity of 228Ra (232Th chain) of \n\n\n\nTransfer of Natural Radionuclides from Soil to Plant\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201466\n\n\n\nTA\nB\n\n\n\nLE\n 2\n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nof\n\n\n\n n\nat\n\n\n\nur\nal\n\n\n\n ra\ndi\n\n\n\non\nuc\n\n\n\nlid\nes\n\n\n\n in\n v\n\n\n\neg\net\n\n\n\nab\nle\n\n\n\n a\nnd\n\n\n\n c\nor\n\n\n\nre\nsp\n\n\n\non\ndi\n\n\n\nng\n so\n\n\n\nil \nsa\n\n\n\nm\npl\n\n\n\nes\n a\n\n\n\nnd\n tr\n\n\n\nan\nsf\n\n\n\ner\n fa\n\n\n\nct\nor\n\n\n\ns f\nro\n\n\n\nm\n S\n\n\n\nav\nar\n\n\n\n, D\nha\n\n\n\nka\n\n\n\n  \n10\n\n\n\n\n\n\n\n             \nTA\n\n\n\nB\nLE\n\n\n\n 2\n \n\n\n\n \nA\n\n\n\nct\niv\n\n\n\nity\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nof\n n\n\n\n\nat\nur\n\n\n\nal\n ra\n\n\n\ndi\non\n\n\n\nuc\nlid\n\n\n\nes\n in\n\n\n\n v\neg\n\n\n\net\nab\n\n\n\nle\n a\n\n\n\nnd\n c\n\n\n\nor\nre\n\n\n\nsp\non\n\n\n\ndi\nng\n\n\n\n so\nil \n\n\n\nsa\nm\n\n\n\npl\nes\n\n\n\n a\nnd\n\n\n\n tr\nan\n\n\n\nsf\ner\n\n\n\n fa\nct\n\n\n\nor\ns f\n\n\n\nro\nm\n\n\n\n S\nav\n\n\n\nar\n, D\n\n\n\nha\nka\n\n\n\n\n\n\n\nSa\nm\n\n\n\npl\ne \n\n\n\nC\nod\n\n\n\ne \nA\n\n\n\nct\niv\n\n\n\nity\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n \n\n\n\npl\nan\n\n\n\nts\n (B\n\n\n\nq/\nkg\n\n\n\n) \nA\n\n\n\nct\niv\n\n\n\nity\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n so\n\n\n\nil \n(B\n\n\n\nq/\nkg\n\n\n\n) \nTr\n\n\n\nan\nsf\n\n\n\ner\n fa\n\n\n\nct\nor\n\n\n\n (T\nF)\n\n\n\n\n\n\n\n22\n6 R\n\n\n\na \n22\n\n\n\n8 R\na \n\n\n\n40\nK\n\n\n\n \n22\n\n\n\n6 R\na \n\n\n\n22\n8 R\n\n\n\na \n40\n\n\n\nK\n \n\n\n\n22\n6 R\n\n\n\na \n22\n\n\n\n8 R\na \n\n\n\n40\nK\n\n\n\n \nAm\n\n\n\nar\nan\n\n\n\nth\nus\n\n\n\n tr\nic\n\n\n\nol\nor\n\n\n\n (S\n1)\n\n\n\n \n6\u00b1\n\n\n\n4 \n21\n\n\n\n\u00b11\n3 \n\n\n\n12\n43\n\n\n\n\u00b18\n4 \n\n\n\n25\n\u00b17\n\n\n\n \n37\n\n\n\n\u00b18\n \n\n\n\n48\n3\u00b1\n\n\n\n42\n \n\n\n\n0.\n23\n\n\n\n5 \n0.\n\n\n\n56\n3 \n\n\n\n2.\n57\n\n\n\n2 \nC\n\n\n\nol\noc\n\n\n\nas\nia\n\n\n\nes\ncu\n\n\n\nle\nnt\n\n\n\na(\nS 2\n\n\n\n) \n19\n\n\n\n\u00b11\n4 \n\n\n\n13\n\u00b11\n\n\n\n2 \n15\n\n\n\n28\n\u00b11\n\n\n\n45\n \n\n\n\n21\n\u00b17\n\n\n\n \n37\n\n\n\n\u00b12\n \n\n\n\n40\n8\u00b1\n\n\n\n35\n \n\n\n\n0.\n92\n\n\n\n6 \n0.\n\n\n\n34\n8 \n\n\n\n3.\n74\n\n\n\n1 \nM\n\n\n\nom\nor\n\n\n\ndi\nca\n\n\n\nch\nar\n\n\n\nan\ntia\n\n\n\n(S\n3) \n\n\n\n2\u00b1\n1 \n\n\n\n7\u00b1\n4 \n\n\n\n71\n2\u00b1\n\n\n\n69\n \n\n\n\n25\n\u00b17\n\n\n\n \n43\n\n\n\n\u00b16\n \n\n\n\n38\n6\u00b1\n\n\n\n38\n \n\n\n\n0.\n08\n\n\n\n2 \n0.\n\n\n\n15\n3 \n\n\n\n1.\n84\n\n\n\n7 \nIp\n\n\n\nom\noe\n\n\n\na \naq\n\n\n\nua\ntic\n\n\n\n (S\n4) \n\n\n\n11\n\u00b14\n\n\n\n \n22\n\n\n\n\u00b11\n6 \n\n\n\n50\n3\u00b1\n\n\n\n95\n \n\n\n\n23\n\u00b14\n\n\n\n \n46\n\n\n\n\u00b16\n \n\n\n\n39\n5\u00b1\n\n\n\n40\n \n\n\n\n0.\n47\n\n\n\n7 \n0.\n\n\n\n47\n5 \n\n\n\n1.\n27\n\n\n\n4 \nTr\n\n\n\nic\nho\n\n\n\nsa\nnt\n\n\n\nhe\nsa\n\n\n\nng\nui\n\n\n\nna\n(S\n\n\n\n5) \n8\u00b1\n\n\n\n9 \n16\n\n\n\n\u00b11\n8 \n\n\n\n60\n0\u00b1\n\n\n\n65\n \n\n\n\n26\n\u00b16\n\n\n\n \n41\n\n\n\n\u00b14\n \n\n\n\n36\n9\u00b1\n\n\n\n38\n \n\n\n\n0.\n29\n\n\n\n9 \n0.\n\n\n\n39\n9 \n\n\n\n1.\n62\n\n\n\n5 \nA\n\n\n\nve\nra\n\n\n\nge\n \n\n\n\n9\u00b1\n7 \n\n\n\n16\n\u00b16\n\n\n\n \n91\n\n\n\n7\u00b1\n44\n\n\n\n5 \n24\n\n\n\n\u00b12\n \n\n\n\n 4\n1\u00b1\n\n\n\n4 \n40\n\n\n\n8\u00b1\n40\n\n\n\n \n0.\n\n\n\n40\n4 \n\n\n\n0.\n38\n\n\n\n8 \n 1\n\n\n\n.6\n25\n\n\n\n\n\n\n\n\n\n\n\nGaffar, S., M. J. Ferdous, A. Begum and S.M. Ullah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 67\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nof\n\n\n\n n\nat\n\n\n\nur\nal\n\n\n\n ra\ndi\n\n\n\non\nuc\n\n\n\nlid\nes\n\n\n\n in\n v\n\n\n\neg\net\n\n\n\nab\nle\n\n\n\n a\nnd\n\n\n\n c\nor\n\n\n\nre\nsp\n\n\n\non\ndi\n\n\n\nng\n so\n\n\n\nil \nsa\n\n\n\nm\npl\n\n\n\nes\n a\n\n\n\nnd\n tr\n\n\n\nan\nsf\n\n\n\ner\n fa\n\n\n\nct\nor\n\n\n\ns f\nro\n\n\n\nm\n M\n\n\n\nan\nik\n\n\n\nga\nnj\n\n\n\n, D\nha\n\n\n\nka\n\n\n\n  \n11\n\n\n\n\n\n\n\n        \nTA\n\n\n\nB\nLE\n\n\n\n 3\n \n\n\n\n \nA\n\n\n\nct\niv\n\n\n\nity\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nof\n n\n\n\n\nat\nur\n\n\n\nal\n ra\n\n\n\ndi\non\n\n\n\nuc\nlid\n\n\n\nes\n in\n\n\n\n v\neg\n\n\n\net\nab\n\n\n\nle\n a\n\n\n\nnd\n c\n\n\n\nor\nre\n\n\n\nsp\non\n\n\n\ndi\nng\n\n\n\n so\nil \n\n\n\nsa\nm\n\n\n\npl\nes\n\n\n\n a\nnd\n\n\n\n tr\nan\n\n\n\nsf\ner\n\n\n\n fa\nct\n\n\n\nor\ns f\n\n\n\nro\nm\n\n\n\n M\nan\n\n\n\nik\nga\n\n\n\nnj\n, \n\n\n\nD\nha\n\n\n\nka\n \n\n\n\n\n\n\n\nSa\nm\n\n\n\npl\ne \n\n\n\nC\nod\n\n\n\ne \nA\n\n\n\nct\niv\n\n\n\nity\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n p\n\n\n\nla\nnt\n\n\n\n \n(B\n\n\n\nq/\nkg\n\n\n\n) \nA\n\n\n\nct\niv\n\n\n\nity\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n so\n\n\n\nil \n(B\n\n\n\nq/\nkg\n\n\n\n) \nTr\n\n\n\nan\nsf\n\n\n\ner\n fa\n\n\n\nct\nor\n\n\n\n (T\nF)\n\n\n\n\n\n\n\n22\n6 R\n\n\n\na \n22\n\n\n\n8 R\na \n\n\n\n40\nK\n\n\n\n \n22\n\n\n\n6 R\na \n\n\n\n22\n8 R\n\n\n\na \n40\n\n\n\nK\n \n\n\n\n22\n6 R\n\n\n\na \n22\n\n\n\n8 R\na \n\n\n\n40\nK\n\n\n\n \nC\n\n\n\nor\nch\n\n\n\nor\nus\n\n\n\nca\nps\n\n\n\nul\nra\n\n\n\nis\n(M\n\n\n\n1)\n \n\n\n\n13\n\u00b11\n\n\n\n0 \n33\n\n\n\n\u00b13\n7 \n\n\n\n32\n0\u00b1\n\n\n\n10\n9 \n\n\n\n29\n\u00b12\n\n\n\n \n41\n\n\n\n\u00b15\n \n\n\n\n43\n3\u00b1\n\n\n\n42\n \n\n\n\n0.\n45\n\n\n\n5 \n0.\n\n\n\n80\n6 \n\n\n\n0.\n73\n\n\n\n8 \nAm\n\n\n\nar\nan\n\n\n\nth\nus\n\n\n\nliv\nid\n\n\n\nus\n(M\n\n\n\n2) \n8\u00b1\n\n\n\n3 \n29\n\n\n\n\u00b13\n6 \n\n\n\n73\n8\u00b1\n\n\n\n10\n3 \n\n\n\n22\n\u00b15\n\n\n\n \n42\n\n\n\n\u00b17\n \n\n\n\n41\n1\u00b1\n\n\n\n41\n \n\n\n\n0.\n34\n\n\n\n3 \n0.\n\n\n\n69\n7 \n\n\n\n1.\n79\n\n\n\n4 \nC\n\n\n\nuc\nur\n\n\n\nbi\nta\n\n\n\n m\nax\n\n\n\nim\na \n\n\n\n(M\n3) \n\n\n\n5\u00b1\n3 \n\n\n\n3\u00b1\n0.\n\n\n\n3 \n67\n\n\n\n8\u00b1\n63\n\n\n\n \n30\n\n\n\n\u00b15\n \n\n\n\n43\n\u00b13\n\n\n\n \n45\n\n\n\n2\u00b1\n41\n\n\n\n \n0.\n\n\n\n15\n9 \n\n\n\n0.\n06\n\n\n\n1 \n1.\n\n\n\n50\n1 \n\n\n\nC\nuc\n\n\n\nur\nbi\n\n\n\nta\n m\n\n\n\nax\nim\n\n\n\na \n(L\n\n\n\nea\nf) \n\n\n\n(M\n4)\n\n\n\n \n4\u00b1\n\n\n\n3 \n4\u00b1\n\n\n\n7 \n70\n\n\n\n6\u00b1\n85\n\n\n\n \n23\n\n\n\n\u00b14\n \n\n\n\n34\n\u00b15\n\n\n\n \n36\n\n\n\n6\u00b1\n32\n\n\n\n \n0.\n\n\n\n18\n9 \n\n\n\n0.\n11\n\n\n\n9 \n1.\n\n\n\n92\n9 \n\n\n\nBe\nni\n\n\n\nnc\nas\n\n\n\nah\nis\n\n\n\npi\nda\n\n\n\n(M\n5) \n\n\n\n2\u00b1\n1 \n\n\n\n16\n\u00b12\n\n\n\n1 \n70\n\n\n\n1\u00b1\n86\n\n\n\n \n26\n\n\n\n\u00b16\n \n\n\n\n39\n\u00b17\n\n\n\n \n36\n\n\n\n0\u00b1\n36\n\n\n\n \n0.\n\n\n\n08\n7 \n\n\n\n0.\n41\n\n\n\n7 \n1.\n\n\n\n94\n9 \n\n\n\nA\nve\n\n\n\nra\nge\n\n\n\n \n6\u00b1\n\n\n\n4 \n17\n\n\n\n\u00b11\n4 \n\n\n\n62\n8\u00b1\n\n\n\n17\n4 \n\n\n\n26\n\u00b13\n\n\n\n \n40\n\n\n\n\u00b13\n \n\n\n\n40\n4\u00b1\n\n\n\n40\n \n\n\n\n0.\n24\n\n\n\n7 \n0.\n\n\n\n42\n \n\n\n\n1.\n58\n\n\n\n2 \n  \n\n\n\n\n\n\n\nTransfer of Natural Radionuclides from Soil to Plant\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201468\n\n\n\nsoils from Manikganj was found to be 40\u00b13 Bq/kg with a range of 34\u00b15 to 43\u00b13 \nBq/kg. The radioactivity of 40K (non-chained) ranged from 360\u00b136 to 452\u00b11 \nBq/kg with an average value of 404\u00b140 Bq/kg which is very close to the world \naverage value of 400 Bq/kg (UNSCEAR 2000). Results show that activity of \ninvestigated radioactive elements varied in their activity depending on plant \ntypes. The concentration of 226Ra in the vegetable samples ranged from 2\u00b11 to \n13\u00b110 Bq/kg with an average value of 6\u00b14 Bq/kg. The maximum value was \nfound in Corchorus capsulrais (13\u00b110 Bq/kg, M1) and minimum value of 226Ra \nwas found in Benincasa hispida (2\u00b11 Bq/kg, M5). The activity of 228Ra in plants \nranged from 3\u00b10.3 to 33\u00b137 Bq/kg with an average of 17\u00b114 Bq/kg. 228Ra activity \nin vegetables was comparatively higher than that of 226Ra. Corchorus capsulrais \n(33\u00b137 Bq/kg, M1) and Cucurbita maxima (3\u00b10.3 Bq/kg, M3) were the highest \nand the lowest accumulator of 228Ra, respectively. Like in Savar, 40K were also \nhigh in plants of Manikganj. 40K concentration ranged from 320\u00b1109 to 738\u00b1103 \nBq/kg with an average value of 628\u00b1174 Bq/kg (Table 3). The activity of 40K was \nfound to be relatively high in all samples. Amaranthus lividus was the highest \n(738\u00b1103 Bq/kg, M2) accumulator of 40K. Minimum value of 40K was found in \nCorchorus capsulrais (320\u00b1109 Bq/kg, M1).\n\n\n\nFigure 2 shows that the activity of 228Ra (232Th chain) was higher than that of  \n226Ra (238U Chain) in soils of Savar and Manikgonj, which is evident from the fact \nthat Thorium is 1.5 times higher than that of Uranium in the Earth\u2019s crust (Kabir \net al. 2009).  It was also observed that the measured activity of 40K (non-chained) \nmarkedly exceeded the values of both 228Ra and 226Ra as it is the most abundant \nradioactive element present in the environment. It can be seen from Figure 4 \nthat 40K activity in plants was much higher than the activity of 226Ra and 228Ra in \nSavar and Manikgonj. This high accumulation may be due to higher biological \nrequirements of 40K; also plants have the tendency to take up soluble potassium \nfar in excess of their needs if sufficiently large quantities are present, termed as \nluxury consumption (Brady and Weil 2002).  Radioactive potassium is also taken \nalong with non-radioactive potassium. Hence, the activity of 40K in vegetables \ntested was very much higher than that in soils. \n\n\n\nTransfer Factor (TF) of the Radionuclides in Different Plants of Savar and \nManikgonj \nThe TF of 226Ra of plants collected in Savar ranged from 0.082 to 0.926 with an \naverage of 0.404. The highest and the lowest TF of 226Ra was found in Colocasia \nesculenta (0.926, S2) and Momordica charantia (0.082, S3).  The results of 228Ra \nshowed that the TF of 228Ra in different plants collected from Savar ranged from \n0.153 to 0.563 with an average of 0.388. Amaranthus tricolor (0.563, S1) showed \nthe highest value while the lowest value was found in Momordica charantia \n(0.0153, S3). The TF of 40K in different plants of Savar ranged from 1.274 to \n3.741 with an average of 2.212. Ipomoea aquatic (1.274, S4) showed the lowest \nTF whileas Colocasia esculenta had the highest TF (3.741, S2) (Table 2). \n\n\n\nGaffar, S., M. J. Ferdous, A. Begum and S.M. Ullah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 69\n\n\n\nTransfer of Natural Radionuclides from Soil to Plant\n\n\n\nFigure 3: Activity concentration of natural radionuclides in plants of Savar and \nManikgonj, Dhaka.\n\n\n\n\n\n\n\n 14 \n\n\n\n0\n\n\n\n100\n\n\n\n200\n\n\n\n300\n\n\n\n400\n\n\n\n500\n\n\n\n600\n\n\n\n S1 S2  S3 S4 S5 M1 M2 M3 M4 M5\n\n\n\nSample code\n\n\n\nAc\ntiv\n\n\n\nity\n C\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n S\n\n\n\noi\nl \n\n\n\n(B\nq/\n\n\n\nkg\n)\n\n\n\nRa-226 Ra-228 K-40\n\n\n\n\n\n\n\nFigure 2: Activity concentration of natural radionuclides in soils of Savar and Manikgonj, \nDhaka. \n\n\n\n\n\n\n\n0\n\n\n\n200\n\n\n\n400\n\n\n\n600\n\n\n\n800\n\n\n\n1000\n\n\n\n1200\n\n\n\n1400\n\n\n\n1600\n\n\n\n1800\n\n\n\n S1 S2  S3 S4 S5 M1 M2 M3 M4 M5\n\n\n\nSample Code\n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n C\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nin\n\n\n\n P\nla\n\n\n\nnt\n (B\n\n\n\nq/\nkg\n\n\n\n)\n\n\n\nRa-226 Ra-228 K-40\n\n\n\n\n\n\n\nFigure 3: Activity concentration of natural radionuclides in plants of Savar and Manikgonj, \nDhaka. \n\n\n\n\n\n\n\n\n\n\n\n 14 \n\n\n\n0\n\n\n\n100\n\n\n\n200\n\n\n\n300\n\n\n\n400\n\n\n\n500\n\n\n\n600\n\n\n\n S1 S2  S3 S4 S5 M1 M2 M3 M4 M5\n\n\n\nSample code\n\n\n\nAc\ntiv\n\n\n\nity\n C\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nin\n S\n\n\n\noi\nl \n\n\n\n(B\nq/\n\n\n\nkg\n)\n\n\n\nRa-226 Ra-228 K-40\n\n\n\n\n\n\n\nFigure 2: Activity concentration of natural radionuclides in soils of Savar and Manikgonj, \nDhaka. \n\n\n\n\n\n\n\n0\n\n\n\n200\n\n\n\n400\n\n\n\n600\n\n\n\n800\n\n\n\n1000\n\n\n\n1200\n\n\n\n1400\n\n\n\n1600\n\n\n\n1800\n\n\n\n S1 S2  S3 S4 S5 M1 M2 M3 M4 M5\n\n\n\nSample Code\n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n C\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nin\n\n\n\n P\nla\n\n\n\nnt\n (B\n\n\n\nq/\nkg\n\n\n\n)\n\n\n\nRa-226 Ra-228 K-40\n\n\n\n\n\n\n\nFigure 3: Activity concentration of natural radionuclides in plants of Savar and Manikgonj, \nDhaka. \n\n\n\n\n\n\n\nFigure 2: Activity concentration of natural radionuclides in soils of Savar and \nManikgonj, Dhaka.\n\n\n\nIn Manikganj, the TFs of 226Ra in different vegetable samples varied between \n0.087 (Benincasa hispida, M5) to 0.455 (Corchorus capsulrais, M1). The average \nvalue was found to be 0.247. The TF of 228Ra ranged from 0.061 to 0.806 with \nan average of 0.42. Corchorus capsulrais showed the highest TFs for 226Ra and \n228Ra at 0.455 and 0.806, M1, respectively, while the lowest values were found in \nBenincasa hispida (0.087, M5) and Cucurbita maxima (0.061, M3) for 226Ra and \n228Ra, respectively. The TFs of 40K ranged from 0.738 to 1.949 with an average of \n1.582. Corchorus capsulrais (0.738, M1) showed the lowest TF whereas Benincasa \nhispida  had the highest TF (1.949, M5) (Table 3).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201470\n\n\n\nFigure 4: Transfer Factor (TF) of the radionuclides in different plants of Savar and \nManikgonj, Dhaka\n\n\n\nTF value of 40K in different plants of Savar was about 7 times higher than \nthose of other radionuclides while in Manikganj it was 2 to 10 times higher \n(Figure 4). The high TF of 40K was probably due to its high mobility in soil and \nits subsequent uptake by plants. High TF values ranging from 5 to 8 for 40K have \nalso been reported by Ababneh et al. (2009).\n\n\n\nRelationship between Activity, Transfer Factors (TF) and Soil Parameters\nIt was possible to get an overview of the influence of the soil parameters on the \nactivity concentrations in soil and plant and the TFs by correlating the activity \nconcentrations and TF with soil parameters. \n\n\n\nAmong the soil properties studied, sand content, K content and Ca content \nin the soils of Savar were found to have significant positive correlation with the \nactivity concentration of 40K in soil. This indicates that the availability of 40K in \nsoil increased with an increase in sand, K and Ca content. A significant negative \ncorrelation was observed between activity concentration of 228Ra in the soil of \nSavar and soil CEC which might be due to the fact that higher soil CEC reduces \nthe availability of 228Ra (Table 4). \n\n\n\nNo significant correlation was found between soil properties and activity \nconcentrations of 226Ra, 228Ra and 40K in soils of Manikganj. However, the activity \nconcentration of 40K in plants showed a significant but negative correlation with \nsoil Ca and Mg content and TF of 40K with that of soil Mg content. This means \n40K uptake decreased with increasing soil Ca and Mg content. It can be said from \nthe investigations that the availability and uptake of certain natural radionuclides \nparticularly 40K is inextricably related to the presence of Ca and Mg concentrations \nin soil (Table 5). \n\n\n\n\n\n\n\n 15 \n\n\n\n0\n0.5\n\n\n\n1\n1.5\n\n\n\n2\n2.5\n\n\n\n3\n3.5\n\n\n\n4\nR\n\n\n\ned\nA\n\n\n\nm\nar\n\n\n\nan\nth\n\n\n\nu\ns\n\n\n\nE\nle\np\nh\nan\n\n\n\nt\u2019\ns\n\n\n\nE\nar\n\n\n\nB\nit\n\n\n\nte\nr \n\n\n\nG\no\n\n\n\nu\nrd\n\n\n\nB\nin\n\n\n\nd\n W\n\n\n\nee\nd\n\n\n\nS\nn\n\n\n\nak\ne\n\n\n\nG\no\n\n\n\nu\nrd\n\n\n\nJu\nte\n\n\n\n L\nea\n\n\n\nf \n\n\n\nG\nre\n\n\n\nen\nA\n\n\n\nm\nar\n\n\n\nan\nth\n\n\n\nu\ns \n\n\n\nP\nu\n\n\n\nm\np\n\n\n\nki\nn\n\n\n\n\n\n\n\nP\nu\n\n\n\nm\np\n\n\n\nki\nn\n\n\n\nL\nea\n\n\n\nf \n\n\n\nW\nax\n\n\n\n G\no\n\n\n\nu\nrd\n\n\n\n\n\n\n\nS1 S2 S3 S4 S5 M1 M2 M3 M4 M5\n\n\n\nTr\nan\n\n\n\nsf\ner\n\n\n\n F\nac\n\n\n\nto\nr\n\n\n\nRa-226 Ra-228 K-40\n\n\n\n\n\n\n\nFigure 4: Transfer Factor (TF) of the radionuclides in different plants of Savar and Manikgonj, \nDhaka \n\n\n\nGaffar, S., M. J. Ferdous, A. Begum and S.M. Ullah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 71\n\n\n\nTransfer of Natural Radionuclides from Soil to Plant\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nC\nor\n\n\n\nre\nla\n\n\n\ntio\nn \n\n\n\nof\n a\n\n\n\nct\niv\n\n\n\nity\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nof\n n\n\n\n\nat\nur\n\n\n\nal\n ra\n\n\n\ndi\non\n\n\n\nuc\nlid\n\n\n\nes\n in\n\n\n\n so\nils\n\n\n\n, p\nla\n\n\n\nnt\ns a\n\n\n\nnd\n tr\n\n\n\nan\nsf\n\n\n\ner\n fa\n\n\n\nct\nor\n\n\n\ns (\nTF\n\n\n\n) w\nith\n\n\n\n so\nil \n\n\n\npr\nop\n\n\n\ner\ntie\n\n\n\ns i\nn \n\n\n\nSa\nva\n\n\n\nr\n\n\n\n  \n12\n\n\n\n\n\n\n\n        \nTA\n\n\n\nB\nLE\n\n\n\n 4\n \n\n\n\n \nC\n\n\n\nor\nre\n\n\n\nla\ntio\n\n\n\nn \nof\n\n\n\n a\nct\n\n\n\niv\nity\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nof\n\n\n\n n\nat\n\n\n\nur\nal\n\n\n\n ra\ndi\n\n\n\non\nuc\n\n\n\nlid\nes\n\n\n\n in\n so\n\n\n\nils\n, p\n\n\n\nla\nnt\n\n\n\ns a\nnd\n\n\n\n tr\nan\n\n\n\nsf\ner\n\n\n\n fa\nct\n\n\n\nor\ns (\n\n\n\nTF\n) w\n\n\n\nith\n so\n\n\n\nil \npr\n\n\n\nop\ner\n\n\n\ntie\ns i\n\n\n\nn \nSa\n\n\n\nva\nr \n\n\n\n \n So\n\n\n\nil \nPr\n\n\n\nop\ner\n\n\n\ntie\ns \n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n o\nf 22\n\n\n\n6 R\na \n\n\n\n(B\nq/\n\n\n\nkg\n) \n\n\n\nTF\n o\n\n\n\nf \n22\n\n\n\n6 R\na \n\n\n\nin\n \n\n\n\npl\nan\n\n\n\nt \n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n o\nf 22\n\n\n\n8 R\na \n\n\n\n(B\nq/\n\n\n\nkg\n) \n\n\n\nTF\n o\n\n\n\nf \n22\n\n\n\n8 R\na \n\n\n\nin\n \n\n\n\npl\nan\n\n\n\nt \n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n o\nf 40\n\n\n\nK\n \n\n\n\n(B\nq/\n\n\n\nkg\n) \n\n\n\nTF\n o\n\n\n\nf \n40\n\n\n\nK\n in\n\n\n\n \npl\n\n\n\nan\nt \n\n\n\nSo\nil \n\n\n\n \nPl\n\n\n\nan\nt \n\n\n\nSo\nil \n\n\n\n \nPl\n\n\n\nan\nt  \n\n\n\nSo\nil \n\n\n\n \nPl\n\n\n\nan\nt  \n\n\n\nSa\nnd\n\n\n\n (%\n) \n\n\n\n0.\n15\n\n\n\n7 \n-0\n\n\n\n.3\n83\n\n\n\n \n-0\n\n\n\n.1\n89\n\n\n\n \n-0\n\n\n\n.5\n27\n\n\n\n \n0.\n\n\n\n35\n7 \n\n\n\n0.\n52\n\n\n\n8 \n0.\n\n\n\n92\n3*\n\n\n\n \n0.\n\n\n\n39\n4 \n\n\n\n0.\n19\n\n\n\n5 \nSi\n\n\n\nlt \n(%\n\n\n\n) \n-0\n\n\n\n.0\n59\n\n\n\n \n0.\n\n\n\n49\n3 \n\n\n\n0.\n27\n\n\n\n1 \n0.\n\n\n\n36\n5 \n\n\n\n-0\n.0\n\n\n\n61\n \n\n\n\n-0\n.2\n\n\n\n06\n \n\n\n\n-0\n.8\n\n\n\n4 \n-0\n\n\n\n.3\n61\n\n\n\n \n-0\n\n\n\n.1\n78\n\n\n\n \nC\n\n\n\nla\ny \n\n\n\n(%\n) \n\n\n\n-0\n.3\n\n\n\n00\n \n\n\n\n0.\n02\n\n\n\n34\n \n\n\n\n-0\n.0\n\n\n\n35\n \n\n\n\n0.\n57\n\n\n\n0 \n-0\n\n\n\n.7\n09\n\n\n\n \n-0\n\n\n\n.8\n42\n\n\n\n \n-0\n\n\n\n.6\n55\n\n\n\n \n-0\n\n\n\n.2\n73\n\n\n\n \n-0\n\n\n\n.1\n36\n\n\n\n \npH\n\n\n\n \n-0\n\n\n\n.6\n31\n\n\n\n \n0.\n\n\n\n24\n1 \n\n\n\n-0\n.0\n\n\n\n43\n \n\n\n\n0.\n20\n\n\n\n0 \n0.\n\n\n\n24\n3 \n\n\n\n0.\n17\n\n\n\n9 \n0.\n\n\n\n39\n2 \n\n\n\n0.\n19\n\n\n\n5 \n0.\n\n\n\n13\n0 \n\n\n\nC\nEC\n\n\n\n (m\neq\n\n\n\n/1\n00\n\n\n\ng)\n \n\n\n\n-0\n.3\n\n\n\n37\n \n\n\n\n0.\n21\n\n\n\n2 \n0.\n\n\n\n69\n1 \n\n\n\n-0\n.8\n\n\n\n82\n* \n\n\n\n-0\n.4\n\n\n\n58\n \n\n\n\n-0\n.2\n\n\n\n09\n \n\n\n\n0.\n33\n\n\n\n5 \n0.\n\n\n\n83\n9 \n\n\n\n0.\n85\n\n\n\n1 \nO\n\n\n\nrg\nan\n\n\n\nic\n M\n\n\n\nat\nte\n\n\n\nr (\n%\n\n\n\n) \n0.\n\n\n\n61\n8 \n\n\n\n-0\n.7\n\n\n\n90\n \n\n\n\n-0\n.3\n\n\n\n53\n \n\n\n\n-0\n.1\n\n\n\n24\n \n\n\n\n-0\n.5\n\n\n\n69\n \n\n\n\n-0\n.4\n\n\n\n68\n \n\n\n\n-0\n.0\n\n\n\n06\n \n\n\n\n-0\n.1\n\n\n\n25\n \n\n\n\n-0\n.1\n\n\n\n62\n \n\n\n\nK\n (m\n\n\n\neq\n/1\n\n\n\n00\ng)\n\n\n\n \n-0\n\n\n\n.2\n11\n\n\n\n \n0.\n\n\n\n02\n2 \n\n\n\n0.\n22\n\n\n\n7 \n-0\n\n\n\n.7\n69\n\n\n\n \n0.\n\n\n\n33\n1 \n\n\n\n0.\n54\n\n\n\n6 \n0.\n\n\n\n96\n3*\n\n\n\n* \n0.\n\n\n\n73\n3 \n\n\n\n0.\n57\n\n\n\n3 \nC\n\n\n\na \n(m\n\n\n\neq\n/1\n\n\n\n00\ng)\n\n\n\n \n-0\n\n\n\n.8\n45\n\n\n\n \n0.\n\n\n\n74\n85\n\n\n\n \n0.\n\n\n\n60\n37\n\n\n\n \n-0\n\n\n\n.4\n47\n\n\n\n \n0.\n\n\n\n33\n1 \n\n\n\n0.\n47\n\n\n\n6 \n0.\n\n\n\n54\n9 \n\n\n\n0.\n71\n\n\n\n9 \n0.\n\n\n\n68\n4 \n\n\n\n M\ng \n\n\n\n(m\neq\n\n\n\n/1\n00\n\n\n\ng)\n \n\n\n\n-0\n.4\n\n\n\n29\n \n\n\n\n0.\n20\n\n\n\n5 \n0.\n\n\n\n29\n3 \n\n\n\n-0\n.6\n\n\n\n79\n \n\n\n\n0.\n40\n\n\n\n0 \n0.\n\n\n\n57\n8 \n\n\n\n0.\n95\n\n\n\n2*\n \n\n\n\n0.\n76\n\n\n\n1 \n0.\n\n\n\n61\n3 \n\n\n\n*C\nor\n\n\n\nre\nla\n\n\n\ntio\nn \n\n\n\nis\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n a\nt 0\n\n\n\n.0\n5 \n\n\n\nle\nve\n\n\n\nl \n**\n\n\n\nC\nor\n\n\n\nre\nla\n\n\n\ntio\nn \n\n\n\nsi\ngn\n\n\n\nifi\nca\n\n\n\nnt\n a\n\n\n\nt 0\n.0\n\n\n\n1 \nle\n\n\n\nve\nl \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201472\n\n\n\nTA\nB\n\n\n\nLE\n 5\n\n\n\nC\nor\n\n\n\nre\nla\n\n\n\ntio\nn \n\n\n\nof\n a\n\n\n\nct\niv\n\n\n\nity\n c\n\n\n\non\nce\n\n\n\nnt\nra\n\n\n\ntio\nn \n\n\n\nof\n n\n\n\n\nat\nur\n\n\n\nal\n ra\n\n\n\ndi\non\n\n\n\nuc\nlid\n\n\n\nes\n in\n\n\n\n so\nils\n\n\n\n, p\nla\n\n\n\nnt\ns a\n\n\n\nnd\n tr\n\n\n\nan\nsf\n\n\n\ner\n fa\n\n\n\nct\nor\n\n\n\ns (\nTF\n\n\n\n) w\nith\n\n\n\n so\nil \n\n\n\npr\nop\n\n\n\ner\ntie\n\n\n\ns i\nn \n\n\n\nM\nan\n\n\n\nik\nga\n\n\n\nnj\n\n\n\n  \n13\n\n\n\n\n\n\n\n             \nTA\n\n\n\nB\nLE\n\n\n\n 5\n \n\n\n\n \nC\n\n\n\nor\nre\n\n\n\nla\ntio\n\n\n\nn \nof\n\n\n\n a\nct\n\n\n\niv\nity\n\n\n\n c\non\n\n\n\nce\nnt\n\n\n\nra\ntio\n\n\n\nn \nof\n\n\n\n n\nat\n\n\n\nur\nal\n\n\n\n ra\ndi\n\n\n\non\nuc\n\n\n\nlid\nes\n\n\n\n in\n so\n\n\n\nils\n, p\n\n\n\nla\nnt\n\n\n\ns a\nnd\n\n\n\n tr\nan\n\n\n\nsf\ner\n\n\n\n fa\nct\n\n\n\nor\ns (\n\n\n\nTF\n) w\n\n\n\nith\n so\n\n\n\nil \npr\n\n\n\nop\ner\n\n\n\ntie\ns i\n\n\n\nn \nM\n\n\n\nan\nik\n\n\n\nga\nnj\n\n\n\n\n\n\n\n So\nil \n\n\n\nPr\nop\n\n\n\ner\ntie\n\n\n\ns \nA\n\n\n\nct\niv\n\n\n\nity\n o\n\n\n\nf 22\n6 R\n\n\n\na \n(B\n\n\n\nq/\nkg\n\n\n\n) \nTF\n\n\n\n o\nf \n\n\n\n22\n6 R\n\n\n\na \nin\n\n\n\n \npl\n\n\n\nan\nt \n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n o\nf 22\n\n\n\n8 R\na \n\n\n\n(B\nq/\n\n\n\nkg\n) \n\n\n\nTF\n o\n\n\n\nf \n22\n\n\n\n8 R\na \n\n\n\nin\n \n\n\n\npl\nan\n\n\n\nt \n\n\n\nA\nct\n\n\n\niv\nity\n\n\n\n o\nf 40\n\n\n\nK\n \n\n\n\n(B\nq/\n\n\n\nkg\n) \n\n\n\nTF\n o\n\n\n\nf \n40\n\n\n\nK\n in\n\n\n\n \npl\n\n\n\nan\nt \n\n\n\nSo\nil \n\n\n\n \nPl\n\n\n\nan\nt \n\n\n\nSo\nil \n\n\n\n \nPl\n\n\n\nan\nt  \n\n\n\nSo\nil \n\n\n\n \nPl\n\n\n\nan\nt  \n\n\n\nSa\nnd\n\n\n\n %\n) \n\n\n\n0.\n26\n\n\n\n8 \n0.\n\n\n\n62\n9 \n\n\n\n0.\n08\n\n\n\n0 \n0.\n\n\n\n21\n2 \n\n\n\n0.\n78\n\n\n\n8 \n0.\n\n\n\n80\n0 \n\n\n\n0.\n06\n\n\n\n8 \n-0\n\n\n\n.8\n04\n\n\n\n \n-0\n\n\n\n.6\n70\n\n\n\n \nSi\n\n\n\nlt \n(%\n\n\n\n) \n-0\n\n\n\n.5\n15\n\n\n\n \n-0\n\n\n\n.3\n98\n\n\n\n \n-0\n\n\n\n.2\n96\n\n\n\n \n0.\n\n\n\n08\n3 \n\n\n\n0.\n30\n\n\n\n1 \n0.\n\n\n\n31\n2 \n\n\n\n-0\n.4\n\n\n\n96\n \n\n\n\n0.\n46\n\n\n\n7 \n0.\n\n\n\n54\n2 \n\n\n\nC\nla\n\n\n\ny \n(%\n\n\n\n) \n-0\n\n\n\n.0\n05\n\n\n\n \n-0\n\n\n\n.3\n57\n\n\n\n \n-0\n\n\n\n.3\n35\n\n\n\n \n-0\n\n\n\n.2\n12\n\n\n\n \n-0\n\n\n\n.7\n93\n\n\n\n \n-0\n\n\n\n.8\n08\n\n\n\n \n0.\n\n\n\n15\n8 \n\n\n\n0.\n47\n\n\n\n6 \n0.\n\n\n\n33\n0 \n\n\n\npH\n \n\n\n\n \n-0\n\n\n\n.6\n52\n\n\n\n \n-0\n\n\n\n.6\n20\n\n\n\n \n-0\n\n\n\n.4\n67\n\n\n\n \n0.\n\n\n\n01\n0 \n\n\n\n-0\n.0\n\n\n\n75\n \n\n\n\n0.\n07\n\n\n\n5 \n-0\n\n\n\n.4\n27\n\n\n\n \n0.\n\n\n\n85\n3 \n\n\n\n0.\n82\n\n\n\n7 \nC\n\n\n\nEC\n(m\n\n\n\neq\n/1\n\n\n\n00\ng)\n\n\n\n \n0.\n\n\n\n32\n6 \n\n\n\n0.\n37\n\n\n\n6 \n0.\n\n\n\n26\n1 \n\n\n\n0.\n08\n\n\n\n2 \n0.\n\n\n\n55\n3 \n\n\n\n0.\n57\n\n\n\n3 \n-0\n\n\n\n.0\n93\n\n\n\n \n-0\n\n\n\n.7\n15\n\n\n\n \n-0\n\n\n\n.5\n38\n\n\n\n\n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n M\nat\n\n\n\nte\nr (\n\n\n\n%\n) \n\n\n\n0.\n05\n\n\n\n0 \n0.\n\n\n\n69\n9 \n\n\n\n0.\n61\n\n\n\n5 \n0.\n\n\n\n06\n0 \n\n\n\n0.\n05\n\n\n\n7 \n0.\n\n\n\n04\n5 \n\n\n\n0.\n60\n\n\n\n3 \n-0\n\n\n\n.6\n83\n\n\n\n \n-0\n\n\n\n.7\n56\n\n\n\n\n\n\n\nK\n (m\n\n\n\neq\n/1\n\n\n\n00\ng)\n\n\n\n \n0.\n\n\n\n22\n9 \n\n\n\n-0\n.3\n\n\n\n18\n \n\n\n\n-0\n.3\n\n\n\n70\n \n\n\n\n-0\n.2\n\n\n\n84\n \n\n\n\n-0\n.8\n\n\n\n56\n \n\n\n\n-0\n.8\n\n\n\n65\n \n\n\n\n0.\n15\n\n\n\n3 \n0.\n\n\n\n24\n0 \n\n\n\n0.\n14\n\n\n\n7 \nC\n\n\n\na \n(m\n\n\n\neq\n/1\n\n\n\n00\ng)\n\n\n\n \n0.\n\n\n\n56\n4 \n\n\n\n0.\n62\n\n\n\n3 \n0.\n\n\n\n47\n0 \n\n\n\n0.\n06\n\n\n\n5 \n0.\n\n\n\n42\n7 \n\n\n\n0.\n43\n\n\n\n8 \n0.\n\n\n\n20\n5 \n\n\n\n-0\n.9\n\n\n\n46\n* \n\n\n\n-0\n.8\n\n\n\n24\n \n\n\n\nM\ng \n\n\n\n(m\neq\n\n\n\n/1\n00\n\n\n\ng)\n \n\n\n\n0.\n53\n\n\n\n7 \n0.\n\n\n\n86\n9 \n\n\n\n0.\n47\n\n\n\n0 \n0.\n\n\n\n14\n4 \n\n\n\n0.\n38\n\n\n\n8 \n0.\n\n\n\n38\n2 \n\n\n\n0.\n54\n\n\n\n9 \n-0\n\n\n\n.9\n21\n\n\n\n* \n-0\n\n\n\n.9\n29\n\n\n\n* \n*C\n\n\n\nor\nre\n\n\n\nla\ntio\n\n\n\nn \nis\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\n a\n\n\n\nt 0\n.0\n\n\n\n5 \nle\n\n\n\nve\nl \n\n\n\n\n\n\n\nGaffar, S., M. J. Ferdous, A. Begum and S.M. Ullah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 73\n\n\n\nTransfer of Natural Radionuclides from Soil to Plant\n\n\n\nCONCLUSION\nSince the level of activity concentrations of natural radionuclides in the soils under \ninvestigation were within the range of the world average, it might not pose any \nradiation hazard to the population. But continuous intake of radionuclides through \nthe food-chain may have some serious health effects on individuals in the long \nterm. It is important to understand the behaviour of radionuclides with respect \nto mitigation and changes in speciation within the soil, availability for plant \nuptake with time, different agricultural practices and the significance of recycling \nthrough animal manure.  As a higher concentration of radioactive substances in \nthe environment is undesirable, investigations should be undertaken to detect the \nconcentration of radionuclides in soil and their transfer to plants in order to take \nnecessary radiological and dosimetric measures with the aim of minimizing the \nharmful effects of ionizing radiation. It is hoped that the data presented here will \nhelp establish a baseline for radioactivity concentrations and TFs of various plants \nin the Sarvar and Manikganj areas of  Dhaka, Bangladesh. \n\n\n\nREFERENCES\nAbabneh, A. M., S. Maisoun, M. S. Masa\u2019deh, Z. Q. Abaneh,  M. A. Mufeed and A. \n\n\n\nM. Alyassin. 2009. Radioactivity concentration in soil and vegetable from the \nNorthern Jordan Rift Valley and the corresponding dose estimates. Radiation \nProtection Dosimetry. 134(1): 30-37.\n\n\n\nBrady, N.C. and R.P. Weil. 2002. The Nature and Properties of Soils (13edn.). Upper \nSaddle River, New Jersey, USA: Prentice Hall.\n\n\n\nBunzl, K. and M. Trautmannsheimer. 1999. Transfer of 238U, 226Ra, and 210Po from \nslag-contaminated soils to vegetables under field conditions. Science of the \nTotal Environment. 231: 91\u201399.\n\n\n\nChibowski, S. 2000. Studies of Radioactive Contaminations and Heavy Metal Contents \nin Vegetables and Fruit from Lublin, Poland. Polish Journal of Environmental \nStudies. 9(4): 249\u2013253.\n\n\n\nInternational Atomic Energy Agency (IAEA). 1982. Generic Models and Parameters \nfor Assessing the Environmental  Transfer of  Radionuclides from Routine \nReleases. Exposure of Critical Groups. Safety Series, No. 57. IAEA . \n\n\n\nInternational Atomic Energy Agency (IAEA). 1990. The Environmental Behavior of \nRadium. Technical Report Series No. 310. IAEA, Vienna, Austria.\n\n\n\nKabir, K. A., S. M. A.  Islam and  M. M. Rahman. 2009. Distribution of radionuclides \non surface soil and bottom sediment in the district of Jessore, Bangladesh and \nevaluation of radiation hazard. Journal of Bangladesh Academy of Sciences. \n33(1): 117-130.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201474\n\n\n\nNoordijk, H., K. E. Van, J. Lembrechts and M. J. Frissel. 1992. Impact of ageing and \nweather conditions on soil-to-plant transfer of radiocesium and radiostrontium. \nThe Journal of Environmental Radioactivity. 15: 277\u2013286.\n\n\n\nOwono, A. P. 2010. Multi-group approximation, scattering and calibration \ncoefficients, uncertainty estimates and detection limits of a NaI(Tl)-based \ngamma spectrometry setup for low-level activity analysis. The Journal of \nEnvironmental Radioactivity. 101: 692\u2013699.\n\n\n\nStaven, L. H., K. Rhoads, B. A. Napier and D. L. Strenge.  2003. A Compendium of \nTransfer Factors for Agricultural and Animal Products. Richland, USA. Pacific \nNorthwest National Laboratory.\n\n\n\nSheppard, S. C. and W. G. Evenden. 1988. The assumption of linearity in soil and plant \nconcentration ratio: an experimental evaluation. The Journal of Environmental \nRadioactivity. 7: 221\u2013247.\n\n\n\nUnited Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). \n2000. Sources and Effects of Ionizing Radiation. New York, USA . UNSCEAR.\n\n\n\nYassine, T., M. Al-Odat and I. Othman. 2003. Transfer of 137Cs and 90Sr from typical \nSyrian soils to crops. Journal of Food Engineering. XVI: 73\u201379. \n\n\n\nGaffar, S., M. J. Ferdous, A. Begum and S.M. Ullah\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: salwan85@agre.uoqasim.edu.iq \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 107-122 (2021)                     Malaysian Society of Soil Science\n\n\n\nEffects of Arbuscular Mycorrhiza and Organic Wastes on \nSoil Carbon Mineralisation, Actinomycete sand Nutrient \n\n\n\nContent in Maize Plants (Zea Mays L.) \n\n\n\nAl-Maliki, S.*, Al-Amery, A., Sallal, M., Radhi, A and \nAl-Taey, D.K.A.\n\n\n\nSoil and Water Science Department, College of Agriculture\nAl-Qasim Green University, Al Qasim 13239, Iraq \n\n\n\nABSTRACT\nArbuscular mycorrhizal fungi are a fundamental contributor to soil carbon \nmineralisation and nutrient cycling in saline soils. This study aimed to evaluate \nthe interactions between arbuscular mycorrhizal fungi (Glomus mosseae) (G), \ntea residue (T), macroalgae biomass (M) and its subsequent effects on carbon \nmineralisation, actinomycetes counts,nutrients content, chlorophyll content, \nand corn growth (Zea mays L.).T wenty four pots with eight treatments,control \n(C), macroalgae (M), tea residue (T), Glomus mosseae (G), Glomus mosseae + \nmacroalgae (G+M), Glomus mosseae + tea residue (G+T), macroalgae+ tea residue \n(M+T) and Glomus mosseae + tea residue+ macroalgae (G+T+M) were randomly \ndistributed in the field using randomised complete design (RCD). Results showed \nthat only treatment T had the highest value of carbon mineralisation (0.216 mg C \ng-1 soil day), while it was lower (0.177 mg C g-1 soil day) in the G+T treatments. \nAdditionally, the highest values of actinomycetes, chlorophyll, phosphorous \ncontent and roots weight were 4.2 10-6 CFU g-1, 35 SPAD, 0.4 % and 55 g, \nrespectively in G+T treatments. In contrast, the addition of T, M and G alone did \nnot increase phosphorous content as compared to the control. In conclusion,the \ncombination of tea residue and macroalgae biomass with Glomous mossea affected \ncarbon decomposition and increased the number of actinomycetesas as well as \nnutrients content. This can be beneficial to ecosystems through facilitating carbon \nconservation and microbial diversity in arid saline soils.. \n\n\n\nKeyword:    Mycorrhizae, carbon mineralisation, actinomycetes, roots, \n      chlorophyll content, nutrients content.\n\n\n\nINTRODUCTION\nArid and semi-arid zones cover nearly 40% of the land surface (IPCC 2008).  \nThe shortage of rainfall under arid and semi-arid regions could lead to the \naccumulation of salts in soil (Rengasamy 2006) (Rengasamy 2006) resulting in \nhigh quantities of sodium and soil alkalinity problems (Dierickx 2009) (Dierickx \n2009). Arid-region soils have concurrently suffered from the loss of soil fertility \nand soil degradation (Su et al. 2004). The loss of organic carbon and vegetation \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021108\n\n\n\nin arid soils can lead to a deteriorated soil structure (Al-Maliki et al. 2014;Turgut \nand Kose 2016; Al-Maliki 2016; Al-Maliki et al. 2018)\n Arbuscular mycorrhizal (AM) fungi play an important role in litter \ndecomposition and soil aggregate formation (Al-Maliki and Scullion 2013; Al-\nMaliki and Bresam, (2020); Rillig 2004; Al-Zabee and Al-Maliki 2019). AM fungi \nhave been reported to produce excretions which have a vital function for microbial \nactivity and organic matter degradation. These fungi have been found to increase \norganic carbon and glomalin in soil (Wilson et al. 2009). More importantly, \nMukerji et al. (2012) noted that AM fungi participated in the decomposition \nprocess by releasing enzymes such as pectinases, cellulases, and hemicelluloses. \nHowever, its role in the decomposition process is still in debate. Leifheit et al. \n(2015) claimed that AM fungi minimised the process of decomposition in woody \nsubstances. Likewise, Zhang et al. (2016) stated that the release of CO2 was \ndepressed after inoculation of AM fungi, leading to less depletion of carbon in the \nsemi-arid soils. AM fungi are an important part of soil biota that largely engage in \ncrop productivity and are capable of creating symbiotic relationships with some \n80% of the various plant roots. Such a phenomena not only enhances the growth \nof plants through uptake of nutrients but improve schlorophyll content in the \nplant. AM fungi symbiosis is believed to enhance photosynthesis rate (Auge et al. \n2016). It hasbeen reported that the amount of chlorophyll in the inoculated plants \nof mycorrhizal fungi was far more pronounced than in non mycorrhizal plants \n(Arya and Buch 2013).  \n Mycorrhizal fungi are not the only beneficial microbes in soil; \nactinomycetes are predominant microbes in soil that areengaged in many \ndecomposition processes. They are resistant against undesirable soil conditions \nand have an ability to stimulate plant growth (Hamdali et al. 2008) and also may \nalso participate in P solubilisation (Ghorbani-Nasrabadi et al. 2012).\n Tea residue is one of the most important wastes in the global world and \ncan play a critical role in agriculture. The Ministry of Commerce reports that the \namount of imported tea is up about 150,000 tons annually. This tea was teneeds \nto be exploited to improve physical, chemical and biological properties of soil as \nthey contain nutrients, carbon, vitamins and amino acids (Feng et al. 2018) which \ncan enhance plant growth. Tea waste has been shown to increase soil aggregate \nstability of degraded lands (Turgut and Kose 2016). In addition, tea waste can \nimprove saline soils by modifying soil electric conductivity (Abdul ghani 2012).\nLikewise, compost tea has been cited as an optimal option to enhance crop fertility, \nmicrobial activity and soil nutrient retention (Merrill and McKeon 2001).\n Algal biomass is another waste which also maybe used as an important agent \nin increasing soil organic matter and nutrients leading to improved CO2  release \nby stimulating microbes in soil (Gougoulias et al. 2018; Al-Maliki et al., 2019). \nThe experimental hypothesis is that the applied mycorrhizal fungi, tea residue \nand macroalgae biomass have a tendency to improve carbon mineralisation, \nactinomycetes counts, roots density and growth of the plant. Therefore, the current \nstudy has focussed on the interaction between mycorrhizalfungi, tea residues and \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 109\n\n\n\nmacroalgal dried biomass and their effect oncarbon mineralisation, actinomycetes, \nroots weight, and chlorophyll, nutrients content and maize growth.  \n\n\n\nMATERIALS AND METHODS\nExperimental Site   \nThis experiment was conducted in the city of Babylon (32\u00b030\u201902.8\u201dN, \n44\u00b019\u201949.7\u201dE). The study area of the study is considered anarid zone (K\u00f6ppen \n1928) (K\u00f6ppen and Geiger, 1928) as it has suffered from the impact of salinity for \nyears. The high temperature, low rainfall and low humidity are the most current \nclimatic characteristics of the Babylon area. A field area of 2500 m2 with the wheat \nplant (Triticumae stivum. L) being the most dominant cultivated plant was used in \nthis study. The electrical conductivity for the field was 7.8 dSm-1. Soil samples were \ntaken from 0.30 m depth, air-dried and then sieved using a 4-mm diameter sieve. \nPots of 50kg were placed at the greenhouse. Eight treatments were selected for this \nstudy:control (C);macroalgae (M); tea residue (T);Glomus mosseae (G);Glomus \nmosseae + macroalgae (G+M);Glomus mosseae + tea residue (G+T); macroalgae \n+ tea residue (M+T); and Glomus mosseae + tea residue + macroalgae (G+T+M). \nThese treatments were replicated three times to obtain 24 pots.  Using randomised \ncomplete design (RCD), pots were randomly distributed in the greenhouse which \nwas covered with a plastic nylon. Five seeds of the maize plant (Zea mays L.) \nwere sown to a depth of 3 cm. Soil moisture content in pots was maintained close \nto field capacity (25%) (Al-Maliki et al. 2017). Soil samples were collected at the \nend of the experiment when the plant had a maximum growth (90 days) from the \nrhizospheric habitat which was located about 2 mm distance from the roots. The \nanalysis of plant tissue was measured based on Harborne (1998). The percentages \nof nitrogen, phosphorus and potassium in tea waste were 1.5 %, 0.02 % and 1.1 \n% respectively while in algae, they were 1.6%, 0.33 % and 1.45% respectively. \nMoreover, the total carbohydrates and polyphenols were 1.3% and 20% in tea \nwaste respectively, whereas it was 0.7 % and 0.8% in algae respectively. Soil \nproperties were measured as described by Black (1962) (Black 1965). Soil texture \nwas sandy clay (clay 46, silt 7, sand 47) g/kg-1. The spores of mycorrhiza fungi \n(Glomus mosseae) were examined using a wet sieving and decanting method \n(Gerdemann and Nicolson 1963). Mycorrhizalspore density was 42 per one gram \nof soil. The top soil was mixed with 200 g of mycorrhizal fungal inoculums.Tea \nresidue was air-dried and crunched to 1mm in diameter and 350 g of tea residue \nwere mixed at a depth of 30 cm for each pot. Macroalgal biomass was added at a \nrate of 250 g per pot.\n\n\n\nAnalysis\nCarbon mineralisation analyses were performed based on Carter and Gregorich \n(2007) using three replicates for each sample. The measurements were carried \nout at 2, 4, 8, 12 and 30 days for the tea waste, algae, mycorrhizal fungi and their \ncombinations. Hundred grams of fresh soil of 25% water content was put in a \nflask. The released CO2 was trapped in 5 ml of NaOH (1 M) and then titrated with \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021110\n\n\n\n1 M HCl, following the addition of 2 ml of BaCl2 solution (30%) to precipitate \nthe BaCO3 (AL-Maliki 2012; Abvien et al. 2007).The following equations were \ncreated to predicate the amounts of CO2.\n\n\n\nmeq CO2 = meqNaOH - meqHCl(1)  \nCO2 mg = meq CO2 x equivalent weight CO2(2) \nWhere the equivalent weight of CO2 is 22  \nmeq= volume (ml)\u00d7 molarity     (3)\n\n\n\nSubsequently, the carbon mineralisation rate wasestimated by dividing the \nquantity of CO2 released in samples to soil mass (g) and the incubation period \n(days) according to the equation below:\nC mineralisation rate = CO2 released in samples / (soil mass in g \u00d7 \n             incubation time (days)   (4)\n\n\n\n Actinomycetes were enumerated using the serial dilution-spread plate \ntechnique. Ten grams of fresh soil were thrown into a flask containing 90 mL \nof distilled water, which was then vibrated for 30 min. One mL of the created \nculture was put into 10 mL tubes with 9 mL of water resulting in 10-1 dilution. \nOne mL of 10-1 dilution was taken for dilution of 10-2. The dilution process was \nmaintained until dilution 10-8 was achieved. For the actinomycetes, 1mL of the \n10-6 diluted solution was poured onto glycerol-arginin-agar medium (Porter et al. \n1960) and then incubated for 5 days at 30\u00b0C. The colony-forming units (CFU) \nwere estimated based on Equation 5and the velvety colonies were regarded as \nactinomycetes.\n\n\n\n\u03a3colony mL-1 = \u03a3colonies x dilution factor  (5)\n\n\n\n The Kjeldahl method was used to analyse total N and P utilising an \nautomated colorimetry with a Technicon auto-analyzer Technicon Instruments \nCorp (Parkinson and Allen 1975). The chlorophyll percentage in corn leaf was \nmeasured using a chlorophyll meter (SPAD-504) (Dwyer et al. 1991). Using a \nportable chlorophyll meter, 10 readings of the leaf were obtained for each pot.\n\n\n\nStatistical Analysis\nTwo-way analysis of variance (ANOVA) was used to analyse carbon mineralisation \nthat included two factors (treatments and time), eight treatments (control (C), \nmacroalgae (M), tea residue (T), Glomus mosseae (G), Glomus mosseae + \nmacroalgae (G+M), Glomus mosseae + tea residue (G+T), macroalgae + tea \nresidue (M+T) and Glomus mosseae + tea residue + macroalgae (G+T+M) and \nfive incubation periods (0, 4, 8, 12, 30 days). Results for the actinomycetes counts, \nchlorophyll percentage, roots density, plant weight and nutrients were analysed \nby one-way ANOVA. Means differences were noted using Tukey\u2019shonestly \nsignificance difference (HSD) test with a significance level of P < 0.05. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 111\n\n\n\nRESULTS AND DISCUSSION\nEffect of Treatments on Carbon Mineralisation Rate\nThe carbon mineralisation rate for the eight treatments was evidently different \n(Figure1). Tea residue and macroalgal biomass recorded the highest increases in \ncarbon mineralisation but decomposed rapidly during the first 4 days (0.546 mg \nC g-1 soil day) and 0.592 (mg C g-1 soil day) but more slowly at day 30 (0.024 \nmg C g-1 soil day and 0.022 mg C g-1 soil day) for the tea residue and macroalgal \nbiomass respectively. It appears that the lower C/N of tea residue and macroalgal \nbiomass degraded quickly causing an increase in carbon mineralisation. In \naddition, tea residue decomposed faster than macroalgal dried biomass and this \ncould be attributed to the enormous quantity of carbohydrates and nutrients in \ntea residue which might increase soil microbial activity leading to rapid carbon \nmineralisation. It is hypothesised that tea residue, macroalgal biomass and \nGlomous mossea were able to enhance carbon mineralisation as they not only \nprovided a substrate  but also exudates to the microbial community. A study of \nGougoulias et al. (2018) has shown that the mineralisation of soil organic carbon \nincreases after application of macroalgal biomass in the soil. Our findings suggest \nthat the algae and tea residue has ten soil organic matter decomposition resulting \nin improved nutrient availability in the soil.\n To the best of our knowledge, our study is the first to investigate carbon \nmineralisation in a combination of Glomous mossea and tea residue or macroalgal \nbiomass. We expected the inoculation of Glomous mossea to increase carbon \nmineralisation to a greater extent in tea residue and macroalgal dried biomass \ndue to the production of several enzymes like cellulase which converts the more \ndifficult materials of cellulose to a simple sugar which in turn promotes microbial \nactivity to release more CO2. Contrary to our expectations, the inoculation of \nGlomous mossea significantly reduced carbon mineralisation (0.173 mg C g-1 soil \nday) overall compared with tea residue (0.216 mg C g-1 soil day) and macroalgal \nbiomass (0.202 mg C g-1 soil day) treatments. These results can be justified by \nthe possibility of improved soil aggregate formation when Glomous mossea was \nincorporated into the soil leading to an ultimate protection of organic carbon. The \nwell-formed stable aggregates have a priority to protect organic matter from the \ndecomposition process. Six et al. (2004) found that soil structure retained organic \ncarbon from the decomposition process. Likewise, Zhang et al. (2016) outlined that \nthe inoculation of AM fungi in soil lowered CO2 release and its consequences on \na slower organic matter decomposition. Our results suggest that Glomous mossea \nmight improve carbon sequestration and benefitclimate change. (Al-Maliki and \nBresam, (2020); reference to support this statement???) Additionally, at day 2, the \ncombination of Glomous mossea with tea residue showed a significant increase in \ncarbon mineralisation (0.483 mg C g-1 soil day) compared to the single addition \nof Glomous mossea (0.456 mg C g-1 soil day). Moreover, the same scenario \nhappened at day 8 which revealed a consistent increase in carbon mineralisation \n(0.108 mg C g-1 soil day) where AM fungi combined with tea residues compared \nto Glomous mossea alone (0.100 mg C g-1 soil day). The possible reason could be \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021112\n\n\n\nthat the availability of substrates from the tea residue to the microbial community \nmight have served as energy sources to has ten mineralised carbon. Based on \nthe results of this study, it is suggested that the combination of Glomous mossea \nwith tea residue raised carbon mineralisation compared to the single addition of \nGlomous mossea and its consequences for nutrient availability. Vierheilig et al. \n(2000) noted that the mycorrhizal fungal mycelium produced enzymes such as \ncellulase and pectinase which increased organic matter decomposition in the soil.  \n\n\n\nEffect of Treatments on ActinomycetesColony\nThere were clear significant differences in actinomycetescounts between \ntreatments. All treatments maximised actinomycetes significantly compared \nto control. It is known that the application of organic material sources has an \nimportant role in rising microbial community (Al-Maliki et al. 2018; Al-Maliki \n2016) due to their rolein providing the fertile platform for preferential growth \nof actinomycetes. Jedidi et al. (2004) and Bouzaiane et al. (2007) revealed that \nmicrobial biomass increased following the addition of organic residue.The highest \nincrease in actinomycetes was after incorporation of Glomous mossea with tea \nresidue and the probable reason is that Glomous mossea can produce exudates like \nsugars and organic acids (Toljander et al. 2008) which might have contributed to an \nincrease in soil actinomycetes. Furthermore, tea residue contains several nutrients \n\n\n\n10 \n \n\n\n\n225 \n  226 \n\n\n\nFigure 1.Carbon mineralisation rate (mg C g-1 soil day) in  saline soil under different 227 \ntreatments:control (C);macroalgae (M);tea residue 228 \n(T);Glomusmosseae(G);Glomusmosseae +macroalgae (G+M);Glomusmosseae 229 \n+ tea residue (G+T);macroalgae + tea residue (M+T); and Glomusmosseae + 230 \ntea residue+ macroalgae (G+T+M),over anincubation period of 2 days, 4 days, 231 \n8 days, 12 days, and 30 days. The vertical bars represent standard errors. 232 \nMeans with different superscript letters represent statistically significant 233 \ndifferences at p<0.05. 234 \n\n\n\n 235 \n\n\n\nEffect of Treatments on ActinomycetesColony 236 \n\n\n\nThere were clear significant differences in actinomycetescounts between treatments. 237 \n\n\n\nAll treatments maximised actinomycetes significantly compared to control. It is 238 \n\n\n\nknown that the application of organic material sources has an important role in rising 239 \n\n\n\nmicrobial community (Al-Maliki et al. 2018; Al-Maliki 2016) due to their rolein 240 \n\n\n\nproviding the fertile platform forpreferential growth of actinomycetes.Jedidi et al. 241 \n\n\n\n(2004) and Bouzaiane et al. (2007)(both not in ref list???)revealed that microbial 242 \n\n\n\nbiomass increased following the addition of organic residue.The highest increase in 243 \n\n\n\nactinomycetes was after incorporation of Glomousmossea with tea residue and the 244 \n\n\n\nprobable reason is that Glomousmosseacan produce exudates like sugars and organic 245 \n\n\n\nacids (Toljanderet al. 2008)which might have contributed to an increasein soil 246 \n\n\n\na \n\n\n\nb \nb \n\n\n\nc c \nc c c \n\n\n\nd \n\n\n\ne \ne \n\n\n\nf f f \nf f \n\n\n\ng \ng g g g g g g \n\n\n\nh h h h h h h h \ni i i o i i i i \n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\n\n\n\n0.7\n\n\n\nC M T G G+M G+T M+T G+M+T\n\n\n\nm\ng \n\n\n\nC \ng-\n\n\n\n1 \nso\n\n\n\nil \nda\n\n\n\ny \n\n\n\nTreatments \n\n\n\n2 days 4 days 8 days 12 days 30 days\n\n\n\nFigure 1.Carbon mineralisation rate (mg C g-1 soil day) in  saline soil under different \ntreatments:control (C);macroalgae (M);tea residue (T);Glomus mosseae(G);Glomus \nmosseae +macroalgae (G+M);Glomus mosseae + tea residue (G+T);macroalgae + \ntea residue (M+T); and Glomus mosseae + tea residue+ macroalgae (G+T+M), over \nanincubation period of 2 days, 4 days, 8 days, 12 days, and 30 days. The vertical bars \nrepresent standard errors. Means with different superscript letters represent statistically \nsignificant differences at p<0.05\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 113\n\n\n\nand polyphenol components, the impact of which might have ameliorated the \nhabitat of microbes resulting in increased actinomycetes. Interestingly, we found \nhigher roots biomass in these treatments inferring that roots exudates played a \nvital role in developing actinomycetes in soil.\n\n\n\n11 \n \n\n\n\nactinomycetes. Furthermore, tea residuecontains several nutrients and polyphenol 247 \n\n\n\ncomponents, the impact ofwhich might have ameliorated the habitat of microbes 248 \n\n\n\nresulting in increased actinomycetes. Interestingly, we found higher roots biomass in 249 \n\n\n\nthese treatments inferring that roots exudates played a vital role in developing 250 \n\n\n\nactinomycetes in soil. 251 \n\n\n\n 252 \n.  253 \n\n\n\n 254 \n 255 \n\n\n\n 256 \n\n\n\nFigure 2.Soil actinomycetes colony counts under different treatments:control 257 \n(C);macroalgae (M);tea residue (T);Glomusmosseae(G);Glomusmosseae 258 \n+macroalgae (G+M);Glomusmosseae + tea residue (G+T), macroalgae + tea 259 \nresidue (M+T); and Glomusmosseae + tea residue+ macroalgae (G+T+M). 260 \nBars represent standard errors. Different superscript letters represent statistical 261 \ndifference (p< 0.05). 262 \n\n\n\n 263 \n\n\n\nChlorophyll Content 264 \n\n\n\n 265 \n\n\n\nChlorophyll content varied significantly across treatments (Figure 3). The highest 266 \n\n\n\nvalue of chlorophyll content was 35 SPAD in G+T treatment, though not significantly 267 \n\n\n\ndifferent from G+M and M+T treatments. First, it is well knownthat the 268 \n\n\n\ndecomposition of tea residue in saline soil raisesnutrients level in soil resulting in the 269 \n\n\n\npromotion of root density and plant growth leading to higher chlorophyll content 270 \n\n\n\n(Figures 4 and5). Second,it is also well known thatGlomousmosseahas a major role in 271 \n\n\n\nabsorbing nutrients which can boostchlorophyll content. These suppositions are 272 \n\n\n\nsupported by finding a higher carbon mineralisation in G+T or T treatments, 273 \n\n\n\na \n\n\n\nb \nb b \n\n\n\nb \n\n\n\nc \n\n\n\nb \n\n\n\nc \n\n\n\n0\n0.5\n\n\n\n1\n1.5\n\n\n\n2\n2.5\n\n\n\n3\n3.5\n\n\n\n4\n4.5\n\n\n\n5\n\n\n\nC M T G G+M G+T M+T G+M+T\n\n\n\nA\nct\n\n\n\nen\nom\n\n\n\nyc\net\n\n\n\nes\n 1\n\n\n\n06  C\nFU\n\n\n\n /\ng \n\n\n\nTreatments \n\n\n\nChlorophyll Content\nChlorophyll content varied significantly across treatments (Figure 3). The \nhighest value of chlorophyll content was 35 SPAD in G+T treatment, though not \nsignificantly different from G+M and M+T treatments. First, it is well known that \nthe decomposition of tea residue in saline soil raises nutrients level in soil resulting \nin the promotion of root density and plant growth leading to higher chlorophyll \ncontent (Figures 4 and 5). Second,it is also well known that Glomous mossea has \na major role in absorbing nutrients which can boostchlorophyll content. These \nsuppositions are supported by finding a higher carbon mineralisation in G+T or \nT treatments, indicating the importance of the carbon mineralisation outcomes in \nimproving plant growth and chlorophyll content. In addition, Glomous mossea \nmight have increased photosynthetic efficiency of maize plant in saline soil. This \nefficiency might be increased to a greater extent when Glomous mossea was \nincorporated with tea residue resulting in a more greenish plant. In contrast, the \nrate of photosynthesis was  lowest in control plants which had leaves that turned \nyellow. A previous study has confirmed that the chlorophyll rate in mycorrhizal \nplants ishigher than in non- mycorrhizal plants (Gemma et al.,1997) (Gemma \n1997 in ref list???)\n It is noted that treatments with the inoculation of only Glomous mossea \nsignificantly increased chlorophyll content compared with control. Nevertheless, \nthis increase was significantly lower than in G+T and G+M treatments. The \n\n\n\nFigure 2. Soil actinomycetes colony counts under different treatments:control (C) \n;macroalgae (M);tea residue (T);Glomus mosseae(G);Glomus mosseae +macroalgae \n(G+M);Glomus mosseae + tea residue (G+T), macroalgae + tea residue (M+T); and \nGlomus mosseae + tea residue+ macroalgae (G+T+M). Bars represent standard errors. \nDifferent superscript letters represent statistical difference (p< 0.05).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021114\n\n\n\napplication of tea residue and macoalgal biomass in soil was found to increase \nchlorophyll content significantly in comparison to the control treatment. As \nmentioned earlier, this increase might be due to improvements in carbon \nmineralisation and soil structure besides the increase in soil phosphorus content \nafter root colonisation by Glomous mossea which can contribute to an increase \nin the chlorophyll content. Phosphorus is one of the most important constitutes \nof chlorophyll. A higher uptake of nitrogen, phosphorus, and potassium by AM \nfungi can maximise plant tolerance to salinity stress and has a consequence on \nthe production of greater leaves and leaf area, causing higher chlorophyll content. \nSuch a relationship between plant and Glomous mossea can affect stomatal status \nand photosynthesis of host leaves. More over, these symbiotic relationships might \nincrease transpirational and photosynthetic rates as well as chlorophyll content \n(Devi and Reddy 2004).\n \nNutrients Content (Is it Nitrogen or NutrientsContent???)\nNitrogen content was significantly different in the soil after the incorporation of \ntreatments (Figure. 4) (Should it be Figure 4????). The significant increase in \nnitrogen content was at G treatment despite it not being significantly different from \nG+T, G+M and T treatments.(Figure 4 should have been mentioned somewhere \nhere and placed here????)\n Undoubtedly Glomous mossea, tea residue and macroalgal biomass have a \nrole in soil fertilityas they can decrease pH and increase nutrient availability (N, K, \nP and Ca), and organic matter amount. Glomous mossea induced tea residues and \nmacroalgal biomass decomposition by stimulating the bacterial population (Al-\nMaliki and Al-Masoudi 2018) which could have allowed the bacterial community \nto participate in nitrogen fixation. This explainsan increase in nitrogen content \n\n\n\n13 \n \n\n\n\n 298 \n 299 \n\n\n\nFigure 3.Chlorophyll (SPAD) content in plant leaves under different 300 \ntreatments:control (C);macroalgae (M);tea residue (T);Glomusmosseae (G); 301 \nGlomusmosseae +macroalgae (G+M);Glomusmosseae + tea residue 302 \n(G+T);macroalgae + tea residue (M+T); and Glomusmosseae + tea residue+ 303 \nmacroalgae (G+T+M). Vertical bars represent standard errors. Different 304 \nsuperscript letters represent the statistical difference (p< 0.05). 305 \n\n\n\n 306 \nNutrients Content(Is it Nitrogen or NutrientsContent???) 307 \n\n\n\nNitrogen content was significantly different in the soil after the incorporation of 308 \n\n\n\ntreatments (Figure. 4)(Should it be Figure 4????). The highest significant increase 309 \n\n\n\nin nitrogen content was at G treatment despite it not being significantly different from 310 \n\n\n\nG+T, G+M and T treatments.(Figure 4 should have been mentioned somewhere 311 \n\n\n\nhere and placed here????) 312 \n\n\n\n Undoubtedly Glomousmossea, tea residue and macroalgal biomass have a 313 \n\n\n\nrole in soil fertilityas they can decrease pH and increase nutrient availability (N, K, P 314 \n\n\n\nand Ca), and organic matter amount. Glomousmosseainduced tea residues and 315 \n\n\n\nmacroalgal biomass decomposition by stimulating the bacterial population (Al-Maliki 316 \n\n\n\nand Al-Masoudi 2018)which could have allowed the bacterial community to 317 \n\n\n\nparticipate in nitrogen fixation.This explainsan increase in nitrogen content recorded 318 \n\n\n\na \n\n\n\ncb \nb \n\n\n\ncb \n\n\n\nd \nd \n\n\n\nbd \nb \n\n\n\n0\n5\n\n\n\n10\n15\n20\n25\n30\n35\n40\n45\n\n\n\nC M T G G+M G+T M+T G+M+T\n\n\n\nCh\nlo\n\n\n\nro\nph\n\n\n\nyl\n (S\n\n\n\nPA\nD\n\n\n\n) \n\n\n\nTreatments \n\n\n\n\n\n\n\nFigure 3. Chlorophyll (SPAD) content in plant leaves under different treatments:control \n(C);macroalgae (M);tea residue (T);Glomus mosseae (G); Glomus mosseae +macroalgae \n(G+M);Glomus  mosseae + tea residue (G+T);macroalgae + tea residue (M+T); and \nGlomus mosseae + tea residue+ macroalgae (G+T+M). Vertical bars represent standard \nerrors. Different superscript letters represent the statistical difference (p< 0.05).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 115\n\n\n\nrecorded after incorporating the Glomous mossea into the soil. Glomous mossea \ncan supply energy for the microbial community by degrading cellulose into sugars \n(Radford et al. 1996). Moreover, a lower C/N ratio of tea residue and macroalgal \nbiomass might have allowed for rapid decomposition which in turninduced \ncarbon mineralisation, thus enriching the saline soil through nutrient availability. \nContrary to our belief, the application of macroalgal biomass alone did not \nsignificantly increase nitrogen content in soil; it was when Glomous mossea was \nincorporated into the soil with macroalgal biomass, that  a significant increase in \nnitrogen content was detected compared with control treatment. This suggests \nthat the incorporation of macroalgal biomass alone does not contribute to nitrogen \ncontent and it is deduced that it could be that the lower C/N of macroalgal biomass \nwas accessed rapidly by microbes leading to a quick depletion of soil nitrogen.\n It was observed that phosphorous content in soil was significantly \nhigher in G+T treatment, although it was not significantly different from G+M, \nM+T and G+M+T (Figure 5). This observation could be linked to the increase \nin actinomycetes population which might solubilise organic phosphates and \nenhance the decomposition process. Actinomycetes have been reported else \nwhere tohydrolyse organic P in soils (Ghorbani-Nasrabadi et al. 2013) (not in ref \nlist???). It appears that actinomycetesare able to survive under high salinity, thus \nphosphorous increases are closely linked to the higher number of actinomycetes. \nMoreover, improvements in carbon mineralisation might increase the available \nnutrients in soil. In general, the microbial community can use the energy produced \nfrom the decomposition of carbon compounds to release phosphorous (Arcand \nand Schneider 2006). These effects become even more important with Glomous \nmossea which aids nutrients uptake. For instance, Glomous mossea combined \nwith tea residueor macroalgal biomass might encourage the bacterial community \nto solubilise phosphate by producing lactic acid, oxalic acid and glycolic acid, \n\n\n\n15 \n \n\n\n\nencourage the bacterial community to solubilise phosphate by producing lactic acid, 344 \n\n\n\noxalic acid and glycolic acid, or by forming HCO3 acid which has an important role in 345 \n\n\n\nmaintaining PH and solubilising phosphate in the soil. Toljanderet al. (2008) outlined 346 \n\n\n\nthat AM fungal hyphal exudates such assugars and organic acids increase soil bacteria 347 \n\n\n\nwhich have a robust role in organic matter decomposition and nutrient availability. 348 \n\n\n\nMore importantly, Glomousmosseahasa cementing role in protecting soil enzymes 349 \n\n\n\nlike phosphatase enzyme and soil organic matter (Qianet al. 2012).The aim of 350 \n\n\n\nphosphatase enzyme is to remove phosphate molecules from organic compounds 351 \n\n\n\nleading to more phosphorous content in the soil. The application of tea 352 \n\n\n\nresidue,macroalgal biomass and Glomousmosseaalone did not significantly increase 353 \n\n\n\nphosphorous content compared with control. This could be dueto the lower amounts 354 \n\n\n\nof phosphorus in tea residue and macroalgal dried biomass.  355 \n\n\n\n 356 \n\n\n\nFigure 4.Nitrogen content (%)in the soil under different treatments: (control 357 \n(C);macroalgae (M);tea residue (T); Glomusmosseae (G);Glomusmosseae 358 \n+macroalgae (G+M);Glomusmosseae + tea residue (G+T);macroalgae + tea 359 \nresidue (M+T); and Glomusmosseae + tea residue+ macroalgae (G+T+M). 360 \nVertical bars represent standard errors. Different superscript letters represent 361 \nthe statistical difference (p< 0.05). 362 \n\n\n\n 363 \n\n\n\n  364 \n\n\n\n 365 \n 366 \n\n\n\na a \n\n\n\nb \nb \n\n\n\nb b b \nab \n\n\n\n0\n0.5\n\n\n\n1\n1.5\n\n\n\n2\n2.5\n\n\n\n3\n3.5\n\n\n\n4\n\n\n\nC M T G G+M G+T M+T G+M+T\n\n\n\nN\nit\n\n\n\nro\nge\n\n\n\nn \n(%\n\n\n\n) \n\n\n\nTreatments \n\n\n\nFigure 4. Nitrogen content (%)in the soil under different treatments: (control \n(C);macroalgae (M);tea residue (T); Glomus mosseae (G);Glomus mosseae +macroalgae \n(G+M);Glomus mosseae + tea residue (G+T);macroalgae + tea residue (M+T); and \nGlomus mosseae + tea residue+ macroalgae (G+T+M). Vertical bars represent standard \nerrors. Different superscript letters represent the statistical difference (p< 0.05).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021116\n\n\n\n16 \n \n\n\n\n 367 \n 368 \n 369 \n\n\n\n 370 \n\n\n\nFigure 5.Phosphorouscontent (%) in the plant leaves under different treatments: 371 \n(control (C);macroalgae (M);tea residue (T);Glomusmosseae 372 \n(G);Glomusmosseae +macroalgae (G+M);Glomusmosseae + tea residue 373 \n(G+T);macroalgae + tea residue (M+T); and Glomusmosseae + tea residue+ 374 \nmacroalgae (G+T+M). Vertical bars represent standard errors. Different 375 \nsuperscript letters represent the statistical difference (p< 0.05). 376 \n\n\n\n 377 \n\n\n\nPlant Growth 378 \n\n\n\nRoots weight was highly ameliorated by treatments. Roots weight 379 \n\n\n\nincreasedsignificantly in all treatments compared with control. (Figure 6 should be 380 \n\n\n\nmentioned here???)The highest raise in root weight was in a response to 381 \n\n\n\nacombination of Glomousmosseawith tea residuecompared toall treatments except for 382 \n\n\n\nG+M+T treatment which confirmed non-significant differences with G+T. The 383 \n\n\n\nincrease in root weight when Glomousmosseawas combined with tea residuewas 384 \n\n\n\nsupported by enhancements in nitrogen and phosphorous contents which are 385 \n\n\n\nconsidered very important macro-elements in developing root weight. Niuet al. (2012) 386 \n\n\n\nreported that the availability of phosphorous in the soil can benefit root architecture, 387 \n\n\n\nroot length, branching, and root hair progress. Thus, the increase in root weight can 388 \n\n\n\nalso be expecteddue tothe rise in carbon mineralisation from organic matter 389 \n\n\n\ndecomposition, leading to increased nutrient availability. The increase in root growth 390 \n\n\n\na a \n\n\n\nac ac \n\n\n\nc \n\n\n\nc \n\n\n\nc \nc \n\n\n\n0\n0.05\n\n\n\n0.1\n0.15\n\n\n\n0.2\n0.25\n\n\n\n0.3\n0.35\n\n\n\n0.4\n0.45\n\n\n\nC M T G G+M G+T M+T G+M+T\n\n\n\nPh\nos\n\n\n\nph\nor\n\n\n\nou\ns \n\n\n\n(%\n) \n\n\n\nTreatments \n\n\n\nFigure 5. Phosphorouscontent (%) in the plant leaves under different treatments: (control \n(C);macroalgae (M);tea residue (T);Glomus mosseae (G);Glomus mosseae +macroalgae \n(G+M);Glomus mosseae + tea residue (G+T);macroalgae + tea residue (M+T); and \nGlomus mosseae + tea residue+ macroalgae (G+T+M). Vertical bars represent standard \nerrors. Different superscript letters represent the statistical difference (p< 0.05)\n\n\n\nor by forming HCO3 acid which has an important role in maintaining PH and \nsolubilising phosphate in the soil. Toljander et al. (2008) outlined that AM fungal \nhyphal exudates such assugars and organic acids increase soil bacteria which \nhave a robust role in organic matter decomposition and nutrient availability. More \nimportantly, Glomous mossea has a cementing role in protecting soil enzymes \nlike phosphatase enzyme and soil organic matter (Qian et al. 2012). The aim of \nphosphatase enzyme is to remove phosphate molecules from organic compounds \nleading to more phosphorous content in the soil. The application of tea residue, \nmacroalgal biomass and Glomous mossea alone did not significantly increase \nphosphorous content compared with control. This could be dueto the lower \namounts of phosphorus in tea residue and macroalgal dried biomass.\n \nPlant Growth\nRoots weight was highly ameliorated by treatments. Roots weight increased \nsignificantly in all treatments compared with control. (Figure 6 should be mentioned \nhere???) The highest raise in root weight was in a response to acombination of \nGlomous mossea with tea residue compared toall treatments except for G+M+T \ntreatment which confirmed non-significant differences with G+T. The increase in \nroot weight when Glomous mossea was combined with tea residue was supported \nby enhancements in nitrogen and phosphorous contents which are considered very \nimportant macro-elements in developing root weight. Niu et al. (2012) reported \nthat the availability of phosphorous in the soil can benefit root architecture, root \nlength, branching, and root hair progress. Thus, the increase in root weight can \nalso be expected due tothe rise in carbon mineralisation from organic matter \ndecomposition, leading to increased nutrient availability. The increase in root \ngrowth when AM fungi was inoculated in maize is consistent with those reported \nby Dickson et al. (1999).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 117\n\n\n\n There were clear significant differences in plant weight among treatments \n(Figure 6)(or Figure 7). The highest value in plant weight was in G+M+T but \nthis was not significantly different from G+T and M+ T treatments. It was noted \nthat plant weight in tea residue alone was significantly lower than in G+T. The \nincrease in plant weight isa response to acombination of Glomous mossea with tea \nresidue and can be mainly attributed to the development in soil structure and root \nweight which play an important role in absorbing nutrients leading to successful \n\n\n\n17 \n \n\n\n\nwhenAM fungiwasinoculated in maize is consistent withthose reported by Dickson et 391 \n\n\n\nal. (1999). 392 \n\n\n\nThere were clear significant differences in plant weight among treatments(Figure 393 \n\n\n\n6)(or Figure 7????). The highest value in plant weight was in G+M+T but this was 394 \n\n\n\nnot significantly different from G+T and M+ T treatments. It was noted that plant 395 \n\n\n\nweight in tea residue alone was significantly lower than in G+T. The increase in plant 396 \n\n\n\nweight isa response to acombination of Glomousmosseawith tea residueand can be 397 \n\n\n\nmainly attributed to the development in soil structure and root weight which play an 398 \n\n\n\nimportant role in absorbing nutrients leading to successful plant growth. Increases in 399 \n\n\n\nplant growth in response to Glomousmosseahave also been shown in other cereal 400 \n\n\n\ncrops (Wahid et al.2016). 401 \n\n\n\n 402 \n\n\n\n 403 \n\n\n\nFigure 6.Root weight (g) in the plant leaves under different treatments: (control (C); 404 \nmacroalgae (M);tea residue (T);Glomusmosseae (G);Glomusmosseae 405 \n+macroalgae (G+M);Glomusmosseae + tea residue (G+T);macroalgae + tea 406 \nresidue (M+T); and Glomusmosseae + tea residue+ macroalgae (G+T+M). 407 \nVertical bars represent standard errors. Different superscript letters represent 408 \nthe statistical difference (p< 0.05). 409 \n\n\n\n 410 \n\n\n\na \n\n\n\nb b \nb \n\n\n\nb \n\n\n\nc \n\n\n\nb \n\n\n\nc \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\nC M T G G+M G+T M+T G+M+T\n\n\n\nRo\not\n\n\n\ns \nw\n\n\n\nei\ngh\n\n\n\nt \n(g\n\n\n\n) \n\n\n\nTreatments \n\n\n\nFigure 6. Root weight (g) in the plant leaves under different treatments: (control (C); \nmacroalgae (M);tea residue (T);Glomus mosseae (G);Glomus mosseae +macroalgae \n(G+M);Glomus mosseae + tea residue (G+T);macroalgae + tea residue (M+T); and \nGlomus mosseae + tea residue+ macroalgae (G+T+M). Vertical bars represent standard \nerrors. Different superscript letters represent the statistical difference (p< 0.05).\n\n\n\n18 \n \n\n\n\n 411 \n\n\n\n 412 \n\n\n\nFigure 7.Plant weight (g) in the plant leaves under different treatments: (control 413 \n(C);macroalgae (M);tea residue (T);Glomusmosseae (G);Glomusmosseae 414 \n+macroalgae (G+M);Glomusmosseae + tea residue (G+T);macroalgae + tea 415 \nresidue (M+T); and Glomusmosseae + tea residue+ macroalgae (G+T+M). 416 \nVertical bars represent standard errors. Different superscript letters represent 417 \nthe statistical difference (p < 0.05). 418 \n\n\n\n 419 \n 420 \n\n\n\nConclusion  421 \n\n\n\nThis study showed that the carbon mineralisation isinfluenced by tea residue, 422 \n\n\n\nmacroalgae and Glomousmossea. Undoubtedly, carbon decomposition increased 423 \n\n\n\nrapidly in T and M. In contrast, Glomousmosseain combination with T and M 424 \n\n\n\nmitigated carbon release compared with T and M treatments suggesting an important 425 \n\n\n\nrole forGlomousmosseain minimising carbon releaseand constrainingcarbon 426 \n\n\n\ndegradation. The G+T treatment increasedto a greater extent chlorophyll 427 \n\n\n\ncontent,actinomycetes, root weight and phosphorous content. These findings present 428 \n\n\n\nnew insights into the impacts of Glomousmossea with tea residue on carbon 429 \n\n\n\ndecomposition and organic carbon stabilisation in arid saline soils. 430 \n\n\n\na \n\n\n\nb bc c bc \nd d d \n\n\n\n0\n20\n40\n60\n80\n\n\n\n100\n120\n140\n160\n180\n\n\n\nC M T G G+M G+T M+T G+M+T\n\n\n\nPl\nan\n\n\n\nt \nw\n\n\n\nei\ngh\n\n\n\nt \n(g\n\n\n\n) \n\n\n\nTreatments \n\n\n\n\n\n\n\nFigure 7. Plant weight (g) in the plant leaves under different treatments: (control \n(C);macroalgae (M);tea residue (T);Glomus mosseae (G);Glomus mosseae +macroalgae \n(G+M);Glomus mosseae + tea residue (G+T);macroalgae + tea residue (M+T); and \nGlomus mosseae + tea residue+ macroalgae (G+T+M). Vertical bars represent standard \nerrors. Different superscript letters represent the statistical difference (p < 0.05).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021118\n\n\n\nplant growth. Increases in plant growth in response to Glomous mossea have also \nbeen shown in other cereal crops (Wahid et al. 2016).\n\n\n\nConclusion \nThis study showed that the carbon mineralisation is influenced by tea residue, \nmacroalgae and Glomous mossea. Undoubtedly, carbon decomposition increased \nrapidly in T and M. 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"\n\n___________________\n*Corresponding author : E-mail: sunny.goh@gmail.com\n\n\n\nINTRODUCTION\nGenetic algorithm (GA) is one of the metaheuristics. Metaheuristics are defined \nas \u201csolution methods that orchestrate an interaction between local improvement \nprocedures and higher level strategies to create a process capable of escaping \nfrom local optima and performing a robust search of a solution space\u201d (Glover \nand Kochenberger, 2003). Other than GA, there are some other metaheuristics \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 17-32 (2015)                        Malaysian Society of Soil Science\n\n\n\nUse of Simple Genetic Algorithm and Bat Algorithm to Fit \nthe Haverkamp Constitutive Functions to the Soil\n\n\n\nWater Content\n\n\n\nGoh, E.G.1,2* and K. Noborio1\n\n\n\n1School of Agriculture, Meiji University, 1-1-1 Higashimita,\nTama-ku, Kawasaki, 214-8571 Japan \n\n\n\n2School of Ocean Engineering, Universiti Malaysia Terengganu,\n21030 Kuala Terengganu\n\n\n\nABSTRACT\nThe aim of this study was to use simple genetic algorithm (SGA) as an inverse \nmethod to fit Haverkamp constitutive functions, implemented in Richards\u2019 water \nflow equation, to volumetric water content data to predict a globally-optimum set \nof input parameter values. For the purpose of comparison, bat algorithm (BA) \nwas also implemented. Both SGA & BA were coded in FORTRAN to solve \nRichards\u2019 equation. The Haverkamp constitutive functions were used to govern the \nrelationships between hydraulic properties. Richards\u2019 equation was approximated \nby a finite-difference solution and was used to simulate water infiltration into \nYolo light clay. In previous study, uncertainities in the input parameters were \nsubjected to global sensitivity analyses (GSA) to determine the sensitivity indices \nand interactions between parameters by previous studies. GSA was also used to \ndetermine the input parameter that was responsible for the greatest uncertainty \nin the simulation results. In this study, the optimum population size was found to \nbe 50, and the SGA was able to reproduce the water infiltration front, provided \nthat data points of water content at saturation and residual levels were used in \nthe prediction. Under extreme conditions where the uncertainties in the input \nparameter values were significant, a larger population size (greater than 50) would \nbe required to obtain reasonable simulation results. The SGA was found able to \ngenerate comparable results to the bat algorithm at a population size of 50. \n\n\n\nKeywords:\t Richards\u2019\twater\tflow\tequation,\twater\tinfiltration,\tHaverkamp\t\nconstitutive functions, inverse method, global sensitivity \nanalyses\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201518\n\n\n\nGoh, E.G. and K. Noborio\n\n\n\ntools such as ant algorithm, bee algorithm and particle swarm optimisation (PSO) \n(Yang, 2010a).\n\n\n\nGA was first introduced by Holland (1975). Its capability in optimisation \nwas first demonstrated in significant detail by de Jong (1975). Since then, GA \nhas been popularised for various optimisation applications. For instance, GA \nwas used for multi-niche crowding designed for locating multiple peaks in a \nmultimodal function (Cedeno and Vemuri, 1992). Population-based incremental \nlearning, a variant of GA, was used in artificial neural network evolution to evolve \na neuro-controller (Baluja, 1994). GA was also used to locate likely candidates \nfor functional protein signals that participate in some molecular events such as \ndegradation  and  chemical  modification  (Levin, 1992).  A  global  path  planner \nbased on the combination of two parallel GAs was used to drive a robot in a \ndynamic environment where obstacles were moving (Latombe, 1991). Chambers \n(1995) provides examples of problems solved using the application of GA.\n\n\n\nIn soil science, two types of GAs have been used to model hydraulic properties \nin unsaturated soils, namely simple genetic algorithm (SGA) and micro-genetic \nalgorithm (\u03bcGA). SGA was used by Schneider et al., (2013), and micro-GA was \nused by Shin et al., (2012) and Ines and Droogers (2002). This study was limited \nto SGA. Other than SGA, Levenberg\u2013Marquardt (L-M) algorithm was also used \nto predict groundwater hydraulic properties (Kelleners et al., 2005). However, \nSGA\u2019s ability to find a global optimum solution give it an advantage over L-M \nalgorithm, which searches only for local optimum solutions. \n\n\n\nThe motivation of this work was to find a globally-optimised set of input \nparameter values that best describe the infiltration of water into unsaturated soils. It \nis common to observe a discrepancy between simulation results and experimental \ndata or analytical solutions (Goh and Noborio, 2013). The uncertainty range of \neach input parameter was considered and solved using global sensitivity analysis \n(GSA) (Goh and Noborio, 2014), using the Sobol\u2019 variance-based method \n(Sobol\u2019, 1990). The extension of the framework to include the inverse method \n(i.e., SGA) would be a step to complement the existing GSA tool. Hence, the aim \nof this study was to use SGA as a tool to fit Haverkamp constitutive functions, \nimplemented in Richards\u2019 water flow equation, to volumetric water content data. \nThe objectives of this study were to: (1) couple SGA with Richards\u2019 equation, \nwith its constitutive functions from Haverkamp et al., (1977); (2) investigate the \neffects of population size, mutation numbers and input value uncertainty range on \nsimulation results; and (3) determine optimum population size and its limitations.\nAdditionally, bat algorithm (BA), a nature-inspired metaheuristic tool proposed \nby Yang (2010b), was coded for the purpose of comparison with the SGA.Yang \n(2010b) demonstrated that BA out performed GA and particle swarm optimisation \n(PSO) after subjected to a benchmarking function. The choice of algorithm would \nbe case dependent in line with the function. In other words, it depends on function \nas one algorithm could be better (or worse) than another algorithm. This study \naimed to demonstrate that the SGA was comparable to BA.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 19\n\n\n\nFitting Constitutive Functions to the Soil Water Flow\n\n\n\nNumerical Solution to Richards\u2019 Equation and Input Parameters\nThe governing equation for transient water flow in unsaturated soil is the Richards\u2019 \nequation and its \u03b8L-based form, is shown below (Richards, 1931):\n\n\n\n     \n  (1)\n\n\n\nwhere \u03b8L is the volumetric water content (m3 m-3); t is time of simulation (s); z \nindicates the vertical distance of simulation (m); K is the hydraulic conductivity \nof the medium (m s-1); \u03c8m  is the  matric pressure head (m); and k\n\n\n\n\uf076  is the vector unit \npositive for downward vertical flow. \n\n\n\nEq. (1) was approximated numerically and implemented in FORTRAN. The \nspatial discretisation method used was termed as cell-centred finite difference. \nThe finite difference algebra for Eq. (1) that coupled to the SGA is shown below:\n\n\n\n(2)\n\n\n\n\n\n\n\n3 \n \n\n\n\nm\uf079 is the matric pressure head (m); and k is the vector unit positive for downward vertical \nflow.  \n \nEq. (1) was approximated numerically and implemented in FORTRAN. The spatial \ndiscretisation method used was termed as cell-centred finite difference. The finite difference \nalgebra for Eq. (1) that coupled to the SGA is shown below: \n \n\n\n\n \uf028 \uf029 \uf028 \uf029\n\n\n\n\uf028 \uf029 \uf028 \uf029 \uf028 \uf029\uf028 \uf029\n1\n\n\n\n1 12\n1 12\n\n\n\n1\n10.5 0.5\n\n\n\nm\n\n\n\nkn n L kL k L k n n\nL k L k\n\n\n\nk k k\n\n\n\nK\n\n\n\nt z z z\n\n\n\n\uf079\n\uf071\uf071 \uf071\n\n\n\n\uf071 \uf071\n\uf02b\uf02b\n\n\n\n\uf02b\n\uf02b \uf02b\n\n\n\n\uf02b\n\uf02b\n\n\n\n\uf0e6 \uf0f6\uf0b6\n\uf0e7 \uf0f7\uf0b6\uf0e8 \uf0f8\uf02d\n\n\n\n\uf03d \uf02d \uf02d\n\uf044 \uf044 \uf044 \uf02b \uf044\n\n\n\n\n\n\n\n\n\n\n\n\uf028 \uf029 \uf028 \uf029 \uf028 \uf029\uf028 \uf029\n1\n\n\n\n1 1 12 , , , ,1 12 2 2\n1\n\n\n\n1\n\n\n\n\n\n\n\n0.5 0.5\n\n\n\nm\n\n\n\nk\nL k i j k i j kn n\n\n\n\nL k L k\nk k k k\n\n\n\nK K k K k\n\n\n\nz z z z\n\n\n\n\uf079\n\uf071\n\n\n\n\uf071 \uf071\n\uf02d\n\n\n\n\uf02d \uf02b \uf02d\n\uf02b \uf02b\n\n\n\n\uf02d\n\uf02d\n\n\n\n\uf0e6 \uf0f6\uf0b6\n\uf0e7 \uf0f7 \uf02d\uf0b6\uf0e8 \uf0f8\n\n\n\n\uf02d \uf02d\n\uf044 \uf044 \uf02b \uf044 \uf044\n\n\n\n  (2) \n\n\n\n \nwhere k indicates a cell-centred number in the z-direction of the Cartesian \ncoordinate system; t\uf044 (s) is the time-step size; \uf028 \uf029\n\n\n\nn\nL k\uf071 (m3 m-3) and \uf028 \uf029\n\n\n\n1n\nL k\uf071 \uf02b (m3 m-3) \n\n\n\nindicate the volumetric water content at old time level ( n ) and new time level \n( 1n\uf02b ), respectively; 1/2kK \uf02b (m s-1) is the hydraulic conductivity at the interface \nbetween cell k and 1k \uf02b ; 1/2kK \uf02d (m s-1) is the hydraulic conductivity at the interface \nbetween cell 1k \uf02d and k ; \uf028 \uf029 1/2\n\n\n\n/m L k\n\uf079 \uf071\n\n\n\n\uf02b\n\uf0b6 \uf0b6 is the partial derivative of m\uf079 with respect \n\n\n\nto L\uf071 at the interface between the cell k  and 1k \uf02b ; \uf028 \uf029 1/2\n/m L k\n\n\n\n\uf079 \uf071\n\uf02d\n\n\n\n\uf0b6 \uf0b6 is the partial \nderivative of m\uf079  with respect to L\uf071  at the interface between the cell 1k \uf02d  and k ; \n\n\n\n1kz \uf02b\uf044  (m), kz\uf044  (m) and 1kz \uf02d\uf044 (m) are corresponding to the spatial sizes of spacing \nof cell 1k \uf02b , k  and 1k \uf02d . \uf028 \uf029\n\n\n\n1\n1\n\n\n\nn\nL k\uf071 \uf02b\n\n\n\n\uf02b (m3 m-3), \uf028 \uf029\n1n\n\n\n\nL k\uf071 \uf02b (m3 m-3) and \uf028 \uf029\n1\n\n\n\n1\nn\n\n\n\nL k\uf071 \uf02b\n\uf02d (m3 m-3) are \n\n\n\nthe volumetric water contents at new time level of cell 1k \uf02b , k  and 1k \uf02d , \nrespectively. The numerical solution was solved by a partially implicit cell-\ncentred finite-difference scheme. An iterative method was used to solve the \nmathematical algebra. A convergence factor criterion was used to indicate the \ncondition for iteration termination process (i.e,. absolute maximum difference \n\n\n\n\uf028 \uf029 \uf028 \uf029\n1| |n n\n\n\n\nL k L k\uf071 \uf071\uf02b \uf02d  for every single cell).  \n \nThe constitutive functions implemented were fromHaverkamp et al. (1977) are shown below: \n\n\n\n\uf028 \uf029\n1\n\n\n\n210 s r\nm\n\n\n\nL r\n\n\n\nexp\n\uf062\uf061 \uf071 \uf071\n\n\n\n\uf079 \uf061\n\uf071 \uf071\n\n\n\n\uf02d \uf0e9 \uf0f9\uf02d\n\uf03d \uf02d \uf02d\uf0ea \uf0fa\uf02d\uf0eb \uf0fb\n\n\n\n       (3) \n\n\n\n\n\n\n\nFitting Constitutive Functions to the Soil Water Flow \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201520\n\n\n\n \n   \n     \n   (3)\n      \n\n\n\n      \n    (4)\n\n\n\nwhere  \u03b1 , \u03b2 , A and B  are the fitting parameters; \u03b8r (m3 m-3) is the residual \nvolumetric water content; \u03b8s (m\n\n\n\n3 m-3) is the saturated volumetric water content; \nand Ks (m s-1) is the saturated hydraulic conductivity. \n\n\n\nSGA Method\nThere were eight input parameters implemented in SGA. They were: \u03b1 , \u03b2 , A \nand B as fitting parameters; \u03b8r (m\n\n\n\n3 m-3), the residual volumetric water content; \n\u03b8s (m\n\n\n\n3 m-3), the saturated volumetric water content; Ks (m s-1) , the saturated \nhydraulic conductivity; and \u03b8L(initial) (m\n\n\n\n3 m-3), the initial value of volumetric water \ncontent. Uncertainty range for each input parameter was required. The upper and \nlower ends of the range of values gave the low (ai ) and high (bi ) limits, where  \nrepresents input parameters. The limits were translated into integers using Eq. (5) \nshown below:\n           \n (bi - ai) x 10n      (5)\n\n\n\nwhere n represents the number of significant figures. Eq. (5) was also used to \ndetermine number of bits (m ) required for the input parameter as shown below:\n\n\n\n 2m-1 < (bi - ai) x 10n < 2m     (6)\n\n\n\nQuasi-random number from Sobol\u2019 sequence was used as values for the input \nparameter  xi . The m -bits value from Eq. (6) was used with the randomly generated   \nxi value to generate decimal number from Eq. (7) as shown below:\n\n\n\n       (7)\n\n\n\nThe decimal number was then converted (i.e,. encoded) into binary digits with the \nlength of m with each digit represented by either 1 or 0. Other encoding methods \nwere also  possible (Whitley, 1994). Eqs. (5) to (7) were used to generate binary \ndigits for eight input parameters, and they were aligned in a single line to represent a \nchromosome. The schematic diagram of flow and process of the SGA used is shown \nin Figure 1. The Sobol\u2019 quasi-random sequence was used repeatedly to generate \n\n\n\nGoh, E.G. and K. Noborio\n\n\n\nThe constitutive functions implemented were from Haverkamp et al., (1977) are \nshown below:\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 21\n\n\n\na population of chromosomes. This was one of Talbi\u2019s (2009) recommended \nstrategies for population initialisation. A pair of randomly selected chromosomes \nwas subjected to the crossover process in order to produce a pair of offspring, \nwhere each offspring was decoded and evaluated based on a fitness function. The \noffspring with a better fitness value than parent chromosome would replace the \nparent chromosome with its own. In a similar manner, the mutation process was \nalso implemented. This procedure was known as the tournament selection. The \ncrossover procedure was accomplished by randomly selecting a point in the parent \nchromosomes and then swapping all the binary digits before the point in a parent \nchromosome with the other parent\u2019s chromosome (Reeves, 2003). The mutation \nprocess was carried out by randomly selecting binary digit(s) in the offspring\u2019s \nchromosome and changing it from 1 to 0 or vice versa. The mutation process was \nused to help preserve population diversity by preventing a sub-optimal solution at \nan early stage of the global search on the input space (Reeves, 2003). The number \nof crossovers and mutations was based on the size of population. A complete cycle \nof the processes resulted in a new generation of population. Multiple generations \nwould be needed to search for an optimised solution. GA was coded and modified \nto handle eight input parameters and was also coupled with Richards\u2019 equation. \nThe code was improved to allow screening of various mutation numbers. Another \nimprovement was the inclusion of termination criteria. The termination of the \nsimulation was based on pre-set percentage reduction of average fitness function \n(FF) value stated by Reeves (2003), and also based on pre-set number of times a \npercentage reduction value less than the pre-set FF value occcured. The code was \nimproved to include multiple time datasets where the solution (i.e., chromosome) \nwas based upon a sum of FF value of the dataset at various times.  \n\n\n\nFigure 1: Flow and process schematic of simple genetic algorithm (SGA).\n\n\n\nFitting Constitutive Functions to the Soil Water Flow\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201522\n\n\n\nThe FF\u2019s objective was to minimise the error between the experimental data \nand the simulated results. In this study, the FF used was absolute residual errors \nas shown below (Zheng and Bennett, 2002):\n\n\n\n      (8)\n\n\n\nwhere calk is the simulated data at cell k ; and obsk is the analytical solution at \ncell k.\n\n\n\nBA Method\nBA was used to imitate the echo location behavior of bats to search for globally-\noptimum set of input parameters. According to Yang (2010b), BA has three \ndistinctive features which improved its efficiency. They were frequency tuning, \nautomatic zooming, and parameter controlling. A population of bats was used to \nsearch for the optimum condition, and each bat has its position (x_bati) and velocity \n(vi ). The success of BA in finding the optimum condition was analogous to a bat \nfinding, locating and reaching a prey or its roosting crevice. The echolocation \nbehaviour begins by the bat emitting a sound pulse that varies in its frequency (fi ). \nThe loudness of the sound (Ai ) reduces as it reaches its target, but the sound pulse \nrate (ri ) increases.\n\n\n\nThe frequency and wavelength combine to provide a constant sound speed in \nthe air, and thus, if one increased, the other would have to decrease. Yang (2010b) \nprefers the parameterization of sound frequency and requires the specification of \nminimum (fmin ) and maximum (fmax ) limits of frequency as shown shown below:\n\n\n\n  fi = fmin + (fmin - fmax) Rand     (9)\n\n\n\nwhere subscript i refers to an individual bat; and  Rand is the random number from \nthe Sobol\u2019 sequence, which has a value between 0 and 1. \n\n\n\nThe generated frequency would be used to produce a bat\u2019s new velocity (Eq. \n(10)). It would then update the bat\u2019s existing position to generate a new position \n(Eq. (11)):\n\n\n\n vi\nt+1 = vi\n\n\n\nt + (x_bati\nt - x _ batbest ) fi    (10)\n\n\n\n  x_ bati\nt+1  = x _ bati\n\n\n\nt + vi\nt+1     (11)\n\n\n\nwhere superscript t and t+1 refer to the old (or previous) and new iterations, \nrespectively; x_batbest  is the existing globally best bat position; x_bati\n\n\n\nt is the \nselected bat position; vi\n\n\n\nt is the existing bat velocity; vi\nt+1 is the updated bat velocity \n\n\n\nafter considering the distance between the existing bat and globally best bat \nposition; and x_bati\n\n\n\nt+1  is the updated or newly generated bat position that is to be \nsubjected to model simulation and FF evaluation. \n\n\n\nGoh, E.G. and K. Noborio\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 23\n\n\n\nEq. (9) is part of Step 3, refer to Figure 2. Eqs. (10) and (11) are parts of Step 4. \nIn Step 5, the random walk equation provided by Yang (2014) was modified to \nthe following form:\n\n\n\n  x_ bati\nt+1  = x _ batbest (1 + multiplier) Rdn_num  (12)\n\n\n\nwhere multiplier is a fraction value that less than 1; and Rdn_num  is a random \nnumber between -1 and 1. Eq. (12) would be executed, only if, a random number \nthat is generated by Sobol\u2019 sequence is greater than sound pulse rate (ri\n\n\n\nt+1 ) emitted \nby bat as shown below:\n\n\n\n       (13)\n\n\n\nwhere ri\no is the initial pulse rate for a bat; \u03b3bat is a pulse rate enhancement factor \n\n\n\nbetween 0 and 1; and ri\nt+1 is an updated sound pulse rate for a bat. Eq. (13) indicates \n\n\n\nthat the sound pulse rate increases with subsequent updates. Thus, it implies that \nthe likelihood of executing Eq. (12) would be reduced with an increasing number \nof updates. \n\n\n\nStep 7 would be executed only if: (1) the individual bat fitness value resulting \nfrom evaluating either Eqs. (11) or (12), was less than its existing individual bat \nfitness value; and (2) a generated random number (Sobol\u2019 sequence) was lesser \nthan the loudness value. The loudness value decreases with subsequent updates \naccording to the Eq. (14) shown below:\n\n\n\n  Ai\nt+1 = \u03b1bat Ai\n\n\n\nt      (14)\n\n\n\nFigure 2: Flow and process schematic of bat algorithm (BA).\n\n\n\nFitting Constitutive Functions to the Soil Water Flow\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201524\n\n\n\nwhere \u03b1bat is a loudness cooling factor between 0 and 1; Ai\nt is the initial loudness \n\n\n\nof a bat; and Ai\nt+1 is an updated loudness for a bat. The second criterion in \n\n\n\nStep 7 denotes that the probability of replacing an existing individual bat with \nan improved bat position (with better fitness value) would be reduced with an \nincreasing number of updates.\n\n\n\nLastly, in Step 8, the globally-optimum position of the bat in each iteration \nwould be stored and compared with other bats\u2019 fitness values. Steps 4 to 8 were \nrepeated until termination criteria were fulfilled. The termination of simulation \nwas based on a pre-set percentage reduction of average FF value, which was \nsimilar to the SGA, and also based on a pre-set number of times a percentage \nreduction value less than the pre-set FF value was achieved. It was also coded to \nbe able to handle the value of dataset at various times.\n\n\n\nRESULTS AND DISCUSSION\nPopulation size, mutation rate and generation were among the important factors of \nSGA (Reeves, 2003). The simulation results were studied for various population \nsizes (i.e., 20, 50, 100, 200 and 300) and different mutation rates were applied \n(i.e., 0, 4, 9, 13, 15, 18, 27, 36, 54, 72 and 90). SGA was set to continue for \n100 generations. These mutation rates were applied to each population size. The \nfitness value was found to decrease at early generations before reaching a plateau \nas the generations continued to increase as shown in Figure 3(a). Decrease in \nthe fitness value indicated that the simulation results closely matched Philip\u2019s \nsemi-analytical solution from Haverkamp et al., (1977). This trend was observed \nfor different population sizes and also for various mutation rates, as shown in \nFigures 3(b)-(d). It thus indicated that a sufficient allocation of generations was \nrequired to achieve the desired results. However, allowing the SGA process to \ncontinue after the plateauing of the fitness value only wastes computational time. \nHence, the SGA code was improved to include termination criteria as suggested \nby Reeves (2003).\n\n\n\nAdditionally, a greater population size was found to result in a low fitness \nvalue. For instance, a population size of 300 produced fitness values between \n0.1123 and 0.2231, whereas a population size of 20 produced finess values between \n0.1395 and 0.7686. Similarly, the fitness values for population sizes of 50, 100 and \n200 were found located between those of 20 and 300. High population size would \nresult in better simulation results, but required excessive computational time \nbecause increases in the size of the population would also increase the number of \nsimulation execution for each generation. Thus, finding an optimum population \nsize was important. Goldberg (1985) suggests that there is an optimal population \nsize for a given chromosome length, but Grefenstette (1986) found it unnecessary. \nMoreover, for each population size, some mutation rates would result in better \nsimulation results (i.e., low fitness value) as shown in Figures 3(a) to (d). For \nexample, a mutation rate of 18 resulted in the lowest fitness value of 0.1395 and \n0.1239 for population sizes of 20 and 50, respectively. Although previously it was \nobserved that there were reductions in fitness values as population size increased, \n\n\n\nGoh, E.G. and K. Noborio\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 25\n\n\n\nthe mutation rates required to obtain the lowest fitness value were simultaneously \nreduced from 18 for a population size of 100 to 4 for a population size of 300.\nSimilarly, for a population size of 200, the required a mutation rate was only 9.\n\n\n\nFrom the observations, it was evident that the fitness value would continue \nto decrease with increasing population size. Finding an optimum population size \nwould require graphical examination of simulation results. A high population \nsize, 300 for instance, provided a slightly better fit on the upper plain of water \ninfiltration front, whilst a low population size, such as 20, improved the lower \nplain prediction as shown in Figure 4(a). The simulation results in Figure 4(a) \nalso suggested that there were insufficient data to reproduce the level of upper \nand lower plains. Using additional data at the upper and lower plains, a population \nsize of 50 was found to provide a reasonable prediction and had a fitness value of \n0.0122 as shown in Figure 4(b). The SGA unexpectedly failed for a population \nsize of 20, suggesting that the population size used was inadequate. \n\n\n\nThe SGA method was subjected to additional performance tests that \nsubjected each input parameter to random percentage changes. The evaluation \nwas necessary to prove that the SGA method was able to withstand a variety of \ninput parameter conditions. The exact percentage variations used are shown in \nFigure 5(a), whilst Figure 5(b) shows reasonably good prediction results for a \n\n\n\nFigure 3: Average fitness function (FF) value for  population sizes: (a) 20; (b) 50; (c) \n100; and (d) 300. For each population size, different mutation rates (m) were applied as \n\n\n\nstated in the legend.\nNote: inverse modeling was investigated by varying each input parameter by +20%\n\n\n\nfrom the base value in Table 1. Simulation time was 105s.\n\n\n\n\n\n\n\n15 \n \n\n\n\n  (a)       (b) \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n(a)                                                                        (b) \n \n(c)       (d) \n\n\n\n\n\n\n\n\n\n\n\n(b)                                                                        (d) \n \n\n\n\nFigure 3: Average fitness function (FF) value for  population sizes: (a) 20; (b) 50; (c) 100; \nand (d) 300. For each population size, different mutation rates (m) were applied as stated in \n\n\n\nthe legend. Note: inverse modeling was investigated by varying each input parameter by +20% \nfrom the base value in Table 1. Simulation time was 105s. \n\n\n\n\n\n\n\n0.00\n\n\n\n0.10\n\n\n\n0.20\n\n\n\n0.30\n\n\n\n0.40\n\n\n\n0.50\n\n\n\n1 10 100\n\n\n\nAv\ner\n\n\n\nag\ne \n\n\n\nfit\nne\n\n\n\nss\n fu\n\n\n\nnc\ntio\n\n\n\nn \n(F\n\n\n\nF)\n v\n\n\n\nal\nue\n\n\n\nlog (Generation)\n\n\n\n0.00\n\n\n\n0.05\n\n\n\n0.10\n\n\n\n0.15\n\n\n\n0.20\n\n\n\n0.25\n\n\n\n1 10 100\n\n\n\nAv\ner\n\n\n\nag\ne \n\n\n\nfit\nne\n\n\n\nss\n fu\n\n\n\nnc\ntio\n\n\n\nn \n(F\n\n\n\nF)\n v\n\n\n\nal\nue\n\n\n\nlog (Generation)\n\n\n\n0.00\n\n\n\n0.05\n\n\n\n0.10\n\n\n\n0.15\n\n\n\n0.20\n\n\n\n0.25\n\n\n\n1 10 100\n\n\n\nAv\ner\n\n\n\nag\ne \n\n\n\nfit\nne\n\n\n\nss\n fu\n\n\n\nnc\ntio\n\n\n\nn \n(F\n\n\n\nF)\n v\n\n\n\nal\nue\n\n\n\nlog (Generation)\n\n\n\n0.00\n\n\n\n0.05\n\n\n\n0.10\n\n\n\n0.15\n\n\n\n0.20\n\n\n\n0.25\n\n\n\n1 10 100\n\n\n\nAv\ner\n\n\n\nag\ne \n\n\n\nfit\nne\n\n\n\nss\n fu\n\n\n\nnc\ntio\n\n\n\nn \n(F\n\n\n\nF)\n v\n\n\n\nal\nue\n\n\n\nlog (Generation)\n\n\n\nFitting Constitutive Functions to the Soil Water Flow\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201526\n\n\n\npopulation size of 50 and a mutation rate of 18. Its fitness value was found to \nbe 0.0978. Thus, it indicated that the SGA method was able to handle random \npercentage changes in the input parameters.\n\n\n\nFurthermore, the reliability of the SGA method was evaluated with two \nextreme performance tests. The first extreme test reduced all input parameter \nvalues by 100% as shown in Figure 6(a). For instance, \u03b8r was reduced from 0.124 \n\n\n\nFigure 4: Water infiltration front with: (a) 28 data points inverse model by \ndifferent population sizes, i.e., 20, 50 and 300; and (b) 200 data points inverse model \n\n\n\nby population sizes of 20 and 50. The 200 data points were simulated from a numerical \nmodel and used in the inverse modeling.\n\n\n\nNote: p indicates population size and m indicates mutation rate. Simulation time \nwas 105s.\n\n\n\nFigure 5: (a) Random percentage change of the input parameter from the base \nvalue (Table 1). (b) Water infiltration prediction at population size (p) of 50 and mutation \n\n\n\nrate (m) of 18.\nNote: \u03b8r is the residual volumetric water content, \u03b8s is the saturated volumetric \n\n\n\nwater content, Ks is the saturated hydraulic conductivity, \u03b8L (initial)  is the initial value of \nvolumetric water content, and \u03b1, \u03b2, A  and B are the fitting coefficients (Eqs. (3) and (4)).\n\n\n\nSimulation time was 105s.\n\n\n\nGoh, E.G. and K. Noborio\n\n\n\n1 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n(a)                                                                        (b) \n\n\n\nFigure 5: (a) Random percentage change of the input parameter from the base value (Table 1). \n\n\n\n(b) Water infiltration prediction at population size (p) of 50 and mutation rate (m) of 18. Note: \n\n\n\nr\uf071  is the residual volumetric water content, s\uf071  is the saturated volumetric water content, sK  is \n\n\n\nthe saturated hydraulic conductivity, \uf028 \uf029L initial\uf071  is the initial value of volumetric water content, and \n\n\n\n\uf061 , \uf062 , A  and B  are the fitting coefficients (Eqs. (3) and (4)). Simulation time was 105s. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0.00\n0.05\n0.10\n0.15\n0.20\n0.25\n0.30\n\n\n\n0.20 0.30 0.40 0.50\n\n\n\nD\nep\n\n\n\nth\n (m\n\n\n\n)\n\n\n\nVolumetric water content (m3 m-3)\n\n\n\nData\np=50,m=18\n\n\n\n-40 -20 0 20 40\nInput parameter variation from base \n\n\n\nvalue (%)\n\n\n\nIn\npu\n\n\n\nt p\nar\n\n\n\nam\net\n\n\n\ner\n\n\n\n\u03b8L(initial) \nKs\n\n\n\n\n\n\n\nB \nA \n\u03b2  \n\u03b8s \n\u03b8r \n\u03b1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 27\n\n\n\nto zero where this value was used as a low limit (ai ) in Eqs. (5) to (7). The extreme \ntest would require a population size of 300, as shown in Figure 6(b), to provide \nreasonable prediction results at a fitness value of 0.1250. Whilst maintaining the \nuse of the low limit of the first extreme test, the second extreme test increased \nthe high limit (bi ), as shown in Figure 6(c), of Eqs. (5) to (7). The extreme test \nwould require at least a population size of 500, which was barely sufficient to \nprovide a reasonable prediction with a fitness value of 0.1763, as shown by Figure \n6(d). Hence, a wide range of uncertainty in input parameter value would require \na greater population size (i.e., more than 500) to maintain reasonable prediction \nresults.\n\n\n\nIn BA, there were a few extra important factors to consider. Apart from the \npopulation size and the generations, as in SGA, in BA there were: (1) maximum \nlimits of frequency, fmax , after setting fmin to zero, refer Eq. (9); (2) multiplier, in \n\n\n\nFigure 6: (a) Extreme percentage reduction of the input parameter from the base \nvalue (Table 1). (b) Water infiltration prediction based on input range of graph (a) at \n\n\n\npopulation size (p) of 300 and mutation rate (m) of 4. (c) Extreme percentage reduction \nand increase of input parameter from base value (Table 1). (d) Water infiltration \n\n\n\nprediction based on input range of graph (c) at population size (p) of 500 and mutation \nrate (m) of 4. \n\n\n\nNote: \u03b8r is the residual volumetric water content, \u03b8s is the saturated volumetric water \ncontent, Ks  is the saturated hydraulic conductivity, \u03b8L (initial)  is the initial value of \n\n\n\nvolumetric water content, and \u03b1, \u03b2, A  and B  are the fitting coefficients (Eqs. (3) and (4)).\nSimulation time was 105s.\n\n\n\nFitting Constitutive Functions to the Soil Water Flow\n\n\n\n2 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n(a)                                                                        (b) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n(c)                                                                       (d) \n\n\n\nFigure 6: (a) Extreme percentage reduction of the input parameter from the base value (Table \n\n\n\n1). (b) Water infiltration prediction based on input range of graph (a) at population size (p) of \n\n\n\n300 and mutation rate (m) of 4. (c) Extreme percentage reduction and increase of input \n\n\n\nparameter from base value (Table 1). (d) Water infiltration prediction based on input range of \n\n\n\ngraph (c) at population size (p) of 500 and mutation rate (m) of 4. Note: r\uf071  is the residual \n\n\n\nvolumetric water content, s\uf071  is the saturated volumetric water content, sK  is the saturated \n\n\n\nhydraulic conductivity, \uf028 \uf029L initial\uf071  is the initial value of volumetric water content, and \uf061 , \uf062 , A  \n\n\n\nand B  are the fitting coefficients (Eqs. (3) and (4)). Simulation time was 105s. \n\n\n\n\n\n\n\n0.00\n0.05\n0.10\n0.15\n0.20\n0.25\n0.30\n\n\n\n0.20 0.30 0.40 0.50\n\n\n\nD\nep\n\n\n\nth\n (m\n\n\n\n)\n\n\n\nVolumetric water content (m3 m-3)\n\n\n\nData\np=300,m=4\n\n\n\n0.00\n0.05\n0.10\n0.15\n0.20\n0.25\n0.30\n\n\n\n0.20 0.30 0.40 0.50\n\n\n\nD\nep\n\n\n\nth\n (m\n\n\n\n)\n\n\n\nVolumetric water content (m3 m-3)\n\n\n\nData\np=500,m=4\n\n\n\n-100.0\n-100.0\n\n\n\n0.0\n-100.0\n-100.0\n-100.0\n-100.0\n-100.0\n\n\n\n30.0\n5.0\n\n\n\n910.15.0\n35.0\n40.0\n30.0\n22.0\n\n\n\n-1000 -500 0 500 1000\nInput parameter variation from base \n\n\n\nvalue (%)\n\n\n\nIn\npu\n\n\n\nt p\nar\n\n\n\nam\net\n\n\n\ner\n\n\n\n-100.0\n-100.0\n\n\n\n0.0\n-100.0\n-100.0\n-100.0\n-100.0\n-100.0\n\n\n\n103.0\n222.6\n\n\n\n910.1\n400.0\n140.8\n408.5\n\n\n\n12,900.0278.8\n\n\n\n-10000 -5000 0 5000 10000 15000\nInput parameter variation from base \n\n\n\nvalue (%)\n\n\n\nIn\npu\n\n\n\nt p\nar\n\n\n\nam\net\n\n\n\ner\n\n\n\n\u03b8L(initial) \nKs\n\n\n\n\n\n\n\nB \nA \n\u03b2  \n\u03b8s \n\u03b8r \n\u03b1 \n \n\n\n\n\u03b8L(initial) \nKs\n\n\n\n\n\n\n\nB \nA \n\u03b2  \n\u03b8s \n\u03b8r \n\u03b1 \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201528\n\n\n\nEq. (12); (3) pulse rate enhancement factor, \u03b3bat  , in Eq. (13); (4) initial pulse \nrate, ri\n\n\n\no , in Eq. (13); (5) loudness cooling factor, \u03b1bat , in Eq. (14); and (6) initial \nloudness, Ai\n\n\n\nt , in Eq. (14). In this study\u2019s SGA, there was only mutation rate apart \nfrom population size and generations. By following a similar procedure to  SGA, \nthose six parameters of  BA were calibrated to search for the best FF value. The \nfirst stage of calibration was done by repeatedly deviating one parameter at a \ntime from the default values (Table 2), whilst maintaining all other parameters \nunchanged. This was carried out individually on all those six parameters. The \nbest parameter values (i.e., each parameter with the best FF value) were selected \n\n\n\nTABLE 1\nThe base values of input parameters from Haverkamp et al. (1977) for Yolo light clay.\n\n\n\nTABLE 2\nThe base (or default) value of input parameters for Bat Algorithm (BA). Parameter \n\n\n\nvalues for population 20, 50 and 100 were calibrated.\n\n\n\nGoh, E.G. and K. Noborio\n\n\n\n\n\n\n\n12 \n \n\n\n\n\n\n\n\nTABLE 1 \nThe base values of input parameters from Haverkamp et al. (1977) for Yolo light clay. \n \n\n\n\nParameter Base value \n\uf061  739 \n\n\n\nr\uf071  0.124 m3 m-3 \n\n\n\ns\uf071  0.495 m3 m-3 \n\uf062  4 \nA  124.6 \nB  1.77 \n\n\n\nsK  1.23x10-7 m s-1 \n\n\n\n\uf028 \uf029L initial\uf071  0.2376 m3 m-3 \n\n\n\nNotes: r\uf071 is the residual volumetric water content, s\uf071 is the saturated volumetric water content, sK is \nthe saturated hydraulic conductivity, \uf028 \uf029L initial\uf071 is the initial value of volumetric water content, and \uf061 , \n\n\n\n\uf062 , A  and B  are the fitting coefficients (Eqs. (3) and (4)). The upper boundary was set to saturated \nvolumetric water content, i.e.,0.495 m3 m-3, and lower boundary was set permeable inflow and outflow \nof water. The data used by Haverkampet al., (1977) fitted to Eq. (3) were obtained from Philip (1969) \nand data for Eq. (4) were from Philip (1957). \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nNotes:  \u03b8r  is the residual volumetric water content, \u03b8s is the saturated volumetric \nwater content,  Ks is the saturated hydraulic conductivity, \u03b8L (initial) is the initial value of \n\n\n\nvolumetric water content, and \u03b1, \u03b2, A and B and are the fitting coefficients (Eqs. (3) and \n(4)). The upper boundary was set to saturated volumetric water content, i.e.,0.495 m3 m-3, \n\n\n\nand lower boundary was set permeable inflow and outflow of water. The data used by \nHaverkamp et al., (1977) fitted to Eq. (3) were obtained from Philip (1969) and data for \n\n\n\nEq. (4) were from Philip (1957).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 29\n\n\n\nand used. The process was then performed for other individual parameters. This \nprocess was repeated two times. In the second stage of calibration, the fmax and  \nmultiplier  parameters were fixed for convenience. The remaining four parameters \nwere randomly sorted through using the Sobol\u2019 sequence to account for every \npossible quad combination. Goh and Noborio (2014) illustrated the spatial \ncoverage of the Sobol\u2019 sequence in two dimensions for two parameters.  \n\n\n\nIn the first stage of calibration, the default condition was found to generate \nan FF value of 0.1556, and when those six parameters were varied individually \n(or individually-calibrated), the lowest FF value of 0.1276 was found and it was \nsignificantly lower (or improved) than that of the default condition. Another \nround of searching was carried out using the individually-calibrated values, and \nit was found to cause deterioration (or increase) in the FF value to 0.1499. The \nparameter values were not provided in the table. The combined individually-\ncalibrated parameters were unable to generate a good FF value indicating that \nthere were some degrees of interaction between the parameters that could be \nexhibiting either antagonistic or synergistic behaviours. This problem was solved \nin the second stage of calibration by simultaneously varying four input parameters \n(i.e., pulse rate enhancement factor, initial pulse rate, loudness cooling factor and \nthe initial loudness). An improved FF value of  0.1365 was found even for a \npopulation size of 20 (refer Table 2 for calibration-20), and 0.1249 and 0.1198 at \ncalibration-50 and calibration-100, respectively. Compared to SGA\u2019s FF values, \nwhich included 200 data points as showed in Figure 4(b), BA\u2019s FF value of 0.1365 \nwas found to outperform SGA\u2019s FF value of 0.4906, for a population size of 20. \nHowever, for a population of 50, SGA outperformed BA with respective FF values \nof 0.0122 and 0.1249.\n\n\n\nFor SGA, a calibrated mutation rate was found after eleven trial-and-error \niterations, whilst for BA, the best quad combination was found at the 84th, 54th and \n42nd iterations for population sizes of  20, 50 and 100, respectively. Additionally, \nif SGA and BA were compared for a population size of 20 without considering \nthe total number of trial-and-error iterations needed to obtain the calibrated \nparameter values, corresponding total model executions of 1,040 and 540 were \nneeded, whilst for a population size of 50, model executions needed were \n3,400 and 1,650. At this point, BA appears to be more cost efficient than SGA. \nHowever, if the total count number of trial-and-error iterations (i.e., the search for \noptimising parameter values) were included, the SGA would require only 11,440 \nmodel executions, whilst BA would require 45,360 for a population size of 20. \nSimilarly, for a population size of 50, SGA and BA required 37,400 and 89,100 \nmodel executions, respectively.\n\n\n\nCONCLUSION\nThe SGA was found to be an effective method to predict a globally-optimum \nset of input parameter values where the simulation results were found to match \nthe existing data points closely. A large number of generations were unnecessary \nas in most cases minimum fitness value was reached in the early generations \n\n\n\nFitting Constitutive Functions to the Soil Water Flow\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201530\n\n\n\n(i.e., between 5 and 40). The SGA was solved with termination criteria. Large \npopulation size resulted in a low fitness value, thus, suggested better simulation \nresults. SGA required expensive computational time and, therefore, finding \noptimum population size was a better approach. In this study, a population size \nof 50 was sufficient to provide reasonable simulation results, except when there \nwas an extremely uncertain range of input parameter values where it required \nan excessive increase in the population size. Additionally, it was found for each \npopulation size, a particular mutation rate applied in order to obtain the lowest \nfitness value. Although this value varied for each population size, it was observed \nto decrease as the population size increased. Whilst SGA was able to reproduce \nsound simulation results, data points at saturation and residual levels were equally \nimportant as those of the water infiltration front. It was necessary to perform a \nmeaningful search of globally-optimum set of input parameter values. Moreover, \nSGA was found to perform reasonably well under constant increments (+20%) and \nrandom percentage changes in input parameter values. Under extreme cases, as \nmentioned previously, SGA continued to perform reasonably well at the expense \nof higher population sizes and greater computational time. An educated guess \nto narrow the range limit of input uncertainty would result in better simulation \nresults, smaller population size and ultimately, require less computational time. \n\n\n\nComparing the SGA to the BA, the latter appeared to perform well at a \nlow population size (i.e., 20) whilst the former was found better at a population \nsize of 50. SGA was the better choice after considering the number of model \nexecutions needed for model calibration, a reasonably small population size \nwithout sacrificing FF value, and the number of input parameters that must be \ncalibrated in order to search for a small FF value.\n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to acknowledge the financial support from Meiji University, \nJapan and the Ministry of Higher Education, Malaysia and Universiti Malaysia \nTerengganu. Expressions of sincere thanks also goes to the anonymous reviewers \nfor their comments on this manuscript. \n\n\n\nREFERENCES\nBaluja, S. 1994. Population-Based Incremental Learning. A Method for Integrating \n\n\n\nGenetic Search Based Function Optimization and Competitive Learning. Report \nNo.CMU-CS-94-163Pittsburgh, PA: Carnegie-Mellon University.\n\n\n\nCedeno, W., and V. Vemuri. 1992. Dynamic multi-modal function optimization using \ngenetic algorithms. 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Statistics and Computing 4(2): 65-85. \ndoi: 10.1007/BF00175354.\n\n\n\nYang, X.-S. 2014. Chapter 10 - Bat Algorithms. In: Nature-Inspired Optimization \nAlgorithms, ed. X.-S. Yang, pp. 141-154. Oxford: Elsevier.\n\n\n\nYang, X.-S. 2010a. Engineering Optimization: An Introduction with Metaheuristic \nApplications. New Jersey: John Wiley & Sons.\n\n\n\nYang, X.-S. 2010b. a new metaheuristic bat-inspired algorithm. In: Nature Inspired \nCooperative Strategies for Optimization (NICSO 2010), ed. J.R. Gonz\u00e1lez, D.A. \nPelta, C. Cruz, G. Terrazas and N. Krasnogor, Vol. 284, pp. 65-74.  Springer: \nBerlin Heidelberg.\n\n\n\nZheng, C. and G.D. Bennett. 2002. Applied Contaminant Transport Modeling (2nd \n\n\n\ned.). New York: John Wiley & Sons.\n  \n\n\n\nGoh, E.G. and K. Noborio\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 177-194 (2016)                         Malaysian Society of Soil Science\n\n\n\nEffects of Empty Fruit Bunch Biochar and Nitrogen\u2013Fixing \nBacteria on Soil Properties and Growth of Sweet Corn \n\n\n\n1Diyar Kareem Abdulrahman, 1,3Radziah Othman,\n2Halimi Mohd Saud\n\n\n\n   1Department of Land Management,\n2 Department of Agriculture Technology, Faculty of Agriculture, \n\n\n\n3 Institute of Tropical  Agriculture, Universiti Putra Malaysia, 43400 UPM Serdang, \nSelangor\n\n\n\nABSTRACT\nEmpty fruit bunch (EFB) biochar is being used as soil amendment to improve soil \nproductivity of infertile soil for enhanced plant growth. Tropical soils are generally \nunfertile with, low organic matter, plant nutrients, acidic and low microorganisms \nthat affect crop production. Addition of biochar such as empty fruit bunch \n(EFB) could improve the soil fertility.  Laboratory and glasshouse studies were \nconducted to determine the effect of EFB biochar and nitrogen-fixing bacteria \nStenotrophomonas sp. (Sb16) on soil microbial communities, enzyme activity, \nchemical properties and growth of sweet corn. Five rates of EFB biochar (0, 0.25, \n0.5, 0.75 and 1%) were applied to soil either with or without bacteria Sb16 and \nincubated for 40 days under laboratory condition.  Sweet corn were grown in pots \ncontaining 6 kg soil and applied with five rates of EFB biochar (0, 5, 10, 15 and 20 \nt/ha) with or without bacteria Sb16. The experiment was arranged in a randomized \ncomplete block design (RCBD), with 5 replications. Results of laboratory study \nshowed that combination of EFB biochar at 0.5% without inoculation and \n0.25% with bacteria Sb16 in both soil, significantly increased populations of soil \nbacteria, fungi, actinomycetes and N2-fixing bacteria (NFB), enzymes (urease, \nacid phosphatase and fluorescein diacetate (FDA) hydrolysis activity), and soil \nchemical properties (pH, organic carbon, total N, available P and exchangeable K, \nCa and Mg). The glasshouse experiment showed that application of EFB biochar \nat 5 t/ha with bacteria Sb16 significantly (p<0.05) improved growth of corn (shoot \nand root biomass, root length, root volume, plant height, leaf chlorophyll content \nand nutrient uptake). Addition of higher EFB biochar  to soil negatively affected \nall the observed parameters. The studies showed that application of EFB biochar at \n5 t/ha or 0.25% with N2-fixing bacteria Sb16 and 10 t/ha or 0.5% without bacterial \ninoculation improved corn growth and the quality of soil for sustainable corn \nproduction.\n\n\n\nKeywords: Empty fruit bunch (EFB) biochar. N2-fixing bacteria \nStenotrophomonas sp., sweet corn, soil enzymes, soil microbial \nproperties.\n\n\n\n___________________\n*Corresponding author : E-mail: radziah@upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016178\n\n\n\nAbdulrahman et al.\n\n\n\nINTRODUCTION\nTropical soils are generally less fertile for crop growth. The soil can be effectively \nameliorated by applying liming material that improves soil pH and  organic \nmatter or other amendments can be added  to improve physical, chemical and \nbiological properties of soil. Application of biochar such as empty fruit bunches \n(EFB) biochar can be an alternative to  chemical fertilisers to improve soil fertility \n(Norazlina et al., 2014). In general, biochar amendment which is alkaline in \nnature can improve soil pH for better plant growth. Biochar has the potential to \noffer multiple environmental benefits in that they do not only contribute to carbon \nstorage, but at the same time act as a soil ameliorant (Cheng et al., 2007). Biochar \nimproves soil physical properties including pore-size distribution, total porosity, \nsoil density, water holding capacity and soil moisture content (Atkinson et al., \n2010; Sohi et al., 2010). Addition of biochar to highly leached, infertile soils \nhas been shown to increase the availability of basic cations (Glaser et al., 2002; \nLiang et al., 2006), and significantly improve crop yields (Lehmann and Rondon, \n2006). Enzymes are proteins produced by soil microbial community that increase \nreactions involved in soil organic matter and nutrient cycling (Lei et al., 2014). \nSoil enzymes, such as urease, phosphatase and FDA play an important role in the \ndecomposition of organic matter, nutrient cycling for microbial activity and plant \nnutrient uptake (Lammirato et al., 2011).\n\n\n\nBiochar has the potential to release a wide range of organic and inorganic \nmolecules  and may provide a mechanism to protect these enzymes (Castaldi et \nal., 2012; Lehmann et al., 2011), but in general, there is little information on the \npossible impacts of biochar on soil enzymes. Biochar improves plant nutrients \nin soil and biological activity, thus enhancing soil microbial populations (Kim \net al., 2007; Unger and Killorn, 2012) through interactions with soil mineral, \norganic matter and microbial activities (Carson et al., 2007; Nguyen et al., 2008). \nHowever, the relationship between chemical and physical properties of biochar \nand its influence on soil microbial activity and probable concomitant impacts on \nsoil processes are poorly understood. Biochar can be applied as a soil ameliorant \nto enhance soil fertility and crop production in a wide range of soils (Blackwell et \nal., 2009).  Addition of biochar is known to increase biomass of rice and cowpea \n(Lehmann et al., 2003), sweet corn and soybeans (Singer et al., 2007), and maize \ngrain (Major et al., 2010). Increased nutrients retention by biochar may be the \nmost important factor for increasing crop yields on infertile acid soils (Asai et al., \n2009).\n\n\n\nBeneficial bacteria such as nitrogen-fixing bacteria can be used to improve \nsoil fertility by fixing N2 and transferring the fixed N in soil. Several crops such \nas rice, wheat and maize need 20 to 40 kg soil N ha-1 to satisfy the N requirements \nfor each tonne of grain produced (Peoples and Craswell, 1992). To fulfill such \na demand for nitrogen, farmers must apply inorganic N fertilisers that have \nthe potential to pollute the environment or rely on beneficial microbes such as \nbiological nitrogen fixation (BNF) with the input of organic wastes, such as \nbiochar. There are several bacteria that are capable of fixing atmospheric nitrogen.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 179\n\n\n\nEffect of Biochar and N-Fixing Bacteria on Sweet Corn\n\n\n\nThey can be the free-living (non-symbiotic) bacteria associated with cereal crops \nand symbiotic bacteria associated with leguminous plants (Esawy et al., 2013). \nThese bacteria transform atmospheric N2 into ammonium (NH4\n\n\n\n+), a form of N \nthat can be used directly by plants. Nitrogen cycling in natural ecosystems relies \non N2-fixing bacteria for agricultural production. N2-fixing bacteria produce \nnitrogen, which is much more effective and less costly to improve plant growth. \nFree-living N2 bacteria in the soil may provide substantial amounts of nitrogen \n(0 to 60 kg N ha-1 year-1) (Burgmann et al., 2004). This could be important in \norganically amended soils, which typically have a lower concentration of nitrogen \nin available forms. The EFB biochar can be applied with free-living N2 fixing \nbacteria to improve soil fertility, microbial activity and plant growth. Therefore, \nthis study was conducted to determine the effect of oil palm EFB biochar and \nN2-fixing bacteria Stenotrophomonas sp. Sb16 on soil enzyme activity, microbial \npopulation, chemical properties and growth and nutrient uptake of sweet corn. \n\n\n\nMATERIALS AND METHODS\n\n\n\nSoil and EFB Biochar Preparation \nBoth laboratory and glasshouse experiments were conducted at the Faculty \n\n\n\nof Agriculture, Universiti Putra Malaysia (UPM). The soil samples were collected \nfrom the top soil (0 - 15 cm depth) of the UPM farm. Soil samples were air-dried \nfor five days, ground and sieved through a 2.00 mm mesh for laboratory and 4.00 \nmm mesh for glasshouse study. The EFB biochar was made from empty fruit \nbunches of oil palm which was provided by a private company in Selangor. This \nbiomass went through the pyrolysis process at a temperature of between 350-450o \n\n\n\nC to produce EFB biochar. The chemical characteristics of  soil and EFB biochar \nwere analysed in the soil microbiology laboratory and the results are as shown in \nTable 1.  \n\n\n\nPreparation of Free- Living N2-Fixing Bacteria\nThe bacterial culture of  Stenotrophomonas sp. (Sb16) was used for soil inoculation \n(Radziah et al., 2013). The bacteria were obtained from Faculty of Agriculture, \nUPM. The strain diversity of diazotrophs depends on the soil environment.  In \nMalaysia, the  tropical soils  generally have low pH and thus favour low pH \ntolerant diazotrophs. Bacteria Sb16 is an endophyte which plays important roles in \nagricultural production as a plant growth-promoting bacteria or N2-fixing bacteria. \nStenotrophomonas sp. Sb16, formerly Xanthomonas maltophilia is widely found \non or in plants and has a worldwide distribution. The strain was sub-cultured in \n100 ml Erlenmeyer flask with Jensen\u2019s N-free broth and shaken continuously for \n36 h (100 rpm at 28o C) (Jensen, 1951), until reaching 108 (cfu / mL).\n\n\n\nLaboratory Experiment\n A factorial study was conducted using a completely randomised design (CRD) \nwith three replications. Five rates of the oil palm EFB biochar (0, 0.25, 0.5, 0.75 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016180\n\n\n\nand 1%) were applied to 150 g of sterilised soil in 250 mL conical flasks and \nthe contents were mixed properly. Soils were inoculated with one milliliter of \napproximately 108 cfu mL-1 N2-fixing bacteria (Sb16). Flasks were covered with \naluminum foil and incubated for 40 days at room temperature (28 \u00b1 2o C). The \nsoils were analysed for microbial populations, enzyme activity and chemical \nproperties.\n\n\n\nGlasshouse Experiment\nSix kilograms of sieved (4.00mm) soil was mixed thoroughly with EFB biochar at \na rate of 0, 5, 10, 15 and 20 t ha-1 and placed in drained pots, either in the presence \nor absence of N2-fixing bacteria (Sb16). The soil and EFB biochar mixture in pots \nwere left to react for 20 days before planting with corn. Five uniform sweet corn \nseeds were planted 5.0 cm below the surface of soil in the pots and one mL of \napproximately 108cfu mL-1 of N2-fixing bacteria (Sb16) was applied to each seed. \nAll pots were watered daily. All pots were fertilised with urea (60 kg ha-1), triple \nsuperphosphate (TSP) (60 kg ha-1) and muriate of potash (MOP) (90 kg ha-1) two \nweeks after sowing. The treatments were arranged in randomised complete block \ndesign (RC) with five replications. The corn plants were harvested at tasseling \nstage (55 days), separated into plant tops and roots, and were oven dried for 4 days \nat 65o C. The dry weights of plant parts were recorded and tissue was ground for \nchemical analysis. \n\n\n\nChemical Analysis of Soil and Biochar\nSoil and EFB biochar pH were determined using the Beckman Digital pH meter \nin a 1:2.5 (w/v, soil: water) for soil and 1:10 (w/v, biochar:water) for EFB biochar \n(Gaspard  et al., 2007). Nelson and Sommers (1982). Total N was determined \naccording to the  Kjeldahl method (Bremner and Mulvaney, 1982) and  available \nphosphorus using Bray and Kurtz no. 2 method (Bray and Kurtz, 1945) and \nanalysed by the auto analyser (Lachat instruments, Quik Chem\u00ae FIA+ 8000 \nseries). The CEC, K, Ca and Mg in soil were determined using the NH4OAc, \npH7.0, leaching method (Dawid and Dorota, 2014) and analysed by an atomic \nabsorption spectrophotometer (AAS) (Perkin Elmer, 5100 PC). The nutrients in \nEFB biochar were determined by digestion technique and analysed by AAS.\n\n\n\nMeasurement of Leaf Chlorophyll Content Using SPAD-502 Meter\nChlorophyll concentration measurements by SPAD meter makes simple, rapid, and \nnon-destructive measurements, providing a relative indication of leaf chlorophyll \nconcentration compared to the extraction methods. The measurements for sweet \ncorn leaf tissue were determined by taking the average of three readings for each \nleaf.\n\n\n\nDetermination of Plant Growth Parameters\nPlant height was measured before harvesting by measuring the plant from the \nsoil surface to the tip of the main stem. Five plants were harvested from each \n\n\n\nAbdulrahman et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 181\n\n\n\ntreatment and separated into roots and shoot (stem and leaves). The plant parts \nwere then oven dried at 65o C until the weight was stable and the dry weight was \nrecorded using digital balance (QC 35EDES- Sartorius- Germany). Root volume \nwas determined using a water displacement method.\n\n\n\nRoot Measurements\nThe root length was measured by using a root scanner (Model Epsom Expression \n1680) which was connected to a computer program Win RHIZO 2007.\n\n\n\nSoil Microbial Populations\nThe populations of soil microbial community (bacteria, fungi, actinomycetes \nand N2-fixing bacteria) were determined using 10 g of fresh soil following the \ndilution plate technique (Parkinson et al., 1971). An amount of 100 \u00b5l of sample \nat selected dilutions was transferred onto Nutrient Agar (NA) for bacteria, Rose \nBengal Streptomycin Agar (RBSA) for fungi (Martin, 1950), Actinomycete \nIsolation Agar (A.A) for actinomycetes and N2-free media for N2-fixing bacteria, \nrespectively. After incubation at room temperature (28o C \u00b1 2) for 2 days, the \ncolonies were counted and population determined as colony forming unit (cfu) g-1 \ndry soil-1 and then transformed to log10 values for statistical analysis. \n\n\n\nDetermination of Soil Enzyme Activity\nSoil samples were air-dried and sieved through a 2 mm sieve. Soil phosphatase \nactivity was determined using the method described by Tabatabai and Bremner \n(1969). Soil urease activity was determined as described by Tabatabai and \nBremner (1972). Fluorescein diacetate hydrolysis Assay (FDA) was conducted \nfor measuring the enzyme activity of microbial populations which can provide an \nestimate of overall microbial activity in an environmental sample (Schnurer and \nRosswall, 1982). \n\n\n\nChemical Analysis of Plant Tissue\nPlant tissue (0.25 mm sieve) was digested with concentrated sulfuric acid and 50% \nhydrogen peroxide and analysed for P, K, Ca and Mg concentrations (Thomas et \nal., 1967). Total plant N was done according to the Kjeldahl method (Bremner \nand Mulvaney, 1982). Plant nutrient uptake was obtained by multiplying the \nconcentration of nutrients in plant tissue with the total plant dry matter weight \ndivided by 100 (Jones, 1985).\n\n\n\nStatistical Analysis\nThe laboratory study was conducted using CRD with five levels of EFB biochar, \ntwo bacterial treatments with and without N2-fixing bacteria (Sb16) and replicated \nthree times. The glasshouse experiment was carried out using RCBD, five rates of \nsoil EFB biochar with and without of N2-fixing bacteria with five replications. The \ndata were recorded and analysed using two way analysis of variance (ANOVA) \nby Statistical Analysis System (SAS) version 9.3 for Windows. The significant \n\n\n\nEffect of Biochar and N-Fixing Bacteria on Sweet Corn\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016182\n\n\n\ndifference of treatment means was checked by the Tukey\u2019s General Linear Model \nTest (GLM) at the 5% level of confidence.\n\n\n\nTABLE 1\nThe chemical analysis of soil and EFB biochar\n\n\n\nRESULTS\n\n\n\nLaboratory Study\nEffects of EFB biochar and N2-fixing bacteria (Sb16) on soil microbial \npopulations \nThe total soil bacteria, fungi, actinomycetes and NFB increased significantly \n(p<0.05) with EFB biochar and N2-fixing bacteria Sb16 application (Figure \n1). EFB biochar amended soil was observed to stimulate the soil microbial \ncommunity compared to control. Addition of EFB biochar at 0.25% positively \nimproved soil microbial populations compared to non-amended soil.   A lower \nmicrobial population was observed with increasing EFB biochar rates in both soils \nwith or without inoculation. Application of EFB biochar at 0.75% and 1% with \nor without bacteria Sb16 inoculation adversely affected soil microbial activity. \nEnhancement of microbial population could be due to available nutrients in soil, \nwhich was affected by EFB biochar and bacteria Sb16 inoculation. Biochar may \nalso provide a suitable habitat to protect beneficial microbes from predators in \nsoil. The abundance of bacteria in biochar amended soils might be attributed to \nthe beneficial properties and characteristics of EFB biochar (Ingram et al., 2005).\n\n\n\n6 \n \n\n\n\n\n\n\n\nTable 1: The chemical analysis of soil and EFB biochar \n \n\n\n\nProperties   Soil EFB Biochar \n\n\n\npH water w/v 4.6 9.39 \n\n\n\nCarbon (%) 2.01 52 \n\n\n\nTotal N (%) 0.1 1.58 \n\n\n\nAvailable P (mg/kg) 34 -- \n\n\n\nTotal P (%) -- 0.22 \n\n\n\nExch K (cmol + /kg) 0.2 -- \n\n\n\nTotal K (%) -- 4.9 \n\n\n\nExch  Ca (cmol +/kg) 2.3 -- \n\n\n\nTotal Ca (%) -- 0.11 \n\n\n\nExch  Mg (cmol +/kg) 0.8 -- \n\n\n\nTotal Mg (%) -- 0.14 \n\n\n\nCEC (cmol + /kg) 8.1 63.2 \n\n\n\n \n3. RESULTS \n\n\n\n3.1   Laboratory Study \n\n\n\n3.1.1 Effects of EFB biochar and N2-fixing bacteria (Sb16) on soil microbial populations  \n\n\n\nThe total soil bacteria, fungi, actinomycetes and NFB significantly (P<0.05) increased with EFB biochar and N2-fixing bacteria \n\n\n\nSb16 application (Figure 1). EFB biochar amended soil was observed to stimulate the soil microbial community compared to \n\n\n\ncontrol. Addition of EFB biochar at 0.25% positively improved soil microbial populations compared to non-amended soil.  \n\n\n\nLower microbial population was observed with increasing EFB biochar rates in both soils with or without inoculated. \n\n\n\nApplication of EFB biochar at 0.75% and 1% with or without bacteria Sb16 inoculation adversely affected soil microbial \n\n\n\nactivity. Enhancement of microbial population could be due to available nutrients in soil, which was affected by EFB biochar \n\n\n\nand bacteria Sb16 inoculation. Biochar may also provide a suitable habitat to protect beneficial microbes from predators in soil. \n\n\n\nThe abundance of bacteria in biochar amended soils might be attributed to the beneficial properties and characteristics of EFB \n\n\n\nbiochar (Ingram et al., 2005). \n\n\n\n\n\n\n\n\n\n\n\nAbdulrahman et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 183\n\n\n\nFigure 1:  Effect of soil sterilisation, EFB biochar and N2-fixing bacteria (Sb16) on \nsoil bacteria, fungi, actinomycetes and N2-fixing bacterial populations. Vertical bars \n\n\n\nrepresent standard error (S.E), n=3.\n\n\n\nEffects of EFB biochar and N2-fixing bacteria (Sb16) on soil enzyme activity\nEFB biochar and N2-fixing bacteria, Sb16, significantly enhanced (p<0.05) soil \nurease, phosphatase and fluorescein diacetate hydrolysis (FDA) activity in the \nsoil. The effect of EFB biochar and bacteria Sb16 on soil enzymes activity is \nas shown in Figure 2. Addition of EFB biochar at 0.25% EFB biochar with and \nwithout bacteria inoculation significantly increased (p \u2264 0.05) the urease and FDA \nactivities, while 0.5% EFB biochar and bacteria Sb16 significantly improved soil \nphosphatase. In general the presence of bacteria Sb16 increased the production of \nsoil enzymes. The enzymes activities were observed to decrease with increased  \nEFB biochar rates. Presence of organic carbon from the EFB biochar may \nimprove growth of soil microorganisms which can subsequently influence enzyme \nactivities in soil (Anderson et al., 2011).\n\n\n\n7 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1:  Effect of soil sterilization, EFB biochar and N2-fixing bacteria (Sb16) on soil bacteria, fungi, actinomycetes and N2-\n\n\n\nfixing bacterial populations. Vertical bars represent standard error (S.E), n=3.  \n\n\n\n3.1.2 Effects of EFB biochar and N2-fixing bacteria (Sb16) on soil enzyme activity \n\n\n\nEmpty fruit bunch (EFB) biochar and N2-fixing bacteria Sb16 significantly (P<0.05) enhanced soil urease, phosphatase and \n\n\n\nfluorescein diacetate hydrolysis (FDA) activity in the soil. The effect of EFB biochar and bacteria Sb16 on soil enzymes \n\n\n\nactivity is as shown in (Figure 2). Addition of EFB biochar at 0.25% EFB biochar with and without bacteria inoculation \n\n\n\nsignificantly (P \u2264 0.05) increased the urease and FDA activities, while 0.5% EFB biochar and bacteria Sb16 significantly \n\n\n\nimproved soil phosphatase in soil. In general the presence of bacteria Sb16 increased the production of soil enzymes. The \n\n\n\nenzymes activities were observed to decrease with increased in EFB biochar rates. Presence of organic carbon from the EFB \n\n\n\nbiochar may improve growth of soil microorganisms which can then influence enzyme activities in soil (Anderson et al., 2011).  \n\n\n\n\n\n\n\nEffect of Biochar and N-Fixing Bacteria on Sweet Corn\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016184\n\n\n\nFigure 2: Effect of  EFB biochar and N2-fixing bacteria in (a) soil urease, (b) \nphosphatase, and (c) FDA activity. Vertical bars represent standard error (S.E), n=3.\n\n\n\nEffects of EFB biochar and N2- fixing bacteria (Sb16) on soil chemical properties\nApplication of EFB biochar and bacteria Sb16 in soil did not significantly improve \nsoil chemical properties except for soil P (Table 2). Among all EFB biochar rates \nwith or without bacteria Sb16, the treatments 0.25% and 0.5% showed the highest \nvalue of selected soil chemical properties. There was no significant interaction \nbetween EFB biochar and bacteria Sb16 on soil pH, organic carbon, total N and \nexchangeable K. In general, bacterial inoculation was better than non-inoculated \ntreatment. The increase in acid soil chemical composition may be due to the \npresence of these elements in EFB biochar.\n\n\n\nGlasshouse Study\nEffects of N2-fixing bacteria Sb16 and EFB biochar on growth of sweet corn\nApplication of N2-fixing bacteria Sb16 and EFB biochar significantly improved \ngrowth of corn at tasseling stage (as indicated by root and shoot biomass, root \nlength, root volume, plant height and leaf chlorophyll content) and nutrient \nuptake (Figure 3). Plant growth with EFB biochar was better compared to without \nEFB biochar. Among the EFB biochar treatments, 5 t ha-1 showed highest shoot \nbiomass (48.5 g/plant). However, growth decreased with a further increase in \nbiochar levels. Significant improvement in plant height and leaf chlorophyll \ncontent was observed at 10 t ha-1 EFB biochar. Inoculation of soil with N2-fixing \nbacteria further improved growth of corn compared to non-inoculated plants. \nHigher shoot biomass (61.4 g/plant) was found at 5 t/ha EFB biochar with N2-\n\n\n\n8 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Effect of soil sterilization, EFB biochar and N2-fixing bacteria in a) soil urease, b) phosphatase and c) FDA activity.\n\n\n\n  Vertical bars represent standard error (S.E), n=3. \n\n\n\n\n\n\n\n3.1.3 Effects of EFB biochar and N2-fixing bacteria (Sb16) on soil chemical properties \n\n\n\nApplication of EFB biochar and bacteria Sb16 in soil did not significantly improve soil chemical properties except for soil P \n\n\n\n(Table 2). Among all EFB biochar rates with or without bacteria Sb16, the treatments 0.25% and 0.5% showed the highest \n\n\n\nvalue of selected soil chemical properties. There was no significant interaction between EFB biochar and bacteria Sb16 on soil \n\n\n\npH, organic carbon, total N and exchangeable K. In general, bacterial inoculation was better than non-inoculated treatment. \n\n\n\nThe increasing of acid soil chemical composition may be due to the presence of these elements in EFB biochar. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAbdulrahman et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 185\n\n\n\nfixing bacteria Sb16 as compared to 0 t ha-1 EFB biochar. Leaf chlorophyll (SPAD \nreading) increased by144% compared to  non-amended control. The EFB biochar \nwhich is an alkaline product could improve soil properties and thus, improve soil \nnutrient for plant growth. EFB biochar provides a conducive environment for \nbeneficial microbes, especially the N2-fixing bacteria to increase N in available \nform that can be absorbed by plants. \n\n\n\nEffects of EFB biochar and N2-fixing bacteria Sb16 on plant nutrient uptake\nAddition of EFB biochar and N2-fixing bacteria Sb16 positively affected plant \nnutrients concentration (Table 3) and uptake (Table 4). Plant nutrients content \nwas higher in EFB biochar treatments. Application of EFB biochar at 5 t/ha \nand N2-fixing bacteria significantly increased plant nutrients concentration and \nuptake compared to control. Bacterial inoculation showed better nutrient content \nthan non-inoculated plants. Higher EFB biochar content of more than10 t ha-1 in \ntreatments with or without bacteria reduced plant nutrients uptake.9 \n\n\n\n\n\n\n\n\n\n\n\nTable 2: Effect of EFB biochar and N2-fixing bacteria Sb16 on soil chemical properties.    \n\n\n\nEFB Biochar \n(%)      \n\n\n\nBacterial \nInoculation \n\n\n\npH   C (%)   N (%) P(mg kg-1) K(cmol (+) kg-1 \n\n\n\n0   -Sb16  4.5 \u00b1 0.03     1.87 \u00b1 0.01         0.004 \u00b1 0.01         28 \u00b1 0.91 g              0.17 \u00b1 0.01           \n\n\n\n0   + Sb16       4.7 \u00b1 0.03     1.92 \u00b1 0.01         0.006 \u00b1 0.01          31 \u00b1 0.93 g              0.20 \u00b1 0.01            \n\n\n\n0.25 \u2013 Sb16        5.5 \u00b1 0.03       2.71 \u00b1 0.01        0.060 \u00b1 0.01          81 \u00b1 0.90 c              0.80 \u00b1 0.01         \n\n\n\n0.25 + Sb16       5.6 \u00b1 0.03     2.78 \u00b1 0.01         0.090 \u00b1 0.01          96 \u00b1 0.57 a              0.85 \u00b1 0.01          \n\n\n\n0.5   \u2013 Sb16     5.6 \u00b1 0.03     2.73 \u00b1 0.02        0.080 \u00b1 0.01         85 \u00b1 0.57 c              0.75 \u00b1 0.02            \n\n\n\n0.5    + Sb16      5.7 \u00b1 0.03     2.75 \u00b1 0.02        0.110 \u00b1 0.01          91 \u00b1 0.96 b              0.81 \u00b1 0.01          \n\n\n\n0.75 \u2013 Sb16       5.4 \u00b1 0.03     2.20 \u00b1 0.01          0.060 \u00b1 0.01          60 \u00b1 0.98 e               0.65 \u00b1 0.01   \n\n\n\n0.75 + Sb16       5.5 \u00b1 0.03      2.23 \u00b1 0.02        0.080 \u00b1 0.01         69 \u00b1 0.95 d              0.69 \u00b1 0.01             \n\n\n\n1.00 \u2013 Sb16      5.3 \u00b1 0.03      1.96 \u00b1 0.01         0.040 \u00b1 0.01           50 \u00b1 0.97 f              0.44 \u00b1 0.02             \n\n\n\n1.00 + Sb16       5.4 \u00b1 0.03     2.01 \u00b1 0.01        0.006 \u00b1 0.01           56 \u00b1 0.95 e             0.47 \u00b1 0.01                         \n\n\n\nBiochar                                                   0.0001*              0.0001*               0.0001*              0.0001*              0.0001* \nSb16                                                  0.0224*              0.0001*               0.0085*              0.0001*              0.0006* \nBiochar*Sb16                                   0.8452ns              0.2124ns               0.1747ns              0.0133*              0.8252ns  \n____________________________________________________________________________________________               \n                                  * = significant (P<0.05)                                    NS = not significant (P>0.05)    \nMeans in each column with the same letter (s) for each variable are not significantly different according to Tukey 's test at 5%. \n\n\n\n\n\n\n\n3.2    Glasshouse Study \n\n\n\n3.2.1 Effects of N2-fixing bacteria Sb16 and EFB biochar on growth of sweet corn \n\n\n\nApplication of N2-fixing bacteria Sb16 and EFB biochar significantly improved growth of corn at tasseling stage (as indicated \n\n\n\nby root and shoot biomass, root length, root volume, plant height and leaf chlorophyll content) and nutrient uptake  (Figure 3). \n\n\n\nPlant growth with EFB biochar was better compared to without EFB biochar. Among the EFB biochar treatments, 5 t/ha \n\n\n\nshowed highest shoot biomass (48.5 g/plant). However, growth decreased with further increase in biochar levels. Significant \n\n\n\nimprovement in plant height and leaf chlorophyll content was observed at 10 t/ha EFB biochar. Inoculation of soil with N2-\n\n\n\nfixing bacteria further improved growth of corn than non-inoculated plants. Higher shoot biomass (61.4 g/plant) was found at 5 \n\n\n\nt/ha EFB biochar with N2-fixing bacteria Sb16 as compared to 0 t/ha EFB biochar. Leaf chlorophyll (SPAD reading) increased \n\n\n\nby144% compared to unamended control. The EFB biochar which is alkaline product could improve soil properties and thus, \n\n\n\nimproved soil nutrient for plant growth. EFB biochar provides a conducive environment for beneficial microbes, especially the \n\n\n\nTABLE 2\nEffect of EFB biochar and N2-fixing bacteria Sb16 on soil chemical properties   \n\n\n\nEffect of Biochar and N-Fixing Bacteria on Sweet Corn\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016186\n\n\n\nFigure  3: Effect of EFB biochar and N2- fixing bacteria Sb16 on (a) shoot biomass, (b) \nroot biomass, (c) root length, (d) root volume, (e) plant height  and (f) leaf chlorophyll \n\n\n\ncontent. Vertical bars represent standard error (S.E), n=5.\n10 \n\n\n\n\n\n\n\nN2-fixing bacteria to increase N in available form that can be absorbed by plants.  \n\n\n\n\n\n\n\nFigure 3: Effect of EFB biochar and N2-fixing bacteria Sb16 on (a) shoot biomass, (b) root biomass, (c) root length, (d) root \n\n\n\nvolume, (e) plant height  and (f) leaf chlorophyll content. Vertical bars represent standard error (S.E), n=5. \n\n\n\n(a) (b) \n\n\n\n(d) \n\n\n\n(e) (f) \n\n\n\n(c) \n\n\n\nAbdulrahman et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 187\n\n\n\nTABLE 3\nEffect of EFB biochar and N2-fixing bacteria (Sb16) on plant nutrient concentrations\n\n\n\nTABLE 4\nEffect of EFB biochar and N2-fixing bacteria Sb16 on plant nutrient uptake\n\n\n\n11 \n \n\n\n\n\n\n\n\n 3.2.2 Effects of EFB biochar and N2-fixing bacteria Sb16 on plant nutrients uptake \n\n\n\nAddition of EFB biochar and N2-fixing bacteria Sb16 positively affected plant nutrients concentrations (Table 4) and uptake \n\n\n\n(Table 5). Plant nutrients content were higher in EFB biochar treatments. Application of EFB biochar at 5 t/ha and N2-fixing \n\n\n\nbacteria significantly increased plant nutrients concentrations and uptake compared to control. Bacterial inoculation showed \n\n\n\nbetter nutrients content than non-inoculated plants. Higher EFB biochar of more than in treatments 10 t/ha in with or without \n\n\n\nbacteria  reduced plant nutrients uptake.  \n\n\n\nTable 4: Effect of EFB biochar and N2-fixing bacteria (Sb16) on plant nutrients concentrations            \n\n\n\nEFB biochar \nt/ha \n\n\n\nBacterial \ninoculation             \n\n\n\nNutrient Concentration (%) \n \n\n\n\n N P K Ca Mg \n0 - Sb16                       1.16 h                 0.08 e           0.55 g          0.12                 0.09               \n\n\n\n0 +Sb16                       1.22 g                 0.11 e           0.61 f           0.16                 0.11     \n\n\n\n5 - Sb16                       2.81cd                0.25 bc         1.55 b           0.34                0.20                    \n\n\n\n5 + Sb16                       3.03 a               0.37 a           1.74 a           0.45                0.28      \n\n\n\n10 - Sb16                     2.85 c                0.28 b           1.58 b          0.35                0.22      \n\n\n\n10 + Sb16                       2.94 b                 0.35 a           1.69 a           0.42                0.25                                                        \n\n\n\n15  -  Sb16                       2.68 e                 0.22 cd          1.41d e         0.32                0.14 \n\n\n\n15 + Sb16                       2.79 d                  0.27 b           1.48 c         0.38                 0.16 \n\n\n\n20 -  Sb16                       2.62 f                   0.21 d           1.39 e         0.31                 0.15   \n\n\n\n20 + Sb16                       2.73 e                   0.26 b          1.46 cd       0.36                 0.17    \n\n\n\nEFB Biochar                                    0.0001*              0.0001*              0.0001*             0.0001*             0.0001* \nSb16                                                 0.0001*              0.0001*              0.0001*             0.0001*             0.0024* \nEFB Biochar*Sb16                          0.0021*              0.0101*              0.0062*             0.1871ns             0.2647ns    \n\n\n\n                        * = significant (P<0.05)                                    NS = not significant (P>0.05)    \nMeans in each column with the same letter (s) for each variable are not significantly different according to Tukey 's test at 5%. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n12 \n \n\n\n\nTable 5: Effect of EFB biochar and N2-fixing bacteria Sb16 on plant nutrients uptake \n\n\n\nEFB Biochar \nt/ha \n\n\n\nBacterial \ninoculation \n\n\n\nNutrient uptake (g/plant) \n\n\n\n  N P K Ca Mg \n0 \u2013   Sb16          0.24 \u00b1 0.01 g         0.02 \u00b1 0.01 f       0.11 \u00b1 0.01 g         0.02 \u00b1 0.01 g           0.02 \u00b1 0.01 e \n\n\n\n0 +  Sb16           0.28 \u00b1 0.02 g        0.03 \u00b1 0.01 f         0.14 \u00b1 0.01 g          0.04 \u00b1 0.01 g         0.03 \u00b1 0.01 e \n\n\n\n5 \u2013  Sb16         1.36 \u00b1 0.02 d          0.12 \u00b1 0.01cd        0.75 \u00b1 0.03 cd       0.16 \u00b1 0.01 de       0.10 \u00b1 0.01 c   \n\n\n\n5 +  Sb16         1.86 \u00b1 0.04 a          0.23 \u00b1 0.01 a         1.09 \u00b1 0.03 a         0.28 \u00b1 0.01 a        0.18 \u00b1 0.01 a \n\n\n\n10 \u2013 Sb16         1.32 \u00b1 0.03 de        0.13 \u00b1 0.01 c         0.73 \u00b1 0.02 cd      0.16 \u00b1 0.01 de      0.10 \u00b1 0.01 c \n\n\n\n10 + Sb16         1.57 \u00b1 0.02 b         0.19 \u00b1 0.01 b         0.9 \u00b1 0.02 b          0.22 \u00b1 0.02 b       0.13 \u00b1 0.01 b \n\n\n\n15 \u2013 Sb16         1.25 \u00b1 0.04 e          0.10 \u00b1 0.01 de       0.65 \u00b1 0.01 e         0.15 \u00b1 0.01 ef      0.07 \u00b1 0.01 d \n\n\n\n15 + Sb16         1.47 \u00b1 0.01 c          0.14 \u00b1 0.01 c         0.78 \u00b1 0.04 c         0.20 \u00b1 0.01 bc     0.09 \u00b1 0.01cd \n\n\n\n20 -  Sb16        1.08 \u00b1 0.03 f          0.09 \u00b1 0.01 e         0.57 \u00b1 0.01 f          0.13 \u00b1 0.01 f       0.06 \u00b1 0.01 d \n\n\n\n20  + Sb16         1.33 \u00b1 0.04 de       0.13 \u00b1 0.01 c          0.71 \u00b1 0.01 d         0.18 \u00b1 0.02 cd     0.08 \u00b1 0.01cd \n\n\n\nEFB Biochar                              0.0001*                 0.0001*                 0.0001*              0.0001*              0.0001*  \n\n\n\nSb16                                           0.0001*              0.0001*                 0.0001*              0.0001*              0.0024* \n\n\n\nEFB Biochar*Sb16                    0.0001*              0.0001*                 0.0001*              0.0002*              0.0061*             \n\n\n\n                                                       * = significant (P<=0.05)                        NS = not significant (P>0.05)   \nMeans in each column with the same letter (s) for each variable are not significantly different according to Tukey 's test at 5%. \n\n\n\n\n\n\n\n4. DISCUSSION    \n\n\n\nSoil acidity is a serious constraint for crop production in tropical regions. Plants require sufficient quantity of elements for \n\n\n\nhealthy growth. To compensate the nutrients deficiency in soil, EFB biochar with bacteria Sb16 are used as an alternative to \n\n\n\nimprove soil properties and plant growth. The current studies showed that oil palm empty fruit bunch (EFB) biochar, and \n\n\n\napplication of N2-fixing bacteria significantly improved soil chemical compositions, microbial populations and enzymes \n\n\n\nactivity and growth of sweet corn.  In an incubation study the combination of bacteria Sb16 and EFB biochar at 0.25% \n\n\n\nsignificantly improved soil microbial populations. Earlier studies also observed that addition of biochar released nutrients to \n\n\n\nthe soil and thus stimulated microbial growth (Rutigliano et al., 2014; Rondon et al., 2007). Biochar has high surface area and \n\n\n\nporosity that enables it to retain nutrients and also provide a suitable habitat for beneficial microorganisms to flourish (Tejada \n\n\n\net al., 2006). Biochar provides a suitable habitat, where indigenous microorganisms may escape from predators, as well as \n\n\n\nproviding carbon, energy, and nutrients (Thies & Rillig, 2009). The alkaline nature of EFB biochar (pH 9.39) alleviates the soil \n\n\n\nEffect of Biochar and N-Fixing Bacteria on Sweet Corn\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016188\n\n\n\nDISCUSSION   \nSoil acidity is a serious constraint for crop production in tropical regions. Plants \nrequire sufficient quantities of elements for healthy growth. To compensate for \nnutrients deficiency in soil, EFB biochar with bacteria Sb16 were used as an \nalternative to improve soil properties and plant growth. The current studies showed \nthat oil palm EFB biochar, and application of N2-fixing bacteria significantly \nimproved soil chemical compositions, microbial populations and enzyme activity \nand growth of sweet corn.  In an incubation study, the combination of bacteria \nSb16 and EFB biochar at 0.25% significantly improved soil microbial populations. \nEarlier studies also observed that addition of biochar released nutrients into the \nsoil and thus stimulated microbial growth (Rutigliano et al., 2014; Rondon et \nal., 2007). Biochar has a high surface area and porosity that enables it to retain \nnutrients and also provide a suitable habitat for beneficial microorganisms to \nflourish (Tejada et al., 2006). Biochar provides a suitable habitat, where indigenous \nmicroorganisms may escape from predators, as well as provide carbon, energy, \nand nutrients (Thies and Rillig, 2009). The alkaline nature of EFB biochar (pH \n9.39) alleviates soil acidity by increasing the pH for increased microbial activity. \nOrganic carbon rich EFB biochar and free living N2-fixing bacteria significantly \nimprove soil enzyme activities. Biochar has the capacity to adsorb a wide range \nof organic and inorganic molecules which may provide a mechanism to protect \nenzyme activity (Bailey et al., 2010). \n\n\n\nThe positive effects of biochar on soil enzyme activities may be due to the \nhigh pH, surface area, pore size distribution, and charge properties (Nannipieri et \nal., 2012). Tejada et al. (2006) reported that the application of biochar stimulated \nsoil microbes to produce some enzymes. Soil enzymes may positively influence \nsoil quality and play important roles in maintaining soil health and fertility \nmanagement in ecosystems. Addition of EFB biochar and bacteria Sb16 increased \nmost nutrients in the soil that are responsible for improved plant growth. \n\n\n\nAddition of biochar to agricultural soil has recently received much attention \ndue to the apparent benefits it accords to soil functions. Besides increasing plant \nnutrients and soil enzymes, EFB biochar was able to decrease soil acidity which \nreduces liming requirements in soil. Improvement in soil pH by biochar addition \nmay be due to several mechanisms, including proton consumption by functional \ngroups associated with the organic materials (Wu et al., 2014), decarboxylation of \norganic acid anions during residue decomposition (Sarah et al., 2013) and through \nbeneficial bacterial hydrolysis of organic nitrogen or nitrogen fixation that release \nNH4\n\n\n\n+ and decomposition of organic residues (Afeng et al., 2012).  An increase \nin soil pH and CEC may reduce the activity of Fe and Al and thus contribute to \navailable plant nutrients in soil.  A similar finding was observed by Abebe (2012) \nwho observed an improvement in soil chemistry by application of alkaline biochar. \nThe increased value of soil organic C may be explained by the carbon and energy \nsubstrates provided by biochar itself (Sander et al., 2010). The initial increase in \nessential plant nutrients in the soil could be due to the abundance of macronutrients \nin the biochar, N2 fixation by beneficial bacteria and mineralisation of organic \n\n\n\nAbdulrahman et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 189\n\n\n\nnutrients in the biochar (Amonette and Joseph, 2009). The light fraction organic \nmatter in the soil and microbial biomass could have resulted in better nutrient \nrelease from EFB biochar (Carson et al., 2007). Application of EFB biochar with \nand without bacteria Sb16 significantly enhanced a few chemical and biological \nproperties and enzyme activities in acidic soil.  EFB biochar may have potential as \na beneficial soil amendment up to a certain limit for enhancing soil properties but \nat higher rates, was found to have a  negative effect on soil properties.\n\n\n\nApplication of EFB and bacteria Sb16 to sweet corn under glasshouse study \nsignificantly improved shoot and root biomass, root development and nutrients \nuptake. Biochar has been known to improve soil properties, plant growth and \nnutrient uptake (Atkinson et al., 2010; Lehmann et al., 2011). Application of \nbiochar improves soil acidity, pore structure, surface area, essential nutrients \nand changes in microbial populations, thus increasing crop productivity. The \ncombination of EFB biochar with bacteria Sb16 improved leaf chlorophyll which \nis essential for photosynthesis, N uptake and plant productivity. Solubilisation and \nporosity of ash-biochar may control the release of soluble nutrients available for \nplant absorbtion (Brockhoff et al., 2010). Amending soil with EFB biochar at 5 t/\nha and bacteria Sb16 affected soil microbial activities. Biochar application to poor \nfertile soil has been found to provide longer-lasting improvements in soil fertility \n(Xu et al., 2013). Biochar has a positive effect on plant nutrients in the soil, \navailable for plants in two common ways: nutrient addition and nutrient retention.  \n\n\n\nSeveral groups of microorganisms in biochar are known to be able to regulate \nplant growth through nutrient cycling (Rutigliano et al., 2014). Production \nof enzymes and other compounds in biochar could also enhance plant growth. \nApplication of biochar has been observed to generally increase plant root hairs and \neffective root surface areas beyond common root absorption zones causing higher \nnutrient transfer for plant production and nutrient uptake (Steiner et al., 2008). \nThe study also suggests that EFB biochar with beneficial bacteria Sb16 could be \na good soil buffer providing suitable conditions for soil microbial populations, \nenzyme activity and essential nutrients for plant growth. Addition of EFB biochar \nwith or without bacterial inoculation improved growth of sweet corn, nutrient \nuptake and soil chemical properties. However, addition of higher levels (> 10 t/ha \nrate) of EFB biochar resulted in low plant growth and decreased the soil microbial \nand chemical properties. \n\n\n\nAddition of EFB biochar may be an alternative solution in enhancing the \nquality of acid soils. Inoculation of bacteria Sb16 further improves release of \nnutrients from EFB biochar. Incorporation of EFB biochar and bacteria Sb16 \ncan induce changes to chemical and biological properties and enzyme activity of \nacidic soils and improve crop production.\n\n\n\nEffect of Biochar and N-Fixing Bacteria on Sweet Corn\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016190\n\n\n\nREFERENCES\nAbebe, N., Endalkachew, K., Mastawesha, M and Gebermedihin, A. 2012. Effect of \n\n\n\nbiochar application on soil properties and nutrient uptake of lettuces (Lactuca \nsativa) grown in chromium polluted soils. American-Eurasian Journal of                    \nAgricultural & Environmental Sciences. 12: 369-376.\n\n\n\nAsai, H., Samson, B.K., Stephan, H.M., Songyikhangsuthor, K., Homma, K., Kiyono, \nY., Inoue,Y., Shiraiwa,T and Horie, T. 2009. Biochar amendment techniques for \nupland rice production in Northern Laos 1. Soil physical properties, leaf SPAD \nand grain yield. 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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 135-146  (2020)                         Malaysian Society of Soil Science\n\n\n\nStudy of Microbial Respiration in Different Types of \nVermicompost\n\n\n\nBiabani, A.* and Gholizadeh, A. \n\n\n\nDepartment of Crop Production, Faculty Agriculture and Natural Resources, \nGonbad-e-Kavous University, Iran\n\n\n\nABSTRACT\nThe respiration of microorganisms in soil is one of the indexes of microbial \nactivity of the soil. However, it is known that soil microbial respiration has \ndecreased due to heavy use of chemical fertilisers and lack of organic matter in the \nsoil. Hence, this study was carried out to measure the effects of different types of \nvermicompost on microbial respiration. The study was conducted in a completely \nrandomized design with four replications in 2014 in the laboratory of Soil Science, \nCollege of Agriculture and Natural Resource, Gonbad Kavous University, Iran. \nTreatments consisted of different mixtures of vermicompost with soil and without \nsoil that were produced from different manures. The results showed that the type \nof vermicompost with soil and without soil had a significant effect on microbial \nrespiration. The most and least microbial respiration rates were observed in 50% \ncow manure + 50% potato and in 25% straw + 75% horse manure, respectively, \nwhen tested without mixing with soil. Means comparison of microbial respiration \nin treatments by 50% composition (vermicompost + soil)  showed that the \nlowest and highest rates of microbial respiration  was in 25% straw + 75% horse \nmanure with 735 mg carbon per kg composition and 75% straw + 25% poultry \nmanure at  1934 mg carbon per kg composition respectively. Our study found that \nvermicompost increased the organic matter of soil and created a good environment \nfor microorganisms to live in the soil.\n \nKey words: Microbial respiration, cow manure, sheep manure, palm, alfalfa.\n\n\n\n___________________\n*Corresponding author : E-mail: abs346@yahoo.com  or abbas.biabani@gonbad.ac.ir\n\n\n\nINTRODUCTION\nThe sustainability of agricultural systems is considered an important issue around \nthe world. Disturbance of biological activity, reduction of biological nitrogen \nfixation and other nutritive elements have increased by frequent application \nof chemical fertilisers for agriculture. It is necessary to gradually reduce the \nuse of chemical fertilisers and substitute it with organic fertilisers especially \nvermicompost (Palm et al. 2001). Vermicompost benefits the environment by \nreducing the need for chemical fertilisers and decreasing the amount of waste \ngoing to landfills. It also improves soil aeration, microbial activity, water holding \ncapacity and nutrient recycling.\n Soil respiration is one of the oldest parameters to determinate microbial \nactivity in the soil (Kieft et al. 1987). It is one of the most important soil fertility \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020136\n\n\n\nindicators, which activity requires energy (Nanipieri et al. 1990). Therefore, \nrespiration is an overall process that is not confined to microorganisms but is \nperformed by all creatures living in soil. The humidity, temperature, ability \nto access nutrients (Ramnarain and Ansari 2019), carbon storage (Fang and \nMoncrieff 2005), vegetation type, quality and quantity of substrate, biomass and \nmicrobial activity, management and land use especially fertilizer consumption are \namong the factors that affect respiration (Ding et al. 2007; Biabani et al. 2018). \nAnimal manure and vermicompost have been used as fertilisers since centuries \nago.\n Poultry manure which has a high amount of nitrogen has now emerged as \none of the best organic fertilisers (Sloan et al. 2003). Fereidooni Naghani (2009) \nfound that using two types of fertilisers (urea and poultry fertilizer) increased soil \nCO2 during its use, with poultry manure increasing CO2 much more than urea due \nto  its ability to access carbon and essential nutrient quantities. Min et al. (2003) \nalso reported the increase in basal respiration in treatments under cow manure \ncompared with chemical fertilisers in the alfalfa fields. Various factors, such as \namount of carbon, access to nitrogen, moisture content, temperature, ventilation \nand worm population, emissions of CO2, CH4 and N2O, influence vermicompost \nproduction process (Hobson et al. 2005).\n Many researchers have shown that soil organic matter is a source of \nenergy and food for microorganisms. The quantity and quality of organic matter are \namong the factors that have an influence on exacerbating soil biological activities \n(Palm et al. 2001). Thus, for greater productivity of plant residue  in agricultural \nsystems, understanding the process of analysing plant residue, factors affecting \nanalysis of materials and speed of releasing nutrients is necessary and important \n(Palm et al. 2001). Several studies have shown that the speed of decomposition \nand mineralization of plant materials depend on environmental conditions and \nchemical composition of crop residue (Kieft et al. 1987; Martens2000; Bossuyt \net al. 2001; Palm et al. 2001). In terms of differences in the compounds such as \nprotein, cellulose, pectin, hemicellulose and polyphenols, a compound of plant \nspecies in an ecosystem has a significant effect on organic matter decomposition \nrate, the cycle of nutrients and finally soil fertility status (Martens 2000; Bossuyt \net al. 2001; Palm et al. 2001; Hadas et al. 2004). During the process of producing \nvermicompost, earthworms break down organic matter. Moreover, the added \nmucus and enzymes increase microbial activity of earthworms and during this \nprocess microorganisms also degrade organic carbon through release of CO2. \nSothar (2006) reported that earthworms cause loss of organic carbon in the process \nof vermicomposting due to microbial respiration. Garg et al. (2005) reported a \n58.4% reduction in organic carbon in cow manure and 55.4% in equine manure \nfor 90 days during the vermicomposting process.\n Sinha and Heart (2012) argue that organic material convert into \nvermicompost. This, in turn, can cause the entry of a great deal of atmospheric \ncarbon which is absorbed by photosynthesis. Furthermore, it can improve soil \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 137\n\n\n\nfertility and prevent greenhouse gas emissions into the atmosphere that occurs \nthrough burning or abandoning organic matter in nature.\n Borken et al. (2002) conducted a study on changes in microbial and soil \nproperties following compost treatment of degraded temperate forest soils. They \nstate that any increase in temperature and use of compost can give rise to an \nincrease in soil microbial biomass carbon and respiration. Tejada et al. (2006) \nstate that an increase in microbial biomass carbon is generally related to positive \neffects of organic fertilisers in the soil. In addition, the simple materials which \nprovide organic fertilisers stimulate microbial and enzyme activities in the soil. \n Since soil microbes are responsible for decomposition, the type of plant \nresidue in terms of differences in the amount and type of organic compounds \n(quality of substrate) have a great impact on different soil biochemical processes \n(Pascual et al. 1999). A study on atmospheric residue (biomass burning) which \nhas been utilized, show that the residue can be a substrate containing considerable \namounts  of carbon and can be used as a source of energy (Bautista et al. 2011). \nOn the other hand, since the ratio of carbon to residual atmospheric nitrogen is \ngreater than 30, the mix of these materials with soil makes nitrogen organic. Thus, \nmicrobial respiration in soil treated with plant residues shows a significant increase \ncompared to pure soil (Nourbakhsh 2004). Other studies have shown that when \nplant residue is added to the soil, the emission rate of carbon dioxide expresses the \nrespiration activity of soil microorganisms because of a significant increase in the \ndecomposition of organic matter that is added to the soil (Martens 2000; Hadas et \nal. 2004). The application of plant residue to the soil not only affects soil chemical \nand biochemical properties, but also affects the rate of conversion of nutrients into \nusable forms for plants. \n C/N is among the biochemical characteristics of an organic material that \nhas a great influence on its decomposition rate. A material with lower C/N ratio, \nlike soybean residue, compared to a corn residue which has a higher C/N, for \nexample, would be decomposed more easily by microorganisms (Manzoni et al. \n2008.)\n This study was conducted with the aim of investigating the effect of \ndifferent types of vermicompost on soil microbial biomass respiration, in relation \nto the importance of organic compounds in the health of agricultural and non-\nagricultural ecosystems.\n\n\n\nMATERIALS AND METHODS\nThe study was carried out in Gonbad Kavous University (Iran) in 2014 with four \nreplications under laboratory conditions in two completely randomized designs. \nThe treatments were normal soil (control) and vermicompost made from 100% \ncow manure, 75% cow manure + 25% potato, 50% cow manure + 50% potato,  \n75% cow manure + 25% cabbage, 50% cow manure + 50% cabbage, 75% cow \nmanure + 25% palm leaf, 50% cow manure + 50% palm leaf, 25% cow manure + \n75% palm leaf, , pure straw, 50% straw + 50% sheep manure, 25% straw + 75% \nsheep manure,  75% straw + 25% poultry manure, sheep manure, 25% alfalfa + \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020138\n\n\n\nTABLE 1\nAmount of C/N in vermicompost\n\n\n\n16 \n \n\n\n\nTABLE 1 320 \n\n\n\nAmount of C/N in vermicompost 321 \n\n\n\nTreatment  C:N \n\n\n\nOil \n\n\n\n100% cow manure \n\n\n\n9.71 \n\n\n\n11.42 \n\n\n\n75% cow manure + 25% palm leaves 11.01 \n\n\n\n50% cow manure + 50% palm leaves 10.69 \n\n\n\n25% cow manure + 75% palm leaves 11.03 \n\n\n\n75% cow manure + 25% potato 10.33 \n\n\n\n50% cow manure + 50% potato 12.88 \n\n\n\n25%  cabbage +75%  cow manure 14.94 \n\n\n\n%50 cabbage+ 50% cow manure 10.69 \n\n\n\nNet straw  16.82 \n\n\n\nHorse manure 13.84 \n\n\n\nSheep manure 11.89 \n\n\n\n50% horse manure + 50% straw 14.26 \n\n\n\n25% straw + 75% horse manure 13.98 \n\n\n\n50% straw + 50% sheep manure 12.77 \n\n\n\n25% straw  + 75% sheep manure 12.42 \n\n\n\n  25% alfalfa  + 75% sheep manure 11.47 \n\n\n\n50% alfalfa + 50% sheep manure 11.76 \n\n\n\n75% straw + 25% poultry manure 11.84 \n\n\n\n 322 \n\n\n\n323 \n75% sheep manure, 50% alfalfa + 50% sheep manure, horse manure, 50% straw \n+ 50% horse manure, 25% straw + 75% horse manure (Table 1).\n In addition to the above mentioned treatments, another set of compound \nvermicompost mixed with soil in the ratio of 50:50 was prepared. In order to \nmeasure microbial respiration, 50g of different types of vermicompost and 100g \nmixture of vermicompost with soil from each treatment were placed in one liter \nplastic containers. In order to retain soil moisture content, distilled water equal \nto 65% of field capacity was added. Dishes were placed in an incubator at a \ntemperature of about 25-30\u00b0C (lids of plastic containers were closed in order to \nprevent entry and exit of gases). The carbon dioxide (CO2) resulting from microbial \nrespiration was collected in 0.5 N NaOH. To do this, 10 mL of 0.5 N NaOH \nwas poured into a small can (film canister) and was placed on the surface of the \nvermicompost. The microbial respiration of vermicompost was titrated with 0.5 \nN HCl for a duration of 33 days as shown in Table 2. Before titration, 5 mL, 10% \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 139\n\n\n\nbarium chloride (BaCl2) was added so that carbonate ion (CO3\n-2) was precipitated \n\n\n\nin the form of barium carbonate (BaCO3) and the remaining NaOH was titrated \nwith 0.5 N HCl. The container without vermicompost (pure soil) was considered \nas a control by adding NaOH. The C/N of treatments was also measured (Table \n1). The results of this study were analysed using SAS statistical software and the \ndifference between means was calculated using LSD.\n The total reactions that occurred in this experiment were as follows:\n CO2 + 2NaOH                       Na2CO3 +2H2O\n Na2CO3 + BaCl2                   BaCO3 + 2NaCl\n\n\n\n The remaining amount of NaOH was determined with titration by HCl. \nFinally, the amount of CO2 caused by microbial respiration was calculated as \nfollows:\n CO2 or C (mg/kg) = (B-S) \u00d7 N \u00d7 E\n\n\n\nWhere\n B= mL of acid used for control\n S= mL of acid used for sample\n N= normality acid\n E= the highest equivalent weight (22 for CO2 and 6 for C)\n\n\n\n 324 \n\n\n\nTABLE 2 \n\n\n\nTitration time \n\n\n\nTitration \nrow  \n\n\n\nWeek \ndays \n\n\n\nDuration \n(days) \n\n\n\n1 First \nweek 3 \n\n\n\n2 First \nweek 3 \n\n\n\n3 Second \nweek 4 \n\n\n\n4 Third \nweek 5 \n\n\n\n5 Fourth \nweek 6 \n\n\n\n6 Fifth \nweek 6 \n\n\n\n7 Sixth \nweek 6 \n\n\n\n  325 \n\n\n\nTABLE 3 326 \n\n\n\nAnalysis of variance for microbial respiration 327 \n\n\n\nSource of \nchanges \n\n\n\nDegree of \nfreedom \n\n\n\nMean squares \n\n\n\nMicrobial \nrespiration without \nsoil  \n\n\n\nMicrobial \nrespiration with \nsoil \n\n\n\nVermicompost 18 3275191.26** 519308.89** \n\n\n\nError 57 12427.74 699.03 \n\n\n\nCV (%) - 4.75 2.04 \n\n\n\n                        ** Significant at 1% probability level 328 \n\n\n\n 329 \n\n\n\nTABLE 2\nTitration time\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020140\n\n\n\n Since the amount of used vermicompost was 50g and 100g, to convert it \ninto one kg, the calculated value was multiplied by 20 and 10, respectively so that the \namount of respiration was calculated and reported based on mg carbon in kg soil.\n\n\n\nRESULTS\nAs shown in Table 3, the use of different types of vermicompost of animal manure \nand plant residue in composition with soil and non-blending with soil had a \nsignificant effect on microbial respiration at 1% probability level. \n\n\n\n 324 \n\n\n\nTABLE 2 \n\n\n\nTitration time \n\n\n\nTitration \nrow  \n\n\n\nWeek \ndays \n\n\n\nDuration \n(days) \n\n\n\n1 First \nweek 3 \n\n\n\n2 First \nweek 3 \n\n\n\n3 Second \nweek 4 \n\n\n\n4 Third \nweek 5 \n\n\n\n5 Fourth \nweek 6 \n\n\n\n6 Fifth \nweek 6 \n\n\n\n7 Sixth \nweek 6 \n\n\n\n  325 \n\n\n\nTABLE 3 326 \n\n\n\nAnalysis of variance for microbial respiration 327 \n\n\n\nSource of \nchanges \n\n\n\nDegree of \nfreedom \n\n\n\nMean squares \n\n\n\nMicrobial \nrespiration without \nsoil  \n\n\n\nMicrobial \nrespiration with \nsoil \n\n\n\nVermicompost 18 3275191.26** 519308.89** \n\n\n\nError 57 12427.74 699.03 \n\n\n\nCV (%) - 4.75 2.04 \n\n\n\n                        ** Significant at 1% probability level 328 \n\n\n\n 329 \n\n\n\nTABLE 3\nAnalysis of variance for microbial respiration\n\n\n\n Organic matter is not just one compound. The C compound present in \nsoil and vermicompost is basically different in its chemical nature and structure. \nNotably, in order to arrive at a conclusion on the chemistry of the compound, \njust imagine the case where the lignin or chitin carbon is similarly treated with \nthe sugar or cellulose or starch carbon. Decomposition is a process that greatly \ndepends on the activity of microorganisms and the nature of organic C and its \nvulnerability towards microbial activity. Hence, the structure and nature of carbon \ncompounds in soil and vermicompost need to be ascertained first, before a real \npicture on decomposition can be assessed (Chudek 2008).\n The mean comparison of microbial respiration in treatments without soil \nand microbial respiration with soil showed a significant difference in microbial \nrespiration between treatments without soil and treatments with soil (Table 4). \nMicrobial respiration in treatments without soil (2349 mg carbon in kg soil) was \nmore than in treatments mixed with soil (1292 mg carbon in kg soil),  almost two \ntimes greater (Table 4).\n Table 5 shows the mean of microbial respiration in different types of \nvermicompost in comparison with control, mixed with soil and without. In the \ntreatment where it was mixed with soil, microbial respiration rate was variable, \nfrom 336 mg carbon per kg of soil (normal soil) up to 3829 mg carbon per kg \nof composition. The maximum rate of microbial respiration was obtained from \n50% cow manure + 50% potato treatment, while 25% straw + 75% horse manure \ntreatment revealed minimum rate (1404 mg C/kg of microbial respiration).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 141\n\n\n\n18 \n \n\n\n\n 330 \n\n\n\n  331 \n\n\n\nTABLE 4 \n\n\n\nComparison of microbial respiration with soil and microbial respiration without soil in \ntreatments under study \n\n\n\nTreatment Mean SEM t Pr> t \n\n\n\nMicrobial respiration without \nsoil \n\n\n\n2349.24 40.58 31.58 0.0001 \n\n\n\nMicrobial respiration with soil 1292.41 102.30 22.96 0.0001 \n\n\n\n332 \n\n\n\nTABLE 4\nComparison of microbial respiration with soil and microbial respiration without soil in \n\n\n\ntreatments under study\n\n\n\n 333 \n\n\n\nTABLE 5 334 \n\n\n\nComparison of mean of microbial respiration (mg carbon per kg of soil) 335 \n\n\n\nTr\nea\n\n\n\ntm\nen\n\n\n\nt \n\n\n\nTreatment levels Microbial respiration \nwithout soil \n\n\n\nMicrobial \nrespiration with soil \n\n\n\nV\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n \n\n\n\nNormal soil (control) 336.00k 336m \n\n\n\n100% cow manure 3208.50c 1319.25f \n\n\n\n75% cow manure + 25% palm leaves 2595.00e 1389.75e \n\n\n\n50% cow manure + 50% palm leaves 2970.00d 1522.50c \n\n\n\n25% cow manure + 75% palm leaves 2697.00e 1481.25d \n\n\n\n75% cow manure + 25% potato 3250.50c 1545.74c \n\n\n\n50% cow manure + 50% potato 3829.50a 1662b \n\n\n\n25%  cabbage +75%  cow manure 3334.50c 1520.25c \n\n\n\n%50 cabbage+ 50% cow manure 2416.50f 1375.50e \n\n\n\nNet straw  2901.00d 1482d \n\n\n\nHorse manure 1567.50i 1249.50gh \n\n\n\nSheep manure 1813.50gh 1473.75d \n\n\n\n50% horse manure + 50% straw 1693.50hi 1282.50fg \n\n\n\n25% straw + 75% horse manure 1404.00j 735  l \n\n\n\n50% straw + 50% sheep manure 1831.50gh 1123.50i \n\n\n\n25% straw  + 75% sheep manure 1860.00g 901.50k \n\n\n\n25% alfalfa  + 75% sheep manure 1777.50gh 1003.50j \n\n\n\n50% Alfalfa + 50% sheep manure 1590.00i 1218h \n\n\n\n75% straw + 25% poultry manure 3559.50b 1934.25a \n\n\n\nLSD (0.05) 157.85 37.43 \n\n\n\n            Means within each column with at least a letter in common are not significantly different at \u03b1= 0.05 336 \n\n\n\n337 \n\n\n\nTABLE 5\nComparison of mean of microbial respiration (mg carbon per kg of soil)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020142\n\n\n\n Comparing the average of microbial respiration in the treatments in \ncombination with soil,  a significant difference was found between treatments. \nTreatments of 25% straw + 75% horse manure treatment with 735 mg carbon per \nkg of composition and 75% straw + 25% poultry manure treatment with 1934 \nmg carbon per kg of composition had the lowest and highest rates of microbial \nrespiration, respectively(Table 5).\n Comparing the microbial respiration activity of vermicompost of \ndifferent types of animal manure and plant residue mixed with soil and without \nsoil (Figure1), compost treatments in combination with soil had less microbial \nrespiration than treatments without soil. Microbial respiration changes were \nuneven in the treatments, but as shown in Figure 1, in every treatment, changes in \nmicrobial respiration without mixing in soil showed a relatively constant increase \ncompared to the same treatment in combination with soil.\n\n\n\nDISCUSSION\nThe application of plant residue and different types of vermicompost in the soil not \nonly affects soil chemical and biochemical properties, but also the decomposition \nrate of conversion of  nutrients into usable forms for plants. Therefore, any \nprocess that influences soil biological populations will lead to some changes in \nsoil enzyme systems. We can say that the soil-enzyme systems are interconnected \nwith the management of organic waste materials (Michael and Joshua 2003). It \nappears that treatments without mixing with soil provided a more appropriate \nenvironment and sources of energy for microorganism activity. One of the reasons \nfor the decomposition of organic matter is the presence of a lot of available \nnitrogen as well as some toxins that exacerbate microbial activity (USDA 2003). \nOne of the reasons for low microbial respiration in 25% straw treatment + 75% of \nhorse manure was its low nitrogen content (C/N =13.98).\n There is a considerable increase in carbon mineralization rate, respiration \nand microbial biomass of the compost because of the composting process itself. It \nis possible that the  high rate of inorganic carbon in treatments containing organic \nmatter (compost) was due to moisture content and an  increase in microbial \nactivity which resulted in increased readily available and easily analysable energy \nsource for microbes. \n Differences in the impact of various organic substances on the amount of \nmicroorganisms in the whole soil are likely to be related to the complexity of their \nchemical structure and their ability to supply carbon and other nutrients required \nfor soil microorganisms, as well as the C/N ratio. The chemical composition \nor quality of organic matter or the amount of organic matter lignin, microbial \npopulation structure, soil moisture, availability of nutrients, salinity and soil \nstructure are important factors that have a significant effect on the degradation \nof plant remains or microbial respiration. There could be several reasons for \nthe difference in microbial respiration in the different treatments. The amount \nof lignin present in the naturals used for treatments is likely to vary. Speratti et \nal. (2007) reported that earthworms influence the emission of CO2 from the soil \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 143\n\n\n\necosystem. The measurement of respiratory carbon dioxide caused by metabolic \nactivity and enzymes is a common indicator of compost maturity.\n Fereidooni Naghani (2009) observed that within a few weeks of animal \nmanure incubation consumed by earthworms, respiration and enzyme activity \nduring this period was greatly reduced. It was also observed that as time progressed, \nmicrobial activity increased due to addition of urea and poultry manure. The \nresearchers found that the lowest rate of decomposition among organic fertilisers \nwas related to materials that had undergone composting (Morank et al. 2005). \nPerhaps some treatments, such as cow + potato fertiliser, have a lesser composting \nduration but have an improved composting process, in contrast to manure + straw. \nSperatti et al. (2007) believe that earthworms through feeding and replacement \nof materials can increase respiration in aerobic microsites.  In a laboratory scale \nstudy, Atiyeh et al. (2000) found that earthworms cause biochemical changes in \ncow manure leading to accelerated maturity; this makes the organic waste material \nstable with an increase in total nitrogen without large changes in acidity. \n When used the amount of straw for making vermicompost increases \nrespiration. This increase is due to having large amounts of carbon and its easily \ndecomposable analyzable compounds and supply of energy for microorganisms \nin the soil, Microorganism activity increases and CO2 sublimation rate increases \nduring respiration. Chicken manure with higher levels of nutrients, in comparison \nwith other treatments, increased the microbial population. Therefore the higher \namount of organic matter dispersed in the soil, resulting in higher available \nsubstrate for use of soil micro-organisms, increased the CO2 outflow rate of soil. \nThis will result in an increase in microbial activity and living organisms present in \nthe soil which will lead to an improvement in the chemical and physical property \ncomplex of the soil.  It is   recommended that further studies be carried out on the \neffects of different ratios of different vermicompost on soil microorganism life.\n \n\n\n\nREFERENCES\nAtiyeh, R.M., J. Dominguez, S. Subler and C.A. Edwards. 2000. 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Vermicomposting of different organic \nmaterials using the epigeic earthworm Eisenia foetida.  International Journal \nof Recycling of Organic Waste in Agriculture 8(1): 23\u201336.\n\n\n\nSinha, R.K. and S. Heart. 2012. Organic farming: producing chemical-free, nutritive \nand protective food for the society while also protecting the farm soil by \nearthworms and vermicompost \u2013 reviving the dreams of Sir Charles Darwin. \nAgricultural Science Research Journal 2(5): 217-239.\n\n\n\nSloan, D.R., G. Kidder and R.D. Jacobs. 2003. Poultry manure as a fertilizer. PS1 \nIFAS Extension. University of Florida, USA. Available online at: http://edis.\nifas.ufl.edu.\n\n\n\nSperatti, A.B., J.K. Whalen and P. Rochette.2007. Earthworm influence on carbon \ndioxide and nitrous oxide fluxes from an unfertilized corn agro ecosystem. \nBiology and Fertility of Soils 44: 405-409.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020146\n\n\n\nSuthar, S. 2006. Potential utilization of Guar gum industrial waste in vermicompost \nproduction Bioresour Technol. 97: 2474-2477.\n\n\n\nTejada, M., C. Garcia, J.L.  Gonzalez and M.T. Hernandez. 2006. Use of organic \namendment as a strategy for saline soil remediation: influence on the \nphysical, chemical and biological properties of soil. Soil Biol. Biochem. 38: \n1413-1421.\n\n\n\nUSDA Natural Resources Conservation Services 2003. Managing soil organic matter. \nTechnical Note No.5. WWW.soils.usda.gov. 29- www. Worldbank.org.\n\n\n\n \n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: shamshud@upm.edu.my \n\n\n\nINTRODUCTION\nAs soils in the upland areas of the tropics are mostly highly weathered, they \nusually contain limited amounts of basic cations. In Peninsular Malaysia, like \neverywhere else on the globe, the profiles of the soils are deep and the soils are \nleached, with their soil solutions low in basic plant nutrients (K, Ca, Mg), but high \nin Al concentration (Tessens and Shamshuddin 1983; Shamshuddin et al.  1991; \nIsmail et al.  1993; Anda et al.  2008; Shamshuddin and Fauziah  2010). The base \nsaturation of the soils is usually < 35%, while Al saturation is high (>50%) which \nlimits the growth of Al-sensitive crops such as cocoa and corn. The low fertility of \nthe soils is usually alleviated by ground magnesium limestone (GML) application. \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 1-10 (2020)                          Malaysian Society of Soil Science\n\n\n\nCan the Acidic Ultisols in Peninsular Malaysia Be Alleviated \nby Biochar Treatment for Corn Cultivation?\n\n\n\nShamshuddin. J1*., Rabileh, M.A2., and Fauziah, C.I.1\n\n\n\n1Department of Land Management, Faculty of Agriculture\nUniversiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia\n\n\n\n2Department of Natural Resource Science,\nMcGill University, Canada\n\n\n\nABSTRACT\nArable land in Peninsular Malaysia is dominated by highly weathered infertile \nsoils which are taxonomically classified as Ultisols. The production of non-acid \ntolerant sweet corn on these acidic Ultisols is known to be negatively affected \nby soil acidity and/or Al3+ toxicity. However, to some extent, corn is able to \ndefend itself against Al3+ toxicity and/or H+ stress. For Al3+ toxicity problem, the \ndefence mechanism is along this line. The positively-charged Al3+ is attracted to \nthe negatively-charged root surface of the sweet corn. When the Al3+ touches the \nsurface of the root, the corn plant reacts instantly to release oxalic acid that chelates \nthe Al3+. By this mechanism some of the Al3+ at the solution-root interface will be \ndeactivated by the organic acid and rendered unavailable for  uptake by corn. The \nchelation of Al3+ occurring in soil solution by this mechanism is a crucial step to \nhelp sustain the production of corn growing on the Ultisols. For sustainable corn \nproduction, the pH of the Ultisols has to be raised to a level above 5.3 by liming \nor other agronomic means. In the final analysis, Al3+ activity in the soil solution \nis less than the critical level of 10 \u00b5M. The low productivity of the Ultisols can \nbe overcome by applying EFB-biochar at a rate of 10 t biochar/ha, which is an \neconomically viable agronomic practice.\n\n\n\nKeyword: Acid Soils, Al toxicity, biochar treatment, corn production, \n    oxalic acid.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 20202\n\n\n\n Mainly classified as Ultisols (Soil Survey Staff  2014), the highly \nweathered soils are very widespread in South-east Asia (Malaysia, Indonesia, \nThailand, Cambodia, the Philippines and Vietnam), Africa (Democratic Republic \nof Congo, Nigeria, Ivory Coast, Liberia and Ghana) and South America (Brazil, \nColombia, and Ecuador). The area occupied by Ultisols in the tropics is 749 \nmillion ha (Fageria and Baligar 2008).\n Corn is one of the most important cereal crops grown worldwide to feed \nthe increasing population. In the tropics, it has been known for many years that \nlow pH stress and/or Al3+ toxicity are the two most important factors limiting \nits production. Lehmann et al. (2003) had shown that the fertility of acidic soils \ncan be ameliorated by biochar application. The critical Al concentration for the \ngrowth of acid tolerant corn species is 22 \u00b5M, while the critical soil solution pH \nis 4.3 (Shamshuddin et al.  1991). However, the normal concentration of Al in the \nsoil solution of Ultisols in Peninsular Malaysia is much higher than 22 \u00b5M. This \npaper explains how biochar produced from oil palm empty fruit bunches (EFB) \ncan be used to sustain corn production on Ultisols.\n The objectives of this paper are:\n\n\n\n1) To explain how EFB-biochar is able to sustain the growth of a \nnon-acid tolerant sweet corn planted on an Ultisol in Peninsular \nMalaysia; and\n\n\n\n2) To determine the mechanisms by which the sweet corn defends \nitself against Al3+ toxicity and/or low pH stress.\n\n\n\nMineralogy and Chemical Properties of Ultisols\nUltisols occurring in Peninsular Malaysia are leached and highly weathered. \nAccording to Tessens and Shamshuddin (1983) and Shamshuddin and Fauziah \n(2010), the clay fraction of the soils is dominated by kaolinite, gibbsite, goethite \nand hematite; the last two minerals are known as variable charge minerals (Uehara \nand Gillman  1981). Due to their low cation exchange capacity (CEC) of less than \n10 cmol/kg, added basic cations (K, Ca and Mg) via fertilisation or liming are \neasily lost via leaching under a tropical environment. With the low productivity \ncontributed by low pH stress and/or Al3+ toxicity, the soils have to be adequately \nlimed (using GML) to sustain corn production (Shamshuddin et al.  1991; Ismail \nand Shamshuddin  1993). Based on the results of past research (Glaser et al.  \n2002) the low productivity of Ultisols can also be alleviated by application of \nbiochar at an appropriate rate and time.\n\n\n\nCharge Manipulation in Soils\nColloids (minerals) in weathered tropical soils can be classified into permanent \nand variable charge colloids (Uehara and Gillman  1981). Charges occurring on \npermanent charge colloids are derived from isomorphic substitution (Tessens and \nZauyah  1982), which do not change with the change in pH and/or the concentration \nof the ambient solution (e.g. mica, smectite, vermiculite, chlorite). On the other \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 3\n\n\n\nhand, charges on the oxides/hydroxides and broken edges of kaolinite change \nwith pH and/or concentration, and are thus classified as variable charge colloids. \nAt low pH, the colloids are net positively-charged, while at high pH, they are net \nnegatively-charged. \n The charges existing on the surfaces of minerals in soils can be presented \nas follows:\n\n\n\nTotal charge (QT) = Permanent charge (QP) + Variable charge (QV)  (1) \n\n\n\nVariable charge (QV) is described by the Guoy-Chapman Equation (Wann and \nUehara  1978):\n\n\n\nQV = [(2nDkT)/\u03c0]sinh1.15z(pH0-pH)      (2)\n\n\n\nwhere n is the electrolyte concentration, D is the dielectric constants, z is the \nvalency, T is the temperature and pH0 is the pH value where the amounts of \npositive and negative charges are equal (Uehara and Gillman  1980).\n Equation 2 implies that one can increase the charge in a soil by lowering \nits pH0 or by increasing the pH. In reality, the pH0 of highly weathered tropical \nsoils (Tessens and Shamshuddin 1983; Shamshuddin  2011) is not far from \ntheir pH and thus charges contributed by variable charge colloids are small, but \nsignificant in relation to the total charge, which is also small.\n What happens when the soils are mixed with soil amendments, such \nas adding organic matter (e.g. biochar) or being limed with ground magnesium \nlimestone (GML) that increases soil pH (Shamshuddin et al. 1991)? We know \nthat when soil pH is increased, negative charge (CEC) on the exchange complex \nincreases accordingly (Shamshuddin and Fauziah  2010; Shamshuddin  2011; \nShamshuddin et al. 2018). This phenomenon will eventually enhance the \nproductivity of highly weathered soils.\n According to Zulkifli and Shamshuddin (1985), adding or mixing a soil \nhaving variable charge minerals with palm oil mill effluents (POME) lowered \nits pH0. Based on the afore-mentioned Guoy-Chapman equation, lowering pH0 \nresults in an increase in soil negative charge. We therefore can assume that the \nCEC of variable soils can be increased by applying EFB-biochar.\n\n\n\nSoil and Biochar under Investigation\nThe pH of the Ultisol used for the experiment was < 5, with expected high \nexchangeable Al present in the soil of not less than 1.5 cmolc/kg. The soil of the \nabove-mentioned study was Bungor Series, belonging to the clayey, kaolinitic, \nisohyperthermic family of Typic Paleudult (Tessens and Shamshuddin 1983). \nThe topsoil (0-20 cm depth) was sampled and collected from Universiti Putra \nMalaysia farm in Serdang, Malaysia. The concentration of Al in the soil solution \nwas believed to have exceeded the critical level for the healthy growth of corn.\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 20204\n\n\n\n The feedstock of the biochar production was oil palm empty fruit bunch \n(EFB). It was produced via slow pyrolysis of the EFB (size 2-5 mm, moisture \ncontent of 5%) in the absence of O2 at a temperature of 300-350o C. It had a pH of \nabout 9, with ash content of 25% (Rabileh et al. 2015). Total C in the biochar was \n50% and its CEC was 60 cmolc/kg. Based on its chemical properties, the biochar \nis capable of alleviating the problem related to soil acidity and/or Al3+ toxicity \nwhen applied onto an Ultisol at an appropriate rate and time.\n\n\n\nEffects of Biochar Application on Soil Solution Attributes\nThe pH of the soil solution (extracted at field capacity) was plotted against biochar \nrate (Figure 1). The effect of applying biochar in combination with 2 GML/ha \nis also presented for purposes of comparison. Soil solution pH was positively \ncorrelated with the rate of biochar irrespective of whether it was applied alone \nor in combination with 2 t GML/ha (Rabileh et al. 2015).The rate of biochar \nrequired to raise the pH to >5 was about 10 t/ha, which is affordable by the \nfarming communities in the country, considering the positive ameliorative impact \nof its application on acidic Ultisols. This finding is consistent with that of Glaser \net al. (2002).\n \n\n\n\n5 \n \n\n\n\nof comparison. Soil solution pH was positively correlated with the rate of biochar irrespective of \n\n\n\nwhether it was applied alone or in combination with 2 t GML/ha (Rabileh et al. 2015).The rate of \n\n\n\nbiochar required to raise the pH to >5 was about 10 t/ha, which is affordable by the farming \n\n\n\ncommunities in the country, considering the positive ameliorative impact of its application on \n\n\n\nacidic Ultisols. This finding is consistent with that of Glaser et al. (2002). \n\n\n\n\n\n\n\nFigure 1. Relationship between soil solution pH and biochar rate with or without GML \n\n\n\n(Rabileh et al. 2015) \n\n\n\nFigure 1 shows that soil solution pH increased with the biochar rate, with the trend being similar \n\n\n\nto that of lime application. Since the soil is dominated by variable change minerals in the clay \n\n\n\nfraction, its CEC would increase in tandem with the pH increase (Shamshuddin and Fauziah   \n\n\n\n2010). Any increase in soil solution pH to a level above 5, results in the precipitation of Al3+ as \n\n\n\nAl-hydroxides. The phenomenon in a way can be considered as an improvement to soil \n\n\n\nproductivity. \n\n\n\nUsing data available from the experiment, the critical pH for the good growth of sweet corn was \n\n\n\ndetermined. It was done by plotting the graph of the relative corn root length against soil solution \n\n\n\npH. The 10% drop in relative root length corresponded to a pH of 5.3 (Figure 2); this value is \n\n\n\ntermed as the critical pH for the healthy growth of the corn under investigation. The rate of \n\n\n\nbiochar to raise soil solution pH to that level was estimated to be about 10 t biochar/ha (Figure \n\n\n\ny = 4.43 + 0.06x  \nR\u00b2 = 0.96** \n\n\n\ny =  5.25 + 0.09x  \nR\u00b2 = 0.91** \n\n\n\n3\n\n\n\n3.5\n\n\n\n4\n\n\n\n4.5\n\n\n\n5\n\n\n\n5.5\n\n\n\n6\n\n\n\n6.5\n\n\n\n7\n\n\n\n7.5\n\n\n\n8\n\n\n\n0 10 20 30\n\n\n\nSo\nlu\n\n\n\ntio\nn \n\n\n\npH\n  \n\n\n\nRate of biochar (t ha-1) \n \n\n\n\n0GML\n\n\n\n2GML\n\n\n\nFigure 1. Relationship between soil solution pH and biochar rate with or without GML\n(Rabileh et al. 2015)\n\n\n\nFigure 1 shows that soil solution pH increased with the biochar rate, with the trend \nbeing similar to that of lime application. Since the soil is dominated by variable \nchange minerals in the clay fraction, its CEC would increase in tandem with the \npH increase (Shamshuddin and Fauziah   2010). Any increase in soil solution \npH to a level above 5, results in the precipitation of Al3+ as Al-hydroxides. The \nphenomenon in a way can be considered as an improvement to soil productivity.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 5\n\n\n\n Using data available from the experiment, the critical pH for the good \ngrowth of sweet corn was determined. It was done by plotting the graph of the \nrelative corn root length against soil solution pH. The 10% drop in relative root \nlength corresponded to a pH of 5.3 (Figure 2); this value is termed as the critical \npH for the healthy growth of the corn under investigation. The rate of biochar to \nraise soil solution pH to that level was estimated to be about 10 t biochar/ha (Figure \n1). When soil solution pH reached 5.3, Al3+ will be precipitated as amorphous Al-\nhydroxides, which is inert (Shamshuddin and Ismail  1995); therefore, the soil \nenvironment is no longer toxic to corn roots.\n\n\n\nFigure 2. Relationship between relative root length and soil solution pH\n(Rabileh et al.  2015)\n\n\n\nThe critical Al3+ activity for the growth of sweet corn was estimated by a similar \nmethod as that of the critical pH and the value so determined was about 10 \u00b5M \n(Figure 3). Note that the corn under investigation was not an acid tolerant species. \nThe critical level obtained cannot be compared to that of the corn studied by \nShamshuddin et al. (1991), which was reported in terms of Al concentration. \nHowever, we can assume that the sweet corn tested in the study was sensitive \nto Al3+ toxicity. We believe or expect Al3+ activity in the Ultisols under normal \ncircumstances in Peninsular Malaysia to be much more than 10 \u00b5M. So using EFB-\nbiochar to alleviate Al3+ toxicity prevailing in the Ultisols of the humid tropics is \ntimely and justified, with the recommended rate being10 t/ha or thereabouts. The \namelioration of Al3+ toxicity by biochar application is somewhat similar to what \nhas already been studied and/or explained by Hue et al. (1986). \n \n\n\n\n6 \n \n\n\n\ny = -76.14 + 31.21x  \nR\u00b2 = 0.95** \n\n\n\n0\n\n\n\n10\n20\n30\n\n\n\n40\n50\n60\n\n\n\n70\n80\n90\n\n\n\n100\n\n\n\n4 4.5 5 5.5 6\n\n\n\nR\nel\n\n\n\nat\niv\n\n\n\ne \nro\n\n\n\not\n le\n\n\n\nng\nth\n\n\n\n (%\n) \n\n\n\n \n         Solution pH \n\n\n\n1). When soil solution pH reached 5.3, Al3+ will be precipitated as amorphous Al-hydroxides, \n\n\n\nwhich is inert (Shamshuddin and Ismail  1995); therefore, the soil environment is no longer toxic \n\n\n\nto corn roots. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2. Relationship between relative root length and soil solution pH \n\n\n\n(Rabileh et al.  2015) \n\n\n\nThe critical Al3+ activity for the growth of sweet corn was estimated by a similar method as that \n\n\n\nof the critical pH and the value so determined was about 10 \u00b5M (Figure 3). Note that the corn \n\n\n\nunder investigation was not an acid tolerant species. The critical level obtained cannot be \n\n\n\ncompared to that of the corn studied by Shamshuddin et al. (1991), which was reported in terms \n\n\n\nof Al concentration. However, we can assume that the sweet corn tested in the study was \n\n\n\nsensitive to Al3+ toxicity. We believe or expect Al3+ activity in the Ultisols under normal \n\n\n\ncircumstances in Peninsular Malaysia to be much more than 10 \u00b5M. So using EFB-biochar to \n\n\n\nalleviate Al3+ toxicity prevailing in the Ultisols of the humid tropics is timely and justified, with \n\n\n\nthe recommended rate being10 t/ha or thereabouts. The amelioration of Al3+ toxicity by biochar \n\n\n\napplication is somewhat similar to what has already been studied and/or explained by Hue et al. \n\n\n\n(1986).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 20206\n\n\n\nEffects of Biochar Application on Solution\nIt is generally believed that even a non-acid tolerant plant species can, to a \ncertain extent, be able to defend itself against soil acidity and/or Al3+ toxicity. \nThe mechanism of defence against the said problem can also be explained via \na nutrient solution experiment. The results of such a study are given in Figure \n4. It was found that the root length of corn grown in the nutrient solution was \nnegatively correlated with Al concentration. Likewise, corn root surface area was \nnegatively correlated with Al concentration. The critical Al concentration and \ncritical pH can also be estimated using the methods mentioned earlier in this paper \n(Figure 5). Note that the estimated values were not far apart from that reported by \nthe previous experiment (Rabileh et al. 2015).   \n\n\n\n7 \n \n\n\n\n\n\n\n\nFigure 3. Relationship between relative root length and Al3+ activity \n\n\n\n(Rabileh et al. 2015) \n\n\n\nEffects of Biochar Application on Solution \n\n\n\nIt is generally believed that even a non-acid tolerant plant species can, to a certain extent, be able \n\n\n\nto defend itself against soil acidity and/or Al3+ toxicity. The mechanism of defence against the \n\n\n\nsaid problem can also be explained via a nutrient solution experiment. The results of such a study \n\n\n\nare given in Figure 4. It was found that the root length of corn grown in the nutrient solution was \n\n\n\nnegatively correlated with Al concentration. Likewise, corn root surface area was negatively \n\n\n\ncorrelated with Al concentration. The critical Al concentration and critical pH can also be \n\n\n\nestimated using the methods mentioned earlier in this paper (Figure 5). Note that the estimated \n\n\n\nvalues were not far apart from that reported by the previous experiment (Rabileh et al. 2015).    \n\n\n\n\n\n\n\ny = 95.84  - 0.56x  \nR\u00b2 = 0.96** \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n0 10 20 30 40 50 60 70 80\n\n\n\nR\nel\n\n\n\nat\niv\n\n\n\ne \nro\n\n\n\not\n le\n\n\n\nng\nth\n\n\n\n (c\nm\n\n\n\n) \n \n\n\n\n\n\n\n\nAl3+ activity (\u00b5M) \n\n\n\nFigure 3. Relationship between relative root length and Al3+ activity\n(Rabileh et al. 2015)\n\n\n\n8 \n \n\n\n\n\n\n\n\nFigure 4. Relationship between root length and Al concentration (left) and \n\n\n\nroot surface area and Al concentration (right) \n\n\n\n\n\n\n\n\n\n\n\nFigure 5. Determination of the critical Al concentration (left) and critical pH (right) \n\n\n\nHow corn grown on an Ultisol defends itself against Al3+ toxicity is explained in Figure 6. In the \n\n\n\npresence of Al3+ ion, the roots of corn react instantly to excrete oxalic acid, which then chelates \n\n\n\nFigure 4. Relationship between root length and Al concentration (left) and\nroot surface area and Al concentration (right)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 7\n\n\n\n8 \n \n\n\n\n\n\n\n\nFigure 4. Relationship between root length and Al concentration (left) and \n\n\n\nroot surface area and Al concentration (right) \n\n\n\n\n\n\n\n\n\n\n\nFigure 5. Determination of the critical Al concentration (left) and critical pH (right) \n\n\n\nHow corn grown on an Ultisol defends itself against Al3+ toxicity is explained in Figure 6. In the \n\n\n\npresence of Al3+ ion, the roots of corn react instantly to excrete oxalic acid, which then chelates \n\n\n\n Figure 5. Determination of the critical Al concentration (left) and critical pH (right)\n\n\n\n How corn grown on an Ultisol defends itself against Al3+ toxicity is \nexplained in Figure 6. In the presence of Al3+ ion, the roots of corn react instantly \nto excrete oxalic acid, which then chelates the Al3+, rendering it inactive. Likewise, \nthe oxalic acid excreted by the roots of corn is able to help deactivate H+ activity. \nIn the end, the amount of Al3+ and/or H+ concentrating at and/or around the root \nrhizosphere is reduced. As seen in Figure 6, the higher the Al3+ or H+ in the \nsolution, the higher the oxalic acid excreted by the corn roots.\n \n\n\n\n9 \n \n\n\n\nthe Al3+, rendering it inactive. Likewise, the oxalic acid excreted by the roots of corn is able to \n\n\n\nhelp deactivate H+ activity. In the end, the amount of Al3+ and/or H+ concentrating at and/or \n\n\n\naround the root rhizosphere is reduced. As seen in Figure 6, the higher the Al3+ or H+ in the \n\n\n\nsolution, the higher the oxalic acid excreted by the corn roots. \n\n\n\n\n\n\n\nFigure 6. Secretion oxalic acid by corn root \n\n\n\nThe following is an alternative explanation on how the secretion of oxalic acid by corn roots \n\n\n\nreduces the H+ stress that enhances corn growth (Shamshudin et al.  2015). Organic acids (in this \n\n\n\ncase, oxalic acid) are weak acids with pKa value of 4-5. When the solution pH is changed to 3, \n\n\n\nthe corn roots immediately release oxalic acid (Figure 6). The pKa of oxalic acid is above 3 and \n\n\n\ntherefore instead of releasing, it consumes proton, resulting in a slight increase in pH at the \n\n\n\nsolution-root interface. The hydrolysis of oxalic acid is described as follows: \n\n\n\n\n\n\n\n      H2C2O4 + H2O  \u2192  HC2O4\n- + H3O+,  pKa > 4 \n\n\n\n\n\n\n\nWhen the oxalic acid is released by the corn roots, the solution pH will be re-adjusted so that it \n\n\n\nwill approach its pKa value. Hence, the reaction is in the reverse order. In this way, the corn \n\n\n\nunder study would suffer less H+ stress compared to that without. \n\n\n\n\n\n\n\nFigure 6. Secretion oxalic acid by corn root\n\n\n\n The following is an alternative explanation on how the secretion of oxalic \nacid by corn roots reduces the H+ stress that enhances corn growth (Shamshudin et \nal.  2015). Organic acids (in this case, oxalic acid) are weak acids with pKa value \nof 4-5. When the solution pH is changed to 3, the corn roots immediately release \noxalic acid (Figure 6). The pKa of oxalic acid is above 3 and therefore instead of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 20208\n\n\n\nreleasing, it consumes proton, resulting in a slight increase in pH at the solution-\nroot interface. The hydrolysis of oxalic acid is described as follows:\n\n\n\n      H2C2O4 + H2O  \u2192  HC2O4\n- + H3O\n\n\n\n+,  pKa > 4\n\n\n\n When the oxalic acid is released by the corn roots, the solution pH will \nbe re-adjusted so that it will approach its pKa value. Hence, the reaction is in \nthe reverse order. In this way, the corn under study would suffer less H+ stress \ncompared to that without.\n The mechanism of how corn defends itself against Al3+ toxicity can \nfurther be explained by Figure 7, which has already been used by Shamshuddin \net al. (2015) to show how rice root excreted organics acids that chelated Al3+. We \nknow that the cells of plant root (including corn) are negatively-charged (Yang \net al. 2009). Hence, the positively-charged Al3+ present in the soil solution of the \nUltisol under investigation are attracted to the corn root (Figure 7). \n\n\n\n10 \n \n\n\n\nThe mechanism of how corn defends itself against Al3+ toxicity can further be explained by \n\n\n\nFigure 7, which has already been used by Shamshuddin et al. (2015) to show how rice root \n\n\n\nexcreted organics acids that chelated Al3+. We know that the cells of plant root (including corn) \n\n\n\nare negatively-charged (Yang et al. 2009). Hence, the positively-charged Al3+ present in the soil \n\n\n\nsolution of the Ultisol under investigation are attracted to the corn root (Figure 7).  \n\n\n\n\n\n\n\n\n\n\n\nFigure 7. Chelation of Al3+ by organic acids in corn root  \n \nWhen Al3+ touches the root of the corn, the plant reacts instantly to release oxalic acid (Figure 6; \n\n\n\nFigure 7). In the end, some of the Al3+ will be chelated by the organic acids and render \n\n\n\nunavailable for uptake by corn via its roots. The deactivation of Al3+ by this mechanism is a \n\n\n\ncrucial step to sustain not only the production of corn growing on the acidic Peninsular \n\n\n\nMalaysian Ultisols, but also those found elsewhere in the tropics. \n\n\n\n \nConclusion \n\n\n\nThe growth of non-acid tolerant sweet corn planted on Ultisols in Peninsular Malaysia is affected \n\n\n\nnegatively by soil acidity and/or Al3+ toxicity. The critical pH for the healthy growth of the corn \n\n\n\nis 5.3, while the critical Al3+ activity is10 \u00b5M. However, the Ultisols in Peninsular Malaysia \n\n\n\nmostly have a soil pH that is below 5 and Al3+ activity much higher than the said critical level. \n\n\n\nTo some extent, corn grown on acidic Ultisols is able to defend itself against Al3+ toxicity. Under \n\n\n\nstress, its roots excrete oxalic acid that chelates Al3+, rendering it inactive.  One of the ways to \n\n\n\nAl3+\nAl3+\n\n\n\nAl3+\n\n\n\nAl3+\n\n\n\nAl3+Al3+\n\n\n\nOA\n\n\n\nOA\nOA\n\n\n\nOA\n\n\n\nOA\n\n\n\nOAs-Al3+\n\n\n\nFigure 7. Chelation of Al3+ by organic acids in corn root.\n\n\n\n When Al3+ touches the root of the corn, the plant reacts instantly to release \noxalic acid (Figure 6; Figure 7). In the end, some of the Al3+ will be chelated by \nthe organic acids and render unavailable for uptake by corn via its roots. The \ndeactivation of Al3+ by this mechanism is a crucial step to sustain not only the \nproduction of corn growing on the acidic Peninsular Malaysian Ultisols, but also \nthose found elsewhere in the tropics.\n\n\n\nCONCLUSION\nThe growth of non-acid tolerant sweet corn planted on Ultisols in Peninsular \nMalaysia is affected negatively by soil acidity and/or Al3+ toxicity. The critical \npH for the healthy growth of the corn is 5.3, while the critical Al3+ activity is10 \n\u00b5M. However, the Ultisols in Peninsular Malaysia mostly have a soil pH that is \nbelow 5 and Al3+ activity much higher than the said critical level. To some extent, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 9\n\n\n\ncorn grown on acidic Ultisols is able to defend itself against Al3+ toxicity. Under \nstress, its roots excrete oxalic acid that chelates Al3+, rendering it inactive.  One \nof the ways to overcome the problem of sweet corn production on the Ultisols \nin the country is to apply biochar at an appropriate rate. Based on this study, the \nrecommended rate is 10 t EFB-biochar/ha.\n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to thank Universiti Putra Malaysia (UPM) for the financial \nand technical supports during the writing of this review paper. \n\n\n\nREFERENCES\nAnda, M., J. Shamshuddin, C.I. Fauziah and S. R. Syed Omar. 2008. Mineralogy \n\n\n\nand factors controlling charge development of three Oxisols developed from \ndifferent parent materials. 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Charge characteristics of soils with variable and \npermanent charge minerals: 1. Theory. Soil Sci. Soc. Amer. J. 44: 250-252.\n\n\n\nUehara, G. and G.P. Gillman. 1981. Mineralogy, Chemistry and Physics of Tropical \nSoils with Variable Charge Clays. Boulder, USA: Westview Press.\n\n\n\nWann, S.S and G. Uehara. 1978. Surface charge manipulation of constant surface \npotential soil colloids: 1. Relation to sorbed phosphorus. Soil Sci. Soc. Amer. \nJ. 42: 565-570.\n\n\n\nYang, J., Y. Li, Y.  Zhang and S. Zheng, S. 2009. Possible involvement of cell wall \npectic polysaccharides in Al resistance of some plant species. In Proc. 7th Int. \nSymp. Plant-Soil Interaction at Low pH ed. H. Liao X. Yan and L. Kochian \n(pp: 57-58). South China University of Technology Press. \n\n\n\nZulkifli, A. and J. Shamshuddin. 1985. The effects of palm oil mill effluents on the \ncharge characteristics of two weathered soils in Malaysia.  PORIM Bull. 10: \n10-15.  \n\n\n\n.  \n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : masomehmoazallahi@gmail.com\n\n\n\nINTRODUCTION\nPhosphorus (P) is one of the most essential nutrients for plant growth. Deficiency \nof this element, which is frequently related to formation of insoluble complexes \nsuch as calcium-phosphate, is commonly observed in calcareous soils. Several \nresearchers have shown that the processes of adsorption as a monolayer on \ncalcium carbonate and precipitation (octacalcium phosphate or dicalcium \nphosphate) are the main P control mechanisms in low and high concentrations in \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 22: 35-58 (2018)                          Malaysian Society of Soil Science\n\n\n\nDesorption Kinetics and Chemical Forms of Phosphorus in \nCalcareous Soils along a Climotoposequence\n\n\n\nMasomeh Moazallahi1*, Majid Baghernejad1 and Hormazd Naghavi2\n\n\n\n1Department of Soil Science, College of Agriculture, Shiraz University,\nShiraz, Iran\n\n\n\n2Soil and Water Research Department, Kerman Agricultural and Natural\nResources Research and Education Center, AREEO, Kerman, Iran.\n\n\n\nABSTRACT\nSoil phosphorus (P) fertility can be significantly affected by the rate of P desorption \nand fractions in soil. The present study attempted to investigate the comprehensive \nrelationships between different physic-chemical and mineralogical properties of \ndifferent soils with desorption parameters and chemical forms of P in different \nsoil orders of a climotoposequence. For this purpose, the collected soil samples \nwere incubated with 50 \u00b5g P g-1 soil (as KH2PO4) for 90 days. The kinetic data \nobtained from 0.05 M NaHCO3 were used to simulate desorption equations. The \nresults showed that P desorption in different soil samples was similar, and  can \nbe interpreted as an initial rapid release rate followed by a slower rate (biphasic \npattern). Among equations fitted on desorption data, the simple Elovich, power \nfunction and two first-order reaction models had good prediction based on highest \nR2 and lowest SE. In the case of studied soil samples, Ca-bound and residual \nP were found to be the most common chemical forms of P.  Moreover, it was \nobserved that the addition of  P to soil samples increased the concentration of all \nfractions. Compared to unamended soil samples, there was an increase in relative \npercentage of exchangeable-P, Fe- and Al-bound fractions in amended samples. \nHowever, Ca-bound and residual P decreased in these samples. Additionally, the \nresults indicate that apart from OM, CEC, silt, available-P and total-P were the \nsignificant properties which affected desorption of P; also, important minerals like \nkaolinite and illite played an important role in the behaviour of P in the studied \nsamples. \n\n\n\nKeywords: Phosphorus, desorption kinetics, fractionation, clay \nminerals.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201836\n\n\n\nalkaline calcareous soils, respectively (Shariatmadari et al. 2006; Samadi 2010, \nHorta and Torrent 2007; Pierzynski and McDowell 2005). Obtaining information \non dynamics and transformation of P in different soils should lead to better \nmanagement of P fertilisers (Meason et al. 2009; Jalali and Ranjbar 2010). Soil \nbuffering capacity (BC) is regarded as the most important factor in P uptake by \nplants. Phosphorus desorption in soils with low BC is more rapid than in soils \nwith high BC. In most studies, changes in soil physical-chemical properties \nincluding clay minerals (metal oxides and phyllosilicate minerals), organic \nmatter, and calcium content, have been identified as the most important factors \naffecting availability of  P (Pierzynski and McDowell 2005). However, many of \nthese studies did not deal with the important role of clay minerals on P behaviour \nin soils. Several studies have shown that metal oxides have a strong affinity for P, \nespecially in acid soils (Fink et al. 2016; Bortoluzzi et al. 2015). However, a few \nstudies have shown that phyllosilicate minerals play a more important role in P \nsorption of alkaline soils compared to metal oxides (G\u00e9rard 2016). Phyllosilicate \nminerals with higher anion exchange capacity (AEC) have a greater affinity for P \nwhich increases as pH decreases in pH-dependent clay minerals like1:1 type clays. \nPrevious studies done in Iran have identified several clay minerals such as illite, \nchlorite, kaolinite, palygorskite, smectite, and vermiculite originating from various \nclimatic conditions (Khormali and Abtahi 2003; Owliaie et al. 2006). Therefore, it \nappears that any study on behaviour changes of P in relation to these clay minerals \ncan be useful for improving soil P management and crop production. Soil P exists \nin several forms including soil solution and in exchangeable form, Ca-bound and \nFe\u2013Al- bound phases such as solid phases and in residual form (Jalali and Ranjbar \n2010). Investigation of chemical forms through sequential extraction methods can \nprovide important information on labile and non-labile forms of P. Although, the \nuse of sequential extraction techniques can be considered as a useful way for \nevaluating the state of P in soil, a lack of specific chemical extractants and some \nprocesses such as redistribution of ions can affect the accuracy of information \nobtained about chemical forms (Saffari et al. 2009). Using other methods such \nas kinetic modeling techniques can help researchers achieve an understanding \nof the behaviour of elements in soils. Nowadays, kinetic methods have been \npreferred over conventional sequential extraction methods for the evaluation of \nmetal behaviour. Therefore, methods involving kinetic fractionation of metals in \nsoils could be useful to understand the fate of metals (Santos et al. 2010). Several \nstudies have been carried out on the correlation between P and clay minerals in \nacidic and non-calcareous soils, but no specific information has been reported \nin alkaline calcareous soils. In addition, evaluating the state of P through kinetic \nmodeling can complete the information obtained from chemical forms. Therefore, \nthe objectives of the present study were to determine (i) P desorption characteristics \nof selected soils; (ii) chemical fractionation of P;  (iii) the relationships between \nthe different P forms and P desorption properties; and (iv) the relationships \nbetween secondary clay minerals and P desorption kinetic parameters. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 37\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Areas, Sampling Design and Laboratory Analysis\nThe study was conducted across a toposequence transect near the city of Kerman \nIran (Figure 1). \n\n\n\n Figure1: Location of the study area\n\n\n\nThe studied climotoposequence covers an area of about 1200 km2. The \nmean annual precipitation of the area varies from 116.1 to 238.70 mm year-1 \nand the air temperature ranges between 15.7 \u00b0C in Kerman plain (30\u00b0 48\u2019 N, \n57\u00b0 55\u2019 E, in the North of the transect, 1840 m above sea level) and 10.4o C in \nLalehzar (29\u00b0 30\u2019 N, 56\u00b0 12\u2019 E, situated in the South of the transect, 3207 m \nabove sea level), respectively (Meteorological Organization of Iran 2016). Soil \nmoisture regime (MR) and temperature regime (TR) in the North of the transect \nare aridic and mesic, changing to  xeric and mesic in the South of the transect \n(Banaei 1998). Different geomorphic surfaces such as the  Kerman Plain, rock \npediment, piedmont plain, and lowlands of Lalehzar Mountains were evaluated \nto determine their P status (Figure 2). Nine representative pedons on different \ngeomorphic positions were selected. All soil horizons were sampled according \nto Soil Taxonomy (Soil Survey Staff  2014) and selected chemical and physical \nproperties were determined using standard methods (Table 1). Soil texture was \ndetermined  using hydrometer method (Bouyoucos 1962). pH was measured in \nsaturated paste. Percentage of calcium carbonate equivalent (CCE) was determined \nby acid neutralisation (Loeppert and Suarez 1996). Organic matter (OM) content \nwas determined using wet oxidation (Nelson and Sommers 1996).\n\n\n\nCation exchange capacity (CEC) was measured by replacing exchangeable \ncations with sodium acetate (Sumner and Miller 1996). Available P was \ndetermined using bicarbonate (NaHCO30.5 M, pH=8.5)-extractable P (Olsen and \nSommers 1982). Total P was determined after digestion of soil samples in a nitric \nacid\u2013percholoric acid mixture (Olsen and Sommers 1982). The amount of P in the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201838\n\n\n\nextractions was determined using colorimetric ascorbic acid method (Murphy and \nRiley 1962). In order to investigate the effectiveness of clay minerals on desorption \nand chemical forms of P, various minerals were identified in different horizons of \nsome studied orders. To do this, a number of selected soil samples were washed to \nremove gypsum and soluble salts. Fe-oxides, carbonate, and organic matter were \nremoved by citrate-bicarbonate-dithionite (CBD), sodium acetate (adjusted pH \n5), and H2O2 (30%), respectively (Mehra and Jackson 1980). The clay fraction \nof the prepared samples was separated by centrifuge (Kittrick and Hope 1963). \nThe centrifuged clay samples (Kittrick and Hope 1963) were treated with Mg (1N \nMgCl2), Mg/ethylene glycol, K (1N KCl), and K/heated at 550\u00baC for 2 h. The clay \nminerals were analysed (Jackson and Barak 2005) using an X-ray diffractometer \n(Philips, PW 1130/00) and Ni-filtered CuK\u03b1 radiation (40 kV, 30 mA). Semi-\nquantitative estimation of clay minerals was performed using the (001) peak \nintensities of the Mg-saturated and glycerol solvated samples (Johns et al. 1954). \nBefore carrying out a test for desorption kinetics, all soil samples were treated \nwith KH2PO4, because the amounts of the available P (P-Olsen) were relatively \nlow in most of the studied soils. Thus, the soil samples were placed in a plastic \ncontainer and P was added at a rate of 50 \u00b5g Pg-1soil, as KH2PO4 (3.674\u00d710-5 \nM in each soil). The  soil samples were subsequently incubated for 90 days at \n25 \u00b0C. Soil moisture was preserved at field capacity. After incubation, samples \nwere air-dried and used for desorption kinetics and chemical fractionation of \nP. Desorption kinetics of P was studied by batch-type experiments (Jalali and \nAhmadi Mohammad Zinli 2011). One gram of each soil sample, in triplicate, \nwas placed in a polyethylene tube and extracted separately with 25 ml of 0.05 \nM NaHCO3 at pH 8.5. Samples were shaken for 0.5 to 256 h (0.5, 1, 2, 4, 8, 16, \n32, 64, 128, and 256 h) time periods at 25\u00b12 \u00b0C. Finally, they were centrifuged \nimmediately at 4000 rpm. The supernatants were filtered through filter paper and \n\n\n\nFigure 2: Location of each pedon in the  studied geomorphic surfaces of the area\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 39\n\n\n\nP concentration was determined by ammonium molybdate\u2013ascorbic acid method \n(Murphy and Riley 1962). Different kinetic equations were used to describe P \ndesorption in the studied soils (Table 1). \n\n\n\n8 \n \n\n\n\nTABLE 1 \n Equations fitted to describe P release kinetics \n\n\n\n \nModel  Equation  Parameters  \n\n\n\nZero order qt = q 0 \u2013 k0 t K0, zero order rate constant (mg P kg\u22121 h\u22121) \n\n\n\nFirst order ln qt = ln q0 - k1t k1, first-order rate constant (h\u22121) \n\n\n\nSecond order 1 / qt = 1 / q0- k2t k2, second-order rate constant [(mg P kg\u22121)\u22121] \n\n\n\nThird order t1/qt\n2 =1/q 0\n\n\n\n2 - k3 k3, third-order rate constant[(mg P kg\u22121)\u22122 h\u22122] \n\n\n\nSimple Elovich qt = 1 / \u03b2 ln \n(\u03b1s\u03b2s) + (1 / \u03b2s) lnt \n\n\n\n\u03b1s, initial desorption rate (mg P kg\u22121 h\u22121), \u03b2s desorption \nconstant [(mg P kg\u22121)\u22121] \n\n\n\nParabolic diffusion qt = q 0 - kpt1 / 2 kp, diffusion rate constant [(mg P g\u22121)\u22120.5] \n\n\n\nPower function qt = atb a, initial desorption rate constant (mg P kg\u22121 h\u22121)b \nb, desorption rate coefficient [(mg P kg\u22121)\u22121] \n\n\n\nq0 and qt are the amount of P desorption (mg kg\u22121) at time zero and t, respectively. \n 132 \n\n\n\nModel Fitting to Kinetic Data  133 \n\n\n\nA two first-order reaction model can divide elements into three fractions (Santos et al. 2010; 134 \n\n\n\nJalali and Sajadi Tabar 2013; Saffari et al. 2016), Q1, Q2 and Q3, where 135 \n\n\n\n    (       )     (       ) \n\n\n\n\n\n\n\nq (mg P kg-1 h-1): amount of element released at t time. 136 \n\n\n\nQ1(mg kg\u22121): \u201clabile\u201d fraction, readily extractable, associated to the rate constant k1(min-1). 137 \n\n\n\nQ2 (mg kg\u22121): \u201cmoderately labile\u201d fraction, less extractable, associated with the rate constant k2 138 \n\n\n\n(min-1). 139 \n\n\n\nQ3 (mg kg\u22121): P fraction, which is not extractable 140 \n\n\n\nqtotal(mg kg\u22121): total concentration of P in soil 141 \n\n\n\nTABLE 1\n Equations fitted to describe P release kinetics\n\n\n\nModel Fitting to Kinetic Data \nA two first-order reaction model can divide elements into three fractions (Santos \net al. 2010; Jalali and Sajadi Tabar 2013; Saffari et al. 2016), Q1, Q2 and Q3, \nwhere\n\n\n\nq (mg P kg-1 h-1): amount of element released at t time.\nQ1(mg kg-1): \u201clabile\u201d fraction, readily extractable, associated to the rate constant \nk1(min-1).\nQ2 (mg kg-1): \u201cmoderately labile\u201d fraction, less extractable, associated with the \nrate constant k2 (min-1).\nQ3 (mg kg-1): P fraction, which is not extractable\nqtotal(mg kg-1): total concentration of P in soil\n\n\n\nTo detect the best-fitted model, a standard error of estimate was calculated for \neach equation and model. Relatively high values of coefficients of determination \n(R2) and low values of standard errors of estimate (SE) were used as the criteria to \nobtain the best-fitted models. The standard error was calculated as follows:\n\n\n\n8 \n \n\n\n\nSimple Elovich qt = 1 / \u03b2 ln \n(\u03b1s\u03b2s) + (1 / \u03b2s) lnt \n\n\n\n\u03b1s, initial desorption rate (mg P kg\u22121 h\u22121), \u03b2s desorption \nconstant [(mg P kg\u22121)\u22121] \n\n\n\nParabolic diffusion qt = q 0 - kpt1 / 2 kp, diffusion rate constant [(mg P g\u22121)\u22120.5] \n\n\n\nPower function qt = atb a, initial desorption rate constant (mg P kg\u22121 h\u22121)b \nb, desorption rate coefficient [(mg P kg\u22121)\u22121] \n\n\n\nq0 and qt are the amount of P desorption (mg kg\u22121) at time zero and t, respectively. \n 127 \n\n\n\nModel Fitting to Kinetic Data  128 \n\n\n\nA two first-order reaction model can divide elements into three fractions (Santos et al. 2010; 129 \n\n\n\nJalali and Sajadi Tabar 2013; Saffari et al. 2016), Q1, Q2 and Q3, where 130 \n\n\n\n    (       )     (       ) \n\n\n\n\n\n\n\nq (mg P kg-1 h-1): amount of element released at t time. 131 \n\n\n\nQ1(mg kg\u22121): \u201clabile\u201d fraction, readily extractable, associated to the rate constant k1(min-1). 132 \n\n\n\nQ2 (mg kg\u22121): \u201cmoderately labile\u201d fraction, less extractable, associated with the rate constant k2 133 \n\n\n\n(min-1). 134 \n\n\n\nQ3 (mg kg\u22121): P fraction, which is not extractable 135 \n\n\n\nqtotal(mg kg\u22121): total concentration of P in soil 136 \n\n\n\nTo detect the best-fitted model, a standard error of estimate was calculated for each equation and 137 \n\n\n\nmodel. Relatively high values of coefficients of determination (R2) and low values of standard 138 \n\n\n\nerrors of estimate (SE) were used as the criteria to obtain the best-fitted models. The standard 139 \n\n\n\nerror was calculated as follows: 140 \n\n\n\n   [\n\u2211(    ) \n\n\n\n   ]\n   \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201840\n\n\n\nwhere S and S\u2032 are the measured and calculated amounts of P desorption in soil at \ntime t respectively, and n is the number of measurements. \n\n\n\nFractionation of P\nChemical distribution of P in unamended and incubated soils (incubated with 50 \n\u00b5gg-1 P) was determined using a modified version of the Hedley et al. (1982) \nprocedure, as outlined by Zhang and MacKenzie (1997) (Table 2). The procedure \nwas designed to separate P into four fractions (i) soluble and exchangeable P; (ii) \nFe- and Al-bound P; (iii) Ca-bound ; and (iv) residual P. An outline of the method \nis presented in Table 3. The amount of P in each extracted sample was determined \nusing colorimetric ascorbic acid method (Murphy and Riley 1962).\n\n\n\nTABLE 2\nSummary of the sequential extraction procedure used in this study\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nMorphology of Studied Soils\nAs can be seen in Figure 2, Kerman Plain, i.e. the first studied geomorphic \nsurface, has the lowest elevation and precipitation compared to all the studied \ngeomorphic surfaces. In the first Pedon (P1),  located in Kerman Plain, two Bk \nhorizons were observed and due to the arid MR in this landform, the Bt horizon \nhas not been formed. This soil is classified as Typic Haplocalcids. In the second \nstudied geomorphic surface (rock pediment), located on gypsiferous neogene \nformations (Geological Survey of Iran 1995), Gypsic Haplosalids was observed \n(P2). In this pedon, due to the existence of a high percentage of coarse gravel \n(10-80%), gypsum pendants were observed more frequently. The third studied \ngeomorphic surface (Piedmont plain) was divided to two parts based on MR. In \nthe first part, that is P3 - P5, the MR is aridic. On the other side, in the second \npart of this geomorphic surface, the MR is xeric (for P6 and P7). P3, P4, P5 were \nclassified as Typic Natrigypsids, Typic Natrigypsids, and Calcic Haplosalids, \nrespectively. More precipitation in P3  compared to positions in P1 and P2, and \na high value of sodium adsorption ratio (SAR) led to formation of Btnk horizon. \nP4 had a polygon structure at the surface, which is due to this pedon in saline \nsoil. On the other hand, a black surface originating from dissolved organic matter \nwas observed in P5, probably stemming  from a sodic soil. The existence of \nthe salic horizon and petrocalcic horizon in P6 marked this soil as Petrocalcic \n\n\n\n9 \n \n\n\n\nwhere S and S\u2032 are the measured and calculated amounts of P desorption in soil at time t 141 \n\n\n\nrespectively, and n is the number of measurements.  142 \n\n\n\n 143 \n\n\n\nFractionation of P 144 \n\n\n\nChemical distribution of P in unamended and incubated soils (incubated with 50 \u00b5gg-1P) was 145 \n\n\n\ndetermined using a modified version of the Hedley et al. (1982) procedure, as outlined by Zhang 146 \n\n\n\nand MacKenzie (1997) (Table 2). The procedure was designed to separate P into four fractions 147 \n\n\n\n(i) soluble and exchangeable P; (ii) Fe- and Al-bound P; (iii) Ca-bound ; and (iv) residual P. An 148 \n\n\n\noutline of the method is presented in Table 3. The amount of P in each extracted sample was 149 \n\n\n\ndetermined using colorimetric ascorbic acid method (Murphy and Riley 1962). 150 \n\n\n\n 151 \n\n\n\nTABLE 2 \nSummary of the sequential extraction procedure used in this study \n\n\n\ng soil:mL \nsolution \n\n\n\n Extracting solution  \nShaking time \n\n\n\n(h) \n \n\n\n\nChemical form of \nP \n\n\n\n Symbol \n\n\n\n0.5:30 0.5 M NaHCO3 (pH 8.5) 16 soluble and \nexchangeable Exch-P \n\n\n\n0.5:30 0.1M NaOH 16 Fe- and Al-bound P Fe-Al-\nP \n\n\n\n0.5:30 1M HCl 16 Ca-bound P Ca-P \n\n\n\n0.5:30 5:2 mixture of concentrated HNO3 and \nHClO4 16 Residual P Res-P \n\n\n\n \n 152 \n\n\n\nStatistical Analysis 153 \n\n\n\nThe regression of linear, non-linear procedure and other statistical analysis were calculated by 154 \n\n\n\nMicrosoft Excel 2010 and SPSS V19. 155 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 41\n\n\n\nCalcixerepts. Towards the end part of the rock pediment geomorphic surface with \na xeric moisture regime in P7 (Typic Natrixeralfs), the  amount of clay increased \nwith depth, forming the  argillic horizon. In addition, the SAR value increased \nwith depth causing the  ormation of the Btnk horizon due to clay dispersion. P8 \nand P9 are located in the lowland geomorphic surface of  Lalehzar Mountains. \nIn P8, the  Mollic epipedon with about 32.5 % organic matter and a xeric MR as \nwell as  a  water saturated condition (for about 6 months  during normal years), \nmarked this soil as Typic Epiaquolls. In P9, the  amount of organic matter was \nhigher than in P8 (Table 3). This pedon is known as an organic soil because it has \nan  intermediate decomposition of organic matter (Typic Haplohemists). Stream \nflow derived from melting snow and rainfall in the  lowlands did not allow for \ndecomposition of organic matter. Results from this geomorphic surface showed \n\n\n\nTABLE 3\nSelected chemical and physical properties of studied soils\n\n\n\n12 \n \n\n\n\nTABLE 3 \nSelected chemical and physical properties of studied soils \n\n\n\n\n\n\n\n Profile \nnumber Taxonomy Horizon Depth \n\n\n\n(cm)  \npH OM CCE clay sand CEC Olsen-p Total P \n-  %  Cmol (+) kg-1  mg kg-1  \n\n\n\nS1 P1 Aridisols A 0-30 8 0.55 19.4 20 74 10.3 8.6 582 \nS2 P1 Aridisols Bk1 30-51 7.9 0.44 16.5 22 68 11.2 6.1 513 \nS3 P1 Aridisols Bk2 51-85 8 0.34 16.3 14 80 7.1 5.2 478 \nS4 P1 Aridisols C1 85-110 8.3 0.46 12.5 4 94 3.8 4.5 578 \nS5 P1 Aridisols C2 110-140 8.2 0.36 14.3 4 90 2.9 2.4 422 \nS6 P2 Aridisols A 0-10 7.8 0.13 28.3 22 46 12.1 12.2 378 \nS7 P2 Aridisols Byz1 10-50 7.6 0.46 25.1 6 82 4.2 9.6 320 \nS8 P2 Aridisols Byz2 50-80 7.4 0.51 19.5 6 88 4.5 5.5 325 \nS9 P2 Aridisols Byz3 80-110 7.6 0.51 22.1 6 88 4.3 4.2 280 \n\n\n\nS10 P2 Aridisols Byz4 110-140 7.5 0.50 21.8 6 88 4.6 3.1 199 \nS11 P3 Aridisols A 0-20 8.2 0.51 18.9 7 83 4.6 18.6 586 \nS12 P3 Aridisols Bw 20-50 8 0.32 18.1 13 79 6.2 17.2 497 \nS13 P3 Aridisols Btnk 50-90 8.2 0.29 27.2 27 53 16.3 8.3 443 \nS14 P3 Aridisols By 90-140 8 0.24 22 22 58 11.5 2.2 478 \nS15 P3 Aridisols C1 140-160 8 0.36 23.3 23 55 12.3 2.1 425 \nS16 P3 Aridisols C2 160-200 8 0.11 22.9 15 73 7.5 1.4 357 \nS17 P4 Aridisols A 0-20 8.2 0.68 27.5 41 45 22.6 6.3 446 \nS18 P4 Aridisols Bw1 20-40 8.5 0.41 24.1 39 51 20.5 6.1 389 \nS19 P4 Aridisols Bw2 40-65 8.1 0.59 22.5 29 51 16.2 3.1 375 \nS20 P4 Aridisols By 65-95 8 0.41 22 31 43 17.3 2.4 347 \nS21 P4 Aridisols Btny 95-140 8.5 0.42 21.8 38 37 20.4 2.1 298 \nS22 P5 Aridisols A 0-5 7.4 0.8 25.2 29 16 19.1 3.5 378 \nS23 P5 Aridisols Bk 5-30 7.3 0.5 25.6 25 37 15.5 1.8 356 \nS24 P5 Aridisols Bz 30-60 7.5 0.6 19.3 25 75 14 1.8 311 \nS25 P5 Aridisols Btn1 60-90 7.7 0.2 16 31 35 17.1 1.5 287 \nS26 P5 Aridisols Btn2 90-120 7.6 0.45 15.1 47 35 25.3 1.6 248 \nS27 P6 Inceptisols Az 0-30 7.4 0.43 21.7 22 62 14.1 8.5 315 \nS28 P6 Inceptisols Bk1 30-70 7.5 0.35 28.1 16 72 11.4 7.5 288 \nS29 P6 Inceptisols Bk2 70-95 7.8 0.52 34.5 14 72 9.5 5.8 274 \nS30 P6 Inceptisols Bkm 95-132 7.7 0.42 47.7 16 56 10.2 2.1 266 \nS31 P7 Alfisols Az 0-5 7.4 0.85 12.5 19 75 11.3 14.1 413 \nS32 P7 Alfisols Bkz 5-30 7.4 0.38 17.3 21 75 14.2 14.5 389 \nS33 P7 Alfisols Bk 30-65 7.5 0.25 16.8 13 81 6.5 5.2 365 \nS34 P7 Alfisols Btnk 65-100 7.7 0.20 28.5 23 55 11.7 2.4 378 \nS35 P8 Mollisols Ag 0-30 6.1 32.5 10.5 44 34 82.36 22.3 873 \nS36 P8 Mollisols Bwg1 30-60 6.4 11.4 9.8 32 46 43.5 20.5 834 \nS37 P8 Mollisols Bwg2 60-90 7.5 6.1 9.1 16 66 23.5 21.1 854 \nS38 P8 Mollisols Cg1 90-120 6.8 6.05 10.2 18 60 25.8 16.8 812 \nS39 P8 Mollisols Cg2 120-150 7 7.5 8.5 22 56 15.7 15.5 789 \nS40 P9 Histosols Oe1 0-30 7.5 46.3 15.1 14 36 120.1 122.4 1812 \nS41 P9 Histosols Oe2 30-60 7.1 26.7 9 30 28 86.7 102.5 1687 \nS42 P9 Histosols Bwg1 60-90 7.1 18.9 12.6 43 5 70.2 138.6 1477 \nS43 P9 Histosols Bwg2 90-120 7.2 9.5 7.5 33 32 44.3 120.5 1419 \nS44 P9 Histosols Cg 120-150 7 10.3 9.1 26 28 39.1 134.3 1315 \n 196 \n\n\n\nPhysico-chemical Properties of the Studied Soils 197 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201842\n\n\n\nthat parent materials, topography and climate changed following the movement \nup the transect. That is why soil properties as well as soil classifications also \nchanged along with the transect.\n\n\n\nPhysico-chemical Properties of the Studied Soils\nSelected chemical and physical properties of the studied soils are shown in Table \n1. The soils were alkaline (except for Histosols soils). The pH value in these soils \nranged from 6.1to 8.5. The OM and CCE values ranged from 0.11 and 46.3% to 7.5 \nand 47.7%, respectively. Higher OM values were observed in Mollisols (with an \naverage  of 12.71%) and Histosols (with an average of 22.43%). Such conditions \ndo now allow for decomposition of organic matter. The highest and lowest \naverage  CCE values were found in Aridisols (P3) and Mollisols, respectively. \nThe CEC varied from 2.9 to 120.1 cmol (+) kg-1. The lowest and highest average \nof CEC values were found in Aridisols (P2) and Histosols, respectively. Olsen \nP in different soils types was significantly different. Available P (Olsen P) \nconcentration varied from 1.4 to138.6 mg kg-1 with an average of 20.81 mg kg-1. \nThe highest available P was observed in Histosols and Mollisols (at an average \nof 123.66 and 19.24 mg kg-1, respectively) compared to Aridisols, Inceptisols, \nand Alfisols which had an  average of  5.48, 5.97, 9.05 mg P kg-1, respectively). \nHowever, total P concentration in all soil samples varied between 199 and 1812 \nmg kg-1 with an average of 20.81 mg kg-1, with only 20% of soil samples having \nP amounts higher than critical level (above 18 mg kg-1). Generally, diversity of \nphysic-chemical properties of the present soils would be useful for obtaining a \nbetter understanding of  P status which should lead  to better management of  P \nand consequently improved soil responses to P fertilisers.\n\n\n\nClay Mineralogy\nClay minerals are considered an important part of the solid phase of soils as  the \nstructural composition and its qualitative and quantitative identification provide \nresearchers with valuable information about the status of adsorption, fixation and \nrelease of cations and anions. In order to investigate the effectiveness of clay \nminerals on desorption and chemical forms of P, various minerals have been \nidentified in different horizons of some studied orders and their quantity has been \ndetermined using PANalytical-X\u2019Pert software. The quantitative results of various \namounts of clay minerals in different horizons of different orders are found in \nTable 4. As can be seen, the relative abundance of smectite, illite and kaolinite \nclay minerals is similar in the  majority of the studied soils. \n\n\n\nChlorite mineral was observed in all studied soils; however, its relative \namount differed in different soils. The quantity of smectite-vermiculite \ninterstratified minerals (interstratified in Table 4) as observed in some orders \nwas very low (lower than 10%). The palygorskite mineral changes showed a \ndecreasing process from the beginning (Aridisols order) to the end (Histosols \norder) of the studied area, with  the palygorskite clay mineral not being found in \nboth Mollisols and Histosols Orders. This may be due to the fact that any increase \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 43\n\n\n\nin the humidity of the studied area from the beginning (Aridisols) to the end \n(Histosols), destroys palygorskite  due to instability, resulting in the prevalence \nof  smectite and vermiculite minerals (Moazallahi and Farpoor 2012). An increase \nin  the smectite mineral in the  Mollisol and Histosol Orders may be attributed to \nthese changes. \n\n\n\nKinetics of P Desorption\nThe trends in cumulative desorption of  P by 0.05 M sodium bicarbonate in \ndifferent soil samples are shown in Figure 3. Based on the results obtained, biphasic \npatterns of P desorption are found at  the beginning followed by slower desorption. \nSimilar results have been found by others including Jalali and Zinli, (2011) and \nNafiu (2009). Biphasic patterns of P desorption can be due to the adsorption of  \nheterogeneous sites with different sorption affinities (Saha et al. 2004). Initial fast \ndesorption of P could be related to rapid dissolution of amorphous phosphates \nwith lower bonding energy (Jalali and Zinli 2011). On the other hand, P desorption \nin the slower second phase depends on the dissolution of the crystalline phosphate \ncompounds such as octa-Ca-phosphate and Ca-hydroxyl-apatite.\n\n\n\nThus, it seems that the first and second stages of P desorption could be related \nto a labile P and also less mobile forms of P, respectively. In most of the soil samples, \nthe first 8 h had the highest amount of P desorption, followed by a slight decrease \nin P desorption. Elrashidi et al. (1975) explained that in calcareous soils, there are \ntwo forms of P, one of which is rapidly released, while the other  is slowly and \ngradually released. In the process of desorption, these two forms are abandoned \nsimultaneously during the first 6 to 12 h, but after that, only the release of the \nsecond form lasts for up to 72 h until a balance is achieved. The gradual reduction \nin P desorption velocity with time may be due to reducing surface coverage and \nsurface charge, thus reducing the potential energy of adsorbed phosphorous ions \n\n\n\n17 \n \n\n\n\n 243 \n\n\n\nTABLE 4- Semi-quantitative analysis of clay minerals in the clay fraction of some soils under study \nSoil \n\n\n\nnumber Pedon Horizon Smectite Illite Chlorite Interstratified Kaolinite Palygorskite \n\n\n\nS2 1 Bk1 ++ ++ ++ * + ++ \nS6 2 A ++++ ++ + * - - \nS7 2 Byz1 +++++ + - - + * \nS11 3 A ++++ ++ - * + + \nS12 3 Bw ++++ ++ ++ * + - \nS13 3 Btnk +++ +++ - * + + \nS14 3 By ++++ ++ - * + - \nS17 4 A + +++ + * + + \nS18 4 Bw1 +++ +++ + * + + \nS22 5 A ++++ ++ - * + + \nS23 5 Bk +++++ ++ + * + * \nS24 5 Bz ++++ +++ + - + * \nS28 6 Bk1 +++ +++ ++ * + - \nS29 6 Bk2 ++++ +++ + - - * \nS30 6 Bkm +++++ ++ - * + * \nS32 7 Bkz ++++ ++ + - + * \nS35 8 Ag +++ ++ ++ * + * \nS36 8 Bwg1 ++++ +++ + * + * \nS40 9 Oe1 +++++ + + * + * \nS41 9 Oe2 +++++ + - * + * \n\n\n\n*:  not detected, -: < 10%, +: 10-20%, ++:20-30%, +++: 30-45%,  ++++ : 45-55%, +++++: >55% \n\n\n\n \n 244 \n\n\n\nChlorite mineral was observed in all studied soils; however, its relative amount had 245 \n\n\n\ndiffer in different soils. The quantity of smectite-vermiculite interstratified 246 \n\n\n\nminerals (interstratified in Table 4) as observed in some orders was very low 247 \n\n\n\n(lower than 10%). The palygorskite mineral changes showed a decreasing process 248 \n\n\n\nfrom the beginning (Aridisols order) to the end (Histosols order) of the studied 249 \n\n\n\narea, so that the palygorskite clay mineral was not found in both Mollisols and 250 \n\n\n\nHistosols Orders.this may be due to the fact that, anyincreasein humidity of studied 251 \n\n\n\narea from the beginning (Aridisols) to the end(Histosols), palygorskite isdestroyed 252 \n\n\n\nTABLE 4\nSemi-quantitative analysis of clay minerals in the clay fraction of some soils under study\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201844\n\n\n\nFigure 3: Cumulative release of  P with time in treated soils \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 45\n\n\n\n(Mikutta et al. 2006). The total average P released in the studied soils ranged \nfrom  25.94 to 154.94 mg kg-1. The minimum (20.6 mg kg-1) and maximum (172.1 \nmg kg-1) amounts of P (average of profiles in each soil order) were in P5 (Calcic \nHaplosalids) and P9 (Typic Haplohemists), respectively (Figure 3). Phosphorus \ndesorption in Histosols order was, on average, 6 times more than the cumulative \nP released in Aridisols, which could be due to a high amount of total P. Seven \nkinetic equations and one model were used for describing P desorption kinetics \nup to 256 h. Based on the results (Table 5), simple rate equations including zero \norder, first order, second order, and third order equations could not be used for \nthe description of P desorption in the soil samples. Phosphorus desorption was \ncertainly affected by many soil factors; needless to say desorption patterns of P \ncould not be described through simple rate equations. An increase in the order of \n\n\n\nTABLE 5\nParameter derived from seven equations for P desorption in studied soils\n\n\n\n18 \n \n\n\n\nSoil \n\n\n\nnumber \nZero order First order \n\n\n\nSecond \norder \n\n\n\nThird \norder \n\n\n\nParabolic \ndiffusion \n\n\n\nSimple Elovich Two-constant rate \n\n\n\n R2 SE R2 SE R2 SE R2 SE R2 SE \u03b1s  \n(Ln) \u03b2s R2 SE a b R2 SE \n\n\n\nS1 0.74 4.24 0.59 0.27 0.45 0.02 0.34 0.00 0.91 2.47 11.76 0.27 0.98 1.18 12.29 0.19 0.99 0.05 \nS2 0.76 3.63 0.67 0.25 0.57 0.02 0.49 0.00 0.94 1.83 9.13 0.31 0.93 1.95 9.91 0.19 0.97 0.08 \nS3 0.57 4.90 0.47 0.32 0.36 0.02 0.27 0.00 0.81 3.29 10.82 0.30 0.98 0.94 11.07 0.19 0.96 0.08 \nS4 0.67 3.39 0.55 0.26 0.42 0.02 0.32 0.00 0.88 2.09 9.78 0.38 1.00 0.42 10.07 0.17 0.98 0.05 \nS5 0.60 5.26 0.47 0.27 0.35 0.01 0.25 0.00 0.81 3.64 16.19 0.27 0.99 0.81 16.27 0.16 0.95 0.09 \nS6 0.74 3.71 0.64 0.18 0.55 0.01 0.47 0.00 0.91 2.17 16.43 0.31 0.97 1.35 16.88 0.13 0.98 0.04 \nS7 0.82 33.42 0.89 0.29 0.71 0.01 0.47 0.00 0.63 47.38 -2.75 0.05 0.36 62.62 14.66 0.30 0.62 0.52 \nS8 0.62 4.88 0.51 0.28 0.39 0.02 0.28 0.00 0.84 3.16 12.92 0.28 0.98 1.12 13.25 0.17 0.96 0.08 \nS9 0.63 4.64 0.47 0.35 0.33 0.03 0.22 0.01 0.84 3.02 9.41 0.29 1.00 0.44 9.63 0.21 0.96 0.10 \n\n\n\nS10 0.56 3.38 0.47 0.26 0.38 0.02 0.30 0.00 0.80 2.31 9.71 0.44 0.99 0.52 9.84 0.16 0.97 0.07 \nS11 0.57 6.08 0.51 0.17 0.45 0.01 0.39 0.00 0.81 4.07 28.97 0.24 0.99 0.96 29.23 0.11 0.98 0.03 \nS12 0.67 6.27 0.57 0.20 0.47 0.01 0.38 0.00 0.88 3.85 25.15 0.20 0.99 0.90 25.63 0.14 0.99 0.03 \nS13 0.63 4.58 0.56 0.19 0.48 0.01 0.40 0.00 0.85 2.90 19.01 0.30 0.98 1.14 19.33 0.13 0.98 0.04 \nS14 0.54 4.94 0.45 0.28 0.36 0.02 0.27 0.00 0.77 3.46 13.03 0.31 0.97 1.25 13.18 0.17 0.95 0.09 \nS15 0.61 4.81 0.50 0.34 0.40 0.03 0.30 0.00 0.84 3.08 8.92 0.29 0.98 1.02 9.36 0.22 0.98 0.07 \nS16 0.49 5.85 0.41 0.32 0.34 0.02 0.27 0.00 0.73 4.27 13.30 0.28 0.96 1.57 13.37 0.18 0.94 0.10 \nS17 0.55 5.69 0.44 0.33 0.34 0.02 0.26 0.00 0.78 4.00 12.72 0.27 0.98 1.11 12.83 0.19 0.94 0.10 \nS18 0.78 4.02 0.67 0.22 0.54 0.01 0.43 0.00 0.95 1.94 13.11 0.27 0.96 1.72 13.85 0.17 0.99 0.03 \nS19 0.49 6.60 0.39 0.37 0.29 0.02 0.21 0.00 0.73 4.85 12.86 0.24 0.97 1.65 12.77 0.21 0.92 0.14 \nS20 0.65 3.88 0.55 0.26 0.45 0.02 0.35 0.00 0.87 2.39 10.48 0.34 0.98 0.82 10.86 0.17 0.99 0.04 \nS21 0.58 3.78 0.49 0.33 0.39 0.03 0.30 0.01 0.81 2.51 7.36 0.39 0.97 0.99 7.64 0.20 0.97 0.08 \nS22 0.48 4.75 0.43 0.27 0.38 0.02 0.34 0.00 0.73 3.45 12.86 0.35 0.94 1.55 13.02 0.15 0.94 0.09 \nS23 0.63 4.68 0.54 0.25 0.44 0.01 0.36 0.00 0.85 2.97 13.73 0.29 0.99 0.82 14.11 0.16 0.98 0.05 \nS24 0.67 2.21 0.62 0.13 0.56 0.01 0.51 0.00 0.88 1.31 13.54 0.58 0.98 0.58 13.72 0.10 0.98 0.03 \nS25 0.65 3.14 0.55 0.22 0.46 0.02 0.37 0.00 0.86 1.95 11.30 0.42 1.00 0.37 11.54 0.14 0.99 0.03 \nS26 0.63 2.98 0.50 0.24 0.37 0.02 0.26 0.00 0.83 2.01 10.20 0.46 0.99 0.53 10.28 0.15 0.95 0.08 \nS27 0.66 5.12 0.56 0.21 0.46 0.01 0.36 0.00 0.87 3.17 18.59 0.26 0.98 1.08 19.00 0.14 0.98 0.04 \nS28 0.67 4.12 0.56 0.16 0.46 0.01 0.36 0.00 0.86 2.70 22.35 0.31 0.98 0.90 22.52 0.11 0.96 0.05 \nS29 0.56 3.36 0.48 0.15 0.41 0.01 0.35 0.00 0.77 2.41 19.84 0.45 0.98 0.75 19.87 0.09 0.95 0.05 \nS30 0.61 4.54 0.52 0.23 0.44 0.01 0.36 0.00 0.84 2.95 15.16 0.31 0.99 0.74 15.45 0.15 0.98 0.05 \nS31 0.55 4.08 0.49 0.13 0.43 0.00 0.38 0.00 0.77 2.90 28.82 0.37 0.98 0.77 28.88 0.08 0.96 0.03 \nS32 0.60 2.97 0.55 0.10 0.50 0.00 0.45 0.00 0.82 2.00 26.17 0.47 0.99 0.37 26.25 0.07 0.98 0.02 \nS33 0.49 4.08 0.41 0.17 0.33 0.01 0.26 0.00 0.70 3.12 21.58 0.40 0.95 1.29 21.47 0.10 0.89 0.07 \nS34 0.46 5.02 0.37 0.30 0.28 0.02 0.21 0.00 0.69 3.82 14.07 0.33 0.95 1.47 13.86 0.16 0.89 0.12 \nS35 0.54 5.65 0.49 0.15 0.43 0.00 0.37 0.00 0.78 3.95 32.20 0.27 0.99 0.91 32.32 0.09 0.97 0.04 \nS36 0.52 6.41 0.46 0.16 0.40 0.00 0.34 0.00 0.74 4.68 34.56 0.24 0.97 1.57 34.61 0.10 0.95 0.05 \nS37 0.58 5.75 0.52 0.14 0.46 0.00 0.40 0.00 0.80 3.95 36.12 0.25 0.99 1.08 36.28 0.09 0.97 0.03 \nS38 0.54 4.45 0.49 0.13 0.43 0.00 0.38 0.00 0.77 3.17 29.80 0.34 0.98 0.93 29.86 0.08 0.96 0.04 \nS39 0.46 6.31 0.40 0.19 0.35 0.01 0.30 0.00 0.70 4.74 27.91 0.26 0.96 1.74 27.87 0.11 0.93 0.07 \nS40 0.43 6.26 0.42 0.04 0.40 0.00 0.39 0.00 0.66 4.85 138.56 0.28 0.94 1.99 138.54 0.02 0.93 0.01 \nS41 0.49 4.12 0.47 0.03 0.46 0.00 0.45 0.00 0.71 3.08 122.19 0.39 0.96 1.13 122.19 0.02 0.95 0.01 \nS42 0.58 5.47 0.57 0.03 0.55 0.00 0.54 0.00 0.81 3.67 152.16 0.26 0.99 0.87 152.23 0.02 0.99 0.01 \nS43 0.58 4.21 0.56 0.03 0.55 0.00 0.53 0.00 0.80 2.92 134.03 0.34 0.99 0.75 134.06 0.02 0.98 0.01 \nS44 0.68 3.59 0.66 0.02 0.65 0.00 0.63 0.00 0.87 2.26 146.34 0.36 0.96 1.29 146.40 0.02 0.96 0.01 \n\n\n\n 271 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201846\n\n\n\nreaction from zero to third order ended in a decrease in R2 in the studied soils, as \nreported by Saffari et al. (2016) who relates this to desorption of Pb.  R2 and SE \nvalues shown in Table 5 indicate that simple Elovich and power function equations \nare the best-fitted equations for describing desorption of P. Elovich equation \nassumes that the active surfaces of the sorbent are heterogeneous, depending on \nwhich different activation energies for chemisorption are shown (Gupta and Babu \n2006).  Given the  good fitting of desorption data with simple Elovich and power \nfunction equations, it can be concluded that the diffusion process is a limiting step \nin P desorption from soils.\n\n\n\nSimilar results were obtained by Garcia-Rodeja and Gil-sotres (1997) in \nP desorption in some acidic soils in Spain. The results obtained from previous \nexperiments show that the rate of metal desorption increases as the value of \u2018\u03b1s\u2019 \nincreases in simple Elovich equation (Chien and Clayton 1980). Among the \nstudied soils, soil samples of Mollisols and Histosols were found to have the \nhighest \u2018\u03b1s\u2019 value from simple Elovich equation and \u201ca\u201d from power function \nequation.  Kuo and Mikkelsen (1980) reported that an increase in the value of \u201ca\u201d \nincreases the  rate of metal desorption from soils. The model of two first-order \nreactions was used by several researches like Santos et al. (2010), Jalali and Tabar \n(2013)  and Saffari et al.(2016) to predict metal behaviour. This model could \nbe used as a kinetic method for metal speciation in soils and sediments (Saffari \net al. 2015). The model of two first-order reactions exhibited biphasic reaction, \ni.e. rapid extraction followed by slow extraction of metal (Saffari et al. 2015). \nHence, this model is expected to describe desorption of P adequately. This model \napproach indicates the quantity and the extraction rate of metal fractions (Santos \net al. 2010). Table 6 shows the parameters of Q1, K1, Q2, K2, R2, SE, and Q1/Q2. As \nthe results indicated that R2 was higher than 0.94 and the values of SE were lower \nthan that of  SE  obtained from the Elovich equation, it was felt that  this model \ncould suitably describe kinetics of P desorption in soil samples. Moreover, the \nresults showed that K1 (coefficients of P for rapid desorption phase) were higher \nthan K2 (coefficients of P for slower desorption phase). The rate of Q1 (quickly \nextracted) in Histosols and Mollisols was higher than in other soil orders, in fact \nthis was the highest rate of P desorption.   A high ratio of Q1/Q2 in Histosols, \nAlfisols, and Mollisols indicate that the labile metal fractions are higher than the \nless labile fractions. \n\n\n\nFractionation of P in Unamended Soils\nFigure 4 shows the concentration of each chemical fraction of P in the native \nsoils (unamended soils). Based on the results, in all soil samples P is strongly \nassociated with Ca-P and Res-P fractions, which agrees with results obtained by \nmany researchers including Kolahchi and Jalali (2012) and Castillo and Wright \n(2008). Jalali and Ranjbar (2010) reported that P in calcareous soils of western \nIran was predominantly present in the Ca-P (65.9%) and Res-P (28.9%). \n\n\n\nOn the other hand, Exch-P, the most bioavailable form of P, has the lowest \nconcentration among all P chemical forms. Distribution of chemical forms of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 47\n\n\n\nTA\nB\n\n\n\nLE\n 6\n\n\n\nPa\nra\n\n\n\nm\net\n\n\n\ner\n v\n\n\n\nal\nue\n\n\n\ns o\nf t\n\n\n\nw\no \n\n\n\nfir\nst\n\n\n\n-o\nrd\n\n\n\ner\n P\n\n\n\n re\nac\n\n\n\ntio\nn \n\n\n\nm\nod\n\n\n\nel\n in\n\n\n\n e\nxa\n\n\n\nm\nin\n\n\n\ned\n so\n\n\n\nils\n\n\n\n22\n \n\n\n\n So\nil \n\n\n\nnu\nm\n\n\n\nbe\nr \n\n\n\nQ\n1 \n\n\n\nK\n1 \n\n\n\nQ\n2 \n\n\n\nK\n2 \n\n\n\nR\n2  \n\n\n\nSE\n \n\n\n\nQ\n1/Q\n\n\n\n2 \nSo\n\n\n\nil \nnu\n\n\n\nm\nbe\n\n\n\nr \nQ\n\n\n\n1 \nK\n\n\n\n1 \nQ\n\n\n\n2 \nK\n\n\n\n2 \nR\n\n\n\n2  \nSE\n\n\n\n \nQ\n\n\n\n1/Q\n2 \n\n\n\nS1\n \n\n\n\n16\n.7\n\n\n\n72\n \n\n\n\n1.\n32\n\n\n\n6 \n17\n\n\n\n.3\n59\n\n\n\n \n0.\n\n\n\n01\n3 \n\n\n\n0.\n95\n\n\n\n7 \n1.\n\n\n\n71\n4 \n\n\n\n0.\n96\n\n\n\n6 \nS2\n\n\n\n3 \n14\n\n\n\n.3\n97\n\n\n\n \n3.\n\n\n\n54\n2 \n\n\n\n16\n.6\n\n\n\n69\n \n\n\n\n0.\n04\n\n\n\n4 \n0.\n\n\n\n97\n7 \n\n\n\n1.\n17\n\n\n\n3 \n0.\n\n\n\n86\n4 \n\n\n\nS2\n \n\n\n\n10\n.2\n\n\n\n31\n \n\n\n\n5.\n22\n\n\n\n3 \n17\n\n\n\n.5\n27\n\n\n\n \n0.\n\n\n\n02\n2 \n\n\n\n0.\n99\n\n\n\n7 \n0.\n\n\n\n40\n0 \n\n\n\n0.\n58\n\n\n\n4 \nS2\n\n\n\n4 \n13\n\n\n\n.8\n46\n\n\n\n \n5.\n\n\n\n86\n0 \n\n\n\n8.\n60\n\n\n\n1 \n0.\n\n\n\n03\n7 \n\n\n\n0.\n98\n\n\n\n5 \n0.\n\n\n\n47\n1 \n\n\n\n1.\n61\n\n\n\n0 \nS3\n\n\n\n \n12\n\n\n\n.4\n47\n\n\n\n \n2.\n\n\n\n03\n1 \n\n\n\n15\n.1\n\n\n\n03\n \n\n\n\n0.\n04\n\n\n\n1 \n0.\n\n\n\n99\n1 \n\n\n\n0.\n69\n\n\n\n8 \n0.\n\n\n\n82\n4 \n\n\n\nS2\n5 \n\n\n\n12\n.7\n\n\n\n93\n \n\n\n\n2.\n68\n\n\n\n7 \n10\n\n\n\n.8\n94\n\n\n\n \n0.\n\n\n\n03\n1 \n\n\n\n0.\n98\n\n\n\n4 \n0.\n\n\n\n66\n4 \n\n\n\n1.\n17\n\n\n\n4 \n\n\n\nS4\n \n\n\n\n12\n.6\n\n\n\n13\n \n\n\n\n1.\n67\n\n\n\n6 \n11\n\n\n\n.6\n64\n\n\n\n \n0.\n\n\n\n02\n2 \n\n\n\n0.\n97\n\n\n\n3 \n0.\n\n\n\n97\n3 \n\n\n\n1.\n08\n\n\n\n1 \nS2\n\n\n\n6 \n12\n\n\n\n.6\n91\n\n\n\n \n1.\n\n\n\n80\n5 \n\n\n\n8.\n91\n\n\n\n1 \n0.\n\n\n\n02\n5 \n\n\n\n0.\n97\n\n\n\n8 \n0.\n\n\n\n72\n0 \n\n\n\n1.\n42\n\n\n\n4 \nS5\n\n\n\n \n20\n\n\n\n.7\n08\n\n\n\n \n1.\n\n\n\n56\n7 \n\n\n\n14\n.4\n\n\n\n53\n \n\n\n\n0.\n02\n\n\n\n8 \n0.\n\n\n\n97\n1 \n\n\n\n1.\n41\n\n\n\n5 \n1.\n\n\n\n43\n3 \n\n\n\nS2\n7 \n\n\n\n21\n.9\n\n\n\n64\n \n\n\n\n2.\n29\n\n\n\n5 \n17\n\n\n\n.8\n76\n\n\n\n \n0.\n\n\n\n02\n4 \n\n\n\n0.\n99\n\n\n\n1 \n0.\n\n\n\n82\n6 \n\n\n\n1.\n22\n\n\n\n9 \n\n\n\nS6\n \n\n\n\n17\n.5\n\n\n\n64\n \n\n\n\n4.\n35\n\n\n\n1 \n15\n\n\n\n.6\n81\n\n\n\n \n0.\n\n\n\n03\n2 \n\n\n\n0.\n94\n\n\n\n4 \n1.\n\n\n\n70\n4 \n\n\n\n1.\n12\n\n\n\n0 \nS2\n\n\n\n8 \n26\n\n\n\n.1\n94\n\n\n\n \n2.\n\n\n\n36\n4 \n\n\n\n13\n.6\n\n\n\n32\n \n\n\n\n0.\n01\n\n\n\n9 \n0.\n\n\n\n98\n5 \n\n\n\n0.\n88\n\n\n\n2 \n1.\n\n\n\n92\n1 \n\n\n\nS7\n \n\n\n\n16\n.2\n\n\n\n49\n \n\n\n\n4.\n41\n\n\n\n8 \n18\n\n\n\n.6\n31\n\n\n\n \n0.\n\n\n\n07\n4 \n\n\n\n0.\n97\n\n\n\n3 \n1.\n\n\n\n40\n0 \n\n\n\n0.\n87\n\n\n\n2 \nS2\n\n\n\n9 \n21\n\n\n\n.9\n74\n\n\n\n \n2.\n\n\n\n72\n7 \n\n\n\n8.\n94\n\n\n\n1 \n0.\n\n\n\n03\n5 \n\n\n\n0.\n96\n\n\n\n8 \n0.\n\n\n\n89\n9 \n\n\n\n2.\n45\n\n\n\n8 \n\n\n\nS8\n \n\n\n\n14\n.6\n\n\n\n66\n \n\n\n\n2.\n39\n\n\n\n0 \n16\n\n\n\n.2\n73\n\n\n\n \n0.\n\n\n\n03\n7 \n\n\n\n0.\n99\n\n\n\n1 \n0.\n\n\n\n74\n9 \n\n\n\n0.\n90\n\n\n\n1 \nS3\n\n\n\n0 \n16\n\n\n\n.2\n57\n\n\n\n \n3.\n\n\n\n11\n1 \n\n\n\n15\n.1\n\n\n\n69\n \n\n\n\n0.\n04\n\n\n\n2 \n0.\n\n\n\n98\n1 \n\n\n\n0.\n99\n\n\n\n9 \n1.\n\n\n\n07\n2 \n\n\n\nS9\n \n\n\n\n13\n.9\n\n\n\n23\n \n\n\n\n1.\n05\n\n\n\n2 \n13\n\n\n\n.6\n05\n\n\n\n \n0.\n\n\n\n02\n2 \n\n\n\n0.\n98\n\n\n\n8 \n0.\n\n\n\n83\n6 \n\n\n\n1.\n02\n\n\n\n3 \nS3\n\n\n\n1 \n30\n\n\n\n.5\n60\n\n\n\n \n3.\n\n\n\n41\n4 \n\n\n\n11\n.3\n\n\n\n47\n \n\n\n\n0.\n04\n\n\n\n5 \n0.\n\n\n\n97\n4 \n\n\n\n0.\n98\n\n\n\n0 \n2.\n\n\n\n69\n3 \n\n\n\nS1\n0 \n\n\n\n10\n.9\n\n\n\n64\n \n\n\n\n2.\n19\n\n\n\n2 \n10\n\n\n\n.0\n10\n\n\n\n \n0.\n\n\n\n04\n3 \n\n\n\n0.\n98\n\n\n\n4 \n0.\n\n\n\n65\n1 \n\n\n\n1.\n09\n\n\n\n5 \nS3\n\n\n\n2 \n27\n\n\n\n.4\n44\n\n\n\n \n4.\n\n\n\n03\n0 \n\n\n\n9.\n23\n\n\n\n9 \n0.\n\n\n\n03\n8 \n\n\n\n0.\n97\n\n\n\n8 \n0.\n\n\n\n69\n1 \n\n\n\n2.\n97\n\n\n\n0 \n\n\n\nS1\n1 \n\n\n\n30\n.5\n\n\n\n10\n \n\n\n\n3.\n55\n\n\n\n9 \n19\n\n\n\n.2\n20\n\n\n\n \n0.\n\n\n\n04\n3 \n\n\n\n0.\n98\n\n\n\n8 \n1.\n\n\n\n00\n1 \n\n\n\n1.\n58\n\n\n\n7 \nS3\n\n\n\n3 \n24\n\n\n\n.5\n97\n\n\n\n \n2.\n\n\n\n27\n7 \n\n\n\n8.\n88\n\n\n\n7 \n0.\n\n\n\n04\n0 \n\n\n\n0.\n97\n\n\n\n9 \n0.\n\n\n\n82\n8 \n\n\n\n2.\n76\n\n\n\n8 \n\n\n\nS1\n2 \n\n\n\n29\n.3\n\n\n\n41\n \n\n\n\n2.\n49\n\n\n\n4 \n22\n\n\n\n.2\n65\n\n\n\n \n0.\n\n\n\n02\n4 \n\n\n\n0.\n97\n\n\n\n8 \n1.\n\n\n\n63\n0 \n\n\n\n1.\n31\n\n\n\n8 \nS3\n\n\n\n4 \n17\n\n\n\n.2\n57\n\n\n\n \n1.\n\n\n\n40\n7 \n\n\n\n10\n.5\n\n\n\n99\n \n\n\n\n0.\n05\n\n\n\n6 \n0.\n\n\n\n97\n9 \n\n\n\n0.\n99\n\n\n\n7 \n1.\n\n\n\n62\n8 \n\n\n\nS1\n3 \n\n\n\n21\n.4\n\n\n\n09\n \n\n\n\n2.\n81\n\n\n\n1 \n15\n\n\n\n.6\n47\n\n\n\n \n0.\n\n\n\n02\n7 \n\n\n\n0.\n98\n\n\n\n5 \n0.\n\n\n\n92\n3 \n\n\n\n1.\n36\n\n\n\n8 \nS3\n\n\n\n5 \n33\n\n\n\n.5\n64\n\n\n\n \n3.\n\n\n\n55\n5 \n\n\n\n16\n.5\n\n\n\n65\n \n\n\n\n0.\n05\n\n\n\n2 \n0.\n\n\n\n97\n8 \n\n\n\n1.\n25\n\n\n\n2 \n2.\n\n\n\n02\n6 \n\n\n\nS1\n4 \n\n\n\n13\n.8\n\n\n\n28\n \n\n\n\n2.\n59\n\n\n\n2 \n14\n\n\n\n.8\n51\n\n\n\n \n0.\n\n\n\n05\n2 \n\n\n\n0.\n99\n\n\n\n3 \n0.\n\n\n\n61\n9 \n\n\n\n0.\n93\n\n\n\n1 \nS3\n\n\n\n6 \n34\n\n\n\n.1\n88\n\n\n\n \n3.\n\n\n\n92\n8 \n\n\n\n19\n.0\n\n\n\n15\n \n\n\n\n0.\n08\n\n\n\n3 \n0.\n\n\n\n97\n9 \n\n\n\n1.\n33\n\n\n\n9 \n1.\n\n\n\n79\n8 \n\n\n\nS1\n5 \n\n\n\n9.\n54\n\n\n\n0 \n2.\n\n\n\n77\n2 \n\n\n\n16\n.7\n\n\n\n33\n \n\n\n\n0.\n04\n\n\n\n4 \n0.\n\n\n\n99\n3 \n\n\n\n0.\n63\n\n\n\n5 \n0.\n\n\n\n57\n0 \n\n\n\nS3\n7 \n\n\n\n38\n.1\n\n\n\n06\n \n\n\n\n3.\n49\n\n\n\n7 \n17\n\n\n\n.4\n24\n\n\n\n \n0.\n\n\n\n04\n5 \n\n\n\n0.\n98\n\n\n\n3 \n1.\n\n\n\n14\n2 \n\n\n\n2.\n18\n\n\n\n7 \n\n\n\nS1\n6 \n\n\n\n11\n.1\n\n\n\n53\n \n\n\n\n4.\n77\n\n\n\n8 \n18\n\n\n\n.5\n91\n\n\n\n \n0.\n\n\n\n09\n7 \n\n\n\n0.\n98\n\n\n\n8 \n0.\n\n\n\n90\n3 \n\n\n\n0.\n60\n\n\n\n0 \nS3\n\n\n\n8 \n30\n\n\n\n.7\n02\n\n\n\n \n3.\n\n\n\n76\n5 \n\n\n\n12\n.8\n\n\n\n87\n \n\n\n\n0.\n05\n\n\n\n9 \n0.\n\n\n\n98\n6 \n\n\n\n0.\n78\n\n\n\n9 \n2.\n\n\n\n38\n2 \n\n\n\nS1\n7 \n\n\n\n13\n.7\n\n\n\n46\n \n\n\n\n2.\n01\n\n\n\n5 \n16\n\n\n\n.7\n22\n\n\n\n \n0.\n\n\n\n06\n1 \n\n\n\n0.\n97\n\n\n\n0 \n1.\n\n\n\n46\n5 \n\n\n\n0.\n82\n\n\n\n2 \nS3\n\n\n\n9 \n26\n\n\n\n.5\n44\n\n\n\n \n4.\n\n\n\n01\n1 \n\n\n\n18\n.2\n\n\n\n60\n \n\n\n\n0.\n10\n\n\n\n2 \n0.\n\n\n\n97\n1 \n\n\n\n1.\n45\n\n\n\n3 \n1.\n\n\n\n45\n4 \n\n\n\nS1\n8 \n\n\n\n15\n.7\n\n\n\n76\n \n\n\n\n2.\n76\n\n\n\n4 \n19\n\n\n\n.5\n41\n\n\n\n \n0.\n\n\n\n01\n8 \n\n\n\n0.\n99\n\n\n\n5 \n0.\n\n\n\n62\n5 \n\n\n\n0.\n80\n\n\n\n7 \nS4\n\n\n\n0 \n13\n\n\n\n4.\n96\n\n\n\n9 \n8.\n\n\n\n26\n9 \n\n\n\n19\n.1\n\n\n\n32\n \n\n\n\n0.\n15\n\n\n\n7 \n0.\n\n\n\n97\n4 \n\n\n\n1.\n35\n\n\n\n2 \n7.\n\n\n\n05\n5 \n\n\n\nS1\n9 \n\n\n\n12\n.4\n\n\n\n98\n \n\n\n\n2.\n04\n\n\n\n3 \n18\n\n\n\n.9\n21\n\n\n\n \n0.\n\n\n\n08\n7 \n\n\n\n0.\n97\n\n\n\n9 \n1.\n\n\n\n32\n6 \n\n\n\n0.\n66\n\n\n\n1 \nS4\n\n\n\n1 \n12\n\n\n\n3.\n30\n\n\n\n1 \n6.\n\n\n\n23\n8 \n\n\n\n10\n.6\n\n\n\n94\n \n\n\n\n0.\n06\n\n\n\n0 \n0.\n\n\n\n97\n9 \n\n\n\n0.\n82\n\n\n\n8 \n11\n\n\n\n.5\n30\n\n\n\n\n\n\n\nS2\n0 \n\n\n\n11\n.8\n\n\n\n31\n \n\n\n\n2.\n67\n\n\n\n8 \n13\n\n\n\n.8\n91\n\n\n\n \n0.\n\n\n\n03\n3 \n\n\n\n0.\n98\n\n\n\n9 \n0.\n\n\n\n68\n0 \n\n\n\n0.\n85\n\n\n\n2 \nS4\n\n\n\n2 \n15\n\n\n\n1.\n42\n\n\n\n7 \n33\n\n\n\n.3\n80\n\n\n\n \n19\n\n\n\n.0\n13\n\n\n\n \n0.\n\n\n\n05\n8 \n\n\n\n0.\n96\n\n\n\n5 \n1.\n\n\n\n57\n6 \n\n\n\n7.\n96\n\n\n\n4 \nS2\n\n\n\n1 \n7.\n\n\n\n68\n7 \n\n\n\n2.\n91\n\n\n\n8 \n12\n\n\n\n.5\n29\n\n\n\n \n0.\n\n\n\n04\n6 \n\n\n\n0.\n99\n\n\n\n5 \n0.\n\n\n\n40\n5 \n\n\n\n0.\n61\n\n\n\n4 \nS4\n\n\n\n3 \n13\n\n\n\n3.\n85\n\n\n\n7 \n8.\n\n\n\n14\n9 \n\n\n\n13\n.6\n\n\n\n66\n \n\n\n\n0.\n06\n\n\n\n9 \n0.\n\n\n\n94\n6 \n\n\n\n1.\n50\n\n\n\n6 \n9.\n\n\n\n79\n5 \n\n\n\nS2\n2 \n\n\n\n11\n.2\n\n\n\n37\n \n\n\n\n25\n.1\n\n\n\n11\n \n\n\n\n15\n.2\n\n\n\n31\n \n\n\n\n0.\n08\n\n\n\n0 \n0.\n\n\n\n99\n7 \n\n\n\n0.\n37\n\n\n\n9 \n0.\n\n\n\n73\n8 \n\n\n\nS4\n4 \n\n\n\n14\n8.\n\n\n\n48\n2 \n\n\n\n6.\n76\n\n\n\n1 \n13\n\n\n\n.2\n44\n\n\n\n \n0.\n\n\n\n02\n3 \n\n\n\n0.\n99\n\n\n\n6 \n0.\n\n\n\n39\n0 \n\n\n\n11\n.2\n\n\n\n11\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201848\n\n\n\nP in unamended soils followed this order: Ca-P> Res-P>Fe-Al-P>Exch-P. The \nCa-P form varied from 126.3 to 873 mg kg-1 (41.2 to 78.7%). Jalali and Sajadi \nTabar (2013) investigated soil P fractionation in calcareous soils and reported \nthat Exch-P ranged between 0.9% and 1.3% of the total P. In alkaline soils (pH \nvalue above 7), P tends to precipitate as calcium phosphates or co-precipitate with \ncarbonates (Jalali and Ranjbar 2010) as these are more stable than Exch-P and Fe-\nAl-P fractions (Diaz et al. 2006). A higher abundance of Ca-P (64.79%) compared \nwith other chemical forms indicates that P is immobilised within the crystalline \nand secondary minerals; additionally, P is essentially non-labile in most of the \nstudied soils. The main difference between soil orders in P distribution was \nobserved in Ca-P form. The relative percentage of Ca-P in Inceptisols, Aridisols, \nAlfisols, Histosols and Mollisols constituted 73.6%, 66.8%, 66.6%, 50% and 60% \nrespectively. Histosols and Mollisols had the lowest Ca-P concentrations because \nthey had the least CCE among all soil orders. Furthermore, Jalali and Tabar \n(2013) believe that soil irrigation by groundwater containing large quantities of \nsoluble salts such as Ca2+ and Na+, encourage P retention in the Ca-P fraction. \nWhereas the studied Histosols and Mollisols were not under cultivation and the \nmain source of their moisture was melting snow and rainfall, thus it seems that \na high quality of water in this region caused the Ca-P fraction in these Orders to \nbe lower than in others. The concentration of Res-P, Fe-Al-P and Exch-P ranged \nbetween 32.3, 12, 2.1 and724.5 mg kg-1 (12.2 to 42.9%) 152.1 mg kg-1 (4.2 to 9.8 \nand to 156.9 mg kg-1 (0.5 to 11.4%), respectively. Res-P is known for its stable \nand recalcitrant chemical forms. Compared to Ca-P, Histosols and Mollisols \nhad the highest Res-P in all soil orders. Jalali and Tabar (2013) observed that \nland uses with minimal management and no fertilisation such as pasture, had \nmore P in residual fraction. The P bound to amorphous oxyhydroxide surfaces \nand crystalline Fe and Al oxides, for all soil samples had the second lowest P \ncontent of all chemical forms. The concentration of Fe-Al-P form was higher for \n\n\n\nFigure 4:  Concentration of different P fractions in unamended soils\n\n\n\n23 \n \n\n\n\n 313 \n\n\n\n 314 \n\n\n\n 315 \n\n\n\n 316 \n\n\n\n 317 \n\n\n\nFigure 4:  Concentration of different P fractions in unamended soils  318 \n\n\n\n0\n200\n400\n600\n800\n\n\n\n1000\n1200\n1400\n1600\n1800\n2000\n\n\n\ns1 s3 s5 s7 s9 s11 s13 s15 s17 s19 s21 s23 s25 s27 s29 s31 s33 s35 s37 s39 s41 s43\n\n\n\nP \nco\n\n\n\nnc\nen\n\n\n\ntr\nat\n\n\n\nio\nn \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n ) \n\n\n\nSoils \n\n\n\nExch-P Fe-Al-P Ca-p Res-P\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 49\n\n\n\nHistosols (7.95%) and Mollisols (7.4%) as compared to other soil types. Wright \n(2009) studied chemical forms of P in Histosols of Florida. He reported that the \naverage Fe-Al-P in turf grass and sugarcane were 2.9 and 11.4% of the total P, \nrespectively. Jalali and Sajadi Tabar (2013) investigated chemical fractionation of \nP in calcareous soils and showed that the average Fe-Al-P fraction was 4.9 to 7% \nof the total P for different land uses. As explained above, Exch-P has the lowest \nconcentration fraction among all P chemical forms. The Exch-P was higher for \nHistosols and Mollisols (averaged 146.5 and 29.26 mg P kg-1, respectively) than \nAridisols (7.67 mg P kg-1), Inceptisols (8 mg P kg-1), and Alfisols (15.3 mg P kg-1). \nThe Exch-P with an average of 3.25% of total P was the least abundant P fraction \nin the studied soils. \n\n\n\nFractionation of P in Amended Soils\nAddition of P to the soils (50 \u00b5g g-1) increased the concentration of all fractions. \nHowever, the relative percentage of Exch-P and Fe-Al-P fraction in amended \nsoils increased but Ca-P and Res-P decreased as compared to unamended soils \n(Figure 5). Since Exch-P and Fe-Al-P forms represent the least recalcitrant pools, \nP in these forms likely represents recent inputs from fertilisers (Wright 2009). \nExch-P concentration in amended soils was about double in comparison to the \nunamended soil samples. The Exch-P concentration ranged between 16.2 and 24.5 \nmg kg-1 (with an average of 20.6 mg kg-1). These results are in agreement with \nmany previous observations (Jalali and Sajadi Tabar 2013; Andrade 2007) that \nthe addition of P increases Exch-P in soil. The highest increases of Exch-P and \nFe-Al-P fractions were observed in Mollisols. For all amended soil samples, Ca-P \nwas the dominant P form, but with a decreased percentage. The highest and lowest \namounts of the applied P to exchangeable fraction were obtained in Mollisols and \n\n\n\nFigure 5:  Concentration of different P fractions in amended soils\n\n\n\n33 \n \n\n\n\n 420 \n\n\n\nFig. 5.  Concentration of different P fractions in amended soils 421 \n\n\n\n0\n\n\n\n200\n\n\n\n400\n\n\n\n600\n\n\n\n800\n\n\n\n1000\n\n\n\n1200\n\n\n\n1400\n\n\n\n1600\n\n\n\n1800\n\n\n\n2000\n\n\n\ns1 s3 s5 s7 s9 s11 s13 s15 s17 s19 s21 s23 s25 s27 s29 s31 s33 s35 s37 s39 s41 s43\n\n\n\nP \nco\n\n\n\nnc\nen\n\n\n\ntr\nat\n\n\n\nio\nn \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n ) \n\n\n\nSoils \n\n\n\nExch-P Fe-Al-P Ca-p Res-P\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201850\n\n\n\nAridisol soils, respectively. On the other hand, the highest and lowest amounts \nof changing P added to Ca-P were observed in Aridisols and Mollisols soils, \nrespectively.  Exch-P is considered an available form of P for plants; therefore, \nit has higher absorbency and leaching compared to other forms. Considering the \nfact that less than 20% of P fertiliser consumed is transferred to Exch-P fraction, \nwe can say that more than 80% of the applied fertiliser temporarily changed to a \nnon-available form in the studied soils due to the calcareous character and high \npH. A high amount of such P is defined as labile and can be released at various \nstages and introduced as a supplier of the plant\u2019s initial need.\n\n\n\nSong et al. (2011) investigated chemical forms of P in Mollisols and showed \nthat application of P increased relative percentage of Ca-P and Al-P, but decreased \nrelative percentage of occluded P. They concluded that the applied P had been \ntransformed to these sparingly soluble forms for a short time. A sharp decrease in \nCa-P and Res-P fractions were observed in Inceptisols and Mollisols, respectively. \nCorrelation between Desorption and Chemical Forms Indices, Physic-Chemical \nand Mineralogical Properties of the Soils\n\n\n\nA simple correlation coefficient between some physical and chemical \nproperties of the studied soils, available-P, total-P and desorbed-P at the beginning, \nmiddle and the end of P released times is shown in Table 7.\n\n\n\nTABLE 7\nSimple correlation coefficient (r) between available P, total P, released P at different \n\n\n\ntimes and soil properties in studied soils\n\n\n\n28 \n \n\n\n\nSong et al. (2011) investigated chemical forms of P in Mollisols and showed that application of P 375 \n\n\n\nincreased relative percentage of Ca-P and Al-P, but decreased relative percentage of occluded P. 376 \n\n\n\nThey concluded that the applied P had been transformed to these sparingly soluble forms for a short 377 \n\n\n\ntime. A sharp decrease in Ca-P and Res-P fractions were observed in Inceptisols and Mollisols, 378 \n\n\n\nrespectively.  379 \n\n\n\nCorrelation between Desorption and Chemical Forms Indices, Physic-Chemical and 380 \n\n\n\nMineralogical Properties of the Soils 381 \n\n\n\nA simple correlation coefficient between some physical and chemical properties of the studied soils, 382 \n\n\n\navailable-P, total-P and desorbed-P at the beginning, middle and the end of P released times is shown 383 \n\n\n\nin Table7.  384 \n\n\n\nTABLE 7 \nSimple correlation coefficient (r) between available P, total P, released P at different times \n\n\n\nand soil properties in studied soils \n \n\n\n\n pH OM CCE Clay CEC EC Sand Silt Olsen-P Total-P \n\n\n\nOlsen-P -0.321 0.862** -0.520* -0.052 0.877** -0.258 -0.375 0.518*   \nTotal-P -0.408 0.908** -0.665** 0.114 0.930** -0.366 -0.467* 0.521* 0.964**  \nT0.5 -0.335 0.855** -0.509* -0.060 0.871** -0.245 -0.375 0.524* 0.996** 0.956** \n\n\n\nT1 -0.337 0.852** -0.519* -0.063 0.869** -0.246 -0.367 0.515* 0.995** 0.957** \n\n\n\nT32 -0.339 0.865** -0.524* -0.061 0.879** -0.274 -0.382 0.533* 0.995** 0.965** \n\n\n\nT128 -0.304 0.861** -0.530* -0.063 0.874** -0.300 -0.365 0.513* 0.994** 0.965** \n\n\n\n 385 \n\n\n\n 386 \n\n\n\nAs can be seen from the results, there is a significant positive correlation \nbetween OM, CEC, silt, available-P, total-P, and the P parameters. On the \nother hand, among the soil properties, only CCE showed a significant negative \nrelation with the P parameters. Other correlations observed were not statistically \nsignificant.  A positive correlation between OM and available-P and P released \nmight be due to four processes: (i) the formation of organic matter complexes \nwith P, which results in the formation of organic phosphates and an increase in \nP mobility; (ii) organic anions, which can be exchanged with surface-adsorbed-\nP;(iii) Fe-Al oxides coatings on humus formations reduce the soil P sorption; and \n( iv) as a source of P, organic matter can also increase the amount of released P \nthrough the  mineralisation processes. Correlation between parameters extracted \nfrom fitted equations and P desorbed data (Table 8) shows that OM, CEC, silt, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 51\n\n\n\navailable-P, total-P and desorbed-P at the beginning, middle and the end of P \nreleased times had a significant positive correlation with \u201cas\u201d value from the  \nElovich equation, \u201c\u03b1\u201d from the power function equation, and Q1, K2,and Q1/Q2 \nparameters and a significant negative correlation with \u201cb\u201d parameter from the \npower function equation. Also, CCE, unlike other mentioned properties, had a \nsignificant positive correlation with \u201cb\u201d parameter from the power function \nequation, and a significant negative correlation with \u201ca\u201d from the power function \nequation and Q1/Q2.\n\n\n\nTABLE 8\nSimple correlation coefficient (r) between parameters of two first-order reactions, simple \nElovich,  power function and soil properties, available P, total P, released P at different \n\n\n\ntimes, in studied soils.\n\n\n\n30 \n \n\n\n\nTABLE 8 \nSimple correlation coefficient (r) between parameters of two first-order reactions, simple Elovich,  power function \n\n\n\nand soil properties, available P, total P, released P at different times, in studied soils. \n \n\n\n\n pH OM CCE Clay CEC EC Sand Silt Olsen-P Total-P T1 T32 T128 \n\n\n\nbs -0.109 -0.049 -0.013 0.168 -0.007 0.189 -0.024 -0.086 -0.006 -0.041 0.031 -0.033 -0.055 \nas -0.135 0.717** -0.216 -0.205 0.706** -0.164 -0.236 0.445* 0.743** 0.671** 0.721** .735** 0.733** \nb 0.352 -0.588** 0.473* -0.107 -0.605** 0.263 0.257 -0.256 -0.622** -0.618** -0.657** -.630** -0.625** \na -0.336 0.858** -0.516* -0.054 0.874** -0.258 -0.376 0.521* 0.995** 0.959** 1.000** .997** 0.994** \nQ1 -0.323 0.851** -0.519* -0.061 0.866** -0.259 -0.357 0.501* 0.994** 0.955** 0.999** .995** 0.994** \nK1 -0.176 0.115 0.081 0.078 0.145 -0.025 -0.524* 0.620** 0.121 0.117 0.112 0.112 0.089 \nQ2 0.254 0.094 -0.105 -0.043 0.076 -0.434 -0.048 0.092 0.041 0.110 -0.009 0.056 0.094 \nK2 -0.461* 0.708** -0.297 -0.059 0.715** -0.059 -0.448* 0.616** 0.679** 0.679** 0.666** .698** 0.673** \nQ1/Q2 -0.348 0.710** -0.488* 0.007 0.741** -0.149 -0.331 0.421 0.902** 0.861** 0.924** .900** 0.892** \n\n\n\n 407 \n\n\n\nOther relationships obtained were not statistically significant. Jalali and Ranjbar (2010) reported a 408 \n\n\n\nsignificant positive relationship between a parameter from power function equation and preliminary 409 \n\n\n\navailable-P of the soil. The correlation between chemical forms of  P (the results obtained before and 410 \n\n\n\nafter adding P fertiliser were the same), available-P and extracted parameters from two first-order 411 \n\n\n\nreaction models (Table 9) shows that all forms of P indicate a significant positive relationship with 412 \n\n\n\nthree Q1, K2 and Q1/Q2 parameters as well as available-P. It appears that the existence of a significant 413 \n\n\n\nrelationship between all forms of P and available-P indicates a lack of specific extraction from 414 \n\n\n\nrelative forms, because it is  predicted that only Exch-P and Fe-Al-P may have a direct a significant 415 \n\n\n\nrelationship with available-P. 416 \n\n\n\n 417 \n\n\n\n 418 \n\n\n\nOther relationships obtained were not statistically significant. Jalali and \nRanjbar (2010) reported a significant positive relationship between a parameter \nfrom power function equation and preliminary available-P of the soil. The \ncorrelation between chemical forms of  P (the results obtained before and after \nadding P fertiliser were the same), available-P and extracted parameters from \ntwo first-order reaction models (Table 9) shows that all forms of P indicate a \nsignificant positive relationship with three Q1, K2 and Q1/Q2 parameters as well \nas available-P. It appears that the existence of a significant relationship between \nall forms of P and available-P indicates a lack of specific extraction from relative \nforms, because it is  predicted that only Exch-P and Fe-Al-P may have a direct a \nsignificant relationship with available-P\n\n\n\nOn the other hand, unlike chemical forms of P, Q1 as an indicator of \navailable-P had a significant positive relationship with Olsen-P, and Q2 as P with \nlow availability did not have a significant relationship with Olsen-P. Therefore, it \ncan be stated that the study of desorption kinetics of P may have more predictive \nability for P availability compared to the sequential extraction technique. However, \nthere is a need  to investigate the relationship between plant reaction and extracted \nparameters from two first-order reaction models fitted on P desorption data in \nfuture studies. The study of simple correlations between clay minerals of studied \nsoil and parameters fitted on P desorption is shown in Table10.\n\n\n\nAmong the various minerals, significant correlations were observed only \nbetween two kaolinite and illite clays.. A significant negative correlation was \nfound between kaolinite and \u201cas\u201d from simple Elovich equation as well as illite \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201852\n\n\n\nand \u201cas\u201d from simple Elovich equation, and  a\u201d from power function equation, \nQ1 and Q1/Q2. Moreover, the study of correlation between clay minerals and \nchemical forms of P (the results obtained before and after adding P fertiliser were \nthe same) showing that kaolinite and illite have a significant negative relationship \nwith Exch-P (Table 11).\n\n\n\nAlso, illite showed a significant positive relationship with Exch-P. Studies \ncarried out by Penn et al. (2005) showed that sorption and desorption of  P \nhave a good correlation with Al-contained minerals such as hydroxy-interlayer \nvermiculite (HIV)minerals and amorphous Al, while maintenance of  P has a \nnegative relationship with kaolinite content, with this relationship being confirmed \nby isotherms performed on pure clay minerals. The present study shows that apart \nfrom clay content, the effect of clay type should be considered (non-significant \nnegative correlation with desorption parameters) in P desorption. Expandable \nand non-expandable clay minerals have different effects on P desorption rate. In \ncorrelation to clay minerals, smectite mineral showed a non-significant positive \nrelationship with \u201cas\u201d from simple Elovich equation and \u201ca\u201d from power function \nequation. Because of changes in the size of aggregates and their fraction due to \nwetting of the soil and lower surface functional groups, the existence of expandable \nminerals affects the P dispersion pattern with time and increases P release rate \n(Sparks 1998).  With regard to the effect of clay minerals on P kinetic in the \n\n\n\n32 \n \n\n\n\nTABLE 9 \nSimple correlation coefficient (r) between parameters of two first-order reaction models and chemical forms of P \n\n\n\nin studied soils \n\n\n\n pH Q1 K1 Q2 K2 Q1/Q2 Exch-P Fe-Al-P Ca-P Res-P \n\n\n\nQ1 -0.323          \nK1 -0.176 0.089         \nQ2 0.254 -0.012 -0.058        \nK2 -0.461* 0.643** 0.406 0.196       \nQ1/Q2 -0.348 0.928** 0.072 -0.285 0.433      \nExch-P -0.355 0.994** 0.121 0.015 0.673** 0.917**     \nFe-Al-P -0.363 0.954** 0.125 0.151 0.709** 0.823** 0.970**    \nCa-P -0.389 0.909** 0.088 0.208 0.681** 0.772** 0.923** 0.973**   \nRes-P -0.430 0.952** 0.135 0.027 0.647** 0.907** 0.969** 0.961** 0.941**  \nOlsen-P -0.321 0.994** 0.121 0.041 0.679** 0.902** 0.998** 0.970** 0.920** 0.959** \n\n\n\n 431 \n\n\n\n 432 \n\n\n\n On the other hand, unlike chemical forms of P, Q1 as an indicator of available-P had 433 \n\n\n\na significant positive relationship with Olsen-P, and Q2 as P with low availability did 434 \n\n\n\nnot have a significant relationship with Olsen-P. Therefore, it can be stated that the 435 \n\n\n\nstudy of desorption kinetics of P may have more predictive ability for P availability 436 \n\n\n\ncompared to the sequential extraction technique. However, there is a need  to 437 \n\n\n\ninvestigate the relationship between plant reaction and extracted parameters from two 438 \n\n\n\nfirst-order reaction models fitted on P desorption data in future studies. The study of 439 \n\n\n\nsimple correlations between clay minerals of studied soil and parameters fitted on P 440 \n\n\n\ndesorption is shown in Table10.  441 \n\n\n\n 442 \n\n\n\nTABLE 9\nSimple correlation coefficient (r) between parameters of two first-order\n\n\n\nreaction models and chemical forms of P in studied soils\n\n\n\nTABLE 10\nSimple correlation coefficient (r) between parameters of two first-order reaction models, \n\n\n\nsimple Elovich,  power function and clay minerals in studied soils\n\n\n\n38 \n \n\n\n\nOlsen-P. Therefore, it can be predicted that the study of desorption 475 \n\n\n\nkinetic of P may have more ability for P availability as compared to 476 \n\n\n\nsequential extraction technique. However, it is required to investigate the 477 \n\n\n\nrelationship between the plant reaction and extracted parameters from 478 \n\n\n\ntwo first-order reaction models fitted on P desorption data in future 479 \n\n\n\nstudies. The study of simple correlation between clay minerals of studied 480 \n\n\n\nsoil and parameters fitted on P desorption is shown in Table10.  481 \n\n\n\nTABLE 10- Simple correlation coefficient (r) between parameters of two first-order reactions, simple \nElovich,  power function and clay minerals in studied soils \n\n\n\n bs as b a Q1 K1 Q2 K2 Q1/Q2 \n\n\n\nKaolinite 0.103 -0.487* 0.119 -0.325 -0.322 -0.134 0.137 0.333 0.182 \n\n\n\nIllite 0.385 -0.500* -0.003 -0.560* -0.553* -0.179 -0.319 -0.529* -0.441 \n\n\n\nChlorite 0.023 0.035 -0.064 -0.018 -0.011 -0.148 0.125 -0.054 -0.054 \n\n\n\nSmectite -0.022 0.235 -0.221 0.394 0.393 0.092 -0.084 0.314 0.389 \n\n\n\nInterstratified 0.406 -0.112 0.013 -0.162 -0.158 -0.089 -0.189 -0.087 -0.018 \n\n\n\nPalygorskite -0.163 -0.196 0.372 -0.356 -0.358 0.063 0.336 -0.355 -0.388 \n\n\n\n 482 \n\n\n\nThe significant correlations are observed only between two kaolinite and 483 \n\n\n\nillite clays among various minerals. A significant negative correlation 484 \n\n\n\nwas shown between kaolinite and \"as\" from simple Elovich equation as 485 \n\n\n\nwell as illite and \"as\" from simple Elovich, \"a\" from power function 486 \n\n\n\nequation, Q1 and Q1/Q2.Moreover, the study of correlation between clay 487 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 2018 53\n\n\n\nTABLE 11\nSimple correlation coefficient (r) between P chemical forms and clay minerals in studied \n\n\n\nsoils\n\n\n\n32 \n \n\n\n\nTABLE 10 \nSimple correlation coefficient (r) between parameters of two first-order reaction models, simple Elovich,  \n\n\n\npower function and clay minerals in studied soils \n\n\n\n bs as b a Q1 K1 Q2 K2 Q1/Q2 \n\n\n\nKaolinite 0.103 -0.487* 0.119 -0.325 -0.322 -0.134 0.137 0.333 0.182 \n\n\n\nIllite 0.385 -0.500* -0.003 -0.560* -0.553* -0.179 -0.319 -0.529* -0.441 \n\n\n\nChlorite 0.023 0.035 -0.064 -0.018 -0.011 -0.148 0.125 -0.054 -0.054 \n\n\n\nSmectite -0.022 0.235 -0.221 0.394 0.393 0.092 -0.084 0.314 0.389 \n\n\n\nInterstratified 0.406 -0.112 0.013 -0.162 -0.158 -0.089 -0.189 -0.087 -0.018 \n\n\n\nPalygorskite -0.163 -0.196 0.372 -0.356 -0.358 0.063 0.336 -0.355 -0.388 \n\n\n\n 433 \n\n\n\n Among the various minerals, significant correlations were observed only between 434 \n\n\n\ntwo kaolinite and illite clays.. A significant negative correlation was found between 435 \n\n\n\nkaolinite and \"as\" from simple Elovich equation as well as illite and \"as\" from simple 436 \n\n\n\nElovich equation, and  a\" from power function equation, Q1 and Q1/Q2. Moreover, the 437 \n\n\n\nstudy of correlation between clay minerals and chemical forms of P (the results 438 \n\n\n\nobtained before and after adding P fertiliser were the same) showing that kaolinite 439 \n\n\n\nand illite have a significant negative relationship with Exch-P (Table 11). 440 \n\n\n\nTABLE 11 \nSimple correlation coefficient (r) between P chemical forms and clay minerals in \n\n\n\nstudied soils \n\n\n\n Exch-P Fe-Al-P Ca-P Res-P \n\n\n\nKaolinite -0.483* -0.369 -0.373 -0.320 \n\n\n\nIllite -0.585** -0.389 -0.307 0.546* \n\n\n\nChlorite 0.001 0.026 0.119 0.027 \n\n\n\nSmectite 0.368 0.307 0.232 0.308 \n\n\n\nInterstratified -0.170 -0.284 -0.353 -0.258 \n\n\n\nPalygorskite -0.328 -0.233 -0.214 -0.274 \n\n\n\nCairo region of Egypt, Wahba et al. (2002) showed that rate equations as well as \nthe P released for montmorillonite mineral was more than  that from kaolinite. \nDue to the presence of  surface functional groups, non-expandable clay minerals \nsuch as kaolinite are more effective in P sorption, and their P release is delayed. \nThis is confirmed by the results obtained from the present research. On the other \nhand, the sorption processes occur faster in some minerals such as kaolinite \nand smectite compared to vermiculite and mica. This results from the fact that \nkaolinite and smectite have more adsorbent surfaces, but vermiculite and mica \nhave multi-locations such as inter-layer, edge, and plate layer surfaces. Therefore, \nnot only expandability, but also sorbent locations affect the P release, indicating \nthat the more the locations, the more the P sorbed. This has the consequent effect \nof increasing the saved available P in soil and decreasing desorption. In the \npresent study, as the height from sea level increased, there was a higher release of \nexpandable minerals such as OM and montmorillonite.. As a result, there was an \nincrease in \u201cas\u201d from Elovich equation, \u201ca\u201d from power function equation, and \nQ1 from two first-order reaction models, indicating a higher release of expandable \nminerals such as montmorillonite.\n\n\n\nCONCLUSION\nThe present study attempted to investigate the comprehensive relationships \nbetween different physic-chemical and mineralogical properties of various soils \nwith desorption behaviours and chemical forms of P in different soil orders of a \nclimotoposequence.  The results obtained show that P desorption (with biphasic \npattern) had a significantly increasing trend from the beginning (Aridisols order) \ntill towards the end (Histosols order) of the studied area. This may be attributed to \na high content of total P at the first glance. However, the increasing trend of OM \ncontent as well as expandable montmorillonite clay may be another reason for the \nreleased P in the soils at the end of the studied transect. Among the equations fitted \non desorption data, simple Elovich, power function and two first-order reaction \nmodels could have good prediction based on the highest R2 and the lowest SE. The \nstudy of chemical forms of P in the studied area showed that Ca-P and Res-P are \nthe most common forms of important P in the studied soils. The relative percentage \nof Ca-P from the beginning till towards the end of the studied area showed a \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 22, 201854\n\n\n\ndecreasing trend due to calcium carbonate of soil as well as the high quality of \nthe area water. Addition of  P to the studied soils increased the content of all \nchemical forms of P. However, the relative percentage of Ca-P, Res-P decreased, \nand Exch-P and Fe-Al-P increased. The highest and the lowest amount of the P \nadded to the studied soils in Exch-form were observed in Mollisols and Aridisols \norders, respectively. These changes are predictable according to physic-chemical \nmineralogical properties of the soils. Moreover, the obtained results show that, \nin addition to OM, CEC, silt, available-P and total P as the significant properties \naffecting the desorption of P, two important kaolinite and illite minerals also \nplay an important role in the status of P behaviour in the studied soils. Generally \nspeaking, according to topographic-climatic properties, it appears that adding P \nfertilisers to soil in order to access more P at the beginning of the experiment (the \ntransformation of added P to non-adsorbable and mobile forms) must be done \nwith greater caution (fertilisation at several times during cultivation) compared \nto the end of the experiment. 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"\n\n___________________\n*Corresponding author : E-mail: bkhayam@yahoo.com\n\n\n\nINTRODUCTION\nZinc (Zn) is an essential element for human and plant but at high concentration \nit is detrimental for human health and plant growth. Manufacturing and other \nindustries release large quantities of metals, including Zn. The increasing demand \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 17: 69-83 (2013)                          Malaysian Society of Soil Science\n\n\n\nSorption and Desorption of Zinc by Clinoptilolite and \nClinoptilolite-Tridymite\n\n\n\nKhayambashi Babak1*, Anuar Abd Rahim1, Samsuri Abd Wahid1, \nSiva Kumar Balasundram2 and Majid Afyuni3\n\n\n\n \n1Department of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia 43400 Serdang Selangor, Malaysia\n2Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia 43400 Serdang Selangor, Malaysia\n3Department of Soil Science, Faculty of Agriculture, Isfahan University of \n\n\n\nTechnology 85184, Isfahan, Iran\n\n\n\nABSTRACT\nIndustrial wastes and mining related activity are main sources of zinc \ncontamination in soils and groundwater. The quality of soil, crop and water will \nalso be affected by high concentrations of heavy metals. The adsorption behaviour \nof clinoptilolite (natural Iranian zeolite) and clinoptilolite-tridymite (Chinese \nzeolite) at different pHs has been studied in order to find out its applicability in \nagriculture as soil amendment. To elucidate zinc adsorption, batch experiment at \nconstant pH was used. The mineralogical composition, specific surface area and \nCEC, were investigated by X-ray diffraction (XRD), BET-N2 sorption analysis \nand Na-acetate method, respectively. The data indicate that Iranian and Chinese \nzeolites contained 93.21 %, 58.83 % Clinoptilolite-Na, respectively, but high \namount of tridymite (28.04 %) was also present in the Chinese zeolite. The Zn \nsorption isotherm data for both Iranian zeolite and Chinese zeolite were fitted to \nthe Langmuir and Freundlich models. The sorption results at different pH showed \nthat sorption at constant pH=5 and 7 can best be fitted to the Langmuir equation. \nIt was found that qmax of Iranian zeolite was higher than Chinese zeolite at both \npHs. According to the findings, the binding strength of Zn adsorption in Chinese \nZeolite was 0.01, 0.03 (L mg-1) at pH 5 and pH 7 whereas KL for the Iranian zeolite \nwas 0.02 (L mg-1) at both pHs. It is revealed that the affinity of Chinese zeolite for \nzinc adsorption was higher at pH=7. The results indicate that the zinc desorption \npercent at highest loading rates for Iranian zeolite were 36.1%, 41.5%, while for \nChinese zeolite were 45.81%, 36.3% at pH value 5 and 7, respectively\n\n\n\nKeyword: Zeolites, soil amendment, zinc, pH study\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201370\n\n\n\nfor alkaline zinc manganese batteries to replace mercury brings serious problems \nof Zn contamination if those batteries are not disposed off properly (Andres et al. \n2000; Vatistas and Bartolozzi 1999). Another source of Zn contamination is from \nthe flooding of ore mines (Zoumis et al. 2000; Zamzow and Murphy 1992). \n\n\n\nSeveral strategies have been used to immobilize heavy metal ions in soils. \nImmobilization can be achieved by adding natural and synthetic amendments such \nas alkaline materials, phosphate minerals, Fe and Mn hydroxides, aluminosilicates, \nzeolites (Mench et al. 1998) and treatment plant residuals (Abbaspour et al. 2008). \nBecause of large specific surface area, high cation exchange capacity (CEC), \nlow cost and widespread availability, zeolites are probably the most promising \nmaterials interacting with many heavy metal ions in contaminated soils and \nwater (Inglezakis et al. 2007). Numerous investigator have suggested the use of \nnaturally occurring materials with high exchange capacity such as zeolite as being \ncost effective in removing heavy metals from wastewater and soils (Usman et al. \n2006; Kaya and \u00d6ren 2005; Mier 2001). Adsorption and ion exchange are simple \nand effective methods to remove Zn from wastewaters and soils contaminated \nby the element (Bhattacharyya and Gupta 2008; Du and Hayashi 2006). Zeolite \nis a natural superporous mineral which belongs to hydrated aluminosilicates, \nwith unique crystal structures consisting of SiO4 and AlO4 three-dimensional \nframework (Kaya and Durukan 2004). It is negatively charged, and therefore, \nvery suitable as Zn adsorbent. It is used as a water purifier (Rahman 2012) and \nalso as soil amendment (Fu and Wang 2011; Wang and Peng 2010). Utilization \nof zeolite as a substrate in hydroponic crop production systems has also been of \ninterest due to its capability to improve water retention capacity and to provide \nsufficient aeration for plants root (Mohammadi Torkashvand et al. 2013; Turhan \nand Eris 2005).\n\n\n\nAdsorption of metals by aluminosilicate was found to be pH dependent \n(Sajidu et al. 2006; Sheta et al. 2003; Shuman 1998). In rice cultivation system, \nsubmergence of soil typically causes a shift towards a more neutral soil pH. \nThis is a result of the change in chemical compounds when soil is reduced. In \nalkaline soil, final pH decreased and in acidic soil pH tend to increase followed \nby submergence to reach 6.5 -7.2 and remain unchanged during growing season \n(Rostaminia et al. 2011; IRRI, 2004; Spark et al. 1997). Therefore, it is important \nto investigate the sorption capacity of soil amendments at constant pHs.\n\n\n\nThe objective of this study was to determine the efficiency of two natural \nzeolites from Iran and China to adsorb or remove Zn from aqueous solutions as well \nas its release for plant uptake. The effects of solution pH and metal concentrations \non the adsorption and desorption of Zn by the zeolites were examined. \n\n\n\nMETHODOLOGY\nThe Iranian zeolite samples were obtained from Firouzkoh mines in northern Iran \nand the Chinese zeolite were brought into Malaysia. The mineral samples were \npulverized and passed through 1mm sieve. The mineralogical composition of the \nzeolite samples were investigated by X-ray diffraction with PW 3040/60MPD, \n\n\n\nKhayambashi Babak, Anuar Abd Rahim, Samsuri Abd Wahid, Siva Kumar Balasundram and M. Afyuni\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 71\n\n\n\nZeolites as Zn Adsorbent\n\n\n\nX\u2019Pert PRO PANALYTICAL, PHILIPS model, and the data were evaluated by \nthe Rietveld method (program AutoQuan, GE Seifert) (Brown and Brindley 1980). \nExchangeable cation capacity (CEC) was determined by Na-acetate method \n(Jones Jr. 2001; Page 1982). Brunauer, Emmett, Teller (BET) nitrogen adsorption \nand desorption isotherms combined with Barrett, Joyner, Halenda (BJH) methods \nwere used for determining the specific surface area, total pore volume and  pore \ndiameter of the zeolites samples by gas sorption method with AUTOSORB-1 \nSeries, Quantachrome instrument (Hamidpour et al. 2010; Rhoads 1986).\n\n\n\nBatch Adsorption Study\nThe Zn adsorption capacity was determined by batch equilibration method using \n0.2 g of zeolite sample (Hamidpour et al. 2010). The study was conducted at pH \n5 and 7 by using buffer solutions. The pH 5 buffer solution was prepared using \nacetic acid and sodium acetate (tri-hydrate) buffer solution (Jacobsen et al. 1997), \nwhile the pH 7 buffer solution was prepared using mixed Tris ((hydroxymethyl) \naminomethane) and 0.1 M HCl (Robinson and Stokes 1970). A series of 40 mL \nsolutions  containing 5, 10, 20, 40. 60, 80 or 100 mg Zn L-1 of zinc chloride \n(Merck, Germany) in 0.01 M CaCl2 was added to the zeolite in falcon tubes. \nThe tubes were shaken on an end over end shaker for 24 hours at 40 RPM at \n25\u00b1 1 oC. Preliminarily experiment showed that the contact time of 24 h was \nsufficient to reach equilibrium. At the end of the equilibrium time, the samples \nwere centrifuged for 20 min at 5000 RPM and 30mL of the supernatant was \ncollected from each tube. Blanks were prepared without sorbents but otherwise \nsimilarly handled. The amount of zinc sorbed by the sorbents was calculated as \nthe difference between the metal concentration in the blanks and the concentration \nin the solution after equilibration. Zinc in the supernatants was measured by a \nPerkin\u2013Elmer 200 AAS. The metal adsorbed (mg zinc adsorbed per g of sorbent) \nwere calculated from the mass balance equation (Eq. 1). \n\n\n\n        (Eq. 1)\n\n\n\nwhere Vi is the volume of the aliquot in vial (mL), C0 is the initial concentration \n(mg L-1), Ce, the equilibrium concentration (mg L-1) and W, the weight of adsorbent \n(g).\n\n\n\nThe sorption data were fitted to the Langmuir and Freundlich isotherm \nmodels. The Langmuir sorption isotherm is\n\n\n\n       (Eq. 2)\n\n\n\n       (Eq. 3)\n\n\n\nq =\nV (C  - C )i o e\n\n\n\n1000W\n\n\n\nq =\nq     .K  .Cmax L e\n\n\n\ne1 + K  .CL\n\n\n\nq = q     K Cmax L e q     max\n\n\n\n1 1 1+\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201372\n\n\n\nwhere KL and qmax are constants of Langmuir equation which are related to \nbinding strength (L mg-1) and maximum adsorption (L g-1), respectively, while the \nFreundlich\u2019s model is \n\n\n\n       (Eq. 4)\n\n\n\nwhere Kf (mg g-1) and n (g L-1) is Freundlich constant\u2019s that are related to the \nsorption capacity and sorption intensity, respectively (Hamidpour et al., 2010; \nPrasad et al., 2000). The relative coefficients of Langmuir was calculated based \non linearized form (Eq. 3) whereas Freundlich sorption isotherms was calculated \nby using linear least-square method from logarithmic linearized model.\n\n\n\nDesorption Study\nZinc desorption capacity was performed immediately after the completion \nof initial adsorption experiments by adding 30 mL of buffered 0.01 M CaCl2 \nsolution to each vial, and the vials were further shaken for 24 hours. Then, the \nvials were centrifuged for 20 min at 5000 RPM and 30mL of the supernatant was \ncollected from each tube. Zinc concentrations in supernatants were measured by \nAAS as previously described. The metal desorbed from sorbents at each initial \nconcentration was calculated based on mass balance equation (Eq 4).\n\n\n\n      (Eq. 4)\n\n\n\nwhere Vi is initial volume of aliquot (mL), Va is volume of refill aliquot (mL), Ce is \nthe equilibrium concentration at end of adsorption study (mg L-1), and W is mass \nof the absorbent employed(g).\n\n\n\nZn desorption was calculated at each concentration, and the q value for zinc \nsorption of zeolites was calculated as the difference between the initial sorption \nand desorption for each concentration.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nComposition and Properties of Iranian and Chinese Zeolites\nThe XRD diffractograms for Iranian and Chinese zeolites are shown in Fig. 1 and \nFig. 2, respectively. The data indicate that the most abundant component in Iranian \nand Chinese zeolites was clinoptilolite-Na (93.21%, 58.83%, respectively) but \nhigh amount of tridymite (28.04%) was also present in the Chinese zeolite. The \nproportional mineral content of both zeolites are shown in Table 1 and Table 2. The \nCEC of Iranian and Chinese zeolite were 112.7 and 87.5 cmol(+) kg-1, respectively. \nClinoptilolite-Na belongs to the natural zeolite groups. It is a crystalline hydrated \naluminosilicate (Cerri et al. 2002; Ming and Dixon 1987) and the most common \nmineral in natural zeolite. It has a three- dimensional structure (tektosilicates) \nand is sheet-like. The structure of clipnotilolite contains open rings of alternating \n\n\n\nKhayambashi Babak, Anuar Abd Rahim, Samsuri Abd Wahid, Siva Kumar Balasundram and M. Afyuni\n\n\n\nq = K . C /n1\n\n\n\nef\n\n\n\ndesorption = q efV (C  - (       )Ci iV - V\nV\n\n\n\na\ni\n\n\n\n1000W\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 73\n\n\n\neight and ten sides. These rings stack on each other to form channels throughout \nthe crystal structure. Replacement of Si+4 by Al+3 created negative charge in the \nstructural tetrahedra and the charges are counterbalanced by cations such as \nsodium, potassium, calcium and magnesium (Baerlocher et al. 2007; Armbruster \n2001). These charged sites are situated in large structural channels and cavities \nthroughout the structure. As a result, zeolite has high cation exchange capacities \n(CEC), between 100 to 300 cmol(+) kg-1 (Castaldi et al. 2008).\n\n\n\nThe chemical formula of tridymite, SiO2, shows that it is a polymorph \nquartz and often found in cavities of acidic volcanic rocks. It consists of a three-\ndimensional framework of corner-connected SiO tetrahedra. Tridymite is known \nto have no cation exchange capacity either in organic solvents or aqueous solutions \n(Koun et al. 1995). The properties of clinoptilolite-Na and tridymite explain the \ndifferences in CEC between the two zeolites. The CEC of Chinese zeolite is \nclearly dependent on clinoptilolite-Na content. The mineralogical composition \nand CEC of the Iranian and Chinese zeolites are shown in Table 1 and Table 2, \nrespectively.\n\n\n\nThe results of specific surface area, pore volume and pore radius of two \nzeolites are shown in Table 3. The specific surface area of Iranian and Chinese \nzeolite was 24.02 and 27.28   m2 g-1, respectively. The pore volume and pore \nradius were 0.105 cm3 g-1 and 73.75 A\u00ba for the Iranian zeolite, while for the \nChinese zeolite, these physical properties were 0.047 cm3 g-1and 33.58 A\u00ba, \nrespectively. The Iranian zeolite has lower specific surface area than Chinese \nzeolite, but the pore volume and pore radius of Iranian zeolite were higher than \nthe Chinese zeolite. The average particle size of the Iranian zeolite was higher \nthan the Chinese zeolite.\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nTable 1 \nComposition, general chemical formula, proportion of compound and CEC in Iranian zeolite \n\n\n\n \nCompound Name General Chemical Formula % \n\n\n\nClinoptilolite-Na (Na, K, Ca)2-3Al3(Al,Si)2Si13O36\u00b712H2O 93.21 \nStilbite (Na2,Ca)Al2Si6O16.6H20 4.28 \nSilicon Oxide Si O2 1.45 \nCEC (cmol(+) kg-1)  112.7 \n\n\n\n\n\n\n\nTable 2 \nComposition, general chemical formula, proportion of compound and CEC in Chinese zeolite \n\n\n\n \nCompound Name General Chemical Formula % \n\n\n\nClinoptilolite-Na (Na, K, Ca)2-3Al3(Al,Si)2Si13O36\u00b712H2O 58.83 \nTridymite Si O2 28.04 \nCowlesite Ca Al2 Si3 O10, 6H2O 1.25 \nCEC (cmol(+) kg-1)  89.5 \n\n\n\n\n\n\n\nTable 3 \nThe specific surface area, pore volume and pore radius of the Iranian and Chinese zeolites \n\n\n\n \n Specific surface area Pore volume Pore radius \n m2 g-1 cm3 g-1 A\u00ba \n\n\n\nIranian zeolite 24.02 0.105 73.75 \nChinese zeolite 27.28 0.047 33.58 \n\n\n\n\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nTable 1 \nComposition, general chemical formula, proportion of compound and CEC in Iranian zeolite \n\n\n\n \nCompound Name General Chemical Formula % \n\n\n\nClinoptilolite-Na (Na, K, Ca)2-3Al3(Al,Si)2Si13O36\u00b712H2O 93.21 \nStilbite (Na2,Ca)Al2Si6O16.6H20 4.28 \nSilicon Oxide Si O2 1.45 \nCEC (cmol(+) kg-1)  112.7 \n\n\n\n\n\n\n\nTable 2 \nComposition, general chemical formula, proportion of compound and CEC in Chinese zeolite \n\n\n\n \nCompound Name General Chemical Formula % \n\n\n\nClinoptilolite-Na (Na, K, Ca)2-3Al3(Al,Si)2Si13O36\u00b712H2O 58.83 \nTridymite Si O2 28.04 \nCowlesite Ca Al2 Si3 O10, 6H2O 1.25 \nCEC (cmol(+) kg-1)  89.5 \n\n\n\n\n\n\n\nTable 3 \nThe specific surface area, pore volume and pore radius of the Iranian and Chinese zeolites \n\n\n\n \n Specific surface area Pore volume Pore radius \n m2 g-1 cm3 g-1 A\u00ba \n\n\n\nIranian zeolite 24.02 0.105 73.75 \nChinese zeolite 27.28 0.047 33.58 \n\n\n\n\n\n\n\nTable 1\nComposition, general chemical formula, proportion of compound and CEC\n\n\n\nin Iranian zeolite\n\n\n\nTable 2\nComposition, general chemical formula, proportion of compound and CEC in \n\n\n\nChinese zeolite\n\n\n\nZeolites as Zn Adsorbent\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201374\n\n\n\nFig. 2: XRD diffractograms and the most abundance peaks for Chinese zeolite\n\n\n\nKhayambashi Babak, Anuar Abd Rahim, Samsuri Abd Wahid, Siva Kumar Balasundram and M. Afyuni\n\n\n\nFig. 1: XRD diffractograms and the most abundance peaks for Iranian zeolite\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 1: XRD diffractograms and the most abundance peaks for Iranian zeolite \n \n\n\n\n \nFig. 2: XRD diffractograms and the most abundance peaks for Chinese zeolite \n\n\n\n\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 1: XRD diffractograms and the most abundance peaks for Iranian zeolite \n \n\n\n\n \nFig. 2: XRD diffractograms and the most abundance peaks for Chinese zeolite \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 75\n\n\n\nAccording to Table 4, the surface area of both Iranian and Chinese zeolites \nwas lower than other zeolites, but their CECs were higher than other natural \nzeolites except for zeolite rocks and zeolites from Turkey. Since each type of \nzeolites consist of different minerals, it is very difficult to compare the properties \nof zeolites.\n\n\n\nSorption of Zn by the Iranian and Chinese Zeolites\nIn order to determine the effect of pH on the adsorption capacity of Iranian and \nChinese zeolite, solutions were prepared at pH 5 and 7. As the quantities of adsorbed \nmetal ions depend on the equilibrium pH, the experiments were conducted at \nconstant pHs in order to avoid the effect of pH change on Zn adsorption. Fig. 3 \nshows the results of zinc sorption at both pHs. Also the linearized Langmuir and \nFreundlich sorption isotherms for Zn sorption by the Iranian and Chinese zeolites are \nshown in Fig. 4. The fitted model parameters, coefficient of determination (R2) and \nstandard errors (SE) for sorption at pH 5 and 7 are given in Table 5. Zinc adsorption \nby both zeolites increased with the increasing Zn concentrations at pH 5 and 7. \n\n\n\nAccording to Fig. 3 and 4, and Table 5, the R2 of Langmuir and Freundlich \nmodel at pH=5 were 99.7% and 99.1% for Iranian zeolite, while for Chinese \nzeolite, they were 99.1% and 99%, respectively. The coefficient of determination \nfor Iranian zeolite at pH=7 were 96.0% and 92% for Langmuir and Freundlich, \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nTable 1 \nComposition, general chemical formula, proportion of compound and CEC in Iranian zeolite \n\n\n\n \nCompound Name General Chemical Formula % \n\n\n\nClinoptilolite-Na (Na, K, Ca)2-3Al3(Al,Si)2Si13O36\u00b712H2O 93.21 \nStilbite (Na2,Ca)Al2Si6O16.6H20 4.28 \nSilicon Oxide Si O2 1.45 \nCEC (cmol(+) kg-1)  112.7 \n\n\n\n\n\n\n\nTable 2 \nComposition, general chemical formula, proportion of compound and CEC in Chinese zeolite \n\n\n\n \nCompound Name General Chemical Formula % \n\n\n\nClinoptilolite-Na (Na, K, Ca)2-3Al3(Al,Si)2Si13O36\u00b712H2O 58.83 \nTridymite Si O2 28.04 \nCowlesite Ca Al2 Si3 O10, 6H2O 1.25 \nCEC (cmol(+) kg-1)  89.5 \n\n\n\n\n\n\n\nTable 3 \nThe specific surface area, pore volume and pore radius of the Iranian and Chinese zeolites \n\n\n\n \n Specific surface area Pore volume Pore radius \n m2 g-1 cm3 g-1 A\u00ba \n\n\n\nIranian zeolite 24.02 0.105 73.75 \nChinese zeolite 27.28 0.047 33.58 \n\n\n\n\n\n\n\nTable 3\nThe specific surface area, pore volume and pore radius of the Iranian and \n\n\n\nChinese zeolites\n\n\n\nTable 4\nComparison natural and synthetic zeolites properties\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nTable 4 \nComparison natural and synthetic zeolites properties \n\n\n\n \n Surface \n\n\n\nArea \nCEC Method Pore \n\n\n\ndiameter. \nparticle size References \n\n\n\nZeolite type m2 g-1 cmol(+) kg-1  A\u00ba mm  \nClinoptilolite 40 180 - 4.0 - (Klein\u00fcbing and C da Silva, 2011) \nZeolite (Slovakia) 30-60 105 - - - (Shavandi et al., 2012) \nTurkey zeolite 15.88 236 - - - (Motsi et al., 2009) \nZeolite-X (synthetic) 486.62 426  BET-Na method - 0.15 (Abdel Rahman et al., 2012) \ny-Type (synthetic) 700 426 BET, theoretical CEC - 0.002-0.003 (Ahmed et al., 2010) \nClinoptilolite 450 263\u2013267 BET, theoretical CEC - 1-2 (Ahmed et al., 2010) \nZeolite rocks 24.5-41.6 124\u2013142 BET- Na-method  - 0.15-2 (Lee et al., 2010) \nZ1 (natural) 34.3 60 BET - 0.015 (Yukselen-Aksoy, 2010) \nZ2 (natural) 32.0 57 BET - 0.015 (Yukselen-Aksoy, 2010) \nGordes (natural) 95 69 Spot test, Na-method - 0.3-0.053 (\u00d6ren and Kaya, 2006) \nBigadic (natural) 54 57 Spot test, Na-method - 0.3-0.053 (\u00d6ren and Kaya, 2006) \nIranian zeolite 24.02 112.7 BET, Na-method 73.75 <1 This study \nChinese zeolite 27.28 89.5 BET, Na-method 33.58 <1 This study \n\n\n\n\n\n\n\nTable 5 \nThe parameters and coefficients of Langmuir and Freundlich sorption isotherms for Zn sorption by Iranian and \n\n\n\nChinese zeolites at pH 5 and 7 \n \n\n\n\n pH 5 pH 7 \nModel Coefficient Iranian Zeolite Chinese zeolite Iranian Zeolite Chinese zeolite \n\n\n\nLangmuir \n\n\n\nA 14.56*(0.63) 24.58*(0.55) 17.82*(2.40) 15.87*(1.13) \nB 0.30*(0.07) 0.35*(0.06) 0.38(0.29) 0.45*(0.14) \nR\u00b2 0.997* 0.991*  0.96* 0.98* \nqmax 3.33 2.86 2.63 2.24 \nKL 0.02 0.01 0.02 0.03 \n\n\n\nFreundlich \n\n\n\nA 0.75*(0.03) 0.79*(0.02) 0.91*(0.08) 0.72*(0.05) \nB -1.03*(0.05) -1.27*(0.03) -1.27*(0.11) -1.07*(0.07) \nR\u00b2 0.98 0.99* 0.92* 0.97* \nKF 0.09 0.05 0.05 0.08 \nn 1.34 1.26 1.10 1.39 \n\n\n\n*Significant at probability level <0.05 \nValues in parentheses are standard errors associated with the estimated model parameters \n \n \n \n \n \n \n \n \n\n\n\nZeolites as Zn Adsorbent\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201376\n\n\n\nwhile for Chinese zeolite were 98.0 and 97%, respectively. Based on coefficient of \ndetermination, the Langmuir model had higher R2 than the Freundlich model for \nboth zeolites at both pHs. So sorption processes data fitted better to the Langmuir \nthan the Freundlich model. According to Langmuir coefficients the qmax (L g-1) \nvalues of Iranian and Chinese zeolite which were calculated at pH=5 were 3.33 \nand 2.86, and at pH=7 were 2.63 and 2.24, respectively. The results showed that \nqmax value was higher for the Iranian zeolite than the Chinese zeolite at both pH \n5 and 7. The KL of Iranian Zeolite was 0.02 at both pHs, while for the Chinese \nzeolite, the KLs were 0.01 and 0.03, respectively (Table 5).\n\n\n\nFig. 3: Adsorption of zinc in (A) Iranian and (B) Chinese zeolite at pH 5 and 7 with a \nmean of 3 replicates\n\n\n\nKhayambashi Babak, Anuar Abd Rahim, Samsuri Abd Wahid, Siva Kumar Balasundram and M. Afyuni\n\n\n\nFig. 4: Linearized Langmuir isotherm at pH 5 (A); linearized Freundlich isotherm at pH \n5 (B); linearized Langmuir isotherm at pH 7 (C); and linearized Freundlich isotherm\n\n\n\nat pH7 (D)\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFig. 3: Adsorption of zinc in (A) Iranian and (B) Chinese zeolite at pH 5 and 7 with a mean \n\n\n\nof 3 replicates \n \n\n\n\n \nFig. 4: Linearized Langmuir isotherm at pH 5 (A); linearized Freundlich isotherm at pH 5 \n(B); linearized Langmuir isotherm at pH 7 (C); and linearized Freundlich isotherm at pH7 \n\n\n\n(D) \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 77\n\n\n\nTable 5\nThe parameters and coefficients of Langmuir and Freundlich sorption isotherms for Zn \n\n\n\nsorption by Iranian and Chinese zeolites at pH 5 and 7\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nTable 4 \nComparison natural and synthetic zeolites properties \n\n\n\n \n Surface \n\n\n\nArea \nCEC Method Pore \n\n\n\ndiameter. \nparticle size References \n\n\n\nZeolite type m2 g-1 cmol(+) kg-1  A\u00ba mm  \nClinoptilolite 40 180 - 4.0 - (Klein\u00fcbing and C da Silva, 2011) \nZeolite (Slovakia) 30-60 105 - - - (Shavandi et al., 2012) \nTurkey zeolite 15.88 236 - - - (Motsi et al., 2009) \nZeolite-X (synthetic) 486.62 426  BET-Na method - 0.15 (Abdel Rahman et al., 2012) \ny-Type (synthetic) 700 426 BET, theoretical CEC - 0.002-0.003 (Ahmed et al., 2010) \nClinoptilolite 450 263\u2013267 BET, theoretical CEC - 1-2 (Ahmed et al., 2010) \nZeolite rocks 24.5-41.6 124\u2013142 BET- Na-method  - 0.15-2 (Lee et al., 2010) \nZ1 (natural) 34.3 60 BET - 0.015 (Yukselen-Aksoy, 2010) \nZ2 (natural) 32.0 57 BET - 0.015 (Yukselen-Aksoy, 2010) \nGordes (natural) 95 69 Spot test, Na-method - 0.3-0.053 (\u00d6ren and Kaya, 2006) \nBigadic (natural) 54 57 Spot test, Na-method - 0.3-0.053 (\u00d6ren and Kaya, 2006) \nIranian zeolite 24.02 112.7 BET, Na-method 73.75 <1 This study \nChinese zeolite 27.28 89.5 BET, Na-method 33.58 <1 This study \n\n\n\n\n\n\n\nTable 5 \nThe parameters and coefficients of Langmuir and Freundlich sorption isotherms for Zn sorption by Iranian and \n\n\n\nChinese zeolites at pH 5 and 7 \n \n\n\n\n pH 5 pH 7 \nModel Coefficient Iranian Zeolite Chinese zeolite Iranian Zeolite Chinese zeolite \n\n\n\nLangmuir \n\n\n\nA 14.56*(0.63) 24.58*(0.55) 17.82*(2.40) 15.87*(1.13) \nB 0.30*(0.07) 0.35*(0.06) 0.38(0.29) 0.45*(0.14) \nR\u00b2 0.997* 0.991*  0.96* 0.98* \nqmax 3.33 2.86 2.63 2.24 \nKL 0.02 0.01 0.02 0.03 \n\n\n\nFreundlich \n\n\n\nA 0.75*(0.03) 0.79*(0.02) 0.91*(0.08) 0.72*(0.05) \nB -1.03*(0.05) -1.27*(0.03) -1.27*(0.11) -1.07*(0.07) \nR\u00b2 0.98 0.99* 0.92* 0.97* \nKF 0.09 0.05 0.05 0.08 \nn 1.34 1.26 1.10 1.39 \n\n\n\n*Significant at probability level <0.05 \nValues in parentheses are standard errors associated with the estimated model parameters \n \n \n \n \n \n \n \n \n\n\n\nReferring to composition of both zeolites and their CEC in Table 1 and \nTable 2, Iranian zeolite has higher portion of clinoptilolite-Na and CEC than \nChinese zeolite. Thus, the mechanisms of sorption may include electrostatic \ninteraction due to negative charged sites on the surface. \u00d6ren and Kaya (2006) \nreported the mechanism that governs the adsorption characteristics of Gordes and \nBigadic zeolites at pH ranges between 4 and 6 are adsorption and ion exchange. \nFormation of zinc species with hydroxide at high pH may affect zinc adsorption \nand precipitation on the zeolite structure. Depending on the pH and metal \nconcentration, zinc may form complexes with hydroxide, for example Zn2+ at \npH<7.69 but ZnOH+, Zn(OH)2\n\n\n\n\u00ba and Zn(OH)3\n- at pH value more than 7.69 which \n\n\n\nmay participate in the adsorption on zeolite (Cabrera et al. 2005; Lindsay 1979). \nIn the literature, there were ranges of final pHs of less than 7 and up to 9 studied \non various kind of zeolites.  \n\n\n\nAlong with the findings, the affinity of Zn adsorption by Chinese zeolite was \nhigher at pH 7 than pH 5. It is revealed that Iranian zeolite has an advantage to \nbe used at both pHs (similar affinity) while the affinity of Chinese zeolite for zinc \nadsorption is pH dependent. \n\n\n\nThere is evidence that the difference in the adsorption of Zn2+ by different \ntype of zeolites may be due to the difference in the mineralogical compositions \nand associated cations at the exchangeable sites (Sheta et al. 2003; Langella \net al. 2000; Kang and Wada 1988). Also the same mechanism was reported by \nShavandi et al. (2012) that indicated higher R2 of the Langmuir model than the \nFreundlich model for Slovakia zeolite and suggested that the coverage of zinc \nions on the surface of the zeolite might be defined as a monolayer and associated \nwith electrostatic attraction in the outer sphere complex.\n\n\n\nZeolites as Zn Adsorbent\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201378\n\n\n\nDesorption of Zn from Iranian and Chinese Zeolites\nThe Zn desorption at both pH 5 and 7 by the Iranian and Chinese zeolites are \nshown in Fig. 5.\n\n\n\nThe amount of zinc released by Iranian zeolite was higher than Chinese \nzeolite, therefore the q (amount adsorbed) after desorption was lower, especially \nat low Zn loading rates. The difference among sorption and desorption amount of \nzinc by adsorbents was elucidated as a reversible process. This suggest that the \nadsorption of Zn by both zeolites are non-specific because specific adsorption \nhappen at low Zn concentration and desorption of specifically adsorbed Zn would \nbe low (Sen and Gomez 2011; Brown et al. 1999). These results can support the \nstatements made in adsorption study that the sorption mechanism may involves \nelectrostatic interactions by negative charge on the surface of both zeolites.\n\n\n\nCONCLUSION\nBoth Iranian and Chinese zeolites contained clinoptilolite-Na but the Chinese \nzeolite also contained high percentage of tridymite. The high proportion of \nclinoptilolite-Na resulted in higher CEC in the Iranian zeolite. Sorption data at \nboth pH 5 and 7 was best fitted to the Langmuir isotherm. The results indicate \nthat the Iranian zeolite was a better adsorbent for Zn than the Chinese zeolite at \nboth pH 5 and 7. The affinity of Chinese Zeolite to adsorb Zn at pH 5 was higher \nthan pH 7. The desorption data suggest that the sorption of zinc by both zeolites \nare non specific.\n\n\n\nFig. 5: Adsorption and desorption of Zn in Iranian (A) and Chinese (B) zeolites at pH5 \nand Iranian (C) and Chinese (D) zeolites at pH 7.\n\n\n\nKhayambashi Babak, Anuar Abd Rahim, Samsuri Abd Wahid, Siva Kumar Balasundram and M. Afyuni\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                              Malaysian Society of Soil Science \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n\n\n\n\n\n\n\n\nFig. 5: Adsorption and desorption of Zn in Iranian (A) and Chinese (B) zeolites at pH5 and \nIranian (C) and Chinese (D) zeolites at pH 7. \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 79\n\n\n\nACKNOWLEDGMENT\nThe authors acknowledge the financial and technical support by Universiti Putra \nMalaysia and the Ministry of Higher Education Malaysia for Long Term Research \nGrant Scheme (LRGS) fund for food security.\n\n\n\nREFERENCES\nAbbaspour, A., M. Kalbasi, S. Hajrasuliha, and A. Fotovat. 2008. 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"\n\nISSN: 1394-7900\n\n\n\nMalaysian Journal of Soil Science Vol. 13:  13-28                                   Malaysian Society of Soil Science\n\n\n\nChemical Characteristics of Representative High Aluminium \nSaturation Soil as Affected by Addition of Soil Amendments \n\n\n\nin a Closed Incubation System\n\n\n\nJose \u00c1lvaro Cristancho Rodr\u00edguez1, M.M. Hanafi2*, \nS.R. Syed Omar2 &  Y.M. Rafii3\n\n\n\n1Laboratory of Plantation Crops, Institute of Tropical Agriculture, Universiti Putra \n\n\n\nMalaysia, 43400-UPM, Serdang Selangor, Malaysia\n\n\n\n 2Department of Land Management, Universiti Putra Malaysia, 43400-UPM, \n\n\n\nSerdang Selangor, Malaysia\n\n\n\n3Deparment of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, \n\n\n\n43400-UPM, Serdang Selangor, Malaysia\n\n\n\nINTRODUCTION\nSoil acidity is one of the main factors that limits and prevents profitable and sustained \nagricultural production in many parts of the world. In most cases, this condition is \n\n\n\nnot treated or corrected suitably. Approximately 50% of the world\u2019s arable soils are\n\n\n\nABSTRACT\nSoil acidity is one of the main factors that limits profitable and sustained agricultural \nproduction. This study examined the performance of selected amendments in \nimproving soil fertility of acidic tropical soils. The best two acidic tropical soils \n\n\n\nfrom Malaysia, Batu Anam and Durian, were selected to represent acid soils \nfrom Colombia while the five soil amendments selected were ground magnesium \nlimestone (GML), magnesium carbonate (MgCO3), gafsa phosphate rock (GPR), \n\n\n\ngypsum, and kieserite. They were incubated in a closed incubation system for two \n\n\n\nmonths. The measured parameters were soil pH, exchangeable aluminium (Al), \nexchangeable cations, and available P. The treatments were organised in a factorial \ncompletely randomised design (CRD) with three replications. There was a significant \ndifference in response among soils, amendments, rates and their interaction effects \n\n\n\nfor the different soil parameters evaluated, with GML giving a high soil pH (0.339) \n\n\n\neffect and amelioration of the exchangeable Al (-0.838 cmolc/kg) per ton applied.  \nMgCO3 and GPR gave similar effects in neutralising exchangeable Al (~ -0.6 \ncmol\n\n\n\nc\n/kg) per ton ha-1 with a slight increase in soil pH (0.1 unit). Kieserite and \n\n\n\nGypsum had a significant effect on amelioration of aluminum ((~ -0.16 cmol\nc\n/kg) \n\n\n\nin Batu Anam soil. GML was the most cost-effective amendment in increasing soil \npH and neutralising Al at USD$ 118.5 per cmol\n\n\n\nc\n/kg of Al. \n\n\n\nKeywords: Acidic soils, exchangeable aluminium, Gafsa Phosphate Rock,\n     ground magnesium limestone, incubation system, soil amendments\n\n\n\n___________________\n*Corresponding author : Email: mmhanafi@agri.upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200914\n\n\n\nacidic and may be subjected to the effects of aluminium (Al) toxicity; the tropics and\nsubtropics account for 60% of the acid soils in the world (Sumner and Noble \n\n\n\n2003). The negative effects of high levels of soluble Al on plant growth have \n\n\n\nbeen widely reported (Marschner 1986; Zang and Rengel 1999; Matsumoto \n2002; Langer et al. 2009). Liming acid soils is generally practised to reduce \n\n\n\nAl toxicity and is considered by many soil scientists as the first step towards \nproviding a balanced nutrition for cultivated plants (Brown and Stecker 2003; \nEssington 2004).  There are few agricultural practices in the tropics which can \n\n\n\nadd as many valuable advantages to crop development and final yield as liming \nof acidic soils (Prochnow 2008). There are four major factors that affect the \n\n\n\nsuccessful neutralisation of soil acidity by agricultural limestone. They are (i) \n\n\n\nlime rate, (ii) lime purity (compared to pure calcium carbonate and expressed \nas calcium carbonate equivalent (CCE)), (iii) lime particle size distribution or \n\n\n\nfineness of grind, and (iv) degree of incorporation or mixing with the soil (Goh \net al. 1998; Conyers et al. 1996; Anon. 2006b). Lime requirements of soil are \nnot only related to soil pH, but also to its buffer capacity or cation exchange \ncapacity (CEC). Addition of lime raises the soil pH and eliminates two major \n\n\n\nproblems of acid soils: excess (toxic) soluble Al and slow microbial activities. \nLime also has the following benefits: improves the physical condition of the soil, \nreduces excess soluble manganese and iron (as possible toxin) by causing them to \nform insoluble hydroxides, increases the CEC in variable charge soils, increases \navailability of several nutrients, such as calcium and magnesium, improves \n\n\n\nsymbiotic nitrogen fixation by legumes, increases plant-available molybdenum, \nand reduces the solubility and plant uptake of potentially toxic heavy metals, such \nas cadmium, copper, nickel, and zinc (Truog 2004; Prochnow 2008). A variety \nof techniques have been developed to measure the lime requirement of soils. \n\n\n\nThe procedures that involve longer equilibration times for reacting soil with a \n\n\n\nliming material (anywhere from days to months) provide a better estimation for \n\n\n\nthe lime requirement. However, the price for improved accuracy is the loss of time \n\n\n\n(Essington 2004).\n\n\n\n Oil palm is mainly cultivated in tropical regions (Corley and Tinker 2003; \nAnon. 2006a). With the exception of the use of phosphate rock (PR) for direct-\napplication to supply phosphorus (P), adding soil amendments in oil palm plantation \n\n\n\nis not generally practised.  We observed that in acid Colombian soils, there exists \na strong relationship between high Al saturation in soils and the occurrence of \n\n\n\nbud rot disease of mature oil palm. Addition of soil amendments could possibly \n\n\n\nalleviate Al toxicity, increase soil pH, and improve soil fertility status. Generally, \nthese materials are of interest to oil palm growers because of low cost and ready \n\n\n\navailability. However, selection of a suitable soil amendment is not based on \n\n\n\ntechnical criteria (Munevar et al. 2005). There is a lack of information related \n\n\n\nto performance and comparison of amendments with very high Al saturation \n\n\n\non acidic soils. Therefore, the specific objective of this study was to evaluate \nthe effects of selected amendments used in oil palm cultivation to improve soil \n\n\n\nfertility, representing high Al saturation soil, using two acidic Malaysian soils in \n\n\n\na closed-incubation system.\n\n\n\nJose \u00c1lvaro Cristancho Rodr\u00edguez, M.M. Hanafi, S.R. Syed Omar &  Y.M. Rafii\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 15\n\n\n\nMATERIALS AND METHODS\n\n\n\nSoil Sampling and Analysis\n\n\n\nFour acid Malaysian soils were selected for the initial characterisation \n\n\n\naccording to USDA soil taxonomy (Paramananthan, 2000). They were Batu \nAnam (clayey, kaolinitic, isohyperthermic, Aquic Paleudult,), Durian (clayey, \n\n\n\nmixed, Isohyperthermic, Plinthaquic Paleudult,), Bungor (clayey, kaolinitic, \nisohyperthermic, Typic Paleudult), and Melaka (clayey-skeletal, kaolinitic \n\n\n\nisohyperthermic, Xanthic Hapludox,). Thirty-six soil samples were collected from \nthese four soils to give a combination of 3 depths, 3 sampling sites, and 4 soil \n\n\n\nseries. These samples were taken from a five-year-old oil palm estate located in \nNegeri Sembilan. The soil samples were air-dried and passed through a 2-mm sized \n\n\n\nsieve. The following soil characteristics were determined: (i) pH\nwater\n\n\n\n (1:1 soil/water \n\n\n\nratio) and pH\nKCl\n\n\n\n (1:2.5, soil/1M KCl solution ratio); (ii) CEC and exchangeable \ncations (Ca, Mg, K, and Na) extracted with 1M NH\n\n\n\n4\nOAc at pH 7 (Jones 2001) \n\n\n\nand the elements determined using atomic absorption spectrophotometry (AAS); \n(iii) available phosphorus extracted with Bray II (Jones 2001) solution and P \nconcentration analysed using an auto analyser (AAS); (iv) total soil carbon \ndetermined using infrared absorption method (LECO CR \u2013 412); and (v) soil \ntexture determined using the pipette method (Boon Sung and Talib 2006).\n The Al forms in soil were sequentially extracted by the following procedures: \n(i) exchangeable Al was extracted with 1M KCl at 1:10 (soil/solution ratio) by \nshaking for 24 hours, (ii) weakly organically bound Al forms were extracted with \n0.3M CuCl\n\n\n\n2\n at 1:10 (soil/solution ratio) by shaking for 2 hours, and (iii) total \n\n\n\norganically bound Al forms were extracted with 0.1 M Na\n4\nP\n\n\n\n2\nO\n\n\n\n7\n at 1:10 (soil/\n\n\n\nsolution ratio) by shaking for 24 hours.  In all steps, soil solution was separated \n\n\n\nby centrifugation for 20 minutes at 13500 rpm, and when necessary, further \n\n\n\npurified by filtration.  Content of strongly organically bound Al was calculated as \nthe difference between Na\n\n\n\n4\nP\n\n\n\n2\nO\n\n\n\n7\n extracted- and CuCl\n\n\n\n2 \nextracted- Al (Drabek et al. \n\n\n\n2003). The Al in solution was analysed using inductively couple plasma atomic \n\n\n\nemission spectroscopy (ICP AES).\n\n\n\nSoils Selected and Amendments\n\n\n\nBatu Anam and Durian soils were selected for the evaluation of the performance \nof various amendments. Both soils had high concentrations of exchangeable Al \nand Al saturation, clayey textural class and low soil pH (Table 1). The following \namendments were used: (i) ground magnesium limestone, (ii) magnesium \n\n\n\ncarbonate, (iii) Gafsa phosphate rock, (iii) gypsum, and (iv) kieserite. The Ca and \n\n\n\nMg of the materials were extracted by dissolution in 1.0 M HCl and analysed by \nAAS (Conyers et al. 1996). The chemical characteristics of the amendments used \n\n\n\nare given in (Table 2).\n\n\n\nAmendments Applied to High Al Soils\n\n\n\n\n\n\n\n\nM\nalay\n\n\n\nsian\n Jo\n\n\n\nu\nrn\n\n\n\nal o\nf S\n\n\n\no\nil S\n\n\n\ncien\nce V\n\n\n\no\nl. 1\n\n\n\n3\n, 2\n\n\n\n0\n0\n9\n\n\n\n1\n6\n\n\n\nJose \u00c1\nlvaro C\n\n\n\nristancho R\nodr\u00edguez, M\n\n\n\n.M\n. H\n\n\n\nanafi\n, S.R\n\n\n\n. Syed O\nm\n\n\n\nar &\n  Y\n\n\n\n.M\n. R\n\n\n\nafii\n\n\n\nTable 1\n\n\n\nSelected physical and chemical properties of four acidic Malaysian soils\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepth Organic Sum CEC ECEC ** Al *** P Ca/Mg (Ca+Mg)/K\n\n\n\nClay Silt Sand C H2O 1 : 1  KCl 1 : 2.5 Exchangeable Weakly Total Strongly Ca Mg K Na Cation * pH 7 saturation Available\n\n\n\ncm % mg kg\n-1\n\n\n\nBatu anam series\n\n\n\n0 - 15 39.9 38.9 21.2 1.1 3.43 3.66 3.05 5.68 14.08 8.40 0.22 0.14 0.27 0.05 0.68 10.29 3.73 81.71 6.16 1.52 1.33\n\n\n\n15 - 30 43.5 31.3 25.1 0.9 3.77 3.63 4.05 6.21 14.66 8.46 0.02 0.08 0.19 0.03 0.32 9.75 4.37 92.73 5.74 0.27 0.50\n\n\n\n30 - 45 44.0 38.3 17.7 0.5 3.70 3.65 4.95 6.50 14.78 8.28 0.07 0.05 0.23 0.03 0.39 10.50 5.34 92.74 6.81 1.28 0.52\n\n\n\nBungor series\n\n\n\n0 - 15 25.7 6.7 67.7 1.2 4.10 3.75 1.06 2.80 7.89 5.09 0.36 0.06 0.17 0.03 0.62 8.14 1.68 63.07 18.78 6.32 2.40\n\n\n\n15 - 30 36.6 6.0 57.4 0.9 3.75 3.79 2.59 3.43 8.85 5.41 0.40 0.08 0.14 0.03 0.65 6.94 3.23 80.05 11.61 4.95 3.36\n\n\n\n30 - 45 45.7 5.3 48.9 0.5 3.76 3.63 3.30 4.47 10.25 5.78 0.12 0.02 0.12 0.02 0.29 8.71 3.59 91.98 5.35 5.54 1.14\n\n\n\nDurian series\n\n\n\n0 - 15 28.7 19.5 51.7 1.8 4.25 3.74 2.19 4.25 14.86 10.61 0.96 0.20 0.53 0.03 1.73 7.21 3.92 55.92 5.10 4.69 2.19\n\n\n\n15 - 30 53.5 9.2 37.3 1.1 4.18 3.63 3.46 4.98 15.53 10.55 0.27 0.15 0.45 0.03 0.90 10.50 4.36 79.41 4.60 1.76 0.96\n\n\n\n30 - 45 58.3 7.7 34.0 0.9 4.07 3.62 3.86 3.83 12.76 8.93 0.41 0.10 0.42 0.02 0.95 7.71 4.82 80.19 3.76 4.03 1.24\n\n\n\n0 - 15 31.8 50.2 18.0 1.9 4.90 3.98 0.50 2.36 11.33 8.97 1.25 0.26 0.49 0.03 2.03 5.51 2.53 19.80 4.46 4.81 3.06\n\n\n\n15 - 30 27.0 33.0 40.0 1.2 4.40 3.89 0.85 4.29 12.22 7.93 0.62 0.15 0.36 0.02 1.15 7.36 2.00 42.45 5.92 4.01 2.11\n\n\n\n30 - 45 10.5 49.6 39.9 1.1 3.95 3.91 1.66 2.62 13.64 11.01 0.39 0.14 0.33 0.05 0.91 9.03 2.57 64.63 5.35 2.87 1.58\n\n\n\nParticle size pH Exchangeable cations\n\n\n\n------------------%------------------- ----------------------------------------------------------- cmolc/kg --------------------------------------------------------\n\n\n\nAluminum\n\n\n\nMelaka series\n\n\n\n*Sum cation = \u2211 Ca, Mg, K and Na\n\n\n\n** ECEC = \u2211 Ca, Mg, K, Na and Al\n\n\n\n***Al saturation (%) = [Al / (\u2211 Ca, Mg, K, Na and Al) x 100]\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 17\n\n\n\nPhysical Composition of Amendments \n\n\n\nThe commercial soil-amendments with the exception of kieserite were granular.  \nThey were dried and sieved for 5 minutes using a mechanical shaker (British \nstandard sieves of 500, 250, and 100 micrometers aperture). Gypsum had the \n\n\n\nfinest particle sizes of < 0.25 mm (96%) and the coarsest was MgCO\n3\n (52%) \n\n\n\n(Table 2).\n\n\n\nApplication of the Amendments\n\n\n\nAbout 100g of subsoil samples (5 to 30cm depth) were placed in plastic pots and \n\n\n\nthe appropriate amounts of amendments added.  The plastic pots were covered \n\n\n\nwith a cloth material so as to reduce loss of moisture and allow for  the exchange \nof O\n\n\n\n2\n and CO\n\n\n\n2\n (Munevar et al. 2005; Zapata 2004). Soil moisture was adjusted \n\n\n\nevery third day to 90% field capacity, which was measured before the incubation \nstudy; the value for Batu Anam was 22.8% and for Durian, it was 23%.\n\n\n\nExperimental Design \n\n\n\nThe treatments consisted of control (without amendment), five amendments, four \nrates (2.2, 3.5, 4.8, and 6.1) and two soil series. The treatments were organised in \n\n\n\na factorial complete random design (CRD) with three replications. After a 60-day \n\n\n\nincubation period, the soils were air-dried and ground to pass through a 2-mm \n\n\n\nsieve size. The following soil properties were determined: soil pH, exchangeable \nAl, exchangeable cations (Ca, Mg and K), and available P, following the methods \nmentioned earlier.\n\n\n\nThe analysis of variance (ANOVA), polynomial contrast and regression analysis \n\n\n\nwere performed with the software Statistix version 8 (USDA and NRCS 2007)\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Characterisation \n\n\n\nThe topsoils (0 \u2013 15cm) were very strongly acidic to strong acidic (Jones 2003) \n\n\n\nwith pHw values ranging between 3.4 (Batu Anam soil) and 4.9 (Melaka soil). The \nmajority of the subsoils  (15 - 45 cm) evaluated, were very strongly acidic with \n\n\n\npHw values less than 4.4. The pH\nKCl\n\n\n\n showed a similar trend as in pH\nw\n, ranging \n\n\n\nbetween 3.7 (Batu Anam) to 4.0 (Melaka). A significant negative correlation \nbetween pHKCl and exchangeable Al (r = -0.73*) was found indicating that a 4 \nunit increase in the pH\n\n\n\nKCl\n value would allow for the exchangeable Al to be totally \n\n\n\nneutralised (data not shown). Exchangeable Al and Al saturation in the topsoils \nranged from 0.5 (Melaka) to 3.05 (Batu Anam) cmol\n\n\n\nc\n/kg, and 19.8% (Melaka) and \n\n\n\n81.7% (Batu Anam), respectively. In general, the exchangeable Al and saturation \nincreased with the soil depth for all soil series reaching 92.7% of Al saturation in \n\n\n\nBatu Anam soil. Malacca soil showed the lowest Al concentration and saturation \nand the highest pH value, indicating less severe acidity constrains. These results \n\n\n\nwere probably influenced by previous application of oil palm empty fruit bunches \n(EFB) mulching as reflected by a high soil C (Table 1).\n\n\n\nAmendments Applied to High Al Soils\n\n\n\n\n\n\n\n\nM\nalay\n\n\n\nsian\n Jo\n\n\n\nu\nrn\n\n\n\nal o\nf S\n\n\n\no\nil S\n\n\n\ncien\nce V\n\n\n\no\nl. 1\n\n\n\n3\n, 2\n\n\n\n0\n0\n9\n\n\n\n1\n8\n\n\n\nJose \u00c1\nlvaro C\n\n\n\nristancho R\nodr\u00edguez, M\n\n\n\n.M\n. H\n\n\n\nanafi, S.R\n. Syed O\n\n\n\nm\nar &\n\n\n\n  Y\n.M\n\n\n\n. R\nafii\n\n\n\nTable 2\n\n\n\nChemical composition and particle size of the amendments\n\n\n\n\n\n\n\n \nEach value is the mean of three replicates \u00b1 SE.  \n\n\n\n\n\n\n\nMgCO3 CaCO3 SO4 P2O5\n\n\n\n> 0.5 mm 0,25 - 0.5 mm 0.1 - 0.25 mm < 0.1 mm\n\n\n\nGround magnesium  limestone \n\n\n\n(GML)\nMalaysia 32.8 \u00b1 0.12 42.8 \u00b1 0.13 7.00 \u00b1 0.03 4.95 \u00b1 0.04 50.08 \u00b1 0.95 37.98 \u00b1 0.91\n\n\n\nMagnesium carbonate \n\n\n\n(MgCO3)\nAntioquia, Colombia 41.8 \u00b1 0.11 30.53 \u00b1 0.05 17.55 0.02 27.00 \u00b1 0.15 24.93 \u00b1 0.17\n\n\n\nGafsa  phosphate rock (GPR) Tunisia 87.5 \u00b1 0.12 25.2 \u00b1 0.10 15.05 \u00b1 0.12 15.20 \u00b1 0.02 64.78 \u00b1 0.12 4.98 \u00b1 0.02\n\n\n\nGypsum Palmira, Colombia 56.7 \u00b1 0.10 48.8 \u00b1 0.01 1.67 \u00b1 0.05 2.37 \u00b1 0.02 51.13 \u00b1 0.33 44.83 \u00b1 0.35\n\n\n\nKieserite Germany 52.3 \u00b1 0.05 59.9 \u00b1 0.03\n\n\n\nAmendments & abbreviation\nParticle size distribution (%)\n\n\n\n%\nOrigin\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 19\n\n\n\n  The soil CEC qualified as very low with  organic C being very low to \nmoderate. The calcium and magnesium concentration and saturation (data not \n\n\n\nshown) and phosphorus concentration were classified as low to moderate category \nfor the majority of soils studied according to the soil nutrient classification tables \n(Munevar 2001; Fairhurst et al. 2005). The Ca/Mg and (Ca + Mg)/K ratio showed \n\n\n\nhigh variability indicating imbalance among the exchangeable cations (Table 1).\n There was a high variability in particle size among the soil series. The Batu \nAnam and Durian soils were characterised by high clay fraction, the Bungor soil \nby sand fraction, and Melaka soil by silt fraction (Table 1).\n\n\n\nAluminium Forms\n\n\n\nBatu Anam and Durian soils showed the highest value of the different Al forms; \nBatu Anam soil had the highest value of exchangeable Al indicating major \ninfluence of soil acidity with the potential to release exchangeable Al to the soil \nsolution. Bungor and Melaka soils showed the lowest exchangeable Al (Table 1).\n\n\n\nEffect of Amendments on Different Chemical Properties of Soils\n\n\n\nSoil pH\n\n\n\nThere was a significant difference among soils, amendments, and rates and their \nrespective two-way interactions with the exception of the three-way interactions \n(Table 3). The best responses were obtained for Batu Anam soil.  The interaction \nbetween amendments x rates for Batu Anam and Durian soils were significant, \nand the linear and quadratic polynomial contrast for both soils were significant \n(Table 3). GML raised the soil pH by 0.34 and 0.32 units for Batu Anam and \nDurian soils, respectively. MgCO\n\n\n\n3\n and GPR showed an increase in pH of 0.10 \n\n\n\nand 0.11 for Batu Anam and 0.05 and 0.08 unit for Durian. Kieserite and gypsum \nhad a significant negative effect on soil pH (Table 5). This study showed that the \nperformance of GML and gypsum are comparable with the results reported by \n\n\n\nShamshuddin et al (1991); in which the application of limestone increased the soil \npH in the zone of incorporation for maize at harvest but gypsum had no consistent \n\n\n\neffect.  Shamshuddin (1995) found that gypsum application showed a tendency for \n\n\n\na decrease in soil pH on the topsoil in an Ultisol. Suswanto et al. (2007) reported \n\n\n\nthat an application of 4 t GML/ha ameliorated the aluminum toxicity by increasing \nsoil pH from 4.27 to 4.93. Goh et al. (1998) showed that continuous applications \n\n\n\nof GML increased soil pH quadratically from 4.3 to 4.7. The quadratic response \n\n\n\nto lime on this soil could be attributed to its buffering capacity, low CEC, and \n\n\n\ncoarser particles of the GML. In addition to the benefit of adding plant available \nP to the soil, phosphate rock (PR) may also serve as a liming agent.  Sinclair et al. \n\n\n\n(1993) observed that a single superphosphate treatment decreased soil pH by 0.16 \n\n\n\nunits over a 6-year period, while PR treatment kept soil pH level ranging from \n\n\n\n5.5 to 6.0 in a variety of soils.  Liming material had a much greater influence on \nincreasing soil pH compared to the PRs (Sikora, 2002).\n\n\n\nAmendments Applied to High Al Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200920\n\n\n\nExchangeable Aluminium and Aluminium Saturation\n\n\n\nThere was a significant difference in exchangeable Al among soils, amendments, \nand rates, and their respective interactions (Table 3). The best response in \n\n\n\namelioration of Al was obtained for Batu Anam soil.  GML for both soils gave a \nhigh response in neutralising Al exchangeable 0.84 and 0.56 cmol\n\n\n\nc\n/kg, and 0.66 \n\n\n\nand 0.34 cmol\nc\n/kg for GPR per t/ha for Batu Anam and Durian soils, respectively \n\n\n\n(Table 4).  Kieserite and gypsum had been shown to have a significant effect on \nneutralising aluminium in Batu Anam soil (Table 5).  Unlimed Batu Anam soil, \nwhich had the lowest pH, contained the highest exchangeable Al and Al saturation \n(Table 4); most of the exchange sites were saturated with Al. As soil pH was \nincreased from 4.4 to 6.5 with the application of GML, the amount of exchangeable \nAl decreased by 100% from 5.17 to 0.00 cmol\n\n\n\nc\n/kg of soil; a corresponding increase \n\n\n\nin exchangeable Ca and Mg was obtained from 0.57 to 5.40 and 0.10 to 2.13 \ncmol\n\n\n\nc\n/kg of soil, respectively. For MgCO\n\n\n\n3\n, as soil pH increased from 4.4 to 5.0, \n\n\n\nthe amount of exchangeable Al decreased by 80.7% from 5.17 to 1.00 cmol\nc\n/kg \n\n\n\nof soil; a consequent increase in exchangeable Mg was also obtained from 0.10 \nto 1.86 cmol\n\n\n\nc\n/kg of soil. There was a slightly increase in Ca from 0.57 to 0.95 \n\n\n\ncmol\nc\n/kg of soil. For GPR, as the soil pH increased from 4.4 to 4.7, the amount \n\n\n\nof exchangeable Al decreased by 77.4% from 5.17 to 1.17 cmol\nc\n/kg of soil; a \n\n\n\nresultant increase in exchangeable Ca from 0.57 to 4.43 cmol\nc\n/kg of soil was \n\n\n\nalso obtained (Table 4). Lime is the most dominant and most effective practice \n\n\n\nto replenish the soil cation pool. Lime increases soil pH, Ca concentration, CEC, \n\n\n\nand base saturation, simultaneously lowering the Al concentration.  All of these \n\n\n\nchemical changes, provided they are within a favourable range, improve grain \n\n\n\nyield and crop sustainability (Fageria and Baligar 2003).\n\n\n\nTABLE 3\nSummary of the statistical analysis of variance (ANOVA) for the comparison of GML, \n\n\n\nMgCO\n3\n and GPR amendments\n\n\n\n*significant at p \u2264 0.05\nn.s. not significant p > 0.05\n\n\n\n\n\n\n\nAl Al  Saturation Soil pH Ca Mg Ca /Mg\n\n\n\nSoil (S) * n.s. * * * *\n\n\n\nAmendments (A) * * * * * *\n\n\n\nRates (R) * * * * * *\n\n\n\nS X A * n.s. * n.s. * *\n\n\n\nS X R * n.s. * * n.s. *\n\n\n\nA X R * n.s. * * * *\n\n\n\nS X A X R n.s. n.s. n.s. * * *\n\n\n\nLinear contrast * * * * * *\n\n\n\nQuadratic contrast * * * * * *\n\n\n\nFactors & Interactions\nParameters \n\n\n\nJose \u00c1lvaro Cristancho Rodr\u00edguez, M.M. Hanafi, S.R. Syed Omar &  Y.M. Rafii\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 21\n\n\n\n There was a significant difference in Al saturation amelioration among \namendments and rates (Table 3). The three amendments were effective in \n\n\n\nneutralising Al saturation. The amelioration of Al saturation for Batu Anam and \nDurian soils were 13.1 and 13.0% for GML, 9.9 and 9.8% for GPR, and 9.2 \n\n\n\nand 9.4% for MgCO\n3\n  per ton applied (Table 4). Values of Al saturation can be \n\n\n\nused as an index for lime application rate, a rate that varies from soil to soil and \namong crop species as well as within cultivars of the same species. In the low-\n\n\n\nland acid soils of Brazil, a relationship has been found between Al saturation and \nthe relative grain yield of common bean.  With increasing Al saturation, there was \n\n\n\na quadratic decrease in the grain yield (Fageria and Baligar 2003).\n A close relationship (P < 0.05) was found between soil pH and exchangeable \nAl concentration and Al saturation with the trends being different among \n\n\n\namendments. In the case of MgC0\n3\n and GPR, increasing soil pH linearly decreased \n\n\n\nexchangeable Al, and for GML increasing soil pH exponentially decreased \nexchangeable Al (Fig. 1). Similar results for GML were reported by Caires et al. \n\n\n\n(2008). According to Sikora (2002), a 0.1 unit increase in soil pH caused a 10.3 \n\n\n\nmg/kg reduction in 1 N KCl extractable Al for the Copper Basin soil.  Although \nthe PR liming effect may only increase pH a few tenths of a unit, the reduction \n\n\n\nin soluble Al could result in a significant improvement for plants growing in very \nacidic soils.\n\n\n\nExchangeable Ca and Mg\n\n\n\nThere was a significant difference in exchangeable calcium among soils, \namendments, and rates, and the interaction between amendments and rates (Table \n\n\n\n3). The interaction between amendments x rates may be described using the \nlinear polynomial contrast with GML rising to 0.82 and 0.78 cmol\n\n\n\nc\n/kg and GPR \n\n\n\nincreasing Ca to 0.61 and 0.79 cmolc/kg, per ton applied for Batu Anam and \nDurian soils, respectively (Table 4). Gypsum showed a significant increase in \nexchangeable Ca raising 1.76 and 1.71 cmol\n\n\n\nc\n/kg of soil per ton applied on Batu \n\n\n\nAnam and Durian Soils. \n\n\n\n There was a significant difference in exchangeable magnesium among \nmeans of soils, amendments, and rates, and their respective interactions (Table \n\n\n\n3). The best response was achieved in Batu Anam soil. The interaction between \namendments and rates, described by the linear polynomial contrast, was as follows: \n\n\n\nkieserite raised Mg content to 1.23 and 1.03 cmolc/kg of soil; GML increased Mg \nto 0.34 and 0.27 cmol\n\n\n\nc\n/kg of soil and MgCO\n\n\n\n3\n increased to 0.28 and 0.24 cmol\n\n\n\nc\n/kg \n\n\n\nof soil per ton applied for Batu Anam and Durian soils, respectively (Tables 4 and \n5). Total Mg contents were observed to increase exponentially with higher GML \nrates. However, exchangeable Mg contents improved quadratically only, implying \nthat at a high rate of GML, a large proportion of the soil Mg content remains in \n\n\n\nnon-exchangeable form. This might be attributed to the poorer chemical reactivity \nof magnesium carbonate compared to calcium carbonate in soil. Therefore, more \n\n\n\ntime is required for the Mg nutrient in GML to reach its maximum effectiveness \n(Goh et al. 1998).  Heming and Hollis (1995) reported that the kieserite, calcined \n\n\n\nAmendments Applied to High Al Soils\n\n\n\n\n\n\n\n\nM\nalay\n\n\n\nsian\n Jo\n\n\n\nu\nrn\n\n\n\nal o\nf S\n\n\n\no\nil S\n\n\n\ncien\nce V\n\n\n\no\nl. 1\n\n\n\n3\n, 2\n\n\n\n0\n0\n9\n\n\n\n2\n2\n\n\n\nJose \u00c1\nlvaro C\n\n\n\nristancho R\nodr\u00edguez, M\n\n\n\n.M\n. H\n\n\n\nanafi, S.R\n. Syed O\n\n\n\nm\nar &\n\n\n\n  Y\n.M\n\n\n\n. R\nafii\n\n\n\nTable 4\n\n\n\nEffect of GML, MgCO\n3\n and GR amendments on soil pH, exchangeable Al, Al saturation, \n\n\n\nCa, Mg and Ca/Mg and available P on Batu Anam and Durian soil series\nf GML, MgCO3 and GPR amendments on soil pH, exchangeable Al, Al saturation, Ca, Mg, and Ca/Mg and available P on Batu Anam and Duri\n\n\n\nRate Al Al saturation Ca Mg Ca : Mg P\n\n\n\nt ha\n-1 (cmolc/kg) % mg kg\n\n\n\n-1\n\n\n\nBatu Anam MgCO3 0 4.40 5.17 78.90 0.57 0.10 7.55\n\n\n\n2.2 4.58 3.70 63.13 0.73 0.38 2.09\n\n\n\n3.5 4.66 2.47 50.63 0.68 0.67 1.03\n\n\n\n4.8 4.83 1.80 40.53 0.72 1.11 0.65\n\n\n\n6.1 5.00 1.00 21.23 0.95 1.86 0.51\n\n\n\nEquation y= 0.097x+4.373 y= -0.694x+5.113 y= -9.229x+81.27 y= 0.051x+0.560 y= 0.278x-0.093 y=-1.116x+6.042\n\n\n\nR\n2 0.98 0.99 0.98 0.73 0.91 0.78\n\n\n\nDurian MgCO3 0.0 3.97 3.42 77.23 0.24 0.11 2.98\n\n\n\n2.2 4.33 3.06 75.23 0.11 0.21 0.88\n\n\n\n3.5 4.43 2.26 64.67 0.08 0.52 0.15\n\n\n\n4.8 4.53 1.26 40.60 0.14 1.09 0.12\n\n\n\n6.1 4.66 0.67 21.83 0.11 1.54 0.07\n\n\n\nEquation y= 0.109x+4.022 y= -0.481x+3.721 y= -9.419x+86.92 y= -0.017x+0.191 y=0.244x-0.112 y=-0.470x+2.389\n\n\n\nR\n2 0.96 0.94 0.85 0.41 0.88 0.80\n\n\n\nBatu Anam GML 0.0 4.40 5.17 78.90 0.57 0.10 7.55\n\n\n\n2.2 4.91 1.40 28.37 2.45 0.47 5.23\n\n\n\n3.5 5.25 0.33 6.07 3.61 0.95 3.81\n\n\n\n4.8 5.90 0.00 0.33 4.64 1.53 3.03\n\n\n\n6.1 6.46 0.00 0.13 5.40 2.13 2.55\n\n\n\nEquation y=0.339x+4.263 y=-0.838x+4.14 y = -13.11x+ 65.94 y=0.806x+0.677 y=0.338x+0.075 y=-0.841x+7.203\n\n\n\nR\n2 0.96 0.81 0.85 0.99 0.96 0.96\n\n\n\nDurian GML 0.0 3.97 3.42 77.23 0.24 0.11 2.98\n\n\n\n2.2 4.59 1.00 30.87 1.21 0.40 3.28\n\n\n\n3.5 5.05 0.27 7.90 2.12 0.67 3.30\n\n\n\n4.8 5.44 0.03 0.47 3.44 1.21 2.83\n\n\n\n6.1 5.89 0.00 0.03 4.99 1.78 2.79\n\n\n\nEquation y=0.316+3.942 y=-0.556x+2.77 y=-12.98x+66.05 y=0.776x-0.157 y=0.273x-0.067 y=-0.046x+3.187\n\n\n\nR\n2 1.00 0.82 0.88 0.96 0.93 0.20\n\n\n\nBatu Anam GPR 0.0 4.40 5.17 78.90 0.57 0.10 7.55 6.00\n\n\n\n2.2 4.44 2.93 41.33 3.26 0.05 57.44 26.30\n\n\n\n3.5 4.54 2.03 29.97 3.63 0.06 61.10 48.40\n\n\n\n4.8 4.62 1.40 21.83 4.17 0.07 58.71 72.50\n\n\n\n6.1 4.67 1.17 18.17 4.43 0.08 55.33 96.20\n\n\n\nEquation y=0.048x+4.376 y=-0.664x+4.726 y=-9896x+70.62 y=0.612x+1.197 y=-0.002x+0.083 y=7.269x+24.09 y=15.08x+0.232\n\n\n\nR\n2 0.96 0.93 0.90 0.87 0.11 0.57 0.97\n\n\n\nDurian GPR 0.0 3.97 3.42 77.23 0.24 0.11 2.98 5.97\n\n\n\n2.2 4.27 2.93 56.17 1.60 0.08 19.28 46.43\n\n\n\n3.5 4.34 2.71 42.60 2.88 0.10 30.70 47.09\n\n\n\n4.8 4.44 1.98 28.80 4.11 0.11 36.98 73.01\n\n\n\n6.1 4.48 1.31 18.87 4.83 0.11 43.66 97.53\n\n\n\nEquation y=0.082x+4.027 y=-0.342x+3.599 y=-9.764x+76.88 y=0.787x+0.140 y=0.001x+0.095 y=6.766x+4.446 y=14.23x+7.132\n\n\n\nR\n2 0.92 0.94 1.00 0.99 0.13 0.99 0.96\n\n\n\n(cmolc/kg)\nSoil Amendment Soil pH\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 23\n\n\n\n\n\n\n\ny = 4E+06e-3.27x\n\n\n\nR\u00b2 = 0.929\n\n\n\ny = -4.477x + 22.80\n\n\n\nR\u00b2 = 0.809\n\n\n\ny = -4.546x + 22.59\n\n\n\nR\u00b2 = 0.552\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\nE\nx\n\n\n\nch\na\n\n\n\nn\ng\nea\n\n\n\nb\nle\n\n\n\n A\nl  \n\n\n\n(c\nm\n\n\n\no\nlc\n\n\n\n / \nk\n\n\n\ng\n)\n\n\n\nGML\n\n\n\nCarbonate\n\n\n\nGPR\n\n\n\n(a)\n\n\n\nMgCO3\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n3 3.5 4 4.5 5 5.5 6 6.5 7\n\n\n\nA\nl  \n\n\n\nS\na\n\n\n\ntu\nra\n\n\n\nti\no\n\n\n\nn\n (\n%\n\n\n\n)\n\n\n\nSoil pH (1:1 soil water ratio)\n\n\n\n(b)\n\n\n\nFig. 1. Relationship between soil pH (1:1 soil to water ratio) and exchangeable \n\n\n\n Al and Al saturation in Batu Anam and Durian soils for GML, MgCO\n3\n, and \n\n\n\nGPR amendments\n\n\n\nAmendments Applied to High Al Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200924\n\n\n\nmagnesite and magnesian limestone had some effect within 12 months with soil \n\n\n\nMg being raised by at least one index value. Kieserite and calcined magnesite \npowder gave a similar increase in extractable Mg, but granular calcined magnesite \nwas less effective in all soils. \n\n\n\nTABLE 5\nKieserite effects on soil pH, Mg and exchangeable Al, and Gypsum effects on soil pH, \n\n\n\nCa and exchangeable Al on Batu Anam and Durian soils\n\n\n\nAvailable Phosphorus\n\n\n\nA positive response on available P was obtained by GPR. For every ton of GPR \n\n\n\napplied, the level of available P increased to 15.08 and 14.23 mg/kg (Table 4). \n\n\n\nThis value qualifies as an optimum level for the maintenance of oil palm crop \n(Munevar et al. 2001; Fairhurst et al. 2005). A similar increase in available P was \nreported by Munevar et al (2005) in which 25.0 mg/kg of available P was obtained \n\n\n\nwith the application of 1.43 ton/ha of phosphate rock # 3 (Enmienda Fosf\u00f3rica # \n\n\n\n3) from Colombia.\n\n\n\nEconomic Analysis\n\n\n\nThe economic analysis of the various amendments on aluminium amelioration \n\n\n\nwas done according to the price of the materials provided by Agro-Export of \nColombia and Mejisulfatos Colombian Fertilizer Company on May 2008 (Table \n\n\n\n6).\n\n\n\n The GML gave more benefits in contrast with the other sources evaluated, \nbecause it has the capacity to highly increase soil pH and reduce a major quantity \n\n\n\nof exchangeable Al, resulting in a  low Al saturation value.  Additionally, it \nsupplied sufficient levels of Ca and Mg, and was able to maintain a balanced Ca/\nMg ratio in the soil. Economically, use of GML as an amendment, allows for  Al  \n\n\n\nto be neutralised and soil pH increased at a low cost (USD 118.50 per cmol\nc\n/kg) \n\n\n\ncompared to GPR (USD 232.36 per cmol\nc\n/kg).\n\n\n\nJose \u00c1lvaro Cristancho Rodr\u00edguez, M.M. Hanafi, S.R. Syed Omar &  Y.M. Rafii\n\n\n\n\n\n\n\nMg Al Ca Al\n\n\n\nt ha\n-1\n\n\n\nBatu Anam 0.0 3.97 0.10 5.16 3.97 0.57 5.16\n\n\n\n2.2 3.81 1.13 3.73 3.80 4.00 4.75\n\n\n\n3.5 3.76 2.59 4.05 3.76 5.94 4.77\n\n\n\n4.8 3.76 5.43 3.77 3.73 8.28 4.52\n\n\n\n6.1 3.76 9.01 4.04 3.74 11.55 4.10\n\n\n\nEquation y=-0.033x+3.92 y=1.238x+0.632 y = -0.1681x+4.70 y=-0.038x+3.92 y=1.762x+0.26 y=-0.157x+5.17\n\n\n\nR\n2\n\n\n\n0.73* 0.86* 0.30* 0.79* 0.97* 0.35*\n\n\n\nDurian 0.0 4.40 0.11 3.42 4.40 0.24 3.42\n\n\n\n2.2 4.06 1.69 3.09 4.11 3.03 3.29\n\n\n\n3.5 4.01 3.10 2.94 4.02 5.38 2.96\n\n\n\n4.8 4.01 4.82 3.06 4.00 7.41 2.94\n\n\n\n6.1 4.03 6.35 2.75 3.98 10.88 2.83\n\n\n\nEquation y=-0.058x+4.29 y=1.036x+0.1941 y=-0.095x+3.36 y=-0.067x+4.32 y=1.715x+0.255 y=-0.103x+3.42\n\n\n\nR\n2\n\n\n\n0.55* 0.95* 0.09 \nns\n\n\n\n0.74* 0.90* 0.12\nns\n\n\n\nSoil\n\n\n\n(cmol  /kg)\n\n\n\nGypsum\n\n\n\nSoil pH (H2O) Soil pH (H2O)\n(cmol  /kg)\n\n\n\nRate\nKieserite\n\n\n\n* - significant at p \u2264 0.05  \n\n\n\nn.s. - not significant p > 0.05  \n\n\n\nc c\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 25\n\n\n\nTABLE 6\nAmendments effect on exchangeable Al, price of the amendment and the cost per ha per \n\n\n\ncmolc/kg of Al neutralised for Batu Anam soil\n\n\n\nCONCLUSIONS AND RECOMMENDATIONS\nThe GML gave the best agronomic and economic efficiency in neutralising soil \nacidity in Ultisols such as Batu Anam and Durian soils as it increased soil pH, Ca, \nMg, and neutralised the major proportion of exchangeable Al and Al saturation, \nand maintained the Ca/Mg balance ratio. MgCO\n\n\n\n3\n and GPR gave a similar effect on \n\n\n\nsoil pH, exchangeable Al, and Al saturation. However, both provided an additional \neffect in improving the level of Mg for MgCO\n\n\n\n3\n, and Ca and P for GPR. The level \n\n\n\nof Mg and P attained from 1 ton/ha is probably enough to achieve the optimum \n\n\n\nlevel for oil palm maintenance. Kieserite provided an increased proportion of Mg \n\n\n\nand was capable of neutralising Al in Batu Anam soil. The amendments evaluated \nshowed variable effects on acid soil parameters. Therefore, a combination of \n\n\n\ndifferent sources must be used to obtain an adequate balance of the nutrients \n\n\n\nwhich assures greater efficiency of nutrient availability to the plants.  \n     \n\n\n\nACKNOWLEDGMENTS\nThe senior author (CRJA) would like to thank the Colombian Oil Palm Research \n\n\n\nCentre Corporation (Cenipalma) and K+S KALI GmbH \u2013 ESTA Kieserite for the \n\n\n\naward of a postgraduate scholarship, Universiti Putra Malaysia for supporting \n\n\n\nthis research, and Applied Agricultural Resources Sdn. Bhd. (AAR) for technical \nsupport. We wish to thank Mr. Goh Kah Joo for valuable comments that have \n\n\n\nsubstantially improved the paper.\n\n\n\nREFERENCES\nAnonymous. 2006a. The oil palm agro-industry in Colombia, pp. 1-32. Bogot\u00e1.\n\n\n\nAnonymous. 2006b. Lime quality-does it matter? Potash and Phosphate Institute,\n\n\n\n Agri-briefs, agronomic news items, Norcross, GA.\n\n\n\nBoon, S.C.T. and Talib, J. Soil physics analyses volume 1. Universiti Putra\n\n\n\n Malaysia Press.\n\n\n\nAmendments Applied to High Al Soils\n\n\n\n\n\n\n\nAmendments\nAl\n\n\n\n3+ \n(cmolc/kg) \n\n\n\nneutralized  / t CaCO3\n\n\n\nPrice  per  t   $ \n\n\n\nUSD\n\n\n\nCost per Al\n3+ \n\n\n\nneutralized\n\n\n\nMgCO 3 0.69 469.91 681.03\n\n\n\nGML 0.84 99.54 118.50\n\n\n\nGPR 0.66 153.36 232.36\n\n\n\n(cmol  /kg)c\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200926\n\n\n\nBrown, J.R. and J. Stecker. 2003. Liming in Missouri in the 20th century. In \n\n\n\n Missouri soil fertility and fertilizes research update 2003. University of \n\n\n\n Missouri-Columbia, special report 548: 1-81.\n\n\n\nCaires E.F., F.P.R. Pereira., F.R. Zardo and C. Feldhaus. 2008. Soil acidity and \n aluminum toxicity as affected by surface liming and cover oat residues \n under a no till system. J. Soil Use and Manage. 24: 302 \u2013 309.\n\n\n\nConyers, M., B. Scott., R. Fisher. And W. Lill. 1996. Predicting the field \n performance of twelve commercial liming materials from southern \n\n\n\n Australia. J. Fert. Res. 44: 151 \u2013 161.\n\n\n\nCorley. R.H.V.and P.B. Tinker. 2003. The origin and development of the oil palm \n industry. In The oil palm, pp. 1-26. Blackwell science.\n\n\n\nDrabek, O., L. Boruvka, L. Mladkova, and M. Kocarek. 2003. Possible method of \n aluminium speciation in forest soil. J Inorg. Biochem. 97 (1): 8-1.\n\n\n\nEssington, M.E. 2004. Acidity in soil minerals. In Soil and water chemistry, pp. 473-497.\n\n\n\n CRC Press LLC.\n\n\n\nFageria N.K. and V.C. Baligar. 2003. Fertility management of tropical acid soils \n for sustainable crop production. In Handbook of soil acidity, ed. Z. \n Rengel, pp. 359 -38. Madison, New York.\n\n\n\nFairhurst, T., J.P. Caliman, R. Harder. And C. Witt. 2005. Nutrient disorders and \n\n\n\n nutrient management oil palm, series volume 7: 14 \u2013 43. \n\n\n\nGoh, K.J., P.S. Chew. and K.C. Teoh. 1998. Ground magnesium limestone as a source \n\n\n\n of magnesium for mature oil palm on sandy soil in Malaysia. In International oil \n\n\n\n palm conference, pp. 347 - 361. Nusa Dua Bali. \n\n\n\nHeming, S.D. and Hollis, J.F. 1995. Magnesium availability from kieserite and calcined \n\n\n\n magnesite on five soils of different pH. J. Soil Use and Manage. 11: 105 \u2013 109.\n\n\n\nJones, J.B. 2001. Laboratory guide for conducting soil tests and plant analysis.\n\n\n\n CRC Press LLC.\n\n\n\nJones, J.B. 2003. Agronomic handbook, management of crops, soils and their \n\n\n\n fertility. CRC Press LLC.\n\n\n\nLanger, H., M. Cea., G. Curaqueo. and F. Borie. 2009. Influence of aluminum \n on the growth and organic acid exudation in alfalfa cultivars grown in \n nutrient solution. J. plant nut. 32: 618 \u2013 628.\n\n\n\nMatsumoto, H. 2002. Plants under aluminum stress: toxicity and tolerance. In Plant\n\n\n\n roots the hidden half, ed. Y. Waisel, A. Eshel, U. Kafkafi, and M. Dekker, \n pp. 821 \u2013 838. Inc. New York. \n\n\n\nJose \u00c1lvaro Cristancho Rodr\u00edguez, M.M. Hanafi, S.R. Syed Omar &  Y.M. Rafii\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 27\n\n\n\nMarschner, H. 1986.  Mineral nutrition in higher plants. Academic Press.\n\n\n\nMunevar, M.F. 2001. Fertilizaci\u00f3n de la palma de aceite para obtener altos \n\n\n\n rendimientos. Palmas 22: 9 \u2013 17.\n\n\n\nMunevar, M.F., F.A. Romero. and S.M. Cuellar. 2005. Pruebas de reactividad de \n\n\n\n enmiendas (PRE): ensayos de laboratorio que apoyan al palmiculor en la \n\n\n\n selecci\u00f3n de fertilizantes efectivos.  Ceniavances 131: 1-4.\n\n\n\nParamananthan, S. 2000. Soils of Malaysia their characteristics and identification.\n\n\n\n Akademi Sains Malaysia.\n\n\n\nProchnow,  L.I. 2008. Optimizing nutrient use in low fertility soils of the tropics in \n\n\n\n times of high fertilizer prices (Brazil). Better crops number 3: 19 -21.\n\n\n\nShamshuddin, J., I. Che Fauziah, and H.A.H. Sharifuddin. 1991. Effects of \n\n\n\n limestone and gypsum to a Malaysian Ultisol on soil solution and yields\n\n\n\n  of maize and groundnut. Plant Soil 134: 45-52.\n\n\n\nShamshuddin, J. and H. Ismail. 1995. Reactions of ground magnesium limestone \n\n\n\n and gypsum in soils with variable-charge minerals. Soil Sci Soc Am J 59, \n no. 1: 106 \u2013 112.\n\n\n\nSikora, F.J. 2002. Evaluating and quantifying the liming potential of phosphate \n\n\n\n rocks. Nutrient Cycling in Agroecosystems 63: 59-67.\n\n\n\nSinclair, A.G., P.D. Johnstone., L.C. Smith. and W.I.I. Risk. 1993. Effect of reactive \n\n\n\n phosphate rock on the pH of soil under pasture. J. Agr. Res. 36:  381 \u2013 384.\n\n\n\nSumner, M.E. and Noble, A.D. 2003. Soil Acidification: The world story. In \n\n\n\n Handbook of Soil Acidity, ed. Z. Rengel., pp 1 \u2013 28. New York. \n\n\n\nSuswanto, T., J. Shamshuddin., S.R. Syed Omar., P. Mat. And C.B.S. Teh. 2007. \n Effects of lime and fertiliser application in combination with water \n\n\n\n management on rice (Oriza sativa) cultivated on an acid sulphate soil. \n\n\n\n Malays. J. soil sci. 11:17-27.\n\n\n\nTinker, P.B. 1964. Equilibrium cation activity ratios and responses to potassium \n fertilizer of Nigerian oil palms. J. Soil sci.15: 35-4.\n\n\n\nTruog, E. 2004. Acidic soils. In Soils in our environment, ed. D.T. Gardiner and \n\n\n\n R.W. Miller, pp. 239 \u2013 276. Inc. New Jersey.\n\n\n\nUSDA and NRCS. 2007. Statistix 8 user guide for the plant materials program, \n\n\n\n version 2, pp. 1-80.\n\n\n\nZapata, H.R. 2004. Correcci\u00f3n de la toxicidad por aluminio en el suelo. In qu\u00edmica \n\n\n\n de la acidez del suelo, pp. 125-175. Medell\u00edn, Colombia.\n\n\n\nAmendments Applied to High Al Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200928\n\n\n\nZhang, W.H. and Z. Rengel. 1999. Aluminium induces an increase in cytoplasmic \n calcium in wheat root apical cells. J. Plant Physiol. 26: 385 -401.\n\n\n\nJose \u00c1lvaro Cristancho Rodr\u00edguez, M.M. Hanafi, S.R. Syed Omar &  Y.M. Rafii\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nINTRODUCTION\nPlant growth depends very much on the soil for water and nutrients (Foth and \n\n\n\nof the important components in the soil that serve the plant and microorganism. \nAlso, soil moisture is an important component in the water cycle.  Plants depend \nat any time more on soil moisture available at root level than on precipitation \noccurrence. Knowledge of soil moisture content is important for management \n\n\n\nAssociation between Soil Moisture Gradient and Tree \nDistribution in Lowland Dipterocarp Forest at Pasoh, \n\n\n\nMalaysia\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\nForest Research Institute of Malaysia (FRIM), 52109 Kepong\nSelangor, Malaysia\n\n\n\nABSTRACT\n\n\n\nForest Reserve, Negeri Sembilan, Malaysia to assess the variations of moisture \ncontent for different soil types and depths and the association between soil moisture \n\n\n\naverage monthly soil moisture measurement for a two-year  period varied between \n\n\n\nsoils showed highest soil moisture at all soil depths. Soil moisture increased with \n\n\n\nfrom moisture at other depths.  Though soil moisture increased with soil depth, \n\n\n\nEuphorbiaceae, \nLecythidaceae, Myrtaceae and Sapidaceae were abundant at wet alluvial soil. \nAt DA, the abundant species were Annonaceae, Myrsinaceae, and Clusiaceae, \nwhile Burseraceae, Alanggiaceae, Anisophylleaceae, Fabaceae, Ulmaceae and \nSterculiaceae were abundant at RD.  This indicates that species abundance at the \n\n\n\nKeywords: Soil moisture, Pasoh 50-ha demography plot, monitoring, FDR\n\n\n\n___________________\n*Corresponding author : E-mail: \n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\ninformation will be useful to determine species suitability for rehabilitation and \nenrichment planting programmes. \n\n\n\net al.\nthe spatial distribution patterns in tropical species is associated with topography \n\n\n\net al\nDavies et al.\n6-ha plots in the Seraya-Ridge Forest of Peninsular Malaysia (Niiyama et al. \n\n\n\net al.\ncommunity types which are associated with the topography of the sites. Nur \n\n\n\nchosen for its relative uniformity of relief and other physical conditions, many \nspecies vary in distribution across the gentle slope, plateau, and swamp habitats \n\n\n\nthat many of the topographic associations on BCI are not simply the artifact of \nspatial autocorrelation in recruitment patterns (Harms et al.\nvegetation mapping at other large CTFS plots encompassing greater variation in \n\n\n\nstudy of Hirai et al\nPlotkin et al.\n\n\n\nMalaysia in collaboration with Smithsonian Tropical Forest Research Institute and \n\n\n\nand population structure of trees have been described (Manokaran and LaFrankie \net al. et al et al.\n\n\n\nsome analyses on population dynamics have also been attempted (Abd. Rahman \net al.\n\n\n\nand population dynamics of trees from the repeated census data with the assistance \n\n\n\nStudies by Condit et al\nin the population size of slope-specialist tree species is associated with two severe \n\n\n\nand temporal variation in water availability is likely to be important for driving \nplant population dynamics and shaping species distributions across habitats, but \n\n\n\nMany studies have been conducted on soil moisture in tropical forests. \nAt Pasoh, topographic associations have been attributed to differences in soil-\n\n\n\n\n\n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\nmoisture gradient between slopes of wet and dry area habitats (Noguchi et al. \n\n\n\net al.\nwater potentials are maintained at higher levels on the BCI plot slopes compared \nto the principal plateau, while Daws et al. \nshorter duration of drought period during the dry season.  Variation in soil-moisture \navailability across topographic gradients in tropical forests might also maintain a \n\n\n\n(Noguchi et al.\nIt is timely that further in-depth research is carried out to determine the \n\n\n\ninterest of this assessment was to look at the temporal pattern of the soil moisture \n\n\n\nspecies preferences at the site. \n\n\n\nSite Description\nThe study was conducted at Pasoh Forest Reserve, a typical lowland rain forest in \n\n\n\no o\n\n\n\nSimpang Pertang in Negeri Sembilan (Fig.1\n\n\n\nthe data collected from Pasoh climate station, the highest monthly precipitation \n\n\n\nFig. 2\n\n\n\nFig. 1: Location of the study area\n\n\n\n \n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\net al. \n\n\n\ndipterocarp forest which consist of various species of Shorea and Dipterocarpus, \n\n\n\nhuman disturbance.  \n\n\n\nMATERIALS AND METHODS\nSampling Location \n\n\n\nha plot (Fig. 3\n\n\n\nin Table 1.\n\n\n\nrepresentative results. The forest is characterised by the large trees of dipterocarps \nsuch as Dipterocarpus spp., Shorea spp. and Neobalanocarpus heimii, and non-\ndipterocarps such as Dyera costulata, Triomma  malaccensis, Canarium apertum, \nand Koompassia malaccensis\nLecythidaceae, Myrtaceae, Celastraceae and Sapindaceae family. Glyptopetalum \nquadrangulare\n\n\n\nFig. 2:  Average monthly rainfalls at Pasoh F.R for years 2006 and 2007 \n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n300\n\n\n\n350\n\n\n\nJa\nn\nu\na\nry\n\n\n\nF\ne\nb\nru\na\nry\n\n\n\nM\na\nrc\nh\n\n\n\nA\np\nri\nl\n\n\n\nM\na\ny\n\n\n\nJu\nn\ne\n\n\n\nJu\nly\n\n\n\nA\nu\ng\nu\nst\n\n\n\nS\ne\np\nte\nm\nb\ne\nr\n\n\n\nO\nct\no\nb\ne\nr\n\n\n\nN\no\nve\nm\nb\ne\nr\n\n\n\nD\ne\nce\nm\nb\ne\nr\n\n\n\nMonths\n\n\n\nRa\ninf\n\n\n\nall\n (m\n\n\n\nm)\n\n\n\n2006\n\n\n\n2007\n\n\n\n\n\n\n\n\nAnnonaceae, Myrsinaceae, Clusiaceae and Dipterocarpaceae are the abundant \ntree family within the DA habitat, while Shorea maxwelliana is the most dominant \nspecies in the area. In the shale area, seven tree families found in abundance \nare Euphorbiaceae, Burseraceae, Annonaceae, Anisophylleaceae, Fabaceae, \nUlmaceae and Sterculiaceae. The most abundant species associated with this area \nis Anisophyllea corneri from the family of Anisophylleaceae.  Alangiaceae is the \nonly tree family found within the lateritic area. \n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\n\n\n\n\n0 200 400 600 800 1000\n\n\n\n0\n1\n0\n0\n\n\n\n2\n0\n0\n\n\n\n3\n0\n0\n\n\n\n4\n0\n0\n\n\n\n5\n0\n0\n\n\n\ncor.X\n\n\n\nc\no\nr.\nY\n\n\n\nN \n\n\n\nDimensions are in meters and North is facing upward direction. The color code indicates \n\n\n\nFig. 3:  Major soil drainage conditions in Pasoh 50 ha Plot\n\n\n\nTABLE 1\nTABLE 1 \n\n\n\nProfile characteristic of sites in Pasoh 50 ha plot, Pasoh Forest Reserve \n\n\n\nSite Soil Characteristics \nWet Alluvial \n(Floodplain) \n\n\n\n- Thin muck, over mottled pale brown wet layered, over moist \nbrownish less mottled; sandy clay subsoil (Awang series) and \nsandy clay loam subsoil (Alma series). Drainage class: 3-4 \n(Imperfect poor) \n\n\n\n- Thick muck over mottled grey wet layered loam, over moist \nbrownish mottled;  Kampung Pusu series, fine & medium sand. \nDrainage class 1-2 (poorly drained) \n\n\n\nDry Alluvial \n(Low terrace) \n\n\n\n- Thin dark brown topsoil over unmottled yellowish/brownish \nloam to 1 m+, mottled below.  Sand predominantly medium or \nfine (Tebuk series); sand predominantly coarse (Tawar series); \nDrainage class 5-6 (Moderately drained). \n\n\n\nRidge \n(Crest and slopes) \n\n\n\n- Thin dark brown fine loam topsoil over strong brown fine loam \nor clay subsoil. Massive laterite or dense gravel within 50 cm \n(Gajah Mati Series); Many laterite gravel at 50-100 cm (Terap \nSeries); Few laterite gravel below 100 cm (Bungor series). \nDrainage class 7-8 (Well drained) \n\n\n\n Adzmi et al. (2010) \n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\nSoil Moisture Measurement\n\n\n\nare independent of soil conductivity. The sensor was inserted into the polypro \naccess tubes to take soil moisture readings. This tube was constructed of specially \n\n\n\ninto the calibration and moisture reading functions of the probe. Substituting \nother tubes such as PVC pipe will result in inaccurate and erroneous readings.  \nNoguchi et al.\n\n\n\nto determine the association of plant community with the environmental factors \n\n\n\nmonthly at three different plots for two years.\n\n\n\nData Analysis\n\n\n\nsoil depths, and soil moisture and its interaction and differences between soil \n\n\n\nat differing soil depths and soil types using S-Plus version 6.1. Post-hoc multi \n\n\n\nmean differences between the factors. \n\n\n\nRESULTS \n\n\n\nSoil Moisture across the Sites\nFig. 4\n\n\n\nmeans comparison between RD and DA shows that DA was much drier compared \n\n\n\n75 cm. Therefore, soil depth and soil types could be among the factors to account \nFig. 5\n\n\n\nin soil moisture observed at different soil depths with all the soil types combined. \nIt shows an obvious difference in moisture among soil depths. Moisture at 15 cm \n\n\n\nthe moisture of different soil types (Fig. 6\nvariation as compared to DA and RD, but DA seem to have small changes in \nmoisture. Fig.7\n\n\n\n\n\n\n\n\nwere similar in January, February, March and April showing a decrease at 15 cm \n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\nFig. 4:  Diagnostic plot showing mean of soil moisture among soil type (STYPE) and soil \ndepth   (DEPTH)\n\n\n\nFig. 5:   Boxplot of soil moisture measured at different soil depths with all \nsites combined\n\n\n\n \n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\ndepths and increasing at the deeper layers. The DA was well drained and the water \n\n\n\nshowed a relative increase towards deeper layers. \n\n\n\nFig. 6: Boxplot of soil moisture measured for different soil types with all soil depths \ncombined\n\n\n\nFig. 7: Mean monthly soil moisture at dry alluvial area\n\n\n\nSoil Type\n\n\n\nDA RD WA\n\n\n\nM\no\n\n\n\nis\ntu\n\n\n\nre\n (\n\n\n\n%\n)\n\n\n\n2\n0\n\n\n\n6\n0\n\n\n\n4\n0\n\n\n\n8\n0\n\n\n\n1\n0\n\n\n\n0\n\n\n\n\n\n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\nFigure 8 shows the combined mean monthly moisture content at the ridge \n\n\n\nFig. 8: Mean monthly soil moisture at ridge area\n\n\n\nFig. 9: Mean monthly soil moisture at wet alluvial area\n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\nat this depth improved and was able to hold more water compared to the gravel \n\n\n\nsoil because of the drainage situation at higher elevations where water from the \n\n\n\nwas driest at the upper surface due to the evaporation process and wettest at the \ndeepest soil due to percolation.\n\n\n\nrainfall events and poor drainage where water retention time was longer in the \n\n\n\nlayers compared to the deepest soil which was probably due to evaporation at the \n\n\n\nthe increase in the water holding capacity at the deepest layer.\nThe regression analysis output between rainfall and soil moisture shows that \n\n\n\nComparison of Soil Moisture between Different Soil Types and Depths \n\n\n\nby RD and DA. Both data sets have similar soil moisture distribution where the \n\n\n\n\n\n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\nand RD have a strong soil moisture association with soil depth.  This is shown by \n\n\n\nthe RD. DA showed a weak association between soil moisture and soil depth. \n\n\n\nthe variation in moisture content. There could be other environmental factors \nthat contribute to the variations in moisture content such as rooting system, \nsoil properties and climatic and hydrological conditions like evaporation and \ntranspiration. \n\n\n\nsoil. This interaction was similar to the DA where the surface moisture tends to \n\n\n\nsoil was slightly different where moisture took a longer period to increase at the \n\n\n\nDifference in moisture content between plots and depth\n\n\n\nTABLE 3  \nCorrelation between depth and soil moisture for 2006 and 2007  \n\n\n\n              2006          2007  \nWet Alluvial area  0.8884  0.9068  \nRidge area  0.7212  0.8832  \nDry Alluvial area  0.0210  0.4437  \n\n\n\n \nTABLE 2  \n\n\n\nDifference in moisture content between plots and depth \n\n\n\n Difference between (%)  \n\n\n\nSoil  \nDepth  \n\n\n\nWet Alluvial  \n and Ridge area  \n\n\n\nWet Alluvial \nand Dry \n\n\n\nAlluvial area  \nRidge and Dry \nAlluvial area  \n\n\n\n(cm)     \n15  13.86  6.45  -7.41  \n30  -0.94  8.89  9.84  \n45  8.12  23.84  15.72  \n60  25.77  36.01  10.24  \n75  14.42  27.03  12. 61  \n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\nshow differences between soil depths.  However, a comparison of moisture at 45 \ncm and 15 cm depths showed small differences. Generally, 75 cm has the largest \ndifference for all monitored soil depths with the highest being between the 75cm \n\n\n\nTable 7 shows the species distribution based on soil types in Pasoh \nEuphorbiaceae, Lecythidaceae, Myrtaceae and \n\n\n\nSapidaceae are abundant at wet alluvial soil. At DA the abundanct species are \nAnnonaceae, Myrsinaceae, and Clusiaceae, while Burseraceae, Alanggiaceae, \nAnisophylleaceae, Fabaceae, Ulmaceae and Sterculiaceae are abundant at RD.  \n\n\n\nDISCUSSION\n\n\n\ncharacteristics of the undisturbed forest where less surface evaporation occurred \n\n\n\nTABLE 5\n\n\n\nand their interaction\n\n\n\nTABLE 4(a) \nMeans and standard deviation for soil moisture at different soil types and depth in 2006 \n\n\n\nWet Alluvial area Ridge area Dry Alluvial area Depth \nMeans SD Means SD Means SD \n\n\n\n15 54.40 10.54 41.53 4.68 48.13 5.79 \n30 43.84 21.89 49.64 6.21 35.60 9.61 \n45 61.14 23.55 52.47 3.82 32.84 14.19 \n60 76.13 14.13 44.16 9.15 28.47 22.39 \n75 82.98 4.74 66.70 5.10 52.38 10.42  \n\n\n\nTABLE 4(b) \nMeans and standard deviation for soil moisture at different soil types and depth in 2007 \n\n\n\n \nWet Alluvial area Ridge area Dry Alluvial area Depth \nMeans SD Means SD Means SD \n\n\n\n15 65.90 16.79 51.05 13.36 59.27 12.51 \n30 60.78 21.41 56.87 12.46 51.24 10.73 \n45 69.78 23.03 62.22 14.29 50.41 10.61 \n60 79.00 20.25 59.42 13.39 54.64 10.48 \n75 90.77 15.88 78.21 15.29 67.31 12.63 \n\n\n\nTABLE 5 \nAnalysis of variance of soil moisture between soil type (STYPE), soil depth (DEPTH) and their interaction \n\n\n\n df SS MS F value \nSTYPE 2 16006 8003 33.4981*** \nDEPTH 4 16961 4240 17.7386*** \nSTYPE DEPTH   8 6559 820 3.4299*** \n\u2018***\u2019  F < 0.001 \n\n\n\n\n\n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\nin the ridge soils, the study found that RD is more moist compared to DA. Adzmi \net al.\n\n\n\nmakes this horizon slowly permeable (Adzmi et al.\nsoil which mostly contains clay or silt often has poor water penetration because \nof the soil aggregation. Clay particles in the soil may swell as they become wet \nand thereby reduce the size of the pores.  Poorly drained soil has an effect on \n\n\n\nGlyptopetalum quadrangulare are in abundance at the wet alluvial area where the \nmoisture tends to be the highest. Belonging to the Celastraceous family, it is a type \n\n\n\nwater and inorganic solutes upward toward the leaves from the roots, while the \nphloem carries organic solutes throughout the plant. Therefore, this plant tends \nto concentrate within the high moisture area because of self- adaption to the \nsite characteristics. Usually, rooting depth in plants is decreased due to the poor \ndrainage. That is why most dipterocarp species prefer a well-drained soil. \n\n\n\nThe RD and DA are better drained with the soils being generally low \n\n\n\nTABLE 6\nMulti-comparison test on soil moisture between soil depths using Studentized range \n\n\n\nTABLE 6 \nMulti-comparison test on soil moisture be\n\n\n\nTukey's \u2018Honest Significant Difference\u2019 method \n\n\n\nSoil depth        diff  \ncomparison  (%)  \n\n\n\n15 and 30 cm   3.6360  \n30 and 45 cm    5.1402  \n30 and 60 cm     7.4346  \n30 and 75 cm  23.3192  \n15 and 45 cm 1.5042  \n15 and 60 cm 3.7987  \n15 and 75 cm 19.6833  \n45 and 60 cm 2.2944  \n45and 75 cm 18.1790  \n60 and 75 cm  15.8846  \n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\nTA\n\n\n\nB\nLE\n\n\n\n 7\n\n\n\nno\nn-\n\n\n\nm\net\n\n\n\nric\n m\n\n\n\nul\ntid\n\n\n\nim\nen\n\n\n\nsi\non\n\n\n\nal\n sc\n\n\n\nal\nin\n\n\n\ng \nan\n\n\n\nal\nys\n\n\n\nis\n. \n\n\n\nTA\nB\n\n\n\nLE\n 7\n\n\n\n\n\n\n\nFi\nve\n\n\n\n c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\n sp\n\n\n\nec\nie\n\n\n\ns o\nf e\n\n\n\nac\nh \n\n\n\nso\nil \n\n\n\nty\npe\n\n\n\n w\nith\n\n\n\n in\ndi\n\n\n\nca\nto\n\n\n\nr v\nal\n\n\n\nue\n (I\n\n\n\nV\n) o\n\n\n\nf i\nnd\n\n\n\nic\nat\n\n\n\nor\n sp\n\n\n\nec\nie\n\n\n\ns a\nna\n\n\n\nly\nsi\n\n\n\ns, \nan\n\n\n\nd \nPe\n\n\n\nar\nso\n\n\n\nn-\nr f\n\n\n\nor\n A\n\n\n\nxi\ns 1\n\n\n\n (r\n1)\n\n\n\n a\nnd\n\n\n\n A\nxi\n\n\n\ns \n\n\n\nN\no.\n\n\n\n \nSp\n\n\n\nec\nie\n\n\n\ns \nFa\n\n\n\nm\nily\n\n\n\n \nIV\n\n\n\n (%\n) \n\n\n\nr1\n \n\n\n\nr2\n \n\n\n\nSo\nil \n\n\n\nty\npe\n\n\n\n \n1 \n\n\n\nAp\nor\n\n\n\nos\na \n\n\n\ngl\nob\n\n\n\nife\nra\n\n\n\n \nEu\n\n\n\nph\nor\n\n\n\nbi\nac\n\n\n\nea\ne\n\n\n\n \n51\n\n\n\n.4\n00\n\n\n\n \n0.\n\n\n\n76\n8 \n\n\n\n-0\n.9\n\n\n\n72\n \n\n\n\nW\net\n\n\n\n A\nllu\n\n\n\nvi\nal\n\n\n\n\n\n\n\n2 \nBa\n\n\n\nrr\nin\n\n\n\ngt\non\n\n\n\nia\n \n\n\n\nm\nac\n\n\n\nro\nsta\n\n\n\nch\nya\n\n\n\n \nLe\n\n\n\ncy\nth\n\n\n\nid\nac\n\n\n\nea\ne\n\n\n\n \n54\n\n\n\n.4\n00\n\n\n\n \n0.\n\n\n\n52\n4 \n\n\n\n-0\n.9\n\n\n\n79\n \n\n\n\nW\net\n\n\n\n A\nllu\n\n\n\nvi\nal\n\n\n\n \n3 \n\n\n\nEu\nge\n\n\n\nni\na \n\n\n\nce\nra\n\n\n\nsif\nor\n\n\n\nm\nis\n\n\n\n \nM\n\n\n\nyr\nta\n\n\n\nce\nae\n\n\n\n \n50\n\n\n\n.3\n00\n\n\n\n \n0.\n\n\n\n81\n6 \n\n\n\n-0\n.9\n\n\n\n37\n \n\n\n\nW\net\n\n\n\n A\nllu\n\n\n\nvi\nal\n\n\n\n\n\n\n\n4 \nG\n\n\n\nly\npt\n\n\n\nop\net\n\n\n\nal\num\n\n\n\n \nqu\n\n\n\nad\nra\n\n\n\nng\nul\n\n\n\nar\ne\n\n\n\n \nC\n\n\n\nel\nas\n\n\n\ntra\nce\n\n\n\nae\n \n\n\n\n58\n.8\n\n\n\n00\n \n\n\n\n0.\n84\n\n\n\n1 \n-0\n\n\n\n.9\n03\n\n\n\n \nW\n\n\n\net\n A\n\n\n\nllu\nvi\n\n\n\nal\n \n\n\n\n5 \nLe\n\n\n\npi\nsa\n\n\n\nnt\nhe\n\n\n\ns t\net\n\n\n\nra\nph\n\n\n\nyl\nla\n\n\n\n \nSa\n\n\n\npi\nnd\n\n\n\nac\nea\n\n\n\ne\n \n\n\n\n51\n.8\n\n\n\n00\n \n\n\n\n0.\n59\n\n\n\n0 \n-0\n\n\n\n.9\n83\n\n\n\n \nW\n\n\n\net\n A\n\n\n\nllu\nvi\n\n\n\nal\n \n\n\n\n6 \nAn\n\n\n\nax\nag\n\n\n\nor\nea\n\n\n\n ja\nva\n\n\n\nni\nca\n\n\n\n \nA\n\n\n\nnn\non\n\n\n\nac\nea\n\n\n\ne\n \n\n\n\n42\n.1\n\n\n\n00\n \n\n\n\n-0\n.0\n\n\n\n9 1\n \n\n\n\n-0\n.6\n\n\n\n61\n \n\n\n\nD\nry\n\n\n\n A\nllu\n\n\n\nvi\nal\n\n\n\n \n7 \n\n\n\nAr\ndi\n\n\n\nsia\n c\n\n\n\nra\nss\n\n\n\na\n \n\n\n\nM\nyr\n\n\n\nsi\nna\n\n\n\nce\nae\n\n\n\n \n41\n\n\n\n.2\n00\n\n\n\n \n-0\n\n\n\n.9\n30\n\n\n\n \n0.\n\n\n\n78\n6 \n\n\n\nD\nry\n\n\n\n A\nllu\n\n\n\nvi\nal\n\n\n\n \n8 \n\n\n\nM\nes\n\n\n\nua\n fe\n\n\n\nrr\nea\n\n\n\n \nC\n\n\n\nlu\nsi\n\n\n\nac\nea\n\n\n\ne\n \n\n\n\n37\n.8\n\n\n\n00\n \n\n\n\n-0\n.3\n\n\n\n85\n \n\n\n\n-0\n.4\n\n\n\n25\n \n\n\n\nD\nry\n\n\n\n A\nllu\n\n\n\nvi\nal\n\n\n\n \n9 \n\n\n\nSh\nor\n\n\n\nea\n m\n\n\n\nax\nwe\n\n\n\nlli\nan\n\n\n\na\n \n\n\n\nD\nip\n\n\n\nte\nro\n\n\n\nca\nrp\n\n\n\nac\nea\n\n\n\ne\n \n\n\n\n51\n.3\n\n\n\n00\n \n\n\n\n-0\n.6\n\n\n\n83\n \n\n\n\n-0\n.0\n\n\n\n93\n \n\n\n\nD\nry\n\n\n\n A\nllu\n\n\n\nvi\nal\n\n\n\n\n\n\n\n10\n \n\n\n\nXe\nro\n\n\n\nsp\ner\n\n\n\nm\num\n\n\n\n \nno\n\n\n\nro\nnh\n\n\n\nia\nnu\n\n\n\nm\n \n\n\n\nSa\npi\n\n\n\nnd\nac\n\n\n\nea\ne \n\n\n\n34\n.6\n\n\n\n00\n \n\n\n\n0.\n56\n\n\n\n5 \n-0\n\n\n\n.9\n04\n\n\n\n \nD\n\n\n\nry\n A\n\n\n\nllu\nvi\n\n\n\nal\n \n\n\n\n11\n \n\n\n\nAp\nor\n\n\n\nos\na \n\n\n\nbr\nac\n\n\n\nte\nos\n\n\n\na\n \n\n\n\nEu\nph\n\n\n\nor\nbi\n\n\n\nac\nea\n\n\n\ne\n \n\n\n\n40\n.7\n\n\n\n00\n \n\n\n\n-0\n.0\n\n\n\n08\n \n\n\n\n0.\n75\n\n\n\n9 \nR\n\n\n\nid\nge\n\n\n\n \n12\n\n\n\n \nAp\n\n\n\nor\nos\n\n\n\na \npr\n\n\n\nai\nni\n\n\n\nan\na\n\n\n\n \nEu\n\n\n\nph\nor\n\n\n\nbi\nac\n\n\n\nea\ne\n\n\n\n \n38\n\n\n\n.6\n00\n\n\n\n \n-0\n\n\n\n.5\n23\n\n\n\n \n0.\n\n\n\n98\n0 \n\n\n\nR\nid\n\n\n\nge\n \n\n\n\n13\n \n\n\n\nD\nac\n\n\n\nry\nod\n\n\n\nes\n ru\n\n\n\ngo\nsa\n\n\n\n \nB\n\n\n\nur\nse\n\n\n\nra\nce\n\n\n\nae\n \n\n\n\n31\n.9\n\n\n\n00\n \n\n\n\n-0\n.8\n\n\n\n67\n \n\n\n\n0.\n89\n\n\n\n6 \nR\n\n\n\nid\nge\n\n\n\n\n\n\n\n14\n \n\n\n\nO\nnc\n\n\n\nod\nos\n\n\n\ntig\nm\n\n\n\na \nm\n\n\n\non\nos\n\n\n\npe\nrm\n\n\n\na\n \n\n\n\nA\nnn\n\n\n\non\nac\n\n\n\nea\ne \n\n\n\n33\n.3\n\n\n\n00\n \n\n\n\n-0\n.6\n\n\n\n63\n \n\n\n\n0.\n98\n\n\n\n2 \nR\n\n\n\nid\nge\n\n\n\n\n\n\n\n15\n \n\n\n\nPo\npo\n\n\n\nwi\na \n\n\n\npi\nso\n\n\n\nca\nrp\n\n\n\na\n \n\n\n\nA\nnn\n\n\n\non\nac\n\n\n\nea\ne\n\n\n\n \n31\n\n\n\n.5\n00\n\n\n\n \n-0\n\n\n\n.6\n63\n\n\n\n \n0.\n\n\n\n98\n3 \n\n\n\nR\nid\n\n\n\nge\n \n\n\n\n16\n \n\n\n\nAl\nan\n\n\n\ngi\num\n\n\n\n e\nbe\n\n\n\nna\nce\n\n\n\num\n \n\n\n\nA\nla\n\n\n\nng\nia\n\n\n\nce\nae\n\n\n\n \n43\n\n\n\n.1\n00\n\n\n\n \n-0\n\n\n\n.7\n72\n\n\n\n \n0.\n\n\n\n97\n5 \n\n\n\nR\nid\n\n\n\nge\n \n\n\n\n17\n \n\n\n\nAn\niso\n\n\n\nph\nyl\n\n\n\nle\na \n\n\n\nco\nrn\n\n\n\ner\ni\n\n\n\n \nA\n\n\n\nni\nso\n\n\n\nph\nyl\n\n\n\nle\nac\n\n\n\nea\ne\n\n\n\n \n46\n\n\n\n.9\n00\n\n\n\n \n-0\n\n\n\n.4\n33\n\n\n\n \n0.\n\n\n\n95\n7 \n\n\n\nR\nid\n\n\n\nge\n \n\n\n\n18\n \n\n\n\nAr\nch\n\n\n\nid\nen\n\n\n\ndr\non\n\n\n\n b\nub\n\n\n\nal\nin\n\n\n\num\n \n\n\n\nFa\nba\n\n\n\nce\nae\n\n\n\n \n38\n\n\n\n.1\n00\n\n\n\n \n-0\n\n\n\n.8\n14\n\n\n\n \n0.\n\n\n\n95\n1 \n\n\n\nR\nid\n\n\n\nge\n \n\n\n\n19\n \n\n\n\nG\niro\n\n\n\nnn\nie\n\n\n\nra\n p\n\n\n\nar\nvi\n\n\n\nfo\nl ia\n\n\n\n \nU\n\n\n\nlm\nac\n\n\n\nea\ne\n\n\n\n \n42\n\n\n\n.0\n00\n\n\n\n \n-0\n\n\n\n.8\n82\n\n\n\n \n0.\n\n\n\n91\n4 \n\n\n\nR\nid\n\n\n\nge\n \n\n\n\n20\n \n\n\n\nSc\nap\n\n\n\nhi\num\n\n\n\n m\nac\n\n\n\nro\npo\n\n\n\ndu\nm\n\n\n\n \nSt\n\n\n\ner\ncu\n\n\n\nlia\nce\n\n\n\nae\n \n\n\n\n37\n.7\n\n\n\n00\n \n\n\n\n-0\n.5\n\n\n\n55\n \n\n\n\n0.\n98\n\n\n\n0 \nR\n\n\n\nid\nge\n\n\n\n \nN\n\n\n\not\ne:\n\n\n\n T\nhe\n\n\n\n se\nle\n\n\n\nct\ned\n\n\n\n sp\nec\n\n\n\nie\ns a\n\n\n\nre\n th\n\n\n\nos\ne \n\n\n\nw\nith\n\n\n\n th\ne \n\n\n\nfiv\ne \n\n\n\nhi\ngh\n\n\n\nes\nt I\n\n\n\nV\n in\n\n\n\n e\nac\n\n\n\nh \nso\n\n\n\nil \nty\n\n\n\npe\n.\n\n\n\n\n\n\n\n\n\n\n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\nTA\nB\n\n\n\nLE\n 8\n\n\n\net\n a\n\n\n\nl\nTA\n\n\n\nB\nLE\n\n\n\n 8\n \n\n\n\nPe\nni\n\n\n\nns\nul\n\n\n\nar\n M\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noi\n\n\n\nl C\nla\n\n\n\nss\nifi\n\n\n\nca\ntio\n\n\n\nn \nan\n\n\n\nd \nin\n\n\n\nte\nrn\n\n\n\nat\nio\n\n\n\nna\nl c\n\n\n\nor\nre\n\n\n\nla\ntio\n\n\n\nn \nof\n\n\n\n so\nil \n\n\n\nof\n 5\n\n\n\n0 \nha\n\n\n\n p\nlo\n\n\n\nt, \nPa\n\n\n\nso\nh \n\n\n\nFR\n (A\n\n\n\ndz\nm\n\n\n\ni e\nt a\n\n\n\nl.,\n 2\n\n\n\n01\n0)\n\n\n\n\n\n\n\nPh\nys\n\n\n\nio\ngr\n\n\n\nap\nhi\n\n\n\nc \nSo\n\n\n\nil \nFe\n\n\n\nat\nur\n\n\n\nes\n \n\n\n\nW\nes\n\n\n\nt M\nal\n\n\n\nay\nsi\n\n\n\nan\n so\n\n\n\nil \ncl\n\n\n\nas\nsi\n\n\n\nfic\nat\n\n\n\nio\nn \n\n\n\n(D\nO\n\n\n\nA\n 1\n\n\n\n98\n7,\n\n\n\n P\nar\n\n\n\nam\nan\n\n\n\nan\nth\n\n\n\nan\n 1\n\n\n\n98\n9)\n\n\n\n \nM\n\n\n\nai\nn \n\n\n\nin\nte\n\n\n\nrn\nat\n\n\n\nio\nna\n\n\n\nl c\nor\n\n\n\nre\nla\n\n\n\ntio\nns \n\n\n\nG\nro\n\n\n\nup\n \n\n\n\nSu\nbg\n\n\n\nro\nup\n\n\n\n \nSh\n\n\n\nar\ned\n\n\n\n \nD\n\n\n\nist\nin\n\n\n\ngu\nis\n\n\n\nhi\nng\n\n\n\n \nSo\n\n\n\nil \nSe\n\n\n\nrie\ns \n\n\n\nD\nra\n\n\n\nin\nag\n\n\n\ne \ncl\n\n\n\nas\ns \n\n\n\nW\nor\n\n\n\nld\n re\n\n\n\nfe\nre\n\n\n\nnc\ne \n\n\n\nba\nse\n\n\n\n (F\nA\n\n\n\nO\n 2\n\n\n\n00\n6) \n\n\n\nSo\nil \n\n\n\nTa\nxo\n\n\n\nno\nm\n\n\n\ny \n(S\n\n\n\noi\nl s\n\n\n\nur\nve\n\n\n\ny \nst\n\n\n\naf\nf \n\n\n\n19\n99\n\n\n\n, 2\n00\n\n\n\n6)\n \n\n\n\nR\nid\n\n\n\nge\n \n\n\n\nC\nre\n\n\n\nst \nTh\n\n\n\nin\n \n\n\n\nda\nrk\n\n\n\n \nbr\n\n\n\now\nn \n\n\n\nfin\ne \n\n\n\nlo\nam\n\n\n\n t\nop\n\n\n\nso\nil \n\n\n\nov\ner\n\n\n\n s\ntro\n\n\n\nng\n \n\n\n\nbr\now\n\n\n\nn \nfin\n\n\n\ne \nlo\n\n\n\nam\n o\n\n\n\nr \ncl\n\n\n\nay\n \n\n\n\nsu\nbs\n\n\n\noi\nl \n\n\n\nM\nas\n\n\n\nsi\nve\n\n\n\n la\nte\n\n\n\nrit\ne \n\n\n\nor\n d\n\n\n\nen\nse\n\n\n\n \ngr\n\n\n\nav\nel\n\n\n\n w\nith\n\n\n\nin\n 5\n\n\n\n0 c\nm\n\n\n\n \nG\n\n\n\naj\nah\n\n\n\n M\nat\n\n\n\ni \n7-\n\n\n\n8 \n(G\n\n\n\noo\nd)\n\n\n\n \nA\n\n\n\ncr\nic\n\n\n\n P\nlin\n\n\n\nth\nos\n\n\n\nol \nTy\n\n\n\npi\nc \n\n\n\nPl\nin\n\n\n\nth\nud\n\n\n\nul\nt \n\n\n\nor\n P\n\n\n\nlin\nth\n\n\n\nic\n \n\n\n\nK\nan\n\n\n\nha\npl\n\n\n\nud\nul\n\n\n\nt \n\n\n\nslo\npe\n\n\n\ns \nM\n\n\n\nan\ny \n\n\n\nla\nte\n\n\n\nrit\ne \n\n\n\ngr\nav\n\n\n\nel\n a\n\n\n\nt 5\n0-\n\n\n\n10\n0 \n\n\n\ncm\n \n\n\n\nTe\nra\n\n\n\np \n7-\n\n\n\n8 \n(G\n\n\n\noo\nd)\n\n\n\n \nPl\n\n\n\nin\nth\n\n\n\nic\n A\n\n\n\ncr\niso\n\n\n\nl \nPl\n\n\n\nin\nth\n\n\n\nic\n \n\n\n\nK\nan\n\n\n\nha\npl\n\n\n\nud\nul\n\n\n\nt \nFe\n\n\n\nw\n la\n\n\n\nte\nrit\n\n\n\ne \ngr\n\n\n\nav\nel\n\n\n\n b\nel\n\n\n\now\n \n\n\n\n10\n0 \n\n\n\ncm\n \n\n\n\nB\nun\n\n\n\ngo\nr \n\n\n\n7-\n8 \n\n\n\n(G\noo\n\n\n\nd)\n \n\n\n\nH\nap\n\n\n\nlic\n A\n\n\n\ncr\niso\n\n\n\nl \nTy\n\n\n\npi\nc \n\n\n\n(o\nr \n\n\n\nPl\nin\n\n\n\nth\nic\n\n\n\n) \nK\n\n\n\nan\nha\n\n\n\npl\nlu\n\n\n\ndu\nlt \n\n\n\n(&\n \n\n\n\nK\nan\n\n\n\nha\npl\n\n\n\naq\nuu\n\n\n\nlt)\n \n\n\n\nA\nllu\n\n\n\nvi\nal \n\n\n\nLo\nw\n\n\n\n te\nrr\n\n\n\nac\ne \n\n\n\nTh\nin\n\n\n\n d\nar\n\n\n\nk \nbr\n\n\n\now\nn \n\n\n\nto\nps\n\n\n\noi\nl \n\n\n\nov\ner\n\n\n\n u\nnm\n\n\n\not\ntle\n\n\n\nd \nye\n\n\n\nllo\nw\n\n\n\nis\nh/\n\n\n\nbr\now\n\n\n\nni\nsh\n\n\n\n lo\nam\n\n\n\n \nto\n\n\n\n 1\n m\n\n\n\n=,\n m\n\n\n\not\ntle\n\n\n\nd \nbe\n\n\n\nlo\nw \n\n\n\nSa\nnd\n\n\n\n p\nre\n\n\n\ndo\nm\n\n\n\nin\nan\n\n\n\ntly\n \n\n\n\nm\ned\n\n\n\niu\nm\n\n\n\n o\nr f\n\n\n\nin\ne \n\n\n\nTe\nbu\n\n\n\nk \n5-\n\n\n\n6 \n(G\n\n\n\noo\nd \u2013\n\n\n\n \nIm\n\n\n\npe\nrf\n\n\n\nec\nt) \n\n\n\nH\nap\n\n\n\nlic\n A\n\n\n\ncr\niso\n\n\n\nl \nTy\n\n\n\npi\nc \n\n\n\n(o\nr \n\n\n\nO\nxy\n\n\n\naq\nui\n\n\n\nc)\n \n\n\n\nK\nan\n\n\n\nha\npl\n\n\n\nud\nul\n\n\n\nt (\n&\n\n\n\n \nK\n\n\n\nan\nha\n\n\n\npl\naq\n\n\n\nuu\nlt)\n\n\n\n\n\n\n\nSa\nnd\n\n\n\n p\nre\n\n\n\ndo\nm\n\n\n\nin\nan\n\n\n\ntly\n \n\n\n\nco\nar\n\n\n\nse\n \n\n\n\nTa\nw\n\n\n\nar\n \n\n\n\n5-\n6 \n\n\n\n(G\noo\n\n\n\nd \u2013\n \n\n\n\nIm\npe\n\n\n\nrf\nec\n\n\n\nt) \n \n\n\n\n\n\n\n\nA\nllu\n\n\n\nvi\nal \n\n\n\nFl\noo\n\n\n\ndp\nla\n\n\n\nin \nTh\n\n\n\nin\n m\n\n\n\nuc\nk,\n\n\n\n o\nve\n\n\n\nr \nm\n\n\n\not\ntle\n\n\n\nd \npa\n\n\n\nle\n b\n\n\n\nro\nw\n\n\n\nn \nw\n\n\n\net\n l\n\n\n\nay\ner\n\n\n\ned\n, \n\n\n\nov\ner\n\n\n\n m\noi\n\n\n\nst\n b\n\n\n\nro\nw\n\n\n\nni\nsh\n\n\n\n le\nss\n\n\n\n \nm\n\n\n\not\ntle\n\n\n\nd \n\n\n\nSa\nnd\n\n\n\ny \ncl\n\n\n\nay\n s\n\n\n\nub\nso\n\n\n\nil \nA\n\n\n\nw\nan\n\n\n\ng \n3-\n\n\n\n4 \n(I\n\n\n\nm\npe\n\n\n\nrf\nec\n\n\n\nt \u2013\n \n\n\n\npo\nor\n\n\n\n) \nU\n\n\n\nm\nbr\n\n\n\nic\n A\n\n\n\ncr\nic \n\n\n\nSt\nag\n\n\n\nno\nso\n\n\n\nl \nU\n\n\n\nm\nbr\n\n\n\nic\n &\n\n\n\n H\num\n\n\n\nic\n \n\n\n\nEp\nia\n\n\n\nqu\nep\n\n\n\nt (\n&\n\n\n\n \nK\n\n\n\nan\nha\n\n\n\npl\naq\n\n\n\nuu\nlt)\n\n\n\n\n\n\n\nTh\nic\n\n\n\nk \nm\n\n\n\nuc\nk,\n\n\n\n \nov\n\n\n\ner\n \n\n\n\nm\not\n\n\n\ntle\nd \n\n\n\ngr\ney\n\n\n\n w\net\n\n\n\n la\nye\n\n\n\nre\nd \n\n\n\nlo\nam\n\n\n\n, \nov\n\n\n\ner\n \n\n\n\nm\noi\n\n\n\nst\n \n\n\n\nbr\now\n\n\n\nni\nsh\n\n\n\n m\not\n\n\n\ntle\nd \n\n\n\nSa\nnd\n\n\n\ny \ncl\n\n\n\nay\n lo\n\n\n\nam\n su\n\n\n\nbs\noi\n\n\n\nl \nA\n\n\n\nlm\na \n\n\n\n3-\n4 \n\n\n\n(I\nm\n\n\n\npe\nrf\n\n\n\nec\nt \u2013\n\n\n\n \npo\n\n\n\nor\n) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nK\nam\n\n\n\npu\nng\n\n\n\n P\nus\n\n\n\nu \n1-\n\n\n\n2 \n(P\n\n\n\noo\nr) \n\n\n\nU\nm\n\n\n\nbr\nic\n\n\n\n A\ncr\n\n\n\nic\n \n\n\n\nG\nle\n\n\n\nys\nol\n\n\n\n (&\n \n\n\n\nSt\nag\n\n\n\nno\nso\n\n\n\nl) \n\n\n\nH\num\n\n\n\nic \nEn\n\n\n\ndo\naq\n\n\n\nue\npt\n\n\n\n (&\n \n\n\n\nEp\nia\n\n\n\nqu\nep\n\n\n\nt) \n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\nthe DA plot shows that moisture on the surface has a similar pattern with the \nmoisture at the deeper soil layers. According to Adzmi et al.\nhas a thin, friable, crumb structured topsoil which tends to be less dark than those \n\n\n\nwith few or no mottles in the upper meter.  Grey and rust-colored mottles increase \n\n\n\noften pale brown or pale yellow.  The coloration indicates imperfect drainage, \nShorea maxwelliana is the Dipterocarpaceae family \n\n\n\nfound to be abundant at this dry alluvial area. This species prefers the terrestrial \necosystem and is categorized under the IUCN red list as threatened species.\n\n\n\nThe difference in spatial distribution of soil moisture at Pasoh Forest could \nbe affected by the slope of the ground surface, the vegetative canopy cover, water \nretention properties of the soil and rooting system. The difference in canopy \nopenness has an effect on the amount of through fall and evaporation. Therefore, \ncanopy openness is an important factor to be considered in soil moisture study \n\n\n\nspacings as stated in Marryanna et al.\ndecrease at a wider tree spacing. The results from Noguchi et al.\nthat the soil moisture at the valley and riverine areas and the lower part of the \n\n\n\ngeneralist with respect to soil conditions.\nPasoh is well suited to further investigation of responses and adaptations of \n\n\n\ntropical forests to variations in soil drainage and aeration, and also of soil mediation \nof moisture supply in a seasonal but marginal climate (Adzmi et al.\non soil moisture are important for a understanding of the hydrological cycle and \nalso for ecological purposes. Therefore, more related studies should be undertaken \nto have a better understanding of the hydrological cycle and its connectivity to \necological parameters such as interception of trees, water uptake by different \nspecies with regard to their locality etc. As an overall observation, we found \n\n\n\nstudy for a better understanding of the moisture variation in Pasoh. Noguchi et \nal.\n\n\n\nspatial distribution of soil moisture will contribute to the understanding of the \n\n\n\n\n\n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\ntheir AGR on alluvial soil and 4 species had their highest AGR growth on ridge \n\n\n\nspecies may have their own water relation characteristics. Based on soil moisture \ndata, ecologists may make preliminary assumptions on the species distribution \nthroughout the plot. Moisture availability in soil indicates the distribution of \nnutrient availability in soil which, in turn, is associated with the species or habitat \n\n\n\ndipterocarp forest, tree species distribution in the Andulau Forest Reserve, Brunei \nwas associated with a topographic gradient that was correlated with variation in \nsoil nutrient availability (Austin et al.\n\n\n\nin promoting the relative importance of nutrient availability across the plot. It is \n\n\n\nbetween nutrient and soil water availability at Pasoh.\n\n\n\nCONCLUSION\n\n\n\nShorea maxwelliana is the Dipterocarpaceae \nfamily found to be abundant at the dry alluvial area. This species prefers to live \nat the terrestrial ecosystem. The association of trees and sites condition could be \none of the indicators that shall be considered in site-matching for future forest \n\n\n\nchemical properties could also lead to trees and habitat association. Therefore, \nmore detailed studies are suggested to understand the factors underlying species \n\n\n\nplot. \n\n\n\nREFERENCES\n\n\n\n Journal of Tropical \nForest Science.\n\n\n\n\n\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\nBrunei. Journal of Ecology.\n\n\n\nJournal of \nTropical Ecology.\n\n\n\nCondit, R., S.P. Hubbell, and R.B. Foster. 1996. Changes in tree species abundance \nin a neotropical forest: Impact of climate change. Journal of Tropical Ecology. \n\n\n\nresearch plot. In:  Pasoh. Ecology of a Lowland Rain Forest in Southeast Asia, \n\n\n\nTopographic position affects the water regime in a semi-deciduous tropical \nforest in Panama. Plant and Soil\n\n\n\nPanduan Penyiasatan Tanah. Cawangan Tanah dan Analisa, Jabatan \n\n\n\nFundamentals of Soil Science (5th\n\n\n\nGuha, M.M. 1969.  A preliminary assessment of moisture and nutrients in soils as \nMalaysian Forester\n\n\n\nJournal of Ecology. 89: \n947-959.\n\n\n\n\n\n\n\n\n41\n\n\n\nSoil Moisture and Lowland Dipterocarp Distribution\n\n\n\nHirai, H., H. Matsumura, H. Hirotani and K. Sakurai.  1995. Soils and the distribution \nof Dryobalanops aromatica and D. lanceolata. In: Studies of Global \n\n\n\n. Vol \n\n\n\nand implications for conservation. In: Tropical Rain Forest: Ecology and \nManagement, eds\n\n\n\nHubbell, S.P. and R.B. Foster. 1986. Biology, chance, and history and the structure of \ntropical rain forest tree communities. In: Community Ecology, ed. J. Diamond \n\n\n\nIndia. Inside CTFS. \n\n\n\nshrubs in Malaya at regional and local levels. Malayan Nature Journal\n545-554.\n\n\n\nin water availability drive habitat associations in a tropical forest. Ecology. \n\n\n\nfor determining soil-moisture content in a clay loam soil. Journal Water SA. \n\n\n\nlowland rain forest in Peninsular Malaysia. Journal of Tropical Forest Science. \n\n\n\nManokaran, N., J.V. Lafrankie and Roslan Ismail. 1991. Structure and composition of \nthe Dipterocarpaceae in a lowland rain forest in Peninsular Malaysia. In: Fourth \nRound-Table Conference on Dipterocarpaceae. Biotrop, ed. I. Soerianegara, S. \n\n\n\nforest in Peninsular Malaysia. Journal of Tropical Forest Science\n\n\n\nECOMOD 2007 2nd \nRegional Conference\n\n\n\n\n\n\n\n\nMcColl, J. G. 1969. Soil-plant relationships in a Eucalyptus forest on the South coast \nEcology.\n\n\n\nNiiyama, K., K. Abd. Rahman, S. Iida, K. Kimura, R. Azizi and S. Appanah. 1999.  \nSpatial patterns of common tree species relating to topography, canopy gaps and \nunderstorey vegetation in a hill dipterocarp forest at Semangkok Forest reserve, \nPeninsular Malaysia.  Journal of Tropical Forest Science\n\n\n\ndistribution of surface soil moisture in a tropical rain forest, Pasoh Forest \nReserve. Proceedings of the 4th Asian Science and Technology Congress\n\n\n\nPasoh: Ecology of a Lowland Rain \nForest in Southeast Asia\nNiiyama, S.C. Thomas and P.S. Ashton, pp. 114- 195. Tokyo: Springer-Verlag.\n\n\n\n\n\n\n\nits impact on forest structure and species composition in the Pasoh Forest \nReserve - implications for the sustainable management of natural resources and \nlandscapes. In: Ecology and Natural History of a Southeast Asian Tropical Rain \nForest,\nSpringer-Verlag. \n\n\n\nParamananthan S. 1987. Field Legend for Soil Survey in Malaysia. Serdang: Penerbit \nUniversiti Pertanian Malaysia. \n\n\n\nPlotkin, J. B., M.D. Potts, N. Leslie, N. Manokaran, J. Lafrankie and P.S. Ashton.  \n\n\n\ntropical forests. Journal of Theoretical Biology\n\n\n\nSoil Survey Staff. 1999. Soil Taxonomy nd\n\n\n\nKeys to Soil Taxonomy th \n\n\n\nStates Department of Agriculture.\n\n\n\nSvenning, J-C. 1999. Microhabitat specialization in a species-rich palm community \nin Amazonian Ecuador. Journal of Ecology 87:55-65.\n\n\n\nRain Forest PhD. Thesis. Department of Plant and Soil Science, University of \nAberdeen, Scotland. U.K.\n\n\n\nMarryanna, L., A.K. Rahman, S. Siti Aisah and M.S. Mohd\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: velobor.spalevic@gmail.com\n\n\n\nINTRODUCTION\nSoil erosion is as one of the biggest environmental problems the world faces. \nIt is a critical threat to food security and to the environment (Ebrahimpour et \nal. 2011). In Europe, soil erosion is caused mainly by water. Rill- and inter-rill \nerosion affects the largest area, whereas gully erosion and landslides are relatively \nlocalised though often visually striking. Soil losses due to water erosion are high \nin southern Europe (Van Lynden 1995). According to Poesen et al. (2003) in \nthis part of Europe, erosion has led to the formation of extensive degraded areas \ncalled badlands, in which high rates of soil loss is observed (Mathys et al. 2003). \nAccording to the expert-based GLASOD map (Oldeman et al. 1991), the area \nof human-induced soil erosion by water in Europe, excluding Russia, is roughly \nestimated to be 114 million hectares (17% of total land area), of which 80% is \ntopsoil loss and 20% terrain deformation (Gobin et al. 2004).\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 17: 49-68 (2013)                          Malaysian Society of Soil Science\n\n\n\nSoil Erosion Intensity and Runoff on the Djuricka River \nBasin (North of Montenegro)\n\n\n\nSpalevic, V.1, N. Djurovic2, S. Mijovic3, M. Vukelic-Sutoska4 and M. Curovic1* \n\n\n\n1University of Montenegro, Biotechnical Faculty, 81000 Podgorica, Montenegro\n2University of Belgrade, Faculty of Agriculture, 11070 Zemun, Belgrade, Serbia\n\n\n\n3The State Audit Institution of Montenegro, 81000 Podgorica, Montenegro\n4Cyril and Methodius University, Faculty of Agriculture Science and Food, \n\n\n\nMacedonia\n\n\n\nABSTRACT\nEcological factors, which are the basis for the calculation of soil erosion, are \nincluded in the simulation model. Social aspects, such as the attitude of farmers \ntowards practising environmentally sustainable land use techniques, are difficult \nto analyse because of lack of data and the level of difficulty inherent in connecting \nnatural, economic, and social data together. At the level of the river basin, the \nuse of an IntErO model allowed the quantification of the environmental effects \nof erosion and the land use planning measures. Maximal outflow (incidence of \n100 years) from the river basin Qmax, is 240 m3/s suggests the possibility of a large \nflood. The strength of the erosion process was medium, and the erosion type was \nmixed erosion. The predicted soil losses were 645 m\u00b3/km2 per year. To support the \nfaster renewal of the vegetation and slow down the erosion processes, biological \nprotection measures need to be applied, together with technical ones, notably by \nusing shoulders and ditches to partition water fluxes at the land surface. These \nwould reduce runoff velocity and further support reforestation and the renewal of \ngrass, shrubs and trees. \n\n\n\nKeywords: IntErO model, land use, runoff, soil erosion, watershed\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201350\n\n\n\nAccording to Spalevic (2011), Kostadinov et al. (2006), Kadovic (1999) \nand Lazarevic (1996), water erosion has affected 13,135 km2 or 95% of the total \nterritory of Montenegro (13,812 km2). Alluvial accumulation characterises the \nremaining area. Erosion caused by water is dominant in terrain with high slopes \ndue to complex physical and geographical conditions alongside reckless logging \n(Spalevic et al. 2012). \n\n\n\nThe extent and distribution of erosion depend on the specific pattern of \nphysical and geographical factors. The major drivers of water erosion are intense \nrainfall, topography, low soil organic matter content, percentage and type of \nvegetation cover, inappropriate farming practices, and land marginalisation or \nabandonment (Vukelic-Shutoska et al. 2011). \n\n\n\nThe exploitation of forests for the timber industry and the irrational use of \nland has caused a change in land use structure, and the quality of vegetation cover \nin the Djuricka river basin. The soil and geological substrate are more exposed to \nthe impact of various agents, particularly water, wind, temperature, and gravity. \nNatural resources are consumed faster than they can be regenerated.\n\n\n\nA field survey shows that forests are degrading. In many places, numerous \nridges, gullies and ravines have appeared; and around the highest mountain peaks, \nsandbanks are present.\n\n\n\nAll these facts obtained in the process of the field survey led the authors \nto analyse the impact of land use on runoff and soil erosion intensity in this area \nusing a computer-graphic method.\n\n\n\nMETHODOLOGY\nWe studied the area of the Djuricka river basin, a right-hand tributary of the river \nLim, which lies on the slopes of the massive mountain Prokletije in the South and \nMount Kofiljaca on its North-East (Fig. 1). The river basin of the Djuricka rijeka \nencompasses an area of 69.5 km2. In terms of geomorphology and climate, it is a \npart of the natural entity of the Polimlje region (North-East of Montenegro). The \nnatural length of the main watercourse, Lv, is 14.54 km. The shortest distance \nbetween the fountainhead and the mouth, Lm, is 12.12 km. The total length of the \nmain watercourse, with tributaries of I and II class, \u03a3L, is 37.5 km.\n\n\n\nFieldwork was undertaken to collect detailed information on the intensity \nand forms of soil erosion, the status of plant cover, the type of land use, and \nthe measures in place to reduce or alleviate the erosion processes. Morphometric \nmethods were used to determine the slope, the specific lengths, the exposition and \nform of the slopes, the depth of the erosion base and the density of erosion rills.\n\n\n\nWe drew on the earlier pedological work of the Biotechnical Faculty team \n(Fustic et al. 1988), who analysed the physical and chemical properties of all the \nMontenegrin soils including those in the study area of Djuricka rijeka. Furthermore, \nsome pedological profiles had been reopened in the last five years, and soil samples \nwere taken for physical and chemical analysis. The granulometric composition of \nthe soil was determined by the pipette method (Gee and Bauder 1986; Karkanis \net al. 1991); the soil samples were air-dried at 105 \u00b0C and dispersed using sodium \n\n\n\nSpalevic, V., N. Djurovic, S. Mijovic, M. Vukelic-Sutoska and M. Curovic \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 51\n\n\n\npyrophosphate. The soil reaction (pH in H2O and nKCl) was determined with a \npotentiometer. \n\n\n\nTotal carbonates were determined by the volumetric Scheibler method (Thun \nand Herrmann 1949); the content of the total organic matter was determined by \nthe Kotzman method (Jakovljevi\u0107 et al. 1995); easily accessible phosphorous \nand potassium were determined by the Al-method (Egner et al. 1960), and the \nadsorptive complex (y1, S, T, V) was determined by the Kappen method (Kappen \n1929). \n\n\n\nUnderstanding soil erosion processes is essential in appreciating the extent \nand causes of soil erosion and in planning soil conservation (Hashim et al. 1995). \n\n\n\nFig. 1: Study area\n\n\n\nPolimlje: Djuricka river basin:\n43.245703 N, 19.580383 E (North); 42.370071 N, 19.562838 E (North);\n42.508046 N, 19.905853 E (South); 42.301407 N, 20.000849 E (South);\n43.148092 N, 19.485626 E (West);  42.314508 N, 19.570517 E (West); \n42.963960 N, 20.120087 E (East). 42.323475 N, 19.570517 E (East).\n\n\n\nModelling of Soil Erosion Intensity and Runoff\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201352\n\n\n\nSpatial modelling has emerged as an important tool in soil erosion studies, \nespecially at the watershed scale (Memarian et al. 2012). The use of computer-\ngraphics in research on runoff and the intensity of soil erosion have also been \ndemonstrated in Montenegro, specifically in the Region of Polimlje (Spalevic \n1999; Spalevic et al. 2013; 2012, 2011; 2007; 2004; 2003; 2001; 2000a; 2000b; \n1999a). That approach was used in the research on the Djuricka river basin.\n\n\n\nThere are a number of relevant empirical evaluation methods. These methods \ninvolve several steps: data acquisition, model specification and estimation \n(Gavrilovic 1961; 1964; 1965; 1972; Madureiraa et al. 2011). \n\n\n\nMost of those methodologies remain at the qualitative (descriptive) level, \nrelying on empirical evidence and expert subjective evaluation of the conditions. \nIn the South-Eastern European Region, two methodologies have achieved the \nrequired level of standardisation of research procedures to minimise subjective \nerrors of the researchers which allows obtaining uniform results and in tracking \nthe state of changes in erosion intensity over a period of time.\n\n\n\nThe first method is the \u2018Universal Soil Loss Equation\u2013USLE\u2019, of the U.S. \nSoil Conservation Services. This method determines the intensity of erosion on \nagricultural land, but is also successful in very small catchments which are located \non the surfaces with moderate slopes and on the nearly flat terrains.\n\n\n\nAnother method is the \u2018Erosion Potential Method\u2013EPM\u2019 and is in use in \nwatershed management. It was created, developed, and calibrated in Yugoslavia \n(Gavrilovic 1972). \n\n\n\nBoth of these methods are standard for use in agriculture and water \nmanagement, according to its primary purpose, but it should be noted that the \nUSLE method is not that accurate for surfaces with a slope of less than 70 as it \nis developed for determining erosion processes for agricultural production.  The \nEPM covers a wide range of soil erosion intensities. Common to both methods is \nthat they each have clearly defined procedures. Subjective evaluations are reduced \nto a minimum. Any trained expert will obtain matching data for the same area of \nthe research.  According to previous experience, and verifications (Spalevic 2011), \nthe most reliable method for determining the sediment yields and the intensity of \nthe erosion processes for the studied area is the EPM. \n\n\n\nBlinkov and Kostadinov (2010) evaluated applicability of various erosion \nrisk assessment methods for engineering purposes. Factors taken into consideration \ndepended on scale, various erosion tasks as well as various sector needs. The EPM \nwas, according to them, the most suitable on catchment level for the watershed \nmanagement needs in this Region.\n\n\n\nThe use of computer-graphics in research on runoff and the intensity of soil \nerosion have been demonstrated in Montenegro, specifically in the Region of \nPolimlje (Spalevic et al. 2013, 2013a, 2013b, 2013c, 2013d, 2012, 2008, 2007, \n2004, 2003, 2001, 2000, 2000a, 1999, 1999a), Fustic and Spalevic (2000). We \nused the Intensity of Erosion and Outflow (IntErO) programme package (Spalevic \n2011) to obtain data on forecasts of maximum runoff from the basin and soil \nerosion intensity. IntErO - an integrated, second-generation version of the Surface \n\n\n\nSpalevic, V., N. Djurovic, S. Mijovic, M. Vukelic-Sutoska and M. Curovic \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 53\n\n\n\nand Distance Measuring (Spalevic 1999) and River basins programmes (Spalevic \n2000a) - is characterized by simplicity of use in calculating a large number of \ninput data. EPM is embedded in the algorithm of this computer-graphic method.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPhysical-Geographical Characteristics and Erosion Factors\nMany authors have studied the physical-geographical characteristics of this area.  \nCvijic (1921) called attention to the geographical individuality of the region, with \nspecial emphasis on the Prokletije mountain group where the Djuricka river basin \nis located.\n\n\n\nKnezevic and Kicovic (2004) and Kicovic and Dragovic (2000) described \nthe natural characteristics of Mount Prokletije in the Polimlje Region. Additional \nresearch elaborated on issues dealing with the influence of anthropogenic factors \nand the processes of soil erosion in the area.\n\n\n\nThe Djuricka river basin stretches from its inflow to Lim (Hmin, is 907 m) \nto the tops of the massive mountain of Prokletije in the South (Fig. 2), where the \nHmax is 2149 m on Mala Scapica (Scapica Minor) (Fig. 3). The basin is hilly and \nmountainous.\n\n\n\nThere are mild slopes around the village Bogajici and steep slopes \nsurrounding the massive mountains. The average river basin decline, Isr, is \n39.26%; the average river basin altitude, Hsr, is 1476.40 m; the average elevation \ndifference of the river basin, D, is 569.40 m. \n\n\n\nThere are not many places in the Polimlje region that are as steep as the area \nstretching from above the town Plav to the tops of Mt. Prokletije. The relief has \nvery pronounced dynamics at the water-source zones of the Trokuska River and \nthe tributaries.\n\n\n\nFig. 2: Prokletije\n\n\n\nModelling of Soil Erosion Intensity and Runoff\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201354\n\n\n\nClimatic Characteristics\nThe climate and human pressure on the land in the Djuricka river basin is very \nvariable. The climate is determined by the proximity of one large water area (the \nLake of Plav) and the Prokletije Mountain. It is characterised by short, fresh, dry \nsummers; rainy autumns and springs, and cold winters. The absolute maximum air \ntemperature is 35 oC. Winters are severe, so much so that negative temperatures \ncan fall to a minimum of -29.8 oC.\n\n\n\nIn terms of rainfall, there are two characteristically rainy periods of the \nyear: the first-cold period (October-March) and the second-warm period (April-\nSeptember). \n\n\n\nBasic data on the area needed for the calculation of soil erosion, intensity, \nand runoff are presented in Tables l - 6. The amount of torrential rain, hb, is \n89.4 mm. The average annual air temperature, t0, is 8.1\u00b0C. The average annual \nprecipitation, Hyear, is 1345.4 mm.\n\n\n\nThe Geological Structure of the Area\nIn the structural-tectonic sense, the area belongs to the Durmitor geotectonic unit \nof the inner Dinarides of Northern and North-eastern Montenegro (Zivaljevic \n1989). The geological structure of the area consists mainly of Paleozoic clastic, \ncarbonate and silicate volcanic rocks and sediments of the Triassic, Jurassic, \nCretaceous-Paleogene and Neogene sediments and Quaternary.\n\n\n\nThe coefficient of the region\u2019s permeability, S1, is 0.96. The structure of the \nDjuricka river basin, according to the permeable products from rocks is presented \nin Fig. 4. \n\n\n\nFig. 3: Malo Selo (the Small village) in the river basin Djuricka rijeka\n\n\n\nSpalevic, V., N. Djurovic, S. Mijovic, M. Vukelic-Sutoska and M. Curovic \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 55\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n16 \n \n \n\n\n\n\n\n\n\nTABLE 1 \nMonthly precipitation sums in lit/m2 \u2013 Gusinje, Montenegro  \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 304 270 235 259 231 162 165 165 329 553 393 398 \nAv 138 119 105 128 89 74 59 67 91 139 175 164 \nSt.d. 104 80 55 57 47 37 39 43 68 117 92 112 \n\n\n\n                                                                                                                   Year = 1345.4 \n \n\n\n\nTABLE 2 \nMonthly precipitation sums in lit/m2- Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 404 246 167 240 240 246 190 147 214 405 435 311 \nAv 124 101 89 106 82 69 54 62 85 119 156 135 \nSt.d. 101 68 46 55 46 52 40 36 56 94 97 82 \n\n\n\n                                                                                                             Year =1182.3                                                                        \n \n\n\n\nTABLE 3 \nDaily maximum in lit/m2 - Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 89 89 77 59 51 49 46 41 41 70 72 66 \nAv 28 31 30 32 25 19 17 18 25 30 36 34 \nSt.d. 26 21 17 13 15 13 11 10 11 18 22 20 \n\n\n\n \nTABLE 4 \n\n\n\nMonthly average air temperature in OC - Plav, Montenegro \n Jan Feb Mar Apr Ma\n\n\n\ny \nJun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 1.9 2.9 5.8 9.8 14 16 20 20 16 11 6.3 3.4 \nMin -5.2 -4.9 -1.9 6.1 10 13 16 16 10 7.5 -1.3 -3.2 \nAv -1.4 -0.4 3.2 7.6 12 15 17 17 13 9.3 3.2 0.0 \nSt.d. 2.2 2.2 2.3 1.1 1.1 1.0 1.2 1.3 1.5 1.2 2.2 2.0 \n\n\n\n             Annual average air temperature = 8.1 \n \n \n \n \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n16 \n \n \n\n\n\n\n\n\n\nTABLE 1 \nMonthly precipitation sums in lit/m2 \u2013 Gusinje, Montenegro  \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 304 270 235 259 231 162 165 165 329 553 393 398 \nAv 138 119 105 128 89 74 59 67 91 139 175 164 \nSt.d. 104 80 55 57 47 37 39 43 68 117 92 112 \n\n\n\n                                                                                                                   Year = 1345.4 \n \n\n\n\nTABLE 2 \nMonthly precipitation sums in lit/m2- Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 404 246 167 240 240 246 190 147 214 405 435 311 \nAv 124 101 89 106 82 69 54 62 85 119 156 135 \nSt.d. 101 68 46 55 46 52 40 36 56 94 97 82 \n\n\n\n                                                                                                             Year =1182.3                                                                        \n \n\n\n\nTABLE 3 \nDaily maximum in lit/m2 - Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 89 89 77 59 51 49 46 41 41 70 72 66 \nAv 28 31 30 32 25 19 17 18 25 30 36 34 \nSt.d. 26 21 17 13 15 13 11 10 11 18 22 20 \n\n\n\n \nTABLE 4 \n\n\n\nMonthly average air temperature in OC - Plav, Montenegro \n Jan Feb Mar Apr Ma\n\n\n\ny \nJun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 1.9 2.9 5.8 9.8 14 16 20 20 16 11 6.3 3.4 \nMin -5.2 -4.9 -1.9 6.1 10 13 16 16 10 7.5 -1.3 -3.2 \nAv -1.4 -0.4 3.2 7.6 12 15 17 17 13 9.3 3.2 0.0 \nSt.d. 2.2 2.2 2.3 1.1 1.1 1.0 1.2 1.3 1.5 1.2 2.2 2.0 \n\n\n\n             Annual average air temperature = 8.1 \n \n \n \n \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n16 \n \n \n\n\n\n\n\n\n\nTABLE 1 \nMonthly precipitation sums in lit/m2 \u2013 Gusinje, Montenegro  \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 304 270 235 259 231 162 165 165 329 553 393 398 \nAv 138 119 105 128 89 74 59 67 91 139 175 164 \nSt.d. 104 80 55 57 47 37 39 43 68 117 92 112 \n\n\n\n                                                                                                                   Year = 1345.4 \n \n\n\n\nTABLE 2 \nMonthly precipitation sums in lit/m2- Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 404 246 167 240 240 246 190 147 214 405 435 311 \nAv 124 101 89 106 82 69 54 62 85 119 156 135 \nSt.d. 101 68 46 55 46 52 40 36 56 94 97 82 \n\n\n\n                                                                                                             Year =1182.3                                                                        \n \n\n\n\nTABLE 3 \nDaily maximum in lit/m2 - Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 89 89 77 59 51 49 46 41 41 70 72 66 \nAv 28 31 30 32 25 19 17 18 25 30 36 34 \nSt.d. 26 21 17 13 15 13 11 10 11 18 22 20 \n\n\n\n \nTABLE 4 \n\n\n\nMonthly average air temperature in OC - Plav, Montenegro \n Jan Feb Mar Apr Ma\n\n\n\ny \nJun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 1.9 2.9 5.8 9.8 14 16 20 20 16 11 6.3 3.4 \nMin -5.2 -4.9 -1.9 6.1 10 13 16 16 10 7.5 -1.3 -3.2 \nAv -1.4 -0.4 3.2 7.6 12 15 17 17 13 9.3 3.2 0.0 \nSt.d. 2.2 2.2 2.3 1.1 1.1 1.0 1.2 1.3 1.5 1.2 2.2 2.0 \n\n\n\n             Annual average air temperature = 8.1 \n \n \n \n \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n16 \n \n \n\n\n\n\n\n\n\nTABLE 1 \nMonthly precipitation sums in lit/m2 \u2013 Gusinje, Montenegro  \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 304 270 235 259 231 162 165 165 329 553 393 398 \nAv 138 119 105 128 89 74 59 67 91 139 175 164 \nSt.d. 104 80 55 57 47 37 39 43 68 117 92 112 \n\n\n\n                                                                                                                   Year = 1345.4 \n \n\n\n\nTABLE 2 \nMonthly precipitation sums in lit/m2- Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 404 246 167 240 240 246 190 147 214 405 435 311 \nAv 124 101 89 106 82 69 54 62 85 119 156 135 \nSt.d. 101 68 46 55 46 52 40 36 56 94 97 82 \n\n\n\n                                                                                                             Year =1182.3                                                                        \n \n\n\n\nTABLE 3 \nDaily maximum in lit/m2 - Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 89 89 77 59 51 49 46 41 41 70 72 66 \nAv 28 31 30 32 25 19 17 18 25 30 36 34 \nSt.d. 26 21 17 13 15 13 11 10 11 18 22 20 \n\n\n\n \nTABLE 4 \n\n\n\nMonthly average air temperature in OC - Plav, Montenegro \n Jan Feb Mar Apr Ma\n\n\n\ny \nJun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 1.9 2.9 5.8 9.8 14 16 20 20 16 11 6.3 3.4 \nMin -5.2 -4.9 -1.9 6.1 10 13 16 16 10 7.5 -1.3 -3.2 \nAv -1.4 -0.4 3.2 7.6 12 15 17 17 13 9.3 3.2 0.0 \nSt.d. 2.2 2.2 2.3 1.1 1.1 1.0 1.2 1.3 1.5 1.2 2.2 2.0 \n\n\n\n             Annual average air temperature = 8.1 \n \n \n \n \n\n\n\nTABLE 1\nMonthly precipitation sums in litres m2 \u2013 Gusinje, Montenegro \n\n\n\nTABLE 2\nMonthly precipitation sums in litres m2- Plav, Montenegro\n\n\n\nTABLE 3\nDaily maximum in litres m2 - Plav, Montenegro\n\n\n\nTABLE 4\nMonthly average air temperature in OC - Plav, Montenegro\n\n\n\nTABLE 5\nAbsolute maximum of air temperature in OC - Plav, Montenegro\n\n\n\nTABLE 6\nAbsolute minimum of air temperature in OC - Plav, Montenegro\n\n\n\n \n                                                                                                       ISSN: 1394-7990 \n\n\n\nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n \n\n\n\nTABLE 5 \nAbsolute maximum of air temperature in OC - Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 14 18 24 24 28 33 35 35 32 27 22 19 \nAv 12 12 18 21 26 29 32 31 28 24 18 13 \nSt.d. 2.1 2.8 3.4 2.4 2.3 2.7 2.0 1.9 2.7 1.8 3.2 2.4 \n\n\n\n\n\n\n\nTABLE 6 \nAbsolute minimum of air temperature in OC - Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax -30 -22 -18 -11 -1.6 0.0 0.0 1.0 -1.1 -6.4 -17.0 -21 \nAv -16 -14 -9.2 -3.1 0.5 2.3 4.0 3.7 2.3 -3.1 -9.5 -14 \nSt.d. 5.2 3.8 4.8 2.9 1.4 1.6 2.0 1.7 2.1 2.0 3.5 4.6 \n\n\n\n\n\n\n\n\n\n\n\n \n                                                                                                       ISSN: 1394-7990 \n\n\n\nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n \n\n\n\nTABLE 5 \nAbsolute maximum of air temperature in OC - Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax 14 18 24 24 28 33 35 35 32 27 22 19 \nAv 12 12 18 21 26 29 32 31 28 24 18 13 \nSt.d. 2.1 2.8 3.4 2.4 2.3 2.7 2.0 1.9 2.7 1.8 3.2 2.4 \n\n\n\n\n\n\n\nTABLE 6 \nAbsolute minimum of air temperature in OC - Plav, Montenegro \n\n\n\n \n Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec \n\n\n\nMax -30 -22 -18 -11 -1.6 0.0 0.0 1.0 -1.1 -6.4 -17.0 -21 \nAv -16 -14 -9.2 -3.1 0.5 2.3 4.0 3.7 2.3 -3.1 -9.5 -14 \nSt.d. 5.2 3.8 4.8 2.9 1.4 1.6 2.0 1.7 2.1 2.0 3.5 4.6 \n\n\n\n\n\n\n\n\n\n\n\nModelling of Soil Erosion Intensity and Runoff\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201356\n\n\n\nSoil Characteristics of the Area\nSeveral researchers (Pavicevic 1956, 1957; Pavicevic and Tancic 1970) studied \nthe soils of the high mountains in Upper Polimlje, including the Djuricka river \nbasin. Going from the inflow of the Djuricka River past Lim to the surrounding \nmountainous terrain, the most common soil types were alluvial-deluvial soils, \nbrown eutric soils, and brown district (acid) soils. The structure of the Djuricka \nriver basin, according to the soil types is presented in Fig. 5.\n \n\n\n\nA part of the river basin consisted of a very permeable rocks   fp\nA part of the river basin area consisted of medium permeable rocks fpp\nA part of the river basin consisted of poor water permeability rocks fo\n\n\n\nFig. 4: Structure of the Djuricka river basin according to the permeable\nproducts from rocks\n\n\n\nFig. 5: Map of soil types in the \nDjuricka river basin\n\n\n\nFig. 6: An example of the   soil profile \nfound (Brown district soils)\n\n\n\nSpalevic, V., N. Djurovic, S. Mijovic, M. Vukelic-Sutoska and M. Curovic \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 57\n\n\n\nVegetation \nLarge soil loses on sloping land can be attributed to high runoff rates which are \ngenerated on surfaces with no or little contact cover. Disturbance to the soil surface \nresulting in large supplies of easily entrainable material enhances the capacity of \noverland flow to cause erosion (Hashim et al. 1997).\n\n\n\nFor the purposes of calculating the maximum outflow from the Djuricka \nriver basin (Qmax), we analysed the vegetative cover (ratio S2: part of the basin \ncovered by forest, the grasses, orchards, as well as the barren land).\n\n\n\nThe composition of the geological substrate and the soil formed on this \nsubstrate are, for the most part, resistant to erosion where the area is well protected \nby adequate vegetation cover. However, in places where the terrain is free from \nvegetation, runoff is intensive. This terrain is characterised by rill erosion, gully \nerosion and other forms of deep erosion. The arrival of torrents erodes the \nsubstrate, causing soil slides, and in some situations, the substrate itself. Deep \nerosion occurs on the slopes of Prokletije and Kofiljaca, including the intersection \nof gullies and ravines. \n\n\n\nThe studied area is located in Dinaridi Province of the Middle-Southern-East \nEuropean mountainous biogeographical region. The dominant type of vegetation \nis forests, accounting for more than half of the total vegetation cover.\n\n\n\nPlant communities of the area are in the following classes of vegetation:\n \n\n\n\na) Querco-fagetea Br.-Bl. Et Vlieger 37.\nb) Quercetea robori-petreae br.-Bl. Et Tx. 43.\nc) Erico-pinetea Horvat 59. \nd) Vaccinio-picetea Br.-Bl. 39.\ne) Betulo-adenostiletea Br.-Bl. 48.\nf) Epilobietea angustifolii Tx. Et Prsc. 50.\ng) Salicetalia purpureae Moor 58.\nh) Alnetea glutinosae Br.-Bl. et Tx. 43.\ni) Arhenanteretea Br.-Bl. 47.\nj) Festuco brometea Br.-Bl. et Tx. 43.\nk) Plantaginetea majoris Tx. et Prsg. 50.\nl) Secalinetea Br.-Bl. 51.\nm) Caricetea curvulae Br.-Bl. 48.\nn) Elyno-seslerietea Br.-Bl. 48.\no) Salicetea herbacea Br.-Bl. 47.\np) Thlaspetea rotundifolii Br.-Bl. 47.\nq) Asplenietea rupestris Br.-Bl. 34.\nr) Phragmitetea Tx. et Prsg. 49.\ns) Montio-cardaminetea Br.-Bl. et Tx. 43.\n\n\n\n \nOn the vertical profile, the Djuricka river basin is differentiated from the \n\n\n\nfollowing forest communities:\n\n\n\nModelling of Soil Erosion Intensity and Runoff\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201358\n\n\n\n1. Quercetum petraeae-cerridis, Lak. Mostly in the southern exposure of \nthe valleys on the main watercourse, and the lower parts of its tributaries.\n\n\n\n2. Quercetum petraeae montenegrinum, Lak. On the hilly parts of the river \nbasin.\n\n\n\n3. Fagetum montanum. Differentiated into several associations of which \nthe most characteristic is Luzulo - Fagion moesiacae.\n\n\n\n4. Abieti - Fagetum moesiacae Blec and Lak. \n5. Picetum excelsae montanum \n6. Picetum excelsae subalpinum, above 1600m. \n7. Fagetum subalpinum between 1500-1800m at all exposures and different \n\n\n\ngeological substrates.\n8. Pinetum heldreichii between 1500-2000m. \n9. Pinetum peuces:\n\n\n\na. Pinetum peuces montenegrinum Blec. between 1800-2000 m;\nb. Pinetum heldreichii-peuces Lak. between 1700-2000 m;\nc. Pinetum mughi above 2000 m.\n\n\n\nIn the upper part of the river basin, on the eastern slopes of Kosutic, and \nthe western slope of Prokletije, close to the border with Albania, are forests of \nMacedonian pine. Going downstream to Kofiljaca on the southern exposures are \nforests of fir and spruce; in the lower regions, fir, spruce and beech are found. On \nthe southern exposure of Kofiljaca, close to the village of Bogajici are forests of \nfir and spruce, and in the lower regions beech. On the slopes of the sub-basin of \nthe Trokuska river is a beech forest. Below, close to the settlement of  Hoti are \nmixed forests of fir, spruce and beech. On the slopes of Lovnocelo, there is a zone \nof pure beech forests.\n\n\n\nSpalevic, V., N. Djurovic, S. Mijovic, M. Vukelic-Sutoska and M. Curovic \n\n\n\n \n                                                                                                       ISSN: 1394-7990 \n\n\n\nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n \n\n\n\n    \nFig. 7: Pioneer vegetation Endemic ass. Euphorbio-Valerianetum bertiscei Lksic (68) 70 \n\n\n\n\n\n\n\n\n\n\n\n \nFig. 8: Land use in the Djuricka river basin \n\n\n\n\n\n\n\nFig. 7: Pioneer vegetation Endemic ass. Euphorbio-Valerianetum \nbertiscei Lksic (68) 70\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 59\n\n\n\nIn the Jasenicka sub-basin of the river, down the left tributary of the Djuricka \nRiver, on the slopes of Mala Sljapica and Podkobila, are the forests of Macedonian \npine and mixed forests of beech and Macedonian pine. Going downstream, the \narea is covered with sub-alpine beech. Further down, on the slopes of Tumba, \nthere are mixed forests of pine, fir, spruce, and beech.\n\n\n\nAccording to our analysis, the coefficient fs, (part of the river basin under \nforests) is 0.50, ft (grass, meadows, pastures and orchards) is 0.47 and fg (bare \nland, plough-land and ground without grass vegetation) is 0.03. \n\n\n\nThe coefficient of the river basin planning, Xa, is 0.45. Of the total river \nbasin area, related to the river basin structure, (Xa, is 0.45) mountain pastures \nare the most widespread form (35.48%). The proportion is as follows: degraded \nforests -25.24%; well-constituted forests - 25.24%; meadows - 10.96%; plough-\nlands - 2.6%; and orchards - 0.48%. The structure of the Djuricka river basin, land \nuse is presented in Fig. 8.\nThe coefficient of the vegetation cover, S2, is 0.70.\n\n\n\nCharacteristics of the Basin in Relation to Soil Erosion and Runoff\nSoil erosion represent key environmental issues worldwide (e.g., Green, 1982; \nLarson et al. 1983; Stoffel and Huggel 2012) and primary drivers of land \ndegradation (Verheijen et al. 2009). Recent studies dealing with soil conservation \nsubjects have discussed and sometimes questioned, the magnitude of land \ndegradation in the Region, human responses, and the linkages with land use \nand cover (LUC) changes where water is one of the causes of positive but also \nnegative effects on the land and environment (Nyssen et al. 2012).\n\n\n\nWater-induced soil erosion is the result of the complex effect of a whole \ngroup of factors. Several studies (Curovic et al. 1999, Spalevic et al. 2013, \n2013a, 2013b, 2013c, 2013d, 2012, 2008, 2007, 2004, 2003, 2001, 2000, 2000a, \n1999, 1999a, Spalevic 2011; Fustic and Spalevic 2000) have shown that erosion \nintensity is always influenced by the properties and the use of soil, increasingly \nso in the anthropogenous period of their evolution. Over the last thirty years, \nanthropogenic factors have significantly increased pressure on agricultural and \nforest land, degrading the vegetation cover, which eventually results in serious \ndegradation and loss of fertile soil.\n\n\n\nThe relief of the hilly-mountainous terrain is characterised by many steep \nslopes from which the water runs off and flows quickly, which is favourable for \ntriggering the soil erosion process. The dominant erosion form in this area is \n\n\n\nFig. 8: Land use in the Djuricka river basin\n\n\n\nModelling of Soil Erosion Intensity and Runoff\n\n\n\n \n                                                                                                       ISSN: 1394-7990 \n\n\n\nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n \n\n\n\n    \nFig. 7: Pioneer vegetation Endemic ass. Euphorbio-Valerianetum bertiscei Lksic (68) 70 \n\n\n\n\n\n\n\n\n\n\n\n \nFig. 8: Land use in the Djuricka river basin \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201360\n\n\n\nsurface runoff, but more severe forms of erosion such as rills, gullies and ravines \nalso occur.\n\n\n\nThe erosion activities affect some areas of agricultural and forest land, \nbut they are mostly close to roads that connect small rural communities with \nthe town Plav. The erosion causes some places to lose fertile land, resulting in \nsterile alluvial deposits on the fertile soils of the small alluvial terraces close to \nthe main watercourse. It has also resulted in torrents which have flooded roads and \ninterrupted travel and the migration of farmers from and to the katuns (seasonal/\nsummer villages). \n\n\n\nMassive surface runoff of soil results in denudation of slopes, followed by \nthe occurrence of numerous gullies, ravines and landslides. Surface or runoff \nerosion has taken place in all the soils on the slopes, with erosion being most \npronounced on steep slopes with scarce or denuded vegetation cover.\n\n\n\nWe used the software IntErO to process the input data required for calculation \nof the soil erosion intensity and the maximum outflow.\nA complete report for the Djuricka river basin is presented in Table 7.\n(A)symmetry coefficient (0.06) indicates that there is a possibility of large flood \nwaves in the river basin.\n\n\n\nThe value of the G coefficient that was 0.54 indicates that there is a medium \ndensity of the hydrographical network. Maximal outflow (appearance of 100 \nyears) from the river basin, Qmax, is calculated on 240 m3/s. \n\n\n\nThe value of the Z coefficient was 0.554. According to the result of the \nvalue of Z, the river basin belongs to destruction category III. The strength of the \nerosion process is medium, and according to the erosion type, it is mixed erosion. \n\n\n\nSediment yields were calculated with the IntErO model on 347,273 m3/year \nfor the 57 basins of Polimlje in Montenegro, and 44,902 m\u00b3/km\u00b2 for the study on \nthe Djuricka river basin (Spalevic 2011); the calculations for the Polimlje region \ncorresponded to the results obtained by the engineers Muhidin Begic and Milosav \nVranic (0.35 x 106m3) for the Potpec accumulation, which is downstream from \nthe study area. This correspondence suggests that the assessment results of actual \nlosses of soil erosion potential obtained by IntErO model are eligible for the study \narea.\n\n\n\nAccording to Babic et al. (2003) from the \u201cJaroslav \u010cerni\u201d Institute for \nthe Development of Water Resources (JCI), the leading research organisation in \nSerbia\u2019s water sector, real soil losses are 350 m\u00b3/km\u00b2 per year for the Lim river \nbasin (Polimlje, Fig. 1). By using the IntErO software to estimate the soil losses \nper km2 in 57 river basins of Polimlje, we found the average value to be  331.78 \nm\u00b3/km\u00b2 per year (Spalevic 2011), and  645.4 m\u00b3/km\u00b2 year for the studied Djuricka \nriver basin (Table 8 / No1). This correspondence suggests that the results of the \nassessment obtained by IntErO model are eligible for the study area.\n\n\n\nSpalevic, V., N. Djurovic, S. Mijovic, M. Vukelic-Sutoska and M. Curovic \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 61\n\n\n\nINPUT DATA \nRiver basin area F 69.57 km\u00b2 \nThe area of the bigger river basin part Fv 35.87 km\u00b2 \nThe area of the smaller river basin part Fm 33.70 km\u00b2 \nNatural length of the main watercourse Lv 14.54 km \nThe shortest distance between the fountain head and mouth Lm 12.12 km \nThe total length of the main watercourse with tributaries \u03a3L 37.5 km \nRiver basin length measured by a series of parallel lines Lb 15.86 km \nContour line length 1000m liz 10.20 km \nContour line length 1100m liz 20.73 km \nContour line length 1200m liz 26.40 km \nContour line length 1300m liz 28.33 km \nContour line length 1400m liz 31.83 km \nContour line length 1500m liz 32.88 km \nContour line length 1600m liz 32.30 km \nContour line length 1700m liz 29.52 km \nContour line length 1800m liz 27.32 km \nContour line length 1900m liz 19.02 km \nContour line length 2000m liz 12.12 km \nContour line length 2100m liz 2.53 km \nThe area between 907m and contour line 1000 fiz 5.35 km\u00b2 \nThe area between the two neighbouring contour lines 1000-1100 fiz 4.65 km\u00b2 \nThe area between the two neighbouring contour lines 1100-1200 fiz 5.92 km\u00b2 \nThe area between the two neighbouring contour lines 1200-1300 fiz 6.33 km\u00b2 \nThe area between the two neighbouring contour lines 1300-1400 fiz 6.57 km\u00b2 \nThe area between the two neighbouring contour lines 1400-1500 fiz 6.8 km\u00b2 \nThe area between the two neighbouring contour lines 1500-1600 fiz 7.37 km\u00b2 \nThe area between the two neighbouring contour lines 1600-1700 fiz 7.5 km\u00b2 \nThe area between the two neighbouring contour lines 1700-1800 fiz 6.75 km\u00b2 \nThe area between the two neighbouring contour lines 1800-1900 fiz 6.15 km\u00b2 \nThe area between the two neighbouring contour lines 1900-2000 fiz 4.06 km\u00b2 \nThe area between the two neighbouring contour lines 2000-2100 fiz 1.83 km\u00b2 \nThe area between the two neighbouring contour lines 2100- fiz 0.3 km\u00b2 \nAltitude of the first contour line h0 1000 m \nEquidistance \u0394h 100 m \nThe lowest river basin elevation Hmin 907 m \nThe highest river basin elevation Hmax 2149 m \nA part of the river basin consisted of a very permeable rocks  fp 0.03  \nA part of the river basin area consisted of medium permeable rocks fpp 0.06  \nA part of the river basin consisted of poor water permeability rocks fo 0.91  \nA part of the river basin under forests fs 0.50  \nA part of the river basin under grass, and orchards ft 0.47  \nA part of the river basin under bare land and without grass vegetation fg 0.03  \nThe volume of the torrent rain hb 89.4 mm \nIncidence Up 20 years \nAverage annual air temperature t0 8.1 \u00b0C \nAverage annual precipitation Hyear 1345.4 mm \nTypes of soil products and related types Y 1.2  \nRiver basin planning, coefficient of river basin planning Xa 0.45  \nNumeral equivalents of visible and clearly exposed erosion process \u03c6 0.41  \n\n\n\n\n\n\n\nTABLE 7\nPart of the IntErO report for  the Djuricka river basin\n\n\n\nModelling of Soil Erosion Intensity and Runoff\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201362\n\n\n\nTazioli (2009) undertook a comparison between the Gavrilovic EPM model \nand direct measurements of sediment transport.  The study was applied to different \nequipped basins in Italy and Africa. The numerical results obtained for some \nbasins in the Marche region (Italy) were compared with the empirical formula \nof EPM for the calculation of erosion. Tazioli\u2019s research concluded that EPM is \nparticularly useful for small and medium water courses (similar to those of the \nApennine ranges in Italy, but also for the Djuricka river basin that was studied), \nallowing  for an assessment of erosion in the whole watershed. \n\n\n\nThis methodology is in use also in Bosnia & Herzegovina, Croatia, Italy, \nMontenegro, Macedonia, Serbia and Slovenia. The EPM is distinguished by its \nhigh degree of reliability in calculating sediment yields as well as transport and \n\n\n\nTABLE 8\nCalculated soil losses per km2 in 57 river basins of Polimlje (m\u00b3/km\u00b2 year)\n\n\n\nSpalevic, V., N. Djurovic, S. Mijovic, M. Vukelic-Sutoska and M. Curovic \n\n\n\n    \n    \n    \n    \n    \n    \nRESULTS: \nCoefficient of the river basin form A 0.54  \nCoefficient of the watershed development m 0.49  \nAverage river basin width B 4.39 km \n(A)symmetry of the river basin a 0.06  \nDensity of the river network of the basin G 0.54  \nCoefficient of the river basin tortuousness K 1.2  \nAverage river basin altitude Hsr 1476.4 m \nAverage elevation difference of the river basin D 569.4 m \nAverage river basin decline Isr 39.26 % \nThe height of the local erosion base of the river basin Hleb 1242 m \nCoefficient of the erosion energy of the river basin's relief Er 136.89  \nCoefficient of the region's permeability S1 0.96  \nCoefficient of the vegetation cover S2 0.7  \nAnalytical presentation of the water retention in inflow W 0.751 m \nEnergetic potential of water flow during torrent rains 2gDF^\u00bd 881.61 m km s \nMaximal outflow from the river basin Qmax 239.8 m\u00b3 s-1 \nTemperature coefficient of the region T 0.95  \nCoefficient of the river basin erosion Z 0.554  \nProduction of erosion material in the river basin Wyear 115593 m\u00b3 year-1 \nCoefficient of the deposit retention Ru 0.388  \nReal soil losses G year 44902 m\u00b3 year-1 \n\n\n\nReal soil losses per km2 G year/km\u00b2 645 \nm\u00b3 km\u00b2 \nyear-1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n20 \n \n \n\n\n\n\n\n\n\nTABLE 8 \nCalculated soil losses per km2 in 57 river basins of Polimlje (m\u00b3/km\u00b2 year) \n\n\n\n \n1 645,40 11 417,68 21 88,66 31 197,99 41 131,23 51 140,64 \n2 521,84 12 278,33 22 470,42 32 296,45 42 122,48 52 315,28 \n3 104,31 13 427,63 23 324,45 33 255,60 43 194,76 53 216,30 \n4 288,97 14 330,12 24 212,67 34 325,19 44 198,08 54 250,39 \n5 562,60 15 429,10 25 385,41 35 195,47 45 212,39 55 256,39 \n6 399,52 16 403,46 26 492,68 36 264,43 46 254,63 56 269,25 \n7 328,96 17 370,61 27 232,47 37 286,07 47 200,43 57 413,66 \n8 180,22 18 244,32 28 305,76 38 327,04 48 514,60 Average \n\n\n\n331,78 \nm\u00b3/km\u00b2 year \n\n\n\n9 327,69 19 219,39 29 268,09 39 452,92 49 200,56 \n10 298,19 20 286,90 30 266,21 40 210,32 50 247,93 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 63\n\n\n\nreservoir sedimentation (Ristic et al. 2011). EPM is embedded in the algorithm of \nIntErO computer-graphic method.\n\n\n\nCONCLUSION\nMany factors influence the erosion processes in the territory of the Djuricka river \nbasin. The most significant factors are climate, relief, geological substrate and \npedological composition, as well as the condition of the vegetation cover and \nland use.\n\n\n\nMaximal outflow (over 100 years) from the river basin, Qmax, is 240 m3 s-1, \nsuggesting the possibility of a large flood. The strength of the erosion process is \nmedium, and erosion type is mixed erosion. The predicted soil losses are 645 m\u00b3 \nkm\u00b2 year-1.\n\n\n\nThis zone will experience intensive tourism in the future. There is therefore a \nneed to take preventive measures against the possibility of increasing soil erosion \nprocesses. 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The estimate of maximum \noutflow and soil erosion intensity in the Vinicka river basin with application of \ncomputer graphic methods. Agriculture and Forestry. 47(3-4): 95-104. \n\n\n\nSpalevic, V., Fustic, B., Jovovi\u0107, Z., Curovic, M., Spalevic, B., Popovic. V. 2000.  \nCharacteristics of erosion processes and proposal of land reclamation measures \nin the drainage basin of the \u0160ekularska river. Agriculture and Forestry. 46(3-4): \n2-18. \n\n\n\nSpalevic V., A. Dlaba\u010d , B. Spalevic,  B. Fu\u0161ti\u0107 and V. Popovi\u0107. 2000b. Application \nof computer - graphic methods in the research of runoff and intensity of ground \nerosion - I program \u201cRiver basins\u201d. Agriculture and Forestry. 46 (1-2): 19-36.\n\n\n\nSpalevic, V. 1999.  Application of computer-graphic methods in the studies of draining \nout and intensities of ground erosion in the Berane valley. Master thesis, Faculty \nof Agriculture of the University of Belgrade, Serbia. 135p.\n\n\n\nSpalevic, V., D. Dubak, B.  Fu\u0161ti\u0107, Z. Jovovi\u0107 and R.  Risti\u0107. 1999a. The estimate of \nthe maximum outflow and soil erosion intensity in the Kaludra River basin. \nActa Agriculturae Serbica IV(8): 79-89. \n\n\n\nStoffel, M., and C. Huggel. 2012. Effects of climate change on mass movements in \nmountain environments. Progress in Physical Geography. 36: 421\u2013439.\n\n\n\nThun, R. and R. Herrmann. 1949. Die Untersuchung von Boden. Neumann Velag, \nRadebeul und Berlin, pp 15-28.\n\n\n\nModelling of Soil Erosion Intensity and Runoff\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201368\n\n\n\nVan Lynden, G.W.J. 1995. European Soil Resources. Nature and Env. No. 71. Council \nof Europe, Strasbourg.\n\n\n\nVerheijen, F.G.A., R.J.A Jones, R.J. Rickson and C. J. Smith. 2009. Tolerable versus \nactual soil erosion rates in Europe. Earth-Science Reviews. 94: 23\u201338.\n\n\n\nVukelic-Sutoska, M., T. Mitkova and M.  Markoski. 2011.  Management of waters \nand soils in Skopje valley according to sustainable agriculture, Opatija-Croatia. \nProceedings of 5th Croatian Water Conference: Croatian Waters Facing the \nChallenge of Climate Changes, pp. 895-904.\n\n\n\nZivaljevic, M. 1989. Tumac Geoloske karte SR Crne Gore, 1:200 000; Posebna \nizdanja Geoloskog glasnika, Knjiga VIII, Titograd.\n\n\n\n\n\n\n\nSpalevic, V., N. Djurovic, S. Mijovic, M. Vukelic-Sutoska and M. Curovic \n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n164 \n \n\n\n\nExploring Optimum Management Practices in Rainfed Areas to Reduce  \nSoil erosion and Nutrient Losses \n\n\n\n \nAziz Sheikh1, Javaid Hassan1*, Shahzada Sohail Ijaz1, Anwar Zaman2, Tajwar Alam1, Sajid Ali3, \n\n\n\nMuhammad Suliman4, Asad Aslam5, Habib Ullah6 and Janas Khan1 \n1 Institute of Soil Science, PMAS Arid Agriculture University Rawalpindi, Pakistan \n\n\n\n2 Department of Soil and Environmental Sciences, The University of Agriculture, Peshawar, Pakistan \n3State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University,  \n\n\n\n26 Hexing Road, Harbin 150040, P.R. China \n4College of Wildlife and Protected Areas, Northeast Forestry University,  \n\n\n\nNo 26, Hexing Road, Harbin 150040, P.R. China \n5Key Laboratory for Sustainable Forest Ecosystem Management-Ministry of Education,  \n\n\n\nCollege of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, P.R. China \n6School of Forestry, Northeast Forest University Harbin, 150040, P.R. China \n\n\n\n \n*Correspondence: javaid87239@gmail.com \n\n\n\n \nABSTRACT \n\n\n\nThe global sustainability of agroecosystems is severely hindered by soil erosion. Globally, agricultural \nproduction and the sustainability of natural ecosystems are at risk from soil erosion due to heavy rainfall, \nposing a severe threat to environmental conservation. Diverse nutrients, transferred along with sediments \nduring detachment and transport by water, affect soil fertility and productivity. The effects of management \npractices and nutrient losses on soil erosion have remained undefined. A field experiment was conducted \nat University Research Farm, Koont Chakwal Road in the Pothwar Plateau, during the monsoon season \nfrom mid-July to mid-September, 2019 in which fallow-based cropping systems used in conservation tillage \nsystems were compared to double cropping and green manuring systems. There were eight treatments and \nthree replications with a split-plot arrangement design. The 900 m2 plot having 3% slope was split into two \nmajor plots for the tillage treatments: conventional tillage and reduced tillage. Each main plot was then \ndivided into four subplots for the summer crops: (i) fallow, (ii) soybean, (iii) maize fodder, and (iv) sesbania \ngreen manure. A plastic drum was installed at the bottom of each sub plot to collect runoff and sediment. \nThe amount of sediment, nutrient concentration, and soil organic matter was collected and measured in \nrunoff water. In contrast to cropped plots, the results showed that fallow plots had a higher rate of runoff \nwater. Maize fodder and sesbania were among the cropping systems with the lowest sediment losses. \nReduced tillage (chisel) showed less sediment loss than mouldboard plough. Overall, nutrient losses varied \nbetween crops and tillage systems. However, there was no significant difference in organic matter loss \nbetween tillage systems, but there was significant difference among crop systems with fallow plots showing \nthe highest and maize plots having the lowest organic matter loss in different rainfall events. In conclusion, \nreduced tillage (chisel plough) in combination with summer crops, specifically maize fodder, can \nconsiderably reduce water erosion and soil losses in the Pothwar region. \n\n\n\nKey words: Runoff, erosion, sediment, nutrient loss, sustainability, agroecosystem \n\n\n\n \nINTRODUCTION \n\n\n\nWater-induced soil erosion is a major factor in global land degradation and productivity losses \n(Fang et al. 2017; Prosdocimi et al. 2017). Moderate to severe water erosion processes affect 751 \nmillion hectares of land worldwide. The layer of topsoil particles may become detached or \nredistributed, a result which could have a negative effect downstream (Dai et al. 2018). Water and \nsediments transported by water erosion contain nutrients lost from the soil such as nitrogen and \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n165 \n \n\n\n\nphosphorus, which are considered the main nutrients causing eutrophication (Xu et al. 2013). \nRunoff and erosion responses differ under different soil management practices. Studies mostly \nfocus on the effects of different management practices on runoff generation and erosion processes \nsuch as conservation tillage (H\u00f6sl and Strauss 2016), tied ridges (Ngetich et al. 2014), mulching \n(Grum et al. 2017) and hedgerow systems (Xia et al. 2013).  \n\n\n\nIn Pakistan, water erosion of soil is a severe problem, considerably affecting rainfed agriculture. \nCountry wide, 76% of total land mass is affected by erosion, of which 36% is affected by water \nerosion. In rainfed areas, the main causes of erosion are inappropriate land use, abandoned grazing, \nillicit tree cutting, and associated vegetation (Baig, Shahid, and Straquadine 2013) Reduced tillage, \nno-tillage, cover crops, and other sustainable land management (SLM) strategies have the potential \nto minimise organic carbon (OC) and nutrient losses from soil resulting from erosion (Martnez-\nMena et al. 2020). For various types of soils and crops, it has been discovered that incorporating \ngreen manure into the soil improves the soil's hydraulic qualities by boosting macro-porosity and \nhydraulic conductivity (Haruna et al. 2018). The features and dynamics of many semi-arid plant \ncommunities are impacted by soil erosion, which may ultimately limit the land's ability to generate \na variety of commodities and services. Ground cover of 20\u201330% is often the most effective way \nto mitigate water erosion, reducing erosion across a variety of soil types and land uses by 80\u201390% \n(Freebairn et al. 2009). Inadequate crop growth, excessive animal grazing on rangeland and loss \nof forest plant cover are the causes of severe soil degradation in rainfed environments (Irshad et \nal. 2007).  \n\n\n\nThe 5.49 Mha Pothwar plateau has an irregular topography and is dependent on rainfall, 60\u201370% \nof which occurs from June to August. Due to the area's low precipitation and insufficient water \nsupply, it is unsuitable for continuous farming. Nearly 50% of rainwater is lost as runoff in rainfed \nareas, which is one of the biggest challenges of the Pothwar region, along with soil erosion and \ndecreased soil fertility (Anjum et al. 2010). Water erosion is a significant issue in the Pothwar \nregion. Utilisation of stone walls, stubble mulching, cover crops, grass strips, field borders, and \nfilter strips can minimise overflow by improving infiltration (Ali et al. 2018) while soil moisture \ncan be conserved by the use of an intensive tillage system. Limited studies have been carried out \nto measure the effects of conservation tillage on soil erosion. Hence, this study was carried out to \ncompare, conservation tillage systems with conventional tillage practices with each having a \nfallow-based cropping system compared with double cropping and green manuring. This research \nwas based on the following objectives i.e., to compare soil, water and nutrient loss under reduced \ntillage and conventional tillage systems and to determine soil erosion under different summer crops \ncompared with optimum management practices. \n\n\n\n \nMATERIALS AND METHODS \n\n\n\nStudy Site \nThe field experiment was carried out at University Research Farm, Koont Chakwal Road in the \nPothwar Plateau region. Climatically, this research field of Koont research station Chakwal, \nPMAS-Arid Agriculture University, Rawalpindi has a semi-arid to sub humid, sub-tropical \ncontinental climate and is located between 33\u00b0 1\u2019 N to 33\u00b0 6\u2019 N and longitude 73\u00b0 30\u2019 to 73\u00b0 45\u2019 \nE, Southeast of Rawalpindi. The bimodal rainfall occurs in late summer and winter season. \nGenerally, about 60-70% of the rainfall is received in the monsoon season (15- June to 15-\nSeptember). However, winter rainfall occurs as gentle showers of longer duration, and is therefore \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n166 \n \n\n\n\nmore productive for agriculture. The soil texture at the experimental site was sandy clay loam \n(56% sand, 22.8% silt, 21.2% clay) with a pH of 7.7. \n \nExperimental Design \nThe field experiment was conducted in the monsoon season that occurs from July to mid-\nSeptember 2019 with a split-plot design having tillage practices in main plots and summer crops \nin sub-plots. The experiment consisted of eight treatments with three replications. The treatment \nwas arranged on total plot size of 900 m2 with 3% slope, divided into two main plots for tillage \ntreatments: (i) conventional tillage and (ii) reduced tillage. Each main plot was further divided into \nfour subplots for summer crops: (i) fallow, (ii) soybean, (iii) maize fodder, and (iv) sesbania green \nmanure. Sub-plot size was 35m2. Seeds were sown at a rate of 1kg per plot. For sediments and \nrunoff collection, a plastic drum was installed at the lower end of each sub-plot (Figure 1). \n\n\n\n\n\n\n\n Mouldboard plough       Chisel plough \n\n\n\nFallow Soybean Maize Sesbania  Fallow Soybean Maize  Sesbania \n\n\n\nFallow Soybean Maize Sesbania  Fallow Soybean Maize  Sesbania \n\n\n\nFallow Soybean Maize Sesbania  Fallow Soybean Maize  Sesbania \n\n\n\nFigure 1. Schematic diagram of experimental layout and installation of containers \n\n\n\nA pit (3 ft. vertical by1.5 ft. horizontal) was dug at the end of sub plots and collection drums were \nfixed for collection of runoff water. Soil around the pit was pressed and sealed for direct entry of \nrunoff water. The collection drums were stirred and the sediment was collected in 1-L plastic \nbottles.  Then the samples were transferred to enamel bowls to settle down for 24 h. The sediment \nwas separated after being evaporated, dried and weighed. Ground samples were sieved and stored \nfor further analysis of soil texture, soil loss, nutrient concentrations, and organic matter.  \n\n\n\nSoil Loss and Runoff \nThe ratio of the weight of the sediment to the area of the plot was calculated for soil loss (g m\u22122). \nRunoff water was measured manually through 3L small buckets to remove the water from the \ndrum. Soil texture was determined by hydrometer method (Gee and Bauder, 1986) and soil organic \nmatter by Walkley-Black method by multiplying Soil organic carbon (SOC) with Van Bemmelen \nfactor (1.724) (Nelson and Sommers, 1996).  NO3-N was determined by salicylic acid method \n(Vendrell and Zupancic 1990). AB-DTPA extractable phosphorous and potassium was measured \nby the method proposed by Soltanpour and Workman (1979). \n \n \n\n\n\nCollection drums \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n167 \n \n\n\n\na\n\n\n\nb\n\n\n\nb\nb\n\n\n\na\n\n\n\nb\n\n\n\nb b\n\n\n\n3\n\n\n\n3.2\n\n\n\n3.4\n\n\n\n3.6\n\n\n\n3.8\n\n\n\n4\n\n\n\n4.2\n\n\n\n4.4\n\n\n\nFallow Soybean Maize Sesbania\n\n\n\nRu\nno\n\n\n\nff \n (m\n\n\n\nm\n)\n\n\n\nConventional tillage Reduced tillage\n\n\n\nStatistical Analysis \nThe collected data was analysed statistically using Statistix 8.1 software, through analysis of \nvariance (ANOVA) technique. Differences between treatments were tested by using least \nsignificant difference Tukey HSD at 0.05 % probability level (Steel, Torrie and Boston, 1997). \n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\n \nPre-soil Physiochemical Attributes \nThe pre-physio chemical analysis of soil samples collected from different sites of the experimental \nfield are given in Table 1. \n\n\n\n  \nMeteorological and Water Runoff Data \nMeteorological data on rainfall during the experimental period was collected from Agro-met \nCentre Chakwal as shown in Table 2. Averaged over the events, there was no difference in runoff \nwater between the tillage systems. However, cropped plots showed lower runoff than fallow plots \n(Figure 2).  \n \n\n\n\nTable 2: Daily precipitation data of 6 sampling events during monsoon in 2009 at University Research Farm, Koont \nDates 20 July 26 July 2 August 7 August 17 August 7 September \nRainfall (mm) 20.4 27.5 36.6 39.6 39.6 29.3 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2. Comparison of average soil runoff water loss on events under different tillage and \nsummer crops \n\n\n\nTable 1: Soil characteristic of the experimental site \nCharacteristics Value \nTexture Sandy loam \nSand (%) 54.7 \nSilt (%) 16.4 \nClay (%) 28.9 \nSlope (%) 3.0 \nN-NO3 (mg kg-1)  10.2 \nAvailable P (mg kg-1) 9.8 \nExtractable K (mg kg-1)  147.1 \nOrganic matter (%) 0.99 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n168 \n \n\n\n\na\n\n\n\nb\nbc c\n\n\n\na\n\n\n\nb\nbc c\n\n\n\n0.5\n0.7\n0.9\n1.1\n1.3\n1.5\n1.7\n1.9\n\n\n\nFallow Soybean Maize SesbaniaSO\nIL\n\n\n\n L\nO\n\n\n\nSS\n  (\n\n\n\nTO\nN\n\n\n\nS \nH\n\n\n\nA\n-1\n\n\n\n)\n\n\n\nConventional tillage Reduced tillage\n\n\n\nAmong crop systems, maize and sesbania plots showed lower runoff than soybean plots. Event- \nwise details are given in Table 3.  \n\n\n\nNote: Differences among values with similar letters in each column are statistically non-significant. \n\n\n\nIn the first rainfall event, there was no difference in runoff between the tillage systems but \ndifferences were significant among the crop systems with sesbania showing least runoff and fallow \nplots, the highest. In the second rainfall event, all the tillage systems and crops had equal runoff. \nIn the third rainfall event, there was no difference in runoff between the tillage systems but among \ncrop systems, fallow plots showed significantly higher runoff than cropped plots. In the fourth \nrainfall event, runoff was equal in tillage systems but differences were significant among crop \nsystems. Among crop systems, the lowest runoff was from maize fodder and sesbania while the \nhighest runoff was from fallow plots. In the fifth rainfall event, both tillage systems produced equal \nrunoff but among crop systems, fallow plots produced significantly higher runoff than cropped \nplots. In the sixth rainfall event, there was no difference in runoff between tillage systems. \nHowever, differences were significant among crop systems with sesbania showing least runoff \nfollowed by maize fodder and lastly by soybean.   \n\n\n\nSoil Loss \nDue to topography and high rainfall intensity, soil loss and runoff are the main problems in the \nPothwar region especially during the monsoon periods. To overcome the risk of soil erosion, \ndifferent tillage systems and crops are used. Overall, there were significant differences among \ntillage systems and crops in the six rainfall events (Figure 3).   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 3.  Comparison of average soil runoff water loss in six rainfall events under different tillage \nand summer crops \n\n\n\nTable 3: Runoff water loss in six rainfall events under different tillage systems and crops \n  Rainfall events \nTreatments 1 2 3 4 5 6 \n ---------Runoff loss (mm m-2) --------- \nConventional  Fallow 4.00a 4.47a 4.52a 4.33ab 3.80ab 4.19ab \nTillage Soybean 3.90a 4.23a 3.52bc 4.00bc 3.19bc 3.80ab \n Maize 4.00a 3.85a 3.66b 3.57c 3.00c 3.85ab \n Sesbania 3.90a 3.90a 3.52c 3.52 3.00c 3.14cd \nReduced  Fallow 4.09a 4.20a 4.52a 4.28a 4.00a 4.23a \nTillage Soybean 3.71ab 4.04a 3.14bc 3.71c 3.14c 3.50bc \n Maize 3.76ab 4.19a 3.00c 3.38c 2.71c 2.61d \n Sesbania 3.42b 3.80a 3.00bc 3.57c 2.71c 3.66abc \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n169 \n \n\n\n\nBetween the tillage systems, a lower sediment loss was observed in reduced (chisel) than \nconventional (mouldboard) tillage. Among the summer crops, maize fodder resulted in lowest \nsediment loss, while the highest sediment loss was from fallow plots. Among the six rainfall \nevents, the highest soil loss was in the 2nd event followed by 3rd and lowest in the 5th event followed \nby the 1st event (Table 4).  \n\n\n\nTable 4: Soil loss during six rainfall events under different tillage and crops \n Rainfall events \nTreatments  1 2 3 4 5 6 \n ---------Soil loss (tons ha-1) --------- \nConventional  Fallow 1.49a 2.44a 2.30a 1.84a 1.25a 1.98a \nTillage Soybean 1.11a 1.68a 1.07b 0.97b 0.87a 1.56ab \n Maize 1.10a 1.26a 0.94b 1.21b 0.58a 0.99c \n Sesbania 0.90a 1.24a 0.91b 0.92b 0.63a 1.03bc \nReduced  Fallow 1.21a 1.73a 2.37a 1.73a 1.01a 1.98a \nTillage Soybean 0.97a 1.32a 0.79b 0.83b 0.71a 1.00bc \n Maize 1.10a 1.26a 0.99b 1.03b 0.58a 0.75c \n Sesbania 0.86a 1.18a 0.78b 0.83b 0.64a 0.67c \n\n\n\nNote: Difference among values with similar letters in each column are statistically non-significant \n \nThere was no difference in sediment loss between crops and tillage systems in the first rainfall \nevent. A significantly higher sediment loss in follow plots among crop systems and equal sediment \nloss in both tillage systems were observed in the second, third and fourth rainfall events. In the \nfifth rainfall event, there was an equal amount of sediment loss in tillage systems but the difference \nwas significant among crop systems. In the sixth rainfall event, among crop systems the least \nsediment loss was in sesbania plots followed by maize fodder and then soybean. \n \nSoil Nitrate-Nitrogen Loss with Sediment \nNutrient losses are directly associated with sediment loss and runoff water. Soil nitrate nitrogen losses were \ncompared under different tillage systems and crops. There was a significant difference in nitrate-N loss in \ntillage systems and crops systems. Between tillage systems and reduced tillage  (chisel plough) plots \nshowed lower loss of nitrogen. compared to conventional tillage plots (mouldboard plough). Nitrogen loss \nwas highest in fallow plots, decreasing in cropped plots. Overall, more nitrate-N loss was observed in the \nsecond rainfall event followed by the lowest loss in the first rainfall event. Rainfall event details are given \nin Table 5.  \n\n\n\n \nTable 5: Mean loss of N-NO3, available P, extractable K and organic matter in sediment in rainfall events \nunder different tillage and crops \nConventional Fallow 12.12a 10.67a 144.08a 0.77a \nTillage Soybean 10.66b 8.83b 123.01b 0.64ab \n Maize 10.44b 7.85b 123.23b 0.56b \n Sesbania 10.22b 7.25b 114.39c 0.58b \nReduced Fallow 11.21a 9.58a 126.25a 0.57a \nTillage Soybean 9.40b 6.96b 110.32b 0.54ab \n Maize 9.53b 5.32b 119.34b 0.47b \n Sesbania 9.55b 6.27b 104.15c 0.49b \nNote: Difference among values with similar letters in each column are statistically non-significant \n\n\n\nThere was no difference in nitrate nitrogen loss between the first and second rainfall events in both \ntillage and crop systems. In the third rainfall event, a significant difference was observed between \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n170 \n \n\n\n\ntillage and crop systems. Among the crops, the least loss of N was from sesbania crop compared \nto other summer crops. In the fourth rainfall event, a significant difference was found among the \ncrop systems while an equal amount of nutrient loss was found in both tillage systems. In the fifth \nrainfall event, no significant difference was observed between tillage systems; however, a \nsignificant difference of nutrient loss was found among the crop systems. In the sixth rainfall event, \nthere was no difference between tillage systems and crop systems but the same amount of nitrogen \nloss was found in both tillage and crop systems. The highest nitrogen loss was in the second rainfall \nevent followed by the first rainfall event. The high nitrogen loss in the second rainfall event could \nbe explained by the effect of tillage practices that affected soil pores after the first rainfall event, \nwhich could have led to greater runoff than infiltration into the soil. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4. Comparison of soil nitrate nitrogen loss in rainfall events under different tillage and crops \n\n\n\nAvailable Phosphorus Loss with Sediments \nSoil phosphorus loss was measured during the monsoon rainfall in the field under different tillage \nand crops. There were significant differences in phosphorus loss between tillage systems and crop \nsystems in overall events of monsoon rainfall. The highest P loss among crop systems was in \nfallow plots which gradually decreased in cropped plots (Figure 5). Between tillage systems, lower \nP loss was observed in reduced tillage (chisel plough) than in mouldboard plough. Overall, the \nhighest phosphorus loss was in the second rainfall event followed by the sixth rainfall event and \nlowest in the fifth rainfall event. Further event-wise details are given in (Table 5). In the first event, \nthere was significant difference among crop systems and tillage systems. In the second event, there \nwas equal amounts of soil P loss in both tillage systems and crop systems. In the third event, no \ndifference was observed between tillage systems but a significant difference among crop systems \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n171 \n \n\n\n\nwas recorded. In the fourth event, there was difference in P loss among crop systems with the \nlowest loss being in maize fodder followed by sesbania and then soybean. All crops showed lower \nP loss than in fallow plots. In the fifth event, both tillage systems had an equal loss of P but \nsignificant differences were observed among crop systems with the highest loss of P being in \nfallow than in cropped plots. Equal P loss occurred in tillage system while differences among crop \nsystems were observed in the in the sixth event. \n\n\n\n\n\n\n\nFigure 5. Comparison of soil phosphorus loss in rainfall events under different tillage and crops  \n\n\n\nThe highest phosphorus loss was found in the second rainfall event and lowest in the fifth event as \nphosphorus is highly soluble in runoff water and soil loss. The fifth event recorded the lowest \nrunoff because the crops were then at maturity stage, with a canopy and deep root system which \nled to decreased runoff water and sediment loss. Meanwhile in the second event, crop seeds had \nnot germinated with less infiltration of water, resulting in higher nutrient losses.  \n\n\n\nExtractable Potassium Loss with Sediments \nThere was a difference in the amount of potassium loss between tillage systems and crop systems \n(Table 5). In the crop system, overall, the highest loss was observed in fallow plots with lowest \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n172 \n \n\n\n\npotassium loss seen in sesbania compared to other crops. Comparing potassium loss in different \nrainfall events, the highest loss was in the second event and lowest in the fifth event. Potassium \nloss in event-wise details is given in (Figure 6). An equal amount of soil K loss was seen in all \ncrops and tillage systems in the first rainfall event. In the second event, no significant difference \nin soil K loss was seen among crop systems and tillage systems. In the third event, significantly \ndifferent K loss was seen among crop systems with sesbania having the lowest K loss.  However, \nequal K loss was found in both the tillage systems. \n \n\n\n\n\n\n\n\nFigure 6. Comparison of soil potassium loss in rainfall events under different tillage and crops \n\n\n\nIn fourth event, the highest K loss was in follow plots with no significant difference observed in \nthe tillage systems. All plots showed equal loss of K among the summer crops and tillage systems \nin the fifth event. Among crop systems, a significant difference was observed with less K loss \nbeing shown by sesbania followed by soybean and then by maize fodder. The highest K loss was \nfound in fallow plots in the sixth rainfall event.  \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n173 \n \n\n\n\nSoil Organic Matter Loss with Sediment \nSoil organic matter (OM) loss was found to be dependent upon sediment loss. In this study there \nwas no difference in OM loss among tillage systems while significant differences were found \namong crop systems when averaged over all events (Figure 7). The highest OM loss was observed  \nin the first and second rainfall events and the lowest in fifth and sixth rainfall events (Table 5). In \nthe first and second rainfall events, all treatments showed equal OM loss among crop systems and \ntillage systems. In the third event, significant difference in OM loss occurred among crop systems. \nAll cropped plots showed lower OM loss than in fallow plots while no significant difference was \nfound in OM loss between tillage systems. In the fourth event, among crops systems, less OM loss \nwas found in maize fodder than in sesbania and soybean. No significant difference in OM loss \noccurred among tillage systems. In the fifth event, there was significant difference in OM loss \namong the crop and tillage systems while in the sixth event, all plots showed an equal amount of \norganic matter loss.  \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 7. Comparison of soil organic matter loss in rainfall events under different tillage and crop \nsystems \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n174 \n \n\n\n\nDISCUSSION \n\n\n\nRunoff Water \nRunoff water loss in both tillage systems was found to be equal. This shows equal infiltration in \nboth the tillage systems. The ability of the reduced tillage system to infiltrate equal amounts of \nwater without intensive tillage (as required by mouldboard plough) is very useful because it does \nnot need to physically crush the soil and break the aggregates and accelerate the decomposition of \norganic matter. Tang et al. (2015) studied three tillage practices in the purple soil region of China. \nThey examined the scaled plots over four years: bare land with minimum tillage (BL), \nconventional tillage (CT) and seasonal no-tillage ridges (SNTR) which was initially designed to \nstop erosion of soil by contoured ridges and no-tillage techniques. Their findings showed no \ndifferences in the surface runoff and soil erosion between the three practices: BL caused a \ncomparative increase in surface runoff and soil erosion, followed by CT and SNTR. The plots with \nvegetation coverage had much lower runoff than fallow plots. In fact, vegetation coverage is \nassumed to be the main factor affecting rainfall interception, other than bulk density and \ntopography. Runoff speed surges and rate of infiltration decline as the soil vegetation cover \ndeclines, bulk density increases and topography or gradient of the slope rises (El Kateb et al. 2013). \nIn a study by Kirk et al. (1995) the rainfall threshold for runoff water production in different land \nuse types showed this trend: Crop Land<Crop Land-Simple Grass<Crop Land-Abandoned Land< \nAbandoned Land. The increase in runoff during the second rainfall event could be due to lesser \ninfiltration caused by closing of soil pores opened with tillage before the first rainfall event. This \nalso could be linked to lower vegetation coverage during the initial stages of crops. On the other \nhand, the lowest runoff in the fifth event could be attributed to relatively higher vegetation \ncoverage due to the highest vegetative stage of the crop. \n \nSoil Loss \nVarying soil loss in tillage systems (conventional and reduced) occurs because much soil \ndisturbance results from conventional tillage (mouldboard plough) in comparison to reduced \ntillage (chisel plough). Due to increased soil aeration and disruption of soil aggregates, very high \nsoil losses under conventional tillage are caused by the deteriorated soil structure (Moyo 1998). \nSince soil loss is a consequence of runoff, the lack of impediments to slow down runoff is also a \ncontributing factor (Munodawafa 2007). Surface and subsurface drainage contribute similar \namounts of suspended sediments from conservation tillage treatments while in conventional \ntillage, sediment loss in subsurface drainage exceeds surface runoff loss. Subsurface drainage \ncontributes significantly to soil erosion, probably because of preferential flow of surface sediments \nto the drains through extensive cracking of the soil (McKeague et al. 1987). Soil erosion is reduced \nsignificantly by cover crops but conventional tillage systems are least effective to prevent soil \nsediment loss (Novara et al. 2011). Runoff and soil erosion have been found to increase with an \nincrease in maize strover harvest. Reduced tillage with crop residue, straw removal, crop stubble \nand maintaining the ridges and deep tillage year to year increases soil fertility (Zhang et al. 2011). \nThese measures increase water infiltration and storage thus offering less opportunities for soil \nerosion (Adekalu et al. 2006). \n \nNutrient Loss with Sediments \nIncreased rainfall leads to greater runoff resulting in higher sediment loss. The lowest nitrogen loss \nobserved in the third rainfall event was due to less sediment loss from a thicker vegetation cover. \nHigh nutrient loss is  a result of poor management of soil (Bashagaluke et al. 2018) as reflected in \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n175 \n \n\n\n\nhigh N-NO3 loss in  the control plots where there was no alley cropping compared to contour alley \ncropping (Wang et al. 2010) highest nitrogen loss occurs in bare soil in comparison to vegetation \ncovered soil. A higher phosphorus loss due to maximum runoff and soil loss was observed in \nconventional tillage (CT) than in reduced tillage. The differences in runoff, soil loss and \nsubsequent nutrient losses between bare follow (BF)  and CT are mainly due to soil protection \noffered by the crops under CT (e.g., crop cover, addition of organic matter, soil binding through \nroots) (Munodawafa 2007). In terms of various soil management approaches, the highest level of \nphosphorus (P) losses through soil runoff occurred in a no-till system with surface residues intact, \ncompared to a conventional system with desiccated surface residues. (Bertol et al. 2007).  \nMinimum soil erosion and P nutrient loss were seen in chisel and coulter tillage systems. \nConventional tillage had also maximum soil P nutrient loss (Romkens et al. 1973). Maximum P \nloss was seen in bare fallow, beans and intercrop at 24.4%, 15% and 9.3% respectively. The \nconcentration of P loss was in the following order: bare fallow < single crop < intercrop. In \ncomparing tillage systems, there was significant difference in K loss with a lower K loss in reduced \ntillage (chisel plough) than in conventional tillage (mouldboard plough). Potassium loss was found \nto be equal in both conventional tillage and reduced tillage systems. Mulch ripping (MR) and tied \nridging (TR) were efficient in reducing soil erosion and consequently nutrient losses through run-\noff and sediments (Munodawafa 2007). A difference in potassium loss was also seen among crops, \nan indication that with increased vegetation growth, sediment loss is reduced and ultimately less \npotassium loss. This explains why in fallow plots which had no vegetation, potassium loss was \nhigher. The effect of aggregate sediment loss on soil properties under mono-cropping of wheat and \ncorn, barely-legume intercropping and corn legume in comparison with fallow found maximum \nsediment and runoff in fallow, which was even higher compared to other practices (Ali et al. 2006). \nK was found to be significantly associated with a collective soil loss. OM, N, P, and K losses were \nmaximum with increased rainfall intensity (Tejada et al. 2008). \n \nSoil Organic Matter Loss with Sediments \nWhen there was a general increase in the concentrations of Organic Carbon, N, and P in soil under \nReduce Tillage total losses in both crops were lower. This was a direct consequence of the \nreduction in runoff and erosion, as well as of the size of material transported (Napoli et al. 2017). \nVegetative barriers may improve soil quality through organic matter input and N-fixation \ndepending on species composition, but appropriate soil management is required to minimise \ncompetition with adjacent crops (Guto et al. 2012). \n \n\n\n\nCONCLUSION \nThe mountainous and sub mountainous areas of Pakistan experience significant soil erosion due to \nundulating topography, torrential rainfall, conventional agriculture and other anthropogenic \nactivities. It is necessary to explore and identify the land use practices that can reduce soil erosion \nand achieve a certain level of agricultural production. The rate of runoff water was higher in fallow \nplots than in cropped plots. Among the crop systems, the lowest sediment loss was found in maize \nfodder and sesbania compared to other crops. Comparing the tillage systems, a lower sediment \nloss was found in reduced tillage (chisel) than in mouldboard plough. Overall, nutrient losses \ndiffered between crops and tillage systems, and the amount of organic matter loss between tillage \nsystems also differed.  However, a significant difference was found in crop systems with fallow \nplots showing the highest and maize plots the lowest organic matter loss collectively over the \nrainfall events. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 164-178 \n\n\n\n176 \n \n\n\n\nACKNOWLEDGMENTS \nWe wish to extend our sincere thanks to Dr. Shahzada Sohail Ijaz, Associate professor, PMAS arid \nAgriculture University Rawalpindi, for his guidance during the research period and providing \nlaboratory facilities for analysis. We thank field staff of Koont Farm Chakwal for their support. \nWe also thank Mr. Javaid Hassan and Anwar Zaman who helped in writing the manuscript. \n \n\n\n\nCONFLICT OF INTEREST \nWe declare no any conflict of interest. \n \n\n\n\nREFERENCES \nAdekalu, K. O., D. A. Okunade and J. A. Osunbitan. 2006. Compaction and mulching effects on soil loss \n\n\n\nand runoff from two south western Nigeria agricultural soils. Geoderma 137(1-2): 226-230. \nAli, I., F. 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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 49-66 (2016)                         Malaysian Society of Soil Science\n\n\n\nZinc Fractionation of Soils of Different Parent Materials and \ntheir Relationships with Some Soil Properties\n\n\n\nOkoli, N.H.*, B.U. Uzoho, E.U. Onweremadu, U.N. Nkwopara and \nI.F. Irokwe\n\n\n\nDepartment of Soil Science and Technology, Federal University of Technology\nP.M.B. 1526, Owerri, Imo State, Nigeria\n\n\n\nABSTRACT\nUnderstanding the distribution of various fractions of micronutrients in soils \nis important for effective and efficient management of soils for optimum crop \nproduction and environmental conservation. Distribution of various Zn fractions \nin soils of different parent materials (coastal plain sands, alluvium, false bedded \nsandstones and Imo clay shale) in Imo State, South-eastern Nigeria was evaluated \nusing sequential extraction procedure, comprising six fractions: water soluble, \nexchangeable, specifically sorbed/CO3 bound, Fe-Mn bound, organic matter (OM) \nbound and residual bound. Soil samples were sequentially extracted and the various \nZn fractions determined using atomic absorption spectrophotometer. Also, various \nZn fractions were correlated to selected soil properties. On average value basis, \nzinc fractions in mg/kg decreased in the order of residual bound (0.695) > Fe-\nMn oxide bound (0.598) > specifically sorbed/CO3 bound (0.454) > exchangeable \n(0.241) > water soluble (0.209) > OM bound (0.143) in soils of Coastal Plain \nSands; specifically sorbed/CO3 (0.408)> water soluble (0.378) > exchangeable \n(0.375) > residual bound (0.250) > OM bound (0.217) > Fe-Mn oxide bound \n(0.121) in soils of alluvium; specifically sorbed/CO3 bound (0.581)> residual \nbound (0.560) > Fe-Mn oxide bound (0.464) > OM bound (0.402) > exchangeable \n(0.283) > water soluble (0.182) in soils of false bedded sandstones; and residual \n(1.163) > specifically sorbed/CO3 bound (1.086)> exchangeable (0.587) > Fe-Mn \noxide bound (0.389) > OM bound (0.364) > water soluble (0.154) in soils of Imo \nclay shale. Available zinc concentrations were low and varied among the soils of \ndifferent parent materials in a decreasing order of alluvium (0.753 mg kg-1) > Imo \nclay shale (0.741 mg kg-1) > false bedded sandstones (0.464 mg kg-1) > coastal \nplain sands (0.449 mg kg-1) while total zinc concentrations  were in decreasing \norder of  Imo clay shale (3.742 mg kg-1) > falsebedded sandstones (2.471 mg kg-\n\n\n\n1) > coastal plain sands (2.340 mg kg-1) > alluvium (1.749 mg/kg).  Zn fractions \ncorrelated among each other and with pH, OM, ECEC, available P, Ca and clay.\n\n\n\nKeywords: Zinc fractions, parent materials, soil properties, South-eastern \nNigeria\n\n\n\n___________________\n*Corresponding author : E-mail: henrynek34@gmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201650\n\n\n\nOkoli et al.\n\n\n\nINTRODUCTION\nZinc is one of the eight trace elements that are essential for normal healthy growth \nand reproduction of plants. It is required as a structural component of a large \nnumber of proteins such as transcription factors and metallo-enzymes (Figueiredo \net al., 2012). Sadeghzadeh (2013) noted that zinc is required for the functioning \nof all the six enzyme classes (oxidoreductase, transferases, hydrolases, lyases, \nisomerases and ligases) in plants.\n\n\n\nZinc deficiency is prevalent worldwide in temperate and tropical climates \n(Slaton et al., 2005; Prasad, 2006; Fageria et al., 2011). Many cases of its \ndeficiency occur in developing (Third world) countries, where there is an urgent \nneed to increase food production in order to feed their population without relying \non food imports. Efforts are being made to reduce Zn deficiency in soils as it is \nnot only a barrier to achieving crop yield goals but also results in low Zn content \nin grains and straw leading to poor Zn nutrition for humans and animals, a subject \nwhich recently received considerable attention (Schardt, 2006).\n\n\n\nZinc bioavailability is reported to be associated with its transformation in \nsoils and plant continuum through various mechanisms, such as adsorption by \nclay surfaces, hydrous oxide minerals, organic matter and so forth, which affect \nZn uptake by crops (Soltani et al., 2015). For a better understanding, total soil \nZn can be broadly classified into five mechanistic fractions using sequential \nor batch fractionation schemes (Saffari et al., 2009). These fractions include \na water soluble pool, present in the soil solution, exchangeable pool with ions \nbound to soil particles by electrical charges, organically bound pool consisting of \nions adsorbed, chelated or complexed with organic ligands, pool of zinc sorbed \nnon-exchangeably onto clay minerals and insoluble metallic oxides and pool of \nweathering primary minerals (Alloway, 2008). These fractions provide broad \ninformation on the biological, geological and chemical processes occurring in a \nsoil and are useful for predicting the availability of Zn for plant uptake. It has been \nreported that the residual Zn and oxide bound Zn are the more stable fractions \nwhile the exchangeable Zn and water soluble Zn fractions are rather more soluble \nand available to plants (Rahmani, et al. 2012).\n\n\n\nDistributions of micronutrient forms vary with parent materials and profile \ndepths (Verma et al., 2005). It has been reported that soils derived from shale are \nrich in carbonate bound trace metal fractions (Hiller, 2006) while soils derived \nfrom false bedded sandstones are usually high in Fe-Mn oxides bound trace metal \nfractions (Gideon et al., 2014). \n\n\n\nThe extent to which each fraction is present and the transformation in \nequilibrium between fractions  is influenced by soil properties such as pH, cation \nexchange capacity, texture and soil organic matter (Ramzan et al., 2014), Thus, \nthe chemistry and effect of the aforementioned properties appear to be of major \nimportance in determining the concentration of Zn fractions (Naik and Das, 2007). \nFor instance, it has been reported that increasing soil pH increases concentration \nof carbonate bound zinc fraction (Meki et al., 2012), whereas a decrease in \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 51\n\n\n\nPhase Association of Zinc under Different Parent Material\n\n\n\npH increases concentration of water soluble zinc fraction (Kabata-Pendias and \nPendias, 1999). \n\n\n\nDespite several studies on zinc fractionation in soils, there have been few \nstudies on zinc fractionation of soils from different parent materials. Therefore, \nthe major objective of this study was to determine zinc fractionation of soils from \ndifferent parent materials and their relationships with some soil properties.\n\n\n\nMATERIALS AND METHODS\nStudy Area\nThe study was conducted in four different locations in Imo State, Southeastern \nNigeria, where soils are derived from four different parent materials of coastal \nplain sands, alluvium, Imo clay shale and false bedded sandstones (Orajaka, \n1975). The four study locations of different parent materials include Ihiagwa \n(coastal plain sands) located between latitude 50 211 and 50 271 N and longitude \n70 021 and 70 151 E, Egwe (alluvium) located between latitude 50 421 and 50 461 \nN and longitude 60 471 and 60 491 E, Amauro (Imo clay shale) located between \nlatitude 50 481 and 50 531 N and longitude 70 201 and 70 251 E and Mbato (false \nbedded sandstones) located between latitude 50 551 and 50 581 N and longitude 70 \n021 and 70 081. Imo State, South-eastern Nigeria lies between latitude 40 401and \n80 151 N and longitude 60 401 and 80 151 E (Federal Department of Agricultural \nLand Resources, 1985) and is within the humid tropics. Temperatures are high \nand change slightly during the year (mean daily temperature of about 270 C). The \naverage annual rainfall is about 2400 mm and there is a distinct dry season of \nabout 3 months. Imo State has rainforest vegetation characterised by multiple tree \nspecies (Onweremadu et al., 2007). Agriculture and cottage industries are major \nsocio-economic activities in the study area. Agricultural crops mostly cultivated \nin the study area include yam (Dioscorea spp), cassava (Manihot spp), oil palm \n(Elaies guineensis) and maize (Zea mays).\n\n\n\nSoil Sampling and Routine Laboratory Analyses \nA profile pit was dug in each parent material group, namely, coastal plain sands \n(Owerri), alluvium (Egwe), Imo clay shale (Amauro) and falsebedded sandstones \n(Mbato). Siting of profile pits was guided by the geological map of the study \narea. Soil samples were collected from soil horizons identified, starting from the \nbottom to the top to avoid contamination. The soil samples were air-dried, sieved \nusing a 2-mm sieve and subjected to laboratory analyses. Routine analyses were \nconducted for particle size (Gee and Or, 2002) and pH in 1: 2.5 solute/suspension \nratio using glass electrode of a pH meter (Thomas, 1996). Exchangeable cations \n(Ca2+,  Mg2+,  K+,  Na+) were extracted with NH4OAc buffered at pH 7.0 (Thomas, \n1982). Exchangeable K+ and Na+ contents of extracts were read on flame \nphotometer while exchangeable Ca2+ and Mg2+ were determined using atomic \nabsorption spectrophotometer. Exchangeable acidity (Al3+ and H+) was extracted \nwith 1 N KCl (Thomas, 1982) and determined by titrating with 0.5 N NaOH using \nphenolphthalein  indicator. Effective cation exchange capacity was obtained by \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201652\n\n\n\nsummation of basic and acidic cations, organic matter was determined by wet \noxidation method (Nelson and Sommers, 1982) while available P was determined \nby Bray II method (Olson and Sommers, 1982).\n\n\n\nZinc Sequential Fractionation Procedures\nWater soluble, exchangeable, specifically sorbed/carbonate bound, Fe-Mn oxide, \norganic matter bound and residual bound Zn fractions were determined using the \nsequential extraction procedure as described by Salbu et al. (1998). The procedure \nis outlined as follows: \n\n\n\nWater Soluble Fraction (F1): This was extracted as follows: Two grams of soil \nsample were weighed into a 50-mL polycarbonate centrifuge tube and extracted \nusing 20 mL of de-ionised water for 1 h at 200 C on a rolling table. \n\n\n\nExchangeable Fraction (F2): In this procedure, residue obtained from water \nsoluble extraction was washed with 10 mL de-ionised water and the washes \ndiscarded. The washed residue was transferred into a 50-mL polycarbonate \ncentrifuge tube and extracted with 20 mL of 1M NH4OAc solution buffered at \npH 7 for 2 h.\n\n\n\nSpecifically Sorbed and Carbonate Bound Fraction (F3): In this procedure, \nresidue obtained from extraction of exchangeable form was washed with 10 mL \nde-ionised water and the washes discarded. The washed residue was transferred \ninto a 50-mL polycarbonate tube and extracted with 20 mL of 1M NH4OAc \nsolution buffered at pH 5 for 2h. \n\n\n\nFe-Mn Oxide Bound Fraction (F4): In this procedure, the residue obtained from \nthe extraction of specifically sorbed and carbonate bound form was washed with \n10 mL de-ionised water and the washes discarded. The washed residue was \ntransferred into a 50-mL polycarbonate tube and extracted with 20 mL of 0.04 M \nNH2OH.HCl in 25% HOAc for 6 h in a water bath at 600 C.\n \nOrganic Matter Bound Fraction (F5): In this procedure, residue obtained from \nthe extraction of Fe-Mn oxide bound form was washed with 10 mL deionised \nwater and transferred into a 50-mL polycarbonate tube and extracted with 15 mL \nof 30% H2O2 at pH 2 (Adjusted with HNO3) for 5.5 h in a water bath at 800 C. The \ncontent was allowed to cool and 5 mL of 3.2 M NH4OAc in 20% HNO3 was added \nand diluted to 20 mL with deionised water. \n\n\n\nResidual Fraction (F6): In this method, 1g of the residue obtained from the \nextraction of organic matter bound form was dried after which it was digested in a \nconical flask with 10 mL of 7 M HNO3 on a hot plate for 6 h. After evaporation, 1 \nmL of 2 M HNO3 was added and the residue dissolved. Thereafter, it was diluted \nusing 10 mL de-ionised water. \n\n\n\nOkoli et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 53\n\n\n\nAfter each successive extraction, the mixture was centrifuged at 1000 rpm \nfor 30 min and the supernatant decanted into polyethylene bottles, acidified to pH \n<2 and stored for analysis. The various chemical fractions of Zn were determined \nusing ICE 3300 atomic absorption spectrophotometer at 324.8 nm.\n\n\n\nAvailable and Total Zinc Determination\nAvailable zinc was calculated as sum of water soluble and exchangeable fractions \nwhile total zinc was calculated as sum of all the fractions determined (Ramzan et \nal., 2014).\n\n\n\nStatistical Analysis\nData generated were subjected to coefficient of variation to determine variability \nin distribution of Zn fractions in the soil profiles studied and ranked according \nto the method of Wilding et al. (1994). The relationship between selected soil \nproperties and Zn fractions was estimated using simple linear correlation analysis.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPhysico-chemical Properties of the Soils of Different Parent Materials\nThe results of physico-chemical properties of the soils are presented in Table \n1.  Particle size distribution analysis indicated predominance of sand particles \n(94.7%) over clay (4%) and silt (1.3%) particles in the soils of alluvium.  A similar \ndistribution trend was observed for soils of coastal plain sands and false bedded \nsandstones with sand, silt and clay particles having mean values of 85.6%, 3.6% \nand 10.8%, respectively, in soils of coastal plain sand soils and 54.2%, 17.4% and \n28.4%, respectively, in soils of false bedded sandstones. But in soils of Imo clay \nshale, clay (42.8%) particle size was the highest followed by sand (34%) and silt \n(23.2%) The high clay content of the soils of Imo clay shale could be attributed \nto the clayey nature of the shale parent material from which the soils are derived \nfrom. Generally, clay particle size increases with depth and could be due to the  \nilluviation pedogenic process that may have taken place in the location. Soils of \ncoastal plain sands and alluvium were dominated by sandy loam and sand texture, \nrespectively (Table 1). That of soils derived from false bedded sandstones and \nImo clay shale were dominated by sandy clay loam and clay texture, respectively. \nSoil pH ranged from 4.28 - 5.63 in coastal plain sand soils, 5.32 - 5.62 in alluvial \nsoils, 5.12 - 5.41 in false bedded sandstone soils but in the Imo clay shale soils, it \nwas in the range of 5.55 - 5.98.  These values varied from extremely (<4.5) acidic \nto moderately (5.6 - 6.5) acidic (FAO, 2004). The low pH of the soils could be \ndue to high amounts of rainfall in the study area, resulting in leaching of basic \ncations; this has led to the exchange complex of the soils to be dominated by \nacidic cations. Similar pH results have been reported by Eshett et al. (1990) in \nsoils of South-eastern Nigeria. \n\n\n\nPhase Association of Zinc under Different Parent Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201654\n\n\n\nEffective cation exchange capacity (ECEC) of the soils was higher in Imo \nclay shale soils (5.28 cmolc kg-1), attributable to higher clay content of the soils \n(Table 1) followed by false bedded sandstones soils (5.08 cmolc kg-1), coastal plain \nsands soils (4.56 cmolc kg-1) and alluvial soils (4.09 cmolc kg-1). These values are \nlow compared with the critical limit (6 cmolc kg-1) recommended by Esu (1991) \nfor arable crop production.  This property is found to be an important predictor of \nmetal retention, movement (Udom et al., 2004) and extractability (Rieuwerts et \nal, 2005) in soils. Hence, the low ECEC of the soils indicates that the soils have \nlow metal retention capacity. Onweremadu et al. (2011) also reported low ECEC \nin soils derived from coastal plain sands, alluvium and false bedded sandstones in \nSouth-eastern Nigeria which is attributed to parent material, climate and land use \ninteractions. Higher ECEC values were recorded mostly in the surface horizons \n(Ap) and could be due to higher organic matter content of the horizons. Organic \nmatter (OM) content of all the soils studied was low (FAO, 2004) with mean \nvalues of the soils ranging from 2.03 g kg-1 to 46.78 g kg-1. The low organic matter \nconcentrations of the soils could be attributed to high temperature and rainfall in \nthe study area which encourages rapid mineralisation, erosion and leaching of \n\n\n\nTABLE 1 \n\n\n\nPhysico-chemical properties of the soils \n\n\n\nHorizon \n \n\n\n\nSoil Depth \n(cm) \n\n\n\nSand \n \n\n\n\nSilt \n (%) \n\n\n\nClay \n \n\n\n\nOM \n(g/kg) \n\n\n\nCa \n(cmolc kg-1) \n\n\n\npH  \n(H2O) \n\n\n\nAvail. P \n(mg/kg) \n\n\n\nECEC \n(cmolc kg-1) \n\n\n\nTC \n \n\n\n\nCoastal plain sands \nAp 0-17 90 4 6 13.99 1.22 4.28 0.86 4.14 S     \nAB 17-36 88 4 8 1.28 1.61 4.89 0.66 4.7 S \nBt1 36-53 86 2 12 1.28 1.22 5.63 0.73 3.75 LS \nBt2 53-91 84 4 12 6.41 2.03 4.91 0.47 5.11 LS \nBt3 91-150 80 4 16 1.56 1.81 5.26 0.56 5.09 SL \n\n\n\n \nMean 85.6 3.6 10.8 4.99 3.79 0.66 0.66 4.56 \n\n\n\n Alluvium \nAp 0-4 94 2 4 2.27 1.22 5.36 2.19 4.03 S \nBC  4-84 96 0 4 1.91 1.61 5.62 1.44 3.81 S \nC 84-100 94 2 4 1.91 2.01 5.32 2.56 4.67 S \n\n\n\n \nMean 94.7 1.3 4 2.03 1.61 5.43 2.06 4.17 \n\n\n\n False bedded sandstones \nAp 0-9 68 14 18 38.51 3.41 5.41 0.92 6.47 SL \nAB 9\u201428 50 20 30 5.72 1.4 5.41 0.76 4.14 SCL \nBt1 28-49 52 18 30 13.63 1.83 5.21 0.36 4.69 SCL \nBt2 49-73 52 16 32 2.24 1.61 5.12 0.27 4.88 SCL \nBt3 73-170 49 19 32 1.91 1.61 5.31 0.23 5.14 SCL \n\n\n\n \nMean 54.2 17.4 28.4 12.4 1.97 5.29 0.51 5.06 \n\n\n\n Imo clay shale \nAp 0-11 44 28 28 48.87 2.04 5.56 3.36 5.37 CL \nAB 19\u201411 30 26 44 47.95 1.02 5.56 0.39 2.89 C \nBt1 19-36 26 24 50 47.78 3.23 5.55 0.23 6.59 C \nBt2 36-55 28 12 60 43.28 2 5.98 0.96 4.79 C \nBt3 55-83 42 26 32 46.03 2.61 5.85 1.56 6.78 CL \n  Mean 34 23.2 42.8 46.78 2.18 5.7 1.3 5.28   \n\n\n\n \nOM- organic matter, Avail.P- available phosphorus, ECEC-effective cation exchange capacity, TC-textural class,S-sand,LS-loamy sand, SL-sandy loam, SCL- \nsandy clay loam,CL-clay loam, C- clay, \n\n\n\nOkoli et al.\n\n\n\nTABLE 1\nPhysico-chemical properties of the soils studied\n\n\n\nOM- organic matter, Avail. P- available phosphorus, ECEC-effective cation exchange capacity, \nTC-textural class,S-sand,LS-loamy sand, SL-sandy loam, SCL- sandy clay loam,CL-clay loam, \nC- clay,\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 55\n\n\n\norganic matter. Generally, OM decreased with soil depth which is in line with the \nreport of Uzoho et al. (2014).  With the exception of soils derived from Imo clay \nshale where exchangeable calcium attained a moderate level (2.16 cmolc kg-1), \nother soils had low exchangeable calcium (<2 cmolc kg-1).  Onweremadu et al., \n(2011) also reported low exchangeable calcium in soils of South-eastern Nigeria \nderived from coastal plain sands and alluvium and attributed the low results to \nthe sandiness of the soils which encourages leaching of calcium. Available P \nconcentrations of the soils were low (0.23-3.36 mg kg-1) and below the critical \nlimit (10 mg kg-1) recommended by Esu (1999) for arable crop production. The \nlow values of available P in all the soils could be due to low pH of the soils which \ncould have resulted in fixation of P by sesquioxides in the \n\n\n\nFractions and Distribution of Zinc in the Soils\nThe results of zinc fractions distribution of soils of different parent materials \nstudied are presented in Table 2. Water soluble zinc fraction of the soils differed \nand on a mean value basis, it was highest in alluvial soils (0.378 mg kg-1), followed \nby coastal plain sands soils (0.209 mg kg-1), false bedded sandstones soils (0.182 \nmg kg-1) and Imo clay shale soils (0.154 mg kg-1). Compared with other fractions, \nwater soluble Zn was the least fraction in the soils of coastal plain sands (8.93% \nof total Zn), false bedded sandstones (7.36% of total Zn) and Imo clay shale \n(4.11% of total Zn) but was intermediate fraction in soils of alluvium, attaining \n21.61% of the total Zn. Ramzan et al. (2014) also obtained least Zn concentration \nin the water soluble fraction. The low concentration of water soluble zinc when \ncompared with other fractions could be partially due to losses from leaching and \nplant uptake, since this represents the fraction that is most bioavailable and mobile \nin soil (Filgueiras et al., 2002). It could also be due to the poor extractive strength \nof water (Mbila et al., 2001). Kabata-Pendias and Pendias (1999) reported that \nthe concentration of water soluble zinc in soils ranges from 0.004 - 0.27 mg kg-\n\n\n\n1which is very low compared with the average total concentrations of about 50-80 \nmg/kg. However, in very acid soils, soluble concentration of about 7 mg kg-1 has \nbeen obtained, indicating that solubility is strongly, but inversely linked to soil \npH.  Therefore values of water soluble zinc fraction in the soils that were above \nthe soil range reported could be due to the acidic nature of the soils. The least \nconcentration of water soluble zinc fraction recorded in Imo clay shale soils could \nbe due to higher pH of the soils (Table 2). In most of the soils, higher values were \nrecorded in the upper horizons and could be due to highly decomposable organic \nmatter content of the horizons which favoured chelation of Zn2+. Alloway (2008) \nnoted that when soils are rich in rapidly decomposable organic matter, zinc may \nbecome more available due to the formation of soluble organic zinc complexes \nwhich are mobile and also probably capable of absorption into plant roots. Its \ndistribution in the Pedons of different parent materials varied from low to high as \nevident in high coefficient of variations recorded (Table 2).\n\n\n\nExchangeable zinc fraction was higher than water soluble Zn and varied \nfrom 0.147-0.455 mg/kg in coastal plain sands soils, 0.120-0.837 mg kg-1 in \n\n\n\nPhase Association of Zinc under Different Parent Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201656\n\n\n\n \nT\n\n\n\nA\nB\n\n\n\nL\nE\n\n\n\n 2\n: F\n\n\n\nR\nA\n\n\n\nC\nT\n\n\n\nIO\nN\n\n\n\nS \nA\n\n\n\nN\nD\n\n\n\n D\nIS\n\n\n\nT\nR\n\n\n\nIB\nU\n\n\n\nT\nIO\n\n\n\nN\n O\n\n\n\nF \nZI\n\n\n\nN\nC\n\n\n\n (m\ng/\n\n\n\nkg\n) I\n\n\n\nN\n T\n\n\n\nH\nE\n\n\n\n S\nO\n\n\n\nIL\nS \n\n\n\nST\nU\n\n\n\nD\nIE\n\n\n\nD\n \n\n\n\n \n H\n\n\n\nor\niz\n\n\n\non\n \n\n\n\nD\nep\n\n\n\nth\n \n\n\n\n(c\nm\n\n\n\n)  \nW\n\n\n\nat\ner\n\n\n\n \nso\n\n\n\nlu\nbl\n\n\n\ne \n \n\n\n\nE\nxc\n\n\n\nha\nng\n\n\n\nea\nbl\n\n\n\ne \n\n\n\nA\nva\n\n\n\nila\nbl\n\n\n\ne \n\n\n\nsp\nec\n\n\n\nifi\nca\n\n\n\nlly\n \n\n\n\nso\nrb\n\n\n\ned\n/C\n\n\n\nO\n3 \n\n\n\nbo\nun\n\n\n\nd \nFe\n\n\n\n-M\nn \n\n\n\nbo\nun\n\n\n\nd \n \n\n\n\nO\nM\n\n\n\n B\nou\n\n\n\nnd\n  \n\n\n\nR\nes\n\n\n\nid\nua\n\n\n\nl \nB\n\n\n\nou\nnd\n\n\n\n \nT\n\n\n\not\nal\n\n\n\n\n\n\n\nC\noa\n\n\n\nst\nal\n\n\n\n p\nla\n\n\n\nin\n sa\n\n\n\nnd\ns \n\n\n\nA\np \n\n\n\n0\u2014\n17\n\n\n\n  \n0.\n\n\n\n45\n8 \n\n\n\n \n0.\n\n\n\n45\n5 \n\n\n\n \n0.\n\n\n\n91\n3 \n\n\n\n \nN\n\n\n\nD\n \n\n\n\n0.\n57\n\n\n\n0 \n \n\n\n\n0.\n10\n\n\n\n4 \n \n\n\n\n1.\n34\n\n\n\n7 \n \n\n\n\n2.\n93\n\n\n\n4 \n \n\n\n\nA\nB\n\n\n\n  \n17\n\n\n\n-3\n6 \n\n\n\n \n0.\n\n\n\n17\n3 \n\n\n\n \n0.\n\n\n\n22\n7 \n\n\n\n \n0.\n\n\n\n40\n0 \n\n\n\n \n0.\n\n\n\n43\n1 \n\n\n\n \n0.\n\n\n\n31\n7 \n\n\n\n \n0.\n\n\n\n21\n6 \n\n\n\n \n0.\n\n\n\n98\n1 \n\n\n\n \n2.\n\n\n\n34\n5 \n\n\n\n \nB\n\n\n\nt1\n  \n\n\n\n36\n-5\n\n\n\n3 \n \n\n\n\n0.\n12\n\n\n\n5 \n \n\n\n\n0.\n19\n\n\n\n0 \n \n\n\n\n0.\n31\n\n\n\n5 \n \n\n\n\nN\nD\n\n\n\n  \n0.\n\n\n\n35\n9 \n\n\n\n \n0.\n\n\n\n14\n0 \n\n\n\n \n0.\n\n\n\n72\n4 \n\n\n\n \n1.\n\n\n\n53\n8 \n\n\n\n \nB\n\n\n\nt2\n  \n\n\n\n53\n-9\n\n\n\n1 \n \n\n\n\n0.\n13\n\n\n\n6 \n \n\n\n\n0.\n14\n\n\n\n7 \n \n\n\n\n0.\n28\n\n\n\n3 \n \n\n\n\n1.\n34\n\n\n\n2 \n \n\n\n\n1.\n42\n\n\n\n8 \n \n\n\n\n0.\n10\n\n\n\n8 \n \n\n\n\nN\nD\n\n\n\n  \n3.\n\n\n\n16\n1 \n\n\n\n \nB\n\n\n\nt3\n  \n\n\n\n91\n-1\n\n\n\n50\n  \n\n\n\n0.\n15\n\n\n\n1 \n \n\n\n\n0.\n18\n\n\n\n5 \n \n\n\n\n0.\n33\n\n\n\n6 \n \n\n\n\n0.\n49\n\n\n\n5 \n \n\n\n\n0.\n31\n\n\n\n7 \n \n\n\n\n0.\n14\n\n\n\n8 \n \n\n\n\n0.\n42\n\n\n\n5 \n \n\n\n\n1.\n72\n\n\n\n1 \n \n\n\n\n \nM\n\n\n\nea\nn \n\n\n\n0.\n20\n\n\n\n9 \n0.\n\n\n\n24\n1 \n\n\n\n0.\n44\n\n\n\n9 \n0.\n\n\n\n45\n4 \n\n\n\n0.\n59\n\n\n\n8 \n0.\n\n\n\n14\n3 \n\n\n\n0.\n69\n\n\n\n5 \n2.\n\n\n\n34\n0 \n\n\n\n \n%\n\n\n\nT\not\n\n\n\nal\n \n\n\n\n8.\n93\n\n\n\n%\n \n\n\n\n10\n.2\n\n\n\n9%\n \n\n\n\n19\n.1\n\n\n\n9%\n \n\n\n\n19\n.4\n\n\n\n0%\n \n\n\n\n25\n.5\n\n\n\n5%\n \n\n\n\n6.\n11\n\n\n\n%\n \n\n\n\n29\n.7\n\n\n\n0%\n \n\n\n\n\n\n\n\n%\nC\n\n\n\nV\n \n\n\n\n67\n.4\n\n\n\n \n51\n\n\n\n.1\n \n\n\n\n58\n.4\n\n\n\n \n12\n\n\n\n0.\n9 \n\n\n\n79\n.5\n\n\n\n0 \n31\n\n\n\n.4\n \n\n\n\n26\n.4\n\n\n\n \n51\n\n\n\n.9\n \n\n\n\nA\nllu\n\n\n\nvi\num\n\n\n\n \nA\n\n\n\np \n0-\n\n\n\n4 \n \n\n\n\n0.\n46\n\n\n\n1 \n \n\n\n\n0.\n16\n\n\n\n8 \n \n\n\n\n0.\n62\n\n\n\n9 \n \n\n\n\n0.\n13\n\n\n\n6 \n \n\n\n\nN\nD\n\n\n\n  \n0.\n\n\n\n21\n0 \n\n\n\n \n0.\n\n\n\n22\n4 \n\n\n\n \n1.\n\n\n\n19\n9 \n\n\n\n \nB\n\n\n\nC\n  \n\n\n\n4\u2014\n84\n\n\n\n  \n0.\n\n\n\n48\n2 \n\n\n\n \n0.\n\n\n\n83\n7 \n\n\n\n \n1.\n\n\n\n31\n9 \n\n\n\n \n0.\n\n\n\n15\n4 \n\n\n\n \n0.\n\n\n\n18\n7 \n\n\n\n \n0.\n\n\n\n17\n5 \n\n\n\n \n0.\n\n\n\n33\n0 \n\n\n\n \n2.\n\n\n\n16\n5 \n\n\n\n \nC\n\n\n\n  \n84\n\n\n\n-1\n00\n\n\n\n  \n0.\n\n\n\n19\n0 \n\n\n\n \n0.\n\n\n\n12\n0 \n\n\n\n \n0.\n\n\n\n31\n0 \n\n\n\n \n0.\n\n\n\n93\n3 \n\n\n\n \n0.\n\n\n\n17\n6 \n\n\n\n \n0.\n\n\n\n26\n5 \n\n\n\n \n0.\n\n\n\n19\n6 \n\n\n\n \n1.\n\n\n\n88\n0 \n\n\n\n\n\n\n\nM\nea\n\n\n\nn \n0.\n\n\n\n37\n8 \n\n\n\n0.\n37\n\n\n\n5 \n0.\n\n\n\n75\n3 \n\n\n\n0.\n40\n\n\n\n8 \n0.\n\n\n\n12\n1 \n\n\n\n0.\n21\n\n\n\n7 \n0.\n\n\n\n25\n0 \n\n\n\n1.\n74\n\n\n\n9 \n \n\n\n\n%\nT\n\n\n\not\nal\n\n\n\n \n21\n\n\n\n.6\n1%\n\n\n\n \n21\n\n\n\n.4\n4%\n\n\n\n \n43\n\n\n\n.0\n5%\n\n\n\n \n23\n\n\n\n.3\n3%\n\n\n\n \n6.\n\n\n\n92\n%\n\n\n\n \n12\n\n\n\n.4\n1%\n\n\n\n \n14\n\n\n\n.2\n9%\n\n\n\n\n\n\n\n \n%\n\n\n\nC\nV\n\n\n\n \n43\n\n\n\n.1\n \n\n\n\n10\n6.\n\n\n\n9 \n68\n\n\n\n.5\n \n\n\n\n11\n1.\n\n\n\n6 \n86\n\n\n\n.7\n \n\n\n\n20\n.9\n\n\n\n \n28\n\n\n\n.3\n \n\n\n\n9.\n2 \n\n\n\nFa\nls\n\n\n\ne \nbe\n\n\n\ndd\ned\n\n\n\n sa\nnd\n\n\n\nst\non\n\n\n\nes\n \n\n\n\nA\np \n\n\n\n0-\n9 \n\n\n\n \n0.\n\n\n\n22\n6 \n\n\n\n \n0.\n\n\n\n06\n5 \n\n\n\n \n0.\n\n\n\n29\n1 \n\n\n\n \n0.\n\n\n\n72\n2 \n\n\n\n \n0.\n\n\n\n37\n5 \n\n\n\n \n0.\n\n\n\n35\n6 \n\n\n\n \nN\n\n\n\nD\n  \n\n\n\n1.\n74\n\n\n\n3 \n \n\n\n\nA\nB\n\n\n\n  \n9\u2014\n\n\n\n28\n  \n\n\n\n0.\n13\n\n\n\n9 \n \n\n\n\n0.\n75\n\n\n\n5 \n \n\n\n\n0.\n89\n\n\n\n4 \n \n\n\n\n0.\n62\n\n\n\n2 \n \n\n\n\n0.\n61\n\n\n\n8 \n \n\n\n\n0.\n19\n\n\n\n4 \n \n\n\n\n1.\n07\n\n\n\n4 \n \n\n\n\n3.\n40\n\n\n\n2 \n \n\n\n\nB\nt1\n\n\n\n  \n28\n\n\n\n-4\n9 \n\n\n\n \n0.\n\n\n\n23\n4 \n\n\n\n \n0.\n\n\n\n14\n7 \n\n\n\n \n0.\n\n\n\n38\n1 \n\n\n\n \n0.\n\n\n\n55\n6 \n\n\n\n \nN\n\n\n\nD\n  \n\n\n\n0.\n85\n\n\n\n7 \n \n\n\n\n0.\n34\n\n\n\n1 \n \n\n\n\n2.\n13\n\n\n\n5 \n \n\n\n\nB\nt2\n\n\n\n  \n49\n\n\n\n-7\n3 \n\n\n\n \n0.\n\n\n\n16\n6 \n\n\n\n \n0.\n\n\n\n22\n9 \n\n\n\n \n0.\n\n\n\n39\n5 \n\n\n\n \n0.\n\n\n\n97\n3 \n\n\n\n \n0.\n\n\n\n57\n9 \n\n\n\n \n0.\n\n\n\n60\n2 \n\n\n\n \n0.\n\n\n\n38\n0 \n\n\n\n \n2.\n\n\n\n92\n9 \n\n\n\n \nB\n\n\n\nt3\n  \n\n\n\n73\n-1\n\n\n\n70\n  \n\n\n\n0.\n14\n\n\n\n3 \n \n\n\n\n0.\n21\n\n\n\n8 \n \n\n\n\n0.\n36\n\n\n\n1 \n \n\n\n\n0.\n03\n\n\n\n3 \n \n\n\n\n0.\n74\n\n\n\n8 \n \n\n\n\nN\nD\n\n\n\n  \n1.\n\n\n\n00\n6 \n\n\n\n \n2.\n\n\n\n14\n8 \n\n\n\n\n\n\n\nM\nea\n\n\n\nn \n0.\n\n\n\n18\n2 \n\n\n\n0.\n28\n\n\n\n3 \n0.\n\n\n\n46\n4 \n\n\n\n0.\n58\n\n\n\n1 \n0.\n\n\n\n46\n4 \n\n\n\n0.\n40\n\n\n\n2 \n0.\n\n\n\n56\n0 \n\n\n\n2.\n47\n\n\n\n1 \n\n\n\n \n%\n\n\n\nT\not\n\n\n\nal\n \n\n\n\n7.\n36\n\n\n\n%\n \n\n\n\n11\n.4\n\n\n\n5%\n \n\n\n\n18\n.7\n\n\n\n8%\n \n\n\n\n23\n.5\n\n\n\n1%\n \n\n\n\n18\n.7\n\n\n\n8%\n \n\n\n\n16\n.2\n\n\n\n7%\n \n\n\n\n22\n.6\n\n\n\n6%\n \n\n\n\n\n\n\n\n%\nC\n\n\n\nV\n \n\n\n\n25\n.0\n\n\n\n \n96\n\n\n\n.2\n \n\n\n\n52\n.4\n\n\n\n \n59\n\n\n\n.4\n \n\n\n\n62\n.9\n\n\n\n \n83\n\n\n\n.8\n \n\n\n\n24\n.1\n\n\n\n \n63\n\n\n\n.5\n \n\n\n\nIm\no \n\n\n\ncl\nay\n\n\n\n sh\nal\n\n\n\ne \nA\n\n\n\np \n0-\n\n\n\n11\n  \n\n\n\n0.\n14\n\n\n\n6 \n \n\n\n\n0.\n09\n\n\n\n6 \n \n\n\n\n0.\n24\n\n\n\n2 \n \n\n\n\n1.\n18\n\n\n\n4 \n \n\n\n\n0.\n34\n\n\n\n0 \n \n\n\n\n0.\n95\n\n\n\n4 \n \n\n\n\n2.\n43\n\n\n\n1 \n \n\n\n\n5.\n15\n\n\n\n1 \n \n\n\n\nA\nB\n\n\n\n  \n11\n\u2014\n\n\n\n19\n  \n\n\n\n0.\n15\n\n\n\n1 \n \n\n\n\n0.\n06\n\n\n\n5 \n \n\n\n\n0.\n21\n\n\n\n6 \n \n\n\n\n1.\n00\n\n\n\n8 \n \n\n\n\n0.\n50\n\n\n\n4 \n \n\n\n\n0.\n55\n\n\n\n0 \n \n\n\n\n0.\n13\n\n\n\n8 \n \n\n\n\n2.\n41\n\n\n\n6 \n \n\n\n\nB\nt2\n\n\n\n  \n19\n\n\n\n-3\n6 \n\n\n\n \n0.\n\n\n\n16\n6 \n\n\n\n \n0.\n\n\n\n80\n3 \n\n\n\n \n0.\n\n\n\n96\n9 \n\n\n\n \n1.\n\n\n\n00\n7 \n\n\n\n \n0.\n\n\n\n41\n7 \n\n\n\n \n0.\n\n\n\n14\n2 \n\n\n\n \n1.\n\n\n\n31\n0 \n\n\n\n \n3.\n\n\n\n84\n5 \n\n\n\n \nB\n\n\n\nt2\n  \n\n\n\n36\n-5\n\n\n\n5 \n \n\n\n\n0.\n14\n\n\n\n4 \n \n\n\n\n0.\n94\n\n\n\n5 \n \n\n\n\n1.\n08\n\n\n\n9 \n \n\n\n\n1.\n10\n\n\n\n6 \n \n\n\n\n0.\n35\n\n\n\n9 \n \n\n\n\n0.\n17\n\n\n\n4 \n \n\n\n\n0.\n31\n\n\n\n0 \n \n\n\n\n3.\n03\n\n\n\n8 \n \n\n\n\nB\nt3\n\n\n\n  \n55\n\n\n\n-8\n3 \n\n\n\n \n0.\n\n\n\n16\n4 \n\n\n\n \n1.\n\n\n\n02\n4 \n\n\n\n \n1.\n\n\n\n18\n8 \n\n\n\n \n1.\n\n\n\n12\n4 \n\n\n\n \n0.\n\n\n\n32\n4 \n\n\n\n \nN\n\n\n\nD\n  \n\n\n\n1.\n62\n\n\n\n6 \n \n\n\n\n4.\n26\n\n\n\n2 \n \n\n\n\n \nM\n\n\n\nea\nn \n\n\n\n0.\n15\n\n\n\n4 \n0.\n\n\n\n58\n7 \n\n\n\n0.\n74\n\n\n\n1 \n1.\n\n\n\n08\n6 \n\n\n\n0.\n38\n\n\n\n9 \n0.\n\n\n\n36\n4 \n\n\n\n1.\n16\n\n\n\n3 \n3.\n\n\n\n74\n2 \n\n\n\n \n%\n\n\n\nT\not\n\n\n\nal\n \n\n\n\n4.\n11\n\n\n\n%\n \n\n\n\n15\n.6\n\n\n\n9%\n \n\n\n\n19\n.8\n\n\n\n0%\n \n\n\n\n29\n.0\n\n\n\n2%\n \n\n\n\n10\n.3\n\n\n\n9%\n \n\n\n\n9.\n73\n\n\n\n%\n \n\n\n\n31\n.0\n\n\n\n8%\n \n\n\n\n\n\n\n\n%\nC\n\n\n\nV\n \n\n\n\n6.\n6 \n\n\n\n79\n.9\n\n\n\n \n63\n\n\n\n.9\n \n\n\n\n7.\n1 \n\n\n\n18\n.9\n\n\n\n \n10\n\n\n\n6.\n5 \n\n\n\n81\n.8\n\n\n\n \n28\n\n\n\n.4\n \n\n\n\nN\nD\n\n\n\n- n\no \n\n\n\nde\nte\n\n\n\nct\nio\n\n\n\nn\n\n\n\nTA\nB\n\n\n\nLE\n 2\n\n\n\nFr\nac\n\n\n\ntio\nns\n\n\n\n A\nnd\n\n\n\n D\nis\n\n\n\ntri\nbu\n\n\n\ntio\nn \n\n\n\nof\n Z\n\n\n\nin\nc \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n) I\nn \n\n\n\nTh\ne \n\n\n\nSo\nils\n\n\n\n S\ntu\n\n\n\ndi\ned\n\n\n\nOkoli et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 57\n\n\n\nalluvial soils, 0.065-0.755 mg kg-1 in false bedded sandstones soils and 0.065-\n1.024 mg kg-1 in Imo clay shale soils (Table 2). Distribution among the sites on \na mean value basis was in a decreasing order of Imo clay shale (0.587 mg kg-1) \n> alluvium (0.375 mg kg-1) > false bedded sandstones (0.283 mg/kg) > coastal \nplain sands (0.241 mg kg-1). When compared with other zinc fractions, it was low \nin the soils of coastal plain sands and false bedded sandstones, attaining about \n10.29% and 11.45% of total Zn, respectively whereas in soils of alluvium and \nImo clay shale, it was an intermediate fraction, attaining 21.44% and 15.69% of \ntotal Zn, respectively. Schulte (2004) noted that soils contain exchangeable zinc \nfraction between the range of 2-25 mg kg-1, with a larger proportion held in iron \nand manganese oxides. The values of exchangeable Zn recorded in the soils were \nbelow the range and could be due to the sandiness of some of the soils, low pH \nof the soils as decreasing pH decreases cation exchange capacity of soils (Das, \n2011) as well as low organic matter content of the soils, hence exchange sites for \nattraction of Zn2+ were in small quantity. The highest concentration recorded in \nsoils of Imo clay shale could be attributed to higher pH (5.7) and clay (42.8%) \ncontents of the soils as increasing levels of both the soil properties increases cation \nexchange capacity of soils (Brady and Weil, 2010). Except in soils of alluvium and \nfalse bedded sandstones where its distribution in the soil profiles did not follow \na definite pattern, in soils of coastal plain sands, its concentration decreased with \nsoil depth and could be due to decreasing organic matter content down the profile \nwhereas in soils of Imo clay shale, it increased down the profile and could be due \nto increasing clay content with depth (Table 2). Das (2011) noted that organic \nmatter and clay are known to constitute exchange sites in soils. The results further \nindicate high variation in its distribution in the soils of different parent materials \nas evident in the high coefficient of variations recorded (Table 2).\n\n\n\nSpecifically sorbed/CO3 bound zinc fraction differed in the soils and was \ntwo times more than exchangeable Zn fraction. Mean values of 0.454 mg kg-\n\n\n\n1equivalent to 19.40% of total Zn, 0.408 mg kg-1 equivalent to 23.33% of total \nzinc, 0.581 mg kg-1 equivalent to 23.51% of total zinc and 1.086 mg kg-1 equivalent \nto 29.02% of total zinc were recorded in soils of coastal plain sands, alluvium, \nfalse bedded sandstones and Imo clay shale, respectively (Table 2). Distribution \namong the sites decreased as Imo clay shale > false bedded sandstones > coastal \nplain sands > alluvium. It was a dominant fraction in soils of alluvium and Imo \nclay shale, intermediate fraction in soils of coastal plain sands and the highest \nfraction in soils of false bedded sandstones. It was higher in soils of Imo clay \nshale and could be due to high carbonate content of the shale parent material \nfrom which the soils were derived from (Hiller, 2006) as well as higher pH of the \nsoils as increasing pH increases concentration of carbonate bound zinc (Ramzan \net al., 2014). Rajakumar (1994) noted that carbonate bound zinc is usually seen \nin soils with high pH and lime content.   Except in soils of alluvium where its \nconcentration increased with soil depth, its distribution in other soils followed \nan irregular pattern (Table 2). The results further indicated high variation in its \ndistribution in soils of coastal plain sands (CV= 120.9%), alluvium (CV= 111.6%) \n\n\n\nPhase Association of Zinc under Different Parent Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201658\n\n\n\nand false bedded sandstones (CV= 59.4%), an indication of uneven distribution \nwhereas low variation was recorded in soils of Imo clay shale (CV= 7.1%). \n\n\n\nFe-Mn oxide bound zinc fraction, being among the non-residual fraction, \nwas found to be the major Zn fraction with values ranging from 0.317-1.428 mg \nkg-1 in coastal plain sands soils, 0-0.187 mg kg-1 in alluvial soils, 0-0.748 mg kg-1 \n\n\n\nin false bedded sandstones soils and 0.324-0.504 in Imo clay shale soils. When \ncompared with other zinc fractions of the soils, it was dominant in soils of plain \nsands, low in soils of alluvium but intermediate fraction in soils of false bedded \nsandstones and Imo clay shale. However, these values are low when compared \nwith values ranging from 2.18 to 7.65 mg kg-1 (mean= 4.53 mg/kg) reported in \nGangavati taluk soils of Dharwad, India  which is derived from mixed parent \nmaterials (Wijebandara et al., 2011) and the findings of Shober (2007) in soils of \nPennsylvania, U.S.A. derived from limestone (Ciolkosz et al., 1995) and could be \nattributed to low total zinc content of the soils under study. It was lower in soils of \nalluvium, attributable to sandiness of the soils as oxides of Fe and Mn are usually \npresent in the clay fraction of soils (Kabata-Pendias, 2011). Its distribution pattern \nwas irregular in soils of coastal plain sands and Imo clay shale but increased \nand decreased with soil depth in soils of false bedded sandstones and alluvium, \nrespectively (Table 2). The results further indicate moderate variation in its \ndistribution in soil profile of Imo clay shale (CV= 18.9%) whereas high variations \nwere recorded in soil profiles of coastal plain sands (CV= 79.5%), false bedded \nsandstones (CV= 62.5%), and alluvium (CV= 86.7%). Generally, Fe-Mn oxide \nbound zinc fraction of the soils of different parent materials decreased in the order \nof coastal plain sands > false bedded sandstones > Imo clay shale > alluvium.\n\n\n\nOrganic matter bound zinc fraction followed a similar distribution trend with \nwater soluble Zn as it was low in most of the soils. Mean organic matter bound \nZn concentration decreased in the order of false bedded sandstones (0.402 mg \nkg-1) > Imo clay shale (0.346 mg kg-1) > alluvium (0.217 mg kg-1)> coastal plain \nsands (0.143 mg kg-1). It was the least Zn fraction in soils of coastal plain sands \n(6.11% of total zinc), low in soils of alluvium (12.41% of total zinc) and Imo \nclay shale (9.73% of total zinc) but  intermediate  fraction in soils of false bedded \nsandstones (16.27% of total Zn) (Table 2). The values were low when compared \nwith the report of Ideriah et al., (2013) in alluvial soils of Niger Delta, Nigeria \nand could be due to low total zinc contents of the soils (<10 mg kg-1) as well as \nlow organic matter (<50 g kg-1) contents of the soils under study. It was higher \nin the surface horizon and could be due to higher organic matter content of the \nhorizon. Additionally, the results indicated moderate variation in its distribution \nin soils of coastal plain sands (CV= 31.4%) and alluvium (CV= 20.9%) whereas \nits distribution followed high variation in soils of false bedded sandstones (CV= \n83.8%) sandstones and Imo clay shale (CV= 106.5%).\n\n\n\nResidual bound zinc dominated most of the soils. Mean value of 0.695 mg/\nkg equivalent to 29.70% of total zinc was recorded in coastal plain sands soils, \n0.250 mg/kg equivalent to 14.29% of total zinc in soils of alluvium, 0.560 mg \nkg-1 equivalent to 22.66% of total zinc in false bedded sandstone soils and 1.163 \n\n\n\nOkoli et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 59\n\n\n\nmg kg-1 equivalent to 31.08% of total zinc in soils of Imo clay shale. Distribution \namong the sites decreased in the order of Imo clay shale > coastal plain sands \n> falsebedded sandstones > alluvium. It was the highest Zn fraction in soils of \ncoastal plain sands and Imo clay shale but dominant in soils of false bedded \nsandstones and alluvium.  The results are in agreement with the report of Ramzan \net al., (2014) and Aydinalp (2009) who obtained highest concentration of zinc \nin the residual fraction. The greater percentage of Zn in the residual fraction in \nmost of the soils indicated its greater tendency to become unavailable to plants \n(Ramzan et al., 2014). This is because the residual bound fraction represents \nmetals incorporated into the crystalline lattices of clays and appears inactive \n(Kabala and Singh, 2001). It was more in soils of Imo clay shale and could be due \nto higher clay content of the soils but lower in soils of alluvium, attributable to \nlow clay content of the soils as residual bound fraction of metals is known to be \nembedded in silicate clays (Ma and Rao, 1997). Furthermore, in the soil profiles \nof different parent materials, its distribution pattern was irregular and moderate \nvariations were recorded in soils of coastal plain sands (CV= 26.4%), alluvium \n(CV= 28.3%), false bedded sandstones (CV= 24.1%) while high variation was \nnoted in soils of Imo clay shale (CV= 81.8%). \n\n\n\nGenerally, fractional distribution of zinc varied in each of the soil profiles of \ndifferent parent materials. Zinc fractions decreased in the order of residual bound \n> Fe-Mn oxide bound > specifically sorbed/CO3 bound > exchangeable > water \nsoluble > OM bound in coastal plain sands soils, specifically sorbed/CO3  bound \n> water soluble > exchangeable > residual bound > OM bound > Fe-Mn Oxide \nbound in soils of alluvium, specifically sorbed/CO3 bound > residual bound > \nFe-Mn Oxide bound > OM bound > exchangeable > water soluble in soils of \nfalsebedded sandstones and residual bound >  specifically sorbed/CO3 bound> \nexchangeable > Fe-Mn Oxide bound > OM bound >  water soluble in soils of Imo \nclay shale.\n\n\n\nDistribution of Available and Total Zinc in the Soils\nFor available zinc fraction, mean values of 0.241 mg kg-1, 0.753 mg kg-1, 0.464 \nmg kg-1 and 0.741 mg kg-1 were recorded for coastal plain sands, alluvium, false \nbedded sandstones and Imo clay shale, respectively. For arable crop production, \nEsu (1991) classified available zinc as low, medium and high when concentrations \nvaried  in the order <0.8, 0.8-2.0 and >2.0 mg kg-1, respectively. Using the mean \nvalues, all the soils were low in available zinc. The low values of available zinc \nrecorded in the soils could be due to low total zinc concentrations (Table 2) of \nthe soils as total concentration of an essential element is an indication of its \navailability, sandiness of some of the soils, high amount of rainfall in the area \nas well as the acidic nature of the soils which encourages leaching losses of \nzinc.  According to Alloway (2008), sandy soils and acid highly leached soils \nwith low total zinc concentrations are highly prone to zinc deficiency. Eteng et \nal., (2014) also reported low available zinc in coastal plain sand soils of South-\neastern, Nigeria.  It was more in soils of alluvium followed by soils of Imo clay \n\n\n\nPhase Association of Zinc under Different Parent Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201660\n\n\n\nshale, false bedded sandstones and coastal plain sands. The highest concentration \nrecorded in soils of alluvium could be due to very low organic matter content of \nthe soils as increasing organic matter content in soils decreases zinc availability \ndue to increased sorption of zinc by organic ligands and components (Alloway,  \n2004). It could also be due to sandiness of the soils which reduced adsorption sites \nfor Zn2+. Additionally, its distribution in all the soils of different parent materials \ndid not follow a regular pattern. The results further indicate high variation in its \ndistribution in the soil profiles of different parent materials under study (Table 2). \nConcentration of total zinc in the soils ranged from 1.538-3.161 mg/kg (mean= \n2.340 mg kg-1), 1.880 -2.165 mg kg-1 (mean= 1.749 mg kg-1), 1.743-3.402 mg \nkg-1 (mean= 2.471 mg kg-1) and 2.416 - 5.151 mg kg-1 (mean= 3.742 mg kg-1) \nin soils of coastal plain sands, alluvium, false bedded sandstones and Imo clay \nshale, respectively (Table 2). Kabata-Pendias (2011) reported that zinc content of \nagricultural soils varies between 10  - 300 mg kg-1. Based on the report, the soils \nwere considered low in total zinc since total zinc concentrations of the soils were \nbelow the range. This could be due high intensity of rainfall in the area which \nmay have triggered weathering and leaching losses of weathered materials. These \nfindings are in line with the report of Onweremadu et al. (2008) who obtained low \ntotal zinc in coastal plain sands (2 - 5 mg kg-1) and Imo clay shale soils of Imo \nState, South-eastern, Nigeria. It was higher in Imo clay shale soils followed by \nfalse bedded sandstones soils, coastal plain sands soils and alluvial soils. Higher \nvalues recorded in soils of Imo clay shale could be due to high zinc content of \nthe shale parent material as reported by Havlin et al. (2012). However, total zinc \nconcentration of the soils did not attain toxic level since it was below 300 mg/kg \nmaximum permissible agricultural soil concentration of total zinc recommended \nby Kabata-Pendias and Pendias (2001).  The results further indicated that its \ndistribution followed low variation in soils of alluvium (CV= 9.2%), moderate \nvariation in soils of Imo clay shale (CV= 28.4%) and high variation in soils of \ncoastal plain sands (CV= 51.9%) and false bedded sandstones (CV= 63.5%).\n\n\n\nTable 3 shows the relationships existing among zinc fractions and between \nzinc fractions and selected soil properties (OM, ECEC, Clay, Ca and pH) using \nsimple linear correlation analysis. The results of the analysis indicated significant \n(p<0.05) negative correlation between exchangeable zinc and organic matter \nbound zinc (r = 0.46). This implies that a decrease in organic matter bound zinc \nwill significantly result in an increase in exchangeable zinc. However, water \nsoluble zinc was found to have a significant and negative (p<0.05) relationship \nwith organic matter bound zinc (r = -0.46) whereas specifically sorbed/CO3 \nbound zinc correlated significantly (p<0.05) and negatively with water soluble \nzinc (r = -0.55) while it had a significant (p<0.05) and positive relationship with \nCa (r = 0.48) and clay (r = 0.51) (Table 3). In addition, organic matter had a \nsignificant (p<0.05) and positive correlation with exchangeable zinc (r = 0.56) \nand specifically sorbed/CO3 bound zinc (r = 0.47) (Table 3). The implication of \nthese results is that an increase in organic matter content of the soils will result in \nan increase in exchangeable and specifically/CO3 bound zinc fractions. However, \n\n\n\nOkoli et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 61\n\n\n\nT\nA\n\n\n\nB\nL\n\n\n\nE\n 3\n\n\n\n: \nSI\n\n\n\nM\nPL\n\n\n\nE\n L\n\n\n\nIN\nE\n\n\n\nA\nR\n\n\n\n C\nO\n\n\n\nR\nR\n\n\n\nE\nL\n\n\n\nA\nT\n\n\n\nIO\nN\n\n\n\n A\nM\n\n\n\nO\nN\n\n\n\nG\n Z\n\n\n\nIN\nC\n\n\n\n F\nR\n\n\n\nA\nC\n\n\n\nT\nIO\n\n\n\nN\nS \n\n\n\nA\nN\n\n\n\nD\n B\n\n\n\nE\nT\n\n\n\nW\nE\n\n\n\nE\nN\n\n\n\n Z\nIN\n\n\n\nC\n F\n\n\n\nR\nA\n\n\n\nC\nT\n\n\n\nIO\nN\n\n\n\nS \nA\n\n\n\nN\nD\n\n\n\n S\nE\n\n\n\nL\nE\n\n\n\nC\nT\n\n\n\nE\nD\n\n\n\n S\nO\n\n\n\nIL\n \n\n\n\nPR\nO\n\n\n\nPE\nR\n\n\n\nT\nIE\n\n\n\nS \n(n\n\n\n\n= \n18\n\n\n\n) \n\n\n\n \nEx\n\n\n\nch\n. Z\n\n\n\nn \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  W\n\n\n\n-S\n Z\n\n\n\nn \n\n\n\n\n\n\n\nSp\nec\n\n\n\nifi\nca\n\n\n\nlly\n \n\n\n\nso\nrb\n\n\n\ned\n/C\n\n\n\nO\n3 \n\n\n\nbo\nun\n\n\n\nd \nZn\n\n\n\n\n\n\n\nFe\n/M\n\n\n\nn \nbo\n\n\n\nun\nd \n\n\n\nZn\n \n\n\n\n\n\n\n\nO\nM\n\n\n\n b\nou\n\n\n\nnd\n Z\n\n\n\nn \n\n\n\n\n\n\n\nR\nes\n\n\n\nid\nua\n\n\n\nl Z\nn \n\n\n\n\n\n\n\nEx\nch\n\n\n\n. Z\nn \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\nW\n-S\n\n\n\n Z\nn \n\n\n\n0.\n11\n\n\n\nns\n \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\n- \n\n\n\nSp\nec\n\n\n\nifi\nca\n\n\n\nlly\n \n\n\n\nso\nrb\n\n\n\ned\n/C\n\n\n\nO\n3 \n\n\n\nbo\nun\n\n\n\nd \nZn\n\n\n\n \n0.\n\n\n\n11\n n\n\n\n\ns \n-0\n\n\n\n.5\n5*\n\n\n\n \n- \n\n\n\n- \n- \n\n\n\n- \n\n\n\nFe\n/M\n\n\n\nn \nbo\n\n\n\nun\nd \n\n\n\nZn\n \n\n\n\n-0\n.0\n\n\n\n7 \nns\n\n\n\n \n-0\n\n\n\n.3\n6 \n\n\n\nns\n \n\n\n\n0.\n30\n\n\n\n n\ns \n\n\n\n- \n- \n\n\n\n- \n\n\n\nO\nM\n\n\n\n b\nou\n\n\n\nnd\n Z\n\n\n\nn \n-0\n\n\n\n.4\n5*\n\n\n\n \n-0\n\n\n\n.1\n1 \n\n\n\nns\n \n\n\n\n0.\n31\n\n\n\n n\ns \n\n\n\n0-\n.2\n\n\n\n8 \nns\n\n\n\n \n- \n\n\n\n- \n\n\n\nR\nes\n\n\n\nid\nua\n\n\n\nl b\nou\n\n\n\nnd\n Z\n\n\n\nn \n0.\n\n\n\n28\n n\n\n\n\ns \n-0\n\n\n\n.1\n3 \n\n\n\nns\n \n\n\n\n0.\n05\n\n\n\n n\ns \n\n\n\n-0\n.0\n\n\n\n4 \nns\n\n\n\n \n0.\n\n\n\n11\n n\n\n\n\ns \n- \n\n\n\nA\nva\n\n\n\nil.\n P\n\n\n\n  \n-0\n\n\n\n.0\n6 \n\n\n\nns\n \n\n\n\n0.\n24\n\n\n\n n\ns \n\n\n\n0.\n14\n\n\n\n n\ns \n\n\n\n-0\n.3\n\n\n\n9 \nns\n\n\n\n \n0.\n\n\n\n26\n n\n\n\n\ns \n0.\n\n\n\n35\n n\n\n\n\ns \n\n\n\nC\na \n\n\n\n \n0.\n\n\n\n23\n n\n\n\n\ns \n-0\n\n\n\n.2\n4 \n\n\n\nns\n \n\n\n\n0.\n48\n\n\n\n* \n-0\n\n\n\n.0\n1 \n\n\n\nns\n \n\n\n\n-0\n.0\n\n\n\n4 \nns\n\n\n\n \n0.\n\n\n\n10\n n\n\n\n\ns \n\n\n\nC\nla\n\n\n\ny \n \n\n\n\n0.\n39\n\n\n\n n\ns \n\n\n\n-0\n.5\n\n\n\n3*\n \n\n\n\n0.\n51\n\n\n\n* \n0.\n\n\n\n08\n n\n\n\n\ns \n0.\n\n\n\n18\n n\n\n\n\ns \n0.\n\n\n\n16\n n\n\n\n\ns \n\n\n\nEC\nEC\n\n\n\n  \n0.\n\n\n\n25\n n\n\n\n\ns \n-0\n\n\n\n.3\n0 \n\n\n\nns\n \n\n\n\n0.\n42\n\n\n\n n\ns \n\n\n\n0.\n07\n\n\n\n n\ns \n\n\n\n-0\n.1\n\n\n\n2 \nns\n\n\n\n \n0.\n\n\n\n33\n n\n\n\n\ns \n\n\n\nO\nM\n\n\n\n  \n0.\n\n\n\n56\n* \n\n\n\n-0\n.1\n\n\n\n6 \nns\n\n\n\n \n0.\n\n\n\n47\n* \n\n\n\n-0\n.0\n\n\n\n9 \nns\n\n\n\n \n-0\n\n\n\n.0\n7 \n\n\n\nns\n \n\n\n\n0.\n29\n\n\n\n n\ns \n\n\n\npH\n(H\n\n\n\n2O\n)  \n\n\n\n0.\n41\n\n\n\nns\n \n\n\n\n-0\n.3\n\n\n\n2 \nns\n\n\n\n \n0.\n\n\n\n31\n1 \n\n\n\nns\n \n\n\n\n-0\n.3\n\n\n\n3 \nns\n\n\n\n \n0.\n\n\n\n03\n n\n\n\n\ns \n0.\n\n\n\n02\n n\n\n\n\ns \n\n\n\nW\n-S\n\n\n\n- w\nat\n\n\n\ner\n so\n\n\n\nlu\nbl\n\n\n\ne,\n A\n\n\n\nva\nil.\n\n\n\n P\n \u2013\n\n\n\n a\nva\n\n\n\nila\nbl\n\n\n\ne \nph\n\n\n\nos\nph\n\n\n\nor\nus\n\n\n\n, E\nC\n\n\n\nEC\n- e\n\n\n\nff\nec\n\n\n\ntiv\ne \n\n\n\nca\ntio\n\n\n\nn \nex\n\n\n\nch\nan\n\n\n\nge\n c\n\n\n\nap\nac\n\n\n\nity\n, O\n\n\n\nM\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n - \nor\n\n\n\nga\nni\n\n\n\nc \nm\n\n\n\nat\nte\n\n\n\nr, \n* \n\n\n\n- \nsi\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n a\nt 5\n\n\n\n%\n p\n\n\n\nro\nba\n\n\n\nbi\nlit\n\n\n\ny \nle\n\n\n\nve\nl, \n\n\n\n**\n - \n\n\n\nsi\ngn\n\n\n\nifi\nca\n\n\n\nnt\n a\n\n\n\nt 1\n%\n\n\n\n p\nro\n\n\n\nba\nbi\n\n\n\nlit\ny \n\n\n\nle\nve\n\n\n\nl, \nns\n\n\n\n- n\not\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\n, \n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nSi\nm\n\n\n\npl\ne \n\n\n\nLi\nne\n\n\n\nar\n C\n\n\n\nor\nre\n\n\n\nla\ntio\n\n\n\nn \nA\n\n\n\nm\non\n\n\n\ng \nZi\n\n\n\nnc\n F\n\n\n\nra\nct\n\n\n\nio\nns\n\n\n\n a\nnd\n\n\n\n B\net\n\n\n\nw\nee\n\n\n\nn \nZi\n\n\n\nnc\n F\n\n\n\nra\nct\n\n\n\nio\nns\n\n\n\n a\nnd\n\n\n\n S\nel\n\n\n\nec\nte\n\n\n\nd \nSo\n\n\n\nil \nPr\n\n\n\nop\ner\n\n\n\ntie\ns (\n\n\n\nn=\n 1\n\n\n\n8)\n\n\n\nPhase Association of Zinc under Different Parent Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201662\n\n\n\nsoil pH had no serious association with any of the zinc fractions as no significant \ncorrelation between soil pH and zinc fractions was recorded.\n\n\n\nCONCLUSIONS\nThe results of this study indicated that the concentration of Zn fractions varied \namong the soils of different parent materials and decreased in the order of residual \nbound > Fe-Mn Oxide bound > specifically sorbed/CO3 bound > exchangeable > \nwater soluble > OM bound in coastal plain sands soils, specifically sorbed/CO3 \n> water soluble > exchangeable > residual bound > OM bound > Fe-Mn Oxide \nbound in alluvial soils, specifically sorbed/CO3 > residual bound > Fe-Mn Oxide \nbound > OM bound > exchangeable > water soluble in false bedded sandstones \nsoils and residual bound > specifically sorbed/CO3 > exchangeable > Fe-Mn \nOxide bound > OM bound > water soluble in Imo clay shale soils. Available zinc \nconcentrations were low and varied among the soils of different parent materials \nin a decreasing order of alluvium > Imo clay shale > false bedded sandstones \n> coastal plain sands, while  total zinc concentrations were in decreasing order \nof Imo clay shale > falsebedded sandstones > coastal plain sands > alluvium, \nrespectively. Zinc forms correlated among each other and with pH, Ca, clay, \nECEC, available P and OM.\n\n\n\nREFERENCES\nAlloway, B.J. 2004. Zinc in soils and crop nutrition. International Zinc Association \n\n\n\nCommunications IZA Publications, Brussels, Belgium.\n\n\n\nAlloway, B.J. 2008. Zinc in soils and crop nutrition. IZA and IFA, Brussels, Belgium \nand Paris France. Ed (2).\n\n\n\nAydinalp, C. 2009. Concentration and speciation of Cu, Ni, Pb and Zn in cultivated \nand uncultivated soils. Bulgarian Journal of Agricultural Science 15(2): 129\u2013\n134.\n\n\n\nBrady, N.C. and R.R. Weil. 2010. 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PHI Private \nLimited, New Delhi-110001. pp513.\n\n\n\nPhase Association of Zinc under Different Parent Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201664\n\n\n\nIderiah, T.J.K., F.N. Ikpe and F.N. Nwanjoku, 2013. Distribution and speciation of \nheavy metals in crude oil contaminated soils from Niger Delta, Nigeria. World \nEnvironment. 3 (1):18-28.\n\n\n\nKabala, C. and R.B Singh. 2001. Fractionation and mobility of copper lead and zinc \nin soil profiles in the vicinity of a Copper Smelter. J. Environ. Qual. 30: 485-\n492.\n\n\n\nKabata-Pendias, A. 2011. Trace Elements in Soils and Plants. CRC Press, Taylor and \nFrancis Group, Boca Raton, USA.Ed.4. pp534.\n\n\n\nKabata-Pendias, A. and H. Pendias. 2001. Trace Elements in Soils and Plants. 3rd \n\n\n\nedition.CRC Press, Boca Raton, Florida. pp413.\n\n\n\nKabata-Pendias, A. and H. Pendias 1999. Biogeochemistry of Trace Elements. 2nd ed. \nWyd. Nauk PWN, Warsaw, Poland. pp400.\n\n\n\nMa, L.Q. and N. Rao. 1997. 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Chemical fractionation of trace elements in biosolid-amended \nsoils and correlation with trace elements in crop tissue. Communications in Soil \nScience and Plant Analysis. 38: 1029\u20131046, 2007.\n\n\n\nPhase Association of Zinc under Different Parent Material\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201666\n\n\n\nSlaton, N.A.,  R. J. Normon and C.E. Jr Wilson. 2005. Effect of Zn source and \napplication time on Zn uptake and grain yield of flood irrigated rice. Agronomy \nJournal. 92: 272\u201378.\n\n\n\nSoltani, S.M., M.M. Hanafi, S.A.Wahid and S.M.S. Kharidah 2015. Zinc fractionation \nof tropical paddy soils and their relationships with selected soil properties.  \nJournal of Chemical Speciation and Bioavailability. \n\n\n\nThomas, G.W. 1996. Soil pH and soil acidity, In.Sparks, D.L, A.L. Page, P.A. \nHelmke, R.H. Loeppert, P.N. Soltanpour, M.A. Tabatabai, C.T. Johnson and \nM.E. Summer (eds.). Methods of Soil Analysis. Part 3.Chemical Methods. Soil \nScience Society of America, Inc, and American Society of Agronomy, Maidson, \nWI.USA. pp475-490.\n\n\n\nThomas, G.W. 1982. Exchangeable bases. In: page, A.L., R.H. Miller and D.R. \nKeeney (Eds) Part 2. Methods of Soil Analysis. Ameri. Soc. Agron. Madison, \nWisconsin. pp159-165.\n\n\n\nUdom B.E., J.S.C. Mbagwu, J.K. Adesodun, and N.N. Agbin 2004. Distribution \nof Zn, Cd, Cu and Pb in a tropical Ultisol after long term disposal of sewage \nsludge. Enviroment. International. 30: 467-470.\n\n\n\nUzoho, B.U., I.I. Ekpe, C.M. Ahukaemere, B.N. Ndukwu, N.H. Okoli, F.A. Osisi, \nand C.M. Chris-Emenyonu. 2014. Nitrogen status of soils of selected land uses \nof two cropping systems in the humid tropical rainforest, Southeastern Nigeria.  \nAdvances in Life Science and Technology. 25: 2224-7181.\n\n\n\nVerma, V.K., R.K. Setia, P.K. Sharma, C. Singh and K. Ashok. 2005. Pedospheric \nvariations in distribution of DTPA-extractable micronutrients in soils developed \non different physiographic units in Central parts of Punjab India. International \nJournal of Agriculture and Biology. 7: 243-246.\n\n\n\nWijebandara, D.M.D.I., G.S. Dasong and P.L. Patil. 2011. Zinc fractions and their \nrelationships with soil properties in paddy growing soils of Northern dry and \nhilly zones of Karnataka. Journal of the Indian Society of Soil Science. 59: \n141-147.\n\n\n\nWilding, L.P., J. Bouma and D. Gross. 1994. Impact of spatial variability on modeling. \nIn: Bryant, R. and Amold, R.W. (Eds.), Quantitative Modeling of Soil Forming \nProcesses. Soil Science Society of America Special Publication No 39. Madison.\n\n\n\n\n\n\n\nOkoli et al.\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nINTRODUCTION\nGlobally, rice falls within the top three most important cereal crops (Haefele et \nal. 2014). Many households in Nigeria depend on rice to meet their daily food \ndemands. The annual quantity of rice produced locally (3.6 million tonnes) is \nnot sufficient to meet local consumption (5.5 million tonnes) (Liverpool-Tasie \net al. 2015). Consequently, the government of Nigeria has relied largely on rice \nimportation to meet the national consumption rate (Terwase and Madu 2014). \nRecently, there was a ban on rice importation with emphasis on increasing local \nproduction of rice. This led to an increase in the market price of rice because \nproduction is still very low. Hence, the purchase of rice has gradually become \nbeyond the reach of an average Nigerian. While rice farmers try to expand their \nareas of cultivation, the short fall between production and local consumption of \nrice in Nigeria could be partly responsible for the current economic recession in \nthe country. \n\n\n\nThe need to boost local production requires extensive cultivation under a \nmechanised system (Ray et al., 2013). In precision agriculture, both terrain and \nfertility data are required. It is  estimated that 75% of rice land are wetlands where \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 29- 45 (2017)                         Malaysian Society of Soil Science\n\n\n\nRice Production in the Flood Plains of River Benue, Nigeria: \nProspects and Challenges\n\n\n\nAG.A.Ajiboye1, K.A. Adegbite2, J.A. Oyedepo1 and S.A.Mesele1\n\n\n\n1 Federal University of Agriculture Abeokuta, Abeokuta Nigeria\n2 Land Mark University, Omuaran, Nigeria \n\n\n\nABSTRACT\nThere exist opportunities to increase rice production in Nigeria as  currently \nrice production is low and consumption level is high. The floodplains of river \nBenue offer great potential for sustainable large scale rice production.  Twenty-\nfive thousand hectares of land bordering river Benue were selected to assess the \nfertility, texture and drainage status of the soil for rice production, and to suggest \npossible soil management practices for optimising rice production. Standard field \nsampling methodologies and soil laboratory procedures were employed. The soils \nof the area were found suitable for rice production but with certain identified \nconstraints. Apart from the low soil nutrient status of the floodplain, which could \nbe easily mediated with fertiliser application, the most critical limitation related \nto poor drainage and flooding. To avert this problem, it is recommended that a \nproper drainage network be designed and executed along with the construction of \nstrategic embankments to prevent flooding of the  river Benue.\n\n\n\nKeywords: Food security, land evaluation, Nigeria, rice.\n\n\n\n___________________\n*Corresponding author : E-mail: \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201730\n\n\n\nrice grows in flooded fields during part or throughout the growing season. The \ntraditional wetland rice cultivation could be considered sustainable because of \nits moderate but stable yield which has been maintained for many years without \nadverse effects on soil (Haefele et al. 2014; Lawal et al. 2014). Flooding  plains \nare usually regarded as good agricultural land due to seasonal deposits of rich \nalluvium that replenish the soil nutrient status (Kyuma 1985; Patrick 1990; Biller \nand Bruland 2013; Brierley and Fryirs 2013). Wetland rice fields favour the \ngrowth and activities of nitrogen-fixing bacteria (Vignesh et al. 2015). \n\n\n\nFurthermore, the River Niger is reputed to be the longest river in Nigeria and \nWest Africa, the third longest river in Africa behind Rivers Nile and Congo. At \n1400 kilometers, River Benue is the major tributary of River Niger and the second \nlongest river in Nigeria (Andersen and Golitzen 2005). It originates from Adamawa \nPlateau in Cameroon and is of economic importance to Nigeria in several ways. \nGenerally, it is assumed that river banks are fertile due to seasonal deposits from \nthe overflowing river. Due to differences in volume, size, topography and inherent \nsoil fertility, there might be heterogeneity in the distribution of fertility around \nthe floodplain (Brierley and Fryirs, 2013). Sustainable production of rice in this \nfloodplain therefore, requires an understanding of the geospatial pattern of the soil \ncharacteristics (both physical and fertility status). To this extent, 25,000 hectares \nof land adjoining river Benue were selected for this study. The objectives of this \nstudy were to evaluate and map the fertility and textural status of the soil for \nrice production and to suggest possible soil management practices for optimising \nsustainable rice production on the land. This study is expected to provide basic \nsoil management infomation for large scale rice production in the flood plain of \nriver Benue in an effort to reduce the shortfall between demand and supply of rice \nespecially as a result of the economic recession in Nigeria. \n\n\n\nMATERIALS AND METHODS\n\n\n\nDescription of study site\nThe area is located on the southern bank of river Benue in Basa Local Government \nArea of Kogi State, Nigeria (Fig. 1). The study area covers about 25,000 hectares \nin the alluvial plain and lies in the immediate vicinity of the southern bank of the \nriver Benue and ranges in altitude from 35 m to 77 m above sea level (MASL). \nThis area is liable to seasonal flooding from river Benue and has several inland \nbasins and back-swamps. About 70% of the land area that falls within this plain \nis poorly drained.\n\n\n\nThe land use land cover analysis of the study site indicates that the area \nis presently covered by alluvial flood plains in the immediate vicinity of river \nBenue where the rice farmers are mostly found and fresh water back swamps \nwhich retain the bulk of the flood waters from river Benue and the riparian forests. \nImmediately after the back swamps are moderately well drained, arable farm \nlands used for crop production. The common crops grown in the area include \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 31\n\n\n\nIrrigation Systems for Rubber Nursery\n\n\n\nlowland rice, benni seed, bambara nut, groundnut, guinea corn, maize, cassava \nand cashew. \n\n\n\nLying to the South-eastern boundary of the site is a combination of grassland, \ndensely vegetated wood land comprising a variety of hardwoods, shrubs and in \nsome instances big trees. It was also observed that most of the wooded savannah \nin the area has been subjected to serious logging activities. Nomadism is a major \nproblem to farmers around the area. There are reported cases of damage to field \ncrops by the grazing nomadic cattle.\n\n\n\nThere are several villages connected together by motorable roads. Also, \nwithin the confines of the site are several seasonal rivulets and rivers that drain \ninto river Benue.\n\n\n\nStudy Approach\nThe selected area for this study was mapped out from the political map of Kogi \nState, Nigeria to cover a greater part of Basa \u2013 a local government area known for \nrice production (Fig. 1). The geo-referenced map was uploaded on the most recent \nGoogle Earth Imagery and this was interpreted to provide the base map as well as \nthe land-use land cover map (LULC) of the farm site (Fig. 2). \n\n\n\nThe base-map was then divided into regular polygons using ArcGIS 12. The \npolygon centre points within the grid map were designated as the composite \nsampling point (Fig. 3). The composite sampling points were then uploaded into \na hand held geographic positioning system. \n\n\n\nFig. 1: Location of the project site\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201732\n\n\n\n(GPS) for mapping. Composite samples were taken from the surface (0-35 cm \ndepth) at the designated point using an auger. Subsurface samples were also taken \nat 15-cm intervals from 40-120 cm and characterised morphologically by standard \nfield methods (Schoeneberger et al. 2012) to ascertain the subsurface texture and \ndrainage conditions. The composite samples taken were well labelled in the field \nand transported to the laboratory where they were air dried and sieved with a 2 \nmm sieve prior to analyses.\n\n\n\nSoil Analyses\nParticle size distribution was determined mechanically using the hydrometer \nmethod. Soil pH in water (1: 2) was determined using glass electrode pH meter \n(Hanna Instruments UK). Total nitrogen was determined using macro-Kjeldahl \nmethod (Bremner 1996). Phosphorus was determined by Bray 1 method (Bray \nand Kurtz 1945), organic carbon determined using Walkley and Black method \n(Walkley 1947). Exchangeable cations (potassium, calcium, sodium and \nmagnesium) were extracted using 1N ammonium acetate, K and Na in the extract \nwere read on a flame photometer while Ca and Mg were read on atomic absorption \nspectrophotometer (AAS). The available micronutrients were extracted with 0.04 \nM EDTA and read on an atomic absorption spectrophotometer (Fisherscientific, \nKansas, USA).\n\n\n\nGeospatial and Statistical Analyses\n\n\n\nPreparing Excel Data for Importation\nThe results of analyzed soil samples were recorded against the respective \n\n\n\nFig. 2: Land use land cover (LULC) map of project site\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 33\n\n\n\ncoordinates indicating the geographical locations on the field. The spread \nsheet of parameters (field) against specific sample points (records) was then \nsaved in a specified directory on the computer in preparation for importation into \nthe Geographic Information Systems environment. Specifically for Excel 2007, \nthe table was saved as a Text (tab delimited) file format in Universal Transverse \nMercator (UTM) (WGS 1984) Minna datum and Zone 32 meters North of the \nequator. The worksheet containing the analysed soil data was selected and \nimported into the Arcview 3.2 spatial analyst environment.\n\n\n\nIt was important to ensure that the data follow a normal distribution curve \nthat is not skewed to the right or left. The data were therefore graphically displayed \nusing histogram in Arcview 3.2. The data followed a normal distribution pattern.  \nData cleaning was conducted to ensure that any outliers were removed before \nsubjecting the data to spatial analysis.\n\n\n\nSpatial Interpolation \nThe inverse distance weighted (IDW) interpolator was utilised to create a \ncontinuous surface of a soil nutrient map for all parameters analysed. This is \nbecause it is one of the most applied and deterministic interpolation techniques \nin the field of soil science (Zandi et al. 2011; Bhunia et al. 2016). Soil samples \ncollected at very close range are likely to be statistically invariant and as such \nit is good to make estimates based on nearby known locations as does IDW \ninterpolation by assigning weights to the interpolating points in the inverse of \nits distance from the interpolation point. Accordingly, closer points are assigned \nmore weights (so, more impact) than distant points and vice versa. This according \nto Robinson and Metternicht (2006) makes the known sample points implicit to \nbe self-governing from each other as expressed below.\n\n\n\nwhere z(x0) is the interpolated value, n represents the total number of sample \ndata values, xi is the ith data value, hij is the separation distance between \ninterpolated value and the sample data value, and \u00df denotes the weighting \npower.\n\n\n\nThe table of geographic coordinates of soil samples and their corresponding \nresults of analysed parameters were added to the GIS. The points were then added \nto the work space as events theme and then used to produce a continuous map of \neach parameter by selecting Interpolate Grid from the Surface menu choosing \nIDW as the Method, and specific parameters in the Z Value Field. A grid map of \nthe farm boundary was created and utilised as the analysis mask??? or mark in the \nAnalysis Extent in the Analysis Properties dialog of Analysis menu. For this task, \nNearest Neighbour and the default number of 12 neighbours were chosen. \n\n\n\nSpatial Interpolation  \n\n\n\nThe inverse distance weighted (IDW) interpolator was utilised to create a continuous surface \n\n\n\nof a soil nutrient map for all parameters analysed. This is because it is one of the most applied \n\n\n\nand deterministic interpolation techniques in the field of soil science (Zandi et al. 2011; \n\n\n\nBhunia et al. 2016). Soil samples collected at very close range are likely to be statistically \n\n\n\ninvariant and as such it is good to make estimates based on nearby known locations as does \n\n\n\nIDW interpolation by assigning weights to the interpolating points in the inverse of its \n\n\n\ndistance from the interpolation point. Accordingly, closer points are assigned more weights \n\n\n\n(so, more impact) than distant points and vice versa. This according to Robinson and \n\n\n\nMetternicht (2006) makes the known sample points implicit to be self-governing from each \n\n\n\nother as expressed below. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nwhere z(x0) is the interpolated value, n represents the total number of sample \n\n\n\ndata values, xi is the ith data value, hij is the separation distance between \n\n\n\ninterpolated value and the sample data value, and \u00df denotes the weighting \n\n\n\npower. \n\n\n\nThe table of geographic coordinates of soil samples and their corresponding results of \n\n\n\nanalysed parameters were added to the GIS. The points were then added to the work space as \n\n\n\nevents theme and then used to produce a continuous map of each parameter by selecting \n\n\n\nInterpolate Grid from the Surface menu choosing IDW as the Method, and specific \n\n\n\nparameters in the Z Value Field. A grid map of the farm boundary was created and utilised as \n\n\n\nthe analysis mask??? or mark in the Analysis Extent in the Analysis Properties dialog of \n\n\n\nAnalysis menu. For this task, Nearest Neighbour and the default number of 12 neighbours \n\n\n\nwere chosen.  \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\nSoil Characteristics \n\n\n\nBased on the soil depth and drainage characteristics, there were three major soil types viz: \n\n\n\ndeep poorly drained, deep well drained without stones and shallow well drained with gravels. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201734\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Characteristics\nBased on the soil depth and drainage characteristics, there were three major soil \ntypes viz: deep poorly drained, deep well drained without stones and shallow well \ndrained with gravels. The soils in the deep poorly drained unit were developed \nfrom recent alluvium and were poorly drained loamy sand or sandy clay loam \nsurface over sandy clay, sandy clay loam or clay and not gravelly. This soil type \nwas susceptible to seasonal flooding from river Benue but considered suitable for \nrice production with the provision of artificial drainage. \n\n\n\nThe deep well drained soils had brown loamy sand to sandy loam surface \nsoil overlying red sandy clay loam subsurface soils. This soil type had no stones \nwithin 100 cm soil depth. This soil occurred in the immediate surroundings of the \npoorly drained soils and occupied the eastern part of the land from the junction of \nEmi Estumbe through Emi Jacob to Odugbe.\n\n\n\nThe last soil type resembled the second in colour and textural characteristics \nbut was shallow and gravelly. Gravels occurred in this soil type at depths < 30 cm \nin most cases. This soil type was found at the base of the hills which formed the \nsouthern boundary of the land and   ranacross the entire length of the land from \nKara through Gboloko to the south-western boundary after Eluli. The two other \nsoil types were only suitable for arable cropping.\n\n\n\nFig. 3: Composite sampling points\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 35\n\n\n\nSoil Physical Characteristics\nAbout 76% of the surface soils were predominantly loamy sand (LS) in texture, \nwhile about 17% of the surface soils were sandy loam (SL) in texture and the \nremaining 7% were sandy clay loam (SCL) in texture (Fig. 4). The soils were \ngenerally high in sand (sandy skeletal), moderate in clay and very low in silt. The \nsand particle size fraction ranged from 70.2% to 89.4%, while the clay content \nof the soils ranged from 9.6% to 20.4% and the silt content ranged from 0.4% to \n9.8%.\n\n\n\nMost of the areas lying close to river Benue were either sandy loam or sandy \nclay loam in texture. These areas were poorly drained and had extensive back \nswamps or inland basins. The subsurface layers (>50 cm depth) of these areas \nwere either sandy clay loam or sandy clay in texture and highly susceptible to \nflooding because they were within the flood plain of river Benue. All the areas that \nhad loamy sand texture were well drained.\n\n\n\nRice grows on a variety of land;  however there is a marked difference \nbetween the drainage requirements of upland and lowland rice. At the early stage \nof growth, both upland and lowland rice require soils that are well-drained for \ndirect seeding. For transplanted lowland rice, a somewhat poorly drained well \npuddle soil can support the rice seedling. Apart from drainage conditionss, rice \nrequires deep, loamy soils with moderate bulk density (1.2-1.6 g/cm3), total \nporosity (higher than 50%) and a ground water table below 0.75 t0 1.20 m depth.  \nThe soil requirements for upland rice production are similar to those of lowland \nrice but the soil must be moderately well drained with high available water holding \n\n\n\nFig. 4. Spatial distribution of soil texture\n\n\n\n Note: LS = loamy sand, SCL = sandy clay loam, SL = sandy loam\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201736\n\n\n\ncapacity. In upland rice, available water holding capacity of 15% or more (15 cm \nper meter depth of soil is required. However, in terms of soil texture, upland rice \nhas been produced on soils ranging from loam to loamy sand while lowland rice \nrequires soils ranging from clay loam to sandy loam.\n\n\n\nMaintenance of proper physical, chemical and biological conditions of the \nsoil is necessary for realising higher growth, yield and quality of rice. Although, \nthe observed soil texture in this evaluation could be adequately managed with \nappropriate manure to produce good rice yield, it will be necessary to have a \nproper drainage network for the northern part of the land before it can become \nsuitable for lowland rice production. \n\n\n\nChemical Properties of the Soils\nSoil pH ranged from moderately acid to moderately alkaline (6.0 -7.9). The pattern \nof the pH distribution correlates with the distribution of LULC (Fig. 5). The \narea of land consisting of the flood plain and back swamp and which abuts river \nBenue had the lowest pH, ranging from 6.0 to 6.3. The moderately well drained \narable land found immediately after the back swamps had a slightly higher pH \nthat ranged from 6.3 to 6.5. The highly elevated densely vegetated south-eastern \nboundary of the soil had pH ranging between 6.5 and 6.9. Most of the soils had \npH that ranged from slightly acid (6.1-6.5) to neutral (6.6-7.3). Less than 10% of \nthe total land area had pH above 6.8.\n\n\n\nThe optimum soil pH for rice production range from 5.5 to 6.5 but rice can \ntolerate a considerable degree of soil acidity and alkalinity. Hence, it is found \ngrowing in soils with pH in the range of 5 to 8.5. Liming is required if pH is less \nthan 5.0.\n\n\n\nSoil acidity adversely affects rice growth, yield and quality. Under acidic \nconditions, the adverse effects are due to aluminium, iron and manganese toxicity. \n\n\n\nFig. 5: Spatial distribution of pH  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 37\n\n\n\nAluminium toxicity can cause P deficiency symptoms due to precipitation of \nalumino-phosphate complexes within the plant and in the soil. The observed pH \nrange of the soil seems to be adequate for rice production. Liming and the addition \nof gypsum will definitely not be necessary in the present situation.\n\n\n\nThe organic carbon content of the soils ranged from very low (0.32%) to \nvery high (3.58%). The distribution of soil organic carbon (OC) content did not \nfollow a specific pattern as noted for the soil pH (Fig. 6a). However, the area \naround the flood plains, back swamps, riparian forests and highly vegetated area \nhad the highest OC contents. Generally, it was observed that 1, 35, 26, 15 and \n23% of the land had very low (<0.4%), low (0.4 \u2013 1.0%), moderate  (1.0 \u2013 1.5%), \n\n\n\nFig. 6a: Spatial distribution of organic carbon\n\n\n\nFig. 6b:  Percentage rating of organic carbon\n Note: VL= Very low; L = Low; M = Moderate; H = High; VH = Very high\n\n\n\n\n\n\n\nFigure 6a: Spatial distribution of organic carbon \n\n\n\n \nFig. 6b:  Percentage rating of organic carbon \n\n\n\nNote: VL= Very low; L = Low; M = Moderate; H = High; VH = Very high \n\n\n\n\n\n\n\n1 \n\n\n\n35 \n\n\n\n26 \n\n\n\n15 \n\n\n\n23 \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\nVL L M H VH\n\n\n\nPe\nrc\n\n\n\nen\nta\n\n\n\nge\n d\n\n\n\nist\nrib\n\n\n\nut\nio\n\n\n\nn \nof\n\n\n\n O\nC \n\n\n\nOrganic Carbon rating \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201738\n\n\n\nhigh (1.5 \u2013 2.0%)  and very high  (>2.0%) OC content  respectively (Fig. 6b).\nThe total nitrogen content of the soils was also generally very low and ranged \n\n\n\nfrom very low (0.02%) to moderate (0.22%). The spatial distribution of the soil \nnitrogen contents (Fig. 7a) indicates that the nitrogen content of 61% of the total \narea of land is very low (<0.10%), 36% of the total area had low (0.10- 0.20%) \ntotal nitrogen content while only 3% of the total land area had moderate (0.20 \u2013 \n0.50%) nitrogen content(Fig. 7b). \n\n\n\nIn terms of nutrient requirement, rice requires a highly fertile soil for optimum \nperformance. Soils with moderate to high nitrogen content (0.2%\u20131.0%) will be \n\n\n\nFig.  7a:  Spatial distribution of total nitrogen\n\n\n\nFig. 7b: Percentage rating of total nitrogen\n  Note: VL= Very low; L = Low; M = Moderate\n\n\n\n \nFigure 7b: Percentage rating of total nitrogen \n\n\n\nNote: VL= Very low; L = Low; M = Moderate \n\n\n\n\n\n\n\n\n\n\n\nVL \n61% \n\n\n\nL \n36% \n\n\n\nM \n3% \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 39\n\n\n\nmore appropriate for rice production. Only about 3% of the total land area met \nthis requirement. Thus, application of nitrogen either from organic or inorganic \nsources will be required for optimum performance of rice on these soils. Since \nmore than 90% of the soil is currently deficient in nitrogen, a renewable source of \nN, like manure will probably be more viable in the long run. \n\n\n\nThe exchangeable bases (calcium, magnesium, potassium and sodium) \ncontents of the soils were very low.  The exchangeable potassium (K) status of the \nsoil ranged from 0.01 cmolc kg-1 (low) to 0.09 cmolc kg-1 (very low).\n\n\n\nThe spatial distribution of K in the soils (Fig. 8) showed that the northern \n\n\n\nFig. 8:  Spatial distribution of exchangeable potassium\n\n\n\nFig. 9:  Spatial distribution of effective cation exchange capacity\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201740\n\n\n\npart of the area lying close to river Benue contained slightly higher amounts of \nK than the southern part of the area. The southern part with lower K is presently \nmore intensely cultivated than the northern part that is subjected to seasonal \nflooding. This may have been the reason for the higher K content of the northern \npart of the area. For optimum performance, the critical soil K requirement for \nrice cultivation is 0.30 cmolc kg-1 (Isitekhale et al. 2014). As the exchangeable \npotassium (K) status of the soil of the site is lower than the critical requirement \nfor rice production, potassium fertiliser application will be required to upgrade the \nproductive status of the soil from the current very low level (<0.2 cmolc kg-1) to \nmoderate or high level (0.6-1.2 cmolc kg-1). To achieve this, the Federal Fertilizer \nDepartment, Federal Ministry of Agriculture and Rural Development (FFD) \nrecommends that 30-40 kg K2O per hectare be applied (FFD 2011).\n\n\n\nThe exchangeable sodium (Na), calcium (Ca) and magnesium (Mg) contents \nof the soils were also very low. While the values of Ca ranged from 0.03 cmolc \nkg-1 to 1.97 cmolc kg-1, those of Mg ranged from 0.04 to 0.47 cmolc kg-1. The Na \nvalues ranged from 0.03 to 0.17 cmolc kg-1. The range of values observed for \nboth Ca and Mg content of the soils are grossly inadequate for the production of \nrice as these values fall below the critical requirement for rice production. The \nexchangeable sodium percentage (ESP) of the soils are lower than 15% indicating \nthat the soils are not saline. Although the levels of these nutritional elements are \nlow in the soil, care must be taken in their management because of the high pH of \nthe soils (neutral pH).\n\n\n\n The effective cation exchange capacity (ECEC), which is a measure of the \nability of the soil to store plant nutrients either naturally or with applied fertilisers, \nis very low. The ECEC of the soils ranged from 0.29 to 2.83 cmolc kg-1 (Fig. 9). \nThis range of values is suboptimal for rice production. Rice production requires \nsoils with CEC greater than 16 cmolc kg-1. Soils with CEC less than 5 cmolc \nkg-1 are regarded as highly unsuitable for rice production (Table 1). The spatial \ndistribution of the ECEC shows  that the alluvial plain and the adjourning land \narea have the highest ECEC, ranging from1.02 to 2.47cmolc kg-1. The highly \ncultivated areas have lower ECEC values.\n\n\n\nThe micronutrient status of the soil ranged from low to moderate in copper \n(Cu) and zinc (Zn) but ranged from high to very high in iron (Fe) and manganese \n(Mn). The values of Cu in the soils ranged from 0.02 mg kg-1 (low) to 0.58 mg \nkg-1 (high) while the values of Zn ranged also from 0.10 mg kg-1 (low) to 5.42 mg \nkg-1 (very high). Both Fe and Mn had excessively high values that ranged from \n6.21 mg kg-1 to 305.0 mg kg-1 and from 0.51 mg kg-1 to 92.14 mg kg-1 respectively.\n\n\n\nRice is highly sensitive to Mn deficiency, and Zn is the most important \nmicronutrient limiting rice growth and yield (Neue et al. 1998). According to De \nDatta (1989), rice performs best when the zinc content of the soils is in the range \nof 2.0 to 2.5 mg kg-1. Generally, Zn, Fe and Mn deficiency is common on neutral \nand calcareous soil, intensively cropped soils, paddy soils and poorly drained \nsoils. Fertiliser recommendations for rice production in many parts of Africa \noften neglect the importance of micro-nutrients in achieving good yield. Thus, the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 41\n\n\n\nAfrica Rice Center, Cotonou, Benin (Sikirou et al. 2015) accepts the possibility \nof iron and zinc deficiencies occurring between 1-2 and 3-4 weeks after seedling \nemergence respectively. This research institute recommended the application of \nfoliar spray of ferrous sulphate or zinc sulphate, only as a corrective measure.\n\n\n\nAlthough the effect of micronutrient toxicity on rice production has not been \nextensively documented in literature, it is well established that high concentrations \nof iron and manganese under a fluctuating water table can lead to the formation \nof plinthites, which is not desirable in agricultural land (Biller and Bruland 2013). \nAlso, Bouwman et al. (2013) reported that a high concentration of iron under \nreducing soil condition or low soil pH can lead to phosphorus fixation, especially \nwhen the clay content of the soil is also high.\n\n\n\nIt is important however, to note that the availability of iron for plant uptake \nis also controlled by soil pH. In soils with pH values above 5.0, the activity of \niron is highly reduced, but when the pH is less than 3.5, iron becomes insoluble.\n\n\n\nPractical Implication of Soil Fertility Status\n A baseline fertility study such as this is desirable as it helps in determining the \noptimum quantity of macro and micronutrient application. \n\n\n\nChemical constraints in the soils, such as acidity and low fertility, are relatively \neasy to correct or control. However, poor physical conditions like soil compaction \ndue to intense mechanisation, impeded drainage or flooding, when limiting, are \nmuch more difficult to ameliorate. For this reason, the physical properties of \nsoil are considered as critical factor in rice growth. The soil requirements for the \ncultivation of rice suggested by Sys et al. (1991; 1993) and De Datta (1989) are \nfound in Table 1.\n\n\n\nFor  the  production of rice in soils with low fertility status such as these \nsoils, FFD(2011) recommended that nitrogen fertiliser should be applied at a rate \nof 100kg N per hectare (ha) for low land rice either as NPK 20:10:10 (5 bags \nbasal) with urea (2 bags top-dress) and 80 kg N per hectare for upland rice applied \nas NPK 20:10:10 (4 bags basal) with urea (1\u00bd bags top-dress).\n\n\n\nSimilarly, it is recommended that phosphorus be applied at a rate of 30\u201340 \nkg P2O5 per hectare as SSP (167-225 kg or 3\u00bd - 4\u00bd bags) in soils with low native \nphosphorus. The recommended rate of application of potassium is 30-40 kg K2O \nper hectare. Furthermore, in addition to the recommendation for N, P and K, it is \nalso recommended that to enhance  the growth of both upland and lowland rice, \nfoliar spray of Boost Xtra at the rate of 1 litre ha-1 be applied four times starting \nfrom 4 weeks of planting/transplanting using a spray volume of 200l ha-1 of water. \n\n\n\nBecause of the low effective cation exchange capacity of the soils, a large \nquantity of fertiliser cannot be added to the soils at once. Therefore split application \nof fertiliser is suggested for soils with low ECEC (Liverpool-Tasie et al. 2015). \nFurthermore, because of its slower rate of release of nutrients and its effects on the \nimprovement of soil physical and hydrological properties, application of organic \nmanures (FYM at 25 tonnes/ha), growing of green manure crops and turning them \ninto soil is recommended for the cultivation of rice.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201742\n\n\n\nCONCLUSION\nThe study concluded that the area under consideration has great potential \nfor sustainable rice production though not without some physico-chemical \nconstraints. The limitations observed in the studied soil for rice production are \n(i) low soil fertility (macro and micro nutrients), (ii) poor drainage of the alluvial \nplain, (iii) susceptibility to flooding (alluvial plains) and (iv) steep slopes (hilly) \n\n\n\nSource: Sys et al. (1991, 1993); De Datta (1989)\n\u2020 = ratings for lowland rice production; SAB =Sub Angular Blocky; Col = Columnar; Cr = \ncrumb; WD = Well Drained; MWD = Moderately Well Drained; ID= Imperfectly Drained; PD \n= Poorly Drained; L= Loamy; SL= Sandy Loam; LS= Loamy Sand; Lfs = Loamy fine sand; \nSCL= Sandy Clay Loam; Fo =Rarely flooded; F1= Flooding expected; F2= Irregularly flooded; \nF3 = regularly flooded.\n\n\n\nTABLE 1  \nSoil quality characteristic requirement for the production of rice\n\n\n\nTABLE 1   \n\n\n\nSoil quality characteristic requirement for the production of rice \nLand Qualities S11 S12 S2 S3 N1 N2 \n\n\n\nTopography (t): Slope (%) <2 3-4 5 \u2013 6 7 -  8 9 \u2013 10 >10 \n\n\n\nDrainage (s):        \n\n\n\n   Wetness WD  \n\n\n\n(ID)\u2020 \n\n\n\nMWD \n\n\n\n (ID) \u2020 \n\n\n\nMD  ID   \n\n\n\n(WD) \u2020 \n\n\n\nPD    \n\n\n\n(WD) \u2020 \n\n\n\nPD   \n\n\n\n(WD) \u2020 \n\n\n\n   Flooding Fo Fo F1 F1 F2 F3 \n\n\n\nSoil physical properties (s)       \n\n\n\n   Texture L        \n\n\n\n (LC)\u2020 \n\n\n\nLfs \n\n\n\n (SLC) \u2020 \n\n\n\nLS    \n\n\n\n (SL) \u2020 \n\n\n\nS S S \n\n\n\n  Structure Cr   \n\n\n\n (SAB) \u2020 \n\n\n\nC     \n\n\n\n (SAB) \u2020 \n\n\n\nSAB  \n\n\n\n (Cr) \u2020 \n\n\n\nSAB  \n\n\n\n (Cr) \u2020 \n\n\n\nCol      \n\n\n\n(Cr) \u2020 \n\n\n\nCol    \n\n\n\n (Cr) \u2020 \n\n\n\n  Coarse fragments (%) (0-45cm) <3 3 \u2013 5 5 \u2013 10 10 \u2013 15 >15  \n\n\n\n  Soil depth (cm) >75 65 -70 50 \u2013 65 35 \u2013 50 30 \u2013 35 <30 \n\n\n\nFertility (f)       \n\n\n\n  pH 5.5 \u2013 6.5 5.0 - 5.5 4.5 \u2013 5.0 4.0 -4.5 <4.0  \n\n\n\n Cation Exchange Capacity (cmol kg-1) >16.0 12.0 -16.0 8.0 -12.0 5.0 \u2013 8.0 <5.0  \n\n\n\n  Base saturation (%) >80 70 \u2013 80 50  -70 40 \u2013 50 25 -35 <25 \n\n\n\n  Organic carbon (%) (0-30 cm) >2.0 2.0 \u2013 1.5 1.2 \u2013 1.5 1.0 \u2013 1.2 1.0 <1.0 \n\n\n\nMacro- nutrients       \n\n\n\n  Nitrogen (%) >2.0 1.5 \u2013 2.0 1.0 \u2013 1.5 0.5 \u2013 1.0 <0.5  \n\n\n\n  Phosphorus (mg kg-1) >20 15 \u2013 20 8 \u2013 15 5 \u2013 8 3 \u2013 5 <3 \n\n\n\n  Potassium  (cmol/kg)  >0.5 0.3 -0.5 0.2 \u2013 0.3 0.1- 0.2 <0.1  \n\n\n\nMicro-nutrient (0.5 N Hcl)       \n\n\n\n  Iron (mg kg-1) >4.5 3.5 \u2013 4.5 2.5 \u2013 3.5 1.5 \u2013 2.5 1.0 \u2013 1.5 <1.0 \n\n\n\n  Zinc (mg kg-1) 2.0-2.5 1.5 \u2013 2.0 1.0 \u2013 1.5 0.8 \u2013 1.0 0.6 -0.8 <0.6 \n\n\n\n  Manganese (mg kg-1) 1.5 \u2013 1.7 1.0 \u2013 1.5 0.8 \u2013 1.0 0.6 \u2013 0.8 0.5 \u2013 0.6 <0.5 \n\n\n\nSource: Sys et al. (1991, 1993); De Datta (1989) \n\n\n\n\u2020 = ratings for lowland rice production; SAB =Sub Angular Blocky; Col = Columnar; Cr = crumb; WD = Well \n\n\n\nDrained; MWD = Moderately Well Drained; ID= Imperfectly Drained; PD = Poorly Drained; L= Loamy; SL= Sandy \n\n\n\nLoam; LS= Loamy Sand; Lfs = Loamy fine sand; SCL= Sandy Clay Loam; Fo =Rarely flooded; F1= Flooding \n\n\n\nexpected; F2= Irregularly flooded; F3 = regularly flooded. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 43\n\n\n\nin the southern part. The most critical limitation relates to poor drainage and \nflooding. To resolve this problem, we recommend designing a proper drainage \nnetwork along with the construction of strategic dykes to prevent flood waters \nfrom river Benue.\n \n\n\n\nREFERENCES\nAndersen, I. and K.G. Golitzen (Eds.). 2005. The Niger River Basin: A Vision for \n\n\n\nSustainable Management. World Bank Publications.\n\n\n\nBhunia, G.S., P.K. Shit, and R. Maiti. 2016. 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A critical examination of a rapid method for determining organic \ncarbon in soils-Effect of variations in digestion conditions and of inorganic soil \nconstituents. Soil Science 63(4): 251-264.\n\n\n\nZandi, S., A. Ghobakhlou and P. Sallis. 2011. Evaluation of spatial interpolation \ntechniques for mapping soil pH. 19th International Congress on Modelling \nand Simulation, Perth, Australia, 12\u201316 December 2011 http://mssanz.org.au/\nmodsim2011\n\n\n\n\n\n"
"\n\nINTRODUCTION\nGlobal soil nitrogen (N) and carbon (C) sequestration has been estimated since the \n1970s. Early inventories of global soil carbon and nitrogen used a carbon/nitrogen \n(C/N) ratio conversion approach (Burns and Hardy, 1975; Stevenson, 1982). \nHowever, more recently, soil profile measurement approaches have been used to \nestimate soil N and C sequestration (Batjes, 1996, Lin et al., 2010; Abebayehu, \n2013). However, there are many methodological problems with sampling soils for \nC and N sequestration, including accurate measurement of bulk density, accurate \nmeasurement of horizon thickness, organic carbon and/or total nitrogen and the \nmaximum profile depth to which soils should be sampled (Burton and Pregtizer, \n2008). Assessments of the distribution of C and N within and among soils are \ncritical to developing an understanding of the cause and effect relationships between \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 37- 48 (2016)                         Malaysian Society of Soil Science\n\n\n\nSub-Soil Carbon and Nitrogen Sequestration: Soil Profile \nMeasurement Approach \n\n\n\nAhukaemere, C.M\n\n\n\nDepartment of Soil Science and Technology, Federal University of Technology,\n            P.M.B. 1526, Owerri, Imo State, Nigeria.\n\n\n\nABSTRACT\nCarbon sequestration is the process of transferring atmospheric carbon dioxide \ninto the soil and storing it securely so it is not immediately re-emitted into the \natmosphere. A thorough understanding of carbon and nitrogen sequestration in \nvarious horizons of the soil profile would be helpful to comprehend carbon and \nnitrogen cycling from a pedological perspective. The quantity of carbon and \nnitrogen sequestered at different horizons of the soil profile pit was investigated. \nResults showed that the mean organic carbon content of the soils varied from \n6.81 to 37.75 g kg-1. The organic carbon content of the individual horizon varied \nsubstantially within the profiles. Carbon and nitrogen sequestration capacities \nof the soils varied from 3142.60 \u2013 7643.25 g C m-2, 101.33 \u2013 503.55 g N m-2 \nand  increased with horizon thickness in all the soil profiles. Bulk density has \nsignificant positive relationship with the amount of carbon sequestered in the soil. \nThe results of regression analysis showed that with the effect of horizon thickness, \nbulk density and organic carbon on carbon sequestration, the regression coefficient \nof determination (R2) was 0.693 (p<0.001). On N sequestration versus horizon \nthickness, bulk density and total nitrogen, it was found that 67 % of the variation \n(significant at p<0.001) in soil nitrogen sequestration capacity was due to the \naforementioned independent variables in soil.\n\n\n\nKeywords: Coastal plain sand, horizon depth, soil bulk density, multiple \nlinear regression, correlation analysis.\n\n\n\n___________________\n*Corresponding author : E-mail: mildredshine@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201638\n\n\n\nAhukaemere, C.M.\n\n\n\nclimate or land use change and release of carbon dioxide and nitrous oxide to the \natmosphere (Schimel et al., 1994). In addition to understanding the cause and \neffect relationships, knowledge of soil C and N distribution within the soil profile \nis critical when developing C budgets for basic ecosystem characterisation (Davis \net al., 2004). Under ideally equivalent environmental condition and management \npractices, the distribution and sequestration of carbon and nitrogen in soil varies \nwith horizon depth (Jobbagy and Johnson, 2000). Variability in C distribution \nwithin the soil profile is attributed to variations in horizon depth, bulk density and \norganic carbon content. \n\n\n\nIn most developed countries, some regional studies on estimation of soil C \npools using profile data have been conducted (Grossman et al., 1998). Eswaran \net al., (1993) reported the contributions of O horizon and sub-soils of forest soils \nto the carbon pool. Such regional studies are scanty in developing countries like \nNigeria and where available, they are  found to consider too many soil properties. \nIn view of this, this study was aimed at investigating the quantity of C and N \nsequestered at different horizons of soil profile pits.  \n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area \nThe study area (Umuagwo, Ohaji) is located between latitudes 5o 191 N, and \nlongitudes 6o 581 E. The geology of the region is characterised by coastal plain \nsands,   alluvium and Sombreiro-Warri deltaic plains (Atlas of Imo State Geology, \n1984). Coastal plain sands which underlie a major part of the region (including \nUmuagwo) consist of unconsolidated sand materials which are sometimes cross-\nbedded with clays, sandy clays and sometimes, pebbles (FDALR, 1985). The area \nreceives an average of 2500 mm of rainfall distributed over about 139 days of the \nyear. The daily temperature ranges from a minimum of 21o C to a maximum of 30o \nC. The relative humidity reaches a minimum of 60% in January (at the peak of the \ndry season) and rises to 80 - 90 % in July (at the peak of the rains) (NIMET, 2014). \nThe original vegetation of the study area was tropical rainforest (FDALR, 1985). \nThe rain forest has, however, been destroyed largely through human activities and \nsupplanted with what is today referred to as the oil palm bush.\n\n\n\nField Study\nA transect technique was adopted in field sampling. A transect of 200 meters was \ndrawn and four profile pits were dug at a distance of 50 m apart along the transect. \nThese profile pits were examined according to FAO (2006) guidelines. Bulk soil \nsamples were collected from various identified genetic horizons of the profiles. \nThe processed soil samples were analysed for some physico-chemical properties \nfollowing procedures outlined by Van Reeuwijk (2002). Particle size analysis \nwas determined by hydrometer method, soil pH in 1:2.5 water suspension was \nmeasured with a pH meter and organic carbon by Walkley and Black method. \nThe available P was determined according to Bray No. 2 method, total N was \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 39\n\n\n\nProfile Approach of Carbon and Nitrogen Sequestration\n\n\n\ndetermined by microKjeldahl digestion method. Bulk density was determined \nby core method, the exchangeable bases were determined by the use of neutral \nammonium acetate method and base saturation  by calculation.\n\n\n\nCarbon Sequestration (g C m-2) was calculated using the method of  Batjes (1996) \nBD (g cm-3) x OC (g kg-1) x horizon thickness (depth) (cm) \u2248 \u2211Bi x Ci x Di\nwhere Bi is the bulk density of individual layer i (g cm-3), Ci is organic carbon in \nlayer i (g kg-1) and Di is the thickness of this layer (cm).  \n\n\n\nNitrogen Sequestration (g N m-2) = \u2211Di x Bi x TNi where D = depth, B = \nbulk density and TN = total nitrogen (He et al., 2012). The amount of C and \nN sequestered (gram per meter square) (g m-2) in each profile was obtained by \nsumming up the carbon stored in different horizons of the respective profiles.\n\n\n\nStatistical Analysis \nData generated were subjected to coefficient of variation, multiple linear regression \nand correlation analyses using SPSS statistical software. Correlation analysis was \nused to detect the relationships among soil variables while the multiple linear \nregression model was used to determine the contribution of the independent \nvariable to the dependent variable, described as follows: Y = b0 + b1x1+ b2x2 + b3x3, \nwhere Y = the dependent variable, x1,\u2026x3 = independent (predictor) variables, b1,\u2026\nb3 = coefficients that describe the effect of the independent variables on dependent \nvariable, b = the value Y is predicted to have when all independent variables are \nequal to zero (0) (Shaw and Wheeler, 1996). Selection of the dependent (Y) and \nthe independent (X) variables was done using stepwise elimination. The aim of \nselection is to reduce the set of predictor variables to those that are necessary and \naccount for nearly as much of the variance as is accounted for by the total set. The \ncoefficient of variation was ranked according to the procedure of Wilding (1985) \nwhere CV \u2264 15% = low variation, CV >15 \u2264 35% = moderate variation, CV > 35 \n% = high variation. \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nThe descriptions of the sites studied are presented in Table 1. The two major land \nuse types identified were secondary forest and cultivated lands. Although both \nland use types contained mixed vegetation, trees dominated the secondary forest \nwhile edible crops, wild grasses and legumes dominated other plant types in the \ncultivated land use type. However, the nature and distribution of the vegetation \ntypes have obvious pedogenic implications on the soils of the coastal plain \nsand. Several bio-sequence studies show that plant species influence litter layer \nthickness (Johnson-Maynard et al., 2004), activities of soil organisms, carbon \nand nitrogen accumulation (Ahukaemere et al., 2015), and rates of decomposition \n(Quideau et al., 2001). The thickness of the soil horizons varied from 4 - 55 cm \n(profile 1) to 20 - 62 cm (profile 2), 5 - 71 cm (profile 3) and 28 - 65 cm (profile 4) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201640\n\n\n\n(Table 2). The O horizons were thin (4 - 14 cm) but with a  greater likelihood that \nthey mixed with the underlying horizons as a result of pedoturbation.  \n\n\n\nTABLE 1\nDescription of the sites\n\n\n\n The mean bulk density of the soils ranged from 1.09 to 1.34 g cm-3 and \nincreased regularly with depth in all the profile pits possibly due to changes in \norganic matter distribution as well as land use pattern. Least bulk density values \nwere recorded in the surface horizons with corresponding high organic matter \nrevealing the influence of organic matter on soil compaction. Several authors have \nreported significant influence of organic matter on soil bulk density (Ahukaemere \net al., 2015). Results of bulk density were less than the critical limits for root \nrestriction (1.75 \u2013 1.85 g cm-3) (Soil Survey Staff, 2003). The average moisture \ncontent of the soils ranged from 9.29 \u2013 10.48 %. Generally, the deepest horizons \nwere more moist compared to the surface horizons. The surface horizons were \nbetter drained with the horizons being generally low in moisture content and \ndrying out quickly due to the quick passage of water movement. Adzmi et al., \n(2010) and  Marryanna et al.  (2012) have reported on the effect of soil depth and \ncanopy cover on soil moisture. Soil pH ranged from 4.48 \u2013 5.04 and fluctuated \nirregularly with depth in all the profiles (Table 2). Strongly acidic soil reaction is \ncharacteristic of soils of South-east Nigeria and it is the consequence of the acidic \nnature of the parent rocks, coupled with the influence of the leached profile under \nhigh annual rainfall condition (Eshett et al., 1990).\n\n\n\nAhukaemere, C.M.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 41\n\n\n\nTABLE 2\nPhysical and chemical properties of soils\n\n\n\nCarbon and Nitrogen Content and Sequestration\nThe C and N contents and sequestration are presented on Table 3. The mean \norganic carbon content of the soils varied from 6.81 to 37.75 g kg-1.  Generally, the \norganic carbon content of the individual horizon varied substantially within the \nprofiles. Moderate to high variation was recorded in the organic carbon contents \nof the soil horizons in all the profiles. High organic carbon content at surface \nhorizon could be due to more organic material input on the soil surface.  The \namount of organic carbon in the soil results from the net balance between the rate \nof organic material input and rate of mineralisation in soil organic carbon (Post \nand Kwon, 2000; Anikwe, 2010). Organic carbon content was the main factor \nexplaining surface horizon bulk density. The deeper the profile, the stronger the \nmixing with mineral material in the profile and the higher the bulk density (Dale \net al.,2011). Negative significant correlation r = -0.695* (p<0.05) was observed \nbetween soil bulk density and organic carbon content (Table 4). The mean total \nnitrogen content of the soil was generally low in all the profiles (3.85 \u2013 12.76 mg \n\n\n\n2 \n \n\n\n\n \n \n \n \n \n \nTable 2: Physical and chemical properties of soils \nHor. des. HT. \n\n\n\n(cm) \nSand  Silt       \n\n\n\n(g kg-1)  \nClay  BD       \n\n\n\n(g cm-3)  \nMC \n(%)  \n\n\n\npH \n(H2O) \n\n\n\n AV.P \n(mg kg-1) \n\n\n\nTEB   \n(cmol+ kg-1) \n\n\n\nProfile 1          \nOe 4 858 94 48 1.10 10.26 5.01 28.13 9.03 \nOa 14 858 64 78 1.06 8.03 4.78 26.8 7.63 \nA 30 818 74 108 1.36 10.83 4.35 20.00 4.55 \nAB 51 778 54 168 1.38 9.34 4.20 16.10 4.58 \nBt1 55 758 54 188 1.53 10.08 4.20 20.00 4.63 \nBt2 46 748 54 198 1.58 11.85 4.80 17.80 4.21 \nMean  803 65 131 1.34 10.07 4.56 21.47 5.77 \nCV (%)  6.10 24.24 47.33 16.10 12.91 8.00 23.00 35 \nProfile 2          \nAp 20 858 54 88 1.14 8.56 5.52 27.7 4.59 \nAB 28 858 34 108 1.17 7.18 5.13 17.1 4.99 \nBt1 62 778 34 188 1.24 8.57 5.67 15.9 5.48 \nBt2 42 778 24 198 1.48 11.26 4.46 17.5 7.08 \nBt3 48 758 44 198 1.52 10.81 4.40 20.4 5.36 \nMean  806 38 156 1.31 9.27 5.04 19.72 5.50 \nCV (%)  6.00 30.00 34.00 14.00 18.00 11.60 24.12 17.00 \nProfile 3          \nOa 5 804.00 108.00 88.00 0.82 5.60 4.20 10.4 5.03 \nA 12 764.00 108.00 128.00 0.96 10.61 4.21 17.9 4.13 \nAB 45 724.00 128.00 148.00 1.22 12.51 4.81 22.3 2.82 \nBt1 71 724.00 128.00 148.00 1.35 13.20 4.71 17.2 5.22 \nMean   754.00 118.00 128.00 1.09 10.48 4.48 16.95 4.30 \nCV (%)  5.08 9.79 22.09 21.94 32.75 8.73 29.00 25.47 \nProfile 4          \nAp 28 818 94 88 0.96 7.69 4.91 41.6 6.84 \nAB 21 838 34 128 1.15 8.86 4.64 20.0 4.63 \nBt1 65 818 64 118 1.21 13.11 4.18 18.7 9.01 \nBt2 36 778 74 148 1.31 9.49 4.90 19.3 6.62 \nBt3 50 738 94 168 1.54 11.19 4.50 19.5 6.26 \nMean  798 72 130 1.23 10.08 4.63 23.82 6.67 \nCV (%)  5.00 35.00 23.00 17.00 21.00 6.58 42.00 24.00 \n HT = Horizon thickness, Hor. des. = Horizon designation, Oe = Hemic horizon, Oa = Sapric horizon,  \nMC = Moisture content, BD = Bulk density, CV = Coefficient of variation, CV < 15% = Low variation, \nCV > 15 < 35% = Moderate variation, CV >35 = High variation (Wilding, 1985). \n \n \n \n\n\n\nProfile Approach of Carbon and Nitrogen Sequestration\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201642\n\n\n\nkg-1), and the pattern of its distribution closely followed that of organic carbon \nas it decreased down the profiles (Table 2). A positive significant relationship \nwas observed between total nitrogen and organic carbon content of the soil (r = \n0.865*).  \n\n\n\nThe mean carbon and nitrogen sequestration capacities of the soil varied from \n3124.60 \u2013 7643.25 g C m-2, 101.33 \u2013 503.55 g N m-2.  Mba and Idike, (2011) \nreported 2435 - 6429 g C m-2 of carbon in soils of south-eastern Nigeria. From \nthe results, carbon and nitrogen sequestration increased with horizon thickness \nin all the profiles across the soils.  The effect of horizon depth on soil carbon \nsequestration is presented in Figure 1.  \n\n\n\nFigure 1: Profile 4 - Effect of horizon thickness (cm) on carbon sequestration (g C m-2) \n\n\n\nThe subsurface horizons (Bt) with horizon thickness varying from 45-71 \ncm contained the highest quantity of stored carbon and nitrogen compared to \nsurface horizons with horizon thickness of about 4 - 30 cm in all the soil profiles \ninvestigated. This revealed the inherent capacity of these horizons to store more C \nand N. For instance, the O horizons of profiles 1 and 3 contained only about 10-\n24 % of the carbon stored in these profiles. Batjes (1996), Eswaran et al., (1995), \nMba and Idike, (2011) and Abebayehu (2013) reported high carbon sequestration \nin the deeper horizons.  According to Mbah and Idike (2011), carbon has higher \ndensity near the surface but soil organic carbon decomposes rapidly, releasing \nCO2 to the atmosphere, thus some carbon becomes stabilised especially in the \nlower part of the profile. Much of this deeper carbon occurs in more stable forms \nand therefore will not contribute much to the current gaseous emission (IPCC, \n1992). In addition, effect of agricultural activities on carbon and nitrogen was \nlargely restricted to the top soil thus causing carbon stored below this depth to be \nmore stable in all the pits. \n\n\n\n3 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 1: Profile 4 - Effect of horizon thickness (cm) on carbon sequestration. (g C m-2)  \n\n\n\n\n\n\n\n \nTable 3: Carbon and nitrogen content and sequestration of soil \nHorizon. \nDesignation \n\n\n\nHorizon \nthickness (cm) \n\n\n\nOrg.C    \n(g kg-1) \n\n\n\nTotal N. \n(mg kg-1) \n\n\n\nCarbon stock  \n(g C m-2) \n\n\n\nNitrogen       \nstock (g N m-2) \n\n\n\nProfile 1      \nOe 4 42.00 12.00 1848 53 \nOa 14 16.63 4.00 2468 60 \nA 30 8.11 1.00 3309 41 \nAB 51 6.90 2.00 4856 141 \nBt1 55 6.10 2.00 5133 168 \nBt2 46 7.21 2.10 4975 145 \nMean  14.49 3.85 3764.8 101.33 \nCV (%)  96.76 --- 37.73 55.43 \nProfile 2      \nAp 20 8.31 8.00 1895 182 \nAB 28 8.30 8.00 2719 262 \nBt1 62 7.20 8.00 5535 615 \nBt2 42 5.61 6.00 3487 373 \nBt3 48 4.92 7.00 3590 511 \nMean  6.87 7.40 3445.20 388.6 \nCV (%)  22.58 12.09 39.27 45.47 \nProfile 3      \n\n\n\n0 \n\n\n\n1000 \n\n\n\n2000 \n\n\n\n3000 \n\n\n\n4000 \n\n\n\n5000 \n\n\n\n6000 \n\n\n\nAp (28 cm) AB (21 cm) Bt1 (65 cm) Bt2 (36 cm) Bt3 (50 cm) \n\n\n\nAhukaemere, C.M.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 43\n\n\n\nTABLE 3\nCarbon and nitrogen content and sequestration of soil.\n\n\n\nTABLE 4\nCorrelation matrix among soil properties (p<0.05)\n\n\n\n4 \n \n\n\n\nTable 3: Carbon and nitrogen content and sequestration of soil \nHorizon. \nDesignation \n\n\n\nHorizon \nthickness (cm) \n\n\n\nOrg.C    \n(g kg-1) \n\n\n\nTotal N. \n(mg kg-1) \n\n\n\nCarbon stock  \n(g C m-2) \n\n\n\nNitrogen       \nstock (g N m-2) \n\n\n\nProfile 1      \nOe 4 42.00 12.00 1848 53 \nOa 14 16.63 4.00 2468 60 \nA 30 8.11 1.00 3309 41 \nAB 51 6.90 2.00 4856 141 \nBt1 55 6.10 2.00 5133 168 \nBt2 46 7.21 2.10 4975 145 \nMean  14.49 3.85 3764.8 101.33 \nCV (%)  96.76 --- 37.73 55.43 \nProfile 2      \nAp 20 8.31 8.00 1895 182 \nAB 28 8.30 8.00 2719 262 \nBt1 62 7.20 8.00 5535 615 \nBt2 42 5.61 6.00 3487 373 \nBt3 48 4.92 7.00 3590 511 \nMean  6.87 7.40 3445.20 388.6 \nCV (%)  22.58 12.09 39.27 45.47 \nProfile 3      \nOa 5 70.4 21.00 2886 861.00 \nA 12 49.3 16.34 5679 188.24 \nAB 45 19.1 8.09 10314 436.86 \nBt1 71 12.2 5.52 11694 528.13 \nMean   37.75 12.76 7643.25 503.55 \nCV (%)  71.74 56.32 53.43 55.26 \nProfile 4      \nAp 28 11.12 8.00 2989 215 \nAB 21 6.80 7.00 1642 169. \nBt1 65 6.80 7.00 5348 551 \nBt2 36 5.11 7.00 2410 330 \nBt3 50 4.20 6.00 3234 462 \nMean   6.81 7.00 3124.60 345.4  \nCV (%)  39.07 10.10 44.35 47.00 \nOe = Hemic horizon, Oa = Sapric horizon,  CV = Coefficient of variation, CV < 15% = Low variation, \nCV > 15 < 35% = Moderate variation, CV >35 = High variation (Wilding, 1985).  \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 4. Correlation matrix among soil properties (p < 0.05) \n\n\n\n5 \n \n\n\n\nTable 4. Correlation matrix among soil properties (p < 0.05) \n HT OC TN CS NS Clay BD MC pH Avp \nHT - - - - - - - - - - \nOC -0.639* - - - - - - - - - \nTN -0.555* 0.865* - - - -  - - - \nCS 0.615* 0.030 -0.102 - - - - - - - \nNS 0.337 0.250 0.480 0.324 - - - - - - \nClay 0.720* -0.494 -0.421 0.354 0.243 - - - - - \nBD 0.683* -0.695* -0.738* 0.612* -0.092 0.791* - - - - \nMC 0.609* -0.337 -0.393 0.619* -0.006 0.424 0.584* - - - \npH -0.071 -0.218 -0.014 -0.105 -0.073 -0.179 -0.150 -0.265 - - \nAvp -0.271 -0.163 -0.129 -0.239 -0.474 -0.461 -0.259 -0.115 0.336 - \n\n\n\nBD = Bulk density, MC = moisture content, HT = Horizon thickness, OC = Organic carbon, TN \n= Total nitrogen, CS = Carbon sequestration, NS = Nitrogen sequestration.  \n \n\n\n\n \nTable 5. Multiple linear regression models for soil properties.   \n\n\n\nMultiple Linear regression R R2 P value \n\n\n\nHT=28.90-0.44OC-1.77TN+0.003CS +0.046NS 0.947 0.897*** P < 0.001 \n\n\n\nHT=28.899- 0.436 OC-1.768 TN+0.003 CS + 0.046 NS 0.947 0.897*** P < 0.001 \n\n\n\nOC=48.178+ 2.066 TN+0.002 CS+0.004 NS-8.649 HT-8.649 pH 0.946 0.894*** P < 0.001 \n\n\n\nCS =-5540.1+138.388HT+98.90 pH +98.90 OC 0.783 0.612** P < 0.01 \n\n\n\nCS =2394.982 +12.263 TN +18.708 Clay -35.608 Avp 0.365 0.133ns P > 0.05 \n\n\n\nCS =35863.691-38.990Sand-5695.801BD +651.851MC 0.837 0.700** P < 0.01 \n\n\n\nNS= -645.323+9.972HT+10.780 OC+91.765pH 0.712 0.506** P < 0.01 \n\n\n\nNS= -28.439+28.980TN+2.007 Clay-6.880Avp 0.705 0.498** P < 0.01 \n\n\n\nNS= 3673.669 -3.382 Sand-358.187 BD-23.026 MC 0.552 0.305 ns P > 0.05 \n\n\n\nNS= 327.224 +22.148 pH-3.170 TEB+22.148 TN 0.485 0.235 ns P > 0.05 \n\n\n\nNitrogen stock = -544.14 + 7.62HT + 286.15BD + 26.05TN  0.810 0.670*** P < 0.001 \n\n\n\nHT=34.900 -0.869 CS+0.048 NS 0.819 0.670*** P < 0.001 \n\n\n\n BD = Bulk density, MC = moisture content, HT = Horizon thickness, OC = Organic carbon, \nTEB = Total exchangeable bases, TN = Total nitrogen. \n \n\n\n\n\n\n\n\n\n\n\n\nProfile Approach of Carbon and Nitrogen Sequestration\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201644\n\n\n\nCarbon and nitrogen sequestration is a function of horizon thickness, bulk \ndensity, organic carbon and total nitrogen. Abebayehu, (2013) found that carbon \nsequestration capacity was significantly affected by organic carbon concentration, \nhorizon depth and bulk density. He also stated that a unit rise in soil organic carbon \nconcentration, bulk density and sampling depth raises soil carbon sequestration \nby 5.47, 1.53 and 25.64 t ha-1 respectively. Linda et al. (2003) observed increased \ncarbon sequestration with increased horizon thickness, bulk density and organic \ncarbon. From the results, bulk density had positive correlation with the amount of \ncarbon and nitrogen sequestered in the soil (Table 4). \n\n\n\nTable 5 shows the multiple linear regression models of the soil variables.  \nIn the combined effects of horizon thickness, organic carbon and pH on carbon \nsequestration, the regression coefficient of determination (R2) was 0.612 (p< \n0.01). This shows that about 61.2% variation in the quantity of C sequestered \nin soil could be due to the combined influence of horizon thickness, organic \ncarbon and soil pH.  However, about 61 % of variation in carbon sequestration \nis explained by these soil variables. Based on these realities, the selected model \nsignificantly fitted with the existing data meaning that the independent variables \nnamely horizon thickness, organic carbon and pH had a strong relationship with \nsoil carbon sequestration at p<0.05. This is obvious as carbon sequestration is a \nfunction of horizon thickness and organic carbon content of the soil. Similarly, \nfor the effect of soil moisture content, bulk density and sand on soil carbon stock, \nthe  regression coefficient of determination (R2) was 0.700 (p<0.01) indicating \nthat 70% variation in soil carbon sequestration could be due to the combined \ninfluence of these independent soil variables. Soil moisture retention influences \nthe level of carbon dioxide fluxes in the soil which may in one way or the other \naffect soil microbial biomass and potential mineralisation of carbon (Hayney et \nal., 2004). From the regression model, it was ascertained that horizon thickness, \nbulk density and total nitrogen contributed 67%  to the content of nitrogen stock. \nThis indicates that these soil parameters had significant influence on nitrogen \nsequestration and that the selected model significantly fitted with the existing \ndata. Abebayehu, (2013) reported similar results for carbon storage capacity of \nforest soil.  Also, horizon thickness, organic carbon and pH contributed 51% to \nthe content of nitrogen stock (Table 5). Organic carbon, pH and total nitrogen are \nessential soil variables that encourage nitrogen accumulation in soil. \n\n\n\nAhukaemere, C.M.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 45\n\n\n\nTABLE  5\nMultiple linear regression models for soil properties.  \n\n\n\nCONCLUSION\nThe average quantities of carbon and nitrogen sequestered in the soil varied \nfrom 3142.60 \u2013 6139.74 g C m-2 to 101.33 \u2013 388.6 g N m-2 with the thickest \nhorizons containing the highest quantities. Carbon and nitrogen sequestration \nis governed by soil organic carbon, bulk density, horizon thickness and total \nnitrogen. Thus, organic carbon, total nitrogen and bulk density improvement are \nthe most important management interventions to increase carbon and nitrogen \nsequestration capacity in soil. Therefore, farmers should focus on management \nactivities that improve these soil characteristics to boost carbon and nitrogen \nsequestration capacity of the soil. Further more,  it is imperative that researchers \ngenerate adequate information from other similar studies to convince farmers, \npolicy makers and other stakeholders to protect these important soil parameters.\n\n\n\nREFERENCES\nAbebayehu, A. (2013). Evaluating organic carbon storage capacity of forest soil: \n\n\n\ncase study in Kafa zone Bita District Southwest Ethiopia.  American- Eurasian \nJournal of Agric. and Environmental Science. 13(1) 95-100. \n\n\n\nAdzmi, Y., W.C. Suhaimi, M.S., Amir Husni, H. Mohd Ghazali, S.K, Amir and I. \nBaillie. (2010). Heterogeneity of soil morphology and hydrology on the 50 ha. \nlong term ecological research plot at Pasoh, West Malaysia. Journal of Tropical \nForest Science 22(1): 21-35.\n\n\n\nAhukaemere, C.M. Akamigbo, F.O.R. Onweremadu, E.U. Ndukwu, B. N.,  Osisi, \nF.A. (2015). \n\n\n\n5 \n \n\n\n\nTable 4. Correlation matrix among soil properties (p < 0.05) \n HT OC TN CS NS Clay BD MC pH Avp \nHT - - - - - - - - - - \nOC -0.639* - - - - - - - - - \nTN -0.555* 0.865* - - - -  - - - \nCS 0.615* 0.030 -0.102 - - - - - - - \nNS 0.337 0.250 0.480 0.324 - - - - - - \nClay 0.720* -0.494 -0.421 0.354 0.243 - - - - - \nBD 0.683* -0.695* -0.738* 0.612* -0.092 0.791* - - - - \nMC 0.609* -0.337 -0.393 0.619* -0.006 0.424 0.584* - - - \npH -0.071 -0.218 -0.014 -0.105 -0.073 -0.179 -0.150 -0.265 - - \nAvp -0.271 -0.163 -0.129 -0.239 -0.474 -0.461 -0.259 -0.115 0.336 - \n\n\n\nBD = Bulk density, MC = moisture content, HT = Horizon thickness, OC = Organic carbon, TN \n= Total nitrogen, CS = Carbon sequestration, NS = Nitrogen sequestration.  \n \n\n\n\n \nTable 5. Multiple linear regression models for soil properties.   \n\n\n\nMultiple Linear regression R R2 P value \n\n\n\nHT=28.90-0.44OC-1.77TN+0.003CS +0.046NS 0.947 0.897*** P < 0.001 \n\n\n\nHT=28.899- 0.436 OC-1.768 TN+0.003 CS + 0.046 NS 0.947 0.897*** P < 0.001 \n\n\n\nOC=48.178+ 2.066 TN+0.002 CS+0.004 NS-8.649 HT-8.649 pH 0.946 0.894*** P < 0.001 \n\n\n\nCS =-5540.1+138.388HT+98.90 pH +98.90 OC 0.783 0.612** P < 0.01 \n\n\n\nCS =2394.982 +12.263 TN +18.708 Clay -35.608 Avp 0.365 0.133ns P > 0.05 \n\n\n\nCS =35863.691-38.990Sand-5695.801BD +651.851MC 0.837 0.700** P < 0.01 \n\n\n\nNS= -645.323+9.972HT+10.780 OC+91.765pH 0.712 0.506** P < 0.01 \n\n\n\nNS= -28.439+28.980TN+2.007 Clay-6.880Avp 0.705 0.498** P < 0.01 \n\n\n\nNS= 3673.669 -3.382 Sand-358.187 BD-23.026 MC 0.552 0.305 ns P > 0.05 \n\n\n\nNS= 327.224 +22.148 pH-3.170 TEB+22.148 TN 0.485 0.235 ns P > 0.05 \n\n\n\nNitrogen stock = -544.14 + 7.62HT + 286.15BD + 26.05TN  0.810 0.670*** P < 0.001 \n\n\n\nHT=34.900 -0.869 CS+0.048 NS 0.819 0.670*** P < 0.001 \n\n\n\n BD = Bulk density, MC = moisture content, HT = Horizon thickness, OC = Organic carbon, \nTEB = Total exchangeable bases, TN = Total nitrogen. \n \n\n\n\n\n\n\n\n\n\n\n\nProfile Approach of Carbon and Nitrogen Sequestration\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201646\n\n\n\nCarbon and Nitrogen Forms and Sequestration in Relation to Agricultural  Land Use \nTypes  in a Humid Agro-ecosystem. 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Spatial distribution of soil carbon \nin southern New England Hardwood forest landscapes. Soil Sci. Soc. Am. J. 68: \n895-903.  \n\n\n\nEshett, E.T., Omoeti, J.A.I. and Juo, A.S.R. (1990). Physico-chemical, morphological \nand clay mineralogical properties of soils overlying basement complex rocks in \nOgoja, Northern Cross River State of Nigeria. Soil Science and Plant Nutrition \n36(2): 203-214.\n\n\n\nEswaran, H., Berg, E.V.D and Reich, P. (1993). Organic carbon in soils of the world. \nSoil Science Society of American Journal, 57:192-194.\n\n\n\nEswaran, H, Berg, E.V.D., Reich, P. and Kimble J. (1995).  Global soil carbon \nresources. In: soils and  Global change. Eds. R, Lal, J. Kimble, E. Levine and \nB.A Stewart, CRC, Lewis publishers pp 27 \u2013 29.\n\n\n\nFAO (Food and Agricultural Organization) (2006). 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In: Soil spatial Variability. Nielsen, D. R., Bouma, J. \n(Eds). Pudoc. Wageningen, The Netherlands, pp. 166-194.\n\n\n\nAhukaemere, C.M.\n\n\n\n\n\n"
"\n\n__________________\n*Corresponding author : Email : samsuri@agri.upm.edu.my\n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 13: 119-131                               Malaysian Society of Soil Science\n\n\n\nSorption-Desorption Study of a Herbicide 2,4-\n\n\n\nDichlorophenoxyacetic Acid on Acidic Tropical Soils\n\n\n\nAkma, N.M.H.1, Samsuri, A.W.1*, Ainie, H.K.2  & Rosenani, A.B.1.\n\n\n\n1Department of Land Management, Faculty of Agriculture, \n\n\n\nUniversiti Putra Malaysia\n\n\n\n2 Product Development and Advisory Service Division, Malaysian Palm Oil Board\n\n\n\n\n\n\n\nINTRODUCTION\n\n\n\nIn the recent years, 2,4-dichlorophenoxyacetic acid (2,4-D) (Fig. 1) has been \n\n\n\nwidely used as post emergence herbicides to eradicate broadleaf weeds in oil \n\n\n\npalm plantation. This herbicide is also known by the trade name 2,4-D Amine, \n\n\n\nBarrage, Planatox and Weedone. Currently, the herbicides account for about 75.1 \n\n\n\n% of total agrochemicals used in Malaysia. The usage of 2,4-D in agricultural \n\n\n\nsector is expected to rise from 1.0 million litres in 1998 to 1.2 million litres in \n\n\n\n2010 (Malaysian Agriculture Directory and Index 1999), with the expansion of \n\n\n\noil palm hectarage. Extensive use of herbicides has become a concern since it is \n\n\n\nsuspected to exhibit endocrine-disrupting activities (Rawling et al. 1998; Short \n\n\n\nABSTRACT\nThe sorption and desorption of 2,4-dichlorophenoxyacetic acid (2,4-D) was \n\n\n\nevaluated on different soils with different range of organic matter content. The \n\n\n\nbatch equilibrium technique under laboratory condition was used to determine \n\n\n\nthe sorption/desorption behavior of 2,4-D in 4 different soil orders of Malaysia \n\n\n\nviz Histosols (peat), Inceptisols (Selangor and Briah) and Ultisols (Rengam and \n\n\n\nSerdang) and Oxisol (Munchong). Sorption data were fitted to the linear and \n\n\n\nFreundlich equations. The values of K\nd \n\n\n\nand K\nF\n ranged from 1.35 to 35.26 and \n\n\n\n2.70 to 42.04, respectively. Highest sorption was observed in peat soil and the \n\n\n\nlowest was in Rengam soil. According to the sorption and desorption results, \n\n\n\norganic matter and clay seemed to be the most important factor influencing \n\n\n\nthe sorption capacity of 2,4-D. Thus, the contributions of organic matter were \n\n\n\nevaluated by comparing changes in 2,4-D of sorption before and after organic \n\n\n\nmatter removal. After organic matter was removed from the soils, the Kd values \n\n\n\nfor sorption by Selangor and Munchong, which were calculated from linear \n\n\n\nand Freundlich equations, decreased by 26.7 % and 28.0 %, respectively. This \n\n\n\nrevealed that soil organic matter greatly influenced the 2,4-D sorption. Based on \n\n\n\ntheir sorption capacity, the soils can be ranked in the following decreasing order: \n\n\n\nPeat> Selangor> Munchong> Briah> Serdang> Rengam Soil Series.\n\n\n\nKeywords : 2,4-D, linear equation, Freundlich equation, organic matter, \n\n\n\n       clay minerals\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009120\n\n\n\nand Colborn 1990) and has potentials to negatively impact soil and groundwater \n\n\n\nquality. However, little is known about their interaction with soil which might \n\n\n\naffect their environmental fates particularly in tropical acidic soils. \n\n\n\nFig. 1: Chemical structure of 2,4-D.\n\n\n\n Sorption is one of the main processes influencing the mobility of herbicides \n\n\n\nin soil under laboratory (Socias-Viciana et al. 1999), as well as under field \n\n\n\nconditions (Laabs et al. 2000). Sorption tends to decrease the degradation rate \n\n\n\nof herbicides and their bioavailability (Yue et al. 2006). Herbicides behaviour in \n\n\n\nsoils greatly depends on adsorption-desorption phenomena (Gao et al. 1998; de \n\n\n\nJonge and de Jonge, 1999) which is important to predict their potential to leach \n\n\n\ninto groundwater. Soil organic matter content appears to be a predominant factor \n\n\n\ninfluencing their retention (Stevenson 1972; Coquet 2002; Spark and Swift 2002) \n\n\n\ndue to the porous nature and large surface of the humic substances. However, the \n\n\n\nasscociation of 2,4-D with the mineral surfaces, pH, and clay content become \n\n\n\nsignificant in low organic matter content soil (Stevenson, 1976; Brownawell \n\n\n\net al. 1990; Schwarzenbach et al. 1993; Baskaran et al. 1996 Celis et al. 1996, \n\n\n\n1999). Interaction at the interface between organic and inorganic soil colloids via \n\n\n\nsorption may affect the movement of pesticides, resulting in contamination of \n\n\n\ngroundwater. \n\n\n\n Previous study on Malaysian soils was done by Cheah et al. (1997) on \n\n\n\nadsorption, mobility and degradation only on sandy loam and muck soils.  Moreover, \n\n\n\nsorption of weak organic acids which can exist in molecular and anionic forms \n\n\n\nhas been less studied (Dubus et al. 2001) particularly in variable charged soils. \n\n\n\nDesorption process is very important in order to know the release rate and their \n\n\n\npotential mobility in soil. Hence, the objective of this study was to determine the \n\n\n\nsorption and desorption of 2,4-D in Malaysian soils cultivated with oil palm. Data \n\n\n\ngenerated from this research is important to assess the providing information on \n\n\n\nbehaviour of 2,4-dichlorophenoxyacetic acid which is vital to ensure the potential \n\n\n\nof groundwater contamination in Malaysian agroenvironment.\n\n\n\nMATERIALS AND METHODS\n\n\n\nChemicals\n\n\n\nAnalytical standard of 2,4-D (purity 96%) was purchased from Riedel-de Haen \n\n\n\n(Germany). Physico-chemical properties of 2,4-D are shown in Table 1. Stock \n\n\n\nsolution of 2,4-D was prepared by dissolving appropriate amount of  2,4-D in \n\n\n\nAkma, N.M.H., Samsuri, A.W., Ainie, H.K.  & Rosenani, A.B..\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 121\n\n\n\nmethanol and kept refrigerated. Working solutions (0.25, 0.5, 1, 2.5, 5, 7.5, 10 and \n\n\n\n15 mg L-1) were prepared in 0.01 M CaCl\n2\n and 200 mg L-1 HgCI\n\n\n\n2\n as background \n\n\n\nsolution to prevent biological activity. Chemical structure of 2,4-D shown in Fig. 1.\n\n\n\nSoils\n\n\n\nSoil samples were collected from oil palm cultivated areas of West Malaysia. \n\n\n\nThe soils used were from the Order Inceptisol, Ultisol, Oxisol and Histosol. \n\n\n\nThe soil series were peat, Rengam, Serdang,  Munchong, Selangor and Briah \n\n\n\naccording to the USDA Soil Taxonomy.  The peat, Rengam, Serdang,  Munchong, \n\n\n\nSelangor and Briah soils were classified as Typic Haplohemists, Typic Kandiudult, \n\n\n\nTypic Paleudult, Haplic Hapludox, Typic Tropaquept, Fluvaquentic or Typic \n\n\n\nEndoaquepts, respectively (Paramananthan 2000). Surface (0-15 cm) and \n\n\n\nsubsurface (15-30 cm) samples of each soil were air dried and passed through a \n\n\n\n2 mm sieve prior to analysis. Selected physical and chemical properties of these \n\n\n\nsoils are listed in Table 1. Soil pH was determined by a Beckman Digital pH meter \n\n\n\nat soil-water ratio of 1:2.5. Cation Exchange Capacity (CEC) was determined \n\n\n\nwith 1 N ammonium acetate at pH 7 (Thomas 1982). Particles size distribution \n\n\n\nwas determined by pipet method (Day 1965), clay minerals were determined \n\n\n\nusing X-ray diffraction technique. Free Fe/Al oxides were extracted by dithionite-\n\n\n\ncitrate-bicarbonate (Mehra and Jackson 1960) and measured by AAS. In order \n\n\n\nto evaluate contributions of soil organic matter to 2,4-D sorption, organic matter \n\n\n\n(OM)-removed soils were prepared by heating the soils at 750 \u00baC for 2 h in a \n\n\n\nmuffle furnace (Ying, 2005).\n\n\n\nTable 1\n\n\n\nPhysico-chemical properties of 2,4-D.\n\n\n\nSorption-Desorption Experiment\n\n\n\nSorption of 2,4-D was conducted using a batch equilibrium method (OECD, \n\n\n\n2000). The soil samples about 2 g were weighed into a centrifuge tubes. The \n\n\n\nsolution containing either 0, 0.25, 0.50, 1.00, 2.50, 5.00, 7.50, 10.00 or 15.00 mg \n\n\n\nL-1 2,4-D in 20 ml of 0.01 M CaCl\n2\n and 200 mg L-1 HgCl\n\n\n\n2 \nas bioinhibitor were \n\n\n\nadded. The concentrations were prepared in triplicates. The tubes were shaken \n\n\n\nfor 24 h and centrifuged at 3,500 rpm for 15 min. The supernatant was decanted \n\n\n\nand determined for 2,4-D concentration at equiblibrium. The amount of herbicide \n\n\n\nadsorbed (C\ns\n) were calculated by taking the difference between the initial (C\n\n\n\ni\n) \n\n\n\nand the equilibrium (C\ne\n) 2,4-D solution concentrations. Sorption isotherms was \n\n\n\nSorption-Desorption of 2,4-D\n\n\n\n\n\n\n\nChemical \n\n\n\n\n\n\n\nMolecular weight pKa Solubility in water at 25  C(mg L\n-1\n\n\n\n) \n\n\n\n2,4-D \n\n\n\n\n\n\n\n221.0 2.73 23,180 (pH 7) \n\n\n\n                                                                                                                                                          (Source: Tomlin (2001)\n \n\n\n\n O\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009122\n\n\n\nobtained by plotting the amount of chemical sorbed per unit weight of soil at \n\n\n\nequilibrium (Cs, mg kg1) versus chemical concentration at equilibrium (C\ne\n, mg \n\n\n\nL-1). Sorption isotherm data were fitted to both linear and Freundlich equation: \n\n\n\n  C\ns\n = K\n\n\n\nd \nC\n\n\n\ne\n\n\n\nand the Freundlich equation (log-transformed);\n\n\n\n  log C\ns\n = log K\n\n\n\nF\n + nlog C\n\n\n\ne\n\n\n\nwhere C\ns\n is the amount of herbicides sorbed, C\n\n\n\ne\n is the equiblibrium concentration \n\n\n\nin solution and K\nF\n is the Freundlich constant.\n\n\n\nDesorption experiments were performed immediately after supernatant was \n\n\n\ndecanted by adding 20 mL solution 0.01 M CaCl\n2\n and 200 mg L-1 HgCl\n\n\n\n2\n. The \n\n\n\nsoils were resuspended by shaking the tubes for a further 24 h under the same \n\n\n\nexperimental conditions as explained above. \n\n\n\nHPLC Analysis of 2,4-D\n\n\n\nThe analysis of the 2,4-D was performed by HPLC (Waters) equipped with \n\n\n\nautosampler injector and Photodiode Array (PDA) detector. Determination was \n\n\n\ndone with methanol and ammonium formate buffer at pH 4.5 (50:50) using \n\n\n\nisocratic mode, flow rate of 0.6 mL min-1, the column was Zorbax 300 SB-C\n18\n\n\n\n\n\n\n\n(4.6 x 250 mm) and the injection volume used was 20 \u00b5L.\n\n\n\n\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Physico-Chemical Properties\n\n\n\nThe physico-chemical properties of soils used are shown in Table 2. All the soils \n\n\n\nwere acidic. In general, soil pH in the surface is higher than subsurface excluding \n\n\n\nMunchong soil. Peat soil had a greater CEC due to the higher organic matter \n\n\n\ncontent. Its CEC value was almost 2 times more than the mineral soils. In mineral \n\n\n\nsoils, Munchong had a higher content of clay (56 %) than other soils. In general, \n\n\n\nthe physico-chemical data observed were slightly lower than those reported by \n\n\n\nParamananthan (2000).\n\n\n\nAkma, N.M.H., Samsuri, A.W., Ainie, H.K.  & Rosenani, A.B..\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 123\n\n\n\nTable 2\n\n\n\nSelected chemical properties of the soils.\n\n\n\nSoil Mineralogy\n\n\n\nClay mineral analysis showed that kaolinite, montmorillonite and vermiculite is \n\n\n\nthe most abundant mineral in soil samples. The higher content of CEC in Selangor \n\n\n\nand Briah probably due to the higher content of 2:1 layer silicates, especially \n\n\n\nmontmorillonite and vermiculite. Both of the soils were richer in free Fe/Al oxides \n\n\n\nas well as % silt, even the % clay is lower than Munchong soil (Table 3).\n\n\n\nTable 3\n\n\n\nMineralogical content of the soils.\n\n\n\nSorption by Natural Soils\n\n\n\nThe sorption isotherms of 2,4-D on different soils are shown in Fig. 2. In general, \n\n\n\nboth linear and Freundlich equations described the sorption data rather well as \n\n\n\nindicated by the R2 values close to 1 for all sorption isotherms (Table 4). Sorption \n\n\n\ndata for most of the soils fitted well with the Freundlich and values for K\nd\n, K\n\n\n\nF\n, n \n\n\n\nand R2 for selected soils are given in Table 4.  The values for K\nF\n are larger than K\n\n\n\nd\n \n\n\n\nvalues for all isotherms. Most of the soils are L types (n<1) curves (Giles et al., \n\n\n\n1960) indicates that sorption decreases with increasing concentrations of sorbates. \n\n\n\nThe n value close to 1 is observed for sorption of non-ionic organic chemicals to \n\n\n\nsoil with high organic matter content. However, some of the isotherms exhibit a C \n\n\n\n\n\n\n\nSoils Depth Texture \n\n\n\n% \n\n\n\nOC \n\n\n\n% \n\n\n\nOM \n\n\n\npH\n \n\n\n\n(H\n2\nO )\n\n\n\nCEC \n\n\n\n(cmol\n+\n/kg) \n\n\n\n% \n\n\n\nClay \n\n\n\n% \n\n\n\nsilt % sand \n\n\n\nPeat 0-15 organic 17.55 30.54 4.37 20.99 - - - \n\n\n\nMunchong 0-15 Clay 2.92 5.08 4.95 4.94 56.02 10.98 32.90 \n\n\n\n  15-30 Clay 1.93 3.36 5.03 4.56 57.66 9.11 33.14 \n\n\n\nSelangor 0-15 Clay 2.75 4.79 4.30 15.76 54.45 37.70 7.78 \n\n\n\n  15-30 silty clay 1.94 3.38 4.10 12.66 53.47 42.91 3.56 \n\n\n\nRengam 0-15 Sandy clay  2.55 4.43 5.53 5.17 41.02 6.85 52.10 \n\n\n\n  15-30 Sandy clay  1.81 3.15 5.04 5.24 44.90 7.59 47.38 \n\n\n\nSerdang 0-15 sandy loam 1.70 2.96 5.04 9.32 22.02 5.73 72.25 \n\n\n\n  15-30 sandy clay loam 1.64 2.86 4.94 9.33 31.10 6.82 62.08 \n\n\n\nBriah 0-15 silty clay 1.67 2.91 4.59 11.80 48.53 41.97 9.42 \n\n\n\n  15-30 Clay 1.14 1.98 4.41 13.11 50.42 39.85 9.68 \n\n\n\n\n\n\n\nSorption-Desorption of 2,4-D\n\n\n\n \nMineral  \n\n\n\nSoil \n\n\n\n\n\n\n\nDepth \nAl \n\n\n\noxides \n\n\n\nFe \n\n\n\noxides gibbsite quartz illite kaolinite montmorillonite vermiculite \n\n\n\nMunchong 0-15 7.32 20.80 + ++  ++   \n\n\n\n 15-30 10.88 23.03 + + + +++   \n\n\n\nSelangor 0-15 12.65 43.41  + ++ +++  + \n\n\n\n  15-30 12.94 47.11  + ++ ++ +++ +++ \n\n\n\nRengam 0-15 7.69 14.36  +++  +++   \n\n\n\n  15-30 10.38 27.31  +++  +++   \n\n\n\nSerdang 0-15 7.76 10.43 + +++  ++  + \n\n\n\n  15-30 7.99 10.06 + +++  +++  + \n\n\n\nBriah 0-15 20.70 79.79  + + ++ ++ ++ \n\n\n\n  15-30 26.04 92.04  + ++ ++ ++  \n\n\n\n Note:\n \n\n\n\n+++  - abundant \n ++    - present \n\n\n\n +      - traces\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009124\n\n\n\ntypes (e.g. Munchong with OM removed and Serdang with OM removed) curves, \n\n\n\nindicates that a constant partition between soil and solution over the concentration \n\n\n\nrange used.\n\n\n\nFig. 2.  Sorption isotherms of 2,4-D on selected natural soils at the surface \n\n\n\n(0-15 cm) and subsurface (15-30 cm).\n\n\n\nAkma, N.M.H., Samsuri, A.W., Ainie, H.K.  & Rosenani, A.B..\n\n\n\nNatural soil (0-15 cm)\n\n\n\n(a)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 4 8 12 16\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\ntio\n\n\n\n (\nu\ng\n/g\n\n\n\n)\n\n\n\nPeat\nSelangor\nM unchong\nBriah\n\n\n\nSerdang\nRengam\n\n\n\n\n\n\n\nNatural soil (15-30 cm)\n\n\n\n(b)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 4 8 12 16\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\ntio\n\n\n\n (\nu\ng\n/g\n\n\n\n)\n\n\n\nSelangor\n\n\n\nM unchong\n\n\n\nSerdang\n\n\n\nBriah\n\n\n\nRengam\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 125\n\n\n\nTable 4\n\n\n\nLinear coeffient (K\nd\n), Freundlich coefficient (K\n\n\n\nF\n), n and R values for sorption of 2,4-D by \n\n\n\nsoils before and after removal of organic matter.\n\n\n\n As shown by K\nF\n values, peat exhibited a higher sorption capacity, followed \n\n\n\nby Selangor, Munchong, Briah, Serdang and the lowest were observed in Rengam \n\n\n\nsoil (Fig. 3). The differences in sorption capacities of the soils can be attributed \n\n\n\nto the differences in organic carbon content (Table 2). On the other hand, \n\n\n\ncontribution of clay constituents to 2,4-D retention can be dominant in low-OM \n\n\n\nsoils. This is shown in the present research where the Briah soils have the high \n\n\n\nretention of 2,4-D with low OM-content. For all soils, the K\nF\n values in the surface \n\n\n\nhad the highest sorption capacity excluding Serdang and Rengam, the highest \n\n\n\nK\nF\n were observed for the subsurface soils. This could be related to high content \n\n\n\nof organic matter and its CEC in surface appeared to be larger than Serdang and \n\n\n\nRengam even though the organic matter content in surface soils were also high. \n\n\n\nFurthermore, change of sorption with depth can be because of pH. The pH values \n\n\n\nfor all soils were greater for the surface rather than subsurface soils. On the other \n\n\n\nhand, the evaluation of a single soil variable on sorption is always difficult due \n\n\n\nto the correlation among soil properties itself. Contribution of organic carbon to \n\n\n\nsorption of 2,4-D have been reported by other researchers (Reddy and Gambrell \n\n\n\n1987; Kah and Brown 2007).  Intra-particle diffusion into organic matter moieties \n\n\n\ncould have been the mechanism for 2,4-D sorption by soils with high organic \n\n\n\nmatter content (Aksu and Kabasakal 2004). \n\n\n\n The K\nF\n value for 2,4-D sorption in peat is 42.04 L kg -1 (Table 2) and this \n\n\n\nvalue is much higher than the value of 6.95 L kg -1, reported by Cheah et al. \n\n\n\nSorption-Desorption of 2,4-D\n\n\n\n\n\n\n\nSoils Depth  Kd  (L kg\n-1\n\n\n\n) R\n2\n KF R\n\n\n\n2\n n \n\n\n\nPeat 0-15 36.26 0.99 42.04 0.98 0.95 \n\n\n\nSelangor 0-15 6.11 0.99 9.92 1.00 0.79 \n\n\n\n 15-30 6.30 1.00 9.55 1.00 0.79 \n\n\n\nMunchong 0-15 3.64 0.99 5.15 1.00 0.84 \n\n\n\n 15-30 3.52 0.98 5.00 1.00 0.86 \n\n\n\nBriah 0-15 2.77 0.97 5.35 1.00 0.75 \n\n\n\n 15-30 2.00 0.98 4.74 0.99 0.67 \n\n\n\nSerdang 0-15 2.29 0.97 4.69 1.00 0.73 \n\n\n\n 15-30 2.61 0.97 5.74 0.99 0.69 \n\n\n\nRengam 0-15 1.35 0.97 2.70 0.99 0.75 \n\n\n\n 15-30 1.72 0.98 5.04 0.99 0.82 \n\n\n\n\n\n\n\nSelangor, OM-removed  0-15 4.48 0.86 12.14 0.95 0.61 \n\n\n\n 0-15 2.81 0.93 6.77 0.99 0.66 \n\n\n\nMunchong, OM-removed  15-30 2.58 0.71 0.71 0.46 1.30 \n\n\n\n 0-15 2.41 0.64 0.92 0.57 1.03 \n\n\n\nBriah, OM-removed  15-30 4.72 0.96 7.00 0.94 0.87 \n\n\n\n 0-15 4.56 0.96 7.00 0.94 0.87 \n\n\n\nSerdang, OM-removed  15-30 2.45 0.67 1.53 0.72 1.78 \n\n\n\n 0-15 2.20 0.63 1.34 0.53 0.73 \n\n\n\nRengam, OM-removed  15-30 0.00 n/a 0.00 n/a 0.00 \n\n\n\n  0-15 0.00 n/a 0.00 n/a 0.00 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009126\n\n\n\n(1997) for sorption of 2,4-D in muck soil taken from paddy field. Correlation \n\n\n\nstudy revealed high correlation between sorption and total carbon, CEC and % \n\n\n\nclay (Table 5). Although 2,4-D is a weak acid with a pKa around 2.64, results \n\n\n\nshowed that sorption was the highest in peat and Selangor soil (a mineral soil \n\n\n\nwith the highest organic matter content). It could be suggested that 2,4-D sorbs \n\n\n\non organic matter and Fe oxide via electrostatic interaction between negatively \n\n\n\ncharged carboxylic group and positively charged Fe oxide surface. At the high pH, \n\n\n\nthere is little bonding of anionic molecules in soil as a result of the repulsion by \n\n\n\nnegative charge of soil particles. The main sources of positively charged surfaces \n\n\n\nin soils are iron oxides, clay mineral and association with organic matter content.\n\n\n\n Fig. 3.  Sorption isotherms of 2,4-D on selected soils with OM-removed at the surface \n\n\n\n(0-15 cm)   and subsurface (15-30 cm).\n\n\n\nAkma, N.M.H., Samsuri, A.W., Ainie, H.K.  & Rosenani, A.B..\n\n\n\nOM-removed soil (0-15 cm)\n\n\n\n(c)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 4 8 12 16\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\ntio\n\n\n\nn\n (\n\n\n\nu\ng\n/g\n\n\n\n)\n\n\n\nBriah\n\n\n\nSelangor\n\n\n\nM unchong\n\n\n\nSerdang\n\n\n\nRengam\n\n\n\n\n\n\n\nOM-removed soil (15-30 cm)\n\n\n\n(d)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 4 8 12 16\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\ntio\n\n\n\nn\n (\n\n\n\nu\ng\n/g\n\n\n\n)\n\n\n\nBriah\n\n\n\nM unchong\n\n\n\nSerdang\n\n\n\nSelangor\n\n\n\nRengam\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 127\n\n\n\n Table 5\n\n\n\nSimple correlation coefficients between K\nd\n and K\n\n\n\nF\n values and soil properties (p=0.05).\n\n\n\nSorption-Desorption By Soils with OM-Removed\n\n\n\nThe sorption of 2,4-D by soils with OM removed are also well described by \n\n\n\nFreundlich equation (Fig. 2). The respective values for K\nd\n, K\n\n\n\nF\n, n and R2 for soils \n\n\n\nwith OM removed are shown in Table 4. The removal of organic matter from the \n\n\n\nsoils greatly reduced 2,4-D sorption for Selangor and Munchong. Compared with \n\n\n\ntheir original soils, K\nd\n value for soils with OM removed decreased by 26.7 % \n\n\n\nand 28.0 %, respectively (Table 4). For all soils with OM removed, the sorption \n\n\n\ncapacities in surface were higher than subsurface.  As shown in Table 4, the sorption \n\n\n\ncapacity for soils with OM removed were higher in Selangor, followed by Briah, \n\n\n\nSerdang, Munchong and the lowest was observed in Rengam. However, in Briah \n\n\n\nsoil there was 30% increase in sorption capacity after OM removal compared \n\n\n\nto the original soil. The high content of iron oxides which sorbed 2,4-D anions \n\n\n\nthrough electrostatic interactions with positive charges Fe oxides surface enhance \n\n\n\nsorption. This can be explained by the hypothesis of mineral blockage by organic \n\n\n\nmatters which control the sorption process. Therefore, the contribution of mineral \n\n\n\nionic sorption was expected after organic matter removal. This result suggested \n\n\n\nthat soil organic matter provide about 25-30 % of sorption sites. The other 75-80 \n\n\n\n% of sorption took place on metal oxides and layer silicates fractions, indicating \n\n\n\nthe significant contribution of soil mineral to sorption process. This findings \n\n\n\nsupport the study by Boivin et al. (2005), which stated that organic matter content \n\n\n\nappears to control 2,4-D sorption. \n\n\n\nDesorption\n\n\n\nSorption and desorption patterns are shown in Fig. 4. The extent of pesticides \n\n\n\nrelease from the soils can also be seen. As a whole, the desorption were higher in \n\n\n\nRengam, followed by Serdang, Briah, Munchong, Selangor and the lowest was \n\n\n\nobserved in peat. This is related with the high organic matter content in soils \n\n\n\nwhich enhanced the sorption capacity. The desorption of pesticides from the soils \n\n\n\nwere high, thus they will be easily leached or moved into groundwater system \n\n\n\nparticularly in highly weathered soils. The desorption process occur due to the \n\n\n\nlow bonding between 2,4-D molecules and active sites in soil resulting in high \n\n\n\nrelease rate.\n\n\n\n\n\n\n\nSorption-Desorption of 2,4-D\n\n\n\n\n\n\n\n  Total C  pH  CEC  % Clay  % Silt  \n\n\n\nKd 0.983* 0.444 0.852 0.976* 0.968* -0.978* \n\n\n\nKF 0.971* -0.476 0.883* 0.927 0.869 -0.891 \n\n\n\n\n\n\n\n% Sand\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009128\n\n\n\nFig. 4. Sorption-desorption isotherms of natural soils at the surface (0-15 cm).\n\n\n\nAkma, N.M.H., Samsuri, A.W., Ainie, H.K.  & Rosenani, A.B..\n\n\n\nPeat (0-15 cm)\n\n\n\n(a)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 2 4 6 8 10 12 14\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\nti\no\n (\n\n\n\nu\ng\n/g\n\n\n\n)\n\n\n\nsorption\n\n\n\ndesorption\n\n\n\n \nSelangor (0-15 cm)\n\n\n\n(b)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 2 4 6 8 10 12 14\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\nti\no\n (\n\n\n\nu\ng\n/g\n\n\n\n)\n\n\n\nsorption\n\n\n\ndesorption\n\n\n\n\n\n\n\nBriah (0-15 cm)\n\n\n\n(c)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 2 4 6 8 10 12 14\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\nti\no\n (\n\n\n\nu\ng\n/g\n\n\n\n)\n\n\n\nsorption\n\n\n\ndesorption\n\n\n\nSerdang (0-15 cm)\n\n\n\n(d)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 2 4 6 8 10 12 14\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\nti\no\n (\n\n\n\nu\ng\n/g\n\n\n\n)\nsorption\n\n\n\ndesorption\n\n\n\n\n\n\n\nRengam (0-15 cm)\n\n\n\n(e)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 2 4 6 8 10 12\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\nti\no\n (\n\n\n\nu\ng\n/g\n\n\n\n)\n\n\n\nsorption\n\n\n\ndesorption\n\n\n\n\n\n\n\nMunchong (0-15 cm)\n\n\n\n(f)\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0 2 4 6 8 10 12 14\n\n\n\nCe - Solution concentration (ug/mL)\n\n\n\nC\ns\n -\n\n\n\n S\no\nrb\n\n\n\ne\nd\n c\n\n\n\no\nn\nc\ne\nn\ntr\n\n\n\na\nti\no\n (\n\n\n\nu\ng\n/g\n\n\n\n)\n\n\n\nsorption\n\n\n\ndesorption\n\n\n\n\n\n\n\n14\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 129\n\n\n\nCONCLUSIONS\n\n\n\nAs expected, sorption of 2,4-D was closely related with the organic matter content \n\n\n\nand clay. Even the small amounts of OM will contribute to the increase of 2,4-D \n\n\n\nsorption. Apart from that, CEC and clay also gave significant contribution towards \n\n\n\nsorption. It was noted that removal of organic matter from soils would unmasked \n\n\n\nthe sorption sites on soil minerals that were originally covered by organic matter. \n\n\n\nDesorption was highest in soil with low organic matter resulting in the potential \n\n\n\nof groundwater contamination. \n\n\n\nACKNOWLEDGEMENT\n\n\n\nWe thank the Malaysia Palm Oil Board (MPOB) for fellowship support under \n\n\n\nGraduate Student Assistantship Scheme (GSAS). This work was also partially \n\n\n\nsupported by the Fundamental Research Grant Scheme from Universiti Putra \n\n\n\nMalaysia (01-01-07-283FR). Our special thanks also go to Golden Hope Sdn. \n\n\n\nBhd for allowing us to use their plantation site for soil sampling. \n\n\n\nREFERENCES\nAksu, Z. and E. Kabasakal. 2004. 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"\n\n___________________\n*Corresponding author : E-mail: shamshud@upm.edu.my\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 195-218 (2021)                          Malaysian Society of Soil Science\n\n\n\nMg-Rich Synthetic Gypsum Application on Soils in Malaysia \nto Sustain Agricultural Production: A Review \n\n\n\nShamshuddin*, J., C.I. Fauziah, M.A. Mohd Firdaus and A.F. \nAyanda\n\n\n\nDepartment of Land Management, Faculty of Agriculture\nUniversiti Putra Malaysia, Serdang, Selangor\n\n\n\nMalaysia\n\n\n\nABSTRACT\nApplying Mg-rich synthetic gypsum (MRSG) on acid sulfate soils results in a \nconcomitant alleviation of Al3+ and/or Fe2+ toxicity and is known to increase soil \npH and improve rice growth. In the case of the oil palm, S is required in sufficient \nquantities to produce oil in its fruitlets. For all intents and purposes, MRSG can be \nused as Mg- and/or Ca-fertiliser for oil palm and rubber as well as for sustainable \nrice cultivation on acidic soils. Greater use of MRSG which is locally available \nwould reduce imports of fertiliser and at the same time sustain agricultural \nproductivity. This is translated into foreign exchange savings as well as increased \nincome for farming communities. As it is available in large quantities in Malaysia, \nMRSG utilisation would sustain agriculture in the country at a reasonable cost. In \nconclusion, application of MRSG on acidic soils in Malaysia does not contribute \nto environmental degradation. Instead, the MRSG supplies Ca, Mg and S that are \nneeded in high amounts by crops to sustain growth and/or production. Thus, we \ncan turn the otherwise cheap by-product of a chemical plant into a useful fertiliser \nthat contributes to our economic growth.\n\n\n\nKeyword:    Agricultural production, Ca-Fertiliser, gypsum, Mg-fertiliser, soil \nameliorant\n\n\n\nINTRODUCTION\nWords has been going round in the vicinity of Kuantan for years that human health \nwould be negatively affected by a Lynas rare earth processing plant located in \nnearby Gebeng town, Malaysia. People living near the chemical plant are divided \nbetween believing the stories propagated by opponents or proponents and the \nreality of the issue. The chemical plant in Gebeng is dedicated to the production \nof rare earth, which is essential for the development of high-end industries in the \nworld. The production process of extracting the precious materials at the state-of-\nthe-art chemical produces a by-product which can be useful for agriculture in the \ncountry. This is called Mg-Rich Synthetic Gypsum (MRSG).  \n The production of rare earth at Lynas Chemical Plant in Gebeng, Pahang \ninvolves many steps. The process starts with lanthanide ore containing rare earth \nimported from Mount Weld in Australia. In the process of extracting the precious \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021196\n\n\n\nrare earth for the world market, two contrasting by-products are produced. After \nundergoing a series of chemical processes in the plant, a radioactive Water Leach \nPurification (WLP) residue is produced. This is followed by the production of \nMg-Rich Synthetic Gypsum (MRSG), known as Neutralisation Underflow (NUF) \nresidues, which is not radioactive (Figure 1). The final product of the process is \nthe afore-mentioned rare earth which fetches a premier price at the marketplace.\n\n\n\nFigure 1. A photograph showing MRSG being stored at\nLynas chemical plant in Gebeng, Malaysia. (Courtesy of Lynas Malaysia Sdn Bhd) \n\n\n\n2 \n \n\n\n\nGebeng is dedicated to the production of rare earth, which is essential for the development of \n\n\n\nhigh-end industries in the world. The production process of extracting the precious materials \n\n\n\nat the state-of-the art chemical produces a by-product which can be useful for agriculture in \n\n\n\nthe country. This is called Mg-Rich Synthetic Gypsum (MRSG).  \n\n\n\n\n\n\n\n     The production of rare earth at Lynas Chemical Plant in Gebeng, Pahang involves many \n\n\n\nsteps. The process starts with lanthanide ore containing rare earth imported from Mount Weld \n\n\n\nin Australia. In the process of extracting the precious rare earth for the world market, two \n\n\n\ncontrasting by-products are produced. After undergoing a series of chemical processes in the \n\n\n\nplant, a radioactive Water Leach Purification (WLP) residue is produced. This is followed by \n\n\n\nthe production of Mg-Rich Synthetic Gypsum (MRSG), known as Neutralisation Underflow \n\n\n\n(NUF) residues, which is not radioactive (Figure 1). The final product of the process is the \n\n\n\nafore-mentioned rare earth which fetches a premier price at the marketplace. \n\n\n\n\n\n\n\n\n\n\n\nFigure 1. A photograph showing MRSG being stored at \nLynas chemical plant in Gebeng, Malaysia. (Courtesy of Lynas Malaysia Sdn Bhd)   \n\n\n\n \n     Application of MRSG on land to enhance the growth of oil palm and sustain its yield does \n\n\n\nnot result in environmental degradation. This was confirmed by the study of Abd Rahim et al. \n\n\n\n(2019) conducted over a period of three years on an oil palm plantation in Bera, Malaysia. \n\n\n\nWhile the land in the trial area benefitted via alleviation of soil acidity that affects crop \n\n\n\ngrowth, water quality in the channels and rivers in the surrounding areas remained intact. \n\n\n\n Application of MRSG on land to enhance the growth of oil palm and sustain \nits yield does not result in environmental degradation. This was confirmed by the \nstudy of Abd Rahim et al. (2019) conducted over a period of three years on an oil \npalm plantation in Bera, Malaysia. While the land in the trial area benefitted via \nalleviation of soil acidity that affects crop growth, water quality in the channels \nand rivers in the surrounding areas remained intact. Thus, MRSG is not only is an \nexcellent soil ameliorant, but also a source of Ca and Mg for crop growth. \n This paper discusses the production of MRSG, physico-chemical properties \nand its utilisation for agriculture in Malaysia. The objectives are consistent with the \nEleventh Malaysia Plan 2016-2020 initiatives which look at managing chemical \nplant wastes holistically. The initiative states that using wastes as a resource gives \nan economic value; hence, it should be diverted away from landfills towards more \nproductive use. This article will be a useful reference for students in the field of \nagriculture as well as soil scientists and agronomists in Malaysia or even from the \ntropical Asia involved in the cultivation of oil palm, rice and rubber.\n MRSG addition has been known to increase soil pH which in turn alleviates \nAl3+ toxicity in Malaysia if applied repeatedly at suitable rates (Ayanda et al., \n2020). Therefore, the objectives of this paper are: (1) to discuss the effects of \napplying MRSG on acidic soils and to measure the growth/yield of oil palm, rice \nand rubber; and (2) to explain the impact of applying MRSG on land to alleviate \nsoil acidity on the surrounding environment. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 197\n\n\n\nCHARACTERISATION OF Mg-RICH SYNTHETIC GYPSUM\n\n\n\nPhysical Properties of MRSG\n     A huge quantity of MRSG is now stored at the Lynas Chemical Plant in \nGebeng, Malaysia (Figure 1). It is ready to be transported to any destination in the \ncountry for use in agriculture. Currently, the MRSG is left in the open and exposed \nto atmospheric conditions within the chemical plant compound. According to \nMohd Firdaus et al. (2020), about 60% of the by-product is <1 mm in size and \nhydroscopic by nature (contains a moisture content of about 26.7%). The pH of \nthe MRSG (existing in semi-powder form) is 9.28, while its EC is 7.03 (mS cm-1). \nTable 1 shows the Mg and Ca content of the MRSG in comparison with those of \nChina Kieserite and GML (Mohd Firdaus et al. 2020).\n\n\n\nTABLE 1\nMg and Ca content in MRSG compared with those of China Kieserite and GML\n\n\n\n4 \n \n\n\n\nTABLE 1 \n\n\n\nMg and Ca content in MRSG compared with those of China Kieserite and GML \n\n\n\n\n\n\n\nElement China Kieserite GML MRSG \n\n\n\nTotal Mg (mg kg-1) 160,000\u00b18,000 91,000\u00b14,000 55,000\u00b14,500 \n\n\n\nTotal Ca (mg kg-1) 25,000\u00b13,000 190,000\u00b18,000 240,000\u00b117,000 \nSource: Mohd Firdaus et al. (2020) \n\n\n\n\n\n\n\nThe results of a recent test showed that the solubility rate of the fertilisers mentioned \n\n\n\nin Table 1 is in the order of: Kieserite > MRSG > GML (Shamshuddin et al. 2017a). This \n\n\n\nbeing the case, it is imperative to enhance the solubility of MRSG to be comparable with that \n\n\n\nof the China Kieserite. When applied on highly weathered acidic soils of the tropics, it is \n\n\n\npossible for MRSG to be dissolved completely in a matter of a few weeks, releasing Mg, Ca \n\n\n\nand S for crop uptake. As explained by Mohd Firdaus et al. (2020), the solubility of MRSG \n\n\n\ncan be significantly accelerated if it is applied on soils in combination with elemental sulphur \n\n\n\n(S). This is because the oxidation of S in the soils would release acidity and increase the \n\n\n\ndissolution rate of the otherwise alkaline MRSG. However, this mechanism can only work \n\n\n\nwell in soils under well drained conditions, but not for soils of waterlogged areas. \n\n\n\n\n\n\n\nMineralogical Composition of MRSG \n\n\n\nXRD analysis \n\n\n\n     XRD analysis of the MRSG by Ayanda et al. (2020) showed that it contained high \n\n\n\namounts of gypsum (45.4%), indicated by the d-spacing of 7.609 \u00c5 (2 theta 11.63) in the \n\n\n\ndiffractogram (Figure 2). Some calcite was also present in the MRSG, indicated by the d-\n\n\n\nspacing at 3.036 \u00c5 (2 theta of 29.41); however, the XRD peak was too weak to be clearly \n\n\n\nseen on the diffractogram. Note that the analysis done by Golder Associates of Australia \n\n\n\n(courtesy of Lynas) found that the MRSG contained 73-74% gypsum, with fair amounts of \n\n\n\nmagnesium hydroxide (17.1%), calcium hydroxide (4.3%) and calcium carbonate (2.3%). It \n\n\n\nis the presence of Mg (OH)2 and Ca (OH)2 that makes MRSG alkaline in nature. \n\n\n\n The results of a recent test showed that the solubility rate of the fertilisers \nmentioned in Table 1 is in the order of: Kieserite > MRSG > GML (Shamshuddin \net al. 2017a). This being the case, it is imperative to enhance the solubility of \nMRSG to be comparable with that of the China Kieserite. When applied on highly \nweathered acidic soils of the tropics, it is possible for MRSG to be dissolved \ncompletely in a matter of a few weeks, releasing Mg, Ca and S for crop uptake. \nAs explained by Mohd Firdaus et al. (2020), the solubility of MRSG can be \nsignificantly accelerated if it is applied on soils in combination with elemental \nsulphur (S). This is because the oxidation of S in the soils would release acidity \nand increase the dissolution rate of the otherwise alkaline MRSG. However, this \nmechanism can only work well in soils under well drained conditions, but not for \nsoils of waterlogged areas.\n\n\n\nMineralogical Composition of MRSG\nXRD analysis\nXRD analysis of the MRSG by Ayanda et al. (2020) showed that it contained high \namounts of gypsum (45.4%), indicated by the d-spacing of 7.609 \u00c5 (2 theta 11.63) \nin the diffractogram (Figure 2). Some calcite was also present in the MRSG, \nindicated by the d-spacing at 3.036 \u00c5 (2 theta of 29.41); however, the XRD peak \nwas too weak to be clearly seen on the diffractogram. Note that the analysis done \nby Golder Associates of Australia (courtesy of Lynas) found that the MRSG \ncontained 73-74% gypsum, with fair amounts of magnesium hydroxide (17.1%), \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021198\n\n\n\nFigure 2. XRD diffractogram of the MRSG produced by the Lynas chemical plant.\nSource: (Ayanda et al. 2020)\n\n\n\n4 \n \n\n\n\nTotal Ca (mg kg-1) 25,000\u00b13,000 190,000\u00b18,000 240,000\u00b117,000 \nSource: Mohd Firdaus et al. (2020) \n\n\n\n\n\n\n\nThe results of a recent test showed that the solubility rate of the fertilisers mentioned \n\n\n\nin Table 1 is in the order of: Kieserite > MRSG > GML (Shamshuddin et al. 2017a). This \n\n\n\nbeing the case, it is imperative to enhance the solubility of MRSG to be comparable with that \n\n\n\nof the China Kieserite. When applied on highly weathered acidic soils of the tropics, it is \n\n\n\npossible for MRSG to be dissolved completely in a matter of a few weeks, releasing Mg, Ca \n\n\n\nand S for crop uptake. As explained by Mohd Firdaus et al. (2020), the solubility of MRSG \n\n\n\ncan be significantly accelerated if it is applied on soils in combination with elemental sulphur \n\n\n\n(S). This is because the oxidation of S in the soils would release acidity and increase the \n\n\n\ndissolution rate of the otherwise alkaline MRSG. However, this mechanism can only work \n\n\n\nwell in soils under well drained conditions, but not for soils of waterlogged areas. \n\n\n\n\n\n\n\nMineralogical Composition of MRSG \n\n\n\nXRD analysis \n\n\n\n     XRD analysis of the MRSG by Ayanda et al. (2020) showed that it contained high \n\n\n\namounts of gypsum (45.4%), indicated by the d-spacing of 7.609 \u00c5 (2 theta 11.63) in the  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 2. XRD diffractogram of the MRSG produced by the Lynas chemical plant. \n\n\n\nSource: (Ayanda et al. 2020) \ncalcium hydroxide (4.3%) and calcium carbonate (2.3%). It is the presence of Mg \n(OH)2 and Ca (OH)2 that makes MRSG alkaline in nature.\n\n\n\nField Emission Electron Microscope analysis\nDominance of gypsum in the MRSG is evidenced by the presence of the acicular-\nshaped mineral observed under Field Emission Scanning Electron Microscope \n(FESEM) as shown in Figure 3. The average chemical composition of the \nMRSG at any particular spot (e.g. at spectrum 1, 2 and 3) in the micrograph \ncan be determined using the EDX attached to the FESEM. The presence of C \nwas detected by FESEM-EDX, which is consistent with the presence of calcite \nconfirmed by the XRD analysis (Figure 2).\n\n\n\nChemical composition of the MRSG\n    The MRSG discussed in this paper is alkaline in nature with a very high pH \nof 9.28 (Mohd Firdaus 2020). Hence, its application in oil palm and rubber \nplantations or even rice fields would have a positive impact on the chemical \nproperties of the soils, especially in the alleviation of soil acidity. Besides, land \napplication of MRSG would add other plant nutrients and/or beneficial elements \ninto the highly weathered soils (Table 2). This is the known benefit of using the \nby-product in agriculture that farmers should know about.\n The composition of the MRSG can be determined by FESEM-EDX. The \nchemical contents determined via this methodology (Figure 4) are at best the \nrelative measurements normalised to 100%; thus, they are not absolute amounts \nas determined by ICP-OES. In this study, spectrum analysis was done to indicate \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 199\n\n\n\nthe presence of certain elements. However, detailed elemental composition of the \nMRSG was tabulated based on the ICP-OES analysis.\n Ayanda et al. (2020) found Ca (20.99%) to be the most abundant element \nin the MRSG, followed by Mg (7.15%). The values of Ca and Mg in the MRSG \nwere almost similar to that obtained by Shamshuddin and Ismail (1995) using \nGML on a Malaysian Ultisol. Besides, the MRSG contained some essential \nmicronutrients (Mn, Zn and Cu), with small amounts of beneficial elements (Se \nand Si), but significant nevertheless (Table 2). The presence of Si was detected by \nICP-OES, but not by the XRD analysis because it is believed to exist in the form \nof amorphous silica (SiO2). Si, if present in sufficient amounts in oil palm tissues, \ncan prevent certain diseases (Najihah et al. 2015).\n\n\n\nFigure 3. FESEM-EDX micrograph of MRSG under investigation\nSource: Abd Rahim et al. (2019)\n\n\n\nTABLE 2 \nMicronutrients and beneficial elements present in MRSG\n\n\n\n7 \n \n\n\n\nTABLE 2  \n\n\n\nMicronutrients and beneficial elements present in MRSG \n\n\n\n                                  Micronutrient                        Beneficial element \n\n\n\n                          Fe        Mn       Zn      Cu                      Si       Se \n\n\n\n                           ------------------------ mg kg-1 ---------------------------- \n\n\n\n\n\n\n\n                         1368   1175    38.7     127                    19.1     0.4 \n\n\n\n       Source: Ayanda et al. (2020) \n\n\n\n\n\n\n\n     The composition of the MRSG can be determined by FESEM-EDX. The chemical \n\n\n\ncontents determined via this methodology (Figure 4) are at best the relative measurements \n\n\n\nnormalised to 100%; thus, they are not absolute amounts as determined by ICP-OES. In this \n\n\n\nstudy, spectrum analysis was done to indicate the presence of certain elements. However, \n\n\n\ndetailed elemental composition of the MRSG was tabulated based on the ICP-OES analysis. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4. FESEM-EDX diffractogram of the MRSG \nSource: Abd Rahim et al. (2019) \n\n\n\n     Ayanda et al. (2020) found Ca (20.99%) to be the most abundant element in the MRSG, \n\n\n\nfollowed by Mg (7.15%). The values of Ca and Mg in the MRSG were almost similar to that \n\n\n\nobtained by Shamshuddin and Ismail (1995) using GML on a Malaysian Ultisol. Besides, the \n\n\n\nMRSG contained some essential micronutrients (Mn, Zn and Cu), with small amounts of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021200\n\n\n\nUSE OF MRSG IN AGRICULTURE\nMg-rich synthetic gypsum has been found to be a non-radioactive, non-toxic and \nnon-hazardous by-product of the chemical plant producing rare earth. By utilising \nthis special gypsum rich in Mg and Ca for agriculture, the Malaysian government \nwill be able to value add to the economy and reduce the import of Mg- and Ca-\nfertilisers from other countries. On MRSG application to agricultural land, Mg \nand Ca will be released into the soils for uptake by crops to sustain growth and/\nor production.\n\n\n\nFigure 4. FESEM-EDX diffractogram of the MRSG\nSource: Abd Rahim et al. (2019)\n\n\n\n7 \n \n\n\n\nTABLE 2  \n\n\n\nMicronutrients and beneficial elements present in MRSG \n\n\n\n                                  Micronutrient                        Beneficial element \n\n\n\n                          Fe        Mn       Zn      Cu                      Si       Se \n\n\n\n                           ------------------------ mg kg-1 ---------------------------- \n\n\n\n\n\n\n\n                         1368   1175    38.7     127                    19.1     0.4 \n\n\n\n       Source: Ayanda et al. (2020) \n\n\n\n\n\n\n\n     The composition of the MRSG can be determined by FESEM-EDX. The chemical \n\n\n\ncontents determined via this methodology (Figure 4) are at best the relative measurements \n\n\n\nnormalised to 100%; thus, they are not absolute amounts as determined by ICP-OES. In this \n\n\n\nstudy, spectrum analysis was done to indicate the presence of certain elements. However, \n\n\n\ndetailed elemental composition of the MRSG was tabulated based on the ICP-OES analysis. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4. FESEM-EDX diffractogram of the MRSG \nSource: Abd Rahim et al. (2019) \n\n\n\n     Ayanda et al. (2020) found Ca (20.99%) to be the most abundant element in the MRSG, \n\n\n\nfollowed by Mg (7.15%). The values of Ca and Mg in the MRSG were almost similar to that \n\n\n\nobtained by Shamshuddin and Ismail (1995) using GML on a Malaysian Ultisol. Besides, the \n\n\n\nMRSG contained some essential micronutrients (Mn, Zn and Cu), with small amounts of \n\n\n\n A large area of arable land in Malaysia (>5 million ha) is cropped to oil \npalm with great success, with the rest of the area being utilised for the cultivation \nof rubber, cocoa and other food crops (Shamshuddin et al. 2018). According to \nShamshuddin and Fauziah (2010), most of the soils in the areas under oil palm \nand rubber cultivation in the country are mainly highly weathered acidic Ultisols \nand Oxisols which have insufficient basic cations (especially Ca and Mg) required \nfor crop growth. Therefore, fertiliser application is crucial to sustain the growth of \nthe crops and eventually their yield.    \n It is normal practice in plantations in tropical Asia to apply kieserite \n(MgSO4.H2O) as the source of Mg to sustain oil palm growth/production. \nApplying kieserite would also add S into the soil system. S is required for oil \nproduction in the fruitlets. Another equally important source of S is dolomitic \nlimestone [CaMg(CO3)2] (Sidhu et al. 2014), which is otherwise known as ground \nmagnesium limestone (GML). GML dissolves to release Mg and Ca into the soil \nand is needed in high amounts by growing oil palm trees. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 201\n\n\n\n The way forward is to look for a cheaper substitute to replace the expensive \nkieserite or GML as a source of Mg and/or Ca because of their high cost of \npurchase. The industrial by-product MRSG has been found to have beneficial \nproperties and offers potential as an alternative to kieserite or GML as a source of \nMg-fertiliser for oil palm cultivation (Ayanda et al. 2020). There is also evidence \nto suggest that MRSG has the capacity to alleviate the problems of soil acidity \n(low pH stress) and Al3+ or even Fe2+ toxicity that significantly curtails rice \nproduction on acid sulfate soils in Malaysia and other Southeast Asian countries.\n Using information from the recent studies conducted in the glasshouse \nand field, we believe that MRSG has a great potential in agriculture, particularly \nas Mg- and Ca-fertilisers or even as a soil conditioner. However, its utilisation \non agricultural land is prohibited until and unless permission is granted by the \nDepartment of Environment (DoE), Ministry of Science in Putrajaya. Currently \nMSRG is listed as a scheduled waste. \n\n\n\nEffects of MRSG Application on Oil Palm Growth and Production\nSoil survey and soil characterisation\nResearchers from Universiti Putra Malaysia (UPM) and Universiti Kebangsaan \nMalaysia (UKM) collaborated on a study using MRSG for oil palm cultivation. \nMRSG is mainly composed of gypsum (CaSO4.2H2O), enriched with magnesium. \nThis high pH by-product is known to be a good soil ameliorant and is regarded as \na source of Ca and Mg for oil palm planted on highly weathered Malaysian soils \n(Shamshuddin et al. 2017a). A study by Abd Rahim et al. (2019) showed that \nMRSG is not harmful to the environment. \n A soil survey was carried out on a 4-ha oil palm smallholding in Bera, \nPeninsular Malaysia to determine soil type distribution in the area. The condition \nof the 15-year-old palms and soil in the smallholding observed during the survey \nis as shown in Figure 5. Note that the soil is reddish in colour, indicative of the \npresence of high amounts of Fe in the form of hematite. But the major clay mineral \nin the clay fraction of the soil is kaolinite (Ayanda 2017).\n The soil survey was carried out to collect samples for detailed physico-\nchemical analyses. Using the data obtained, the soil at the trial area was \ncharacterised and subsequently classified according to Soil Taxonomy (Soil \nSurvey Staff 2014). Following the Malaysian System of Soil Classification, the \nsoil found at the trial site was identified as the Jempol Series (Shamshuddin et al. \n2017a; Soil Survey Staff 2018). \n\n\n\nGlasshouse Study Using MRSG to Enhance Oil Palm Growth\nEffects of treatments on the chemical properties of the soil\nThe soil used for the glasshouse study was collected from the field experimental site \nat Bera, Malaysia (Figure 5). It was identified as the Jempol Series (Shamshuddin \net al. 2017a) and taxonomically classified as the clayey, kaolinitic, isohyperthermic \nfamily of Typic Paleudults (Soil Survey Staff, 2014). The soil was acidic with a \npH of <5; however, according to Goh et al. (2003), a pH level of <5 is still within \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021202\n\n\n\nFigure 5. Oil palm (left) growing on an Ultisol (right) in Bera, Malaysia\n\n\n\nthe suitable soil pH range for oil palm cultivation. Exchangeable magnesium in \nthe soil was 0.23 cmolc kg-1 (Ayanda 2017). This is considered moderate for oil \npalm growth and/or production. Available P in the soil was very low probably \ndue to immobilisation via specific adsorption by Fe and/or Al oxides, forming \ninsoluble Fe-P and/or Al-P compounds (Fageria and Baligar 2008); the Fe content \nin the soil was very high (Figure 4).  \n The pKa of Al3+ was 5; hence, soil solution pH will move towards 5 to \nachieve the state of maximal equilibrium in the system. High organic matter \ncontent as reflected by high nitrogen and carbon in the topsoil is thought to be \ncontributed by the felled fronds and empty fruit bunches previously laid down in \nthe inter-rows of the palms in the smallholding.\n According to Ayanda et al. (2020), the exchangeable Mg in the soil of the \nglasshouse study at harvest (month 9) indicated the level of Mg in soil treated \nwith MRSG to be comparable to that of the kieserite application (Table 3). The \noriginal exchangeable Mg and Ca in the topsoil of the smallholding was 0.23 and \n0.64 cmolc/kg soil (Ayanda 2017), respectively, and these values are below the \nsufficiency range for optimal oil palm growth (Shamshuddin et al. 2018). Mg \nor even Ca required by oil palm can be supplied by GML application; however, \nthe standard practice of supplying Mg for oil palm consumption is via kieserite \napplication.  \n IPNI (2013) states that Ca is often neglected in oil palm nutrition since its \ndeficiency has been rarely reported by agronomists. This is due to the fact that in \nthe past, Ca was added to the soil through liming using GML. As the area of land \ncultivated with oil palm is large, GML application has become a very expensive \npractice. But the oil palm is known to be an acid-tolerant plant species (Auxtero \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 203\n\n\n\nTABLE 3 \nSoil pH and exchangeable Ca and Mg as affected by MRSG treatments\n\n\n\n11 \n \n\n\n\nand carbon in the topsoil is thought to be contributed by the felled fronds and empty fruit \n\n\n\nbunches previously laid down in the inter-rows of the palms in the smallholding. \n\n\n\n\n\n\n\n     According to Ayanda et al. (2020), the exchangeable Mg in the soil of the glasshouse \n\n\n\nstudy at harvest (month 9) indicated the level of Mg in soil treated with MRSG to be \n\n\n\ncomparable to that of the kieserite application (Table 3). The original exchangeable Mg and \n\n\n\nCa in the topsoil of the smallholding was 0.23 and 0.64 cmolc/kg soil (Ayanda 2017), \n\n\n\nrespectively, and these values are below the sufficiency range for optimal oil palm growth \n\n\n\n(Shamshuddin et al. 2018). Mg or even Ca required by oil palm can be supplied by GML \n\n\n\napplication; however, the standard practice of supplying Mg for oil palm consumption is via \n\n\n\nkieserite application.   \n\n\n\n\n\n\n\nTABLE 3  \nSoil pH and exchangeable Ca and Mg as affected by MRSG treatments \n\n\n\n \nTrt* Month 3 \n\n\n\n \nMonth 6 Month 9 \n\n\n\n pH Exch \nCa \n\n\n\nExch \nMg \n\n\n\npH Exch \nCa \n\n\n\nExch \nMg \n\n\n\npH Exch \nCa \n\n\n\nExch \nMg \n\n\n\n cmolc/kg \nT0 5.32f 0.72d 0.28e 5.27e 0.49f 0.28e 5.23e 0.49d 0.28e \nT1 6.02e 0.86cd 0.39d 6.19d 0.76c 0.45c 6.17d 0.82c 0.51b \nT2 6.48b 1.03c 0.31e 6.41b 1.25b 0.35d 6.25b 1.28a 0.36cd \nT3 6.18d 1.36b 0.34ed 6.20d 1.06cd 0.34d 6.18d 0.92c 0.34d \nT4  6.20cd 1.39ab 0.52c 6.29c 1.02d 0.47bc 6.28cd 1.16b 0.42c \nT5 6.76a 1.61a 0.71a 6.83a 1.44a 0.61a 6.99a 1.39a 0.61a \nT6 6.22c 0.47ab 0.63b 6.34c 1.22bc 0.50bc 6.36bc 1.32a 0.47b \n\n\n\n    Means followed by different letters within the same column are significantly different at p\u22640.05 \nNotes*: T0 = Control (NPK only); T1 = NPK+ kieserite; T2 = NPK+GML; T3 = NPK+MRSG at the \nrecommended rate; T4 = NPK+MRSG at half of the recommended rate; T5 = NPK+MRSG at double the \nrecommended rate; T6 = NPK+MRSG equivalent to Ca in GML \n            Source: Ayanda (2017) \n\n\n\n     IPNI (2013) states that Ca is often neglected in oil palm nutrition since its deficiency has \n\n\n\nbeen rarely reported by agronomists. This is due to the fact that in the past, Ca was added to \n\n\n\nthe soil through liming using GML. As the area of land cultivated with oil palm is large, \n\n\n\nGML application has become a very expensive practice. But the oil palm is known to be an \n\n\n\nacid-tolerant plant species (Auxtero and Shamshuddin 1991) that can tolerate a pH of below \n\n\n\n5. Raising soil pH to a higher level significantly enhances the growth of oil palm seedlings \n\n\n\nand Shamshuddin 1991) that can tolerate a pH of below 5. Raising soil pH to \na higher level significantly enhances the growth of oil palm seedlings (Ayanda \n2017). This is normally achieved through liming, which has been found to be \nexpensive for oil palm cultivation. This is where MRSG could come in to alleviate \nthe problem facing the oil palm industry. Adding MRSG as Mg-fertiliser would \nadd a valuable amount of Ca into the soil. The presence of Ca results in a slight \nincrease in soil pH, which is good for the growth of the oil palm. Thus, there is \nstrong justification to raise the pH of Ultisols in Malaysia or Indonesia for oil \npalm cultivation to a higher level for the oil palm to perform even better.\n In the glasshouse study, it was noted that applying MRSG on the soil \nresulted in a significant increase in soil pH, exchangeable Ca and exchangeable Mg \n(Table 3). Soil pH of the control treatment was almost 5, which is rather unusual \nfor an Ultisol in Peninsular Malaysia (Shamshuddin et al. 2018). As such, there \ncould be possible contamination of the control plots by running water (run-off) \nfrom the treated plots.  Notwithstanding this, the results of the experiment showed \nthat soil pH increased further with increasing rates of the MRSG treatment, with \nvalues exceeding 6 and a concomitant increase in exchangeable Ca and Mg. This \nmeans that applying MRSG somewhat improves soil fertility which is believed \nto have enhanced the growth of the oil palm seedlings planted under glasshouse \nconditions.\n\n\n\nEffects of treatments on oil palm seedlings\nThe growth of oil palm seedlings in the glasshouse in terms of height, stem \ndiameter, root length and root surface area were significantly enhanced by the \naddition of MRSG, giving results comparable to application of other sources \nof Mg-fertiliser (Table 4).  For the vegetative growth of the oil palm seedlings, \nMRSG treatments gave comparable results to that of the kieserite (Ayanda et al. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021204\n\n\n\n2020). This is encouraging as it indicates the possibility of using MRSG to replace \nkieserite as an Mg source to sustain the growth of oil palm seedlings.\n The content of Mg in frond 3 of the oil palm seedlings was within the \nsufficiency range for healthy growth (Table 5). In the case of Ca content, the \nvalues were higher than the sufficiency level for oil palm requirement. This is \nbased on the standard requirement proposed by Von Uexkull and Fairhurst (1991) \nand Fairhurst and Hardter (2003). This indicates that, the higher the content of Mg \nand Ca in the soil due to application of MRSG, the higher the uptake by the oil \npalm seedlings in the glasshouse (Ayanda 2017). \n Using the data obtained from the glasshouse experiment, more agronomic \ninterpretations can be done. For instance, the height of oil palm seedlings was \nplotted against exchangeable Mg. It was found that there was no significant \ncorrelation between the height of oil palm seedlings and the level of exchangeable \nmagnesium in the treated soil. However, when plant height was plotted against \nexchangeable Ca, a significant correlation was obtained. As explained by Ayanda \net al. (2020), a possible explanation could be that Ca is the more limiting nutrient \nin the soil in comparison to Mg. \n\n\n\nTABLE 4 \nEffects of treatments on height and diameter of stem and root growth parameter of oil \n\n\n\npalm seedlings\n\n\n\n12 \n \n\n\n\n(Ayanda 2017). This is normally achieved through liming, which has been found to be \n\n\n\nexpensive for oil palm cultivation. This is where MRSG could come in to alleviate the \n\n\n\nproblem facing the oil palm industry. Adding MRSG as Mg-fertiliser would add a valuable \n\n\n\namount of Ca into the soil. The presence of Ca results in a slight increase in soil pH, which is \n\n\n\ngood for the growth of the oil palm. Thus, there is strong justification to raise the pH of \n\n\n\nUltisols in Malaysia or Indonesia for oil palm cultivation to a higher level for the oil palm to \n\n\n\nperform even better. \n\n\n\n\n\n\n\n    In the glasshouse study, it was noted that applying MRSG on the soil resulted in a \n\n\n\nsignificant increase in soil pH, exchangeable Ca and exchangeable Mg (Table 3). Soil pH of \n\n\n\nthe control treatment was almost 5, which is rather unusual for an Ultisol in Peninsular \n\n\n\nMalaysia (Shamshuddin et al. 2018). As such, there could be possible contamination of the \n\n\n\ncontrol plots by running water (run-off) from the treated plots.  Notwithstanding this, the \n\n\n\nresults of the experiment showed that soil pH increased further with increasing rates of the \n\n\n\nMRSG treatment, with values exceeding 6 and a concomitant increase in exchangeable Ca \n\n\n\nand Mg. This means that applying MRSG somewhat improves soil fertility which is believed \n\n\n\nto have enhanced the growth of the oil palm seedlings planted under glasshouse conditions. \n\n\n\n\n\n\n\nEffects of treatments on oil palm seedlings \n\n\n\n     . \n\n\n\n \nTABLE 4  \n\n\n\nEffects of treatments on height and diameter of stem and root growth parameter of oil palm \nseedlings \n\n\n\n Means followed by different letters within the same column are significantly different at p\u22640.05 \n       Source: Ayanda et al. (2020) \n\n\n\nTreatment Height (cm) \nStem \n\n\n\ndiameter \n(mm) \n\n\n\nRoot length \ncm/plant \n\n\n\nRoot surface area  \ncm2/palm \n\n\n\nT0 131.05b 75.00d 18684b 3296.6c \nT1 164.66a 82.10abc 21856a 3371.2bc \nT2 161.57a 87.35a 23120a 3487.1bc \nT3 166.01a 80.61bcd 22429a 3353.8c \nT4 164.00a 84.40ab 21543a 3746.9ab \nT5 172.83a 77.33cd 21924a 3936.4a \nT6 163.83a 84.08ab 23615a 3650.9abc \n\n\n\nTABLE 5\nMg and Ca content in frond 3 of the oil palm seedlings\n\n\n\n13 \n \n\n\n\n     The content of Mg in frond 3 of the oil palm seedlings was within the sufficiency range \n\n\n\nfor healthy growth (Table 5). In the case of Ca content, the values were higher than the \n\n\n\nsufficiency level for oil palm requirement. This is based on the standard requirement \n\n\n\nproposed by Von Uexkull and Fairhurst (1991) and Fairhurst and Hardter (2003). This \n\n\n\nindicates that, the higher the content of Mg and Ca in the soil due to application of MRSG, \n\n\n\nthe higher the uptake by the oil palm seedlings in the glasshouse (Ayanda 2017).  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 5 \n\n\n\nMg and Ca content in frond 3 of the oil palm seedlings \n\n\n\n            *Von Uexkull and Fairhurst (1991) and Fairhurst and Hardter (2003)  \n             Source: Ayanda et al. (2020) \n\n\n\n     Using the data obtained from the glasshouse experiment, more agronomic interpretations \n\n\n\ncan be done. For instance, the height of oil palm seedlings was plotted against exchangeable \n\n\n\nMg. It was found that there was no significant correlation between the height of oil palm \n\n\n\nseedlings and the level of exchangeable magnesium in the treated soil. However, when plant \n\n\n\nheight was plotted against exchangeable Ca, a significant correlation was obtained. As \n\n\n\nexplained by Ayanda et al. (2020), a possible explanation could be that Ca is the more \n\n\n\nlimiting nutrient in the soil in comparison to Mg.  \n\n\n\n\n\n\n\n     Relative height can be used as an indicator of oil palm seedling growth. The relative plant \n\n\n\nheight (%) values were then calculated and subsequently plotted against exchangeable Ca in \n\n\n\norder to determine the critical level of exchangeable Ca to sustain oil palm growth (Figure 6). \n\n\n\nThe critical level of exchangeable Ca value estimated in this way was 0.9 cmolc/kg (value \n\n\n\nwas taken at 90% relative plant height). This means that the topsoil exchangeable Ca of 0.64 \n\n\n\ncmolc/kg observed in in plantations is insufficient for healthy oil palm growth.  Oil palm \n\n\n\nagronomists are aware of this deficiency and that is why GML is applied. It has been known \n\n\n\nNutrient Data from this study Nutrient sufficiency level \nfor oil palm* \n\n\n\n(%) \n\n\n\nMagnesium 0.29-0.45 0.30-0.42 \n\n\n\nCalcium 0.81-1.21 0.50-0.70 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 205\n\n\n\n Relative height can be used as an indicator of oil palm seedling growth. The \nrelative plant height (%) values were then calculated and subsequently plotted \nagainst exchangeable Ca in order to determine the critical level of exchangeable \nCa to sustain oil palm growth (Figure 6). The critical level of exchangeable Ca \nvalue estimated in this way was 0.9 cmolc/kg (value was taken at 90% relative plant \nheight). This means that the topsoil exchangeable Ca of 0.64 cmolc/kg observed \nin in plantations is insufficient for healthy oil palm growth.  Oil palm agronomists \nare aware of this deficiency and that is why GML is applied. It has been known \nfor a long time that Ultisols and Oxisols in Malaysia contain insufficient Ca and \nMg to sustain crop production (Shamshuddin and Fauziah 2010; Shamshuddin et \nal. 2018).\n As mentioned above, exchangeable Ca of more than 0.9 cmolc/kg is rather \nuncommon for the Ultisols in Malaysia under continuous oil palm cultivation, \nas well as for the more weathered Oxisols endemic in the upland regions of the \ncountry. Note that about 70% of Malaysia is covered by these two soil orders. \nTherefore, exchangeable Ca level of the soil in the oil palm plantations has to be \nraised accordingly via agronomic means. Under normal circumstances, at this \nvalue of exchangeable Ca, soil pH would have been raised to above 5.\n Stepwise regression analyses of soil pH, exchangeable calcium and \nexchangeable magnesium showed that a significant relationship exists between \nthese variables and the height of oil palm seedlings, with R2 equal to 0.9215 \n(Ayanda et al. 2020). This means that about 92 % of the relationship can be \nexplained by the regression line. Looking at plant height, it can be assumed that \nif soil pH, exchangeable Ca and exchangeable Mg are sufficiently increased, the \ngrowth of the oil palm seedlings would be significantly enhanced. It is believed \nthat this can be done via the application of MRSG at appropriate rates and times.\n Soil pH is found to be significantly correlated with height of oil palm \nseedlings (Figure 7). Stepwise regression analysis confirmed that soil pH is the \nmost important factor contributing to the growth of oil palm seedlings in terms \n\n\n\nFigure 6. Relationship between relative plant height and exchangeable Ca\nSource: Shamshuddin et al. (2017a)\n\n\n\n14 \n \n\n\n\nfor a long time that Ultisols and Oxisols in Malaysia contain insufficient Ca and Mg to \n\n\n\nsustain crop production (Shamshuddin and Fauziah 2010; Shamshuddin et al. 2018). \n\n\n\n\n\n\n\n\n\n\n\nFigure 6. Relationship between relative plant height and exchangeable Ca \nSource: Shamshuddin et al. (2017) \n\n\n\n\n\n\n\n     As mentioned above, exchangeable Ca of more than 0.9 cmolc/kg is rather uncommon for \n\n\n\nthe Ultisols in Malaysia under continuous oil palm cultivation, as well as for the more \n\n\n\nweathered Oxisols endemic in the upland regions of the country. Note that about 70% of \n\n\n\nMalaysia is covered by these two soil orders. Therefore, exchangeable Ca level of the soil in \n\n\n\nthe oil palm plantations has to be raised accordingly via agronomic means. Under normal \n\n\n\ncircumstances, at this value of exchangeable Ca, soil pH would have been raised to above 5. \n\n\n\n\n\n\n\n     Stepwise regression analyses of soil pH, exchangeable calcium and exchangeable \n\n\n\nmagnesium showed that a significant relationship exists between these variables and the \n\n\n\nheight of oil palm seedlings, with R2 equal to 0.9215 (Ayanda et al. 2020). This means that \n\n\n\nabout 92 % of the relationship can be explained by the regression line. Looking at plant \n\n\n\nheight, it can be assumed that if soil pH, exchangeable Ca and exchangeable Mg are \n\n\n\nsufficiently increased, the growth of the oil palm seedlings would be significantly enhanced. \n\n\n\nIt is believed that this can be done via the application of MRSG at appropriate rates and \n\n\n\ntimes. \n\n\n\n\n\n\n\nY = 20.06x + 71.78 \nR\u00b2 = 0.69 \n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n110\n\n\n\n0.3 0.6 0.9 1.2 1.5 1.8R\nel\n\n\n\nat\niv\n\n\n\ne \npl\n\n\n\nan\nt h\n\n\n\nei\ngh\n\n\n\nt (\n%\n\n\n\n) \n\n\n\n \nExchangeable Ca (cmolc/kg) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021206\n\n\n\nof height. This is consistent with the expected notion that oil palm growth is \nenhanced if soil pH is raised to a level above 5. \n Stepwise regression analysis was also run on soil pH and exchangeable \nCa and the correlation was found to be significantly positive. After MRSG \napplication, exchangeable Ca in the soil was increased which in turn raised soil \npH to the level dependent on the rate. This is another benefit of applying MRSG \non highly weathered soils in Malaysia or even in the tropics where the pH is \nusually below 5. \n We know that soil solution Al3+ will be precipitated as inert Al-hydroxides \nat a pH above 5 (Shamshuddin et al. 1991; Shamshuddin and Ismail 1995). For \nthe topsoil of Jempol Series under investigation, even though the field soil pH \nwas slightly below 5, the exchangeable Al was still high with a value of 1.41 \ncmolc/kg. It follows that the increase in soil pH results in better growth of the oil \npalm seedlings under glasshouse conditions. The increase in soil pH after MRSG \napplication was partly due to the addition of hydroxyl ions released by Mg and \nthe presence of Ca hydroxides in it. The increase in soil pH was also promoted \nslightly by the reaction of some calcite, a liming agent found in MRSG.  \n\n\n\nField Trial Using MRSG to Sustain Oil Palm Growth and Production\nEffects of treatments on soil properties \n     Soil data in March 2016 (6 months after MRSG application) showed that there \nwas no significant difference in exchangeable Mg or Ca between treatments over \nthe period of the study (Table 6). Application of the MRSG on the soil produced \nresults comparable to that of the kieserite in terms of supplying Mg to sustain oil \npalm growth. MRSG application also supplied Ca, which kieserite was unable to \ndo so. Mg and Ca in the soil were sufficient for the healthy growth of the oil palm \nin the field due to MRSG application (Shamshuddin et al. 2017a). \n\n\n\nFigure 7. Relationship between plant height and soil pH\nSource: Shamshuddin et al. (2017a)\n\n\n\n15 \n \n\n\n\n     Soil pH is found to be significantly correlated with height of oil palm seedlings (Figure 7). \n\n\n\nStepwise regression analysis confirmed that soil pH is the most important factor contributing \n\n\n\nto the growth of oil palm seedlings in terms of height. This is consistent with the expected \n\n\n\nnotion that oil palm growth is enhanced if soil pH is raised to a level above 5.  \n\n\n\n\n\n\n\n \nFigure 7. Relationship between plant height and soil pH \n\n\n\nSource: Shamshuddin et al. (2017a) \n\n\n\n\n\n\n\n     Stepwise regression analysis was also run on soil pH and exchangeable Ca and the \n\n\n\ncorrelation was found to be significantly positive. After MRSG application, exchangeable Ca \n\n\n\nin the soil was increased which in turn raised soil pH to the level dependent on the rate. This \n\n\n\nis another benefit of applying MRSG on highly weathered soils in Malaysia or even in the \n\n\n\ntropics where the pH is usually below 5.  \n\n\n\n\n\n\n\nWe know that soil solution Al3+ will be precipitated as inert Al-hydroxides at a pH \n\n\n\nabove 5 (Shamshuddin et al. 1991; Shamshuddin and Ismail 1995). For the topsoil of Jempol \n\n\n\nSeries under investigation, even though the field soil pH was slightly below 5, the \n\n\n\nexchangeable Al was still high with a value of 1.41 cmolc/kg. It follows that the increase in \n\n\n\nsoil pH results in better growth of the oil palm seedlings under glasshouse conditions. The \n\n\n\nincrease in soil pH after MRSG application was partly due to the addition of hydroxyl ions \n\n\n\nreleased by Mg and the presence of Ca hydroxides in it. The increase in soil pH was also \n\n\n\npromoted slightly by the reaction of some calcite, a liming agent found in MRSG.   \n\n\n\n\n\n\n\nY = 24.22x + 9.46 \nR\u00b2 = 0.89 \n\n\n\n100\n\n\n\n110\n\n\n\n120\n\n\n\n130\n\n\n\n140\n\n\n\n150\n\n\n\n160\n\n\n\n170\n\n\n\n180\n\n\n\n190\n\n\n\n5 5.5 6 6.5 7 7.5\n\n\n\nPl\nan\n\n\n\nt h\nei\n\n\n\ngh\nt (\n\n\n\ncm\n) \n\n\n\nsoil pH \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 207\n\n\n\n The respective soil pH at 12 and 18 months (Table 6) was 3.9 \u2013 4.3 and \n4.1 \u2013 4.4, with no difference among treatments. It appeared to indicate a slight \nincrease in soil pH with time. This can be regarded as an ameliorative impact \nof applying Ca containing MRSG on the soil. Any increase in soil pH resulting \nfrom MRSG application was partly due to: 1) The addition of hydroxyl ions \nreleased by Mg and Ca hydroxides present in it; and 2) By the reaction of calcite \npresent in the MRSG.  Continuous application of MRSG in the long run would \npossibly reduce soil acidity of the Ultisol even further. According to Auxtero and \nShamsuddin (1991), the critical soil pH for healthy oil palm in Malaysia is 4.3 and \nin terms of pH consideration, this explains why oil palm plantations have been \nable to sustain growth and production. Based on the study of Shamshuddin et al. \n(1991), an increase in soil pH to a level above 5 would lower soil exchangeable \nAl, subsequent to which other nutrients will become more available in the soil for \nuptake by the oil palm. \n The study further showed that after 18 months of experimental duration \n(Table 6), total soil C was 1.59\u20132.08%. Organic C in the soil was high, which was \ndue to the proper soil/agronomic management in the field. Oil palm plantations \nin the country place cut fronds and empty fruit bunches in the inter-rows of \nthe palms. When these materials are decomposed or mineralised, C and plant \nnutrients are returned to the soil, which eventually enhances soil fertility slightly. \nThe CEC of the soil at 6 months of experimental duration was 9.59 \u2013 14.43, while \nat 18 months it was observed to be 10.67 \u2013 11.21 cmolc/kg (Table 6) indicating \nthat the CEC was within the range expected for a typical highly weathered Ultisol \n\n\n\nTABLE 6 \nEffects of treatments on soil pH, exchangeable Mg, exchangeable Ca and CEC\n\n\n\n16 \n \n\n\n\nField Trial Using MRSG to Sustain Oil Palm Growth and Production \n\n\n\nEffects of treatments on soil properties  \n\n\n\n     Soil data in March 2016 (6 months after MRSG application) showed that there was no \n\n\n\nsignificant difference in exchangeable Mg or Ca between treatments over the period of the \n\n\n\nstudy (Table 6). Application of the MRSG on the soil produced results comparable to that of \n\n\n\nthe kieserite in terms of supplying Mg to sustain oil palm growth. MRSG application also \n\n\n\nsupplied Ca, which kieserite was unable to do so. Mg and Ca in the soil were sufficient for \n\n\n\nthe healthy growth of the oil palm in the field due to MRSG application (Shamshuddin et al. \n\n\n\n2017a).  \n\n\n\n\n\n\n\nTABLE 6  \n\n\n\nEffects of treatments on soil pH, exchangeable Mg, exchangeable Ca and CEC \n\n\n\nTR\n\n\n\nT \n\n\n\nMonth 6 Month 12 Month 18 \n\n\n\np\n\n\n\nH \n\n\n\nMg Ca CEC pH Mg Ca CEC pH Mg Ca CEC \n\n\n\ncmolc kg-1 cmolc kg-1 cmolc kg-1 \n\n\n\nT0 - 0.46\n\n\n\na \n\n\n\n1.05\n\n\n\na \n\n\n\n9.59ab 4.27\n\n\n\na \n\n\n\n0.87\n\n\n\na \n\n\n\n1.70\n\n\n\na \n\n\n\n15.77\n\n\n\na \n\n\n\n4.37\n\n\n\na \n\n\n\n1.03\n\n\n\na \n\n\n\n0.27\n\n\n\na \n\n\n\n10.67\n\n\n\na \n\n\n\nT1 - 0.46\n\n\n\na \n\n\n\n1.03\n\n\n\na \n\n\n\n10.19a\n\n\n\nb \n\n\n\n3.99\n\n\n\na \n\n\n\n0.25\n\n\n\na \n\n\n\n1.20\n\n\n\na \n\n\n\n15.79\n\n\n\na \n\n\n\n4.26\n\n\n\na \n\n\n\n0.64\n\n\n\na \n\n\n\n0.24\n\n\n\na \n\n\n\n11.17\n\n\n\na \n\n\n\nT2 - 0.38\n\n\n\na \n\n\n\n1.15\n\n\n\na \n\n\n\n11.54a 4.39\n\n\n\na \n\n\n\n0.36\n\n\n\na \n\n\n\n1.13\n\n\n\na \n\n\n\n15.77\n\n\n\na \n\n\n\n4.10\n\n\n\na \n\n\n\n0.71\n\n\n\na \n\n\n\n0.24\n\n\n\na \n\n\n\n11.21\n\n\n\na \n\n\n\nT3 - 0.60\n\n\n\na \n\n\n\n1.52\n\n\n\na \n\n\n\n10.54a\n\n\n\nb \n\n\n\n3.90\n\n\n\na \n\n\n\n0.32\n\n\n\na \n\n\n\n1.07\n\n\n\na \n\n\n\n16.55\n\n\n\na \n\n\n\n4.40\n\n\n\na \n\n\n\n0.88\n\n\n\na \n\n\n\n0.26\n\n\n\na \n\n\n\n10.71\n\n\n\na \n\n\n\nT4 - 0.42\n\n\n\na \n\n\n\n0.98\n\n\n\na \n\n\n\n14.43a 4.31\n\n\n\na \n\n\n\n0.59\n\n\n\na \n\n\n\n1.25\n\n\n\na \n\n\n\n17.84\n\n\n\na \n\n\n\n4.26\n\n\n\na \n\n\n\n0.78\n\n\n\na \n\n\n\n0.24\n\n\n\na \n\n\n\n10.96\n\n\n\na \n           Means followed by different letters within the same column are significantly different at p\u22640.05                      \n\n\n\n           Source: Shamshuddin et al. (2017); Ayanda et al. (2020) \n\n\n\n\n\n\n\n     The respective soil pH at 12 and 18 months (Table 6) was 3.9 \u2013 4.3 and 4.1 \u2013 4.4, with no \n\n\n\ndifference among treatments. It appeared to indicate a slight increase in soil pH with time. \n\n\n\nThis can be regarded as an ameliorative impact of applying Ca containing MRSG on the soil. \n\n\n\nAny increase in soil pH resulting from MRSG application was partly due to: 1) The addition \n\n\n\nof hydroxyl ions released by Mg and Ca hydroxides present in it; and 2) By the reaction of \n\n\n\ncalcite present in the MRSG.  Continuous application of MRSG in the long run would \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021208\n\n\n\ncontaining sufficient amounts of organic matter. Based on the afore-mentioned \nchemical attributes, the soil is in good condition for oil palm growth and/or \nproduction.\n\n\n\nEffects of treatments on oil palm growth and fruit bunch yield\nA significant difference was observed for Ca and Mg in frond 17 among treatments. \nThe Ca and Mg level were within the sufficient range (NSR) for mature oil palm \nunder production, based on the requirement levels proposed by Von Uexkull \nand Fairhurst (1991) and Fairhurst and Hardter (2003). The uptake of NPK by \nthe oil palm was not significantly affected by the MRSG treatments indicating \nthat MRSG application has a similar effect to that of kieserite or GML (control \ntreatments) in terms of nutrient uptake. This finding shows that MRSG is as good \nas kieserite in terms of supplying Mg for the requirement of oil palm, although \nit may take a longer time to release the nutrient into the soil unlike in kieserite. \nMRSG is thus an excellent Mg-fertiliser as well as a source of Ca that is needed \nto sustain oil palm growth.\n The Ca/Mg ratio in oil palm frond 17 of Malaysia is usually monitored \nby owners. The ratio in the leaves should be within 1.5-3.0 range (Fairhurst and \nHardter 2003). Therefore, higher Ca is needed compared to that of the Mg to \nsustain oil palm growth/production. Note that the MRSG contains about 25% Ca \nand 5.5% Mg (Table 1). The respective Ca/Mg ratios for T0, T1, T2, T3 and T4 \nin this study were 2.4, 2.9, 2.9, 2.9 and 2.7. There was no Ca-Mg imbalance due \nto MRSG treatment. Thus, it can be assumed that treating the soil with MRSG or \nkieserite results in the same amount of Ca and Mg uptake by the oil palm.\n The chemical composition of the oil palm tissue at month 6 showed \nno significant differences among treatments for all the parameters measured. \nHowever, Ca and Mg in the tissues were a bit lower than the sufficient range \nfor normal oil palm growth. There was no significant difference in the number \nand weight of FFB among treatments. However, the means separation by month \nshowed a significant yield increment towards the end of 2016. This is mainly \ndue to the effect of increased rainfall occurring towards the end of the year \n(Shamshuddin et al. 2017a). This shows that oil palm requires adequate amounts \nof water for healthy growth and production of fruit bunches as mentioned by \nCorley and Tinker (2003). \n A slight fluctuation in FFB harvested from January to August 2017 was \nobserved between months. However, there were no significant differences in FFB \nyield between treatments for each month. This seems to indicate that T3 (MRSG \ntreatment) produced higher FFB weight compared to that of the control treatments \n(T0 and T4) in January on MRSG application. It seemed to enhance growth of the \noil palm leading to an increase in FFB weight. But it is admitted that this could be \ndue to enhancement of soil fertility as shown by an increase in soil pH as well as \nMg and/or Ca or it could even be due to the presence of extra micronutrients or \nessential elements (Table 2).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 209\n\n\n\n The FFB yield at month 18 showed no significant difference in FFB weight \nand number of fruitlets between MRSG and control treatments. MRSG treatment \nof Ultisols in the field gave comparable results to that of the kieserite in terms of \nweight of FFB and number of fruitlets in each fruit bunch.\n Fruitlets for oil extraction rate (OER) were sampled about 2 years after the \nfirst MRSG application on the soil to ensure validity of the interpretation of the \nresults obtained. The analysis of the OER showed no significant difference among \ntreatments with values ranging from 16.3 to 22% (Table 7). The OER values \ndue to MRSG treatments were comparable to those obtained by the commercial \nplantations in Malaysia. The data in Table 7 indicates that treating the Ultisol with \nMRSG produces higher OER compared to that of the kieserite or even GML. This \nis an important finding of the study. \n As MRSG is a by-product of a chemical plant producing rare earth, it is \nmuch cheaper compared to GML. At present, the cost attached to it is due to the \ntransportation of this material to the sites of application. Also, this by-product will \nbe available in large quantities as long as the chemical plant producing rare earth \nin Malaysia is in operation. \n The contribution of kieserite, GML and MRSG tested in the study towards \nthe enhancement of soil fertility is summarised in Table 8. It is clear that MRSG \nis superior in terms of macronutrient and micronutrient supply to meet oil palm \nrequirements compared to those provided by kieserite or GML. According to \nShamshuddin and Ismail (1995), GML in Malaysia contains some Mn and Zn. \nKieserite does not change soil pH, but both GML and MRSG do. MRSG contains \nS (which is required for oil production in the fruitlets); furthermore, it is cheaper \nthan kieserite or GML. This being the case, as a fertiliser MRSG can be considered \nto be comparable or even better than kieserite (Table 8). \n\n\n\nTABLE 7 \nOil extraction rate as affected by treatments\n\n\n\n19 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 7  \n\n\n\nOil extraction rate as affected by treatments \n\n\n\nTreatment Material Rate (kg/palm) OER (%) \n\n\n\nT0 GML 1.25 16.3 a \n\n\n\nT1 MRSG 1.10 17.1 a \n\n\n\nT2 MRSG 1.45 22.0 a \n\n\n\nT3 MRSG 2.40 18.6 a \n\n\n\nT4 Kieserite 0.50 17.2 a \nMeans followed by different letters within the same column are significantly different at p\u22640.05 \n\n\n\n            Source: Shamshuddin et al.(2017a) \n\n\n\n\n\n\n\n     As MRSG is a by-product of a chemical plant producing rare earth, it is much cheaper \n\n\n\ncompared to GML. At present, the cost attached to it is due to the transportation of this \n\n\n\nmaterial to the sites of application. Also, this by-product will be available in large quantities \n\n\n\nas long as the chemical plant producing rare earth in Malaysia is in operation.  \n\n\n\n\n\n\n\n     The contribution of kieserite, GML and MRSG tested in the study towards the \n\n\n\nenhancement of soil fertility is summarised in Table 8. It is clear that MRSG is superior in \n\n\n\nterms of macronutrient and micronutrient supply to meet oil palm requirements compared to \n\n\n\nthose provided by kieserite or GML. According to Shamshuddin and Ismail (1995), GML in \n\n\n\nMalaysia contains some Mn and Zn. Kieserite does not change soil pH, but both GML and \n\n\n\nMRSG do. MRSG contains S (which is required for oil production in the fruitlets); \n\n\n\nfurthermore, it is cheaper than kieserite or GML. This being the case, as a fertiliser MRSG \n\n\n\ncan be considered to be comparable or even better than kieserite (Table 8).  \n\n\n\n\n\n\n\nTABLE 8  \n\n\n\nKey differences in chemical properties among tested fertilisers \n\n\n\n Fertiliser      Formula                Macronutrients      Micro/Essential \n\n\n\nnutrient      \n\n\n\nChange in pH \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021210\n\n\n\nTABLE 8 \nKey differences in chemical properties among tested fertilisers\n\n\n\n20 \n \n\n\n\nTABLE 8  \n\n\n\nKey differences in chemical properties among tested fertilisers \n\n\n\n Fertiliser      Formula                Macronutrients      Micro/Essential \n\n\n\nnutrient      \n\n\n\nChange in pH \n\n\n\nKieserite MgSO4.H2O                 Mg, S                         -                             No change in pH \n\n\n\nGML           Ca,Mg(CO3)2               Ca, Mg               Mn, Zn                          Soil pH increase \n\n\n\nMRSG CaSO4.2H2O + \n\n\n\nMg      \n\n\n\nCa, Mg, S           Mn, Zn, Se                    Soil pH increase \n\n\n\nSource: Ayanda et al. (2020) \n\n\n\n\n\n\n\nEffects of treatments on environment and palm oil quality \n\n\n\n     Abd Rahim et al. (2019) determined soil quality in the above-mentioned trial site using \n\n\n\nvarious indices. These were Biological Accumulation Coefficient (BAC), Geological Index \n\n\n\n(I-geo), Contamination Factor (CF) and Pollution Load Index (PLi). The results of the \n\n\n\nevaluation exercises showed that the BAC was low to moderate for As, Cd and Se, and was \n\n\n\nintensive for Zn. However, the intensive level obtained for Zn is probably not due to the \n\n\n\nresult of applying MRSG as only a small amount of Zn was present in the MRSG used in the \n\n\n\ntrial (Table 2).    \n\n\n\n\n\n\n\n     Heavy metals and other elements of concern in the soil under investigation and surface \n\n\n\nwater in the trial and surrounding areas were determined. The impact of applying MRSG on \n\n\n\nthe surrounding environment using the data so obtained were also assessed by Abd Rahim et \n\n\n\nal. (2019) during the period of the field trial. The level of heavy metals present in the treated \n\n\n\nsoil was lower than the values of the control soil. It is noted that there was no indication of an \n\n\n\nincrease in heavy metal content in the soils treated with MRSG. \n\n\n\n\n\n\n\n     Further results of the assessment showed that the heavy metals contents and other \n\n\n\nelements of concern in the soil of the experimental plots were below the soil investigation \n\n\n\nlevel of Zarcinas et al. (2004) or the Eco-SSL of USEPA (2007). Heavy metals and other \n\n\n\nelements of concern in the surface water before and after MRSG application were below the \n\n\n\nEffects of treatments on environment and palm oil quality\nAbd Rahim et al. (2019) determined soil quality in the above-mentioned trial site \nusing various indices. These were Biological Accumulation Coefficient (BAC), \nGeological Index (I-geo), Contamination Factor (CF) and Pollution Load Index \n(PLi). The results of the evaluation exercises showed that the BAC was low to \nmoderate for As, Cd and Se, and was intensive for Zn. However, the intensive \nlevel obtained for Zn is probably not due to the result of applying MRSG as only \na small amount of Zn was present in the MRSG used in the trial (Table 2).   \n Heavy metals and other elements of concern in the soil under investigation \nand surface water in the trial and surrounding areas were determined. The impact \nof applying MRSG on the surrounding environment using the data so obtained \nwere also assessed by Abd Rahim et al. (2019) during the period of the field trial. \nThe level of heavy metals present in the treated soil was lower than the values of \nthe control soil. It is noted that there was no indication of an increase in heavy \nmetal content in the soils treated with MRSG.\n Further results of the assessment showed that the heavy metals contents \nand other elements of concern in the soil of the experimental plots were below the \nsoil investigation level of Zarcinas et al. (2004) or the Eco-SSL of USEPA (2007). \nHeavy metals and other elements of concern in the surface water before and after \nMRSG application were below the bench mark value of the Ministry of Health \nMalaysia.  Note that the drinking water standard of Malaysia is adopted from the \nWorld Health Organizations standard. The environmental risk analysis assessment \nby Abd. Rahim et al. (2019) shows clearly that the soil is not contaminated as \nevidenced by the low contamination factor with low pollution load index. This \nresult indicates that MRSG is safe for application on agricultural land in Malaysia. \n Heavy metal content and other elements of concern in the soil of the \nresearch plots in the study area were not affected by the application of MRSG, \nkieserite and GML, with no significant differences among treatments (Sahibin \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 211\n\n\n\net al., 2019). Both MRSG and kieserite treatments had produced comparable or \nsimilar results. This is good as MRSG can be promoted as Mg- and Ca-fertiliser.\n The critical toxic concentration of La and Ce in soils throughout the world \nis unavailable at the moment. However, the mean concentration of the two rare \nearth elements in some common soils of Japan and China have been determined. \nThe concentration in Japan is 18 and 40 mg/kg, while in the soils of China, it \nis 44 and 86 mg/kg (Sahibin et al., 2019). It is believed that their concentration \nin the soil under study due to MRSG application would not exceed the amount \npresent in the soils of Japan or China. As for Sr in soils, no credible information \nis available in the literature. \n The ultimate test on the impact of applying MRSG on soil cropped to oil \npalm is the quality of its oil. Palm oil quality was determined on oil palm fruitlets \nabout 2 years after MRSG was first applied to ensure the credibility of the results \n(Shamshuddin et al. 2017a). The oil was analysed for the presence of common \nheavy metals (As, Cd, Pb, Zn, Mn, Ni, Cu and Fe) as well as the other metals of \nconcern (Th, Cr and Hg). The latter could be harmful for human consumption, if \npresent above the critical level. \n The concentration of the said elements in the palm oil of the study was \ncompared to those of the edible oils on sale at the marketplace. The results of the \ncomparison did not show any indication of the accumulation of heavy metals and \nother elements of concern in the palm oil under investigation. It is also noted that \nthe heavy metals found in the extracted oil were much lower than those found \nand/or reported by other researchers (Abd Rahim et al. 2019). Palm oil quality \nfrom palms grown on soil applied with MRSG is similar to that of oil from palms \ngrown on soil applied with kieserite.\n\n\n\nSUSTAINING RICE PRODUCTION ON ACIDIC SOILS USING MRSG\n\n\n\nAcidic Soils in the Kelantan Plains, Malaysia\nSome rice production in Malaysia is on soils with low pH, that is, the acid sulfate \nsoils (Shamshuddin et al. 2014; Shamshuddin et al. 2017b). Hence, the rice plants \nare subjected not only to low pH stress, but also suffer from Al3+ and/or Fe2+ \ntoxicity (Panhwar et al. 2014a; Panhwar et al. 2014b; Alia et al. 2017). Without \nalleviating the low pH stress as well as the toxicity caused by the two afore-\nmentioned acidic metals, rice yields have been < 2 t/ha/season in comparison to \nthe national average of 4-5 t/ha/season. According to Elisa et al. (2014), using \nground magnesium limestone as a liming agent to increase soil pH, rice yields \ncomparable to that of the national average can be obtained. The soils could be \nfurther treated with GML in combination with a bio-fertiliser, fortified with \nbeneficial microorganisms (Panhwar et al. 2014a). This agronomic practice has \nbeen tested by Panhwar et al. (2015) and Panhwar et al. (2016) on the acid sulfate \nsoils in the Kelantan Plains, Malaysia.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021212\n\n\n\n The acid sulfate soils of the Kelantan Plains are notorious for being very \nacidic and containing high amounts of Fe (Shamshuddin 2006; Enio et al. 2011). \nOn flooding the soils for rice cultivation, water in the fields would turn reddish in \ncolour, indicating the presence of a high quantity of Fe (Figure 8). The pH of the \nwater would be < 4 and the Al concentration would exceed the critical level of 20 \n\u00b5M. Note that the critical pH for the health growth of rice is 6 (Alia et al. 2015). \nThe problem of soil acidity and Al3+ and Fe2+ toxicity in the area can be overcome \nby GML application. Based on the above discussion, we believe that the problem \nof soil acidity can be alleviated by MRSG application, albeit at a lower cost. On \naccount of its proven ameliorative attributes, agronomists are now contemplating \nthe use of MRSG to enhance rice production on the acid sulfate soils in the Muda \nAgricultural Development Authority (MADA), Kedah (Kedah-Perlis Plains). This \narea is regarded as the granary of the country as it is an important rice growing \narea. \n\n\n\nAcidic Soils in the Kedah-Perlis Plains, Malaysia\nBased on geological records, the Kedah-Perlis plains were once inundated by \nsea water when the sea level rose to its highest level some 4,300 years ago \n(Shamshuddin, 2017; Shamshuddin et al. 2017b). During that period of geological \nhistory, mineral pyrite (FeS2) was formed and remained in the sediments where \nthe MADA area is. That geological episode has left its fingerprint in affecting soil \nfertility negatively. During dry spells, the water table drops and exposes the pyrite \nwhich is subsequently oxidised to release acidity and toxic iron. The phenomenon \nhas a negative impact on rice production in the long run. This seems to be the \ncase in certain rice fields in Pendang, Kedah (under MADA\u2019s jurisdiction). The \n\n\n\nFigure 8. Rice fields in an acid sulfate area of the Kelantan Plains, Malaysia\nSource: Shamshuddin (2006)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 213\n\n\n\nproblem of high acidity and iron toxicity for rice production has to be rectified \nvia agronomic means. Plans are underway to alleviate the low pH stress and Fe2+ \n\n\n\ntoxicity of rice fields by using MRSG. \n\n\n\nUSING MRSG FOR SUSTAINABLE RUBBER CULTIVATION\nIt is reported that liming an acidic soil in Malaysia at a rate of 1 t GML/ha results \nin enhanced rubber growth (Shamshuddin and Fauziah 2010). This is probably \ndue to the increased soil pH as well as additional Ca and Mg from the liming \nagent. The soil tested in the above study was an Oxisol at an advanced degree \nof weathering, known to have a low soil pH of <5 and insufficient Ca and Mg to \nsustain rubber growth (Shamshuddin et al. 2018). Due to liming, rubber in the \nresearch plots grew so fast that it could be tapped one year ahead of schedule. This \nindicates that liming using GML at specific rates helps improve rubber growth \nsignificantly. Latex flow was not in any way affected by GML application. The \nMRSG produced by Lynas Malaysia can be an important source of Ca and Mg for \nrubber requirement. \n A glasshouse study is on-going in Malaysia to promote the use of MRSG \nto enhance the growth of rubber seedlings. However, the high Mg content in \nMRSG is a cause for concern when rubber trees in plantations mature and are \nready for tapping. The subject of debate among rubber planters in the country for \na long time is that the presence of high Mg in the mature rubber trees might have \nan adverse effect on latex flow. Latex tends to coagulate if Mg concentration is \npresent above a certain critical level, which has yet to be determined. It is one of \nthe objectives of the study to determine the said critical concentration of Mg in \nthe trees so that latex flow after tapping is not curtailed via coagulation. Research \nis being carried out to determine the critical rate of MRSG application in the field. \nThis is to ensure that rubber roots only take up the required amount of Mg present \nin the soil for growth and latex production.  Notwithstanding this concern, Mg is a \nmacronutrient that is needed in sufficient quantities to sustain rubber growth and/\nor latex production. \n Despite this reservation, MRSG can be an alternative source of Ca and \nMg for the rubber tree to sustain growth, just like it does for oil palm (Ayanda et \nal. 2020). Applying MRSG would raise soil pH to the level required for positive \nrubber growth in the long run. The research hopes to identify a suitable rate \nof MRSG application that helps enhance rubber growth, yet is able to sustain \nlatex flow at the expected level. The ultimate aim is to extend the results of the \nglasshouse study to rubber cultivation in the plantations. \n\n\n\nUSING MRSG TO SUSTAIN AGRICULTURE IN\nSOUTHEAST ASIA\n\n\n\nBased on the results of our review, we have come to the conclusion that MRSG \nhas a great potential as Mg- and/or Ca-fertiliser to sustain the production of oil \npalm, rubber and rice planted on soils under tropical environment. The countries \nin Southeast Asia have almost similar soil types \u2013 Ultisols, Oxisols, Inceptisols \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021214\n\n\n\nand Entisols. The first 2 soil orders are cropped to oil palm and rubber. Many of \nthe soils in the last 2 soil orders belong to acid sulfate soils where rice is grown.  \nBeside Malaysia, other Southeast Asian countries producing the crops mentioned \nabove include Indonesia, Thailand, Vietnam and the Philippines (Shamshuddin \n2006; Shamshuddin et al. 2014). MRSG can be promoted to these countries for \nsustainable production of the crops.\n In Indonesia, it is almost certain that most of the oil palm and rubber are \ngrown on the highly weathered soils of Sumatra and Kalimantan (Shamshuddin et \nal. 2018). The soils planted with these crops are similar in their physico-chemical \nproperties to those of Malaysia. On the other hand, Anda et al. (2009) mention in \ntheir study that rice is grown in some areas with mixed success on the acid sulfate \nsoils of Kalimantan, not far from Banjarmasin, Indonesia. \n The acid sulfate soils in the Bangkok Plains (Thailand), Mekong Delta \n(Vietnam) or in some islands of the Philippines are in dire need of alleviation of \nsoil acidity as well as Al3+ or Fe2+ toxicity to sustain rice production (Shamshuddin \net al. 2014). Liming materials at these places can be costly or even unavailable. \nThus, the problem facing rice production can be alleviated via the application of \nMRSG at a reasonable cost for the farmer.  \n\n\n\nCONCLUSION\nOur studies show that application of Mg-rich synthetic gypsum on land \nsupplies sufficient amounts of Ca and Mg to highly weathered soils lacking \nthese macronutrients, with a concomitant increase in soil pH that enhances oil \npalm and rubber growth. Also, of equal importance, our study shows that land \napplication of MRSG does not result in environmental degradation. For oil palm \nplantations in the tropics, application of MRSG provides the necessary S for oil \nproduction in the fruitlets. We also know that rice production on acid sulfate soils \nin Southeast Asia is often limited by low pH stress and Al3+ and/or Fe2+ toxicity. \nThese agronomic problems can be alleviated cheaply and/or effectively via the \napplication of MRSG at suitable rates and times. \n To sustain agricultural production in the long run, MRSG, a cheap \nchemical plant waste produced in Malaysia offers a viable alternative to GML and \nkieserite which are imported. We know that MRSG is available in large quantities \nin the country (Malaysia) and is more cost effective than that of the GML. In \nturning to MRSG, the nations\u2019s fertiliser import could be reduced, yet agricultural \nproductivity is expected to be sustained. This effectively translates into increased \nfarmers\u2019 income and foreign exchange savings.\n\n\n\nACKNOWLEDGEMENTS\nThe authors would like to acknowledge the financial and technical support \nprovided by Lynas Malaysia Sdn Bhd and Universiti Putra Malaysia without \nwhich it would not have been possible to obtain adequate data for this paper.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 215\n\n\n\nREFERENCES\nAbd Rahim, S., J. Shamshuddin, C.I. Fauziah, O. Radziah, I. Wan Mohd Razi and \n\n\n\nM.S.K. Enio. 2019. Impact of Mg rich synthetic gypsum application on the \nenvironment and palm oil quality. Science of the Total Environment 652: 573-\n582.\n\n\n\nAlia, F., J. Shamshuddin, C.I. Fauziah, M. H. A. Husni and Q. A. Panhwar. 2015. \nEffects of aluminum, iron and/or low pH on rice seedlings grown in solution \nculture. Int. J. Agric. Biol. 17: 702-710. \n\n\n\nAlia, F., J. Shamshuddin, C.I. Fauziah, M. H. A. Husni and Q. A. 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In: Selected \nAnalytical Methods for Environmental Remediation and Recovery (SAM). \nhttps://www.epa.gov/homeland-security-research/epa-method-3050b-acid-\ndigestion-sediments-sludges-and-soils,1996. Accessed 5 February, 2018.\n\n\n\nVon Uexkull H.R. and T. H. Fairhurst. 1991. Fertilizing for high yield and quality - the \noil palm. IPI-Bulletin No. 12. Switzerland: International Potash Institute. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021218\n\n\n\nZarcinas, B.A., C.I. Fauziah, M. J. Mclaughlin and G. Cozens. 2004. Heavy metals in \nsoils and crops in Southeast Asia. 1. Peninsular Malaysia. Environ Geochem. \nHealth. 26(4): 343-357.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: ecsuhaimi@salam.uitm.edu.my\n\n\n\nINTRODUCTION\nPolycyclic aromatic hydrocarbons (PAHs) are widespread and are recalcitrant \norganic compounds that might pose a threat to mankind and the environment \n(Harvey 1991; Mrozik et al. 2003). Significant levels of PAHs have been detected \nin air, water, soils and sediments (Harvey 1991; Yang and Hildebrand 2006). \nPAHs pollution in soil differs from water. In water, PAHs may be diluted when \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 17: 111-126 (2013)                     Malaysian Society of Soil Science\n\n\n\nDegradation of Phenanthrene by Corynebacterium \nurealyticum in Liquid Culture and Sand Slurry\n\n\n\nNor Amani Filzah Mohd-Kamil1,2, Salina Alias1, Norzila Othman2 and \nSuhaimi Abdul-Talib1*\n\n\n\n \n1Institute for Infrastructure, Engineering & Sustainable Management, Faculty of \n\n\n\nCivil Engineering, Universiti Teknologi MARA, 40450 Shah Alam, \nSelangor, Malaysia\n\n\n\n2Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn \nMalaysia, 86400 Batu Pahat, Johor, Malaysia\n\n\n\nABSTRACT\nMost studies on biodegradation of Polycyclic Aromatic Hydrocarbons (PAHs) \nevaluate the degradation potential of indigenous microorganisms in either liquid or \nsolid media. There are limited studies on evaluation of the same microorganisms \nin degrading PAHs under non-indigenous condition in both liquid or solid media. \nThis study investigated the potential of the bacteria, Corynebacterium urealyticum \nisolated from municipal sludge in degrading phenanthrene in both liquid and solid \nmedia. The study also evaluated the performance of the strain when subjected to \nlow and high initial concentration of PAHs. Batch experiments were conducted \nover 20 days in reactors containing artificially contaminated phenanthrene \nminimal media and sand slurry inoculated with a bacterial culture. Phenanthrene \ndegradation in liquid culture and sand slurry were found to be 82.15% and 27.71%, \nrespectively. The degradation activity of bacteria in liquid culture remained active \nthroughout the duration of the experiment, but this was not the case in the sand \nslurry. A significant difference was observed in the amount of phenanthrene \nremaining in the sand slurry when the bacteria was inoculated into the low and \nhigh phenanthrene concentrations. Percentages of phenanthrene remaining for \nboth initial concentrations in liquid culture were not significant. From the bacteria \ngrowth curve plotted through viable count analysis, it was observed that the bacteria \ncould immediately adapt to PAH-contaminated sand and had better capability to \ndegrade phenanthrene in liquid culture compared to sand slurry.\n\n\n\nKeywords: Biodegradation, Corynebacterium urealyticum, liquid cultures, \nphenanthrene, sand slurry\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013112\n\n\n\na large volume of water is present. PAHs may be transported to other parts of \nthe environment following the flow of water bodies. However, in soil, PAHs are \ngenerally adsorbed to soil particles and degradation of PAHs is limited (Madsen \n2003). Phenanthrene is a three-ring PAH commonly found in water bodies and \ncontaminated soil. This compound is normally utilized as a model compound in \nPAH biodegradation studies (Woo et al. 2004; Tang et al. 2005; Lors et al. 2010). \n\n\n\nBiodegradation appears to be the main process responsible for the removal \nof PAHs from the natural environment (Wilson and Jones 1993; Mohan et al. \n2006). Bacteria constitute one of the more frequently used microorganisms in \nbiodegradation as they have the capability to transform PAHs to other organic \nor inorganic end products (Cerniglia 1984; Rakesh et al. 2005). Many bacteria \nare capable of consuming PAHs as energy and carbon sources or cometabolism, \nleading to the biodegradation and reduction processes (Atlas 1995; Rakesh et al. \n2005). Various studies have identified these bacteria to be mainly from the genera \nPseudomonas sp., Rhodococcus sp., Mycobacterium sp., Sphingobacterium sp., \nSphingomonas sp., Bacillus cereus, Flavobacterium sp., Beijerinckia sp. and \nBurkholderiacepacia (Samanta et al. 1999; Moody et al. 2001; Abdul-Ghani \n2008; Chauhan et al. 2008; Zhao et al. 2008; Janbandhu and Fulekar 2011).\n\n\n\nReports on use of bacteria strains in PAHs biodegradation in either liquid \nor solid media are available in the literature. Studies in liquid medium have been \nreported by Moody et al. (2001), Abdul-Ghani (2008), Zhao et al. (2008) and \nJanbandhu and Fulekar (2011). Bacteria used in these studies were capable of \ndegrading more than 95% of PAHs in less than 14 days. However, for certain \ncases where the bacteria were exposed to high concentrations of PAH, less than \n60% were degraded (Janbandhu and Fulekar 2011).  \n\n\n\nThe capability of bacteria to degrade PAHs in solid medium was investigated \nby Kwok and Loh (2003), Sheng and Gong (2006), Gottfried et al. (2010) and \nKaramalidis et al. (2010). Incomplete degradation of PAHs was observed in most \nof these studies, that is, less than 85%. Moreover, a long duration was required to \nachieve a high percentage of removal (Karamalidis et al. 2010). Lower removal \nand longer duration are due to the complex processes involved such as solubility, \nphysico-chemical sorption, concentration of PAH and low bioavailability of PAHs \nin solid medium (Boopathy 2000). \n\n\n\nReported degradation studies on the capability of bacteria under non-\nindigenous condition in both liquid and solid media are limited.  Evaluation \nof PAHs biodegradation in different media by using similar bacteria could \nenhance our understanding of the performance of bacteria for the remediation \nof PAHs contaminated sites. This will lead to better implementation of effective \nremediation strategies. \n\n\n\nThe specific aim of this present work is to evaluate the potential of \nCorynebacterium urealyticum in degrading phenanthrene in liquid culture and in \nsand slurry. This study also compared the trend of degradation at different initial \nphenanthrene concentrations in both media. In addition, the survival of bacteria in \nsand slurry during the phenanthrene degradation was also investigated.\n\n\n\nNor Amani Filzah Mohd-Kamil, Salina Alias, Norzila Othman and Suhaimi Abdul-Talib\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 113\n\n\n\nPhenanthrene Degradation in Liquid Culture and Sand Slurry\n\n\n\nMETHODOLOGY\n\n\n\nMaterials\nAll chemicals used for extraction, preparation of minimal media and bacteria \nculture were of analytical grade and supplied by Merck, Germany. Phenanthrene \nstandard for Gas Chromatography Mass Spectrometer (GCMS) was obtained \nfrom Merck (Augsburg, Germany). For extraction in liquid culture, Solid \nPhase Micro Extraction (SPME) fibre holder assembly equipped with a 7 \u03bcm \npolydimethylsiloxane (PDMS) SPME fibre was purchased from Supelco, Sigma-\nAldrich Chemie. Ultra-pure water (UPW) used in this study was purchased from \nAlga Purelab Ultra (18.2M\u2126, United Kingdom).\n\n\n\nSamples Preparation\nMinimal media solution was prepared by dissolving 8.5 g Na2HPO4, 3.0 g KH2PO4, \n0.5 g NaCl, 1.0 g NH4Cl, 0.5 g MgSO4.7H2O, 0.0147 g CaCl2, 0.0004 g CuSO4, \n0.001 g KI, 0.004 g MnSO4.H2O, 0.004 g ZnSO4, 0.005 g H3BO3 and 0.002 g \nFeCl3 in 1 L ultra pure water. \n\n\n\nFor the biodegradation study in liquid culture, 20 ml of minimal media \nsolution and 30 ml of UPW was transferred into 250 ml conical flasks. For the \nbiodegradation study in sand slurry, sand was sampled and washed in sand: water \nratio of 1:2 (w: v) four times. Then it was dried in an oven at 60\u00b0C for 5 days. \nThe cleaned and dried sand was sieved through 1.18 mm wire mesh. Then, 20 ml \nof minimal media solution and 40 g of cleaned sand was transferred into 500 ml \nconical flasks. The flasks for both tests were then subjected to steam sterilization \nfor 15 min (Hirayama, HVE-50, Japan) to eliminate indigenous microorganisms. \n\n\n\nAfter sterilization, both flasks were spiked with phenanthrene at different \nconcentrations: 100 mg L-1 and 350 mg L-1 in minimal media solution while 100 \nmg kg-1 and 350 mg kg-1 in sand, respectively. Hexane was evaporated under \ncontinuous mixing to ensure homogenous distribution of phenanthrene in samples. \nSamples were stored under 4\u00baC until its use in biodegradation studies. The initial \nconcentration of phenanthrene was verified in triplicate before being used for the \nbiodegradation study.\n\n\n\nBacteria Strain Preparation\nCorynebacterium urealyticum used in this study was isolated from municipal sludge \nby Othman et al. (2010) and preserved at -80oC in microbeads (microbankTM). \nThe bacteria was revived by transferring a few of the frozen beads into universal \nbottles containing 20 mL of nutrient broth with the broth being incubated at 30\u00baC \nfor three days. A series of dilution streaking was performed and the strain was sub-\ncultured three times to attain active bacteria before it was used in biodegradation \nstudies.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013114\n\n\n\nBiodegradation Study\nFlasks comprising artificially contaminated minimal media and artificially \ncontaminated sand were inoculated with 5 ml bacterial inoculums. The inoculated \ninoculum in liquid culture and sand slurry would produce bacteria ranging in \nnumber from 107 - 108 colony forming unit (CFU) per mL or per gram of sample, \nrespectively. All tests were conducted at an initial pH and temperature of 7.0 and \n30oC, respectively. These conditions were adopted from optimisation studies by \nOthman et al. (2010). \n\n\n\nAfter inoculation, all flasks were shaken in an incubation shaker at 150 \nrpm. Phenanthrene degradation was determined every day until the degradation \nwas completed or the remaining concentrations stabilized over the 20-day study \nduration. This duration was determined from a preliminary study. Biodegradation \nstudies were performed in triplicate. Three control reactor flasks without inoculum \nwere also used during the study. Viable count analysis was conducted in all \nsamples and no colony was found. In sand slurry, sterilized water was supplied \nat 2% remaining weight every day. Water was supplied to compensate for drying \neffects on samples during the experiment. \n\n\n\nPAHs Extraction and Analysis\nPAHs in liquid culture were extracted by SPME method optimised by Othman \net al. (2010). A 20 mL sample was withdrawn and transferred into a 25 mL glass \nbottle with septum cap. The SPME fibre holder was immersed in the 25 mL \nglass bottle. Then, the sample was heated at 60\u00baC in a water bath for 60 min. The \nfibre was then retracted and transferred to a heated injection port of the GCMS \n(Perkin Elmer Clarus 600) for analysis. In this analysis, each sample was tested in \ntriplicate with efficiency recovery from this method being 70%.\n\n\n\nFor sand slurries, 500 mg of the sand sample was dissolved in 25 mL \nof n-hexane and acetone 7:3 (v/v). The extractions were performed with the \npressurized microwave extraction system (MAE) Multiwave 3000 (Anton Paar, \nAustria) at 10 bars for 40 minutes. When the extraction period was completed, \nthe equipment was cooled to room temperature. Subsequently, the samples were \nfiltered with Whatman fibre filter with pore size of 11 \u00b5m. The samples were \nconcentrated by means of a rotary evaporator to about 1 mL. The extraction \nmethod was modified from Gfrerer and Lankmayr (2001). \n\n\n\nExtracted supernatant was analyzed by using GCMS (Perkin Elmer Clarus \n600). Elite Column 5MS with 30 m long, 0.25 mm internal diameter and 0.25 \n\u03bcm thickness was used to separate the compounds. The injector was operated \nat 250oC in the splitless mode with 2-min splitless period. Helium was used as \nthe carrier gas with 1 mL min-1 constant flow rate. The column temperature was \ninitially set at 50oC for 1 min, increased to 150oC at a rate of 15oC min-1 and held \nat 1 min, and finally ramped at 5oC min-1 to 300oC and held constant until the end \nof the 35 min total run time.\n\n\n\nNor Amani Filzah Mohd-Kamil, Salina Alias, Norzila Othman and Suhaimi Abdul-Talib\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 115\n\n\n\nSurvival of Bacteria in Sand Slurry\nConcentration of bacteria from sand slurry was quantified by mixing 1 g of sand \nwith 9 mL of sterile phosphate buffered saline and homogenized at high speed \nof 1min using a vortex mixer. Successive 1/10 dilutions were made by adding \n1 mL of the sand suspension to 9 mL of phosphate buffered saline solution. An \naliquot (0.1 mL) of each dilution was transferred to nutrient agar on petri dish. \nThe dishes were incubated at 30\u00b0C at an inverted position. After four days, the \nnumber of bacterial colonies was counted using a plate counter. Plates with \ndifferent dilutions were prepared and those with colonies in the range of 30 to 300 \nwere used to estimate the number of bacteria. This number of colonies was then \nmultiplied by the dilution factor to find the total number of bacteria per 1 g of the \nsand. The number of colonies was expressed as colony-forming units per gram of \nsand (CFU/g). All tests were conducted in triplicate.\n\n\n\nStatistical Analysis and Biodegradation Kinetics \nThe difference in concentration of phenanthrene for both conditions was \ndetermined by using analysis of variance (ANOVA) test with time as co-variance. \nAll statistical analyses were performed with Microsoft Excel software. The \nbiodegradation kinetics of phenanthrene was described using the first order rate \nmodel (Equation 1):\n\n\n\n      (1)\n\n\n\nwhere S is the phenanthrene concentration in the medium at time, t, S0 is the \ninitial phenanthrene concentration and k is the first order rate constants of the \ndegradation process. A linear plot of ln S against t is applied to provide the value \nof k from its slope (Equation. 2). \n\n\n\n  Ln S = -kt + ln So (2)\n\n\n\nBased on Equation 1, values of first order rate constant (k) was determined. The \nhalf-life, t1/2 is the time required for concentration of substrate to reach one-half of \nits initial value and calculated as Equation 3: \n \n\n\n\n    (3)\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPhenanthrene Degradation in Liquid Cultures and Sand Slurry\nFig. 1 shows phenanthrene degradation by C. urealyticum in liquid culture and \nsand slurry at an initial phenanthrene concentration of 350 mg L-1 and 350 mg kg-1 \n\n\n\nrespectively. In both media, the degradation was observed in three phases with \n\n\n\nPhenanthrene Degradation in Liquid Culture and Sand Slurry\n\n\n\nS = S e0\n-kt\n\n\n\no\n\n\n\nt   = /21\n\n\n\n/21In- k\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013116\n\n\n\nPhase 1 being characterized by rapid degradation, Phase 2 with slow degradation \nand Phase 3 with very slow or almost no degradation. Rapid degradation of \nphenanthrene in Phase 1 showed that the bacteria easily adapted to the new \nenvironment, artificially phenanthrene-contaminated media. Degradation rates in \nliquid culture were determined to be 23.80 mg L-1 day-1, 14.31 mg L-1 day-1 and \n3.88 mg L-1 day-1 for phases 1, 2 and 3, respectively. These degradation rates \nwere higher than the degradation rates in sand slurry which were 13.82 mg kg-1 \nday-1, 1.45 mg kg-1 day-1 and 0.15 mg kg-1 day-1 for phase 1, phase 2 and phase 3, \nrespectively. \n\n\n\nIn this study, the bacteria were inoculated in nutrient broth, which is a \nliquid medium. In the liquid cultures, phenanthrene was dissolved in minimal \nmedia during sample preparation. As the liquid culture is a homogenous media, \nphenanthrene is readily available to the bacteria. Phenanthrene in sand slurry \nwas entrapped and had strong adsorption to sand particles, contributing to low \nbioavailability of phenanthrene to the bacteria (Boopathy 2000).\n\n\n\nPAHs degrading bacteria are able to degrade PAHs by the action of \nintracellular dioxygenases, meaning both PAHs and atoms of molecular oxygen \nmust be transferred into the cell to begin PAHs degradation (Johnsen et al. 2005). \nThus, the low bioavailability in sand slurry slowed and to some extent limited the \ntransfer of phenanthrene into the cell and subsequently retarded the degradation \nprocess. PAHs form strong bonds with solid particles (Pizzul et al. 2007). \nFurthermore, PAHs are highly hydrophobic and have low solubility leading to their \nlimited microbial degradation in solid media (Johnsen et al. 2005). In general, the \nresults show that C. urealyticum was capable of degrading phenanthrene better in \nliquid culture than in sand slurry medium.\n\n\n\nFig. 1: Degradation of phenanthrene in liquid culture and sand slurry at initial \nphenanthrene concentration of 350 mg L-1 in liquid culture and 350 mg kg-1 in \nsand slurry. Data plotted are mean of three replicates; error bars represent SD\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n1 \n\n\n\n\n\n\n\n \nFig. 1: Degradation of phenanthrene in liquid culture and sand slurry at initial phenanthrene \nconcentration of 350 mg L-1 in liquid culture and 350 mg kg-1 in sand slurry. Data plotted are \n\n\n\nmean of three replicates; error bars represent SD \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 2b: Phenanthrene degradation \nby Corynebacterium urealyticum in \n\n\n\nsand slurry \n \n\n\n\nFig. 2a: Phenanthrene degradation \nby Corynebacterium urealyticum in \n\n\n\nliquid cultures \n \n\n\n\nNor Amani Filzah Mohd-Kamil, Salina Alias, Norzila Othman and Suhaimi Abdul-Talib\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 117\n\n\n\nEffect of Different Phenanthrene Concentration in Both Media\nPhenanthrene degradation at different initial concentrations in both media are \nshown in Fig. 2a and 2b. In both media, similar trends of degradation were observed \nfor both initial phenanthrene concentrations. More than 90.11% phenanthrene \nwas degraded at 100 mg L-1, while only 82.15% phenanthrene was degraded at \n350 mg L-1 in liquid cultures. In sand slurry, 69.79% and 27.71% of phenanthrene \nwas degraded at 100 mg kg-1 and 350 mg kg-1, respectively. Overall, the remaining \nphenanthrene was found lower at low phenanthrene concentration (100 mg kg-1) \ncompared to a high concentration (350 mg kg-1). These results show that bacteria \nperformed better in degrading phenanthrene at low phenanthrene concentration \ncompared to high concentration. In liquid culture, almost complete degradation \nwas observed at both initial phenanthrene concentrations. On the other hand, no \ndegradations were observed after day 6 in sand slurry. \n\n\n\nGenerally, bacteria consume organic carbon as a substrate to produce energy \nduring the  metabolism process (Boopathy 2000). However, when the substrate \nis present in very high concentrations, the bacteria cell will be saturated with the \nsubstrate. The high concentration of substrate may become toxic to the bacteria \nand this reduces the efficiency of the degradation process. In a separate but related \nstudy, Othman et al. (2010) conducted a biodegradation study using the same \nbacteria at an initial phenanthrene concentration of less than 100 mg L-1 and found \nthat the percentage of phenanthrene remaining was lower compared to the results \nof the present study. Thus, the degradation process of phenanthrene by the bacteria \nwas optimum or attained steady state at 100 mg L-1. At this point the strain has \nsufficient carbon sources for energy and growth. After the steady state, substrate \nsaturation occurs and the degradation process starts decreasing, resulting in a \nlower percentage of degradation. Similar observations were reported by Bouchez \net al. (1995) and Romero et al. (1998).\n\n\n\nFig. 2a: Phenanthrene degradation by \nCorynebacterium urealyticum in liquid \n\n\n\ncultures\n\n\n\nFig. 2b: Phenanthrene degradation by \nCorynebacterium urealyticum in sand \n\n\n\nslurry\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n1 \n\n\n\n\n\n\n\n \nFig. 1: Degradation of phenanthrene in liquid culture and sand slurry at initial phenanthrene \nconcentration of 350 mg L-1 in liquid culture and 350 mg kg-1 in sand slurry. Data plotted are \n\n\n\nmean of three replicates; error bars represent SD \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 2b: Phenanthrene degradation \nby Corynebacterium urealyticum in \n\n\n\nsand slurry \n \n\n\n\nFig. 2a: Phenanthrene degradation \nby Corynebacterium urealyticum in \n\n\n\nliquid cultures \n \n\n\n\nPhenanthrene Degradation in Liquid Culture and Sand Slurry\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013118\n\n\n\nThe degradation trend in liquid culture from this study was consistent with \nresults from a study conducted by Abdul-Ghani (2008). Table 1a and 1b show \nthe performances of PAHs degrading bacteria in liquid culture and solid media, \nrespectively. Abdul-Ghani (2008) spiked his samples with mixed PAHs, whereas \nsamples from this study were only spiked with individual PAHs. Irrespective of \nwhether single or multi PAHs were used, the complete degradation of PAHs was \nobserved in both studies. Meanwhile, the effect of different initial phenanthrene \nconcentrations established in this study was consistent with the study conducted \nby Janbandhu and Fulekar (2011), which found that percentage degradation was \nreduced when initial phenanthrene concentrations exceeded 100 mg L-1. In using \na bacteria consortium, Janbandhu and Fulekar (2011) reported a degradation rate \nof 56.9% (from 250 mg L-1) phenanthrene. In contrast, this study which used \nonly single bacteria, namely, C. urealyticum degraded 82.15% of 350 mg L-1 in \nliquid media. The percentage degradation in this study was higher compared to \nthat reported by Janbandhu and Fulekar (2011). Thus, this study showed that C. \nurealyticum is a better phenanthrene degrader.  A study conducted by Zhao et al. \n(2008) also discovered a bacteria, namely Sphingomonas sp. that degraded 100% \nof 250 mg L-1 phenanthrene. \n\n\n\nIn sand slurry or solid medium, the degradation trend established in this \nstudy was consistent with the study reported by Sheng and Gong (2006), which \nalso found that the degradation rate decreased with an increase in time. In \naddition, complete degradation was not achieved even over an extended duration. \nSimilar findings have been reported by Kwok and Loh (2003), Gottfried et al. \n(2010) and Karamalidis et al. (2010). Results on effect of initial phenanthrene \nconcentrations from this study concurred with the findings reported by Sheng \nand Gong (2006), which stated that as the initial concentration increased, \nthe percentage of degradation was reduced. In general, it is observed that the \npercentage of degradation in studies conducted in solid media is lower compared \nto liquid culture studies. These studies show that complete degradation is not \nachievable in solid media.\n\n\n\nGrowth of C. urealyticum in Sand Slurry\nIncomplete and slow degradation of PAH observed in sand slurry might be \ndue to the inability of bacteria to attain and metabolise PAH as substrate for its \ngrowth. The inability of utilising a substrate is possibly due to the substrate being \nunavailable and/or toxic to the bacteria. Therefore, experiments on growth of the \nbacteria were conducted to determine its survival in sand slurry.\n\n\n\nThe growth curves of the bacteria in sand slurry at different initial \nphenanthrene concentrations are shown in Fig. 3. For the sample with an initial \nconcentration of 100 mg kg-1, the number of colonies increased from an average of \n9.94 x 106 CFU g-1 at day 0 to 61-fold (6.13 x 108 CFU g-1) at day 3. Furthermore, \nthe colonies increased 14-fold (1.82 x 108 CFU g-1) in the sample with initial \nconcentration of 350 mg kg-1 at day 2. The bacteria continued to multiply for both \nconcentrations until an early stationary phase was achieved at day 3 and day 2 \n\n\n\nNor Amani Filzah Mohd-Kamil, Salina Alias, Norzila Othman and Suhaimi Abdul-Talib\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 119\n\n\n\nfor 100 mg kg-1 and 350 mg kg-1, respectively. Then, the bacteria concentration \nbegan to decline until the end of the experiment. This reduction shows that the \nbacteria are unable to survive longer in sand slurries and subsequently retarded \nthe degradation process.\n\n\n\nOverall, Fig. 3 shows that the bacteria can grow better in 100 mg kg-1 \n\n\n\ncompared to 350 mg kg-1, indicating  that substrate concentrations can affect the \ngrowth of bacteria. Bacteria growth was optimum at a low substrate concentration \nof 100 mg kg-1. Substrates at high concentrations (350 mg kg-1) may be toxic to \n\n\n\nTABLE 1a\nDegradation of PAHs by different bacteria in liquid culture\n\n\n\nTABLE 1b\nDegradation of PAHs by different bacteria in solid media\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n1 \n\n\n\n\n\n\n\nTABLE 1a \nDegradation of PAHs by different bacteria in liquid culture \n\n\n\n \nReferences Bacteria PAHs Initial \n\n\n\nconc. \n(mg L-1) \n\n\n\n % \ndegradation \n\n\n\nDuration \n(day) \n\n\n\n \nAbdul-\nGhani \n(2008) \n\n\n\n \nPseudomonas stutzeri \nbacterium HS-D36 \nUncultured \nPseudomonas sp. clone \n2-A \n \nPseudomonas stutzeri \nbacterium LS401 \n \n\n\n\n \ndibenzothiophene \n\n\n\nphenanthrene \nanthracene \n\n\n\n(Mixed) \n\n\n\n \n100 \n\n\n\n \n95 \n\n\n\n \n14 \n\n\n\nZhao et al. \n(2008) \n \n\n\n\nSphingomonas sp. phenanthrene 250 100 8 \n\n\n\nJanbandhu \nand Fulekar \n(2011) \n\n\n\nSphingobacterium sp., \nBacillus cereus  \nAchromobacterin solitus \n \n\n\n\nphenanthrene 100 \n250 \n500 \n\n\n\n100 \n56.9 \n25.8 \n\n\n\n14 \n\n\n\nThis study Corynebacterium \nurealyticum \n\n\n\nphenanthrene 100 \n350 \n\n\n\n90 \n82 \n\n\n\n20 \n\n\n\n\n\n\n\nTABLE 1b \nDegradation of PAHs by different bacteria in solid media \n\n\n\n \nReferences Bacteria PAHs Initial \n\n\n\nconc. \n(mg kg-1) \n\n\n\n % \ndegradation \n\n\n\nDuration \n(day) \n\n\n\n \nKwok and Loh \n(2003) \n \n\n\n\n \nPseudomonas putida \n(ATCC 17484) \n\n\n\n \nphenanthrene \n\n\n\n \n300 \n\n\n\n \n70 \n\n\n\n \n20 \n\n\n\nSheng and \nGong (2006) \n \n\n\n\nPseudomonas sp. GF3 \n+ wheat (planted) \n \n\n\n\nphenanthrene 100 \n200 \n\n\n\n84.8 \n70.2 \n\n\n\n80 \n\n\n\nKaramalidis et \nal. (2010) \n \n\n\n\nPseudomonas \naeruginosa \n\n\n\n16 PAHs \n(mixed) \n\n\n\n58 70 191 \n\n\n\nGottfried et \nal.(2010) \n \n\n\n\nPseudomonas putida \n(ATCC 17484) \n\n\n\nphenanthrene 32 68 10 \n\n\n\nThis study Corynebacterium \nurealyticum \n\n\n\nphenanthrene 100 \n350 \n\n\n\n70 \n27 \n\n\n\n20 \n\n\n\n\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n1 \n\n\n\n\n\n\n\nTABLE 1a \nDegradation of PAHs by different bacteria in liquid culture \n\n\n\n \nReferences Bacteria PAHs Initial \n\n\n\nconc. \n(mg L-1) \n\n\n\n % \ndegradation \n\n\n\nDuration \n(day) \n\n\n\n \nAbdul-\nGhani \n(2008) \n\n\n\n \nPseudomonas stutzeri \nbacterium HS-D36 \nUncultured \nPseudomonas sp. clone \n2-A \n \nPseudomonas stutzeri \nbacterium LS401 \n \n\n\n\n \ndibenzothiophene \n\n\n\nphenanthrene \nanthracene \n\n\n\n(Mixed) \n\n\n\n \n100 \n\n\n\n \n95 \n\n\n\n \n14 \n\n\n\nZhao et al. \n(2008) \n \n\n\n\nSphingomonas sp. phenanthrene 250 100 8 \n\n\n\nJanbandhu \nand Fulekar \n(2011) \n\n\n\nSphingobacterium sp., \nBacillus cereus  \nAchromobacterin solitus \n \n\n\n\nphenanthrene 100 \n250 \n500 \n\n\n\n100 \n56.9 \n25.8 \n\n\n\n14 \n\n\n\nThis study Corynebacterium \nurealyticum \n\n\n\nphenanthrene 100 \n350 \n\n\n\n90 \n82 \n\n\n\n20 \n\n\n\n\n\n\n\nTABLE 1b \nDegradation of PAHs by different bacteria in solid media \n\n\n\n \nReferences Bacteria PAHs Initial \n\n\n\nconc. \n(mg kg-1) \n\n\n\n % \ndegradation \n\n\n\nDuration \n(day) \n\n\n\n \nKwok and Loh \n(2003) \n \n\n\n\n \nPseudomonas putida \n(ATCC 17484) \n\n\n\n \nphenanthrene \n\n\n\n \n300 \n\n\n\n \n70 \n\n\n\n \n20 \n\n\n\nSheng and \nGong (2006) \n \n\n\n\nPseudomonas sp. GF3 \n+ wheat (planted) \n \n\n\n\nphenanthrene 100 \n200 \n\n\n\n84.8 \n70.2 \n\n\n\n80 \n\n\n\nKaramalidis et \nal. (2010) \n \n\n\n\nPseudomonas \naeruginosa \n\n\n\n16 PAHs \n(mixed) \n\n\n\n58 70 191 \n\n\n\nGottfried et \nal.(2010) \n \n\n\n\nPseudomonas putida \n(ATCC 17484) \n\n\n\nphenanthrene 32 68 10 \n\n\n\nThis study Corynebacterium \nurealyticum \n\n\n\nphenanthrene 100 \n350 \n\n\n\n70 \n27 \n\n\n\n20 \n\n\n\n\n\n\n\nPhenanthrene Degradation in Liquid Culture and Sand Slurry\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013120\n\n\n\nthe bacteria, slowing the bacteria replication rate. This result was also supported \nby Boopathy (2000), whose study found that one of the factors influencing the \nsurvival of bacteria in soil is the concentration of the substrates. \n\n\n\nFor 100 mg kg-1 phenanthrene, the bacteria concentration began to reduce on \nday 4, that is, the day on which is no degradation was observed. On the other hand, \nthe bacteria concentration began to reduce on day 3 for 350 mg kg-1 phenanthrene, \nwhich differed from the no degradation day, that is, day 8. After day 8, low \nbioavailability of phenanthrene may have contributed to the retardation of the \nbiodegradation process. Therefore, both toxicity and bioavailability affected the \ndegradation process at 350 mg kg-1, whereas at 100 mg kg-1 only the bioavailability \nfactor influenced the degradation process.\n\n\n\nFindings on the growth of bacteria from this study contradicted observations \nmade in liquid culture (Zhao et al. 2008; Janbandhu and Fulekar 2011). Janbandhu \nand Fulekar (2011) found that bacteria concentration increased within 14 days of \nexperiment and remained constant towards the end of the experiment. Although \nsamples were spiked with high initial phenanthrene concentrations, no reductions \nwere detected in the bacteria concentration. Findings from Janbandhu and Fulekar \n(2011) were consistent with a study in liquid culture conducted by Zhao et al. \n(2008).\n\n\n\nStatistical Analysis and Biodegradation Kinetic\nStatistical analysis using ANOVA was conducted to establish the correlation \nbetween the degradation of phenanthrene at different initial phenanthrene \nconcentrations, as shown in Table 2a and 2b. From ANOVA, at a 5% level of \nsignificance, it was found that the percentage of phenanthrene remaining for both \nconcentrations in liquid culture was not significant (p>0.05). Thus, the result \n\n\n\nFig.  3: The growth of the bacteria in sand slurry\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n2 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig.  3: The growth of the bacteria in sand slurry \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNor Amani Filzah Mohd-Kamil, Salina Alias, Norzila Othman and Suhaimi Abdul-Talib\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 121\n\n\n\nconfirms that the different concentrations of phenanthrene in liquid cultures from \nthis study did not affect the degradation process. A significant correlation was \nobserved (p<0.05) on percentage of phenanthrene degradation in sand slurry for \nboth concentrations from the ANOVA analysis. This analysis showed that the \ndegradation of phenanthrene by C. urealyticum in sand slurry was influenced by \nthe initial phenanthrene concentration. \n\n\n\nIn this study, the corelation between phenanthrene concentration of 100 \nmg kg-1 and 350 mg kg-1 to the bacteria concentration in sand slurry was also \nevaluated using ANOVA, as shown in Table 3a and 3b. At 5% level of significance, \nconcentrations of phenanthrene and bacteria for both concentrations were found \nto be significant (p<0.05). As a result, it can be stated that bacteria concentration \nhad influenced the degradation of phenanthrene. \n\n\n\nExperiments in liquid culture fitted well to first order rate model (R2\n100 mg L\n\n\n\n-1= \n0.97 and R2\n\n\n\n350 mg L\n-1 = 0.99). Fig. 4 shows the plots of biodegradation data fit to the \n\n\n\nfirst-order kinetics model. First-order rate constant for low initial concentration, \n100 mg L-1 (k = 0.118d-1) was higher compared to high initial concentration, 350 \nmg L-1 (k= 0.092d-1). The half-life values for low initial concentration, 100 mg \nL-1 (t1/2 = 5.87 day) were significantly shorter than high initial concentration, 350 \nmg L-1 (t1/2 = 7.53 day). Thus, more rapid degradation occurred at 100 mg L-1 \n\n\n\ncompared to 350 mg L-1. \nExperiments in sand slurry were fitted well to first order rate model in the \n\n\n\nphase 1 with R2\n100 mg kg\n\n\n\n-1= 0.95 and R2350 mg kg-1= 0.98, as shown in Fig. 5. For \nphases 2 and 3, degradation of phenanthrene in sand slurry was very slow, with \nalmost no degradation occurring, and this trend did not fit any law on rate reaction. \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n1 \n\n\n\n\n\n\n\nTABLE 2a \nANOVA analysis on the effect of different phenanthrene concentrations on the \n\n\n\nbiodegradation process in liquid cultures \n \n\n\n\nSource of variation SS df MS F P-value F crit \nBetween groups 69.97178 1 69.97178 0.077671 0.782866 4.259677 \nWithin groups 21620.92 24 900.8715    \n       \nTotal 21690.89 25     \n*Groups: 350 mg L-1 and 100 mg L-1 \n\n\n\n\n\n\n\nTABLE 2b \nANOVA analysis on the effect of different phenanthrene concentrations on the  \n\n\n\nbiodegradation process in sand slurry \n \n\n\n\nSource of variation SS df MS F P-value F crit \nBetween groups 6069.522 1 6069.522 16.10301 0.00051 4.259677 \nWithin groups 9046.043 24 376.9185    \n       \nTotal 15115.56 25     \n*Groups: 350 mg kg-1 and 100 mg kg-1 \n \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n1 \n\n\n\n\n\n\n\nTABLE 2a \nANOVA analysis on the effect of different phenanthrene concentrations on the \n\n\n\nbiodegradation process in liquid cultures \n \n\n\n\nSource of variation SS df MS F P-value F crit \nBetween groups 69.97178 1 69.97178 0.077671 0.782866 4.259677 \nWithin groups 21620.92 24 900.8715    \n       \nTotal 21690.89 25     \n*Groups: 350 mg L-1 and 100 mg L-1 \n\n\n\n\n\n\n\nTABLE 2b \nANOVA analysis on the effect of different phenanthrene concentrations on the  \n\n\n\nbiodegradation process in sand slurry \n \n\n\n\nSource of variation SS df MS F P-value F crit \nBetween groups 6069.522 1 6069.522 16.10301 0.00051 4.259677 \nWithin groups 9046.043 24 376.9185    \n       \nTotal 15115.56 25     \n*Groups: 350 mg kg-1 and 100 mg kg-1 \n \n\n\n\nTABLE 2a\nANOVA analysis on the effect of different phenanthrene concentrations on the \n\n\n\nbiodegradation process in liquid cultures\n\n\n\nTABLE 2b\nANOVA analysis on the effect of different phenanthrene concentrations on the  \n\n\n\nbiodegradation process in sand slurry\n\n\n\nPhenanthrene Degradation in Liquid Culture and Sand Slurry\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013122\n\n\n\nTABLE 3a\nANOVA analysis on the effect of bacteria concentration grown in 100 mg kg-1  \n\n\n\nphenanthrene to biodegradation process in sand slurry\n\n\n\nTABLE 3b\nANOVA analysis on the effect of bacteria concentration grown in 350 mg kg-1 \n\n\n\nphenanthrene to biodegradation process in sand slurry.\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n1 \n\n\n\n\n\n\n\nTABLE 2a \nANOVA analysis on the effect of different phenanthrene concentrations on the \n\n\n\nbiodegradation process in liquid cultures \n \n\n\n\nSource of variation SS df MS F P-value F crit \nBetween groups 69.97178 1 69.97178 0.077671 0.782866 4.259677 \nWithin groups 21620.92 24 900.8715    \n       \nTotal 21690.89 25     \n*Groups: 350 mg L-1 and 100 mg L-1 \n\n\n\n\n\n\n\nTABLE 2b \nANOVA analysis on the effect of different phenanthrene concentrations on the  \n\n\n\nbiodegradation process in sand slurry \n \n\n\n\nSource of variation SS df MS F P-value F crit \nBetween groups 6069.522 1 6069.522 16.10301 0.00051 4.259677 \nWithin groups 9046.043 24 376.9185    \n       \nTotal 15115.56 25     \n*Groups: 350 mg kg-1 and 100 mg kg-1 \n \n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n1 \n\n\n\n\n\n\n\nTABLE 2a \nANOVA analysis on the effect of different phenanthrene concentrations on the \n\n\n\nbiodegradation process in liquid cultures \n \n\n\n\nSource of variation SS df MS F P-value F crit \nBetween groups 69.97178 1 69.97178 0.077671 0.782866 4.259677 \nWithin groups 21620.92 24 900.8715    \n       \nTotal 21690.89 25     \n*Groups: 350 mg L-1 and 100 mg L-1 \n\n\n\n\n\n\n\nTABLE 2b \nANOVA analysis on the effect of different phenanthrene concentrations on the  \n\n\n\nbiodegradation process in sand slurry \n \n\n\n\nSource of variation SS df MS F P-value F crit \nBetween groups 6069.522 1 6069.522 16.10301 0.00051 4.259677 \nWithin groups 9046.043 24 376.9185    \n       \nTotal 15115.56 25     \n*Groups: 350 mg kg-1 and 100 mg kg-1 \n \n\n\n\nFig. 4: First order biodegradation kinetic \nin liquid culture. Data plotted are mean of \n\n\n\nthree replicates; error bars \nrepresent SD (\u00b13%)\n\n\n\nFig. 5: First order biodegradation kinetic \nin sand slurry. Data plotted are \n\n\n\nmean of three replicates; error bars \nrepresent SD (\u00b16%)\n\n\n\n                                                                                                       ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                 Malaysian Society of Soil Science \n \n\n\n\n3 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 4: First order biodegradation \nkinetic in liquid culture. Data \n\n\n\nplotted are mean of three \nreplicates; error bars represent SD \n\n\n\n(\u00b13%) \n \n\n\n\nFig. 5: First order biodegradation \nkinetic in sand slurry. Data plotted \nare mean of three replicates; error \n\n\n\nbars represent SD (\u00b16%) \n \n\n\n\nNor Amani Filzah Mohd-Kamil, Salina Alias, Norzila Othman and Suhaimi Abdul-Talib\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 123\n\n\n\nThe reaction rate constant in sand slurry at 100 mg kg-1 (k = 0.184 d-1) was higher \ncompared to 350 mg kg-1 (k = 0.046 d-1), as similarly observed in liquid culture. \nThe phenanthrene was reduced to half of its initial concentration on day 3.76 for \n100 mg kg-1. Meanwhile, at 350 mg kg-1 of initial phenanthrene concentration, \nthe bacteria was unable to degrade half of its initial concentration. Therefore, the \nhalf-life of 350 mg kg-1 phenanthrene cannot be determined by using first order \nrate model. \n\n\n\nThe degradation of phenanthrene in liquid culture fitted well with first-order \nrate model throughout the experiments. This was consistent with the findings made \nby Okpokwasili and Nweke (2005) and Kwon et al. (2009). Meanwhile, studies \nconducted in solid media such as in sediment slurry (Chen et al., 2008) and PAHs \ncontaminated soil (Yang et al. 2011) were consistent with the results on sand \nslurry from this study. The first order fitted well only for a certain period, such as \n24 hours by Chen et al. (2008) and 8 days by Yang et al. (2011). Subsequently, the \ndegradation became very slow or no degradation was detected and in this period, \nit did not fit with the first order. \n\n\n\nCONCLUSIONS\nAs a conclusion, the performance of C. urealyticum was better in liquid culture \ncompared to sand slurry. It was observed that phenanthrene degradation at high \ninitial concentration was lower compared to low initial concentration in both \nmedia. Degradation trends in both media established in this study were consistent \nwith those reported in the literature. Complete degradation was observed in \nliquid culture, whereas in solid media, this was not the case. The bacteria \nwere unable to survive longer in sand slurry, and this affected the degradation \nprocess. Percentages of phenanthrene remaining for both initial concentrations \nin liquid culture were not significant. This is in contrast with observations in \nsand slurry where the remaining phenanthrene was significantly higher. Bacteria \nconcentration had influenced the degradation of phenanthrene in sand slurry at \n5% level of significance. Phenanthrene degradation in liquid culture fitted well to \nthe first-order rate model throughout the experiment, whereas in sand slurry, the \nfirst-order rate model only fitted well with phase 1.\n\n\n\nACKNOWLEDGEMENTS\nFinancial support from the Ministry of Higher Education, Malaysia under the \nFundamental Research Grant Scheme (FRGS) and Long-Term Research Grant \nScheme (LRGS) is gratefully acknowledged. 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"\n\n\uf0d7\uf0cd\uf0cd\uf0d2\uf0e6\uf020\uf0ef\uf0ed\uf0e7\uf0ec\uf0f3\uf0e9\uf0e7\uf0f0\uf0f0\n\n\n\n\uf0db\uf0b2\uf0b8\uf0bf\uf0b2\uf0bd\uf0b7\uf0b2\uf0b9\uf020\uf0ce\uf0b7\uf0bd\uf0bb\uf020\uf0db\uf0ad\uf0ac\uf0bf\uf0be\uf0b4\uf0b7\uf0ad\uf0b8\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b7\uf0b2\uf020\uf0df\uf0b2\uf0bf\uf0bb\uf0ae\uf0b1\uf0be\uf0b7\uf0bd\uf020\uf0dc\uf0b7\uf0ae\uf0bb\uf0bd\uf0ac\uf020\uf0cd\uf0bb\uf0bb\uf0bc\uf0b7\uf0b2\uf0b9\uf020\n\n\n\n\uf0cc\uf0b8\uf0ae\uf0b1\uf0ab\uf0b9\uf0b8\uf020\uf0dd\uf0b1\uf0b2\uf0ac\uf0ae\uf0b1\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0c9\uf0bb\uf0bb\uf0bc\uf0a7\uf020\uf0ce\uf0b7\uf0bd\uf0bb\n\n\n\n\uf0ca\uf0f2\uf0d6\uf0bb\uf0a7\uf0bf\uf0b2\uf0b2\uf0a7\uf0ef\uf0f6\uf0f4\uf020\uf0cd\uf0f2\uf0ce\uf0f2\uf020\uf0cd\uf0a7\uf0bb\uf0bc\uf020\uf0d1\uf0b3\uf0bf\uf0ae\uf0ee\uf0f4\uf020\uf0d3\uf0f2\uf020\uf0df\uf0a6\uf0b3\uf0b7\uf0ed\uf020\uf020\uf0fa\uf020\uf0df\uf0f2\uf0cd\uf0f2\uf020\uf0d6\uf0ab\uf0ae\uf0bf\uf0b7\uf0b3\uf0b7\uf0ee\n\n\n\n\uf0ef\uf0da\uf0b1\uf0ae\uf0bb\uf0ad\uf0ac\uf020\uf0ce\uf0bb\uf0ad\uf0bb\uf0bf\uf0ae\uf0bd\uf0b8\uf020\uf0d7\uf0b2\uf0ad\uf0ac\uf0b7\uf0ac\uf0ab\uf0ac\uf0bb\uf020\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf020\uf0f8\uf0da\uf0ce\uf0d7\uf0d3\uf0f7\uf0f4\uf020\uf0eb\uf0ee\uf0ef\uf0f0\uf0e7\uf020\uf0d5\uf0bb\uf0b0\uf0b1\uf0b2\uf0b9\uf0f4\uf020\uf0cd\uf0bb\uf0b4\uf0bf\uf0b2\uf0b9\uf0b1\uf0ae\uf020\n\n\n\n\uf0ee\uf0da\uf0bf\uf0bd\uf0ab\uf0b4\uf0ac\uf0a7\uf020\uf0b1\uf0ba\uf020\uf0df\uf0b9\uf0ae\uf0b7\uf0bd\uf0ab\uf0b4\uf0ac\uf0ab\uf0ae\uf0bb\uf0f4\uf020\uf0cb\uf0b2\uf0b7\uf0aa\uf0bb\uf0ae\uf0ad\uf0b7\uf0ac\uf0b7\uf020\uf0d0\uf0ab\uf0ac\uf0ae\uf0bf\uf020\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0f4\uf020\uf0ec\uf0ed\uf0ec\uf0f0\uf0f0\uf020\uf0cb\uf0d0\uf0d3\uf020\uf020\uf0cd\uf0bb\uf0ae\uf0bc\uf0bf\uf0b2\uf0b9\uf0f4\uf020\n\n\n\n\uf0cd\uf0bb\uf0b4\uf0bf\uf0b2\uf0b9\uf0b1\uf0ae\uf0f4\uf020\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\n\n\n\n\uf0ed\uf0ce\uf0b7\uf0bd\uf0bb\uf020\uf0ce\uf0bb\uf0ad\uf0bb\uf0bf\uf0ae\uf0bd\uf0b8\uf020\uf0dc\uf0b7\uf0aa\uf0b7\uf0ad\uf0b7\uf0b1\uf0b2\uf0f4\uf020\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf020\uf0df\uf0b9\uf0ae\uf0b7\uf0bd\uf0ab\uf0b4\uf0ac\uf0ab\uf0ae\uf0bf\uf0b4\uf020\uf0ce\uf0bb\uf0ad\uf0bb\uf0bf\uf0ae\uf0bd\uf0b8\uf020\uf0fa\uf020\uf0dc\uf0bb\uf0aa\uf0bb\uf0b4\uf0b1\uf0b0\uf0b3\uf0bb\uf0b2\uf0ac\uf020\n\n\n\n\uf0d0\uf0b7\uf0b2\uf0bf\uf0b2\uf0b9\uf0f4\uf020\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\n\n\n\n\uf0df\uf0de\uf0cd\uf0cc\uf0ce\uf0df\uf0dd\uf0cc\n\n\n\n\uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0ad\uf0bb\uf0bb\uf0bc\uf0ad\uf020\uf0b1\uf0b2\uf020\uf0bd\uf0ab\uf0b4\uf0ac\uf0b7\uf0aa\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0a9\uf0bb\uf0bb\uf0bc\uf0a7\uf020\uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0bb\uf0ad\uf0ac\uf0bf\uf0be\uf0b4\uf0b7\uf0ad\uf0b8\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b7\uf0b2\uf020\uf0bc\uf0b7\uf0ae\uf0bb\uf0bd\uf0ac\uf020\uf0ad\uf0bb\uf0bb\uf0bc\uf0b7\uf0b2\uf0b9\uf020\uf0b0\uf0b4\uf0bf\uf0b2\uf0ac\uf0b7\uf0b2\uf0b9\uf020\n\n\n\n\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\uf0f2\uf020\uf0cc\uf0ae\uf0bb\uf0bf\uf0ac\uf0b3\uf0bb\uf0b2\uf0ac\uf0ad\uf020\uf0bf\uf0b0\uf0b0\uf0b4\uf0b7\uf0bb\uf0bc\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0a9\uf0bb\uf0ac\uf020\uf0ad\uf0bb\uf0bb\uf0bc\uf0b7\uf0b2\uf0b9\uf020\uf0f8\uf0cc\uf0ef\uf0f7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0a9\uf0bf\uf0ac\uf0bb\uf0ae\uf020\uf0f8\uf0bf\uf0b2\uf0bf\uf0bb\uf0ae\uf0b1\uf0be\uf0b7\uf0bd\uf0f7\uf020\uf0ad\uf0bb\uf0bb\uf0bc\uf0b7\uf0b2\uf0b9\uf020\n\n\n\n\uf0aa\uf0b7\uf0bf\uf0be\uf0b7\uf0b4\uf0b7\uf0ac\uf0a7\uf020\uf0b1\uf0ba\uf020\uf0bd\uf0ab\uf0b4\uf0ac\uf0b7\uf0aa\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0bd\uf0b1\uf0b3\uf0b0\uf0bf\uf0ae\uf0bb\uf0bc\uf020\uf0ac\uf0b1\uf020\uf0a9\uf0bb\uf0bb\uf0bc\uf0a7\uf020\uf0ae\uf0b7\uf0bd\uf0bb\uf0f2\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0a9\uf0bb\uf0bb\uf0bc\uf0a7\uf020\uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0b0\uf0b1\uf0b0\uf0ab\uf0b4\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0a9\uf0bf\uf0ad\uf020\n\n\n\n\uf0b0\uf0ae\uf0bb\uf0f3\uf0ac\uf0ae\uf0bb\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0ad\uf0bb\uf0bb\uf0bc\uf0ad\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0bf\uf0b2\uf0bf\uf0bb\uf0ae\uf0b1\uf0be\uf0b7\uf0bd\uf020\uf0bc\uf0b7\uf0ae\uf0bb\uf0bd\uf0ac\uf020\uf0ad\uf0bb\uf0bb\uf0bc\uf0b7\uf0b2\uf0b9\uf020 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\uf0cd\uf0bb\uf0bb\uf0bc\uf020\n\n\n\n\uf0cd\uf0bd\uf0b7\uf0f2\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0cc\uf0bb\uf0bd\uf0b8\uf0b2\uf0b1\uf0b4\uf0f2\uf020\uf0ed\uf0f0\n\n\n\n\uf0d7\uf0b2\n\n\n\n\uf0ad\uf0bb\uf0bb\uf0bc\uf0b4\uf0b7\uf0b2\uf0b9\uf0ad\uf020\uf0b7\uf0b2\uf020\uf0a9\uf0bf\uf0ac\uf0bb\uf0ae\uf020\uf0bf\uf0ac\uf020\uf0bd\uf0b1\uf0b2\uf0ac\uf0ae\uf0b1\uf0b4\uf0b4\uf0bb\uf0bc\uf020\uf0b1\uf0a8\uf0a7\uf0b9\uf0bb\uf0b2\uf020\uf0b4\uf0bb\uf0aa\uf0bb\uf0b4\uf0ad\uf0f2\uf020\uf0df\uf0b9\uf0ae\uf0b1\uf0b2\uf0b1\uf0b3\uf0a7\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0e9\uf0ed\n\n\n\n\uf0d3\uf0ab\uf0bc\uf0bf\uf020\uf0df\uf0b9\uf0ae\uf0b7\uf0bd\uf0ab\uf0b4\uf0ac\uf0ab\uf0ae\uf0bf\uf0b4\uf020\uf0dc\uf0bb\uf0aa\uf0bb\uf0b4\uf0b1\uf0b0\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0df\uf0ab\uf0ac\uf0b8\uf0b1\uf0ae\uf0b7\uf0ac\uf0a7\uf0f2\uf020\uf0ee\uf0f0\uf0ee\uf020\uf0b0\uf0b0\uf0f2\n\n\n\n\uf0c9\uf0b7\uf0b4\uf0bc\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0a9\uf0bb\uf0bb\uf0bc\uf0a7\uf020\uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0b7\uf0b2\uf020\uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0bb\uf0bd\uf0b1\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\uf0ad\uf020\uf0b7\uf0b2\uf020\uf0df\uf0ad\uf0b7\uf0bf\uf0f3\uf020\uf0df\uf020\n\n\n\n\uf0ae\uf0bb\uf0aa\uf0b7\uf0bb\uf0a9\uf0f2\uf020\uf0d7\uf0b2\uf0ac\uf0bb\uf0ae\uf0b2\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0bf\uf0b4\uf020\uf0ce\uf0b7\uf0bd\uf0bb\uf020\uf0ce\uf0bb\uf0ad\uf0bb\uf0bf\uf0ae\uf0bd\uf0b8\uf020\uf0d7\uf0b2\uf0ad\uf0ac\uf0b7\uf0ac\uf0ab\uf0ac\uf0bb\uf020\uf0f8\uf0d7\uf0ce\uf0ce\uf0d7\uf0f7\uf0f2\n\n\n\n\uf0d6\uf0bf\uf0b0\uf0bf\uf0b2\uf020\uf0d7\uf0b2\uf0ac\uf0bb\uf0ae\uf0b2\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0bf\uf0b4\uf020\n\n\n\n\uf0ce\uf0bb\uf0ad\uf0bb\uf0bf\uf0ae\uf0bd\uf0b8\uf020\uf0dd\uf0bb\uf0b2\uf0ac\uf0bb\uf0ae\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0df\uf0b9\uf0ae\uf0b7\uf0bd\uf0ab\uf0b4\uf0ac\uf0ab\uf0ae\uf0bf\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf0ad\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf0f2\uf020\uf0ee\n\n\n\n\n\n"
"\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nFractionation Study of Heavy Metals in Sewage Sludge and Soils Applied with Sewage Sludge\n\n\n\n81\n\n\n\nISSN: 1394-7990\nMalaysian Society of Soil ScienceMalaysian Journal of Soil Science Vol.11 : 81-95 (2007)\n\n\n\nDomestic Sewage Sludge Application to an Acid Tropical\nSoil: Part III. Fractionation Study of Heavy Metals in Sewage\n\n\n\nSludge and Soils Applied with Sewage Sludge\n\n\n\nA. Rosazlin1, I. Che Fauziah2*, A.B. Rosenani2 &  S. Zauyah2\n\n\n\n1Forestry Division, Forest Research Institute of Malaysia (FRIM)\n52109 Kepong, Selangor\n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra\nMalaysia, 43400 UPM, Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nSoil  fractionation studies of heavy metals can provide insight into their\nsolubility and chemical reactivity in terms of labile and non-labile pools of\nthese metals. The fractionation of cadmium (Cd), copper (Cu), nickel (Ni),\nlead (Pb) and zinc (Zn) in domestic sewage sludge, soil, soil applied with\nNH4SO4 and soil applied with sewage sludge was studied after the first and\nthird maize cycles. The second cycle did not give significant yield results\nbecause of the dry period during this cycle. The correlation between differ-\nent forms of heavy metals in the soil and content in maize grain was also\ninvestigated. Fractionation of heavy metals in sewage sludge showed that\nmost soil metals were associated with the less soluble or non-labile soil\nmoieties (carbonate, Fe-Mn oxides, organic and residual fraction).  The domi-\nnant form of all heavy metals was the residual form (non-phytoavailable\nform) except for Cu. Leaving the residual fraction aside, Cd and Pb  were\ndominant in exchangeable (labile pool) form, Ni in carbonate form and Zn in\nFe-Mn oxide form. For the untreated and treated soils, the residual fraction\nwas also the dominant fraction except for Cd and Pb. The organic form is the\ndominant form for Cu in sludge treated soil. In general, the percentage of\nwater soluble content was less than 5%. Also, in general, there was no\nsignificant difference between the different metal fractions of the inorganic\nfertiliser treatment compared to the control, except for exchangeable Pb and\nZn associated with Fe-Mn oxide fraction. The addition of sewage sludge\ntended to shift the solid phase forms of the metals away from the residual to\nthe Fe-Mn oxide form. Significant correlations were only obtained between\nCd content in maize grain and the organic forms in soil (n=30 , r =0.378 , and\np <0.05), Ni content in grain with total metal in the soil (n=30, r= 0.406, p<0.05)\nand between Cu content in maize grain and the carbonate, Fe-Mn oxide and\norganic forms in soils (n=30, r= 0.475, p<0.01; n=30, r=0.539, p<0.01; and,\nn=30, r=0.545, p<0.01, respectively).\n\n\n\nKeywords: Sewage sludge, fractionation study, geochemical forms, labile\nand non-labile  pool, correlation study\n\n\n\n* Corresponding author: Email:fauziah@agri.upm.edu.my\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM81\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200782\n\n\n\nA. Rosazlin, I. Che Fauziah, A.R. Rosenani & S. Zauyah\n\n\n\nINTRODUCTION\nHeavy metals in soils may be present in many forms. The fractionation of heavy\nmetals  in sludge-treated soils is important in order to evaluate their behaviour in\nthe environment. Information on the distribution of these different chemical forms\nof  heavy  metals  in soils can provide insight into their solubility, chemical\nreactivity, and the fractions that can serve as a pool of available forms for plant\nuptake (Stumm and Morgan 1981). However, knowledge of the total content of\nheavy metals present in soil only provides limited information on the mobility and\navailability.  Identifying the chemical forms in which the metals are retained in\nsoil is helpful to predict their potential mobility to water sources, plant availability\nand the amount of metal cycling through the food chain. Consequently, it is\nnecessary to evaluate the ecotoxicological potential of these elements in a more\naccurate way. Compared to single extractants, multi-step extraction methods are\nfound to furnish relatively more detailed information about heavy metals specia-\ntion in soils.\n\n\n\nThe application of sewage sludge to soils may alter the geochemical forms\nor metal fractions, which, in turn, may affect its availability to plants. Sewage\nsludge, with its organically bound nutrients and large fraction of organic matter,\nbuilds soil integrity and feeds plants over a longer period of time. Hence, this\nstudy was conducted to (i) investigate the different forms of Cd, Cu, Ni, Pb and\nZn in sewage sludge using different chemical extractants; (ii) investigate whether\nthere are any shifts in the metal fractions in soil applied with sewage sludge after\nthe first and third maize cycles using different chemical extractants; and (iii)\ncorrelate between different forms of heavy metals in the soil with uptake by\nsweet maize.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSewage Sludge and Soil Samples\nSewage sludge was collected from Indah Water Konsortium (IWK) Sdn. Bhd. treat-\nment plant at Taman Sri Gombak, Selangor. The sewage sludge was applied before\nplanting of the first, second and third crop cycles. The second cycle was excluded in\nthis study because of crop failure due to very dry weather. Soil samples used in this\nstudy were taken from 0-20 cm depth after maize harvest, from the plots of three\ntreatments: T1- inorganic fertiliser (140 kg N/ha ammonium sulphate; T2- no inor-\nganic fertiliser and sludge application; and T3 - 28 t ha\u20131 sewage sludge (560 kg N/\nha) with five replicates (for details, please refer to Part I of the paper ( Rosazlin et al.\n2005).\n\n\n\nPreparation of Soil Samples\nIn the laboratory, the soil samples were air-dried at room temperature. Each soil\nsample was sieved through a 2-mm sieve. About 10 g of each sample was finely\nground to pass through the <250-mm sieve. The heavy metal concentrations\nwere determined using the atomic absorption spectrophotometer (AAS).\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM82\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nFractionation Study of Heavy Metals in Sewage Sludge and Soils Applied with Sewage Sludge\n\n\n\n83\n\n\n\nChemical Analyses\nThe extraction procedure was carried out using 30 mL centrifuge tubes. The\ntubes were thoroughly cleaned and acid-washed (10% HCl) prior to use. The\nmethod of the sequential extraction procedure used in this study was that of\nTessier et al. (1979) and followed by the modified version of Yang and Kimura\n(1995) and Chlopecka et al. (1996) as put forward by Salas et al. (1998). The\nfive heavy metals (Cd, Cu, Ni, Pb and Zn) were partitioned into six operationally\ndefined fractions. The forms of heavy metals are water soluble, exchangeable,\ncarbonate bound, Fe-Mn oxides bound, organically bound and the residual form.\nWater soluble and exchangeable fraction is the plant available fraction.  Carbon-\nate, Fe-Mn oxides and organic fraction are considered as the \u2018inactive\u2019 and \u2018non-\nlabile\u2019 pool, which will become available if soil condition changes.  The residual\nfraction is part of the clay matrix and will not take part in the chemical reaction\nin the soil.  The reagents employed and chemical forms solubilised with 5 g\nsample weights are listed below:\n\n\n\n1. Water Soluble Fraction - extracted by adding 20 mL of distilled water to the\ntube containing 5 g of air-dried soil and then shaken for 2 hours at room\ntemperature.\n\n\n\n2. Exchangeable Fraction - extracted with 20 mL of 1.0 M magnesium chlo-\nride, followed by shaking for 2 hours at room temperature.\n\n\n\n3. Carbonate Fraction - extracted with adding 20 mL of 1.0 M sodium acetate,\nadjusted to pH 5.0 using acetic acid, followed by shaking for 2 hours at\nroom temperature.\n\n\n\n4. Fe-Mn Oxides Fraction - residue from the carbonate fraction extraction was\nextracted using 20 mL of 0.04 M hydroxylamine hydrochloride in 25% ace-\ntic acid (v/v) for 8 hours at 65oC with occasional agitation.\n\n\n\n5. Organic Fraction - residue obtained from the extraction of the Fe-Mn oxides\nfraction was treated with 2 mL of 0.02 M nitric acid and 5 mL of 30%\nhydrogen peroxide. Then, the solution was adjusted to pH 2 with nitric acid.\nThe mixture was heated up to 65oC for 5 hours with occasional agitation.\nThree mL aliquot of 30% hydrogen peroxide was added and the mixture was\nadjusted to pH 2 with nitric acid. Then, the sample was heated again to 65oC\nfor 7 hours with agitation.  After cooling, 5 mL of 3.2 M ammonium acetate\nin 20% (v/v) nitric acid was added and the resulting solution was diluted to\n20 mL with distilled water and agitated continuously for 30 minutes at room\ntemperature.\n\n\n\n6. Residual Fraction - final residue was transferred into a 250 mL beaker. Then,\n50 mL aqua-regia (3:1 HCl : HNO3) was added and left to react overnight\nafter which the mixture was heated at 90\u00b0C for 2 hours, then made up to\n100 mL.\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM83\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200784\n\n\n\nA. Rosazlin, I. Che Fauziah, A.R. Rosenani & S. Zauyah\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nForms of Heavy Metals in Sewage Sludge\nAll the domestic sewage sludge contained varying amounts of Cd, Pb, Zn, Ni and\nCu. To understand the chemical form of the various metals in municipal sewage\nsludge, one must first determine the form these metals exist as after wastewater\ntreatment processes. Each metal may be distributed between the soluble and\nsolid phase depending upon which complex equilibrium controls the wastewater\ncomposition.  However, in most cases, Cd, Pb, Zn, Ni and Cu have been found\nto be predominantly associated with the solid phase in wastewater influents (Page\net al. 1981). The distribution of these metals among the various forms is depen-\ndent on properties of the specific metal and the characteristics of the sludge. The\ntotal metal concentrations in the Taman Sri Gombak sewage sludge sample were\n2.68 mg kg-1 Cd, 73.29 mg kg-1 Pb,  2300 mg kg-1 Zn, 23.61 mg kg-1 Ni and 116\nmg kg-1 Cu.  However, the total concentrations of heavy metals in sewage sludge\ntreated soil do not indicate the amounts that are available for plant uptake.\n\n\n\nOverall, a higher percentage of all the heavy metals, except for Cu was\nobtained in the residual forms (Fig. 1).  Sequential chemical fractionation of the\nsludge sample showed that extractable Cd occurred mainly in the residual form\n(34%), followed by the exchangeable form (24%), as well as in the carbonate,\nFe-Mn oxides and organic form. Similarly, Pb occurred mainly in the residual\nform (84 %), followed by exchangeable form (9%).  According to Silviera and\nSommers (1977), higher exchangeable Pb was obtained in air-dried sewage sludge\nthan in wet sewage sludge. High Zn content in the residual form (43%) was\nobtained and followed by Fe-Mn oxides (27%).  As most of the Zn was adsorbed\non the surfaces of hydrous oxides of Fe and Al, they would probably co-precipi-\ntate with these compounds during sewage treatment (Sommers, 1977).  Nickel\noccurred in the residual form (55%), followed by carbonate form (22%).  Cop-\nper (57%) occurred predominantly in the organic form. This is in agreement\n\n\n\nFig. 1:  Forms of Ni, Cd, Zn, Pb and Cu in sewage sludge\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM84\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nFractionation Study of Heavy Metals in Sewage Sludge and Soils Applied with Sewage Sludge\n\n\n\n85\n\n\n\nwith the previous findings (Emmerich et al. 1982) where the major forms of Cu\nextracted were in the organically bound form. The order of Cu contents in the\nfraction is organic>residual>Fe-Mn oxide.  It is similar to that reported by\nSchalscha et al. (1981) and is consistent with the known affinity of Cu for\norganic matter ligands. Generally, the percentage of water-soluble metals in sludge\nis approximately proportional to the total amount present (Fig. 1). This trend is\nespecially apparent for Zn (7%), Cu (2 %) and Ni (2%) in the sludge studied.\nWater-soluble form of Cd and Pb made up only 1% of the total metal present as\nthey might be attached to negatively-charged exchange sites or through relatively\nweak adsorption, and it is anticipated that this fraction would tend to be propor-\ntional to the total metal content present.  Thus, the distribution of the metals in\nthe sludge is variable, and this influences their behaviour after application to the\nsoil.\n\n\n\nForms of Heavy Metals in Soil, Soil Applied with NH4SO4, and Soil Applied with\nSewage Sludge\nTable 1 shows the mean total heavy metals under investigation for the different\ntreatments after the first and third maize cycles. Application of sewage sludge\nresulted in an increase in metal concentrations of the soil. The mean pH of the\nsoil for the control treatment was 5.37 after the first maize cycle, increasing to\n5.79 in the third cycle. For the soil applied with NH4SO4, the mean pH was 4.96\nafter the first cycle, remaining the same after the third cycle. Application of\nNH4SO4 resulted in increased soil acidity. This might have an effect on the water\n\n\n\nTABLE 1\nMean total concentration of heavy metals (n=5) for the different\n\n\n\ntreatments after the first and third maize cycle\n\n\n\nTreatment Cycle Cd Cu Ni Pb Zn\n\n\n\nmg kg-1\n\n\n\nT1 \u2013 Inorganic  After 1st 0.60 7.96 9.20 18.04 12.44\nfertiliser ( NH4SO4) maize cycle\n\n\n\nAfter 3rd 1.24 6.56 10.04 21.40 15.00\nmaize cycle\n\n\n\nT2-  control After 1st 0.64 7.82 9.76 20.52 13.68\nmaize cycle\n\n\n\nAfter 3rd 1.24 7.04 10.48 21.40 14.65\nmaize cycle\n\n\n\nT3- 560kg N/ha\nsewage sludge After 1st 0.72 7.72 8.60 18.28 16.24\n\n\n\n maize cycle\n\n\n\nAfter 3rd 2.19 9.52 11.12 24.04 33.00\nmaize cycle\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM85\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200786\n\n\n\nA. Rosazlin, I. Che Fauziah, A.R. Rosenani & S. Zauyah\n\n\n\nsoluble and exchangeable fraction, which can be seen for Pb. There also seemed\nto be higher amounts of Zn associated with Fe-Mn oxide fraction in the inorganic\nfertiliser treatment compared to the control. Otherwise, in general, there is no\nsignificant difference between the different metal fractions of the inorganic\nfertiliser treatment compared to the control. Application of sewage sludge re-\nsulted in a slight increase in soil pH from 5.68 (first cycle) to 5.86 (third cycle).\n\n\n\nCadmium\nFor Cd, the increased application rates of sewage sludge increased its content in\nthe soil in both the water soluble and exchangeable forms, whereas the residual\nforms had decreased (Fig. 2a). However, there was no difference in the ex-\nchangeable form of the first cycle (38%) and third cycle (37 %) treated with\nsewage sludge. According to Brummer (1986), the exchangeable form can be\ncategorised as mobile and can be used to estimate the total available heavy metals.\n\n\n\nCd content was also high in the carbonate form.  There was a significant\ndifference (p<0.05) between the two cycles treated with sewage sludge. The\nusage of sewage sludge may have influenced the increase in Cd associated with\nthe carbonate fraction in the soil. However, no significant differences between\nthe treatments were found in the percentage of Cd associated with the carbonate\nfraction and the range was 21 \u2013 24 %. Chlopecka (1993) showed that Cd added\nto soil as carbonate is relatively mobile in acidic conditions and within a few\nyears or less, may change to the exchangeable form.\n\n\n\nLowest Cd content was found in the organic form.  As Cd is loosely bound\nto organic matter, it could have been extracted at the first level of extraction.\nThis is consistent with the low adsorption constant of Cd to organic matter and\nprovides evidence that Cd does not appear to form strong organic complexes\n(Sposito et al. 1982; Keefer et al. 1984). Salas et al. (1998) reported a low\npresence of Cd in the soil organic matter of Tanashi series which was treated\nwith sewage sludge. In an acid soil, organic matter and sesquioxides are impor-\ntant in controlling the solubility of Cd (Kabata-Pendias and Pendias 2002).\n\n\n\nThe exchangeable Cd was the dominant form in the sewage sludge.  Further-\nmore, Cd was also dominant in exchangeable form in soils applied with sewage\nsludge. More Cd was in the exchangeable form than in the oxidised form, in\nwhich Cd was adsorbed on the surface of the clay and easily released (Hickey\nand Kittrick 1984; Tuin and Tels 1990). There was a significant difference (p<0.05)\nin exchangeable form between the inorganic fertiliser treatment and the control.\nMost of the soluble Cd might have shifted to other fractions and increased the\npercentage of exchangeable Cd.  However, there was no geochemical change\nfrom sludge to soil amended with sludge between the first and third cycles. It\nalso means that Cd is more easily available for plant uptake.\n\n\n\nCopper\nOverall, addition of the sludge increased with significant changes in all forms\nexcept for the residual fractions (Fig. 2b). A higher percentage of Cu in the\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM86\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nFractionation Study of Heavy Metals in Sewage Sludge and Soils Applied with Sewage Sludge\n\n\n\n87\n\n\n\nFig. 2 (a-e): Forms of Cd, Cu, Ni, Pb and Zn  in soil after first  (C1) and\nthird (C3) maize cycles\n\n\n\nT1 -  inorganic fertiliser , T2 \u2013 control ( no fertilizer) , T3 \u2013 28 t ha-1 sewage sludge\n\n\n\n(a) (b)\n\n\n\n(c) (d)\n\n\n\n(e)\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM87\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200788\n\n\n\nA. Rosazlin, I. Che Fauziah, A.R. Rosenani & S. Zauyah\n\n\n\norganic form was obtained in treatment with sewage sludge of both the first\n(43%) and third crop cycle (45%) compared to other forms.  This might be\nattributed to the fact that soluble organics from the decomposition of the sludge\nsolids had a greater affinity for Cu ions. The 2% increase in organic form be-\ntween the crop cycles indicate that the distribution pattern of heavy metals ex-\ntracted by sequential extraction procedure depended on heavy metal fraction-\nation, the qualities and the application rates of sewage sludge.   This is in agree-\nment with previous findings (Salas et al. 1998; Robert et al. 1995) which also\nreported Cu associated with organic fraction to be the dominant form.  Between\nthe crop cycles, there were no difference in water-soluble Cu and no trend was\nfound based on the different treatments. This suggests that these forms of Cu\nhave been removed by plants and/or have leached from the surface soil profile,\nwhile that remaining behind have transformed to the less readily soluble form.\n\n\n\nThe sewage sludge treatment gave the highest concentration of Cu associ-\nated with the organic fraction and also increased after the third cycle.  In the\nthird cycle, there was no significant difference between the inorganic fertiliser\ntreatment and the control.  The contamination of control plots with sewage sludge\nthrough surface run-offs could have contributed to this situation.  The most\nfrequent chemical form of heavy metals in sewage sludge-treated soils was the\norganically bound form (Chang et al. 1984).  However, this study showed that\nthe organically bound form or of heavy metals in soil was relatively low, except\nfor Cu.  This result also shows that the chemical forms of heavy metals may\ndepend on the soil characteristics and experimental conditions.\n\n\n\nAfter the third crop, the water-soluble and exchangeable forms in the soil\ntreated with sewage sludge had decreased.  Water soluble Cu decreased with\ntime from 3 to 2% and 4 to 2% for the exchangeable form. Possible explanations\nfor the decrease in water soluble and exchangeable Cu are (i) formation of stable\ncomplexes between Cu and soil organic matter, (ii) sorption by hydrous oxides\nin soil, and (iii) formation of precipitates (Silviera and Sommers, 1977). Thus,\nthe mobility and bioavailability of Cu may be controlled by the binding of Cu to\nsoluble organic matter (Kabata-Pendias and Pendias 2002).\n\n\n\nCopper in the organic form was dominant in both sewage sludge and soils\ntreated with sewage sludge.  No transformation of Cu in the sludge to soil amended\nwith sludge was obtained between first  and third cycles. The dominant forms of\nCu in both the crop cycles were the residual and organic forms (Fig. 2b). The\nresidual forms played little role in the soil chemical reactions. The residual form\nis the heavy metals bound to mineral lattice and can be considered the inactive\nfraction in terms of chemical processes in the soil.  The residual form is divided\ninto two types: (a) heavy metal found in between the mineral layers; (or b)\nspecifically adsorbed on the edge of the clays\u2019 layers and unable to be extracted\nby reagents (Calvet et al. 1989).  According to Brummer (1986), the organic and\nresidual forms are stable in the soils and not available to plants.\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM88\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nFractionation Study of Heavy Metals in Sewage Sludge and Soils Applied with Sewage Sludge\n\n\n\n89\n\n\n\nNickel\nThe dominant form of nickel is residual, followed by carbonate form, exchange-\nable form, organic form and water-soluble form.  There was significant increase\nin the concentration of the carbonate form and organic forms of Ni in the sludge-\ntreated soils (Fig. 2c).  Ni associated with the carbonate fraction is higher than\nNi associated with the organic fraction.  In terms of the total Ni (sum of all\nfractions), there was a decrease in the percentage of residual Ni but an increase\nin the organic and carbonate fractions as a result of sludge application.  It ap-\npears that Ni carbonate and to some extent organic Ni, were the principal chemi-\ncal forms of Ni that were formed after sludge application.\n\n\n\nThe concentration of exchangeable Ni of sewage sludge treatments in both\nthe first and second treatments, were 8% and 7%, but there was no significant\ndifference compared to the control treatment.  It is possible that the sewage\nsludge did not influence the concentration of exchangeable Ni.  Overall, the\nsewage sludge application had increased all the Ni forms in both the first and\nthird cycles.  Though Ni associated with the carbonate fraction is the dominant\nform, its percentage is not more than 20%.  Nickel in the carbonate form was\ndominant in both the sewage sludge and soils treated with sewage sludge.  Thus,\nthere was no transformation of geochemical form from sludge to soil amended\nwith sludge in the first and third cycles.  This may suggest that liming did not\nstrongly influence the increase of Ni in the carbonate form as sewage sludge\ncontains a high concentration of Ni-CO3.\n\n\n\nLead\nLead was surprisingly dominant in the exchangeable form in all the treatments\nfor both crop cycles.  This was followed by the residual form, Fe-Mn oxide,\ncarbonate form and water soluble form (Fig. 2d).  Normally, in highly weathered\nsoils, Pb can form carbonates and is also incorporated into clay minerals, in Fe\nand Al hydroxides, Mn oxides and in organic matter (Kabata-Pendias and Pendias\n2002). The content of Pb in exchangeable form has exceeded 50 %.  However,\nthere was no significant difference between the sewage sludge treatment com-\npared to the inorganic and control treatment in the first cycle.  In the third  cycle,\nthe organic treatment exhibited the highest exchangeable Pb. There was signifi-\ncant difference between sewage sludge treatment and control. This makes it\ndifficult to explain why Pb extracted from sludge-treated or control soils were\nnot taken up by the maize plants in sufficiently high amounts (for details, refer to\nPart II of the paper (Rosazlin et al. 2006)). Indeed, there is more general evi-\ndence to suggest that Pb is very immobile in the soil-plant pathway (Koeppe\n1981).  Pichtel and Anderson (1996) reported Pb in composted sewage sludge to\nbe dominant in the carbonate form.  Other findings reported Pb to be dominantly\nassociated with the oxide fraction (Sposito et al. 1982; Dudka and Chlopecka\n1990).\n\n\n\nThere was significant difference between the Pb water-soluble form in soil\namended sludge compared to the control and inorganic fertiliser for the first and\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM89\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200790\n\n\n\nA. Rosazlin, I. Che Fauziah, A.R. Rosenani & S. Zauyah\n\n\n\nthird cycles. The results obtained suggest that the presence of Pb did not influ-\nence the forms of Pb in all treatments.  The presence of Pb may not be due to the\nusage of sewage sludge.  A factor such as parent material may have contributed\nto the Pb content in Oxisol or the red soil of the tropics.  This is in agreement\nwith previous studies (Wan Noridah 2000) where soils applied with sewage sludge\nshowed no influence on the forms of Pb. The exchangeable form of Pb was\ndominant in both sewage sludge and soils amended with sewage sludge.  How-\never, no shift in the geochemical form from sludge to soil amended with sludge\nbetween the first  and third cycles was observed in this study.\n\n\n\nZinc\nBased on the results obtained (Fig. 2e), the dominant geochemical form of Zn is\nthe residual form followed by Fe-Mn oxide after the first and third maize cycles.\nThis is in agreement with previous findings (Iyenger et al. 1981).  This may\nindicate the presence of a strong interaction between the metals derived from\nsludge and soil metal components which leached out of the soil. The Fe-Mn\noxide bound Zn was higher than the other metals in the sludge treated plots.\nSeveral researchers also reported higher Zn in the form of Fe-Mn oxide (Tessier\net al. 1979; Hickey and Kittrick et al. 1984; Tuin and Tels 1990; Krishnamurty et\nal. 1995). Zinc was also present in the forms of organic, exchangeable and\nwater-soluble in the order: residual > Fe-Mn oxide > organic > exchangeable >\nwater soluble.\n\n\n\nThe amount of Zn in the form of water-soluble is not more than 1%.  How-\never, the water-soluble Zn in the sewage sludge treatment has increased in both\ncrop cycles.  Lindsay (1972) reported that the solubility of Zn in soil solutions\nincreased 100-fold for each unit decrease in pH. Therefore, a unit decrease in pH\nafter addition of the sludge could be responsible for the increase in water soluble\nZn. Zinc may also have been released from organic complexes during the de-\ncomposition of organic matter in the sludge.\n\n\n\nIn the long-term, Zn in the form of Fe-Mn oxide increased with application\nof sewage sludge.  A 10 % increase of Zn associated with Fe-Mn oxide fraction\nfrom 29% of total Zn after the first crop cycle to 39% after the second  cycle\nwas obtained.  This increase was significant for both inorganic and control treat-\nments.  This shows that sewage sludge addition could greatly affect Zn concen-\ntration in the soil. It could be concluded that pollutant inputs of Zn may have\nbeen in both residual and Fe-Mn Oxide forms. Even though the trend is less clear\ncompared with Cd and Pb, it is possible that mobilised Zn becomes more strongly\nassociated when adsorbed onto Mn (Loganathan et al. 1977) or iron and alu-\nminium hydrous oxide surfaces (Shuman 1977).\n\n\n\nZinc in the Fe-Mn oxide extractable form in the first cycle was 24% in the\ninorganic fertiliser treatment, 20% in the control and 29% in the sewage sludge\ntreated soil. In the third cycle, the percentage was 21% in the soil with the\ninorganic fertiliser, 17% in the control soil and 39% in soil treated with sewage\nsludge. This increase was significant in sewage sludge treated soil. This indi-\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM90\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nFractionation Study of Heavy Metals in Sewage Sludge and Soils Applied with Sewage Sludge\n\n\n\n91\n\n\n\ncates that sewage sludge has a strong influence on Fe-Mn oxide form.  Accord-\ning to Miller et al. (1986), organic matter and oxides of Fe and Mn are the main\ncomponents that hold heavy metals. It appears that Fe-Mn oxide was the domi-\nnant form in both sewage sludge and soils treated with sewage sludge. Results\nshowed a shift from residual form (first cycle) to Fe-Mn oxide form (third cycle)\nfor the soil amended with sludge (Fig. 2e).\n\n\n\nCorrelation Study\nResearch on the possible adverse effects of heavy metal accumulation in agricul-\ntural soils amended with sewage sludge has focused on the available form of\nthese heavy metals in relation to their uptake by plants (Silviera and Sommers\n1977).  The heavy metal contents of sludge constitute less than 1% of the dry\nweight of the sludge solids. But long-term applications may significantly increase\nthe heavy metal contents of soils that could persist for several years (Kirkham\n1975).  Total heavy metals content of plants is influenced by many factors.\n\n\n\nSignificant correlation (p<0.05) was obtained between Cd in grains with the\norganically bound Cd in the soil (Table 2). No significant correlation was ob-\ntained between Cd in other forms with content in grains of maize. There was no\nsignificant difference between all forms of Zn and the content of maize crops.\nThe types of extracting solutions might have contributed to this correlation.\nPrevious findings suggest apositive correlation between Zn concentration in roots\nand Zn bound with Fe-Mn oxide.  Furthermore, Zn in the form of Fe-Mn oxide\nis more available for plant uptake (Zhang et al. 1998).  Positive correlations were\nfound between water-soluble, exchangeable and organic Zn in the plant tissues.\nThis is in agreement with previous findings (Rupa and Shukla 1999). However,\nReneau et al. (1990) reported negative correlation because Zn in soils is not\neasily transported into the plant because of migration characteristics despite pres-\nence of a high content of heavy metals in soils.\n\n\n\nTABLE 2\nCorrelation coefficient (r) between heavy metals in the soil after the first and third\n\n\n\nmaize cycles and  heavy metals content  in the maize grain (n=30)\n\n\n\nElement Water Exchan Carbonate Fe-Mn Organic Residual Total\nsoluble geable bound  Oxide  bound Metal\n\n\n\nCd 0.1515 0.2763 0.2690 0.1207 0.3779* 0.0005 0.0299ns\nPb -0.0105 -0.0837 -0.2899 -0.1514 0.2350 -0.0394 0.1173ns\nZn 0.2356 0.1778 0.2783 0.2745 0.1051 0.0612 0.0814ns\nNi 0.3047 -0.0800 0.3091 -0.2849 0.0922 -0.0291 0.4059*\n\n\n\nCu 0.0565 -0.2354 0.4746** 0.5392** 0.5452** 0.2887 0.1402ns\n\n\n\nns - not significant\n*   - significant at p<0.05\n** - significant at p<0.01\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM91\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 11, 200792\n\n\n\nA. Rosazlin, I. Che Fauziah, A.R. Rosenani & S. Zauyah\n\n\n\nIn the case of Cu, the organically bound forms in soils were positively cor-\nrelated with the Cu concentration in the maize grains.  A higher portion of or-\nganic form in the soils was also observed compared to other heavy metal frac-\ntions. Significant correlation (p<0.01) was obtained for Cu in grains with car-\nbonate and Fe-Mn oxide forms.\n\n\n\nThe heavy metal concentration of Pb in the maize grains was negatively\ncorrelated with all the chemical forms of Pb in the soil, indicating non-availability\nof Pb for plant uptake.\n\n\n\nThere was significant correlation (p<0.05) between Ni in grain with total Ni\nin the soil. The total concentrations of heavy metals in soil do not indicate the\namounts that are available for plant uptake (Srikanth and Reddy 1991).  Chukuma\n(1993) observed that the total concentrations in the leaves of bilinga (Nauclea\npopeeguinei L.) and cogongrass (Imperata cylinderica L. Beauv.) did not reflect\ntotal concentrations of the elements in the soil, suggesting a gap between\nbioavailable forms and total soil concentrations of the elements.\n\n\n\nCONCLUSIONS\nA fractionation study of sewage sludge showed that for all the heavy metals\ndetermined (Cd, Pb, Ni and Zn), the most dominant form was the residual form,\nexcept for Cu. The residual form is the heavy metals bound to mineral lattices\nthat can be considered as the inactive fraction in terms of chemical processes in\nthe soil. As for relatively more active fractions, Cd and Pb were mostly in the\nexchangeable form.  Nickel was dominant in the carbonate form, Cu dominant\nin the organic form, while Zn was bound to the Fe-Mn oxides.\n\n\n\n In general, there is no real difference between the different metal fractions\nof the inorganic fertiliser treatment compared to the control except for exchange-\nable Pb and Zn associated with the Fe-Mn oxide fraction.  No shift in the geochemi-\ncal forms of sludge to soil amended with sludge was observed for all the metals\nexcept for Zn. The application of sewage sludge tended to shift the solid phase\nforms of the metals away from those extractable with aqua-regia (residual frac-\ntion) to those extractable with hydroxylamine hydrochloride and acetic acid mix-\nture (Fe-Mn oxide fraction).  From the fractionation data and total elemental\nanalyses from the first and third maize cycles, Cu and Zn increased with addition\nof sewage sludge. On the other hand, Cd, Pb and Ni did not show an increase in\ntrend from the first to third cycles.\n\n\n\nFrom the correlation study, relationships were found between Cu in grain\nand the carbonate, Fe-Mn oxide and organic bound Cu in soil.  Meanwhile, Cd\ncontent in maize grain showed a positive correlation with organic bound form.\nNi content in maize grain showed a positive correlation with total metal in soil.\nThere was no correlation between Zn and Pb content in maize grain with these\nmetal fractions in the soil.  It can be concluded that the sequential extraction\nscheme that was used in this study revealed clear differences between the geochemi-\ncal forms of metals in sludge-treated and inorganic fertiliser\u2013treated soil and\ncontrol.\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM92\n\n\n\n\n\n\n\n\nMalaysian Journal of  Soil Science Vol. 11, 2007\n\n\n\nFractionation Study of Heavy Metals in Sewage Sludge and Soils Applied with Sewage Sludge\n\n\n\n93\n\n\n\nACKNOWLEDGEMENTS\nWe would like to express our sincere gratitude to Indah Water Konsortium Sdn.\nBerhad (IWK) for their generous funding of this project, UPM for giving us the\npermission to carry out this study and the staff of Soil and Plant Analytical Sec-\ntion, Department of Land Management, UPM for their assistance in the labora-\ntory analyses.\n\n\n\nREFERENCES\nBrummer, G.W. 1986. Heavy metals species, mobility and availability in soils. 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Sequently extraction procedure for\nthe speciation of particulate trace metals. Anal. Chem.  51: 38-44.\n\n\n\nTessier, A.P., G.C. Campbell and M. Biosson. 1979. Sequential extraction procedure for\ndifferent soil clay fractions and goethite. Geoderma 34: 17-35.\n\n\n\nTuin, B.J.W. and M. Tels. 1990. Distribution of six heavy metals in contaminated clay\nsoils before and after extractive cleaning. Environ. Tech. 11: 935-948.\n\n\n\nWan Noridah, W.A. 2000. Fractionation of heavy metals in sewage sludge treated\nsoils. Final year project. Department of Soil Management, Faculty of Agriculture,\nUniversiti Putra Malaysia, Malaysia\n\n\n\nYang, Z.Q. and M. Kimura. 1995. Solubility fractionation of Zn, Cu, and Cd in soils\napplied with sewage sludge and their potential availability to plant. Environ. Sci.\n8 (4): 369-378.\n\n\n\nZhang, D., X. Shan and F. Li. 1998. Comparison of wo?? sequential extraction proce-\ndures for speciation analysis of metalsin soils and plant availability. Soil Sci. Plant\nAnal. 29(7&8): 1023-1034.\n\n\n\nMJ of Soil Science 081-095.pmd 08-Apr-08, 10:49 AM95\n\n\n\n\n\n"
"\n\nINTRODUCTION\nMan has applied non-hazardous industrial wastes on land such as sewage sludge \n\n\n\nimprove the soil via recycling of nutrients and addition of organic matter. Due to \n\n\n\nmethods, interest in land application of industrial wastes has continued to grow. \n\n\n\nFormulation of Coal Fly Ash and Sewage Sludge Mixtures \nto Reduce Impacts on the Environment When Used as Soil \n\n\n\nAmeliorant for Acidic Tropical Soils\n\n\n\nM. Nur Hanani, I. Che Fauziah*, A.W. Samsuri & S. Zauyah\n\n\n\nABSTRACT\n\n\n\nmacronutrients in the ash, besides, there are also concerns about its high \nconcentration of microelements, especially boron. Sewage sludge (SS) on the \n\n\n\nit also contains high concentrations of micronutrients especially Zn. This study \nwas carried out to evaluate the potential of CFA as a soil ameliorant to immobilize \nheavy metals from SS-treated soil and whether the SS could provide supplementary \nmacronutrients for maize growth requirement. A laboratory soil incubation study \n\n\n\nZn, Cu and B. A similar experiment was conducted in a glasshouse but using CFA \n\n\n\n-1. \nThe reduction in Cu and B concentrations in the soil solution were not apparent in \n\n\n\n-1\n\n\n\nsewage sludge mixture can be used as a soil ameliorant provided attention is given \n\n\n\nsuch as maize.\n\n\n\nKeywords: acidic tropical soil, glasshouse study, maize plants, soil solution\n      study \n\n\n\n___________________\n*Corresponding author : E-mail: \n\n\n\n\n\n\n\n\n54\n\n\n\nRecycling of these wastes to agricultural land can supply the soil with essential \n\n\n\nmay also have adverse effects due to the presence of heavy metal concentrations \nwhich will affect soil health, food quality as well as the environment.  Therefore, \nthis move to recycle these waste on land is of great public concern.\n\n\n\nOne of the reasons for the poor acceptance of waste materials such as CFA \nand SS as soil ameliorant is because it may cause plant nutrient imbalance, either \n\n\n\namorphous aluminosilicate material, a by-product of coal combustion and is \n\n\n\nalternative natural resources used for the production of electricity in Malaysia. \nThe increasing use of coal for electric power generation will generate large \nquantities of CFA. Kapar power station in Selangor, Malaysia, produces around \n\n\n\nfound application as a liming agent (Adriano et al\nin trace elements and has been successfully applied to alleviate micronutrients \n\n\n\npotential in the future. The application of SS to agricultural land is generally the \nmost economical means of waste disposal and also provides an opportunity to \n\n\n\nThe value of SS as N and P fertilizer replacement for example, has been frequently \nreported (Kelling et al. et al\nsupply other plant nutrients such as Mg, Ca, Zn and Cu, but their importance \n\n\n\nin SS can increase crop productivity by improving soil physical and chemical \nproperties (Adriano et al\n\n\n\nand plants and can cause heavy metal toxicity (McGrath et al.\nmetals concentrations in soils can pose detrimental effects such as metal toxicity \nto plants. \n\n\n\nIn many cases, a single byproduct may not be ideal by separate from itself for \nland application, but through co-utilization of byproducts, more useful agronomic \n\n\n\nbalance, reduction in toxins or contaminants, improved moisture content, improved \neconomic value, improved soil conditioning effects, etc. Therefore, the objectives \n\n\n\n\n\n\n\n\n55\n\n\n\nof this study were to evaluate whether CFA can be utilized as soil ameliorant to \nimmobilize heavy metals from SS-treated soil and whether the SS could provide \nadequate macronutrients for plant growth requirement.\n\n\n\nMATERIALS AND METHODS\n\n\n\nKapar, Selangor, Malaysia and the SS was collected from Indah Water wastewater \n\n\n\nmm sieve before the laboratory analyses were conducted.\n\n\n\nThe chemical properties of the CFA, SS and soil analyzed are listed in Table 1. \n\n\n\nmeter. The EC was measured from a solution collected from the saturated paste \nusing the Radiometer EC meter. Soil exchangeable bases (K+, Ca  and Mg ) \n\n\n\n4OAc, buffered \n\n\n\n4\n+ was then displaced with K SO4 solution and determined \n\n\n\nUSA). \n\n\n\ncarbon analyser (LECO Corporations, St. Joseph, USA). Total nitrogen was \n\n\n\n3\n\n\n\nBoron was extracted with hot water and extracts determined by the colorimetric \n\n\n\nwith calcium carbonate. The ANC, which was expressed as calcium carbonate \nequivalence (CCE), was calculated based on the amount of acid required to obtain \n\n\n\nused.\n\n\n\nCoal Fly Ash and Sewage Sludge as Soil Ameliorant\n\n\n\n\n\n\n\n\nstudies (Rosazlin et al.\nSS + CFA) was placed in a pot. Twenty-four pots were arranged in a completely \nrandomized design (CRD), which consisted of six treatments and four replicates. \n\n\n\nTABLE 1\nludge and Bungor series soil used \n\n\n\nin this study\n\n\n\nThe in-situ soil solutions from the incubation study were sampled using a \n\n\n\nporous tip of the rhizon soil moisture sampler was pushed into the soils. After 48 \n\n\n\nThe study was conducted in a glasshouse unit of the Faculty of Agriculture, Universiti \nPutra Malaysia, Malaysia. Twenty pots were arranged in a completely randomized \n\n\n\nfour times. The treatments were similar to the treatments used in the soil incubation \n\n\n\n\n\n\n\nCFA  Sewage sludge Bungor Series Soil\npH  8.34  4.42  4.72  \nE.C. (dS m 1.31  n.d.  n.d.  \nCEC (cmol (+)  kg 0.04  n.d.  6.07  \nANC %  0.50  n.d.  n.d.  \nTotal C %  5.32  31.02  0.97  \nTotal N %  0.32  3.33  0.06  \nTotal P %  0.15  0.48  n.d.  \nExtract. P (mg kg n.d.  n.d.  3.12  \nExch. K %  0.34  3.75  0.31  \nExch. Ca %  0.97  0.21  0.52  \nExch. Mg %  0.44  0.87  0.17  \nTotal Zn (mg kg 82.27  2300  n.d.  \nTotal Cu (mg kg 43.73  107  n.d.  \nExtract. Zn (mg kg n.d.  n.d.  1.80  \nExtract Cu (mg kg n.d.  n.d.  0.40  \nExtract B (mg kg 727.50  n.d.  n.d.  \n\n\n\n          n.d. - not determined  \n\n\n\n-1) \n\n\n\n-1) \n\n\n\n-1) \n\n\n\n-1) \n-1) \n\n\n\n-1) \n-1) \n\n\n\n-1) \n\n\n\n\n\n\n\n\nhigh for practical application. Maize (Zea mays L.) was used as the test crop and \n\n\n\n-1as triple superphosphate (TSP) and K was \n-1 as muriate of potash (MOP) for all treatments.\n\n\n\nO ratio), \nO).\n\n\n\ndry plant weights were determined. Then the maize tissue was dried in an oven \noC) until a constant weight was recorded. The dried tissue was ground to \n\n\n\nand Zn) in the tissue were extracted out using the dry ashing method. Between \noC \n\n\n\noC for a further four hours. The cool ash was digested \n\n\n\noC for one \n\n\n\nwith distilled water. The macronutrients (K, Ca and Mg) and micronutrients (Zn \n\n\n\nKjeldahl digestion method while B was determined by the azomethine method \n\n\n\nplants were calculated by multiplying the concentrations of these elements in the \nplant tissue with the dry weight of the plant.\n\n\n\nThe validity of the dry ashing procedure and the AAS operating parameters \nwas established using the National Institute of Standard and Technology (NIST) \nmaize grain standard reference material, NIST 8433. The data obtained were \nsubjected to analysis of variance (ANOVA) using the Statistical Analysis System \n\n\n\nusing Duncan Multiple Range Test (DMRT).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nThe chemical properties of the CFA, SS and Bungor Series soil are shown in Table \n\n\n\nCoal Fly Ash and Sewage Sludge as Soil Ameliorant\n\n\n\n\n\n\n\n\n58\n\n\n\nmaterial (Adriano et al.\n\n\n\npathogen and heavy metals availability (McGrath et al. et al\nThe CEC value for CFA has been not widely discussed in previous studies. Cation \n\n\n\nas well as in assessing their fertility status. Since CFA is not considered a soil, \nthis maybe one of the reason why CEC of CFA has not been widely discussed. \n\n\n\n-1. This \nindicates that this form of heavy metal complexes in CFA amended soil would not \n\n\n\n-1 et al\nThe ANC is usually expressed as CaCO3 equivalent and one of the most \n\n\n\nimportant factors used to evaluate the value of industrial byproducts for use as a \nliming agent on acidic soil. The neutralization of acid by CFA is a relatively slow \nprocess that mainly involves the particle surfaces (Wong et al\n\n\n\n3 equivalent \n(CCE). Based on the low level of Ca in this CFA (Table 1), it is considered only as \n\n\n\net al\n\n\n\nlevel. \n\n\n\nbe oxidized into gaseous constituents during the combustion, it is usually present \net al.\n\n\n\nvolatilized during the process of coal combustion which drastically reduces the \n\n\n\net \nal\nby CFA applications (Elseewi et al\n\n\n\ncomplexes (Adriano et al\n\n\n\nrespectively. These results were within the range of a   previously reported study \n\n\n\nK, respectively (Page et al.\nAll the heavy metals listed in Table 1 fall within the ranges found in most \n\n\n\nsoils and allowable level of the European Communities Standards (ECS) for land \n\n\n\n\n\n\n\n\nAmending SS with CFA has been reported to reduce the availability of heavy \n\n\n\nagricultural soils should be critically evaluated in view of plant growth problems \nassociated with B toxicity. According to numerous researchers, the only limitation \nin using CFA as a soil amendment is its high soluble B concentrations. Since B \ncompound in soils is water soluble, the weathering of CFA by allowing adequate \ndrainage should reduce the detrimental effect of B on plants (Elseewi et al. \n\n\n\net al. \n\n\n\nwater. This study showed that B concentration in leached CFA was much lower \n-1 -1), proving that B \n\n\n\nin the CFA was highly leachable by water.\n\n\n\nelements is mostly a function of solubility, therefore, a reliable technique to assess \n\n\n\nsolution from the soil (Zhang et al.\n\n\n\npresented in Fig.1\n\n\n\nand precipitation reactions would decrease their solubilities (Sajwan et al\n\n\n\net al.\n\n\n\nlow and therefore the CFA was not a good liming agent. This CFA is considered \n\n\n\nIncreasing the CFA ameliorant rates clearly reduced the Zn concentrations in soil \nFig. 2). The reduced concentration of Zn \n\n\n\nprobably can be explained by the higher adsorption and precipitation of Zn with an \net al.\n\n\n\n-1 to \n\n\n\nCoal Fly Ash and Sewage Sludge as Soil Ameliorant\n\n\n\n\n\n\n\n\n-1\n\n\n\n-1\n\n\n\n-1) compared to \n-1 \n\n\n\nwhich were less than 1 mg L-1. This indicates that CFA is feasible as a stabilization \nagent to reduce heavy metal toxicity in the SS-treated soil. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n3.50\n\n\n\n4.00\n\n\n\n4.50\n\n\n\n5.00\n\n\n\n5.50\n\n\n\n6.00\n\n\n\n6.50\n\n\n\n7.00\n\n\n\n7.50\n\n\n\n8.00\n\n\n\n8.50\n\n\n\n1 2 3 4 5 6 7 8 9 10\n\n\n\nTIME (week)\n\n\n\np\nH\n\n\n\n0% 2.5% 5% 10% 20% 40%\n\n\n\nTime (week)\n\n\n\nFig. 1 :  pH value of soil solution for 10 weeks of CFA incubation          \n \n\n\n\nFig. 2 : Soluble Zn at d ifferent rates of CFA treatments\n\n\n\n\n\n\n\n\nThe soluble Cu concentrations in the incubated soil mixture are shown in \nFig. 3. The results indicate that Cu concentrations in the soil solutions were very \n\n\n\n-1. The \n\n\n\nmg L-1) compared to other treatments. \n\n\n\nThe concentration of  B in CFA amended soils appears to be the most serious \nconstrain associated with land application of CFA to soil. Boron concentration \nespecially in unweathered CFA has been reported to be very high and readily \navailable to plants (Adriano et al.\nweathering of CFA by allowing the adequate drainage should alleviate the \ndetrimental effect of B on plants. The soluble B concentrations in the soil mixture \nfrom different rates of CFA are shown in Fig. 4. The soluble B concentrations \n\n\n\nlowest concentrations of B in the soil solution for the whole period of incubation. As \nthe rates of CFA increased, the soluble B concentrations also increased drastically. \n\n\n\n-1 B. It has been reported that most crops require only \n-1 of hot water extractable soil B for normal growth, while 5 mg L-1 \n\n\n\ncan be toxic for many plants (Ponnamperuma et al\n\n\n\nFig. 3 : S oluble Cu at d ifferent rates of CFA treatments\n\n\n\nCoal Fly Ash and Sewage Sludge as Soil Ameliorant\n\n\n\n\n\n\n\n\na soil ameliorant.\n\n\n\nGlasshouse Study\n\n\n\nMaize Dry Weight\nThe dry weight of maize grown in the SS-treated soil with different rates of CFA \nis shown in . There was no reduction in dry weight of maize in soils treated \n\n\n\nmacronutrients by maize in all CFA treatments except for N. Thus, the reduction in \n\n\n\n(EC), which is a measure of the amount of soluble salts in the soil, was measured \nto determine the possible presence of salinity problems in the soil due to sludge \n\n\n\n-1) will have an adverse effect on plant growth \n\n\n\n-1, respectively, and these \nvalues were below the soil salinity threshold value of 4 dS m-1. \n\n\n\nrange (NSR) of maize to assess if CFA application has an effect on the adequacy \n\n\n\n\n\n\n\nFig. 4 : Soluble B at d ifferent rates of CFA treatments  \n\n\n\n\n\n\n\n\nfor maize growth. Insoluble forms of P may result due to the high levels of Ca \n\n\n\n-1) was \n\n\n\nand therefore no additional P was added. It has been reported that soil P and \n\n\n\nprecipitation of soil P (Ponnamperuma et al.\n\n\n\n for maize\n\n\n\n      Note: *Data showed adequate range for maize growth based on Mills and Jones \n\n\n\nCoal Fly Ash and Sewage Sludge as Soil Ameliorant\n\n\n\nNote : Similar letters above  the  bars indicate that the y are not significantly \ndifferent at 99 % confidence level, according to the Duncan New Multiple \nRange Test (DMRT)  \n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\n\n\n\n\n \nTABLE 2\n\n\n\nComparisons of macronutrients in maize  and Nutrient Sufficiency Range (NSR) \nfor maize  \n\n\n\nParameter  Rates  of CFA  \n\n\n\nN (%)\nP (%)\nK (%)\n\n\n\nCa (%)\nMg (%)\n\n\n\n0%  \n12.01\n0.15\n1.66\n0.82\n0.33\n\n\n\n2.5%\n13.72\n0.15\n1.60\n0.76\n0.47\n\n\n\n5%  \n11.61\n0.22\n1.70\n0. 73\n0.48\n\n\n\n10%  \n9.93\n0.27\n1.93\n0.57\n0.55\n\n\n\n20%  \n9.11\n0.30\n2.27\n0.91\n0.71\n\n\n\nNSR*  \n(%)  \n\n\n\n3.50 -5.00\n0.30 -0.50 \n2.50 -4.00\n0.30 -0.70 \n0.15 -0.45\n\n\n\nNote:  * D ata showed adequate range for maize growth based on Mills and  Jones (1996)\n \n\n\n\n\n\n\n\n\nFig. 6a\n\n\n\nfor maize growth. A mixture of a large amount of CFA to SS increased the C:N \nratio which slowed down the N mineralization process (Zhang et al.\n\n\n\n\n\n\n\n0\n50\n\n\n\n100\n150\n200\n250\n300\n350\n400\n450\n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\nU\npt\n\n\n\nak\ne \n\n\n\nof\n N\n\n\n\n (\nm\n\n\n\ng \npo\n\n\n\nt-1\n)\n\n\n\na\n\n\n\nbb\n\n\n\naa\n\n\n\na)\n\n\n\n0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\nU\npt\n\n\n\nak\ne \n\n\n\nof\n P\n\n\n\n (\nm\n\n\n\ng \npo\n\n\n\nt-1\n)\n\n\n\na\n\n\n\nb\n\n\n\nb\n\n\n\ncc\n\n\n\nb)\n\n\n\n\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\nU\npt\n\n\n\nak\ne \n\n\n\nof\n K\n\n\n\n (\nm\n\n\n\ng \npo\n\n\n\nt-1\n)\n\n\n\naa\n\n\n\naaa\n\n\n\nc)\n\n\n\n0\n2\n4\n6\n8\n\n\n\n10\n12\n14\n16\n18\n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\nU\npt\n\n\n\nak\ne \n\n\n\nof\n M\n\n\n\ng \n(m\n\n\n\ng/\npo\n\n\n\nt-1\n)\n\n\n\nbb\n\n\n\na\n\n\n\na\n\n\n\nb\n\n\n\nd)\n\n\n\n\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\nU\np\nta\n\n\n\nke\n o\n\n\n\nf C\na\n (\n\n\n\nm\ng\n p\n\n\n\no\nt-1\n) a\n\n\n\nb\n\n\n\na\naa\n\n\n\ne)\n\n\n\n  Note : Similar letters above the bars indicate that they are not significantly different at 99% confidence \n level, according to the Duncan New Multiple Range Test (DMRT)\n\n\n\n\n\n\n\n\nFig. 6b\n\n\n\nby maize was not from CFA, since CFA was a poor source of P. The addition of \n\n\n\nAs shown in \n\n\n\nmixture was low compared to the adequate level for maize growth indicating that \n\n\n\nFig. \n6d\n\n\n\nright proportion of CFA should be used to supply an adequate amount of Mg for \n\n\n\nFig. 6e). This shows that addition of CFA as  soil \n\n\n\ngrowth. This suggests that CFA can be a good source of Ca but a proper amount \nshould be added to avoid the Ca and Mg imbalance. \n\n\n\ntreatments. Copper concentrations in all treatments were also found to be within \nthe adequate level for maize growth.\n\n\n\n. Overall, the \n\n\n\nreduced Zn concentration in maize.  This shows that the CFA is useful as a soil \n\n\n\nCoal Fly Ash and Sewage Sludge as Soil Ameliorant\n\n\n\n\n\n\n\n\nTABLE 3\nComparis\n\n\n\nfor Maize\n\n\n\nby the maize plants compared to the control \n\n\n\navoid Cu toxicity. \n\n\n\n\n\n\n\n\n\n\n\n0.0\n\n\n\n1.0\n\n\n\n2.0\n\n\n\n3.0\n\n\n\n4.0\n\n\n\n5.0\n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\nu\np\nta\n\n\n\nk\ne\n o\n\n\n\nf \nZ\n\n\n\nn\n (\n\n\n\nm\ng\n p\n\n\n\no\nt-1\n\n\n\n)\n\n\n\na a\n\n\n\nb\n\n\n\nc\nc\n\n\n\na)\n\n\n\n0.00\n\n\n\n0.05\n\n\n\n0.10\n\n\n\n0.15\n\n\n\n0.20\n\n\n\n0.25\n\n\n\n0.30\n\n\n\n0.35\n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\nu\np\nta\n\n\n\nk\ne\n o\n\n\n\nf \nC\n\n\n\nu\n m\n\n\n\ng\n p\n\n\n\no\nt-1 a\n\n\n\nb\nb\n\n\n\nb\n\n\n\nab)\n\n\n\n0.00\n\n\n\n0.50\n\n\n\n1.00\n\n\n\n1.50\n\n\n\n2.00\n\n\n\n2.50\n\n\n\n3.00\n\n\n\n3.50\n\n\n\n0% 2.5% 5% 10% 20%\n\n\n\nu\np\nta\n\n\n\nk\ne\n o\n\n\n\nf \nB\n\n\n\n m\ng\n p\n\n\n\no\nt-\n\n\n\n1\n\n\n\nc\n\n\n\nb\nab\n\n\n\nb\n\n\n\nac)\n\n\n\n \nNote : Similar letters above the bars indicate that they are not significantly \n\n\n\ndifferent at 99% confidence level, according to the Duncan New Multiple \nRange Test (DMRT)  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n TABLE 3\nComparisons of micronutrients in maize  and Nutrient Sufficiency Range (NSR) \n\n\n\nfor maize  \n\n\n\n\n\n\n\nParameter  Rates  of CFA  \n0%   \n\n\n\nZn (mg/kg) 153\nCu (mg/kg) 8.0\nB (mg/kg) 36\n\n\n\n2.5%  \n129\n2.7\n66  \n\n\n\n5%\n1 01  \n2.0  \n81  \n\n\n\n10%  \n67\n5.3\n88\n\n\n\n20%  \n43\n\n\n\n12.0\n120\n\n\n\nNSR*\n(mg/kg)\n\n\n\n20 -60\n5 -20  \nn.a.  \n\n\n\nn.a. \u2013  data not available; \nNote: * Data showed adequate range for maize growth based on Mills  and  Jones  (1996)\n\n\n\n\n\n\n\n\nThe most common adverse effects of using CFA as a soil amendment is the high \n-1 in maize tissue \n\n\n\n-1 is reported to cause toxicity to the maize plants (Jones et al\n\n\n\n-1. \nrates of CFA treatments. The B concentration apparently increased with the usage \n\n\n\n-1 with the exception \n\n\n\nhowever it should not be applied excessively to avoid the phytotoxic effect of B. It \n\n\n\nas a soil ameliorant.\n\n\n\nCONCLUSION\nThe amount of major macronutrients in the CFA is considerably low for plant \ngrowth and therefore this byproduct cannot be a good fertilizer supplement if it \n\n\n\nsince the CCE was low. All the heavy metals in CFA with the exception of B were \nwithin ranges found in most soils and the allowable level for land application of \na waste byproduct. When mixed with sewage sludge, all the macronutrients were \n\n\n\nToxic levels of B were detected in the foliar tissues of maize at the highest rate \n\n\n\nThis study indicates that land applications of CFA can be recommended as soil \nameliorant because of their effectiveness in reducing heavy metals in the SS-treated \n\n\n\ncorrect dose and has to be weathered or lagooned to remove excess B before it can \n\n\n\nthe utilisation of CFA as a soil ameliorant for crop production in tropical acid soils. \n\n\n\nACKNOWLEDGEMENTS\n\n\n\nstation in Kapar, Selangor, Malaysia, and Indah Water Konsortium, for supplying \n\n\n\nUPM for giving us the permission to carry out this project under the Fundamental \n\n\n\nCoal Fly Ash and Sewage Sludge as Soil Ameliorant\n\n\n\n\n\n\n\n\nWe are grateful to Ms. Rosazlin Abdullah for typing this manuscript.\n\n\n\nREFERENCES\n\n\n\nVerlag.\n\n\n\nAdriano, D. C., J. Weber, N. Bolan, S. Paramasivam,  S. Bon-Jun Koo and K.S.Sajwan. \n\n\n\nquality.  139\n\n\n\nB\n\n\n\nBi\nSoil Amendments and Environmental Quality. Boca Raton, Florida: Lewis \nPublisher.\n\n\n\nB\n\n\n\nBrown S, B. Christensen, E. Lombi, M. McLaughin, S.  McGrath, J. Colpaert and  J. \n\n\n\nto reduce the availability of Cd, Pb and Zn in situ.  138: \n34 - 45.\n\n\n\nC\ncropped soil: bioavailability and chemical form of zinc. \n\n\n\n 30:\n\n\n\nE\nplant.  7\n\n\n\nInda\nSewerage Sludge System for Malaysia. (A phamplet) \n\n\n\nJac\n\n\n\n 28:\n\n\n\nJala\nreview. 97(9)\n\n\n\nJohn\n\n\n\n3rd \n\n\n\n\n\n\n\n\nJo\n\n\n\n3rd ed. SSSA. Madison, WI. \n\n\n\nKelling, K. A., L.M. Walsh, D.\nstudy of agriculture use of sewage sludge. Effect on soil N and P. \n\n\n\n 6:\n\n\n\nMar 12: \n\n\n\nMc\nin sewage sludge on soils, microorganisms and plants. \n\n\n\n 14\n\n\n\nM\nMicroMacro Publishing, Inc.\n\n\n\nMol\ncomposition of corn on acid sandy soils. In  Proceeding of Soil and Crop Science \nSociety of Florida 41\n\n\n\nPage, \nConsequence of Trace Element Enrichment of Soil and Vegetation from the \nCombustion Of Fuels Used In Power Generation. S. Calif. Edison Res. and Dev. \n\n\n\nPonnam\nproduction in the humid tropics. In: Soil related constrain to food Production in \n\n\n\nPon\nan extractant for available zinc, copper and boron in rice soils.  \n61\n\n\n\nRos\nsludge application to an acid tropical soil: Part I. Sludge as an N fertilizer for \nmaize.  9\n\n\n\nRos\n\n\n\nand accumulation in the soil.  10\n\n\n\nSajwa\n\n\n\nmixtures.  8:\n\n\n\nSA\nInc.\n\n\n\nCoal Fly Ash and Sewage Sludge as Soil Ameliorant\n\n\n\n\n\n\n\n\nSchu\n 28\n\n\n\nSims,  \nmetals in soil amended with composed sewage sludge. \nQuality 13\n\n\n\nSu\n\n\n\n 29\n\n\n\nTessen\nSelangor. Technical Bulletin, Faculty of Agriculture, Universiti Pertanian \nMalaysia, pp 88.\n\n\n\nTho\n\n\n\nWong, \nagricultural soils of the Pearl River Delta, South China.  \n119:33-44.\n\n\n\n 20:\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 95-110 (2016)                         Malaysian Society of Soil Science\n\n\n\nN2O and CO2 Emissions From Arable and Grassland Soils \nUnder Various Moisture Regimes: A Microcosm Study\n\n\n\nShah, A.1, 2,* and R. Gaebler1\n\n\n\n1 Institute of Soil Science and Land Evaluation, Biogeophysics (310), \nUniversity of Hohenheim, 70593 Stuttgart, Germany \n\n\n\n2 Sindh Agriculture Research Institute, Tando Jam Pakistan\n\n\n\nABSTRACT\nGreenhouse gas emissions have increased during the last century due to human \nactivities such as agricultural practices, fossil fuel burning and industrial practices. \nHowever, the formation of greenhouse gases, in particular N2O or CO2 is strongly \ncontrolled by both soil temperature and soil moisture. A laboratory experiment \nwas conducted to assess the response of grassland and arable soils with regard \nto N2O and CO2 flow and mineral nitrogen concentration; soils were exposed to \nvarious drying- rewetting cycles at different gravimetric water contents (\u03b8wt) \nunder controlled conditions for a duration of 60 days. In total, four treatments \nwere conducted: soils under continuously moist conditions (control) at 32% \u03b8wt; \nsoils received short drying-rewetting cycles (SDWC) of between 32 to 21% \u03b8wt; \nsoils exposed to medium drying rewetting cycles (MDWC) of between 32 to 18% \n\u03b8wt and a treatment with long drying-rewetting cycles (LDWC) of between 32 to \n5% \u03b8wt. Short, medium and long drying-rewetting cycle treatments went through \n6, 4 and 2 drying-rewetting cycles (DWC) (0.1, 0.07 and 0.03 drying-rewetting \nfrequencies). Soil samples of arable and grassland soils were analyzed for NH4\n\n\n\n+ \n\n\n\nand NO3- at the different stages of incubation in order to compare changes over \ntime. The results indicated that arable and grassland soils reduced N2O-N flow in \nthe long drying-rewetting treatments. For the grass soil, the short drying-rewetting \ncycle treatment yielded the highest cumulative N2O-N flow (325 \u03bcg kg-1). In arable \nsoil, however, the long drying-rewetting cycles receiving treatment released 69% \nless N2O-N flow as compared to the other treatments. For the CO2-C flow, soils \nshowed differing patterns, with the shortly dried-rewetted cycle treatment of \ngrassland soils yielding the highest (130 \u03bcg kg-1) cumulative flow that was 25% \nhigher than LDWC. Drying-rewetting cycles (DWC) on grass soils had no effect. \nThe stressed treatments emitted only 19% higher CO2-C flow than the control. \nThe treatment with 5% (\u03b8wt) successfully reduced N2O-N flow in grassland and \narable soils. Soil net nitrogen mineralization (NNM) and nitrification (NNN) rates \nof arable soils were significantly higher than in grassland soils.\n\n\n\nKeywords: Grassland soils, arable soils, N2O, CO2, moisture regimes.\n\n\n\n___________________\n*Corresponding author : E-mail: ambreennaz74@hotmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201696\n\n\n\nShah and Gaebler\n\n\n\nINTRODUCTION\nSoils are one of the major sources of atmospheric greenhouse gases, in particular \nCO2 (Raich et al., 2002) and 70 % of the total N2O emissions (Wei et al., 2010; \nConrad 1996). N2O and CO2 are the consequences of microbial mediated processes, \nenhanced by physico-chemical characteristics of soil (Batjes and Bridges 1992) \nand land management practices (Ugalde et al., 2007). Nitrous oxide is very stable \nand persists in the atmosphere for approximately 120 years. It has a large global \nwarming potential that is 296 times greater than CO2 in a 100-year period and is \nrelated to the catalytic destruction of the stratospheric ozone (Diz-Mu\u00f1oz et al., \n2010). Atmospheric CO2 concentration has increased by almost 100\u00b5L L-1 since \npre-industrial levels, reaching as high as 379\u03bcL L-1 in 2005. The mean annual \ngrowth rate during 2000-2005 was higher than in the 1990s and will continue to \nrise in Europe, Caucasus and Central Asia by more than 40% until 2030 (IPCC, \n2007).\n\n\n\nFierer et al., (2003) predicted that mineral soils are expected to receive long \ndrying periods during summers in this century because of climate change (Muhr et \nal., 2008). Surface soils get more exposure to drying-rewetting events (Fierer and \nSchimel, 2002)  and this have a significant impact on the microbial community \n(Smith et al., 2003) by providing physiological stress (Fierer et al., 2003). \n\n\n\nFew studies have underlined how the frequency of stress events (drying-\nrewetting) control soil biochemical processes related to C and N cycles. Wang et \nal., (2010) indicated that the C mineralisation rate was not significantly affected \nby moisture.  However, several reports suggest that repeated drying-rewetting has \nan effect on nitrogen and carbon turnover in the soil (Kruse et al., 2004; Butterly, \n2008) and on the microbial community (Fierer and Schimel, 2002; Smith et al., \n2003). Nitrifier activity may generally be sensitive to low moisture (Fierer and \nSchimel, 2002). Maximum N2O was emitted when soil was rewetted (Ruser et \nal., 2005), yet no differences between constantly moist (Beare et al., 1999) and \nfrequently dried soils (Kruse et al., 2004) were found. Shortage of water can \nretard microbial activity by lowering intracellular water potential and in solid \nmatrices by restricting substrate supply, leading to a decline in nitrification rates \n(Stark and Firestone, 1995). However, nitrification rates  were found to increase \nin the soils that were exposed to soil drying for short periods (Fierer and Schimel, \n2002; Kessavalou et al., 1998). \n\n\n\nNitrous oxide emissions are significantly affected by soil moisture during the \nwheat growing season (Liu et al., 2011). Normally, wheat crops require adequate \nirrigation and fertilisers during the entire growth period. High N fertilisation \nstimulates N2O emission by providing substrate as NH4\n\n\n\n+ and NO3\n- for the \n\n\n\nmicrobes, enhancing nitrification and denitrification (Duxbury, 1994). In winter \nwheat, nitrogen fertiliser application (Mancino and Torello, 1986) and soil organic \ncarbon (Huang et al., 2002a) did not affect the denitrifier population and N2O \nemission. Chen et al., (2007) even postulated that N input is not the parameter \nthat can predict seasonal N2O emission from the soil. The release of CO2 through \naerobic respiration becomes a function of water content when the soil dries out \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 97\n\n\n\nGreenhouse Gas Emissions from Arable and Grass Soils\n\n\n\n(Smith et al., 2003). Variability in moisture content can also affect respiration \nrates (Fierer and Schimel 2002). Thomson et al. (2010) detected significantly \nhigher respiration rates in dried and rewetted microcosms. \n\n\n\nGlobal circulation models indicate that an increase in global warming is due \nto C cycle feedbacks (Smith et al., 2003) because it is more sensitive to rewetting \nfrequency than N (Miller et al., 2005). Its uptake and storage by plants can be \nincreased through improved agricultural management practices (Haney and \nHaney, 2010). However, the influence of autotrophic or heterotrophic activity to \nchanges in CO2 evolution is not verified yet (Kim et al., 2011); especially in grass \nsoils which contain more labile carbon than annually cropped soils regardless of \ntillage regimes (Carpenter-Boggs et al., 2003). \n\n\n\nHowever, the net N mineralization is considerably dominated by nitrification \nin grassland soils (Zhang et al., 1998) as seen in the higher N2O emission due \nto increased nitrification rates and organic C availability that is used as an \nenergy source for heterogenous microorganisms. The influence of drying-\nrewetting frequencies on overall ecosystem nutrient budgets is still unclear. In \nthis connection, an effort was made to quantify the impact of various drying-\nrewetting cycles (DWC) on grassland and arable soils, specifically in relation to \nCO2 and N2O emissions, NH4\n\n\n\n+ and NO3- contents at different stages by means of \na microcosm experiment under controlled conditions.\n\n\n\nMATERIALS AND METHODS\n\n\n\nSoil Sampling\nTwo soils under different management practices were chosen for incubation, that \nis, grassland and arable soil from Heidfeldhof Research Station at the University of \nHohenheim, 13 km south of Stuttgart, Germany (48\u00b0 43\u2019 00\u201d N; 9\u00b0 11\u2019 40\u201d E) where \nannual average temperature and total rainfall are 8.7\u00b0C and 685 mm, respectively. \nThe soil is classified as Alfisols (USDA, 2010). Soil samples were taken from a \ndepth of 0-15 cm by removing upper vegetation and then well integrated to ensure \nhomogeneity. After sampling, soil was immediately transported to the laboratory \nand sieved through a 4 mm sieve (Thomson et al., 2010).\n\n\n\nExperimental Setup\nSoil weighing 130 g was packed into columns (100 cm3) resulting in a bulk density \nof. 1.3 g cm-3. The soils were artificially saturated and adjusted to pF 1.8 i.e. \n32% gravimetric water content (\u03b8wt) by weight through pressure plates (Loveday, \n1974). After pF adjustment, soil cores were placed into microcosms (0.85l) and \nsealed with plastic lids containing rubber septa. In total, 80 microcosms were \nprepared. The microcosms were then transferred to a climate chamber, set at a \nconstant temperature of 20\u00b0C in darkness for 60 days. \n\n\n\nAdding deionised water with a graduate pipette compensated evaporation \nlosses. The experimental setup included four treatments with ten replicates each. \nDuring the experiment, soils were incubated for a period of 60 days and exposed \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201698\n\n\n\nto multiple drying-rewetting cycles with target moisture content (Figures 2c, 4c, \n5c and 6c). The control soil remained at constant moisture of 32% \u03b8wt. Treatments \nreceiving short drying-rewetting cycles (SDWC) were exposed to six evenly \ndistributed DWC; the soil was rewetted to 32% \u03b8wt after reaching a water content \nof 21% \u03b8wt; soils with medium drying-rewetting cycles (MDWC) were exposed \nto 15% \u03b8wt and brought back to 32% \u03b8wt, while soils with long drying-rewetting \ncycles (LDWS) were exposed to drying until 5% \u03b8wt and were rewetted back to \ntheir previous moisture state.\n\n\n\nSDWC, MDWC and LDWC went through 6, 4 and 2 DWC i.e. 0.1, 0.07 \nand 0.03 drying-rewetting frequencies (drying-rewetting frequencies means \nthe number of cycles in 60 days. Six drying-rewetting cycles are divided by 60 \nresulting  in 0.1 drying-rewetting frequencies and so on).\n\n\n\nAll of the treatments received the final drying-rewetting cycles for a 2-month \nperiod. Soil drying was accomplished by removing the microcosm lids to allow \nfor evaporation. Rapid rewetting was performed by dripping deionised water \ncautiously onto the soil surface with the use of a graduated syringe.\n\n\n\nN2O and CO2 Flow Measurements\nFor measuring N2O and CO2 flows, the microcosms were tightly closed and a \nrubber septum was fixed in the lid with a 2-way Luer-Lock valve. Gas sampling \nwas carried out at 0-, 30- and 60-min  time intervals by connecting the microcosm \natmosphere to the vacutainer with a mounted septum, using a surgical syringe \n(Smith et al., 1995) and then stored in the evacuated vacutainers of 0.25 L volume.\n\n\n\nTo ensure the absence of N2O prior to gas sampling, all vacutainers were \nevacuated and rinsed with N2 thrice shortly before sampling to avoid contamination. \nNitrous oxide and CO2 concentrations were analysed by N63 electron capture \ndetector (ECD) and flame ionisation detector (FID) respectively, using a gas \nchromatograph (AutoSystem XL Perkin Elmer) coupled with an auto-sampler. The \ninstrumental conditions were as follows: oven temperature 65\u00b0C, ECD operation \ntemperature 100-450\u00b0C, carrier gas for ECD and FID CH4/Ar (10%/90) and He \n(95%) respectively. Calibration was done with three external standards (0.0003, \n0.0015 and 0.003 \u03bcl L-1 for N2O and 400, 1500 and 3000 \u03bcl L-1 for CO2). Gas \nflow rates were calculated by measuring the change of gas concentration in the \nheadspace of the microcosm using linear regression. Cumulative flow rates were \ncalculated by linear interpolation. To estimate cumulative N2O and CO2 flows \nthroughout measuring period, sum curves were created by multiplying mean flow \nrates of two sequential gas flow rates with the consistent time period and summing \nup these time-weighted means afterwards as described by Goldberg et al., (2009).\n\n\n\nSoil Analysis\nSoil cores were harvested at the end of  the pre-incubation period and thereafter \nevery two weeks in order to compare changes over time. Total carbon (TC) \nwas detected by a LECO 2000 CN analyser. The particle size distribution was \ndetermined by the pipette method (Gee and Bauder, 1986). Soil pH was measured \n\n\n\nShah and Gaebler\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 99\n\n\n\nin 1: 2.5 (soil:0.01 M CaCl2) using glass electrode pH meter. Soil mineral N (NH4\n+ \n\n\n\nand NO3\n-) was extracted in a 1 M KCl solution (soil/liquid ratio 1: 5 w/w) as \n\n\n\nreferred to by Keeney and Nelson (1982). The filtrates were then analysed on an \nautomated flow injection analysis (Brann en LuebbeTrAAcs 800 Auto analyzer). \n\n\n\nNet nitrogen nitrification (NNN) mg kg-1 was calculated as final concentration \nof NO3\n\n\n\n--N minus initial concentration of NO3\n-. Likewise, net nitrogen \n\n\n\nmineralisation (NNM) mg kg-1 was assumed as final concentration of NO3\n- plus \n\n\n\nNH4\n+, minus initial concentration of NO3\n\n\n\n- plus NH4\n+.\n\n\n\nPhysico-Chemical Properties of Soil\nThe arable soil was cultivated with spring wheat (Triticum aestivum L. cv. Triso) \nand fertilised with 140 kg N, 60 kg K and 30 kg P ha-1. Arable soil contained 1.2% \ntotal carbon (TC) and pH value of 6.5 (CaCl2). Grassland soil exhibited 2% TC \nand pH value of 6.0 (CaCl2), respectively. \n\n\n\nParticle size distribution of the arable soil was 12.6% sand, 58% silt and \n29.4% clay, while the grassland soil was 17.3% sand, 61.7% silt and 21% clay. \nAmmonium and NO3\n\n\n\n- contents of the soil before incubation were 5.3 and 6.1 mg \nkg-1 for arable and 4.3 and 2.4 mg kg-1 for grassland soils, respectively. \n\n\n\nStatistical Analysis\nThe experimental results were statistically evaluated by one way analyses of \nvariance (ANOVA) using the software IBM SPSS Statistics Version 19. The \nleast significant difference (LSD) test (\u03b1=0.05) was used to identify significant \ndifferences among treatments.\n\n\n\nRESULTS\nN2O Cumulative Flow \nOn a cumulative basis, the response of the grassland soils to frequent stress \n(SDWC) with regard to N2O flow was significantly (\u03b1=0.05) higher than those of \ncontrol, MDWC and LDWC treatments (Figure 1). Arable soil behaved differently \nto DWC, where the control (245 \u00b5g kg-1), SDWC (242 \u00b5g kg-1) and MDWC(220.7 \n\u00b5g kg-1) treatments produced significantly higher cumulative flows compared to \nthe LDRW (75 \u00b5g kg-1). \n\n\n\nTemporal Dynamics of N2O over the 2-Month Incubation\nTemporal N2O evolution patterns of both soils by different DWC are depicted in \nFigure 2. With regard to arable and grassland soils against different \u03b8wt, the flows \nstarted rising after the 38th day of incubation. Such an increase was negligible \nin the control (0-10 \u00b5g kg-1 hr-1) treatments of arable soils, but continued to rise \nto 24.09 \u00b5g kg-1hr-1 in grassland soils. During periods of background emission, \nSDWC treatment induced a N2O-N peak of 30.78 \u00b5g kg-1 hr-1 and 24.81 \u00b5g kg-1 \n\n\n\nhr-1 grassland and arable soils, respectively, and only appeared on the 50th day of \nincubation when the treatment returned to the actual moisture content of 32% \u03b8wt \n(Figure 2).\n\n\n\nGreenhouse Gas Emissions from Arable and Grass Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016100\n\n\n\nShah and Gaebler\n\n\n\nFigure 1: Cumulative flow of N2O-N (\u00b5g kg-1) in arable and grassland soils during the \ntwo months\u2019 incubation, when drying-rewetting frequencies were manipulated. Means of \n\n\n\nthree replicates per treatment with standard error.\n\n\n\nFigure 2: Temporal dynamics of N2O-N (\u00b5g kg-1 hr-1) in grassland (a) and arable (b) soils \nagainst different moisture regimes (c). Data points represent means of three replicates \n\n\n\nper treatment with standard error.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 101\n\n\n\nThe flows were within the range of 0.4-1.8 \u00b5g kg-1 hr-1 during the first month \nof all treatments (Figure 2). MDWC and LDWC yielded two higher peaks (16. and \n14.11 \u00b5g kg-1hr-1, respectively) in arable soils within the 2nd month of incubation \n(Figure 2). In MDWC of grassland soils, the flow also rose to 13.08 \u00b5g kg-1 hr-1 \n\n\n\nwhen brought back to 15% \u03b8wt.\n\n\n\nCO2 Cumulative Flow\nBased on the cumulative flow illustrated in Figure 3, after  a 2-month incubation \nperiod, statistically significant (\u03b1=0.05) differences were found between the \ncumulative flows of CO2 by the grass soil receiving LDWC (97.2 mg kg-1) \nand SDWC (130.50 mg kg-1), where SDWC was 25.38% higher than LDWC. \nFurthermore, the arable soil (Figure 3) showed no statistically significant \ndifference in response to the frequency of stress events compared to the control \nwhile the stressed treatments emitted CO2 flows that were only 19.85% higher \nthan the control.\n\n\n\nCO2 Temporal Dynamics over the 2- Month Incubation Period\nEvolution of CO2 was initially higher in all arable soil treatments (Figure 4). \nSDWC behaved similarly to the control during the entire period of incubation. \nFlows dropped from 2 to 0 mg kg-1 hr-1when exposed to a second drying cycle in \nMDWC. The flows of LDWC treatments reduced to nearly 1 from 3.5 mg kg-1 hr-1 \n\n\n\nwhen exposed to the first drying cycle and then remained low. With regard to the \ngrassland soils, a significant increase was detected between 30 and 40 days, about \nhalf way in the incubation period. Flows significantly reduced to 0.5 mg kg-1 hr-1 \n\n\n\nfrom 2.5mg kg-1 hr-1 when the SDWC soils were brought back (Figure 4) to their \n\n\n\nGreenhouse Gas Emissions from Arable and Grass Soils\n\n\n\nFigure 3: Cumulative flow of CO2-C (mg kg-1) in arable and grassland soils during the \ntwo months\u2019 incubation, when drying-rewetting frequencies were manipulated. Means of \n\n\n\nthree replicates per treatment with standard error.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016102\n\n\n\ninitial \u03b8wt (32%). An immediate response in the MDWC treatment occurred when \nexposed to the third drying cycle. Long drying-rewetting events significantly \nsuppressed CO2 flows during the entire incubation period. \n\n\n\nFigure 4: Temporal dynamics of CO2-C (mg kg-1 hr-1) in grassland (a) and   arable \n(b) soils against different moisture regimes (c). Data points represent means of three \n\n\n\nreplicates per treatment with standard error.\n\n\n\nNitrogen Concentration\nNitrogen concentration was affected in all treatments regardless of stress events. \nAmmonification increased significantly in MDWC and LDWC treatments in both \nsoils gradually with time in the control treatment of arable soils (Figures 5 and \n6). Ammonium content in the SDWC treatment of arable soils steadily declined \n(Figure 5), followed by a rapid increase in nitrate content (4.75-36.51 mg kg-1). In \ncontrast, in the grassland soils (Figure 6) NH4\n\n\n\n+ content decreased thereby increasing \nNO3\n\n\n\n-, but only minutely. \nWith regard to MDWC treatments of grass and arable soils, NH4\n\n\n\n+ increased \nslightly and decreased sharply at the end, but NO3\n\n\n\n- continued to rise in arable soils \n(Figure 5) yet remained constant in grassland soils (Figure 6). In grass soils, a \nsignificant increase in NH4\n\n\n\n+-N content took place in MDWC (2.32-4.01 mg kg-1) \n\n\n\nShah and Gaebler\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 103\n\n\n\nGreenhouse Gas Emissions from Arable and Grass Soils\n\n\n\nFigure 5: Figure 5: NH4\n+ and NO3-(mg kg-1) contents of different arable soil (a) \n\n\n\ntreatments against different moisture regimes (b). Data points represent means of three \nreplicates per treatment with standard error.\n\n\n\nFigure 6: Figure 6: NH4\n+ and NO3- (mg kg-1) contents of different grassland soil (a) \n\n\n\ntreatments against different moisture regimes (b). Data points represent means of three \nreplicates per treatment with standard error.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016104\n\n\n\nand LDWC (1.47-3.46 mg kg-1) and in the LDWC treatment of arable soil as well \n(2.5-4.0 mg kg-1). However, NO3\n\n\n\n- contents of arable soils were significantly higher \nthan in grassland soils in all cases (Figures 5 and 6).\n\n\n\nDISCUSSION\n\n\n\nN2O Flow During a Two-Month Incubation Period and N concentration\nDistinct temporal patterns of N2O flows from control and SDWC treatments of \ngrass soils, as well as the results obtained for N mineralisation, when using different \nmoisture conditions, reveal that grass soil induced N2O flows were weakly related \nto the NO3\n\n\n\n- content that might have been denitrified. Nevertheless, the presence \nof only NO3\n\n\n\n- does not ensure denitrification (Kunickis et al., 2010). Thus, it can \nbe postulated that N2O flows were not only due to microbial nitrification, but \nalso due to the limited availability of total organic carbon(McCarty and Bremner, \n1992). On the other hand, such short and temporary drying-rewetting frequency \nenhanced denitrifier activity by availing physically protected organic matter \n(Fierer and Schimel, 2002). The behaviour of both soils (Figures 5 and 6) under a \nconstant moisture regime (32% \u03b8wt) is different in the second half of the incubation \nperiod, where significantly higher N2O emission was observed.\n\n\n\nBateman and Baggs (2005) and Ciarlo et al., (2008) pointed out the \npredominance of nitrification between 18-21% volumetric water content in the \nsoil and that continuous moisture treatments influenced release of N2O. It can \nbe assumed that in arable soils, most of the organic carbon was lost as CO2 at \nthe beginning of the incubation. However, NO3\n\n\n\n--N content of arable soils was \nsignificantly higher, but organic carbon was not available for the denitrifiers. The \nactivity of denitrifiers might have accelerated when the MDWC finished its first \nand second drying cycle. \n\n\n\nLong drying significantly reduced N2O temporal and cumulative flows in both \nsoils indicating that the activity of nitrifying and denitrifying microorganisms was \ndisrupted (Bottner 1985). Significantly lower N2O production from nitrification \nat low water content (below 40% WFPS) has been reported previously (Dalal et \nal., 2003). Likewise, Smith and Parsons (1985) postulated that drying results in a \nlarge decrease in the number of denitrifiers and their membrane bound denitrifying \nenzyme system. \n\n\n\nMineral Nitrogen Concentration Affected by Soil Moisture and Temperature\nAmmonium content decreased due to a continuous moist state, but not in the \nstressed treatments of the grassland soils (Figure 6). The other possibility could \nbe that NH4\n\n\n\n+ might have been  promptly converted to NO3\n-. The favourable \n\n\n\ntemperature (20\u00b0C) had a measurable effect on the oxidation of NH4\n+ to NO3\n\n\n\n- \n\n\n\n(Parker and Larson, 1962). Moisture manipulation enhanced the ammonification \nprocess in arable and grassland soils and was significantly higher in MDWC and \nLDWC (Figure 5). It can be concluded that ammonification in the LDWC and \nMDWC treatments responded significantly to drying-rewetting events in the \n\n\n\nShah and Gaebler\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 105\n\n\n\ngrass soils. The considerable increase in NH4\n+ content in MDWC and LDWC \n\n\n\nis likely due to the mineralisation of the amount of organic N because of soil \ndrying (Appel, 1998). The NH4\n\n\n\n+ content of arable soil was initially higher than \nin grass soil, which might be due to fertiliser application to the crop in the field. \nNitrate concentration shot up significantly between days 12-24 in all treatments \nregardless of stress events; either the nitrifiers were in a more active state (Abera \net al., 2012) or they were stress resistant (Fierer and Schimel, 2002). In arable \nsoils, the NNM and NNN were significantly higher than in the grassland soils \nin all treatments revealing the internal cycling of nitrogenous compounds into \nbiologically available forms (Strauss, 2000). Higher NH4\n\n\n\n+ contents indicate the \nslow mineralisation or reduced availability of indigenous N in grassland soils \nrather than arable soils. On the contrary, Fierer and Schimel (2002) found no \nsignificant differences between stressed and unstressed oak and grassland soils \nwith regard to NO3\n\n\n\n- concentration. They proposed that nitrifier population might \nbe able to survive during the drying periods. With regard to NNN, the NO3\n\n\n\n- has \nbeen denitrified. However, under natural grassland conditions, the quantification \nof available N is difficult as NH4\n\n\n\n+ is rapidly converted to NO3\n- and both are taken \n\n\n\nup by developed grass root systems.\nThe gradual increase in NO3\n\n\n\n- with time (Figures 5 and 6) in manipulated \nand control soils can be justified in both cases by explaining that NH4\n\n\n\n+ has been \ntransformed to NO3. The most likely explanation is that nitrifiers were stress \nresistant and survived (Fierer and Scimel,  2002) in arable soils. \nTemporal Dynamics of CO2. \n\n\n\nCarbon mineralisation trends observed in arable and grassland soils in \nrelation to CO2\n\n\n\n-C flow are opposite. The manipulations affected grassland soils \nwith different VWC during the first month of incubation, while arable soils proved \nmore responsive with different \u03b8wt during the second month of incubation. The \nflows associated with arable soils were initially higher and afterwards decreased \n(Figure 4) under all treatments. When brought back to the actual moisture content \n(32% \u03b8wt), LDWC was quite efficient in its evolution of CO2\n\n\n\n-C flows. Later, an \nincrease in CO2 flows in the SDWC and MDWC of grass soils (Figure 4) were \nidentical to the control. Microbes managed to survive at 21% \u03b8wt and 15 % \u03b8wt.\n\n\n\nThe lower temporal and cumulative flow related to the treatment with LDWC \nin grassland soils may be ascribed to the reduction in microbial activity at a \nmoisture regime of 0.05% \u03b8wt due to moisture deficiency (Stark and Firestone, \n1995; De Nobili et al., 2006) or even death of microbes (Chen and Alexander \n1973). But it is noted that the flows were initially higher as seen in the other \ntreatments. \n\n\n\nThe findings related to the grass soils are supported by the results published \nby Thomson et al. (2010), who detected significantly higher CO2 emission from \nshortly dried and rewetted microcosms than those of longer dried. Significantly \nhigher CO2 flows in arable soils as compared to grassland soils show the availability \nof carbon as a substrate (Brant et al., 2006) due to the decomposition of organic \n\n\n\nGreenhouse Gas Emissions from Arable and Grass Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016106\n\n\n\nmatter, likely because of higher microbial activity due to N fertiliser application \nat sowing time and possibly its re-mineralisation afterwards. \n\n\n\nA late appearance of peaks in the control (32 %), SDWC (21 %), MDWC \n(18 %) and LDWC (5 %) after one month of incubation was a result of the \ndecomposition of very minute roots (mixed with soil), which might have been \na major source of organic C (Herman et al., 1977). Furthermore, no statistically \nsignificant differences were found between the cumulative flow of the control and \nthe stressed soils (Figure 3). The results are in agreement with Muhr et al. (2008), \nwho found no differences among CO2 flows of constantly moist soil and drying-\nrewetting treatments. \n\n\n\nCONCLUSION\nThe factors that influence carbon and nitrogen mineralisation leading to N2O \nand CO2 emissions are influenced by a range of factors: temperature, fertiliser \napplication and organic carbon availability. Moreover, for CO2 and N2O \nproduction, we can conclude that the variation between soils at different periods \nof emission was accounted for by differences in organic carbon availability. \nGravimetric water content (5%) appeared to be effective in reducing CO2 and N2O \nemissions in both soils. However, NO3\n\n\n\n- content was significantly higher in arable \nsoils than in grass soils. Thus, it is concluded that long drying and rewetting can \nincrease NH4\n\n\n\n+ but not NO3\n-.\n\n\n\nHigh doses of nitrogen fertiliser result in low N utilisation by denitrifiers \nand a high risk of water contamination by nitrates leading to further research \non the judicious use of N on wheat crops. Grassland soils can be N retaining if \nnot frequently moistened. As far as the control treatment is concerned, it can be \nargued that the soils under continuous moist conditions, or frequently rewetted, \ncould undergo nitrification thereby increasing N losses as N2O or as ground water \nleaching losses.\n\n\n\nREFERENCES\nAbera G., Wolde-Meskel E., Beyene S., Bakken L.R. (2012): Nitrogen mineralization \n\n\n\ndynamics under different moisture regimes in tropical soils. International \nJournal of Soil Science. 7: 132-145.\n\n\n\nAppel T. (1998): Non-biomass soil organic N. 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"\n\n___________________\n*Corresponding author : E-mail: irfan.afip@gmail.com\n\n\n\nINTRODUCTION\nGiven rapid urbanisation all over the world, peat is likely to become one of the \nsoil foundations for development. That is why it is important to understand the \ncharacteristics and structure embedded inside peat, because these play a vital role \nin its tensile strength. This information is necessary for geotechnical engineers \nand developers to help them understand foundation laying of buildings. In this \nresearch, the focus was more on characteristics that provide tensile strength for \npeat. Peat is known for its low shear strength and tensile strength, thus developers \ntend to permanently remove it, rather than working on peat as a foundation for \nconstruction. This research will also help to provide further information on \nstabilising peat for geotechnical engineers.\n Peat originates from inhibited decomposition of various plant materials \nin a waterlogged environment of marshes, bogs and swamps (Asadi et al. 2010). \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 1-12 (2019)                          Malaysian Society of Soil Science\n\n\n\nProperties of a Tropical Sapric Peat Soil in Sarawak\n\n\n\nI.A. Afip1 and K. Jusoff2\n\n\n\n1 Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia \nSarawak, 94300 Kota Samarahan, Sarawak, Malaysia.\n\n\n\n2 Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, \n94300 Kota Samarahan, Sarawak, Malaysia.\n\n\n\nABSTRACT\nKnowing peat characteristics and its microstructure is of grave importance to \nagriculturists, engineers and developers. This paper aimed to determine the degree \nof humification of tropical peat in the state of Sarawak, Malaysia. The peat under \nstudy was extracted from Sarawak and the degree of humification was determined \nbased on its physical properties. Data was collected using seven parameters based \non the American Society for Testing and Materials (ASTM) and British Standard \n(BS) method while peat assessment was done using the scanning electron \nmicroscope (SEM). The micrograph of peat shows colloidal granular particles \nwith no visible hollow cellular connections. Our findings show that the peat index \nproperties reflect the effect of decomposition and influence of fabric composition \non its geotechnical properties. The degree of humification was successfully \nidentified as H7. This implies that the peat sample is highly decomposed in the \nsubsurface. Further research using X-Ray diffraction should be able to correlate \nthe degree of humification with the shear strength of the peat to obtain a better \nunderstanding of peat\u2019s microstructure.\n\n\n\nKeywords: SEM, microstructure, peat, humification\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 20192\n\n\n\nNatural peats that are formed by complex humification processes from plant residues \nare important reservoirs of refractory organic carbon in aquatic environments. \nPeat also has high organic content as it is a result of full decomposition of plant \nremains. The organic materials range from woody coarse-fibres (fibrous) to \nfine-fibres (hemic) and amorphous (sapric). The materials depend highly on the \nparent plant, environmental condition and degree of decomposition (Gylland \net al. 2013). The high rainfall and hot temperature of Southeast Asia allows for \npeat deposits to form and decompose rapidly. Then, there is also the influence of \npenetrating air and the combination of immense heat with humidity (Huat et al. \n2013). Besides, microbes, for instance, fungi, bacteria and microflora, hasten the \nbreakdown of plant remains in aerobic conditions while the accumulation of plant \nremains intensifies during anaerobic conditions (Pastor et al. 2017). When oxygen \nis reduced in a water-saturated environment, an anaerobic condition prevails. \nTemperature, acidity and nitrogen level also affect the rate of decomposition \n(Kazemian et al. 2011). As studies on microstructure and decomposition effects \nare limited, good data is not available for the study of peat settlement and strength \nbehaviour.\n Due to its high content of humic substances, natural peats exhibit favourable \nchemical-physical properties enabling its useful application in various areas, for \nexample, wastewater treatment, pollution monitoring, fuel production, and soil \nfertilising. Past research has established that the ability of humic substances to \nbind heavy metal ions can be attributed to its high content of oxygen that contains \nfunctional groups such as carboxyl, phenol, hydroxyl, enol and structures of \ncarbonyl (Xiao et al. 2017). Humic substances are classified according to their \nsolubility in water as humic acids, fulvic acids and humin. The fractions with \na high porous level exhibit a surface area that is very large. The pores available \nfor the internal surface are useful in the adsorption process (Mutalib et al. 1991). \nDue to its heterogeneity and natural variety, chemical attributes of peat can vary \nwidely among peat deposits.  The characterisation of isolated peat samples and its \nmanifold effects is still a challenging task in environmental analytical chemistry \nas it requires efficient combinations of powerful chemical and spectroscopic \nmethodologies. Further, peat is composed mostly of humus which can generate \na lot of methane gas (CH4), which is a contributor to greenhouse gases that are \nconsidered a major cause of global warming. The emission from peat to the \natmosphere is dependent on the rates of methane production and consumption \nand the ability of plants and soil to act as a medium that transports the gas to the \nsurface (Lulie et al. 2005).\n Our study on soil index properties and microstructure of decomposed \npeat was conducted in the laboratory. Soil index properties involve moisture \ncontent, particle density, pH, loss on ignition, and organic and fibre content test. \nThe microstructure of Sarawak peat samples and its properties were studied \nthrough scanning electron microscope (SEM). A multi-method concept is an \nimportant prerequisite for a reliable assessment of beneficial peat effects on the \nenvironment.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 3\n\n\n\nMATERIALSAND METHODS\n\n\n\nSite Description \nOn Monday, 6th March 2017 at 10.00 am in the morning, the samples were \nretrieved from Kampung Meranek, Kota Samarahan, Sarawak. The weather \ncondition during the extraction of the samples was sunny and the coordinates for \nthe extraction point was 1\u00b025\u201937\u00b0N, 110\u00b027\u201933\u00b0E. The area where the peat was \nextracted was surrounded by a pineapple plantation.\n\n\n\nSoil Sampling\nAn auger was used to extract the sample peat from 2m depth. An in-situ Von \nPost Scale test was conducted by squeezing the peat to determine the scale of \nhumification. Subsequent to extraction of the peat, samples were kept in hard \nplastic bags that were completely sealed to retain the moisture of the soil and to \navoid any external contamination that might affect soil properties.\n\n\n\nLaboratory Analysis\nMoisture Content\n This analysis was based on BS 1377: Part 2 1990. An empty crucible \nwas weighed and labelled w3. Then the soil specimen was also weighed at an \napproximate weight of 20g and placed into an empty crucible labelled w1. The \ncrucible that was filled with the soil specimen was placed in an oven set at 105oC \nand this was labelled as w2. The specimen was dried in the oven for a night. The \nprocedure was repeated three times to collect the average percentage moisture \ncontent. Finally, the percentage of moisture content was calculated using the \nfollowing equation:\n  \n w% = [(w1 - w2)/(w2 - w3)] x 100%  (1)\n\n\n\nwhere\nw1 = weight of empty crucible + saturated soil (g)\nw2 = weight of empty crucible + oven dried soil (g)\nw3 = weight of empty crucible\n\n\n\nMoisture content of undisturbed peat sample:\n\n\n\nTime placed in an oven  14.05 pm 6 March 2017\n\n\n\nTime taken out of an oven 14.15 pm 7 March 2017\n\n\n\nTotal time in an oven   24 h and 10 min\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 20194\n\n\n\nMoisture content:\n1st Attempt: 1220%\n2nd Attempt: 1131.25%\n3rd Attempt: 1058.82%\nAverage: 1136.69%\n\n\n\nParticle Density\nParticle density was determined by referring to BS1377: 1990 Test 2. Particle \ndensity is the ratio of a unit of dry soil sample to a unit of water. A small pycnometer \nwas used for this analysis. The formula below was used to calculate the particle \ndensity of the soil.\n\n\n\n  GS = [(M2-M1)/[(M4-M1)-(M3-M2)]]  (2)\nwhere\nm1 = mass of pycnometer +stopper (g)\nm2 = mass of pycnometer + stopper + dry soil (g)\nm3 = mass of pycnometer + stopper + soil with water (g)\nm4 = mass of pycnometer + stopper when full of water (g)\n\n\n\nParticle Density Data:\n\n\n\n-Density of Kerosene = 810 kg/m3 = 0.81 mg/m3\n\n\n\nPycnometer number    A B C\n\n\n\nMass of bottle + peat + kerosene (m3)(g)  75 75 75\n\n\n\nMass of bottle + peat  (m2)(g)  49 49 49\n\n\n\nMass of bottle full of kerosene (m4)(g)  70 71 71\n\n\n\nMass of bottle   (m1)(g)  31 32 31\n\n\n\nMass of peat   (m2-m1)(g) 18 17 18\n\n\n\nMass of kerosene in full bottle (m4-m1)(g) 39 39 40\n\n\n\nMass of kerosene used  (m3-m2)(g) 26 26 26\n\n\n\nVolume of peat particles  (m4-m1)-\n    (m3-m2)(ml) 13 13 14\n\n\n\n Particle density, GS (mg/m3)  1.38 1.31 1.29\n\n\n\n \nParticle density   GS  1.33\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 5\n\n\n\nLoss on Ignition\nThe loss on ignition of peat can be determined by the percentage of oven dried \nmass. The calculation of loss on ignition can be retrieved by applying this formula:\n \n N% = [(m2 - m3)/(m2 - m1)]  x100% (3)\n\n\n\nwhere\nm1 = mass of crucible (g)\nm2 = mass of crucible and dry soil (g)\nm3 = mass of crucible and dry soil after ignition (g)\n\n\n\nOrganic Content\nThe analysis was based on BS 1377: 1990 Test 3. The percentage of organic \ncontent was calculated using this formula:\n  OC% = 1-1.04(C-N)   (4)\nwhere\nC = 1 for 4400o or 1.04 for 5500o temperature\nN = ignition loss\n\n\n\nLoss on ignition and organic content of dried peat sample\nMass of crucible after 1 h in furnace\nm1a = 38.97 g m1b = 35.98 g m1c = 39.95 g\n\n\n\nMass of crucible and dried oven sample\nm2a = 46.34 g m2b = 42.53 g m2c = 46.22 g\n\n\n\nMass of crucible and sample after 5 h in furnace\nm3a = 39.62 g m3b= 36.38 g m3c = 40.56 g\n\n\n\nLoss on ignition:\n1st attempt: 91.18%\n2nd attempt: 93.89%\n3rd attempt: 90.27%\nAverage: 91.78% & Organic content: 91.27%\n\n\n\nFibre Content\nThe fibre content can be determined from the dry weight of the fibres retained \non the 150\u00b5m sieve. Approximately about 100g of soil specimen was placed into \na sieve and soaked into the water for 24 h and washed until all the soil particles \npassed through the sieve. The sample specimen was then kept in the oven for one \nnight and weighed. The fibre content was calculated by applying this formula.\n \n Fibre Content nt = [(M2 - M1)/100] x 100% (5)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 20196\n\n\n\nwhere\nm1 = mass of 150 \u00b5m sieve\nm2 = mass of 150 \u00b5m sieve + retained soil\n\n\n\nMass of 150 \u03bcm sieve\nm1a= 30.1 g m1b = 30.1 g m1c = 30.1 g\n\n\n\nMass of 150 \u03bcm sieve + retained soil\nm2a = 35.8 g m2b = 35.8 g m2c = 35.8 g\n\n\n\nFibre content\n1st attempt: 5.7%\n2nd attempt: 5.7%\n3rd attempt: 5.7%\nAverage: 5.7%\n\n\n\nBulk Density\nFor determination of bulk density of soil samples, the simplest method is to \nmeasure volume and mass or weight.\n\n\n\n P\u03b2 = M/Va \n\n\n\nwhere P\u03b2 is bulk density (g/cm3), M is mass (g), and Va is the bulk volume \nincluding both solid and pore volume (cm3). If mass M is measured using dry peat \nsample, P\u03b2 is the bulk dry density while bulk wet density is determined by the \nmass of the peat sample at a water saturated state.\n\n\n\nPeat pH Value\nPeat is naturally acidic, and this test proved that it is acidic in its natural state. The \ndata was extracted on site by submerging the pH meter into the subsoil to retrieve \nthe readings.\n\n\n\nMicrostructure Analysis\nThe peat samples were first air dried at room temperature for approximately two \nweeks. This was to prevent the organic material from crumbling when exposed to \ndrying in the oven at a high temperature. Then, the air-dried sample was smashed \nusing a rubber hammer and kept in an airtight polythene bag. Next, the sample \nwas sprinkled on 5 mm aluminium stubs, and overlaid with double-sided carbon \ntape as a form of adhesive to attach the sample to the stubs. The sample was \ncoated with a thin layer of gold in a sputtering diode system for 10 min to allow \nrepulsion of the scattered electron that hits the surface of the sample when it is \nplaced into the SEM.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 7\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPeat Properties\nThe scale of humification for the retrieved peat sample was H7 which can be \ncategorised as sapric type of peat, as it contained less than 33% of fibre content \nand its capacity to trap water was small compared to fibric or hemic type of peat. \nFurthermore, it had low permeability, compressibility and fiction angle while its \ncoefficient of earth pressure increased at rest. Table 1 gives the moisture content, \nloss of ignition, organic content, fibre content, bulk density, particle density and \npH values as 1136.69%, 91.78% 91.271% 5.7% 1.15 mg/m3, 1.33, and pH 3.57, \nrespectively. \n\n\n\nTABLE 1\nPhysical properties index for sapric type peat\n\n\n\nParameters Value\nVon post degree of humification H7\nMoisture content 1136.69%\nLoss on ignition (LOI) 91.78%\nOrganic content 91.27%\nFibre content 5.7%\nBulk density 1.15 mg/m3\n\n\n\nParticle density 1.33\npH 3.57\n\n\n\nMoisture Content\nMoisture content of the peat samples was not affected or influenced by the \nsurrounding environment. The results are presented in Table 2 which shows that \nthe average moisture content for the peat samples is 1136.69% which is more than \n200%.  Thus, the peat samples can be categorised as sapric peat.\n\n\n\nTABLE 2\nMoisture content of peat samples\n\n\n\nSample number 1 2 3\nMoisture content, \n%\n\n\n\n1220 1131.25 1058.82\n\n\n\nAverage moisture, \n%\n\n\n\n1136.69\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 20198\n\n\n\nLoss on Ignition (LOI) and Organic Content\nThe above two tests were done after conducting a moisture content test because \ndried samples are needed to conduct these two tests. The results are shown in Table \n3. The samples collected from the study area (Kampung Meranek) possessed a \nhigh organic content range of above 75%. The LOI percentage depends on the \ncapability of a soil sample to ignite when it is exposed to high temperatures. \nCertain peat samples can easily ignite when exposed to high temperatures and \ntend to lose a lot of their mass in this manner.\n\n\n\nTABLE 3\nLoss on ignition and organic content of peat sample\n\n\n\nTABLE 4\nParticle density of peat sample\n\n\n\nSample number 1 2 3 Average\n\n\n\nLoss on ignition, % 91.18 93.89 90.27 91.78\n\n\n\nOrganic content, % 90.27 93.65 89.88 91.27\n\n\n\nFibre Content\nThe fibre content test was only 5.7% indicating that that soil samples did not \ncontain much fibre.  As this result is rated below 33%, this peat sample is \nconsidered a sapric type of peat. It is also known that this type of peat has organic \nmatter composition ranging from medium to high.\n\n\n\nBulk Density\nThe bulk density value of 1.15 mg/m3 had been obtained during soil sample \nretrieval. When the bulk density obtained ranges between 0.8 and 1.2 mg/m3, it is \ncategorised as a typical bulk density of peat.\n\n\n\nParticle Density\nThe data collected for particle density test are shown in Table 4. The average \nvalue for particle density was 1.33 while the range for the density of the peat \nparticle from Samarahan, Sarawak peat was from 1.07 to 1.63.\n\n\n\nSample number 1 2 3\n\n\n\nParticle density, GS (mg/m3) 1.38 1.31 1.29\n\n\n\nAverage value, GS 1.33\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 9\n\n\n\npH Value\nAn average pH value of 3.57 was obtained. Table 5 shows that the values settled \nbetween the range of pH 3.56 to pH 3.58 which is an average pH value for \nSarawak peat.\n\n\n\nTABLE 5\npH test of peat sample\n\n\n\npH Test number pH value, pH\n\n\n\n1 3.57\n\n\n\n2 3.58\n\n\n\n3 3.56\n\n\n\nAverage 3.57\n\n\n\nMicrostructure Study and Analysis\nScanning electron microscope (SEM) is an electron microscope that is able to \ncapture and observe an image of a sample by scanning its surface with a focused \nbeam of electrons. The electrons tend to interact with the atoms in the sample. It \nwill then form a raster scan pattern, and the position of the beam will be combined \nwith the signal detected to produce the image. SEM can reach a resolution of \nbetter than 1 nanometre. The specimen can be observed in a high or low vacuum \ncondition, or even in wet conditions under multiple pressure. The micrograph \nmagnification range is from x100\u00b5 to x 500\u00b5.\n The images were taken at various micrograph magnifications of the \nmicrostructures (Figures 1, 2, 3). The microstructure is homogeneous non-\ncrystalline micro-particles that are colloidal in structure and jelly-like in texture. \nFigures 2, 4 and 5 show that peat is slightly compact, and it can be observed in \nFigures 4, 5 and 6 that the particles have a honeycomb and poriferous structure. \nThis condition shows peat to be spongy and fragile and can rupture due to the void \nwhich can be exploited for an increase in water retention. This is attributed to its \ncomposition of fibric organic content that has more porous space. The colloidal \nstructure in Figures 7 and 8 do not indicate any intra-assemblage pore space, \nbut surfaces seem to be bonded by pore water. The colloidal microstructure in \npeat indicates a highly humified granular material that ranges from H7 to H10 \nin the Von-Post classification. Organic colloids are very small in size (less than \n2 \u00b5m) because it is formed from humified peat with less fibre. It is a product of \nsecondary synthesis of peat and is chemically active due to its small size and high \nelectrical charge. Furthermore, no hollow perforated cellular structure was found \nin the SEM micrographs. The intra-particle space in peat was reduced, and the \nwater storage capacity was hindered because only water can fill the inter-particle \nvoids. Moreover, the moisture content in peat is lower than that of fibrous peat. \nThe fibrous peat can store water within the hollow cellular fibres and in the inter \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201910\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 11\n\n\n\nparticle voids between the fibres. This will cause the peat particle to become more \ngelatinous and compact with a relatively high particle density.\n\n\n\nCONCLUSIONS\nIt can be concluded that the samples were dominated by highly decomposed \norganic contents using the soil index properties and SEM. The particle density of \n1.33 is higher than the other types of peat, and the decrease in water content shows \na reduction in fibrosity. The SEM micrograph of the fibrous peat showed a hollow \ncellular structure which had disintegrated into a colloidal texture. Hence, the \ngelatinous, compact texture of peat resulted in non-availability for water storage \nin the intra-particle arrangement. Therefore, this peat had a high void ratio where \nits compressibility can be assumed to be as high as its void spaces. This research \nmakes a contribution to areas of soil sciences and agricultural engineering as \nit provides enhanced understanding of peat characteristics. It is suggested that \nfuture studies use techniques such as the X-Ray Diffraction (XRD) to improve the \naccuracy of the current work.\n\n\n\nConflict of Interest\nThe authors declare that there is no conflict of interests regarding the publication \nof this paper.\n\n\n\nFunding Statement\nThis research did not receive any funds.\n\n\n\nREFERENCES\nAsadi, A., B.K. Huat, M.M. Hanafi, T.A. Muhammad and N. Shariatmadani. 2010. \n\n\n\nPhysicochemical sensitivities of tropical peat to electrokinetic environment. \nGeoscience Journal 14(1): 67-75.\n\n\n\nBritish Standard BS 1377.1990. British standard methods of test for soils for civil \nengineering purposes part 1\u20139. British Standard Institution London. BS 1377-\n2:1990, ICS: 93.020, ISBN: 0 580 17867 6.BSI, 6pp..\n\n\n\nGylland, A.S., M. Long, A. Emdaland R. Sandven. 2013. Characterization and \nengineering properties of Tiller clay. Engineering Geology 164:86-100.\n\n\n\nHuat B.B.K., A. Prasad, A. Asadi and S. Kazemian. 2013. Geotechnics of organic \nsoils and peat. Croydon: CREC Press/Balkema. Assessed on 26 Feb. 2017 from \n\n\n\n h t t p s : / / s c h o l a r . g o o g l e . c o m / c i t a t i o n s ? v i e w _ o p = v i e w _\ncitation&hl=en&user=ycDHr-wAAAAJ&citation_for_view=ycDHr-\nwAAAAJ:kNdYIx-mwKoC. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201912\n\n\n\nKazemian S., B.B.K. Huat, A.Pasad and M. Barghchi.2011. A state of art review \nof peat: Geotechnical engineering perspective. International Journal of the \nPhysical Science 6 (8):1974-1981.\n\n\n\nMutalib A. Ahmad, J.S. Lim,M.H. Wong and I. Koonvai. 1991. Characterization, \ndistribution and utilization of peat in Malaysia. Symposium on Tropical Peat \nLand, pp. 6-10, 8-10 May 1991, Kuching Sarawak.\n\n\n\nLulie M., H. Ryusuke and J.G. Kah 2005. Methane fluxes from three ecosystems in \ntropical peatland of Sarawak, Malaysia. \n\n\n\nXiao W., Y. Gui and G.  Xu.  2017. Effect of organic content and frequency on \ndegradation and pore pressure in marine organic soils. Marine Georesources \nand Geotechnology Journal 36(1):108-122.\n\n\n\n\n\n"
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"\n\n___________________\n*Corresponding author : E-mail: saber.heidari@yahoo.com\n\n\n\nINTRODUCTION\nContamination of soils by heavy metals is one of the most important environmental \nissues throughout the world, and the cleanup of these soils is a difficult task. \nOne possible decontamination technique is ex-situ soil washing using a variety \nof agents such as acids, surfactants, electrolytes and chelating agents. Chelating \nagents are the most popular extraction reagents for soil washing. Since chelating \nagents such as ethylene-diaminetetraacetic acid (EDTA), ethylenediamine-\nN,N\u2019-disuccinic acid (EDDS), diethylene triaminepentaacetic acid (DTPA) \nand nitrilotriacetic acid (NTA) form stable complexes with most heavy metals \nover a broad pH range, they have proven to be the most efficient at heavy metal \nremoval. Unfortunately, they also have disadvantages such as persisting in \nthe environment (particularly EDTA), adversely affecting health (particularly \nNTA) and being expensive (particularly EDDS), which have excluded their \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 141-155 (2015)                     Malaysian Society of Soil Science\n\n\n\nMobilisation of Heavy Metals from a Contaminated \nCalcareous Soil Using Organic Acids\n\n\n\nHeidari, S.*, S. Oustan, M.R. Neyshabouri and A.Reyhanitabar\n\n\n\nDepartment of Soil Science, Faculty of Agriculture, University of Tabriz. Iran\n\n\n\nABSTRACT\nEthylenediaminetetraacetic acid (EDTA) is an effective chelating agent in \nremoving heavy metals from contaminated soils, although extraction efficiency \ndepends on many factors. Moreover, extraction of heavy metals by low molecular \nweight organic acids (LMWOAs) of  citrate, oxalate, and acetate is likely to be \nmore representative of the available fraction to plants. This study examined the \npotential of these two chelating agents to decontaminate a calcareous soil (with \na total heavy metal concentration of 80.6 mmol kg-1) from the zinc-lead smelting \nplant area in Zanjan Province, Iran. This was carried via 12 successive washings \nof soils with 0.01 M concentration chelating agent. For EDTA and citric acid, the \norders of extraction efficiency of heavy metals (in decreasing order) were found to \nbe lead (Pb)>zinc (Zn)>cadmium (Cd), and Zn > Pb > Cd, respectively. Oxalic acid \nremoved more Zn than Cd. Acetic acid removed more Cd than Zn. Neither showed \nany efficiency for Pb removal. Therefore, EDTA and citric acid were efficient \nextraction agents for Pb and Zn, respectively. However, both of these agents \nwere unable to efficiently remove Cd from the soil, even at high concentrations.  \nThis study found that chelating agents showed different efficiencies in removing \na variety of heavy metals from contaminated soils and more than one chelating \nagent may be necessary to optimise cleanup efforts. \n \nKeywords: Citiric acid, oxalic acid, acetic acid, chelating agent, EDTA.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015142\n\n\n\nuse in remediation of metal contaminated sites (Tandy et al., 2004). There are \nadvantages and disadvantages associated with the use of EDTA. EDTA is able to \nform stable complexes with a wide variety of metals (non-selective nature). The \nextraction is pH-independent and takes place quickly. Moreover, EDTA has been \nrecognised to improve phytoextraction. Additionally, EDTA solution offers good \npossibilities for recovery and recycling. Among the disadvantages are the high \ncost, non-selective nature and low degree of biodegradability which have limited \nits use. Moreover, the washing efficiency of EDTA is dependent on the source of \nmetal contamination in the soil and on metal distribution among the soil fractions \n(Barona et al., 2001).\n\n\n\nLow molecular weight organic acids (LMWOAs) are alternative agents for \nthe extraction of heavy metals from the soils. In contrast to strong acids, LMWOAs \ncause less damage to the soil\u2019s crystalline structure over extended contact times \n(Yu and Klarup, 1994). Natural LMWOAs include oxalic, citric, acetic, lactic and \nmalic acids which are natural products of root exudates, microbial secretions, \nand plant and animal residue decomposition in soils (Naidu and Harter, 1998).\nThus, metal dissolution by organic acids is likely to be more representative of a \nmobile metal fraction that is available to plants (Labanowski et al., 2008). Metals \nextracted by a mixture of organic acids are well-correlated with the mobile metal \nfraction in the soil solution (Naidu and Harter, 1998). The chelating LMWOAs \nare able to dislodge the exchangeable, carbonate, and reducible fractions of heavy \nmetals via washing procedures (Peters, 1999). Citrate has been reported to be one \nof the LMWOAs in soil solution. Among the LMWOAs used to simulate metal \nmobilisation, citric acid presents a high metal complexation strength (Labanowski \net al., 2008; Huang et al.,2008).\n\n\n\nMany researchers have studied various natural and synthetic chelating agents \nfor their ability to remediate soils contaminated by heavy metals. Determining the \neffectiveness of a chelating agent for washing soils contaminated by heavy metals \nhas commonly been accomplished in one-step batch extractions at the laboratory-\nscale (Andrade et al., 2007). Such extractions often encounter some limitations \nor problems. For example, reverse reactions and precipitation of the released \nspecies must be taken into account in the data analysis. For these reasons multi-\nstep (successive) extractions are preferred. The objective of this study was to \nassess the potential of four organic acids to decontaminate a highly contaminated, \ncalcareous soil via successive washings.\n\n\n\nMATERIALS AND METHODS\nThe zinc (Zn), lead (Pb) and cadmium (Cd) contaminated soil used in this research \nwas sampled from an orchard located near a local zinc-lead smelting plant in \nZanjan Province, Iran. The soil sample was air dried and ground to pass through \na 2-mm sieve, then homogenised and stored for further analyses. Soil pH was \ndetermined in a suspension with a soil to water ratio of 1:1 (McLean, 1982). \nElectrical conductivity was measured in a saturation extract (Richards, 1954). \nParticle size analysis was performed using an ASTM 152-H type hydrometer (Gee \n\n\n\nHeidari, S., S. Oustan, M.R. Neyshabouri and A. Reyhanitabar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 143\n\n\n\nand Or, 2002). Calcium carbonate equivalent (CCE) content was determined by the \ntitration method (Jackson, 1958). Organic carbon content was determined using \nthe method by Nelson and Sommers (1982). Cation exchange capacity (CEC) was \nmeasured using the Bower method (Chapman, 1965). The total contents of Zn, Pb \nand Cd in the soil were determined by acid digestion with 4 M HNO3 (Sposito \net al., 1982). The heavy metal fractionation in the contaminated soil sample was \ndetermined by a sequential extraction procedure (Sposito et al., 1982) in five \nfractions, namely, exchangeable (0.5M KNO3 for 16h), sorbed (distilled H2O for \n2h, repeated three times ), organic-bound (0.5M NaOH for 16h), carbonate-bound \n( 0.05 M Na2-EDTA for 6h), and residual (4M  HNO3 at 80\u00b0C for 16h).\n\n\n\nFour organic acids were chosen, namely, Na2H2EDTA (or EDTA), citric acid, \noxalic acid, and acetic acid. Batch extractions of heavy metal contaminants using \na common extractant concentration of 0.01M were conducted. The extraction \ntests were conducted in 50 mL polyethylene tubes. The tubes containing 1.00 \ng soil sample and 20 ml of 0.01 M EDTA (or citric acid) were agitated using an \nend-over-end shaker at a speed of 140 revolutions per minute (RPM) at room \ntemperature for 25 min. The suspensions were centrifuged at 3000 RPM for 5 \nmin and the supernatants were then filtered through a Whatman-42 filter paper \nfor heavy metal analysis. Then new extracting solution was added to the treated \nsoil sample and the tubes were returned to the shaker. Twelve consecutive series \nof 25 min long extractions were carried out. The concentrations of Zn, Pb and Cd \nwere measured by a Shimadzu model 6300 flame atomic absorption spectrometer \n(FAAS). The pH of the solutions after washing was measured using a pH meter. \nAdditionally, the effect of different concentrations (0.001, 0.005, 0.01, 0.05 and \n0.1 M) of organic acids on the removal efficiency of heavy metals was investigated. \nAll tests were performed in duplicates and the results were presented as averages \nof the duplicate extracts. Percent of each metal removed was calculated using an \nequation similar to the one used by Reddy and Chinthamereddy (2000) and is \nshown below: \n\n\n\n Percent metal removed (%) =(ClVl /C S mS) \u00d7 100\n\n\n\nwhere Cl and CS are the metal concentrations in supernatant (mmol L-1) and soil \n(mmol kg-1), respectively; Vl is the volume of supernatant (L) and mS is the dry \nmass of the soil (kg).\n\n\n\nRESULTS AND DISCUSSION\nSelected properties of the soil used in this study are presented in Table 1. The soil \nwas moderately fine textured, slightly alkaline (calcareous),and had a low level \nof organic matter. The major heavy metals of concern in this soil were Pb, Cd and \nespecially Zn, which had a very high concentration.\n\n\n\nAccording to the fractionation analysis (Figure 1), Zn was present mainly in \nthe residual and carbonate-bound fractions. Lead and Cd were mainly accumulated \nin the carbonate-bound fraction. There was a clear trend of migrating heavy metals \n\n\n\nOrganic Acids  to Mobilise of Heavy Metals \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015144\n\n\n\nfrom the carbonate-bound fraction to the residual fraction. The order of migration \nwas Zn >Pb > Cd. Only traces of all the three investigated metals were detected in \nthe exchangeable fraction in the order of Cd > Zn >Pb. This was consistent with \nthe pH of the soil, since other studies have already reported that when soil pH is \nhigher than 5.6, the exchangeable amount of metals becomes negligible (Kim \net al., 2003).\n\n\n\nAccording to materials and methods, the amount of chelating agents added \n(1200 mmol kg-1) was much higher than the total amount of heavy metals in the \nsoil (80.6 mmol kg-1). Kim et al. (2003) showed that if a sufficiently large amount \nof EDTA was applied (EDTA:Pb stoichiometric ratio greater than 10), most of the \nPb was extracted for all soils tested, except for soil from a Superfund site from \na Pb mining area. Percentages of Zn, Pb and Cd removed from the soil using \nsuccessive soil washings with 0.01 M solutions of EDTA, citric acid, oxalic acid, \nand acetic acid are presented in Figures 2, 3, 4 and 5, respectively. \n\n\n\nEDTA and citric acid were able to remove all three heavy metals (Zn, Pb and \nCd) from the soil, while oxalic and acetic acids did not extract Pb from the soil. \n\n\n\nHeidari, S., S. Oustan, M.R. Neyshabouri and A. Reyhanitabar\n\n\n\nTABLE 1\nCharacteristics of the studied soil\n\n\n\n\n\n\n\n10 \n \n\n\n\nTABLE 1 \nCharacteristics of the studied soil \n\n\n\n \nSoil properties Value \nTexture SCL \npH 7.6 \nEC (mS cm-1) 5.0 \nCEC (cmolc kg-1) 15.0 \nCCE (g kg-1) 109 \nClay(g kg-1) 230 \nOC(g kg-1) 3.0 \nZn (mmol kg-1) 74.3 \nPb (mmol kg-1) 5.21 \nCd (mmol kg-1) 1.14 \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Fractionation of heavy metals in the studied soil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 145\n\n\n\nOrganic Acids  to Mobilise of Heavy Metals \n\n\n\nFigure 2: Removal percentage of heavy metals vs. number of extractions by EDTA\n\n\n\nFigure 3: Removal percentage of heavy metals vs. number of extractions by oxalic acid\n\n\n\nFigure 4:. Removal percentage of heavy metals vs. number of extractions by citric acid\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015146\n\n\n\nFigures 2 and 3 indicate that renewing the extracting solutions greatly increased \nthe removal of metals, but not to the same extent.  The cumulative releases of \nZn, Pb and Cd from the soil for EDTA and citric acid were 19.5%, 27% and \n18.4%, and 20.2%, 2.5% and 13.6%, respectively, at the first cycle of extraction. \nThe corresponding figures for Zn, Pb, and Cd extracted in the last (twelfth) cycle \nfor EDTA and citric acid were 63.9%, 80.5%, and 36%, and 63.4%, 39.3%, and \n32.9%, respectively. The cumulative releases of Zn and Cd from the soil for oxalic \nacid and acetic acid were only 1.8% and 2.4%, and 3% and 4.9%, respectively. \nThe corresponding figures for Zn, Cd extracted in the last (twelfth) cycle for oxalic \nacid and acetic acid were 34.3% and 2.4%, and 20.4% and 29.1%, respectively. \nThe trends in removal of the heavy metals with successive washings are expressed \nby the equations listed in Table 2.\n\n\n\nIt can be seen that all of the extraction curves, except those of the acetic \nacid, are mostly described properly by second order polynomial equations. The \nextraction of Pb by citric acid is described well by a third order polynomial \nequation. The power equation has a proper fit to the extraction curves of acetic \nacid.\n\n\n\nHeidari, S., S. Oustan, M.R. Neyshabouri and A. Reyhanitabar\n\n\n\nFigure 5: Removal percentage of heavy metals vs. number of extractions by acetic acid\n\n\n\nTABLE 2\nThe trends in removing of the heavy metals with successive washings\n\n\n\n\n\n\n\n11 \n \n\n\n\nTABLE 2 \nThe trends in removing of the heavy metals with successive washings \n\n\n\n \n Heavy metals Equations \n\n\n\nEDTA \nZn y = -0.38x2 + 8.8815x + 11.628       R2 = 0.998 \nPb y = -0.618x2 + 12.696x + 15.592      R2 = 0.998 \nCd y = -0.239x2 + 4.4149x + 16.398        R2 = 0.966 \n\n\n\nCitric acid \nZn y = -0.5293x2 + 10.431x + 12.262    R2 = 0.990 \nPb y = -0.0428x3 + 0.619x2 + 1.9761x - 0.8369    R2 = 0.991 \nCd y = -0.2452x2 + 4.6846x + 10.84                R2 = 0.979 \n\n\n\nOxalic acid Zn y = -0.046x3 + 0.903x2 - 1.570x + 2.850    \nR2 = 0.998 \n\n\n\nAcetic acid \nZn y = 2.864x0.765     R2 = 0.997 \n\n\n\nCd y = 4.743x0.733        \nR2 = 0.999 \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 147\n\n\n\nFor EDTA, the order of extractionwas Pb > Zn > Cd which agreed with the \norder of stability constant of EDTA complexes (Palma and Mecozzi, 2007):\n\n\n\nLogKPb-EDTA (18.04) > logKZn-EDTA (16.5) >logKCd-EDTA (16.42)\n\n\n\nHowever, the higher amount of Zn extracted could be due to higher amounts \nof Zn compared to Cd in the soil sample. Huong and Ohtsubo (2007) found a \nreverse order of extraction using EDTA (Cd > Zn >Pb). The findings of this study \nwith respect to extraction order was similar to that observed by Peters (1999). \nAbumaizar and Smith (1999) found that EDTA preferentially extracts Pb over Zn \nand Cd. Moreover, Finzgar and Lestan (2007) found that EDTA extracts Pb more \nefficiently than Zn from three of the four soils they studied. Chang et al. (2007) \nalso reported that EDTA does not show considerable preference for the chelation \nof any particular Zn species during extraction. Huong and Ohtsubo (2007) found a \nlarge proportion of Cd present in the residual fraction of the soils they studied. In \ncontast, the Cd soil sample in this study was mainly accumulated in the carbonate-\nbound fraction. Others found the EDTA to be a strong extractant for Pb due to its \nhigh log K value (Tandy et al., 2004).The results of this study found no general \nefficiency order for chelating agents.\n\n\n\nThe mobility of heavy metals when extracted by citric acid was Zn>Pb>Cd. \nLobanowski et al. (2008) noted that that citric acid is better at extracting Zn than \nPb. This study\u2019s results showed that EDTA extracted greater amounts of heavy \nmetals from the soil than citric acid. In contrast to EDTA, citric acid extracted \nmore Cd than Pb at early cycles of extraction. After the seventh cycle, the reverse \noccurred.\n\n\n\nOxalic and acetic acids were ineffective in extracting Pb from the soil as \npreviously reported by Nascimento (2006). However, Reddy et al., (2006) showed \nthe effectiveness of acetic acid at high concentrations, up to 2M. The reason for \nthe low removal of Pb by oxalic acid was presumably due to the formation of a \nlow-solubility lead oxalate precipitate (Ksp=2.74\u00d710-11). Oxalic acid extracted Cd \nonly at first extraction cycle. The solubilities of cadmium oxalate (Ksp=9.00\u00d710-\n\n\n\n8) as well as zinc oxalate (Ksp=1.35\u00d710-9) were sufficiently high compared to \nthat of lead oxalate. It could be that oxalate ions in calcareous soils react with \ncalcium (Ca) to form calcium oxalate. The occurrence of this precipitate hindered \nthe removal of heavy metals from these soils. Oxalate could inhibit the dissolution \nof calcium carbonate through the coating of calcium oxalate. Such a coating \naround calcium carbonate may provide protection from releasing co-precipitated \nor occluded cadmium carbonate. Co-precipitation of Cd with calcium carbonate \nhas been well demonstrated (Kumagai and Matsui, 1992).\n\n\n\nChanges in pH during the successive extraction cycles are presented in \nFigure 6. The pH of soils treated with the two chelating agents decreased abruptly \n(about 4 units for EDTA and 6 units for citric acid) between the fifth and seventh \nextraction cycles. The removal of Pb increased abruptly at the fifth extraction \ncycle. This indicates that the extraction strength of citric acid for Pb increased \n\n\n\nOrganic Acids  to Mobilise of Heavy Metals \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015148\n\n\n\nwith decreasing pH. Since Pb was mostly bound to carbonates (Figure.1), and \nunder alkaline conditions precipitation of PbCO3 is favoured (Lo and Yang, 1999), \nthe decrease in pH at fifth cycle played a dominant role in the extraction of Pb. \nThe decrease in pH showed no effect on the extraction of Zn and Cd.\n\n\n\nThe percentage of Pb removed at each extraction cycle is shown in Figure 7. \nFor EDTA, after a quick decrease in heavy metal extraction, a plateau was reached \nat about the fourth extraction cycle, which was followed by a second decrease and \na plateau at the tenth extraction cycle. The second decrease in the extraction of Pb \nmay be due to the decrease in pH creating competition between Pb and iron (Fe). \n\n\n\nTrivalent Fe forms very stable complexes with EDTA (log K EDTA-Fe = 25.1).\nFor citric acid an increase in extraction was followed by a decrease. The early low \nefficiency of citric acid is attributed to co-dissolution of soil calcium carbonates \nand release of huge amounts of Ca which forms calcium citrate precipitate. After a \nfew cycles of extraction, sufficient amounts of citrate ions are available to remove \nPb from the soil. The late decrease in the extraction may be due to a decrease in pH \n\n\n\nHeidari, S., S. Oustan, M.R. Neyshabouri and A. Reyhanitabar\n\n\n\nFigure 6: Changes in pH during the extractions using EDTA and citric acid\n\n\n\nFigure 7: Removal percentage of Pb at each successive cycle of extraction\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 149\n\n\n\nand a release of huge amounts of Fe, which in turn formed stable complexes with \ncitrate and reduced Pb extraction efficiency. Palma and Mecozzi (2007) observed \nFe dissolution up to about the 39% of the total Fe content in the sediment under \ncitric acid treatment. It seems that the removal heavy metals from the soils should \nincrease with decreasing pH, but this is offset by an increase in the extraction of \nFe and Ca (Tandy et al., 2004).\n\n\n\nOxalic acid extracted more Zn than Cd and the reverse occurred for acetic \nacid. This was not expected, because of the theoretically higher extraction strength \nof oxalic acid compared to acetic acid. A higher removal efficiency of Cd than that \nof Zn by oxalic acid has been reported previously (Peters, 1999). \n\n\n\nChanges in pH during the successive extraction cycles are presented \nin Figure 8. As  can be seen, the pH of soil treated with oxalic acid decreased \nabruptly (6 units) between the fifth and seventh extraction cycles. Meanwhile, \nthe removal of Zn increased abruptly at the fifth extraction cycle. This indicated \nthat the extraction strength of oxalic acid for Zn increased with decreasing pH. \nThis decrease in pH was not effective at all for the removal of Cd from the soil. \nAs shown earlier (Figure 1), Zn and Cd in the studied soil were mainly bound \nto residual and carbonate fractions, respectively, which is in agreement with the \nliterature (Ramos et al., 1994: Khanmirzaei et al., 2013).\n\n\n\nThe sequential extraction procedure by Sposito et al. (1982) failed to separate \nthe Fe and manganese (Mn) oxides bound fractions which were found within the \nresidual fraction. Han and Banin (1995) found that in eight calcareous soils from \narid and semi-arid regions of China, Zn bound to the Fe oxides accounted for 20% \nto 30% of total Zn, which was higher than Zn bound to carbonates (< 5%). It might \nbe possible to consider the decrease in soil pH values as favouring solubilisation \nof co-precipitated Zn from Fe and Mn oxides. Another explanation for the higher \nremoval of Zn compared to Cd in oxalic acid extraction could be due to the higher \ncomplex stability of Zn (log K=3.4) compared to Cd (log K=2.73). According \nto this explanation, as pH decreases, the amount of both Zn and Cd released \n\n\n\nOrganic Acids  to Mobilise of Heavy Metals \n\n\n\nFigure 8: Changes in pH during the extractions using oxalic and acetic acids\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015150\n\n\n\nincreases with increasing calcium carbonate dissolution. Therefore, because of \nthe higher complex stability, the removal of Zn is favoured.\n\n\n\nFor the removal of heavy metals with acetic acid (Figure 5), similar trends \nof removal for Zn and Cd were observed. A slight decrease in pH coincided with \na slight increase in the extraction of both Zn and Cd.\n\n\n\nThe effect of concentration of chelating agents (EDTA and citric acid) on the \nremoval efficiency of heavy metals in the studied soil is shown in Figures 9 and \n10. Increasing the concentration of both chelating agents considerably favoured \nthe extraction of Pb from the soil. Kim et al., (2003) state that the extraction of \nPb from the Pb-contaminated soils was dependent on the quantity of the EDTA \npresent. Nevertheless, increasing the concentrations of both chelating agents over \n0.01M showed no further effect on the extraction level.\n\n\n\nHeidari, S., S. Oustan, M.R. Neyshabouri and A. Reyhanitabar\n\n\n\nFigure 10: Effect of citric acid concentration on the percentage of heavy metals removed\n\n\n\nFigure 9: Effect of EDTA concentration on the percentage of heavy metals removed\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 151\n\n\n\nOrganic Acids  to Mobilise of Heavy Metals \n\n\n\nOther workers reported that a 0.01M EDTA solution was less effective \nthan a 0.05M or a 0.1M EDTA solution at extracting heavy metals (Lo and \nYang, 1999). However, Zhang et al., (2010) recommended a very dilute 0.0005 \nM EDTA solution. A more concentrated citric acid solution was ineffective in \nremoving Cd from the soil. Extraction of Cd decreased for concentrations over \n0.01M of this chelating agent. This may be the result of calcium citrate formation \nin the soil which removes citrate ions from the solution. Furthermore, raising the \nconcentration of EDTA caused only a slight increase in the removal efficiency of \nCd. This indicates that the effectiveness of supplying more concentrated solutions \nof chelating agents for removal of heavy metals depends on the stability constant \nof their complexes. \n\n\n\nThe high stability constant is consistent with more effectiveness. Both the \nchelating agents were unable to efficiently remove Cd from the soil, even at high \nconcentrations. Considering the presence of Cd in the carbonate-bound fraction \n(easily soluble under acid attack), this could be due to competition from other \nmajor (Ca and Fe) or trace (Pb and Zn) metals. Potential benefits of increased \nconcentration of chelating agents in removing Zn were more than that of Cd and \nless than that of Pb.\n\n\n\nFigures 11 and 12 show the effect of concentration of oxalic and acetic acids \non the removal efficiency of Cd and Zn in the studied soil, respectively. As can \nbe seen, increasing the concentration of oxalic acid was ineffective in Cd removal \nfrom the soil. Furthermore, raising the concentration of oxalic acid caused only a \nslight increase in the removal efficiency of Zn. This may be the result of calcium \noxalate formation in the soil which removes oxalate ions from the solution.In \ncontrast, as the concentration of acetic acid increased, the removal of Cd and Zn \nincreased considerably. \n\n\n\nAs can be seen in Figures 11 and 12, the removal of Cd was not easy. \nThis may have been due to the presence of  Cd in less removable fractions (i.e., \nresidual fraction) as reported by other workers (Chen et al., 2008). Surprisingly, \nan increase in the concentration of organic acids had no effect on the extraction \nefficiency of Pb.\n\n\n\nFigure 11: Effect of oxalic acid concentration on the percentage of heavy metals removed\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015152\n\n\n\nThe results of our study indicate that no general efficiency order for organic \nacids could be distinguished and there is a need for further fractionation studies \nto improve understanding of heavy metal mobilisation in and removal from soils. \nIt should be noted that heavy metals behave differently after the ageing of treated \nsoils. For example, it has been observed that the mobility of Pb, Cd and Zn, \nincreased, decreased, and did not change, respectively, after ageing (Udovic and \nLestan, 2009). \n\n\n\nCONCLUSION\nEDTA and citric acid showed high but different efficiencies for removing Pb \nand Zn from a contaminated soil. Moreover, both the two chelating agents were \nineffective in removing Cd from the soil. Oxalic acid and acetic acid were mild \nextractants, but effective for removing Zn and Cd, respectively. The complexity \nof soils and the presence of multiple heavy metals make soil remediation efforts \ndifficult. Furthermore, heavy metals behave differently after the ageing of treated \nsoils. For this reason, the fate of soil fractions after remediation should be \ninvestigated more thoroughly.\n\n\n\nACKNOWLEDGMENT\nThis study was supported by the Soil Science Department, Faculty of Agriculture, \nUniversity of Tabriz.\n\n\n\nREFERENCES\nAbumaizar, R.J. and E.H. Smith.1999. Heavy metal contaminants removal by soil \n\n\n\nwashing. Journal of Hazardous Materials 70: 71\u201386.\n\n\n\nAndrade, M.D., S.O. Prasherand W.H. Hendershot. 2007. Optimizing the molarity \nof a EDTA washing solution for saturated-soil remediation of trace metal \ncontaminated soils. 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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n125 \n \n\n\n\nFertility Index of Industrial Polluted Land and Plant Response to Heavy \nMetal Contamination \n\n\n\n \nWanti Mindari1, Purnomo Edi Sasongko1, Haidar Fari Aditya1, \n\n\n\nDaljit Singh Karam2*, Intan Nadhirah Masri3 \n \n\n\n\n1Universitas Pembangunan Nasional \u201cVeteran\u201d Jawa Timur, Surabaya, Indonesia \n2Department of Land Management, Faculty of Agriculture, UPM, Malaysia \n3Soil Science, Water and Fertilizer Research Centre, MARDI, Malaysia \n\n\n\n \nCorrespondence:*daljitsingh@upm.edu.my  \n\n\n\n\n\n\n\nABSTRACT \nIndustrial waste in the Sidoarjo Regency, Java, Indonesia has polluted the agricultural land \naround it. The high level of heavy metal pollution in these fields affects the physical, chemical, \nand biological characteristics of the soil and plant growth. This study aimed to examine the \nsoil fertility index (SFI) of four agricultural plots of land around the paper, pharmaceutical, \nanimal feed, and leather industries in the Sidoarjo region and the response of food crops to \nheavy metal contamination. The research was carried out in two stages, Stage (1): Evaluation \nof the soil fertility index (SFI) based on physical, chemical, and biological characteristics of \nthe main soils; Stage (2):  The response of rice plants and maize to heavy metal contamination. \nThe SFI value is derived from the minimum soil chemical-physical-biological characteristic \nindicator values which include pH, EC, cation exchange capacity (CEC), K, Na, Ca, Mg, heavy \nmetals (Fe, Mn, Zn, Cu, Pb, Hg, Cd) clay, and C-organic soils. The results showed that the \nvalue of the soil fertility index of agricultural land around the industries in the Sidoarjo area \nwas low to moderate. The value of SFI is obtained through factor analysis of highly correlated \nsoil features. The main factors determining the low value of SFI are soil pH, soil texture, and \nheavy metal content of Pb, Cd, Fe, and Zn. Therefore, it is not advisable to cultivate rice and \ncorn in the region due to the significant uptake of high levels of heavy metal elements by these \nplants, which not only compromises their growth but also human health. \n\n\n\nKey words: industry, fertility, heavy metals, plants, pollution \n\n\n\n \nINTRODUCTION \n\n\n\nFuture industrial development  is likely to harm agriculture because industrial waste pollutes \nrivers and land (Zwolak et al. 2019). Industrial waste plays the most important role in \npollution, especially heavy metal pollution. Many hazardous wastes contain chemicals rich in \nheavy metals that can pollute agricultural land (Rosariastuti and Supriyadi 2020). \nEnvironmental pollution can reduce health of plants. Heavy metals that pollute the soil reduce \nthe pH of the soil, making the soil acidic (Napitupulu 2008;  Adamczyk-Szabela et al. 2015; \nAbdu et al. 2017). Plants absorb and accumulate any heavy metals present in the soil (Tangahu \net al. 2011). The soil quality in the Sidoarjo Regency has decreased because it contains large \namounts of heavy metals. Heavy metals such as cadmium (Cd), mercury (Hg), lead (Pb), \ncopper (Cu), chromium (Cr) and zinc (Zn) are considered 'serious' pollutants due to their toxic \nproperties such as the tendency to enter the food chain, and the ability to reside (residence \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n126 \n \n\n\n\ntime) in the environment for a long time. Removal of heavy metals from soil is time consuming \nand costly (Putranto 2011). The liquid waste from the batik industry in Sidokare, Sidoarjo \ncontains Pb 0.173 mg kg-1, Cd 0.009 mg kg-1, and Cr 0.004 mg kg-1 while the maximum \nthreshold for the discharge of Pb content into a water body is 0.1 ppm according to the \nGovernor Regulation of The Republic of Indonesia in 2014. Soil with chemical concentrations \nover this threshold level contain dangerous components or heavy metals (Lenart and Wolna-\nKoadka 2013). If the land is under agriculture, the plants will uptake these harmful chemicals \nand human and animal health is likely to be affected if they depend on these plants for foods. \n \nAgriculture, through reusing wastewater for irrigation, contributes to spreading and \nreintroducing these contaminants into the aquatic environment. Adhani plants carrying heavy \nmetals and pose a risk to people if ingested (Oliver and Gregory 2015; Zwolak et al. 2019). \nThe level of Fe, Cd, Zn and Pb do meet the standards of drinking water quality. However, \nmanganese (Mn) in three locations was found to exceed drinking water quality standards (> \n0.4 mg/l) (Sholehhudin et al. 2019). The cadmium content in the rice fields of the Taman sub-\ndistrict was 1.90 ppm, while in the soil it was 2.43 ppm. Therefore, the quality of the soil at \nthe four locations was declared to exceed the environmental quality standards. The distribution \nmap shows that the Taman sub-district area has the highest levels compared to other sub-\ndistricts (Fitrianah et al. 2022). There are indications that the agricultural land around the \nindustries in the Sidoarjo area is contaminated with heavy metals from industrial waste \ndisposal (Khasanah et al. 2021). Ameliorating the physical and chemical properties of the soil \ncan reduce associated risks, increase soil productivity, and improve food security (Wuana and \nOkieimen 2011). Applying a straw powder concentration of 40,000 mg/L and a contact time \nof 180 min resulted in a significant reduction of 50.35% in the liquid waste's initial \nconcentration of Pb (Dini et al. 2013). Planting of Fimbristylis globulosa which inoculates \nbacteria producing Azotobacter exopolysaccharides and using activated charcoal, can reduce \nthe content of Cd and Pb in the soil as F. globulosa increases its uptake resulting in increased \nPb content in its roots (Dewi dan Hindersah 2009). \n \nEvaluating soil fertility is crucial for identifying soil features and the external variables that \ncan help create a more sustainable agricultural system. With carefully selected indicators, the \nfuzzy technique may adequately evaluate soil fertility and offer helpful information for \ndecision making (Khaki et al. 2017). In organic management methods, the soil fertility index \n(SFI) is Class 4, or Extremely High, while in conventional management systems, it is Class 3, \nor high. Manure that is applied for extended periods increases the SFI and promotes plant \nnutrition (Prastiwi et al. 2021). The reduction in soil fertility of agricultural land near industrial \nregions has motivated academics to investigate the relationship between alterations in soil \nproperties and plant development in greater depth. Based on the preceding information, this \nstudy aims to investigate the SFI of agricultural land surrounding the industrial area in the \nSidoarjo region and plant response to heavy metal pollution. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n127 \n \n\n\n\nMATERIALS AND METHODS \nThe research was conducted on farmers\u2019 agricultural plots around the industries in Sidoarjo \nRegency which is located between 112.50 and 112.9 0 E longitude and between 7.3 0 N latitude \nand 7.5 0 S latitude. The study was conducted from June to September 2019. Research methods \nincluded identifying the physical-chemical-biological characteristics of the soil to determine \nthe SFI value and observing plant response to metal absorption, particularly growth.  \n \n \n\n\n\n \nFigure 1. Map of the study sites \n\n\n\nEvaluation of Soil Fertility Index of Four Farmlands Around the Industry \nA site survey was first carried out to determine the location of the study and the determination \nof sampling points adjacent to the industries, residents' homes, highway access, and areas \nirrigated by water from rivers polluted with industrial waste. Soil sampling was conducted \nadjacent to the plastics (TR), pharmaceutical (TF), animal feed (TP), and paper (TC) industries.  \n \nSoil Sampling \nAt five sampling locations, 0-20 cm soil samples were collected, placed in plastic bags, and \nlabelled with the sampling location. Soil samples were transported to the laboratory for air-\ndrying and sifting using 2-mm and 1-mm sieves.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n128 \n \n\n\n\nSoil Analyses \nThe pH of the soil samples was determined using the 1:2 saturation technique while soil texture \nwas determined using the pipette method. One mm soil samples were analysed for organic \ncarbon using Walkey and Black method, cation exchange capacity (CEC) of potassium, \ncalcium, magnesium, and sodium with ammonium acetate 1 N at pH 7, soil total nitrogen by \nKjeldahl method, available phosphorus by Olsen method, and heavy metal content of iron zinc, \ncopper, manganese, lead, and cadmium with 25%HCl extraction. Descriptive statistics \nexamined the findings of soil analysis to determine data flow, regression analysis to determine the \nrelationship between components, factor analysis to determine the most influential factors, and the \nSFI to determine the degree of soil fertility. The SFI value was computed on a scale of 0 to 1, using \nthe method of Mukashema (2007), while its classification is based on Bagherzadeh et al. (2018). \n\n\n\n \n                                          \u2026 (1) \n \nSci= c j x pc \u2026 (2) \npc= 1/nc, \u2026 (3) \ncj= w i x si \u2026 (4) \n \nwhere \n\n\n\nSFI = soil fertility index \nSci= MSFI value, minimum soil fertility indicator (MSFI) \nN= number of minimum soil fertility indicators (MSFI) \npc= probability of SFI class \nnc= SFI class number \nwi= weight index \nsi= score index  \n\n\n\nThe sum of minimum soil fertility indicators (N) was determined based on the value of the \ncorrelation between each variable and has two asterisks (**) (sig < 0.01) or an asterisk (*) (sig \n< 0.05).  Sci is the value of the minimum soil fertility indicator (MSFI) calculated by \nmultiplying the probability of the soil fertility index class (pc) with the weight index  (wi) and \nthe characteristic score index of soil (si). The score value of each condition depends on the \nagreement of several components. The SFI class (pc) probability is one-sixth of the SFI class. \nHence, if the SFI class (nc) is equal to 5, the value of p=1/5. The number of SFI classes, \naccording to Bagherzadeh et al. (2018) are presented in Table 1.  \n\n\n\nTABLE 1  \n\n\n\nSoil Fertility Index class according to Bagherzadeh et al. (2018) \n\n\n\nSFI class SFI value \n\n\n\nVery Low 0.00-0.25 \nLow 0.25-0.50 \nKeep 0.50-0.75 \nTall 0.75-0.90 \nVery High 0.90- 1.00 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n129 \n \n\n\n\nHeavy Metal Uptake by Plant \nPlant response to heavy metal Pb and Hg contamination was evaluated against the quantum of \nmetals absorbed by rice and maize plants and the effect on their growth. The experiment was \narranged in a Randomized Group Design with the first factor being four plots polluted by \nindustry, the second factor being rice and maize crops as indicators of plant growth. Rice and \nmaize crops were planted in four selected agricultural land in the vicinity of the plastics, \npharmaceutical, cattle fodder and paper industries under Stage 1. Heavy metals Hg and Pb \nuptake by plants are indicators of contamination that harm human health. The crop was \nharvested 30 days after planting (DAP).  \n\n\n\n \nPlant analyses \nThe entire plant tissue was dried between 60 and 70\u00b0C and then strained through an 80-mm \nmesh screen. Fine plant samples were digested with H2SO4 and HNO3 in water. The Pb and \nHg content of the clear extract was determined using atomic absorption spectrophotometer \n(AAS). Soil samples were taken in the same field planted with rice and maize at a depth of 0-\n20 cm, and then analysed for Pb and Hg content using the same method as previously. Data \nfrom Pb and Hg measurements of plants and soils were tabulated for statistical descriptive \nanalysis, ANOVA, and correlations between treatments. \n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\nThe results of the statistical descriptive analysis, as presented in Table 2, show that part of the \nsoil is neutral to alkaline with a very low salinity level. All soil cation exchange values are \nlow. N availability is low, but P availability is relatively high. All heavy metals are classified \nas high. The soil texture is dominated by dust with a low clay content.  \n \nEvaluation of Soil Fertility Index of Four Farm Land Around the Industries \nSoil texture, CEC, pH, and organic matter content can affect the presence of heavy metals in \nthe soil. The texture of clay and organic matter can determine the absorbency of nutrients or \nheavy metals on negative charges or fixation of the structure of the interlayer silicate \naluminate. The texture of clay can affect attractiveness on negative charges such as C-organic, \nwhile silt textures attract fewer ions than clay. The amount and soil texture type determines \nthe magnitude of ion adsorption  (Purbalisa et al. 2019). The number of cations on a negative \ncharge is equal to CEC. Heavy metals are closely related to soil organic matter levels and soil \npH. Organic matter causes chelation in metal cations, making nutrients available to plants. If \nthe pH is low, the concentration of heavy metals is high and is easily absorbed by plants. This \ncondition also indicates a possible influence on the development of microorganisms \n(Komarawidjaja 2017). \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n130 \n \n\n\n\nTABLE 2 \n\n\n\nPhysical and chemical characteristics of industrially polluted farmland soil \n\n\n\nParameters Rate Description \n\n\n\npH in H2O 7.52\u00b10.34 Neutral - alkaline \npH in KCl 6.23\u00b10.47 Low-high \nEC (mS cm-3) 0.51\u00b10.23 Very Low \nOrg-C (%) 1.85\u00b10.72 Very Low - high \nCEC (cmolc kg-1) 5.92\u00b11.35 Very Low-Low \nP-Olsen (ppm) 73.84\u00b142.06 High \u2013 Very high \nN-total (%) 0.29\u00b10.01 Very low \nC/N ratio 22.23  \nClay (%) 7.75\u00b11.83 Low \nExchangeable-K (cmolc kg-1) 0.97\u00b10.01 Low-very high \nExchangeable-Na (cmolc kg-1) 0.34\u00b10.10 Very low-medium \nExchangeable-Ca (cmolc kg-1) 0.70\u00b10.43 Very low \nExchangeable-Mg (cmolc kg-1) 0.94\u00b10.38 Very low-medium \nBase saturation (%) 50.31\u00b116.90 Low-Very high \nZn (ppm) 82.28\u00b151.83 Very high \nCu (ppm) 54.70\u00b114.43 Very high \nFe (ppm) 1711.43 Very high \nMn (ppm) 844.25 Very high \nHg (ppm) 0.92\u00b10.27 Very high \nPb (ppm) 1.47\u00b10.29 Very high \nCd (ppm) 0.37\u00b10.12 Very high \n\n\n\nSource: Soil Chemical, Crops, Water and Fertilizer Analysis (Indonesia Soil Research Institute, 2005) \n\n\n\nThe results of the correlation analysis between observation variables are presented in Table 3. \nThe variable that has the highest correlation value determines the factors of soil fertility \nthrough factor analysis. Variables analysed include H2O pH, KCl pH, C-org, C/N N-total, P-\nOlsen, CEC, Exch. K, Exch. Na, Exch. Mg, Zn, Cu, Fe, Mn, and soil clay. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n131 \n \n\n\n\nTABLE 3  \n\n\n\nCorrelation matrix of soil physical-chemical indicators \n\n\n\n  pH H2O C-org CEC N-total Exch. K  Exch. Na Exch. Mg Zn Cu Fe pH KCl C/N Cd P-Olsen Clay Mn \n\n\n\npH H2O 1,00 -.564** 0,12 -0,15 -0,01 -0,41 0,37 -0,40 -.607** 0,02 .914** -0,06 -0,42 -.460* -.460* -0,07 \nC-Org  1,00 -0,41 -0,35 -0,29 0,25 -.686** .826** .854** -0,10 -0,41 .637** .609** .527* .527* 0,43 \nCEC   1,00 0,41 .554* 0,19 0,30 -0,30 -0,23 0,02 0,03 -0,40 -0,19 -0,09 -0,09 0,05 \nN-total    1,00 0,43 .590** .590** -0,38 -0,23 -0,03 -0,28 -.908** -.526* 0,28 0,28 -0,24 \nExch. K     1,00 -0,01 0,29 -0,30 -0,17 0,08 -0,19 -0,36 -0,23 0,10 0,10 0,16 \nExch. Na      1,00 0,05 0,23 0,31 -0,10 -.474* -0,37 -0,07 0,44 0,44 -0,02 \nExch. Mg       1,00 -.592** -.687** -0,11 0,24 -.738** -.649** -0,18 -0,18 -0,36 \nZn        1,00 .881** -.528* -0,20 .615** .486* 0,26 0,26 .520* \nCu         1,00 -0,36 -.466* .510* .489* 0,31 0,31 .648** \nFe          1,00 -0,17 0,02 0,14 0,08 0,08 -0,40 \npH KCl           1,00 0,05 -0,36 -0,40 -0,40 -0,06 \nC/N            1,00 .687** -0,08 -0,08 0,39 \nCd             1,00 -0,01 -0,01 0,29 \nPolsen              1,00 1.00** 0,05 \nClay               1,00 0,05 \nMn                1,00 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n132 \n \n\n\n\nThe factor analysis of selected soil characteristic variables resulted in five main components \n(PC1 - PC5) with a composite value of 85.68%. The results of the matrix correlation analysis \nled to the grouping of soil characteristic variables into the main components (PC). \nFurthermore, PC integration is carried into the formula for calculating SFI. The weight index \n(wi) is calculated based on the division of the proportional value of the variable in the PCA \ncolumn, with the highest cumulative value having an eigenvalue greater than 1, which is \n85.68% (Table 4). \nwi= proportion/ cumulative......................... (5) \n\n\n\n \nTABLE 4 \n\n\n\nDetermining component factors of SFI of polluted land  \n\n\n\n Main Component (PC) \n PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 \nEigenvalue 5.48 3.66 1.85 1.67 1.05 0.76 0.51 0.38 0.28 0.14 \nProportion 34.24 22.85 11.59 10.44 6.57 4.76 3.21 2.35 1.73 0.86 \nCumulative 34.24 57.09 68.68 79.11 85.68 90.45 93.65 96.00 97.73 98.59 \n\n\n\n\n\n\n\nThe results of the factor analysis of the 16 soil characteristics showed that the first level of \ndeterminants were C-organic, C/N ratio, total-Fe, total-Cu, and total-Cd.  N-total and Exch-Na \nwere at the second level, clay-texture and available-P at the third level, pH in KCl, total-Zn, \nand total-Mn at the fourth level, and pH in water, CEC, Exch. K, and Exch. Mg soil at the fifth \nlevel of determining factors. The sixteen variants (wi)of the soil features were calculated and \nmultiplied by the value of si (variable score) to obtain the score of Sci (equation 6) The variable \nsoil characteristics were scored 1-5 according to the method of Bagherzadeh et al. (2018). \n\n\n\nSci= wi x si............ ........................... ... (6) \n\n\n\nThe SFI value is calculated based on a formula presented in Equation 1. The results of the \nsimulation of SFI calculations with different soil characteristic variables are presented in Table \n7. The selected determinants of soil characteristic indicators greatly affect the value of SFI. If \nall high-correlated elements are included in the SFI calculation, then the SFI value is lower, \nabout 40-45%. If the correlation of soil characteristics is low, Ca and some microelements are \nnot included in the SFI assessment. This will lead to a greater SFI value (Table 5). \n\n\n\nTABLE 5  \n\n\n\nSFI values of industrial polluted land \n\n\n\nSFI Industries \nPlastic Pharmacy Feed Paper \n\n\n\nValue  0.45- 0.65 0.44-0.63 0.42-0.52 0.44-0.55 \n Low -Medium Low -Medium Low -Medium Low -Medium \n\n\n\nNote: Data processing with factor analysis; SFI, soil fertility index \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n133 \n \n\n\n\nOverall, the soil fertility index in a land polluted with industrial waste is low to moderate, \ndepending on the determinants of soil characteristics indicators. The variety of heavy metals \ngreatly affects the availability of essential plant nutrients. N, P, K, Ca and Mg availability \nbecomes low because the soil trap is filled with heavy metal ions. Although the P-Olsen \nmeasurement results are high, its position in the trap is easily bound by soil metals because it \nis positively charged, causing P to be unavailable to plants.  \n \nThe content of organic matter plays an essential role in the concentration of heavy metals. The \nlow content of organic matter in the soil causes an increase in concentration of metals This is \nbecause organic matter  is one of the most important components in the soil. It plays a role in \nthe development of soil structure, regulates the transfer of pollutants in the soil, and plays an \nimportant role in the cycle of turnover and storage of nutrients and water. Humic compounds \nalso play a role in forming complex bonds with metals. The presence of complex formations \naffects the reactivity and toxic effects of the metal (Adhani and Husaini 2017). \n\n\n\nPlant Response to Heavy Metal Contamination \nThe research was arranged in a Randomized Group Design with two factors. The first factor \nwas two types of food crops (rice and maize as indicators of metal uptake). The second factor \nwas four kinds of agricultural land near the pollutant source, namely from waste in the paper, \npharmaceutical, animal feed, and leather industries. Each treatment was repeated thrice. Soil \nsamples were taken at 0-20 cm depth and then dried and ground. The soil samples were sifted \nwith a 2-mm sieve. The soil subsamples were weighed to 5 kg TKO and put into a pot for metal \nuptake and plant growth tests. NPK fertilizer equivalent to 350 kg ha-1 was added to meet the \nnutritional needs. Maize and rice crops were planted on all four lands. Each treatment was \nrepeated thrice. So, the total number of treatments was 2x4x3= 24. Plants were harvested after \n30 days of planting, and all plant parts were dried at a temperature of 60-70oC, then mashed \nand sifted to pass a 0.1-mm sieve. HCLO 4 was added to the finely meshed plants at a ratio of \n1g: 4 ml (plant material: digestion solution) and boiled until the solution was clear. This \nsolution was used to determine Pb and Hg metal levels using AAS. The results of the \ndescriptive analysis of metal levels in the soil and plant tissues are presented in Table 6.  Soil \nPb content was greater than the soil Hg content. Both food crops were found to absorb heavy \nmetals in small quantities. \n\n\n\nTABLE 6  \n\n\n\nDescriptive analysis of metal levels in plant tissue and soil  \n\n\n\nNo Plant Descriptive \nstatistics \n\n\n\nPb (ppm) Hg (ppm) \nPlant Soil Plant Soil \n\n\n\n1 Corn \n\n\n\nMean 0.01 \u00b10.01 0.03\u00b10.01 0.01\u00b10.00 0.03\u00b10.01 \nMedian 0.01 0.03 0.01 0.03 \nMinimum 0.01 0.02 0.00 0.02 \nMaximum 0.01 0.04 0.01 0.05 \n\n\n\n2 Rice \n\n\n\nMean 0.01\u00b10.00 0.05\u00b10.01 0.01\u00b10.00 0.03\u00b10.01 \nMedian 0.01 0.05 0.01 0.03 \nMinimum 0.01 0.03 0.00 0.02 \nMaximum 0.01 0.07 0.01 0.05 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n134 \n \n\n\n\n\n\n\n\n\n\n\n\n \nFigure 2. Soil Pb and Hg levels in rice and maize crops \n\n\n\nLevels of Pb and Hg elements in maize and rice plants aged 30 DAP (Figure 2), show that rice \nabsorbed a higher amount of metal than corn. Though rice growth as measured by plant length \ndiffered between polluted lands, the difference was more clearly seen in maize plants than in \nrice plants. The number of leaves and the length of the plant did not differ markedly between \ntreatments.  \n \nRice plants can absorb more soil metals compared to maize. Rice has root fibres that have a \nhigh ability to absorb nutrients in the soil. The maize plant, although rooted in fibres, has fewer \nroot hairs causing its absorption ability to be less than the maximum. Rice plants are considered \nthe most tolerant to cultivation in polluted land, and it may be suspected that rice can adapt to \nmetal-polluted environments. However, the accumulation of metals in rice plants is harmful \nwhen consumed by humans. Root growth and leaf count positively correlated with the amount \nof metal in the soil. Plant growth is inhibited if the metallic elements in the soil exceed their \nneeds. A high level of almost all metals was found in the soil. \n \nThe results of the correlation between plant growth attributes show that plant root length was \nnegatively correlated with plant length (Table 8). All plant growth attributes were negatively \ncorrelated to soil Hg levels but positively correlated with soil Pb except for plant length. This \nshows that Hg is very dangerous to plant life in soil, especially leaf growth which becomes \ninhibited. However, the existing level of Pb in soil is still safe for plant growth.  \n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\nT1 T2 T3 T4\n\n\n\nPb Jagung\n\n\n\nPb\n \n\n\n\nTanah awal eksisting kehilangan\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\nT1 T2 T3 T4\n\n\n\nHg jagung\n\n\n\nPb\n \n\n\n\nTanah awal eksisting kehilangan\n\n\n\n0\n0.2\n0.4\n0.6\n0.8\n\n\n\n1\n1.2\n1.4\n1.6\n1.8\n\n\n\nT1 T2 T3 T4\n\n\n\nPb padi\n\n\n\nPb\n, p\n\n\n\npm\n \n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\nT1 T2 T3 T4\n\n\n\nHg padi\n\n\n\nHg\n, p\n\n\n\npm\n\n\n\nTanah awal eksisting kehilangan\n\n\n\nHg Rice \n\n\n\nHg Corn Pb Corn \n\n\n\nPb Rice \n\n\n\nSoil  Soil  \n\n\n\nSoil  existing  \n\n\n\nexisting  existing  \n\n\n\nlost  \n\n\n\nlost  lost  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n135 \n \n\n\n\nTABLE 7  \n\n\n\nResponse of maize and rice plant growth to heavy metal contamination under various field \nconditions \n\n\n\n Farmland near \nRoot \n\n\n\nlength \nNumber \nof leaves \n\n\n\nPlant \nlength \n\n\n\nPlant Length \nCorn Rice \n\n\n\n(cm)  (cm) (cm) \nPaper mill 43.25 11.67 30 9.80 c 0.00 a \nPharmaceutical factory 39.65 21.33 59.95 9.40 c 0.00 a \nAnimal feed mill 30.65 12.33 61.93 10.43 c 0.00 a \nLeather factory 32.9 13.17 57.38 7.03 b 0.00 a \n\n\n\nLSD 5% ns ns 54 1.38 ns \nNote: ns= not significance, LSD = least square design  \n\n\n\nTABLE 8 \n\n\n\n Correlation test between heavy metal levels and plants \n\n\n\n  \nRoot \n\n\n\nlength \nNumber \nof leaves \n\n\n\nPlant \nlength \n\n\n\nExisting \nPb Existing Hg \n\n\n\nRoot length 1     \nNumber of leaves 0.25 1.00    \nPlant Length 0.76 0.43 1.00   \nExisting Pb 0.46 0.49 0.12 1.00  \nExisting Hg 0.30 0.64 0.16 0.24 1 \nNote: Pb, lead; Hg, mercury  \n\n\n\nCONCLUSION \nThe degree of heavy metal pollution in agricultural land near industries affects the physical, \nchemical, and biological characteristics of the soil and plant growth. The value of SFI is \nobtained through factor analysis of highly correlated soil features. The main factors \ndetermining the low value of SFI are soil pH, soil texture, and heavy metal content, mainly Fe. \nThe SFI value of agricultural land around the industries in the Sidoarjo area is low to moderate, \ndepending on the various variables used for assessment. Growth of rice and maize crops in the \nregion was inhibited because of relatively high metal absorption.  \n\n\n\n \nACKNOWLEDGEMENTS \n\n\n\nWe express our deepest gratitude to the Ministry of Education, Culture, Research and \nTechnology, the Ministry of Higher Education for providing thesis grants and the Head of the \nInstitute for Research and Community Service.  \n\n\n\n \nREFERENCES \n\n\n\nAbdu, N., A.A. Abdullahi and A. Abdulkadir. 2017. Heavy metals and soil microbes. Environmental \nChemistry Letters 15(1):65\u201384. https://doi.org/10.1007/s10311-016-0587-x \n\n\n\nAdamczyk-Szabela, D., J. Markiewicz and W.M.Wolf. 2015. Heavy metal uptake by herbs. iv. influence \nof soil pH on the content of heavy metals in Valeriana officinalis l. Water, Air, and Soil Pollution \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n136 \n \n\n\n\n226(4): 106. https://doi.org/10.1007/s11270-015-2360-3 \nAdhani, R. and Husaini. 2017. Logam Berat Sekitar Manusia ed. S. Kholishotunnisa. Lambung Mangkurat \n\n\n\nUniversity Press. \nBagherzadeh, A., A. Gholizadeh and A. Keshavarzi. 2018. Assessment of soil fertility index for potato \n\n\n\nproduction using integrated fuzzy and AHP approaches in Northeast of Iran. Eurasian Journal of Soil \nScience 7(3): 203\u2013212. https://doi.org/10.18393/ejss.399775 \n\n\n\nDewi, T. and R. Hindersah. 2009. Konsentrasi kadmium dan timbal di tanaman mendong yang ditanam di \ntanah sawah dengan aplikasi azotobacter dan arang aktif. 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Mapping and modelling landscape-based soil fertility change in relation to human \ninduction. Enschede: International Institute for Geo-information Science and Earth Observation, pp. \n10\u201319. \n\n\n\nOliver, M.A. and P.J. Gregory. 2015. Soil, food security and human health: a review. European Journal of \nSoil Science 66(2): 257\u2013276. https://doi.org/10.1111/ejss.12216 \n\n\n\nPergub Jatim no. 52. 2014. Baku Mutu Air Limbah Bagi Industri dan/atau Kegiatan Usaha Lainnya. \nPerubahan Atas Peraturan Gubernur Jawa Timur Nomor 72 Tahun 2013 Tentang Baku Mutu Air \nLimbah Bagi Industri Dan/Atau Kegiatan Usaha Lainnya, August, 15. https://dlh.ponorogo.go.id/wp-\ncontent/uploads/2018/05/pergub-jatim-nomor-52-tahun-2014-tentang-baku-mutu-limbah.pdf \n\n\n\nPrastiwi, P., S. Sumani, M.Slamet, S. Suntoro and S.Suntoro. 2021. The assessment of Soil Fertility Index \nfor evaluation of rice production in Karanganyar Regency. Modern Applied Science 15(2): 63. \nhttps://doi.org/10.5539/mas.v15n2p63 \n\n\n\nPurbalisa, W.S, O. Handayani, C. and S. Harsanti, E. 2019. Assessment of heavy metal contamination risk \nin rice field and rice plant in Brantas watershed, Jombang Regency. Jurnal Tanah dan Sumberdaya \nLahan 06(01): 1033\u20131042. https://doi.org/10.21776/ub.jtsl.2019.006.1.2 \n\n\n\nPutranto,T.T. 2011. Pencemaran logam berat merkuri (Hg) pada air tanah Thomas Triadi Putranto.  Teknik \n32(1): 62\u201371. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 125-137 \n\n\n\n137 \n \n\n\n\nRosariastuti R. and W.W. Supriyadi. 2020. Fitoremediation technology for heavy metal pollution handling \nin agricultural land at Kebakkramat sub-district Karanganyar city MMA.Jurnal Litbang Provinsi \nJawa Tengah 18: 25\u201336. \n\n\n\nSholehhudin, M., B.H. Susanto, R. Adriyani and D. Laksono. 2019. Heavy metals contamination in \ngroundwater around Sidoarjo mud volcano area, East Java, Indonesia. Malaysian Journal of Medicine \nand Health Sciences 15: 86\u201389. \n\n\n\nTangahu, B.V., S.R. Sheikh Abdullah, H. Basri, M. Idris, N. Anuar and M.Mukhlisin. 2011. A review on \nheavy metals (As, Pb, and Hg) uptake by plants through phytoremediation. International Journal of \nChemical Engineering. Article ID 939161. https://doi.org/10.1155/2011/939161 \n\n\n\nTriyani Dewi dan Reginawanti Hindersah. (n.d.). Konsentrasi kadmium dan timbal di tanaman mendong \nyang ditanam di tanah sawah dengan aplikasi azotobacter dan arang aktif. Agrikultura 20(3): 185\u2013\n190. Retrieved 19 October 2021. http://jurnal.unpad.ac.id/agrikultura/article/view/953/997 \n\n\n\nWuana, R.A. and F.E.Okieimen. 2011. Heavy metals in contaminated soils: a review of sources, chemistry, \nrisks and best available strategies for remediation. ISRN Ecology 2011: 1\u201320. \nhttps://doi.org/10.5402/2011/402647 \n\n\n\nZwolak, A., M.Sarzy\u0144ska, E, Szpyrka and K.Stawarczyk. 2019. Sources of soil pollution by heavy metals \nand their accumulation in vegetables: a Review. Water, Air, and Soil Pollution 230: 164 . \nhttps://doi.org/10.1007/s11270-019-4221-y \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 69-86 (2019)                         Malaysian Society of Soil Science\n\n\n\nSoil Management Systems Based on Soil Quality at Three \nDifferent Toposequences in Cross River State,\n\n\n\nSouth-Eastern Nigeria \n\n\n\nOgban*, P. I., Abang, P. O. Achi, E. A. and Nweke, C. \n\n\n\nDepartment of Soil Science and Land Resources Management,\nUniversity of Uyo, Uyo, Nigeria\n\n\n\nABSTRACT\nThe morphological, physical and chemical characteristics of three toposequences \nwere studied in order to offer suggestions on soil management systems that would \nincrease crop production in South-eastern Nigeria. The study was conducted at \nthe upper slope, middle/lower slope, and valley bottom on three parent materials, \nnamely, basement complex (BC), sandstone and sandstone/shale. The Ap horizon \nwas generally shallow. A hue of 7.5YR typified the landscapes, and colour value \nranged from light-brown to brown. Low chroma in the matrix and high value \nmottles were common in the valley bottom. The soil profiles were similar in soil \nstructure and consistency, generally weak, medium sub-angular blocky and friable \ntopsoil to very sticky and very plastic subsoil. Sand was the dominant particle-size \nfraction. Soil texture was either sandy loam or loamy sand overlying sandy clay \nor sandy clay loam. Soil density varied with the soil profiles. Macropores were \ngenerally greater than 50% of total pore space. Saturated hydraulic conductivity \n(Ksat) was 2.16, 1.50 and 1.28 cm h-1 in the respective toposequences. Water \nstable aggregates > 2.0 mm averaged 28.8%, 26.5% and 33.5%, while >0.5mm \naveraged 52.1%, 60.1% and 51.5%, respectively in the topographic positions of \nthe toposequences. The soil profiles were slightly acidic. Soil organic C, averaged \nover all toposequences was 11.6, 12.5 and 10.5 g kg-1, while available phosphorus \nwas 9.32, 6.77 and 20.04 m kg-1, respectively. Similarly, CEC averaged 7.21, \n10.61 and 12.48cmolkg-2, while base saturation was 45.4%, 67.8% and 78.6%, \nrespectively. Based on the soil characteristics, three soil management units (SMU) \nwere identified as SMU A (Upper slope), SMU B (Middle/lower slope) and SMU \nC (Valley bottom). The corresponding soil management systems (SMS) are dry \nland farming with agroforestry and planted fallow in SMU A, wetland and dry land \nfarming in SMU B, and wetland, and wetland/dry land farming in SMU C. These \nSMUs and SMS will facilitate the use of the soils on the slopes and lead to an \nincrease in crop production and farmers\u2019 income in the study area and other areas \nwith similar soil and ecological conditions. These SMS are being recommended \nfor use by agricultural extension agents to assist farmers cultivate their sloping \nlands in a more efficient and productive manner.\n\n\n\nKeywords: Soil characteristics, topographic position, soil management unit, \nsoil management system\n\n\n\n___________________\n*Corresponding author : cgulser@omu.edu.tr\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201970\n\n\n\nINTRODUCTION\nSoils and their properties are known to be determined by the configuration of \nthe state factors, the clorpt (climate, organisms, parent material, topography, and \ntime) at a given location (Jenny 1980; Jungerius 1985). Beckett and Webster \n(1971) associated soil variation with landscape position, the clorpt, and/or soil \nuse and management. Olatunji et al. (2007) observed a strong association between \ntopographic position and soil properties rather than parent material. This is because \ntopography through its slope element drives geomorphic processes (Evans 1972) \nthrough its influence on the speed of water flow, velocity of colluvial material \nand infiltration into soil. Topography significantly affects earth\u2019s surface water \ncharacteristics, endogenic and exogenic soil forming factors, and plays a crucial \nrole in the spatial distribution of soils and their properties (Moore and Hutchinson \n1991; Schaetzl and Anderson 2005; Seibert et al. 2007; Behrens et al., 2010).  \nConsequently, in an undulating landscape, the influence of topography on soil \ncharacteristics and hence the potential for crop production can be substantial.\n Soils frequently occur in well defined and regular sequences related to \nrelief or topography (Ahn1970; Okusami et al. 1985). Moormann (1981) defined \na toposequence as a succession of sites from the summit to the valley bottom. \nSome soils on the toposequence (e.g., crest \u2013 upper and middle slope) may be \nsedentary, while others (e.g., lower slope and valley bottom) may have developed \nfrom transported materials derived from the underlying geology of the area (Smyth \nand Montgomery 1962) Similarly, differences in parent material may occur on a \ntoposequence even though the underlying geology may be uniform.\n Differences among soils on a toposequence are also related to differences \nin drainage which are related to relief (Ahn 1970). Well-drained, imperfectly \ndrained, and poorly drained soils are often found closely associated along a \ntoposequence. Colour differences have also been used to separate soils on \ntoposequences in West Africa. Gerrard (1981)reported that upland, well-drained \nsoils are usually reddish brown or brownish red, middle and lower slope soils are \nbrown or brownish yellow due to slower drainage, while poorly drained valley \nbottom soils are bluish grey, etc., and may be mottled (Brady and Weil 2002) \nbecause of fluctuating water tables. Generally, their colours range from reddish \nhues upslope to yellowish hues in the valley bottom (Fagbami and Udo1982).\n Smyth and Montgomery (1962), Gerrad (1981)and Carsky (1992) \nobserved that soils formed along a slope often varied greatly in pedological, \nchemical and mineralogical characteristics. De Souza et al. (2009) reported that \nvariations in landscape shape promoted  differentiated variability of the physical \nand mineralogical soil properties. Walker et al. (1968), Ahn (1970) and Stoop \n(1987) found that soil fertility and organic matter content were mostly low in the \nupper and middle slopes and soil degradation could be rapid when cultivation is \nintensified, compared to the higher organic matter content and fertility status, low \nerosion hazard and adequate soil water availability in the valley bottom.\n Odemerho (1980) stated that the distribution of individual soils on a \ntoposequence as well as the spatial distribution of toposequences have considerable \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 71\n\n\n\ninfluence on land use utilisation, pattern, and soil management. Conversely, \nmanagement practices affect significantly soil variability (Castringano et al. \n2000), especially successive agricultural activities and erosion. Eshett (1985), \nvan Stavern and Stoop (1985) and Carsky and Masajo (1992) reported that the \nlocal farmers often cleverly utilise the spatially variable conditions along the \ntoposequence to diversify their crops. Consequently, because of the diversity of \nsoil types and ecological conditions, a range of technological options may be \nrequired on a toposequence (Stoop et al. 1982; van Staveren and Stoop, 1985).\n The toposequence concept has been studied intensively in Nigeria (Smyth \nand Montgomery, 1962; Juo, 1980; Juo and Moormann, 1980). Ogunwale et al. \n(1986) found soils on the upper and middle slope to be sandy to sandy clay loam, \nand sandy in all horizons in the lower slope and valley bottoms on coastal plain \nsands in South-western Nigeria. Fagbami and Udo (1980) reported that soils on \nthe upper and middle slopes were coarse-textured while they were fine-textured \nin the lower slope and valley bottom on the basement complex in the Abuja area, \nNigeria.\n Juo and Moormann (1980) and Eshett (1985) identified three topographic \npositions/land types along toposequences in South-eastern Nigeria, namely, the \nupper and middle slope land, the lower slope land, and the valley bottom land. \nHowever, while Juo and Moormann (1980) considered the land types unsuitable \nfor extensive food crop production on account of their shallow soil depth, sandy \ntexture and erosion, Eshett (1985) reported that the upper, middle and lower slope \nand valley bottom soils of  basaltic parent material are extensively used for yam-\nbased cropping, and wetland rice cultivation, respectively. All this indicate that \nthe soil-landscape relationship could be used in assessing potential land uses \n(Carsky and Masajo 1992). As yet, specific and suitable land and soil and crop \nmanagement systems have not been developed for the sloping lands in the area \nof the current study and similar places elsewhere. The objectives of this study \nwere (i) to characterise the soils occurring on physiographic positions along \ntoposequences, and (ii) to propose appropriate management systems for the land \ntypes in Cross River State to improve agricultural production.\n\n\n\nMATERIALS AND METHODS\n\n\n\nDescription of Study Area \nThe study was conducted in Assiga in central, and Adim and Odukpani in southern \nCross River State, South-eastern Nigeria. The areas lie between latitudes 5\u00b0 30\u2032 \nand 6\u00b0 00\u2032 N and longitudes 8\u00b0 00\u2032 and 8\u00b0 45\u2032 E. The climate of the area is tropical, \nhot and humid with high temperatures and pronounced wet and dry seasons. Mean \nannual temperature is near uniform, varying between 26\u00b0C and 28\u00b0C, while mean \nannual rainfall varies from 2500 to 3000 mm. High relative humidity is common, \naveraging about 80%. \n The area is situated over several geological formations, the predominant \nbeing the Quaternary coastal plain sands, the Cretaceous Cross River sandstone \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201972\n\n\n\ngroup, and the Pre-Cambrian Basement complex. The most extensive formation is \nthe Cretaceous Cross River sandstone group. Assiga is located on the Cretaceous \nsandstone plains; Adim is on the crystalline basement complex, while Odukpani \nis on the Cretaceous sandstone/shale (Odukpani Formation) parent materials. The \nsoils show very strong influence of the underlying lithology. The depth of the soils \nvaries considerably from shallow, due to underlying concretionary materials, to \ndeep on well-developed soil profiles. Soil texture varies from loamy sand and \nsandy loam in the surface to sandy clay and clay in the subsurface layers. The soils \nare structurally unstable, generally low in inherent physical and chemical fertility, \nand susceptible to high erosion rates.\n The vegetation in the area comprises the southern Guinea Savanna, the \nrain forest, and the mangrove and freshwater swamp forest. Much of the area \nhas been farmed and the secondary forest that is now common is a degradation \nof the original natural rain forest vegetation. A variety of agricultural and non-\nagricultural activities contribute to modifying the native vegetation. A wide variety \nand diversity of tress, shrubs and grasses are common in the landscape. The area, \nas in other parts of the State, supports a wide range of agricultural crops, both for \nfood and raw materials. The major food crops include yams, maize, plantains and \nbananas, rice and a variety of vegetables. The major plantation crops are oil palm, \ncocoa and rubber. The land use systems are upland/dry land or wetland uses, and \nbased on traditional shifting cultivation with the associated bush or natural fallow \nrotation. But the fallow period has become shortened to about 4 years from 7-15 \nyears (Ogban et al. 2004: 2005) and this has left the vegetation immature and \nunable to restore soil quality attributes (Areola 1990). Continuous cultivation has \ntherefore become common in most places in the low external inputs production \nsystems.\n\n\n\nFields Methods \nThe study was conducted at three topographic positions, namely upper slope, \nmiddle/lower slope and valley on three toposequences. At each site, and at each \ntopographic position, soil profile pits were dug, described and sampled according \nto FAO/WSRI (2006). In the valley bottom, soil samples were collected with an \nauger according to designated depths of 0-15, 15-30, 30-50, 50-75 and 75-100cm \nbecause the soils were wet (at the time of study).   \n Bulk soil samples were collected from the depth zones for physical and \nchemical analyses. Undisturbed soil samples were also collected with metal \ncylinders measuring 7.2 cm long and 6.8 cm internal diameter for the determination \nof hydraulic conductivity and bulk density as described in Dane and Topp (2002). \nAnother set of disturbed samples was collected from the top two soil depths for \nthe determination of wet aggregate stability (Dane and Topp 2002).\n\n\n\nLaboratory Methods \nThe samples were processed and used in the following analyses. Mechanical \nanalysis, bulk density and hydraulic conductivity were determined as described \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 73\n\n\n\nin Dane and Topp (2002). Soil texture was designated according to Soil Survey \nStaff (1999). Total porosity was calculated from soil bulk density and an assumed \nparticle density of 2.65 Mg m-3, and macroporosity was computed from total \nporosity and field capacity water content (at 50 cm tension). Percent sand-free \nwater stable aggregates (WSA) >0.25 mm were determined by the wet sieving \nmethod of Yoder (1936) as also described in Dane and Topp (2002). Soil pH was \nmeasured in a 1:2.5 soil:water suspension using a pH meter. Organic carbon (SOC) \nwas determined by the dichromate wet oxidation method of Walkley and Black \n(1934). Total N was determined by the Kjedahl digestion method. Available P \nwas estimated by the Bray-1 method. The exchangeable bases, Ca, Mg, K and Na, \nwere determined according to the methods described in IITA (1979) and Sparks \n(1996). Exchangeable acidity was also determined using routine procedures. The \neffective cation exchange capacity (ECEC) was obtained from the summation \nof exchangeable bases and exchangeable acidity. Base saturation was calculated \nfrom total exchangeable bases and ECEC (IITA 1979).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nEffect of Physiographic Position on Soil Morphological and Physical \nCharacteristics \nThe Ap horizon was about 10cm thick at the upper and middle slope positions, \nand 15cm thick in the bottom land (Tables 1-3). The depth in the valley bottom \nwas rather contrary to the findings of Philips (2007) who reported that erosional \nand depositional processes favour the accumulation of colluvial materials at the \nlowest members of the toposequence. The average soil depth in all slope positions \nwas less than the 30 cm effective rooting depth of common cultivated crops. \nThe shallow depth of the Ap horizon on the upper physiographic positions could \nbe attributed to the interaction between topographic attributes and soil erosion. \nErosional processes could inhibit the accumulation and formation of soil.\n Slope is an important element of topography (Schaetzl, and Anderson \n2005; Seibert et al. 2007) because it drives geomorphic processes (Evans, 1972) \nthrough its influence on soil water infiltration, flow rate of water and colluvial \nmaterials, and by controlling the rate of expenditure of energy or the stream power \navailable to drive the flow. It influences the amount of sediment that moves down \nslope and also the amount of water that is available for eluviation and illuviation, \nand leaching in the soil profile. Consequently, it influences the process of soil \nformation both laterally and vertically, and has a significant influence on a wide \nrange of soil physical and chemical properties (Garrard 1981).\n Surface water flow or stream power directly relates to soil erosion, in \nterms of its detachment and transportation power. In other words, the stream \npower on these generally <5% slopes may be such that could have inhibited soil \nformation, but soil destruction on the slopes in this study environment where soil \nerodibility (susceptibility of soil to erosion) and rainfall erosivity (potential of \nrainwater to cause erosion) were high. The shallow depth of the Ap horizons of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201974\n\n\n\nthe physiographic positions may therefore be attributed to erosional processes in \nthe study area, and therefore require specific best management practices for soil \nand water conservation and increases in agricultural production.\n The soil in the upper and middle slope positions was dominated by a \nhue of 7.5YR, while the valley bottom soil had a hue of 10YR, generally (Tables \n1-3). Similarly, the soil in the upper and middle slope had high chroma of  > \n4 while the soil in the valley bottom had a chroma \u2264 2. The upper and middle \nslope positions had little or no mottles while the soil in the valley bottom had \nmottles with high value and chroma. The observed colour patterns and prevalence \nof mottles from the upper slope to the valley bottom were attributed to the parent \nmaterials and episodic drainage condition. Presence of mottles in the soil profiles \nof the upper and/or middle slope positions are characteristic in some basement \ncomplex materials. These imply that while soils on the upper and middle slope \nmay usually be well-drained and somewhat well-drained or imperfectly drained \nrespectively, and oxidising and therefore exhibit a lower hue and high chroma, \nsoils in the valley bottom are under the influence of a perennially high or seasonal \nfluctuating water table, poorly drained and reducing and therefore have a higher \nhue and lower chroma in the matrix as observed in this study. The cyclical nature \nof the ground water table directly influences the dynamics of iron in soil water \nor the oxidising and reducing conditions and prevalence of mottles in valley \nbottom soils, and could be capitalised in managing the soils for crop production \nby adapting the uses of the soils to their wet conditions.\n Soil structure was weak and medium sub-angular blocky. Soil consistency \nranged from moist and friable in the surface horizons to very sticky and very \nplastic at lower depths of all topographic positions and landscapes (Tables 1-3). \nThe weak structure of the soil indicated poor state of aggregation, fragility and \nsusceptibility to erosion. Although erosion hazards were low or could be non-\nexistent in the bottom land, the soil may remain slaked, dispersive and easily \nsorted, and this could explain the coarse-texture of the soil. Weak soil structure \ncan be an erosion-susceptibility factor at the upper and middle slopes where the \nstream power index could be high, aggravating the severity of soil erosion and \ndegradation.\n Particle-size distribution differed both within and among the \ntoposequences (Tables 1-3). Percent sand separate was generally higher in the \nsurface soil depth and decreased down the soil profiles and from the upper to \nthe bottomland, and was apparently similar among the toposequences/parent \nmaterials. The underlying lithology was sandstone, which may be responsible for \nthe similarity in the magnitude of the sand fraction. The respective contents of silt \nand clay fractions generally increased with depth and from the upper to the bottom \nland. The particle-size fractions varied irregularly with depth at all physiographic \npositions. But, while the sand fraction generally decreased with depth, the silt and \nclay separates increased with depth in all soil profiles and parent materials. At the \nupper and middle slopes of the basement complex, clay content was highest at \nabout 30-40cm depth. Particle-size distribution among the topographic positions \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 75\n\n\n\nand parent materials was similar, indicating that the parent materials/geomorphic \nunits were essentially similar or have a common lithology. \n Soil texture was therefore either sandy loam or loamy overlying sandy \nclay loam, or coarse-textured overlying medium-textured soil. The bottomland \nwas generally more coarse-textured than soil at the other toposequence positions, \nprobably due to sorting and deposition of colluvial materials from the upper \nslope positions. The soil profiles were different in texture and suggest that each \ntoposequence comprises a group of soils that occur in a catenary sequence. \nThe topographic positions could therefore form soil management units on each \ntoposequence.\n Bulk density was moderately high, ranging from 1.42 to 1.59 Mg m-3, and \nvaried irregularly with depth at all topographic positions in the landscapes (Tables \n1-3). Also, average soil density was generally higher at the upper slope than in the \nmiddle slope of the parent materials/toposequences. Bulk density is an index of \nsoil compaction, resistance to root growth and penetration, and water infiltration \n(Hillel 1998). The observed soil density may not adversely affect plant root \ngrowth and development since the soils were generally friable and could easily \nbe penetrated by plant roots, and infiltrated by rain water (the latter) depending \non the position of the soil on the landscape. The pattern of differences in total and \nmacro-porosity was similar to bulk density. Total pore space averaged 40% of the \nsoil volume while water-free pore space averaged 50% of total pore space. The \nhigh total and air-filled porosities reflect the medium to coarse textures of the soils \nand may favour a high rate of water transmission or internal drainage in the soils, \nbut may be diminished by the effect of slope on water infiltration and overland \nflow. The apparently high micro-porosity may, however, favour water retention \nin the soils. Management practices such as mulching may be adopted to enhance \nrainwater infiltration and conserve soil water against evaporative losses.\n Hydraulic conductivity (Ksat) was higher in the surface horizons and \ndecreased with depth in all soil profiles (Table 1-3). In each location, average \nvalues were higher at the upper than at the middle slope positions. The values \nobtained varied from rapid in the top soil layers to moderate at lower depths in \nall topographic positions. Although there was no significant relationship between \nKsat and other soil properties, the observed Ksat may be associated with the \npattern of increases in clay distribution with depth in the soils (organic carbon vs \nKsat, r = 0.31, p<0.05, clay vs Ksat, r = 0.77, p< 0.05). Hydraulic conductivity is \nan index of soil resistance to fluid flow and a characteristic of soil for drainage. \nThe Ksat determined in this study indicates that the soil could be well drained \nexcept where the water table is high and perennial.\n The data in Tables 1-3 show that percent water stable aggregates (WSA) \nin the top 30cm soil depth was greater in the 2.0 - 0.5 mm diameter range than \nin the >2.0 mm range, within and across the toposequences/landscapes. That is, \naggregate stability in the size range >2.0 mm was less than in the size range <0.5 \nmm. Overall, however, there was a preponderance of aggregates >0.5 mm (2.0 + \n0.5 mm), averaging more than 70%, in all landscapes. This indicates that the soils \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201976\n\n\n\ncould be less readily disposed to splashing and structural damage, for according \nto Bryan (1969), soils with a greater proportion of aggregates <0.5mm are easily \nerodible. There is however no guarantee that the aggregates would not disintegrate \nand erosion accentuated when the lands are intensively cultivated, as soil organic \nmatter (SOM) decomposition rate would also increase. Although the stability of \nthe aggregates could not be attributed to soil organic carbon (SOC) (Org. C vs. \nWSA (2.0 to 0.5mm) was not significant), even as organic matter is reputed to be \nthe main agent of aggregate stabilisation in soils of the humid tropics. The lack \nof a significant SOC vs. WSA relationship may be attributed to the interacting \neffect of other aggregation and stabilisation agents (mainly inorganic substances \nnot determined in this study), which may have obscured the importance of \nSOC in the soil. It could also be attributed to the constantly changing content \nof organic matter and its derivatives that are important as stabilising agents, \ndue to the favourable decomposition environment (high moisture and elevated \ntemperatures). Bronick and Lal (2005) reported that the effectiveness of SOC \nin forming stable aggregates is related to its decomposition rate. Niklaus et al. \n(2001) stated that increased decomposition rates due to increased temperatures, \nmoisture and microbial activities may have a greater influence on rapid turnover of \nSOC pool and further losses as radiative gas, and may be deleterious to aggregate \nstability. Consequently, the structural stability and shear strength of the soil may \nbe low, under the prevailing high intensity rainfall and rainfall erosivity in the \narea.\n\n\n\nEffect of Physiographic Position on Soil Chemical  Properties \nThe data in Tables 4-6 show that soil pH was slightly acidic and with very low \nvariability both within and across the toposequences. This is attributed to the \nacidic parent material of the soils, leaching losses of basic cations, and to the \nshortened fallow which reduces the effectiveness of nutrient cycling between \nplant and soil, depressing the soil pH.\n SOC content differed widely in its distribution from the upper slope to \nthe valley bottom, average profile values being generally higher in the valley \nbottom and lower in the middle slope positions of the toposequences (Tables 4-6). \nSimilarly, its variability ranged from low in the basement complex to very high in \nthe sandstone parent material. The data show that percent organic C was higher \nin the topsoil layers, and decreased to lower values with depth in all topographic \npositions. The higher values in the surface layers were attributed to supplies \nof organic matter from the above and below ground plant biomass. However, \nthe observed SOC contents, which were generally below 20 g kg-1, the critical \nvalue for the area (FMANR 1990), was attributed to favourable decomposition \nprocesses. Besides, the length of the bush fallow period has been drastically \nreduced (Ogban et al 2004; 2005), the vegetation is immature (Areola 1990), \nand therefore evergreen. The vegetation may therefore be acting as an effective C \nsink. Also, the slash-and burn plant residue management destroys organic matter \nin the soil and may have contributed to the low SOC content. The low SOC \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 77\n\n\n\nstorage has important environmental effects. First, the atmosphere may become a \nsink for organic C. Second, degraded SOC contributes significantly to increasing \nthe tropospheric concentration of one of the radiatively active greenhouse gasses, \ncarbon dioxide (CO2). This is accentuated by the slash-and-burn system of plant \nresidue management common in the area, and in all traditional farming systems. \nThe overall effect is the trapping of earth radiation and increasing the scourge of \nclimate change and environmental degradation.\n The trend in percent total N was similar to that of  SOC. Thus recapitalising \nSOC will increase the soil N content. Similarly, it will reduce the C/N ratio which \nvaries from very low to very high among the topographic positions and locations, \nas can be inferred from Tables 4-6. The values of soil N in this study were low \nand lower than 2.0g kg-1, the critical N value for the area (FMANR, 1990). The \ndeficiency of soil N is one of the factors constraining agricultural production in \nthe area. Therefore, agronomic or soil management practices that increase soil \nN levels will improve the productive capacity of the soil, crop performance and \nfarmers\u2019 income.\n Available P varied irregularly with depth, ranging from about 8 to 57 \nmg kg-1 at all physiographic positions (Tables 4-6). Average profile values were \ngenerally low except at the upper and middle slope of the sandstone with 21.55 \nmg kg-1 and 28.86mg kg-1, respectively. Available P content was less than 15.0 mg \nkg-1, the critical value for southeastern Nigerian soils (FMANR, 1990). Also, there \nwas no definite pattern of differences in P content among the parent materials. The \nhigh values at the two topographic positions of the sandstone parent material could \nbe attributed to fertilisation. However, the generally low P content was probably \ndue to low inorganic P content of the parent materials, weathering and erosion \nlosses, the low SOC content, and the slash-and-burn system of plant residue \nmanagement or post-clearing soil management, as well as insufficient fertiliser \napplication which is associated with a high cost and often, non-availability.\n Exchangeable Ca and Mg increased from the upper slope to the valley \nbottom in all parent materials (Tables 4-6). Also, the variability in the profile \ndistribution of the exchangeable bases increased down the slope, generally \nranging from low to very high. The higher values down slope, especially in the \nvalley bottom, indicates the erosion of the exchangeable bases from the upper \nmembers to the lower members of the toposequence on all parent materials, since \nthe basic cations are easily mobilised in soil water or runoff. Therefore, agronomic \npractices to be adopted in intensive cultivation, should include mulching, routine \nfertilisation and hedge row planting to minimise nutrient runoff. The trend in the \ndistribution of exchangeable K and Na was almost similar to exchangeable Ca \nand Mg (Tables 4-6). While exchangeable K may be said to be fairly adequate in \nthe soil, but not the case with exchangeable Na. However, both basic cations will \nneed supplementation when the soils are intensively cultivated.\n The profile distribution of exchangeable acidity and Al varied among the \nparent materials, especially in the sandstone where variability was as high as 60%, \nbut differed from the pattern of distribution of exchangeable Ca and Mg (Tables \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201978\n\n\n\n4-6). However, the toxicity of Al in particular is generally not feasible because \nof the high rainfall which will always flush the rooting zone, and mitigate its \ninfluence on the availability of other plant nutrients. Effective cation exchange \ncapacity (CEC) varied irregularly with depth, and profile variability range from \nlow (15.9%) to very high (42.7%). Effective cation exchange capacity (CEC) \nvaried among the parent materials, and was generally low in status. It is probable \nthat the parent materials were poor in nutrient status or it could be the result of \ncycles of weathering or erosion and/or cultivation. The pattern of base saturation \nwas similar to the effective CEC. The results also indicate that the exchange \ncomplex in the sandstone parent material was dominated by non-basic cations \ncompared to the basement complex and shale.\n\n\n\nPROPOSED SOIL MANAGEMENT SYSTEMS\nLack of suitable cultivable land is one reason resource-poor farmers cultivate \nintensively and dig deep into their agricultural capital, the soil quality or its \nphysical and chemical fertility, to produce more food (IITA 1989/90). The quality \nof vast areas of the uplands has substantially declined in the attempts to increase \nfood production due partly to the shortening of the soil-restoring fallow period \nbetween crops (Areola 1990; Ogban et al. 2004). Loss of soil quality is accentuated \nby soil erosion and degradation or loss of soil capacity to function, limiting land \nuse to extensive crop production. There is also a dearth of information on the \nproperties of the soils and how these properties change under cultivation. In \naddition, regenerating and productive soil management/conservation technologies \nare not components of the agricultural systems. For this reason, soil use does not \ninclude management based on soil\u2019s inherent properties and characteristics, and \ntheir specific needs.\n A soil-scape is the catena or gradient of soils along a toposequence or \nacross the landscape, that is, from valley bottoms, slopes and fringe, to the uplands \n(IITA 1989/90). It is farming by soil, which is, matching management practices or \ntechnological options to specific soil and landscape characteristics (Pierce and Lal \n1991).\n The study demonstrated that the topographic positions of the landscapes \nwere different in morphological, physical, hydrological, and chemical \ncharacteristics. The Ap horizon, soil zone of intense plant root activities and \nsoil process interactions, was shallow in all topographic units. Soil texture was \nsandy or loamy sand or medium, to coarse, and generally permeable. The upper \nslope is most likely to be droughty and the agricultural potential will depend \non the soil depth in relation to water availability and susceptibility to erosion \nand degradation. On the other hand, the middle slope and valley bottom may be \ndroughty during the dry season, due to low water table, inducing soil water stress, \nor poorly drained during the wet or rain season and likely to  impair cultivation. \nThe soils were generally low in physical and chemical quality attributes, and \nmay easily be degraded when cultivation is intensified without best management \npractices suitable to the soils. Management systems must aim at manipulating \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 79\n\n\n\nthe soils\u2019 properties and characteristics to increase their productivity. One such \npractice is increasing soil organic carbon stock which will invariably improve \nthe C/N ratio in each soil management unit (SMU), because of its relation to soil \nphysical quality through soil structure stability and chemical quality through its \nnutrient holding capacity.\n In a similar study, Juo and Moormann (1980) considered the catena in the \narea unsuitable for extensive food production. They also found valley bottoms in \nthe area poor in soil quality attributes for lowland rice production. Although the \nsoils in this study were equally of low physical and chemical quality, their use can \nbe adapted to their conditions by adopting the soil management unit and systems \nproposed here. The soil management units are the physiographic positions, that is, \nthe upper slope, middle slope and valley bottom. Basically, it is farming by soil-\nscape or soil ecosystem or soil condition, and adapting soil management systems \nto the prevailing conditions as outlined hereunder.\n\n\n\nSoil Management Unit (SMU) A \nUpper slope:  This soil management unit occurs on rolling topography with the \nslope varying from 0 to 5% in all landscapes. Top soil is shallow with sandy \nsurface and sandy clay loam and concretionary subsoil especially in the basement \ncomplex and sandstones parent materials (Fagbami 1981; Obi and Akinbola \n2010). The soils have weak structural properties, low inherent fertility and acidity \nproblems. Present land use is crop production under shifting cultivation. Common \nfood crops are cassava, yams and maize. Slash-and-burn practice is common even \nas the fallow vegetation is immature. \n The soil is susceptible to erosion and degradation, and its position on \nthe landscape and shallow soil depth may limit available soil water, especially as \nsoil conservation measures such as residue mulching and fertiliser application are \nnot commonly practised in the low input-output production systems in the area. \nDependence on the natural fallow to restore soil quality and resilience is the rule.\n Dryland farming in the rain season is an appropriate soil management \nsystem (SMS) and the common practice in the area. But continuous and intensive \nfood crop production requires the adoption of modified agro-forestry, the planting \nof acid-tolerant leguminous tree species and encouragement of growth of adapted \nshrubs and tree species. This will also provide adequate stakes for yam culture, \ncommon in the area. Intensifying mixed-cropping by widespread use of melon \nand related crops, and including leguminous crops such as cowpea, will greatly \nimprove soil quality attributes by recycling nutrient elements and improving soil \nfertility properties such as soil organic matter content, bulk density, aggregation \nand soil structural stability, soil water infiltration and plant available water. These \nmeasures will at the same time reduce soil erosion. These practices will increase \nthe residence time of soil water and the wetness index, allowing the water to \npercolate through and influence soil formation processes down the profile, and \nultimately increase the crop rooting depth.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201980\n\n\n\nSoil Management Unit (SMU) B \nMiddle slope: The soils are sandy over sandy clay loam subsoil and could be \nimperfectly drained. The physical and analytical data in this management unit \nare similar to those of SMU A, and although on a different topographic position, \nsomewhat similar management practices may be adopted.\n However, the soils have a high wetness index, because of seasonal \ngroundwater fluctuation which results in capillary infringement of the rooting \ndepth and the soil being occasionally (seasonally or perennially) saturated, creating \nphreatic or hydromorphic upland conditions (Garity 1984). However, the shallow \nground-water table makes the soil suitable for rice culture during the rainy season. \nCultivating this soil unit with upland dry food crops, for example, yam, cassava \nand maize, during the rainy season (dry land farming) requires managing the \ngroundwater table by using shallow drainage furrows constructed across the slope \nto intercept runoff; by not letting them become erosion channels, the groundwater \ndoes not inhibit root activity, that is, water and nutrient uptake functions of the \nplant. The use of moderate residue mulching will allow evaporation and reduce \nweed growth as well as improve soil physical and chemical quality attributes. \nHydromorphic processes will also mobilise nutrients in the soil.\n Therefore, the recommended SMS for this SMU is the upland or dry \nland farming, but which will require a greater mulch application rate, and only \nwhen soil strength or bearing capacity has improved, so that evaporative drying is \nnot impeded and that crop roots follow the receding groundwater table (Cornish, \n1987). This soil management unit is usually under-utilised because of the wetness \ncondition of the rooting zone of the soil.\n\n\n\nSoil Management Unit (SMU) C \nValley bottom: Soil in this SMU is either seasonal or perennially strongly \nhydromorphic (Andriesse 1986; Carsky and Masajo 1992; Ogban, 1999) with a \nhigh wetness index, which may impede cultivation during the rain season. Soil is \nsandy throughout the profile, low in plant nutrient supply and present land use is \nmainly rice cultivation (wetland farming). Hydromorphic processes are excellent \nfor the nutrition of the rice crop, and the SMU is already being exploited in all \nthree landscapes by adapting the farming of the land to the wet condition (Ogban \nand Babalola 2003). In spite of the low inherent chemical fertility, soils are more \nproductive than adjacent upland soils due to adequate water supply, nutrient \ntransformation under wet soil conditions, and low erosion hazard.\n Soil in this topographic position has potential for wetland/dryland and \ndryland farming or SMS which can be utilised to maximise crop production. \nWetland/dryland farming involves simultaneous cultivation to wetland and \ndryland crops. A system of mound-tillage will be required to grow a toposequence \nof water-tolerant crops (e.g., rice) at the base, progressing to crops with aerobic \nedaphic requirement (e.g., maize, yam etc) at the apex (Ogban and Babalola (2003 \n&2009). The mounds will be large, more than 1m height and diameter. Ponding \nor flooding is controlled by drainage through space among mounds, although, a \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 81\n\n\n\nsystem of shallow ditch drainage to a 20cm depth will remove flood or overland \nflow without increasing production cost (Ogban and Babalola 2002).  Dry land \nfarming involves cultivating the land types with maize, cassava and vegetable \ncrops in the dry season, similar to the practice in SMU B. The land will however \nbe cultivated when soil strength or soil workability has been improved by natural \ndrainage (internal drainage or evaporation). This approximates the second rate \nstage of evaporation. Plant residues collected at sites may be applied as mulch \nduring the dry land cropping phase, to minimise evaporation, reduce weed growth, \nconserve soil water, improve SOC sequestration and chemical fertility as well as \ncounteract radiative emission of methane (CH4) when the soils are intensively \nused. Adopting these soil management practices or SMS will enhance intensive \nuse of the land type and increase the productivity of the low resource farmers.\n\n\n\nCONCLUSION\nThree toposequences were investigated for their soil quality attributes to guide \nthe design of soil management practices for increases in crop production in the \nstudy area. The three toposequences were generally low in soil physical and \nchemical fertility and therefore not suitable for intensive food cultivation without \nthe adoption of appropriate quality soil (best) management practices. Based on the \nstatus of the soil characteristics, particularly, soil water availability, three suitable \nSMUs have been identified, corresponding to the topographic upper slope, middle \nslope and valley bottom, in the landscapes. Also, three SMS have been designed \nfor the topographic positions in the agro-ecological zones, namely, dryland \nfarming for the upper slope, and wetland, wetland and dryland farming for the \nmiddle slope and valley bottom SMUs. Maximising increases in food production \nnecessitates adopting the recommended soil management systems to the SMUs. \nThese SMS can also be adopted in other similar agro-ecological conditions. These \nSMS are being recommended for use by agricultural extension agents to assist \nfarmers to cultivate their sloping lands more efficiently and productively.\n\n\n\nREFERENCES\nAhn, P. M. 1970. West African Soils (3rd ed.), pp. 60-78. Oxford: Oxford University \n\n\n\nPress.\n\n\n\nAndriessse, W. 1986. Area and distribution. 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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 1-7 \n \n\n\n\n1 \n \n\n\n\nThe potential of Plant Residues and Industrial Sewage Sludge as Organic \nFertilizers, and their Effect on Soil pH and Moisture of Acid Sulfate Soil \n\n\n\n \nVu, Thi Quyen 1 \n\n\n\n \n1Faculty of Applied Technology, School of Technology, Van Lang University,  \n\n\n\nHo Chi Minh City, Vietnam  \n \n\n\n\n*Corresponding email: quyen.vt@vlu.edu.vn  \n \nABSTRACT \nIntensive farming is expanding rapidly, making organic fertilizer development vital to achieve \nsustainable agriculture. Thus, the purpose of this study is to understand the potential of decaying plants \nand industrial sewage sludge as organic soil amendments. This study was conducted from December \n2019 to May 2021 in Ho Chi Minh City. Decaying plants and sewage sludge from wastewater treatment \nenterprises were collected. Manure and probiotics were also added according to the semi-fermentation \nmethod to develop different composting formulas. The quality of fertilizer was evaluated based on \nnational standards. The last step was to observe the compost applications potential in improving acidic \nsoil moisture and soil pH. The study found that the composts containing 70% plant waste + 20% cow \nmanure + 10% dried sludge + microbial (0.25 liters/1m3) met the nutrient criteria of the Vietnamese \nGovernment on fertilizer management as follows: pH 6.7, total organic matter 31.08%; total N 2.37%; \n5.67% P2O5, 8.97% K2O; humic acid 2.59%; fulvic acid 1.24%; and C:N ratio 11.03. After applying \nthe compost fertilizer (10,000 kg/ha), a significant improvement in acidic sulfate soil pH and moisture-\nholding capacity was recorded compared to the conventional methods of farming. \n \nKeywords: decayed plants, sewage sludge, organic fertilizer, acidic sulfate soil \n \nINTRODUCTION \nConventional agriculture that frequently uses chemical fertilizers and pesticides has been \nknown to cause hazardous residues in agricultural products, jeopardizing both environmental \nand human health. Thanks to the benefits of organic farming, innovative and environmentally \nfriendly methods of producing organic fertilizers are gradually being developed. In 2018, the \nPrime Minister of Vietnam stated that \u201cFertilizers and soil amendments must be produced from \nmaterials and methods that conform to organic agricultural standards\u201d. Therefore, Vietnam is \nfocused on identifying organic fertilizer sources that meet the requirements of state regulations.  \n\n\n\nTo achieve sustainable agriculture, diverse types of organic fertilizers from different \nsources of biological waste should be utilized. Based on the current agricultural land area in \nVietnam, about 10 million hectares of cultivated land, and about 200 million tons of organic \nfertilizer are needed each year. However, the total organic fertilizer from domestic production \nand importation is only about 3 million tons per year (MARD, 2018). \n\n\n\nOrganic raw materials are needed to produce organic fertilizers. Sources of materials \ninclude by-products from farming, animal husbandry, food production and manufacturing. \nP\u00f6yki\u00f6 et al. (2019) analyzed that the concentrations of nutrients (P, Ca, Na, K, Mg, and Zn) in \nFinnish sludge were relatively high. This supports the production of fertilizer from sludge. \nFaozi et al. (2018) have proved that compost from banana stem can be used as a soil conditioner \nas well as a source of nutrients to increase the growth and yield of soybean crops. Banana stem \nis a potential material of compost because it is rich in minerals and organic materials: C \n(21.85%), N (0.28%), P (0.98%), K (3.30%), and C: N (78). Applying compost from banana \nstem at a dose of 15 tons/ha for Artemisia vulgris provides an average yield of 18 tons per ha, \nwhile increasing soil pH from 5.26 to 6.77 (Quyen and Bao, 2021). Thus, organic fertilizer can \nto improve the physical and chemical properties of soil. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 1-7 \n \n\n\n\n2 \n \n\n\n\nThis study was conducted to determine the feasibility of producing and using organic fertilizers \nfrom plant residues and industrial sewage sludge through the decomposition mechanism of \nmicroorganisms. The study also evaluates the effect of organic fertilizers on soil pH and soil \nmoisture. \n \nMATERIALS AND METHODS  \n \nSampling \nThe study was carried out from December 2019 to May 2021 at the laboratory of the Faculty \nof Environment and Biotechnology, Van Lang University, and Sen Viet Farm. The materials \nin this research were collected from different areas of Ho Chi Minh City, Vietnam. The sewage \nsludge was gained from municipal wastewater treatment plants. The sludge was analyzed and \ndetermined to be free of hazardous substances (pH=8.8; Pb = 0.67 mg/kg; Cd = 0.075 mg/kg; \nAs = 0.018 mg/kg), which was dried at 105oC for 8 hours. Banana stems and fruits, and \nvegetable remains were collected from markets in Ho Chi Minh City. They were chopped and \ndried to have a moisture content of 20%. Cow manure purchased from free-range cow farms \ncontained total organic matter (68.6%), nitrogen (1.57%), P2O5 (2.29%), and K2O (1.08%). \nMicrobial (Bacillus subtilis, Streptomyces spp) for agriculture usage were provided by the \nSouthern Research Centre for Soil, Fertilizer and Environment, Institute of Soil Agro-\nchemistry. Acidic soil was taken from the basil cultivation area of Senviet Farm (pH-KCl 3.2; \nOC=3.69%; N total=0.177%; P2O5 total=107mg/kg; K2O5 total = 813 mg/kg). \n \nCompost treatments preparation \nThe fertilizer formulas were established as shown in Table 1. After the fermentation process, \nthe materials were kept in semi-aerobic incubation conditions at room temperature. The \nmoisture content was periodically checked and maintained at 50%. The compost was mixed \nevery 7 days. \n \n\n\n\nTable 1. The compost compositions \nFormula Decayed plants Cow manure Dried sewage sludge Microbial \n\n\n\nF1 80% 20% 0% 0.25 liters/1m3 mixture \nF2 75% 20% 5% 0.25 liters/1m3 mixture \nF3 70% 20% 10% 0.25 liters/1m3 mixture \nF4 65% 20% 15% 0.25 liters/1m3 mixture \nF5 60% 20% 20% 0.25 liters/1m3 mixture \n\n\n\n \nThe indicators of fertilizer quality according to Vietnam\u2019s National technical regulation \n\n\n\ninclude pHKCl, OM (%); Ntotal (%); P2O5; K2O; fulvic acid; humic acid; lead (Pb); cadmium \n(Cd); arsenic (As); mercury (Hg); presumptive E. coli and Salmonella spp. The most optimal \nformula was selected to be used in the experiment of improving acidic soil. \n \nEffect of organic compost on soil moisture and soil pH \nThe experiment was conducted in pots containing 5 kg of soil. The amount of fertilizer for each \npot was calculated using the formula: weight (Db) x depth of an acidic soil (20cm) = soil mass \nof 1 ha/20cm. Each treatment was replicated 3 times. After fertilizing, the fertilizer was mixed \nevenly into the soil and 3.75 liters of water was added. The pots for the trail were arranged \noutdoors but protected from rainwater. The pots have a depth of 30cm and the soil contained \nin them with a thickness of 20cm. Table 2 shows the composting dosage of organic compost \nused.  \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 1-7 \n \n\n\n\n3 \n \n\n\n\nTable 2. Composting dosage for different treatments \nTreatment Dosage \nT1 (control) Normal farmer\u2019s process: lime: 10,000 kg/ha + phosphorus 600 kg/ha \n\n\n\nT2 Composting 8000 kg/ha \nT3 Composting 10,000 kg/ha \nT4 Composting 12,000 kg/ha \nT5 Composting 14,000 kg/ha \n\n\n\nNational standard TCVN 6651:2000 was applied to determine the moisture saturation: \nA metal tube (D = 100cm, H = 10cm) was used to collect the soil samples. In the laboratory, \nthe soil mass from the tube was gently removed and weighed from the tube to prevent the soil \nfrom breaking and losing its texture, then weigh the soil mass and proceed to put the soil into \na plastic tube with D = 100 cm and H = 20 cm, one end is sealed by a thin cloth, hang the \nplastic tube on the holder. Use the measuring glass to measure the correct amount of water, \npour slowly into the plastic tube containing the soil to allow the water to penetrate evenly into \nthe soil, stop for about 5 minutes for the water to penetrate, and pour again when the water is \nclear. If the surface overflow occurs, stop for 5 minutes for the water to absorb completely and \ncontinue pouring until the first drop of water is seen through the thin film covering the bottom, \nat this time the soil has reached saturated moisture; determine the amount of water poured into \nthe soil (the soil has the maximum moisture capacity). Thereby, determining the amount of \nwater used to saturate the experimental soil is 3.75 liters per 5 kg of soil. \n \nExperimental follow-up: \nDetermination of soil moisture in experimental treatments: determine soil moisture before the \nexperiment and after 14 days of incubation with manure. Using a moisture meter to determine \nthe moisture content of soil samples for testing and taking soil samples for drying also follows \nthe drying method in an oven. The method is to mix the soil, pick up all the roots. Then take \n20g into a cup and dry it at 1050C, in 8 hours take it out and put it in a desiccator to cool, \nreweigh the weight, this job is repeated 2 to 3 times when the soil mass is not double. Calculate \nthe complete dried moisture content of the soil (%) (National standards TCVN 5979:2007). \n\n\n\nDetermination of soil pH was done before and 14 days after composting treatments, done \nby glass electrode in soil suspension - 1M KCl solution (National standards TCVN 5979:2007). \nThe collected data were analyzed for variance (ANOVA) and the mean values were tested \naccording to LSD (least significant differences) with a level of p \u2264 0.05 using Stagraphic 15.0 \nsoftware. \n \nRESULTS AND DISCUSSION \n \nEffect of dried sewage sludge on time and quality of compost \nAfter 5 weeks, the moisture content of compost in the incubator was always maintained at 50%.  \nThe changes in pH and incubation temperature are shown in Table 3. When fermentation \noccurs, the enzymes of microorganisms and fungi consume organic compounds and release \norganic acids. During the early stages of composting, these acids accumulate and result in a \ndecrease in pH (after 2-3 weeks) (Quyen and Bao, 2021). The change in pH in this study was \ndue to the organic matter in the plant waste being decomposed by enzymes and the influence \nof sewage sludge. All treatments with decayed plants and dried sludge had increased pH, from \n6.5 to 6.7 (higher values received in formula F3, F4, F5). All of these treatments were higher \nthan the control treatment (pH = 6.0). However, there is no significant statistical difference was \nobserved. The above results of pH increase in formula F3, F4, and F5 can be explained due to \nbeing supplemented with dried sludge (the initial pH of dried sludge is 8.8). \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 1-7 \n \n\n\n\n4 \n \n\n\n\nTable 3. Effect of the dried sludge on the pH of the compost \n\n\n\n \nTable 4. Effect of dried sludge content on compost mass temperature \n\n\n\nThe formulas with the addition of dried sludge gave the composting time up to 5 weeks. \nThe result had a statistically significant difference compared to the control. This explains the \neffect of the dried sludge as a buffer, serving both as a habitat and as a source of nutrients for \nmicroorganisms. The decay time of compost in this experiment is similar to that of compost \nfrom banana stems and biochar in the research of Quyen and Bao (2021). In terms of \norganoleptic, formulas F3, F4, and F5 gave a well-balanced and smooth compost, with a dark \nbrown color. This can be explained by the impact of the sludge content on the quality of the \ncompost as well as the ripening time of the compost materials. \n \nAnalysis results of nutritional indicators in compost after composting \nTable 5 shows that pHKCl of organic fertilizer after composting get highest in treatment F4 and \nlowest in treatment F1, this difference was statistically significant (p<0.05). The results were \nnoted, when composting plant wastes supplemented with manure and sewage sludge in the \nratio of 60:20:20, improved the pH of organic fertilizer better than the remaining treatments. \nThe drying sludge has an initial pH of 8.8 - This is also an important scientific basis for the \nresearch team to include this material as a research object and experimentally has demonstrated \nits ability to increase the pH of the sludge when added to the composting mixture. \n\n\n\nTotal organic matter (OM) content: OM content was highest in treatment control (F1), \nfollowed by treatment F2, and lowest in treatment F4. The results of the lowest humification \nrate in treatment F1 (control - without the addition of sludge) show that the role of supporting \nthe decomposition of organic matter in the sludge material is quite clear. The highest rate of \nhumification in the composting process belonged to treatment F4 (mixing 15% of sludge), \nfollowed by treatment F3 (10% sludge) and treatment F5 (20% sludge). However, according \nto Duncan's ranking in statistical analysis, all 3 treatments F3, F4, and F5 did not have a clear \ndifference in this indicator at statistically significant; therefore, in terms of economic \nefficiency, treatment F3 will be selected in this experiment. \n\n\n\n\n\n\n\n\n\n\n\nFormula Composting duration \n1 weeks 2  weeks 3 weeks 4 weeks 5 weeks \n\n\n\nF1 (control) 6.0 5.5 5.6 6.0 6.0 \nF2 6.5 5.6 5.9 6.0 6.5 \nF3 6.8 5.6 6.0 6.5 6.7 \nF4 7.0 5.6 6.0 6.7 6.7 \nF5 7.0 5.6 6.4 6.7 6.7 \nCV, % 0.65 1.23 3.67 1.86 3.33 \n\n\n\na 0.676  \n(> 0.05) \n\n\n\n0.868  \n(> 0.05) \n\n\n\n0.001  \n(< 0.05) \n\n\n\n0,543  \n(> 0,05) \n\n\n\n0,623   \n(> 0,05) \n\n\n\nFormula \nComposting duration \n\n\n\n1 weeks 2 weeks 3 weeks 4 weeks 5 weeks 6 weeks \nF1 30.5 43.5 50.5 51.5 43.0 33.5 \nF2 32.6 52.5 53.5 45.0 33.2  \nF3 33.3 52.7 50.7 44.5 30.0  \nF4 33.3 53.2 51.5 43.5 29.0  \nF5 33.3 53.5 50.5 45.5 28.7  \nCV, % 0.55 1.02 5.72 1.53 2.22  \n\n\n\na 0.8859 \n(> 0.05) \n\n\n\n0.4539 \n(> 0.05) \n\n\n\n0.008 \n(< 0.05) \n\n\n\n0.2539  \n(> 0.05) \n\n\n\n0.2356  \n(> 0.05) \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 1-7 \n \n\n\n\n5 \n \n\n\n\nTable 5. Results of analysis of nutrients in compost \n\n\n\nCriteria Results \nUnit Formula \n\n\n\n  F1 (control) F2 F3 F4 F5 \npHKCl  6.14 6.25 6.7 7.23 7.4 \nTotal nitrogen % 2,23 2,32 2,37 2,34 2,35 \nP2O5 % 4,27 4,03 5,67 5,73 5,76 \nK2O % 5,11 7,25 8,97 8,99 8,99 \nAcid humic % 1,41 2,06 2,59 2,72 2,61 \nAcid fulvic % 0,17 0,94 1,24 1,21 1,21 \nC:N - 12,34 11,49 11,03 12,54 12,68 \nE. coli MPN/g Not detected Not detected Not detected Not detected Not detected \nSamonella spp /25g Not detected Not detected Not detected Not detected Not detected \nTotal organic matter % 34,8 33,91 31,08 30,61 30,76 \n\n\n\n \nTotal nitrogen was highest in treatment F3 and lowest in treatment F1, this difference was not \nstatistically significant. This shows that the total nitrogen content in organic fertilizer through \nthe composting process does not change significantly. Total potassium content was highest in \ntreatments F4 and F5; lowest in treatment F1, this difference is statistically significant; This \ncould explain the source of potassium added from the sludge material. The highest total \nphosphorus content in treatments F4 and F5, followed by treatment F3 and lowest in treatment \nF1, this difference was statistically significant. However, compared with the control, the \nremaining treatments had the total phosphorus content gradually increase with the mixed \nbiochar content, the biochar control improved the phosphorus content in the manure. \n\n\n\nHumic acid content was highest in treatment F4, followed by treatments F3 and F5; \nlowest in the control treatment. The humic acid content in the post-experiment treatments was \nimproved as compared to before experiment. According to Decree 108/2017/CP of the \nGovernment, it is required that bio-organic fertilizers must have a humic acid content greater \nthan 2.5%. Thus, treatments F3, F4, and F5 all had humic acid content that met the standards \nof bio-organic fertilizers. In terms of economic efficiency, treatment F3 will be selected for \nproduction. \n\n\n\nFurthermore, fulvic acid content was found to be highest in treatment F3, followed by \ntreatments F4 and F5 with the lowest level of fulvic acid was found in treatments F1 and F2.  \n\n\n\nC:N ratio, which is one of the important criteria to assess the quality standards of \norganic fertilizers according to the Government's Decree 108/2017/CP: organic or bio-organic \nfertilizers must have a C:N ratio of less than 12. According to this regulation, only two \ntreatments F2 and F3 met the requirements of organic fertilizer standards according to TCVN. \n\n\n\nThe results in Table 5 showed that E.coli and Samonella spp. in all treatments were not \ndetected in this experiment. \n The results of the analysis of quality indicators of organic fertilizers based on the \nstandards of the Government's Decree 108/2017 ND-CP on fertilizer management showed that \ntreatment F3 met the standards of bio-organic fertilizers. And treatment F3 with the \ncomposition: plant waste (70%) + cow manure (20%) +  dry sludge (10%) + microbial (0.25 \nliters/1m3) will be selected to be included in the efficacy assessment for improving the pH of \nacid soil in Binh Quoi area, Binh Thanh District, Ho Chi Minh City, Viet Nam (Senviet Farm). \nThe ability of organic compost to improve soil pH and soil moisture holding capacity \nThe results in Table 6 shows that organic fertilizer produced from plant residues in combination \nwith manure and sewage sludge had a higher effect on improving the pH of acid soil than the \nmethod of using lime and phosphorus of the farm (treatment T1-control); however, there was \nno statistical difference in the level of additional application of 8000 kg/ha compared with the \ncontrol. With application from 10,000 kg to 16,000 kg per ha, there was a statistically \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 1-7 \n \n\n\n\n6 \n \n\n\n\nsignificant difference (p<0.05) in the experimental treatments T3, T4, and T5 compared with \nthe control (T1). Besides, the soil moisture was also improved most clearly in the organic \nfertilizer treatments. This shows the important role of organic matter in maintaining soil \nmoisture. Treatments T3, T4, and T5 gave the best moisturizing results and was a statistically \nsignificant difference compared with NT1 and control (p<0.05). This result can be compared \nwith the study of Faozi et al. (2018) when applying compost from banana stem to soybean \nplants in coastal sandy areas, the authors also noted: compost from banana stem makes \nincreased organic matter content in coastal sandy soils, improved soil water and nutrient \nstorage capacity; at the same time, the level of fertilization from 20 to 60 tons/ha increased the \nyield of experimental soybean varieties. From the results obtained, treatment T3 with a \nfertilizer application rate of 10,000 kg/ha was selected as optimal when applied to improve the \nacid soil of SenViet Farm. \n\n\n\nTable 6. Soil pH and moisture holding capacity of acidic soil after treatment \n\n\n\nTreatment pH-KCl Moisture \n\n\n\nT1 (control) 4,97c 10,68c \n\n\n\nT2 5,50b 17,50b \n\n\n\nT3 6,10a 20,14a \n\n\n\nT4 6,12a 20,32a \n\n\n\nT5 6,20a 20,27a \n\n\n\nLSD 0.05 0,48 2,13 \nCV% 3,8 6,6 \n\n\n\nNote: CV% stands for coefficient of variation, unit is percent \nCONCLUSION \nThe organic fertilizer that made from plant waste (70%) + cow manure (20%) + dry sludge \n(10%) + microbial (0.25 liters/1m3) are composted in 5 weeks reach the standards of the \nVietnam Government's Decree 108/2017 ND-CP on fertilizer management. Applying organic \nfertilizer (compost) to acidic soil has had a significant impact on improving soil pH and \nmoisture holding capacity in the soil compared to the method of liming and phosphorus \napplication of the people; the level of additional fertilizer application for acidic soil of Senviet \nFarm is recommended 10,000 kg per ha. It is necessary to popularize the process of composting \nagricultural by-products and non-hazardous industrial wastes to make fertilizers for sustainable \nagricultural production. Also, further studies and evaluation of the compost on the effectiveness \nfor specific plants is needed. \n \nACKNOWLEDGEMENTS \nI would like to thank Van Lang University, Vietnam for funding this work. I am also grateful \nto Director of Sen Viet Farm and Dr. Nguyen Luong Lam Anh for their assistance throughout \nthis research. \n \nREFERENCES  \nFaozi, K., Yudono, P., Indradewa, D. and Ma\u2019as, A. 2018. Banana stem Bokashi and its effect to \n\n\n\nincrease soybean yield (Glycine max L. Merrill) in Coastal Sands area. Agrotechnology 7:2. \nMinistry of Agriculture and Rural Development. 2018. Summary report on the tasks of 2018. Published \n\n\n\nby Office of Ministry of Agriculture and Rural Development, Viet Nam. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 1-7 \n \n\n\n\n7 \n \n\n\n\nMinistry of Science, Technology and Environment 2007. National standards TCVN 5979:2007 on soil \nquality \u2013 determination of pH. Ministry of Science and Technology and Environment, The \ngovernment of The Socialist Republic of Vietnam. \n\n\n\nMinistry of Science, Technology and Environment 2008. Vietnamese standard TCVN 6651:2000 (ISO \n11274: 1998) on soil quality. Ministry of Science, Technology and Environment, The government \nof The Socialist Republic of Vietnam. \n\n\n\nP\u00f6yki\u00f6, R., Watkins, G. and Dahl, O. 2019. Characterisation of municipal sewage sludge as a soil \nimprover and a fertilizer product. Ecological Chemistry and Engineering S 26(3):547-557. \n\n\n\nVietnamese Government 2017. Decree 108/2017/ND-CP on fertilizer management. The government of \nThe Socialist Republic of Vietnam, Ministry of Agriculture and Rural Development. \n\n\n\nVietnamese Government 2018. Decree No. 109/2018/ND-CP on organic agriculture. The government \nof The Socialist Republic of Vietnam. \n\n\n\nVietnamese Government 2018. National technical regulation QCVN 01-189:2019/BNNPTNT on \nfertilizer quality. The government of The Socialist Republic of Vietnam. \n\n\n\nQuyen, V. T. and Bao, L. Q. 2021. Effect of organic fertilizer from banana stem on growth and yield of \nMugwort (Artemisia vularis). Journal of Agriculture and Rural Development 16(2):62-66. \n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : Email: momayeziir@gmail.com\n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 13:  59- 75                                 Malaysian Society of Soil Science\n\n\n\nAgronomic Characteristics and Proline Accumulation of \n\n\n\nIranian Rice Genotypes at Early Seedling Stage under \n\n\n\nSodium Salts Stress \n\n\n\nMomayezi, M.R. *, 1, 2, Zaharah, A.R.2, Hanafi, M.M.2 &  \n\n\n\nMohd Razi, I.3\n\n\n\n1 Faculty of Agriculture, Islamic Azad University, Varamin, Tehran, Iran \n\n\n\n2Land Resource Management Department, Universiti Putra Malaysia, Serdang, \n\n\n\nMalaysia \n\n\n\n3Institute of Tropical Agriculture, Universiti Putra Malaysia, Serdang, Selangor, \n\n\n\nMalaysia \n\n\n\nINTRODUCTION\n\n\n\nSalinity is one of the important abiotic stresses limiting rice productivity. The \n\n\n\ncapacity to tolerate salinity is a key factor in plant productivity. Rice is a species \n\n\n\nnative to swamps and freshwater marshes and its cultivated varieties provide one \n\n\n\nof the world\u2019s most important food crops.  In south and south-east Asia, about 100 \n\n\n\nABSTRACT\nSalt composition can affect rice (Oryza sativa L.) growth at germination and early \n\n\n\nseedling stages. The response of eleven rice genotypes to sodium salt compositions \n\n\n\n(NaCl and Na\n2\nSO\n\n\n\n4\n with the ratio of 1:1, 2:1 and 1:2 molar concentrations) and \n\n\n\nconcentrations (2.5, 5.0, 7.5 and 10 dS m-1 salt concentrations) was investigated in \n\n\n\nthe laboratory for 10 days. Effects due to salinity, genotype, and their interaction \n\n\n\nwere observed for most of the measured parameters during the germination and \n\n\n\nearly seedling stages. Mean germination time increased and germination index \n\n\n\ndecreased with increasing salt stress. Measured agronomic characteristics were \n\n\n\ninfluenced by salinity stress with the extent differing  with salt treatments. The 2:1 \n\n\n\nmolar ratio compared to the other salt compositions showed the greatest effect on \n\n\n\nrice germination. The results also confirmed that Cl- toxicity effects decreased as \n\n\n\nSO\n4\n\n\n\n2- increased in the solution. There was a non-significant relationship between \n\n\n\nwater content and proline accumulation. The anion associated with Na+ may play a \n\n\n\nfunctional role in the responses of rice seedlings and the degree of proline synthesis \n\n\n\nin stressed plants. According to mean germination time and germination index, \n\n\n\nTarom-e-Hashemi and Shirodi can be classified into salt sensitive and salt tolerant \n\n\n\ngroups, respectively. \n\n\n\nKeywords: Germination, germination index, mean germination time,   \n\n\n\n      proline content, sodium salt composition\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200960\n\n\n\nmillion hectares of land suited to rice production are not utilised because of soil \n\n\n\nproblems such as salinity, alkalinity, strong acidity, or excess organic matter (IRRI \n\n\n\n2005).  There has been great interest in developing varieties of rice that are resistant \n\n\n\nto salinity. The physiological basis for salt resistance is not completely understood.  \n\n\n\nPlants which grow in salt affected soils tend to show differences in physiological \n\n\n\nand biochemical activities from those grown on non-salt affected soils (Lutts et al. \n\n\n\n1995).  Jamil et al. (2007) reported that salinity delayed germination and decreased \n\n\n\nseedling growth.  \n\n\n\n Growth reduction depends on the period of time over which the plants were \n\n\n\ngrown in saline conditions. No visual differences were observed between control \n\n\n\nand stressed plants for tolerant and sensitive genotypes by a gradual increase in \n\n\n\nsalinity (Walia et al. 2005). Salinity reduced leaf growth more than root growth \n\n\n\n(Munns, 2002).  Studying the physiology of salt tolerance in rice plants may throw \n\n\n\nlight on a number of different aspects of plant stress physiology and may help to \n\n\n\ndevelop a unified model of stress tolerance in plants (Orcuttd et al. 2000). \n\n\n\n The commonly characterised biochemical response of plant cells to osmotic \n\n\n\nstress is the synthesis of special organic solutes (osmolytes) which accumulate \n\n\n\nat high cytoplasm concentrations (Serrano et al. 1994).  Plant cells accumulate \n\n\n\nproline as an osmo-protectant to conserve osmotic stability and to prevent \n\n\n\ndamage. There is a general acceptance that under salt stress, many plants tend to \n\n\n\naccumulate proline as a defense mechanism against osmotic challenge by acting \n\n\n\nas a compatible solute (Liu and van Staden. 2000 and Ghoulam et al. 2002) \n\n\n\n Many studies have been carried out to elucidate the physiological responses \n\n\n\nof rice plants to NaCl salinity stress (Chowdhury et al. 1995; and Hoai et al. \n\n\n\n2003). Gregorio and Senadhira (1993) and Zeng et al. (2000 and 2002) reported \n\n\n\nthat NaCl and calcium chloride (CaCl\n2\n) salts affected plant growth. Therefore, the \n\n\n\nobjectives of the present study were: (i) to determine the influence of both NaCl \n\n\n\nand Na\n2\nSO\n\n\n\n4\n concentrations and composition on seed germination and proline \n\n\n\naccumulation in eleven rice genotypes during early growth and (ii) to examine \n\n\n\nthe relationships between proline content and morphological and physiological \n\n\n\ncharacteristics of the rice seedlings.\n\n\n\nMATERIALS AND METHODS\n\n\n\nGrowth Condition and Salt Treatments\n\n\n\nRice seeds (Oryza sativa L.), from 11 genotypes selected from the widely \n\n\n\ncultivated cultivars in Iran namely Pouya, Shafag, Neda, Kadous, Tabesh, Tarom-\n\n\n\ne-Hashemi, Sahel, Khazar, Shirodi, Fajr and Nemat were used for the present \n\n\n\nstudy.  \n\n\n\n Twenty seeds were placed on a petri dish of 9.0 cm diameter lined with \n\n\n\na filter paper.  Salt treatments of NaCl and Na\n2\nSO\n\n\n\n4\n (1:1, 2:1 and 1:2 molar \n\n\n\nconcentrations) were dissolved in distilled water at 2.5, 5.0, 7.5 and 10 dS m-1 \n\n\n\nelectrical conductivity.  The salt solutions as well as 20 ml distilled water as a \n\n\n\ncontrol were applied to 39 different petri dishes.  The experiment was conducted \n\n\n\nMomayezi, M.R., Zaharah, A.R., Hanafi, M.M. &  Mohd Razi, I.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 61\n\n\n\nin laboratory using a complete randomize design (CRD) in 3 replications at room \n\n\n\ntemperature (27\u00b12\u00b0C) under dark conditions. The number of germinated seeds \n\n\n\nwas counted for 7 days (from 3rd to 9th day after soaking).  Ten-day-old rice \n\n\n\nseedlings were evaluated for root length, shoot height, dry and fresh weight, \n\n\n\nwater content as agronomic characteristics and proline content as a biochemical \n\n\n\ncharacteristic.  Significant differences between treatments were determined using \n\n\n\nthe Student-Newman-Keuls test.\n\n\n\n Germination was observed daily according to recommendations by \n\n\n\nInternational Seed Testing Association (ISTA, 1993).  Mean germination time \n\n\n\n(MGT) was calculated according to the equation of Ellis and Roberts (1981).  \n\n\n\nGermination index (GI) was calculated as described in the Association of Official \n\n\n\nSeed Analysis (1983).  In order to evaluate salt tolerance of 11 rice genotypes \n\n\n\nand investigate the relationship between the measured parameters at germination \n\n\n\nstage and growth parameters at early seedling stage, the following were also \n\n\n\ndetermined: root length, shoot height, dry weight, water content percentage and \n\n\n\nproline content. Three seedlings from each replicate were randomly sampled and \n\n\n\nroot length, shoot height, and seedlings fresh weight were measured. Root and \n\n\n\nshoot length were measured with a ruler. Seedlings were dried in a forced-air oven \n\n\n\n(70\u00b0C) for 72 hours and then measured for dry weight.\n\n\n\n Proline was measured as described by Bates et al. (1973).  The amount \n\n\n\nof proline was determined from a standard curve and presented in \u00b5mol g-1 fresh \n\n\n\nweight.\n\n\n\nRESULTS  AND DISCUSSION\n\n\n\nSeed Germination\n\n\n\nSeed germination was significantly (P\u22640.01) affected by salinity both in rice \n\n\n\ngenotypes and salt treatments. There was significant interaction between genotype \n\n\n\nand salt treatment for most of the parameters; however, this interaction was \n\n\n\nnot reflected in the final germination percentage, dry weight and water content \n\n\n\npercentages. \n\n\n\n Germinated rice seeds demonstrated that mean germination time (MGT) \n\n\n\nwas significantly increased by increasing salinity levels up to 10 dS m-1 compared \n\n\n\nto control (Fig. 1). The lowest and the highest MGT were recorded for Shirodi and \n\n\n\nTarom-e-Hashemi genotypes, respectively (Fig. 2). Significant (p\u22640.01) effect \n\n\n\nof salinity was seen on germination index (GI). Germination index decreased \n\n\n\nwith increasing salinity level (Fig. 3) and the genotypes responded differently to \n\n\n\nsalinity stress (Fig. 4). However, different salt compositions did not affect final \n\n\n\ngermination percentage (Fig. 5). The highest and the lowest GI were observed for \n\n\n\nShirodi and Tarom-e-Hashemi genotypes.\n\n\n\n Significant differences in salt sensitivity were observed at the germination \n\n\n\nstage of eleven tested rice genotypes. The germination of rice seeds declined \n\n\n\nsteadily when external salinity increased in contrast to the salt tolerant genotypes \n\n\n\nwhich attain a faster growth rate under saline conditions (Walia et al. 2005). \n\n\n\nProline in Different Rice Genotypes\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200962\n\n\n\nConsequently, mean germination time and germination index can be reliable \n\n\n\nparameters for evaluation of salt tolerance during germination stage because the \n\n\n\nsalt tolerant genotype has the lowest MGT and the highest GI. Based on these \n\n\n\nparameters, eleven genotypes can be assigned to three groups: tolerant (with \n\n\n\nthe lowest MGT and the highest GI), sensitive (with the highest MGT and the \n\n\n\nlowest GI) and moderate (those that fall outside the first 2 groups) Shirodi, Fajr \n\n\n\nand Shafag genotypes, with the lowest MGT and the highest GI, were classified \n\n\n\ninto the salt tolerant group.  Hence Tarom-e-Hashemi genotype which had \n\n\n\nthe highest MGT and the lowest GI was assigned to the salt sensitive group.  \n\n\n\nOther genotypes were categorised into the moderate group (Figs. 2 and 4). \n\n\n\nFig. 1: Mean Germination time (MGT) at three different sodium salt compositions \n\n\n\nincluding NaCl:Na2SO4 (1:1, 2:1 and 1:2 molar concentrations). Note: Values are  \n\n\n\nmeans of eleven genotypes with three replications and vertical bars represent SE.\n\n\n\nFig. 2: Effect of salinity on mean germination time (MGT) in different salt compositions \n\n\n\nincluding NaCl:Na2SO4 (1:1, 2:1 and 1:2 molar concentrations) and different \n\n\n\ngenotypes. Note: Values are means of three replications and vertical bars represent SE.\n\n\n\nMomayezi, M.R., Zaharah, A.R., Hanafi, M.M. &  Mohd Razi, I.\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n0 2.5 5 7.5 10 12.5\n\n\n\n1:1\n\n\n\n2:1\n\n\n\n1:2\n\n\n\nM\nG\n\n\n\nT\n \n(\nd\n\n\n\na\ny\n\n\n\n)\n\n\n\nSalt levels (dS m   )\n-1\n\n\n\n0\n\n\n\n0.5\n\n\n\n1\n\n\n\n1.5\n\n\n\n2\n\n\n\n2.5\n\n\n\n3\n\n\n\nTarom Sahel Neda Tabesh Khazar Kadous Pouya Nemat Fajr Shafag Shirodi\n\n\n\nRice genotypes\n\n\n\n1:1 2:1 1:2\n\n\n\na\n\n\n\na\n\n\n\na\n\n\n\nb\n\n\n\nbc bcd\n\n\n\ncd\nd\n\n\n\nb\n\n\n\nb\nbc\n\n\n\nb b\n\n\n\nbcd\n\n\n\ncd cd\n\n\n\nbc\n\n\n\nbcd de\n\n\n\nbcde\nbc\n\n\n\ncde\n\n\n\ne\n\n\n\nbcb\nbc\n\n\n\nde\n\n\n\nbc cde de\nde\n\n\n\ncde\n\n\n\ne\n\n\n\nM\nG\n\n\n\nT\n \n(\nd\n\n\n\na\ny\n\n\n\n)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 63\n\n\n\nFig. 3: Effect of salinity on germination index. Note: Values are means of eleven \n\n\n\ngenotypes with three replications and vertical bars represent SE.\n\n\n\nFig. 4: Effect of salinity on germination index of eleven rice genotypes in different \n\n\n\nsodium salt compositions. Note: Values are means of three replications and vertical bars \n\n\n\nrepresent SE.\n\n\n\nFig. 5: Final germination percentage (FGP) at different salt levels and salt \n\n\n\ncompositions. Note: Values are  mean of eleven genotypes with three replications and \n\n\n\nvertical bars represent SE.\n\n\n\nProline in Different Rice Genotypes\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\n16\n\n\n\n18\n\n\n\n20\n\n\n\n0 2.5 5 7.5 10 12.5\n\n\n\n1:1\n\n\n\n2:1\n\n\n\n1:2\n\n\n\nG\ne\nr\nm\n\n\n\ni\nn\na\nt\ni\no\nn\n \ni\nn\nd\ne\nx\n\n\n\nSalt levels (dS m   )\n-1\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\nTarom Sahel Neda Tabesh Khazar Kadous Pouya Nemat Fajr Shafag Shirodi\n\n\n\n1:1 2:1 1:2\n\n\n\ne\n\n\n\nd\ncd cd cd cd\n\n\n\nbcd\n\n\n\nabc\nab\n\n\n\na\na\n\n\n\ng\n\n\n\nf\nef ef\n\n\n\ncde\n\n\n\ndef\n\n\n\nbcd\n\n\n\nabc\n\n\n\nab\n\n\n\nab\n\n\n\na\n\n\n\nab\n\n\n\na\n\n\n\nabc\n\n\n\nbcd\n\n\n\nbcdbcde\n\n\n\nde\ncdede\n\n\n\ne\n\n\n\nf\n\n\n\nG\ne\nr\nm\n\n\n\ni\nn\na\nt\ni\no\nn\n \nI\nn\nd\ne\nx\n\n\n\nRice genotype\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n0 2.5 5 7.5 10 12.5\n\n\n\n1:1\n\n\n\n2:1\n\n\n\n1:2\n\n\n\nF\nG\n\n\n\nP\n \n(\n%\n\n\n\n)\n\n\n\nSalt levels (dS m   )\n-1\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200964\n\n\n\nEarly Seedling Growth\n\n\n\nRice seedling growth were significantly (P\u22640.01) influenced by salt levels. Root \n\n\n\nlength of rice seedlings was reduced by increasing the levels of salt concentration \n\n\n\n(Fig. 6). These finding are consistent with those of Jamil et al. (2007) and \n\n\n\nRodr\u00edguez et al. (1997). Maximum root length occurred at 2.5 dS m-1 when salt \n\n\n\ncompositions were applied at 1:1 and 2:1 molar ratio; whereas exposure of the \n\n\n\nseedlings to 1:2 molar concentrations resulted in the highest root length at 5 dS \n\n\n\nm-1. Besides, a relation was observed between variations in root length and sodium \n\n\n\nsalt composition. The root length of seedlings slightly increased as sulphate and \n\n\n\nchloride concentration changed (Fig. 7). Root length increased up to 2.5 dS m-1 \n\n\n\nfor 1:1 and 2:1 molar ratios and thereafter decreased up to 10 dS m-1. For 1:2 salt \n\n\n\ncomposition, there was an upward tendency to 5.0 dS m-1, decreasing as salinity \n\n\n\nconcentration increased. In general, Fajr and Tabesh genotypes had the longest \n\n\n\nand the shortest root lengths, respectively (Fig. 7). \n\n\n\n The analysed data show that in terms of shoot height, rice seedlings \n\n\n\nresponded differently to different salt mixtures and levels. There was a significant \n\n\n\n(P\u2264 0.01) increase in the shoot height at 2.5 dS m-1 compared to the control (Fig. \n\n\n\n8). This process continued up to 5 dS m-1 while salinity composition was at 2:1 \n\n\n\nmolar concentration. However, this significant increase in shoot height was not \n\n\n\nobserved when the salt composition was changed to 1:1 or 1:2 molar ratios.  The \n\n\n\nshoot height decreased when salinity was increased above 2.5 dS m-1 when the \n\n\n\nrice seedlings were treated by 1:1 and 1:2 molar concentrations.  However, there \n\n\n\nwas a downward tendency in shoot height when salt composition reached 1:1 and \n\n\n\n1:2 ratios. Conversely, rice seedlings tended to increase their shoot height when \n\n\n\nthey were exposed to 2:1 salt ratio (Fig. 8).  The data revealed that Fajr and Shafag \n\n\n\ngenotypes had the tallest and shortest shoot heights, respectively (Fig. 9). \n\n\n\nFig. 6: Effect of salinity on rice seedling root length. Note: Values are means of eleven \n\n\n\ngenotypes with three replications and vertical bars represent SE.\n\n\n\nMomayezi, M.R., Zaharah, A.R., Hanafi, M.M. &  Mohd Razi, I.\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n0 2.5 5 7.5 10 12.5\n\n\n\n1:1\n\n\n\n2:1\n\n\n\n1:2\n\n\n\nR\no\n\n\n\no\nt\n \nl\ne\nn\n\n\n\ng\nt\nh\n\n\n\n \n(\nc\nm\n\n\n\n)\n\n\n\nSalt levels (dS m   )\n-1\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 65\n\n\n\nFig. 7: Effect of salinity on root length of eleven rice genotypes in different sodium salt \n\n\n\ncompositions. Note: Values are means of three replications and vertical bars represent SE.\n\n\n\nFig. 8: Effect of different salt mixtures on rice seedling shoots length. Note: Values are \n\n\n\nmeans of eleven genotypes with three replications and vertical bar represent SE.\n\n\n\nFig. 9: Effect of salinity on shoot length of eleven rice genotypes in different sodium salt \n\n\n\ncompositions. Note: Values are means of three replications and vertical bars represent SE.\n\n\n\nProline in Different Rice Genotypes\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\nFajr Sahel Shafag Shirodi Kadous Neda Tarom Pouya Nemat Khazar Tabesh\n\n\n\n1,1 2,1 1,2\n\n\n\na\nab\n\n\n\nbc\n\n\n\nabc\n\n\n\na a\n\n\n\nab\n\n\n\nab\n\n\n\nbc\n\n\n\nabc\n\n\n\nb\n\n\n\nbcd\n\n\n\nabc\n\n\n\nbcde\n\n\n\nabc\n\n\n\nbcd\n\n\n\ncde\n\n\n\nabc\n\n\n\ncdecde\n\n\n\nd\n\n\n\nbc\n\n\n\ndef\n\n\n\nbcd\nbcd\n\n\n\nef\n\n\n\ncd\n\n\n\ne\ned\n\n\n\nf\nf\n\n\n\ndd\n\n\n\nR\no\no\nt\n l\n\n\n\ne\nn\ng\nt\nh\n (\n\n\n\nc\nm\n\n\n\n)\n\n\n\nRice genotypes\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n0 2.5 5 7.5 10 12.5\n\n\n\n1:1\n\n\n\n2:1\n\n\n\n1:2\n\n\n\nSalt levels (dS m   )-1\n\n\n\nS\nh\no\no\nt\n \nh\n\n\n\ne\ni\ng\nh\nt\n \n(\nc\nm\n\n\n\n)\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\nFajr Tarom Shirodi Tabesh Kadous Pouya Khazar Sahel Neda Shafag Nemat\n\n\n\n1:1 2:1 1:2\n\n\n\na\n\n\n\na\n\n\n\na\n\n\n\nb\n\n\n\nc\nc c\n\n\n\nc c\nc\n\n\n\nc\n\n\n\nab\n\n\n\nbc bc\n\n\n\nb bcbc\nbcd\n\n\n\nbcdebcde\n\n\n\nb\ncd\ne\n\n\n\ncde\nde\n\n\n\ne\n\n\n\nab\n\n\n\nab\n\n\n\nab\n\n\n\nab\n\n\n\nb\n\n\n\nb\nb b\n\n\n\nb\n\n\n\nS\nh\n\n\n\no\no\n\n\n\nt \nh\n\n\n\ne\nig\n\n\n\nh\nt \n\n\n\n(\nc\nm\n\n\n\n)\n\n\n\nRice genotypes\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200966\n\n\n\n Dry matter weight increased when salinity stress was raised to 7.5 dS m-1 \n\n\n\n(Fig. 10). But above 7.5 dS m-1 when salt compositions were 2:1 and 1:2 molar \n\n\n\nratios, there was a decrease in dry matter weight.   However, a slight increase \n\n\n\nwas observed after 7.5 dS m-1 when salt composition was 1:1 molar composition \n\n\n\n(Fig. 10). Tabesh and Fajr genotypes had the highest and the lowest dry matter \n\n\n\nweight (Fig. 11). The percentage of water content was influenced by salt stress. \n\n\n\nA downward tendency in water content percentage was observed above 2.5 dS \n\n\n\nm-1, when the sodium salts ratio was changed to 1:2 and 2:1 molar compositions. \n\n\n\nAs the duration of salinity stress increased, a significant increase in water content \n\n\n\noccurred at 2.5 dS m-1; thereafter, a decrease was observed (Fig. 12). Fajr and \n\n\n\nShafag genotypes demonstrated the highest and lowest water content respectively \n\n\n\n(Fig. 13). Generally, root length and water content significantly decreased by \n\n\n\nraising salt stress particularly at 7.5 dS m-1. A similar conclusion was made by \n\n\n\nAkbar et al. (1974) and Basra et al. (2006).  Shoot height and dry weight were \n\n\n\nobserved to increase up to 7.5 dS m-1 but when salt concentration exceeded 7.5 \n\n\n\ndS m-1, seedling growth was inhibited.  These parameters were similarly affected \n\n\n\nby different salt compositions.  Seedling growth was acutely restrained by salt \n\n\n\nstress at 10 dS m-1 because osmotic pressure of medium was increased by high \n\n\n\nsalt concentration. As the seedlings could not absorb water, survival in this hard \n\n\n\ncondition became difficult.  Similar results have been documented by Kim et al. \n\n\n\n(2005). \n\n\n\nFig. 10: Dry weight at different salt compositions and concentrations \n\n\n\nNote: Values are means of eleven genotypes with three replications and vertical bars \n\n\n\nrepresent SE.\n\n\n\nMomayezi, M.R., Zaharah, A.R., Hanafi, M.M. &  Mohd Razi, I.\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n0 2.5 5 7.5 10 12.5\n\n\n\n1:1\n\n\n\n2:1\n\n\n\n1:2\n\n\n\nSalt levels (dS m   )-1\n\n\n\nD\nr\ny\n\n\n\n \nw\n\n\n\ne\ni\ng\n\n\n\nh\nt\n \n(\nm\n\n\n\ng\n)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 67\n\n\n\nFig. 11: Dry weight of eleven rice genotypes at different salt compositions\n\n\n\nFig. 12: Water content percentage at different salt compositions and concentrations\n\n\n\nFig. 13: Water content percentage of eleven rice genotypes at different salt stress and salt \n\n\n\ncompositions\n\n\n\nProline in Different Rice Genotypes\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\nTabesh Nemat Neda Kadous Khazar Sahel Pouya Shirodi Shafag Tarom Fajr\n\n\n\n1,1 2,1 1,2\n\n\n\na\na\n\n\n\na\n\n\n\nab\n\n\n\nbc bc bc\nbcd\n\n\n\ncd\ncde\n\n\n\nde\n\n\n\ne\n\n\n\naba\n\n\n\nbc\nbc\n\n\n\ncdecde\nde\n\n\n\ncde\n\n\n\nef\n\n\n\nf\n\n\n\na\na\n\n\n\nab\n\n\n\nbc\nbc bcdbcd cdcd\n\n\n\nde\n\n\n\ne\n\n\n\nRice genotypes\n\n\n\nD\nr\ny\n\n\n\n \nw\n\n\n\ne\ni\ng\n\n\n\nh\nt\n \n(\nm\n\n\n\ng\n)\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\nFajr Tarom Shirodi Pouya Sahel Kadous Khazar Neda Tabesh Nemat Shafag\n\n\n\n1,1 2,1 1,2\n\n\n\na\n\n\n\naa\n\n\n\na\na a a a\n\n\n\na\n\n\n\nab ab ab\n\n\n\nb\n\n\n\na\n\n\n\na\na\n\n\n\naa\naa\n\n\n\na\n\n\n\na\n\n\n\nb\n\n\n\naa\n\n\n\na\n\n\n\na\naa\n\n\n\na\n\n\n\na\n\n\n\nab\n\n\n\nb\n\n\n\nW\na\n\n\n\nt\ne\nr\n \nc\no\n\n\n\nn\nt\ne\nn\n\n\n\nt\n \n(\n%\n\n\n\n)\n\n\n\nRice genotypes\n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n0 2.5 5 7.5 10 12.5\n\n\n\nSalt levels\n\n\n\n1:1\n\n\n\n2:1\n\n\n\n1:2\n\n\n\nW\na\nt\ne\nr\n \nc\no\nn\nt\ne\nn\nt\n \n(\n%\n\n\n\n)\n\n\n\n(dS m   )-1\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200968\n\n\n\n Fajr and shirodi genotypes with low MGT and high GI at germination stage \n\n\n\nwere clustered into the salt tolerant group. These genotypes also demonstrated long \n\n\n\nroot length, high shoot height, low dry weight and high water content percentage \n\n\n\nat early seedling stage. It is seen that this group was able to adapt to the limiting \n\n\n\nconditions because it had the lowest dry weight and the highest water content \n\n\n\npercentage (Table 1). Consequently, these genotypes were better able to take up \n\n\n\nwater from the media compared to other genotypes. \n\n\n\nTarom Hashemi genotype, which was classified as salt sensitive at germination \n\n\n\nstage, had long root length and dry weight, high water content percentage and \n\n\n\nshoot height; consequently it was clustered into salt tolerant group at early seedling \n\n\n\nstage. It appears that these genotypes are more sensitive at germination stage than \n\n\n\nat early seedling stage (Tables 1 and 2).\n\n\n\n However, Khazar genotypes which were categorised into moderate group \n\n\n\nat germination stage, had short root length, medium shoot height, high dry weight \n\n\n\nand low water content; therefore it was assayed into the salt sensitive group \n\n\n\nindicating that  they were more sensitive at seedling stage than at germination \n\n\n\nstage (Tables 1 and 2). \n\n\n\n The accumulation of proline was significantly (P\u22640.01) related to salt levels \n\n\n\nand sodium salt compositions (Fig. 14). With the exception of 2:1 salt composition, \n\n\n\nthere was a slight increase in proline content at 2.5 dS m-1 compared to control, at \n\n\n\nsalt composition of 1:1 and 1:2 molar concentrations. The rice seedlings tended \n\n\n\nto accumulate proline up to 5 dS m-1 when the applied sodium salt composition \n\n\n\nwas at 1:1 and 2:1 molar concentrations. Thereafter, the amount of proline within \n\n\n\nseedlings was reduced by salinity up to 7.5 dS m-1 at 1:1 and 2:1 salt compositions. \n\n\n\nThe considerable decrease at 10 dS m-1 (1:1 salt composition) might be due to \n\n\n\ninhibition of eedling growth by salinity (Fig. 14). Shafag and Tabesh genotypes \n\n\n\naccumulated the maximum amount of proline while, Fajr and Sahel genotypes \n\n\n\nhad the minimum amount of proline in their shoot tissue (Fig. 15).\n\n\n\n The different salt combinations diversely affected growth parameters. Dry \n\n\n\nweights increased irrespective of salt composition. As shown in Table 3, the \n\n\n\ncomparison of two rice salt tolerant and salt sensitive groups revealed that the response \n\n\n\nof rice seedlings varied in different salt compositions. At germination stage, salt \n\n\n\nconcentration in the medium appears to be more important for emergence than salt \n\n\n\ncomposition because the osmotic pressure of medium is dominated by salt \n\n\n\nconcentration. A diverse relationship between water and proline content was\n\n\n\nobserved in salt tolerant and salt sensitive groups, for example, Fajr genotype \n\n\n\nclassified as salt tolerant showed a upward tendency in water content as proline \n\n\n\ndecreased at 1:1 molar ratio but this relation was not observed in other genotypes. \n\n\n\nIt was also noted that the decrease in water content was accompanied by an  \n\n\n\nincrease in proline content in sensitive genotypes.  There is a likelihood that the \n\n\n\nanion associated with Na+ plays an important role in salt tolerance.\n\n\n\nMomayezi, M.R., Zaharah, A.R., Hanafi, M.M. &  Mohd Razi, I.\n\n\n\n\n\n\n\n\nM\nalay\n\n\n\nsian\n Jo\n\n\n\nu\nrn\n\n\n\nal o\nf S\n\n\n\no\nil S\n\n\n\ncien\nce V\n\n\n\no\nl. 1\n\n\n\n3\n, 2\n\n\n\n0\n0\n9\n\n\n\n6\n9\n\n\n\nP\nro\n\n\n\nlin\ne in\n\n\n\n D\nifferen\n\n\n\nt R\nice G\n\n\n\nen\no\nty\n\n\n\np\nes\n\n\n\nTABLE 1  \n\n\n\nEffects of different salt compositions on characteristics of salt tolerant (Shirodi and Fajr) and salt sensitive\n\n\n\n  (Khazar and Tarom-e-Hashemi) rice genotypes\n  \n\n\n\nGI MGT Root length Shoot height Dry weight \nWater \n\n\n\ncontent \nSalt composition \n\n\n\nratio \n (day) (cm) (mg) (%) \n\n\n\n1:1 18.4\u00b10.3a\n#\n 1.13\u00b10.02a 8.1\u00b10.6a 7.9\u00b10.4b 20.8\u00b10.4a 74.5\u00b10.9a \n\n\n\n2:1 18.0\u00b10.5ab 1.22\u00b10.06a 9.6\u00b10.5a 8.5\u00b10.3b 21.4\u00b10.4a 76.6\u00b10.7a \n\n\n\n1:2 18.4\u00b10.2a 1.13\u00b10.02a 9.2\u00b10.4a 11.4\u00b10.5a 21.6\u00b10.3a 74.5\u00b10.5a \n\n\n\n1:1 18.0\u00b10.4a\n \n\n\n\n1.21\u00b10.04b 10.4\u00b10.6a 9.7\u00b10.5b 18.2\u00b10.4a 78.9\u00b10.7a \n\n\n\n2:1 16.1\u00b10.7b 1.37\u00b10.08a 11.2\u00b10.4a 11.2\u00b10.6a 18.3\u00b10.4a 80.3\u00b10.8a \n\n\n\n1:2 17.4\u00b10.4a 1.26\u00b10.05ab 11.2\u00b10.4a 12.7\u00b10.4a 19.0\u00b10.3a 79.5\u00b10.6a \n\n\n\n1:1 15.5\u00b10.7a 1.41\u00b10.08bc 4.9\u00b10.3c 7.1\u00b10.6b 22.2\u00b10.5a 70.8\u00b11.8a \n\n\n\n2:1 13.7\u00b10.8a 1.66\u00b10.09a 6.3\u00b10.3ab 8.2\u00b10.4ab 20.9\u00b10.5a 75.1\u00b10.9a \n\n\n\n1:2 15.0\u00b10.6a 1.48\u00b10.07ab 6.6\u00b10.4a 9.5\u00b10.4a 21.4\u00b10.5a 73.6\u00b10.7a \n\n\n\n1:1 8.9\u00b10.5a 2.3\u00b10.2bc 6.8\u00b10.4a 9.4\u00b10.5a 23.6\u00b10.5ab 76.4\u00b11.2ab \n\n\n\n2:1 8.3\u00b10.7b 2.8\u00b10.2a 7.1\u00b10.4a 9.4\u00b10.6a 25.0\u00b10.5a 75.0\u00b11.1b \n\n\n\n1:2 8.0\u00b10.8b 2.6\u00b10.1ab 6.0\u00b10.6a 8.8\u00b10.5a 20.5\u00b10.5b 79.0\u00b11.3a \n\n\n\n\n\n\n\n\n\n\n\nRice\n\n\n\ngenotype\n\n\n\nShirodi\n\n\n\nFajr\n\n\n\nKhazar\n\n\n\nTarom-e\n\n\n\nHaahemi\n\n\n\n# Results are mean values of three replications \u00b1 SE. \n\n\n\nMeans with the same letter in the column are not significantly different in each rice genotype \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200970\n\n\n\nFig. 14: Differing effects of salt levels with different compositions on proline content of \n\n\n\nrice seedlings \n\n\n\nFig. 15: Salinity effects on proline accumulation in different rice genotypes\n\n\n\n Proline accumulation in plants is one of the most commonly reported \n\n\n\nmodifications, and cytoplasmic accumulation of proline is thought to be involved \n\n\n\nin osmotic adjustment of stressed tissues. Therefore, proline content as a main \n\n\n\ncompatible solute of rice was measured. Salt tolerant cultivated rice accumulated \n\n\n\nless free-proline than salt sensitive ones (Lutts et al. 1996). Nakamura et al. (2002) \n\n\n\nrevealed that there was a positive correlation between free proline content and \n\n\n\nosmotic potential in salt tolerant rice species. Proline participates in decreasing \n\n\n\nosmotic potential. But in this study, it was observed that there was no correlation \n\n\n\nbetween the amount of proline and water content percentage, at least in these rice \n\n\n\ngenotypes (r = -0.11) (data not shown).  It is feasible that proline, with other free \n\n\n\namino acids, is involved in ameliorating salt tolerance.  These results support those \n\n\n\nof Hoai et al. (2003), where it was found that free amino acids were influenced \n\n\n\nby salt concentration.  The lowest and the highest reduction of proline were at \n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\n\n\n\n0.7\n\n\n\n0.8\n\n\n\n0.9\n\n\n\nshafag tabesh pouya tarom khazar kadous nemat neda shirodi fajr sahel\n\n\n\n1:1 2:1 1:2\n\n\n\na\n\n\n\na\n\n\n\na\nb\nc\n\n\n\nab ab\n\n\n\na\nb\nc\n\n\n\nab\n\n\n\na\nb\n\n\n\naab\n\n\n\nbcd\n\n\n\nc\n\n\n\nab\n\n\n\na\n\n\n\na\n\n\n\nab\n\n\n\na\n\n\n\na\n\n\n\nabc\n\n\n\ne\n\n\n\nc\n\n\n\nabc\n\n\n\nbcd\n\n\n\nab\n\n\n\ncd\n\n\n\nab\n\n\n\nc\n\n\n\nd\ncd\n\n\n\nc\n\n\n\nd\n\n\n\nc\n\n\n\nd\n\n\n\nRice genotypes\n\n\n\nP\nr\no\n\n\n\nli\nn\n\n\n\ne\n (\n\n\n\n\n\n\n\nm\no\n\n\n\nl \ng\n\n\n\n\n\n\n\nF\nW\n\n\n\n)\n-\n1\n\n\n\n\u00b5\n \n\n\n\nMomayezi, M.R., Zaharah, A.R., Hanafi, M.M. &  Mohd Razi, I.\n\n\n\n0\n\n\n\n0.2\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n0.8\n\n\n\n1\n\n\n\n1.2\n\n\n\n0 2.5 5 7.5 10 12.5\n\n\n\n1:1\n\n\n\n2:1\n\n\n\n1:2\n\n\n\nSalt levels (dS m   )\n\n\n\n-\n1\n\n\n\nP\nr\no\n\n\n\nl\ni\nn\n\n\n\ne\n \n(\n \n \n \n \nm\n\n\n\no\nl\n \ng\n\n\n\n \n \nF\n\n\n\nW\n)\n\n\n\n\u00b5\n \n \n \n \n\n\n\n-1\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 71\n\n\n\n1:1 and 1:2 molar salt ratios, respectively (Table 2).  Proline content increases \n\n\n\nwhen the anion ratio (chloride to sulphate) decreases (Fig. 14).  This demonstrates \n\n\n\nthat chloride had a more toxic effect  on rice seeds than sulphate at germination \n\n\n\nstage.  A similar effect was recorded in most measured agronomic characteristics. \n\n\n\nThis suggests that there is a difference in plants species response to salt stress \n\n\n\nand the degree of proline synthesis in stressed plant can be controlled by Na+ \n\n\n\nconcentration and anion associated with Na+ in solution (Cl- and SO\n4\n\n\n\n2-).\n\n\n\nTABLE 2 \n\n\n\nVariation percentage of measured parameters in rice genotypes under different salt \n\n\n\ncompositions at 10 dS m-1 compared to control\n\n\n\n\n\n\n\nGI \n1 \n\n\n\nMGT \n2 Root \n\n\n\nlength \n\n\n\nShoot \n\n\n\nheight \n\n\n\nDry \n\n\n\nweight \n\n\n\nWater \n\n\n\ncontent \nProline \n\n\n\nRice \n\n\n\ngenotypes \n\n\n\nSalt \n\n\n\ncomposition \n\n\n\nrate  (day) cm (mg) (%) \n(\u00b5mol g\n\n\n\n-\n\n\n\n1\nFW) \n\n\n\n1:1 4.2 -6.5 -38.3 -13.0 5.9 -3.9 -98.3 \n\n\n\n2:1 43.1 -25.8 -14.2 -14.9 9.4 -4.4 35.3 \nShirodi \n\n\n\n1:2 7.4 -9.7 8.7 43.8 11.5 2.0 0.2 \n\n\n\n1:1 31.7 -13.1 -16.2 -11.8 20.6 -5.0 -60.7 \n\n\n\n2:1 68.3 -30.4 -7.3 -1.2 18.4 -0.2 -0.2 \nFajr \n\n\n\n1:2 44.1 -19.5 -6.0 2.5 12.8 -2.3 -5.8 \n\n\n\n1:1 58.3 -34.5 3.2 -31.7 14.4 -9.9 -77.5 \n\n\n\n2:1 44.7 -33.5 30.8 -25.2 5.7 -6.0 19.4 \nKhazar \n\n\n\n1:2 38.2 -29.4 24.7 -4.9 4.2 -4.1 42.5 \n\n\n\n1:1 43.4 -39.5 -5.0 12.1 2.3 -3.0 -44.3 \n\n\n\n2:1 64.1 -45.6 21.7 5.8 0.6 -0.6 -23.7 \nTarom \n\n\n\nHashemi \n1:2 22.9 -20.8 -19.1 -6.0 4.2 -4.4 -37.0 \n\n\n\n1\n Germination index; \n\n\n\n2\n mean germination time \n\n\n\n # Values are percentage of change in observations at EC 10 dS m\n-1\n\n\n\n compared with \n\n\n\nco ntrol. Values  are means (n=9). \n\n\n\nProline in Different Rice Genotypes \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200972\n\n\n\nMomayezi, M.R., Zaharah, A.R., Hanafi, M.M. &  Mohd Razi, I.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 73\n\n\n\nCONCLUSION\n\n\n\nThere was no relationship between growth parameters of different rice genotypes \n\n\n\nand MGT and GI. The salt tolerant and salt sensitive groups showed no significant \n\n\n\nrelationship between proline production and salt tolerance as the proline content \n\n\n\nalso increased in salt tolerant genotypes. Our results confirm that rice responds \n\n\n\ndifferently to salt stress as the salt composition is changed. Since the soil solution \n\n\n\nconsists of a mixture of solutes, application of different salt compositions can \n\n\n\nshed light on the response of plants to salt in natural conditions. \n\n\n\nACKNOWLEDGMENTS\n\n\n\nThe work presented here is a part of the PhD thesis of the first author. We wish \n\n\n\nto gratefully acknowledge Dr. Anuar Abd. Rahim for his excellent help in the \n\n\n\nstatistical analysis and interpretation of the data. The authors would also like to \n\n\n\nthank Mr. Alagie Bah for helping with the paper.\n\n\n\nREFERENCES\nAkbar, M. and Y. Yabuno. 1974. Breeding for saline-resistant varieties of rice. II. \n\n\n\n Comparative performance of some rice varieties to salinity during early \n\n\n\n developing stages. Jap. J. Breed. 25,176-181\n\n\n\nAssociation of Official Seed Analysis (AOSA). 1983. Seed vigor testing handbook.\n\n\n\n Contribution No. 32 to The Handbook on Seed Testing. Association of \n\n\n\n Official Seed Analysis. Springfield. IL.\n\n\n\nBasra, S.M.A., M. Farooq, I. Afzal and M. Hussain. 2006. Influence of \n\n\n\n osmopriming on the germination and early seedling growth of coarse and \n\n\n\n fine rice. Int. J. Agri. Boil., 8(1):19-22\n\n\n\nBates, L.S., R.P. Waldren and I.D. Teare. 1973. Rapid determination of free proline\n\n\n\n  for water stress studies. Plant and Soil, 39:205-207\n\n\n\nBhumbla, D.R. and I.P. Abrol. 1977. Saline and sodic soil. pp 719-734. In: \n\n\n\n Symposium on Soils and Rice. IRRI. Los Banos. Philippines;\n\n\n\nChowdhury, M.A.M., B. Moseki and D.J.F. Bowling. 1995. A method for screening\n\n\n\n rice plants for salt tolerance. Plant and Soil. 171: 317-322\n\n\n\nEllis, R.A. and E.H. Robert. 1981. The quantification of ageing and survival in \n\n\n\n orthodox seeds. Seed Sci. and Technol., 9,373-409.\n\n\n\nGhoulam, C., A. Foursy and K. Fares. 2002. Effects of salt stress on growth, \n\n\n\n inorganic ions and proline accumulation in relation to osmotic adjustment \n\n\n\n in five sugar beet cultivars. Environ. Exp. Bot. 47: 39-50\n\n\n\nGregorio, G.B. and D. Senadhira. 1993. Genetic analysis of salinity tolerance in \n\n\n\n rice (Oryza sativa L.). Theor Appl Genet. 86:333-338; \n\n\n\nProline in Different Rice Genotypes\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200974\n\n\n\nHoai, N.T.T., I.S. Shim, K. Kobayashi and U. Kenji. 2003. Accumulation of some \n\n\n\n nitrogen compounds in response to salt stress and their relationships with \n\n\n\n salt tolerance in rice (Oryza sativa L.) seedlings. Plant Growth \n\n\n\n Regulation. 41:159-164\n\n\n\nInternational Rice Research Institute (IRRI). 2005. Rice around the world. http://\n\n\n\n irri.org/science/cnyinfo/index.asp . Accessed on 15th October 2007\n\n\n\nInternational Seed Testing Association (ISTA). 1993. International rules for seed \n\n\n\n testing. Seed Sci. Technol. 13. Pp. 480-490  \n\n\n\nJamil, M. and E.S. Rha. 2007. Response of transgenic rice at germination and \n\n\n\n early seedling growth under salt stress. Pakistan Journal of Biological \n\n\n\n Science. 10(23):4303-4306\n\n\n\nKim, D-W., R. Rakwal, G. K. Argrawal, Y-H. Jung, J. Shibato, N-S. Jwa, Y. \n\n\n\n Iwahashi, H. Iwahashi, D. H. Kim, I-S. Shim and K. Usui. 2005. A \n\n\n\n hydroponical rice seedling culture model system for investigating \n\n\n\n proteome of salt stress in rice leaf. Electrophoresis. 26:4521-4539\n\n\n\nLiu, T. and J. van Staden. 2000. Selection and characterization of sodium chloride\n\n\n\n tolerant callus of Glycine max (L.) Merr cv. Acme. Plant Growth Regul. \n\n\n\n 31:195-207\n\n\n\nLutts, S., J.M. Kinet and J. Bouharmont. 1995. Change in plant response to \n\n\n\n NaCl during development of Rice (Oryza sativa L.) varieties differing in \n\n\n\n salinity resistance. J. Exp. Bot. 46: 1843-1852\n\n\n\nLutts, S., J.M. Kinet and J. Bouharmont. 1996. Effects of salt stress on growth, \n\n\n\n  mineral nutrition and proline accumulation in relation to osmotic \n\n\n\n  adjustment in rice (Oryza sativa L.) cultivars differing in salinity\n\n\n\n  resistance. Plant Growth Regul. 19: 207-218. \n\n\n\nMunns, R. 2002. Comparative physiology of salt and water stress. Plant, Cell and \n\n\n\n environment. 25: 239-250\n\n\n\nNakamura, I., S. Murayama, S. Tobita, B.B. Bong, S. Yanagihara, Y. Ishimine and \n\n\n\n Y. Kawamitsu. 2002. Effect of NaCl on the photosynthesis, water relations \n\n\n\n and free proline accumulation in the wild Oryza species. Plant Prod. Sci. \n\n\n\n 5(4): 305-310.\n\n\n\nOrcuttd, M. and E.T. Nilsen. 2000. Physiology of plants under stress. John Wiley \n\n\n\n and SonsRodr\u00edguez, H.G., J.K.M. Roberts, W.R. Jordan and M.C. Drew. \n\n\n\n 1997. Growth, water relations, and accumulation of organic and inorganic \n\n\n\n solutes in roots of maize seedlings during salt stress. Plant Physiology. \n\n\n\n 113:881-893.\n\n\n\nSerrano R. and R. Gaxiola. 1994. Microbial models and salt stress tolerance in \n\n\n\n plants. Critical Rev. Plant Sci. 13:121-138\n\n\n\nMomayezi, M.R., Zaharah, A.R., Hanafi, M.M. &  Mohd Razi, I.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 75\n\n\n\nWalia H., C. Wilson, P. Condamine, X. Liu, A.M. Ismail, L. Zeng, S.I. Wanamaker,\n\n\n\n J. Mandal, J. Xu, X. Cui and T.J. Close. 2005. Comparative \n\n\n\n transcriptional profiling of two contrasting rice genotypes under salinity \n\n\n\n stress during the vegetative growth stage. Plant Physiol. 139:822-835.\n\n\n\nZeng L. and M.C. Shannon. 2000. Salinity effects on seedling growth and yield \n\n\n\n components of rice. Crop Sci. 40: 996-1003\n\n\n\nZeng L., M.C. Shannon and C.M. Grieve. 2002. Evaluation of salt tolerance in rice \n\n\n\n genotypes by multiple agronomic parameters. Euphytica. 127: 235-245\n\n\n\n\n\n\n\n\n\n\n\nProline in Different Rice Genotypes\n\n\n\n\n\n"
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"\n\nINTRODUCTION\nSoil structure has important effects on different soil properties and these affect its \nperformance in the environment. Soil structure cannot be considered as a direct \ncomponent of plant growth but should be considered among the characteristics \nthat are effective in plant growth factors. Robot et al. (2018) state that soil \nstructure controls several soil processes such as water infiltration and storage, gas \nexchange, organic matter, soil nutrients, root penetration and erosion potential. \nSoil structure also serves as a habitat for thousands of living organisms  whose \nvariety and activities is dependent on soil structure.  Researchers such as Horn \net al. (1994), Kode\u0161ova et al. (2009; 2011) carried out studies on hydraulic and \nphysical properties affecting soil structure. Soil structure affects the physical and \nhydraulic properties of the soil (Mohawesh et al. 2017). Physical properties of \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 43-54 (2019)                         Malaysian Society of Soil Science\n\n\n\nEvaluation and Comparison of Physical and Hydraulic \nProperties in Different Soil Structures \n\n\n\nRamezani, N.*, A. Landi, A. R. Barzegar and G. A. Sayyad\n\n\n\nDepartment of Soil Science, College of Agriculture, Shahid Chamran \nUniversity of Ahwaz, Ahwaz, Iran\n\n\n\nABSTRACT\nPhysical properties and various compounds of soil structure are reflected \nin different soil properties which affect the hydraulic properties of soil and \nconsequently the flow of water and movement of contaminants. Therefore, this \nstudy was conducted to measure physical and hydraulic properties in different \nstructures of Silakhore Bala plain located in Lorestan province of Iran. The study \nwas conducted under laboratory conditions on nine  undisturbed soil columns with \nthree different structures of  granular, blocky and massive with three replications \nfor each column in a completely randomised design with different structures of \nthe soil as the main factor.  Physical parameters like soil particle size distribution, \nbulk density, total porosity, mean weight diameter and also hydraulic parameters \nlike saturated and unsaturated hydraulic conductivity and the number of water-\nconducting active pores in each column were measured. Chemical properties \nincluding organic matter content were measured too in different soil samples. The \nresults showed that soil structure had a significant effect on physical and hydraulic \nparameters. Due to higher organic matter content, porosity and stability of the \nstructure, granular soils had the  highest value of for saturated and unsaturated \nhydraulic conductivity compared to  blocky and massive structures. Soils with \nmassive structure had the weakest structure in terms of physical and hydraulic \nproperties.\n\n\n\nKeywords: Bulk density, hydraulic conductivity, mean weight diameter.\n\n\n\n___________________\n*Corresponding author : Ramezani_nooshin@yahoo.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201944\n\n\n\nthe different soil structures have an important role in the movement of water and \nthe transport of solution which in turn affect the  hydraulic properties of the soil. \nThe formation of soil aggregates results in numerous active water-conducting \npores. These pores lead to a  preferential pathway of water movement in the soil \nand consequently alter its hydraulic properties (Jiang and Shao 2014). Karahan \nand Er\u015fahin (2016) state that minor changes in soil structure have a significant \neffect on saturated hydraulic conductivity, which in turn is strongly controlled by \nsoil pores (geometry, size, and direction of the pores). Soil structure significantly \naffects the flow of water and the transport of pollutants in the soil. Activities such \nas soil compaction and intensive ploughing can affect soil quality resulting in \nthe destruction of soil structure, leading to undesirable effects on soil fertility. \nTherefore, it is necessary to study physical and hydraulic properties of soils in \norder to apply appropriate management methods on different types of soil. The \npurpose of this study was to investigate and compare physical and hydraulic \nproperties of different soil  structures under laboratory conditions.\n\n\n\nMATERIALS AND METHODS\n\n\n\nDescription of Study Area\nThe study area was the Bayatan Village of Borujerd County in Silakhore Bala \nplain within the Lorestan Province of Iran. This region was chosen because of the \nexistence of three different types of structures recognisable at different depths, but \nhaving the same climate, parent materials, and texture. Therefore, physical and \nhydraulic properties of the  different soil structures under similar conditions were \ncompared and evaluated quantitatively. Bayatan is located at latitude 34 \u00b0 4\u2019 N, \nlongitude 48 \u00b0 39\u2019 E of Iran (Figure 1)\n\n\n\nFig. 1: Location of studied area in Lorestan- Iran\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 45\n\n\n\n The study area has relatively cold winters and mild summers with annual \nprecipitation and temperature of 664.09 mm (Xeric moisture regime) and 16.4\u00b0C \n(Mesic temperature regime), respectively.  In the International Classification, the \nBayatan soil series are classified as Inceptisol soils, the great group of Xerochrepts \nand the CalciXerollic Xerochrepts subgroup, with its counterpart class in FAO \nsystem being Calcic Cambisols.\n\n\n\nImplementation Method\nThe study was conducted under laboratory conditions on nine undisturbed soil \ncolumns with three different structures namely: granular, blocky and massive \nwith three replications for each column in a completely randomised design with \ndifferent structures of the soil as the main factor. Different samples were labeled as \nG1, G2, G3, B1, B2, B3 and M1, M2, M3. G, B and M to indicate Granular, Blocky and \nMassive structures respectively, and each of their subscript number represents the \nnumber of soil samples. The sampling of granular, blocky and massive structures \nwas done from 0 to 25 cm, 25 to 50 cm and 50 to 75 cm depths respectively in \nthe sampling area. Statistical analysis was performed using Generalized Linear \nModel in SAS (ver. 9.4) while mean comparison was performed using Duncan\u2019s \ntest at a 0.05 level of significance (P <0.05). The effects of soil structure on the \nphysical and hydraulic properties were investigated and compared.\n\n\n\nSampling and Preparation of Undisturbed Columns\nFirst, the soil profile was dug to a depth of 100cm in the field, and the depth of \neach layer and soil structure was determined. In order to compare the physical and \nhydraulic properties of different soil structures under the same conditions, samples \nfrom different depths and parts of the region were kept intact. Thick polyethylene \ncolumns of 3 mm size with an inner diameter of 25 cm and a uniform height of \n25 cm were established. Before the sampling, the inner wall of the columns was \nstained with paraffin to (i) prevent the formation of preferential flow in the soil \nand column surface and (ii) reduce the preferential flow between the soil and the \ninner wall of the column during sampling. In order to prevent  soil from falling, \nthe opening of the columns was covered with a net. The columns were transferred \nto the laboratory and placed on an assembly mounted on a metal tripod (Figure 2).\n \nMeasurement of Hydraulic Properties\nThe hydraulic properties of various soil structures were measured by a diskin \nfiltrometer device (Soil Measurement Systems LLC, TUCSON, ARIZONA \n85704 USA) that had a diameter of 20cm and matric suction of 14, 10, 4 and \n1 cm. Saturated hydraulic conductivity (Ks), unsaturated hydraulic conductivity \n(Kh), number of water-conducting active pores (N)( average number of pores \nper unit area (N) in two classes of pore size:  macropore (0.375 mm< radius) \nand mesopore (0.107<radius < 0.375 mm)) were measured based on equations \npreviously developed (Gardner 1958; Wooding1968; Watson and Luxmoore \n1986; Ankeny et al.1991).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201946\n\n\n\nMeasurement of Physical and Chemical Properties\nOn completion of the measurement of hydraulic properties, the infiltrometer \ndevice was removed from the soil column. After removing sand with less than \n100 \u00b5m diameter from the bottom of the infiltrometer plate in each undisturbed \nsoil column, intact samples were prepared in steel cylinders for bulk density \nmeasurements while the disturbed samples were prepared in three replications \nfor measuring other characteristics. The disturbed samples were air dried and \nsieved after collection. In total, 27 samples (9 undisturbed columns with different \nstructures) were taken in 3 replications for measuring each parameter.\n Soil texture and particle size distribution were measured by the hydrometer \nmethod (Beretta et al. 2014). To determine bulk density and porosity, three intact \nsamples were prepared from each depth using steel cylinders (4.5 cm in diameter \nand 5 cm in height (Sultani et al. 2007). In order to evaluate mean weight diameter \n(MWD) of aggregates,wet sieving was done  using sieves 4, 2, 1, 0.5 and 0.25 \nmm in diameter (Barzegar et al. 2004). In order to better interpret the results, \nsome chemical properties such as organic matter, pH and electric conductivity \nwere measured. The percentage of organic matter was measured by oxidation \nwith chromic acid, followed by titration with ferrous ammonium sulfate (Walkley \nand Black 1934). The pH (in water) of the saturated mud was measured by a pH \nmeter and electric conductivity (EC) of the saturated mud extract was measured \nby the EC meter.\n\n\n\n5 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2.  Placement of infiltrometer device on the soil column \n\n\n\nMeasurement of Hydraulic Properties \n\n\n\nThe hydraulic properties of various soil structures were measured by a diskin filtrometer \n\n\n\ndevice (Soil Measurement Systems LLC, TUCSON, ARIZONA 85704 USA) that had a \n\n\n\ndiameter of 20cm and matric suction of 14, 10, 4 and 1 cm. Saturated hydraulic conductivity \n\n\n\n(Ks), unsaturated hydraulic conductivity (Kh), number of water-conducting active pores (N)( \n\n\n\naverage number of pores per unit area (N) in two classes of pore size:  macropore (0.375 \n\n\n\nmm< radius) and mesopore (0.107<radius < 0.375 mm)) were measured based on equations \n\n\n\npreviously developed (Gardner 1958; Wooding1968; Watson and Luxmoore 1986; Ankeny et \n\n\n\nal.1991). \n\n\n\nFig. 2:  Placement of infiltrometer device on the soil column\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 47\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nPhysical and Chemical Properties\nThe results did not show any significant difference between the mean distribution \nof particle size in different structures (Table 1). Soil texture was loam in all three \ntypes of structures (according to the soil texture triangle). The average pH of \nthe studied soils was about neutral ranging from 7.09 to 7.56. The electrical \nconductivity of the studied soils was 0.4-0.5 ds/m.\n There was a significant difference between the amounts of organic matter \nin different structures (Table 2) as indicated by a comparison of mean values of \norganic matter.  Based on the measured values of the organic matter, granular \n\n\n\nTABLE 1\nMean comparison of particle size between different structures\n\n\n\nTABLE 2\nMean comparison of some chemical and physical properties between different structures\n\n\n\n7 \n\n\n\n\n\n\n\n(according to the soil texture triangle). The average pH of the studied soils was about neutral \n\n\n\nranging from 7.09 to 7.56. The electrical conductivity of the studied soils was 0.4-0.5 ds/m. \n\n\n\nTABLE 1 \nMean comparison of particle size between different structures \n\n\n\n\n\n\n\nSample Sand (%) Silt (%) Clay (%) \n\n\n\nG1 40.00a 38.33a 21.66b \nG2 40.63a 38.39a 20.97b \nG3 40.30a 37.87a 21.82b \nB1 39.55a 34.24a 26.20a \nB2 40.32a 33.72a 26.29a \nB3 39.65a 33.75a 26.59a \nM1 40.06a 33.97a 25.96a \nM2 39.73a 33.75a 26.30a \nM3 40.23a 33.77a 25.99a \n\n\n\n\n\n\n\nTable 2 shows the comparison of the mean values of some soil properties in different \n\n\n\nstructures. The effect of structure on these properties is significant at 0.01. \n\n\n\nTABLE 2 \n\n\n\nMean comparison of some chemical and physical properties between different structures \n\n\n\nSample Organic matter \n(%) \n\n\n\nBulk density \n(Mg/m3) Porosity(%) MWD(mm) \n\n\n\nG1 0.83a 1.32b 50.19b 0.73b \n\n\n\nG2 0.84a 1.12c 57.61a 0.90a \n\n\n\nG3 0.80a 1.13c 57.23a 0.91a \n\n\n\nB1 0.56b 1.24b 52.95b 0.88a \n\n\n\nB2 0.58b 1.25b 52.54b 0.90a \n\n\n\nB3 0.63b 1.23b 53.46b 0.90a \nM1 0.32c 1.39a 47.29c 0.26c \nM2 0.29c 1.44a 45.53c 0.30c \nM3 0.26c 1.47a 44.53c 0.27c \n\n\n\nIn each column, the dis-similar letters indicate significant difference and same letters show non-significant \ndifference between various structures (p<0.05 Duncan). (G, B and M indicate Granular, Blocky and Massive \nstructure respectively, and each of their subscript number represents the  number of soil samples) \n \n\n\n\nThere was a significant difference between the amounts of organic matter in different \n\n\n\nstructures (Table 2) as indicated by a comparison of mean values of organic matter.  Based on \n\n\n\n7 \n\n\n\n\n\n\n\n(according to the soil texture triangle). The average pH of the studied soils was about neutral \n\n\n\nranging from 7.09 to 7.56. The electrical conductivity of the studied soils was 0.4-0.5 ds/m. \n\n\n\nTABLE 1 \nMean comparison of particle size between different structures \n\n\n\n\n\n\n\nSample Sand (%) Silt (%) Clay (%) \n\n\n\nG1 40.00a 38.33a 21.66b \nG2 40.63a 38.39a 20.97b \nG3 40.30a 37.87a 21.82b \nB1 39.55a 34.24a 26.20a \nB2 40.32a 33.72a 26.29a \nB3 39.65a 33.75a 26.59a \nM1 40.06a 33.97a 25.96a \nM2 39.73a 33.75a 26.30a \nM3 40.23a 33.77a 25.99a \n\n\n\n\n\n\n\nTable 2 shows the comparison of the mean values of some soil properties in different \n\n\n\nstructures. The effect of structure on these properties is significant at 0.01. \n\n\n\nTABLE 2 \n\n\n\nMean comparison of some chemical and physical properties between different structures \n\n\n\nSample Organic matter \n(%) \n\n\n\nBulk density \n(Mg/m3) Porosity(%) MWD(mm) \n\n\n\nG1 0.83a 1.32b 50.19b 0.73b \n\n\n\nG2 0.84a 1.12c 57.61a 0.90a \n\n\n\nG3 0.80a 1.13c 57.23a 0.91a \n\n\n\nB1 0.56b 1.24b 52.95b 0.88a \n\n\n\nB2 0.58b 1.25b 52.54b 0.90a \n\n\n\nB3 0.63b 1.23b 53.46b 0.90a \nM1 0.32c 1.39a 47.29c 0.26c \nM2 0.29c 1.44a 45.53c 0.30c \nM3 0.26c 1.47a 44.53c 0.27c \n\n\n\nIn each column, the dis-similar letters indicate significant difference and same letters show non-significant \ndifference between various structures (p<0.05 Duncan). (G, B and M indicate Granular, Blocky and Massive \nstructure respectively, and each of their subscript number represents the  number of soil samples) \n \n\n\n\nThere was a significant difference between the amounts of organic matter in different \n\n\n\nstructures (Table 2) as indicated by a comparison of mean values of organic matter.  Based on \n\n\n\nIn each column, the dis-similar letters indicate significant difference and same letters show \nnon-significant difference between various structures (p<0.05 Duncan). (G, B and M indicate \nGranular, Blocky and Massive structure respectively, and each of their subscript number \nrepresents the  number of soil samples)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201948\n\n\n\nstructure had the highest amount of organic matter and was significantly different \ncompared to the amounts of organic matter in soils with blocky and massive \nstructures. The lowest percentage of organic matter was observed in massive \nsoils. Blocky soils had a higher percentage of organic matter than massive soils, \nindicating improved and higher stability of blocky structures in comparison to \nmassive structures. Martinez et al. (2008) observed that plant debris increases \norganic matter especially on soil surface, which can lead to a more stable layer of \nsoil. Ahad et al. (2015) showed that organic matter plays an important role in the \nnutrition cycle by contributing to an improved  soil structure.\n The difference in mean bulk density and total porosity was significant \namong treatments with granular, blocky and massive structures (Table 2). Soil \nstructure had a significant effect on bulk density. The trend of soil bulk density \nwas as follows: granular < blocky <massive. Total porosity percentage of soils \nwas as follows: granular > blocky> massive structures. This indicates that soil \nstructure has an effect on bulk density and porosity percentage of total soil. G2 and \nG3 soils, which had the highest soil porosity, had the lowest bulk density. As the \ntextures in the studied soils were the same, it can be concluded that soil structure \nis independent of its texture in its effects on soil hydraulic properties. Kelishadi \net al. (2014) showed that high values of bulk density of arable soils are associated \nwith a weak structure and the degradation of the soil pore system. The presence of \nthe highest amount of organic matter in G2 and G3 with granular structure (0.82) \njustifies the high porosity (57.42%) and low bulk density (1.125 Mg/m3) in this \nstructure compared to other structures. Ahad et al. (2015) noted that bulk density \ndepends on the amount of soil organic matter, particle size distribution and soil \nporosity. There was a significant difference in the values of bulk density and soil \nporosity between G1 and two other granular soil structures (G2 and G3). Due to the \nposition of the G1 soil and the fact that this soil sample was taken near the road, \nthe probability of passing tractors and agricultural machinery could have led to \nsoil compaction and increased bulk density and reduced soil porosity relative to \nthe other two granular (G2 and G3) structures. According to Behfar et al. (2012), \ncompaction resulting from the passage of tractors and agricultural machinery \nleads to reduced porosity and number of soil pores, as well as increased bulk \ndensity and soil resistance.\n As shown in Table 2, there is a significant difference in average mean \nweight diameter (MWD) of the aggregates in the granular structures (G2 and G3) \nand blocky (B1, B2, B3) compared to massive structures (M1, M2, M3). Soils \nwith massive structures (M1, M2, M3) had the lowest MWD index. There was \nno significant difference between granular and blocky structures. The presence \nof organic matter has a significant effect on the improvement and formation of \nthe soil structure. Aziz and Karim (2016) stated that the percentage of clay and \norganic matter plays a very important role in the stability of the soil structure. \nDue to the presence of organic matter, M1, M2, M3 soils had low soil stability \nand a weak structure with a minimum MWD of about 0.3 mm. The development \nof soil structure in granular (G2 and G3) and blocky structures (B1, B2, B3) was \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 49\n\n\n\ncompletely visible and the MWD of the aggregates at 0.73-0.91 mm indicates \nthe good stability of these soils. In granular structure, a significant difference was \nobtained between G1 and the other two granular soils of  G2 and G3 (Table 2). The \nposition of the G1 soil near the road and the passing of agricultural machinery \ncould have led to the breakdown of aggregates and a decrease in the MWD of the \naggregates. Barzegar et al. (2004) reported that passing agricultural machinery, \nespecially when the soil is dry, could lead to the collapse of aggregates and the \nformation of smaller sized aggregates.\n\n\n\nHydraulic Properties\nThe results of statistical analysis showed that structure had a significant effect \non hydraulic properties (Table 3). As  the textures in the studied treatments were \nthe same (Table 1), it can be concluded that soil structure affects soil hydraulic \nproperties, independent of  its texture. Kelishadi et al. (2014) studied soil \nhydraulic properties in different landuse management systems and concluded that \nhydraulic properties such as saturated and unsaturated hydraulic conductivity and \nmacroscopic capillary length were not influenced by soil texture. They also showed \nthe significant difference in different landuse patterns that was independent of  \nsoil texture.\n\n\n\nTABLE 3\nStatistical analysis of structure effect on some hydraulic parameters\n\n\n\n10 \n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nStatistical analysis of structure effect on some hydraulic parameters \n\n\n\nSouce of \nvariation df \n\n\n\nF value \n\n\n\nK14 K10 K4 K1 Ks Nmacropore Nmezopore \n\n\n\nSoil \nstructure 8 462.43ns 137.02** 842.29** 1505.26** 3088.04** 3231.62** 51959** \n\n\n\nNotes: Letter K shows hydraulic conductivity; subscripts display various matric suctions; letter N\" \n            indicates number of pores. \n\n\n\n\n\n\n\nTABLE 4. \n\n\n\nMean comparison of macropore and mesopore number per square meter in different structures \n\n\n\nPore size  \n\n\n\nMesopore \n\n\n\n0.107< \nradius<0.375(mm) \n\n\n\nMacropore \n\n\n\n0.375 (mm) < radius \n\n\n\nSample \n\n\n\n20167b 140c G1 \n\n\n\n27261a 216a G2 \n\n\n\n27306a 214a G3 \n\n\n\n18430c 147b B1 \n\n\n\n18552c 148b B2 \n\n\n\n18554c 149b B3 \n\n\n\n2429d 14d M1 \n\n\n\n2489d 14d M2 \n\n\n\n2425d 15d M3 \n\n\n\nNotes: In each column, the dissimilar letters indicate significant difference between various structures (p<0.05 \nDuncan). (G, B and M indicate Granular, Blocky and Massive structure respectively, and each of their subscript \n\n\n\nnumber  represents the  number of soil samples) \n \n\n\n\nThe number of mesopores was manifold higher than the macropores in all treatments. \n\n\n\nThe highest number of macropores and mesopores was observed in G3 and G2 and the lowest \n\n\n\nwas seen in massive M1, M2 and M3 soils. The number of macropores and mesopores in G1 \n\n\n\n The average number of pores per unit area (N) in two classes of pore size \nincluding mesopore and macropore of each are given in Table 4. In calculating \nthe number of pores in each class, the smallest radius in each class was used in \ntwo successive matric suctions to calculate the maximum number of pores in each \nclass. Our results of statistical analysis show the effect of soil structure on both \npore size classes to be significant.\n The number of mesopores was manifold higher than the macropores in all \ntreatments. The highest number of macropores and mesopores was observed in \nG3 and G2 and the lowest was seen in massive M1, M2 and M3 soils. The number \nof macropores and mesopores in G1 soil was significantly less than in the G3 \nand G2 soils. Despite its high organic matter content and granular structure, G1 \nhad a  significantly lower number of pores compared to the two other granular \nstructures due to soil compaction resulting from passing agricultural machinery. \nThe passing of agricultural machinery and a significant increase in bulk density, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 201950\n\n\n\nwith its consequent  decrease in the porosity of the total soil, led to a significant \ndecrease in the number of pores. In G1, the number of macropores and mesopores \nwere respectively 35% and 26% lesser compared to the number of pores in \nthe G3 and G2. As compaction increased, macropores were more affected than \nmesopores despite their lesser quantity. Lipiec and H\u00e5kansson (2000) showed \nthat the compaction caused by the transport of agricultural machinery affects \nthe volume of macropores (the pores that are active in the saturation flow) more \nthan micropores (\u00b5m>30). The presence of  greater organic matter, low bulk \ndensity (high porosity) and more stable aggregates in the granular class indicate \nthat this class has a higher number of macropores and mesopores than the other \ntwo soil structures. Dorner et al. (2010) who studied the role of soil structure on \npore performance stated that unstructured soils have a smaller number and less \ncontinuous pores than the structured soils.\n  The effect of structure on saturated and unsaturated hydraulic conductivity \nin various potentials is shown in Figure 3.\n  In all structures, saturated and unsaturated hydraulic conductivity \nincreased with matric suction reduction (from 14 to 1 cm). By reducing the matric \nsuction to near-saturation conditions, the macropores involved in the water flow \n\n\n\nTABLE 4.\nMean comparison of macropore and mesopore number per square meter in different \n\n\n\nstructures\n\n\n\n10 \n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nStatistical analysis of structure effect on some hydraulic parameters \n\n\n\nSouce of \nvariation df \n\n\n\nF value \n\n\n\nK14 K10 K4 K1 Ks Nmacropore Nmezopore \n\n\n\nSoil \nstructure 8 462.43ns 137.02** 842.29** 1505.26** 3088.04** 3231.62** 51959** \n\n\n\nNotes: Letter K shows hydraulic conductivity; subscripts display various matric suctions; letter N\" \n            indicates number of pores. \n\n\n\n\n\n\n\nTABLE 4. \n\n\n\nMean comparison of macropore and mesopore number per square meter in different structures \n\n\n\nPore size  \n\n\n\nMesopore \n\n\n\n0.107< \nradius<0.375(mm) \n\n\n\nMacropore \n\n\n\n0.375 (mm) < radius \n\n\n\nSample \n\n\n\n20167b 140c G1 \n\n\n\n27261a 216a G2 \n\n\n\n27306a 214a G3 \n\n\n\n18430c 147b B1 \n\n\n\n18552c 148b B2 \n\n\n\n18554c 149b B3 \n\n\n\n2429d 14d M1 \n\n\n\n2489d 14d M2 \n\n\n\n2425d 15d M3 \n\n\n\nNotes: In each column, the dissimilar letters indicate significant difference between various structures (p<0.05 \nDuncan). (G, B and M indicate Granular, Blocky and Massive structure respectively, and each of their subscript \n\n\n\nnumber  represents the  number of soil samples) \n \n\n\n\nThe number of mesopores was manifold higher than the macropores in all treatments. \n\n\n\nThe highest number of macropores and mesopores was observed in G3 and G2 and the lowest \n\n\n\nwas seen in massive M1, M2 and M3 soils. The number of macropores and mesopores in G1 \n\n\n\nNotes: In each column, the dissimilar letters indicate significant difference between \nvarious structures (p<0.05 Duncan). (G, B and M indicate Granular, Blocky and \nMassive structure respectively, and each of their subscript number  represents the  \nnumber of soil samples)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 51\n\n\n\nincreased the rate of saturated and unsaturated hydraulic conductivity in the \nsoil. The difference in the values of all three parameters measured at the matric \nsuction of 1 cm was higher than that of the matric suction at 14 cm among the \ndifferent structures. This is due to the difference between macropores and their \nnon-uniformity in near-saturated suction in different structures. Moosavi and \nSepaskhah (2012) reported that the higher coefficient of variation of Ks is related \nto the size, the number and heterogeneity of macropores compared to unsaturated \nhydraulic conductivity values. They also stated that the low coefficient of variation \nof the flow and hydraulic conductivity parameters in the higher matric suction in \nrelated to the smaller and uniform sizes of these pores. The highest rate of flow \nand saturated and unsaturated hydraulic conductivity through macropores and \nmesopores were observed in granular structures (G3 and G2) and the lowest value \nof the mentioned parameters was calculated in massive structures (M1, M2, M3) \n(Figure 3), which had the lowest number of active water-conducting pores. The \npresence of high organic matter and low bulk density in granular soils resulted in \nmore active water conductive pores, which consequently resulted in saturated and \nunsaturated hydraulic conductivity in these soils.\n In G1 soil, there was a significant difference between the values of \nsaturated and unsaturated hydraulic conductivity compared to G3 and G2 soils. \nThe position of G1 near the road and the passing of tractors led to increased \nbulk density and reduced porosity which resulted in a significant reduction in \nthe flow rate and consequently a decrease in the saturated and unsaturated \nhydraulic conductivity relative to G3 and G2. Changes in bulk density resulted in \nsaturated hydraulic conduction changes which indicate soil structure sensitivity \n\n\n\nFig. 3: Mean saturated and unsaturated hydraulic conductivity in different matric \nsuctions and various structures\n\n\n\n12 \n\n\n\n\n\n\n\n \nFigure 3: Mean saturated and unsaturated hydraulic conductivity in different matric suctions and various \n\n\n\nstructures \nNotes: The dissimilar letters in each suction indicate significant difference between various structures (p<0.05 Duncan). (G, \nB and M indicate Granular, Blocky and Massive structure respectively, and each of their number represents the number of \nsoil samples. Letter K shows hydraulic conductivity and numbers display various matric suctions). \n\n\n\nIn all structures, saturated and unsaturated hydraulic conductivity increased with matric \n\n\n\nsuction reduction (from 14 to 1 cm). By reducing the matric suction to near-saturation \n\n\n\nconditions, the macropores involved in the water flow increased the rate of saturated and \n\n\n\nunsaturated hydraulic conductivity in the soil. The difference in the values of all three \n\n\n\nparameters measured at the matric suction of 1 cm was higher than that of the matric suction \n\n\n\nat 14 cm among the different structures. This is due to the difference between macropores and \n\n\n\ntheir non-uniformity in near-saturated suction in different structures. Moosavi and Sepaskhah \n\n\n\n(2012) reported that the higher coefficient of variation of Ks is related to the size, the number \n\n\n\nand heterogeneity of macropores compared to unsaturated hydraulic conductivity values. \n\n\n\nThey also stated that the low coefficient of variation of the flow and hydraulic conductivity \n\n\n\nparameters in the higher matric suction in related to the smaller and uniform sizes of these \n\n\n\npores. The highest rate of flow and saturated and unsaturated hydraulic conductivity through \n\n\n\nmacropores and mesopores were observed in granular structures (G3 and G2) and the lowest \n\n\n\nvalue of the mentioned parameters was calculated in massive structures (M1, M2, M3) (Figure \n\n\n\n3), which had the lowest number of active water-conducting pores. The presence of high \n\n\n\n0.00\n2.00\n4.00\n6.00\n8.00\n\n\n\n10.00\n12.00\n14.00\n\n\n\nG1 G2 G3 B1 B2 B3 M1 M2 M3\n\n\n\na a a b b b c c c \n\n\n\na a a b b b \nc c c \n\n\n\nb \na a \n\n\n\nc c c \n\n\n\nd d d \n\n\n\nb \n\n\n\na a \n\n\n\nc c c \n\n\n\nd d d \n\n\n\nb \n\n\n\na a \n\n\n\nc c c \n\n\n\nd d d \n\n\n\n h\nyd\n\n\n\nra\nul\n\n\n\nic\n c\n\n\n\non\ndu\n\n\n\nct\niv\n\n\n\nity\n (c\n\n\n\nm\n/h\n\n\n\n)  \n\n\n\nsoil structure \n\n\n\nK14\n\n\n\nK10\n\n\n\nK4\n\n\n\nK1\n\n\n\nks\n\n\n\nNotes: The dissimilar letters in each suction indicate significant difference between \nvarious structures (p<0.05 Duncan). (G, B and M indicate Granular, Blocky and Massive \nstructure respectively, and each of their number represents the number of soil samples. \nLetter K shows hydraulic conductivity and numbers display various matric suctions).\n\n\n\n\n\n\n\n\nin soil management practices. Soracco et al. (2015) who studied the effects of \nagricultural machinery movement on the shape and distribution of soil pores, \nstated that soil compaction, by decreasing the number of soil pores, significantly \nchanged physical and hydraulic properties, especially hydraulic conductivity. In \nsoil structure, continuity and distribution of pore size, especially the volume and \ncontinuity of conductive active pores (macropores and mesopores), have a major \neffect on soil hydraulic conductivity. According to Verwoort and Cattle (2003) \na meaningful relationship exists between the continuity of pores and hydraulic \nconductivity which indicates the importance of soil structure and the continuity of \npores in water movement and transport of solutes in the soil. In near-zero matric \nsuction, the water and solute flow occur mainly in the macropores and mesopores, \nwhich are the preferred water paths in the soil. Due to the presence of a high \nnumber of macropores and mesopores, soils with G3 and G2 granular structures \nhave preferential pathways and are more likely to have a more preferential flow \nthan other soils.\n\n\n\nCONCLUSION\nOur study showed that soil structure has significant effects on hydraulic and \nphysical properties. Considering physical and hydraulic properties, granular \nsoils had the best structure compared to blocky and massive soils. Blocky soils \nranked second in terms of physical and hydraulic conditions, followed by massive \nsoils. Massive soils had the weakest structure in terms of physical and hydraulic \nproperties. In general, the presence of organic matter, as one of the important \nfactors in aggregation, caused the formation of more stable aggregates and a lower \nbulk density in granular soils. The formation of more active water conductive \npores in these soils led to more saturated and unsaturated hydraulic conduction, \nwhich increased the preferential flow in this structure compared to the blocky and \nmassive structure.\n Compaction had a significant effect on soil structure. The passing of \nagricultural machinery resulted in reduced porosity and number of pores and \nincreased bulk density, which resulted in significant changes in the physical and \nhydraulic parameters in compacted soils compared to other soils. \n\n\n\nREFERENCES\nAhad, T., T.A. Kanth and S. Nabi. 2015. 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International Journal of environmental \nScience & Technology 4(1):109-118.\n\n\n\nVervoort, R.W. and S.R. Cattle. 2003. Linking hydraulic conductivity and tortuosity \nparameters to pore space geometry and pore-size distribution. Journal of \nHydrology 272(1-4): 36-49.\n\n\n\nWalkley, A. and I.A .Black. 1934. An examination of the Degtjareff method for \ndetermining soil organic matter, and a proposed modification of the chromic \nacid titration method. Soil Science 37(1): 29-38.\n\n\n\nWatson, K.W. and R.J. Luxmoore. 1986. Estimating macroporosity in a forest \nwatershed by use of a tension infiltrometer 1. Soil Science Society of America \nJournal 50(3): 578-582.\n\n\n\nWooding, R.A. 1968. Steady infiltration from a shallow circular pond. Water \nResources Research 4(6):1259-1273.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : Email: mmhanafi@agri.upm.edu.my\n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 13: 29-42                                  Malaysian Society of Soil Science\n\n\n\nEarthworm Populations and Cast Properties in the Soils of \n\n\n\nOil Palm Plantations\n\n\n\nD.T. Sabrina1, M. M. Hanafi2*, A.A. Nor Azwady3 & \n\n\n\nT. M. M. Mahmud4\n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia\n\n\n\n2Institute of Tropical Agriculture, Universiti Putra Malaysia \n\n\n\n3Department of Biology, Faculty of Science, Universiti Putra Malaysia\n\n\n\n4Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, \n\n\n\n43300 UPM, Serdang Selangor, Malaysia\n\n\n\nABSTRACT\nOil palm plantations generate a substantial amount of agricultural by-products, \n\n\n\nsuch as oil palm empty fruit bunches (EFB) and fronds.  These by-products are \n\n\n\ncommonly recycled in the oil palm plantations in order to obtain plant nutrients \n\n\n\nthrough decomposition.  Information on earthworm species and populations and \n\n\n\ntheir cast properties in oil palm plantations in different soil types and oil palm \n\n\n\ntree ages is still lacking.  The population and diversity of earthworms, casts and \n\n\n\nsoils were surveyed in 10 m transects using 5 of 25 cm2 quadrat.  In all sampling \n\n\n\nsites, only an endogeic species, Pontoscolex corethrurus M\u00fcller discovered.  \n\n\n\nThe earthworm population densities were influenced by the age of the oil palm \n\n\n\ntrees and soil types.  Under similar soil types and different oil palm ages, the \n\n\n\nearthworm population densities were inversely related.  Four major factors which \n\n\n\ndictated the heterogeneity of earthworm population in oil palm plantation were: \n\n\n\n(i) food and soil physical habitat, (ii) exchangeable calcium, (iii) pH, and (iv) \n\n\n\nexchangeable potassium as determined by principal component analysis (PCA).  \n\n\n\nThe earthworm population was positive significantly related to the CEC and \n\n\n\nexchangeable Ca in the soil  (R2=0.66*, n=100).  With the exception of the soil C: \n\n\n\nN ratio, all other soil chemical properties (pH, C, N, total P, plant available P, total \n\n\n\nK, total Mg, CEC, exchangeable- K, Ca and Mg) were  significantly correlated \n\n\n\nwith the earthworm cast properties.  Available P was 509 % higher in casts than \n\n\n\nin the surface soil (r=0.63*, n=100).  The cast CEC and exchangeable Ca were \n\n\n\nstrongly correlated with the soil CEC and exchangeable Ca in soil. However, \n\n\n\nthe increase in CEC and exchangeable Ca were 67 and 98%,  respectively.  The \n\n\n\nearthworm population was highly correlated with soil CEC and exchangeable Ca.\n\n\n\nKeywords:  Oil palm, earthworm, Pontoscolex corethrurus, soil factors, \n\n\n\n        principal component analysis\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200930\n\n\n\nINTRODUCTION\n\n\n\nOil palm (Elaeis guineensis Jacq) is a major plantation crop in Malaysia. The \n\n\n\ntotal oil palm plantation area was about 4.01 million ha in 2005 and is expected \n\n\n\nto increase to 5.10 million ha in 2020 (Anon. 2005).  The oil palm tree is a highly \n\n\n\ndemanding crop for nutrients both for early growth and production, hence requiring \n\n\n\nhigh inherent soil fertility status.  When planted on soils with low inherent fertility, \n\n\n\noil palm requires more agronomic inputs to ensure adequate yields.  The addition \n\n\n\nof nutrient-rich palm oil mill by-products and agricultural by-products to replace \n\n\n\nsome of these inorganic nutrients is an environmentally friendly option.\n\n\n\n Oil palm plantations churn out a huge amount of by-products. An average \n\n\n\nof 24 fronds is pruned per plant per year which is equivalent to 11.7 tonnes ha-1 \n\n\n\nyear-1 (Chan et al. 1980; Chan et al. 1984).  The empty fruit bunches constitute 6-7 \n\n\n\ntonnes per 10 tonnes of fresh fruit bunch.  Palm oil mills also generate additional \n\n\n\nindustrial by-products. For every tonne of crude palm oil produced, three tonnes \n\n\n\nof palm oil mill effluent cake are generated during the oil extraction process. \n\n\n\nThis huge amount of oil palm by-products are potential sources of nutrients to \n\n\n\nthe oil palm trees.  However, oil palm by-products are difficult to manage as \n\n\n\nthey decompose slowly.  Soil micro and macro-organisms are required to \n\n\n\nenhance the decomposition process.  Soil macro-organisms such as earthworms \n\n\n\nhave considerable potential to increase the decomposition, thus, increasing soil \n\n\n\nproductivity in oil palm plantations.  Furthermore, application of agricultural \n\n\n\nby-products and earthworms are economically and ecologically feasible in the \n\n\n\nplantations.\n\n\n\n Earthworms contribute to soil turnover, structure formation and serve as \n\n\n\na fertility enhancer in various ways.  Earthworms and their casts are useful in \n\n\n\nland improvement, reclamation and in organic waste management (Edwards and \n\n\n\nBaker 1992; Johnson 1997; Lavelle and Martin 1992; Villenave et al. 1999).  Soil \n\n\n\nproductivity can be improved by manipulating the community of earthworms in \n\n\n\nthe soil (Brown et al. 1999).\n\n\n\n The distribution and population density of various species of earthworm \n\n\n\nhave been correlated with soil type, and agricultural land use (El-Duweini and \n\n\n\nGhabbour 1964; Haynes et al. 2003).  In oil palm plantations, the earthworm \n\n\n\npopulation and the diversity are expected to differ with soil type and palm tree \n\n\n\nage, with the cast properties also being affected by soil properties.\n\n\n\n This research was done in oil palm plantations in Malaysia with the aim of (i) \n\n\n\nidentifying earthworm diversity and density and its cast properties in different soil \n\n\n\ntypes and oil palm ages and (ii) studying the relationships between soil properties \n\n\n\nand earthworm populations in the oil palm plantations.\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Location\n\n\n\nThe study was carried out at five oil palm plantations with different soil types.  \n\n\n\nThese plantations enjoy a warm and humid climate, with a mean annual rainfall of \n\n\n\n2,141 mm and a mean annual temperature of 260C.  Five soil series were chosen \n\n\n\nD.T. Sabrina, M. M. Hanafi, A.A. Nor Azwady & T. M. M. Mahmud\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 31\n\n\n\nfor this study.  They were Rengam, Serdang, Jerangau, Bungor and Munchong, \n\n\n\nwhich are the common soil series in the oil palm plantations (Department of \n\n\n\nAgriculture 2004).  General properties of the soils are summarised in Table 1.  For \n\n\n\neach soil type, the samples were collected from oil palm of 3 different ages: (i) <7 \n\n\n\nyears old, (ii) 7-14 years old, and (iii) >14 years old.  These ages correspond to the \n\n\n\ngrowing phase of the oil palm tree (Henson 2003).  During the first six years after \n\n\n\nplanting, the growth of the palm tree is extremely fast.  The age of 6 to 12 years is \n\n\n\na period of steadily rising yield, and the peak of FFB production.  Thereafter, the \n\n\n\nyield stabilises and then slows down.\n\n\n\nTABLE 1\n\n\n\nSome physico-chemical characteristics of the soils used in this study and their \n\n\n\nclassifications\n\n\n\nSampling Technique\n\n\n\nEarthworm casts, earthworm and topsoil (depth 0-25 cm) were sampled in \n\n\n\nFebruary to July 2004.  At each location, five quadrats of 25 cm2 were taken \n\n\n\nrandomly from a 10 m transect, along  palm rows or palm inter-rows or across the \n\n\n\npalm inter-row with the frond heaps being included.\n\n\n\n At each sampling site, the surface casts were removed.  After collecting \n\n\n\nthe top soil, sampling sites were poured with 500 mL 10% of formalin in order \n\n\n\nto induce the worms to come up to the surface of the soil for easy collection and \n\n\n\nthen taken back to the laboratory.  Also, the soil was dug to 25 cm depth and the \n\n\n\naggregates broken up for earthworm collection.  The earthworms were identified \n\n\n\nfollowing the procedure of Bouch\u00e9 (1977) and the checklist was made according \n\n\n\nto Blakemore (2002).\n\n\n\n At every site, soil samples were also taken for soil chemical and physical \n\n\n\nanalyses.  Soil and cast samples were air-dried, ground and sieved to pass through \n\n\n\na 2 mm sieve size.  Soil pH was determined in water using a soil (cast) to solution \n\n\n\nratio of 1:2.5; organic carbon was analysed using the Walkley and Black method \n\n\n\n(1934); total nitrogen was determined by the Kjeldahl method (Bremner, 1960); \n\n\n\ntotal P, K, Ca and Mg were measured after digesting the sample using the aqua-\n\n\n\nregia method (1:3 ratio of HNO\n3\n to HCl) (Mehlich, 1953); available P in soil \n\n\n\nand cast were analysed using the Bray 2 method (Bray and Kurtz, 1945); and \n\n\n\nexchangeable Na, K, Ca, Mg and CEC were determined after leaching the sample \n\n\n\nwith neutral 1 M ammonium acetate (Blakemore et al. 1987).  Concentrations \n\n\n\nEarthworms in Oil Palm Ecosystem\n\n\n\n\n\n\n\nSoil Soil series \n\n\n\ncharacteristics Serdang Rengam Jerangau Bungor Munchong \n\n\n\npH \n\n\n\nC (%) \n\n\n\nN (%) \n\n\n\nCEC (cmol (+) kg-1 soil) \n\n\n\nTexture \n\n\n\nSoil taxonomy \n\n\n\nFAO classification \n\n\n\n4.8 \u00b1 0.8 \n\n\n\n1.15 \u00b1 0.71 \n\n\n\n0.12 \u00b1 0.06 \n\n\n\n3.69 \u00b1 0.96 \n\n\n\nsandy loam \n\n\n\nTypic Kandiudults \n\n\n\nHaplic Nitisols \n\n\n\n4.9 \u00b1 0.7 \n\n\n\n1.25 \u00b1 0.25 \n\n\n\n0.13 \u00b1 0.01 \n\n\n\n4.33 \u00b1 1 \n\n\n\nsandy loam \n\n\n\nTypic Kandiudults \n\n\n\nHaplic Nitisols \n\n\n\n5.1\u00b1 0.8 \n\n\n\n1.90 \u00b1 0.32 \n\n\n\n0.18 \u00b1 0.09 \n\n\n\n7.98 \u00b1 1.25 \n\n\n\nclay loam \n\n\n\nTypic Hapludox \n\n\n\nGeric Ferrasols \n\n\n\n4.7 \u00b1 0.8 \n\n\n\n1.22 \u00b10.94 \n\n\n\n0.12 \u00b10.06 \n\n\n\n6.04 \u00b11.00 \n\n\n\nsandy loam \n\n\n\n\n\n\n\nHaplic Nitisols \n\n\n\n4.6 \u00b10.5 \n\n\n\n1.97 \u00b10.97 \n\n\n\n0.18 \u00b10.07 \n\n\n\n9.28 \u00b11.2 \n\n\n\nheavy clay \n\n\n\nHaplic Hapludox \n\n\n\nHaplic Ferrasols \n\n\n\n\n\n\n\nTypic Paleudults\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200932\n\n\n\nof N, P and K in solution were determined using the Lachat QuickChem FIAT \n\n\n\nauto-analyser, and Ca and Mg was measured using the Perkin Elmer PE 5100 \n\n\n\natomic absorption spectrophotometer in the presence of 1000 mg Sr(NO\n3\n)\n\n\n\n2\n L-1, as \n\n\n\nan ionisation suppressant.\n\n\n\nStatistical Analysis\n\n\n\nThe effects of soil type and palm age on earthworm population were examined \n\n\n\nusing a factorial design, soil type and palm age as a factor. The soil physical \n\n\n\nand chemical properties affecting the heterogeneity of earthworm population \n\n\n\nwere identified using the principal component analysis.  Soil properties extracted \n\n\n\nfrom PCA were used as independent variables and earthworm population as the \n\n\n\ndependent variable.  Stepwise multiple regression analysis was used to investigate \n\n\n\nrelationships between soil properties and population of earthworms.  The \n\n\n\nrelationship between the soil and the cast properties was determined by simple \n\n\n\ncorrelation analyses.  The data were analysed using the statistical analysis system \n\n\n\n(SAS) version 8e (SAS 1999).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nDistribution and Population of Earthworms\n\n\n\nOnly one endogeic species Pontoscolex corethrurus M\u00fcller was found at all the \n\n\n\nsampling sites.  This earthworm species is a pigmentless geophagus type from \n\n\n\nthe Glossoscolecidae family. The population of earthworms varied significantly \n\n\n\n(P\u22640.01) with soil type and oil palm age.  The highest population found was 42 \n\n\n\nindividual m-2 in Rengam soil in areas of oil palm > 14 years (Fig. 1).  This \n\n\n\nwas significantly higher than the population of earthworms for oil palm age of \n\n\n\n<7 and 7-14 years in the same soil.  In Rengam and Munchong soils, with an \n\n\n\nincrease in the age of the oil palm trees, there was an  increase in the abundance of \n\n\n\nearthworms. The patterns of earthworm density under Jerangau, and Bungor soils \n\n\n\nwere similar, with the highest number of earthworm being found under 7\u201314 year \n\n\n\nold trees.  The number of earthworms in the oil palm plantation >14 years was \n\n\n\nsignificantly (P\u22640.01) different from those in plantations <7 and 7\u201314 years old.  \n\n\n\nThe young oil palm tree field contained relatively low organic matter (2.22%) \n\n\n\ncompared to an old palm oil tree field (3.29%), thus, contributing to the lower \n\n\n\ndensity of earthworms (Table 2).\n\n\n\nD.T. Sabrina, M. M. Hanafi, A.A. Nor Azwady & T. M. M. Mahmud\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 33\n\n\n\nTABLE 2\n\n\n\nThe number of earthworm and organic matter content under fronds heap and \n\n\n\nopen soil surface\n\n\n\nCommon letters within soil types and palm age indicate no significant differences according to Duncan test \n\n\n\n\u03b1\u22640.05\n\n\n\nFig. 1: Earthworm population in five soil types (Serdang, Rengam, Jerangau, Bungor, \n\n\n\nMunchong) and three different palm oil age (< 7, 7 \u2013 14, > 14).  \n\n\n\n Pontoscolex corethrurus is a native species in these oil palm plantations.  \n\n\n\nThe lack of earthworm population diversity in oil palm plantations  might be due \n\n\n\nto the intensive management systems.  Pontoscolex corethrurus exhibits wide \n\n\n\nclimatic and edaphic tolerances (Fragoso et al. 1999a).  The difference in survival \n\n\n\nof Malaysian species and/or the invasion of exotic species in India compared to \n\n\n\nAfrica or to Mexico-Central America may be related to management practices \n\n\n\n(Fragoso et al., 1999b).  Schmidt et al. (2003) emphasised that land use is a factor \n\n\n\nthat influences the diversity and populations of earthworms.  Soil management \n\n\n\naffects the chemical and physical properties of soils.  The effect of organic matter \n\n\n\nremoval during land preparation of the young oil palm plantation is more apparent \n\n\n\n\n\n\n\nEarthworm number Organic matter Soil Type Palm age \n\n\n\n Under fronds Open surface Under fronds Open surface \n\n\n\n Year   \u2500\u2500\u2500\u2500\u2500\u2500 % \u2500\u2500\u2500\u2500\u2500 \n\n\n\nSerdang \n\n\n\n\n\n\n\n\n\n\n\nRengam \n\n\n\n\n\n\n\n\n\n\n\nJerangau \n\n\n\n\n\n\n\n\n\n\n\nBungor \n\n\n\n\n\n\n\n\n\n\n\nMunchong \n\n\n\n\n\n\n\n\n\n\n\n< 7 \n\n\n\n7 -  14 \n\n\n\n> 14 \n\n\n\n< 7 \n\n\n\n7 -  14 \n\n\n\n> 14 \n\n\n\n< 7 \n\n\n\n7 -  14 \n\n\n\n> 14 \n\n\n\n< 7 \n\n\n\n7 -  14 \n\n\n\n> 14 \n\n\n\n< 7 \n\n\n\n7 -  14 \n\n\n\n> 14 \n\n\n\n5 \n\n\n\n2 \n\n\n\n6 \n\n\n\n1 \n\n\n\n8 \n\n\n\n24 \n\n\n\n8 \n\n\n\n13 \n\n\n\n5 \n\n\n\n5 \n\n\n\n8 \n\n\n\n4 \n\n\n\n10 \n\n\n\n12 \n\n\n\n14 \n\n\n\n2 \n\n\n\n1 \n\n\n\n3 \n\n\n\n0 \n\n\n\n7 \n\n\n\n18 \n\n\n\n7 \n\n\n\n10 \n\n\n\n5 \n\n\n\n3 \n\n\n\n8 \n\n\n\n3 \n\n\n\n7 \n\n\n\n10 \n\n\n\n13 \n\n\n\n2.56 \n\n\n\n1.78 \n\n\n\n2.10 \n\n\n\n1.96 \n\n\n\n2.49 \n\n\n\n2.39 \n\n\n\n2.60 \n\n\n\n4.14 \n\n\n\n2.96 \n\n\n\n2.10 \n\n\n\n2.34 \n\n\n\n2.32 \n\n\n\n2.35 \n\n\n\n4.02 \n\n\n\n3.10 \n\n\n\n2.01 \n\n\n\n1.74 \n\n\n\n1.73 \n\n\n\n1.69 \n\n\n\n2.30 \n\n\n\n2.25 \n\n\n\n2.43 \n\n\n\n3.37 \n\n\n\n3.37 \n\n\n\n1.96 \n\n\n\n2.06 \n\n\n\n2.32 \n\n\n\n2.23 \n\n\n\n3.61 \n\n\n\n3.23 \n\n\n\n\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n45\n\n\n\nSerdang Rengam Jerangau Bungor Munchong\n\n\n\nSoil Type\n\n\n\n< 7\n\n\n\n 7 - 14\n\n\n\n> 14\n\n\n\nd\nd\n\n\n\nd\ne\n\n\n\ne\n\n\n\na\n\n\n\nc\n\n\n\nb\n\n\n\nd\ndd\n\n\n\nc c\n\n\n\nb\n\n\n\nb\n\n\n\nEa\nrth\n\n\n\nwo\nrm\n\n\n\n / m\n2\n\n\n\nEarthworms in Oil Palm Ecosystem\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200934\n\n\n\nthan in the older oil palm plantation.  Mostly, the young oil palm plantations \n\n\n\nconsist of low organic matter, except for the sites that are fully maintained with \n\n\n\ncover crops.  Organic matter content in the development of sustainable earthworm \n\n\n\npopulations is very important especially in the restored site (Lowe and Butt 2002).  \n\n\n\nFurthermore, the crown of the younger oil palm tree do not fully cover the soil \n\n\n\nsurface, thus, the sunlight may directly radiate on the soil surface which results \n\n\n\nin a lower soil moisture content (11.5-20.9%) compared to the soil under older \n\n\n\npalm tree (14.1-32.1%).  During the phase of fast growth or the first six years after \n\n\n\nplanting, oil palm trees absorb more water and nutrients compared to those trees \n\n\n\nwhich are steadily growing.  The oil palm by-products from young oil palm tree \n\n\n\nare also less than the older trees.  Therefore, the effect of oil palm by-products \n\n\n\nwere excluded as a factor for a fewer number of earthworms in the oil palm \n\n\n\nplantation.  Populations of P. corethrurus in tropical tree plantations in Hawaii \n\n\n\nare influenced strongly by tree species, particularly the palatability of leaves to \n\n\n\nearthworms.  Although Eucalyptus trees produce more litter, Albizia trees produce \n\n\n\nfiner litter fall and earthworm densities are correlated positively with the nitrogen \n\n\n\ncontent and concentration of fine litter fall (Zou 1993).  The young oil palm tree \n\n\n\nreceived less organic matter compared to the old oil palm tree,  a factor which \n\n\n\nmight affect the population of earthworms.  The influence of organic matter in \n\n\n\nthe development of sustainable earthworm populations is of vital importance, \n\n\n\nespecially in the restored site (Lowe and Butt 2002).  The abundance and biomass \n\n\n\nof earthworms were higher in the improved maize with a Mucuna prupriens cover \n\n\n\ncrop than in continuous conventional maize, which was caused by the amount \n\n\n\nof organic matter and N content (Otiz-Ceballos and Fragoso, 2004).  However, \n\n\n\nthis study found that not all the species of earthworms prefer the high organic \n\n\n\nmatter and nitrogen; one of the native species, Balanteodrilus pearsei, was found \n\n\n\nto have a negative correlation with nitrogen content in the soil.  Besides, the other \n\n\n\npossibility is that the crowns of the young oil palm trees do not fully cover the soil \n\n\n\nsurface; hence direct sunlight contact with the soil surface contributes to higher \n\n\n\nsoil temperature.  Higher soil temperature might be the cause of lower earthworm \n\n\n\npopulation (Edwards and Bohlen 1996).  The management of a young oil palm tree \n\n\n\ndiffers from that of a old or mature tree, which also has an effect on the population \n\n\n\nof earthworms beside the diversity mentioned earlier.  Curry et al. (2002) found \n\n\n\nthat the impact of intensive cultivation for potato production resulted in a drastic \n\n\n\nreduction in the earthworm population.\n\n\n\n Microclimate may be a further reason for low earthworm diversity.  \n\n\n\nEarthworm communities in the tropics are dominated by endogeics, while in the \n\n\n\ncolder environment epigeics predominate (Fragoso et al. 1999b).  The number \n\n\n\nof P. corethrurus was higher under the oil palm frond heaps compared to the \n\n\n\nopen soil surface.  Soil under frond heaps is moist and humid where plenty of \n\n\n\nfood is available for the earthworms.  Even though P. corethrurus is classified \n\n\n\nas endogeic and geophagus, the endogeics communities in the tropical regions \n\n\n\ncan shift to epigeic communities if soil nutrients and seasonality of rains are low \n\n\n\nD.T. Sabrina, M. M. Hanafi, A.A. Nor Azwady & T. M. M. Mahmud\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 35\n\n\n\n(Fragoso and Lavelle 1992).  However, according to field observations during this \n\n\n\nstudy, the presence of termites under the frond heaps and wild boar (recognized \n\n\n\nfrom its footprint) might have reduced the population of earthworm drastically.\n\n\n\n Organic matter content in the development of sustainable earthworm \n\n\n\npopulations is very important especially in the restored site (Lowe and Butt 2002).  \n\n\n\nThe effect of organic matter removal during land preparation for the young oil \n\n\n\npalm plantation has not yet been investigated.\n\n\n\nSoil Factors Affecting the Distribution of Earthworm Population\n\n\n\nSoil properties can affect the distribution of earthworm population in soil under \n\n\n\ndifferent oil palm ages.  Six soil factors have been identified by principal component \n\n\n\nanalysis which explained 71% of total variance (Table 3).  The first factor (27.9%) \n\n\n\ncomprised of C, N, CEC, clay, silt and water content; and bulk density and sand \n\n\n\ncontent.  This first principal component was considered to be a \u2018physical habitat \n\n\n\nand food\u2019 factor.  The second component involved the concentration of calcium \n\n\n\nin the soil (Fig. 2).  The signs of coefficients of total and exchangeable Ca were \n\n\n\nopposite to each other.  Increments of exchangeable Ca in the soil probably caused \n\n\n\na reduction in total Ca.  The close relationships between Ca content in the soil \n\n\n\nand population of P. corethrurus may be because earthworms need Ca to produce \n\n\n\ntheir calciferous glands.  The third component was related strongly to pH and \n\n\n\ntotal Mg, and moderately to total P and available P.  The soil pH and total Mg \n\n\n\nexhibited a negative correlation, while total P, available P and pH were positively \n\n\n\ncorrelated.  The availability of P in the soil is controlled by soil pH.  The fourth \n\n\n\ncomponent is apparently the \u2018K\u2019 factor (Fig. 3).  Clay content in the study area \n\n\n\nappeared to be a factor influencing earthworm populations.  The fifth and sixth \n\n\n\ncomponent contributed 7% and 6% of the total variation (Table 3). The factors \n\n\n\nthat caused a higher variation in the principal component 1 and 2 were used as \n\n\n\nan independent variable for regression analysis.  They were organic C, N, CEC, \n\n\n\nclay, sand, bulk density, water content, total Ca and exchangeable Ca.  The most \n\n\n\nappropriate model obtained at a 5% significant level was:\n\n\n\n  Y = 1.42 X\n1 \n+ 0.006 X\n\n\n\n2\n (R2=0.66* n=100)\n\n\n\n  where \n\n\n\n   Y   = earthworm/m2\n\n\n\n   X\n1   \n\n\n\n= CEC \n\n\n\n   X\n2   \n\n\n\n= exchangeable calcium\n\n\n\nThe PCA produced some probable answers in the variation of earthworm \n\n\n\npopulations in term of soil type and oil palm age.  Although P. corethrurus is \n\n\n\nan endogeic earthworm species, which is a soil feeder, our survey showed that \n\n\n\norganic C and N caused the variability in earthworm population.  Soil physical \n\n\n\nconditions have been shown to affect the activities of earthworms.  For example, \n\n\n\nStovold et al. (2004) showed that earthworm burrows were longer in the loose soil \n\n\n\nEarthworms in Oil Palm Ecosystem\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200936\n\n\n\nthan in compact soil.  Our study showed that the physical habitat/soil conditions \n\n\n\ninfluenced populations of earthworms in the same proportion as food.  The limited \n\n\n\nareas for earthworms to move or burrow in order to obtain food might serve as \n\n\n\na disturbance for the reproduction capability of the earthworms.  There was a \n\n\n\npositive linear correlation between soil CEC and organic C content and clay \n\n\n\ncontent, which indicates that the effect of CEC on earthworm populations was \n\n\n\nsimilar to the effects of organic C and clay.\n\n\n\nTABLE 3\n\n\n\nEigenvalues and proportions of variance to the total variance for derived \n\n\n\nprincipal component\n\n\n\nRelationship between Soil Properties and Earthworm Cast\n\n\n\nEarthworm casts significantly affect plant growth through their effects on micro-\n\n\n\norganisms, aggregation of soil, and nutrient supply (Table 4).  The amounts of \n\n\n\ncast varied from 0 to 262 g dry weight m-2 (Table 5).  Earthworms ingest selected \n\n\n\nsoil particles, soil organic matter, dead plant material, seeds or seedlings, and \n\n\n\nmicro-organisms.  This may affect the chemical and physical properties of cast as \n\n\n\ncompared to the surrounding soils. \n\n\n\n      The cast pH was higher than the soil pH. This might be due to the \n\n\n\ndifference in Ca content and OM.  Calcium and OM of certain residue are able to \n\n\n\ncorrect the acidity of cast, thus the pH of cast becomes higher.  The 69% increase \n\n\n\nin organic C in casts compared to soil showed that P. corethrurus consumes \n\n\n\norganic C. A similar trend in the amounts of total N in earthworm casts occurred \n\n\n\nin comparison to the N in the associated soils.  Carbon and nitrogen are major \n\n\n\nnutrients for earthworms, so the utilisation of C and N by earthworms gave the \n\n\n\nlowest value of 69 and 52% in the cast compared to other nutrients (Table 4).\n\n\n\n\n\n\n\nD.T. Sabrina, M. M. Hanafi, A.A. Nor Azwady & T. M. M. Mahmud\n\n\n\n\n\n\n\nPC1  \n\n\n\nPC2  \n\n\n\nPC3  \n\n\n\nPC4  \n\n\n\nPC5  \n\n\n\nPC6  \n\n\n\nPC7  \n\n\n\nPC8  \n\n\n\nPC9  \n\n\n\nPC10  \n\n\n\n5.02  \n\n\n\n2.90  \n\n\n\n1.92  \n\n\n\n1.68  \n\n\n\n1.20  \n\n\n\n1.11  \n\n\n\n0.83  \n\n\n\n0.65  \n\n\n\n0.60  \n\n\n\n0.55  \n\n\n\n0.28  \n\n\n\n0.16  \n\n\n\n0.11  \n\n\n\n0.09  \n\n\n\n0.07  \n\n\n\n0.06  \n\n\n\n0.05  \n\n\n\n0.04  \n\n\n\n0.03  \n\n\n\n0.03  \n\n\n\n28 \n\n\n\n44  \n\n\n\n55 \n\n\n\n64  \n\n\n\n71  \n\n\n\n77  \n\n\n\n82  \n\n\n\n85  \n\n\n\n89  \n\n\n\n92  \n\n\n\n\n\n\n\nPrincipal component Eigenvalue Proportion Cumulative percentage\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 37\n\n\n\nFig .2: Principal component analysis 1 (horizontal) and 2 (vertical) of earthworm \n\n\n\ndistribution pattern related to soil properties.\n\n\n\nFig.3: Principal component analysis 3 (horizontal) and 4 (vertical) of earthworm \n\n\n\ndistribution pattern related to soil properties.\n\n\n\nTABLE 4\n\n\n\nRelationships between soil and earthworm cast properties and its relative percentage \n\n\n\nincrease in value between cast and soil properties (n=100)\n \n\n\n\n\n\n\n\nProperties \n\n\n\n\n\n\n\nLinear regression equation  \n\n\n\nCorrelation \n\n\n\ncoefficient (r) \n\n\n\nRelative \n\n\n\nincrease \n\n\n\n\n\n\n\npH \n\n\n\nOrganic C \n\n\n\nN \n\n\n\nC/N \n\n\n\nPtotal \n\n\n\nPavailable \n\n\n\nKtotal \n\n\n\nCatotal \n\n\n\nMgtotal \n\n\n\nCEC \n\n\n\nKexchangeable \n\n\n\nCaexchangeable \n\n\n\nMgexchangeable \n\n\n\n\n\n\n\npH cast = 2.05 + 0.65 pH soil \n\n\n\nOrg-C cast = 1.27 + 0.63 org-C soil \n\n\n\nN cast = 0.05 + 1.11 N soil \n\n\n\n------ \n\n\n\nPtotal cast = 165 + 1.18 Ptotal soil \n\n\n\nPavailablecast = 55.4 + 1.21 Pavailable soil \n\n\n\nKtotalcast = 96.2 + 1.32 Ktotal soil \n\n\n\nCatotal cast = 838 + 0.46 Catotal soil \n\n\n\nMgtotalcast = 303 + 0.26 Mgtotal soil \n\n\n\nCEC cast = 4.31 + 0.84 CEC soil \n\n\n\nKexchangeable cast = 0.23 + 1.36 Kexchangeable soil \n\n\n\nCaexchangeablecast = 195 + 1.66 Caexchangeablesoil \n\n\n\nMgexchangeablecast = 41.6 + 1.43 Mgexchangeable soil \n\n\n\n\n\n\n\n0.61** \n\n\n\n0.57** \n\n\n\n0.66** \n\n\n\n-0.03\nns\n\n\n\n\n\n\n\n0.55* \n\n\n\n0.63* \n\n\n\n0.57* \n\n\n\n0.42* \n\n\n\n0.60* \n\n\n\n0.72** \n\n\n\n0.66** \n\n\n\n0.82** \n\n\n\n0.71** \n\n\n\n% \n\n\n\n5 \n\n\n\n69 \n\n\n\n52 \n\n\n\n--- \n\n\n\n302 \n\n\n\n509 \n\n\n\n116 \n\n\n\n117 \n\n\n\n110 \n\n\n\n67 \n\n\n\n165 \n\n\n\n98 \n\n\n\n241 \n\n\n\n\n\n\n\nEarthworms in Oil Palm Ecosystem\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200938\n\n\n\nMost of the chemical properties of the casts were greater than in the surrounding \n\n\n\nsoils, except for the C:N ratio.  Earthworms ingest dead plant materials of varying \n\n\n\nbut relatively wide C:N ratios and convert them to earthworm tissues with lower \n\n\n\nC:N ratio, which are released on the death of the earthworm.  Earthworms ingest \n\n\n\nthe selected soil particles, soil organic matter, dead plant material, seeds or \n\n\n\nseedlings, and microorganisms.  Surface casts usually have lower C:N ratios than \n\n\n\nassociated soils, and no correlations were obtained between the C:N ratio in soil \n\n\n\nand C/N ratio in cast (Syers and Springett 1984).  The amounts of total P and \n\n\n\navailable P were higher by 302% and 509%, respectively, than in soils.  However, \n\n\n\nthe correlation coefficients between these P properties and corresponding cast and \n\n\n\nsoil were low (r=0.55* and r=0.63*).  As for the P status in soil, the increases in \n\n\n\ntotal P and available P in casts were probably caused by modifications of the pH \n\n\n\n(5% improvement) in earthworm casts.  The percentage of plant-available P in \n\n\n\ncasts was nearly twice that of the percentage increase in total P.  This indicates \n\n\n\nthat the P produced by earthworms in their casts was the labile P in inorganic \n\n\n\nfractions.  Increases in activity of microorganisms in the casts were probably \n\n\n\nresponsible for increases in phosphatase activity in earthworm casts compared \n\n\n\nto the underlying soil (Jim\u00e9nez et al. 2003).  The total K and exchangeable K \n\n\n\ncontents in casts were 116 and 165% greater than the surrounding soil, as reported \n\n\n\nby Basker et al. (1992).  They further stated that earthworm activity increased \n\n\n\namounts of exchangeable K, and concluded that the increase in exchangeable \n\n\n\nK in cast must be due to the displacement of K+ from the wedge sites of clay \n\n\n\nminerals by NH\n4\n\n\n\n+ ions generated by enhanced mineralisation of organic N.  The \n\n\n\namounts of exchangeable Ca in cast were correlated strongly with the amounts of \n\n\n\nexchangeable Ca in the soil, and the exchangeable Ca in the casts was 98% higher \n\n\n\nthan the exchangeable Ca in the soil. The amounts of total Ca released in earthworm \n\n\n\ncasts were high although earthworms use it in their metabolism (Laverack 1963).\n\n\n\n\n\n\n\nTABLE 5\n\n\n\nThe weight of surface earthworm casts\n\n\n\n\n\n\n\n\n\n\n\n Earthworm casts  \n\n\n\nPalm age Serdang Rengam Jerangau Bungor Munchong \n\n\n\n \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 g \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \n\n\n\n< 7 182.02\u00b138.6 90.11\u00b138.6 174.72\u00b118.6 59.61\u00b115.8 116.75\u00b112.2 \n\n\n\n7  - 14 127.91\u00b18.7 129.95\u00b11.5 95.65\u00b156.6 227.78\u00b16.4 123.62\u00b136.3 \n\n\n\n> 14 186.04\u00b132.9 84.80\u00b118.3 389.39\u00b17.1 273.07\u00b123 223.17\u00b131.8 \n\n\n\nMean and standard error of unequal replicates \n\n\n\nD.T. Sabrina, M. M. Hanafi, A.A. Nor Azwady & T. M. M. Mahmud\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 39\n\n\n\nTotal Mg and exchangeable Mg in the cast were significantly (p \u2264 0.05) correlated \n\n\n\nwith the content of total Mg and exchangeable Mg in the soil.  The concentration \n\n\n\nof exchangeable Ca, Mg and K are usually significantly greater in earthworm casts \n\n\n\nthan in undigested soil (Tiwari et al. 1992).  The effects of soil Mg on earthworms \n\n\n\nis unknown; however, the increasing concentrations of total Mg and exchangeable \n\n\n\nMg in the casts were probably caused by the modification of pH and P content in the \n\n\n\ncast.\n\n\n\n The clay content in the casts was greater than in soil, and the sand and silt \n\n\n\ncontents were lower than in the soil (Table 6).  Soil particles in the earthworm gizzard \n\n\n\nare ground up, thus increasing the clay content.\n\n\n\nTABLE 6\n\n\n\nSoil and earthworm cast particles components\n\n\n\nCONCLUSIONS\nThe CEC and exchangeable Ca were the most important factors influencing earthworm \n\n\n\npopulations in soils of oil palm plantations. Most of the chemical and physical \n\n\n\nproperties of earthworm casts under oil palm plantations were closely related to those \n\n\n\nof the soil. Overall, nutrient avaialability was higher in casts than in the surrounding \n\n\n\nsoils. The content of available P in earthworm cast was five-fold higher compared to \n\n\n\nthat of the soil.\n\n\n\nREFERENCES\nAnonymous.  2005.  Malaysian Oil Palm Statistics, 25th edition.  Malaysian Palm \n\n\n\n Oil Board.  Ministry of Plantation Industries and Communities.\n\n\n\nBasker, A., A.N. Macgregor, and J.H. Kirkman.  1992.  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Rep. 80:102.\n\n\n\nEarthworms in Oil Palm Ecosystem\n\n\n\n \nSample Clay Sand Silt \n\n\n\n \uf8e7\uf8e7\uf8e7\uf8e7 % \uf8e7\uf8e7\uf8e7\uf8e7 \n\n\n\nEarthworm cast \n\n\n\nSurface soil \n\n\n\n30.6 \n\n\n\n28.3 \n\n\n\n15 \n\n\n\n18 \n\n\n\n52.1 \n\n\n\n53.4 \n\n\n\nT test , Pr>t \n\n\n\nStandard error, cast \n\n\n\nStandard error, soil \n\n\n\n0.3558 \n\n\n\n2.51 \n\n\n\n2.20 \n\n\n\n0.1145 \n\n\n\n2.89 \n\n\n\n2.93 \n\n\n\n0.7161 \n\n\n\n1.33 \n\n\n\n2.15 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200940\n\n\n\nBouch\u00e9, M. B.  (1997)  Strat\u00e9gies lombriciennes.  In:  Soil Organisms as \n\n\n\n Components of Ecosystems (eds. U. Lohm, and T. Persson).  Ecol. Bul. \n\n\n\n (Stockholm) 25: 32-122.\n\n\n\nBray, R.H. and L.T. Kurtz.  1945.  Determination of total, organic, and available \n\n\n\n forms of phosphorus in soils.  Soil Sci. 59: 39-45.\n\n\n\nBremner, J.M.  1960.  Determination of nitrogen in soil by the Kjeldahl method.  J.\n\n\n\n Agric. 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Biology and Ecology of Earthworms.  \n\n\n\n Chapman and Hall, 426 pp.\n\n\n\nFragoso, C., and P. Lavelle.  1992.  Earthworm communities of tropical rain forests.\n\n\n\n Soil Biol. Biochem. 24: 1397-1408.\n\n\n\nD.T. Sabrina, M. M. Hanafi, A.A. Nor Azwady & T. M. M. Mahmud\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 41\n\n\n\nFragoso, C., J. Kanyonyo,  A. Moreno,  B.E. Senapati, E. Blanchart, and C. \n\n\n\n Rodriguez.  1999a.  A survey of tropical earthworms: taxonomy, \n\n\n\n biogeography and environmental plasticity.  In: Earthworms Management \n\n\n\n in Tropical Agroecosystems (eds. P. Lavelle, L. Brussaard, and P. \n\n\n\n Hendrix), pp. 1-26. CAB International Publishing.\n\n\n\nFragoso, C., P. Lavelle, E. Blanchart, B.K. Senapati, J.J. Jimenez, M. Martinez, \n\n\n\n T. Deca\u0451ns, and J. Tondoh.  1999b.  Earthworm communities of tropical \n\n\n\n agroecosystems: origin, structure and influence of management practices.  \n\n\n\n In: Earthworms Management in Tropical Agroecosystems (eds. P. Lavelle, \n\n\n\n L. Brussard, and P. Hendrix), pp. 27-55. CAB International Publishing. \n\n\n\nHaynes, R.J., C.S. Dominy, and M.H. Graham.  2003.  Effect of agricultural land \n\n\n\n use on soil organic matter and the composition of earthworm communities \n\n\n\n in KwaZulu-Natal, South Africa.  Agric. Ecosyst. Environ. 95: 453-464.\n\n\n\nHenson, I. E.  2003.  The Malaysian National average oil palm: concept & \n\n\n\n evaluation.  Oil Palm Bul. 46: 15-77.\n\n\n\nJohnson, D.L.  1997.  Earthworms casts reflect soil conditions.  Agric. Res. 45:19.\n\n\n\nJim\u00e9nez, J.J., A. Cepeda, T. Deca\u00ebns, A. Oberson, and D.K. Friesen.  2003.  \n\n\n\n Phosphorus fractions and dynamics in surface earthworm casts under \n\n\n\n native and improved grasslands in a Colombian savanna Oxisol.  Soil \n\n\n\n Biol. Biochem. 35: 715-727.\n\n\n\nLaverack, M. S.  1963.  The Physiology of Earthworms.  Pergamon Press, London.\n\n\n\nLavelle, P. and A. Martin.  1992.  Small-scale and large-scale effects of endogeic \n\n\n\n earthworms on soil organic matter dynamics in soils of the humid tropics.  \n\n\n\n Soil Biol. Biochem. 24: 1491-1498. \n\n\n\nLowe, C. N. and K.R. Butt.  2002.  Influence of organic matter on earthworm \n\n\n\n production and behaviour: a laboratory-based approach with applications \n\n\n\n for soil restoration.  Eur. J. Soil Biol. 38: 173-176.\n\n\n\nMehlich, A.  1953 . Determination of P, Ca, Mg, K, Na, and NH4 (Mineo).  North\n\n\n\n  Carolina Soil testing Division, Releigh, NC.\n\n\n\nOrtiz-Ceballos, A.I. and C. Fragoso.  2004.  Earthworm populations under tropical \n\n\n\n maize cultivation: the effect of mulching with velvebean.  Biol. Fertil. \n\n\n\n Soils 39: 438-445.\n\n\n\nSAS Institute Inc.  1999.  SAS@ User\u2019s Guide: Statistic: SAS Institute Inc., Cary, NC.\n\n\n\nSchmidt, O., R.O. Clements, and G. Donaldson.  2003.  Why do cereal-legume \n\n\n\n intercrops support large earthworm populations.  Appl. Soil Ecol. 22:181-\n\n\n\n 190.\n\n\n\nEarthworms in Oil Palm Ecosystem\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200942\n\n\n\nStovold, R.J., W.R. Whalley, P.J. Harris, and R.P. White.  2004.  Spatial variation \n\n\n\n in soil compaction, and the burrowing activity of the earthworm \n\n\n\n Aporrectodea caliginosa.  Biol. Fertil. Soils 39: 360-366.\n\n\n\nSyers, J.K. and J.A. Springett.  1984.  Earthworms and soil fertility.  Plant Soil 76:\n\n\n\n  93-104. \n\n\n\nTiwari, S.C., B.K. Tiwari, and R.R. Mishra.  1989.  Microbial populations, enzyme\n\n\n\n activities and nitrogen-phosphorus-potassium enrichment in earthworm \n\n\n\n casts an in the surrounding soil of a pineapple plantation.  Biol. Fertil. \n\n\n\n Soils 8: 178-182.\n\n\n\nVillenave, C., F. Charpentier, P. Lavelle, C. Feller, L. Brussard, B. Pashanas, \n\n\n\n I. Barois, A. Albrecht, and J.C. Patr\u00f3n.  1999.  Effects of earthworms on \n\n\n\n soil organic matter and nutrient dynamics following earthworm \n\n\n\n inoculation in field experiment situations.  In P. Lavelle, L. Brussard and \n\n\n\n P.Hendrix (Eds.) Earthworms Management in Tropical Agroecosystems. \n\n\n\n pp. 173-198. CAB International Publishing.\n\n\n\nWalkley, A. and C.A. Black.  1934.  An examination of the Degtjareff method for \n\n\n\n determining soil organic matter and a proposed modification of the \n\n\n\n chromic acid titration method.  Soil Sci. 37: 29-38.\n\n\n\nZou, X.  1993.  Species effects on earthworm density in tropical tree plantations in\n\n\n\n Hawaii.  Biol. Fertil. Soils 15: 35-38.\n\n\n\n\n\n\n\nD.T. Sabrina, M. M. Hanafi, A.A. Nor Azwady & T. M. M. Mahmud\n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\nINTRODUCTION\n\n\n\nFertilizer management is a major consideration in agricultural production. \n\n\n\nInadequate fertilizer application limits crop yield, results in nutrient mining and \n\n\n\ncauses soil fertility depletion. An excessive or imbalanced application not only \n\n\n\nwastes a limited resource, but also pollutes the environment. With consideration \n\n\n\nof both economic optimization and environmental concerns, farmers are forced to \n\n\n\nface with an ever-increasing demand for effective soil fertility management. An \n\n\n\napproach towards justifying such concerns is site specific nutrient management \n\n\n\n\u2013 which takes into account spatial variations in nutrients status cutting down the \n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 14: 27-39 (2010)                        Malaysian Society of Soil Science\n\n\n\nSpatial Variability of Selected Chemical Characteristics of \n\n\n\nPaddy Soils in Sawah Sempadan, Selangor, Malaysia\n\n\n\nA.W. Aishah, S. Zauyah*, A.R. Anuar & C.I. Fauziah\n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia,43400 UPM, Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nA study was conducted to evaluate the spatial variability of selected soil chemical \n\n\n\nproperties of paddy soils in the Barat Laut Paddy Project area in Selangor. A \n\n\n\ntotal of 138 geo-referenced soil samples were collected from the area at 0-20 cm \n\n\n\ndepth after harvest, at an interval of 80-90 m to determine the selected chemical \n\n\n\nproperties: pH, organic carbon, total nitrogen (N), available phosphorus (P) and \n\n\n\nexchangeable potassium (K). Geostatistical analyses were applied to examine the \n\n\n\nwithin-field spatial variability using semivariograms and kriged maps. Kriged \n\n\n\nmaps for each property were prepared using geostatistical software package based \n\n\n\non the results of spatial dependence. The effective ranges for the areas were about \n\n\n\n6 km for pH, 1 km for organic carbon, 8 km for total N and available P and 9 \n\n\n\nkm for exchangeable K, respectively. Kriged maps produced showed that most \n\n\n\nof the area have pH values within the range of 4-4.5 (moderately acidic) and high \n\n\n\namount of organic carbon content (3-5%). The kriged maps also showed that a \n\n\n\nlarge portion of the study area (66%) have high total N (0.30-0.40%), with low \n\n\n\namount of  available P (< 40 mg kg-1) covering 70% of the total study area, while \n\n\n\nmost of the area have optimum content of exchangeable K (> 0.10 cmol(+) kg-1). \n\n\n\nThese results suggest the need for a site specific approach in managing paddy soils \n\n\n\nparticularly with regard to nutrient management. The results also suggested that \n\n\n\nfuture soil sampling in these area can be carried out by increasing the sampling \n\n\n\ninterval depending on the soil properties, and appropriate management should be \n\n\n\napplied according to the variations which exist. \n\n\n\nKeywords: Spatial variability, Geostatistics, Paddy soil, Chemical properties\n\n\n\n___________________\n\n\n\n*Corresponding author : E-mail: zauyah@agri.upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201028\n\n\n\npossibility of over or under use of fertilizer. There have been growing interests \n\n\n\nin the study of spatial variation of soil characteristics using geostatistics since \n\n\n\n1970s, as geostatistics were well developed and successful in characterizing the \n\n\n\nspatial variations of heavy metals (Steiger et al. 1996; White et al. 1997; Yu et \n\n\n\nal. 2001; Romic and Romic 2003), micronutrients (Webster and Oliver 2001; Liu \n\n\n\net al. 2004)  and other soil characteristics (Yost et al. 1982; Yanai et al. 2001; \n\n\n\nCorwin et al. 2003; Mueller et al. 2003; Gilbert and Wayne 2008; Liu et al. 2008). \n\n\n\nRelatively few studies have thoroughly investigated the spatial variability of soil \n\n\n\nchemical characteristics in paddy field on a large scale (Yanai et al. 2000, 2001, \n\n\n\n2002; Chen et al. 2002; Liu et al. 2008) and little information is available on the \n\n\n\nsoil-related crop yield potential for monsoon Asia (Yanai et al. 2002).  It was \n\n\n\nshown that the soil status of the major nutrients as well as organic matter and clay \n\n\n\ncontents is spatially variable within a single small sized paddy field (Moritsuka et \n\n\n\nal. 2004). This author also found a spatial auto-correlation for most investigated \n\n\n\nsoil parameters and concluded that a site-specific soil management would increase \n\n\n\nnutrient efficiency and crop productivity. The objectives for this study were (1) to \n\n\n\ndetermine the spatial dependency of the measured characteristics; and (2) to map \n\n\n\nthe spatial distribution of each characteristic.\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy area\n\n\n\nSawah Sempadan is located in the Barat Laut Paddy Project within the north of \n\n\n\nSelangor state which is in the southeast part of Malaysia (Fig. 1). The paddy soil \n\n\n\nis classified as Sedu Series (Typic Sulfaquepts) which is developed over brackish \n\n\n\nwater deposits (Paramananthan, 2000). The selected area comprised of plots \n\n\n\nbelonging to 54 farmers with a total hectarage of 70 ha with an average plot size \n\n\n\nof 1.2 ha or less. Fertilizer applications are limited to the farmers\u2019 perception, or at \n\n\n\nbest, based on general recommendations provided by agricultural agencies. Global \n\n\n\nPositioning System (GPS) coordinates for the area is 3.730467oN, 101.029567oE \n\n\n\n(Fig. 1). A digital map for the area was constructed using DGPS Trimble Pro \n\n\n\nXR and GIS software. The GPS was used to record geographic coordinates of \n\n\n\neach corner of the plots and fence line during a site walk-through. The area map \n\n\n\nexcluded housing area and other areas which were not cultivated with paddy. \n\n\n\nEvery irrigation and drainage canals in the plot was also mapped. Sampling \n\n\n\nlocations were later on-screen digitized on the area map with an average sampling \n\n\n\ndistance of 80-90 m, to make sure that each sampling location are well distributed \n\n\n\n(Fig. 2). \n\n\n\nSoil sampling and analysis\n\n\n\nGeo-referenced soil samples were taken from 138 locations within Sawah \n\n\n\nSempadan area after harvesting season, prior to the area being burned to avoid \n\n\n\nerrors from accumulation of ashes or effects of burning. The practice of burning is \n\n\n\nto prevent pest or disease outbreak from soil-borne pathogens. The soils has been \n\n\n\nA.W. Aishah, S. Zauyah, A.R. Anuar & C.I. Fauziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 29\n\n\n\nFig. 1: Location of study area\n\n\n\nFig. 2: Location of sampling points in paddy plots\n\n\n\nSpatial Variability of Chemical Characteristics of Paddy Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201030\n\n\n\ndrained, remain wet but non-saturated with 0-3 cm of the soils surface already \n\n\n\nbeginning to dry. Another 30 geo-referenced soil samples were also randomly \n\n\n\ncollected from the same area for data validation test. All the soil samples were \n\n\n\ntaken at a depth of 0-20 cm with the soil surface cleared of coarse rice straw \n\n\n\ndebris. Soil samples were air-dried, thoroughly mixed and ground to pass a 2 mm \n\n\n\nsieve, then stored in plastic containers prior to the analysis of soil pH, organic \n\n\n\ncarbon (OC), total nitrogen (TN), available phosphorus (AP) and exchangeable \n\n\n\npotassium (EK). Soil pH was measured in a 1:2.5 (w/v) ratio of soil to water. \n\n\n\nOrganic carbon was determined using the Walkley and Black method (Nelson \n\n\n\nand Sommers, 1982) and total N in the samples was determined by the Kjeldahl \n\n\n\ndigestion method (Bremner and Mulvaney 1982). Available P was extracted using \n\n\n\nthe Bray and Kurtz No. II extractant and determined using the Quickchem, FIA \n\n\n\n8000 auto-analyzer (Lachat Instruments, Milwaukee, WI, USA). Exchangeable \n\n\n\nK was extracted with 1 M NH\n4\nOAc, pH 7.0 using the leaching method and \n\n\n\ndetermined using the Perkin Elemer 5010 atomic absorption spectrophotometer. \n\n\n\nClassical statistics\n\n\n\nData that were not normally distributed were logarithmically transformed in this \n\n\n\nstudy. It is shown in Table 1 that the data sets for all soil properties were normally \n\n\n\ndistributed. Descriptive statistics, including the mean, range, standard error (SE), \n\n\n\nskewness and coefficient of variation (CV), were determined for each set of data. \n\n\n\nPearson correlation coefficients were calculated to determine the relationship \n\n\n\nbetween soil properties. \n\n\n\nGeostatistical analysis\n\n\n\nGeostatistics is based on the theory of a regionalized variables (Matheron, \n\n\n\n1963), which is distributed in space (with spatial coordinates) and shows \n\n\n\nspatial autocorrelation. Semivariogram were developed in this study to evaluate \n\n\n\nthe degree of spatial continuity of each soil property. Information generated \n\n\n\nthrough variogram was used to calculate sample weighted factors for spatial \n\n\n\ninterpolation by a Kriging procedure using the nearest 16 sample points and a \n\n\n\nmaximum searching distance equal to the range distance of the variables (Isaacs \n\n\n\nand Srivastava 1989; Lark and Ferguson 2004). Kriging is a linear interpolation \n\n\n\nprocedure that provides a best linear unbiased estimation for quantities, which \n\n\n\nvary in space. Kriging estimates are calculated as weighted sums of the adjacent \n\n\n\nsampled concentrations that is, if the data appeared to be highly continuous in \n\n\n\nspace, then the points closer to those estimated received higher weight age than \n\n\n\nthose further away (Cressie 1990).\n\n\n\nSemivariogram, y(h), is computed as half the average squared difference \n\n\n\nbetween the components of data pairs (Burgess and Webster, 1980; Wang  1999; \n\n\n\nGoovaerts 1999), and is expressed using the following formula: \n\n\n\n\n\n\n\n( ) [ ]\n2)(\n\n\n\n1\n\n\n\n)()(\n)(2\n\n\n\n1\n\u2211\n=\n\n\n\n+\u0396\u2212\u0396=\nhN\n\n\n\ni\n\n\n\nii\nhxx\n\n\n\nhN\nh\u03b3  \n\n\n\nA.W. Aishah, S. Zauyah, A.R. Anuar & C.I. Fauziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 31\n\n\n\nwhere N(h) is the total number of data pairs separated by a distance h; Z represents \n\n\n\nthe measured value for soil property at the location of x. Several standard models \n\n\n\nare available to fit the experimental semivariogram, e.g., spherical, exponential, \n\n\n\nGaussian, linear and power models (Oliver 1987; Isaaks and Srivastava 1989; \n\n\n\nWang 1999). Using the fitted models, spatial interpolation was accomplished with \n\n\n\na point kriging approach. \n\n\n\n The levels for each variable on the spatial maps were set based on the \n\n\n\nstandard range for paddy soil, recommended by Malaysian Agriculture Research \n\n\n\nand Development Institute (MARDI) in 2000. Cross-validation of kriged values \n\n\n\nwere carried out based on the criteria proposed by Delhomme (1978) and Dowd \n\n\n\n(1984) and as explained in detail by Balasundram et al. (2008).  Kriging was \n\n\n\ncarried out using geostatistical software package to map the spatial patterns of \n\n\n\neach soil property. The validation test to evaluate the quality of produced maps \n\n\n\nwas also conducted by comparing data values of each element from the 30 extra \n\n\n\ngeo-referenced soils samples collected with the kriged values. The kriged values \n\n\n\ndeviates from the samples\u2019 original values by only 1.76%, 3.41%, 1.9%, 4.12% \n\n\n\nand 2.23% for pH, organic carbon, total nitrogen, available P and exchangeable \n\n\n\nK, respectively. \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nDescriptive statistics\n\n\n\nDescriptive statistics results for each soil characteristic for 138 samples are \n\n\n\npresented in Table 1. These results indicated that all characteristics were normally \n\n\n\ndistributed and showed wide variations. Except for pH and organic carbon, \n\n\n\nall other variables have CV values greater than 4%, the highest being 31% in \n\n\n\nthe case of total N, suggesting that they had greater variation in the soils. This \n\n\n\nvariation of soil chemical properties might be due to errors in measurements, soil \n\n\n\nproperties, paddy variety, tillage practices, soil mineralogy, clay content, pesticide \n\n\n\napplications and moisture availability (Anuar et al. 2001).\n\n\n\nIn comparison to the optimum values of chemical characteristics for paddy \n\n\n\nrequirement, as recommended by MARDI (Table 2), it is shown that the mean \n\n\n\npH for the area is lower than the optimum range (5.5-6.5), while the mean \n\n\n\nconcentration of organic carbon have already exceeded the optimum level (2-3%). \n\n\n\nThe mean concentration of total nitrogen, available P and exchangeable K are \n\n\n\nwithin the optimum level for total nitrogen (0.2-0.3%), available P (> 40 mg kg-1) \n\n\n\nand exchangeable K (> 0.1 cmol(+) kg-1). \n\n\n\nGeostatistical analysis results\n\n\n\nThe semivariograms and fitted models for each soil characteristic are presented \n\n\n\nin Fig. 3.  The attributes of the semivariograms for each soil characteristic are \n\n\n\nsummarized in Table 3. Nugget variance represents the experimental error and \n\n\n\nfield variation within the minimum sampling spacing. \n\n\n\nSpatial Variability of Chemical Characteristics of Paddy Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201032\n\n\n\nTABLE 1\n\n\n\nDescriptive statistics of soils pH, organic carbon (OC), total Nitrogen (TN), \n\n\n\navailable P (AP) and exchangeable K (EK)\n\n\n\nTABLE 2\n\n\n\nOptimum soils chemicals properties values for paddy requirement\n\n\n\nSpatial class ratios (Nugget/Sill ratio) similar to those presented by \n\n\n\nCambardella et al. (1994) were adopted to define distinctive classes of spatial \n\n\n\ndependence. A variable is considered to have a strong spatial dependency if the\n\n\n\nratio is less than 25%, moderate spatial dependency if the ratio is between 25-75% \n\n\n\nand weak spatial dependency if the Nug/Sill ratio is greater than 75%.  In addition, \n\n\n\nspatial dependence is defined as weak if the best-fit semivariogram model has an \n\n\n\nR2 < 0.5 (Duffera et al. 2007). Cambardella et al. (1994) also reported that strong \n\n\n\nspatial dependency of soil characteristics can be attributed to intrinsic factors (soil \n\n\n\nformation factors, such as parent materials), and weak spatial dependency can be \n\n\n\nattributed to extrinsic factors (soil management practices, such as fertilization). \n\n\n\nA.W. Aishah, S. Zauyah, A.R. Anuar & C.I. Fauziah\n\n\n\nCharacteristic  Sample size  Mean  Skewness  SE  CV (%)  \n\n\n\npH  138  4.70  0.051  0.014  4  \n\n\n\nOC (%)  138  4.06  2.587  0.003  1  \n\n\n\nTN (%)  138  0.43  1.54  0.010  31  \n\n\n\nAP (mg kg -1\n) 138  43.18  0.487  0 .009  29  \n\n\n\nEK (cmol(+) kg 138  0.25  -0.69  0.004  24  \n\n\n\n     SE standard error, CV coefficient of variation\n\n\n\n-1\n)\n\n\n\n1. pH  5.5 \u2013  6.0  \n\n\n\n2. Organic Carbon (%) 2  \u2013  3  \n\n\n\n3. Total Nitrogen (%) 0.2 \u2013  0.3\n\n\n\n4. Available P (mg kg > 40  \n\n\n\n5. Exchangeable K (cmol + kg > 0.1  \n\n\n\n            (Source: MARDI, 2000)\n\n\n\n-1\n)\n\n\n\n-1\n)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 33\n\n\n\nFig. 3: The semivariograms of soil pH, organic carbon (OC), total N (TN), available P \n\n\n\n(AP) and exchangeable K (EK) at Sawah Sempadan\n\n\n\nThe semivariograms for soil pH, total N, available P and exchangeable K \n\n\n\nwere all fitted to exponential model and their Nugget/Sill ratios were 50, 58, 50 \n\n\n\nand 50%, respectively, indicating the existence of moderate spatial dependency.  \n\n\n\nThese suggest that the extrinsic factors such as fertilization, plowing and other \n\n\n\nsoil management practices weakened their spatial correlation after a long history \n\n\n\nof cultivation. Soil pH, total nitrogen (TN), available P and exchangeable K (EK) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpatial Variability of  Chemical Characteristics of Paddy Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201034\n\n\n\nall had long effective spatial correlation range. The spatial ranges of soil pH \n\n\n\nand total nitrogen in Sawah Sempadan were 6320 m and 8390 m, respectively. \n\n\n\nFor available P and exchangeable K, its effective spatial correlation distances \n\n\n\nwere 8200 m and 9330 m, respectively.  This result indicates a rational sampling \n\n\n\ndistance for soil pH, total nitrogen, available P and exchangeable K within their \n\n\n\neffective spatial correlation ranges in Sawah Sempadan. \n\n\n\nHowever, soil organic carbon did not show a scale of dependency, which \n\n\n\nindicates that soil management practices greatly affected soil organic carbon \n\n\n\nand reduced spatial dependency at the sampling intervals. This indicates that \n\n\n\n138 samples are not sufficient to describe their true characteristics. Therefore, it \n\n\n\nwas reasonable to predict the spatial distribution of all soil characteristics with \n\n\n\nthe exception of organic carbon at Sawah Sempadan due to their high spatial \n\n\n\ndependency. In other words, the results also showed that with the exception of \n\n\n\nsoil organic carbon, the range of exponential models which exceeded 80-90 m \n\n\n\nindicated the presence of spatial structure beyond the original average sampling \n\n\n\ndistance. When cross-validated, data from each soil property measured showed \n\n\n\nacceptable accuracy.\n\n\n\nSpatial Distributions\n\n\n\nThe main application of geostatistics to soil science has been the estimation \n\n\n\nand mapping of soil attributes in unsampled areas. Fig. 4 presents the spatial \n\n\n\ndistributions of each soil characteristic in Sawah Sempadan area generated from \n\n\n\ntheir semivarograms. The prediction maps of soil pH, organic carbon, total \n\n\n\nnitrogen, available P and exchangeable K were generated using ordinary Kriging \n\n\n\nmethods with original values of soil pH, organic carbon, total nitrogen, available \n\n\n\nP and exchangeable K.\n\n\n\nA.W. Aishah, S. Zauyah, A.R. Anuar & C.I. Fauziah\n\n\n\nTABLE 3\n\n\n\nBest-fitted semivariogram models for soil pH, organic carbon (OC), total Nitrogen\n\n\n\n (TN), available P (AP) and exchangeable K (EK) and their parameters\n\n\n\nCharacteristic Model Nugget \n\n\n\nCo \n\n\n\nSill \n\n\n\nCo+C \n\n\n\nNug/Sill \n\n\n\nratio (%) \n\n\n\nSpatial \n\n\n\nClasses \n\n\n\nEffective \n\n\n\nRange \n\n\n\n(m) \n\n\n\nR\n2\n \n\n\n\npH  Exponential 0.026 0.052 0.50 M 6327 0.39 \n\n\n\nOC (%)  Linear 0.002 0.002 1 - 1137 0.42 \n\n\n\nTN (%)  Exponential 0.014 0.033 0.58 M 8394 0.95 \n\n\n\nAP (mg kg\n-1\n\n\n\n) Exponential 132.4 264.9 0.50 M 8205 0.47 \n\n\n\nEK (cmol+ kg\n-1\n\n\n\n) Exponential 0.003 0.006 0.50 M 9330 0.59 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 35\n\n\n\nFig. 4: The distribution maps of soil pH, organic carbon (OC), total N (TN), \n\n\n\navailable P (AP) and exchangeable K (EK) at Sawah Sempadan\n\n\n\nThe correlation between each soil characteristics were analyzed to understand \n\n\n\ntheir effect on each other (Table 4). Organic carbon (r=0.23) were found to be \n\n\n\npositively correlated with total nitrogen and exchangeable K were in positive \n\n\n\ncorrelations with available P (r=0.37).\n\n\n\nThe maps show that 100% of the area has pH values of 4.5-5.0 which is \n\n\n\nconsidered as moderately acid. The optimum pH values as recommended \n\n\n\nby MARDI are within the range of 5.5-6.5. The area has high organic carbon \n\n\n\ncontent within the range of 3-5%. The addition of organic carbon was through the \n\n\n\naddition of organic matter in the form of plant residue (rice straws) and the slow \n\n\n\ndecomposition of organic matter owing to the soil always remaining wet most of \n\n\n\nthe time. Of the total area, 46.5 ha showed very high total nitrogen content while \n\n\n\nthe other 23.5 ha have high total nitrogen content which ranges between 0.3-0.4%. \n\n\n\nHigh content of total nitrogen are also related to the addition of organic matter in \n\n\n\nthe form of plant residues. \n\n\n\nSeventy percent (49.01 ha) of the total area shows available P values lower \n\n\n\nthan 40 mg kg-1. The other 20.99 ha shows available P values higher than 40 mg \n\n\n\nkg-1 which is considered as optimum based on the recommendation by MARDI. \n\n\n\nThe whole study area shows optimum conditions for exchangeable K (> 0.1 \n\n\n\nSpatial Variability of  Chemical Characteristics of Paddy Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 201036\n\n\n\ncmol(+) kg-1). The variability among all these soil characteristics might probably \n\n\n\nbe due to the difference between various soil management practices by farmers.  \n\n\n\nTABLE 4\n\n\n\nPearson correlation coefficients between soil characteristics\n\n\n\nFrom the maps of soil characteristics, information about their spatial \n\n\n\ndistribution over long distances could be clearly achieved. The rice fields in \n\n\n\nSawah Sempadan may be classified into soil groups according to similar soil \n\n\n\nnutrients concentrations (N, P and K). Appropriate fertilization is recommended \n\n\n\nfor different groups, which will make the soil management more scientific. The \n\n\n\nresults of this study can also be used for soil survey and evaluation.\n\n\n\nIt is also found that the sampling interval could be increased in future studies \n\n\n\ndepending on the soil characteristics. Based on the variability existed, it is \n\n\n\nstrongly recommended that site specific nutrient management should be carried \n\n\n\nout in Sawah Sempadan area with more emphasis on P nutrient and increasing pH \n\n\n\nvalues to the optimum level of pH required for paddy production which is between \n\n\n\npH 5.5 to 6.0. As suggested, the variability of each soil characteristic existed due \n\n\n\nto the differences in management practices by farmers. Therefore, the fertilization \n\n\n\nplan of an individual farmer should take into account this variability to optimize \n\n\n\nnutrient application rates for better yield and economics of crop production. \n\n\n\nCONCLUSIONS\n\n\n\nThis study reveals major variability in terms of soil nutrients status in the 70 ha \n\n\n\narea in Sawah Sempadan. Spatial maps produced showed that the area in Sawah \n\n\n\nSempadan has high organic carbon content (3-5%), with total nitrogen value \n\n\n\nCharacteristic pH OC TN AP EK \n\n\n\npH      \n\n\n\nOC 0.004ns     \n\n\n\nTN 0.025ns 0.216*    \n\n\n\nAP 0.086ns 0.049ns 0.037ns   \n\n\n\nEK -0.023ns -0.075ns 0.049ns 0.175*  \n\n\n\n        * P<0.05  \n\n\n\nA.W. Aishah, S. Zauyah, A.R. Anuar & C.I. Fauziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 14, 2010 37\n\n\n\nalready exceeded the optimum values required for paddy (2-3%) and optimum \n\n\n\nconditions for exchangeable K (> 0.1 cmol(+) kg-1). However, pH ranges for the \n\n\n\nwhole area is still below the optimum pH ranges (5.50 \u2013 6.50) required, and 70% \n\n\n\nof the area still has available P values lower than the optimum value (< 40 mg \n\n\n\nkg-1). \n\n\n\nThe results suggested that pH, total N, available P and exchangeable K had \n\n\n\nmoderate spatial dependence over a long distance; suggesting that extrinsic factors \n\n\n\nsuch as fertilization, plowing and other soil management practices weakened their \n\n\n\nspatial correlation after a long history of cultivation. The spatial ranges for pH, \n\n\n\ntotal N, available P and exchangeable K were about 6, 8, 8 and 9 km, respectively. \n\n\n\nHowever, the semivariogram for organic carbon did not show any scale of \n\n\n\ndependency which could be due to fertilization practices. This indicates that more \n\n\n\nsamples should be taken at smaller sampling intervals in the area to determine the \n\n\n\nspatial dependency for heterogeneous data. 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"\n\n\uf020 \uf020 \uf020 \uf020 \uf020 \uf020 \uf020 \uf0d7\uf0cd\uf0cd\uf0d2\uf0e6\uf020\uf0ef\uf0ed\uf0e7\uf0ec\uf0f3\uf0e9\uf0e7\uf0f0\uf0f0\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\uf020\uf0e6\uf020\uf0ef\uf0f3\uf0ef\uf0e8\uf020\uf020\uf0f8\uf0ee\uf0f0\uf0f0\uf0e8\uf0f7\uf020 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\uf0bd\uf0b1\uf0b2\uf0ad\uf0b7\uf0ad\uf0ac\uf0ad\uf020\n\n\n\n\uf0ac\uf0ab\uf0ba\uf0ba\uf0bf\uf0bd\uf0bb\uf0b1\uf0ab\uf0ad\uf020\uf0ad\uf0b8\uf0bf\uf0b4\uf0bb\uf020\uf0f8\uf0cd\uf0bf\uf0b2\uf0ab\uf0bc\uf0b7\uf0b2\uf020\uf0ef\uf0e7\uf0e8\uf0e7\uf0f7\uf0f2\uf020\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0ad\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b9\uf0ae\uf0bf\uf0b0\uf0b8\uf0a7\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0cc\uf0bf\uf0be\uf0b7\uf0b2\uf020\uf0bf\uf0ae\uf0bb\uf0bf\uf020\uf0b4\uf0b1\uf0bd\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\n\n\n\n\uf0da\uf0b7\uf0b9\uf0f2\uf020\uf0ee\uf0f2\uf020\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0d3\uf0bb\uf0b4\uf0bf\uf0b2\uf0b9\uf0bb\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0d4\uf0b7\uf0be\uf0b1\uf0b2\uf0b9\uf020\uf0d3\uf0bb\uf0b3\uf0be\uf0bb\uf0ae\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0bb\uf020\n\n\n\n\uf0cc\uf0bf\uf0be\uf0b7\uf0b2\uf020\uf0ca\uf0b7\uf0ae\uf0b9\uf0b7\uf0b2\uf020\uf0d6\uf0ab\uf0b2\uf0b9\uf0b4\uf0bb\uf020\uf0ce\uf0bb\uf0ad\uf0bb\uf0ae\uf0aa\uf0bb\uf020\uf0a9\uf0b8\uf0bb\uf0ae\uf0bb\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0d4\uf0b7\uf0b0\uf0bf\uf0bc\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0cc\uf0bf\uf0be\uf0b7\uf0b2\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf0bb\uf0ad\uf020\uf0bf\uf0ae\uf0bb\uf020\uf0b4\uf0b1\uf0bd\uf0bf\uf0ac\uf0bb\uf0bc\uf020\n\n\n\n\uf0f8\uf0cd\uf0bf\uf0b2\uf0ab\uf0bc\uf0b7\uf0b2\uf020\uf0ef\uf0e7\uf0e8\uf0e7\uf0f7\uf0f2\n\n\n\n\uf020\n \n\n\n\nFig. 1: Location of Tabin Wildlife Reserve (Tabin)  \n\uf020\n\n\n\n\n\n\n\n\n\uf0ed\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0ef\uf0e7\uf0e8\uf0e7\uf0f7\uf0f2\uf020\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0a9\uf0bf\uf0ac\uf0bb\uf0ae\uf020\uf0b9\uf0bb\uf0b2\uf0bb\uf0ae\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0ba\uf0ae\uf0b1\uf0b3\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0bf\uf0bd\uf0ac\uf0b7\uf0aa\uf0b7\uf0ac\uf0a7\uf020\uf0b7\uf0ad\uf020\uf0ad\uf0bf\uf0b4\uf0ac\uf0a7\uf020\uf0a9\uf0b8\uf0bb\uf0ae\uf0bb\uf020\uf0b2\uf0b1\uf020\uf0b0\uf0b4\uf0bf\uf0b2\uf0ac\uf0ad\uf020\n\n\n\n\uf0bd\uf0bf\uf0b2\uf020\uf0b9\uf0ae\uf0b1\uf0a9\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ac\uf0b8\uf0bb\uf0ae\uf0bb\uf0ba\uf0b1\uf0ae\uf0bb\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b7\uf0bd\uf020\uf0bf\uf0ae\uf0bb\uf0bf\uf020\uf0b7\uf0ad\uf020\uf0bf\uf0b4\uf0a9\uf0bf\uf0a7\uf0ad\uf020\uf0be\uf0bf\uf0ae\uf0ae\uf0bb\uf0b2\uf020\uf0f8\uf0d8\uf0bf\uf0b7\uf0b4\uf0bb\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0c9\uf0b1\uf0b2\uf0b9\uf020\n\n\n\n\uf0d4\uf0b7\uf0b0\uf0bf\uf0bc\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf020\uf0f8\uf0d4\uf0d3\uf0ca\uf0f7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0cc\uf0bf\uf0be\uf0b7\uf0b2\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf020\uf0f8\uf0cc\uf0d3\uf0ca\uf0f7\uf020\uf0bf\uf0ae\uf0bb\uf020\uf0ad\uf0b7\uf0ac\uf0ab\uf0bf\uf0ac\uf0bb\uf0bc\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf0b7\uf0b2\uf020\n\n\n\n\uf0da\uf0b7\uf0b9\uf0f2\uf020\uf0ee\uf0f7\uf0f2\uf020 \uf020\uf0de\uf0b1\uf0ac\uf0b8\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf0bb\uf0ad\uf020\n\uf0b1\n\n\n\n\uf0ef\uf0ef\uf0e8\uf0b1\n\n\n\n\uf0bf\uf0b2\uf020\uf0bf\uf0bd\uf0ac\uf0b7\uf0aa\uf0bb\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf0f2\uf020\uf020\uf0d4\uf0d3\uf0ca\uf020\uf0b8\uf0bf\uf0ad\uf020\uf0b1\uf0b2\uf0bb\uf020\uf0bf\uf0ae\uf0bb\uf0bf\uf020\uf0a9\uf0b8\uf0bb\uf0ae\uf0bb\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b9\uf0bb\uf0b2\uf0ac\uf0b4\uf0bb\uf020\uf0bd\uf0b1\uf0b2\uf0ac\uf0b7\uf0b2\uf0ab\uf0b1\uf0ab\uf0ad\uf020\uf0bf\uf0bd\uf0ac\uf0b7\uf0aa\uf0b7\uf0ac\uf0a7\uf020\n\n\n\n\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf020\uf0b1\uf0bd\uf0bd\uf0ab\uf0ae\uf0ae\uf0bb\uf0bc\uf020\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b3\uf0b7\uf0bc\uf0bc\uf0b4\uf0bb\uf020\uf0b1\uf0ba\uf020\uf0b7\uf0b2\uf0b2\uf0bb\uf0ae\uf020\uf0a6\uf0b1\uf0b2\uf0bb\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf020\uf0bf\uf020\uf0bc\uf0b7\uf0bf\uf0b3\uf0bb\uf0ac\uf0bb\uf0ae\uf020\uf0b1\uf0ba\uf020\uf0ab\uf0b0\uf020\uf0ac\uf0b1\uf020\n\uf0b1 \uf0b1\n\n\n\n\uf0d9\uf0bb\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ae\uf0b7\uf0be\uf0ab\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0db\uf0b4\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf0ad\n\n\n\n\uf020\n\uf020\n\n\n\n\uf020\n\n\n\nFig. 2: Stratigraphy summary of Dent Peninsula, Sabah (Sanudin 1989)\n\n\n\n\n\n\n\n\n\uf0ec 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\uf0bf\uf0b2\uf0bc\uf020 \uf0e8\uf0f7\uf0f2\uf020 \uf020\uf0cc\uf0b8\uf0bb\uf020 \uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020\n\n\n\n\uf0d9\uf0bb\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ae\uf0b7\uf0be\uf0ab\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0db\uf0b4\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf0ad\n\n\n\n\n\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0b7\uf0b2\uf020 \uf0ac\uf0b8\uf0bb\uf020\uf0b1\uf0ab\uf0ac\uf0bb\uf0ae\uf020\uf0a6\uf0b1\uf0b2\uf0bb\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0b3\uf0b1\uf0ae\uf0bb\uf020\uf0bf\uf0b0\uf0b0\uf0bf\uf0ae\uf0bb\uf0b2\uf0ac\uf020 \uf0bf\uf0ac\uf020\uf0cc\uf0d3\uf0ca\uf0f2\uf020 \uf020\uf0de\uf0b1\uf0ac\uf0b8\uf020\uf0bf\uf0ae\uf0bb\uf0bf\uf0ad\uf020 \uf0b8\uf0bf\uf0bc\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0b4\uf0b1\uf0a9\uf0bb\uf0ad\uf0ac\uf020\uf0d3\uf0b9\uf020\n\n\n\n\uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0bb\uf0ad\uf020\uf0ee\uf0f0\uf0f0\uf020\uf0b3\uf020\uf0bf\uf0a9\uf0bf\uf0a7\uf020\uf0ba\uf0ae\uf0b1\uf0b3\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b0\uf0bb\uf0ae\uf0b7\uf0b0\uf0b8\uf0bb\uf0ae\uf0a7\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf0bb\uf0ad\uf020\n\n\n\n\uf0b7\uf0b2\uf020\uf0cc\uf0d3\uf0ca\uf020\uf0f8\uf0da\uf0b7\uf0b9\uf0ad\uf0f2\uf020\uf0e7\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ef\uf0f0\n\n\n\n\uf0cc\uf0b8\uf0bb\uf020\uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0d2\uf0bf\uf020\uf0bf\uf0ac\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b7\uf0b2\uf0b2\uf0bb\uf0ae\uf020\uf0a6\uf0b1\uf0b2\uf0bb\uf020\uf0b1\uf0ba\uf020\uf0d4\uf0b7\uf0b0\uf0bf\uf0bc\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0cc\uf0bf\uf0be\uf0b7\uf0b2\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf0bb\uf0ad\uf020\n\n\n\n\uf0b7\uf0b2\uf020\uf0d2\uf0bf\uf020\uf0bd\uf0b1\uf0b2\uf0bd\uf0bb\uf0b2\uf0ac\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0a9\uf0b7\uf0ac\uf0b8\uf0b7\uf0b2\uf020\uf0ac\uf0bb\uf0b2\uf020\uf0b3\uf0bb\uf0ac\uf0bb\uf0ae\uf0ad\uf020\uf0ba\uf0ae\uf0b1\uf0b3\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b0\uf0bb\uf0ae\uf0b7\uf0b0\uf0b8\uf0bb\uf0ae\uf0a7\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b7\uf0b2\uf0b2\uf0bb\uf0ae\uf020\uf0a6\uf0b1\uf0b2\uf0bb\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0be\uf0b1\uf0ac\uf0b8\uf020\n\n\n\n\uf0b1\uf0ab\uf0ac\uf0bb\uf0ae\uf020\uf0a6\uf0b1\uf0b2\uf0bb\uf0ad\uf0f2\uf020\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0b4\uf0b1\uf0a9\uf0bb\uf0ad\uf0ac\uf020\uf0aa\uf0bf\uf0b4\uf0ab\uf0bb\uf0ad\uf020\uf0a9\uf0bb\uf0ae\uf0bb\uf020\uf0ae\uf0bb\uf0bd\uf0b1\uf0ae\uf0bc\uf0bb\uf0bc\uf020\uf0bf\uf0ac\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ee\uf0f0\uf0f0\uf020\uf0b3\uf020\uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0b7\uf0b2\uf0b9\uf020\uf0b0\uf0b1\uf0b7\uf0b2\uf0ac\uf0ad\uf020\uf0a9\uf0b8\uf0bb\uf0ae\uf0bb\uf020\n\n\n\n\uf0ae\uf0bb\uf0ad\uf0b0\uf0bb\uf0bd\uf0ac\uf0b7\uf0aa\uf0bb\uf0b4\uf0a7\uf0f2\uf020\uf020\n\n\n\n\uf0da\uf0b7\uf0b9\uf0ad\uf0f2\uf020\uf0ef\uf0ef\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ef\uf0ee\uf0f7\uf0f2\n\n\n\n\uf0b1\uf0ab\uf0ac\uf0bb\uf0ae\uf020\uf0a6\uf0b1\uf0b2\uf0bb\uf020\uf0b1\uf0ba\uf020\uf0be\uf0b1\uf0ac\uf0b8\uf020\uf0b3\uf0ab\uf0bc\uf020\uf0aa\uf0b1\uf0b4\uf0bd\uf0bf\uf0b2\uf0b1\uf0bb\uf0ad\uf0f2\uf020\uf0df\uf0ac\uf020\uf0ad\uf0bf\uf0b3\uf0b0\uf0b4\uf0b7\uf0b2\uf0b9\uf020\uf0b0\uf0b1\uf0b7\uf0b2\uf0ac\uf0ad\uf020\uf0ee\uf0f0\uf0f0\uf020\uf0b3\uf020\uf0ba\uf0ae\uf0b1\uf0b3\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b0\uf0bb\uf0ae\uf0b7\uf0b0\uf0b8\uf0bb\uf0ae\uf0a7\uf020\uf0b1\uf0ba\uf020\n\n\n\n\uf0da\uf0b7\uf0b9\uf0ad\uf0f2\uf020\uf0ef\uf0ed\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ef\uf0ec\uf0f7\uf0f2\uf020\uf020\n\n\n\n\uf020\n\n\n\n\n\n\n\n\n\uf0e9\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0d9\uf0bb\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ae\uf0b7\uf0be\uf0ab\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0db\uf0b4\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf0ad\n\n\n\n\uf020\n\n\n\n \nFig. 3:  Distribution of Fe at the north, south, east and west transects of Lipad Mud \n\n\n\nVolcano      \n\n\n\n\uf020\n\n\n\nFig. 4: Distribution of Fe at the north, south, east and west transects of Tabin Mud \nVolcano  \n\n\n\n\n\n\n\n\n\uf0e8 \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\nFig. 5 : Distribution of Mn at the north, south, east and west transects of Lipad Mud \nVolcano \n\n\n\nFig. 6 : Distribution of Mn at the north, south, east and west transects of Tabin Mud \nVolcano \n\n\n\n\n\n\n\n\n\uf0e7\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0d9\uf0bb\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ae\uf0b7\uf0be\uf0ab\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0db\uf0b4\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf0ad\n\n\n\nFig. 7 : Distribution of Mg at the north, south, east and west transects of Lipad Mud \nVolcano \n\n\n\nFig. 8: Distribution of Mg at the north, south, east and west transects of Tabin Mud \nVolcano  \n\n\n\n\n\n\n\n\n\uf0ef\uf0f0 \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\nFig. 9 : Distribution of Na at the north, south, east and west transects of Lipad Mud \nVolcano \n\n\n\nFig. 10 : Distribution of Na at the north, south, east and west transects of Tabin Mud \nVolcano \n\n\n\n\n\n\n\n\n\uf0ef\uf0ef\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0d9\uf0bb\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0b7\uf0bd\uf0bf\uf0b4\uf020\uf0dc\uf0b7\uf0ad\uf0ac\uf0ae\uf0b7\uf0be\uf0ab\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0b1\uf0ba\uf020\uf0db\uf0b4\uf0bb\uf0b3\uf0bb\uf0b2\uf0ac\uf0ad\n\n\n\nFig. 11 : Distribution of Ca at the north, south, east and west transects of Lipad Mud \nVolcano  \n\n\n\n \nFig. 12 : Distribution of Ca at the north, south, east and west transects of Tabin Mud \n\n\n\nVolcano  \n\n\n\n\n\n\n\n\n\uf0ef\uf0ee \uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\nFig. 13 : Distribution of K at the north, south, east and west transects  Lipad Mud \nVolcano \n\n\n\nFig. 14: Distribution of K at the north, south, east and west transects of Tabin Mud \nVolcano 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\uf0d7\uf0b2\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\n\n\n\n\uf0d9\uf0bb\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0b7\uf0ad\uf0ac\uf0ae\uf0a7\n\n\n\n\uf0d0\uf0ae\uf0b7\uf0b2\uf0bd\uf0b7\uf0b0\uf0b4\uf0bb\uf0ad\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0df\uf0b0\uf0b0\uf0b4\uf0b7\uf0bd\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0ad\uf020\uf0b1\uf0ba\uf020\uf0d7\uf0b2\uf0b1\uf0ae\uf0b9\uf0bf\uf0b2\uf0b7\uf0bd\uf020\n\n\n\n\uf0d9\uf0bb\uf0b1\uf0bd\uf0b8\uf0bb\uf0b3\uf0b7\uf0ad\uf0ac\uf0ae\uf0a7\n\n\n\n\uf0cd\uf0bf\uf0be\uf0bf\uf0b8\uf020\uf0cd\uf0b1\uf0bd\uf0b7\uf0bb\uf0ac\uf0a7\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0ed\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : Email: sunny.goh@gmail.com\n\n\n\nISSN: 1394-7990\n\n\n\nMalaysian Journal of Soil Science Vol. 13: 93-104                                 Malaysian Society of Soil Science\n\n\n\nModelling the Spatial and Temporal Change in Diffusion \n\n\n\nRates of Molasses in Sand Medium\n\n\n\nE.G. Goh & I. Athira\n\n\n\n Department of Engineering Science, Faculty of Science and Technology, \n\n\n\nUniversiti Malaysia Terengganu, Mengabang Telipot, \n\n\n\n21030 Kuala Terengganu, Terengganu, Malaysia\n\n\n\nINTRODUCTION\n\n\n\nIn natural environment, there is spatial imbalance in distribution of natural \n\n\n\nsubstances in terms of concentration. However, eventually any differences in \n\n\n\nconcentration at separate locations will naturally be levelled off in time. This \n\n\n\nnatural phenomenon is known as diffusion. Diffusion from the physical point \n\n\n\nof view is basically the result of random movement of molecule. This random \n\n\n\nmovement is a function of temperature, and hence, increasing temperature would \n\n\n\nincrease the speed of random movement. \n\n\n\n Mass flux rate (kg s-1) through a unit area (m2) due to molecular diffusion is \n\n\n\ncorrelated to concentration gradient with a constant D that is known as diffusion \n\n\n\ncoefficient (m2 s-1). This relationship is described by first Fick\u2019s law as follows \n\n\n\n(Demonico and Schwartz, 1990):\n\n\n\nABSTRACT\nDiffusion is one of the important parameters in groundwater study. In a relatively \n\n\n\nslow moving groundwater, diffusion could be a dominant factor in transporting \n\n\n\ncontaminants between liquid-solid interface and liquid-liquid interchange. The \n\n\n\ndiffusion coefficient of dissolved substance is normally tabulated as a constant \n\n\n\nvalue, irrespective of the influence of space and time. In this study, molasses was \n\n\n\ntaken as a dissolved organic carbon (DOC) representation, and it was injected \n\n\n\ninto a basin filled with porous medium (sand) in which it was allowed to diffuse \n\n\n\nhorizontally and vertically in space and time. Diffusion coefficient was determined \n\n\n\nfrom first and second Fick\u2019s law, in which the later model was solved with \n\n\n\npolynomial equation. Diffusion coefficient was observed with respect to changes \n\n\n\nin space and time. A large fluctuation of diffusion coefficient was more apparent at \n\n\n\nthe initial stage of diffusion. Changes of DOC concentration eventually stabilized \n\n\n\nafter a longer time period. Diffusion coefficient from second Fick\u2019s law was found \n\n\n\nto be more informative than the first Fick\u2019s law. From graphical observation, four \n\n\n\ntypes of concentration-distant relation curve were proposed to classify an observed \n\n\n\nrelation of concentration and distant.\n\n\n\nKeywords: molasses, dissolved organic carbon, diffusion coefficient, Fick\u2019s\n\n\n\n                    law, porous medium\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200994\n\n\n\n         (1)\n\n\n\nwhere x  is the horizontal distance (m), F is the mass flux rate per unit area (kg m-2 \n\n\n\ns-1) and C is solute concentration (kg m-3). \n\n\n\n The transport of a chemical substance from a higher concentration to \n\n\n\nlower concentration region is a continuous process. Hence, the difference of \n\n\n\nconcentration between locations will also vary with time. This suggests that \n\n\n\nEq. (1) only modelled diffusion mechanism at a specific time. In order to model \n\n\n\ndiffusion for both distant and time, second Fick\u2019s law was used as follows (Appelo \n\n\n\nand Postma, 2005):\n\n\n\n        (2)\n\n\n\nwhere t  is for time (s).\n\n\n\n Diffusion is one of the few important mechanisms in governing contaminant \n\n\n\ntransport in groundwater. For instance, there are mechanical dispersion, \n\n\n\ndegradation, adsorption, and advection. In groundwater modelling software, for \n\n\n\nexample, SUTRA program on solute transport in subsurface system employed \n\n\n\ndiffusion as a constant input parameter (Voss and Provost 2003). Diffusion is \n\n\n\ninfluenced by various factors such as water content, compaction on soils, porosity \n\n\n\ndistribution, types of chemical substance (hydrophilic, hydrophobic, anion, cation, \n\n\n\netc), and tortuosity (Mott and Weber 1991; Shackelford and Daniel 1991; Myrand \n\n\n\net al. 1992; Cotten et al. 1998). The current work was limited to spatial and time \n\n\n\nfactors, which was based on Eq. (2).\n\n\n\n Arsenic pollution in the groundwater of east and west Bengal is a well-\n\n\n\nknown problem. Study from Islam et al. (2004) had shown that simultaneous \n\n\n\norganic carbon oxidation and reduction of arsenic-bearing Fe(III) compounds \n\n\n\nmay be implicated with the release of arsenic from sediment to groundwater. The \n\n\n\navailability of organic matter either from surface-derived infiltration or naturally \n\n\n\nembedded in sediment with a slow release from sediment, or a combination of \n\n\n\nboth has an important implication in the management of arsenic contamination \n\n\n\nin groundwater. In this context, diffusion coefficient has an important practical \n\n\n\ncontribution on the movement of organic mass from sediment or ground surface \n\n\n\ninto groundwater. \n\n\n\n The objectives of this work were to: (1) solve second Fick\u2019s law using \n\n\n\npolynomial equations; and (2) illustrate and classify diffusion phenomenon into \n\n\n\ndifferent types of Concentration (C)-Distant (x) relation. In this study, molasses \n\n\n\nwas used as a representation of natural organic matter. Molasses-induced arsenic \n\n\n\nin groundwater was observed in the work of Harvey et al. (2002) in east Bengal. \n\n\n\nOther workers chose to use acetate to induce arsenic release from sediment (Van \n\n\n\nGeen et al. 2004; Coker et al. 2006; Lear et al. 2007).\n\n\n\nE.G. Goh & I. Athira\n\n\n\n\n\n\n\nx\n\n\n\nC\nDF\n\u2202\n\n\n\n\u2202\n\u2212=\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 95\n\n\n\nMATERIALS AND METHODS\n\n\n\nExperimental Setting \n\n\n\nSand was collected from a seaside of Kuala Terengganu, Terengganu, Malaysia, \n\n\n\nand the sand was subjected to a sieving machine in which it consisted of sieve sizes \n\n\n\nranging from 0.063 \u03bcm to 4.0 mm. Sand ranging from 0.43 to 2.0 mm (medium \n\n\n\nto very coarse sand, respectively) was chosen to allow reasonable measurement \n\n\n\nof solute diffusion process in the timeframe of laboratory experimentation. This \n\n\n\nis because smaller sand size would decrease the diffusion process which would \n\n\n\neventually require a longer period of experimentation, and vice versa for a larger \n\n\n\nsand size. A diffusion testing model was constructed as shown in Fig. 1. A basin \n\n\n\nof 36 cm was filled with 6 cm depth of sand and then, filled with demineralised \n\n\n\nwater until it was slightly above the surface of sand. Dissolved organic carbon \n\n\n\n(DOC) was represented by molasses (C\n6\nH\n\n\n\n12\nNNaO\n\n\n\n3\nS) and was prepared to a \n\n\n\nconcentration of 30 mg L-1. The DOC was injected into the sand at 3 cm below \n\n\n\nthe water table in the middle of the basin. For every 15 minutes, water sample \n\n\n\nwas collected at 3 cm depth from horizontal distance of 3, 6, 9, and 12 cm from \n\n\n\nthe injection point. The concentration of DOC was measured with a TOC-V\nCPH\n\n\n\n\n\n\n\nShimadzu Analyzer in triplicates. An average value was calculated and was used \n\n\n\nfor graphical illustration as well as the determination of diffusion coefficients in \n\n\n\nfirst and second Fick\u2019s law.\n\n\n\nFig. 1: A diffusion model testing unit on porous medium.\n\n\n\nMathematical Solution to the Diffusion Coefficient of the First Fick\u2019s Law\n\n\n\nDiffusion coefficient was calculated at discrete location from 6 to 12 cm (distant \n\n\n\nfrom the injection point) and at discrete time from 15 to 105 minutes. To calculate \n\n\n\ndiffusion coefficient (D), Eq. (1) can be rearranged into the following form:\n\n\n\nMolasses Diffusion in Sand\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200996\n\n\n\n\n\n\n\n        (3)\n\n\n\n\n\n\n\n      (4)\n\n\n\n\n\n\n\n\n\n\n\n      (5)\n\n\n\n\n\n\n\nwhere: \n11 ,txC   is the concentration (mg L-1) at x\n\n\n\n1\n  distance from injection point at \n\n\n\ntime t\n1\n ; \n\n\n\n12 ,txC  is the concentration (mg L-1) at x\n2\n  distance from injection point at \n\n\n\ntime t\n1\n ; \n\n\n\n22 ,txC  is the concentration (mg L-1) at x\n2\n  distance from injection point at \n\n\n\ntime t\n2\n ; and   and  x\n\n\n\n1\n < x\n\n\n\n2\n and t\n\n\n\n1\n < t\n\n\n\n2\n\n\n\n Eq. (4) was proposed to approximate Eq. (3), and it can be reduced to Eq. (5). \n\n\n\nFrom Eq. (4), its numerator denotes mass flux rate per unit area and denominator \n\n\n\ndenotes concentration gradient. The numerator was derived from the following \n\n\n\nequation:   \n\n\n\nwhere: \n.\n\n\n\nm , mass flux rate, kg s-1 (      = m /t); V, volume of water, m3 (V = Ax); C, \n\n\n\nsolute concentration, kg m-3 (C = m/V). Eq. (5) was used to calculated diffusion \n\n\n\ncoefficient of molasses at specific time.\n\n\n\nMathematical Solution to the Diffusion Coefficient of the Second Fick\u2019s Law\n\n\n\nDiffusion coefficient of second Fick\u2019s law was calculated at discrete location from \n\n\n\n3 to 12 cm and at discrete time from 0 to 120 minutes. Polynomial equations were \n\n\n\nused to correlate concentration (C) of DOC with time (t) and DOC travel distant \n\n\n\n(x) as shown below:\n\n\n\n     (6)\n\n\n\n\n\n\n\n    (7)\n\n\n\n\n\n\n\nwhere: a\nt\n, a\n\n\n\nx\n, b\n\n\n\nt\n, b\n\n\n\nx\n, c\n\n\n\nt\n, c\n\n\n\nx\n, d\n\n\n\nt\n, d\n\n\n\nx\n, e\n\n\n\nt\n, and f\n\n\n\nt\n  are coefficients of polynomial equations. \n\n\n\nThe fitted curves and standard deviation on experimental data are shown in Figs. \n\n\n\n2(a) and 2(b), respectively, generated from Eqs. (6) and (7). \n\n\n\n In order to avoid a possible unwanted generation of \u201cbump\u201d in the curve \n\n\n\nprediction, linear interpolation between experimental data was carried out to \n\n\n\nestimate data points and these data were used in the curve-fitting, as shown in \n\n\n\nFigs. 2(a) and 2(b).  \n\n\n\nE.G. Goh & I. Athira\n\n\n\n \n( ) ( ) ( ) ( ) ( )txCtxVmxAxtmAmF \u00d7=\u00d7=\u00d7\u00d7== 1\n\n\n\n.\n\n\n\n.\n\n\n\nm\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 97\n\n\n\nFig. 2: Curve-fitting results: (a) the correlation between concentration (C) and time \n\n\n\n(t) which has the lowest R2 from 0.936. The plotted DOC data in-between 0, 15, 30, \n\n\n\n45, 60, 75, 90, 105 and 120 min were generated from linear interpolation; and (b) the \n\n\n\ncorrelation between concentration (C) and distant (x) which has the lowest R2 from \n\n\n\n0.934. The plotted DOC data in-between 0.03, 0.06, 0.09 and 0.12 m were generated \n\n\n\nfrom linear interpolation.\n\n\n\n Note that a minimum requirement of estimation on any equation parameters \n\n\n\nis to have a number of experimental data equal or more than the number of \n\n\n\nparameter required to be estimated from proposed equation. In the current study, \n\n\n\nMolasses Diffusion in Sand\n\n\n\n\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n0 20 40 60 80 100 120 140\n\n\n\nTime (min)\n\n\n\nD\nO\n\n\n\nC\n (\n\n\n\nm\ng\n\n\n\n L\n-1\n\n\n\n)\n\n\n\n0.03 m\n\n\n\n0.06 m\n\n\n\n0.09 m\n\n\n\n0.12 m\n\n\n\nPoly. (0.03 m)\n\n\n\nPoly. (0.06 m)\n\n\n\nPoly. (0.09 m)\n\n\n\nPoly. (0.12 m)\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n0 0.02 0.04 0.06 0.08 0.1 0.12 0.14\n\n\n\nDistance from Injection Point (m)\n\n\n\nD\nO\n\n\n\nC\n (\n\n\n\nm\ng\n\n\n\n L\n-1\n\n\n\n)\n\n\n\n30\n\n\n\n60\n\n\n\n90\n\n\n\n120\n\n\n\nPoly. (30)\n\n\n\nPoly. (60)\n\n\n\nPoly. (90)\n\n\n\nPoly. (120)\n\n\n\n(a)\n\n\n\n(b)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 200998\n\n\n\nEq. (6) with six equation parameters was estimated by nine experimental data \n\n\n\nand combined fitted to additional twenty four interpolated data, and Eq. (7) with \n\n\n\nfour parameters was estimated by four experimental data and combined fitted to \n\n\n\nadditional nine interpolated data. Hence, the orders of polynomial used in the \n\n\n\nstudy are considered reasonable. \n\n\n\n By taking the derivative of  C with respect to t , and second derivative of C  \n\n\n\nwith respect to x, they become the following forms:\n\n\n\n           (8)\n\n\n\n\n\n\n\n       (9)\n\n\n\n The diffusion coefficient of second Fick\u2019s law can be solved by rearranging \n\n\n\nEq. (2) and then, employ solution from Eqs. (8) and (9). The solution is shown \n\n\n\nbelow:\n\n\n\n\n\n\n\n       (10)\n\n\n\n\n\n\n\n\n\n\n\n     (11)\n\n\n\n\n\n\n\n Eq. (11) is not intended as an ultimate analytical equation in solving for \n\n\n\nthe diffusion coefficient of second Fick\u2019s law, but it was rather proposed as one \n\n\n\nof the easiest and practical solution in achieving the first objective of the current \n\n\n\nwork.\n\n\n\n\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nDOC Diffusion in Space and Time\n\n\n\nImmediately after the injection of DOC (molasses) into the middle of the basin \n\n\n\nin which it was filled with sand and demineralised water, there was a distinctive \n\n\n\nregion of high and low (or very low) DOC concentration. As time increased, DOC \n\n\n\nbecame increasingly uniform throughout the system (Fig. 3). \n\n\n\n The concentration of DOC reached its peak at 3 cm in its first 30 minutes. \n\n\n\nIt was detected as 29.7 mg L-1 which approximates the original injected DOC \n\n\n\nconcentration. A gradual decrease of DOC concentration was observed after 30 \n\n\n\nminutes, which was mainly due to the spread and diffusion of DOC throughout \n\n\n\nthe medium/system. After 90 minutes, the DOC concentration has started to show \n\n\n\na gradual increase for most locations and it was approaching stabilization at 120 \n\n\n\nminutes (see Fig. 2(a)). \n\n\n\nE.G. Goh & I. Athira\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 99\n\n\n\n Overall, diffusion had created regions in which there were the highest \n\n\n\nand the lowest concentration regions of DOC. However, after a longer time of \n\n\n\ndiffusion (approximately at 120 minutes), a levelling off of DOC concentration \n\n\n\nfor all locations was observed which ranged from 3.8 to 4.9 mg L-1 with a \n\n\n\nstandard deviation of 0.5 mg L-1. At this region the system was approximating a \n\n\n\nhomogenous DOC concentration distribution. Further evaluation was carried out \n\n\n\nwith the determination of diffusion coefficient from the first and second Fick\u2019s \n\n\n\nlaw.\n\n\n\nFig. 3: Concentration of molasses in porous medium in space and time.\n\n\n\nFig. 4. Diffusion coefficient of molasses in porous medium in space and time which was \n\n\n\ncalculated from first Fick\u2019s law.\n\n\n\nMolasses Diffusion in Sand\n\n\n\nTime (min)\n\n\n\nDiffusion Coefficient \n\n\n\n(x 10\n-5\n\n\n\n m\n2\n\n\n\n s\n-1\n\n\n\n)\n\n\n\n0.0-4.0\n\n\n\n-4.0-0.0\n\n\n\n-8.0--4.0 D\nis\n\n\n\nta\nn\n\n\n\nc\ne\n\n\n\n (\nm\n\n\n\n)\n\n\n\n0.09\n\n\n\n0.12\n\n\n\n0.06\n\n\n\n15 30 45 60 75 90 105\n\n\n\n\n\n\n\nDOC (mg L\n-1\n\n\n\n)\n\n\n\n26.6-30.4\n\n\n\n22.8-26.6\n\n\n\n19.0-22.8\n\n\n\n15.2-19.0\n\n\n\n11.4-15.2\n\n\n\n7.6-11.4\n\n\n\n3.8-7.6\n\n\n\n0.0-3.8\n\n\n\nD\nis\n\n\n\nta\nn\n\n\n\nc\ne\n\n\n\n f\nro\n\n\n\nm\n i\nn\n\n\n\nje\nc\nti\no\n\n\n\nn\n\n\n\n p\no\n\n\n\nin\nt \n\n\n\n(m\n)\n\n\n\nTime (min)\n\n\n\n0 15 30 45 60 75 90 105 120\n\n\n\n0.03\n\n\n\n0.06\n\n\n\n0.09\n\n\n\n0.12\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009100\n\n\n\nDiffusion Coefficient of the First Fick\u2019s Law\n\n\n\nDiffusion coefficient determined from the first Fick\u2019s law can be broadly \n\n\n\ncategorized into those of positive and negative values. A positive value of the \n\n\n\ndiffusion coefficient is referring to DOC flux that is diffusing away from the \n\n\n\ninjection point, and the DOC concentration is increasing with increasing time. \n\n\n\nA positive value could also be given by DOC flux that is diffusing towards \n\n\n\ninjection point, and the DOC concentration is decreasing with increasing time. A \n\n\n\nnegative diffusion coefficient indicates DOC flux that is diffusing away from the \n\n\n\ninjection point, and the DOC concentration is decreasing with increasing time. \n\n\n\nAlternatively, negative diffusion coefficient could also indicate DOC flux that is \n\n\n\ndiffusing towards the injection point, and the DOC concentration is increasing \n\n\n\nwith increasing time. \n\n\n\n It is observed that within the two hours of diffusion, the initial and the last \n\n\n\n15 minutes have given positive values of diffusion coefficients (Fig. 4), whereas \n\n\n\nin between it was dominated by negative diffusion coefficients. At 12 cm and 30 \n\n\n\nminutes, a large magnitude of negative diffusion coefficient was found as - 6.6 x \n\n\n\n10-5 m2 s-1. This is because of a smaller concentration gradient that was required to \n\n\n\ncause a larger amount of DOC flux, which could be due to the non-homogeneous \n\n\n\ndistribution of sand. The following diffusion coefficients of D\n12cm,75mins\n\n\n\n, D\n9cm,90mins\n\n\n\n\n\n\n\nand D\n6cm,90mins \n\n\n\nwhere a sudden change of sign in diffusion coefficients was indicated. \n\n\n\nThis was due to a change in solute mass accumulation from DOC concentration \n\n\n\nthat decreases with increasing time to DOC concentration which increases with \n\n\n\nincreasing time. Diffusion at 12 cm and 90 minutes was a result of reversal in \n\n\n\nsolute mass transport direction that was diverted from DOC flux which diffuses \n\n\n\naway from injection point to DOC flux which diffuses towards the injection \n\n\n\npoint. \n\n\n\nDiffusion Coefficient of the Second Fick\u2019s Law\n\n\n\nSimilar to first Fick\u2019s law, the diffusion coefficient determined from second \n\n\n\nFick\u2019s law also can be broadly divided into those of positive and negative values. \n\n\n\nHowever, it has more characteristics than the diffusion coefficient of the first Fick\u2019s \n\n\n\nlaw, and thus, it carries more information. As a result, four graphical illustration as \n\n\n\nshown in Fig. 5 is proposed to categorize the observation of experimental results. \n\n\n\nSince the second Fick\u2019s law accounted for both space and time, the positive value \n\n\n\nof diffusion coefficient indicates an increasing DOC concentration with time, and \n\n\n\nDOC flux is diffusing either towards or away from injection point with C - x   \n\n\n\ncurve concaves upwards. Also, a positive value could indicate a decreasing DOC \n\n\n\nconcentration with time, and DOC flux is diffusing either towards or away from \n\n\n\ninjection point with C - x  curve concaves downwards. For negative value of \n\n\n\ndiffusion coefficient, the DOC concentration is increasing with time, and DOC \n\n\n\nflux is diffusing either towards or away from injection point with C - x  curve \n\n\n\nconcaves downwards. Alternatively, negative could be due to decreasing DOC \n\n\n\nconcentration with time, and DOC flux is diffusing either towards or away from \n\n\n\ninjection point with C - x  curve concaves upwards.\n\n\n\nE.G. Goh & I. Athira\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 101\n\n\n\nFig. 5: Types of  C - x  curve: (a) Decreasing DOC concentration with distant (concave \n\n\n\nupwards); (b) Increasing DOC concentration with distant (concave upwards); (c) \n\n\n\nIncreasing DOC concentration with distant (concave downwards); and (d) Decreasing \n\n\n\nDOC concentration with distant (concave downwards).\n\n\n\n At the initial stage of diffusion, between 0 and 15 minutes, a large fluctuation \n\n\n\nof diffusion coefficients was observed from 3 to 12 cm (Fig. 6). This observation \n\n\n\ndeviates from the diffusion coefficient that was determined from first Fick\u2019s law. \n\n\n\nThis could be explained by the method of calculation proposed in Eq. (5) in which \n\n\n\nits diffusion coefficient is subjected to the influence of adjacent concentration \n\n\n\nin subsequent time of diffusion. As a result, the estimated value was unable to \n\n\n\nillustrate a definite change of diffusion coefficient, and also subjected to limitation \n\n\n\nin the availability of experimental data.\n\n\n\n As shown in Fig. 6, the first 30 minutes of diffusion has given a positive \n\n\n\ndiffusion coefficient, except at D\n3cm,30mins\n\n\n\n, D\n9cm,0mins\n\n\n\n and D\n12cm,0-30mins\n\n\n\n. This region has \n\n\n\nshown a dominant increasing (or accumulating) DOC concentration with time, \n\n\n\nwhich was an indication of DOC flux from high concentration to low concentration \n\n\n\nregions. \n\n\n\n The region from 45 to 90 minutes has diffusion coefficient in negative value, \n\n\n\nexcept at D\n12cm,45-90mins\n\n\n\n. This region was governed by diffusion in which its DOC \n\n\n\nconcentration decreases with increasing time and the DOC flux was diffusing \n\n\n\naway from the injection point. A decreasing DOC concentration during diffusion \n\n\n\nis an indication of mass dispersion that would lead towards a homogenous DOC \n\n\n\nconcentration. \n\n\n\nMolasses Diffusion in Sand\n\n\n\n\n\n\n\na) \n\n\n\nx \n\n\n\nC \n\n\n\nb) \n\n\n\nx \n\n\n\nC \n\n\n\nx \n\n\n\nC \n\n\n\nd) \n\n\n\nx \n\n\n\nC \n\n\n\nc) \n\n\n\nType A \n\n\n\nType C \n\n\n\nType B \n\n\n\nType D \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009102\n\n\n\nFig. 6: Diffusion coefficient of molasses in porous medium in space and time, calculated \n\n\n\nfrom second Fick\u2019s law.\n\n\n\n From 105 to 120 minutes, it was found mainly positive diffusion coefficient, \n\n\n\nexcept at D\n6cm,120mins\n\n\n\n and D\n12cm,120mins\n\n\n\n. Also, it has half of its DOC flux diffuses \n\n\n\ntowards the injection point and approximately, half of its diffusion with DOC \n\n\n\nconcentration increases with increasing time. The balance of back and forward \n\n\n\nof DOC flux that either diffuses towards or away from the injection point and \n\n\n\nwith the balance of increasing and decreasing DOC concentration with time, these \n\n\n\nobservations could be used to indicate the onset of diffusion system stabilization \n\n\n\nin which it showed a lower DOC concentration (Fig. 3) and a lower diffusion \n\n\n\ncoefficient with less fluctuation in values. \n\n\n\n An average diffusion coefficient of 1.2 x 10-2 m2 s-1 (\u00b1 9.3 x 10-3) at 0 minute, \n\n\n\nit gradually decreases to an average of 1.1 x 10-4 m2 s-1 (\u00b1 7.4 x 10-5) at 120 \n\n\n\nminutes. At 0 minute, diffusion coefficient was the highest with respective 1.9 x \n\n\n\n10-2 and 2.1 x 10-2 m2 s-1 at 3 and 6 cm. This was caused by a high concentration \n\n\n\ngradient that generates greater DOC flux at the early stage of DOC injection. A \n\n\n\nnegative diffusion coefficient of D\n9cm,0mins\n\n\n\n and D\n12cm,0-30mins\n\n\n\n was due to its respective \n\n\n\ntypes C and D (concaves downwards) of C- x  curves which generates negative \n\n\n\nvalue on the second derivative of C with respect to x  (i.e., d2C/dx2= -1 ) (see Fig. \n\n\n\n5). For D\n3cm,30mins, \n\n\n\nand D\n12cm,120mins\n\n\n\n, the negative diffusion coefficient was due to the \n\n\n\ndecreasing DOC flux with time which generates negative value on concentration \n\n\n\nrate (i.e., dC/dt = -1 ), whereas at D\n6cm,120mins\n\n\n\n it was caused by its type D of C - x  \n\n\n\ncurves.\n\n\n\nCONCLUSIONS\n\n\n\nDiffusion of molasses in porous medium (sand) dynamically varies in space and \n\n\n\ntime. The fluctuation of DOC concentration in space and time causes  fluctuation of \n\n\n\ndiffusion coefficient which was apparently observed from the calculated diffusion \n\n\n\ncoefficient obtained from second Fick\u2019s law. The injected DOC has gone through \n\n\n\na rapid random motion which causes a quick jump in DOC concentration at the \n\n\n\ninitial 30 minutes of diffusion and gradually decreasing the DOC concentration in \n\n\n\nE.G. Goh & I. Athira\n\n\n\n0 15 30 45 60 75 90 105 120\n\n\n\n0.03\n\n\n\n0.06\n\n\n\n0.09\n\n\n\n0.12\n\n\n\nDiffusion Coefficient \n\n\n\n(x 10\n-2\n\n\n\n m\n2\n s\n\n\n\n-1\n)\n\n\n\n1.5-3.0\n\n\n\n0.0-1.5\n\n\n\n-1.5-0.0 D\nis\n\n\n\nta\nn\n\n\n\nc\ne\n\n\n\n (\nm\n\n\n\n)\n\n\n\nTime (min)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009 103\n\n\n\nthe remaining time, before stabilizing at the last 15 to 30 minutes of diffusion. The \n\n\n\nproposed four types of C - x curve appeared sufficient in classifying the relation of \n\n\n\nconcentration and distant from the injection point. Types A and D were the most \n\n\n\ndominant C - x curves in the current laboratory scale diffusion system and it is \n\n\n\nlimited to the observation of 120 minutes and to a maximum of 12 cm observation \n\n\n\ndistance from the injection point.    \n\n\n\nREFERENCES\nAppelo, C.A.J. and D. Postma. 2005. Geochemistry, groundwater and pollution. 2nd edn. \n\n\n\n A.A. Balkema Publisher, Laiden.\n\n\n\nCoker V.S., A.G. Gault, C.I. Pearce, G. van der Laan, N.D. Telling, J.M. Charnock, D.A. \n\n\n\n Polya and J.R. Lloyd. 2006. XAS and XMCD evidence for species-dependent \n\n\n\n partitioning of arsenic during microbial reduction of ferrihydrite to magnetite. \n\n\n\n Environ Sci Technol. 40: 7745-7750.\n\n\n\nCotten, T.E., M.M. Davis and C.D. Shackelford. 1998. Effects of test duration and specimen\n\n\n\n  length on diffusion testing of unconfined specimens. Geotechnical Testing \n\n\n\n   Journal. 21: 79-94.\n\n\n\nDomenico, P.A. and F.W. Schwartz. 1990. Physical and Chemical Hydrogeology. John \n\n\n\n Wiley and Sons, Inc., Canada.\n\n\n\nHarvey, C.F., C.H. Swartz, A.B.M. Badruzzaman, N. Keon-Blute, W. Yu, M.A. Ali, J. Jay, R.\n\n\n\n Beckie, V. Niedan, D. Brabander, P.M. Oates, K.N. Ashfaque, S. Islam, H.F. \n\n\n\n Hemond, and M.F. Ahmed. 2002. Arsenic Mobility and Groundwater Extraction in \n\n\n\n Bangladesh. Science. 298: 1602-1606.\n\n\n\nIslam, F.S., A.G. Gault, C. Boothman, D.A. Polya, J.M. Charnock, D. Chatterjee and J.R. \n\n\n\n Lloyd. 2004. Role of metal-reducing bacteria in arsenic release from Bengal delta \n\n\n\n sediments. Nature. 430: 68-71.\n\n\n\nLear G., B. Song, A.G. Gault, D.A. Polya and J.R. Lloyd. 2007. Molecular analysis of \n\n\n\n arsenate-reducing bacteria within Cambodian sediments following amendment \n\n\n\n with acetate. Appl Environ Microbiol. 73:1041\u20131048.\n\n\n\nMott, H.V. and W.J. Weber. 1991. Factors influencing organic contaminant diffusivities in \n\n\n\n soil-bentonite cutoff barriers. Environmental Science and Technology. 25: 1708-715.\n\n\n\nMyrand, D., R.W. Gillham, E.A. Sudicky, S.F. O\u2019Hannesin and R.L. Johnson. 1992. \n\n\n\n Diffusion of volatile organic compounds in natural clay deposits: laboratory tests. \n\n\n\n Journal of Contaminant Hydrology. 10: 159-177.\n\n\n\nShackelford, C.D. and D.E. Daniel. 1991. Diffusion in saturated soil. II. Results for \n\n\n\n compacted clay. Journal of Geotechnical Engineering. 117: 485-506.\n\n\n\nMolasses Diffusion in Sand\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 13, 2009104\n\n\n\nVan Geen, A., J. Rose, S. Thoral, J.M. Garnier, Y. Zheng and J.Y. Bottero. 2004. Decoupling\n\n\n\n of As and Fe release to Bangladesh groundwater under reducing conditions. Part \n\n\n\n II: Evidence from sediment incubations. Geochem Cosmochem Acta. 68: 3475-\n\n\n\n 3486.\n\n\n\nVoss, C.I. and A.M. Provost. 2003. SUTRA: A Model for Saturated-Unsaturated, Variable-\n\n\n\n Density Ground-Water Flow with Solute or Energy Transport. U.S. Geological \n\n\n\n Survey. Water Resources Investigations Report 02-4231.\n\n\n\nE.G. Goh & I. Athira\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : hhnurjanto@ugm.ac.id\n\n\n\nINTRODUCTION\nSoil in the tropics is fragile. It can be easily degraded (Fearnside, 2006), which is \ndefined as the process of decreasing soil quality in relation to plant productivity \n(Lal, 1986). An important factor that contributes to the soil degradation process \nis a rapid decline in soil organic matter due to continuously high temperatures in \nthe tropics, soil cultivation which favours rapid organic matter oxidation, surface \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 1-18 (2016)                          Malaysian Society of Soil Science\n\n\n\nSmectite under Heavy Clay Soils Development at FRE \nWanagama Forest Area \n\n\n\nNurjanto, H.H,a*, H. Supriyoa, S. M. Widyastutib, S. Kabirunc, \nE. Johand, N. Matsued\n\n\n\na Laboratory of Tree Physiology and Forest Soil, Faculty of Forestry, Universitas \nGadjah Mada, Yogyakarta 55281 Indonesia\n\n\n\nb Laboratory of Forest Protection, Faculty of Forestry, Universitas Gadjah Mada, \nYogyakarta 55281 Indonesia\n\n\n\nc Laboratory of Soil Microbiology, Faculty of Agriculture, Universitas Gadjah \nMada, Yogyakarta 55281 Indonesia\n\n\n\nd Laboratory of Applied Chemistry for Environmental Industry, Faculty of \nAgriculture, Ehime University, 3-5-7 Tarumi, Matsuyama,790-8566, Japan\n\n\n\nABSTRACT\nA degraded area in Forest Research and Education (FRE) Wanagama 1 was \nsuccessfully rehabilitated with Gliricidia sepium, a fast growing pioneer species \ncapable of producing a great amount of organic matter, and which facilitates the \ndevelopment of a shallow clay soil. Since rehabilitation, this area has received \nminimal human disturbances, and is thus a suitable area for studying soils \ndeveloped on/or in association with limestone parent material. Such studies, which \nare rather limited, contribute to knowledge on soil development in the tropics. \nSoil samples from six plots representing two different stand ages (44 and 28 \nyears old) of G. sepium, three slope positions, and soil depths were collected and \nanalysed for selected physical, chemical and mineralogical properties. Results of \nthis study showed that the soil is dark brown to very dark grey in color, contains a \nhigh amount of organic matter, is pH neutral to alkaline and is dominated by clay \nparticles which mainly consist of smectite; the soil can  therefore be classified \nas Vertisols. The smectite-dominated soil is developed from dissolution and \nsubsequent precipitation of limestone parent materials. Soil forming factors, \nnamely monsoonal climate, dense vegetation which causes builtup of soil organic \nmatter content, and terracing were responsible for the formation processes.\n\n\n\nKeywords: Soil development, smectite, organic matter, limestone, \nrehabilitated area\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 20162\n\n\n\nrun-off and erosion which are induced by the opening of forest canopy (Lal 1986; \nSmiley and Kroschel, 2008; Laganiere et al., 2013). \n\n\n\nThere is growing concern on rehabilitation of tropical forest area and its \nsoil conditions. Forest rehabilitation can improve soil conditions but different \ntree species may affect soil development differently. Fisher and Binkley (2000) \nsuggest eight mechanisms by which vegetation may vary in their effects on soil, \nincluding quantity and quality of carbon compound addition, nitrogen input, soil \norganisms, mineral weathering, pedogenesis, physical properties, water loss and \natmospheric deposition. Several researchers have reported an improvement in \nsoil organic carbon and nutrient content status following forest rehabilitation, \nfor example, Macedo et al. (2008) in Rio de Janeiro and Deng et al. (2013) in \nChina.  However, there is no information on mineralogical components of soil \nthat develops subsequent to forest rehabilitation, especially on calcareous rock \n(limestone) parent material. Sedov et al. (2008) gathered  information from \nvarious studies on tropical ecosystem and found several types and descriptions \nof soils developed on calcareous parent rock material. They were widely diverse \nand ranged from shallow profile soils with low weathering status and mollic \nepipedon (found in South-eastern China), to young Entisols and Mollisols,  more \ndeveloped soils like Alfisols and Ultisols (in the tropical islands of the Pacific \nand Caribbean), even deeply weathered \u2018Red Ferrallitic\u2019 soils rich in kaolinite (in \nCuba) and Oxisols (in Jamaica). Soils in the humid tropics and the monsoonal \nregion of Indonesia have been reviewed by Tan (2008) who described that soils \ndeveloped on or from calcareous material can vary from Vertisols (previously \nnamed Grumusols), lowland Alfisols (previously named red Mediterranean soil), \nand in some cases upland Ultisols (red-yellow Podzolic soils). Mella and Mermut \n(2010) describe the genesis and mineralogy of reddish Alfisols and black Mollisols \ndeveloped in an uplifted coral reef in West Timor, Indonesia. Indeed, tropical soils \nare very diverse even within small areas because the soil forming factors vary \nwithin small localities (Fisher and Binkley, 2000; Tan, 2008). \n\n\n\nMATERIALS AND METHODS\nIn this paper, young clayey soils developed on calcareous (limestone) parent \nmaterial at an ex-degraded forest area rehabilitated with Gliricidia sepium are \nassessed. Prior to rehabilitation, the forest area was characterised by scarce and \nscattered soil patches between rocks. Rehabilitation of the degraded forest area \nwas started in 1969 using a biological approach involving pioneer vegetation \n(Acacia vilosa, Leucaena leucocepala and Gliricidia sepium) and terrace \nestablishment which was conducted by arranging the rocks in the terrace edge. \nThe initial rehabilitation program was conducted in compartment 5 in 1969 and \nextended to compartment 6 in 1985 (Suseno, 2004). At present, these areas are \nmostly occupied by G. sepium with the stand age being approximately 44 and 28 \nyears, respectively. Supriyo (2004) classified the soil that developed in these areas \nas Entisols (Lithosol) on the basis that it has shallow soil depth (< 20 cm).\n\n\n\nNurjanto et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 3\n\n\n\nSmectite under Heavy Clayey Soils Development\n\n\n\nStudy Site\nThis study was performed in Forest Research and Education (FRE) of Wanagama \n1, Gunung Kidul District, Yogyakarta Province, approximately 35 km south-east \nof the city of Yogyakarta, Indonesia. The topography is hilly with the altitude \nbeing about 115 \u2013 205 m above sea level. The climate is monsoonal, characterised \nby a distinct dry season with dry months (rainfall < 60 mm/month)  and more \nthan 6 months with an annual rainfall of 1,500 - 1,900 mm year-1 (Supriyo, 2004).  \nAfter rehabilitation with G. sepium, the area had minimal exposure to human \ndisturbance, making the area a good site for studying tropical pedogenesis on \ncalcareous parent materials. \n\n\n\nSix plots representing two different ages of G. sepium, 44 and 28 years old, \nand threee slope positions were established in compartments 5 and 6, respectively. \nBoth compartments are adjacent to each other and are separated by the Oya river. \nTopography of both compartments is similar with the slope averaging from 20 \nto 40 % downward to the river, thus they have an opposite sloping direction. \nThe 44- and 28-year old G. sepium have a stand density of 1208 \u2013 1550 ha-1 and \n2341 \u2013 4650 ha-1, respectively, with an annual litter production of 5.04 ton ha-1 \n\n\n\nand 6.16 ton ha-1, respectively. As comparison, a control plot was established in a \nbare area without vegetation except for several stands of Melaleuca cajuputi and \nSantalum album. Grasses grew to cover the control plot soil due to the availability \nof sufficient sunlight. This was in contrast to soils under G. sepium stands which \nwere devoid of grass cover.\n\n\n\nSample Collection and Analysis\nSoil samples were taken from each plot at three positions (two diagonals and \ncentre) at depths of 0-10 cm, 10-20 cm and 20-30 cm soil. At the Laboratory of \nPlant Physiology and Forest Soil, Faculty of Forestry, Universitas Gadjah Mada, \nsoil samples from similar depths were mixed and used as sample composite. The \nsamples were air dried and passed through a 2-mm pore sieve. These samples \nwere used for determining pH using a mixture of soil sample and distilled water \n(1:2.5, w/v) with a glass electrode (McLean, 1982) Hanna Instrument HI 8314 \nand soil organic carbon contents using Walkey and Black wet combustion method \n(Nelson and Sommers, 1982); the samples were then converted to soil organic \nmatter content by multiplying the value by 1.724 (Supriyo, 1992). Another set of \nsamples was taken for determining soil bulk density. This was carried out using \nclod method, where undisturbed soil clods were taken, trimmed with a sharp knife \nto make nearly spherical clods (approximate diameter 4 \u2013 5 cm) and coated with \na Parafilm\u00ae. The volume was then determined by displacement and sample mass \nby weighing (Blake and Hartge, 1986). Soil colour of moist soils was determined \nby reference to Munsell Soil Colour Charts.\n\n\n\nThe air dried and sieved soil samples were quantitatively separated into sand \n(2\u22120.05 mm), silt (0.05\u22120.002 mm) and clay (< 0.002mm) fractions after micro-\naggregate destruction following the method by Kunze and Dixon (1986) with \nlittle modification. This was carried out by treating the samples with 1 N hydrogen \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 20164\n\n\n\nNurjanto et al.\n\n\n\nchloride solution, instead of 0.5 M Na-acetate buffer, to remove carbonates and \nwith 30% hydrogen peroxide to remove organic matter. Prior to treatments, the \nsamples were suspended in distilled water and agitated using a shaker; the organic \ndebris that floated were carefully collected, oven dried and weighed. Samples free \nof carbonates and organic matter were separated into sand fraction using a sieve \nwhile silt and clay by gravity sedimentation. Soil samples at each stage were \noven dried and weighed to obtain weight of calcium carbonate, organic matter \nand inorganic particles and expressed as a percentage of total soils. Soil particle \nfractions (sand, silt and clay) were expressed as percentage of the fraction of total \nsoil minerals. \n\n\n\nClay mineralogical analysis was carried out using the facilities of Laboratory \nof Applied Chemistry for Environmental Industry, Faculty of Agriculture, Ehime \nUniversity, Japan. Clay samples for this analysis were prepared using the method \ndescribed above except that carbonate removal was achieved using 0.5 M Na-\nacetate buffer adjusted to pH 5 with acetic acid. Rigaku X-Ray Diffractometer \nwas used to obtain X-ray diffraction (XRD) pattern of the clay samples mounted \non a glass (oriented aggregate) and treated with the following treatments: (1) Mg \nsaturated, air dried sample; (2) Mg saturated, glycerol solvated; (3) K saturated, \nair dried sample; (4) K saturated, heated to 100o C; (5) K saturated, heated to 350o \n\n\n\nC; and (6) K saturated, heated to 550o C. Identification of the clay mineral was \nthen carried out using several references, such as Brown and Brindley (1980) \nand Whittig and Allardice (1986). Basically, it is obtained by detecting X-ray \ndiffraction spacing produced by a certain mineral with peak height of the XRD \npattern indicating the relative proportion of the mineral (Whittig and  Allardice, \n1986). \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nSoil Colour, Organic Matter Content, pH and Soil Bulk Density\nAn important and easily recognised characteristic of soil is soil colour; it is therefore  \nused to differentiate soil horizons and classification (Fisher and Binkley, 2000; \nBuol et al., 2003). The colour of soil depends on parent material and pedogenic \nprocesses by which the soil is developed. Mineral soils generally have light \ncolour while a dark colour is usually associated with organic matter, manganese \ncompound and reduced (poor aeration) conditions (Fisher and Binkley, 2000). \nThe colour of the soils in the studied area was dark brown (10YR 3/3) to very \ndark grey (10YR 3/1) as shown in Table 1. The colour of the soils from different \nplots was not different and generally the upper layer soils were somewhat darker \nthan the soil at lower depths. The dark colour indicates that the soils contain a high \nproportion of organic matter, as shown in Table 2. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 5\n\n\n\nSmectite under Heavy Clayey Soils Development\n\n\n\nTABLE 1\nColour of soils under 44- and 28-year old G. sepium in FRE Wanagama 1 \n\n\n\nSoil organic carbon at 0 \u2013 10 cm depth was very high (5.04 - 6.98 %) and generally \nsignificantly greater than that found at lower depths (2.73 - 3.63 %  for 10 \u2013 \n20 cm and 2.06 \u2013 3.47 % for 20 \u2013 30 cm). This rate is  very high compared to \nother studies, for instance the study carried out in Brazil (Macedo et al., 2008) \nwhere a degraded area rehabilitated with several leguminous tree species was \nfound to contain 1.5 \u2013 1.85 % soil organic carbon at 0 \u2013 10 cm and 1.35 % at \n10 \u2013 20 cm after 13 years; another study by Deng et al. (2013) observed  that \nsoil organic carbon in Loess Plateau China at 0 \u2013 20 cm was as much as 2 \u2013 2.4 \n%. Various factors may have contributed to  the markedly higher soil organic \ncarbon content in this study compared to other studies.  The most important \nfactor controlling soil organic matter in this area is probably capability of soil to \nretain and accumulate organic matter which is determined by soil types and the \nabsence of soil cultivation/tillage. Soils in this area are dominated by clay and \nthe capability of clay soils to retain organic matter has been reported previously \n(Hassink et al., 1997; Koutika et al., 1999; Mikutta et al., 2006; Laganiere et \nal., 2013). Soil tillage can substantially reduce soil organic matter by favouring \n\n\n\nTable 1. Color of soils under 44 and 28 year old G.sepium in FRE Wanagama 1  \n\n\n\nSlope Depth (cm) Color  \n44 year old G. sepium \n\n\n\nUpper \n0 \u2013 10 10YR 2/2 Very Dark Brown to 10YR 3/1 Very Dark Grey \n\n\n\n10 \u2013 20 10YR 3/3 Dark Brown to 10YR 3/1 Very Dark Grey \n20 \u2013 30 10YR 3/3 Dark Brown to 10YR 2/2 Very Dark Brown \n\n\n\nMiddle  \n0 \u2013 10 10YR 2/2 Very Dark Brown to 10YR 3/1 Very Dark Grey \n\n\n\n10 \u2013 20 10YR 3/2 Very Dark Grayish Brown to 10YR 3/1 Very Dark Grey \n20 \u2013 30 10YR 2/2 Very Dark Brown \n\n\n\nLower \n0 \u2013 10 10YR 3/1 Very Dark Grey \n\n\n\n10 \u2013 20 10YR 3/2 Very Dark Grayish Brown to 10YR 3/1 Very Dark Grey \n20 \u2013 30 10YR 3/3 Dark Brown to 10YR 3/2 Very Dark Grayish Brown \n\n\n\n28 year old G. sepium  \n\n\n\nUpper \n0 \u2013 10 10YR 2/2 Very Dark Brown to 10YR 3/1 Very Dark Grey \n\n\n\n10 \u2013 20 10YR 2/2 Very Dark Brown to 10YR 3/1 Very Dark Grey \n20 \u2013 30 10YR 3/1 Very Dark Grey \n\n\n\nMiddle   \n0 \u2013 10 10YR 2/2 Very Dark Brown to 10YR 3/1 Very Dark Grey \n\n\n\n10 \u2013 20 10YR 2/2 Very Dark Brown to 10YR 3/1 Very Dark Grey \n20 \u2013 30 10YR 3/2 Very Dark Grayish Brown \n\n\n\nLower \n0 \u2013 10 10YR 4/2 Dark Grayish Brown to 10YR 3/1 Very Dark Grey \n\n\n\n10 \u2013 20 10YR 4/2 Dark Grayish Brown to 10YR 3/1 Very Dark Grey \n20 \u2013 30 10YR 3/2 Very Dark Grayish Brown to 10YR 3/1 Very Dark Grey \n\n\n\nControl (Plot without G. sepium)* \n\n\n\n \n0 \u2013 10 10YR 2/2 Very Dark Brown to 10YR 4/2 Dark Grayish Brown  \n\n\n\n  10 \u2013 20 10YR 3/2 Very Dark Grayish Brown to 10 YR 5/2 Grayish Brown \nNote: * The depth of soils in control plots are < 20 cm  \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 20166\n\n\n\nNurjanto et al.\n\n\n\noxidation and leaching (Machado and Silva, 2001; Bayer et al., 2002; Smiley \nand Kroschel, 2008). Soil depth affects accumulation of soil organic matter by \naffecting its allocation in the soil. Organic matter is produced by vegetation and \naccumulated in the upper part of the soil. With time, further production of organic \nmatter may affect soil organic matter content in the lower soil layer.  A study by \nDeng et al. (2013) showed that soil organic carbon in 0 \u2013 20 cm was significantly \nhigher than in 20 \u2013 40 cm and 40 \u2013 60 cm soil layer only at the early (< 50 year) \nsuccession stage while at the later stage (>50 year), soil organic carbon in both \nlayers were not different but significantly higher than in 40 \u2013 60 cm layer. The \nlower soil organic carbon reported by Macedo et al. (2008) probably was due to \nthe shorter period of reforestation where the soil was  dominated by sand and the \nsteep sloping topography.  A study by Supriyo et al. (2013)  in compartment 17 \nof Wanagama 1 which have Alfisols soil (clay fraction is dominated by kaolinite) \nand deeper (up to 90 cm) soil depth (Supriyo, 1992) found soil organic carbon \ncontent in 0 \u201310 cm layer under various forest stands to vary from 1.3 to 2.8 %.\n\n\n\nThe study by Deng et al. (2013) also clearly shows that period of rehabilitation \nof an area also affects soil organic matter content. In compartments 5 and 6 of \nFRE Wanagama 1 which had been reforested for 44 and 28 years, there was, \nhowever, no difference in soil organic matter content between stand ages. This is \nprobably due to the characteristics of G. sepium, which is a fast growing pioneer \ntree species; it produces a great amount of litter which decomposes rapidly with \nthe organic matter being distributed in the shallow soil depth (both compartments \nhave soil depth varying from only 20 to 30 cm).\n\n\n\nSoil pH was alkaline (> 7), indicating the effects of the monsoonal climate \nand limestone. In the long dry season of the monsoon season,  water movement \ncan be in an upward direction causing some calcification (Tan, 2008) which can \nincrease pH. Bulk density of the soils varied from 0.94 \u2013 1.08 g cm-3 at the upper \n(0\u201310 cm) layer and was generally lower than at the 10\u201320 cm layer which had \na bulk density of 1.04 \u2013 1.13 g cm-3, thus reflecting the contents of soil organic \nmatter. Hossain et al. (2015) states that bulk density of forest soils are positively \ncorrelated with soil organic carbon content. Result of this research is also in \nagreement with McLaren and Cameron (2005) and Deng et al. (2013). A volcanic \nash soil in New Zealand which typically contains 15 \u2013 20 % organic matter would \nhave a bulk density of 0.66 g cm-3 in the upper layer and 0.73 g cm-3 in the lower \nlayer (McLaren and Cameron, 2005). Similarly, forest soils at 0 \u2013 20 cm layer \nwhich have 2.2 \u2013 2.3 %  soil organic carbon have a bulk density of 1.1 g cm-3, \nlower than at 40 \u2013 60 cm layer where the  soil organic carbon is 1.4 \u2013 1.5 % and  \na bulk density of 1.2 \u2013 1.27 g cm-3 (Deng et al. 2013).   \n\n\n\nSoil Fraction \nResults of this study (Table 3) showed that the soil contained a high proportion of \ncalcium carbonate and the deepest soil layer contained higher calcium carbonate, \nconfirming that the soils were developed from limestone parent materials. Soils \nwith a high proportion of calcium carbonate  and which originated from weathering \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 7\n\n\n\nSmectite under Heavy Clayey Soils Development\n\n\n\nof limestone parent materials have been  previously reported in uplifted coral reef \nforests in tropical Taiwan (Liao et al. 2006). \n\n\n\nTABLE 2\nSoil organic matter, pH and bulk density of soils under 44- and 28- year old G. sepium\n\n\n\nin FRE Wanagama 1 \n\n\n\nThe soils also contained a high proportion of soil organic matter which was \npresent as organic debris and other forms (Table 3). Tropical forest soils with high \norganic matter content have also been reported, for example, in Brazil (Machado \nand Silva, 2001; Macedo et al., 2008) and in an uplifted coral reef of tropical \nTaiwan (Liao et al. 2006). These reports support the observation by Fisher and \nBinkley (2000) that many tropical soils contain high amounts of organic matter \nand nutrients. \n  \n\n\n\nTable 2. Soil organic matter, pH and bulk density of soils under 44 and 28 year old \nG. sepium in FRE Wanagama 1  \n\n\n\n\n\n\n\nSlope Depth \n(cm)  \n\n\n\nSoil organic  \ncarbon (%) pH Bulk density \n\n\n\n(g cm-3) \n44 year old G. sepium stand \n\n\n\n     \nUpper \n\n\n\n0 \u2013 10 6.51 + 0.92a 7.47 + 0.21 1.02 + 0.01 \n10 \u2013 20 3.08 + 0.39b 7.46 + 0.36 1.12 + 0.04 \n20 \u2013 30 2.90 + nd b 6.60** \n\n\n\n   \nMiddle \n\n\n\n0 \u2013 10 5.04 + 1.08a 7.50 + 0.32 1.05 + 0.08 \n10 \u2013 20 2.73 + 0.56b 7.42 + 0.88 1.06 + 0.07 \n20 \u2013 30 3.47 + nd b 6.41** \n\n\n\n   \nLower \n\n\n\n0 \u2013 10 6.98 + 1.16a 7.26 + 0.17 1.03 + 0.09 \n10 \u2013 20 3.63 + 0.34b 7.17 + 0.25 1.11 + 0.06 \n20 \u2013 30 2.06 + 0.93b 7.23 + 0.25 \n\n\n\n   28 year old G. sepium stand            \n\n\n\nUpper \n0 \u2013 10 5.45 + 0.13a 7.35 + 0.23 1.08 + 0.03 \n10 \u2013 20 3.27 + 0.55b 7.38 + 0.28 1.13 + 0.02 \n20 \u2013 30 2.69 + nd b 6.86** \n\n\n\n   \nMiddle \n\n\n\n0 \u2013 10 5.51 + 1.00a 7.72 + 0.01 1.02 + 0.09 \n10 \u2013 20 3.38 + 0.65b 7.73 + 0.02 1.11 + 0.03 \n20 \u2013 30 3.14 + 0.51b  7.68 + 0.19 \n\n\n\n   \nLower \n  \n\n\n\n0 \u2013 10 5.22 + 0.91a 7.29 + 0.28 0.94 + 0.14 \n10 \u2013 20 3.22 + 0.21b 7.30 + 0.34 1.04 + 0.06 \n20 \u2013 30 2.97 + 1.44b 6.62 + 0.15       \n\n\n\nControl (Plot without G. sepium)* \n\n\n\n 0 \u2013 10 5.39 + 0.15a 7.8 1.17 + 0.04 \n\n\n\n \n10 \u2013 20 2.08 + 0.08b 8.0 1.36 + 0.02 \n\n\n\nNote:  *   The depth of soils in control plots are < 20 cm \n ** Data is obtained from 1 plot, the other 2 replication plots have soil depth  \n      < 20 cm \n \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 20168\n\n\n\nNurjanto et al.\n\n\n\nTABLE 3\nOrganic debris, CaCO3, soil organic matter and inorganic particle content of soils under \n\n\n\n44- and 28- year-old G.sepium in FRE Wanagama 1\n\n\n\nInorganic particles, as expected, constituted the highest proportion of the soil, \nvarying from 82 to 86% for soils under G. sepium, markedly higher than in the \ncontrol soils which had inorganic content of 70 to 76 % (Table 3). Separation of \nsoil inorganic particles revealed that the clay fraction dominated the  inorganic \npart of the soil with only a small proportion of silt and sand fraction (Table 4) \nthus indicating that the soil textural class was heavy clay. Soils under G. sepium \nhad clay content  of 87 \u2013 90 %, markedly higher than the control soils which had \na clay content of 79 to 82 %. This indicates that stands of G. sepium  in  the area \nfacilitated formation of soil mineral particles, in particular, the clay fraction. The \nmechanism of clay formation is discussed below.\n\n\n\nTABLE 4\nSand, silt and clay content of soils under 44- and 28-year-old G. sepium in FRE \n\n\n\nWanagama 1\n\n\n\nTable 3. Organic debris, CaCO3, soil organic matter and inorganic particle content of \nsoils under 44 and 28 year old G. sepium in FRE Wanagama 1 \n\n\n\n\n\n\n\nPlot Depth (cm) Organic debris \n(%) CaCO3 (%) Organic matter \n\n\n\n(%) \nInorganic \n\n\n\nparticles (%) \n44 year \nold G. \nsepium  \n\n\n\n0 \u2013 10 0.88 + 0.05a 9.48 + 1.02a 6.64 + 0.64a 82.99 + 0.44a \n10 \u2013 20 0.40 + 0.11b 9.13 + 0.91a 3.85 + 1.09b 86.62 + 0.58b \n20 \u2013 30 0.16 + 0.12c 15.47 + 5.34b 2.53 + 0.52b 81.84 + 5.66a \n\n\n\n28 year \nold G. \nsepium  \n\n\n\n0 \u2013 10 1.07 + 0.44a 9.05 + 1.29a 7.35 + 0.91a 82.53 + 0.11a \n10 \u2013 20 0.67 + 0.28b 8.07 + 1.47a 4.59 + 0.59b 86.68 + 0.68b \n20 \u2013 30 0.06 + 0.04c 12.98 + 5.12b 4.72 + 1.30b 82.24 + 4.70a \n\n\n\nControl 0 \u2013 10 0.56 21.00 2.27 76.17 \n10 \u2013 20 0.15 27.46 1.57 70.82 \n\n\n\nNote: Relative proportion of the soil components are estimated on the basis of \ncomponent weight \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 4. Sand, silt and clay content of soils under 44 and 28 year old G. sepium in \nFRE Wanagama 1 \n\n\n\n \nPlot Depth (cm) Sand (%) Silt (%) Clay (%) \n\n\n\n44 year old \nG. sepium  \n\n\n\n0 \u2013 10 6.35 + 0.86 6.68 + 1.56 86.97 + 2.14 \n10 \u2013 20 5.89 + 0.69 6.40 + 1.59 87.71 + 2.11 \n20 \u2013 30 3.68 + 0.99 8.28 + 1.19 88.04 + 0.35 \n\n\n\n28 year old \nG. sepium \n\n\n\n0 \u2013 10 3.40 + 1.31 9.06 + 4.08 87.54 + 5.38 \n10 \u2013 20 6.50 + 0.69 5.83 + 0.28 87.67 + 0.46 \n20 \u2013 30 4.68 + 0.91 4.95 + 0.70 90.36 + 0.92 \n\n\n\nControl 0 \u2013 10 10.85 9.74 79.41 \n\n\n\n10 \u2013 20 7.02 11.01 81.97 \n\n\n\nNote: Relative proportion of the soil particles are estimated on the basis of particle \nweight \n\n\n\n\n\n\n\n\n\n\n\nK saturated, 20oC \n\n\n\nK saturated, 100oC \n\n\n\nK saturated, 350oC \n\n\n\nK saturated, 550oC \n\n\n\nMg saturated, 20oC \n\n\n\nMg saturated, Gly \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 9\n\n\n\nSmectite under Heavy Clayey Soils Development\n\n\n\nClay Mineralogy \nXRD patterns of all soil samples were similar. Figure 1A and 1B show XRD \npatterns of soil samples from the area under 44- and 28- year- old G. sepium stands \nat 0 \u2013 10 cm soil layer after treatment with K saturation. Those A major peaks \noccurred at 12.80 \u1ea2 which resembles smectite mineral, and this was followed by \n7.17 \u1ea2 (kaolinite mineral), 6.28 \u1ea2 (second order smectite), and 3.58 \u1ea2 and 3.34 \u1ea2 \nwhich resembles a quartz mineral. \n\n\n\nFigure 1: X-Ray Diffraction (XRD) pattern of clay soil from 28- (A) and 44- (B) year-old \nG. sepium stands at 0 \u2013 10 cm layer after the soil samples were mounted on glass slides \n\n\n\nand treated with K saturation and dried at room temperature (20o C).\n\n\n\nFigure 2 is  a compilation of XRD patterns after the prepared samples were treated \nwith either Mg saturated and air dried, Mg saturated and glycerol solvated, K \nsaturated and air dried, heated to 100o C, 350o C or 550o C, flat background. This \nfigure confirms the presence of smectite mineral following this pattern: XRD peak \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201610\n\n\n\nNurjanto et al.\n\n\n\nof Mg saturated air dried clay occurred at 15.71 \u1ea2 which shifted to 18.32 \u1ea2 in Mg \nsaturated glycerol solvated clay. The peak collapsed to 12.80, 12.34 and 12.20 \u1ea2 \nin K saturated air dried, K saturated heated to 100o C and K saturated heated to \n350o C clay respectively, which then contracted to 9.97 \u1ea2 in K saturated heated \nto 550o C clay. This behaviour is  in agreement with the description given by \nSupriyo (1992) for smectite mineral in Black Grumusol (Vertisol) soil found in \na flat agricultural area in Gading Village, approximately 8 km away from the \nstudied plot and in teak forests in East Java (Supriyo et al., 1992). \n\n\n\nFigure 2: X-Ray Diffraction (XRD) pattern of clay soils after the mounted samples \nwere treated with either Mg saturated air dried, Mg saturated and glycerol solvated, K \n\n\n\nsaturated air dried, heated to 100o C, 350o C or 550o C, flat background  \n\n\n\nTable 5 shows that the studied soils are composed of mainly smectite (proportions \nof smectite were more than 42% with smaller proportions of kaolinite and quartz). \nSoils under G. sepium stands at 0 \u2013 10 cm layer contained a greater proportion of \nsmectite than the lower layer. Similarly, soils under 44-year- old G. sepium stand \nalso had a greater smectite proportion than the 28-year-old stands. Control soils \nhad equal smectite proportions to surface (0 \u2013 10 cm) layer, however, considering \nthat the control soil was composed of markedly smaller proportions of inorganic \nparticles and clay fraction than surface soil, the control soil probably contained \nlesser amounts of smectite. These results support the suggestion that  the existence \nof G. sepium stands facilitated the formation of the soil through formation of the \nsmectite mineral. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 11\n\n\n\nSmectite under Heavy Clayey Soils Development\n\n\n\nTABLE 5\nMajor clay mineral content of soils under 44- and 28-year-old G. sepium in FRE \n\n\n\nWanagama 1\n\n\n\nSmectite is formed in situ from dissolution of limestone parent material. \nLimestone is a sedimentary rock which is mainly composed of calcium carbonate \nand weathers relatively easily (McLaren and Cameron, 2005). Dissolution of \ncarbonaceous materials (limestone) by organic acid produced by soil organisms, \nor derived from organic matter decomposition or root exudates have been shown \nby many researchers (Chang and Li, 1998; Blum et al., 2002; Rosling et al., \n2004; Bonneville et al., 2011; De la Rosa-Garcia et al., 2011; Smits et al., 2012). \nThe weathering process causes various ions to be released which then become \navailable for plant absorption, lost through leaching or are transformed into other \nminerals (McLaren and Cameron, 2005). Borchardt (1989) has described several \nefforts to synthesis smectite and the first effort to synthesis smectite under room \ntemperature and pressure was conducted by Sedletski in 1937. It was carried out by \nreacting Na silicate and Na aluminate, washing with MgCl2 solution and distilled \nwater and leaving the mixture under moist condition for 4 years (Borchardt 1989). \nIn nature, smectite can be found in soils at various environmental conditions \nwhich support its formation and preservation, that is, in soils containing high \namounts of Si and basic cations, especially Mg, and a poorly drained condition \nwhich prevents loss of those elements through leaching (Borchardt, 1989; Buol \net al., 2003; Chittamart et al., 2010). Formation of smectite under this condition \nis termed as silicification process (Tan, 2008), as in the case of the  smectite \nformation in the studied areas. \n\n\n\nLimited leaching is a prerequisite for formation and preservation of smectite. \nThis can be achieved in basin areas (Borchardt, 1989), in areas with an arid or \nsemi-arid climate, in areas where a soil layer of slow permeability at shallow \ndepth occurs and/or the soil material possesses a self-preserving property \n(slow permeability of the smectite-dominated soil) (Buol et al., 2003). Periodic \nwaterlogging in paddy cultivation is an important condition for formation of \nsmectite-rich vertisols in Thailand (Chittamart et al., 2010). In our study, in \n\n\n\nTable 5. Major clay mineral content of soils under 44 and 28 year old G. sepium in \nFRE Wanagama 1 \n\n\n\n \nPlot Depth (cm) Smectite (%) Kaolinite (%) Quartz (%) \n\n\n\n44 year old \nG. sepium \n\n\n\n0 \u2013 10 80.02 + 4.41a 7.40 + 2.64a 10.00 + 3.47 \n10 \u2013 20 68.64 + 5.10b 12.20 + 1.59b 16.50 + 2.24 \n20 \u2013 30 64.02 + 15.3b 15.59 + 8.75b 16.68 + 5.19 \n\n\n\n28 year old \nG. sepium \n\n\n\n0 \u2013 10 67.78 + 9.22b 15.29 + 4.90b 13.48 + 4.24 \n10 \u2013 20 52.47 + 1.81c 27.06 + 0.63c 15.59 + 0.99 \n20 \u2013 30 41.59 + 14.9c 32.67 + 11.6c 17.16 + 1.16 \n\n\n\nControl \n0 \u2013 10 79.99 4.00 13.21 \n10 \u2013 20 83.24 2.84 13.92 \n\n\n\nNote: Relative proportion of the minerals are estimated on the basis of XRD peak \nheight \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201612\n\n\n\nNurjanto et al.\n\n\n\ncompartments 5 and 6 FRE Wanagama 1, the smectite was formed in sloping \nareas with  a degree varying from 20 to 40%. High soil organic matter status \nand terracing might play a role in the formation of smectite under this condition. \nMavris et al. (2010) stated that acidity and availability of (organic) ligands \npromote dissolution reaction of primary minerals and govern its transformation \ninto secondary minerals. In the presence of humic acid and other organic acids, \nsilica is easily dissolved and forms complexes or chelates causing the silica to \nremain soluble and free in water (Mavris et al., 2010). In a well-drained area, \nthis favours desilification (Tan, 2008). However, when leaching is inhibited and \nwater tends to flow upward, which occurs in the long dry season of an  area \nunder a monsoon climate, concentration of ions in the near surface facilitates \nincreased formation of smectite. Limited leaching in this area can be maintained \nbecause the clay soil is dominated by smectite, contains high soil organic matter \nand the area is densely vegetated. Smectite has slow permeability (Buol et al., \n2003) and contains high negative charges that can control leaching of essential \ncations (Chittamart et al., 2010). High soil organic matter content enhances this \nproperty (Lal, 1989). Yang et al. (2014) state that high soil organic matter content \nin the alpine grassland of China is  the dominant factor controlling high water \nretention of the soil. Two mechanisms are suggested for this condition. Firstly, \nsoil organic matter affects soil bulk density and soil porosity thus increasing the \ncapacity to store water. Secondly, soil organic matter influences soil absorption \ncapacity. Based on these mechanisms, therefore, soils at 0 \u2013 10 cm layer which \nhave higher soil organic matter content have a higher proportion of smectite \nthan the lower layers which have a lower soil organic matter content. Also root \nsuction of the densely growing G. sepium also allow for greater water retention \nof the soil. Leung et al. (2015) demonstrated that in a wetting event vegetated \nsoil had greater root suction by 100 to 160% compared to bare soil. Additionally, \nterrace construction and possibly the presence of parent rock material just under \nthe shallow soil with slow permeability may also contribute to the formation of \nsmectite.\n\n\n\nIn situ soil formation in tropical areas through dissolution of limestone parent \nmaterial has also been  reported by Bautista et al. (2011) and Sedov et al. (2008) in \nthe Yucatan Peninsula of Mexico and by Mella and Mermut (2010) in West Timor, \nIndonesia.  Soils in the Yucatan Peninsula, Mexico have shallow soil depth (lesser \nthan 35 cm) and have the following characteristics:  coloured reddish dark brown \nto very dark brown; often found mixed with gravel; pH variying from mild acid to \nalkaline; high in organic matter content (up to 15%); and dominated by 2:1 type \nclay and Fe-oxide (Sedov et al., 2008). In the study by Mella and Mermut (2010) \nsmectite-mica-dominated Mollisols were found in mixed-deciduous forest areas \nwhile kaolinite-dominated Alfisols were found in savannah areas. Soil organic \nmatter content of both soils was not described but the Mollisols had lower bulk \ndensity than the Alfisols.\n\n\n\nTable 5 shows that kaolinite and quartz are also found in the soils. These \nmineral are probably derived from the limestone parent material. According to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 13\n\n\n\nSmectite under Heavy Clayey Soils Development\n\n\n\nMcLaren and Cameron (2005), the weathering processes, beside releasing soluble \nmaterials (ions), also release secondary minerals, such as kaolinite, and resistant \nprimary minerals, such as quartz. Thus both kaolinite and quartz found in this \nstudy were from the soils forming sedimentary bedrocks (parent materials).  \n\n\n\nSoils which developed under G. sepium stands in compartments 5 and 6 of \nFRE Wanagama 1, can be classified as Vertisols due to the predominant proportion \nof smectite and the expanding 2:1 clay.  This is based on the statement of Buol et \nal. (2003) who cited  the definition of Dixon and Nash (1968) that soil with a high \ncontent of clay (>30%) which predominantly consists of 2:1 expanding clay is \nclassified as Vertisols. Similarly Tan (2008) describes Vertisols as  generally  been  \ncharacterised by a clay fraction containing smectite or montmorillonite which \nare 2:1 layer lattice-type clays. Additionally, Tan (2008) states that Vertisols in \nIndonesia may have smectite content varying from 70 to 100% but in some cases \ncan be as low as 50% (mixed with kaolinite). However, considering that these \nareas have a  slope varying in degree from 20 \u2013 40%, Vertisols that developed \nin these areas may represent intermediate stages of soil development and can \ntransform to different soil orders because in sloping areas, 2:1 type expanding \nclay can transform to 1:1 type non-expanding clay (Borchardt, 1989; Buol et \nal., 2003). Improvements in drainage which occur in sloping topography, will \nenhance the weathering process causing transformation of smectite to kaolinite \n(Borchardt 1989) through the desilicification process  in which silica is released \nfrom soil silicates (Tan, 2008). A study by Supriyo (1992) found that soil in \ncompartment 17, approximately 2 km away from the studied compartments of \n5 and 6) was classified as Mediterranean soil (presently is termed Alfisols) and \nwas composed mainly of  kaolinite with only a fair proportion of smectite. This \narea has been planted with Eucalyptus while the forest floor is used for crop \ncultivation, such as peanut and corn. Soil depth can be 110 cm deep with the \norganic matter content at the A layer (0-19 cm) can be 2.6 %. Reddish Alfisols \nare also commonly found in nearby areas with similar topographic condition but \nare intensively cultivated. In West Timor, Indonesia, both kaolinite-dominated \nAlfisols and Mollisols, dominated by smectite and mica, exist in savannah and \nmixed-deciduous forest areas, respectively, and here the  topography  is less than \n10 % (Mella and Mermut, 2010).   \n\n\n\nThis study shows that rehabilitation of degraded forest areas enables built up \nof soil organic matter and could facilitate the development of soil.  Accumulation \nof organic matter in the soil surface and activities of organisms that carry out its \nconversion are crucial for soil development processes (Sourkova et al., 2005), \nbecause they will significantly increase the soil organic component and affect \nthe inorganic mineral of the soil through weathering of parent rock material and \nregulating transformation of the minerals (Fisher and Binkley, 2000; Buol et al. \n2003; van Breemen and Buurman, 2002).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 201614\n\n\n\nNurjanto et al.\n\n\n\nCONCLUSIONS\nThe soil described in this study was developed after efforts at rehabilitation were \ncarried out by successfully planting G. sepium and constructing terraces. The soil \nformed is dark brown to very dark grey in colur, contains high amouns of organic \nmatter, is pH neutral to alkaline and is dominated by clay particles which mainly \nconsist of smectite. The soil can therefore be classified as Vertisols. The smectite-\ndominated clay is developed from dissolution and subsequent precipitation of \nlimestone parent materials. Monsoonal climate, high soil organic matter content, \ndense vegetation and terrace construction contributed to the formation and \npreservation of the smectite mineral. \n\n\n\nACKNOWLEDGEMENTS\nThe authors gratefully extend their thanks to Forest Research and Education \nWanagama1 Yogyakarta, Indonesia and Laboratory of Applied Chemistry for \nEnvironmental Industry, Faculty of Agriculture, Ehime University, Japan for \npermission and support towards the completion of this research.  \n\n\n\nREFERENCES\nBayer, C., J. Mielniczuk, L. Martin-Neto and P.R. Ernani. 2002. Stocks and \n\n\n\nhumification degree of organic matter fractions as affected by no-tillage on a \nsubtropical soil. Plant and Soil 238: 133\u2013140\n\n\n\nBlake, G.R. and K.H. Hartge. 1986. Bulk density.  In A. Klute (Ed.) Methods of Soil \nAnalysis. Part 1. 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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 95-106  (2020)                         Malaysian Society of Soil Science\n\n\n\nEffects of Free-Living Diazotrophs on Plant Growth and \nRoot Colonisation of Pak Choi\n\n\n\nA.M. Asilah \n\n\n\nDepartment of Agricultural Science, Faculty of Technical and Vocational,\nUniversiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia\n\n\n\nABSTRACT\nAn experiment was conducted to determine the effects of free-living diazotrophs \non the growth and root colonisation of pak choi. Free-living diazotrophs were \nisolated from soil samples obtained from the selected areas of the university \ncampus. The isolation method used Jensen\u2019s liquid (N-free medium) and a \nspectrophotometer to determine diazotrophic bacterial growth and check the level \nof turbidity, respectively. Bacteria that grew in the N-free liquid medium were \nassumed to be diazotrophs. Three diazotrophic strains were selected from the \nisolation in the N-free liquid medium for a pot experiment. Growth of the strains \nin the Jensen\u2019s liquid medium was an indicator for selection of strains in this study. \nThe level of turbidity was compared with that of the control. Results showed that \nshoot biomass was significantly affected by the diazotrophs. Plants inoculated \nwith Bacterium 3 produced significantly larger shoots (F2.35 = 2.10, P < 0.001) than \nthose grown with Bacterium 1, 2 and 0 (uninoculated). However, the fresh weight \nof root and root colonisation analyses were unaffected by treatment. The plants did \nnot grow well and were stressed for most of the experiment. The combined effects \nof autoclaving and nutrient limitation possibly adversely affected plant health. \nNevertheless, plants grew sufficiently well for the experiment to be continued and \nfor testing the efficacy of the bacteria.\n \nKey words: Pak choi, diazotrophs, N-free liquid medium.\n\n\n\n___________________\n*Corresponding author : E-mail: asilah@ftv.upsi.edu.my\n\n\n\nINTRODUCTION\nThe dramatic increase in the level of global warming and climate change since \nthe last century has led to a huge strain on the environment (Alburquerque et \nal. 2013). The agriculture sector contributes a large amount of greenhouse gases \nwhich have an effect on climatic change (Montzka et al. 2011; Vermeulen et al. \n2012; Pittelkow et al. 2014). At times, agricultural practices may have harmful \neffects on the environment such as the process of anthropogenic emissions of \nnitrous oxide (N2O) and methane (CH4) (Pittelkow et al. 2013). Attempts are \nbeen made to meet world demand for food by raising food production in a \nsustainable manner. Fertiliser usage plays a vital role in protecting food security \nand reducing environmental impacts. Therefore, several methods must be adopted \nto increase crop production whilst keeping environmental impact low. There is a \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202096\n\n\n\npotential approach to implement the method which is the utilisation of beneficial \nmicroorganisms to reduce usage of chemical fertilisers. Therefore, in this study, \nthe effects of plant growth-promoting bacteria (PGPB) such as diazotrophs, on \ngrowth and root colonisation on pak choi were studied. Diazotrophs are beneficial \nmicroorganisms that play a crucial role in sustainable agriculture. Examples of \nbacteria under the PGPB category are nitrogen fixers and phosphate solubilisers. \nThese bacteria have beneficial effects on growth enhancement of plants and \neliminate their dependence on fertilisers (Prakamhang et al. 2009; Weyens et \nal. 2009; Yang et al. 2009). Phosphate solubilisation and nitrogen fixation are \nprocesses which can increase N and P uptake by PGPB such as nitrogen fixers \nand phosphate solubilisers (\u00c7akmak\u00e7i et al. 2006; Pineda et al. 2010). Root \ncolonisation is a considerable factor in successful interactions between PGPB \nand plants that lead to beneficial responses in plant growth enhancement (Bashan \n1986). Exceptional colonisation may lead to reduced use of chemical fertilisers \nfor plant production (Bloemberg and Lugtenberg 2001; \u00c7akmak\u00e7i et al. 2006). \nTherefore, this study aims to isolate free-living diazotrophs from various sources \nand determine their root colonisation rates and effects on plant growth.\n\n\n\nMATERIALS AND METHODS\nA pot experiment was conducted to quantify the effects of three bacterial isolates \nwhich are potential nitrogen fixers (based on their ability to grow in an N-free \nmedium) on pak choi. Prior to the experiment, bacteria were isolated from soil \nsamples as described in the method below.\n\n\n\nProcedure of Soil Sampling for Diazotrophic Isolation\nSoil samples were collected from under pine trees, field edge, grass and nettle \nareas of the arboretum in the Sutton Bonington Campus of the University of \nNottingham. The samples were randomly collected and taken to the laboratory for \nisolation and estimation of PGPB populations. \n\n\n\nDiazotrophic Isolation from Soil \nOne gram (fresh weight) of soil sample was placed in a sterile universal tube. \nA series of 10-fold dilutions was conducted for each collected soil sample and \nreplicated three times. The universal tubes containing soil samples were shaken, \nand 0.1 mL aliquot was plated onto a spread plate containing Jensen\u2019s agar to \ndetermine diazotrophic bacterial growth. Jensen\u2019s agar is nominally N-free. \nJensen\u2019s agar medium comprised 20 g sucrose, 1 g K2HPO4, 0.5 g MgSO4, 0.5 \ng NaCl, 0.1 g FeSO4, 0.005 g Na2MoO4 and 20 g agar in 1 L. Bacterial colonies \nthat grew were isolated into single species cultures and streaked onto the Jensen\u2019s \nmedium. Given that the agar present in the medium is likely to contain nitrogen, \neach bacterium was inoculated into a 50 mL Falcon tube containing a Jensen\u2019s \nliquid medium (i.e. the agar was absent). All the tubes were shaken for three \ndays at room temperature prior to determining the turbidity level by using \nspectrophotometer (500 nm wavelength). All the samples were compared with \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 97\n\n\n\nJensen\u2019s liquid medium (without bacteria) as a medium of control. Bacteria that \nshowed growth in the N-free liquid medium were assumed to be N-fixers and \nwere maintained on Jensen\u2019s agar medium. \n\n\n\nGrowth Medium Preparation and Seed Germination \nSand was washed a few times by using deionised water. The washed sand was \nair-dried and mixed with top soil (3:1). Subsequently, the mixture of sand and \nsoil was sterilised using an autoclave (121\u00b0C for 20 min on two consecutive \ndays) and placed in pots (120 g mixture of soil and sand per pot). Five pak choi \nseeds were sown in each pot. The plant pots were watered daily with sterilised \ndeionised water until the first harvest. Approximately 20 mL of Hoagland N-free \nnutrient solution was applied on alternate days to the remaining plant pots. After \ntwo weeks, the Hoagland solution was changed to low nutrient fertiliser NPK \ncompound. Approximately 30 mL of the nutrient fertiliser was applied daily to the \nplant pots. The pots were located in a growth room with 12 h light-dark cycle with \ndaytime and night-time temperatures of 20\u00b0C and 18\u00b0C, respectively. After seed \ngermination, the plants were thinned to one plant per pot. The plants were also \nleft to recover for two days before inoculating bacteria. This experiment of four \ntreatments (three bacterial isolates and one control) \u00d7 three sampling times with \nfour replications was established in factorial randomised complete block design.\n\n\n\nPreparation and Application of Inocula\nThree diazotrophic strains isolated from N-free liquid medium were selected for \nthis experiment. Three sterile falcon tubes were used by filling each with 35 mL \nsterile deionised water. For each bacterium, a sterile loop was used to scrap cells \nfrom the surface of the agar plates. The sterilised loop of bacteria was added to the \nfalcon tubes containing sterilised deionised water. This method was continued until \nthe water was cloudy with the cells. Each plant was inoculated with approximately \n2 \u00d7 102 cfu/mL of live bacterial cells. The control pots (non-inoculated treated) \nwere applied with the same amount of sterilised deionised water (2 mL). The \nnumber of cells applied to each plant for each bacterium was determined using \na dilution series and plating onto Jensen\u2019s agar medium. A spectrophotometer \n(500 nm wavelength) was also used to determine the diazotrophic population by \nmeasuring the level of turbidity.\n\n\n\nProcedure for Sampling Plants\nPlants were harvested on three occasions, namely, 13, 34 and 47 days, after \ninoculating with the bacteria.\n\n\n\nDetermination of Shoot Biomass \nShoots were cut with a scalpel just above the soil line, and each shoot was put into \nan individual envelope. Each envelope was labelled according to the treatments. \nAll these envelopes were dried in an oven at 57\u00b0C to 60\u00b0C for three days until \nconstant weight was achieved.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 202098\n\n\n\nDetermination of Rhizosphere Population \nThe root system was removed from the soil and shaken to remove the soil. Soil \nremaining on the roots after shaking was considered as the rhizosphere fraction. \nThe entire root system was then placed into 10 mL of sterilised deionised water \nin a sterile tube (universal tube). Subsequently, the tube was vortexed for 10 s to \nremove the root system. The roots were then placed into a clean and dry universal \ntube which was kept to one side in the fridge. The \u2018rhizosphere solution\u2019 was \nserially diluted to a 10-2 dilution. Approximately, 0.1 mL of each dilution was \nplated onto a plate with Jensen\u2019s medium, and the plates were incubated at 20\u00b0C.\n\n\n\nDetermination of Endosphere Population \nThe root system was taken from the fridge (which was maintained after washing \nto remove the rhizosphere soil) and rinsed three times thoroughly in sterilised \ndeionised water, to remove any residual soil particles. The roots were then surface \nsterilised in 6% sodium hypochlorite for 20 min. The roots were dipped into 70% \n(v/v) for 30 sec (the method generally used for secondary surface sterilisation). The \nroots were then rinsed in sterile deionised water twice and macerated separately \nin universal tubes in 9 mL of sterilised deionised water. Lastly, approximately \n0.1 mL of the solution (which contained root fragments) was placed on plates \ncontaining Jensen\u2019s medium and were incubated at 20\u00b0C.\n\n\n\nRESULTS\n\n\n\nRoot Fresh Weight\nRoot fresh weight was unaffected by treatment, but significantly large roots were \nfound at harvest 3 (F1.23 = 0.17, P \u02c2 0.001) (Figure 1). Therefore, expressing the \ndata by using root weight as a covariate is important when observing the bacteria \n(log CFU/mL).\n\n\n\nFigure 1. Effect of bacterial inoculation at different sampling times in inoculated and \nuninoculated plants on root fresh weight production. \n\n\n\n Note: The data were log-transformed to satisfy the requirements for ANOVA (the \ndistribution should be normal). Data are mean \u00b1 standard error (n = 4).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 99\n\n\n\nShoot Biomass\nShoot biomass was significantly affected by bacteria (F3.35 = 4.37, P = 0.01) (Figure \n2). Furthermore, plants inoculated with Bacterium 3 produced significantly \nlarger shoots (F2.35 = 2.10, P \u02c2 0.001) than those grown with Bacterium 1, 2 or 0 \n(uninoculated).\n\n\n\nFigure 2. Effect of bacterial inoculation at different sampling times in inoculated and \nuninoculated plants on shoot biomass production. \n\n\n\nNote: The data were log-transformed to satisfy the requirements for ANOVA (the \ndistribution should be normal). Data are mean \u00b1 standard error (n = 4).\n\n\n\nRhizosphere Population\nThe rhizosphere population (log CFU g-1 root) was unaffected by treatment (Figure \n3), but fewer bacteria were observed at harvest 3 than that at harvest 2 (F1.23 = 0.01, \nP = 0.002). When data were expressed on per mL of extraction liquid basis, the \nrhizosphere population also remained unaffected by treatment (F3.22 = 2.49, P = \n0.087). However, the P value might indicate a trend. The CFU in the rhizosphere, \nwhether expressed as CFU/mL or as per gram, was insignificant. Root weight \nwas used as a covariate in the analysis of these data to account for the different \nroot sizes associated with each plant. Expressing the data on a per weight basis \ndid not account for any treatment-related changes in root architecture (e.g. altered \nlength, root hair or branching), which may have affected bacterial colonisation \nbut not necessarily root biomass. Therefore, expressing the data on the basis of \nthe amount of extraction fluid was arguably appropriate, particularly because \nroot weight was used as a covariate. The difference highlights the importance \nof expressing the data correctly. Future studies will quantify root architectural \nchanges, particularly root length. Furthermore, understanding which portion of \nthe root is colonised by the N2 fixers is important.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020100\n\n\n\nFigure 3. Changes in number of rhizosphere bacteria at different sampling times in \ninoculated and uninoculated plants, expressed on a per unit weight of root basis (a) and \n\n\n\nper mL of solution used for root preparation (b). \nNote: The data were log-transformed to satisfy the requirements for ANOVA (the \ndistribution should be normal). Data are means \u00b1 standard error (n = 4).\n\n\n\nEndosphere Population\nThe endosphere population (log CFU g-1 root) was unaffected by treatment (Figure \n4), but significantly fewer bacteria were observed at harvest 3 than that at harvest \n2 (F1.23 = 1.07, P \u02c2 0.001). In contrast, when data were expressed on per mL of \nextraction liquid basis, a large number of cells were isolated from roots inoculated \nwith bacterium B3 and few from the uninoculated control roots (Treatment as a \nsingle factor, F3.22 = 22.92, P \u02c2 0.001). A similar number of bacteria were isolated \nfrom all treatments that received bacterial inoculum at harvest 2 and 3, but \nnumbers extracted from the uninoculated roots were lower at harvest 3 compared \nwith those at harvest 2 (F3.22 = 3.14, P = 0.046). \n\n\n\n(a)\n\n\n\n(b)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 101\n\n\n\nRoot weight was used as a covariate in the analysis of these data to account for \nthe different root sizes associated with each plant. Expressing the data on a per \nweight basis did not account for any treatment-related changes in root architecture \n(e.g. altered length, root hair or branching), which may have affected bacterial \ncolonisation but not necessarily root biomass. \n Therefore, expressing the data on the basis of the amount of extraction \nfluid is arguably appropriate, particularly because root weight was used as \na covariate. The difference highlights the importance of expressing the data \ncorrectly. Future studies will quantify root architectural changes, particularly root \nlength. Furthermore, understanding which portion of the root is colonised by the \nendophytes is important.\n\n\n\n(a)\n\n\n\n(b)\n\n\n\nFigure 4. Changes in number of endosphere bacteria at different sampling times in \ninoculated and uninoculated plants expressed on a per unit weight of root basis (a) and \n\n\n\nper mL of solution used for root preparation (b). \nNote: The data were log-transformed to satisfy the requirements for ANOVA (the \ndistribution should be normal). Data are mean \u00b1 standard error (n = 4).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020102\n\n\n\nDISCUSSION\nDiazotrophs were isolated from the rhizosphere and endosphere of all plant \nroots regardless of the treatment. Previous literature also proved that several \nspecies of PGPB can colonise the rhizosphere of plant and simultaneously grow \nendophytically (Darbyshire and Greaves 1973; Quadt-Hallman et al. 1997; Sturz \nand Nowak 2000). These bacteria are facultative endophytes and include genera \nsuch as Azospirillum. The free-living nitrogen-fixing bacteria or diazotrophs, \nincluding the Azotobacter spp., are usually associated with the rhizosphere or \nthe rhizoplane where they generally live epiphytically. Obligate endophytes are \nonly found within the plant and are not free-living. Endophytes can access plant \nroots, either through active or passive mechanisms and colonise the host tissue \nwithout damaging the host, internally (Hallman et al. 1997; Reinhold-Hurek and \nHurek 1998; Hardoim et al. 2008). Previous literature suggests that this kind of \nbacteria can be isolated from surface-disinfected plant tissues or that some can \nbe extracted from internal plant tissues. Besides, the aforementioned bacteria are \ncapable of invading inner tissues such as xylem vessels and spread systemically \n(James and Olivares 1998).\n Fewer bacteria were isolated from the control treatments, but their \npresence in the uninoculated soil reflected the limited effectiveness of the \nautoclaving procedure. However, another possible effective sterilisation method, \nsuch as gamma (\u03b3) irradiation, should be used after autoclaving to confirm the \nabsence of microorganisms in the uninoculated soil. Previous researchers also \nshowed that sterilisation procedures used for soil were autoclaved (121\u00b0C, 1 h) or \nunderwent gamma (\u03b3) irradiation (50 kGy) (Mahmood et al. 2014). The plants did \nnot grow well and were stressed for most of the experiment. \n Consequently, nitrogen was given to the plants after the first harvest, \nbut their continued lack of growth may be due to the autoclaving process which \nhas previously been reported to release toxic compounds. The combined effects \nof autoclaving and nutrient limitation may have adversely affected plant health. \nNevertheless, plants grew sufficiently well for the experiment to be continued and \nfor testing the efficacy of the bacteria. \n Endosphere population results showed that plants were unaffected by \ntreatment. However, numerous existing works showed that endophytes benefit \nplants. Most endophytic microbes offer benefits to the plants (Compant et al. 2005; \nSessitsch et al. 2005; Sun et al. 2009; Rashid et al. 2012). Moreover, bacterial \nendophytes could colonise the plant interior and enhance plant growth through \ndifferent mechanisms (Glick 1995; Hallman et al. 1997; Reiter and Sessitsch \n2006; Rashid et al. 2012). The mechanisms involved include nitrogen fixation \n(Compant et al. 2005), induced systemic resistance (ISR) (Ait Barka et al. 2002), \nantibiotic production (Ezra et al. 2004) and phytohormone production (Lee et \nal. 2004). In addition, these mechanisms also increase phosphate solubilisation \n(Wakelin et al. 2004) and nutrient availability (Puente et al. 2009), in the case \nof facultative endophytes. Previous researchers also showed that plant-growth \npromoting rhizobacteria, such as diazotrophs, are important in plant growth \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 103\n\n\n\nenhancement through one or more mechanisms, including enhancement of \nnutrient uptake, direct stimulation of plant growth, elimination of plant pathogens \nand/or induction of pathogen resistance in plant hosts (Tailor and Joshi 2014). \nHowever, in this study, diazotrophic population was reduced towards the end of \nthe experiment. This phenomenon could be due to diazotrophs not maintaining \ntheir viability over time.\n\n\n\nCONCLUSION\nResults of this study showed that root colonisation of these diazotrophs on the \nsurface (rhizosphere) and interior (endosphere) of pak choi roots significantly \naffected plant growth enhancement, particularly shoot biomass. However, the \nfresh weight of root and root colonisation analyses were unaffected by treatment. \nFuture research should consider the potential effects of the diazotrophs on other \nanalyses, such as plant biomass, enzyme extraction, hormone determination and \nbacterial identification. Therefore, the plant roots must be reinoculated to increase \npopulation, biological activity and diazotroph viability.\n\n\n\nACKNOWLEDGEMENTS\nThe work was supported by Malaysian Ministry of Higher Education, Universiti \nPendidikan Sultan Idris and the University of Nottingham, United Kingdom \nduring the writing of this article.\n\n\n\nREFERENCES\nAit Barka, E., S. Gognies, J. Nowak, J. Audran and A. Belarbi. 2002. Inhibitory effect \n\n\n\nof endophyte bacteria on Botrytis cinerea and its influence to promote the \ngrapevine growth. Biological Control 24: 135-142.\n\n\n\nAlburquerque, J.A., P. Salazar, V. Barr\u00f3n, J. Torrent, M.C. del Campillo, A. Gallardo \nand R. Villar. 2013. 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"\n\n___________________\n*Corresponding author : E-mail: pawhar107@yahoo.com\n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 87-106 (2021)                          Malaysian Society of Soil Science\n\n\n\nAssessment of Potential Bacterial Isolates for Enhancing \nPlant Nutrient Uptake and Growth of Wheat \n\n\n\nSarki, M.S1, Panhwar, Q.A.2*, Ali, A.2, Jamali, M.S.1, Rajpar, I.1 \nand Depar, N.2\n\n\n\n1Department of Soil Science, Faculty of Crop Production, Sindh Agriculture \nUniversity, Tandojam\n\n\n\n2Soil & Environmental Sciences Division, Nuclear Institute of Agriculture (NIA), \nTandojam\n\n\n\nABSTRACT\nWheat is the major cereal crops in Pakistan and inoculation of beneficial microbes \nplays a major role in crop growth enhancement. This study was conducted to \nevaluate bacteria with potential to contribute to enhanced plant nutrient uptake \nand wheat growth. A total of 10 bacterial strains, isolated from the wheat crop, \nwere characterised. The results showed that selected isolates were able to produce \nIndole-3-acetic acid (IAA) and biofilm, fix atmospheric nitrogen and solubilise \ninorganic phosphate. However, NIA-2 and NIA-5 were the most efficient among \nthe isolated bacteria in regard to N2-fixation, biofilm production, P-solubilisation \n(57.32 and 45.38 %) and IAA production (4.28 and 3.49 mg L-1). A pot study was \nconducted on wheat crop to investigate the effect of NIA-2 and NIA-5 isolates \nwith full NP fertilisers (120 kg N ha-1 in the form of urea, 90 kg P2O5 ha-1 in the \nform of DAP) and at half rates of NP fertilizers (60 kg N ha-1 in the form of urea, \n45 kg P2O5 ha-1 in the form of DAP). The highest plant height (34.49 cm) and \nroot length (10.38 cm) were observed in half NP fertiliser application inoculated \nwith NIA-5 inoculated treatments. Similarly, the highest plant dry biomass (1.270 \ng plant-1) was recorded in half fertiliser application inoculated with NIA-5. All \nbacterial inoculated treatments showed the existence of microbes in the soil after \n45 days of sowing. Nevertheless, the highest bacterial population was recorded \nin half NP fertiliser with NIA-05 (5.560 log CFU g-1 soil). Significantly highest \nplant nitrogen (1.344 %), phosphorus (0.697 %) and potassium uptake (0.310 %) \nwere observed in half NP fertiliser with NIA-05. Overall, the half rate of the NP \nfertiliser inoculated with bacterial isolate NIA-5 improved nutrient uptake and \ngrowth of the wheat crop. Thus, this study suggests that these bacterial isolates \nmight be used as an inoculum for enhancing plant growth by supplying plant \nnutrient and phytohormones. \n\n\n\nKeyword: Bacterial population, inoculation, plant uptake, potential, wheat \ngrowth inoculation.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202188\n\n\n\nINTRODUCTION\nWheat (Triticum-aestivum L.) is grown in most parts of the world. China is the \nlargest wheat grower (30 million hectares), followed by the Russian Federation, \nIndia, the USA, Australia, Canada, Turkey and Pakistan. Wheat is one of the \nimportant agricultural cereal crops in Pakistan, contributing about 10 % of value \nadded to agriculture and 2.1 % to GDP of the country. Wheat is Pakistan\u2019s dietary \nstaple food and its flour accounts for 72 % of Pakistan\u2019s daily caloric intake with \na per capita wheat consumption of around 124 kg per year (PES 2017). Wheat is \ncultivated all over the world in various environments. It takes up 36 % of the total \ncultivated land, 30 % of the value added by major crops and 76 % of the total \nproduction of food grains. It offers livelihood to 43.5% of the rural population \n(PARC 2013). Wheat supplies high energy to the average diet and is the main food \ncereal crop which is better from the nutritional point of view than most cereals \nand other food staples (Abedi et al. 2010). \n Wheat requires nutrients for growth improvement and yield. These \nnutrients exist in soil but are depleted during the cultivation of various crop \nplants. Hence, to attain better growth and higher yields of wheat and other \nagricultural crops, fertilisers are applied for nutrient restoration (Ramteke and \nShirgave 2012). The mineral nutrients in soil are solubilised in water and taken \nup by plant roots. However, nutrient contents in soil are mostly irregular and not \nsufficient for plants growth. The major nutrients (NPK) which are taken up by \nplants in higher amounts by crops are frequently found in mixed fertilisers (Khan \net al. 2009). Increased flux into plant stem could probably deliver supplementary \nmicronutrients for seed biofortification through several mechanisms that enhance \nuptake of micronutrients by plant roots (Rana et al. 2011). NPK influences growth \nand development of the plant. Nitrogen performs a vital role in plant development \nas it makes a portion of amino acids, proteins, enzymes and chlorophyll molecules. \nPhosphorus plays a role in many life processes like photosynthesis, synthesis and \nbreakdown of carbohydrates and the transference of energy in the plant (Obreza \n2001). Potassium is crucial for primary physiological purposes e.g. development \nof sugars and starch, synthesis of proteins and cell division and growth (Abbas \nand Fares 2009). \n Chemical fertilisers in excess have great adverse effects on the soil and \nenvironment. One possible way to optimise crop production and maintain a \nhealthy environment is the usage of organic sources and soil microorganisms, \nmainly bacteria that perform several biological processes in plant growth and \nnutrient cycles. Biofertilisers comprise microorganisms which are helpful to plant \ngrowth and enhance crop yield. Several free living bacteria, useful for plant growth \nand for high yields, are known as plant growth promoting rhizobacteria (PGPR) \n(Kloepper 1994). The beneficial microbes contribute to plant growth through \nthe production of Indole-3-acetic acid (IAA), gibberellic acid, indole-3 butyric \nacid, siderophore, ammonia, HCN and solubilisation of inorganic phosphate. \nAnand and Nikhilesh (2015) showed that application of isolated microbes was \nable to promote growth of wheat resulting in higher productivity. These beneficial \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 89\n\n\n\nbacteria perform a vital role in improving growth and yield of crop plants \n(Ahemad and Khan 2009). Nitrogen fixating bacteria are important and perform a \npositive role in supplying nitrogen to the soil. The sensible utilisation of chemical \nfertilisers including that of N2 fixing inoculants and rhizobium can be an alternate \nor supplementary source for crop production (Dobrei et al. 2001). In this case, \nsoil beneficial microbes can be an alternative method for viable crop production \n(Panhwar et al. 2014). The use of several beneficial microbes such as Azospirillum \nsp. and Azotobacter sp. as bio-inoculants for plant growth has been confirmed by \nstudies (Naher et al. 2016; Panhwar et al. 2014; Ribaudo et al. 2006). The use \nof biofertilisers in agriculture offers several benefits: (i) is an organic source ii) \nminimises environmental pollution; (iii) improves soil health; and (iv) reduces \ninput cost of agriculture (Shamshuddin et al. 2016). In addition, the cumulative \neffect of humic substances and biofertilisers in soil enhances nutrient absorption \nby increasing the availability of nutrients to improve the physical structure of \nsoil. Biofertilisers are safe substitutes to the use of chemical fertilisers as they \nare environmentally friendly, have less impact on animals and human beings and  \nreduce pollution of the environment (Naher et al. 2016). \n The application of biofertilisers increases the absorption accessibility of \nseveral minerals to the plant and allows the plant to develop resistance to; this \nresults in a 25% reduction in nitrogen requirement to the plants (Kannaiyan 2002). \nIn addition, the combined application of beneficial microbes results in a significant  \nupsurge in  spikes, number of tillers, grain weight, grain size, spikelet per plant, \nspike length etc. Thus the use of 75% mineral N and beneficial microbes as \nbiofertiliser enhanced all the growth components in wheat (Chauhan et al. 2011). \nThese microbes (Azotobacter etc.) enhance the yield of many agricultural crops \nby about 10-12 % (Jaga and Singh 2010). A study by Kaushik et al. (2012) found \nthat inoculation of beneficial microbes improved nitrogen fixation and phosphate \nsolubilisation as well as enhanced straw and grain yield of plants compared \nto non-inoculated treatments. Mehnaz et al. (2010) found that the use of soil \nmicroorganisms such as Rhizobium and Azotobacter can fix atmospheric N2 and \nsynthesise growth enhancing substances which provide higher amounts of humus \nin soils in an environment friendly soil ecosystem. Panhwar et al. (2011) found that \nthe use of beneficial bacteria in soil rhizosphere improved plant growth and yield. \nThere has been much research interest in the potential of beneficial microbes and \nthere is now an increasing number of microbes being commercialised for various \ncrops. Several reviews have discussed specific features of plant growth promotion \nby these microbes (Saharan and Nehra 2011). Hence, the study was conducted to \nscreen out, characterise and assess the potential of selected bacterial isolates to \nimprove wheat growth.\n\n\n\nMATERIALS AND METHODS\nThe research was conducted in the Soil Microbiology Laboratory at the Nuclear \nInstitute of Agriculture (NIA) Tandojam, Sindh, Pakistan during the 2016-17 \nwheat season. A total of 10 bacteria isolated from wheat crop were selected to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202190\n\n\n\nassess their potential. The preliminary study was conducted under laboratory \nconditions for characterisation of isolates while the second study was done in a \nnet house.\n\n\n\nLaboratory Experiment\n\n\n\nScreening and characterisation of potential bacteria for improving wheat growth \nThe experiment was conducted in the Soil Microbiology Laboratory at Nuclear \nInstitute of Agriculture (NIA) Tandojam in three replications. The following \nobservations were recorded.\n\n\n\nDetermination of Indole acetic acid (IAA) production\nThe bacterial strains were cultured on nutrient media containing 2 mg mL-1 \n\n\n\ntryptophan and incubated at 28 \u00b1 2\u00b0C for 48 h. The cultures were centrifuged at \n7000 rpm for 7 m and one mL of the supernatant was added to 2 mL of Salkowsky\u2019s \nreagent (Gordon and Weber 1951). The IAA-concentration was determined using \nspectrophotometer at 535 nm.\n\n\n\nDetermination of phosphate solubilisation activity\nThe P-solubilising activity of the bacteria was determined by spotting 10 \u03bcl of \n48 h cultures on NBRIP media plates. The media plates were incubated at 30\u00b0C \nfor one week and observed for halo zone formation. The solubilising activity was \ncalculated by following the formula of Nguyen et al. (1992).\n\n\n\n\n\n\n\n100\n)(\n\u00d7=\n\n\n\ncolonyofdiametergrowth\n\n\n\nzonehalodiameterilizationso\nefficiencybilizationSoP\n\n\n\nlub\nlu\n\n\n\nDetermination of nitrogen fixation activity\nThe nitrogen fixing activity of the bacteria was determined by culturing one loop \nof fully-grown bacterial culture in Nfb semi-solid liquid medium (Gyaneshwar et \nal. 2001). Results were confirmed by pellicle formation.\n\n\n\nBiofilm production\nThe inoculated broth cultures of the bacteria were incubated at 30\u00b0C on a Kotter \nman 4020-shaker at a medium speed of 80 cycle\u2019s min-1. After 72 h, the culture was \nobserved for biofilm production. Detection of biofilm production was done using \nthe tissue culture plate method (TCPM). The development of film is described as \nbiofilm productive bacteria (Sultan and Nabiel 2018). \n\n\n\nNet House Study \nThe soil for the experiment was taken from the NIA Farm Tandojam and soil \nphysico-chemical properties were determined before the experiment. Soil texture \nwas assessed by Bouyoucos hydrometer method (Bouyoucos 1962); organic \nmatter by Walkely and Black (1934) method; electrical conductivity was measured \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 91\n\n\n\nby an electrical conductivity meter at a ratio of 1:5-soil water extract; soil pH \nwas measured in soil:water (1:5) extract using PHM210 Standard pH meter at \n30\u00b0C (Benton 2001); total nitrogen was determined by Kjeldahl digestion method \n(Bremner and Mulvaney 1982) and available P and K by AB-DTPA method \n(Soltanpur and Schwab1977).\n\n\n\nTreatment and experimental design \nThe soil was air dried, ground and passed through a 2-mm sieve. A total of 5 \nkg of sieved soil was sterilised at 121\u00b0C for 60 m by autoclaving. The sterilised \nsoil was transferred into plastic pots (17 cm diameter \u00d7 23 cm height) and the \nexperiment was conducted in a net house at the Nuclear Institute of Agriculture \n(NIA) Tandojam during the 2016-2017 season. The experimental set up involved \nthree replications in a complete randomised design (CRD) with factorial (bacteria \nand chemical fertiliser) arrangements (Table 1). Chemical fertilisers consisting \nof 120 kg of N ha-1 in the form of urea, 90 kg of P2O5 ha-1 in the form of DAP as \na full recommended dose and 60 kg of N ha-1  in the form of urea, 45 kg of P2O5 \nha-1 in the form of DAP was the half rate of recommended fertilizer were applied. \nHowever, 5 kg of Zinc in the form of zinc sulfate were applied in all treatments.\n\n\n\n21 \n \n\n\n\n                                           TABLE 1 652 \n 653 \n\n\n\n                                Description of treatments in pots 654 \n 655 \n\n\n\nTreatment Description \n\n\n\nT1 NF + PF without Bacteria \n\n\n\nT2 NH + PF without Bacteria \n\n\n\nT3 NF + PH without Bacteria \n\n\n\nT4 NH + PH without Bacteria \n\n\n\nT5 NF + PF NIA-02 \n\n\n\nT6 NH + PF NIA-02 \n\n\n\nT7 NF + PH NIA-02 \n\n\n\nT8 NH + PH NIA-02 \n\n\n\nT9 NF + PF NIA-05 \n\n\n\nT10 NH + PF NIA-05 \n\n\n\nT11 NF + PH NIA-05 \n\n\n\nT12 NH + PH NIA-05 \n\n\n\nNH= nitrogen half; NF = nitrogen full; PH= phosphorus half; NF = phosphorus full  656 \n 657 \n\n\n\n 658 \n 659 \n 660 \n 661 \n 662 \n 663 \n 664 \n 665 \n 666 \n 667 \n 668 \n 669 \n 670 \n 671 \n 672 \n 673 \n 674 \n 675 \n 676 \n 677 \n 678 \n 679 \n 680 \n 681 \n\n\n\nTABLE 1\nDescription of treatments in pots\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202192\n\n\n\nInoculum preparation and seed inoculation\nThe two best bacteria with potential were selected from the previous study to \nassess its effect on growth and nutrient uptake of wheat. A pure culture of bacterial \nstrains was cultured in nutrient broth for 48 h. The bacterial cells were collected \nby centrifugation at 13500-rev min-1 for 10 min in Eppendorf tubes and washed \nwith 0.85% sterilised phosphate buffer saline (PBS). The optical density (OD600) \nof washed cells was checked and adjusted consequently. The bacterial population \nwas confirmed by using drop plate method on nutrient agar (NA). The wheat \nseeds were soaked in the bacterial solution at a population of 109 CFU mL-1 for 2 h \nprior to planting. The non-inoculated seeds were given the same amount of killed \ncells (autoclaved for 30 min at 121\u00b0C).\n\n\n\nDetermination of total bacterial population\nAfter 45 days of sowing, rhizospheric bacterial population was determined. \nApproximately 10g of soil sample was added into a conical-flask containing \n90 mL sterilised distilled water. The mixture was shaken vigorously on a rotary \nshaker for 10-m to suspend bacterial cells. A serial dilution was prepared and the \ntotal bacterial population was determined following spread plate count method on \nnutrient agar plate (Somasegaran and Hoben 1985).\n\n\n\nAgronomic data collection \nPlant height, root length and plant leaf numbers were observed fortnightly. After \n45 days plant samples were harvested and cleaned and dried in an oven at 70\u00b0C \nfor three days.\n\n\n\nNutrient uptake (NPK)\nTotal plant nitrogen was determined by Kjeldhahl method (Bremner and Mulvaney \n1982) P was analysed by wet digestion method of Havlin and Soltanpur (1980) \nand exchangeable K was extracted using 1 mol L-1 NHOAc buffered at pH 7.0 \n(Benton 2001).\n\n\n\nStatistical analysis\nThe collected data were analysed statistically using STATISTIX 8.1. Tukey\u2019s \nLSD test was employed for multiple comparisons.\n\n\n\nRESULTS\n\n\n\nLaboratory Experiment\nBiochemical characterisation \nA total of 10 bacteria strains with potential were screened, characterised and \nstudied further based on their different colony morphological-characteristics \n(Table 2). Futher table 2 shows eleven of the strains had N2 fixation ability while \nNIA-4, NIA-7, NIA-8 and NIA-10 did not;  for phosphate solubilisation ability, \nonly NIA-1 and NIA-3 did not possess this ability. All strains, except for NIA-4 \nand NIA-9, were able to produce biofilm (Table 2).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 93\n\n\n\nDetermination of IAA production by bacteria\nAll bacterial isolates were examined for IAA production and all strains were able \nto produce IAA in the nutrient broth culture in the range of 1.87\u22124.28 mg L-1. The \nhighest IAA production of 4.28 mg L-1 was observed in NIA-2 followed by NIA-5 \nwith 3.49 mg L-1 (Figure 1).\n\n\n\nFigure 1. Production of IAA by potential bacteria isolated from wheat crop\nMeans within the same column followed by the same letters are not significantly different \n\n\n\nat P<0.05\n\n\n\nTABLE 2\nBiochemical properties of potential bacteria isolated from the wheat crop\n\n\n\n22 \n \n\n\n\n 682 \nTABLE 2 683 \n\n\n\nBiochemical properties of potential bacteria isolated from the wheat crop 684 \n\n\n\nNote: +ve = positive, -ve = negative 685 \n\n\n\n 686 \n\n\n\n 687 \n\n\n\n 688 \n\n\n\n 689 \n\n\n\n 690 \n\n\n\n 691 \n\n\n\n 692 \n\n\n\nTABLE 3 693 \nSoil physic-chemical properties of experimental soil 694 \n\n\n\nParameters      *EC        pH               *OM           *OC           *N            *P              *K \n\n\n\n                    (dSm-1)           ------------------%----------------           ------(mg kg1)------ \n\n\n\n       1.125  7.7        0.88      0.512 0.03    4.17      127 \n           *OM = Organic matter, *OC = Organic carbon 695 \n\n\n\nS. No. Isolates Colony morphology Nitrogen \nfixing ability \n\n\n\nPhosphate- \nsolubilising \nability \n\n\n\nBiofilm \nproduction \n\n\n\n1 NIA-1 Circular,  translucent, dull +ve -ve +ve \n2 NIA-2 Transparent, oval +ve +ve +ve \n3 NIA-3 Translucent, gummy +ve -ve +ve \n4 NIA-4 Circular, translucent, off white, \n\n\n\nshiny \n-ve +ve -ve \n\n\n\n5 NIA-5 Irregular, off white +ve +ve +ve \n6 NIA-6 Circular, off white +ve +ve +ve \n7 NIA-7 Circular, translucent -ve +ve +ve \n8 NIA-8 Circular, light yellow, gummy -ve +ve +ve \n9 NIA-9 Brown, circular, sticky +ve +ve -ve \n10 NIA-10 Oval, light yellow -ve +ve +ve \n\n\n\n26 \n \n\n\n\n 46 \n\n\n\n 47 \n\n\n\nFigure 1. Production of IAA by potential bacteria isolated from wheat crop 48 \n\n\n\nMeans within the same column followed by the same letters are not significantly different at P<0.05 49 \n 50 \n\n\n\n 51 \n\n\n\n 52 \n\n\n\n 53 \n\n\n\n 54 \n\n\n\n 55 \n\n\n\n 56 \n\n\n\n 57 \n\n\n\n 58 \n\n\n\n 59 \n\n\n\n 60 \n\n\n\n 61 \n\n\n\n 62 \n\n\n\n 63 \n\n\n\n 64 \n\n\n\n 65 \n\n\n\ng \n\n\n\na \n\n\n\ne \n\n\n\nc \nb \n\n\n\nf \n\n\n\nc \n\n\n\ne \n\n\n\ng \n\n\n\nd \n\n\n\n0.00\n\n\n\n0.50\n\n\n\n1.00\n\n\n\n1.50\n\n\n\n2.00\n\n\n\n2.50\n\n\n\n3.00\n\n\n\n3.50\n\n\n\n4.00\n\n\n\n4.50\n\n\n\n5.00\n\n\n\nNIA-1 NIA-2 NIA-3 NIA-4 NIA-5 NIA-6 NIA-7 NIA-8 NIA-9 NIA-10\n\n\n\nIA\nA\n\n\n\n (m\ng \n\n\n\nL\n-1\n\n\n\n) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202194\n\n\n\nDetermination of phosphate solubilising activities by bacteria\nThe selected bacteria (10) were observed for phosphate solubilising ability on the \nNBRIP agar media plates. The P solubilisation proficiency of the selected isolates \nwas different as they produced a different diameter of halo zones (Figure 2). The \nhighest P solubilisation efficiency was found by NIA-2 (57.32 %) followed by \nNIA-5 (45.38 %). The lowest solubilisation efficiency was recorded by NIA-8 \n(16.42 %).\n\n\n\nFigure 2.Phosphate solubilisation by potential bacteria isolated from wheat crop\nMeans within the same column followed by the same letters are not significantly different \n\n\n\nat P<0.05\n\n\n\n27 \n \n\n\n\n 66 \n\n\n\nFigure 2.Phosphate solubilisation by potential bacteria isolated from wheat crop 67 \nMeans within the same column followed by the same letters are not significantly different at P<0.05 68 \n 69 \n\n\n\n 70 \n\n\n\n 71 \n\n\n\n 72 \n\n\n\n 73 \n\n\n\n 74 \n\n\n\n 75 \n\n\n\n 76 \n\n\n\n 77 \n\n\n\n 78 \n\n\n\n 79 \n\n\n\n 80 \n\n\n\n 81 \n\n\n\n 82 \n\n\n\n 83 \n\n\n\n 84 \n\n\n\n 85 \n\n\n\n 86 \n\n\n\n 87 \n\n\n\n 88 \n\n\n\nc \n\n\n\na \n\n\n\ne \nd \n\n\n\nb \n\n\n\ng \n\n\n\nf \n\n\n\nh \n\n\n\ne \n\n\n\ng \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\nNIA-1 NIA-2 NIA-3 NIA-4 NIA-5 NIA-6 NIA-7 NIA-8 NIA-9 NIA-10\n\n\n\nP-\nso\n\n\n\nlu\nbi\n\n\n\nliz\nat\n\n\n\nio\nn \n\n\n\n(%\n) \n\n\n\nNet house study\nSoil physico-chemical properties\nTable 3 shows the physic-chemical properties of the soil used for the pot \nexperiment. The soil was non-saline and slightly alkaline in nature. However, the \nsoil was low in organic matter (0.88 %), organic carbon (0.512%) and nitrogen \npercentage (0.03 %) but levels of phosphorus and potash were low to adequate.\n\n\n\nEffect of beneficial bacteria on the agronomic parameters of wheat\nThere were significant differences observed among the various treatments (Table \n4) after 15 days of sowing. The highest plant height (16 cm) was observed in NH + \nPH NIA 02, and NF + PH NIA-05 followed by NF + PH NIA 02 (15 cm). The longest \nroot was found in NH + PH NIA-05 (4.73 cm) followed by NH + PH NIA 02(4.50 \ncm).  However, there was no significant difference in leaf numbers (3 plant-1) after \n15 days of sowing among the treatments. The highest plant dry biomass (0.071 \ng plant-1) was reported in NH + PH NIA 05 followed by NH + PH NIA-02 (0.060 g \nplant-1). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 95\n\n\n\nTABLE 3\nSoil physic-chemical properties of experimental soil\n\n\n\n22 \n \n\n\n\n 682 \nTABLE 2 683 \n\n\n\nBiochemical properties of potential bacteria isolated from the wheat crop 684 \n\n\n\nNote: +ve = positive, -ve = negative 685 \n\n\n\n 686 \n\n\n\n 687 \n\n\n\n 688 \n\n\n\n 689 \n\n\n\n 690 \n\n\n\n 691 \n\n\n\n 692 \n\n\n\nTABLE 3 693 \nSoil physic-chemical properties of experimental soil 694 \n\n\n\nParameters      *EC        pH               *OM           *OC           *N            *P              *K \n\n\n\n                    (dSm-1)           ------------------%----------------           ------(mg kg1)------ \n\n\n\n       1.125  7.7        0.88      0.512 0.03    4.17      127 \n           *OM = Organic matter, *OC = Organic carbon 695 \n\n\n\nS. No. Isolates Colony morphology Nitrogen \nfixing ability \n\n\n\nPhosphate- \nsolubilising \nability \n\n\n\nBiofilm \nproduction \n\n\n\n1 NIA-1 Circular,  translucent, dull +ve -ve +ve \n2 NIA-2 Transparent, oval +ve +ve +ve \n3 NIA-3 Translucent, gummy +ve -ve +ve \n4 NIA-4 Circular, translucent, off white, \n\n\n\nshiny \n-ve +ve -ve \n\n\n\n5 NIA-5 Irregular, off white +ve +ve +ve \n6 NIA-6 Circular, off white +ve +ve +ve \n7 NIA-7 Circular, translucent -ve +ve +ve \n8 NIA-8 Circular, light yellow, gummy -ve +ve +ve \n9 NIA-9 Brown, circular, sticky +ve +ve -ve \n10 NIA-10 Oval, light yellow -ve +ve +ve \n\n\n\n After 30 days of sowing there were significant diverse observations between \nthe various treatments (Table 4). Significantly (P<0.05), the highest plant height \n(28.84 cm) was observed in NH + PH NIA 05, followed by NH + PH NIA 02 (28.57 \ncm). The highest root length (7.30 cm) was found in NH + PH NIA 05 followed by \nNH + PH NIA 02 (7.29 cm).  However, no differences were found in leaf numbers \nof plant (5 plant-1) after 30 days of sowing. The highest plant dry biomass (0.093 \ng plant-1) was reported in NH + PH NIA 05 followed by NH + PH NIA 02 (0.085 g \nplant-1).\n After 45 days of sowing, the highest plant height (34.50 cm) was observed \nin NH + PH NIA 05 followed by  NH + PH NIA 02 treatment (33.47 cm) (Table 4) \nwhile the highest root length was found in NH + PH NIA 05 (10.38 cm) followed \nby NF + PH NIA 05 (10.30 cm). However, no difference was found in number of \nleaves (7 plant-1) after 45 days of sowing. The highest plant biomass (1.270 g \nplant-1) was reported in NH + PH NIA 05 followed by NH + PH NIA 02 (1.210 g \nplant-1).\n\n\n\nTotal bacterial population trend during the wheat growth\nTable 5 shows the bacterial population during the growth period after 15, 30 \nand 45 days of sowing wheat inoculated by the beneficial bacteria. There were \nsignificant varied observations among the treatments. The bacterial population \ninitially showed an increasing trend up to 30 days of sowing but after 45 days, \nthe bacterial population began to show a slightly decreasing trend among all \ntreatments. However, the highest total bacteria population after 15 days (4.840 \nlog CFU g-1 soil) was observed in NF + PH NIA-05; in 30 days,  (6.910 log-CFU \ng-1 soil) was observed in in NF + PF NIA-02; and after 45 days (5.560 log CFU-g-1 \nsoil) was observed in NF + PF NIA-02 followed by NH + PH NIA-05 treatment \n(5.340 log CFU-g-1 soil).   \n\n\n\nEffect of beneficial bacteria on the plant nutrient uptake of wheat\nTable 6 shows that significantly (P<0.05) high nitrogen uptake (1.353 %) was \nnoted in NH + PH NIA-05 followed by NH + PH NIA-02 (1.344%). However, the \nhighest phosphorus uptake (0.697 %) was found in the NH + PH NIA-05 treatment \nfollowed by NF + PH NIA 02 treatment with 0.696 %. Significantly (P<0.05) high \npotassium (0.310 %) plant uptake was observed in NH + PH NIA-05 followed \nby NF + PH and NF + PF inoculated with NIA-05 treatments. The non-inoculated \ntreatments received less plant uptake among all treatments.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202196\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nEf\nfe\n\n\n\nct\n o\n\n\n\nf b\nen\n\n\n\nefi\nci\n\n\n\nal\n b\n\n\n\nac\nte\n\n\n\nria\n o\n\n\n\nn \nag\n\n\n\nro\nno\n\n\n\nm\nic\n\n\n\n p\nar\n\n\n\nam\net\n\n\n\ner\ns o\n\n\n\nf w\nhe\n\n\n\nat\n\n\n\n23\n \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\n \nEf\n\n\n\nfe\nct\n\n\n\n o\nf b\n\n\n\nen\nef\n\n\n\nic\nia\n\n\n\nl b\nac\n\n\n\nte\nria\n\n\n\n o\nn \n\n\n\nag\nro\n\n\n\nno\nm\n\n\n\nic\n p\n\n\n\nar\nam\n\n\n\net\ner\n\n\n\ns o\nf w\n\n\n\nhe\nat\n\n\n\n\n\n\n\nM\nea\n\n\n\nns\n w\n\n\n\nith\nin\n\n\n\n th\ne \n\n\n\nsa\nm\n\n\n\ne \nco\n\n\n\nlu\nm\n\n\n\nn \nfo\n\n\n\nllo\nw\n\n\n\ned\n b\n\n\n\ny \nth\n\n\n\ne \nsa\n\n\n\nm\ne \n\n\n\nle\ntte\n\n\n\nrs\n a\n\n\n\nre\n n\n\n\n\not\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\nly\n d\n\n\n\niff\ner\n\n\n\nen\nt a\n\n\n\nt P\n<0\n\n\n\n.0\n5\n\n\n\nTr\nea\n\n\n\ntm\nen\n\n\n\nts\n \n\n\n\nPl\nan\n\n\n\nt h\nei\n\n\n\ngh\nt  \n\n\n\n(c\nm\n\n\n\n) \nR\n\n\n\noo\nt l\n\n\n\nen\ngt\n\n\n\nh \n(c\n\n\n\nm\n) \n\n\n\nN\num\n\n\n\nbe\nr o\n\n\n\nf l\nea\n\n\n\nve\ns  \n\n\n\n(p\nla\n\n\n\nnt\n-1\n\n\n\n) \nPl\n\n\n\nan\nt d\n\n\n\nry\n b\n\n\n\nio\nm\n\n\n\nas\ns \n\n\n\n \n(g\n\n\n\n p\nla\n\n\n\nnt\n-1\n\n\n\n) \n\n\n\n \n15\n\n\n\n D\nA\n\n\n\nS \n30\n\n\n\n D\nA\n\n\n\nS \n45\n\n\n\n D\nA\n\n\n\nS \n15\n\n\n\n D\nA\n\n\n\nS \n30\n\n\n\n D\nA\n\n\n\nS \n45\n\n\n\n D\nA\n\n\n\nS \n15\n\n\n\n D\nA\n\n\n\nS \n \n\n\n\n30\n D\n\n\n\nA\nS \n\n\n\n45\n D\n\n\n\nA\nS \n\n\n\n15\n  D\n\n\n\nA\nS \n\n\n\n \n30\n\n\n\n D\nA\n\n\n\nS \n45\n\n\n\n D\nA\n\n\n\nS \n\n\n\nN\nH\n\n\n\n \n+  \n\n\n\nP H\n \n\n\n\nw\nith\n\n\n\nou\nt  \n\n\n\nba\nct\n\n\n\ner\nia\n\n\n\n \n13\n\n\n\n.5\n0c\n\n\n\nd \n26\n\n\n\n.6\n63\n\n\n\nde\n \n\n\n\n31\n.1\n\n\n\n60\ne \n\n\n\n3.\n58\n\n\n\ne \n5.\n\n\n\n65\ne \n\n\n\n8.\n59\n\n\n\ng \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n03\n\n\n\n57\nf \n\n\n\n0.\n05\n\n\n\n56\ne \n\n\n\n0.\n88\n\n\n\n3d\n \n\n\n\nN\nH\n\n\n\n \n+  \n\n\n\nP F\n \n\n\n\nw\nith\n\n\n\nou\nt  \n\n\n\nba\nct\n\n\n\ner\nia\n\n\n\n \n12\n\n\n\n.0\n0e\n\n\n\n \n24\n\n\n\n.0\n00\n\n\n\ng \n31\n\n\n\n.0\n00\n\n\n\ne \n3.\n\n\n\n40\nef\n\n\n\n \n3.\n\n\n\n21\nh \n\n\n\n8.\n31\n\n\n\ng \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n02\n\n\n\n03\nj \n\n\n\n0.\n03\n\n\n\n59\ni \n\n\n\n0.\n47\n\n\n\n0h\n \n\n\n\nN\nF \n\n\n\n+  \nP H\n\n\n\n \nw\n\n\n\nith\nou\n\n\n\nt  \nba\n\n\n\nct\ner\n\n\n\nia\n \n\n\n\n14\n.0\n\n\n\n0c\n \n\n\n\n26\n.4\n\n\n\n70\ne \n\n\n\n31\n.5\n\n\n\n03\nde\n\n\n\n \n3.\n\n\n\n47\nef\n\n\n\n \n5.\n\n\n\n60\ne \n\n\n\n8.\n48\n\n\n\ng \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n03\n\n\n\n28\ng \n\n\n\n0.\n05\n\n\n\n04\nf \n\n\n\n0.\n81\n\n\n\n0e\n \n\n\n\nN\nF \n\n\n\n+  \nP F\n\n\n\n \nw\n\n\n\nith\nou\n\n\n\nt  \nba\n\n\n\nct\ner\n\n\n\nia\n \n\n\n\n12\n.0\n\n\n\n0e\n \n\n\n\n23\n.8\n\n\n\n33\ng \n\n\n\n30\n.0\n\n\n\n00\nf \n\n\n\n2.\n50\n\n\n\ng \n5.\n\n\n\n19\ng \n\n\n\n8.\n30\n\n\n\ng \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n01\n\n\n\n61\nk \n\n\n\n0.\n03\n\n\n\n31\nj \n\n\n\n0.\n33\n\n\n\n3i\n \n\n\n\nN\nH\n +\n\n\n\n P\nH\n  N\n\n\n\nIA\n-0\n\n\n\n2 \n16\n\n\n\n.0\n0a\n\n\n\n \n28\n\n\n\n.5\n73\n\n\n\na \n33\n\n\n\n.4\n73\n\n\n\nb \n4.\n\n\n\n50\nb \n\n\n\n7.\n29\n\n\n\na \n9.\n\n\n\n22\nbc\n\n\n\nd \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n06\n\n\n\n07\nb \n\n\n\n0.\n08\n\n\n\n47\nb \n\n\n\n1.\n21\n\n\n\n0a\n \n\n\n\nN\nH\n\n\n\n +\n P\n\n\n\nF  \nN\n\n\n\nIA\n-0\n\n\n\n2 \n13\n\n\n\n.0\n0d\n\n\n\n \n26\n\n\n\n.4\n27\n\n\n\nef\n \n\n\n\n30\n.9\n\n\n\n93\ne \n\n\n\n3.\n29\n\n\n\nf \n5.\n\n\n\n33\nf \n\n\n\n8.\n92\n\n\n\ndc\n \n\n\n\n3 \n5 \n\n\n\n7 \n0.\n\n\n\n02\n39\n\n\n\ni \n0.\n\n\n\n04\n03\n\n\n\nh \n0.\n\n\n\n54\n6g\n\n\n\n\n\n\n\nN\nF \n\n\n\n+  \nP H\n\n\n\n  N\nIA\n\n\n\n-0\n2 \n\n\n\n15\n.0\n\n\n\n0b\n \n\n\n\n27\n.4\n\n\n\n97\nc \n\n\n\n32\n.6\n\n\n\n67\nbc\n\n\n\n \n3.\n\n\n\n90\nd \n\n\n\n6.\n12\n\n\n\nc \n9.\n\n\n\n11\ncd\n\n\n\n \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n04\n\n\n\n77\nd \n\n\n\n0.\n07\n\n\n\n14\nc \n\n\n\n1.\n08\n\n\n\n3b\n \n\n\n\nN\nF +\n\n\n\n P\nF \n\n\n\n N\nIA\n\n\n\n-0\n2 \n\n\n\n13\n.0\n\n\n\n0d\n \n\n\n\n26\n.4\n\n\n\n40\ne \n\n\n\n30\n.9\n\n\n\n97\ne \n\n\n\n3.\n45\n\n\n\nef\n \n\n\n\n5.\n50\n\n\n\ne \n8.\n\n\n\n65\nef\n\n\n\n \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n03\n\n\n\n38\ng \n\n\n\n0.\n05\n\n\n\n04\nf \n\n\n\n0.\n76\n\n\n\n0e\n \n\n\n\nN\nH\n\n\n\n +\n P\n\n\n\nH\n  N\n\n\n\nIA\n-0\n\n\n\n5 \n16\n\n\n\n.0\n0a\n\n\n\n \n28\n\n\n\n.8\n40\n\n\n\na \n34\n\n\n\n.4\n90\n\n\n\na \n4.\n\n\n\n73\na \n\n\n\n7.\n30\n\n\n\na \n10\n\n\n\n.3\n8a\n\n\n\n \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n07\n\n\n\n05\na \n\n\n\n0.\n09\n\n\n\n33\na \n\n\n\n1.\n27\n\n\n\n0a\n \n\n\n\nN\nH\n\n\n\n +\n P\n\n\n\nF  \nN\n\n\n\nIA\n-0\n\n\n\n5 \n13\n\n\n\n.0\n0d\n\n\n\n \n26\n\n\n\n.1\n60\n\n\n\nf \n31\n\n\n\n.0\n20\n\n\n\ne \n3.\n\n\n\n35\nf \n\n\n\n5.\n41\n\n\n\nf \n9.\n\n\n\n46\nb \n\n\n\n3 \n5 \n\n\n\n7 \n0.\n\n\n\n02\n90\n\n\n\nh \n0.\n\n\n\n04\n33\n\n\n\ng \n0.\n\n\n\n64\n6f\n\n\n\n\n\n\n\nN\nF \n\n\n\n+  \nP H\n\n\n\n  N\nIA\n\n\n\n-0\n5 \n\n\n\n16\n.0\n\n\n\n0a\n \n\n\n\n28\n.1\n\n\n\n70\nb \n\n\n\n32\n.6\n\n\n\n60\nbc\n\n\n\n \n4.\n\n\n\n14\nc \n\n\n\n6.\n36\n\n\n\nb \n10\n\n\n\n.3\n0a\n\n\n\n \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n05\n\n\n\n19\nc \n\n\n\n0.\n07\n\n\n\n32\nc \n\n\n\n1.\n13\n\n\n\n6b\n \n\n\n\nN\nF \n\n\n\n+  \nP F\n\n\n\n  N\nIA\n\n\n\n-0\n5 \n\n\n\n13\n.6\n\n\n\n6c\nd \n\n\n\n26\n.8\n\n\n\n27\nd \n\n\n\n32\n.1\n\n\n\n60\ncd\n\n\n\n \n3.\n\n\n\n63\nd \n\n\n\n5.\n85\n\n\n\nd \n9.\n\n\n\n30\nbc\n\n\n\n \n3 \n\n\n\n5 \n7 \n\n\n\n0.\n04\n\n\n\n10\ne \n\n\n\n0.\n06\n\n\n\n05\nd \n\n\n\n0.\n96\n\n\n\n0c\n \n\n\n\nLS\nD\n\n\n\n \n1.\n\n\n\n66\n \n\n\n\n0.\n27\n\n\n\n4 \n0.\n\n\n\n84\n6 \n\n\n\n0.\n22\n\n\n\n8 \n0.\n\n\n\n17\n1 \n\n\n\n0.\n32\n\n\n\n6 \n1.\n\n\n\n68\n5 \n\n\n\n1.\n68\n\n\n\n5 \n2.\n\n\n\n14\n8 \n\n\n\n0.\n00\n\n\n\n14\n \n\n\n\n0.\n00\n\n\n\n28\n \n\n\n\n0.\n62\n\n\n\n1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 97\n\n\n\nTABLE 6\nEffect of beneficial bacteria on nutrients uptake by  wheat plant\n\n\n\nTABLE 5\nTotal bacterial population trend during wheat growth\n\n\n\n24 \n \n\n\n\nTABLE 5 1 \nTotal bacterial population trend during wheat growth 2 \n\n\n\n 3 \n \nTreatment \n\n\n\n15 days after sowing 30 days after sowing 45 days after sowing \n --------------------------(log CFU g-1 soil) ---------------------------------- \n\n\n\nNH + PH Without \nbacteria \n\n\n\n0.000 0.000 0.000 \n\n\n\nNH + PF without \nbacteria \n\n\n\n0.000 0.000 0.000 \n\n\n\nNF + PH without \nbacteria \n\n\n\n0.00 0.000 0.000 \n\n\n\nNF + PF without \nbacteria \n\n\n\n0.000 0.000 0.000 \n\n\n\nNH + PH  NIA-02 4.520d 6.690b 5.240d \nNH + PF  NIA-02 4.360e 6.430f 5.080c \nNF + PH NIA-02 4.533d 6.603d 5.253d \nNF + PF NIA-02 4.767b 6.910a 5.560a \nNH + PH NIA-05 4.620c 6.690b 5.340b \nNH + PF NIA-05 4.580cd 6.650c 5.300c \nNF + PH NIA-05 4.840a 6.546e 5.196e \nNF + PF NIA-05 0.0389 0.0430 0.0854 \nLSD 4.520d 6.690b 5.240d \nMeans within the same column followed by the same letters are not significantly different at 4 \nP<0.05 5 \n\n\n\n 6 \n\n\n\n 7 \n\n\n\n 8 \n\n\n\n 9 \n\n\n\n 10 \n\n\n\n 11 \n\n\n\n 12 \n\n\n\n 13 \n\n\n\n 14 \n\n\n\n 15 \n\n\n\n 16 \n\n\n\n 17 \n\n\n\n 18 \n\n\n\n 19 \n\n\n\n 20 \n\n\n\n 21 \n\n\n\n25 \n \n\n\n\nTABLE 6 22 \nEffect of beneficial bacteria on nutrients uptake by  wheat plant 23 \n\n\n\n 24 \nTreatment N  P  K  \n\n\n\n      --------------------------------  (%)  -------------------------------- \nNH + PH without bacteria 1.288b 0.555e 0.160ef \nNH + PF without bacteria 1.190cd 0.617c 0.160ef \nNF + PH without bacteria 1.055e 0.645b 0.230c \nNF + PF without bacteria 1.050e 0.476f 0.230c \nNH + PH NIA-02 1.344a 0.685a 0.190d \nNH + PF NIA-02 1.022e 0.584d 0.150f \nNF + PH NIA-02 1.288b 0.696a 0.280b \nNF + PF NIA-02 1.218c 0.547dc 0.280b \nNH + PH NIA-05 1.353a 0.697a 0.310a \nNH + PF  NIA-05 0.924f 0.629bc 0.170e \nNF + PH NIA-05 1.204cd 0.684a 0.300a \nNF + PF NIA-05 1.171d 0.574d 0.300a \nLSD 0.046 0.026 0.0169 \nMeans within the same column followed by the same letters are not significantly different at 25 \nP<0.05 26 \n\n\n\n 27 \n\n\n\n 28 \n\n\n\n 29 \n\n\n\n 30 \n\n\n\n 31 \n\n\n\n 32 \n\n\n\n 33 \n\n\n\n 34 \n\n\n\n 35 \n\n\n\n 36 \n\n\n\n 37 \n\n\n\n 38 \n\n\n\n 39 \n\n\n\n 40 \n\n\n\n 41 \n\n\n\n 42 \n\n\n\n 43 \n\n\n\n 44 \n\n\n\n 45 \n\n\n\nDISCUSSION\nBeneficial bacteria are a cluster of microorganisms which vigorously colonise \nplant roots and improve plant growth and yield. The mechanism of these bacteria \nby which they stimulate plant growth is their capability to produce phytohormones, \nfix N2, synthesise antibiotics and enzymes and solubilise phosphates and \nmicronutrients. The bacteria mostly show adequate persistence and survival in \nthe plant rhizosphere. The significant propagation in plant growth and  increased  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202198\n\n\n\nnitrogen level both in shoot and root on bacterial isolates application is vibrant, \nsuggestive of the fact that the bacteriological isolates could be capable of \ndelivering improved nutrient flux to the host plant resulting in an upsurge of plant \nbiomass and increased N. The development in root length due to the inoculation \nof useful isolates which enhanced N uptake in plant shoot. \n The ten selected bacteria with potential were screened from wheat crop and \ncharacterised for their beneficial traits. Most of the bacteria had nitrogen fixation \nabilities. The application of soil microbes such as Rhizobium and Azotobacter \ncan fix-atmospheric N2 and promote production of growth enhancing abilities by \ndecomposition of plant-residues for better plant nutrient management (Mehnaz \net al. 2010). Similarly, Panhwar et al. (2012) reported that several soil microbes \nhave beneficial plant traits. Furthermore, PGPR are free-living soil microbes \nwhich vigorously inhabit the plant rhizosphere and stimulate the growth and yield \nof several plants after their application to seed or plants (Kumar et al. 2014). In \nthe case of biofilm formation, all bacterial strains isolated in our study were able \nto form biofilm with the exception of NIA-4 and NIA-9. Earlier we had already \nreported that biofilm was formed by the most of the isolates (Bacillus followed by \nPseudomonas and Azotobacter) (Table 2). Further assessment of the Bacillus for \ntheir efficiency on survival in the rhizopshere showed better survival compared \nto control. Previous studies have also shown that in vitro biofilm formation has \nsignificantly positive correlation with plant root colonisation (Panhwar et al. \n2011; 2015). Therefore, such beneficial microbes with their biofilm formation \nshould play an effective role in preventing competing organisms, nutrient uptake, \nquick reactions, and adaptating to changing environmental conditions. An earlier \nstudy by Seneviratne et al. (2011) showed that plant associated biofilms has a \nsignificant capability to defend themselves from external stresses and other \nmicrobial competitors in the rhizosphere, and to create beneficial effects on plant \ngrowth. \n Most of the bacteria isolated in our study had the capability to produce \nIAA, a major beneficial characteristic of beneficial microbes. Several mechanisms \nhave been assumed to clarify how microbes affect positively the plant host. These \ncomprise the capability to produce plant growth regulators as indole acetic acid \n(IAA), cytokinins and gibberellins (Marques et al. 2010). Furthermore, IAA \nproduction by beneficial bacteria screened from the rhizosphere of wheat, maize, \npeanut, and rice had been previously defined in a number of research studies (Ali \net al. 2016; Naher et al. 2016; Panhwar et al. 2014). \n The selected beneficial bacteria (10) were found to be positive for P \nsolubilising activity on the Pikovskaya agar media plates, while their efficiency \nvaried as they produced various hallo zones around their colonies. The highest P \nsolubilisation efficiency was exhibited by NIA-2 (57.32 %) while the minimum \nwas recorded in NIA-8 (16.42 %). Many bacteria with potential to contribute \nto plant growth isolated from various crops belong to Pseudomonas, Bacillus, \nEnterobacter, Serretia, Pantoea, Azospirullum, Azotobacter, Rhizobium, \nBurkholderia and Flavobacterium (Deepa et al. 2010). Among the 23 bacterial \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 99\n\n\n\nisolates Singh et al. (2017) isolated, they found 17 isolates exhibited IAA \nproduction, three showed siderophore production capability and 13 showed \nNH4 production ability and exhibited a clear halo zone around the colonies on \ntricalcium phosphate containing Pikovaskaya\u2019s agar plates. P solubilisation is \ngenerally production of bacteriological metabolites containing organic-acids that \ndecreases the pH of the culture medium (Shahid et al. 2012). \n Wheat plant-growth in the pot study benefited from the bacterial inoculation \nand inoculated treatments as it was reflected in better plant enhancement compared \nto the non-inoculated treatments. The maximum plant height, number of leaves, \nroot length and plant dry biomass was observed in NIA-05 followed by NIA-02 \nat half rate (60 kg of N and 45 kg of P2O5 ha-1) of fertilised treatments throughout \nthe planting period. The inoculation of beneficial microbes to the plants showed \nsignificant correlation of weight of panicles and grains, plant biomass, N, P and \niron (Fe) with acetylene reduction (ARA) activities, demonstrating the impact of \nN2 fixation in whole crop productivity (Rana et al. 2012). \n Plant growth promoting microorganisms vigorously inhabit plant \nrhizosphere and increase growth and yield of plant when smeared on seed or \ncrops (Kumar et al. 2014). Additionally, use of chemical fertilisers and plant \ngrowth promoting bacteria enhance urease actions of the soil. The occurrence \nof beneficial bacteria in arrangement of various doses (25 and 50 % reduced) of \nchemical fertilisers improved the effect on soil enzymatic activities. Maximum \nplant growth and leaf protein, and maximum upsurge in root length was developed \nby Azotobacter and Azospirillum microbes with a mixture of chemical fertilisers \n(25 and 50 % reduced). Leaf area and chlorophyll content significantly increased \nthrough Azotobacter combined with half dosage (50 % reduced) of chemical \nfertilisers (Nosheen and Ban 2014). The beneficial bacterial strains of P. sp. NUU-\n1 and P. fluorescens NUU-2 considerably enhanced shoot and root length and dry \nweight of wheat. According to Egamberdieva (2010), the inoculation of the wheat \nplant with Pseudomonas strains is able to increase plant growth in calcareous soils \nbut this growth also depends on the wheat cultivar used. \n Our study showed that bacterial inoculums were capable of inhabiting the \nplant roots as well as inducing a significant optimistic effect on enhancing plant \ngrowth. However, there were significant variations among the various bacterial \npopulations during the planting period. The bacterial population initially showed \nan increasing trend up to 30 days of sowing but after 45 days, the population \nshowed a slightly decreasing trend among all the treatments. \n The application of positive microbes to seeds is an effective mechanism for \nemployment of bacterial inoculation into the soil where it will colonise roots of \nseedlings and defend the plant against several infections and pests (O\u2019Callaghan \n2016). Philippot et al. (2013) states that inoculation of beneficial microbial \ninoculants to the  rhizosphere zones of soil surrounding the roots allow the  plants \nto act directly with microbes resulting in beneficial effects on plant growth and \ndevelopment The plant rhizosphere is a region of strong bacterial activity  that \npromotes plant growth. Furthermore, beneficial microbes rely on the reciprocal \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021100\n\n\n\nfacility of plant nutrients and a wide range of supplementary traits including plant \ngrowth regulators and antibiotics. Several microbes have significant agricultural \nsignificance in the rhizosphere as they have the capability to enhance plant-growth \nthrough a range of mechanisms (Babalola 2010).\n The inoculation of bacterial isolates promotes plant growth by providing \nadequate nutrients. Comparatively, all beneficial bacterial isolates improved \nnutrient uptake compared to non-inoculated treatments. Significantly (P<0.05) \nthe highest N (1.344 %), P (0.684 %) and K (0.310%) uptake was found in \nNIA-05 inoculated treatment with half dose (60 kg of N and 45 kg of P2O5 ha-1) \nof the fertiliser. Comparable results were obtained by Turan et al. (2015) who \nfound that bacterial inoculations along with fertiliser applications significantly \nenhanced wheat growth, total biomass yield and nutrients compared with control. \nFurthermore, inoculation of a bacterial consortium of OSU - 142 + M - 13 + \nAzospirillum sp. 245 significantly increased grain yields of crop at half rate (60 \nkg of N ha-1) of nitrogenous fertiliser doses compared with a full dose of nitrogen. \nIn addition, inoculation of a bacterial consortium has been found to significantly \nimprove uptake of major nutrients  such as N, P, K, S, Ca and Mg including micro-\nnutrients of  Fe, Mn, Cu and Zn in the wheat plant  as reflected in  grains, leaves, \nand straw parts of the plants.\n Bacterial strains have several useful effects on enhancing plant growth \nas observed by developments in seed germination, increase in roots, shoot & \nroot weight, leaf area, yield, hydraulic activities, chlorophyll content, protein \ncontent and nutrient uptake. The use of helpful microorganisms in agricultural \nproduction systems has long been in use and there is strong evidence that \nhelpful microorganisms can increase plant tolerance to adverse environmental \nstresses including salt stresses (Egamberdieva 2010). Several research studies \n(Rana et al. 2015; Pnahwar et al. 2014; Panhwar et al. 2011) have reported the  \nbeneficial effect of bacteria on plant growth when inoculated with  rhizobacterial \nstrains AW-1 Bacillus sp., AW-5 Providencia-sp. and AW-7 Brevundimonas sp. \nInoculated along with 2/3-recommended dosage of N and full dose of P & K \nfertilizer applications. About 14 to 34 % in plant agronomic parameters and 28 \nto 60 % in micronutrient contents were enhanced in treatments inoculated with a \nmixture of rhizobacterial isolates with a full rate of fertiliser application. These \ntreatments, including inoculation using rhizobacterial strains, were analysed for \nthe maximum percent of P & N and found a two-fold improvement in P and 66.7 \n% enhancement in N with full P and K fertiliser application. Likewise a substantial \nassociation was reported in plant biomass, grain weight, panicle weight, N & \nP, and Fe with decreased activity in acetylene, showing the consequence of N2 \nfixation for crop production (Rana et al. 2015).\n\n\n\nCONCLUSION\nBacterial isolates with potential, selected from the wheat crop, exhibited various \nbeneficial traits such as N2 fixation, P-solubilisation, IAA production and biofilm \nformation. Inoculation of the bacterial isolates enhanced  growth of the wheat \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 101\n\n\n\ncrop. The results showed that at half the NP fertilizer dose (60 kg of N and 45 kg \nof P2O5 ha-1) wheat plants inoculated with NIA-05 exhibited the highest beneficial \neffects on plant growth parameters and nutrient uptake. Based on the results of \nour study, it is concluded that isolates (NIA-2 and NIA-5) which produced the \nhighest amount of IAA offered maximum effect on enhancing growth of the wheat \ncrop. This study suggests that the bacterial isolates isolated in our study have \nthe potential for IAA production, besides having other beneficial traits such as; \nNitrogen fixation, P-solubilsation which could be used for developing biofertiliser \nproducts for plant growth enhancement and as a supplementary source of plant \nnutrients.\n\n\n\nFunding\nThe authors did not receive any grant from any funding agency.\n\n\n\nDeclaration of Conflict of Interests\nThe authors have no conflict of interest.\n\n\n\nREFERENCES\nAbbas, F. and A. Fares. 2009. 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"\n\n\uf0d7\uf0cd\uf0cd\uf0d2\uf0e6\uf020\uf0ef\uf0ed\uf0e7\uf0ec\uf0f3\uf0e9\uf0e7\uf0f0\uf0f0\n\n\n\n\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\uf020\uf0e6\uf020\uf0e8\uf0e9\uf0f3\uf0ef\uf0f0\uf0ee\uf020\uf020\uf0f8\uf0ee\uf0f0\uf0f0\uf0e8\uf0f7\uf020 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\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf\uf0b2\uf020\uf0d6\uf0b1\uf0ab\uf0ae\uf0b2\uf0bf\uf0b4\uf020\uf0b1\uf0ba\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0cd\uf0bd\uf0b7\uf0bb\uf0b2\uf0bd\uf0bb\uf020\uf0ca\uf0b1\uf0b4\uf0f2\uf0ef\uf0ee\n\n\n\n\uf0ae\uf0bb\uf0b4\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ac\uf0b1\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b0\uf0ae\uf0b1\uf0bc\uf0ab\uf0bd\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ad\uf0bd\uf0ae\uf0bb\uf0bb\uf0b2\uf0b7\uf0b2\uf0b9\uf020\uf0b1\uf0ba\uf020\uf0ad\uf0bf\uf0b4\uf0ac\uf020\uf0ac\uf0b1\uf0b4\uf0bb\uf0ae\uf0bf\uf0b2\uf0ac\uf020\uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0aa\uf0bf\uf0ae\uf0b7\uf0bb\uf0ac\uf0b7\uf0bb\uf0ad\uf0f2\uf020\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0ad\uf0b7\uf0a6\uf0bb\uf020\uf0b1\uf0ba\uf020\n\n\n\n\uf0ee\uf020\uf0f8\uf0da\uf0b7\uf0b9\uf0f2\uf020\uf0ef\uf0f7\uf0f2\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0bf\uf0be\uf0ad\uf0b1\uf0b4\uf0ab\uf0ac\uf0bb\uf020\uf0bd\uf0b1\uf0b2\uf0ac\uf0ae\uf0b1\uf0b4\uf020\uf0f8\uf0df\n\uf0f0\n\uf0d9\uf0a9\n\n\n\n\uf0f0\n\uf0de\uf0cd\n\n\n\n\uf0f0\n\uf0f7\uf020\uf0ac\uf0ae\uf0bb\uf0bf\uf0ac\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b1\uf0ba\uf020\uf0ac\uf0b8\uf0bb\uf020\n\n\n\n\uf0bb\uf0a8\uf0b0\uf0bb\uf0ae\uf0b7\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0b8\uf0bf\uf0ad\uf020\uf0be\uf0bb\uf0bb\uf0b2\uf020\uf0bb\uf0a8\uf0bd\uf0b4\uf0ab\uf0bc\uf0bb\uf0bc\uf020\uf0bc\uf0ab\uf0bb\uf020 \uf0ac\uf0b1\uf020\uf0b2\uf0b1\uf0ac\uf020\uf0b8\uf0bf\uf0aa\uf0b7\uf0b2\uf0b9\uf020\uf0bc\uf0bf\uf0ac\uf0bf\uf020\uf0b1\uf0b2\uf020\uf0bf\uf0b9\uf0ae\uf0b1\uf0b2\uf0b1\uf0b3\uf0b7\uf0bd\uf020\uf0b0\uf0bf\uf0ae\uf0bf\uf0b3\uf0bb\uf0ac\uf0bb\uf0ae\uf0ad\uf020\n\n\n\n\uf0b1\uf0ba\uf020 \uf0ae\uf0b7\uf0bd\uf0bb\uf020\uf0a9\uf0b8\uf0b7\uf0bd\uf0b8\uf020\uf0a9\uf0bf\uf0ad\uf020 \uf0bf\uf0ac\uf0ac\uf0ae\uf0b7\uf0be\uf0ab\uf0ac\uf0bb\uf0bc\uf020 \uf0ac\uf0b1\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0ad\uf0bb\uf0aa\uf0bb\uf0ae\uf0bb\uf020 \uf0bf\uf0bd\uf0b7\uf0bc\uf0b7\uf0ac\uf0a7\uf020 \uf0bf\uf0b2\uf0bc\uf020 \uf0ad\uf0bf\uf0b4\uf0b7\uf0b2\uf0b7\uf0ac\uf0a7\uf020 \uf0b0\uf0ae\uf0b1\uf0be\uf0b4\uf0bb\uf0b3\uf0ad\uf020 \uf0b1\uf0ba\uf020 \uf0ac\uf0b8\uf0bb\uf020\n\n\n\n\uf0ad\uf0b1\uf0b7\uf0b4\uf0f2\uf020 \uf020 \uf0d7\uf0ac\uf020\uf0a9\uf0bf\uf0ad\uf020 \uf0bf\uf0b4\uf0ad\uf0b1\uf020\uf0b3\uf0bb\uf0b2\uf0ac\uf0b7\uf0b1\uf0b2\uf0bb\uf0bc\uf020 \uf0bb\uf0bf\uf0ae\uf0b4\uf0b7\uf0bb\uf0ae\uf020 \uf0ac\uf0b8\uf0bf\uf0ac\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0a9\uf0bf\uf0ad\uf020 \uf0bd\uf0b1\uf0b4\uf0b4\uf0bb\uf0bd\uf0ac\uf0bb\uf0bc\uf020 \uf0ba\uf0ae\uf0b1\uf0b3\uf020 \uf0ac\uf0b8\uf0bb\uf020 \uf0ba\uf0bf\uf0b4\uf0b4\uf0b1\uf0a9\uf020\n\n\n\n\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\n\n\n\n\uf0ee\uf0f0\n\uf0d9\uf0a9\n\n\n\n\uf0f0\n\uf0de\uf0cd\n\n\n\n\uf0ef\uf0f0\n\uf020\uf0f8\uf0cc\n\n\n\n\uf0ef\n\uf0f7\uf020\uf0ac\uf0ae\uf0bb\uf0bf\uf0ac\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0a9\uf0bf\uf0ad\uf020\n\n\n\n\uf0ab\uf0ad\uf0bb\uf0bc\uf020\uf0bf\uf0ad\uf020\uf0bb\uf0a8\uf0b0\uf0bb\uf0ae\uf0b7\uf0b3\uf0bb\uf0b2\uf0ac\uf0bf\uf0b4\uf020\uf0bd\uf0b1\uf0b2\uf0ac\uf0ae\uf0b1\uf0b4\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0b0\uf0ae\uf0bb\uf0ad\uf0bb\uf0b2\uf0ac\uf020\uf0ad\uf0b7\uf0b3\uf0ab\uf0b4\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ad\uf0ac\uf0ab\uf0bc\uf0a7\uf0f2\uf020\uf0cc\uf0b8\uf0bb\uf020\uf0bb\uf0a8\uf0b0\uf0bb\uf0ae\uf0b7\uf0b3\uf0bb\uf0b2\uf0ac\uf020\uf0a9\uf0bf\uf0ad\uf020\n\n\n\n\uf0a9\uf0b7\uf0ac\uf0b8\uf020 \uf0ac\uf0b8\uf0ae\uf0bb\uf0bb\uf020 \uf0ae\uf0bb\uf0b0\uf0b4\uf0b7\uf0bd\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0ad\uf020 \uf0ba\uf0b1\uf0ae\uf020 \uf0bb\uf0bf\uf0bd\uf0b8\uf020 \uf0ac\uf0ae\uf0bb\uf0bf\uf0ac\uf0b3\uf0bb\uf0b2\uf0ac\uf0f2\uf020\uf0cc\uf0b8\uf0bb\uf020 \uf0ac\uf0bb\uf0b3\uf0b0\uf0bb\uf0ae\uf0bf\uf0ac\uf0ab\uf0ae\uf0bb\uf0ad\uf020 \uf0bc\uf0ab\uf0ae\uf0b7\uf0b2\uf0b9\uf020\uf0bb\uf0a8\uf0b0\uf0bb\uf0ae\uf0b7\uf0b3\uf0bb\uf0b2\uf0ac\uf020\n\n\n\n\uf0ad\uf0ac \uf0b2\uf0bc\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0ed\uf0ae\uf0bc\n\n\n\nFig.  1:  Design of concrete tank used for the simulation study \n\n\n\n\uf0e8\uf0f0\uf020\uf0bd\uf0b3\uf020\n\n\n\n60 cm 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\uf0ad\uf0ab\uf0ae\uf0aa\uf0bb\uf0a7\uf0ad\uf0f2\uf020 \uf0ee\uf0b2\uf0bc\n\n\n\n\uf0bf\uf0b2\uf0bc\uf020\uf0d6\uf0f2\uf020\uf0cd\uf0ab\uf0b9\uf0b7\uf0f2\uf020\uf0ef\uf0e7\uf0e7\uf0ee\uf0f2\uf020\uf0cd\uf0b1\uf0b7\uf0b4\uf020\uf0bf\uf0b3\uf0bb\uf0b4\uf0b7\uf0b1\uf0ae\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf020\uf0ac\uf0ae\uf0b7\uf0bf\uf0b4\uf0ad\uf020\uf0b7\uf0b2\uf020\uf0b0\uf0bb\uf0bf\uf0ac\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0bf\uf0bd\uf0b7\uf0bc\uf020\uf0ad\uf0ab\uf0b4\uf0ba\uf0bf\uf0ac\uf0bb\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf0ad\uf0f2\uf020\uf0d7\uf0b2\uf020\uf0dd\uf0b1\uf0bf\uf0ad\uf0ac\uf0bf\uf0b4\uf020\uf0b4\uf0b1\uf0a9\uf020\n\n\n\n\uf0b4\uf0bf\uf0b2\uf0bc\uf020\uf0bb\uf0bd\uf0b1\uf0ad\uf0a7\uf0ad\uf0ac\uf0bb\uf0b3\uf0ad\uf020\uf0b7\uf0b2\uf020\uf0ac\uf0b8\uf0bb\uf020\uf0ad\uf0b1\uf0ab\uf0ac\uf0b8\uf0bb\uf0ae\uf0b2\uf020\uf0cc\uf0b8\uf0bf\uf0b7\uf0b4\uf0bf\uf0b2\uf0bc\uf020\uf0bf\uf0b2\uf0bc\uf020\uf0d3\uf0bf\uf0b4\uf0bf\uf0a7\uf0ad\uf0b7\uf0bf \uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\n\n\n\n\uf0d7\uf0b2\n\n\n\n\uf0d3\uf0bb\uf0ac\uf0b8\uf0b1\uf0bc\uf0ad\uf020\uf0ba\uf0b1\uf0ae\uf020\uf0bf\uf0ad\uf0ad\uf0bb\uf0ad\uf0ad\uf0b3\uf0bb\uf0b2\uf0ac\uf020\n\n\n\n\uf0b1\uf0ba\uf020\uf0ad\uf0b1\uf0b7\uf0b4\uf020\uf0bc\uf0bb\uf0b9\uf0ae\uf0bf\uf0bc\uf0bf\uf0ac\uf0b7\uf0b1\uf0b2\uf0f2\uf020\uf0db\uf0bc\uf0f2\uf020\uf0ce\uf0f2\uf020\uf0d4\uf0bf\uf0b4\uf020\uf0bb\uf0ac\uf020\uf0bf\uf0b4\uf0f2\n\n\n\n\n\n"
"\n\nINTRODUCTION\n\n\n\nis to transform food agribusiness industry into a competitive sector producing safe \n\n\n\norganic farming is an agriculture system that promotes environmentally, socially \n\n\n\ninputs by refraining from the use of chemo-synthetic fertilizers, pesticides and \n\n\n\nCommercial Organic Fertilizers and their Labeling\nin Malaysia   \n\n\n\n \nD.R. Kala1, A.B. Rosenani1*, C.I. Fauziah1, S.H. Ahmad2, \n\n\n\nO. Radziah1 and  A. Rosazlin3\n\n\n\n1Department of Land Management and  2Department of Crop Science, Faculty of \nAgriculture, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan\n\n\n\n3Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 \nKuala Lumpur, Malaysia\n\n\n\nABSTRACT\n\n\n\nfertilizer manufacturer would be able to designate products that are suitable for \n\n\n\nespecially on adjustment of fertilizer terms and information on the labels. This \n\n\n\norganic fertilizers for organic production in Malaysia. \n\n\n\nKeywords: labeling, organic fertilizer, organic farming, Malaysian Organic\nn\n\n\n\n___________________\n*Corresponding author : Email: rosenani@agri.upm.edu.my\n\n\n\n\n\n\n\n\n148\n\n\n\nnumber of organic farmers, processors, retailers, consumers and suppliers. The \nSijil Organik Malaysia\n\n\n\ncompetitiveness of Malaysian organic produce in the local and foreign markets. \n\n\n\nmainly on production of vegetables, herbs, medicinal plants and fruits and a few in \n\n\n\ncomprising mainly of crop residues, animal manures, compost, green manures \nand residues from processing of plant, animal products and sewage sludge. \n\n\n\nenhance soil biological activity which improves nutrient mobilization from organic \n\n\n\net al. 1998; Herrick and \nWander 1998; Seybold et al.\nand variable and behaves differently when applied to soil due to its contents. The \n\n\n\nUndecomposed OF, that is, municipal waste sludge, which has high ammonium \n(NH4\n\n\n\n+\n\n\n\nphytotoxic when consumed (Zucconi et al.\n\n\n\net al.\nCurrently in Malaysia, composting process is recognized as the most \n\n\n\nfew manufacturers who formulate fertilizers for organic agriculture systems that \nare composed of dehydrated granular by-products of animal production such as \nfeather meal, bone meal and poultry litter, plus other mined sources of P, K and \nsome micronutrients.\n\n\n\nAccording to Bernal et al.\n\n\n\nsalts and xenobiotiocs. However, the characterization of composted materials is \n\n\n\nfertilizer. As organic fertilizer source becomes more diverse, this problem becomes \n\n\n\nD.R. Kala, A.B. Rosenani, C.I. Fauziah, S.H. Ahmad, O. Radziah and  A. Rosazlin\n\n\n\n\n\n\n\n\n149\n\n\n\nusage are becoming more complex to establish. The problem is compounded by \nthe common practice of mixing diverse wastes types, in order to achieve optimal \ncarbon, nitrogen and phosphorus as nutrient source. \n\n\n\nInorganic chemical fertilizers have N, P, K content on the labels according \nto rules established more than half a century ago. However, organic fertilizers or \ncompost, products that contains nutrients and organic matter, is not subjected to \n\n\n\nno labeling rules and no published guidelines established by the Department of \n\n\n\n\u201cthat the claim of the product, compound, mix of compounds or constituent to be \norganic has been allowed or allowed with restriction by the SOM regulations\u201d. \n\n\n\nare adopted, fertilizer manufacturers would be able to designate products that \nare suitable for organic production. Therefore buyers are clearly in a position \n\n\n\ninput for organic production and the physico-chemical properties of OFs varying \n\n\n\nMATERIALS AND METHODS\n\n\n\ndried at 65 o\n\n\n\nsamples were stored at room temperature until analysis. The physico-chemical \nproperties analyzed were as follows; pH and EC was determined in the suspension \n\n\n\nremoved from the oven and placed at room temperature to cool off. The weight \nof the oven-dried fertilizer was recorded. Organic C was determined according \n\n\n\nC for an hour. The temperature was \n\n\n\nOrganic Fertilizer Labeling\n\n\n\n\n\n\n\n\nC and left for 5 hours. The remaining ash was weighed and \norganic C was calculated from the loss in weight during the ashing process. \n\n\n\nHCl and HNO3\n\n\n\nRESULTS AND DISCUSSION\nDetails of the commercial organic fertilizers and information on the labels are \npresented in Table 1. The OFs generally had gone through composting process \nespecially oil palm waste compost and vermicompost. Out of 35 bags, 51% of \n\n\n\nbio-organon, respectively on their lables. Though 74% of the OFs collected had \ninformation on nutrient contents, only 14% had the fertilizer grades stated on \nthe labels. In relation to trace elements, 64% had such information and 94% had \nrecommended use and rates of application, for example, for vegetables, fruits, \nnursery, greens and potted plants. However only 74 % of the OFs had additional \n\n\n\nhave meaningful labels. This could be due to the fact that the consumers that \npurchase these OFs are big scale farmers either from organic or sustainable \n\n\n\ninformation on the labels for the use of farmers. Small farmers whose acreage is \n\n\n\nare especially vulnerable to misleading or confusing fertilizer labels. Therefore, it \net al. \n\n\n\nof a product that has a combined concentration of N, P, and K that is lower than \n\n\n\nand provide guidelines for use of fertilizer terminologies and information on the \n\n\n\nD.R. Kala, A.B. Rosenani, C.I. Fauziah, S.H. Ahmad, O. Radziah and  A. Rosazlin\n\n\n\n\n\n\n\n\n151\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nD\net\n\n\n\nai\nls\n\n\n\n o\nf t\n\n\n\nhe\n c\n\n\n\nom\nm\n\n\n\ner\nci\n\n\n\nal\n o\n\n\n\nrg\nan\n\n\n\nic\n fe\n\n\n\nrti\nliz\n\n\n\ner\n c\n\n\n\nol\nle\n\n\n\nct\ned\n\n\n\n a\nnd\n\n\n\n in\nfo\n\n\n\nrm\nat\n\n\n\nio\nn \n\n\n\non\n th\n\n\n\ne \nla\n\n\n\nbe\nls\n\n\n\nOrganic Fertilizer Labeling\n\n\n\n In\ngr\n\n\n\ned\nie\n\n\n\nnt\ns a\n\n\n\nnd\n c\n\n\n\nom\npo\n\n\n\nst\nin\n\n\n\ng \nde\n\n\n\nta\nils\n\n\n\n \nor\n\n\n\nga\nni\n\n\n\nc \nte\n\n\n\nrm\n \n\n\n\nnu\ntri\n\n\n\nen\nt \n\n\n\nin\nfo\n\n\n\nrm\nat\n\n\n\nio\nn \n\n\n\ntra\nce\n\n\n\n e\nle\n\n\n\nm\nen\n\n\n\nts\n \n\n\n\npu\nrp\n\n\n\nos\ne \n\n\n\nan\nd \n\n\n\nra\nte\n\n\n\ns \nad\n\n\n\ndi\ntio\n\n\n\nna\nl \n\n\n\nin\nfo\n\n\n\nrm\nat\n\n\n\nio\nn \n\n\n\nfe\nrti\n\n\n\nliz\ner\n\n\n\n \nco\n\n\n\nde\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\ned\n, o\n\n\n\nil \npa\n\n\n\nlm\n w\n\n\n\nas\nte\n\n\n\n, m\ned\n\n\n\niu\nm\n\n\n\n g\nra\n\n\n\nde\n \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\n\n\n\n\n\n\n\n\nO\nP1\n\n\n\n \nC\n\n\n\nom\npo\n\n\n\nst\ned\n\n\n\n, o\nil \n\n\n\npa\nlm\n\n\n\n w\nas\n\n\n\nte\n, \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n\n\n\n\n\n\n\n\n \nO\n\n\n\nP2\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\ned\n, o\n\n\n\nil \npa\n\n\n\nlm\n w\n\n\n\nas\nte\n\n\n\n \nO\n\n\n\nrg\nan\n\n\n\nic\n \n\n\n\n\n\n\n\n\n\n\n\nO\nP3\n\n\n\n \nC\n\n\n\nom\npo\n\n\n\nst\ned\n\n\n\n, o\nil \n\n\n\npa\nlm\n\n\n\n w\nas\n\n\n\nte\n, \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  \nO\n\n\n\nP4\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\ned\n, o\n\n\n\nil \npa\n\n\n\nlm\n w\n\n\n\nas\nte\n\n\n\n, \nbi\n\n\n\no-\nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  \nO\n\n\n\nP5\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\ned\n, o\n\n\n\nil \npa\n\n\n\nlm\n w\n\n\n\nas\nte\n\n\n\n , \nco\n\n\n\nm\nm\n\n\n\ner\nci\n\n\n\nal\n g\n\n\n\nra\nde\n\n\n\n \nbi\n\n\n\no-\nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nO\n\n\n\nP6\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\ned\n, o\n\n\n\nil \npa\n\n\n\nlm\n w\n\n\n\nas\nte\n\n\n\n, w\nith\n\n\n\n m\nyc\n\n\n\noh\nriz\n\n\n\nae\n \n\n\n\nse\nm\n\n\n\ni-o\nrg\n\n\n\nan\nic\n\n\n\n  \n8-\n\n\n\n2-\n5 \n\n\n\n\n\n\n\n \nO\n\n\n\nP7\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\ned\n, o\n\n\n\nil \npa\n\n\n\nlm\n w\n\n\n\nas\nte\n\n\n\n \nbi\n\n\n\no-\nor\n\n\n\nga\nni\n\n\n\nc \nK\n\n\n\n \n2-\n\n\n\n6-\n6 \n\n\n\n\n\n\n\n \nO\n\n\n\nP8\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\ned\n, o\n\n\n\nil \npa\n\n\n\nlm\n w\n\n\n\nas\nte\n\n\n\n, \nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nO\n\n\n\nP9\n \n\n\n\nC\nom\n\n\n\npo\nst\n\n\n\ned\n, o\n\n\n\nil \npa\n\n\n\nlm\n w\n\n\n\nas\nte\n\n\n\n, p\nH\n\n\n\n 1\n0 \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nno\nn \n\n\n\n2-\n3-\n\n\n\n11\n \n\n\n\n\n\n\n\n \nO\n\n\n\nP1\n0 \n\n\n\nV\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n, 7\n\n\n\n0%\n sp\n\n\n\nen\nt m\n\n\n\nus\nhr\n\n\n\noo\nm\n\n\n\n a\nnd\n\n\n\n 3\n0%\n\n\n\n c\nat\n\n\n\ntle\n m\n\n\n\nan\nur\n\n\n\ne \nbi\n\n\n\no-\nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nV\n\n\n\nC\n1 \n\n\n\nV\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n, e\n\n\n\nm\npt\n\n\n\ny \nfr\n\n\n\nui\nt b\n\n\n\nun\nch\n\n\n\nes\n a\n\n\n\nnd\n c\n\n\n\nat\ntle\n\n\n\n m\nan\n\n\n\nur\ne \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\n\n\n\n\n\n\n\n\nV\nC\n\n\n\n2 \nV\n\n\n\ner\nm\n\n\n\nic\nom\n\n\n\npo\nst\n\n\n\n, 6\n0%\n\n\n\n sa\nw\n\n\n\n d\nus\n\n\n\nt a\nnd\n\n\n\n 4\n0%\n\n\n\n c\nat\n\n\n\ntle\n m\n\n\n\nan\nur\n\n\n\ne \nbi\n\n\n\no-\nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nV\n\n\n\nC\n3 \n\n\n\nV\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n, 7\n\n\n\n0%\n sa\n\n\n\nw\ndu\n\n\n\nst\n a\n\n\n\nnd\n 3\n\n\n\n0%\n c\n\n\n\nat\ntle\n\n\n\n m\nan\n\n\n\nur\ne \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nN\nA\n\n\n\n \nN\n\n\n\nA\n  \n\n\n\n\n\n\n\nN\nA\n\n\n\n \nV\n\n\n\nC\n4 \n\n\n\nV\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n, 6\n\n\n\n0%\n sa\n\n\n\nw\n d\n\n\n\nus\nt a\n\n\n\nnd\n 1\n\n\n\n0%\n g\n\n\n\noa\nt m\n\n\n\nan\nur\n\n\n\ne \nor\n\n\n\nga\nni\n\n\n\nc \nN\n\n\n\nA\n \n\n\n\nN\nA\n\n\n\n\n\n\n\n \nN\n\n\n\nA\n \n\n\n\nV\nC\n\n\n\n5 \nV\n\n\n\ner\nm\n\n\n\nic\nom\n\n\n\npo\nst\n\n\n\n, 5\n0%\n\n\n\n m\nan\n\n\n\nur\ne \n\n\n\nan\nd \n\n\n\n50\n%\n\n\n\n p\nad\n\n\n\ndy\n st\n\n\n\nra\nw\n\n\n\n \nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nV\n\n\n\nC\n6 \n\n\n\nV\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n, 9\n\n\n\n0%\n c\n\n\n\nat\ntle\n\n\n\n m\nan\n\n\n\nur\ne \n\n\n\nan\nd \n\n\n\nsa\nw\n\n\n\n d\nus\n\n\n\nt  \nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nV\n\n\n\nC\n7 \n\n\n\nV\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n, 9\n\n\n\n0%\n c\n\n\n\nat\ntle\n\n\n\n m\nan\n\n\n\nur\ne \n\n\n\nan\nd \n\n\n\nsa\nw\n\n\n\n d\nus\n\n\n\nt (\nad\n\n\n\nde\nd \n\n\n\nm\nic\n\n\n\nro\nb)\n\n\n\n \nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nV\n\n\n\nC\n8 \n\n\n\nV\ner\n\n\n\nm\nic\n\n\n\nom\npo\n\n\n\nst\n, 9\n\n\n\n0%\n c\n\n\n\nat\ntle\n\n\n\n m\nan\n\n\n\nur\ne \n\n\n\n,sa\nw\n\n\n\n d\nus\n\n\n\nt, \nP \n\n\n\nfe\nrti\n\n\n\nliz\ner\n\n\n\n \nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nV\n\n\n\nC\n9 \n\n\n\nPl\nan\n\n\n\nt b\nas\n\n\n\ne,\n m\n\n\n\nat\nur\n\n\n\ned\n c\n\n\n\nom\npo\n\n\n\nst\n \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\n\n\n\n\n\n\n\n\nP1\n \n\n\n\nPl\nan\n\n\n\nt b\nas\n\n\n\ne,\n p\n\n\n\nad\ndy\n\n\n\n st\nra\n\n\n\nw\n fe\n\n\n\nrm\nen\n\n\n\nte\nd \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nN\nA\n\n\n\n \nN\n\n\n\nA\n  \n\n\n\nN\nA\n\n\n\n \nN\n\n\n\nA\n \n\n\n\nP2\n \n\n\n\nPl\nan\n\n\n\nt b\nas\n\n\n\ne,\n m\n\n\n\nul\nti \n\n\n\nst\nra\n\n\n\nin\n \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\n \nN\n\n\n\nA\n \n\n\n\nN\nA\n\n\n\n\n\n\n\nP3\n \n\n\n\nPl\nan\n\n\n\nt b\nas\n\n\n\ne \n \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nN\nA\n\n\n\n \nN\n\n\n\nA\n \n\n\n\n \nN\n\n\n\nA\n \n\n\n\nP4\n \n\n\n\nPl\nan\n\n\n\nt b\nas\n\n\n\ne \nbi\n\n\n\no-\nor\n\n\n\nga\nni\n\n\n\nc \n8-\n\n\n\n8-\n8 \n\n\n\n\n\n\n\nN\nA\n\n\n\n \nP5\n\n\n\n \nPl\n\n\n\nan\nt b\n\n\n\nas\ne \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\n4-\n3-\n\n\n\n2 \n \n\n\n\n\n\n\n\nP6\n \n\n\n\nFe\nrm\n\n\n\nen\nte\n\n\n\nd \npl\n\n\n\nan\nt a\n\n\n\nnd\n a\n\n\n\nni\nm\n\n\n\nal\n w\n\n\n\nas\nte\n\n\n\ns  \n b\n\n\n\nio\n-o\n\n\n\nrg\nan\n\n\n\nic\n \n\n\n\nN\nA\n\n\n\n \nN\n\n\n\nA\n  \n\n\n\n \nN\n\n\n\nA\n \n\n\n\nPM\n1 \n\n\n\nPl\nan\n\n\n\nt a\nnd\n\n\n\n a\nni\n\n\n\nm\nal\n\n\n\n w\nas\n\n\n\nte\n \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nN\nA\n\n\n\n \nN\n\n\n\nA\n  \n\n\n\n \nN\n\n\n\nA\n \n\n\n\nPM\n2 \n\n\n\nC\now\n\n\n\n M\nan\n\n\n\nur\ne \n\n\n\nan\nd \n\n\n\nPl\nan\n\n\n\nt W\nas\n\n\n\nte\n C\n\n\n\nom\npo\n\n\n\nst\n \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nN\nA\n\n\n\n \nN\n\n\n\nA\n  \n\n\n\n\n\n\n\nPM\n3 \n\n\n\nFa\nrm\n\n\n\n y\nar\n\n\n\nd \nm\n\n\n\nan\nur\n\n\n\ne \nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\n\n\n\n\n \nM\n\n\n\n1 \nPo\n\n\n\nul\ntry\n\n\n\n m\nan\n\n\n\nur\ne \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nN\nA\n\n\n\n \nN\n\n\n\nA\n  \n\n\n\n\n\n\n\nM\n2 \n\n\n\nG\noa\n\n\n\nt M\nan\n\n\n\nur\ne \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\n\n\n\n\n\n\n\n\nM\n3 \n\n\n\nG\noa\n\n\n\nt M\nan\n\n\n\nur\ne \n\n\n\n(1\n00\n\n\n\n%\n) \n\n\n\nbi\no-\n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\n\n\n\n\n\n\n\n\nM\n4 \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n fe\nrti\n\n\n\nliz\ner\n\n\n\n \nor\n\n\n\nga\nni\n\n\n\nc \nN\n\n\n\nA\n \n\n\n\nN\nA\n\n\n\n\n\n\n\nN\nA\n\n\n\n \nU\n\n\n\nK\nN\n\n\n\n1 \nO\n\n\n\nrg\nan\n\n\n\nic\n fe\n\n\n\nrti\nliz\n\n\n\ner\n b\n\n\n\nlu\ne \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\n \nN\n\n\n\nA\n  \n\n\n\n \nN\n\n\n\nA\n \n\n\n\nU\nK\n\n\n\nN\n2 \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n fe\nrti\n\n\n\nliz\ner\n\n\n\n \nor\n\n\n\nga\nni\n\n\n\nc \n \n\n\n\nN\nA\n\n\n\n\n\n\n\n \nU\n\n\n\nK\nN\n\n\n\n3 \n N\n\n\n\nA\n - \n\n\n\nN\non\n\n\n\n A\nva\n\n\n\nila\nbl\n\n\n\ne\n\n\n\n\n\n\n\n\nfrom residues of plant and animals whereas the by-product fertilizers are mainly \n\n\n\nwastes. \nThe range of physico-chemical properties of the 35 commercial organic \n\n\n\nno regulations on the type and amount of organic matter for organic fertilizers. \n\n\n\nC of commercial organic fertilizers ranged from 8.4 to 45 % and total N values \n\n\n\nand not the nutrients; however standards for organic fertilizers are focused mainly \n\n\n\nO5 > 1% and K\n\n\n\nAccording to the US labeling rules (Eggerth et al.\n\n\n\nMoreover traces of Co, Cd and Pb were not detectable in the OFs samples. \nCommercial organic fertilizers in this study had lignin and cellulose content \n\n\n\n1, respectively. The OFs were \n\n\n\nproperties of OFs in this study did not vary greatly when grouped according to the \n\n\n\nthe pH, EC, TOC, lignin, cellulose, K, Ca, Mg and Mn content among the fertilizers. \n\n\n\nD.R. Kala, A.B. Rosenani, C.I. Fauziah, S.H. Ahmad, O. Radziah and  A. Rosazlin\n\n\n\n\n\n\n\n\n153\n\n\n\nR\nan\n\n\n\nge\n o\n\n\n\nf p\nhy\n\n\n\nsi\nco\n\n\n\n- c\nhe\n\n\n\nm\nic\n\n\n\nal\n p\n\n\n\nro\npe\n\n\n\nrti\nes\n\n\n\n o\nf 3\n\n\n\n5 \nco\n\n\n\nm\nm\n\n\n\ner\nci\n\n\n\nal\n o\n\n\n\nrg\nan\n\n\n\nic\n fe\n\n\n\nrti\nliz\n\n\n\ner\ns i\n\n\n\nn \nM\n\n\n\nal\nay\n\n\n\nsi\na.\n\n\n\nOrganic Fertilizer Labeling\n\n\n\n \nEC\n\n\n\n  \nC\n\n\n\n/N\n \n\n\n\nO\n.M\n\n\n\n  \nTO\n\n\n\nC\n  \n\n\n\nLi\ngn\n\n\n\nin\n \n\n\n\nTN\n \n\n\n\nC\nel\n\n\n\nlu\nlo\n\n\n\nse\n \n\n\n\nP \nK\n\n\n\n  \nC\n\n\n\na \nM\n\n\n\ng \n \n\n\n\nZn\n  \n\n\n\nC\nu \n\n\n\n \nM\n\n\n\nn \nFe\n\n\n\n\n\n\n\n \npH\n\n\n\n \n(1\n\n\n\n:5\n) \n\n\n\n(d\nS \n\n\n\nm\n-1\n\n\n\n) \n \n\n\n\n%\n \n\n\n\nm\ng \n\n\n\nkg\n -1\n\n\n\n \nM\n\n\n\nax\nim\n\n\n\num\n \n\n\n\n9.\n8 \n\n\n\n12\n.2\n\n\n\n8 \n42\n\n\n\n.7\n \n\n\n\n79\n \n\n\n\n45\n.0\n\n\n\n \n67\n\n\n\n.0\n \n\n\n\n4.\n4 \n\n\n\n29\n.1\n\n\n\n \n8.\n\n\n\n85\n \n\n\n\n6.\n94\n\n\n\n \n12\n\n\n\n.0\n0 \n\n\n\n3.\n3 \n\n\n\n35\n3 \n\n\n\n88\n \n\n\n\n82\n7 \n\n\n\n24\n74\n\n\n\n0 \nM\n\n\n\nin\nim\n\n\n\num\n \n\n\n\n4.\n5 \n\n\n\n0.\n08\n\n\n\n \n3.\n\n\n\n8 \n25\n\n\n\n \n8.\n\n\n\n4 \n5.\n\n\n\n3 \n0.\n\n\n\n7 \n9.\n\n\n\n3 \n0.\n\n\n\n04\n \n\n\n\n1.\n29\n\n\n\n \n0.\n\n\n\n12\n \n\n\n\n0.\n3 \n\n\n\n45\n \n\n\n\n17\n \n\n\n\n89\n \n\n\n\n91\n2 \n\n\n\nA\nve\n\n\n\nra\nge\n\n\n\n \n6.\n\n\n\n9 \n2.\n\n\n\n10\n \n\n\n\n13\n.0\n\n\n\n \n46\n\n\n\n \n19\n\n\n\n.1\n \n\n\n\n16\n.8\n\n\n\n \n1.\n\n\n\n7 \n19\n\n\n\n.1\n \n\n\n\n1.\n70\n\n\n\n \n2.\n\n\n\n82\n \n\n\n\n1.\n68\n\n\n\n \n1.\n\n\n\n0 \n13\n\n\n\n4 \n47\n\n\n\n \n31\n\n\n\n5 \n86\n\n\n\n02\n \n\n\n\nSD\na  \n\n\n\n1.\n3 \n\n\n\n2.\n76\n\n\n\n \n7.\n\n\n\n2 \n16\n\n\n\n \n7.\n\n\n\n9 \n10\n\n\n\n.8\n \n\n\n\n0.\n8 \n\n\n\n6.\n0 \n\n\n\n2.\n02\n\n\n\n \n1.\n\n\n\n70\n \n\n\n\n3.\n00\n\n\n\n \n0.\n\n\n\n8 \n70\n\n\n\n \n17\n\n\n\n \n20\n\n\n\n0 \n56\n\n\n\n91\n \n\n\n\n a - S\nta\n\n\n\nnd\nar\n\n\n\nd \nD\n\n\n\nev\nia\n\n\n\ntio\nn\n\n\n\n\n\n\n\n\n154\n\n\n\n \nTA\n\n\n\nB\nLE\n\n\n\n 3\nPh\n\n\n\nys\nic\n\n\n\no-\nch\n\n\n\nem\nic\n\n\n\nal\n p\n\n\n\nro\npe\n\n\n\nrti\nes\n\n\n\n o\nf c\n\n\n\nom\nm\n\n\n\ner\nci\n\n\n\nal\n o\n\n\n\nrg\nan\n\n\n\nic\n fe\n\n\n\nrti\nliz\n\n\n\ner\ns a\n\n\n\ncc\nor\n\n\n\ndi\nng\n\n\n\n to\n so\n\n\n\nur\nce\n\n\n\n o\nf m\n\n\n\nat\ner\n\n\n\nia\nls\n\n\n\nD.R. Kala, A.B. Rosenani, C.I. Fauziah, S.H. Ahmad, O. Radziah and  A. Rosazlin\n\n\n\n  \nO\n\n\n\nP \n(n\n\n\n\n=1\n0)\n\n\n\n \nV\n\n\n\nC\n \n\n\n\n(n\n=9\n\n\n\n) \nP \n\n\n\n(n\n=6\n\n\n\n) \nP+\n\n\n\nM\n \n\n\n\n(n\n=3\n\n\n\n) \nM\n\n\n\n (n\n=4\n\n\n\n) \nU\n\n\n\nK\nN\n\n\n\n (n\n=3\n\n\n\n) \npH\n\n\n\n (1\n:5\n\n\n\n) \n7.\n\n\n\n3a\nb \n\n\n\n6.\n1b\n\n\n\n \n6.\n\n\n\n6a\nb \n\n\n\n7.\n5a\n\n\n\nb \n6.\n\n\n\n9a\nb \n\n\n\n8.\n2a\n\n\n\n \nEC\n\n\n\n  (\ndS\n\n\n\n m\n-1\n\n\n\n) \n1.\n\n\n\n1a\n \n\n\n\n1.\n4a\n\n\n\n \n4.\n\n\n\n1a\n \n\n\n\n2.\n4a\n\n\n\n \n2.\n\n\n\n2a\n \n\n\n\n3.\n1a\n\n\n\n \nC\n\n\n\n/N\n \n\n\n\n11\n.6\n\n\n\nab\n \n\n\n\n9.\n8b\n\n\n\n \n20\n\n\n\n.9\na \n\n\n\n13\n.3\n\n\n\nab\n \n\n\n\n10\n.5\n\n\n\nb \n14\n\n\n\n.1\nab\n\n\n\n \nO\n\n\n\n.M\n (%\n\n\n\n) \n40\n\n\n\nbc\n \n\n\n\n31\nc \n\n\n\n63\na \n\n\n\n47\nab\n\n\n\n \n57\n\n\n\na \n58\n\n\n\na \nTO\n\n\n\nC\n (%\n\n\n\n) \n15\n\n\n\na \n18\n\n\n\na \n26\n\n\n\na \n16\n\n\n\na \n23\n\n\n\na \n19\n\n\n\na \nLi\n\n\n\ngn\nin\n\n\n\n (%\n) \n\n\n\n15\na \n\n\n\n19\na \n\n\n\n23\na \n\n\n\n13\na \n\n\n\n14\na \n\n\n\n11\na \n\n\n\nTN\n (%\n\n\n\n) \n1.\n\n\n\n4b\n \n\n\n\n1.\n9a\n\n\n\nb \n1.\n\n\n\n8a\nb \n\n\n\n1.\n2b\n\n\n\n \n2.\n\n\n\n6a\n \n\n\n\n1.\n6a\n\n\n\nb \nC\n\n\n\nel\nlu\n\n\n\nlo\nse\n\n\n\n (%\n) \n\n\n\n19\n.9\n\n\n\na \n18\n\n\n\n.2\na \n\n\n\n17\n.8\n\n\n\na \n16\n\n\n\n.4\na \n\n\n\n20\n.2\n\n\n\na \n23\n\n\n\n.1\na \n\n\n\nP \n(%\n\n\n\n) \n0.\n\n\n\n8b\n \n\n\n\n3.\n9a\n\n\n\n \n0.\n\n\n\n9b\n \n\n\n\n0.\n8b\n\n\n\n \n1.\n\n\n\n3b\n \n\n\n\n1.\n1b\n\n\n\n \nK\n\n\n\n (%\n) \n\n\n\n1.\n32\n\n\n\na \n3.\n\n\n\n30\na \n\n\n\n2.\n99\n\n\n\na \n1.\n\n\n\n76\na \n\n\n\n1.\n30\n\n\n\na \n3.\n\n\n\n15\na \n\n\n\nC\na \n\n\n\n(%\n) \n\n\n\n1.\n01\n\n\n\na \n0.\n\n\n\n90\n a\n\n\n\n \n3.\n\n\n\n06\na \n\n\n\n2.\n55\n\n\n\na \n2.\n\n\n\n61\na \n\n\n\n6.\n94\n\n\n\na \nM\n\n\n\ng \n(%\n\n\n\n) \n0.\n\n\n\n34\n a\n\n\n\n \n1.\n\n\n\n3 \na \n\n\n\n0.\n5 \n\n\n\na \n0.\n\n\n\n6 \na \n\n\n\n0.\n9 \n\n\n\na \n1.\n\n\n\n0 \na \n\n\n\nZn\n (m\n\n\n\ng \nkg\n\n\n\n-1\n) \n\n\n\n97\nb \n\n\n\n10\n7b\n\n\n\n \n12\n\n\n\n9b\n \n\n\n\n17\n0b\n\n\n\n \n16\n\n\n\n5b\n \n\n\n\n26\n8a\n\n\n\n \nC\n\n\n\nu \n(m\n\n\n\ng \nkg\n\n\n\n-1\n) \n\n\n\n56\na \n\n\n\n38\nab\n\n\n\n \n48\n\n\n\nab\n \n\n\n\n56\na \n\n\n\n31\nb \n\n\n\n48\nab\n\n\n\n \nM\n\n\n\nn \n(m\n\n\n\ng \nkg\n\n\n\n-1\n) \n\n\n\n24\n0a\n\n\n\n \n31\n\n\n\n8a\n \n\n\n\n26\n5a\n\n\n\n \n52\n\n\n\n5a\n \n\n\n\n44\n3a\n\n\n\n \n27\n\n\n\n5a\n \n\n\n\nFe\n (m\n\n\n\ng \nkg\n\n\n\n-1\n) \n\n\n\n10\n96\n\n\n\n0 \na \n\n\n\n85\n48\n\n\n\n b\n \n\n\n\n76\n06\n\n\n\n b\n \n\n\n\n11\n03\n\n\n\n0 \na \n\n\n\n52\n68\n\n\n\n b\n \n\n\n\n49\n12\n\n\n\n b\n \n\n\n\nPb\n (m\n\n\n\ng \nkg\n\n\n\n-1\n) \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nC\no \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n) \nnd\n\n\n\n \nnd\n\n\n\n \nnd\n\n\n\n \nnd\n\n\n\n \nnd\n\n\n\n \nnd\n\n\n\n \nC\n\n\n\nd \n(m\n\n\n\ng \nkg\n\n\n\n-1\n) \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n \n\n\n\nnd\n - \n\n\n\nno\nt d\n\n\n\net\nec\n\n\n\nta\nbl\n\n\n\ne\n \n\n\n\nM\nea\n\n\n\nns\n w\n\n\n\nith\n d\n\n\n\niff\ner\n\n\n\nen\nt l\n\n\n\net\nte\n\n\n\nrs\n w\n\n\n\nith\nin\n\n\n\n th\ne \n\n\n\nro\nw\n\n\n\n in\ndi\n\n\n\nca\nte\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\n d\n\n\n\niff\ner\n\n\n\nen\nce\n\n\n\ns (\np \n\n\n\n< \n0.\n\n\n\n05\n)\n\n\n\nus\nin\n\n\n\ng \nD\n\n\n\nun\nca\n\n\n\nn\u2019\ns M\n\n\n\nul\ntip\n\n\n\nle\n R\n\n\n\nan\nge\n\n\n\n T\nes\n\n\n\nt.\n\n\n\n\n\n\n\n\n155\n\n\n\ncompared to other OFs and the increase could be due to the addition of phosphate \n\n\n\nand total P in vermicompost after 75 days. The NPK of an OF is a function of \nthe NPK of the wastes from which the fertilizer or compost is produced. In this \nstudy, due to low nutrient content in these fertilizers, it would be misleading \n\n\n\nensure the most effective and satisfactory utilization of the product. Low graded \n\n\n\nhigh grade fertilizer could be applied for crop production. Guidelines have been \nset by DOA on the fertilizer inputs safe to be used for organic farming systems; \n\n\n\ncontents were below the levels permitted by EEC-1998 organic rules for organic \nproduction, the macro nutrient content were still low and could only be graded \nas compost, soil improver or soil conditioner and not as an organic fertilizer. \n\n\n\nCONCLUSION\nThe Department of Agricultural of Malaysia should set guidelines for the labeling \nof organic fertilizers, such as the fertilizer terms, guaranteed analysis, fertilizer \ngrade, application rates, especially for organic fertilizers. By adopting uniform \n\n\n\nsuitable for organic production and buyers would be able to clearly determine \n\n\n\nfor organic production in Malaysia. \n\n\n\nREFERENCES\n\n\n\nsoil organic matter in relation to phytoremediation. In: Navarro-Avi\u00f1o, J.P. \n\n\n\nhttp://www.\n\n\n\nB\nMethods of Soil Analysis\n\n\n\nBri\nFinal Report to the New York State Association of Recyclers, Woods End \nLaboratory. \n\n\n\n\n\n\n\nOrganic Fertilizer Labeling\n\n\n\n\n\n\n\n\n156\n\n\n\nEEC-Rule. 1998. EU Regulation Organic Farming\n\n\n\nE\nIn: Diaz, L.F., et al. Compost Science and Technology.  8:1-364.\n\n\n\nFa\n\n\n\nin Rural Poverty Alleviation, United Nations Conference Complex, Bangkok, \nThailand.\n\n\n\nHerrick, J.E., and M.M. Wander. 1998. Relationships between soil organic carbon \n\n\n\nIF http://www.\n  \n\n\n\nKal\npalm wastes and sewage sludge for use in potting media of ornamental plants. \nMalaysian Journal of Soil Science\n\n\n\nLal, R., J. Kimble and R.F. Follett. 1998. Need for research and need for action. In: \n\n\n\nMcKeague, J. A. 1976. Manual on soil sampling and methods of analysis. Canadian \nSociety of Soil Science, Ottawa.\n\n\n\nMyung, H.U. and L. Youn. 1999. Quality control for commercial compost in Korea. \nFood and Fertilizer Technology Center International Workshop.\n\n\n\nOf\n\n\n\nJou\naward of the Community Eco-Label to soil improvers.\n\n\n\nSe\n\n\n\nTo\norganic waste compost. Bioresource Technology\n\n\n\nV\nfeeds. Determination of plant cell wall constituents. Journal of the Association \n\n\n\nD.R. Kala, A.B. Rosenani, C.I. Fauziah, S.H. Ahmad, O. Radziah and  A. Rosazlin\n\n\n\n\n\n\n\n\n157\n\n\n\nVi\nphosphate solubilizing bacteria. Bioresource Technology. 76. pp 173-175.\n\n\n\nWong, M.H. 1985. Phytotoxicity of refuses compost during the process of maturation. \nEnvironnemental Pollution\n\n\n\nZucconi, F., A. Monaco, M. Forte and M. De Bertoldi. 1981. Biological evaluation of \ncompost maturity. BioCycle\n\n\n\nZucconi, F, A. Monaco, M. Forte and M. De Bertoldi. 1985. Phytotoxins during the \nstabilization of organic matter. In: J.K.R. de Gassek, Editor, Composting of \nAgricultural and Other Wastes\n\n\n\nand characterization of compost from municipal solid waste. In: Bertoldi M.D., \n\n\n\nOrganic Fertilizer Labeling\n\n\n\n\n\n"
"\n\nINTRODUCTION\nPeat lands are some of the most important and largest stores of carbon, which  \n\n\n\norganic matter in peat lands is primarily caused by slow rates of litter decomposition. \nIn the tropics, the accumulation rates of peat can be rapid, averaging 4-5 mm/\n\n\n\nTropical peat lands are normally associated with both high production and \nrapid decomposition rates due to the hot and often humid conditions that prevail in \n\n\n\nDecomposition of Leaf and Fine Root Residues of Three \nDifferent Crop Species in Tropical Peat under \n\n\n\nControlled Condition\n\n\n\nH. Nahrawi1, M. H. A. Husni2*, R. Othman2 and A. Bah2\n\n\n\n1Department of Plant Science and Environmental Ecology, Faculty of Resource \nScience and Technology, Universiti Malaysia Sarawak, \n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 UPM, Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\n\n\n\npalm (Elaeis guineensis Metroxylon sagu Ananas comosus\ncrops in peatland soils was conducted under controlled conditions. The fourteen- \nmonth study showed that sago leaf residue was the most resistant to decomposition \n\n\n\nof the different plants were in the order: pineapple > oil palm > sago drained = \nsago undrained for leaf residues, and, sago drained  = sago undrained > oil palm > \n\n\n\nKeywords: C:N ratio, mass loss, oil palm, sago, pineapple, tropical peat\n\n\n\n___________________\n*Corresponding author : E-mail: husni@agri.upm.edu.my\n\n\n\n\n\n\n\n\n64\n\n\n\nH. Nahrawi, M. H. A. Husni, R. Othman and A. Bah\n\n\n\nlignin and high nutrient concentration tend to decompose relatively fast (Limpens \n\n\n\ndecomposition. Logically, a lowered water level will increase oxygen availability \nin the soil surface, thus resulting in accelerated rates of decomposition. \n\n\n\nRecently, vast areas of tropical peat land in Southeast Asia have been \ndeveloped for large scale agricultural production of oil palm, sago and pineapple \n\n\n\nsago and oil palm are often stacked on the ground surface to be decomposed. The \n\n\n\nC that is conserved during the decomposition process as crop residues are added \nto soils. \n\n\n\nlands under different land use practices, there is a need to know the decomposition \nrate of crop residues on such soils. This is because rapid decomposition of peat \n\n\n\ndecomposition rate.\n\n\n\nMATERIALS AND METHODS\n\n\n\nExperimental Site and Design  \n\n\n\nfour replications. Decomposition of the residues was studied using the litter bag \nprocedure (Fosu et al.\n\n\n\nSample Collection and Preparation\nOil palm and pineapple residues were collected from Peninsula Plantation at \nSimpang Renggam, Johor, Malaysia, while sago residues were obtained from \nDalat Sago Plantation, Mukah, Sarawak, Malaysia. Oil palm and sago leaves were \ncollected from freshly fallen fronds, while pineapple leaves were collected from \nthe harvested plant. Fine root samples from each crop were collected from about \n1-year-old plants. All the samples were washed and air-dried. The leaves were cut \n\n\n\nwere used in the study.\n\n\n\nSoil and Pot Preparation\n\n\n\n\n\n\n\n\n65\n\n\n\nCrop Residue Decomposition in Peat\n\n\n\nMalaysia within 1 m depth and mixed into a single composite sample for the \n\n\n\nfragments and allow microbial activities. Then 3 litter bags each containing leaf \n\n\n\nsamples were placed horizontally on the soil surface, while those containing root \nsamples were placed vertically 5 cm under the soil surface to simulate the real \ncondition of roots distribution in the soil. A basin was placed underneath each \npot for the undrained peat to prevent water loss via drainage. Water level was \n\n\n\nMalaysia, Serdang, Selangor, Malaysia, under 75% shading simulating the real \n\n\n\nFollowing four months of decomposition, two litterbags from each pot (one \n\n\n\nLeaves and Roots Analyses\nPercent dry mass loss for each crop residues was calculated using the following \n\n\n\no - Mf o\n\n\n\nWhere:     Mo is the initial litter dry mass\n            Mf, the dry mass of the remaining litter in the collected litter bag at\n                 sampling time.\n\n\n\nresidues. Statistix 8.1 for Windows and Sigma plot softwares were used for the \nstatistical analysis of the data.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nEnvironmental Variables\n\n\n\nperiod was variable. The high precipitation and high temperature recorded might \n\n\n\n\n\n\n\n\n66\n\n\n\nH. Nahrawi, M. H. A. Husni, R. Othman and A. Bah\n\n\n\nshow that favorable moisture conditions with higher temperature result in more \nrapid decomposition (Domisch et al.\n\n\n\nChemical Characteristics of Soil, Leaves and Roots \n\n\n\n-1\n\n\n\nK (393.7 mg kg-1\n\n\n\nwas found in pineapple roots. Leaf litter had higher lignin content compared to \nroots except for pineapple, which gave the lowest concentration among all crop \n\n\n\nMass Losses of Decomposing Leaf and Fine Root Residues\n\n\n\nroots were similar with the pot trial after 4 and 7 months of decomposition. Thus, \nit is suggested that the pot experiment can be used to predict the decomposition \n\n\n\ntime (Figs. 1-4)\n\n\n\nto be more appropriate to describe litter decomposition as compared to single \n\n\n\nTABLE 1\nInitial chemical characteristics of oil palm, pineapple and sago leaves\n\n\n\n \nNutrient Properties \n\n\n\nCrop Crop Parts \nC (%) N (%) C:N \n\n\n\nProximate \n% Lignin \n\n\n\nLeaf 43.20 (0.03) 1.88 (0.01) 22.99 (0.10) 17.5 \nOil Palm \n\n\n\nFine roots 38.94 (0.34) 0.81 (0.01) 47.99 (0.78) 12.5 \nLeaf 36.61 (0.03) 1.30 (0.03) 28.17 (0.56) 5.0 \n\n\n\nPineapple \nFine roots 43.49 (0.10) 0.56 (0.03) 78.99 (4.21) 17.5 \nLeaf 44.67 (0.04) 1.25 (0.01) 35.66 (0.29) 30.0 \n\n\n\nSago \nFine roots 38.81 (0.13) 0.97 (0.01) 40.22 (0.36) 17.5 \n\n\n\nNote: Values in parenthesis are standard error of the means. \n \n\n\n\n\n\n\n\n\n67\n\n\n\nCrop Residue Decomposition in Peat\n\n\n\ndecomposition over 14 months\n\n\n\ndecomposition over 14 months\n\n\n\n\n\n\n\nOil Palm\n\n\n\n0 100 200 300 400 500\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\nLeaves Remaining Mass (Pot) \nTime vs Leaves Remaining Mass (Pot) \nRoots Remaining Mass (Pot) \nTime vs Roots Remaining Mass (Pot) \nLeaves Remaining Mass (Field)  \nRoots Remaining Mass (Field)  \n\n\n\n)40.66(exp)59.47(expy\n0.98R\n\n\n\n)x)10((3.19(0.01x)\n\n\n\n2\n\n\n\n13\n\n\n\n)(exp31.23)(exp92.76y\n0.99R\n\n\n\n)x)10((2.16(0.01x)\n\n\n\n2\n\n\n\n13\n\n\n\n\n\n\n\n\n\n\n\nRemaining Mass (%)\n\n\n\nTime (days)\n\n\n\nPineapple\n\n\n\n0 100 200 300 400 500\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\nLeaves Remaining Mass (Pot) \nTime vs Leaves Remaining Mass (Pot) \nRoots Remaining Mass (Pot) \nTime vs Roots Remaining Mass (Pot) \nLeaves Remaining Mass (Field)  \nRoots Remaining Mass (Field)  \n\n\n\n)99.49(expy\n0.97R\n\n\n\n0.01x)(\n\n\n\n2\n\n\n\n)41.70(exp(0.005x)59.04(exp(y\n0.95R\n\n\n\n)x)10((7.77\n\n\n\n2\n\n\n\n13\n\n\n\n\n\n\n\n Time (days) \n\n\n\nRemaining Mass (%)\n\n\n\n\n\n\n\n\n68\n\n\n\nH. Nahrawi, M. H. A. Husni, R. Othman and A. Bah\n\n\n\n\n\n\n\nSago (Drained)\n\n\n\n0 100 200 300 400 500\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\nLeaves Remaining Mass (Pot) \nTime vs Leaves Remaining Mass (Pot) \nRoots Remaining Mass (Pot) \nTime vs Roots Remaining Mass (Pot) \nLeaves Remaining Mass (Field)  \nRoots Remaining Mass (Field)  \n\n\n\n)(exp23.66)(exp45.43y\n0.83R\n\n\n\n)x)10((3.93(0.005x)\n\n\n\n2\n\n\n\n13\n\n\n\n)(exp29.25)74.77(expy\n0.99R\n\n\n\n)x)10((6.03(0.01x)\n\n\n\n2\n\n\n\n12\n\n\n\nRemaining Mass (%)\n\n\n\nTime (days)\n\n\n\n\n\n\n\nSago (Undrained)\n\n\n\n0 100 200 300 400 500\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\nLeaves Remaining Mass (Pot) \nTime vs Leaves Remaining Mass (Pot)\nRoots Remaining Mass (Pot) \nTime vs Roots Remaining Mass (Pot) \n\n\n\n)(exp99.59)(exp69.40y\n0.91R\n\n\n\n)x)10((4.22(0.004x)\n\n\n\n2\n\n\n\n13\n\n\n\n)(exp98.23)(exp07.76y\n0.97R\n\n\n\n)x)10((4.26(0.01x)\n\n\n\n2\n\n\n\n12\n\n\n\nRemaining Mass (%)\n\n\n\nTime (days)\n\n\n\ndecomposition over 14 months\n\n\n\ndecomposition over 14 months\n\n\n\n\n\n\n\n\n69\n\n\n\nCrop Residue Decomposition in Peat\n\n\n\nfunction is used to model a relationship in which a constant change in the \nindependent variable gives the same proportional change (i.e. percentage increase \n\n\n\nconstant is raised to the power of an exponential function.\nUsually these compounds contain easily degradable substrates such as simple \n\n\n\nrecalcitrant compounds accumulate. \n\n\n\nresidue remained. Overall, most of the crop residue showed a similar decay rate \n-1 -1\n\n\n\n-1 -1\n\n\n\nlitter with low concentration of soluble C compounds, high concentration of lignin \n\n\n\nThe double exponential model showed that, pineapple leaves and sago roots \n\n\n\nfaster than the roots suggesting that roots could be more important to peat \naccumulation in the tropics. However, differences in litter types and the age of the \nsago roots used in this study may cause these differences. Sago roots that were \n\n\n\nto Nicolardot et al.\n\n\n\nParameter estimates for the exponential decomposition model\n \n\n\n\nCrops Types k1 w1 k2 w2 \n\n\n\nOil Palm Leaves 0.01 59.47 3.91x10-13 40.66 \n  Roots 0.01 67.92 2.16x10-13 32.22 \nPineapple Leaves 0.01 99.49 Na na \n  Roots 0.005 59.04 7.77x10-13 41.7 \nSago Leaves 0.005 34.45 3.93x10-13 66.23 \nDrained Roots 0.01 74.77 6.03x10-12 25.29 \nSago  Leaves 0.004 40.69 4.22x10-13 59.99 \nUndrained Roots 0.01 76.07 4.26x10-12 23.98 \n\n\n\n  k1 and k2 are rate constants for rapidly (easily degradable) and slowly (recalcitrant)  \ndecomposing fractions of the residues, respectively.  \nw1, and w2 are the amount of easily degradable and recalcitrant fractions, respectively.  \nna = not available. \n \n\n\n\n\n\n\n\n\nH. Nahrawi, M. H. A. Husni, R. Othman and A. Bah\n\n\n\nheterogeneity for fungal than for bacterial communities.\n\n\n\nTissues Nutrient Concentrations of Decomposing Leaf and Fine Root Residues\n\n\n\nexpresses N concentration in organic matter and gives a good relationship to mass \n\n\n\n(Figs. 5 - 8). The appeal of the double exponential model is based \non the fact that the litter can be partitioned into two components (relatively easily \n\n\n\nIn leaf residues, C:N ratio decreased as soon as the decomposition started in \noil palm and pineapple leaves until its minimum level before it started to increase. \nHowever, in sago leaves, the C:N ratio slightly increased at the very early stage \nof decomposition and started to decrease at about 75 days after decomposition \nstarted. In root residues, oil palm and sago roots showed a similar trend of C:N \nratio over time in which the C:N ratio decreased as soon as the decomposition \nstarted until its reached a minimum level before it started to increase. However, in \npineapple roots, as in the case of sago leaves, the C:N ratio increased at the very \nbeginning of the decomposition before it started to decrease. The decrease in the \nC:N ratio is a result of increasing total N in the decomposing tissue. \n\n\n\nThe initial C:N ratio is important in the decomposition process, because \n\n\n\nK during the early stage of decomposition and low concentration of these nutrients \nin the litter may lead to low decomposition rate (Taylor et al. 1989; Thormann et \nal\nchemical predictor of litter decomposability. \n\n\n\nCONCLUSIONS\n\n\n\nsago leaves and pineapple roots have higher C:N ratio and lower N content as \ncompared to other crop residue, thus resulting in a slow decomposition rate. \nDecomposition of leaf residue showed an increase in decomposition rate values in \n\n\n\nresidues: sago drained = sago undrained  > oil palm > pineapple.  Crop residues \nstudies on peat land may highlight issues that are related to the environment and \nsustainability of tropical peat. This is because the decomposition processes that \noccurred from crop residues that are placed on the ground surface of the tropical \npeat will enhance carbon dioxide emission from the peat land ecosystem to the \n\n\n\n\n\n\n\n\n71\n\n\n\nCrop Residue Decomposition in Peat\n\n\n\n\n\n\n\nOil Palm\n\n\n\n0 100 200 300 400 500\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\nLeaves C:N (Pot) \nTime vs Leaves C:N (Pot) \nRoots C:N (Pot) \nTime vs Roots C:N (Pot) \nLeaves C:N (Field)  \nRoots C:N (Field)  \n\n\n\n2\n\n\n\n2\n\n\n\n0.0001x0.08x23.09y\n0.99R\n\n\n\n2\n\n\n\n2\n\n\n\n0.0001x0.05x48.20y\n0.83R\n\n\n\nTime (days)\n\n\n\n C:N \n\n\n\nPineapple\n\n\n\n0 100 200 300 400 500\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\nLeaves C:N (Pot) \nTime vs Leaves C:N (Pot) \nRoots C:N (Pot) \nTime vs Roots C:N (Pot)\nLeaves C:N (Field)  \nRoots C:N (Field)  \n\n\n\n2\n\n\n\n2\n\n\n\n0.0001x0.05x28.85y\n0.81R\n\n\n\n2\n\n\n\n2\n\n\n\n0.001x0.16x81.67y\n0.83R\n\n\n\nTime (days)\n\n\n\n C:N \n\n\n\n\n\n\n\n\nH. Nahrawi, M. H. A. Husni, R. Othman and A. Bah\n\n\n\nSago (Drained)\n\n\n\n0 100 200 300 400 500\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n45\n\n\n\n50\nLeaves C:N (Pot) \nTime vs Leaves C:N (Pot) \nRoots C:N (Pot) \nTime vs Roots C:N (Pot) \nLeaves C:N (Field)  \nRoots C:N (Field)  \n\n\n\n2\n\n\n\n2\n\n\n\n0.0001x0.01x37.33y\n0.67R\n\n\n\n2\n\n\n\n2\n\n\n\n0.0002x0.14x39.24y\n0.95R\n\n\n\nTime (days)\n\n\n\n C:N \n\n\n\n\n\n\n\nSago (Undrained)\n\n\n\n0 100 200 300 400 500\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n45\nLeaves C:N (Pot) \nTime vs Leaves C:N (Pot) \nRoots C:N (Pot) \nTime vs Roots C:N (Pot) \n\n\n\n2\n\n\n\n2\n\n\n\n0.0001x0.01x35.76y\n0.98R\n\n\n\n2\n\n\n\n2\n\n\n\n0.0002x0.11x39.41y\n0.88R\n\n\n\nTime (days)\n\n\n\n C:N \n\n\n\n\n\n\n\n\n73\n\n\n\nCrop Residue Decomposition in Peat\n\n\n\natmosphere. However, the residue that decomposes relatively slowly might help \nenhance peat formation.\n\n\n\nACKNOWLEDGEMENT\nWe wish to thank Mr. Koh Soo Koon and his workers at the Peninsula Plantation \nand Ms. Law Mei Ching and staff at Department of Land Management, Faculty of \n\n\n\nalso wish to thank the Ministry of Science Technology and Innovation, Malaysia \n\n\n\nREFERENCES\nAndriesse, J. 1988. Nature and Management of Tropical Peat Soils. FAO Soils \n\n\n\nBullletin 59, Food and Agricultural Organization of the United Nations, Rome. \nDecomposition, Humus Formation, \n\n\n\nCarbon Sequestration\npp. 338. \n\n\n\nBremner, J.M. 1965. Total Nitrogen, in: Methods of Soil Analysis\n\n\n\nCh A tropical freshwater wetland: II. Production, \ndecomposition, and peat formation. Wetlands Ecology and Management. 13: \n671-684.\n\n\n\nCl\n\n\n\nDo\ndynamics of litter in peat soils from two climatic regions under different \ntemperature regimes. European Journal of Soil Biology\n\n\n\nFo\nDynamics During Decomposition of Four Leguminous Residues. Journal of \nBiological Sciences.\n\n\n\nLa\nresults on the impact of lowered water levels. Soil Biology and Biochemistry. \n\n\n\nLi\nfrom decomposing sphagnum. Oikos.\n\n\n\nMaltby, E. and P. Immirzi. 1993. Carbon dynamics in peatlands and other wetland \nsoils. Regional and global perspectives. Chemosphere\n\n\n\n\n\n\n\n\n74\n\n\n\nH. Nahrawi, M. H. A. Husni, R. Othman and A. Bah\n\n\n\nMaas, A. 1996. A note on the formation of peat deposits in Indonesia. In: Maltby E, \n\n\n\nAsia. Proceedings of a Workshop on Integrated Planning and Management of \nTropical Lowland Peatlands. IUCN, Gland, Switzerland.\n\n\n\nNi\n\n\n\ndecomposition and genetic structure of soil microbial communities. Soil Biology \nand Biochemistry.\n\n\n\nOlson, J. S.1963. Energy storage and the balance of producers and decomposers in \necological systems. Ecology.\n\n\n\nTaylor, B. R., D. Parkinson  and W.F.J. Parsons. 1989. Nitrogen and lignin content as \npredictors of litter decay rates: a microcosm test. Ecology.\n\n\n\nT\nof belowground and aboveground plant litters in peatlands of boreal Alberta, \nCanada. Canadian Journal of Botany\n\n\n\nVa\nJournal of the Association of \n\n\n\n\n\n"
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"\n\nINTRODUCTION\nClinoptilolite is one of the most abundant natural zeolites because it retains its \nresidual mineral phases during weathering of volcanic parent materials (Ming and \nDixon 1986). The fundamental building block of natural zeolite is a tetrahedron \nof four oxygen atoms surrounding silicon (Si4+) or aluminium (Al3+) atom. The \nphysical structure of a zeolite is porous with interconnected cavities in which \nexchangeable cations (Na+, K+, Mg2+, Ca2+) and water molecules are present. The \nadsorption of counter cations contributes to the net negative charge of natural \nzeolites derived from isomorphous substitution of Si4+ by Al3+ (Dyer and White \n1999). Natural zeolites are classified as low cost adsorbents and have  been \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 105-112  (2017)                         Malaysian Society of Soil Science\n\n\n\nDetermination of Cation Exchange Capacity of Natural \nZeolite : A Revisit \n\n\n\nSukor, A*., A. Z. A. Azira, and M.H.A. Husni\n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra Malaysia\n43400 UPM Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nNatural zeolite has been widely used as an ion exchanger since the 1950s. The \npurpose of this study was to quantify the cation exchange capacity (CEC) of natural \nzeolite from different locations (Bayah and Cikembar in West Java, Indonesia) \nbased on  particle sizes of 0.15  and 0.079 mm, using different displacement \nsolutions of 0.5M cesium chloride (CsCl) and 0.5 M potassium chloride (KCl). \nHigher CEC was observed in Cikembar100 compared to Bayah100 due to its \nhigher surface area (31%) and total pore volume (11%) compared to Bayah100. \nCikembar100 had 11% higher clinoptilolite mineral content compared to Bayah \n100. The low CEC measured for Bayah100 and Bayah200 may be due to the \nlower percentage purity of the clinoptilolite mineral content in those samples. \nThe natural zeolite samples displaced with 0.5M CsCl had 6% higher CEC \ncompared to 0.5M KCl, which means that Cs+ had more strength compared to \nK+ in displacing NH4\n\n\n\n+ into the solution from the nanocavity site of the zeolitic \nframework into the solution. In both displacement solutions (0.5M CsCl and KCl), \nCikembar100 had 10% more net negative charge compared to Bayah100 due to its \nisomorphous substitution properties in natural zeolite.  Isomorphous substitution \nin natural zeolite affects its negative charge and the capacity to retain NH4\n\n\n\n+ in the \nzeolitic framework, thus increasing its CEC and making natural zeolite with the \nparticle size of 0.079 mm (Cikembar100) a promising material for cation removal, \nparticularly Cs from aqueous solution. \n\n\n\nKeywords: Natural zeolite, Clinoptilolite, cesium chloride, potassium \nchloride, isomorphous substitution \n\n\n\n___________________\n*Corresponding author : E-mail: shairah@upm.edu.my\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017106\n\n\n\nwidely used in the management of radioactive wastes from nuclear reactors, gas \npurification, petroleum production and wastewater treatment (Ames 1961; Hor \net al. 2016). One of the important properties of zeolite is its cation exchange. \nAmong the factors that affect cation exchange of natural zeolite from aqueous \nsolutions are washing frequency, chemical conditioning, contact time, type \nof displacement solution and the presence of soluble and competing ions in an \naqueous solution. Based on the study by Ames (1960)(1961 in the ref list), the \ncation selectivity series in natural zeolite is described as cesium, Cs+>ammonium, \nNH4\n\n\n\n+>potassium, K+>sodium, Na+>calcium, Ca2+ >magnesium, Mg2+. The cation \nselectivity of natural zeolite is advantageous for the efficient storage of fission \nproducts Cs137generated by  a  nuclear power plant. The  11th Malaysia Plan 2016-\n2020, calls for exploring the usage of nuclear power as an alternative energy \nresource (Khattak et al. 2016). Natural zeolite is abundant worldwide in huge \ndeposits, readily available and inexpensive compared to synthetic zeolite. Natural \nzeolites have been widely used as an ion exchanger but this is often limited to \ncountries having their own natural zeolite deposits such as the United States of \nAmerica, Russia, China, Turkey and Indonesia. The purpose of this study was \nto quantify the cation exchange capacity (CEC) of natural zeolite from different \nlocations (Bayah and Cikembar in West Java, Indonesia) based on  particle sizes \nof 0.15  and 0.079 mm, using different displacement solutions of 0.5M cesium \nchloride (CsCl) and 0.5 M potassium chloride (KCl). \n\n\n\nMATERIALS AND METHODS\nNatural zeolite samples  were selected from from two locations inWest Java, \nIndonesia (Bayah and Cikembar) with different particle sizes of 0.079 mm and \n0.15 mm. X-Ray diffraction analysis was conducted to identify the clinoptilolite \nmineral at the Department of Land Management, Faculty of Agriculture, UPM. \nThe pore structure, surface area (micropore and mesopore), total pore volume, \nand pore size of natural zeolite were measured using Brunauer-Emmett-Teller \n(BET) method (Brunauer  et al.1938) at INFRA Laboratory, Universiti Malaya. \n\n\n\nParticle Size\nTo determine the CEC of natural , the shaking method was conducted in four \nbatches under laboratory conditions with a constant room temperature of 270C. \nA total of six experimental units comprising one location and one particle size \nwith three replications were analysed per batch. In each batch, six samples were \nmicrowaved and washed with 50 mL of distilled water. Samples were shaken \nwith an end-to-end shaker and then centrifuged. The supernatant was decanted for \nNa+, K+, Ca2+, and Mg2+ determination using atomic absorption spectrophotometer \n(AAS). \n\n\n\nContact Time\nFive g of samples with 50 mL of 0.5M NH4Cl were shaken with an end-to-end \nshaker and left equilibrated for 24 h. After 24 h, the sample was mixed with \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 107\n\n\n\n50 mL of 95% denatured alcohol and agitated using an end-to-end shaker and \nthen centrifuged. Samples were decanted to determine exchangeable bases (Na+, \nK+, Ca2+, Mg2+) using AAS. A 50 mL of fresh solution of 0.5M NH4Cl was then \nreplaced every 24 h (Mackenzie 1951) for five consecutive days to imitate a \ndynamic system of ion exchange (Noda, 1980; Bain and Smith 1987). \n\n\n\nDisplacement Solution\nAmmonium was then displaced using two displacement solutions of 0.5M CsCl \nand 0.5M KCl. Ammonium concentration in solution extract was analysed using \nan autoanalyser. The value of NH4\n\n\n\n+ concentration obtained in mg L-1was then \nconverted to cmolc kg-1.The sensitivity of natural zeolite CEC with the F-ratio \nvalue in relation  to experimental factors (location, displacement solution and \nparticle size) was tested using analysis of variance model using the MIXED \nprocedure with SLICE function. All respective experimental factors were \nincluded in the model as independent variables and CEC as the response variable. \nF-ratio from the model output was used to assess the relative magnitude of CEC \nsensitivity to the  experimental factors at 99% confidence level.\n\n\n\nRESULTS AND DISCUSSION\nWashing with Distilled Water and Presence of Soluble Cations\nOne of the factors that affect cation selectivity is the presence of other cations \n(Na+, K+, Ca2+, and Mg2+) that could reduce the value of NH4\n\n\n\n+ adsorbed on the \nnatural zeolite. In our study, across location and particle sizes, washing with \ndistilled water prior to ionic saturation resulted in higher exchange of Na+(from \n0.10 to 0.49 cmolc kg-1; Table 4  compared to other cations in the solution. Sodium \nions in zeolitic framework are held by weak electrostatic interaction and thus they \ncan be replaced by other cations (Sprynskyy et al. 2005). An undetectable Ca2+ \nand low release of Mg2+ (Table 4) may suggest that much of Ca and Mg may be \nfixed in the natural zeolite framework (Kitsoupoulos1999). \n\n\n\nContact Time of Saturating Solution and Exchangeable Cations\nWithout any pre-treatment of chemical conditioning, the natural zeolites generally \nhave low CEC and therefore are frequently treated by increasing contact time \nby saturating zeolite samples with an ionic solution. In our study, 93-96% of the \nexchangeable cations were released in the first three days of saturation with 0.5M \nNH4Cl (data not shown),  in comparison to five days (Kitsoupoulos, 1999; Noda, \n1980; Sprynskyy et al. 2005) , where the values were within range of  the study \nconducted by Kitsopoulos(1999) and Hulbert (1987). Ammonium adsorption into \nthe zeolite nanocavities can be achieved with time due to the process of slow \ndiffusion of NH4\n\n\n\n+ ions into the channels and central nanocavities of natural zeolite \n(Kithome et al. 1998). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017108\n\n\n\nEffects of Displacement Solution on the CEC of Natural Zeolite\nNatural zeolite samples displaced with 0.5M CsCl had 6% higher CEC compared \nto 0.5M KCl (P<0.0001), which means that Cs+ had more strength compared to \nK+ in displacing NH4\n\n\n\n+ from the nanocavity site of the zeolitic framework into \nthe solution. Yoshida et al. (2013) reported that the ionic radius of Cs+ (0.17 nm), \nNH4\n\n\n\n+ (0.14 nm), K+ (0.13 nm) and Na+ (0.10 nm) plays a functional role in cationic \nselectivity. An increase in ionic radius results in an  increase in selectivity. Based \non the lyotropic series, the cation selectivity series in natural zeolite is described \nas Cs+> NH4\n\n\n\n+> K+> Na+> Ca2+ > Mg2+ (Ames 1960). \n\n\n\nLocation\nF-ratio from the ANOVA models represents the magnitude of the effect of the \nexperimental factors (location, displacement solution and particle size) on the CEC \nof natural zeolite. Greater sensitivity is reflected by a larger F-ratio. In this study, \nlocation (Cikembar and Bayah) had a larger F-ratio than displacement solution and \nparticle size (Table 2). In both the displacement solutions, Cikembar100 recorded \nsignificantly higher CEC compared to Bayah100, 21% and 11%, respectively \n(Table 3).  The higher CEC observed in Cikembar100 compared to Bayah100 was \ndue to its higher surface area (31%) and total pore volume (11%) (Table 1). The \nCEC of natural zeolite, particularly clinoptilolite is  within the  range of  the value \nas  reported by Ahmed et al. (2006) and Kithome et al. (1998).\n \nTable numbers are not right. Should be in numerological order???\n\n\n\nPurity of Clinoptilolite and Particle Size on CEC of Natural Zeolite\nThe composition and purity of natural zeolites depend on the amount of \nclinoptilolite minerals in the natural deposits. Cikembar100 had 11% higher \nclinoptilolite mineral compared to Bayah 100 (Table 1). The low CEC measured \nfor Bayah100 and Bayah200 may be due to the lower percentage purity of the \nclinoptilolite mineral content in those samples (Table1). According to Ferguson and \nPepper (1987), clinoptilolite zeolite is characterised by a rigid three dimensional \nlattice with tunnels nm in size and which contain internal exchange sites that have \nan affinity for NH4\n\n\n\n+. A  25% decrease in pore size of Cikembar100 compared \nto Bayah100 (Table 1) increased NH4\n\n\n\n+ retention in the zeolitic framework and \nresulted in significantly higher CEC in Cikembar100 compared to Bayah100 \n(Table 3). \n\n\n\nIsomorphous substitution and Cation Exchange in Natural Zeolite\nIn both displacement solutions (0.5M CsCl and KCl), Cikembar100 had 10% more \nnet negative charge compared to Bayah100 due to its isomorphous substitution \nproperties in natural zeolite (Table 4). Isomorphous substitution in natural \nzeolite affects its negative charge and the capacity to retain NH4\n\n\n\n+ in the zeolitic \nframework. This  thus increases its CEC and makes natural zeolite a promising \nmaterial for cation removal from aqueous waste streams. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 109\n\n\n\nTABLE 1: \nKey properties of natural zeolite from two different locations (Bayah and Cikembar) with \n\n\n\ndifferent particle sizes (0.15 and 0.079 mm) in West Java, Indonesia.\n\n\n\n1 \n \n\n\n\nTABLES \n\n\n\n\n\n\n\nTable 1: Key properties of natural zeolite from two different locations (Bayah and Cikembar) with \n\n\n\ndifferent particle sizes (0.15 and 0.079 mm) in West Java, Indonesia. \n\n\n\n\n\n\n\nSample \n\n\n\nEstimated \n\n\n\nClinoptilolite \n\n\n\nmineral (%) \n\n\n\nParticle size  \n\n\n\n(mm) \n\n\n\nSurface area  \n\n\n\n(m2/g) \n\n\n\nTotal pore volume \n\n\n\n(cm3/g) \n\n\n\nPore size* \n\n\n\n(nm) \n\n\n\nBayah100 78 0.15  56.9 0.083 5.2 \n\n\n\nCikembar100 89 0.15  82.2 0.093 3.9 \n\n\n\nCikembar200 80 0.079 -- -- -- \n\n\n\nBayah 200 71 0.079   -- -- -- \n\n\n\n*Classification by the International Union of Pure and Applied Chemistry. Width of pore size \n\n\n\nbetween 2 \u2013 50 nm = Mesopores and < 2 nm = Micropores (Sing et al.,1985). \n\n\n\n\n\n\n\nTable 2: The F-ratio and P-values of sources of variation from experimental factors at 99% confidence \n\n\n\nlevel (n=24). \n\n\n\nExperimental Factors F-ratio P-value \n\n\n\nParticle size 24.35 0.0001 \n\n\n\nLocation 179.95 <0.0001 \n\n\n\nDisplacement solution 31.70 <0.0001 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1 \n \n\n\n\nTABLES \n\n\n\n\n\n\n\nTable 1: Key properties of natural zeolite from two different locations (Bayah and Cikembar) with \n\n\n\ndifferent particle sizes (0.15 and 0.079 mm) in West Java, Indonesia. \n\n\n\n\n\n\n\nSample \n\n\n\nEstimated \n\n\n\nClinoptilolite \n\n\n\nmineral (%) \n\n\n\nParticle size  \n\n\n\n(mm) \n\n\n\nSurface area  \n\n\n\n(m2/g) \n\n\n\nTotal pore volume \n\n\n\n(cm3/g) \n\n\n\nPore size* \n\n\n\n(nm) \n\n\n\nBayah100 78 0.15  56.9 0.083 5.2 \n\n\n\nCikembar100 89 0.15  82.2 0.093 3.9 \n\n\n\nCikembar200 80 0.079 -- -- -- \n\n\n\nBayah 200 71 0.079   -- -- -- \n\n\n\n*Classification by the International Union of Pure and Applied Chemistry. Width of pore size \n\n\n\nbetween 2 \u2013 50 nm = Mesopores and < 2 nm = Micropores (Sing et al.,1985). \n\n\n\n\n\n\n\nTable 2: The F-ratio and P-values of sources of variation from experimental factors at 99% confidence \n\n\n\nlevel (n=24). \n\n\n\nExperimental Factors F-ratio P-value \n\n\n\nParticle size 24.35 0.0001 \n\n\n\nLocation 179.95 <0.0001 \n\n\n\nDisplacement solution 31.70 <0.0001 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n*Classification by the International Union of Pure and Applied Chemistry. Width of pore \nsize between 2 \u2013 50 nm = Mesopores and < 2 nm = Micropores (Sing et al.,1985).\n\n\n\nTABLE 2\nThe F-ratio and P-values of sources of variation from experimental factors at 99% \n\n\n\nconfidence level (n=24).\n\n\n\n2 \n \n\n\n\nTable 3: Comparison of cation exchange capacity (CEC) of natural zeolite using two displacement \n\n\n\nsolutions (0.5M CsCl and 0.5M KCl) (n=24). \n\n\n\n Displacement Solution \n\n\n\n\n\n\n\nSample \n\n\n\n0.5M CsCl \n\n\n\nMean \u00b1 Standard error \n\n\n\n0.5M KCl \n\n\n\nMean \u00b1 Standard error \n\n\n\nCikembar100          107 a \u00b1 0.2              95 a \u00b1 0.6 \n\n\n\nCikembar200          101 a \u00b1 1.3              89 a \u00b1 1.2 \n\n\n\nBayah100            85 b \u00b1 1.1              84 b \u00b1 0.4 \n\n\n\nBayah 200            80 b \u00b1 0.4              77 b \u00b1 0.9 \n\n\n\nMSD (99% level)            11              10 \n\n\n\nMeans with different letters within column showed significant difference at 99% confidence level. \n\n\n\nMinimum significant difference (MSD) based on Tukey\u2019s Honestly Significant Difference (HSD) at \n\n\n\n99% confidence level. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 3\nComparison of cation exchange capacity (CEC) of natural zeolite using two displacement \n\n\n\nsolutions (0.5M CsCl and 0.5M KCl) (n=24).\n\n\n\nMeans with different letters within column showed significant difference at 99% \nconfidence level. Minimum significant difference (MSD) based on Tukey\u2019s Honestly \nSignificant Difference (HSD) at 99% confidence level.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017110\n\n\n\nCONCLUSION\nOur study found d that the natural zeolite exchangeable cations, Na+ adsorbed \nweakly at the edge of the clinoptilolite structure during washing prior to saturating \nwith an ionic solution. Our results indicate that  0.5M CsCl is a more promising \ndisplacement solution for higher CEC determination of natural zeolite than \n0.5M KCl. Due to its low cost and easily accessible raw materials from the \nneighbouring country, natural zeolite from Cikembar with a particle size of 0.079 \nmm (Cikembar100) from West Java has potential for cost-effective removal of Cs \n(including Cs137) from aqueous solution. \n\n\n\nACKNOWLEDGEMENTS\nWe would like to thank Mr. Rizal Taslim from PT Khatulistiwa Zeolite Prima \nfor providing the natural zeolite samples from West Java, Indonesia. Heartfelt \nthanks goes to Ms Norasma Zaki, Mr. Jamil Omar, and Mr. Fuzi Shariff from the \nDepartment of Land Management, Faculty of Agriculture, UPM for providing \nassistance in laboratory works.\n\n\n\nTABLE 4\nSoluble cations from washing and isomorphous substitution of natural zeolite samples (n=24).\n\n\n\n3 \n \n\n\n\nTable 4: Soluble cations from washing and isomorphous substitution of natural zeolite samples \n\n\n\n(n=24).  \n\n\n\n ---------------Soluble Cations (cmolc kg-1)--------------- \n\n\n\nElement Cikembar100 Cikembar200 Bayah100 Bayah200 \n\n\n\nCa ND\u01c2 0.02 c ND\u01c2 ND\u01c2 \n\n\n\nMg 0.01 c 0.01 c 0.01 c 0.01 c \n\n\n\nNa 0.41 a 0.37 a 0.49 a 0.14 a \n\n\n\nK 0.05 b 0.04 b 0.06 b 0.06 b \n\n\n\n\n\n\n\nDisplacement Solution \n\n\n\n-----------------Isomorphous Substitution (%)--------------- \n\n\n\n(100 - Percent Base Saturation) \n\n\n\n0.5M CsCl 87 91 77 77 \n\n\n\n0.5M KCl 86 89 76 76 \n\n\n\n\u01c2ND = Not Detected. Means with different letters within column showed significant difference at 99% \n\n\n\nconfidence level. Minimum significant difference (MSD) based on Tukey\u2019s Honestly Significant \n\n\n\nDifference (HSD) at 99% confidence level. \n\n\n\n\n\n\n\n\n\n\n\n\u01c2ND = Not Detected. Means with different letters within column showed \nsignificant difference at 99% confidence level. Minimum significant difference \n(MSD) based on Tukey\u2019s Honestly Significant Difference (HSD) at 99% \nconfidence level.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 111\n\n\n\nREFERENCES\nAhmed, O.H., H. Aminuddin, and M. H. A. Husni. 2006. Reducing ammonia loss from \n\n\n\nurea and improving soil-exchangeable ammonium retention through mixing \ntriple superphosphate, humic acid and zeolite. Soil Use and Management 22: \n315-319.\n\n\n\nAmes, L. L. 1960. The cation sieve properties of clinoptilolite. The American \nMineralogist. 45: 689-700.\n\n\n\nAmes, L. L. 1961. 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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 99-108 (2019)                         Malaysian Society of Soil Science\n\n\n\nEffects of Organic Amendments and Incubation Time on the \nAmelioration of Saline Soils \n\n\n\nM. Z. Hossain, F. Akter and K.Q. Kibria\n\n\n\nSoil, Water and Environment Discipline, Khulna University\nKhulna-9208, Bangladesh\n\n\n\nABSTRACT\nRice hull and saw dust at rates of 3, 6 and 9 t ha-1 (designated as T1, T2 and T3 \nfor rice hull and T4, T5 and T6 for saw dust, respectively) were applied separately \nto Bajoa and Dumuria soils to evaluate their reclamation potentiality of salinity \nthrough estimation of physico-chemical attributes of the soils. The pH of both \nsoils was not significantly influenced by the amendments. The EC values of both \nthe soils were significantly (p\u22640.05) decreased and the reductions were more \npronounced in the Bajoa soil. The values of sodium adsorption ratio (SAR) were \nfound to decrease for all the treatments in both the soils and followed the order of \nT6 >T3 > T5> T2 > T4>T1 in the Bajoa soil and T2 > T3 > T6 > T1 > T5 > T4 in \nthe Dumuria soil. Sodium removal efficiency was reduced significantly (p\u22640.05) \nat the highest dose (9 t ha-1). The treatments showed significant (p\u22640.01) effects \non exchangeable cations at different periods of incubation. During the incubation, \nthe concentration of Na showed a remarkable decrease while K, Ca and Mg \nconcentrations showed an increasing trend in both soil types. \n\n\n\nKeywords: Saline Soil, Management, Rice Hull, Saw Dust, Physico-\nChemical Attributes\n\n\n\n___________________\n*Corresponding author : falgunissku@gmail.com  \n\n\n\nINTRODUCTION\nSalinity is a major threat to irrigated agriculture because many of the soils and \nirrigation waters contain significant amounts of dissolved salts. Salt-affected \nsoils are widely distributed throughout the world, and about 20% of the world\u2019s \ncultivated land is salt-affected (Sumner 2000). An estimated 1.056 million ha of \nsalt affected soils (SRDI 2001) occur in Bangladesh which have high agricultural \npotential if they were to be cultivated after proper reclamation (Khan 2015). It \nis inevitable that marginal and problem soils  be used or to improve the yield \npotential for rice per unit of land under various soil stresses. The reclamation of \nthe problem soils is a very important goal throughout the world, especially in the \ncase of saline soils. There are many effective ways of improving salt-affected \nland such as water leaching, chemical remediation and phytoremediation (Khan \net al. 2008). The amelioration of saline soils with chemical amendments is an \nestablished technology which is costly for subsistence farmers in the developing \ncountries because of increased use by industry and reductions in government \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019100\n\n\n\nsubsidy to farmers (Qadir and Oster 2002). The influence of organic matter on crop \ngrowth and productivity is not just a matter of nutrient supply; it also influences \nthe physical characteristics and chemical properties of the soil. In this regard, \nAbdurrahman et al. (2004) reported that the application of compost decreased soil \npH (from 9.75 to 8.22), electrical conductivity (EC) (from 12.35 to 2.25 dS m-1) \nand exchangeable sodium percentage (ESP) (from 44.75 to 6.61 %). Cl\u00e1udio et al. \n(2007) found that the efficiency of compost depends on compost characteristics \nas well as on the rate and time of application. The selection of reclamation agents \nshould take into account, not only for their influence on the soil itself, but also \ntheir price and environmental hazards. The application of rice hull and sawdust \nincreases organic matter content in the soil which increases N in soil as well \nas in plant tissues (Kaniz and Khan 2013). Furthermore, the adverse effects of \nsalinity on soil physical properties e.g. water movement, may be improved by \nthe application of rice hull and sawdust. Besides this, cellulose, hemicelluloses \nand lignin may be released from rice straw to the soil through the mineralisation \nprocesses which may be available for subsequent crop growth (Byous et al. 2004). \nThe aims of the present research were: (1) to evaluate the potential of different soil \namendments for the reclamation of saline soils and (2) to evaluate the efficiency \nof organic amendments in relation to the improvement of the physico-chemical \ncharacteristics of saline soils.\n\n\n\nMATERIALS AND METHODS\nBajoa and Dumuria soils were collected from Raingamari and Fultala of \nBatiaghata upazila in Khulna district (both of which were Typic Endoaquepts \naccording to USDA soil taxonomy) at a depth of 0-10 cm and then air-dried and \nsieved through a 2-mm sieve prior to incubation and soil analysis. Sub-samples \nof the air-dried soil were used for estimation of pH (soil: water ratio of 1: 2.5) as \nsuggested by Jackson (1973), EC (USDA 2004) and organic carbon by Walkley \nand Black\u2019s wet oxidation methods as outlined by Jackson (1973). Organic matter \nwas determined by multiplying the percentage of organic carbon with conventional \nVan-Bemmelen\u2019s factor of 1.724 (Piper 1950). Exchangeable sodium and cation \nexchange capacity (CEC) was determined by 1M NaOAc method (Rhoades \n1982). Particle size distribution was determined by the hydrometer method (Day \n1965). ESP and sodium adsorption ratio (SAR) were calculated as described by \nBrady and Weil (2017). Sodium removal efficiency (RSE) in percentage of Na-\nremoved from soils at end of the experiment was calculated as:\n\n\n\n RSE = [(ESPi \u2013 ESPf)/ESPi] \u00d7 100\n\n\n\nwhere ESPi = exchangeable sodium percentage before the soil amendments \napplication \nESPf = exchangeable sodium percentage after the soil amendments application at \nthe end of the experiment\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 101\n\n\n\n Rice hull and saw dust as soil amendments were well-mixed after the \napplication with 2 kg soil in each pot giving rise to seven treatments such as T0 \n(control), T1 (3 t ha-1 rice hull), T2 (6 t ha-1 rice hull), T3 (9 t ha-1 rice hull), T4 \n(3 t ha-1 saw dust), T5(6 tha-1saw dust) and T6 (9 t ha-1saw dust). Each treatment \nwas replicated three times and pots were arranged in a completely randomised \nway. Total incubation period was 180 days and data of respective parameters were \ntaken at 30, 90 and 180 days. The level of significance of the different treatment \nmeans was calculated by two way ANOVA technique. The main properties of the \nsoils are shown in Table 1.\n\n\n\nTABLE 1\nCharacteristics of the soils and amendments used\n\n\n\n(Piper 1950). Exchangeable sodium and cation exchange capacity (CEC) was determined by 1M \n\n\n\nNaOAc method (Rhoades 1982). Particle size distribution was determined by the hydrometer \n\n\n\nmethod (Day 1965). ESP and sodium adsorption ratio (SAR) were calculated as described by \n\n\n\nBrady and Weil (2017). Sodium removal efficiency (RSE) in percentage of Na-removed from \n\n\n\nsoils at end of the experiment was calculated as: \n\n\n\nRSE = [(ESPi \u2013 ESPf)/ESPi] \u00d7 100 \n \nwhere ESPi= exchangeable sodium percentage before the soil amendments application  \nESPf = exchangeable sodium percentage after the soil amendments application at the end of the \nexperiment \n \nRice hull and saw dust as soil amendments were well-mixed after the application with 2 kg soil \n\n\n\nin each pot giving rise to seven treatments such as T0 (control), T1(3 tha-1 rice hull), T2 (6 tha-1 \n\n\n\nrice hull), T3 (9 tha-1 rice hull), T4 (3 tha-1saw dust), T5(6 tha-1saw dust) and T6(9 tha-1saw \n\n\n\ndust). Each treatment was replicated three times and pots were arranged in a completely \n\n\n\nrandomised way. Total incubation period was 180 days and data of respective parameters were \n\n\n\ntaken at 30, 90 and 180 days. The level of significance of the different treatment means was \n\n\n\ncalculated by two way ANOVA technique. The main properties of the soils are shown in Table \n\n\n\n1. \n\n\n\nTABLE 1 \nCharacteristics of the soils and amendments used \n\n\n\nParameter Bajoa soil series Dumuria soil series Rice hull Saw dust \nLocation N 22\u00ba 48.233\u0384 \n\n\n\nE 89\u00ba 29.832\u0384 \nN 22\u00ba 42.740\u0384 \nE 89\u00ba 31.476\u0384 \n\n\n\n-- -- \n\n\n\npH (H2O) 7.83 \u00b1 0.005 7.95 \u00b1 0.15 7.46 \u00b1 0.02 7.37 \u00b1 0.01 \nECe (dSm-1) 6.34 \u00b1 0.04 13.34 \u00b1 0.03 0.09\u00b1 0.01 0.12\u00b1 0.03 \nCEC (cmolckg-1) 12.56 \u00b1 0.20 13.32 \u00b1 0.03 -- -- \nClay (%) 28 \u00b1 2.65 42 \u00b1 2.0 -- -- \nSilt (%) 51 \u00b1 3.61 33 \u00b1 1.73 -- -- \nSand (%) 21 \u00b1 3.21 25 \u00b1 5.19 -- -- \nTexture Silty Clay Clay Loam -- -- \nOrganic C (%) 1.28 \u00b1 0.09 1.31 \u00b1 0.06 29.21\u00b10.69 27.47\u00b11.10 \nOrganic Matter (%) 2.21 \u00b1 0.01 2.26 \u00b1 0.07 -- -- \nAvailable N (mgkg-1) 46.12 \u00b1 0.84 54.51 \u00b1 5.60 -- -- \nAvailable P (mgkg-1) 2.68 \u00b1 0.03 2.16 \u00b1 0.04 -- -- \nTotal N (mgkg-1) -- -- 118.16\u00b12.11 74.98\u00b11.33 \nTotal P (mgkg-1) -- -- 53.11\u00b10.83 43.19\u00b11.07 \nTotal K (mgkg-1) -- -- 688.37\u00b18.59 797.07\u00b112.72 \nSAR (%) 19.16 \u00b1 1.76 26.14 \u00b1 1.11 -- -- \nESP (%) 12.56 \u00b1 2.84 15.61 \u00b1 0.12 -- -- \n% water content at field \ncapacity (%) \n\n\n\n56.49 48.59 -- -- \n\n\n\nCaCO3 (%) 1.83 1.61 -- -- \n \n\n\n\nRESULTS \n\n\n\nChanges in Soil pH \nChanges in soil pH are shown in Table 2. The data revealed insignificant differences \namong the treatments of the soils used. The pH of Dumuria soil was less affected \nthan the  pH of Bajoa soil after the application of organic amendments. pH \ndecreased from 8.11 to 7.70 in Bajoa soil and 8.26 to 8.04 in Dumuria soil after \n180 days of incubation. RSE or percentage of sodium removed from the soils at \nthe end of the experiment (Table 2) indicated 52.71% reduction in sodium from \nthe exchange site of Bajoa soil for rice hull and 55.51% for saw dust application \nwhereas in Dumuria soil it was 44.33% and 50.48% respectively. The results \nshowed that rice hull was an effective amendment for sodium removal in both the \nsoils studied which was confirmed by the values of ESPf of the soil that reached \na value lower than the critical value (>13) for saline soils with all the treatments \nexcept for control and T1 treatment in Dumuria soil (Table 2). However, the \nchanges in soil pH was statistically insignificant with time.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019102\n\n\n\nChanges in Soil ECe \nFigure 1 shows that a significant (p\u22640.05) decrease in the electrical conductivity \nof the saturated soil paste extract (ECe) occurred with the increased rates of \namendment and with the days of incubation in both the soils compared with the \ncontrol where no amendment was applied. Initially, the ECe value of Dumuria \nclay loam soil was twice than that of Bajoa silty clay soil. After the amendment \napplication, the ECe value was reduced from 6.34 to 2.29 dSm-1 in Bajoa soil \nand 13.30 to 3.7 dSm-1 in Dumuria soil. The reduction was greater in Dumuria \n(reduced up to 72.18%) soil than in Bajoa soil (63.88%) which was prominent at \n90 days of incubation period. At the end of the experiment, the reduction was not \nsignificant among the treatments though it showed lower values compared to the  \n90 days incubation period. \n\n\n\nTABLE 2\npH, exchangeable sodium percentage (ESP), and amount of exchangeable sodium \n\n\n\nremoved following application of amendments to the soils \n\n\n\n\n\n\n\nRESULTS  \n \n\n\n\nChanges in Soil pH  \n\n\n\nChanges in soil pH are shown in Table 2. The data revealed insignificant differences among the \n\n\n\ntreatments of the soils used. The pH of Dumuria soil was less affected than the  pH of Bajoa soil \n\n\n\nafter the application of organic amendments. pH decreased from 8.11 to 7.70 in Bajoa soil and \n\n\n\n8.26 to 8.04 in Dumuria soil after 180 days of incubation.   RSE or percentage of sodium \n\n\n\nremoved from the soils at the end of the experiment (Table 2) indicated 52.71% reduction in \n\n\n\nsodium from the exchange site of Bajoa soil for rice hull and 55.51% for saw dust application \n\n\n\nwhereas in Dumuria soil it was 44.33% and 50.48% respectively. The results showed that rice \n\n\n\nhull was an effective amendment for sodium removal in both the soils studied which was \n\n\n\nconfirmed by the values of ESPf of the soil that reached a value lower than the critical value \n\n\n\n(>13) for saline soils with all the treatments except for control and T1 treatment in Dumuria soil \n\n\n\n(Table 2). However, the changes in soil pH was statistically insignificant with time. \n\n\n\n \nTABLE 2 \n\n\n\npH, exchangeable sodium percentage (ESP), and amount of exchangeable sodium removed following application of \namendments to the soils  \n\n\n\n \nBajoa soil (ESPi = 12.56) Dumuria soil (ESPi = 15.61) \n\n\n\nTreatment pH ESPf Na removed (%) pH ESPf Na removed (%) \nT0 8.11\u00b10.03 13.09 \u00b1 0.05 5.92 8.26\u00b10.04 16.60 \u00b1 0.31 2.09 \nT1 7.75\u00b10.03 10.25 \u00b1 0.31 25.48 8.23\u00b10.03 13.29 \u00b1 0.54 21.60 \nT2 7.72\u00b10.02 8.02 \u00b1 0.18 41.73 8.20\u00b10.02 11.33 \u00b1 0.09 33.17 \nT3 7.72\u00b10.04 6.51 \u00b1 0.11 52.71 8.15\u00b10.01 9.44 \u00b1 0.15 44.33 \nT4 7.72\u00b10.03 10.17 \u00b1 0.11 26.13 8.26\u00b10.01 12.85 \u00b1 0.27 24.19 \nT5 7.70\u00b10.04 8.77 \u00b1 0.08 36.29 8.12\u00b10.02 11.67 \u00b1 0.12 31.20 \nT6 7.70\u00b10.05 6.12 \u00b1 0.05 55.51 8.04\u00b10.04 8.40 \u00b1 0.06 50.48 \n\n\n\n \n \nChanges in Soil ECe  \n\n\n\nFigure 1 shows that a significant (p\u22640.05) decrease in the electrical conductivity of the \n\n\n\nsaturated soil paste extract (ECe) occurred with the increased rates of amendment and with the \n\n\n\ndays of incubation in both the soils compared with the control where no amendment was applied. \n\n\n\nInitially, the ECe value of Dumuria clay loam soil was twice than that of Bajoa silty clay soil. \n\n\n\nAfter the amendment application, the ECe value was reduced from 6.34 to 2.29 dSm-1in Bajoa \n\n\n\nFig. 1: Effect of rice hull and saw dust at different incubation times on ECe \nin the soils. \n\n\n\nsoil and 13.30 to 3.7 dSm-1 in Dumuria soil. The reduction was greater in Dumuria (reduced up \n\n\n\nto 72.18%) soil than in Bajoa soil (63.88%) which was prominent at 90 days of incubation \n\n\n\nperiod. At the end of the experiment, the reduction was not significant among the treatments \n\n\n\nthough it showed lower values compared to the  90 days incubation period.  \n\n\n\n  \nFigure 1. Effect of rice hull and saw dust at different incubation times on ECe in the soils.  \nNote:T0=Control; T1, T2, T3=3, 6 and 9tha-1 rice hull respectively; T4, T5 and T6=3, 6 and 9tha-1 saw dust \nrespectively. \n \nSoil Sodicity  \n\n\n\nThe sodium adsorption ratios (SAR) of the soils are shown in Figure 2. The SAR significantly \n\n\n\ndecreased with the application of the amendments used in the soils. The result showed that the \n\n\n\nhighest SAR was found in control which was 23.87 in Bajoa soil and 32.23 in Dumuria soil \n\n\n\nrespectively. The lowest SAR was found at T3 and T6 treatments in Bajoa soil which were 10.85 \n\n\n\nand 10.0 respectively and in Dumuria soil the lowest SAR was 10.96 at T2 and 13.18 at T6. The \n\n\n\nresults indicated that the highest rates of the amendments were effective in reducing the SAR for \n\n\n\nboth the soils. \n\n\n\n0.00\n\n\n\n1.00\n\n\n\n2.00\n\n\n\n3.00\n\n\n\n4.00\n\n\n\n5.00\n\n\n\n6.00\n\n\n\n7.00\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nEC\ne \n\n\n\n(d\nSm\n\n\n\n-1\n) \n\n\n\nTreatment \n\n\n\nBajoa Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\n0\n\n\n\n2\n\n\n\n4\n\n\n\n6\n\n\n\n8\n\n\n\n10\n\n\n\n12\n\n\n\n14\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nEC\ne \n\n\n\n(d\nSm\n\n\n\n-1\n) \n\n\n\nTreatment \n\n\n\nDumuria Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\nNote:T0 = Control; T1, T2, T3 = 3, 6 and 9 t ha-1 rice hull respectively; T4, T5 and T6 = \n3, 6 and 9 t ha-1 saw dust respectively.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 103\n\n\n\nSoil Sodicity \nThe sodium adsorption ratios (SAR) of the soils are shown in Figure 2. The SAR \nsignificantly decreased with the application of the amendments used in the soils. \nThe result showed that the highest SAR was found in control which was 23.87 in \nBajoa soil and 32.23 in Dumuria soil respectively. The lowest SAR was found at \nT3 and T6 treatments in Bajoa soil which were 10.85 and 10.0 respectively and \nin Dumuria soil the lowest SAR was 10.96 at T2 and 13.18 at T6. The results \nindicated that the highest rates of the amendments were effective in reducing the \nSAR for both the soils.\n   \n\n\n\nFig. 2: Effect of rice hull and saw dust at different incubation times on SAR \nin the soils. \n\n\n\n  \nFigure 2. Effect of rice hull and saw dust at different incubation times on SAR in the soils.  \nNote: T0=Control; T1, T2, T3=3, 6 and 9tha-1 rice hull respectively; T4, T5 and T6=3, 6 and 9tha-1 saw dust respectively. \n \n \n\n\n\nChanges in Exchangeable Sodium and Potassium  \n\n\n\nFigure 3 shows that the initial amount of sodium in both soils was very high due to their high \n\n\n\nsalinity. At the end of the experiment, sodium content in Bajoa soil was reduced from 350 mgkg-\n\n\n\n1 at control to 150 mgkg-1 at T3 and T6 treatments. On the other hand, in Dumuria soil sodium \n\n\n\ncontent was reduced to 200 mgkg-1at T2 treatment which was 475 mgkg-1 at control.  \n\n\n\n\n\n\n\n  \nFigure 3. Effect of rice hull and saw dust at different incubation times on exchangeable sodium content in the soils.  \nNote: T0=Control; T1, T2, T3=3, 6 and 9tha-1 rice hull respectively; T4, T5 and T6=3, 6 and 9tha-1 saw dust respectively. \n \nThe exchangeable potassium showed an increasing trend for both the soils after the addition of \n\n\n\namendment throughout the incubation period (Figure 4). In Bajoa soil, the highest K content \n\n\n\nfound was 236.33 mgkg-1 and 243.45 mgkg-1in soil amended with rice hull and saw dust \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nSA\nR \n\n\n\n(%\n) \n\n\n\nTreatment \n\n\n\nBajoa Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nSA\nR \n\n\n\n(%\n) \n\n\n\nTreatment \n\n\n\nDumuria Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n300\n\n\n\n350\n\n\n\n400\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nN\na \n\n\n\n(m\ngk\n\n\n\ng-1\n) \n\n\n\nTreatment \n\n\n\nBajoa Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n300\n\n\n\n350\n\n\n\n400\n\n\n\n450\n\n\n\n500\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nN\na \n\n\n\n(m\ngk\n\n\n\ng-1\n) \n\n\n\nTreatment \n\n\n\nDumuria Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\nChanges in Exchangeable Sodium and Potassium \nFigure 3 shows that the initial amount of sodium in both soils was very high due \nto their high salinity. At the end of the experiment, sodium content in Bajoa soil \nwas reduced from 350 mg kg-1 at control to 150 mg kg-1 at T3 and T6 treatments. \nOn the other hand, in Dumuria soil sodium content was reduced to 200 mg kg-1 at \nT2 treatment which was 475 mg kg-1 at control. \n The exchangeable potassium showed an increasing trend for both the \nsoils after the addition of amendment throughout the incubation period (Figure \n4). In Bajoa soil, the highest K content found was 236.33 mg kg-1 and 243.45 mg \nkg-1 in soil amended with rice hull and saw dust respectively whereas rice hull and \nsaw dust amended Dumuria soil had K values of 358.78 mg kg-1 and 349.98 mg \nkg-1 respectively.\n\n\n\nNote: T0=Control; T1, T2, T3=3, 6 and 9 t ha-1 rice hull respectively; T4, T5 and T6=3, \n6 and 9 t ha-1 saw dust respectively.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019104\n\n\n\nChanges in Exchangeable Calcium and Magnesium\nFigure 5 showed the changes in exchangeable calcium and magnesium throughout \nthe incubation period with different rates of amendments. The results indicated \nthat the concentration of calcium significantly (p\u22640.05) increased from 332 mg \nkg-1 at control to 1023 mg kg-1 at T6 treatment in Bajoa soil while in Dumuria soil, \nit increased from 338mg kg-1 at control to 712 mg kg-1 at T6 treatment. On the \nother hand, the magnesium content in Bajoa soil increased from 97.88 mg kg-1 at \ncontrol to 130.72 mg kg-1 at T3 for rice hull and 140.42 mg kg-1 at T6 for saw dust. \nIn Dumuria soil, the magnesium increment was 172.27 mg kg-1 at T3 in rice hull-\ntreated soil and 168.91 mg kg-1 at T6 in saw dust-treated soil. However, at the end \nof the experiment, calcium and magnesium content did not show any significant \nchange for the amendments used for both the soils.\n\n\n\nFig. 3: Effect of rice hull and saw dust at different incubation times on exchangeable \nsodium content in the soils. \n\n\n\nNote: T0=Control; T1, T2, T3=3, 6 and 9t ha-1 rice hull respectively; T4, T5 and T6=3, 6 and 9t \nha-1 saw dust respectively.\n\n\n\n  \nFigure 2. Effect of rice hull and saw dust at different incubation times on SAR in the soils.  \nNote: T0=Control; T1, T2, T3=3, 6 and 9tha-1 rice hull respectively; T4, T5 and T6=3, 6 and 9tha-1 saw dust respectively. \n \n \n\n\n\nChanges in Exchangeable Sodium and Potassium  \n\n\n\nFigure 3 shows that the initial amount of sodium in both soils was very high due to their high \n\n\n\nsalinity. At the end of the experiment, sodium content in Bajoa soil was reduced from 350 mgkg-\n\n\n\n1 at control to 150 mgkg-1 at T3 and T6 treatments. On the other hand, in Dumuria soil sodium \n\n\n\ncontent was reduced to 200 mgkg-1at T2 treatment which was 475 mgkg-1 at control.  \n\n\n\n\n\n\n\n  \nFigure 3. Effect of rice hull and saw dust at different incubation times on exchangeable sodium content in the soils.  \nNote: T0=Control; T1, T2, T3=3, 6 and 9tha-1 rice hull respectively; T4, T5 and T6=3, 6 and 9tha-1 saw dust respectively. \n \nThe exchangeable potassium showed an increasing trend for both the soils after the addition of \n\n\n\namendment throughout the incubation period (Figure 4). In Bajoa soil, the highest K content \n\n\n\nfound was 236.33 mgkg-1 and 243.45 mgkg-1in soil amended with rice hull and saw dust \n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nSA\nR \n\n\n\n(%\n) \n\n\n\nTreatment \n\n\n\nBajoa Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n30\n\n\n\n35\n\n\n\n40\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nSA\nR \n\n\n\n(%\n) \n\n\n\nTreatment \n\n\n\nDumuria Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n300\n\n\n\n350\n\n\n\n400\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nN\na \n\n\n\n(m\ngk\n\n\n\ng-1\n) \n\n\n\nTreatment \n\n\n\nBajoa Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n300\n\n\n\n350\n\n\n\n400\n\n\n\n450\n\n\n\n500\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nN\na \n\n\n\n(m\ngk\n\n\n\ng-1\n) \n\n\n\nTreatment \n\n\n\nDumuria Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\nFig. 4: Effect of rice hull and saw dust at different incubation times on exchangeable \npotassium content in the soils. \n\n\n\nNote: T0=Control; T1, T2, T3=3, 6 and 9t ha-1 rice hull respectively; T4, T5 and T6=3, 6 and \n9t ha-1 saw dust respectively.\n\n\n\nrespectively whereas rice hull and saw dust amended Dumuria soil had K values of 358.78 mgkg-\n\n\n\n1 and 349.98 mgkg-1respectively. \n\n\n\n\n\n\n\n  \nFigure 4. Effect of rice hull and saw dust at different incubation times on exchangeable potassium content in the soils.  \nNote: T0=Control; T1, T2, T3=3, 6 and 9tha-1 rice hull respectively; T4, T5 and T6=3, 6 and 9tha-1 saw dust respectively. \n \n \n\n\n\nChanges in Exchangeable Calcium and Magnesium \n\n\n\nFigure 5 showed the changes in exchangeable calcium and magnesium throughout the incubation \n\n\n\nperiod with different rates of amendments. The results indicated that the concentration of \n\n\n\ncalcium significantly (p\u22640.05) increased from 332 mgkg-1 at control to 1023 mgkg-1 at T6 \n\n\n\ntreatment in Bajoa soil while in Dumuria soil, it increased from 338mgkg-1 at control to 712 \n\n\n\nmgkg-1 at T6 treatment. On the other hand, the magnesium content in Bajoa soil increased from \n\n\n\n97.88 mgkg-1 at control to 130.72 mgkg-1 at T3 for rice hull and 140.42 mgkg-1 at T6 for saw \n\n\n\ndust. In Dumuria soil, the magnesium increment was 172.27 mgkg-1 at T3 in rice hull-treated soil \n\n\n\nand 168.91 mgkg-1 at T6 in saw dust-treated soil. However, at the end of the experiment, calcium \n\n\n\nand magnesium content did not show any significant change for the amendments used for both \n\n\n\nthe soils. \n\n\n\n\n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n300\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nK \n(m\n\n\n\ngk\ng-1\n\n\n\n) \n\n\n\nTreatment \n\n\n\nBajoa Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6 0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n300\n\n\n\n350\n\n\n\n400\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nK \n(m\n\n\n\ngk\ng-1\n\n\n\n) \n\n\n\nTreatment \n\n\n\nDumuria Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 105\n\n\n\nDISCUSSION\nEffects of rice hull and saw dust application as organic amendments were evaluated \nin the reduction of salinity of two soils. Changes in different soil parameters such \nas pH, ECe, soil sodicity and exchangeable cations were studied. The reduction \nin pH may be due to the production of organic acids upon decomposition of \norganic amendments in soil. However, the overall changes in pH for both the \nsoils were insignificant. This is due to the high calcium carbonate content which \nacted as a buffer and resisted any appreciable change in soil pH in the alkaline \nrange. Hossain and Sarker (2015) observed that a decrease in pH of salt affected \nsoils could be due to the addition of rice straw applied as organic amendments \nwhich adsorbed H+ by their specific negative surface areas. Cates et al. (1982) \nreported a decrease in soil pH after the addition of soil amendments, especially \ngypsum, during reclamation of a calcareous saline-sodic soil. The RSE or the \npercentage of Na removed from the soils at the end of the incubation study (180 \ndays) also showed a significant reduction after the application of amendments. \nRSE values for the highest dose of amendments showed the highest reduction \nin both soils followed by other treatments. The data obtained from  the present \nstudy indicate that the amendments applied have a high reclaiming capacity. \nThis may due to the increase in organic matter content of both soils which upon \n\n\n\n\n\n\n\n  \n \nFigure 5. Effect of rice hull and saw dust at different incubation times on exchangeable calcium and magnesium in the soils. \nNote: T0=Control; T1, T2, T3=3, 6 and 9tha-1 rice hull respectively; T4, T5 and T6=3, 6 and 9tha-1 saw dust respectively. \n \n \n\n\n\nDISCUSSION \n\n\n\nEffects of rice hull and saw dust application as organic amendments were evaluated in the \n\n\n\nreduction of salinity of two soils. Changes in different soil parameters such as pH, ECe, soil \n\n\n\nsodicity and exchangeable cations were studied. The reduction in pH may be due to the \n\n\n\nproduction of organic acids upon decomposition of organic amendments in soil. However, the \n\n\n\noverall changes in pH for both the soils were insignificant. This is due to the high calcium \n\n\n\ncarbonate content which acted as a buffer and resisted any appreciable change in soil pH in the \n\n\n\nalkaline range. Hossain and Sarker (2015) observed that a decrease in pH of salt affected soils \n\n\n\ncould be due to the addition of rice straw applied as organic amendments which adsorbed H+ by \n\n\n\ntheir specific negative surface areas. Cates et al. (1982) reported a decrease in soil pH after the \n\n\n\naddition of soil amendments, especially gypsum, during reclamation of a calcareous saline-sodic \n\n\n\nsoil. The RSE or the percentage of Na removed from the soils at the end of the incubation study \n\n\n\n0\n\n\n\n200\n\n\n\n400\n\n\n\n600\n\n\n\n800\n\n\n\n1000\n\n\n\n1200\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nCa\n (m\n\n\n\ngk\ng-1\n\n\n\n) \n\n\n\nTreatment \n\n\n\nBajoa Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6 0\n\n\n\n100\n\n\n\n200\n\n\n\n300\n\n\n\n400\n\n\n\n500\n\n\n\n600\n\n\n\n700\n\n\n\n800\n\n\n\n900\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nCa\n (m\n\n\n\ngk\ng-1\n\n\n\n) \n\n\n\nTreatment \n\n\n\nDumuria Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n140\n\n\n\n160\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nM\ng \n\n\n\n(m\ngk\n\n\n\ng-1\n) \n\n\n\nTreatment \n\n\n\nBajoa Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6 0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n140\n\n\n\n160\n\n\n\n180\n\n\n\n200\n\n\n\n30 Days 90 Days 180 Days\n\n\n\nM\ng \n\n\n\n(m\ngk\n\n\n\ng-1\n) \n\n\n\nTreatment \n\n\n\nDumuria Soil \n\n\n\nT0\n\n\n\nT1\n\n\n\nT2\n\n\n\nT3\n\n\n\nT4\n\n\n\nT5\n\n\n\nT6\n\n\n\nNote: T0 = Control; T1, T2, T3 = 3, 6 and 9t ha-1 rice hull respectively; T4, T5 and T6 = 3, \n6 and 9 t ha-1 saw dust respectively.\n\n\n\nFig. 5: Effect of rice hull and saw dust at different incubation times on exchangeable \ncalcium and magnesium in the soils. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019106\n\n\n\ndecomposition increases the partial pressure of CO2 and produces organic acids \nthat mobilise calcium through enhancing the solubility of soil calcite. Tajeda et \nal. (2006) observed a significant decrease in ESP using organic amendments on \nsaline soil. The reduction in ECe values was probably related to the increased \nsoil porosity resulting from the addition of organic amendments. An increase in \nthe Ca2+ concentration in the soil solution resulted in the replacement of Na+ by \nCa2+ at the cation exchange sites on the soil particles. Meanwhile, it was found \nthat the greater the total porosity, the greater the leaching of the exchanged Na+ \nand the greater the subsequent reduction in soil salinity (as indicated by the ECe \nvalue). A similar finding was observed by Qadir and Oster (2004). The SAR value \nreduced up to 10 units in Bajoa and 10.96 units in Dumuria soil. The highest rate \nof the respective amendments was the most effective amendment rate in reducing \nthe SAR of soil (Figure 2) and had high adsorptive capacity of sodium by rice \nhull and saw dust. However, during 30 days of incubation, the  SAR value did \nnot produce any significant variation with amendment application. During the \n90 and 180 days of incubation, it also showed similar  results which confirm that \nincubation with 90 days is sufficient for reducing the SAR value in both the soils. \nThe positive effects of all the treatments for reducing SAR in Bajoa soil followed \nthe order: T6 >T3 > T5> T2 > T4>T1. Meanwhile in Dumuria soil, the order \nwas: T2 > T3 > T6 > T1 > T5 > T4. With increasing rates of organic amendment, \nthe value of sodium ions decreased in both the soils studied. During the start of \nthe incubation, sodium content showed an insignificant increase and with time \nthe sodium ions decreased significantly compared to control for both the soils. \nThis decrease in sodium content may be due to the leaching loss of sodium ions \nupon increased soil porosity after the application of organic amendments in the \nsoil. The results also showed increased potassium content in the soil that may \nbe a result of organic matter decomposition which released some K in the soil \nsolution. Warman and Termeer (2005) observed increased available potassium in \norganic amended saline soil. Calcium and magnesium content increased after the \naddition of rice hull and saw dust with incubation time. This may be due to the \ndissolution of calcite and magnesium containing minerals in soil which results \nfrom the low soil pH. This calcium and magnesium ions displace the sodium ions \nfrom its exchange sites. A similar finding was observed by Prapagar et al. (2012).\n\n\n\nCONCLUSION\nThe results of this study showed that application of rice hull and saw dust acted \nas agents for reclaiming soil salinity for both the soils under study. Both rice hull \nand saw dust performed effectively at the highest dose to ameliorate the Bajoa \nsoil whereas rice hull served as  a better ameliorant than saw dust at a lower dose \nfor Dumuria soil. Soil chemical properties were improved by reducing the EC, \nSAR and pH while RSE and other exchangeable cations were increased except \nfor sodium, irrespective of the soil under study. However, field tests are required \nto draw a final conclusion.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 107\n\n\n\nREFERENCES\nAbdurrahman, H., B. Fatih, M. Fatih and Y. Mustafa.  2004. Reclamation of saline-\n\n\n\nsodic soils with gypsum and  MSW compost. Journal of Compost science & \nUtilization 12(2): 175 \u2013 179.\n\n\n\nBrady, N.C. and R.R. Weil. 2017. The Nature and Properties of Soils(15th ed.).  \nEngland: Pearson Education Limited, 456p.  \n\n\n\nByous, E.W., J.F. Williams, G.E. Jones, W.R. Horwath and C. Kessel. 2004. Nutrient \nrequirements of rice with alternative straw management. Better Crops 36: 6-11.\n\n\n\nCates, R.L., V.A. Haby, E.O. Skogley  and H. Ferguson. 1982. Effectiveness of by-\nproduct sulfuric acid for reclaiming calcareous, saline-sodic soils. Journal of \nEnvironmental Quality 11 : 299 \u2013 302.\n\n\n\nCl\u00e1udio, P.J., C.N. Cl\u00e9sia, L.F. Renildes, R.C. Paulo and L.P. Jos\u00e9. 2007. Effects of \ncomposted urban solid waste addition on yield and metal contents of lettuce. \nJournal of the Brazilian Chemical Society 18(1): 195-204.\n\n\n\nDay, P.R. 1965. Particle fraction and particle size analysis. In: Methods of Soil \nAnalysis, Part I, ed. Black A.C., D.D. Evans, L.E. Ensminger, J.L.White and \nF.E. Clark. Madison: American Society of Agronomy, Madison, pp.545 \u2013 566.\n\n\n\nJackson, M.L. 1973. Soil Chemical Analysis (2nd ed). Englewood Cliffs, NJ:Prentice-\nHall Inc. \n\n\n\nHossain, M.B. and R.R. Sarkar. 2015. Organic and inorganic amendments on \nrice (Oryza sativa) and soil in salt affected areas of Bangladesh, Journal of \nEnvironmental Science and Natural Resources 8(2): 109-113.\n\n\n\nKhan, H.R.  2015. Development of sustainable agriculture in the coastal problem \nsoils of Bangladesh. SUSTAIN Congress 23 \u2013 26 Sept.\u201915, University of Kiel, \nKiel, Germany. \n\n\n\nKhan, H.R., S.M. Kabir, M.M.A. Bhuiyan, H.P. Blume, Y. Oki and T. Adachi. 2008. \nAmelioration of Cheringa acid sulfate soil and screening of acidity-salinity \ntolerant rice varieties  in a simulation study. Malaysian Journal of Soil Science \n12: 87 \u2013 102.\n\n\n\nKaniz, F. and H.R. Khan. 2013. Reclamation of saline soil using gypsum, rice hull, \nand sawdust in relation to rice production, Journal of Advanced Scientific \nResearch 4(3): 1\u20135.\n\n\n\nPiper, C.C. 1950. Soil and Plant Analysis (3rd ed.). Australia: Adelaide University. \nPress, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019108\n\n\n\nPrapagar, K., S.P. Indraratne and P. Premanandharajah. 2012. Effect of soil amendments \non Reclamation of Saline-Sodic Soil. Tropical Agricultural Research 23(2): 168 \n\u2013176.\n\n\n\nQadir, M. and J.D. Oster. 2004. Crop and irrigation management strategies for saline-\nsodic soils and waters aimed at environmentally sustainable agriculture. Science \nof the Total Environment 323: 1\u201319.\n\n\n\nQadir, M. and J.D. Oster. 2002. Vegetative bioremediation of calcareous sodic soils: \nhistory, mechanisms, and evaluation. Irrigation Science 21: 91 \u2013 101.\n\n\n\nRhoades, J.D. 1982. Cation exchange capacity. In: Methods of Soil Analysis, ed. \nA.L.Page, R.H. Miller and D.R. Keeney.vMadison: American Society of \nAgronomy, pp.149 \u2013 157.\n\n\n\nSumner, M. 2000. Handbook of Soil Science. Boca Raton: CRC Press, - 2148pp.\n\n\n\nSRDI (Soil Resource Development Institute). 2001. Soil Resources in Bangladesh: \nAssessment and Utilization, 105p.\n\n\n\nTajeda, M., C. Garcia, J. L. Gonzalez and M. T.  Harnandez. 2006. Use of organic \namendment as a strategy for saline soil remediation: Influence on the physical, \nchemical and biological properties of soil. Soil Biology and Biochemistry 38(6): \n1413 \u2013 1421.\n\n\n\nUSDA (United States Department of Agriculture). 2004. Soil Survey Laboratory \nManual, Soil Survey Investigation Report No. 42, version 4.0, Nebraska, \nUSDA: USDA-NRCS.\n\n\n\nWarman, P.R. and W.C.Termeer. 2005. Evaluation of sewage sludge, septic waste and \nsludge compost applications to corn and forage: yields and N, P and K content \nof crops and soils. Bioresource Technology 96: 955 \u2013 961.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: ainiazuraali@gmail.com \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 29-44 (2021)                     Malaysian Society of Soil Science\n\n\n\nCadmium and Zinc Content in Oil Palm Seedlings and \ntheir Phase Associations in Jawa Series Soil Applied with \n\n\n\nPhosphate Rock and Amended with Palm Oil Mill Effluent \nSludge and Lime \n\n\n\nAini, A. A.1*, Fauziah, C. I.2 and Samsuri, A. W.2\n\n\n\n1Estates Research & Advisory Services (ERAS), RISDA Estates Sdn. Bhd. \nNo. 8 Persiaran Institusi, 43000 Kajang, Selangor, Malaysia\n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 UPM Serdang, Selangor, Malaysia\n\n\n\n\n\n\n\nABSTRACT\nThis study was conducted to determine the mobility and availability of Cd, \nZn and P  to oil palm seedlings and their soil phase association (water soluble, \nexchangeable, carbonate, Fe-Mn oxide, organic and residual fractions) after the \nsoil was applied with a phosphate rock fertiliser and amended with palm oil mill \neffluent (POME) or lime. The potential of these amendments in reducing Cd \nuptake by oil palm seedlings from phosphate rock fertiliser application was also \ninvestigated. Four rates of POME (0, 5, 10 and 20 t/ha) and lime (0, 2, 4 and 8 t/ha) \nwere applied on a Jawa Series soil (Sulfic Endoaquepts) planted with 3-month-old \noil palm seedlings until the 9th month. In lime amended soil, 65% of Cd in the soil \nwas in the immobile phase and Cd content in the root decreased with increasing \nrates of lime application. Meanwhile, application of POME sludge amendment \nresulted in 44% of Cd being in the mobile fraction and 56% in the immobile \nphase. The mobile fractions, which comprised the exchangeable and water soluble \nfractions, were found to increase with increasing POME sludge rates. However, \nthere was no influence on the Cd content in plant parts. Application of POME \nsludge also increased Zn content in roots and leaves as exchangeable Zn fraction \nalso increased with increasing application rates. As for P, the content increased in \nall plant parts as a result of POME sludge application. Cadmium in the soil was \ndominant in the mobile fraction (exchangeable), while Zn and P were dominant \nin the immobile fractions (residual and organic, respectively) after being amended \nwith POME and lime. Thus, lime was found to be a better amendment in reducing \nCd uptake from PR fertiliser application in comparison to POME. \n\n\n\nKeyword: Sequential fractionation study, potential acid sulfate soil, \nphosphate rock fertiliser, heavy metals, alkaline mineral and \nbiosolid amendments.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202132\n\n\n\nINTRODUCTION\nHigh rates of phosphate fertilisers (PF) are required at the early stage of oil palm \ngrowth to build P stock in soils and also to overcome high P-fixing capacity of \ntropical soils (Corley and Tinker 2003). However, phosphate rocks (PR) that are \ncommonly used as PF contain Cd as an impurity which could be taken up by oil \npalm seedlings and consequently might enter the human body through the edible \nparts. It is not a common practice to lime the soil at the nursery stage or while the \nplant is growing in the field. However, soil amendments such as alkaline minerals \n(liming materials) and biosolid byproduct of palm oil mill (POME sludge) is \nspeculated to be effective in reducing heavy metal availability and phytotoxicity.\n Soil organic matter has been of particular interest in studies of heavy metal \nsorption by soils because of its significant contribution towards CEC and more \nimportantly, the tendency of transition metal cations to form stable complexes \nwith organic ligands. Organic matter of biosolids with suitable reactive groups \nsuch as hydroxyl, phenoxyl and carboxyl can effectively control the adsorption \nand complex formation of heavy metals with soil, and hence affect the activity \nof metals in soils (Lee et al. 2004). Therefore, high organic matter content or \naddition of organic matter decreases solution concentration of Cd due to its high \nCEC that enhances chelation of organic colloids.\n Liming the contaminated soil to reduce the bioavailability of heavy metals \nis the most widely used remediation treatment. Lime may increase soil pH because \nof the release of hydroxyl ions by the hydrolysis reaction of calcium carbonate, \nand thus, significantly decreases the exchangeable and water soluble fraction \nof metals in contaminated soil; it also induces and increases the residual metal \nform as well as heavy metal precipitation as metal-carbonates (Lombi et al. 2002; \nSantona et al. 2006; Garau et al. 2007 and Lee et al. 2004).\n Knowledge of both the total concentration and chemical forms or phase \nassociation of metal is necessary to understand their behaviour in the soil system \n(Zauyah et al. 2008). However, it has long been accepted that the total soil metal \ncontent alone is not a good measure of bioavailability and not a very useful tool in \ndetermining the potential risks associated with soil contamination (Prokop et al. \n2003). Khan and Frankland (1983) state that the total concentration may indicate \nthe potential toxicity, but the proportion of such metals in the various chemical \nforms will determine the actual toxicity in the soil-plant system at a particular \npoint in time. Sequential chemical extraction techniques have been widely used to \nexamine physico-chemical forms  to better understand the processes that influence \nan element\u2019s potential mobility and availability. Speciation or fractionation refers \nto both the process and the quantification of the different defined species, forms and \nphases of a trace metal. This process in soils is related to the soil biogeochemical \nreactivity and several physico-chemical conditions of the soil (Kabata- Pendias \n2004). To more fully understand the dynamics of the heavy metal in agriculture, \nit is important to identify the forms/fractions of the heavy metal in the soil.\n As soils consist of a heterogeneous mixture of different organic and organo-\nmineral substances, clay mineral, oxides of Fe, Al, Mn and other solid components \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 33\n\n\n\nas well as a variety of soluble substances, the binding mechanisms for metals \nin soils are manifold and vary with the composition of soils and their physical \nproperties. The metal associated with different binding sites can be defined by \nthe selective extraction method (Chen et al. 2000; Krishnamurti and Naidu 2003; \nKim and Fergusson 1991). Metal fractions can be categorised as shown in Table 1. \nWater soluble and exchangeable fractions, commonly regarded as the most mobile \nbioavailable form of soil elements comprise free ions. This form would have a \ndirect bearing on heavy metals uptake by plants (Table 2) (Ahnstrom and Parker \n1999). Meanwhile, carbonates, Fe-Mn oxides, organic and residual fractions are \nconsidered as immobile and become mobile and phytoavailable with time.\n Hence, this study was conducted to determine whether palm oil mill \neffluent (POME) and lime amendment can help reduce Cd uptake by oil palm \nseedlings as these soil amendments are commonly used in the oil palm plantations \nof Malaysia. The study also aimed to determine whether these amendments  affect \nP and Zn uptake, which is highly present in PR fertilisers and serves as one of the \nmicronutrients required by oil palm. The fractionation study was carried out as \nwell to determine the metals and nutrients phase associations.\n\n\n\nTABLE 1\nGeneral Cd fractions in soil\n\n\n\nFraction Explanation \nWater soluble Most loosely held by soil \nExchangeable Easily exchange \nCarbonate Bound to carbonates \nFe-Mn oxide Bound to iron and manganese oxide \n\n\n\nOrganic \nResidual \n\n\n\nBound to organic matter \nAssociated with residual matter  \n\n\n\nSources: Kim and Fergusson (1991)\n \n \n \n\n\n\nTABLE 2 \n\n\n\nEvaluation of the sequential extraction reagents for each fraction \n \n\n\n\nFraction Reagent Evaluation of the sequential \nextraction \n\n\n\n\n\n\n\nWater soluble/exchangeable \n\n\n\n\n\n\n\nDistilled water/1.0 MgCl \n\n\n\n \nExtract most mobile, \nbioavailable form comprised \nfree ions and soluble \ncomplexes \n\n\n\n \nCarbonate \n\n\n\n \n1.0 M NaOAc, pH=5.0 \n\n\n\nDissolutions of soil \ncarbonates. Solubilised \nspecifically sorbed Cd \n\n\n\n 0.04M NH2OH.HCl in 25% \nNaOAc \n\n\n\nDissolved reducible oxides \nFe-Mn  \n\n\n\n \nOrganic \n\n\n\n0.02M HNO3, 30% H2O2, \npH=2, 3.2 M NH4OAc in \n20% HNO3 \n\n\n\nReleased substantial amounts \nof trace metals bound to \norganic matter \n\n\n\nResidual Aqua-regia (3:1 HNO3: HCl) Dissolution of any remaining \nmineral matter \n\n\n\nSource: Kim and Fergusson(1991) and Ahnstrom and Parker (1999) \n \n \n \n \n \n \n \n \n\n\n\nTABLE 3 \nCadmium and Zn concentrations (mg/kg) in soil, fertilisers and amendment used \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202134\n\n\n\nMATERIALS AND METHODS\nA glasshouse study was conducted where oil palm seedlings were planted in \nunfertilised Jawa Series (Sulfic Endoaquepts), a potential acid sulfate soil, \nwhich represents coastal soils largely planted with oil palm in the West coast \nof Peninsular Malaysia. The soil was collected at Pulau Indah, Klang. Two \nexperimental units were set-up simultaneously,  with only POME biosolids being \napplied at different rates in one experiment and for the other, only lime at different \nrates being applied. The rates of POME sludge were 0, 5, 10 and 20 t/ha or 0, \n66.6, 133 and 266.6 g per 20 kg soil. Meanwhile, the rates for CaCO3 were 0, 2, \n4 and 8 t/ha or 0, 13.34, 26.6 and 53.4 g per 20 kg of soil. The rate of 2 t/ha is \nthe recommended lime requirement of highly weathered soils in Malaysia which \nwas obtained experimentally by Shamshuddin  (1989). Both amendments were \nreplicated four times which made a total number of 32 polybags. Nitrogen, P and \nK fertiliser were applied based on the manuring program of Sime Darby Seed & \nAgriculture Services Sdn. Bhd (one of the main oil palm plantation companies \nin Malaysia). The amendments and fertilisers were applied once and mixed \nhomogenously with the soil. Then, 3-month-old seedlings of the same size and \nheight were planted in each polybag. The experimental design for this experiment \nwas a complete randomised design (CRD). After 9 months  (12-month-old oil \npalm seedlings), the plants were harvested. The whole plant of each treatment \nand replicate were sampled. The plants were separated into root, rachis and leaf \nportions (leaf spikelets with the mid-rib removed). \n\n\n\nTABLE 2\nEvaluation of the sequential extraction reagents for each fraction\n\n\n\nFraction Explanation \nWater soluble Most loosely held by soil \nExchangeable Easily exchange \nCarbonate Bound to carbonates \nFe-Mn oxide Bound to iron and manganese oxide \n\n\n\nOrganic \nResidual \n\n\n\nBound to organic matter \nAssociated with residual matter  \n\n\n\nSources: Kim and Fergusson (1991)\n \n \n \n\n\n\nTABLE 2 \n\n\n\nEvaluation of the sequential extraction reagents for each fraction \n \n\n\n\nFraction Reagent Evaluation of the sequential \nextraction \n\n\n\n\n\n\n\nWater soluble/exchangeable \n\n\n\n\n\n\n\nDistilled water/1.0 MgCl \n\n\n\n \nExtract most mobile, \nbioavailable form comprised \nfree ions and soluble \ncomplexes \n\n\n\n \nCarbonate \n\n\n\n \n1.0 M NaOAc, pH=5.0 \n\n\n\nDissolutions of soil \ncarbonates. Solubilised \nspecifically sorbed Cd \n\n\n\n 0.04M NH2OH.HCl in 25% \nNaOAc \n\n\n\nDissolved reducible oxides \nFe-Mn  \n\n\n\n \nOrganic \n\n\n\n0.02M HNO3, 30% H2O2, \npH=2, 3.2 M NH4OAc in \n20% HNO3 \n\n\n\nReleased substantial amounts \nof trace metals bound to \norganic matter \n\n\n\nResidual Aqua-regia (3:1 HNO3: HCl) Dissolution of any remaining \nmineral matter \n\n\n\nSource: Kim and Fergusson(1991) and Ahnstrom and Parker (1999) \n \n \n \n \n \n \n \n \n\n\n\nTABLE 3 \nCadmium and Zn concentrations (mg/kg) in soil, fertilisers and amendment used \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 35\n\n\n\nSoil and Plant Analyses\nSoil pH was determined in a soil-water suspension using 1:2.5 soil solution ratio. \nCadmium, Zn and P in plant parts were determined by the dry ashing method. All \nthe elements in soil and plant tissue were determined using Perkin Elmer 5100 \nflame atomic absorption spectrophotometry  except for Cd in plant parts which \nused PE 5100 graphite furnace atomic absorption spectrophotometry.\n\n\n\nSequential Extraction of Cadmium, Zinc and Phosphorus.\nAfter the oil palm seedlings were harvested, the soil was homogenised before \nbeing air-dried. The soil was then ground to pass through a 2-mm sieve. The \nphase-associated forms of Cd, Zn and P (water soluble, exchangeable, carbonate, \nFe-Mn oxide, organic and residual fractions) in the soil were determined through \nsequential extraction. The use of selective extractants to quantify the elemental \ncontent in a particular phase is described by the concept of pools in soils, of \nelements of different solubilities and mobilities that can be selectively sampled \nby extractants of different strengths. In this method, a sample is treated with a \nseries of progressively harsher reagents to dissolve increasingly refractory forms. \nIdeally, the reagents are chosen to selectively attack a specific soil compartment \nwith the minimal dissolution of a non-targeted phase (Ahnstrom and Parker1999; \nObrador et al. 2007). The method of sequential extraction procedure used in this \nstudy originated from Tessier et al. (1979), which was then modified by Yang \nand Kimura (1995) and Chlopecka et al. (1996). This method has been used to \ndetermine Cd or other heavy metal fractions in contaminated soils by  several \nresearchers (Kim and Fergusson1991; Chowdhury et al. 1997; Ahnstrom and \nParker 1999; Chen et al. 2000). For the fractionation analysis, 5 g of each soil \nsample was weighed into the centrifuge tube and sequential extraction was \nconducted as shown in Figure 1. All the elements in the different soil fractions \nwere analysed using the PE 5100 flame atomic absorption spectrophotometry.\n\n\n\nStatistical Analysis\nData obtained from the dry ashing and fractionation methods were subjected to  \nANOVA    analysis using Tukey for means comparison.\n\n\n\nRESULTS AND DISCUSSION \nCadmium and Zinc Content in Soils, Fertilisers and Amendments Used\nAnalyses on Jawa Series soil show that Zn was present but Cd was not detectable \nas shown in Table 3. Zinc was present in both amendments (POME and lime) \nand 9.71 mg kg-1 Cd existed in lime whilst it was not detectable in POME. \nUrea fertiliser was free from Cd and Zn contamination while muriate of potash \n(MOP) contained 2.65 and 5.87 mg kg-1 of Cd and Zn, respectively. Zinc in Gafsa \nphosphate rock (PR), used in this study, was higher (291 mg kg-1) than Cd (89 mg \nkg-1) with the Zn:Cd ratio of approximately 3:1. Thus, the main source of Cd was \nfrom the Gafsa PR fertiliser.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202136\n\n\n\n\n\n\n\nonized H2O shaking for 2 h, \nd, filter \n\n\n\n\n\n\n\n\n\n\n\n20 mL 1.0 M MgCl2, shaking for 2 h, \ncentrifuged, filter, wash \n\n\n\n\n\n\n\n20 mL 1.0 M NaOAc, pH=5.0, shaking for \n2 h, centrifuged, filter, wash \n\n\n\n\n\n\n\n20 mL 0.04M NH2OH.HCl in 25% (v/v) \nNaOAc, shaking for 2 h, agitated, \ncentrifuged, filter, wash \n\n\n\n\n\n\n\n2 mL 0.02M HNO3 + 30% H2O2, pH=2, agitated for 5 \nh at 650C. + 3 mL aliquot, 30% H2O2, pH=2, agitated \nfor 7 h at 650C, cooling, + 5 mL 3.2 M NH4OAc in \n20%(v/v) HNO3, dilute 20 mL with dI H2O, agitated \u00bd \nh at room temperature, centrifuged, filter, wash \n\n\n\n\n\n\n\nOrganic \n50 mL aqua regia (3:1 HNO3: HCl) \n\n\n\n\n\n\n\n\n\n\n\nResidual \n \nFigure 1. Sequential extraction schemes for cadmium in soil. Method of Tessier et al. (1979) and \nfollowed by the modified version of Yang and Kimura (1995) and Chlopecka et al. (1996) \n\n\n\nsupernatant \n\n\n\n \nWater soluble \n\n\n\nresidue \n\n\n\nresidue \n\n\n\nsupernatant \n\n\n\nsupernatant \n\n\n\n \nExchangeable \n\n\n\nresidue supernatant \n\n\n\nCarbonates \n\n\n\nresidue supernatant \n\n\n\nFe-Mn oxides \n\n\n\nresidue supernatant \n\n\n\n 5.0 g soil  \n\n\n\n 20 mL dei \ncentrifuge \n\n\n\n\n\n\n\nFigure 1. Sequential extraction schemes for cadmium in soil. Method of Tessier et al. \n(1979) and followed by the modified version of Yang and Kimura (1995) and Chlopecka \n\n\n\net al. (1996)\n\n\n\nTABLE 3\nCadmium and Zn concentrations (mg/kg) in soil, fertilisers and amendment used\n\n\n\n Jawa PR POME Lime MOP \n\n\n\n Series \nSoil \n\n\n\n \n(Gafsa) \n\n\n\n\n\n\n\n \nCd \n\n\n\n \n*nd \n\n\n\n \n89 \n\n\n\n \nnd \n\n\n\n \n9.71 \n\n\n\n \nnd \n\n\n\n \n2.65 \n\n\n\nZn 3.59 291 50 7.62 nd 5.87 \n*Not detectable (nd) \n\n\n\nUrea\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 37\n\n\n\nCadmium, Zinc and Phosphorus Content in Plant Parts Amended with POME\nThere was no significant difference (p>0.05) in Cd content of root, leaf and rachis \nof plants grown in Jawa Series among the four rates of POME as shown in Figure \n2(a). Meanwhile, there were significant differences (p<0.05) for Zn content in \nroot and leaf between the treatment rates T2 to T4. However, the rachis showed \nno significant difference (p>0.05) among the rates, but the content generally \nincreased from T1 to T3 as shown in Figure 2(b). Phosphorus content significantly \nincreased (p<0.05) throughout the rates in all plant parts (root, leaf and rachis) as \nshown in Figure 2 (c).\n\n\n\nCadmium, Zinc and Phosphorus Content in Plant Parts Amended with Lime.\nSoil amended with lime showed significant difference (p<0.05) in Cd content \nin root among four rates of amendments in which Cd concentration obviously \ndecreased with increased lime rates from T1 to T4. Meanwhile, there was no \nsignificant difference (p>0.05) in Cd content of other plant parts (leaf and rachis) \nas shown in Figure 2(d). Zinc showed no significant difference in all plant parts \namong the four rates of lime as shown in Figure 2(e). Also, Figure 2(f) showed no \nsignificant difference (p>0.05) in P  content of all plant parts.\n\n\n\nSoil pH Amended with POME and Lime.\nSoil pH amended with POME showed no significant difference (p>0.05) among \nthe four rates of POME amendment as shown in Figure 3(a). However, soil pH was \nhighest in rates T3 (4.76) and decreased slightly in rates T4 (4.71). Meanwhile, \nsoil pH amended with lime showed a significant difference (p<0.05) with the soil \npH increasing from rates T2 to T4 as shown in Figure3(b). Increasing soil pH \ngenerally decreases metal availability through a precipitation reaction.\n\n\n\nCadmium, Zinc and Phosphorus Fractionation Study in Soil Amended with POME\nThe distribution of Cd in six different types of extractants for four rates of POME \ntreatments is shown in Figure 4(a). The exchangeable Cd (mobile) and carbonates \nfraction (immobile) were the dominant fractions (average of 0.55 mg/kg and 0.54 \nmg/kg, respectively). The decreasing order of Cd fraction was exchangeable = \ncarbonate > water soluble > residual > organic > Fe-Mn form.\n Increasing addition of POME from 0, 5, 10 to 20 t/ha resulted in significant \ndifferences (p<0.05) for all Cd fractions  except  for  the organic  fraction  (Figure \n4(a)). The mobile fraction (exchangeable) generally increased with increasing \nPOME rates, with the higher rates of POME addition (T3 and T4) exhibiting \nhigher Cd concentration compared to the other rates. The increase in water \nsoluble fraction is presumably due to the formation of a soluble organic matter \nmetal complex. However, the concentrations of mobile fractions were very low, \nranging from 0.05 to 0.75 mg/kg Cd which might be the reason for the absence \nof a significant difference (p>0.05) of Cd content between T1 to T4 in all plant \nparts (Figure2(a)). Cadmium in the carbonate fraction was also high in this \nsoil treatment and generally, there was a significant decrease (p<0.05) with an  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202138\n\n\n\n\n\n\n\nb \nb \n\n\n\nab\n a\n\n\n\n \nab\n\n\n\n b \n\n\n\n \nab\n\n\n\n a \na  \n\n\n\n60\n \n\n\n\n20\n \n\n\n\nab\n a  \n\n\n\n80\n \n\n\n\nb \nab\n\n\n\n\n\n\n\n \nab\n\n\n\n a\n \n\n\n\n\n\n\n\n40\n  \n\n\n\nb \nb \n\n\n\nab\n a\n\n\n\n \nb \n\n\n\nb \n\n\n\na \na \n\n\n\na \na \n\n\n\n a\n a\n\n\n\n a\n \n\n\n\na \na \n\n\n\na \na \n\n\n\nT2\n  \n\n\n\nT4\n  \n\n\n\nT4\n \n\n\n\n\n\n\n\nT4\n \n\n\n\n\n\n\n\nT4\n \n\n\n\n     \n20\n\n\n\n\n\n\n\n15\n \n\n\n\n10\n 5 0 \n\n\n\nro\not\n\n\n\n \nle\n\n\n\naf\n \n\n\n\nra\nch\n\n\n\nis\n \n\n\n\n \nso\n\n\n\nil \nse\n\n\n\nri\nes\n\n\n\n\n\n\n\n(a\n) \n\n\n\n \n3.\n\n\n\n0 \n \n\n\n\n2.\n0 \n\n\n\n \n1.\n\n\n\n0 \n \n\n\n\n\n\n\n\n0.\n0 \n\n\n\n\n\n\n\nro\not\n\n\n\n\n\n\n\nle\naf\n\n\n\n \nra\n\n\n\nch\nis \n\n\n\nso\nil \n\n\n\nse\nri\n\n\n\nes\n \n\n\n\n(b\n) \n\n\n\n \n1 1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nro\not\n\n\n\n\n\n\n\nle\naf\n\n\n\n \nra\n\n\n\nch\nis \n\n\n\nso\nil \n\n\n\nse\nri\n\n\n\nes\n \n\n\n\n(c\n) \n\n\n\n\n\n\n\nFi\ngu\n\n\n\nre\n 2\n\n\n\n. (\na)\n\n\n\n C\nad\n\n\n\nm\niu\n\n\n\nm\n, (\n\n\n\nb)\n Z\n\n\n\nin\nc,\n\n\n\n a\nnd\n\n\n\n (c\n) P\n\n\n\nho\nsp\n\n\n\nho\nru\n\n\n\ns c\non\n\n\n\nte\nnt\n\n\n\n in\n ro\n\n\n\not\n, l\n\n\n\nea\nf a\n\n\n\nnd\n ra\n\n\n\nch\nis\n\n\n\n fo\nr J\n\n\n\naw\na \n\n\n\nSe\nrie\n\n\n\ns s\noi\n\n\n\nl a\nm\n\n\n\nen\nde\n\n\n\nd \nw\n\n\n\nith\n fo\n\n\n\nur\n ra\n\n\n\nte\ns o\n\n\n\nf \nPO\n\n\n\nM\nE.\n\n\n\n L\net\n\n\n\nte\nrs\n\n\n\n w\nith\n\n\n\n th\ne \n\n\n\nsa\nm\n\n\n\ne \nal\n\n\n\nph\nab\n\n\n\net\n o\n\n\n\nn \nth\n\n\n\ne \nba\n\n\n\nrs\n w\n\n\n\nith\nin\n\n\n\n th\ne \n\n\n\npl\nan\n\n\n\nt p\nar\n\n\n\nts\n a\n\n\n\nre\n n\n\n\n\not\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\nly\n d\n\n\n\niff\ner\n\n\n\nen\nt a\n\n\n\nt p\n>0\n\n\n\n.0\n5.\n\n\n\n\n\n\n\n20\n \n\n\n\n15\n \n\n\n\n10\n 5 0 \n\n\n\nro\not\n\n\n\n \nle\n\n\n\naf\n \n\n\n\nra\nch\n\n\n\nis\n \n\n\n\n \nso\n\n\n\nil \nse\n\n\n\nri\nes\n\n\n\n\n\n\n\n(d\n) \n\n\n\n3.\n0 \n\n\n\n \n2.\n\n\n\n0 \n \n\n\n\n\n\n\n\n1.\n0 \n\n\n\n\n\n\n\n0.\n0 \n\n\n\n\n\n\n\nro\not\n\n\n\n\n\n\n\nle\naf\n\n\n\n \nra\n\n\n\nch\nis \n\n\n\nso\nil \n\n\n\nse\nri\n\n\n\nes\n \n\n\n\n(e\n) \n\n\n\n16\n0 \n\n\n\n12\n0 80\n\n\n\n\n\n\n\n40\n 0 \n\n\n\n\n\n\n\n\n\n\n\nro\not\n\n\n\n\n\n\n\nle\naf\n\n\n\n \nra\n\n\n\nch\nis \n\n\n\nso\nil \n\n\n\nse\nri\n\n\n\nes\n \n\n\n\n(f\n) \n\n\n\n\n\n\n\nFi\ngu\n\n\n\nre\n 2\n\n\n\n. (\nd)\n\n\n\n C\nad\n\n\n\nm\niu\n\n\n\nm\n, (\n\n\n\ne)\n Z\n\n\n\nin\nc,\n\n\n\n a\nnd\n\n\n\n (f\n) P\n\n\n\nho\nsp\n\n\n\nho\nru\n\n\n\ns c\non\n\n\n\nte\nnt\n\n\n\n in\n ro\n\n\n\not\n, l\n\n\n\nea\nf a\n\n\n\nnd\n ra\n\n\n\nch\nis\n\n\n\n fo\nr J\n\n\n\naw\na \n\n\n\nSe\nrie\n\n\n\ns s\noi\n\n\n\nl a\nm\n\n\n\nen\nde\n\n\n\nd \nw\n\n\n\nith\n fo\n\n\n\nur\n ra\n\n\n\nte\ns o\n\n\n\nf \nlim\n\n\n\ne.\n L\n\n\n\net\nte\n\n\n\nrs\n w\n\n\n\nith\n th\n\n\n\ne \nsa\n\n\n\nm\ne \n\n\n\nal\nph\n\n\n\nab\net\n\n\n\n o\nn \n\n\n\nth\ne \n\n\n\nba\nrs\n\n\n\n w\nith\n\n\n\nin\n th\n\n\n\ne \npl\n\n\n\nan\nt p\n\n\n\nar\nts\n\n\n\n a\nre\n\n\n\n n\not\n\n\n\n si\ngn\n\n\n\nifi\nca\n\n\n\nnt\nly\n\n\n\n d\niff\n\n\n\ner\nen\n\n\n\nt a\nt p\n\n\n\n>0\n.0\n\n\n\n5.\n \n\n\n\n\n\n\n\na \na \n\n\n\na \na \n\n\n\na \na \n\n\n\na \na \n\n\n\na \na  \n\n\n\n\n\n\n\nT2\n  \n\n\n\nT4\n \n\n\n\n\n\n\n\na \na \n\n\n\n\n\n\n\na \na \n\n\n\na \na  \n\n\n\na \n \n\n\n\na \na \n\n\n\na \n\n\n\n \na \n\n\n\na \nab\n\n\n\nab\n b\n\n\n\n \na \n\n\n\na \na \n\n\n\na \n \n\n\n\n\n\n\n\nT4\n \n\n\n\nCd (\u00b5g/plant part) Cd (\u00b5g/plant part) \n\n\n\nZn (mg/plant part) Zn (mg/plant part) \n\n\n\nP (mg/plant part) \nP (mg/plant part) \n\n\n\nFi\ngu\n\n\n\nre\n 2\n\n\n\n. (\na)\n\n\n\n C\nad\n\n\n\nm\niu\n\n\n\nm\n, (\n\n\n\nb)\n Z\n\n\n\nin\nc,\n\n\n\n a\nnd\n\n\n\n (c\n) P\n\n\n\nho\nsp\n\n\n\nho\nru\n\n\n\ns c\non\n\n\n\nte\nnt\n\n\n\n in\n ro\n\n\n\not\n, l\n\n\n\nea\nf a\n\n\n\nnd\n ra\n\n\n\nch\nis\n\n\n\n fo\nr J\n\n\n\naw\na \n\n\n\nSe\nri\n\n\n\nes\n so\n\n\n\nil \nam\n\n\n\nen\nde\n\n\n\nd \nw\n\n\n\nith\n fo\n\n\n\nur\n ra\n\n\n\nte\ns o\n\n\n\nf \nPO\n\n\n\nM\nE.\n\n\n\n L\net\n\n\n\nte\nrs\n\n\n\n w\nith\n\n\n\n th\ne \n\n\n\nsa\nm\n\n\n\ne \nal\n\n\n\nph\nab\n\n\n\net\n o\n\n\n\nn \nth\n\n\n\ne \nba\n\n\n\nrs\n w\n\n\n\nith\nin\n\n\n\n th\ne \n\n\n\npl\nan\n\n\n\nt p\nar\n\n\n\nts\n a\n\n\n\nre\n n\n\n\n\not\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\nly\n d\n\n\n\niff\ner\n\n\n\nen\nt a\n\n\n\nt p\n>\n\n\n\n0.\n05\n\n\n\n.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 39\n\n\n\nincrease in POME rates. However, POME rates had little influence on the Fe-Mn \nand residual fraction. This is similar to the findings of Shuman (1998) on Typic \nKandiudult soils amended with organic waste. Low non-significantly different \nCd concentrations in the organic fraction was found for control and at all POME \ntreated rates. Since Cd is loosely bound to organic matter (forms a weak complex \nwith organic matter), this fraction could have been extracted at the first level of \nextraction. Cadmium added to soils through anthropogenic sources associates \nwith organic matter more than mineral surfaces, but becomes available upon \ndecomposition of the organic matter and when soils are acidified (Shuman1998).\nMeanwhile, the highest amount of Zn fraction was found in the residual form \n(immobile) as shown in Figure 4(b), probably because the soil is an Inceptisols, \nwhich is an extremely young and relatively unweathered soil. This fraction is \nmade up of primary and secondary silicate minerals and therefore extremely inert \nand completely unavailable for plant uptake (Chowdhury et al. 1997). Next are \nthe exchangeable and Fe-Mn fractions which are of similar values (average 3.23 \nand 3.2 mg/kg, respectively). The decreasing order of Zn fractions were residual \n> exchangeable = Fe-Mn > organic > carbonates > water soluble fractions.\n\n\n\nFigure 3. Soil pH of Jawa Series soil amended by (a) POME (b) lime\n\n\n\nLetters with the same alphabet on the bars within the rates of treatment are not \nsignificantly different at p>0.05.\n\n\n\n\n\n\n\n\n\n\n\n6 \n \n\n\n\n5.5 \n \n\n\n\n5 \n \n\n\n\n4.5 \n \n\n\n\n4 \nT1 T2 T3 T4 \n\n\n\nTreatments \n \n \n\n\n\n \n6 \n\n\n\n \n5.5 \n\n\n\n \n5 \n\n\n\n \n4.5 \n\n\n\n \n4 \n\n\n\nT1 T2 T3 T4 \n\n\n\nTreatments \n \n \n \nFigure 3. Soil pH of Jawa Series soil amended by (a) POME (b) lime \n\n\n\n \nLetters with the same alphabet on the bars within the rates of treatment are not \nsignificantly different at p>0.05. \n\n\n\n\n\n\n\n\n\n\n\nab \n\n\n\n\n\n\n\nSo\nil \n\n\n\npH\n \n\n\n\nSo\nil \n\n\n\npH\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202140\n\n\n\nFigure 4. Concentrations of (a) Cd, (b) Zn, and (c) P fractions in Jawa Series soil \namended with four  rates of POME. Letters with the same alphabet on the bars within the \n\n\n\nsame soil fractions are not significantly different at p>0.05.\n\n\n\n4 \n \n3 \n \n2 \n\n\n\n\n\n\n\n\n\n\n\n0.8 \n \n\n\n\n0.6 \n \n\n\n\n0.4 \n \n\n\n\n0.2 \n\n\n\nT1\n T\n2 \n\n\n\nT3 T4 \n\n\n\n \n0 \n\n\n\nWS EXC CAR Fe-Mn ORG RES \nCd fractions \n\n\n\n\n\n\n\n \n8.0 \n\n\n\nT1\n T\n2 \n\n\n\nT3 T4 \n\n\n\n \n6.0 \n\n\n\n \n4.0 \n\n\n\n\n\n\n\n2.0 \n \n\n\n\n0.0 \nWS EXC CAR Fe-Mn ORG RES \n\n\n\nZn fractions \n \n\n\n\n\n\n\n\n500 \n \n\n\n\n00 \n \n\n\n\n00 \n \n\n\n\n00 \n \n\n\n\n100 \n \n\n\n\n0 \n\n\n\n\n\n\n\nWS\n EX\nC \n\n\n\nT1 T2 \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nCAR \n\n\n\nT3 T4 \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nFe-Mn ORG RES \n\n\n\nP fractions \n \n\n\n\nFigure 4. Concentrations of (d) Cd, (e) Zn, and (f) P fractions in Jawa Series soil amended with four rates of \n\n\n\nlime. Letters with the same alphabet on the bars within the same soil fractions are not significantly different at \n\n\n\np>0.05. \n\n\n\n a  \n\n\n\n\n\n\n\n ab \n\n\n\n\n\n\n\nab \n \n\n\n\na a \n \n\n\n\na ab a a \n \n\n\n\n \na a a a \n\n\n\nb b \n \n\n\n\n \na \n\n\n\na \na \n\n\n\nab a \nab  \n\n\n\n \nb b b \n\n\n\n  \n a a \n\n\n\na a c b b \n\n\n\na a \n\n\n\n\n\n\n\na a a \n\n\n\n\n\n\n\na a a a \na a a a  b b b  \n\n\n\na a a a \n\n\n\nP \n(m\n\n\n\ng/\nkg\n\n\n\n) \nZ\n\n\n\nn \n(m\n\n\n\ng/\nkg\n\n\n\n) \nC\n\n\n\nd \n(m\n\n\n\ng/\nkg\n\n\n\n)  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 41\n\n\n\n Zinc in exchangeable and Fe-Mn and organic fractions showed significant \ndifferences (p<0.05) between the T4 and T2 rates of POME amendment, and no \nsignificant difference (p>0.05) for the rest of the Zn fractions (water soluble, \ncarbonates and residual) as shown in Figure 4(b). There was no significant \ndifference of exchangeable Zn concentration between T2 and control (T1); \nhowever, the concentration significantly increased with an increase in POME \nrates for T2 and T4. The Zn water soluble fraction concentrations were not \nstatistically significant. Thus, the exchangeable fraction was the mobile fraction \nthat might have caused a significant increase (p<0.05) in Zn content of roots and  \nleaves  as shown in Figure 2(b). Also, the concentration of Fe-Mn fractions was \nhigh in this soil treatment, possibly due to the soluble organic matter that led \nto Zn redistribution among fractions (Shuman 1998). Heavy metals in Fe-Mn \noxides and organic fraction were of low solubility and high stability for biological \nactivity and would not have a direct bearing on their uptake by plant (Ahnstrom \nand Parker 1999). The low Zn concentrations of organic fractions were similar to \nthose reported by Zauyah et al. (2008) which indicates that the association may be \nrelatively unstable (Kashem and Singh 1999). Kashem and Singh (1999), in their \nstudy on contaminated soils, reported that organic matter had little effect on Zn \npartitioning although the soils contained a higher amount of organic matter.\n In this POME treated soil, P concentrations in the organic fractions were \nthe highest, followed by residual > Fe-Mn > carbonates = exchangeable = water \nsoluble fractions (Figure 4(c)). From this figure, P in the mobile fractions (water \nsoluble and exchangeable) was much less than in the immobile fractions (residual, \norganic, Fe-Mn and carbonates). For P, only T4 residual fraction showed a \nsignificant increase (p<0.05) in comparison to T2 (Figure 4(c)) whilst other \nfractions were not statistically different (p>0.05).\n\n\n\nCadmium, Zinc and Phosphorus Fractionation Study in Soil Amended with Lime\nSoil amended with 0, 2, 4 and 8 t/ha of lime showed dominance of Cd in the \nexchangeable fraction followed by carbonates > residual = Fe-Mn = organic \n> water soluble fraction (Figure 4(d)). Carbonates and exchangeable fractions \nwere obviously higher than other fractions. The result obtained was similar to \nsoil amended with POME. There were significant differences (p<0.05) for all Cd \nfractions among the four rates of lime amendment except for water soluble and \nFe-Mn as shown in Figure 4(d). The exchangeable fraction concentrations in T2 \nto T4 were significantly lower than in control (T1) while the residual fractions in \nT2 to T4 were significantly higher than in control (T1). This study revealed that \nincreasing soil pH (Figure 3(b)) might have increased the concentration of the \nresidual fraction which reduced Cd availability to the oil palm seedlings (Garau \net al. 2007). The Cd concentrations in water soluble fractions of lime treated soil \nwere lower (average 0.05 mg kg-1) than in soil amended with POME (average \n0.10 mg kg-1). The decrease in concentrations of mobile fractions (water soluble \nand exchangeable) might be the reason for the significant decrease (p<0.05) in Cd \ncontent of the roots (Figure 2(d)). Additionally, competition between Cd and Ca \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202142\n\n\n\n(from lime) at root surfaces led to less Cd uptake from soils (Ramachandran and \nD`Souza 1998). The carbonates and organic fractions decreased with an increase \nin lime rates.\n The highest fraction of Zn concentrations in the lime treated soil was found \nto be the residual form and which decreased in the order residual > exchangeable \n= Fe-Mn > organic > carbonates > water soluble fraction (Figure 4(d)). In this \nlime treated soil, the exchangeable fraction had a similar value with Fe-Mn \nfraction (average 4.14 and 4.13 mg kg-1, respectively) but was higher than the \nother immobile fractions (carbonate and organic). Zinc showed a significant \ndifference (p<0.05) for exchangeable, carbonate and Fe-Mn fraction, while there \nwas no significant difference (p>0.05) for water soluble, organic and residual \nfractions (Figure 4(e)). The exchangeable Zn decreased with increasing lime rates \nfrom T2 to T4. Zn concentrations in water soluble fractions were not statistically \ndifferent. The immobile fraction (Fe-Mn) significantly increased for the highest \nlime treatment of 8 t/ha compared to the other treatments. This clearly showed \nthat lime amendment decreased the Zn mobile fraction. The pH increase induced \nby the lime treatment favoured heavy metal precipitation and also could have \nresulted in an increase in heavy metal sorption by variable charged colloids such \nas organic matter and Fe-Mn oxides, finally resulting in reduced concentration of \navailable metal (Garau et al. 2007).\n Phosphorus concentrations in organic fractions exhibited the highest levels \ncompared to other fractions. The decreasing order of P concentration in the soil \nfractions for lime treated soil was organic > residual > Fe-Mn > carbonates > \nwater soluble = exchangeable (Figure 4(f)). Immobile fractions (residual and \norganic fraction) were much higher than in water soluble and exchangeable \nfractions (mobile fractions). In summary, for the sequential fractionation study, \nPOME amendment resulted in 44% of Cd in mobile fraction with 56% associated \nwith the immobile fractions, while lime amendment caused 35% of Cd to be in the \nmobile and 65% in the immobile fractions. However, the highest Cd concentrations \nwere in the mobile fraction (exchangeable) for POME, and the immobile fraction, \ncarbonate (which can turn to mobile fraction when soil condition changes) with \nlime amendment.\n About 70% of Zn was associated with immobile fractions in soil amended \nwith POME and lime, with 30% being in the mobile fractions. Soil amended with \nPOME and lime resulted in less than 1% of P being in the mobile fractions and the \nrest in the immobile fraction. Table 4 shows the summary of decreasing order of \nCd, Zn and P fractions for Jawa Series soil amended with POME sludge and lime.\nThus, in this study, it was found that lime was a better soil amendment in reducing \nCd uptake from PR application in comparison to POME.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 43\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\n \nD\n\n\n\nis\ntri\n\n\n\nbu\ntio\n\n\n\nn \nof\n\n\n\n C\nd,\n\n\n\n Z\nn \n\n\n\nan\nd \n\n\n\nP \nfr\n\n\n\nac\ntio\n\n\n\nns\n in\n\n\n\n Ja\nw\n\n\n\na \nSe\n\n\n\nrie\ns s\n\n\n\noi\nl a\n\n\n\nm\nen\n\n\n\nde\nd \n\n\n\nw\nith\n\n\n\n P\nO\n\n\n\nM\nE \n\n\n\nan\nd \n\n\n\nlim\ne \n\n\n\n \nSo\n\n\n\nil \nSe\n\n\n\nrie\ns \n\n\n\nA\nm\n\n\n\nen\ndm\n\n\n\nen\nt \n\n\n\nEl\nem\n\n\n\nen\nt \n\n\n\nFr\nac\n\n\n\ntio\nn \n\n\n\n\n\n\n\n \nC\n\n\n\nd \n \n\n\n\nEx\nch\n\n\n\nan\nge\n\n\n\nab\nle\n\n\n\n =\n C\n\n\n\nar\nbo\n\n\n\nna\nte\n\n\n\n >\n W\n\n\n\nat\ner\n\n\n\n S\nol\n\n\n\nub\nle\n\n\n\n >\n R\n\n\n\nes\nid\n\n\n\nua\nl >\n\n\n\n O\nrg\n\n\n\nan\nic\n\n\n\n >\n F\n\n\n\ne-\nM\n\n\n\nn \n \n\n\n\nPO\nM\n\n\n\nE \nsl\n\n\n\nud\nge\n\n\n\n \nZn\n\n\n\n \nR\n\n\n\nes\nid\n\n\n\nua\nl >\n\n\n\n E\nxc\n\n\n\nha\nng\n\n\n\nea\nbl\n\n\n\ne \n= \n\n\n\nFe\n-M\n\n\n\nn \n> \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n >\n C\n\n\n\nar\nbo\n\n\n\nna\nte\n\n\n\n >\n w\n\n\n\nat\ner\n\n\n\n S\nol\n\n\n\nub\nle\n\n\n\n\n\n\n\n \nP \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n >\n re\n\n\n\nsi\ndu\n\n\n\nal\n >\n\n\n\n F\ne-\n\n\n\nM\nn \n\n\n\n> \nC\n\n\n\nar\nbo\n\n\n\nna\nte\n\n\n\n =\n E\n\n\n\nxc\nha\n\n\n\nng\nea\n\n\n\nbl\ne \n\n\n\n= \nW\n\n\n\nat\ner\n\n\n\n S\nol\n\n\n\nub\nle\n\n\n\n \nJa\n\n\n\nw\na \n\n\n\n\n\n\n\n\n\n\n\n \nC\n\n\n\nd \nEx\n\n\n\nch\nan\n\n\n\nge\nab\n\n\n\nle\n >\n\n\n\n C\nar\n\n\n\nbo\nna\n\n\n\nte\ns >\n\n\n\n R\nes\n\n\n\nid\nua\n\n\n\nl =\n F\n\n\n\ne-\nM\n\n\n\nn \n= \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n >\n W\n\n\n\nat\ner\n\n\n\n S\nol\n\n\n\nub\nle\n\n\n\n\n\n\n\nlim\ne \n\n\n\nZn\n \n\n\n\nR\nes\n\n\n\nid\nua\n\n\n\nl >\n E\n\n\n\nxc\nha\n\n\n\nng\nea\n\n\n\nbl\ne \n\n\n\n> \nFe\n\n\n\n-M\nn \n\n\n\n> \nO\n\n\n\nrg\nan\n\n\n\nic\n >\n\n\n\n C\nar\n\n\n\nbo\nna\n\n\n\nte\n >\n\n\n\n w\nat\n\n\n\ner\n S\n\n\n\nol\nub\n\n\n\nle\n \n\n\n\n\n\n\n\nP \nO\n\n\n\nrg\nan\n\n\n\nic\n >\n\n\n\n re\nsi\n\n\n\ndu\nal\n\n\n\n >\n F\n\n\n\ne-\nM\n\n\n\nn \n> \n\n\n\nC\nar\n\n\n\nbo\nna\n\n\n\nte\n >\n\n\n\n W\nat\n\n\n\ner\n S\n\n\n\nol\nub\n\n\n\nle\n =\n\n\n\n E\nxc\n\n\n\nha\nng\n\n\n\nea\nbl\n\n\n\ne \n  \n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nD\nis\n\n\n\ntri\nbu\n\n\n\ntio\nn \n\n\n\nof\n C\n\n\n\nd,\n Z\n\n\n\nn \nan\n\n\n\nd \nP \n\n\n\nfr\nac\n\n\n\ntio\nns\n\n\n\n in\n Ja\n\n\n\nw\na \n\n\n\nSe\nrie\n\n\n\ns s\noi\n\n\n\nl a\nm\n\n\n\nen\nde\n\n\n\nd \nw\n\n\n\nith\n P\n\n\n\nO\nM\n\n\n\nE \nan\n\n\n\nd \nlim\n\n\n\ne\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202144\n\n\n\nCONCLUSION\nJawa Series amended with four rates of POME resulted in an increase in the  \nuptake of Zn and P (nutrients) by plant parts. Meanwhile, Cd (heavy metal) \ncontent decreased in roots when amended with lime. Cadmium was dominant in \nthe mobile fraction (exchangeable) in Jawa Series amended with both POME and \nlime. Furthermore, the Cd concentration generally increased with an increase in \nPOME rates while it decreased for lime throughout the rates. Meanwhile, Zn and \nP were dominant in the immobile fractions (residual and organic, respectively) for \nJawa Series amended with both POME and lime.\n\n\n\nACKNOWLEDGEMENT\nWe acknowledge the Golden Hope Agrotech. Consultancy Services Sdn. Bhd for \npermission to purchase oil palm seedlings and the Soil Resource Management \nDivision and Department of Agriculture, Selangor, for permission to collect \nthe samples. All staff of the Soil and Plant Analytical Services, Department of \nLand Management, Faculty of Agriculture, Universiti Putra Malaysia are also \nacknowledged.\n \nThis research was funded by the Fundamental Research Grant Scheme (FRGS) \n(Project no. 01-0197-238FR) of the Ministry of Higher Education of Malaysia.\n\n\n\nREFERENCES\nAhnstrom, Z. S. and D.R. Parker. 1999. Development and assessment of a sequential \n\n\n\nextraction procedure for the fractionation of soil cadmium. Soil Sci. Soc. Am. \nJ. 63: 1650-1658.\n\n\n\nChen, H. M., C. R. Zheng, C. Tu and Z. G. Shen. 2000. Chemical methods and \nphytoremediation of soil contaminated with heavy metals. Chemosphere 41: \n229-234.\n\n\n\nChen, Z. S., G.J. Lee and J.C. Liu. 2000. The effects of chemical remediation treatments \non the extractability and speciation of cadmium and lead in contaminated soils. \nChemosphere 41: 235-242.\n\n\n\nChlopecka, A., J.R. Bacon, M.J. Wilson and J. Kay. 1996. Forms of cadmium, lead \nand zinc in contaminated soils from Southwest Poland. J. Environ. Qual. 25: \n69-75.\n\n\n\nChowdhury, A. K., R.G. McLaren, K.C. Cameron and R.S. Swift. 1997. Fractionation \nof zinc in some New Zealand soils. Commun. Soil Sci. Plant Anal. 28: 301-\n312.\n\n\n\nCorley, R. H. V. and P.B. Tinker. 2003. The Oil Palm (4th ed.) Oxford: Blackwell \nScience Ltd. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 45\n\n\n\nGarau, G., P. Castaldi, L. Santona, P. Deiana and P. Melis. 2007. Influence of red \nmud, zeolite and lime on heavy metal immobilization, culturable heterotropic \nmicrobial populations and enzyme activities in a contaminated soil. Geoderma \n142: 47-57.\n\n\n\nKabata-Pendias.2004. Soil-plant transfer of trace elements-an environmental issue. \nGeoderma 122: 143-149.\n\n\n\n \nKashem, M. A. and B.R. Singh. 1999. Heavy metal contamination of soil and \n\n\n\nvegetation in the vicinity of industries in Bangladesh. 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Relationships of soil properties with Mn and Zn distribution in \nacidic and their uptake by a barley crop. Geoderma 137: 432-443.\n\n\n\nProkop, Z., P. Cupr, J. . Zlevorova-Zlamalikova, J. Komarek, L. Dusek and I. \nHoloubek. 2003. Mobility, bioavailability, and toxic effects of cadmium in soil \nsamples. Environ. Rese. 91:119-126.\n\n\n\nRamachandran, V. and T.J. D`Souza. 1998. Plant uptake of Cadmium, Zinc, and \nManganese in soils amended with sewage sludge and city compost. Bull. \nEnviron. Contam. Toxico. 61: 347-354.\n\n\n\nSantona, L., P. Castalti and P. Melis. 2006. Evaluation of the interaction mechanisms \nbetween red muds and heavy metal. J. Hazard. Mater. B136: 324-329.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 202146\n\n\n\nShamshuddin, J. 1989. Lime requirements of highly weathered Malaysian soils. \nPertanika, 12(1): 107-112.\n\n\n\nShuman, L. M. 1998. Effect of organic waste amendments on cadmium and lead in \nsoil fractions of two soils. Commum. Soil Sci. Plant Anal. 29: 2939-2952.\n\n\n\nTessier, A., P.G.C. Campbell and M. Bissom. 1979. Sequently extraction procedure \nfor the speciation of particulate trace metals. Anal. Chem. 51: 844.\n\n\n\nYang, Z. Q. and M. Kimura. 1995. Solubility fractionation of Zn, Cu and Cd in soils \napplied with sewage sludge and their potential availability to plant. Environ. \nSci. 8 (4)): 369-378.\n\n\n\nZauyah, S., B. Juliana. and C.I.Fauziah. 2008. Concentrations and chemical forms of \nheavy metals in some Ultisols in Johore, Peninsular Malaysia. Malaysian J. \nSoil Sc. 12: 113-124.\n\n\n\n.\n\n\n\n. \n\n\n\n\n\n"
"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: samsudin@agri.upm.edu.my\n\n\n\nINTRODUCTION\nThe shoreline of Peninsular Malaysia is found to be prograding towards the sea \n\n\n\na result, two or three sandy beach ridges (one ridge represents one level of drop in \n\n\n\nsandy beach ridges due to eustatic sea effect has been well described by Tanner \net al\n\n\n\nFertility and Suitability of the Spodosols Formed on Sandy \nBeach Ridges Interspersed with Swales in the Kelantan - \n\n\n\nTerengganu Plains of Malaysia for Kenaf Production\n \n\n\n\nI. Roslan, J. Shamshuddin*, C.I. Fauziah and A.R. Anuar\n\n\n\nDepartment of Land Management, Faculty of Agriculture\nUniversiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nA study was conducted to evaluate the suitability of sandy soils in the Kelantan - \n\n\n\nfertility for kenaf production. The coastal landscape is scarcely utilized for crop \nproduction due to the inherently low soil fertility, nutrient imbalance and water \n\n\n\nhumus - rich spodic horizon were found, locally named as Rhu Tapai, Rudua and \nc kg-1\n\n\n\ngibbsite, hematite and feldspars in trace amounts that result from high degree of \nweathering. The Corg\n\n\n\nas 4Dnt where D stand for drainage, n for nutrient and t for texture. The major \nlimitations were found to be excessive drainage, nutrient imbalance and sandy \ntexture. Therefore, management practices recommended to improve the soils are \n\n\n\nagronomic practices are carried out, these soils could be productively used for \ngrowing kenaf. \n\n\n\n                     agronomic package for kenaf\n\n\n\n\n\n\n\n\nSandy beach ridges are very common in the Kelantan - Terengganu Plains.  \n\n\n\nby soils having sand texture which are locally named as BRIS Soils. The acronym \nBRIS stands for beach ridges interspersed with swales. The swales are found in \nbetween the ridges; they sit in the depression areas, and are therefore inundated \nby water for most part of the year. It is known that the soils on the beach ridges \n\n\n\nsand (Roslan et al.\n\n\n\nfound the presence of a spodic horizon at varying depths, which usually occur \nimmediately above the groundwater water-table. The spodic horizon is very \ncommon in the temperate regions of the world, but not in the tropical climate \n\n\n\nthe presence of Spodosols in the lowland areas of tropical regions, but available \ndata is not complete. The spodic horizon is known to be rich in organic matter, \nwhich can be exploited for agriculture production. The organic matter is often \n\n\n\npresence of this spodic horizon that is rich in organic matter indicates that it could \nbe used for crop production.  \n\n\n\nmainly in the east coast states of Peninsular Malaysia, especially in the Kelantan \n- Terengganu Plains. BRIS Soils in these plains consist of many soil series of \nwhich the most common are the Baging, Rhu Tapai, Rudua and Jambu Series; \n\n\n\nmorphology and chemical properties. The Rhu Tapai, Rudua and Jambu Series \n\n\n\n(Paramananthan 1987; Roslan et al.\n\n\n\nThe BRIS Soils in Malaysia are not well utilized for crop production due to \n\n\n\nsoils over the years is tobacco. However, the government of Malaysia wants to \nreplace tobacco with other eco-friendly crops, such as kenaf (Hibiscus cannabinus \nL\nproductivity of the sandy Spodosols for kenaf production. Hence, this study \n\n\n\nIn order to replace tobacco with kenaf, a study is needed to determine its \n\n\n\nand automotive industry. It can grow both in the tropical and temperate regions \n(Ogbonnaya et al.\n\n\n\n-1\n\n\n\nintroduced into Malaysia only recently as a new cash crop, and hence, information \n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\n\n\n\n\n\n3\n\n\n\nabout its agronomic practice for sustainable production is not available or at best, \nscarce. \n\n\n\nStudies in African countries have indicated that sandy soils have potential for \net al. \n\n\n\nThe productivity of the Spodosols in the Kelantan - Terengganu Plains is \nlow. Under normal circumstances, these sandy soils cannot retain the nutrients \n\n\n\nthese sandy soils are known to be extremely high as they occur in areas with high \nrainfall. Roslan et al.\n\n\n\ncapacity. Added nutrients are, therefore, easily leached into the groundwater and \nas such the soils have to be properly managed for kenaf production. \n\n\n\nAccording to Zaharah et al\n\n\n\nHowever, with good management practices, chili, yam, bean, sweet potato and \nsome other vegetative crops have been grown with success. In the area, tobacco \n\n\n\neasy to convince the farmers in the area to replace tobacco with kenaf.  For sure, \nan acceptable agronomic package for kenaf production should be put in place \nbefore farmers are asked to grow this crop. \n\n\n\nInitial studies have shown that kenaf grew poorly on BRIS soils with yields \n-1\n\n\n\nevaluate the suitability of the Spodosols formed on the sandy beach ridges in the \n\n\n\nproduction of kenaf.\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Area Description\nThe study area is the Kelantan - Terengganu Plains, Malaysia (Fig. 1\nbetween 5.65o o o o East \nlongitudes. In the northern part, it is separated from Thailand by Golok River. The \nland areas are mostly covered with grasses, small shrubs and casuarina species. \nThis vegetation provides a fresh supply of organic matter to the soils. Due to \n\n\n\nwhich over a period of time has accumulated in the topsoil. It is this humus that \nhelps promote the podzolisation process, leading to the formation of the spodic \nhorizon.\n\n\n\n\n\n\n\n\n4\n\n\n\no C, dropping to \no\n\n\n\naccompanied by abundant rainfall throughout the year. There is no area in the \n\n\n\nof the annual precipitations is brought by the Northeast Monsoon winds, during \nthe months of November-January throughout the year. The high precipitation \n\n\n\nField Observations and Data Collection\n\n\n\nThe dominant agricultural activity is tobacco production, followed by livestock \nrearing. As we moved further inland, we observed that the presence of acid sulfate \n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\nFig. 1: A map showing Kelantan-Terengganu Plains, dominated by sandy beach \nridges interspersed with swales (A-B: the presence of ridges with sandy Spodosols \n\n\n\nand swales in depression area)\n\n\n\n\n\n\n\n\n5\n\n\n\nThe preliminary information and essential data of the area under study were \n\n\n\net al.\nresurveyed in detail to get further information. Based on the survey carried out, \nsoils on the ridges were found to contain the spodic horizon, and are therefore \n\n\n\n(Fig.2). To carry out detailed analyses of the soils, soil pits \nwere dug in the areas undisturbed by human activities and samples were collected \nbased on their respective horizon.\n\n\n\nAs the soils occurring on the ridge nearest to the shoreline had no spodic \nhorizon, they were not used for this study. It was found that Rhu Tapai and Rudua \n\n\n\n(Fig. 1). As \n\n\n\nobservation points were recorded by global positioning system (GPS Garmin \n\n\n\nSoil and Data Analyses \nThe collected soil samples were subjected to standard chemical and physical \nanalyses in the laboratory. The sand, silt and clay fractions in the soils were \nseparated by successive sedimentation. Soil pH was determined in water (at a soil \n\n\n\nFig. 2: Pictures showing two types of Spodosols with spodic horizon: (a) Rhu Tapai \nSeries (P1) - not cemented, with water-table just below the spodic horizon, \nand (b) Rudua Series (P2) \u2013 cemented; this is also known as hardpan, with \n\n\n\nthe watertable located at more than 200 cm\n\n\n\n\n\n\n\n\n6\n\n\n\nusing 1 M NH4OAc\nthe NH4OAc extract were determined by atomic absorption spectrophotometer \n\n\n\ncations. Other analyses include the determination of exchangeable Al and free \niron oxides (van Ranst et al\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nChanges in the Landscape of the Coastal Plains\nA cross-section of the study area shows an undulating topography pattern (Fig. \n1). The soils are severely depleted of nutrients with soil temperatures ranging \n\n\n\no\n\n\n\nFebruary to May/June every year. The present undulating and gentle landscape \nthat characterises the coastal plains was formed by eustatic effect (Shamshuddin \n\n\n\n et al.\naction with auxillary forces acting on the beach shoreline. This effect brings \nmarine deposits to the shoreline, washing away much of the lighter fraction (like \n\n\n\nOver time, the action created ridges and swales with the ridges being the \nelevated section of the landscape, while the swales formed the depression area. \nThe ridge has a lower water table compared to the swales which are inundated \n\n\n\neconomical. \n\n\n\nthe one closest to the shoreline are Entisols and those further away are Spodosols \n(Fig. 2)\nsoils. The Spodosols can be suitable for agricultural purposes due to the presence \nof a conspicuous organic layer in the B horizon, called the spodic horizon (Fig. \n2\net al.\nSeries (Fig. 3).  \n\n\n\nPhysico-chemical Properties of the Soils\nThe physical and chemical properties of the soils are given in Table 1. It is seen \nthat the CEC value is low (< 5 cmolc kg-1\n\n\n\ncan be a threat to crop growth. However, Al is only present in trace amounts in the \nsoils. It is believed  that the Al in the soils have been leached out over a period of \n\n\n\nhorizons.  This leaves very little Al in the topsoil. This phenomenon contradicts \nthe popular belief that Spodosols in the area have Al toxicity that limits crop \ngrowth. \n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\n\n\n\n\n\n7\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nPh\nys\n\n\n\nic\nal\n\n\n\n a\nnd\n\n\n\n c\nhe\n\n\n\nm\nic\n\n\n\nal\n p\n\n\n\nro\npe\n\n\n\nrti\nes\n\n\n\n o\nf t\n\n\n\nhe\n sa\n\n\n\nnd\ny \n\n\n\nSp\nod\n\n\n\nos\nol\n\n\n\ns i\nn \n\n\n\nth\ne \n\n\n\nK\nel\n\n\n\nan\nta\n\n\n\nn-\nTe\n\n\n\nre\nng\n\n\n\nga\nnu\n\n\n\n P\nla\n\n\n\nin\ns\n\n\n\nSe\nrie\n\n\n\ns  \nG\n\n\n\nra\nnu\n\n\n\nlo\nm\n\n\n\net\nric\n\n\n\n \nco\n\n\n\nm\npo\n\n\n\nsit\nio\n\n\n\nn \n(%\n\n\n\n) \n  \n\n\n\npH\n \n\n\n\nbd\na   \n\n\n\nE.\nC\n\n\n\n \nEx\n\n\n\nch\nan\n\n\n\nge\nab\n\n\n\nle\n c\n\n\n\nat\nio\n\n\n\nns\n \n\n\n\n B\nS \n\n\n\nA\nva\n\n\n\nil.\n \n\n\n\nP \nC\n\n\n\n or\ng \n\n\n\n(%\n) \n\n\n\n N\n \n\n\n\n(%\n) \n\n\n\nC\n/N\n\n\n\n \nra\n\n\n\ntio\n \n\n\n\nFr\nee\n\n\n\n \nFe\n\n\n\n \nA\n\n\n\nva\nil.\n\n\n\n \nA\n\n\n\nl \n \n\n\n\n\n\n\n\n \nH\n\n\n\n2O\n \n\n\n\n(g\n/c\n\n\n\nm\n3 ) \n\n\n\n(d\nS \n\n\n\nm\n-1\n\n\n\n) \nC\n\n\n\na \nM\n\n\n\ng \n \n\n\n\nK\n \n\n\n\nN\na \n\n\n\nC\nEC\n\n\n\n \n (%\n\n\n\n) \n(p\n\n\n\npm\n) \n\n\n\n(p\npm\n\n\n\n(p\npm\n\n\n\n) \nC\n\n\n\nla\ny \n\n\n\nSi\nlt \n\n\n\n \nSa\n\n\n\nnd\n \n\n\n\n  \n(c\n\n\n\nm\nol\n\n\n\nc k\ng-1\n\n\n\n)  \n \n\n\n\nRh\nu \n\n\n\nTa\npa\n\n\n\ni  \n(P\n\n\n\n1)\n: S\n\n\n\nof\nt h\n\n\n\num\nus\n\n\n\n p\nod\n\n\n\nzo\nls \n\n\n\nfo\nrm\n\n\n\ned\n o\n\n\n\nn \nth\n\n\n\ne \nin\n\n\n\nte\nrm\n\n\n\ned\nia\n\n\n\nte\n ri\n\n\n\ndg\ne \n\n\n\n\n\n\n\nA\np \n\n\n\n(0\n-4\n\n\n\n1)\n \n\n\n\n2.\n50\n\n\n\n \n1.\n\n\n\n50\n \n\n\n\n96\n.0\n\n\n\n0 \n \n\n\n\n5.\n10\n\n\n\n \n1.\n\n\n\n12\n \n\n\n\n0.\n01\n\n\n\n \n1.\n\n\n\n30\n \n\n\n\n0.\n40\n\n\n\n \n0.\n\n\n\n02\n \n\n\n\n0.\n01\n\n\n\n \n2.\n\n\n\n12\n \n\n\n\n85\n.0\n\n\n\n0 \n11\n\n\n\n.4\n0 \n\n\n\n1.\n70\n\n\n\n \n0.\n\n\n\n36\n \n\n\n\n5.\n00\n\n\n\n \n0.\n\n\n\n56\n \n\n\n\n0.\n08\n\n\n\n \nB\n\n\n\ns (\n41\n\n\n\n-9\n8)\n\n\n\n \n1.\n\n\n\n20\n \n\n\n\n1.\n30\n\n\n\n \n97\n\n\n\n.5\n0 \n\n\n\n \n5.\n\n\n\n10\n \n\n\n\n2.\n65\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n04\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n03\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n32\n\n\n\n \n38\n\n\n\n.0\n0 \n\n\n\n5.\n60\n\n\n\n \n0.\n\n\n\n82\n \n\n\n\n0.\n06\n\n\n\n \n20\n\n\n\n.5\n0 \n\n\n\n0.\n21\n\n\n\n \n0.\n\n\n\n12\n \n\n\n\nC\n (+\n\n\n\n98\n) \n\n\n\n0.\n70\n\n\n\n \n0.\n\n\n\n10\n \n\n\n\n98\n.2\n\n\n\n0 \n \n\n\n\n4.\n90\n\n\n\n \n1.\n\n\n\n45\n \n\n\n\n0.\n01\n\n\n\n \n0.\n\n\n\n03\n \n\n\n\n0.\n04\n\n\n\n \n0.\n\n\n\n02\n \n\n\n\n0.\n02\n\n\n\n \n0.\n\n\n\n16\n \n\n\n\n69\n.0\n\n\n\n0 \n2.\n\n\n\n10\n \n\n\n\n0.\n08\n\n\n\n \n0.\n\n\n\n04\n \n\n\n\n2.\n00\n\n\n\n \n0.\n\n\n\n07\n \n\n\n\n0.\n05\n\n\n\n\n\n\n\nRu\ndu\n\n\n\na \n (P\n\n\n\n2)\n: H\n\n\n\num\nus\n\n\n\n p\nod\n\n\n\nzo\nl (\n\n\n\nwi\nth\n\n\n\n h\nar\n\n\n\ndp\nan\n\n\n\n) f\nor\n\n\n\nm\ned\n\n\n\n o\nn \n\n\n\nth\ne \n\n\n\nin\nte\n\n\n\nrm\ned\n\n\n\nia\nte\n\n\n\n ri\ndg\n\n\n\ne \nA\n\n\n\np \n(0\n\n\n\n-3\n3)\n\n\n\n \n2.\n\n\n\n80\n \n\n\n\n0.\n90\n\n\n\n \n96\n\n\n\n.3\n0 \n\n\n\n \n5.\n\n\n\n10\n \n\n\n\n1.\n20\n\n\n\n \n0.\n\n\n\n13\n \n\n\n\n0.\n87\n\n\n\n \n0.\n\n\n\n51\n \n\n\n\n0.\n06\n\n\n\n \n0.\n\n\n\n07\n \n\n\n\n1.\n75\n\n\n\n \n70\n\n\n\n.0\n0 \n\n\n\n15\n.9\n\n\n\n0 \n2.\n\n\n\n12\n \n\n\n\n0.\n14\n\n\n\n \n15\n\n\n\n.0\n0 \n\n\n\n0.\n33\n\n\n\n \n0.\n\n\n\n07\n \n\n\n\nA\n1 \n\n\n\n(3\n3-\n\n\n\n52\n) \n\n\n\n1.\n50\n\n\n\n \n1.\n\n\n\n00\n \n\n\n\n97\n.5\n\n\n\n0 \n \n\n\n\n5.\n20\n\n\n\n \n1.\n\n\n\n42\n \n\n\n\n0.\n01\n\n\n\n \n0.\n\n\n\n04\n \n\n\n\n0.\n32\n\n\n\n \n0.\n\n\n\n07\n \n\n\n\n0.\n04\n\n\n\n \n1.\n\n\n\n02\n \n\n\n\n52\n.0\n\n\n\n0 \n20\n\n\n\n.5\n0 \n\n\n\n0.\n87\n\n\n\n \n0.\n\n\n\n11\n \n\n\n\n8.\n00\n\n\n\n \n0.\n\n\n\n13\n \n\n\n\n0.\n09\n\n\n\n \nB\n\n\n\n (5\n2-\n\n\n\n84\n) \n\n\n\n0.\n60\n\n\n\n \n0.\n\n\n\n40\n \n\n\n\n99\n.0\n\n\n\n0 \n \n\n\n\n4.\n90\n\n\n\n \n1.\n\n\n\n75\n \n\n\n\n0.\n01\n\n\n\n \n0.\n\n\n\n19\n \n\n\n\n0.\n07\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n06\n\n\n\n \n0.\n\n\n\n92\n \n\n\n\n36\n.0\n\n\n\n0 \n8.\n\n\n\n40\n \n\n\n\n0.\n91\n\n\n\n \n0.\n\n\n\n04\n \n\n\n\n23\n.0\n\n\n\n0 \n0.\n\n\n\n19\n \n\n\n\n0.\n10\n\n\n\n \nB\n\n\n\ns (\n+8\n\n\n\n4)\n \n\n\n\n0.\n80\n\n\n\n \n0.\n\n\n\n10\n \n\n\n\n99\n.1\n\n\n\n0 \n \n\n\n\n5.\n00\n\n\n\n \n2.\n\n\n\n80\n \n\n\n\n0.\n01\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n03\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n06\n\n\n\n \n0.\n\n\n\n31\n \n\n\n\n35\n.0\n\n\n\n0 \n11\n\n\n\n.0\n0 \n\n\n\n0.\n72\n\n\n\n \n0.\n\n\n\n05\n \n\n\n\n14\n.4\n\n\n\n0 \n0.\n\n\n\n16\n \n\n\n\n0.\n11\n\n\n\n\n\n\n\nJa\nm\n\n\n\nbu\n  (\n\n\n\nP3\n):\n\n\n\n H\num\n\n\n\nus\n-r\n\n\n\nic\nh \n\n\n\npo\ndz\n\n\n\nol\n (w\n\n\n\nith\n h\n\n\n\nar\ndp\n\n\n\nan\n) f\n\n\n\nor\nm\n\n\n\ned\n o\n\n\n\nn \nth\n\n\n\ne \nol\n\n\n\nde\nst \n\n\n\nrid\nge\n\n\n\n \nA\n\n\n\np \n(0\n\n\n\n-1\n6)\n\n\n\n \n1.\n\n\n\n80\n \n\n\n\n0.\n50\n\n\n\n \n97\n\n\n\n.7\n0 \n\n\n\n5.\n00\n\n\n\n \n1.\n\n\n\n35\n \n\n\n\n0.\n09\n\n\n\n \n2.\n\n\n\n86\n \n\n\n\n0.\n65\n\n\n\n \n0.\n\n\n\n05\n \n\n\n\n0.\n07\n\n\n\n \n4.\n\n\n\n52\n \n\n\n\n74\n.0\n\n\n\n0 \n2.\n\n\n\n40\n \n\n\n\n2.\n58\n\n\n\n \n0.\n\n\n\n42\n \n\n\n\n6.\n00\n\n\n\n \n0.\n\n\n\n08\n \n\n\n\n0.\n08\n\n\n\n \nE \n\n\n\n(1\n6-\n\n\n\n10\n9)\n\n\n\n \n1.\n\n\n\n00\n \n\n\n\n2.\n00\n\n\n\n \n97\n\n\n\n.0\n0 \n\n\n\n4.\n50\n\n\n\n \n1.\n\n\n\n42\n \n\n\n\n0.\n02\n\n\n\n \n0.\n\n\n\n87\n \n\n\n\n0.\n03\n\n\n\n \n0.\n\n\n\n02\n \n\n\n\n0.\n05\n\n\n\n \n2.\n\n\n\n38\n \n\n\n\n38\n.0\n\n\n\n0 \n0.\n\n\n\n85\n \n\n\n\n0.\n30\n\n\n\n \n0.\n\n\n\n05\n \n\n\n\n6.\n00\n\n\n\n \n0.\n\n\n\n02\n \n\n\n\n0.\n05\n\n\n\n \nB\n\n\n\nhs\n (1\n\n\n\n09\n-1\n\n\n\n50\n) \n\n\n\n0.\n80\n\n\n\n \n2.\n\n\n\n30\n \n\n\n\n96\n.9\n\n\n\n0 \n4.\n\n\n\n40\n \n\n\n\n3.\n20\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n20\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n01\n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n0.\n56\n\n\n\n \n52\n\n\n\n.0\n0 \n\n\n\n2.\n50\n\n\n\n \n1.\n\n\n\n50\n \n\n\n\n0.\n06\n\n\n\n \n25\n\n\n\n.0\n0 \n\n\n\n0.\n12\n\n\n\n \n0.\n\n\n\n15\n \n\n\n\nB\ns(\n\n\n\n+1\n50\n\n\n\n) \n0.\n\n\n\n80\n \n\n\n\n0.\n60\n\n\n\n \n98\n\n\n\n.6\n0 \n\n\n\n5.\n10\n\n\n\n \n3.\n\n\n\n00\n \n\n\n\n0.\n01\n\n\n\n \n0.\n\n\n\n05\n \n\n\n\n0.\n02\n\n\n\n \n0.\n\n\n\n01\n \n\n\n\n0.\n06\n\n\n\n \n0.\n\n\n\n38\n \n\n\n\n37\n.0\n\n\n\n0 \n5.\n\n\n\n10\n \n\n\n\n0.\n87\n\n\n\n \n0.\n\n\n\n30\n \n\n\n\n3.\n00\n\n\n\n \n0.\n\n\n\n13\n \n\n\n\n0.\n12\n\n\n\n \na \nBu\n\n\n\nlk\n d\n\n\n\nen\nsi\n\n\n\nty\n. \n\n\n\nb \nn.\n\n\n\nd=\n N\n\n\n\not\n d\n\n\n\net\nec\n\n\n\nte\nd.\n\n\n\n \n\n\n\n\n\n\n\n\n8\n\n\n\nSilt together with clay was X-rayed to determine the mineralogical \ncomposition of the soil fraction. X-ray diffraction analysis showed the dominance\n\n\n\n(Roslan et al.\nThe spodic horizons of the soils contain higher amounts of organic carbon (1 - \n\n\n\nthat the spodic horizon is rich in soil organic matter and has the potential to be \n\n\n\nthe horizon. However, we believe that with improved soil management practices, \nsuch as land leveling (Fig. 3)\nsandy Spodosols can be used for agriculture. Plenty of rice straw can be found \n\n\n\nkenaf-based material as a growth media and found that such a media can improve \nwater retention and nutrient availability.   \n\n\n\nEvaluation of the Spodosols for Kenaf Production\nAs less and less arable land are available for agriculture, new land clearing and \ndeforestation which is a common practice is not a sustainable option; both practice \nrelease much of CO stored in the soil carbon to the atmosphere. This, in return, \nincreases green house effect in the environment. In Peninsular Malaysia, there are \n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\nFig. 3: A cross section of the landscape from A to B: (i) before land leveling, \nand (ii) after land leveling practice by the farmers (P1, P2 and P3 are soils \n\n\n\nwith the spodic horizon at different depths)\n\n\n\n\n\n\n\n\n9\n\n\n\nareas which are less utilized, for example, the soils on the sandy beach ridges in \nthe Kelantan - Terengganu Plains, which is the focus of this study. In a scenario of \ndecreasing arable land, utilizing the sandy coastal soils becomes more necessary \n\n\n\nan eco-friendly crop has further increased the need to assess the suitability of \nSpodosols in the Kelantan - Terengganu Plains for crop production.\n\n\n\nWith the availability of new data, the prospects of using the Spodosols for \nkenaf production is good. In this paper, we propose two methods of evaluating the \nsoils for kenaf production. Firstly, due to high local interest in utilizing the land, \n\n\n\nused them for the evaluation of the soils. \n\n\n\nhas been used to evaluate the fertility status of soils in Peninsular Malaysia. This \n\n\n\nrectify together with chemical limiting properties. No climatic conditions have \nbeen taken into account because there are no four seasons in Malaysia; therefore, \nthe crop is not affected by seasonal limitations as in the temperate regions. The \nclimate in Malaysia is hot and humid throughout the year with abundant rainfall, \n\n\n\nIn order to understand the land characteristics of the sandy beach ridges, a \nsite survey was conducted and three soil pits within the spodic horizon were dug \n\n\n\n(Fig. 3)\nbeing closest to the present shoreline and the last furthest away.  Note that the \n\n\n\net al.\n\n\n\nand structure limitations. Class 4 states that soils have more than one serious \nlimitation for crop growth:\n\n\n\n\n\n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\nN\no.\n\n\n\nSy\nm\n\n\n\nbo\nl \n\n\n\nTy\npe\n\n\n\n \nV\n\n\n\nal\nue\n\n\n\n fo\nr P\n\n\n\n1 \n(li\n\n\n\nm\nita\n\n\n\ntio\nn \n\n\n\nfo\nr c\n\n\n\nro\np \n\n\n\ngr\now\n\n\n\nth\n ) \n\n\n\nV\nal\n\n\n\nue\n fo\n\n\n\nr P\n2 \n\n\n\n(li\nm\n\n\n\nita\ntio\n\n\n\nn \nfo\n\n\n\nr c\nro\n\n\n\np \ngr\n\n\n\now\nth\n\n\n\n) \nV\n\n\n\nal\nue\n\n\n\n fo\nr P\n\n\n\n3 \n(li\n\n\n\nm\nita\n\n\n\ntio\nn \n\n\n\nfo\nr c\n\n\n\nro\np \n\n\n\ngr\now\n\n\n\nth\n) \n\n\n\n1  \nA\n\n\n\n  \nD\n\n\n\nep\nth\n\n\n\n to\n a\n\n\n\nci\nd \n\n\n\nsu\nlp\n\n\n\nha\nte\n\n\n\n \nla\n\n\n\nye\nr \n\n\n\n- \n- \n\n\n\n- \n2  \n\n\n\nC\n  \n\n\n\nD\nep\n\n\n\nth\n to\n\n\n\n c\nom\n\n\n\npa\nct\n\n\n\ned\n \n\n\n\nla\nye\n\n\n\nr \n- \n\n\n\n- \n- \n\n\n\n3 \nD\n\n\n\n  \nD\n\n\n\nra\nin\n\n\n\nag\ne \n\n\n\n \nM\n\n\n\nod\ner\n\n\n\nat\nel\n\n\n\ny \nw\n\n\n\nel\nl d\n\n\n\nra\nin\n\n\n\ned\n  \n\n\n\n(S\ner\n\n\n\nio\nus\n\n\n\n) \nSo\n\n\n\nm\new\n\n\n\nha\nt e\n\n\n\nxc\nes\n\n\n\nsi\nve\n\n\n\nly\n d\n\n\n\nra\nin\n\n\n\ned\n  \n\n\n\n(S\ner\n\n\n\nio\nus\n\n\n\n) \nEx\n\n\n\nce\nss\n\n\n\niv\nel\n\n\n\ny \ndr\n\n\n\nai\nne\n\n\n\nd \n(S\n\n\n\ner\nio\n\n\n\nus\n) \n\n\n\n  \nd \n\n\n\n  \n- \n\n\n\n- \n- \n\n\n\n4 \nG\n\n\n\n \nG\n\n\n\nra\ndi\n\n\n\nen\nt \n\n\n\n- \n- \n\n\n\n- \n5 \n\n\n\nN\n \n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\n im\nba\n\n\n\nla\nnc\n\n\n\ne \n- \n\n\n\n- \n- \n\n\n\n  \nn \n\n\n\n  \nC\n\n\n\nEC\n <\n\n\n\n 5\n m\n\n\n\neq\n/1\n\n\n\n00\ngm\n\n\n\n so\nil \n\n\n\nC\nEC\n\n\n\n <\n 5\n\n\n\n m\neq\n\n\n\n/1\n00\n\n\n\ngm\n so\n\n\n\nil \nC\n\n\n\nEC\n <\n\n\n\n 5\n m\n\n\n\neq\n/1\n\n\n\n00\ngm\n\n\n\n so\nil \n\n\n\n\n\n\n\n  \n(S\n\n\n\ner\nio\n\n\n\nus\n) \n\n\n\n(S\ner\n\n\n\nio\nus\n\n\n\n) \n(S\n\n\n\ner\nio\n\n\n\nus\n) \n\n\n\n6 \n \n\n\n\n\n\n\n\no \n \n\n\n\n\n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n h\nor\n\n\n\niz\non\n\n\n\n \nth\n\n\n\nic\nkn\n\n\n\nes\ns \n\n\n\n\n\n\n\nBs\n la\n\n\n\nye\nr a\n\n\n\nt 4\n1-\n\n\n\n98\ncm\n\n\n\n d\nep\n\n\n\nth\n \n\n\n\n\n\n\n\nBs\n la\n\n\n\nye\nr a\n\n\n\nt  \n> \n\n\n\n84\ncm\n\n\n\n d\nep\n\n\n\nth\n \n\n\n\n\n\n\n\nBh\ns l\n\n\n\nay\ner\n\n\n\n (c\nem\n\n\n\nen\nte\n\n\n\nd)\n a\n\n\n\nt 1\n09\n\n\n\n-\n15\n\n\n\n0c\nm\n\n\n\n, a\nnd\n\n\n\n B\ns l\n\n\n\nay\ner\n\n\n\n (c\nem\n\n\n\nen\nte\n\n\n\nd)\n a\n\n\n\nt \n> \n\n\n\n15\n0c\n\n\n\nm\n d\n\n\n\nep\nth\n\n\n\n \n7  \n\n\n\nR\n  \n\n\n\n%\n S\n\n\n\nto\nni\n\n\n\nne\nss\n\n\n\n to\n 1\n\n\n\n00\n c\n\n\n\nm\n \n\n\n\nde\npt\n\n\n\nh \n- \n\n\n\n- \n- \n\n\n\n  \nr \n\n\n\n  \n- \n\n\n\n- \n- \n\n\n\n8 \ns \n\n\n\nSa\nlin\n\n\n\nity\n \n\n\n\n- \n- \n\n\n\n- \n9  \n\n\n\nT  \nTe\n\n\n\nxt\nur\n\n\n\ne \nan\n\n\n\nd \nst\n\n\n\nru\nct\n\n\n\nur\ne \n\n\n\n \nM\n\n\n\nod\ner\n\n\n\nat\nel\n\n\n\ny \nto\n\n\n\n c\noa\n\n\n\nrs\ne \n\n\n\nte\nxt\n\n\n\nur\ned\n\n\n\n  \nan\n\n\n\nd \nw\n\n\n\nea\nkl\n\n\n\ny \nst\n\n\n\nru\nct\n\n\n\nur\ned\n\n\n\n (S\ner\n\n\n\nio\nus\n\n\n\n) \n- \n\n\n\nFi\nne\n\n\n\n to\n v\n\n\n\ner\ny \n\n\n\nfin\ne \n\n\n\nte\nxt\n\n\n\nur\ned\n\n\n\n a\nnd\n\n\n\n \nst\n\n\n\nro\nng\n\n\n\nly\n c\n\n\n\nem\nen\n\n\n\nte\nd \n\n\n\n(V\ner\n\n\n\ny \nse\n\n\n\nri\nou\n\n\n\ns)\n \n\n\n\n  \nt  \n\n\n\n  \n- \n\n\n\nM\nod\n\n\n\ner\nat\n\n\n\nel\ny \n\n\n\nto\n fi\n\n\n\nne\n te\n\n\n\nxt\nur\n\n\n\ned\n a\n\n\n\nnd\n \n\n\n\nm\nod\n\n\n\ner\nat\n\n\n\nel\ny \n\n\n\nst\nru\n\n\n\nct\nur\n\n\n\ned\n (M\n\n\n\nod\ner\n\n\n\nat\ne)\n\n\n\n\n\n\n\n-  \n10\n\n\n\n \nH\n\n\n\n \nH\n\n\n\num\nan\n\n\n\n \n- \n\n\n\n- \n- \n\n\n\nC\nla\n\n\n\nss\nifi\n\n\n\nca\ntio\n\n\n\nn:\n \n\n\n\n4D\nnT\n\n\n\n \n4D\n\n\n\nn(\nt) \n\n\n\n4D\nnT\n\n\n\n \n\n\n\n\n\n\n\n\n11\n\n\n\n(i)   Drainage condition (D)\n\n\n\ndrainage of the area varies from somewhat highly drained to excessively drained \n\n\n\nelevations of up to 5 m, depending on the localities. The climatic regime of the \n\n\n\npercolation is very fast owing to the sandy nature of the soils (Fig. 4). From the \n\n\n\nsand, with the soils being  loosely structured and  very permeable.    \n  \n(ii)    Nutrient imbalance (n)\n\n\n\nvery low (1-5 cmolc kg-1\n\n\n\nsoils; normally, soils with high clay content have higher CEC value. Increasing the \nclay content is almost impossible; however, we can increase the CEC somewhat \nvia mulching with organic materials.\n\n\n\n(iii)    Texture and structure (t)\n\n\n\nweakly structured for all the Spodosols. In the spodic horizon, the sand was found \nto be pseudo-coated with organic materials. This layer is compact, cemented by \noxides of Al and/or Fe, which is normally referred to as hardpan or orstein. Due to \nthe compaction, root penetration is almost impossible. \n\n\n\nIn a recent study at the Kelantan - Terengganu Plains, it was found that the \nroot of kenaf was deformed when it reached the spodic horizon. The deformation \n\n\n\no in the soil; despite this \n\n\n\nroots were found in Bs and Bhs layer, believed to be originated from the past \nlitter deposition.  Microbial activity by rock - eating mycorrhizal fungi and/or \ndecomposition in the Spodosol as suggested by some researchers as among the \nkey players in spodic horizon formation (van Breemen et al\nto be involved here. If such a process has taken place actively in the soil, there \n\n\n\nsuggests that if the microbial activity does occur in the spodic horizon, it would \nbe extremely low. \n\n\n\n\n\n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\n\n\n\n\nSo\nil \n\n\n\ncr\nite\n\n\n\nria\n \n\n\n\nV\nal\n\n\n\nue\n (S\n\n\n\npo\ndo\n\n\n\nso\nl i\n\n\n\nn \nth\n\n\n\ne \nsa\n\n\n\nnd\ny \n\n\n\nso\nil \n\n\n\nof\n \n\n\n\nth\ne \n\n\n\nco\nas\n\n\n\nta\nl p\n\n\n\nla\nin\n\n\n\ns)\n \n\n\n\n\n\n\n\n\n\n\n\n   \n C\n\n\n\nro\np \n\n\n\nsu\nita\n\n\n\nbi\nlit\n\n\n\ny \n  \n\n\n\n\n\n\n\n  \nC\n\n\n\noc\non\n\n\n\nut\n \n\n\n\nR\nub\n\n\n\nbe\nr  \n\n\n\nPa\nlm\n\n\n\n O\nil \n\n\n\nC\noc\n\n\n\noa\n \n\n\n\nPa\ndd\n\n\n\ny \n  \n\n\n\n  \n(G\n\n\n\nro\nup\n\n\n\n 2\n) \n\n\n\n(G\nro\n\n\n\nup\n 5\n\n\n\n) \n(G\n\n\n\nro\nup\n\n\n\n 3\n) \n\n\n\n(G\nro\n\n\n\nup\n 4\n\n\n\n) \n(G\n\n\n\nro\nup\n\n\n\n 2\n4)\n\n\n\n \nSl\n\n\n\nop\ne \n\n\n\n0-\n2 \n\n\n\n(>\n6-\n\n\n\n12\no  sl\n\n\n\nop\ne)\n\n\n\n \nS \n\n\n\nS \nS \n\n\n\nS \nS \n\n\n\nD\nra\n\n\n\nin\nag\n\n\n\ne \nSo\n\n\n\nm\new\n\n\n\nha\nt h\n\n\n\nig\nhl\n\n\n\ny \ndr\n\n\n\nai\nne\n\n\n\nd-\nex\n\n\n\nce\nss\n\n\n\niv\nel\n\n\n\ny \ndr\n\n\n\nai\nne\n\n\n\nd \nS  \n\n\n\nS  \nM\n\n\n\n/d\n \n\n\n\n \nS  \n\n\n\nU\n  \n\n\n\nEf\nfe\n\n\n\nct\niv\n\n\n\ne \nso\n\n\n\nil \nde\n\n\n\npt\nh \n\n\n\n50\n- 1\n\n\n\n50\n++\n\n\n\n  c\nm\n\n\n\n  \nS \n\n\n\nS \nS \n\n\n\nS \nS \n\n\n\nTe\nxt\n\n\n\nur\ne \n\n\n\nan\nd \n\n\n\nst\nru\n\n\n\nct\nur\n\n\n\ne \n  \n\n\n\nC\noa\n\n\n\nrs\ne \n\n\n\nte\nxt\n\n\n\nur\ned\n\n\n\n a\nnd\n\n\n\n w\nea\n\n\n\nkl\ny \n\n\n\nst\nru\n\n\n\nct\nur\n\n\n\ned\n (A\n\n\n\n h\nor\n\n\n\niz\non\n\n\n\n) a\nnd\n\n\n\n c\nem\n\n\n\nen\nte\n\n\n\nd \nsp\n\n\n\nod\nic\n\n\n\n h\nor\n\n\n\niz\non\n\n\n\n (B\n h\n\n\n\nor\niz\n\n\n\non\n) \n\n\n\nM\n/t   \n\n\n\nU\n   \n\n\n\nU\n   \n\n\n\nU\n   \n\n\n\nU\n   \n\n\n\nSa\nlin\n\n\n\nity\n \n\n\n\nn.\nd \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\n- \nD\n\n\n\nep\nth\n\n\n\n to\n a\n\n\n\nci\nd \n\n\n\nsu\nlfa\n\n\n\nte\n  \n\n\n\nla\nye\n\n\n\nr \nn.\n\n\n\nd \n- \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\nPe\nat\n\n\n\n T\nhi\n\n\n\nck\nne\n\n\n\nss\n \n\n\n\nn.\nd \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\n- \nSt\n\n\n\non\nin\n\n\n\nes\ns \n\n\n\nN\no \n\n\n\nst\non\n\n\n\nin\nes\n\n\n\ns \nS \n\n\n\nS \nS \n\n\n\nS \nS \n\n\n\nN\nut\n\n\n\nrie\nnt\n\n\n\n im\nba\n\n\n\nla\nnc\n\n\n\ne \nM\n\n\n\nod\ner\n\n\n\nat\ne \n\n\n\nto\n se\n\n\n\nrio\nus\n\n\n\n \nM\n\n\n\n/n\n \n\n\n\nM\n/n\n\n\n\n \nM\n\n\n\n/n\n \n\n\n\nM\n/n\n\n\n\n \nM\n\n\n\n/n\n \n\n\n\n\n\n\n\nM\n/tn\n\n\n\n \nM\n\n\n\n/n\n \n\n\n\nM\n/d\n\n\n\nn \nM\n\n\n\n/n\n \n\n\n\nU\n/d\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nN\n\n\n\not\ne:\n\n\n\n S\n= \n\n\n\nSu\nita\n\n\n\nbl\ne,\n\n\n\n U\n= \n\n\n\nU\nns\n\n\n\nui\nta\n\n\n\nbl\ne,\n\n\n\n M\n= \n\n\n\nM\nar\n\n\n\ngi\nna\n\n\n\nlly\n su\n\n\n\nita\nbl\n\n\n\ne \n(r\n\n\n\neq\nui\n\n\n\nre\n so\n\n\n\nil \nim\n\n\n\npr\nov\n\n\n\nem\nen\n\n\n\nt),\n a\n\n\n\nnd\n t=\n\n\n\n te\nxt\n\n\n\nur\ne,\n\n\n\n n\n=n\n\n\n\nut\nrie\n\n\n\nnt\n im\n\n\n\nba\nla\n\n\n\nnc\ne \n\n\n\nan\nd,\n\n\n\n d\n=d\n\n\n\nra\nin\n\n\n\nag\ne.\n\n\n\n\n\n\n\n\n\n\n\n\n\n13\n\n\n\nwater through the soil horizon. Water percolates from surface to adjacent or lower \nground much faster than the root system can take up the water (Fig. 4). In addition, \nfertilizers applied onto the soils have less time to be available for plant uptake. \n\n\n\n-1 in extreme \n\n\n\nimbalance of the soils.\nThe sandy texture soil has large pore spaces; these conditions shift and \n\n\n\ndisperse the fertilizers to the adjacent lower elevation via lateral water movement \n(Fig. 4).  For instance, when fertilizer is applied onto the soils on ridge 3, the \n\n\n\nridge 1. The fertilizer undergoes dilution effect and is removed from the ridges, \n\n\n\nSeries present in swale 1 clearly indicates the enrichment of organic matter and N, \nP and K. Rainfall data of the area shown in Fig. 5 give an idea of water availability. \n\n\n\nThe lateral movement of water is possible due to the cemented Bs and Bhs layer \nin the Spodosols. \n\n\n\nthe soil pits was dug slightly wider and water was poured from the surface. The \nwater ran downwards in a few seconds and later moved horizontally parallel to the \nspodic horizon. The drying out process of water was very fast. There was no water \npenetration through the cemented Bs and Bhs layers.  A similar scenario of water \n\n\n\nIn order to better understand crop suitability of the Spodosols, a crop suitability \n\n\n\nare marginally suitable for kenaf production.\nSome of the Spodosols have undergone the process of land leveling over \n\n\n\nthe years (Fig. 3) and the ongoing human disturbance of the sandy soils makes it \n\n\n\nFig. 4: Lateral movement of water (side-ways rather than percolating downward) from the \nridge to swales due to the presence of the hardpan (Bs and/or Bhs) in B horizon\n\n\n\n\n\n\n\n\n14\n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\nscraping the topsoil, and in doing so, the spodic horizon comes closer to the top \n\n\n\nfarmers in the area cultivated chili, yam, bean, brinjal and okra with mixed \nsuccess. The land leveling activity has altered the depth of spodic horizon; hence, \nthis can change the soil series name as the names were based on the depth of \n\n\n\nEvaluation Based on Agronomic Requirements\n\n\n\ntherefore, it is necessary to refer to past literature to determine the minimum \n\n\n\nindicated that kenaf can be grown in areas from latitude 16oS to latitude 41oN \n\n\n\noC during the growing season. The climatic condition in Peninsular Malaysia is \nhot and humid throughout the year with little seasonal variations, and hence it \n\n\n\ngrowth:\n\n\n\n(i)    Geographical location\n\n\n\no o North latitudes, and \no o  East longitudes. It means the location of the study is \n\n\n\nFig. 5: Monthly rainfall distribution (mm) for Bachok (Kelantan) and \n\n\n\ntemperature  of 28 oC\n\n\n\n\n\n\n\n\n15\n\n\n\n(ii)    Temperature\nA study by Ogbonnaya et al.\n\n\n\no\n\n\n\no oC (Fig 5) and therefore, is suitable \n\n\n\n(iii)    Water availability\n\n\n\nfor optimal growth and production.  The Kelantan-Terengganu Plains fall under \n\n\n\n(Fig. 5). \n\n\n\nFig. 6: A typical landscape of the sandy beach ridges interspersed with \nswales:a) Jambu Series (oldest ridge) and; b) Rudua Series and \n\n\n\nRhu Tapai Series (intermediate ridge)\n\n\n\n\n\n\n\n\n16\n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\n(iv)    Rooting condition\n\n\n\nan elevation of up to 5 m. The soils in the ridges have undergone extensive \nleaching process, washing out of clay, leaving behind the sand fraction. Sand has \n\n\n\nSpodosols, initial rooting is not much of a problem. However, once it reaches the \no; despite that, the plants grew well \n\n\n\n(v)    Kenaf Growth\nIn kenaf cultivation, plant height and amount of rainfall are major yield contributors \nto the total stalk yield (Ching et al.\n\n\n\no\n\n\n\nHazandy et al\nthe growth and biomass of kenaf grown under wet conditions.  This shows the \nimportance of water supply for kenaf growth; when water supply is limited, tugor \npressure will force the plant cell wall to concave, thus reducing growth. The point \nis we should conserve the rain-fed water. So we propose rice straw mulching to \nimprove water holding capacity as well as the soil structure. \n\n\n\n(vi)    Al toxicity\nA common belief among the farmers is that Spodosols suffer from Al toxicity, but \nfortunately, the Spodosols in the study area are not. The soil pH was 5.5 or slightly \nbelow; hence, it does not promote the release of Al into the soil solution. As seen \nin Table 1, the exchangeable Al ranged from low to non-detectable. This is related \nto the sandy nature of the soils. This suggests that the Al in the spodic horizon is \nnot readily available for dissolution into the soil colloid system; it exists as Al-\nhydroxides or is chelated by organic acids. Therefore, Al toxicity is not a threat \nto kenaf growth. \n\n\n\n(vii)    Flood hazard\nThe oldest ridges have elevations of up to 5 m and therefore Spodosols on these \n\n\n\n(Fig. \n4)\n\n\n\nsoils which are used for growing rice. \n\n\n\n(viii)    Diseases \n\n\n\n\n\n\n\n\n17\n\n\n\net \nal.\nbe grown continuously. The use of chemicals does not affect seed germination; \n\n\n\nand improve stand establishment by protecting the seeds and seedlings from seed-\nand soil-borne pathogens (Cook et al.\n\n\n\nattacked by mealybugs (Fig. 7). These white bugs feed on kenaf leaves and stems \n\n\n\nyellow and the presence of high number of ants than normal is another sign of \ninfection (Malisa et al.\n\n\n\nwithin days, the crop will be destroyed. \n\n\n\n(ix)    Kenaf cultivar\n\n\n\nclimatic conditions, kenaf grows up to a height of 5 - 6 m in 6 - 8 months, \n-1\n\n\n\nFig. 7: Mealybugs attacking kenaf leaves (this retards \ngrowth and the leaves turn yellowish)\n\n\n\n\n\n\n\n\n18\n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\nto climatic variability and produce high yield (Ching et al. 1993; Webber and \n\n\n\nnon-irrigated conditions. In order to obtain a cultivar with good yield for any given \nclimate and soil type, a breeding programme to produce hardy cultivars is needed. \nIn Malaysia, breeding programmes are well underway, but a programme suited \nto sandy soils has yet to be started. Despite this setback, there are efforts to grow \nkenaf on the sandy Spodosols of the Kelantan - Terengganu Plains. However, this \n\n\n\nfertility?  \nBased on the above review we have constructed a table containing the \n\n\n\nthe suitability of the Spodosols for kenaf production using these data. Again, we \nfound that the Spodosols were marginally suitable for kenaf production. Hence, \na special agronomic practice is needed to make the soils suitable for kenaf \nproduction on the soils under study.\n\n\n\nCan Kenaf be Planted on Sandy Soils?\nIt is known that kenaf is well-adapted to a wide range of soil types, from clayey \nto sandy clay loam soils. However, best kenaf growth is obtained on well drained \nfertile soils. Soil texture should be heavier than sandy clay loam. Studies conducted \nby Ogbonnaya et al. et al.\nwell on sandy clay loam soils. In our case, the soil texture is sand; with very little \nclay, the soils are not able to retain nutrients for kenaf uptake. However, with the \npresence of the spodic horizon rich in organic matter, it may be possible to grow \n\n\n\nin water collection or a perched water table, etc. \nKenaf can be grown on sandy Spodosols provided that soil improvement is \n\n\n\nsoils have shown promising results (Mat Daham et al. et \nal.\noptimal kenaf growth given in Table 4. Thus, in this paper, we highlight a few \nsuggestions for soil improvement.\n\n\n\nImprovement of the Spodosols for Kenaf Production\n\n\n\nof Kenaf on the Spodosols of the Kelantan - Terengganu Plains. Having said this, \n\n\n\n\n\n\n\n\n19\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\n\n\n\n\n\n\n\n\nLa\nnd\n\n\n\n c\nha\n\n\n\nra\nct\n\n\n\ner\nist\n\n\n\nic\ns \n\n\n\n \nV\n\n\n\nal\nue\n\n\n\n1  \n \n\n\n\nK\nen\n\n\n\naf\n p\n\n\n\nar\nam\n\n\n\net\ner\n\n\n\n2  \n \n\n\n\nTe\nm\n\n\n\npe\nra\n\n\n\ntu\nre\n\n\n\n (t\nC\n\n\n\n) \n  \n\n\n\n\n\n\n\n   \n  M\n\n\n\nea\nn \n\n\n\nte\nm\n\n\n\npe\nra\n\n\n\ntu\nre\n\n\n\n (o C\n) \n\n\n\n\n\n\n\n 2\n8-\n\n\n\n42\n \n\n\n\n\n\n\n\n35\n.5\n\n\n\n\u00b10\n.9\n\n\n\n5 \n23\n\n\n\n-3\n0 \n\n\n\n\n\n\n\n O\ngb\n\n\n\non\nna\n\n\n\nya\n e\n\n\n\nt a\nl, \n\n\n\n19\n97\n\n\n\n. \n C\n\n\n\nra\nne\n\n\n\n 1\n94\n\n\n\n7.\n \n\n\n\n  \nW\n\n\n\nat\ner\n\n\n\n a\nva\n\n\n\nila\nbi\n\n\n\nlit\ny \n\n\n\n(w\na)\n\n\n\n\n\n\n\n\n\n\n\n   \n  R\n\n\n\nai\nnf\n\n\n\nal\nl (\n\n\n\nm\nm\n\n\n\n/y\nea\n\n\n\nr)\n \n\n\n\n\n\n\n\n 2\n50\n\n\n\n0-\n35\n\n\n\n00\n \n\n\n\n\n\n\n\n50\n0-\n\n\n\n60\n0 \n\n\n\n78\n0-\n\n\n\n12\n00\n\n\n\n \n\u00b1 \n\n\n\n30\n0 \n\n\n\n C\nra\n\n\n\nne\n 1\n\n\n\n94\n7.\n\n\n\n \n B\n\n\n\na\u00f1\nue\n\n\n\nlo\ns e\n\n\n\nt a\nl. \n\n\n\n20\n02\n\n\n\n. \n C\n\n\n\nhi\nng\n\n\n\n e\nt a\n\n\n\nl, \n19\n\n\n\n93\n. \n\n\n\nN\num\n\n\n\nbe\nr o\n\n\n\nf d\nry\n\n\n\n m\non\n\n\n\nth\ns  \n\n\n\n \n(m\n\n\n\non\nth\n\n\n\n) \n  \n\n\n\n <\n 3\n\n\n\n (9\n0 \n\n\n\nda\nys\n\n\n\n) \n   \n\n\n\n- 5-\n6 \n\n\n\n(1\n50\n\n\n\n-1\n80\n\n\n\nda\nys\n\n\n\n) \n  \n\n\n\n G\nha\n\n\n\nza\nli \n\n\n\nan\nd \n\n\n\nN\nie\n\n\n\now\nul\n\n\n\nt 1\n98\n\n\n\n2.\n \n\n\n\n C\nra\n\n\n\nne\n 1\n\n\n\n94\n7.\n\n\n\n\n\n\n\nO\nxy\n\n\n\nge\nn \n\n\n\nav\nai\n\n\n\nlib\nili\n\n\n\nty\n (o\n\n\n\na)\n \n\n\n\n\n\n\n\n\n\n\n\n  D\nra\n\n\n\nin\nag\n\n\n\ne \n \n\n\n\n S\nom\n\n\n\new\nha\n\n\n\nt e\nxc\n\n\n\nes\nsi\n\n\n\nve\n/ \n\n\n\n e\nxc\n\n\n\nes\nsi\n\n\n\nve\n \n\n\n\nW\nel\n\n\n\nl d\nra\n\n\n\nin\ned\n\n\n\n\n\n\n\nO\ngb\n\n\n\non\nna\n\n\n\nya\n e\n\n\n\nt a\nl. \n\n\n\n19\n97\n\n\n\n; H\naz\n\n\n\nan\ndy\n\n\n\n e\nt a\n\n\n\nl. \n20\n\n\n\n09\n. \n\n\n\n \nR\n\n\n\noo\ntin\n\n\n\ng \nco\n\n\n\nnd\niti\n\n\n\non\n (r\n\n\n\nc)\n \n\n\n\n\n\n\n\n\n\n\n\n  T\nex\n\n\n\ntu\nre\n\n\n\n\n\n\n\n S\nan\n\n\n\ndy\n so\n\n\n\nil \n(>\n\n\n\n95\n%\n\n\n\n) \n   \n\n\n\nSa\nnd\n\n\n\ny \nso\n\n\n\nil \n(\u00b1\n\n\n\n 5\n0%\n\n\n\n) \nSa\n\n\n\nnd\ny \n\n\n\ncl\nay\n\n\n\n lo\nam\n\n\n\n\n\n\n\nH\nsia\n\n\n\no \n19\n\n\n\n73\n; O\n\n\n\ngb\non\n\n\n\nna\nya\n\n\n\n e\nt a\n\n\n\nl. \n19\n\n\n\n97\n \n\n\n\nC\noo\n\n\n\nk \nan\n\n\n\nd \nSm\n\n\n\nar\nt 1\n\n\n\n99\n5.\n\n\n\n\n\n\n\n   \n  R\n\n\n\nou\ngh\n\n\n\n m\nat\n\n\n\ner\nia\n\n\n\nls\n (%\n\n\n\n) \n M\n\n\n\nin\nor\n\n\n\n ro\not\n\n\n\ns \n - \n\n\n\n - \n   \n\n\n\n  S\noi\n\n\n\nl d\nep\n\n\n\nth\n (c\n\n\n\nm\n) \n\n\n\n >\n50\n\n\n\n++\n \n\n\n\n>7\n5 \n\n\n\n(o\npt\n\n\n\nim\num\n\n\n\n) \nPa\n\n\n\nra\nm\n\n\n\nan\nan\n\n\n\nth\nan\n\n\n\n 1\n98\n\n\n\n7;\n M\n\n\n\nA\nR\n\n\n\nD\nI \n\n\n\n20\n10\n\n\n\n. \nN\n\n\n\nut\nrie\n\n\n\nnt\n re\n\n\n\nte\nnt\n\n\n\nio\nn \n\n\n\n(n\nr)\n\n\n\n\n\n\n\n\n\n\n\n   \n  C\n\n\n\nla\ny \n\n\n\nC\nEC\n\n\n\n (c\nm\n\n\n\nol\n/k\n\n\n\ng)\n \n\n\n\n 0\n-5\n\n\n\n (<\n 1\n\n\n\n in\n m\n\n\n\nos\nt l\n\n\n\noc\nal\n\n\n\niti\nes\n\n\n\n) \n N\n\n\n\nut\nrie\n\n\n\nnt\n d\n\n\n\nef\nic\n\n\n\nie\nnc\n\n\n\ny \n A\n\n\n\nbu\n B\n\n\n\nak\nar\n\n\n\n 1\n98\n\n\n\n5;\n L\n\n\n\nim\n 1\n\n\n\n98\n9.\n\n\n\n\n\n\n\n   \n  B\n\n\n\nas\ne \n\n\n\nsa\ntu\n\n\n\nra\ntio\n\n\n\nn \n(%\n\n\n\n) \n 3\n\n\n\n7-\n85\n\n\n\n (<\n40\n\n\n\n in\n sp\n\n\n\nod\nic\n\n\n\n \nho\n\n\n\nriz\non\n\n\n\n) \n <\n\n\n\n15\n (v\n\n\n\ner\ny \n\n\n\nlo\nw\n\n\n\n) \n A\n\n\n\nbu\n B\n\n\n\nak\nar\n\n\n\n 1\n98\n\n\n\n5.\n  \n\n\n\n   \n  p\n\n\n\nH\n H\n\n\n\n2O\n \n\n\n\n \n 4\n\n\n\n.0\n - \n\n\n\n5.\n4 \n\n\n\n(a\nci\n\n\n\ndi\nc)\n\n\n\n\n\n\n\n N\not\n\n\n\n su\nita\n\n\n\nbl\ne \n\n\n\nun\nde\n\n\n\nr a\nci\n\n\n\ndi\nc \n\n\n\nco\nnd\n\n\n\niti\non\n\n\n\n \nM\n\n\n\nA\nR\n\n\n\nD\nI 2\n\n\n\n01\n0.\n\n\n\n\n\n\n\n   \n  O\n\n\n\nrg\nan\n\n\n\nic\n C\n\n\n\n (%\n) \n\n\n\n \n 0\n\n\n\n.7\n \u2013\n\n\n\n 2\n \n\n\n\n \n<1\n\n\n\n , \nan\n\n\n\nd \n3.\n\n\n\n8 \n(s\n\n\n\npo\ndi\n\n\n\nc \nho\n\n\n\nriz\non\n\n\n\n) \n \n\n\n\nA\nbu\n\n\n\n B\nak\n\n\n\nar\n 1\n\n\n\n98\n5.\n\n\n\n\n\n\n\nTo\nxi\n\n\n\nci\nty\n\n\n\n (A\nl) \n\n\n\nN\not\n\n\n\n fo\nun\n\n\n\nd \n \n\n\n\n B\nel\n\n\n\now\n p\n\n\n\nH\n 5\n\n\n\n.5\n \n\n\n\n D\nO\n\n\n\nA\n 2\n\n\n\n01\n0.\n\n\n\n \nFl\n\n\n\noo\nd \n\n\n\nha\nza\n\n\n\nrd\n (f\n\n\n\nh)\n \n\n\n\n\n\n\n\n\n\n\n\n  I\nnu\n\n\n\nnd\nat\n\n\n\nio\nn \n\n\n\n1 \n- \n\n\n\nR\nos\n\n\n\nla\nn \n\n\n\net\n a\n\n\n\nl. \n20\n\n\n\n10\n. \n\n\n\n\n\n\n\n\n\n\n\nN\not\n\n\n\ne:\n T\n\n\n\nhe\n st\n\n\n\nat\ned\n\n\n\n d\nat\n\n\n\na \nar\n\n\n\ne \nre\n\n\n\npr\nes\n\n\n\nen\nta\n\n\n\ntiv\ne \n\n\n\nfo\nr: \n\n\n\n 1\n) V\n\n\n\nal\nue\n\n\n\n1 : t\nhe\n\n\n\n fi\nel\n\n\n\nd \nda\n\n\n\nta\n fr\n\n\n\nom\n th\n\n\n\ne \nst\n\n\n\nud\ny \n\n\n\nar\nea\n\n\n\n, a\nnd\n\n\n\n 2\n) K\n\n\n\nen\naf\n\n\n\n p\nar\n\n\n\nam\net\n\n\n\ner\n2 : t\n\n\n\nhe\n d\n\n\n\nat\na \n\n\n\nfro\nm\n\n\n\n o\nth\n\n\n\ner\n st\n\n\n\nud\nie\n\n\n\ns. \n\n\n\n\n\n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\n(i)    Intercropping kenaf with other crops \n\n\n\nyam bean (Saka et al.\nIntercropping is referred to as poly-culture farming. We found that rotational crop \n\n\n\nsmall farmers in the study area. Hence, intercropping of these crops with kenaf \ncan be a good option for utilizing the sandy soils. Furthermore, kenaf with its deep \ntap root and lateral root system is considered to be an excellent user of residual \nnutrients from the previous crops. After harvest, kenaf residue decomposes and \nadds nitrogen into the soils, sustaining the soil fertility. Therefore, it is suggested \nthat intercropping is the best possible option for the small farmers in the study \narea. \n\n\n\n(ii)    Good management farming soil practices \n\n\n\nsuch as the National Tobacco and Kenaf Board, Malaysia, and Kemasin-Semarak \nIntegrated Agricultural Development Project members, we propose the following \nagricultural practices:\n\n\n\nthat can improve nutrient retention, improve water holding capacity and \nsupport root growth. Rice straws are available in the vicinity of the study \n\n\n\no\n\n\n\nsandy Spodosols can be mixed with the more clayey soils from the swales. \nThis will help increase CEC. Organic manures (for example, chicken \n\n\n\nsoil fertility. In addition, a study by Lima et al\nVertisol with sandy soil in Ap horizon, improves nutrient retention and water \n\n\n\nkg ha-1.\n\n\n\nCONCLUSIONS\n\n\n\nfeldspars and mica, indicating a high degree of weathering. The soils are low in \n\n\n\n\n\n\n\n\nnutrients and CEC, and suffer from excessive drainage, leading to serious nutrient \nimbalance. During the dry season, kenaf suffers from water stress that inhibits \nits growth. A well planned growing calendar based on rainfall data can minimize \n\n\n\nthey are, these Spodosols are marginally suitable for kenaf production. The soils \ncan be made suitable for growing kenaf by mulching with rice straw, irrigation \nduring dry period and mixing with more clayey soils available nearby. To further \nimprove soil productivity, organic matter should be added into the soils. \n\n\n\nACKNOWLEDGEMENTS\n\n\n\nand technical support during the conduct of the research. This research was \n\n\n\nREFERENCES\nAbdul Wahab, N. 1984. BRIS soil temperature. MARDI Research Bulletin\n\n\n\n179.\n\n\n\nAbu Bakar, O. 1985. A comparison of selected Entisols and Spodosols occurring in \n\n\n\nAndriesse, J.P. 1968. 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Marine Geology\n161.\n\n\n\nT (Hibiscus cannabinus L.) core and rice hulls as \ncomponents of container media for growing Pinus halepensis M. seedlings. \nBioresource Technology.  97: 1631-1639.\n\n\n\nW\nIndustrial Crops and Products. 16: 81-88. \n\n\n\nW\nof Agriculture, Ministry of Agriculture and Rural Development, Kuala Lumpur, \nMalaysia. \n\n\n\nW\nof Agriculture, Ministry of Agriculture and AgroBased Industry, Putrajaya, \nMalaysia. \n\n\n\nW\nhttp://www.newscrops.uq.edu.au/newslett/ncn10212.htm.\n\n\n\nv\npodzolization via rock-eating mycorrhizal fungi? Geoderma. 94: 161-169.\n\n\n\nvan Ranst, E., M. Verloo, A. Demeyer,  and J.M. Pauwesl. 1999. Manual for the Soil \nChemistry and Fertility Laboratory. University of Ghent, Gent, Belgium. \n\n\n\nZ\n\n\n\non Small Scale vegetable production and Horticulture Economics in Developing \nCountries. Acta Hoticulturae No. 369. Leuven, Belgium. Intern. Soc. Hort. Sci. \n\n\n\nI. Roslan, J. Shamshuddin, C.I. Fauziah and A.R. Anuar\n\n\n\n\n\n"
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"\n\n___________________\n*Corresponding author : E-mail: husni@upm.edu.my\n\n\n\nINTRODUCTION\nBiochar is widely regarded as a stable material with long resident time ranging \nfrom centuries to millenniums (Lehmann et al. 2007). However, biochar is still \nvulnerable to decomposition and diminishes over time due to abiotic and biotic \nreactions. The stability of biochar depends on the pyrolysis condition and type of \nfeedstock. Hamer et al. (2004) reported 0.005%/day loss from the initial C, after \n60 days of incubation, for oak biochar produced at 800oC. Meanwhile, wheat \nstraw biochar produced at 225oC recorded 0.06%/day loss from the initial C \n(Bruun et al. 2009). Initial massive mass loss is usually reported for short-term \ndecomposition studies, and mainly attributed to the early abiotic changes on the \nlabile fraction of biochar such as carbohydrates and volatiles (Cheng et al. 2006). \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 17: 85-97 (2013)                          Malaysian Society of Soil Science\n\n\n\nShort-term Field Decomposition of Pineapple Stump Biochar \nin Tropical Peat Soil\n\n\n\nCheah, P.M.1, M.H.A. Husni1*,  A.W. Samsuri1 and A. Luqman Chuah2\n\n\n\n \n1Department of Land Management, Faculty of Agriculture, Universiti Putra \n\n\n\nMalaysia, 43400 Serdang, Selangor, Malaysia\n2Department of Chemical Engineering, Faculty of Engineering, Universiti Putra \n\n\n\nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nThe transformation of biochar on tropical peat is yet to be studied as all previous \nstudies have been conducted on mineral or forest soils. The objectives of this study \nwere to investigate the physical and chemical changes experienced by pineapple \nstump biochar (PSB) in tropical peat and to determine the short-term decomposition \nmodel of PSB in a C-rich environment. Elemental composition was determined \nusing CHNS-O analyzer and surface area with Brunauer-Emmett-Teller (BET) \nmethod. Surface chemistry and structural study were conducted with Fourier \nTransform Infrared (FTIR) spectroscopy and 13C solid state Nuclear Magnetic \nResonance (NMR) spectroscopy, respectively. The PSB short-term decomposition \nwas conducted with a litter bag study and best fitted into the hyperbolic decay \nmodel compared to exponential decay model because no significant mass loss was \ndetected after 4 months. The stagnant phase was probably due to interaction with \nmetals from peat. Redox reaction was prominent on the surface and structural \nchemistry. Surface oxidation of PSB produced more O-functionalities (hydroxyl, \ncarboxylic and phenolic) and achieved chemical recalcitrance after 12 months. The \ncarbon structure was reduced or saturated causing a decrease in electronegativity. \nFurther PSB decomposition probably depends on biotic decomposition. \n\n\n\nKeywords: FTIR, hyperbolic decay model, litter bag study, NMR, \norganic C\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201386\n\n\n\nAfter incorporation in the soil, the outer surfaces of biochar are vulnerable \nto rapid surface oxidation (Lehmann 2007). The surface of aged biochar collected \nfrom historical charcoal blast furnaces in US was dominated by hydroxyl bonds, \ncarboxylic acid groups and phenolic acids proving the increase in non-aromatic \nfunctionality over time (Cheng et al. 2008). The increase in non-aromatic \nfunctionality on the surface of biochar can affect the elemental composition of \nbiochar. The increase in O and H content is associated with the acquisition of \nan acid functional group on the surface of biochar due to the oxidation process \n(Cheng et al. 2008; Hockaday et al. 2007). Moreover, the increase in acid \nfunctional groups of biochar also change the biochar into more hydrophilic in \nnature due to the evolution of surface positive charge compared to negatively \ncharged functionality (Cheng et al. 2006). \n\n\n\nThis phenomenon promotes the adsorption of readily available organic C \nin soil that is rich in functional groups on biochar and decrease the surface area \nover time (Carcaillet 2001; Cheng et al. 2006). Heavy molecular weight and \ncomplex organic matter like humic acid are responsible for obstructing pores \nsmaller than 2nm on the external surfaces of powdered wood biochar (Pignatello \net al. 2006). The blocked pores might deprive the microbial activities on biochar, \ninhibiting C mineralization and promoting biochar stability. Further, biochar-\nmineral interaction might contribute to its stability by reducing the bioavailability \nof biochar. Higher levels of Fe, Al and Si have been detected within biochar \nstructures after 10 years of application to soil (Nguyen et al. 2008). Aged biochar \nis more negatively charged (Cheng et al. 2008) and has higher affinity to form \nbonding with positive charges of metals or their oxides. \n\n\n\nThus, the decomposition of biochar in a C-rich environment could be slower \nthan expected. In this study, the biochar decomposition study was conducted \non a tropical peat soil. Lawful open-burning is permitted within the Malaysian \nEnvironmental Quality Act (2009) limitation and is usually enacted to manage \nthe pineapple waste on peat, thus producing biochar with the burning process. \nApproximately 0.78 Mg/ha biochar are produced from burning pineapple leaves \nper harvest (Leng et al. 2011). Biochar produced in situ from open-burning \nadds more C into peat organic C pool and accumulates at the top or surface of \npeat. Stable biochar could reduce C emission of peat over particular timescales. \nHowever, the stability of biochar in peat is relatively unknown because all the \nbiochar decomposition studies up to date have been conducted on mineral soils or \nforest soils (Cheng et al. 2008; Wardle et al. 2008). Low pH and the presence of \nsoluble organic matter in peat soil might affect the biochar resident time in peat. \nThe presence of Bronsted acid such as humic acid in tropical peat contributes \nto high amounts of free H+ that could saturate and keep biochar in reduced \nform. Besides, a high amount of dissolved organic matter is likely to encourage \nsurface adsorption on aged biochar which could shield the biochar from further \ndegradation. However, biochar behaves differently in acidic mineral soil. Thus, \nstudying biochar transformation on peat can provide a lead in understanding the \nrecalcitrance of biochar in a C-rich environment. The objectives of this study were \n\n\n\nCheah, P.M., M.H.A. Husni,  A.W. Samsuri and A. Luqman Chuah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 87\n\n\n\nto determine the decay model, physico-chemical and structural transformations of \na short-term field study of pineapple stump biochar (PSB) in tropical peat. \n\n\n\nMETHODOLOGY\nThe PSB used in this study was laboratory-produced at 250oC for 3 h with a \nCarbolite ELF 11/23 type furnace. Pineapple stump (PS) raw feedstock was \ncollected from Peninsula Plantation, Simpang Renggam, Malaysia. The feedstock \nwas air-dried and chipped into small pieces (3-5 cm) before pyrolysis. The PS \nchar obtained was ground and sieved to 2 mm. Field decomposition study was \nbased on 2 mm PS char.\n\n\n\nChemical Composition and Surface Area\nThe C, H, N and O content of PS at 0, 4, 8, 12 and 16 months (M) was analysed using \nCHNS-O analyser (LECO TruSpec CHN). Iron (Fe) content was determined using \nthe dry ashing method (Mitra, 2003) and Atomic Absorption Spectrophotometry \n(Thermo Scientific S-Series). The surface area was determined using a surface \narea analyser (Quantachrome Autosorb-1) applying the Brunauer, Emmet and \nTeller method.\n\n\n\nSurface Chemistry and C Skeletal Structure \nThe infrared spectral properties of PSB were determined by Perkin-Elmer \nSpectrum 100 Spectrometer with a Perkin-Elmer Universal Attenuated Total \nReflectance (ATR) sampling accessory. Data collection and processing were \nconducted using Spectrum version 6.2.0.0055 software. The C skeletal structure \nof PSB was determined by a 13C solid state NMR (Bruker Avance 400 MHz).\n\n\n\nField Decomposition Study\nThe litterbag method (Wardle et al. 2008) was used for this field decomposition \nstudy. Approximately 6 g of PS biochar were placed in a 6.5 x 10.5 cm nylon \nbags of 250 \u00b5m mesh. The weight of the nylon bags was taken before they were \nburied in peat soil. A total of 32 nylon bags were randomly placed at the Peninsula \nPlantation, Simpang Rengam, Malaysia after the plot was cleared after 1 month \nfor replanting. The nylon bags were buried vertically at 0-10 cm depth and a total \nof 8 bags were recovered at intervals of four months till end of the decomposition \nstudy of 16 months. The harvested nylon bags were cleared of foreign materials \nand oven-dried at 60\u00b0C till constant weight to obtain the dry weight. The results \nof the remaining PSB mass from the initial mass over 16 months were plotted and \nbest fitted into several decay models.\n\n\n\nDecomposition Model and Statistical Analysis\nThe field decomposition data over 16 months were fitted into a hyperbolic decay \nmodel, single exponential decay model and double exponential decay model to \nselect the best representation of short-term PSB decomposition in tropical peat. \nThe decomposition model was generated by SigmaPlot version 11.0 using the \n\n\n\nField Decomposition of Pineapple Stump Biochar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201388\n\n\n\nMarquardt-Levenberg algorithm to obtain the coefficients of the independent \nvariables. The trend of PSB mass remaining over time was determined using \nregression analysis of SigmaPlot version 11.0\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nElemental Composition and Surface Area\nThe elemental composition and BET surface area of PSB over the course of 16 \nmonths are presented in Table 1. The N content of the PSB remained unchanged or \nconcentrated over 16 months in tropical peat due to PSB mass loss. The C content \ndecreased and H content showed no specific pattern throughout the 16 months. \nThe drastic decrease in BET surface area (0-8 month) indicated the adsorption \nof organic matter on PSB. However, the decreasing C content conflicted with \nthe adsorption of dissolved organic matter. The atomic O/C ratio increased over \n16 months, indicating a higher degree of oxidation. Atomic H/C ratio increased \ninitially (PS 0M-4M) from 0.98 to 1.60 and remained nearly idle afterwards. The \nincrease in atomic H/C for the first four months could be due to the rapid loss of \ncarbon. Since the PSB atomic H/C ratio remained unchanged after the first four \nmonths, this indicated no major structural reformation. The high atomic H/C ratio \nhinted at the aliphaticity nature of PSB. The atomic H/C ratio of peas biochar \nproduced at 700oC was 0.22 due to its high aromatic content (Braadbaart et al. \n2004). Besides, Fe content in PSB accumulated over 16 months, indicating PSB \nadsorption of Fe from the peat. This biochar-Fe interaction could protect PSB \nfrom degradation and explain the slow PSB weight loss (Fig. 1). \n\n\n\nCharacteristics of tropical peat are presented in Table 2. The pH value was \nbelow 4 and the Fe content was high at 0.1%. Besides, the peat was drained for \nongoing pineapple cultivation. Thus, top or surface peat was heavily oxidized due \nto exposure to the atmosphere.\n\n\n\n                                                                                                 ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                         Malaysian Society of Soil Science \n \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n9 \n\n\n\n\n\n\n\nTABLE 1 \nElemental composition and surface area of PSB \n\n\n\n \nSamples C (%) \n\n\n\n \nH (%) \n\n\n\n \nN (%) \n\n\n\n \nO (%) \n\n\n\n \nFe (%) \n\n\n\n \nAtomic \n\n\n\nH/C \nAtomic \n\n\n\nO/C \nSurface \n\n\n\nArea (m2 \ng-1) PS biochar \n\n\n\nmonth (M) \n0 \n \n\n\n\n52.04 \n\u00b10.18 \n\n\n\n4.27 \n\u00b10.15 \n\n\n\n1.27 \n\u00b10.012 \n\n\n\n32.96 \n\u00b11.54 \n\n\n\n0.011 \n\u00b10.002 \n\n\n\n0.99 0.47 24.46 \n\n\n\n4 \n \n\n\n\n44.60 \n\u00b10.65 \n\n\n\n5.97 \n\u00b10.50 \n\n\n\n1.63 \n\u00b10.015 \n\n\n\n33.96 \n\u00b11.62 \n\n\n\n0.062 \n\u00b10.008 \n\n\n\n1.60 0.57 4.02 \n\n\n\n8 \n \n\n\n\n41.28 \n\u00b10.68 \n\n\n\n5.20 \n\u00b10.47 \n\n\n\n1.65 \n\u00b10.016 \n\n\n\n36.51 \n\u00b11.98 \n\n\n\n0.045 \n\u00b10.007 \n\n\n\n1.51 0.66 n.a* \n\n\n\n12 \n \n\n\n\n40.16 \n\u00b10.42 \n\n\n\n5.13 \n\u00b10.56 \n\n\n\n1.73 \n\u00b10.010 \n\n\n\n37.08 \n\u00b11.54 \n\n\n\n0.067 \n\u00b10.007 \n\n\n\n1.53 0.69 0.653 \n\n\n\n16 32.10 \n\u00b10.52 \n\n\n\n4.32 \n\u00b10.48 \n\n\n\n1.86 \n\u00b10.013 \n\n\n\n36.47 \n\u00b11.66 \n\n\n\n0.081 \n\u00b10.010 \n\n\n\n1.61 0.85 0.335 \n\n\n\n*The BET surface area for PS 8M was outside of the valid range. The surface area was too small. \n \u00b1SE  = Standard error of mean \n \n\n\n\nTABLE 2 \nCharacteristics of tropical peat soil \n\n\n\n \nParameters Readings Reference \n\n\n\npH > 4 Lucas (1982) \nC (%) 58 Kanapathy (1976) \nN (%) 0.5-2 Tie and Lim (1976) \nK (%) 0.04 Lucas (1982) \nFe (%) 0.1 Kyuma (1991) \nCa (%) 0.3 Lucas (1982) \n\n\n\n\n\n\n\nTABLE 1\nElemental composition and surface area of PSB\n\n\n\nCheah, P.M., M.H.A. Husni,  A.W. Samsuri and A. Luqman Chuah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 89\n\n\n\nSurface Chemistry\nThe FTIR spectrum of PSB over 16 months in tropical peat soil is shown in Fig. \n2. The IR wavelength assignment was based on Lampman et al. (2010). The H \nbonded O-H detected at 3407 cm-1 indicated the polymeric nature of fresh PSB \n(PS 0M). This implied the orderly arrangement of the crystalline phase of PSB. \nAlkene C=C stretch group was detected at 1584 cm-1. Since no N-H group was \ndetected, the presence of C-N stretch at 1369 cm-1 implied tertiary amines. The \nC-O stretch (1245 cm-1) combined with H bonded O-H suggested the presence of \nan alcohol group in PS 0M.\n\n\n\nAfter 4 months, PSB (PS 4M) showed signs of oxidation as carbonyl (C=O) \ngroup was detected at 1694 cm-1. The H bonded O-H was not detected and C-O \n\n\n\n                                                                                                 ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                         Malaysian Society of Soil Science \n \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n9 \n\n\n\n\n\n\n\nTABLE 1 \nElemental composition and surface area of PSB \n\n\n\n \nSamples C (%) \n\n\n\n \nH (%) \n\n\n\n \nN (%) \n\n\n\n \nO (%) \n\n\n\n \nFe (%) \n\n\n\n \nAtomic \n\n\n\nH/C \nAtomic \n\n\n\nO/C \nSurface \n\n\n\nArea (m2 \ng-1) PS biochar \n\n\n\nmonth (M) \n0 \n \n\n\n\n52.04 \n\u00b10.18 \n\n\n\n4.27 \n\u00b10.15 \n\n\n\n1.27 \n\u00b10.012 \n\n\n\n32.96 \n\u00b11.54 \n\n\n\n0.011 \n\u00b10.002 \n\n\n\n0.99 0.47 24.46 \n\n\n\n4 \n \n\n\n\n44.60 \n\u00b10.65 \n\n\n\n5.97 \n\u00b10.50 \n\n\n\n1.63 \n\u00b10.015 \n\n\n\n33.96 \n\u00b11.62 \n\n\n\n0.062 \n\u00b10.008 \n\n\n\n1.60 0.57 4.02 \n\n\n\n8 \n \n\n\n\n41.28 \n\u00b10.68 \n\n\n\n5.20 \n\u00b10.47 \n\n\n\n1.65 \n\u00b10.016 \n\n\n\n36.51 \n\u00b11.98 \n\n\n\n0.045 \n\u00b10.007 \n\n\n\n1.51 0.66 n.a* \n\n\n\n12 \n \n\n\n\n40.16 \n\u00b10.42 \n\n\n\n5.13 \n\u00b10.56 \n\n\n\n1.73 \n\u00b10.010 \n\n\n\n37.08 \n\u00b11.54 \n\n\n\n0.067 \n\u00b10.007 \n\n\n\n1.53 0.69 0.653 \n\n\n\n16 32.10 \n\u00b10.52 \n\n\n\n4.32 \n\u00b10.48 \n\n\n\n1.86 \n\u00b10.013 \n\n\n\n36.47 \n\u00b11.66 \n\n\n\n0.081 \n\u00b10.010 \n\n\n\n1.61 0.85 0.335 \n\n\n\n*The BET surface area for PS 8M was outside of the valid range. The surface area was too small. \n \u00b1SE  = Standard error of mean \n \n\n\n\nTABLE 2 \nCharacteristics of tropical peat soil \n\n\n\n \nParameters Readings Reference \n\n\n\npH > 4 Lucas (1982) \nC (%) 58 Kanapathy (1976) \nN (%) 0.5-2 Tie and Lim (1976) \nK (%) 0.04 Lucas (1982) \nFe (%) 0.1 Kyuma (1991) \nCa (%) 0.3 Lucas (1982) \n\n\n\n\n\n\n\nTABLE 2\nCharacteristics of tropical peat soil\n\n\n\n\n\n\n\nMonth (t)\n\n\n\n0 2 4 6 8 10 12 14 16 18\n\n\n\n%\n W\n\n\n\nei\ngh\n\n\n\nt r\nem\n\n\n\nai\nni\n\n\n\nng\n (W\n\n\n\nr)\n\n\n\n75\n\n\n\n80\n\n\n\n85\n\n\n\n90\n\n\n\n95\n\n\n\n100\n\n\n\n105\n\n\n\nMonth vs % Weight remaining \nMonth vs % Weight remaining\n95% Confidence Band \n\n\n\nFig. 1: Short-term decomposition model of PSB\n\n\n\nField Decomposition of Pineapple Stump Biochar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201390\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  M\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n \n11\n\n\n\n\n\n\n\n\n\n\n\n40\n00\n\n\n\n.0\n36\n\n\n\n00\n32\n\n\n\n00\n28\n\n\n\n00\n24\n\n\n\n00\n20\n\n\n\n00\n18\n\n\n\n00\n16\n\n\n\n00\n14\n\n\n\n00\n12\n\n\n\n00\n10\n\n\n\n00\n80\n\n\n\n0\n60\n\n\n\n0\n40\n\n\n\n0\n28\n\n\n\n0.\n0\n\n\n\ncm\n-1\n\n\n\n%\nT\n\n\n\n\n\n\n\n34\n07\n\n\n\n.2\n8\n\n\n\n29\n26\n\n\n\n.8\n2\n\n\n\n21\n99\n\n\n\n.7\n719\n\n\n\n88\n.0\n\n\n\n4\n\n\n\n15\n84\n\n\n\n.3\n3\n\n\n\n13\n69\n\n\n\n.3\n3\n\n\n\n12\n45\n\n\n\n.7\n4\n\n\n\n76\n4.\n\n\n\n11\n\n\n\n32\n2.\n\n\n\n91\n\n\n\n37\n80\n\n\n\n.5\n3\n\n\n\n32\n06\n\n\n\n.9\n8\n\n\n\n29\n24\n\n\n\n.2\n0\n\n\n\n16\n94\n\n\n\n.9\n6 15\n\n\n\n93\n.5\n\n\n\n0\n\n\n\n13\n65\n\n\n\n.0\n8\n\n\n\n12\n16\n\n\n\n.4\n4\n\n\n\n75\n8.\n\n\n\n90\n\n\n\n37\n85\n\n\n\n.8\n0\n\n\n\n36\n97\n\n\n\n.5\n0\n\n\n\n31\n99\n\n\n\n.5\n9\n\n\n\n29\n24\n\n\n\n.3\n6\n\n\n\n16\n91\n\n\n\n.0\n7 15\n\n\n\n98\n.2\n\n\n\n6\n12\n\n\n\n19\n.0\n\n\n\n6\n\n\n\n10\n47\n\n\n\n.9\n1\n\n\n\n76\n4.\n\n\n\n31\n\n\n\n32\n0.\n\n\n\n78\n\n\n\n33\n79\n\n\n\n.7\n5\n\n\n\n29\n73\n\n\n\n.6\n7\n\n\n\n29\n22\n\n\n\n.9\n3\n\n\n\n22\n32\n\n\n\n.1\n1 20\n\n\n\n36\n.5\n\n\n\n7\n\n\n\n16\n45\n\n\n\n.3\n5\n\n\n\n15\n94\n\n\n\n.9\n815\n22\n\n\n\n.2\n6 14\n\n\n\n33\n.7\n\n\n\n1 13\n51\n\n\n\n.8\n012\n77\n\n\n\n.8\n9\n\n\n\n11\n53\n\n\n\n.8\n9\n\n\n\n11\n11\n\n\n\n.4\n2\n\n\n\n10\n71\n\n\n\n.2\n497\n\n\n\n2.\n9991\n\n\n\n4.\n76\n\n\n\n83\n1.\n\n\n\n11 76\n6.\n\n\n\n2869\n5.\n\n\n\n12\n\n\n\n42\n6.\n\n\n\n52\n30\n\n\n\n5.\n13\n\n\n\n33\n67\n\n\n\n.2\n4\n\n\n\n29\n73\n\n\n\n.6\n5\n\n\n\n29\n25\n\n\n\n.4\n8\n\n\n\n26\n02\n\n\n\n.4\n4\n\n\n\n21\n69\n\n\n\n.9\n4\n\n\n\n16\n44\n\n\n\n.6\n7\n\n\n\n15\n87\n\n\n\n.5\n5\n\n\n\n15\n21\n\n\n\n.7\n4 14\n\n\n\n33\n.2\n\n\n\n713\n50\n\n\n\n.5\n0 12\n78\n\n\n\n.4\n3\n\n\n\n12\n32\n\n\n\n.1\n1 11\n\n\n\n51\n.2\n\n\n\n5\n\n\n\n97\n2.\n\n\n\n01\n83\n\n\n\n0.\n1076\n\n\n\n4.\n01 69\n\n\n\n6.\n28\n\n\n\n42\n4.\n\n\n\n66\n\n\n\n\n\n\n\nPS\n 0\n\n\n\nM\n \n\n\n\nPS\n 4\n\n\n\nM\n \n\n\n\nPS\n 8\n\n\n\nM\n \n\n\n\nPS\n 1\n\n\n\n2M\n \n\n\n\nPS\n 1\n\n\n\n6M\n \n\n\n\nFi\ng.\n\n\n\n 1\n: F\n\n\n\nTI\nR \n\n\n\nsp\nec\n\n\n\ntr\num\n\n\n\n o\nf P\n\n\n\nS \nbi\n\n\n\noc\nha\n\n\n\nrs\n (P\n\n\n\nSB\ns)\n\n\n\n o\nve\n\n\n\nr 1\n6 \n\n\n\nm\non\n\n\n\nth\ns \n\n\n\nFi\ng.\n\n\n\n 2\n: F\n\n\n\nTI\nR \n\n\n\nsp\nec\n\n\n\ntr\num\n\n\n\n o\nf P\n\n\n\nS \nbi\n\n\n\noc\nha\n\n\n\nrs\n (P\n\n\n\nSB\ns)\n\n\n\n o\nve\n\n\n\nr 1\n6 \n\n\n\nm\non\n\n\n\nth\ns\n\n\n\nCheah, P.M., M.H.A. Husni,  A.W. Samsuri and A. Luqman Chuah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 91\n\n\n\nintensity decreased compared to PS 0M. Besides, without hydroxyl group, the \nC-O stretch (1216 cm-1) indicated an ether group. The absence of an alkyne group \n(C\u2261C) from PS 4M might be due to reaction with free acids of tropical peat \nconverting the alkyne group into an alkene group (Vollhardt et al. 2007). The \nalkene group (C=C) was detected at 1593 cm-1.\n\n\n\nNo major alterations in PSB were detected after 8 months (PS 8M) compared \nto PS 4M as carbonyl, alkene and ether groups were also detected. However, there \nwas one difference as secondary amines were found in PS 8M instead of tertiary \namines with the detection of N-H group (3199 cm-1). \n\n\n\nAfter 12 months, PSB (PS 12M) acquired oxygen functionalities like the \nhydroxyl, carboxylic and phenolic groups as reported by Cheng et al. (2008). \nThe sorting of aromaticity (1594 cm-1 and 1433 cm-1) and carbonyl group (1645 \ncm-1)  with hydroxyl group (3379 cm-1) indicated phenolic and carboxylic group, \nrespectively. In general, PS 12M became negatively charged or more hydrophilic \nwith the gain of the hydroxyl, carboxylic and phenolic groups. Similar findings \nwere reported by Cheng et al. (2006) in an incubation study. \n\n\n\nThe PSB (PS 16M) surface transformation halted at 16 months. The spectrum \nof PS 16M was similar to the PS 12M spectrum. Thus, PSB developed chemical \nrecalcitrance after 12 months in a tropical peat soil. Further action of biochar \ndecay might be mainly dependent on biotic reaction. \n\n\n\nChemical Structure\nThe 13C NMR spectrum of fresh PSB (PS 0M) is presented in Fig. 3. All 13C \nNMR chemical shift assignments were based on Lampman et al. (2010). Fresh \nPSB (PS 0M) was aliphatic in nature due to the absence of aryl C or aromaticity. \nBesides, the peak at 28.14 ppm was relative to aliphatic sp2 C-H(R2CH2) or alkyl \nC. The O-alkyl C (C-O) peak at 73.55 ppm implied the cellulosic origin of PS 0M. \nO-alkyl C is usually associated with carbohydrate degradation (Marin-Spiotta et \nal. 2008). This could be attributed to the thermal degradation of cellulose during \nthe pyrolysis process. The peak at 83.50 ppm was relative to alkyne C (C\u2261C). \n\n\n\nThe 13C NMR spectrum of PSB after 8 months (PS 8M) is shown in Fig. \n4. Both alkyl C and O-alkyl C (C-O) were still present in PS 8M at 29.73 ppm \nand 72.19 ppm, respectively. Aromaticity was formed after 8 months as aryl C \nwas found at 128.63 ppm. The loss of alkyne C could be attributed to alkyne \ntrimerisation, forming an aromatic ring in return (Agenet et al. 2007).\n\n\n\nAfter 16 months in tropical peat, PS 16M lost the aromaticity which hinted \nat the breakdown of the aromatic ring. (Fig. 5). The alkene (C=C) detected at \n121.57 ppm could be the product of aromaticity breakdown. The presence of a \nstrong activating group like hydroxyl (O-H) and amine (N-H) on the surface of \nPSB (Fig. 2) or in the tropical peat could destabilize the aromatic ring by donating \nits electron density into the aromatic \u03c0 system breaking the cyclic structure \n(Pocius 2002). Besides, a high amount of Bronsted acid such as humic acid could \nprovide free H+ to saturate aromaticity. Alkyl C (R2CH2) and O-alkyl C (C-O) was \ndetected at 27.47 ppm and 72.53 ppm, respectively. \n\n\n\nField Decomposition of Pineapple Stump Biochar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201392\n\n\n\nFig. 4: The 13C NMR spectrum of PS 8M\n\n\n\nFig. 5: The 13C NMR spectrum of PS 16M\n\n\n\n                                                                                         \n        ISSN: 1394-7990 \n\n\n\nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                         Malaysian Society of Soil Science \n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n\n\n\n\n\n\n\n15 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 5: The 13C NMR spectrum of PS 16M \n\n\n\nCheah, P.M., M.H.A. Husni,  A.W. Samsuri and A. Luqman Chuah\n\n\n\nFig. 3: The 13C NMR spectrum of PS 0M\n\n\n\n                                                                                                 ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                         Malaysian Society of Soil Science \n \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n13 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 3: The 13C NMR spectrum of PS 0M \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 93\n\n\n\nShort-term Decomposition Model \nThe results of normality test, constant variance test, coefficient of determination \n(R2) and mean square error (MSE) are presented in Table 3. PSB was best fitted \ninto a hyperbolic decay with three parameters (recalcitrant fraction, labile fraction \nand decay rate) model after 16 months of residence in tropical peat soil (Fig. 1). It \npassed the normality test with the lowest MSE (6.28%2/t2), but failed the constant \nvariance test, while the double exponential and single exponential decay model \nfailed both the tests. The failure of the hyperbolic decay model to fit with the \nconstant variance test could be attributed to the long period of unchanged mass. \nThe recalcitrant fraction (Wr) or asymptotic value was 84% of weight remaining, \nand the decomposable fraction (Wd) was 15.84%. The decay constant (k) was \n0.99. According to the hyperbolic decay model, PSB short-term decomposition \nwas divided into two phases; initial rapid loss of labile fraction (0-4 months) and \nthe near-inert mass loss that reflects the recalcitrant fraction (4-16 months). \n\n\n\nThe hyperbolic decay model differed from the double exponential decay \nmodel suggested by Lehmann et al. (2009). This could be largely attributed to \nthe insensitiveness to detect significant mass loss in a short-term decomposition \nstudy. Thus the short-term PSB mass loss appeared stagnant while for the double \nexponential decay model, the mass loss progressed continuously at a very slow \nrate. This hinted the stability of PSB on peat but a longer decomposition study \nis necessary to further highlight the long-term stability of PSB. The hyperbolic \ndecay equation generated by SigmaPlot version 11 is stated in Fig. 1.\n                                                                         \n\n\n\n W at t  = percentage of PSB weight at t (t in months)\n Wr = percentage of PSB recalcitrance fraction\n Wd = percentage of PSB decomposable fraction\n k = decay constant\n\n\n\nTABLE 3\nComparison of 3 different decay models fitted with PSB field decomposition data\n\n\n\n                                                                                                 ISSN: 1394-7990 \nMalaysian Journal of Soil Science Vol. 17: x \u2013x ( 2013)                                                         Malaysian Society of Soil Science \n \n \n \n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 \n \n\n\n\n10 \n\n\n\nTABLE 3 \nComparison of 3 different decay models fitted with PSB field decomposition data \n\n\n\n \nDecay model Normality test Constant-variance \n\n\n\ntest \nR2 Mean Square \n\n\n\nError (MSE) \n(%2 t2) \n\n\n\nHyperbolic decay \nwith 3 parameters \n\n\n\nPassed Failed 0.83 6.23 \n\n\n\nDouble \nexponential decay \nwith 4 parameters \n\n\n\nFailed Failed 0.86 6.57 \n\n\n\nSingle exponential \ndecay with 3 \nparameters \n\n\n\nFailed Failed 0.83 6.38 \n\n\n\n\n\n\n\n\n\n\n\nWat t   = Wr +  \n \n\n\n\nField Decomposition of Pineapple Stump Biochar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201394\n\n\n\nTransformation of PSB in Tropical Peat\nShort-term decomposition of PSB can be further explained by examining the \nchanges on the physico-chemical and structural properties. The mass loss of the \ninitial 4 months (PS 0M-PS 4M) was accompanied by a decrease in C content. \nThe decreasing mass and C content could be attributed to the loss of labile C-like \ncarbohydrate. Traces of degraded carbohydrates were picked up by the 13C NMR \nspectrum of fresh PSB (PS 0M) (Fig. 3). The decrease in C-O intensity and \nelimination of O-H on the FTIR spectrum of PS 4M further proved the diminution \nof carbohydrates (Fig. 2). Increasing H/C ratio (Table 1) hinted saturation of C \ndue to higher C-H bonds. This could be attributed to the free H+ from Bronsted \nacid such as humic acid which is abundant in peat.\n\n\n\nAfter the end of the initial rapid loss of labile fraction, the near-inert stage \nthat represents the recalcitrant fraction continued from 4th-16th months. Although \nthere were no significant mass changes during this stage, the abiotic reactions \nwere vigorous on the PSB surface until the 12th month when the PSB achieved \nchemical recalcitrance. The little mass change could be attributed to adsorption on \nthe surface of PSB as indicated by the decreasing BET surface area. In a similar \nlitterbag study of biochar decay in an organic horizon of a boreal forest, no loss \nof mass was detected after 10 years due to the adsorption of dissolved organic \nmatter (Wardle et al. 2008). Abiotic reactions like oxidation and hydrolysis \naltered the surface charges of PSB. Oxidation preceded throughout the 16 months \nas shown by the increasing O/C ratio. As a result, more oxygen functionalities \n(hydroxyl, carboxylic and phenolic) were developed on the surface of PS 12M \ncompared to fresh PSB (PS 0M) (Fig. 2) rendering PSB hydrophilic and leading \nto interactions with the peat constituents. However, adsorption of organic C onto \nPSB is unlikely or insignificant as C content of PSB decreased over 16 months. \nInstead, interactions of PSB with free metals and their oxides are more prominent. \nIron (Fe) content was high at the experimental site (0.1%) and could interact with \nthe negatively charged PSB by ligand exchange or cation bridging. The Fe content \nin PSB was concentrated and increased over time. This could be attributed to \nFe adsorption and contributed to the low PSB weight loss.  High amounts of \nionic Fe and Al were discovered in the highly stable humic fractions of biochar \n(Nakamura et al. 2007). Thus, complexation between PSB surfaces and metal ions \nwould reduce bioavailability of PSB and improve the stability or recalcitrance of \nbiochar. According to Hockaday et al. (2006), aged biochar is less susceptible \nto enzymatic degradation than fresh deposited biochar and this could be due to \nbiochar-mineral interactions. The high H/C ratio was generally unchanged after \nthe first 4 months (PS 4M-PS 16M) and this showed PSB remained aliphatic or \nunaltered (Table 1). \n\n\n\nThe chemical structures of PSB indicated PSB was saturated over time \nas shown by the 13C NMR spectrum. The increase in aromaticity (PS 8M) is \nunexplainable. Alkynes detected in PS 0M could be further degraded to alkene \n(PS 16M). This was a sign of reduction reaction due to free H+ in acidic peat. \n\n\n\nCheah, P.M., M.H.A. Husni,  A.W. Samsuri and A. Luqman Chuah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 95\n\n\n\nElectronegativity was reduced and this might affect the reactivity of PSB; slowing \ndown further degradation.\n\n\n\nCONCLUSION\nThe PSB displayed hyperbolic decay over 16 months in tropical peat. Oxidation \nincreased the O- functionalities (hydroxyl, carboxylic and phenolic) on the surface \nand might have promoted biochar-mineral interaction that protected PSB from \nfurther degradation. The reactivity of PSB decreased over time as the C skeletal \nstructure became saturated due to the low pH and reduced condition of the peat. \n\n\n\nACKNOWLEDGEMENT\nThis research was supported by the Fundamental Research Grant Scheme (FRGS) \nunder Ministry of Higher Education in collaboration with Universiti Putra \nMalaysia. We wish to thank Ms Nor Azma Zaki for help rendered in the non-\nroutine sample analysis.\n\n\n\nREFERENCES\nAgenet, N., O. Buisine, F. Slowinski, V. Gandon, C. Aubert and M. Malacria. 2007.  \n\n\n\nCotrimerisation of acetylenic compounds. Organic Reactions. 68: 1-302.\n\n\n\nBraadbaart, F., J.J. Boon, H. Veld, P. David and P.F. Van Bergen. 2004. 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Natural oxidation of black carbon \nin soils: Changes in molecular form and surface charge along a climosequence. \nGeochimica et Cosmochimica Acta .72: 1598-1610.\n\n\n\nEnvironmental Quality Act and Regulations: Law of Malaysia. 2009. International \nLaw Book  Services. Kuala Lumpur.\n\n\n\nHamer, U., B. Marschner, S. Brodowski and W. Amelung. 2004. Interactive priming \nof black carbon and glucose mineralisation. Organic Geochemistry. 35: 823-\n830.\n\n\n\nField Decomposition of Pineapple Stump Biochar\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201396\n\n\n\nHockaday, W.C., A.M. Grannas, S. Kim and P.G. Hatcher. 2006. Direct molecular \nevidence for the degradation and mobility of black carbon in soils from ultra \nhigh-resolution mass pectral analysis of dissolved organic matter from a fire-\nimpacted forest soil. Organic  Geochemistry. 37: 501-510.\n\n\n\nHockaday, W.C., A.M. Grannas, S. Kim and P.G. Hatcher. 2007. The transformation \nand mobility of charcoal in a fire-impacted watedshed. Geochimica et \nCosmochimica Acta. 71: 3432-3445.\n\n\n\nKanapathy, K. 1976. Fertilizer requirement on peat soils. Malaysian Agricultural \nJournal. 50: 292-307.\n\n\n\nKyuma, K. 1991. Problems related to reclamation and development of swampy \nlowlands in the tropics. Japan Journal of Soil Physical Condition Plant Growth \n63: 43-49.\n\n\n\nLampman, G.M., D.L. Pavia, G.S. Kriz and J.R. Vyvyan. 2010. Spectroscopy. \nInternational edition. USA: Brooks/Cole.\n\n\n\nLehmann, J. 2007. A handful of carbon. Nature 447: 143-144.\n\n\n\nLehmann, J., C. Czimzik, D. Laird and S. Sohi. 2009. Stability of biochar in the soil \nIn: Biochar for Environmental Management: Science and Technology. U.K: \nEarthscan.\n\n\n\nLeng, L.Y., M.H.A. Husni and A.W. Samsuri. 2011. Comparison of the carbon-\nsequestering abilities of pineapple leaf residue chars produced by controlled \ncombustion and by field burning. Bioresource Technology. 102: 10759-10762.\n\n\n\nLucas, R.E. 1982. Organic soils (Histosols). Formation, Distribution, Physical and \nChemical Properties and Management for Crop Production. Michigan State \nUniversity.\n\n\n\nMarin-Spiotta, E., C.W. Swanston, M.S. Torn, W.L. Silver and S.D. Burton. 2008. \nChemical and mineral control of soil carbon turnover in abandoned tropical \npastures. Geoderma. 143: 49-62.\n\n\n\nMita, S. 2003. Sample Preparation Techniques in Analytical Chemistry. New Jersey: \nJohn Wiley & Sons Inc. \n\n\n\nNakamura, S., M. Hiraoka, E. Matsumoto, K. Tamura and T. Higashi. 2007. Humus \ncomposition of Amazonian dark earths in the middle Amazon, Brazil. Soil \nScience and Plant Nutrition. 53: 229-235.\n\n\n\nNguyen, B., J. Lehmann, J. Kinyangi, J. Smernik and M.H. Engelhard. 2008. Long-\nterm black  carbon dynamics in cultivated soil. Biogeochemistry. 89: 295-308.\n\n\n\nCheah, P.M., M.H.A. Husni,  A.W. Samsuri and A. Luqman Chuah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 97\n\n\n\nPignatello., J.J., S. Kwon and Y. Lu. 2006. Effect of natural organic substances on \nthe surface and adsorptive properties of environmental black carbon(char): \nAttenuation of surface activity of humic and fulvic acid. Environmental Science \nand Technology. 40: 7757-7763. \n\n\n\nPocius, A.V. 2002. Adhesion and Adhesives Technology: An Introduction. USA: \nHanser Gardner Publications. \n\n\n\nTie, Y.L. and Lim, C.P. 1979. Sarawak Land Capability Classification and Evaluation \nfor Agricultural Crops. Technical Paper No 5. Soil Division, Department of \nAgricultural, Sarawak, Malaysia. \n\n\n\nWardle., D.A., M.C. Nilsson and O. Zackrisson. 2008. Fire-derived charcoal causes \nloss of forest humus. Science. 320: 629.\n\n\n\nVollhardt, K., C. Peter and N.E. Schore. 2007. Organic chemistry: Structure and \nFunction. New York: W.H. Freeman and Company.\n\n\n\nField Decomposition of Pineapple Stump Biochar\n\n\n\n\n\n"
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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 135-147 (2019)                         Malaysian Society of Soil Science\n\n\n\nCommunity of Indigenous Arbuscular Mycorrhizal Fungi \n(Amf) of Chili Rhizosphere and Natural Forest Ecosystem \n\n\n\nBaiq Azizah Haryantini1, Yuyun Yuwariah1, Suseno Amien1, Betty \nNatalie Fitriatin2, Mieke Rochimi Setiawati2, Anne Nurbaity2,  \n\n\n\nNadia Nuraniya Kamaluddin2 and Tualar Simarmata2*\n\n\n\n1Universitas 45 Mataram, Department of Agronomy, Faculty of Agriculture, JI \nImam Bonjol, Cakranegara, Mataram, 83239 West Nusa Tenggara, Indonesia.\n\n\n\n1Universitas Padjadjaran, Department of Agronomy, Faculty of Agriculture,  Jl. \nRaya Bandung  Sumedang km 45,Jatinangor 45363,West Java, Indonesia\n\n\n\n2Universitas Padjadjaran,Department of Soil Science, Faculty of Agriculture, Jl. \nRaya Bandung  Sumedang km 45,Jatinangor 45363 -, West Java, Indonesia\n\n\n\nABSTRACT\nArbuscular mycorrhizal fungi (AMF) play a significant role in enhancing soil \nhealth, nutrient uptake and availability in soils. This research aimed to determine \nthe status of indigenous AMF on an intensively cultivated agricultural soil \necosystem (chili rhizosphere) and a natural forest ecosystem in Garut district, well \nknown as a central chili producer in West Java. High tillage of agricultural soil \nmay lead to destruction of the soil microbial community in general, therefore a \nforest ecosystem representing an untilled natural soil was used as a comparison \nSoil sampling was done in transects with the length of an ordinate point in every \n100 m on chili cultivated areas and in the natural forest soil ecosystem of Gunung \nPutri.  Five composite soil samples (0-20 cm depth) from each ecosystem were \ntaken based on coordinate points. The number of indigenous AMF spores and \nroots colonisation was determined and mycorrhiza species were identified using a \nmolecular analysis of the AMF DNA.  The research results revealed that  number of \nAMF spores in the chili rhizosphere soil was greater than in the natural forest soil. \nHowever, the degree of mycorrhizal colonisation in the rhizosphere under both \necosystems was not significantly different. The indigenous Glomus etunicatum \nwas identified to be the dominant species in both soil ecosystems. Further research \nneeds to investigate the potential  of this  indigenous AMF that could develop as \nbiofertilizer for cultivation of chili.\n\n\n\nKeywords: agricultural soils, soil healthh, beneficial microbes.\n\n\n\n___________________\n*Corresponding author : tualar.simarmata@unpad.ac.id    \n\n\n\nINTRODUCTION\nChili (Capsicum annum L.) is an important horticultural crop. Recent studies \nindicate that most chili areas are cultivated intensively and depend on a high dosage \nof inorganic fertilisers. The intensive and extensive use of inorganic fertilisers \naccelerates mineralisation of soil organic matter (SOM) and also contributes to \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019136\n\n\n\nenvironmental problems that include deterioration of soil quality or soil health, \nsurface water runoff,  biodiversity loss and a less efficient  ecosystem (Sullivan \n2004; Simarmata 2013, Simarmata et al. 2017).  Recent studied reveal that soil \nhealth and yield losses of chili are subject to soil borne diseases (Gopal et al. 2005; \nMendes et al. 2013; Srinivasamurthy et al. 2014). The beneficial microorganism \npopulation in soil correlates strongly with soil organic matter (Simarmata et al. \n2017).  Low Som will lead to a decline in the beneficial microorganism population \nof the soil food web (Ingham 2004; Sullivan 2004). Arbuscular mycorrhizal fungi \n(AMF) in soil and plant roots may suppress pathogens and reduce the domination \nsoil borne diseases significantly (Mendes et al. 2013). Several AMF species have \nbeen found to control soil -borne pathogens and mycorrhizal colonisation was \nfound to increase growth parameters significantly (fresh and dry shoot and root \nweight, shoot and root length and leaf area) in the absence of pathogens (Al-Askar \nand Rashad 2010). AM fungi may contribute to bioprotection against plant soil \nborne pathogens. Bioprotection within AM fungal-colonised plants is the outcome \nof complex interactions between plants, pathogens and AM fungi (Harrier and \nWatson 2004). Microbial parameters are a more effective and consistent indicator \nof management-induced change to soil quality than biochemical parameters \n(Bending et al. 2004; Lumley and Abbott 2014).\n Mycorrhizal fungal hyphae occur both inside roots and the soil surrounding \nthe roots. Therefore, the mycorrhizal fungi could be used as biofertiliser for \nincreasing the fertiliser efficiency, the growth  and yield  of chili.  Biofertilisers \ncontain large populations of specific or a group of beneficial organisms which \nactivate the biological process  to form a fertiliser compound or make available, \nunavailable forms of elements or facilitate nutrient availability for plants (Singh \nand Purohit, 2011; Simarmata, 2013; Jehangir et al. 2017; Nicholas et al. 2017).  \nArbuscular mycorrhizal (AM) fungi are well known for their plant growth \npromoting efficiency and providing bio-protection against soil-borne pathogens \n(bacterial, fungal and parasitic nematodes). The inoculation of chili with AMF \nshowed that the  Phosphorus content in the shoot was highest at 150% of organic \nmanure application with AM plants recording significantly more phosphorus \ncontent than the non-mycorrhizal plants (Dai et al. 2011).\n AMF are a group of microorganism that form symbiotic associations with \na wide range of plant species that enhance plant nutrition and growth (Brundrett \n2009; Torres-Ariasa et al. 2017).  Moreover, AMF are able to enhance nutrients \nand water availability, availability of P in acid soils ecosystem up to 75 % and \nare known as a bioprotector of roots (Brundrett, 2009; Ofili et al. 2014). Several \nstudies indicate that application of AMF inoculation increases P availability, \nfertiliser efficiency and productivity of various crops, for example, maize, tomato \nand bean) (Singh and Purohit, 2011;  Nicholas et al. 2017; Jehangir et al. 2017).  \n The population of AMF inoculants is  influenced or correlates with plant \ntype as host crop and edaphic factors such as soil pH, organic matter content, \nand moisture (Solaiman and Mickan 2014; Brundrett, 2002).The degree of plant \ngrowth change associated with AM colonisation is expressed as mycorrhizal \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 137\n\n\n\ndependency (MD) and this indicates that the cultivated plant species shows a lower  \nMD than the wild species (Tawaraya 2003). The diversity of microbes associated \nwith plant roots is enormous, in the order of tens of thousands of species as in \nforest soils. This complex plant-associated microbial community, also referred \nto as the second genome of the plant, is crucial for plant health. Recent advances \nin plant\u2013microbe interactions research reveal that plants are able to shape their \nrhizosphere microbiome, as evidenced by the fact that different plant species host \nspecific microbial communities when grown on the same soil (Berendsen et al. \n2012). Soil trapping method (Walker 1999) is one of the ways to obtain an AMF \nculture. By this method, a single or mixed culture of AMF can be obtained from \neach plant trap. In this study, AMF populations from a chili rhizosphere and forest \nsoil were isolated using this method and identified using DNA barcode.  This \nresearch provides a comparison of AMF populations in two rhizosphere types, \ntrapping methods, and information of potential species that can be applied as a \nbioagent in biofertilisers.\n\n\n\nMATERIALS AND METHODS\n\n\n\nLocation Characteristics and Soil Sampling\nThe study to explore and isolate the indigenous AMF was conducted from October \n2016 to February 2017. Soil samples from red chili rhizosphere were obtained \nfrom a vegetable farm in Rancabango village, Garut (Figure 1), with red chili as \nthe main commodity. Forest soil samples were obtained from the mountain forest \nof Gunung Putri, Garut. The mountain slope is covered with dense vegetation \nand is also a conservation area, with the range of the slope ranged 0 to 40%. The \nslope of 71.42% of the overall area (approximately 218,924 ha) is between 8 to \n25%. The soils sample were taken based on transects and coordinate points with \na distance about 100 m from each sampling (Table 1). Two hundred grams of soil \n(0-20 cm depth) were taken from five location points to obtain one kilogram of \ncomposite sample. \n Initial soil analysis (Table 2) revealed that the chili rhizosphere had a \nslightly acidic to slightly neutral pH (6.1-6.6). Both carbon (C) and nitrogen (N) \nwere moderate with high total and available phosphate (P) content. The forest soil \necosystem had a slightly neutral pH (6.2) and moderate C, N, and P contents.\n\n\n\nAMF Spore Trapping and Enumeration\nInitial spore analysis was done on a 25-g fresh soil sample and the AMF were \ncultured by the trapping method (Brundett et al. 1996).  Fifty gram of soil sample \nwas mixed with 150 g of zeolite (1:3 ratio) to obtain a growth medium. Sorghum \nwas planted in the aforementioned medium for six weeks. Three replicates were \nprepared for each soil sample. Upon harvesting, the final number of mycorrhizal \nspores were counted and root colonisation was examined. The next step was to \nuse the soil media of the first trapping and mix it with zeolite at a ratio of 3: \n1, (Brundrett 2009; Brundrett 2002). The number of mycorhiza was enumerated \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019138\n\n\n\nTABLE 1\nThe altitude and coordinate points (longitude and latitude) soil sampling location\n\n\n\n5 \n \n\n\n\nm from each sampling (Table 1). Two hundred grams of soil (0-20 cm depth) were taken from \n\n\n\nfive location points to obtain one kilogram of composite sample.  \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nThe altitude and coordinate points (longitude and latitude) soil sampling location \n\n\n\nSample Sample code Ordinate point \n\n\n\n\n\n\n\nChili rhizosphere \n\n\n\nGC1 Latitude -7.11487 \nLongitude 107.515256 \nAltitude 799 m \n\n\n\nGC2 Latitude -7.196860 \n\n\n\nLongitude 107.864573 \nAltitude 799 m \n\n\n\nGC3 Latitude -7.196961 \nLongitude 107.864974 \nAltitude 788 m \n\n\n\nGC4 Latitude -7.196897 \nLongitude 107.864539 \nAltitude 796 m \n\n\n\nGC5 Latitude -7.191058 \nLongitude 107.863771 \nAltitude 797 m \n\n\n\n\n\n\n\n \nForest ecosystem \n\n\n\n \nGF1 \n\n\n\nLatitude -7.20161 \nLongitude 107.86857 \nAltitude 1020.2 m \n\n\n\n\n\n\n\nGF2 \n\n\n\nLatitude -7.18181 \nLongitude 107.85605 \nAltitude 1020.2 m \n\n\n\n \nGF3 \n\n\n\nLatitude -7.23162 \nLongitude 107.80679 \nAltitude 1022.5 m \n\n\n\n \nGF4 \n\n\n\nLatitude -7.18176 \nLongitude 107.85611 \nAltitude 1022.5 m \n\n\n\n \nGF5 \n\n\n\nLatitude -7.18166 \nLongitude 107.85595 \n\n\n\n \nAltitude \n\n\n\n998.1 m \n\n\n\n\n\n\n\n by wet sieving and sugar centrifugation method. Analysis of samples from each \nlocation was repeated three times (Brundett 2002). \n In the sugar centrifugation method, 25 g of soil sample was mixed with \n100-500 ml of water, stirred, and allowed to settle for 30 sec. The suspension was \nfiltered with 355, 15, and 45 mesh and rinsed thoroughly with sterilised distilled \nwater to release attached spores. Twenty milliliters of suspension was transferred \ninto a centrifuge tube and added with 15 ml of water (75%). The tubes were \ncentrifuged at 2000 rpm for 5 min. The centrifuged suspension was filtered using \nNo. 42 Whatman filter paper into a conical flask and rinsed 2-3 times to release \nall the sugar from the spores. AMF spores were placed on top of a disk plate in \nthe same direction and observed through a binocular microscope with 100 times \nmagnification. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 139\n\n\n\nFig. 1: The regional map of research and soil sampling area of Rancabago village \nand Gunung Putri Forest of Garut, West Java\n\n\n\n6 \n \n\n\n\n\n\n\n\nFigure 1. The regional map of research and soil sampling area of Rancabago village and \nGunung Putri Forest of Garut, West Java \n\n\n\nInitial soil analysis (Table 2) revealed that the chili rhizosphere had a slightly acidic to slightly \n\n\n\nneutral pH (6.1-6.6). Both carbon (C) and nitrogen (N) were moderate with high total and \n\n\n\navailable phosphate (P) content. The forest soil ecosystem had a slightly neutral pH (6.2) and \n\n\n\nmoderate C, N, and P contents.  \n\n\n\nTABLE 2. \nInitial soil analysis of chili rhizosphere and forest soil \n\n\n\nSoil sample pH N  \n(%) \n\n\n\nOrganic-C  Phosphate   CEC \n(cmolckg-1) (%) Total \n\n\n\n(mg kg-1) \nAvailable \n(mg kg-1) \n\n\n\nChili rhizosphere \n\uf0b7 GC1 6.67 0.25 2.67 633.2 21.33 32.62 \n\n\n\n           Garut District \n\n\n\n7 \n \n\n\n\nTABLE 2. \nInitial soil analysis of chili rhizosphere and forest soil \n\n\n\n \nSoil sample pH N  \n\n\n\n(%) \nOrganic-C  Phosphate   CEC \n\n\n\n(cmolckg-1) (%) Total \n(mg kg-1) \n\n\n\nAvailable \n(mg kg-1) \n\n\n\nChili rhizosphere \n\uf0b7 GC1 6.67 0.25 2.67 633.2 21.33 32.62 \n\n\n\n\uf0b7 GC2 6.28 0.16 2.52 1167.8 1.02 28.14 \n\n\n\n\uf0b7 GC3  6.45 0.13 2.47 1167.8 7.56 25.41 \n\n\n\n\uf0b7 GC 4   6.52 0.16 3.02 1167.8 28.20 17.48 \n\n\n\n\uf0b7 GC5 6.13 0.13 4.00 528.8 40.35 30.06 \n\n\n\n\uf0b7 GF1 6.63 0.12 2.87 544.2 14.35 26.15 \n\n\n\n\uf0b7 GF2 6.55 0.15 2.75 544.4 13.33 27.45 \n\n\n\n\uf0b7 GF3 6.43 0.13 2.88 545.3 22.32 25.33 \n\n\n\n\uf0b7 GF4 6.50 0.15 2.77 555.6 23.22 26.32 \n\n\n\n\uf0b7 GF5 6.55 0.14 2.68 554.4 22.23 25.34 \n\n\n\n \nAMF Spore Trapping and Enumeration \n\n\n\nInitial spore analysis was done on a 25-g fresh soil sample and the AMF were cultured by the \n\n\n\ntrapping method (Brundett et al. 1996).  Fifty gram of soil sample was mixed with 150 g of \n\n\n\nzeolite (1:3 ratio) to obtain a growth medium. Sorghum was planted in the aforementioned \n\n\n\nmedium for six weeks. Three replicates were prepared for each soil sample. Upon harvesting, the \n\n\n\nfinal number of mycorrhizal spores were counted and root colonisation was examined. The next \n\n\n\nstep was to use the soil media of the first trapping and mix it with zeolite at a ratio of 3: 1, \n\n\n\n(Brundrett 2009; Brundrett 2002). The number of mycorhiza was enumerated by wet sieving and \n\n\n\nsugar centrifugation method. Analysis of samples from each location was repeated three times \n\n\n\n(Brundett 2002).  \n\n\n\nTABLE 2.\nInitial soil analysis of chili rhizosphere and forest soil\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019140\n\n\n\nAMF Colonisation in Roots\nAMF colonisation was examined according to the method described by \n(Gerdemann and Nicolson 1963).  Five root samples were cleaned, and segmented \ninto 1 cm fragments.  Two g of samples were weighed and placed in test tubes \ncontaining 10% KOH solution. Subsequently, the samples were heated for 30-60 \nmin at 70\u00baC. After removing the KOH solution from the test tube, the root samples \nwere rinsed with distilled water until the water became clear. Hydrochloric acid \n(1%) was added to the test tubes with the sample to obtain good coloration and \nthis was followed by the addition of Fuchsin dye until the roots were submerged. \nFinally, glycerin droplets were added to the root surface to improve colonisation \nobservation. The AMF colonisation rate was calculated and data were presented \nin percent (%), as described by the following formula (Walker 1999);\n\n\n\n Rate of Colonization = (A/B) x 100 %\n\n\n\nwhere A is the number of infected fragmented roots and B is the total number of \nobserved fragmented roots.\n\n\n\nMolecular Analysis\nDNA barcoding analysis of AMF began with DNA isolation of fungi with Tiangen \nPlant Genomic DNA kit on a 100-mg sample tissue (500 spores). Subsequently, \nthe quantity of DNA was calculated following this sequence: electrophoresis, \nspectrophotometer, PCR, PCR qualification and sequencing.\n\n\n\nStatisticalAnalysis\nThe analysis of variance and the differences between the means of number of \nspores and root colonisation were tested using the SPSS 16.0 statistical software \nby Duncan\u2019s test at  95% level of significance. Mycorrhizal spore morphological \nidentification was done visually according to the method of Goswani et al. (2018).\n\n\n\nRESULTS AND DISCUSSION\nNumber of Spores\nThe number of spores of chili rhizosphere obtained from agricultural soils and \nnatural forest soils (Figure 2) was influenced by the sampling location (coordinate \npoints) (Figures 3 and 4).\n\n\n\n In the case of the chili rhizosphere, a relatively high number of  spores \n(>200 spore 25 g-1) was obtained at GC1, GC2 and GC3, while the lowest spore \nnumber was obtained at GC4.  But in the forest rhizosphere, a high number \nof spores was only obtained at GF1. At the beginning of the inspection on the  \nrhizosphere soil, chili plants showed an average spore count that was as high \nas found in the  natural forest land but after trapping, the average number of \nspores on the rhizosphere soil showed a decrease compared to before trapping \nbut the spore count was still  quite high (> 200 spores 25 g-1 chili rhizosphere) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 141\n\n\n\nFig. 2:  Sampling of soil rhizosphere: (a) soils amples were takenfrom 4 sites around the \nplant up to 20 cm depth; (b) trapping of AFM using sorghum as the trapping plant\n\n\n\n(a) (b)\n\n\n\nFig. 3: Number of spores in the indigenous AFM  of the chili rhizosphere\n\n\n\n10 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Number of spores in the indigenous AFM  of the chili rhizosphere \n\n\n\n\n\n\n\nFigure 4. Total AMF spores compared to total AMF obtained by trapping in a natural forest \n\n\n\necosystem \n\n\n\ncompared to the spore count of the natural forest land which increased after \ntrapping but the number of mycorrhizal spores was almost identical to the number \nof spores in the rhizosphere. This is an indication that the number of spores is \ncorrelated to soil properties (Table 1) and the plant ecosystem (Brundrett 2009).  \nThe rhizosphere soil of the chili plants had a lower spore count compared to after \ntrapping because the initial condition of the rhizosphere soil was better suited \nto mycorrhizal development because it had a symbiotic relationship with chili \nplants as its host. Moreover mycorrhizae utilise the exudates from plant roots \nto live and produce more spores. A recent study revealed that various factors \ninfluence the plant response and benefits from mycorrhiza such as host-crop \ndependency on mycorrhizal colonisation, tillage, fertiliser application and \npotential inoculant application  (Brundrett 2009; Solaiman and Mickan 2014).  \nAs shown in Figure 3, the lowest number of spores was obtained at the GC4 site \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019142\n\n\n\nof the rhizosphere soil of the chili plant.  Moreover, there is the possibility that \nspore number after trapping with sorghum plant may be more or lower than the \ninitial examination. It is known that colonisation and formation of AMF spores \nare affected by each host plant root exudate and other factors that affect spore \nformation and  plant growth (Brundrett 2009 ). In addition, trapping with sorghum \nplant in forest soils increased the number of indigenous AMF spores.  The \nincreasing number of trapped AMF spores is an indication of mutual symbiosis \nbetween plant roots andAMF (Brundrett 2002; Jacott et al. 2017). The method \nof trapping spores can stimulate the formationof AMF spores.. The presence of \nmycorrhizal fungi, especially AM, is able to promote the symbiotic association \nwith terrestrial plants including agricultural crops. Tillage practices will change \nsoil environmental conditions (micro climate) which affect root growth and C and \nN mineralisation. Soil microbial community are subject to and respond quickly to \nchange. Consequently, these lead to shifts in soil microbial community structure, \nbiodiversity and microbial metabolic activities (Brundrett 1996; Sullivan 2004; \nMendes et al. 2013; Guo et al. 2016).\n\n\n\nRoot Colonisation by Trapping Plants\nThe degree of roots colonisation of the chili rhizosphere and natural forest soils \nwas relatively similar (Figure 5) with no significant difference in degree of root \ninfection  within both ecosystems. In general, the colonisation rate in the chili \nrhizosphere was relatively higher than in the natural forest soils. It appears that \nroot type and diameter do influence mycorrhizal colonisation.  The limited nutrient \nresources (Table 1) and high density of chili roots may stimulate the infection of \nAMF on the host plant (Brundrett 2009; Nicholas et al. 2017).  Moreover, the \n\n\n\n10 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3. Number of spores in the indigenous AFM  of the chili rhizosphere \n\n\n\n\n\n\n\nFigure 4. Total AMF spores compared to total AMF obtained by trapping in a natural forest \n\n\n\necosystem \n\n\n\nFig. 4: Total AMF spores compared to total AMF obtained by trapping\nin a natural forest ecosystem\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 143\n\n\n\nvesicles were also found in sorghum roots which was used as a trapping plant \n(Figure 6).  Thus, the isolated indigenous mycorrhizae are able to colonise the \nplant and offer the potential to be tested and to developed into a biological agent \nor biofertiliser.\n The morphology of spores is a basis for AMF fungi identification because \nthe hyphae and the organs such as arbuscles and vesicles are not specific to species \n(Brundrett 2009; Goswami et al. 2018). The morphological identification of AMF \nwas done according to (Goswami et al. 2018). Based on their characteristics such \nas form of spore, colour and ornament of surface spore, the indigenous AMF \nspecies found  were identified as Glomus sp.(Figure 7 ABC) and Gigaspora \nmargarita  (Figure 7 D).\n\n\n\nFig. 5:  The degree of mycorrhizal colonisation in (a) chili rhizosphere and \n(b) natural forest soils ecosystem\n\n\n\nFig. 6: Mycorhizal colonisation of  roots of sorghum which was used as a trapping plant, \n(A) vesicle image and spore form (a). (B) and (C) are mychorrhizal vesicle forms.\n\n\n\n13 \n \n\n\n\na.  b.  \n\n\n\nFigure 5.  The degree of mycorrhizal colonisation in (a) chili rhizosphere and (b) natural forest \n\n\n\nsoils ecosystem \n\n\n\n\n\n\n\nFigure 6. Mycorhizal colonisation of  roots of sorghum which was used as a trapping plant, (A) \n\n\n\nvesicle image and spore form (a). (B) and (C) are mychorrhizal vesicle forms. \n\n\n\nThe morphology of spores is a basis for AMF fungi identification because the hyphae and \n\n\n\nthe organs such as arbuscles and vesicles are not specific to species (Brundrett 2009; Goswami et \n\n\n\nal. 2018). The morphological identification of AMF was done according to (Goswami et al. \n\n\n\n2018). Based on their characteristics such as form of spore, colour and ornament of surface \n\n\n\nspore, the indigenous AMF species found  were identified as Glomus sp.(Figure 7 ABC) and \n\n\n\nGigaspora margarita  (Figure 7 D). \n\n\n\n\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\nGC1 GC2 GC3 GC4 GC5\n\n\n\nC\nol\n\n\n\non\niz\n\n\n\nat\nio\n\n\n\nn \n(%\n\n\n\n) \n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\nGF1 GF2 GF3 GF4 GF5\n\n\n\nC\nol\n\n\n\non\niz\n\n\n\nat\nio\n\n\n\nn \n(%\n\n\n\n) \n\n\n\n\n\n\n\n\n\n\n\nA C B \n\n\n\nB A C D \n\n\n\n(a)\n\n\n\n(a)\n\n\n\n(b)\n\n\n\n(b) (c)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019144\n\n\n\nREFERENCES \nAl-Askar, A.A. and Y.M. Rashad. 2010. Arbuscular mycorrhizal fungi: A biocontrol \n\n\n\nagent against common bean Fusarium root rot disease. Plant Pathology Journal \n9(1): 31-38. \n\n\n\nBerendsen, R.L., C.M. Pieterse and P.A. Bakker. 2012. 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"\n\n___________________\n*Corresponding author : E-mail: drbasseyudoh@yahoo.com\n\n\n\nINTRODUCTION\nThe ability of any soil to supply the required quantity of plant nutrients is mostly \naffected by the soil genetic composition (parent material), the degree to which the \nparent material has been altered by the forces of weathering and the management \nof the soil by man. Therefore, the soil productive potential and its resilience to \namendment and management for sustainable agricultural production depend \nlargely on the soil parent material (Ajiboye and Ogunwale 2010). \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 17: 17-37 (2013)                          Malaysian Society of Soil Science\n\n\n\nAgricultural Potential of the Beach Ridge Soils of the Niger \nDelta, Nigeria\n\n\n\nUdoh, B.T.*1, I. E. Esu2, T. O. Ibia1, E. U. Onweremadu3 and S. E. Unyienyin1\n\n\n\n1 Department of Soil Science, and Land Resources Management University of Uyo, \nUyo, Nigeria\n\n\n\n2 Department of Soil Science, University of Calabar, Calabar, Nigeria\n3 Department of Soil Science and Technology, Federal University of  Technology, \n\n\n\nOwerri, Nigeria\n\n\n\nABSTRACT\nThis study aimed to characterize, classify and evaluate the agricultural potentials \nof soils formed from the beach ridge sands parent material in the Niger Delta \narea of Akwa Ibom State, Nigeria. Three toposequences were used as study \nsites. Along with each toposequence, three profile pits were studied \u2013 one at the \ncrest, middle slope and valley bottom. Results of laboratory analysis and fertility \ncapability classification (FCC) showed that the soils were predominantly sandy in \ntexture, strongly acidic (pH 3.3 \u2013 4.3) and low in the following fertility parameters \n\u2013 organic matter content (1.00-1.28%), total nitrogen (N) (0.043 \u2013 0.057%), \neffective cation exchange capacity (ECEC) (2.38-6.02 cmolc kg-1), base saturation \n(56-71%), exchangeable K (0.038-0.090 cmolc kg-1) and available phosphorus \n(P) (4.60-13.12 mgkg-1). Based on Soil Taxonomy, soils in the area belonged to \ntwo soil orders \u2013 Entisols (44.4%) and Inceptisols (55.6%). Also, results of land \nsuitability evaluation (LSE) revealed the land to be marginally suitable (S3) for oil \npalm, rubber and upland rice cultivation, moderately suitable (S2) for cocoa and \ncashew and highly suitable (S1) for coconut cultivation. Major crop production \nconstraints were soil physical characteristics (texture/structure) and fertility. To \nraise land productivity, management techniques should include application of \norganic fertilizers to enhance nutrient holding capacity of the soils and supply \ndeficient basic cations. Regular soil testing for proper fertilizer application to \nensure a balance nutrient application is also recommended. \n\n\n\nKeywords: Agricultural potential, beach ridge sands, Niger Delta, \nNigeria, soil characteristics \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201318\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\nIn order to accurately classify a soil and make recommendations for utilitarian \npurposes, soils occupying any particular agro-ecological zone must be properly \ncharacterized (Brady 1990; Esu 2005). Information on the kinds of soils in an area \nis obtained through soil survey activities. Soil survey identifies, characterizes and \nclassifies the soils in the survey areas, showing their extent and distribution on a \nmap (SCS News 1984). Land evaluation is the process of estimating the potential \nof a land for alternative uses (FAO 1976). Application of the Food and Agriculture \nOrganization (FAO) framework for land evaluation can identify the most limiting \nland qualities and characteristics and provide a good basis for advising farmers \non appropriate management practice for optimum production in a particular agro-\necological zone (Chinene 1992). \n\n\n\nMost agricultural soils in Akwa Ibom State, Nigeria, put to arable crop \nproduction are developed from parent materials which are grouped into coastal \nplain sands, beach ridge sands, sandstone and alluvial deposits. The characteristics \nof these soils are largely determined by these original materials and influenced by \nclimate, topography and the general agricultural land use pattern and management \n(Ibia and Udo 2009). \n\n\n\nThe parent materials of the beach ridge sands are fluvio-marine deposits of \nunconsolidated sands deposited by tidal waters along the fringes of the Atlantic \nocean and in estuaries of the various rivers. They are therefore found in those states \n(Rivers, Akwa Ibom and Cross River) which border the coast (FMANR 1990). \nTahal Consultants (1982) observed that in the southern coastal areas along the bight \nof Bonny, fine sandy coastal beach ridges occupy about 560 square kilometers \nwithin the Qua Iboe River Basin. \n\n\n\nThe beach ridge sands soils, like other \u2018acid sands\u2019 of southern Nigeria are \nfragile, acidic and low in native fertility (Udo and Sobulo 1981); nevertheless, \nthey support a very high population density in the country. Due to the very \npoor agricultural productivity of the beach ridge sands, they are not intensively \ncultivated by farmers who seem to regard these areas as marginal lands because \nof lack of knowledge and appropriate technology for managing them for optimum \nproductivity. \n\n\n\nTherefore, the very low fertility status of these soils, harsh climatic conditions, \nproneness to pollution due to oil exploration and the dense population they support, \ncall for special attention to their proper management for agriculture and human \nsettlement. The current shortage of food and the increasing food requirements of \nthe rapidly expanding population necessitate that marginal lands such as the beach \nridges hitherto left under-utilized, be brought under intensive agricultural land use, \nand commercially oriented permanent farming as opposed to shifting cultivation \n(Ojanuga 2006). \n\n\n\nHowever, available information on the beach sands soils is insufficient for \nefficient scientific planning for the future use of the soils for agriculture. A clear \nunderstanding of the relationship of land qualities/characteristics to land use is \nessential for the formulation of meaningful guidelines for efficient land use policies \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 19\n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\nand ultimately increased productivity of the reach ridge sands and conservation of \nnatural resources. \nIn view of the above, the present study was therefore carried out with the following \nobjectives: \n\n\n\na. To characterize and classify soils derived from the beach ridge sands \nin the coastal (southernmost) areas of Akwa Ibom State, Nigeria\n\n\n\nb. To carry out fertility capability classification of soils identified in the \narea.\n\n\n\nc. To evaluate the agricultural potential of the soils in terms of their \nsuitability for the cultivation of certain crops of economic importance, \nnamely, oil palm (Elaeis guineensis),  cashew nut (Anacardium \noccidentalis)  cocoa (Theobroma cacao),  coconut palm (cocos \nnucifera), rubber (Hevea brassilienesis) and upland rice (Oryza sativa) \n\n\n\nd. To recommend measures that would ensure optimum and sustainable \nagricultural productivity of these soils.\n\n\n\nMETHODOLOGY\nStudy Area \nThe study was conducted in the southernmost part of Akwa Ibom State of Nigeria, \ncomprising mainly the coastal areas of Ikot Abasi, Eastern Obolo, Esit Eket, Eket, \nMkpat Enin, Onna, Ibeno and Mbo Local Government Areas. The area is located \nwithin latitudes 4o35\u2019 and 4o40\u2019 N and between longitudes 7o30\u2019 and 8o15\u2019 E. (Fig. 1).\n\n\n\nThe climate is humid tropical with an annual rainfall of about 3000 mm. \nThe area has a bimodial rainfall pattern with peaks around July and September \nwith almost no month without rainfall. Mean annual maximum and minimum \n\n\n\n \nFig. 1: Akwa Ibom State showing the beach ridge sands and other parent materials\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201320\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\ntemperatures are about 29 oC and 24 oC, respectively (Petters et al. 1989). Relative \nhumidity ranges from 80 to 90%. The natural rainforest vegetation has lost its \noriginal nature due to anthropogenic activities arising from population increase. \nIn the narrow valleys where the soils are hydromorphic, the terrain is covered by \nnatural vegetation of shrubs and bush. Where the soils are better drained, there are \ncultivated areas, mainly cassava. The area generally comprises a low lying delta \nplain underlain mainly by beach ridge sands and Holocene Fluviomarine deposits \n(Udo and Sobulo 1981). \n\n\n\nField Studies \nThree study sites (toposequences) were selected to represent the area of study \nas follows: Ikot Okwo (IKW) Etebi (ETB) and Ibaka (IBK) in Ikot Abasi, Esit \nEket and Mbo Local Government Areas, respectively (Fig. 1). Along each \ntoposequence, profile pits were located, one each at the crest, middle slope and \nvalley bottom, respectively. Each pit was described according to FAO Guidelines \nfor soil description (FAO 2006) and sampled by genetic horizons for laboratory \nanalysis. \n\n\n\nLaboratory Analysis and Soil Classification\nLaboratory analyses of soil samples were carried out using appropriate standard \nprocedures (IITA 1979; Udo and Ogunwale 1986; Udo et al. 2009). The following \nparameters were analysed: particle size distribution, soil reaction (pH), electrical \nconductivity, organic carbon, total nitrogen, available phosphorus, exchangeable \nbases, exchangeable acidity and available micronutrients. Effective cation \nexchange capacity (ECEC) was determined as the summation of exchangeable \ncations (Ca, Mg, K, Na) and exchangeable acidity (Al3+ + H+). Using appropriate \nformulas/methods, base saturation (BS), exchangeable sodium percentage (ESP) \nand carbon/nitrogen (C/N) ratio were also determined. \n\n\n\nFrom the results of the laboratory analyses and field morphological \nproperties, the nine pedons identified in the study area were classified following \nSoil Taxonomy (Soil Survey Staff 2010) and correlated with FAO/UNESCO \nLegend (IUSS / WRB 2006). \n\n\n\nLand Evaluation \nThe potential and limitations of five land qualities / characteristics (climate, \ntopography, wetness, soil physical characteristics and soil fertility) in determining \nthe suitability of the nine pedons (identified in the study area) for oil palm, coconut \npalm, cashew nut, cocoa, rubber and upland rice cultivation were evaluated using \nthe FAO Land Suitability Evaluation (LSE) (FAO 1976) system. Also, the Fertility \nCapability Classification (FCC) system was used to classify the soils according \nto the kinds of problems they present for agronomic management of the chemical \nand physical properties. The FCC system adopted was the Sanchez et al. (1982) \nversion.                     \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 21\n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nLand Qualities/Characteristics of Pedons in the Study Area \nSome important morphological, physical and chemical properties of soils derived \nfrom the beach ridge sands (BRS) are presented in Tables 1 (a,b,c) and 4. The soils \nwere generally deep (>200 cm) irrespective of site, except for the valley bottom soils \nwhich had a high water table. They were non-concretionary and fairly well drained. \nThe soil colours for Ikot Okwo pedons were dark brown (7.5YR 4/4) topsoil over \nreddish yellow (7.5YR 6/8) subsoil at the crest and dark brown over yellow (10YR \n7/8) subsoil at both the middle slope and valley. For Etebi (ETB), the topsoils were \nvery dark brown (10YR 3/3) over brownish yellow (10YR 6/6) subsoil at the crest; \ndark brown (7.5 YR 3/2) topsoil over yellowish brown (10YR 5/6) subsoil at the \nmiddle slope and dark yellowish brown (10YR 4/4) topsoil/ subsoil at the valley \n(Table 1). Finally, for Ibaka (IBK), the colours were very pale brown (10YR 7/3) \ntopsoil over brownish yellow (10YR 6/8) subsoil at the crest, brown (10YR 6/8) \nat the middle slope and dark brown (10YR 3/3) over brown (10 YR 4/3) subsoil. \nVariation in soil colour indicates differences in soil moisture and drainage conditions \nas influenced by topography (Buol et al. 1989; Tahal Consultants 1982).\n\n\n\nThe soils were weak/moderately well structured. The topsoils had either fine, \nmedium or coarse granular or crumb structure, while the subsoils had a medium \nsubangular blocky structure (Table 4). All the pedons belonged to the sand textural \nclass. Most of the pedons (67%) were dominated by fine sand fraction (ranging \nfrom 47 to 84%), while three pedons (33%) were dominated by coarse sand fraction \n(ranging from 68 to 81%). These results are in line with earlier observations on \nthis area by Tahal Consultants (1982) and Udo (2001) that the texture of the soils \nof the beach ridge sands is characterized by very fine loose sands having a high \ninfiltration capacity. \n\n\n\nThe results in Table 4 also show that soil pH in the study area ranged from \n3.27 to 4.35 indicating strongly acidic soils.  The very low pH is explained by the \nfact that these soils are influenced by salt water marshes (of the Atlantic Ocean). \nThus when air penetrates, the pyrites are oxidized to basic ferric sulphates and \nH2SO4 producing acid sulphate soils (Ojanuga et al. 2003). Organic matter was \nlow (<2.00%) in most of the soils. Available P was low to medium (6.33 \u2013 13.11 \nmgkg-1). Exchangeable cations (K, Ca, Mg, and Na) were low resulting in low \nECEC (ranging from 2.89 to 5.27 cmolc kg-1) and low base saturation (ranging \nfrom 56.18 to 71.16%).\n\n\n\nThis result confirms earlier findings and observations of other workers \nconcerning the characteristics of soils in the area (Udo and Sobulo 1981; Tahal \nConsultants 1982; FMANR 1989; Petters et al. 1989 and Udo 2001). The result \nshows the effect of high rainfall experienced in the area combined with coarse and \nloosed textured soils which are highly susceptible to leaching. This has resulted in \nthe leaching of most of the basic cations resulting in the observed low ECEC, low \nbase status, high exchangeable acidity and the overall low nutrient status of the \nsoils.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201322\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\na \nSo\n\n\n\nm\ne \n\n\n\npr\nof\n\n\n\nile\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\ns o\nf s\n\n\n\noi\nls\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns \n\n\n\n(I\nK\n\n\n\nW\n) \n\n\n\n Pe\ndo\n\n\n\nn \n \n\n\n\nH\nor\n\n\n\niz\non\n\n\n\n \nde\n\n\n\nsi\ngn\n\n\n\nat\nio\n\n\n\nn \nD\n\n\n\nep\nth\n\n\n\n \n(c\n\n\n\nm\n) \n\n\n\nB\n. S\n\n\n\nat\n \n\n\n\n(%\n) \n\n\n\nEC\nEC\n\n\n\n \ncm\n\n\n\nol\nc \nkg\n\n\n\n-1\n \n\n\n\nC\noa\n\n\n\nrs\ne \n\n\n\nsa\nnd\n\n\n\n \n(%\n\n\n\n) \nFi\n\n\n\nne\n sa\n\n\n\nnd\n \n\n\n\n(%\n) \n\n\n\nTo\nta\n\n\n\nl s\nan\n\n\n\nd \n(%\n\n\n\n) \n%\n\n\n\n S\nlit\n\n\n\n \nC\n\n\n\nla\ny \n\n\n\n(%\n) \n\n\n\nTe\nxt\n\n\n\nur\nal\n\n\n\n \ncl\n\n\n\nas\ns \n\n\n\nSo\nil \n\n\n\nco\nlo\n\n\n\nur\n \n\n\n\n(to\nps\n\n\n\noi\nl \n\n\n\n \nsu\n\n\n\nbs\noi\n\n\n\nl  \n \n\n\n\nIK\nW\n\n\n\n 1\n \n\n\n\nA\nP \n\n\n\nA\nB\n\n\n\n \nB\n\n\n\nI \nB\n\n\n\n2 \nC\n\n\n\n1 \nC\n\n\n\n2 \n\n\n\n0 \n-  \n\n\n\n20\n \n\n\n\n20\n - \n\n\n\n52\n \n\n\n\n52\n -7\n\n\n\n2 \n72\n\n\n\n -1\n17\n\n\n\n \n11\n\n\n\n7-\n14\n\n\n\n7 \n14\n\n\n\n7.\n20\n\n\n\n2 \n\n\n\n69\n.8\n\n\n\n7 \n68\n\n\n\n.3\n1 \n\n\n\n61\n.8\n\n\n\n1 \n61\n\n\n\n.3\n0 \n\n\n\n71\n.5\n\n\n\n0 \n62\n\n\n\n.2\n3 \n\n\n\n2.\n69\n\n\n\n \n2.\n\n\n\n43\n \n\n\n\n3.\n06\n\n\n\n \n3.\n\n\n\n07\n \n\n\n\n2.\n95\n\n\n\n \n3.\n\n\n\n39\n \n\n\n\n23\n 8\n\n\n\n2 \n30\n\n\n\n.4\n8 \n\n\n\n46\n.7\n\n\n\n8 \n47\n\n\n\n.1\n2 \n\n\n\n45\n.2\n\n\n\n0 \n46\n\n\n\n.0\n0 \n\n\n\n65\n.5\n\n\n\n0 \n56\n\n\n\n.8\n6 \n\n\n\n36\n.5\n\n\n\n4 \n42\n\n\n\n.2\n0 \n\n\n\n46\n.0\n\n\n\n6 \n43\n\n\n\n.2\n6 \n\n\n\n89\n.3\n\n\n\n2 \n87\n\n\n\n.3\n2 \n\n\n\n83\n.3\n\n\n\n2 \n89\n\n\n\n.3\n2 \n\n\n\n91\n.2\n\n\n\n6 \n89\n\n\n\n.2\n6 \n\n\n\n5.\n94\n\n\n\n \n5.\n\n\n\n94\n \n\n\n\n5.\n94\n\n\n\n \n5.\n\n\n\n94\n \n\n\n\n3.\n94\n\n\n\n \n5.\n\n\n\n94\n \n\n\n\n4.\n74\n\n\n\n \n6.\n\n\n\n74\n \n\n\n\n10\n.7\n\n\n\n4 \n4.\n\n\n\n74\n \n\n\n\n4.\n80\n\n\n\n \n4.\n\n\n\n80\n \n\n\n\nS LS\n \n\n\n\nLS\n \n\n\n\nS S \n \n\n\n\nS \n\n\n\n7.\n5Y\n\n\n\nR\n4/\n\n\n\n4 \n 7.\n\n\n\n5Y\nR\n\n\n\n6/\n8 \n\n\n\n\n\n\n\n IK\nW\n\n\n\n 2\n \n\n\n\n A\nP B\n1 \n\n\n\nB\n2 \n\n\n\nB\n3 \n\n\n\nC\n1 \n\n\n\nC\n2 \n\n\n\n 0 \n-2\n\n\n\n \n21\n\n\n\n-5\n6 \n\n\n\n56\n-8\n\n\n\n5 \n85\n\n\n\n-1\n10\n\n\n\n \n11\n\n\n\n0-\n15\n\n\n\n0 \n15\n\n\n\n0-\n20\n\n\n\n5 \n\n\n\n \n56\n\n\n\n.7\n3 \n\n\n\n59\n.5\n\n\n\n2 \n56\n\n\n\n.8\n5 \n\n\n\n70\n.4\n\n\n\n0 \n70\n\n\n\n.4\n0 \n\n\n\n75\n.0\n\n\n\n8 \n\n\n\n \n3.\n\n\n\n33\n \n\n\n\n3.\n56\n\n\n\n \n2.\n\n\n\n96\n \n\n\n\n2.\n38\n\n\n\n \n2.\n\n\n\n38\n \n\n\n\n2.\n81\n\n\n\n\n\n\n\n \n23\n\n\n\n.5\n2 \n\n\n\n40\n.7\n\n\n\n6 \n30\n\n\n\n.4\n2 \n\n\n\n35\n.7\n\n\n\n8 \n35\n\n\n\n.2\n4 \n\n\n\n29\n.1\n\n\n\n8 \n\n\n\n \n71\n\n\n\n.6\n0 \n\n\n\n46\n.5\n\n\n\n0 \n48\n\n\n\n.8\n4 \n\n\n\n51\n.4\n\n\n\n8 \n54\n\n\n\n.0\n2 \n\n\n\n58\n.0\n\n\n\n8 \n\n\n\n \n95\n\n\n\n.1\n4 \n\n\n\n87\n.2\n\n\n\n6 \n79\n\n\n\n.2\n6 \n\n\n\n87\n.2\n\n\n\n6 \n89\n\n\n\n.2\n6 \n\n\n\n87\n.2\n\n\n\n6 \n\n\n\n \n0.\n\n\n\n06\n \n\n\n\n5.\n94\n\n\n\n \n5.\n\n\n\n94\n \n\n\n\n3 \n94\n\n\n\n \n3.\n\n\n\n94\n \n\n\n\n5 \n94\n\n\n\n\n\n\n\n \n4.\n\n\n\n80\n \n\n\n\n6.\n80\n\n\n\n \n14\n\n\n\n.8\n0 \n\n\n\n6.\n80\n\n\n\n \n6.\n\n\n\n80\n \n\n\n\n6.\n80\n\n\n\n\n\n\n\n S LS\n \n\n\n\nSL\n \n\n\n\nLS\n \n\n\n\nS L \nS \n\n\n\n  7.\n5Y\n\n\n\nR\n 3\n\n\n\n/3\n \n\n\n\n  10\nY\n\n\n\nR\n 7\n\n\n\n/8\n \n\n\n\n \n IK\n\n\n\nW\n 3\n\n\n\n  \n A\nP \n\n\n\nB\nA\n\n\n\n \nB\n\n\n\n1 \nB\n\n\n\n2 \nC\n\n\n\n1 \nC\n\n\n\n2 \n\n\n\n 0-\n24\n\n\n\n \n24\n\n\n\n-5\n0 \n\n\n\n50\n-5\n\n\n\n7 \n67\n\n\n\n-1\n06\n\n\n\n \n10\n\n\n\n6-\n14\n\n\n\n5 \n14\n\n\n\n5-\n15\n\n\n\n2 \n\n\n\n \n72\n\n\n\n.2\n9 \n\n\n\n62\n.2\n\n\n\n6 \n61\n\n\n\n.7\n5 \n\n\n\n81\n.6\n\n\n\n1 \n81\n\n\n\n.6\n1 \n\n\n\n62\n.0\n\n\n\n9 \n\n\n\n \n2.\n\n\n\n92\n \n\n\n\n3.\n39\n\n\n\n \n3.\n\n\n\n77\n \n\n\n\n3.\n15\n\n\n\n \n3.\n\n\n\n15\n \n\n\n\n3.\n80\n\n\n\n\n\n\n\n \n32\n\n\n\n.8\n8 \n\n\n\n20\n.1\n\n\n\n8 \n21\n\n\n\n.3\n6 \n\n\n\n38\n.5\n\n\n\n6 \n32\n\n\n\n.5\n4 \n\n\n\n14\n.9\n\n\n\n4 \n\n\n\n \n56\n\n\n\n.4\n4 \n\n\n\n71\n.1\n\n\n\n4 \n67\n\n\n\n.9\n6 \n\n\n\n48\n.7\n\n\n\n6 \n56\n\n\n\n.7\n8 \n\n\n\n70\n.3\n\n\n\n8 \n\n\n\n \n89\n\n\n\n.3\n2 \n\n\n\n91\n.3\n\n\n\n2 \n89\n\n\n\n.3\n2 \n\n\n\n87\n.3\n\n\n\n2 \n89\n\n\n\n.3\n2 \n\n\n\n85\n.3\n\n\n\n2 \n\n\n\n \n5 \n\n\n\n94\n \n\n\n\n3 \n94\n\n\n\n \n3 \n\n\n\n94\n \n\n\n\n5 \n94\n\n\n\n \n5 \n\n\n\n94\n \n\n\n\n5 \n94\n\n\n\n\n\n\n\n \n4.\n\n\n\n74\n \n\n\n\n4.\n74\n\n\n\n \n6.\n\n\n\n74\n \n\n\n\n6.\n74\n\n\n\n \n6.\n\n\n\n74\n \n\n\n\n8.\n74\n\n\n\n\n\n\n\n LS\n \n\n\n\nS LS\n \n\n\n\nLS\n \n\n\n\nLS\n \n\n\n\nLS\n \n\n\n\n 7.\n5Y\n\n\n\nR\n4/\n\n\n\n2 \n 7.\n\n\n\n5Y\nR\n\n\n\n7/\n8 \n\n\n\n\n\n\n\nS \n= \n\n\n\nsa\nnd\n\n\n\n, L\nS \n\n\n\n= \nlo\n\n\n\nam\ny \n\n\n\nsa\nnd\n\n\n\n; I\nK\n\n\n\nW\n =\n\n\n\n Ik\not\n\n\n\n O\nkw\n\n\n\no;\n 1\n\n\n\n =\n h\n\n\n\nill\n c\n\n\n\nre\nst\n\n\n\n; 2\n =\n\n\n\n m\nid\n\n\n\ndl\ne \n\n\n\nsl\nop\n\n\n\ne,\n 3\n\n\n\n =\n v\n\n\n\nal\nle\n\n\n\ny\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\na\nSo\n\n\n\nm\ne \n\n\n\npr\nofi\n\n\n\nle\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\ns o\nf s\n\n\n\noi\nls\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns (\n\n\n\nIK\nW\n\n\n\n)\n\n\n\nS \n= \n\n\n\nsa\nnd\n\n\n\n, L\nS \n\n\n\n= \nlo\n\n\n\nam\ny \n\n\n\nsa\nnd\n\n\n\n; I\nK\n\n\n\nW\n =\n\n\n\n Ik\not\n\n\n\n O\nkw\n\n\n\no;\n 1\n\n\n\n =\n h\n\n\n\nill\n c\n\n\n\nre\nst\n\n\n\n; 2\n =\n\n\n\n m\nid\n\n\n\ndl\ne \n\n\n\nsl\nop\n\n\n\ne,\n 3\n\n\n\n =\n v\n\n\n\nal\nle\n\n\n\ny \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 23\n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nb\nSo\n\n\n\nm\ne \n\n\n\npr\nofi\n\n\n\nle\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\ns o\nf s\n\n\n\noi\nls\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns (\n\n\n\nET\nB\n\n\n\n)\n\n\n\nS \n= \n\n\n\nsa\nnd\n\n\n\n, L\nS \n\n\n\n= \nlo\n\n\n\nam\ny \n\n\n\nsa\nnd\n\n\n\n; E\nTB\n\n\n\n =\n E\n\n\n\nte\nbi\n\n\n\n; 1\n =\n\n\n\n h\nill\n\n\n\n c\nre\n\n\n\nst\n, 2\n\n\n\n =\n m\n\n\n\nid\ndl\n\n\n\ne \nsl\n\n\n\nop\ne,\n\n\n\n 3\n =\n\n\n\n v\nal\n\n\n\nle\ny.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nb \n \n\n\n\nPe\ndo\n\n\n\nn \n \n\n\n\nH\nor\n\n\n\niz\non\n\n\n\n \nde\n\n\n\nsi\ngn\n\n\n\nat\nio\n\n\n\nn \nD\n\n\n\nep\nth\n\n\n\n \n(c\n\n\n\nm\n) \n\n\n\nB\n. S\n\n\n\nat\n \n\n\n\n(%\n) \n\n\n\nEC\nEC\n\n\n\n \ncm\n\n\n\nol\nc \nkg\n\n\n\n-1\n \n\n\n\nC\nou\n\n\n\nrs\ne \n\n\n\nsa\nnd\n\n\n\n (%\n) \n\n\n\nFi\nne\n\n\n\n sa\nnd\n\n\n\n \n(%\n\n\n\n) \nTo\n\n\n\nta\nl \n\n\n\nsa\nnd\n\n\n\n (%\n) \n\n\n\nSl\nit \n\n\n\n(%\n) \n\n\n\nC\nla\n\n\n\ny \n(%\n\n\n\n) \nTe\n\n\n\nxt\nur\n\n\n\nal\n \n\n\n\ncl\nas\n\n\n\ns \nSo\n\n\n\nil \nco\n\n\n\nlo\nur\n\n\n\n (t\nop\n\n\n\nso\nil \n\n\n\n \nSu\n\n\n\nbs\noi\n\n\n\nl) \n  \n\n\n\nET\nB\n\n\n\n 1\n \n\n\n\nA\nP \n\n\n\n0-\n33\n\n\n\n \n65\n\n\n\n.7\n4 \n\n\n\n6.\n02\n\n\n\n \n81\n\n\n\n.3\n4 \n\n\n\n7.\n32\n\n\n\n \n88\n\n\n\n.6\n6 \n\n\n\n6.\n24\n\n\n\n \n5.\n\n\n\n10\n \n\n\n\nS \n \n\n\n\n \nB\n\n\n\n1 \n33\n\n\n\n-6\n2 \n\n\n\n39\n.4\n\n\n\n9 \n5.\n\n\n\n31\n \n\n\n\n70\n.1\n\n\n\n6 \n18\n\n\n\n.5\n0 \n\n\n\n88\n.6\n\n\n\n6 \n4.\n\n\n\n30\n \n\n\n\n7.\n04\n\n\n\n \nLS\n\n\n\n \n10\n\n\n\nY\nR\n\n\n\n 2\n/3\n\n\n\n\n\n\n\n \nB\n\n\n\n2 \n62\n\n\n\n-1\n10\n\n\n\n \n60\n\n\n\n.9\n0 \n\n\n\n5.\n73\n\n\n\n \n50\n\n\n\n.5\n4 \n\n\n\n36\n.1\n\n\n\n2 \n86\n\n\n\n.6\n6 \n\n\n\n6.\n30\n\n\n\n \n10\n\n\n\n.7\n4 \n\n\n\nLS\n \n\n\n\n\n\n\n\nB\nC\n\n\n\n \n11\n\n\n\n0-\n16\n\n\n\n5 \n57\n\n\n\n.3\n6 \n\n\n\n4.\n50\n\n\n\n \n74\n\n\n\n.0\n2 \n\n\n\n16\n.6\n\n\n\n4 \n90\n\n\n\n.6\n6 \n\n\n\n0.\n30\n\n\n\n \n9.\n\n\n\n04\n \n\n\n\nS \n10\n\n\n\nY\nR\n\n\n\n 6\n/6\n\n\n\n\n\n\n\n \nC\n\n\n\n \n16\n\n\n\n5-\n20\n\n\n\n0 \n57\n\n\n\n.3\n7 \n\n\n\n4.\n88\n\n\n\n \n53\n\n\n\n.6\n6 \n\n\n\n23\n.0\n\n\n\n0 \n86\n\n\n\n.6\n6 \n\n\n\n2.\n30\n\n\n\n \n11\n\n\n\n.0\n4 \n\n\n\nLS\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nET\n\n\n\nB\n 2\n\n\n\n \nA\n\n\n\np \n0-\n\n\n\n30\n \n\n\n\n69\n.8\n\n\n\n3 \n5.\n\n\n\n30\n \n\n\n\n86\n.0\n\n\n\n0 \n8.\n\n\n\n78\n \n\n\n\n94\n.7\n\n\n\n8 \n0.\n\n\n\n24\n \n\n\n\n4.\n98\n\n\n\n \nS \n\n\n\n\n\n\n\n \nB\n\n\n\n \n30\n\n\n\n-6\n0 \n\n\n\n60\n.8\n\n\n\n4 \n5.\n\n\n\n31\n \n\n\n\n82\n.1\n\n\n\n0 \n7.\n\n\n\n15\n \n\n\n\n89\n.2\n\n\n\n6 \n3.\n\n\n\n76\n \n\n\n\n6.\n98\n\n\n\n \nS \n\n\n\n7.\n5Y\n\n\n\nR\n 3\n\n\n\n/2\n \n\n\n\n \nB\n\n\n\nC\n \n\n\n\n60\n-7\n\n\n\n7 \n57\n\n\n\n.2\n9 \n\n\n\n4.\n50\n\n\n\n \n75\n\n\n\n.0\n0 \n\n\n\n9.\n66\n\n\n\n \n83\n\n\n\n.6\n6 \n\n\n\n6.\n30\n\n\n\n \n9.\n\n\n\n04\n \n\n\n\nLS\n \n\n\n\n\n\n\n\nC\n \n\n\n\n77\n-1\n\n\n\n40\n \n\n\n\n67\n.6\n\n\n\n9 \n4.\n\n\n\n46\n \n\n\n\n51\n.7\n\n\n\n0 \n32\n\n\n\n.9\n6 \n\n\n\n84\n.6\n\n\n\n6 \n4.\n\n\n\n30\n \n\n\n\n11\n.0\n\n\n\n4 \nLS\n\n\n\n \n10\n\n\n\nY\nR\n\n\n\n 5\n/6\n\n\n\n\n\n\n\nET\nB\n\n\n\n 3\n  \n\n\n\nA\np 1\n\n\n\n \n0-\n\n\n\n10\n \n\n\n\n61\n.2\n\n\n\n3 \n4.\n\n\n\n95\n \n\n\n\n89\n.2\n\n\n\n6 \n4.\n\n\n\n56\n \n\n\n\n93\n.8\n\n\n\n2 \n0.\n\n\n\n72\n \n\n\n\n5.\n46\n\n\n\n \nS \n\n\n\n\n\n\n\nA\nP 2\n\n\n\n \n10\n\n\n\n-2\n0 \n\n\n\n63\n.7\n\n\n\n5 \n4.\n\n\n\n77\n \n\n\n\n67\n.9\n\n\n\n6 \n15\n\n\n\n.3\n0 \n\n\n\n83\n.2\n\n\n\n6 \n9.\n\n\n\n28\n \n\n\n\n7.\n46\n\n\n\n \nLS\n\n\n\n \n10\n\n\n\nY\nR\n\n\n\n 4\n/4\n\n\n\n\n\n\n\n \nA\n\n\n\nB\n \n\n\n\n20\n-3\n\n\n\n2 \n66\n\n\n\n.6\n7 \n\n\n\n5.\n22\n\n\n\n \n71\n\n\n\n.8\n0 \n\n\n\n17\n.4\n\n\n\n6 \n89\n\n\n\n.2\n6 \n\n\n\n5.\n28\n\n\n\n \n5.\n\n\n\n46\n \n\n\n\nS \n \n\n\n\n \nB\n\n\n\n \n32\n\n\n\n-5\n1 \n\n\n\n61\n.0\n\n\n\n9 \n5.\n\n\n\n35\n \n\n\n\n72\n.6\n\n\n\n0 \n12\n\n\n\n.6\n6 \n\n\n\n85\n.2\n\n\n\n6 \n7.\n\n\n\n28\n \n\n\n\n7.\n46\n\n\n\n \nLS\n\n\n\n \n10\n\n\n\nY\nR\n\n\n\n 4\n/4\n\n\n\n\n\n\n\nB\nC\n\n\n\n \n51\n\n\n\n-6\n2 \n\n\n\n65\n.5\n\n\n\n3 \n4.\n\n\n\n64\n \n\n\n\n78\n.5\n\n\n\n6 \n8.\n\n\n\n70\n \n\n\n\n87\n.2\n\n\n\n6 \n5.\n\n\n\n76\n \n\n\n\n6.\n98\n\n\n\n \nLS\n\n\n\n\n\n\n\nC\n \n\n\n\n62\n-1\n\n\n\n00\n \n\n\n\n51\n.3\n\n\n\n2 \n4.\n\n\n\n60\n \n\n\n\n60\n.5\n\n\n\n6 \n34\n\n\n\n.2\n2 \n\n\n\n94\n.7\n\n\n\n8 \n0.\n\n\n\n24\n \n\n\n\n4.\n98\n\n\n\n \nS \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201324\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nc\nSo\n\n\n\nm\ne \n\n\n\npr\nofi\n\n\n\nle\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\ns o\nf s\n\n\n\noi\nls\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns (\n\n\n\nIB\nK\n\n\n\n)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 1\n\n\n\nc \nSo\n\n\n\nm\ne \n\n\n\npr\nof\n\n\n\nile\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\ns o\nf s\n\n\n\noi\nls\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns \n\n\n\n(I\nB\n\n\n\nK\n) \n\n\n\n Pe\ndo\n\n\n\nn \n \n\n\n\nH\nor\n\n\n\niz\non\n\n\n\n \nde\n\n\n\nsi\ngn\n\n\n\nat\nio\n\n\n\nn \nD\n\n\n\nep\nth\n\n\n\n \n(c\n\n\n\nm\n) \n\n\n\nB\n. S\n\n\n\nat\n \n\n\n\n(%\n) \n\n\n\nEC\nEC\n\n\n\n \ncm\n\n\n\nol\nc \nkg\n\n\n\n-1\n \n\n\n\nC\nou\n\n\n\nrs\ne \n\n\n\nsa\nnd\n\n\n\n \n(%\n\n\n\n) \nFi\n\n\n\nne\n sa\n\n\n\nnd\n \n\n\n\n(%\n) \n\n\n\nTo\nta\n\n\n\nl s\nan\n\n\n\nd \n(%\n\n\n\n) \nSl\n\n\n\nit \n \n\n\n\n(%\n) \n\n\n\nC\nla\n\n\n\ny \n(%\n\n\n\n) \nTe\n\n\n\nxt\nur\n\n\n\nal\n \n\n\n\ncl\nas\n\n\n\ns \nSo\n\n\n\nil \nco\n\n\n\nlo\nur\n\n\n\n \n(to\n\n\n\nps\noi\n\n\n\nl \n \n\n\n\nsu\nbs\n\n\n\noi\nl )\n\n\n\n  \nIB\n\n\n\nK\n 1\n\n\n\n \nA\n\n\n\np \n0-\n\n\n\n22\n \n\n\n\n61\n.1\n\n\n\n5 \n3.\n\n\n\n86\n \n\n\n\n16\n.3\n\n\n\n6 \n76\n\n\n\n.9\n0 \n\n\n\n93\n.2\n\n\n\n6 \n1.\n\n\n\n94\n \n\n\n\n4.\n80\n\n\n\n \nS \n\n\n\n10\n Y\n\n\n\nR\n7/\n\n\n\n3 \n \n\n\n\nB\nA\n\n\n\n \n22\n\n\n\n-4\n6 \n\n\n\n57\n.2\n\n\n\n6 \n4.\n\n\n\n49\n \n\n\n\n55\n.1\n\n\n\n2 \n40\n\n\n\n.0\n2 \n\n\n\n95\n.3\n\n\n\n2 \n0.\n\n\n\n06\n \n\n\n\n4.\n62\n\n\n\n \nS \n\n\n\n\n\n\n\nB\n \n\n\n\n46\n-8\n\n\n\n7 \n61\n\n\n\n.1\n8 \n\n\n\n3.\n84\n\n\n\n \n45\n\n\n\n.5\n2 \n\n\n\n49\n.6\n\n\n\n2 \n95\n\n\n\n.1\n4 \n\n\n\n0.\n06\n\n\n\n \n4.\n\n\n\n8 \nS \n\n\n\n10\n Y\n\n\n\nR\n 6\n\n\n\n/8\n \n\n\n\n \nC\n\n\n\n1 \n87\n\n\n\n-1\n36\n\n\n\n \n56\n\n\n\n.7\n7 \n\n\n\n3.\n70\n\n\n\n \n42\n\n\n\n.1\n6 \n\n\n\n53\n.1\n\n\n\n4 \n95\n\n\n\n.3\n2 \n\n\n\n0.\n06\n\n\n\n \n4.\n\n\n\n62\n \n\n\n\nS \n \n\n\n\n \nC\n\n\n\n2 \n13\n\n\n\n6-\n20\n\n\n\n8 \n60\n\n\n\n.6\n5 \n\n\n\n3.\n58\n\n\n\n \n10\n\n\n\n.1\n6 \n\n\n\n84\n.9\n\n\n\n8 \n95\n\n\n\n.1\n4 \n\n\n\n0.\n06\n\n\n\n \n4.\n\n\n\n8 \nS \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nIB\n\n\n\nK\n 2\n\n\n\n \nA\n\n\n\np \n0-\n\n\n\n26\n \n\n\n\n61\n.1\n\n\n\n5 \n3.\n\n\n\n86\n \n\n\n\n21\n.3\n\n\n\n6 \n71\n\n\n\n.7\n0 \n\n\n\n93\n.2\n\n\n\n6 \n1.\n\n\n\n94\n \n\n\n\n4.\n8 \n\n\n\nS \n \n\n\n\n \nA\n\n\n\nB\n \n\n\n\n26\n-5\n\n\n\n9 \n68\n\n\n\n.0\n5 \n\n\n\n5.\n10\n\n\n\n \n34\n\n\n\n.7\n2 \n\n\n\n56\n.5\n\n\n\n4 \n91\n\n\n\n.2\n6 \n\n\n\n3.\n94\n\n\n\n \n4.\n\n\n\n8 \nS \n\n\n\n10\n Y\n\n\n\nR\n4/\n\n\n\n3 \n \n\n\n\nB\n \n\n\n\n59\n-1\n\n\n\n11\n \n\n\n\n68\n.9\n\n\n\n7 \n4.\n\n\n\n02\n \n\n\n\n31\n.1\n\n\n\n0 \n58\n\n\n\n.3\n4 \n\n\n\n89\n.4\n\n\n\n4 \n5.\n\n\n\n84\n \n\n\n\n4.\n62\n\n\n\n \nS \n\n\n\n\n\n\n\nB\nC\n\n\n\n \n11\n\n\n\n1-\n15\n\n\n\n0 \n64\n\n\n\n.1\n5 \n\n\n\n4.\n01\n\n\n\n \n16\n\n\n\n.8\n8 \n\n\n\n74\n.5\n\n\n\n6 \n91\n\n\n\n.4\n4 \n\n\n\n3.\n94\n\n\n\n \n4.\n\n\n\n62\n \n\n\n\nS \n10\n\n\n\n Y\nR\n\n\n\n 6\n/8\n\n\n\n\n\n\n\nC\n \n\n\n\n15\n0-\n\n\n\n21\n3 \n\n\n\n59\n.2\n\n\n\n1 \n3.\n\n\n\n58\n \n\n\n\n1.\n30\n\n\n\n \n96\n\n\n\n.4\n4 \n\n\n\n97\n.7\n\n\n\n4 \n0.\n\n\n\n06\n \n\n\n\n4.\n8 \n\n\n\nS \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIB\nK\n\n\n\n 3\n  \n\n\n\nA\np 1\n\n\n\n \n0-\n\n\n\n12\n \n\n\n\n61\n.8\n\n\n\n6 \n3.\n\n\n\n07\n \n\n\n\n31\n.6\n\n\n\n4 \n63\n\n\n\n.6\n8 \n\n\n\n95\n.3\n\n\n\n2 \n0.\n\n\n\n06\n \n\n\n\n4.\n62\n\n\n\n \nS \n\n\n\n\n\n\n\nA\np 2\n\n\n\n \n12\n\n\n\n-3\n0 \n\n\n\n51\n.8\n\n\n\n1 \n4.\n\n\n\n58\n \n\n\n\n27\n.8\n\n\n\n6 \n63\n\n\n\n.5\n8 \n\n\n\n91\n.4\n\n\n\n4 \n3.\n\n\n\n94\n \n\n\n\n4.\n62\n\n\n\n \nS \n\n\n\n10\n Y\n\n\n\nR\n3/\n\n\n\n3 \n \n\n\n\nB\n \n\n\n\n30\n-4\n\n\n\n8 \n68\n\n\n\n.9\n7 \n\n\n\n4.\n63\n\n\n\n \n31\n\n\n\n.4\n0 \n\n\n\n58\n.0\n\n\n\n4 \n89\n\n\n\n.4\n4 \n\n\n\n5.\n94\n\n\n\n \n4.\n\n\n\n62\n \n\n\n\nS \n \n\n\n\n \nC\n\n\n\n \n48\n\n\n\n-8\n0 \n\n\n\n67\n.0\n\n\n\n7 \n3.\n\n\n\n49\n \n\n\n\n38\n.9\n\n\n\n4 \n54\n\n\n\n.3\n2 \n\n\n\n93\n.2\n\n\n\n6 \n1.\n\n\n\n94\n \n\n\n\n4.\n8 \n\n\n\nS \n10\n\n\n\n Y\nR\n\n\n\n 4\n/3\n\n\n\n \n S \n\n\n\n= \nsa\n\n\n\nnd\n; I\n\n\n\nB\nK\n\n\n\n =\n Ib\n\n\n\nak\na;\n\n\n\n 1\n =\n\n\n\n c\nre\n\n\n\nst\n; 2\n\n\n\n =\n m\n\n\n\nid\ndl\n\n\n\ne \nsl\n\n\n\nop\ne;\n\n\n\n 3\n =\n\n\n\n v\nal\n\n\n\nle\ny \n\n\n\n\n\n\n\n \n  S \n= \n\n\n\nsa\nnd\n\n\n\n; I\nB\n\n\n\nK\n =\n\n\n\n Ib\nak\n\n\n\na;\n 1\n\n\n\n =\n c\n\n\n\nre\nst\n\n\n\n; 2\n =\n\n\n\n m\nid\n\n\n\ndl\ne \n\n\n\nsl\nop\n\n\n\ne;\n 3\n\n\n\n =\n v\n\n\n\nal\nle\n\n\n\ny \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 25\n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\nSoil Classification \nThe classification of the nine pedons from the three study sites (IKW, ETB and IBK) \nrepresenting the area of study is shown in Table 2, while some characteristics of \nthe pedons are shown in Tables 1a, b, c. The nine pedons were classified following \nthe criteria outlined in the USDA Soil Taxonomy (Soil Survey Staff 2010) and \ncorrelated with World Reference Base (WRB) for Soil Resources (IUSS/WRB \n2007) system. \n\n\n\nPedons were classified into order, suborder, great group and subgroup, \nmainly on the basis of diagnostic horizons, the properties of the soils that reflect \nthe nature of the soil environment and the dominant pedogenic processes that \nare responsible for the soil formation (Ajiboye and Ogunwale 2010). Generally, \nthe results of field study of profile pits and laboratory analysis showed that all \nthe soils were relatively young (Unyienyin 2010). They all lacked argillic or \nkandic horizons. However, based on the stage of profile development, soils in the \nentire  area could be placed in either  the Inceptisoils or Entisols soil orders (Soil \nSurvey Staff 2010) which correlate with Cambisols and Regosols, respectively \n(IUSS/WRB, 2007). Five pedons (or 56% of the area), representing the crest and \nmiddle slope in IKW, and all pedons (crest, middle  slope and valley) in ETB with \nmoderate weathering but having features of cambic  B horizon were classified \nas Inceptisols (Cambisols). On the other hand, four pedons (or 44% of the area), \nrepresenting the valley in IKW and all the pedons (crest, middle slope and valley) \nin IBK qualified as Entisols (Regosols), being very young soils with no diagnostic \nhorizon development.\n\n\n\nThree of the Inceptisols were placed in Typic Dystrudepts subgroup (Table \n2), based on the moisture regime, low pH  and low base (Soil Survey Staff 2010), \nwhile two Inceptisols qualified as Aeric Endoaquepts as they had a relatively \nhigher water table resulting in poor drainage conditions. Similarly, the Entisols \nwere divided into two subgroups \u2013 Typic Psammaquent and Typic Udipsamments \n(Table 2). Both had sandy texture, but whereas the Psammaquents had high, water \ntable, poor drainage and were wet at certain periods of the year, the Udipsamments \nwere relatively drier (under humid conditions). Earlier workers had similarly \ndescribed beach ridge soils as young soils derived from recently deposited \nmaterials (Jungerius 1964). Similarly, Petters et al. (1989) classified beach ridge \nsoils as Typic Tropopsamments, Typic Tropoaquent (Dystric Regosols) and Oxic \nDystropepts (Dystric Cambisols).\n\n\n\nAgricultural Potential of Soils of the Beach Ridge Sands\n\n\n\nFertility Capability Classification \nThe result of fertility capability classification of the soils in the study area \nis shown in Table 3. The conversion data used in evaluating the soils are as \noutlined by Sanchez et al. (1982). The system consists of three categorical \nlevels: \u2018type\u2019 (texture of plough layer or top 20 cm); substrata type\u2019 (texture of \nsubsoils) and \u2018modifiers\u2019 (soil properties or conditions which act as constraints\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201326\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\nTA\nB\n\n\n\nLE\n 2\n\n\n\nC\nla\n\n\n\nss\nifi\n\n\n\nca\ntio\n\n\n\nn \nof\n\n\n\n so\nils\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n  \nTA\n\n\n\nB\nLE\n\n\n\n 2\n \n\n\n\nC\nla\n\n\n\nss\nifi\n\n\n\nca\ntio\n\n\n\nn \nof\n\n\n\n so\nils\n\n\n\n o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  C\n\n\n\nla\nss\n\n\n\nifi\nca\n\n\n\ntio\nn \n\n\n\n \nU\n\n\n\nSD\nA\n\n\n\n(S\noi\n\n\n\nl S\nur\n\n\n\nve\ny \n\n\n\nSt\naf\n\n\n\nf, \n20\n\n\n\n10\n)  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  W\n\n\n\nR\nB\n\n\n\n (F\nA\n\n\n\nO\n) \n\n\n\n\n\n\n\nPe\ndo\n\n\n\nn*\n  \n\n\n\n\n\n\n\n  O\nrd\n\n\n\ner\n \n\n\n\n\n\n\n\nSu\nbg\n\n\n\nro\nup\n\n\n\n\n\n\n\n\n\n\n\nIK\nW\n\n\n\n 1\n \n\n\n\nIK\nW\n\n\n\n 2\n \n\n\n\nIK\nW\n\n\n\n 3\n \n\n\n\nET\nB\n\n\n\n 1\n \n\n\n\nET\nB\n\n\n\n 2\n \n\n\n\nET\nB\n\n\n\n 3\n \n\n\n\nET\nB\n\n\n\n 1\n \n\n\n\nET\nB\n\n\n\n 2\n \n\n\n\nET\nB\n\n\n\n 3\n \n\n\n\nIn\nce\n\n\n\npt\nis\n\n\n\nol\ns \n\n\n\nIn\nce\n\n\n\npt\nis\n\n\n\nol\ns \n\n\n\nEn\ntis\n\n\n\nol\ns \n\n\n\nIn\nce\n\n\n\npt\nis\n\n\n\nol\ns \n\n\n\nIn\nce\n\n\n\npt\nis\n\n\n\nol\ns \n\n\n\nIn\nce\n\n\n\npt\nis\n\n\n\nol\ns \n\n\n\nEn\ntis\n\n\n\nol\ns \n\n\n\nEn\ntis\n\n\n\nol\ns \n\n\n\nEn\ntis\n\n\n\nol\ns \n\n\n\nTy\npi\n\n\n\nc \nD\n\n\n\nys\ntru\n\n\n\nde\npt\n\n\n\ns \nTy\n\n\n\npi\nc \n\n\n\nD\nys\n\n\n\ntru\nde\n\n\n\npt\ns \n\n\n\nTy\npi\n\n\n\nc \nPs\n\n\n\nam\nm\n\n\n\naq\nne\n\n\n\nnt\ns \n\n\n\nTy\npi\n\n\n\nc \nD\n\n\n\nys\ntru\n\n\n\nde\npt\n\n\n\ns \nAe\n\n\n\nri\nc \n\n\n\nEn\ndo\n\n\n\naq\nue\n\n\n\npt\ns \n\n\n\nAe\nri\n\n\n\nc \nEn\n\n\n\ndo\naq\n\n\n\nue\npt\n\n\n\ns \nTy\n\n\n\npi\nc \n\n\n\nU\ndi\n\n\n\nps\nam\n\n\n\nm\ner\n\n\n\nts \nTy\n\n\n\npi\nc \n\n\n\nU\ndi\n\n\n\nps\nam\n\n\n\nm\ner\n\n\n\nts \nTy\n\n\n\npi\nc \n\n\n\nPs\nam\n\n\n\nm\naq\n\n\n\nue\nnt\n\n\n\ns \n\n\n\nH\nap\n\n\n\nlic\n  C\n\n\n\nam\nbi\n\n\n\nso\nls \n\n\n\n(D\nys\n\n\n\ntri\nc)\n\n\n\n \nH\n\n\n\nap\nlic\n\n\n\n   \nCa\n\n\n\nm\nbi\n\n\n\nso\nls\n\n\n\n (D\nys\n\n\n\ntri\nc)\n\n\n\n \nEn\n\n\n\ndo\ngl\n\n\n\ney\nic\n\n\n\n R\neg\n\n\n\nos\nol\n\n\n\ns (\nD\n\n\n\nys\ntri\n\n\n\nc)\n \n\n\n\nH\nap\n\n\n\nlic\n  C\n\n\n\nam\nbi\n\n\n\nso\nls \n\n\n\n(D\nys\n\n\n\ntri\nc)\n\n\n\n \nEn\n\n\n\ndo\ngl\n\n\n\ney\nic\n\n\n\n C\nam\n\n\n\nbi\nso\n\n\n\nls\n (D\n\n\n\nys\ntr\n\n\n\nic\n) \n\n\n\nEn\ndo\n\n\n\ngl\ney\n\n\n\nic\n  C\n\n\n\nam\nbi\n\n\n\nso\nls\n\n\n\n (D\nys\n\n\n\ntri\nc)\n\n\n\n \nH\n\n\n\nap\nlic\n\n\n\n R\neg\n\n\n\nos\nol\n\n\n\ns (\nD\n\n\n\nys\ntri\n\n\n\nc)\n \n\n\n\nH\nap\n\n\n\nlic\n  R\n\n\n\neg\nos\n\n\n\nol\ns (\n\n\n\nD\nys\n\n\n\ntri\nc)\n\n\n\n \nEn\n\n\n\ndo\ngl\n\n\n\ney\nic\n\n\n\n  R\neg\n\n\n\nos\nol\n\n\n\ns (\nD\n\n\n\nys\ntri\n\n\n\nc)\n \n\n\n\n *I\nK\n\n\n\nW\n  \n\n\n\n= \nIk\n\n\n\not\n O\n\n\n\nkw\no;\n\n\n\n E\nTB\n\n\n\n =\n E\n\n\n\nte\nbi\n\n\n\n;  \nIB\n\n\n\nK\n =\n\n\n\n Ib\nak\n\n\n\na \n1 \n\n\n\n= \nhi\n\n\n\nll \ncr\n\n\n\nes\nt, \n\n\n\n2=\n m\n\n\n\nid\ndl\n\n\n\ne \nsl\n\n\n\nop\ne,\n\n\n\n \n 3\n\n\n\n =\n v\n\n\n\nal\nle\n\n\n\ny.\n \n\n\n\n *I\nK\n\n\n\nW\n  \n\n\n\n= \nIk\n\n\n\not\n O\n\n\n\nkw\no;\n\n\n\n E\nTB\n\n\n\n =\n E\n\n\n\nte\nbi\n\n\n\n;  \nIB\n\n\n\nK\n =\n\n\n\n Ib\nak\n\n\n\na\n1 \n\n\n\n= \nhi\n\n\n\nll \ncr\n\n\n\nes\nt, \n\n\n\n2=\n m\n\n\n\nid\ndl\n\n\n\ne \nsl\n\n\n\nop\ne,\n\n\n\n \n 3\n\n\n\n =\n v\n\n\n\nal\nle\n\n\n\ny.\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 27\n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nFe\nrti\n\n\n\nlit\ny \n\n\n\nca\npa\n\n\n\nbi\nlit\n\n\n\ny \ncl\n\n\n\nas\nsi\n\n\n\nfic\nat\n\n\n\nio\nn \n\n\n\n* \n(F\n\n\n\nC\nC\n\n\n\n) o\nf p\n\n\n\ned\non\n\n\n\ns d\ner\n\n\n\niv\ned\n\n\n\n fr\nom\n\n\n\n b\nea\n\n\n\nch\n ri\n\n\n\ndg\ne \n\n\n\nsa\nnd\n\n\n\ns\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\n \nFe\n\n\n\nrti\nlit\n\n\n\ny \nca\n\n\n\npa\nbi\n\n\n\nlit\ny \n\n\n\ncl\nas\n\n\n\nsi\nfic\n\n\n\nat\nio\n\n\n\nn \n* \n\n\n\n(F\nC\n\n\n\nC\n) o\n\n\n\nf p\ned\n\n\n\non\ns d\n\n\n\ner\niv\n\n\n\ned\n fr\n\n\n\nom\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns \n\n\n\n \nPe\n\n\n\ndo\nn \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  T\n\n\n\nyp\ne1  \n\n\n\nSu\nbs\n\n\n\ntra\nta\n\n\n\n 2  \nTy\n\n\n\npe\n  \n\n\n\n\u2026\n\u2026\n\n\n\n\u2026\n\u2026\n\n\n\n. C\non\n\n\n\ndi\ntio\n\n\n\nn \nm\n\n\n\nod\nifi\n\n\n\ner\ns3 \u2026\n\n\n\n\u2026\n\u2026\n\n\n\n\u2026\n\u2026\n\n\n\n\u2026\n\u2026\n\n\n\n..\u2026\n \n\n\n\ng \n   \n\n\n\n   \nd \n\n\n\n\n\n\n\nk \n   \n\n\n\n   \ne \n\n\n\n   \n a\n\n\n\n   \n  h\n\n\n\n\n\n\n\nb \n  i\n\n\n\n   \nx \n\n\n\n  v\n   \n\n\n\ns  \n n\n\n\n\n   \nc \n\n\n\n   \n%\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n F\n\n\n\nC\nC\n\n\n\n U\nni\n\n\n\nt  \n\n\n\nIK\nW\n\n\n\n 1\n   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  S\n\n\n\n \nIK\n\n\n\nW\n 2\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  S\n \n\n\n\nIK\nW\n\n\n\n 3\n   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  S\n\n\n\n \nET\n\n\n\nB\n 1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  S\n \n\n\n\nET\nB\n\n\n\n 2\n   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  S\n\n\n\n \nET\n\n\n\nB\n 3\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  S\n \n\n\n\nIB\nK\n\n\n\n 1\n   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  S\n\n\n\n \nIB\n\n\n\nK\n 2\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  S\n \n\n\n\nIB\nK\n\n\n\n 3\n   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n  S\n\n\n\n\n\n\n\nS S S S S S S S S \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  0\n\n\n\n-2\n \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  6\n\n\n\n-1\n3 \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  0\n\n\n\n-2\n \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  0\n\n\n\n-2\n \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  4\n\n\n\n-6\n \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  2\n\n\n\n-4\n \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  0\n\n\n\n-2\n \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  6\n\n\n\n-1\n3 \n\n\n\n-  \n   \n\n\n\n   \n-  \n\n\n\n\n\n\n\n+ \n   \n\n\n\n +\n   \n\n\n\n  -\n   \n\n\n\n   \n+ \n\n\n\n   \n - \n\n\n\n   \n-  \n\n\n\n - \n  -\n\n\n\n   \n-  \n\n\n\n  -\n   \n\n\n\n - \n  0\n\n\n\n-2\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \n S\n\n\n\neh\nk \n\n\n\n *A\nfte\n\n\n\nr S\nan\n\n\n\nch\nez\n\n\n\n e\nt a\n\n\n\nl. \n(1\n\n\n\n98\n2)\n\n\n\n  \n1.\n\n\n\n \nTy\n\n\n\npe\n  =\n\n\n\n te\nxt\n\n\n\nur\ne \n\n\n\nof\n to\n\n\n\nps\noi\n\n\n\nl; \n  \n\n\n\n2.\n \n\n\n\nSu\nbs\n\n\n\ntra\nte\n\n\n\n T\nyp\n\n\n\ne \n =\n\n\n\n te\nxt\n\n\n\nur\ne \n\n\n\nof\n s\n\n\n\nub\nso\n\n\n\nil \n \n\n\n\n3.\n \n\n\n\nC\non\n\n\n\ndi\ntio\n\n\n\nn \nm\n\n\n\nod\nifi\n\n\n\ner\ns  \n\n\n\n= \nC\n\n\n\nro\np \n\n\n\npr\nod\n\n\n\nuc\ntio\n\n\n\nn \nco\n\n\n\nns\ntra\n\n\n\nin\nts\n\n\n\n ( \nk \n\n\n\n= \nex\n\n\n\nch\nan\n\n\n\nge\nab\n\n\n\nle\n p\n\n\n\not\nas\n\n\n\nsi\num\n\n\n\n (K\n) d\n\n\n\nef\nic\n\n\n\nie\nnc\n\n\n\ny;\n  e\n\n\n\n =\n lo\n\n\n\nw\n c\n\n\n\nat\nio\n\n\n\nn \nex\n\n\n\nch\nan\n\n\n\nge\n c\n\n\n\nap\nac\n\n\n\nity\n (C\n\n\n\nEC\n); \n\n\n\nh \n= \n\n\n\nac\nid\n\n\n\nic\n re\n\n\n\nac\ntio\n\n\n\nn \n; %\n\n\n\n =\n sl\n\n\n\nop\ne \n\n\n\nof\n th\n\n\n\ne \nla\n\n\n\nnd\n.  \n\n\n\n        *A\nfte\n\n\n\nr S\nan\n\n\n\nch\nez\n\n\n\n e\nt a\n\n\n\nl. \n(1\n\n\n\n98\n2)\n\n\n\n \n1.\n\n\n\n \nTy\n\n\n\npe\n  =\n\n\n\n te\nxt\n\n\n\nur\ne \n\n\n\nof\n to\n\n\n\nps\noi\n\n\n\nl; \n \n\n\n\n2.\n \n\n\n\nSu\nbs\n\n\n\ntra\nte\n\n\n\n T\nyp\n\n\n\ne \n =\n\n\n\n te\nxt\n\n\n\nur\ne \n\n\n\nof\n su\n\n\n\nbs\noi\n\n\n\nl \n3.\n\n\n\n \nC\n\n\n\non\ndi\n\n\n\ntio\nn \n\n\n\nm\nod\n\n\n\nifi\ner\n\n\n\ns  \n= \n\n\n\nC\nro\n\n\n\np \npr\n\n\n\nod\nuc\n\n\n\ntio\nn \n\n\n\nco\nns\n\n\n\ntra\nin\n\n\n\nts\n ( \n\n\n\nk \n= \n\n\n\nex\nch\n\n\n\nan\nge\n\n\n\nab\nle\n\n\n\n p\not\n\n\n\nas\nsi\n\n\n\num\n (K\n\n\n\n) d\nefi\n\n\n\nci\nen\n\n\n\ncy\n;  \n\n\n\ne \n= \n\n\n\nlo\nw\n\n\n\n c\nat\n\n\n\nio\nn \n\n\n\nex\nch\n\n\n\nan\nge\n\n\n\n c\nap\n\n\n\nac\nity\n\n\n\n (C\nEC\n\n\n\n); \nh \n\n\n\n= \nac\n\n\n\nid\nic\n\n\n\n re\nac\n\n\n\ntio\nn \n\n\n\n; %\n =\n\n\n\n sl\nop\n\n\n\ne \nof\n\n\n\n th\ne \n\n\n\nla\nnd\n\n\n\n. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201328\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nLa\nnd\n\n\n\n q\nua\n\n\n\nlit\nie\n\n\n\ns /\nch\n\n\n\nar\nac\n\n\n\nte\nris\n\n\n\ntic\ns o\n\n\n\nf p\ned\n\n\n\non\ns o\n\n\n\nf t\nhe\n\n\n\n b\nea\n\n\n\nch\n ri\n\n\n\ndg\ne \n\n\n\nsa\nnd\n\n\n\ns\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\n \nLa\n\n\n\nnd\n q\n\n\n\nua\nlit\n\n\n\nie\ns /\n\n\n\nch\nar\n\n\n\nac\nte\n\n\n\nris\ntic\n\n\n\ns o\nf p\n\n\n\ned\non\n\n\n\ns o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns \n\n\n\n  \nLa\n\n\n\nnd\n q\n\n\n\nua\nlit\n\n\n\nie\ns/\n\n\n\n c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\ns \n\n\n\nPe\ndo\n\n\n\nns\n  \n\n\n\n1.\n C\n\n\n\nlim\nat\n\n\n\ne \n( c\n\n\n\n): \nU\n\n\n\nni\nt \n\n\n\nIK\nW\n\n\n\n 1\n \n\n\n\nIK\nW\n\n\n\n 2\n \n\n\n\nIK\nW\n\n\n\n3 \nET\n\n\n\nB\n 1\n\n\n\n \nET\n\n\n\nB\n 2\n\n\n\n \nET\n\n\n\nB\n 3\n\n\n\n \nIB\n\n\n\nK\n 1\n\n\n\n \nIB\n\n\n\nK\n 2\n\n\n\n \nIB\n\n\n\nK\n 3\n\n\n\n\n\n\n\n A\nnn\n\n\n\nua\nl r\n\n\n\nai\nnf\n\n\n\nal\nl \n\n\n\nm\nm\n\n\n\n  \n25\n\n\n\n84\n.2\n\n\n\n \n25\n\n\n\n84\n.2\n\n\n\n \n25\n\n\n\n84\n.2\n\n\n\n \n25\n\n\n\n84\n.2\n\n\n\n \n25\n\n\n\n84\n.2\n\n\n\n \n25\n\n\n\n84\n.2\n\n\n\n \n25\n\n\n\n84\n.2\n\n\n\n \n25\n\n\n\n84\n.2\n\n\n\n \n25\n\n\n\n84\n.2\n\n\n\n\n\n\n\n M\nea\n\n\n\nn \nte\n\n\n\nm\npe\n\n\n\nra\ntu\n\n\n\nre\n  \n\n\n\n0 C\n \n\n\n\n27\n.8\n\n\n\n \n27\n\n\n\n.8\n \n\n\n\n27\n.8\n\n\n\n \n27\n\n\n\n.8\n \n\n\n\n27\n.8\n\n\n\n \n27\n\n\n\n.8\n \n\n\n\n27\n.8\n\n\n\n \n27\n\n\n\n.8\n \n\n\n\n27\n.8\n\n\n\n\n\n\n\n R\nel\n\n\n\nat\niv\n\n\n\ne \nhu\n\n\n\nm\nid\n\n\n\nity\n  \n\n\n\n%\n \n\n\n\n88\n \n\n\n\n88\n \n\n\n\n88\n \n\n\n\n89\n \n\n\n\n89\n \n\n\n\n89\n \n\n\n\n89\n \n\n\n\n89\n \n\n\n\n89\n \n\n\n\n   \n S\n\n\n\nol\nar\n\n\n\n ra\ndi\n\n\n\nat\nio\n\n\n\nn \n \n\n\n\n N\nJM\n\n\n\n-2\nda\n\n\n\ny \n-1\n\n\n\n \n11\n\n\n\n4 \n11\n\n\n\n4 \n11\n\n\n\n4 \n11\n\n\n\n4 \n11\n\n\n\n4 \n11\n\n\n\n4 \n11\n\n\n\n4 \n11\n\n\n\n4 \n11\n\n\n\n4 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n2.\n S\n\n\n\noi\nl p\n\n\n\nhy\nsi\n\n\n\nca\nl  \n\n\n\n   \nch\n\n\n\nar\nac\n\n\n\nte\nris\n\n\n\ntic\ns (\n\n\n\ns)\n: \n\n\n\n   \n S\n\n\n\noi\nl D\n\n\n\nep\nth\n\n\n\n  \ncm\n\n\n\n  \n20\n\n\n\n2 \n20\n\n\n\n5 \n15\n\n\n\n2 \n20\n\n\n\n0 \n14\n\n\n\n0 \n10\n\n\n\n0 \n20\n\n\n\n8 \n21\n\n\n\n3 \n80\n\n\n\n\n\n\n\n F\nin\n\n\n\ne \nsa\n\n\n\nnd\n \n\n\n\n%\n \n\n\n\n63\n.2\n\n\n\n7 \n47\n\n\n\n.4\n0 \n\n\n\n63\n.2\n\n\n\n5 \n20\n\n\n\n.0\n6 \n\n\n\n13\n.8\n\n\n\n2 \n10\n\n\n\n.3\n2 \n\n\n\n83\n.6\n\n\n\n6 \n68\n\n\n\n.0\n5 \n\n\n\n70\n.2\n\n\n\n6 \n   \n\n\n\nC\noa\n\n\n\nrs\ne \n\n\n\nsa\nnd\n\n\n\n \n%\n\n\n\n \n23\n\n\n\n.8\n2 \n\n\n\n40\n.1\n\n\n\n2 \n27\n\n\n\n.1\n0 \n\n\n\n68\n.3\n\n\n\n5 \n74\n\n\n\n.6\n0 \n\n\n\n80\n.8\n\n\n\n8 \n9.\n\n\n\n60\n \n\n\n\n23\n.6\n\n\n\n5 \n22\n\n\n\n.6\n2 \n\n\n\n   \n T\n\n\n\not\nal\n\n\n\n sa\nnd\n\n\n\n \n%\n\n\n\n \n87\n\n\n\n.0\n9 \n\n\n\n87\n.5\n\n\n\n2 \n90\n\n\n\n.3\n5 \n\n\n\n88\n.4\n\n\n\n1 \n88\n\n\n\n.4\n2 \n\n\n\n91\n.2\n\n\n\n0 \n93\n\n\n\n.2\n6 \n\n\n\n91\n.7\n\n\n\n0 \n92\n\n\n\n.8\n8 \n\n\n\n   \n S\n\n\n\nilt\n \n\n\n\n%\n \n\n\n\n5.\n99\n\n\n\n \n3.\n\n\n\n70\n \n\n\n\n4.\n72\n\n\n\n \n5.\n\n\n\n27\n \n\n\n\n5.\n26\n\n\n\n \n1.\n\n\n\n95\n \n\n\n\n1.\n86\n\n\n\n \n3.\n\n\n\n60\n \n\n\n\n2.\n49\n\n\n\n\n\n\n\n C\nla\n\n\n\ny \n \n\n\n\n%\n \n\n\n\n6.\n92\n\n\n\n \n8.\n\n\n\n78\n \n\n\n\n4.\n93\n\n\n\n \n6.\n\n\n\n32\n \n\n\n\n6.\n32\n\n\n\n \n6.\n\n\n\n85\n \n\n\n\n4.\n88\n\n\n\n \n4.\n\n\n\n70\n \n\n\n\n4.\n63\n\n\n\n\n\n\n\n T\nex\n\n\n\ntu\nre\n\n\n\n  \n- \n\n\n\nSa\nnd\n\n\n\n \nsa\n\n\n\nnd\n \n\n\n\nsa\nnd\n\n\n\n \nsa\n\n\n\nnd\n \n\n\n\nsa\nnd\n\n\n\n \nSa\n\n\n\nnd\n \n\n\n\nsa\nnd\n\n\n\n \nsa\n\n\n\nnd\n \n\n\n\nsa\nnd\n\n\n\n\n\n\n\n S\ntru\n\n\n\nct\nur\n\n\n\ne \n \n\n\n\n \nf,g\n\n\n\nr;m\n;s\n\n\n\nbk\n \n\n\n\nM\n,g\n\n\n\nr,s\nbk\n\n\n\n \nc,\n\n\n\ngr\n,m\n\n\n\nsb\nk \n\n\n\nf,c\nr;m\n\n\n\nsb\nk \n\n\n\nf,c\nr,c\n\n\n\nsb\nk \n\n\n\nc,\ngr\n\n\n\n \nf,c\n\n\n\nr,m\n,sb\n\n\n\nk \nf,c\n\n\n\nr;m\nsb\n\n\n\nk \nf,c\n\n\n\nr;m\n,sb\n\n\n\nk \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n3.\n T\n\n\n\nop\nog\n\n\n\nra\nph\n\n\n\ny \n(t)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n S\nlo\n\n\n\npe\n \n\n\n\n%\n \n\n\n\n0-\n2 \n\n\n\n6-\n13\n\n\n\n \n0-\n\n\n\n2 \n0-\n\n\n\n2 \n3-\n\n\n\n8 \n0-\n\n\n\n2 \n0-\n\n\n\n2 \n6-\n\n\n\n13\n \n\n\n\n0-\n2 \n\n\n\n 4.\n W\n\n\n\net\nne\n\n\n\nss\n (w\n\n\n\n) (\nor\n\n\n\n G\nro\n\n\n\nun\nd \n\n\n\nW\nat\n\n\n\ner\n T\n\n\n\nab\nle\n\n\n\n) \n   \n\n\n\n D\nra\n\n\n\nin\nag\n\n\n\ne \n \n\n\n\n- \n2 \n\n\n\n3 \n3 \n\n\n\n3 \n3 \n\n\n\n3 \n2 \n\n\n\n3 \n3 \n\n\n\n   \n F\n\n\n\nlo\nod\n\n\n\n d\nur\n\n\n\nat\nio\n\n\n\nn \nm\n\n\n\non\nth\n\n\n\ns \n- \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\n- \n- \n\n\n\n   \n G\n\n\n\nro\nun\n\n\n\nd \nw\n\n\n\nat\ner\n\n\n\n ta\nbl\n\n\n\ne \ncm\n\n\n\n \nN\n\n\n\nE \nN\n\n\n\nE \n15\n\n\n\n2 \nN\n\n\n\nE \n14\n\n\n\n0 \n10\n\n\n\n0 \nN\n\n\n\nE \nN\n\n\n\nE \n80\n\n\n\n\n\n\n\n\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 29\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nLa\nnd\n\n\n\n q\nua\n\n\n\nlit\nie\n\n\n\ns /\nch\n\n\n\nar\nac\n\n\n\nte\nris\n\n\n\ntic\ns o\n\n\n\nf p\ned\n\n\n\non\ns o\n\n\n\nf t\nhe\n\n\n\n b\nea\n\n\n\nch\n ri\n\n\n\ndg\ne \n\n\n\nsa\nnd\n\n\n\ns (\nco\n\n\n\nnt\nin\n\n\n\nue\nd)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\n \nLa\n\n\n\nnd\n q\n\n\n\nua\nlit\n\n\n\nie\ns /\n\n\n\nch\nar\n\n\n\nac\nte\n\n\n\nris\ntic\n\n\n\ns o\nf p\n\n\n\ned\non\n\n\n\ns o\nf t\n\n\n\nhe\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns (\n\n\n\nco\nnt\n\n\n\nin\nue\n\n\n\nd)\n \n\n\n\n \nLa\n\n\n\nnd\n q\n\n\n\nua\nlit\n\n\n\nie\ns/\n\n\n\n c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\ns \n\n\n\nPe\ndo\n\n\n\nns\n  \n\n\n\n \nU\n\n\n\nni\nt \n\n\n\nIK\nW\n\n\n\n 1\n \n\n\n\n \nIK\n\n\n\nW\n 2\n\n\n\n\n\n\n\nIK\nW\n\n\n\n3 \n \n\n\n\nET\nB\n\n\n\n 1\n \n\n\n\nET\nB\n\n\n\n 2\n \n\n\n\nET\nB\n\n\n\n 3\n \n\n\n\nIB\nK\n\n\n\n 1\n \n\n\n\nIB\nK\n\n\n\n 2\n \n\n\n\nIB\nK\n\n\n\n 3\n \n\n\n\n5.\n F\n\n\n\ner\ntil\n\n\n\nity\n (f\n\n\n\n) \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n  p\n\n\n\nH\n \n\n\n\n- \n3.\n\n\n\n71\n \n\n\n\n4.\n35\n\n\n\n \n3.\n\n\n\n42\n \n\n\n\n4.\n04\n\n\n\n \n3.\n\n\n\n96\n \n\n\n\n3.\n86\n\n\n\n \n3.\n\n\n\n54\n \n\n\n\n3.\n27\n\n\n\n \n3.\n\n\n\n61\n \n\n\n\n  T\not\n\n\n\nal\n N\n\n\n\n \n%\n\n\n\n \n0.\n\n\n\n04\n3 \n\n\n\n0.\n52\n\n\n\n \n0.\n\n\n\n04\n3 \n\n\n\n0.\n04\n\n\n\n5 \n0.\n\n\n\n04\n6 \n\n\n\n0.\n04\n\n\n\n9 \n0.\n\n\n\n04\n8 \n\n\n\n0.\n04\n\n\n\n5 \n0.\n\n\n\n04\n0 \n\n\n\n  O\nrg\n\n\n\nan\nic\n\n\n\n M\nat\n\n\n\nte\nr \n\n\n\n%\n \n\n\n\n1.\n91\n\n\n\n \n2.\n\n\n\n08\n \n\n\n\n1.\n76\n\n\n\n \n1.\n\n\n\n82\n \n\n\n\n1.\n85\n\n\n\n \n2.\n\n\n\n94\n \n\n\n\n1.\n98\n\n\n\n \n1.\n\n\n\n79\n \n\n\n\n2.\n00\n\n\n\n \n  A\n\n\n\nva\nila\n\n\n\nbl\ne \n\n\n\nP \nm\n\n\n\ngk\ng-1\n\n\n\n \n6.\n\n\n\n33\n \n\n\n\n12\n.1\n\n\n\n1 \n8.\n\n\n\n61\n \n\n\n\n13\n.1\n\n\n\n1 \n8.\n\n\n\n28\n \n\n\n\n10\n.4\n\n\n\n8 \n9.\n\n\n\n59\n \n\n\n\n8.\n03\n\n\n\n \n10\n\n\n\n.4\n8 \n\n\n\n  E\nxc\n\n\n\nha\nng\n\n\n\nea\nbl\n\n\n\ne \nK\n\n\n\n \ncm\n\n\n\nol\nc \nkg\n\n\n\n-1\n \n\n\n\n0.\n07\n\n\n\n7 \n0.\n\n\n\n07\n5 \n\n\n\n0.\n06\n\n\n\n8 \n0.\n\n\n\n07\n6 \n\n\n\n0.\n06\n\n\n\n9 \n0.\n\n\n\n07\n1 \n\n\n\n0.\n05\n\n\n\n8 \n0.\n\n\n\n03\n2 \n\n\n\n0.\n06\n\n\n\n0 \n  E\n\n\n\nxc\nha\n\n\n\nng\nea\n\n\n\nbl\ne \n\n\n\nC\na \n\n\n\ncm\nol\n\n\n\nc \nkg\n\n\n\n-1\n \n\n\n\n1.\n00\n\n\n\n \n1.\n\n\n\n11\n \n\n\n\n1.\n25\n\n\n\n \n2.\n\n\n\n49\n \n\n\n\n2.\n66\n\n\n\n \n1.\n\n\n\n98\n \n\n\n\n1.\n45\n\n\n\n \n1.\n\n\n\n69\n \n\n\n\n1.\n30\n\n\n\n \n  E\n\n\n\nxc\nha\n\n\n\nng\nea\n\n\n\nbl\ne \n\n\n\nM\ng \n\n\n\ncm\nol\n\n\n\nc \nkg\n\n\n\n-1\n \n\n\n\n0.\n73\n\n\n\n \n0.\n\n\n\n31\n \n\n\n\n0.\n72\n\n\n\n \n1.\n\n\n\n15\n \n\n\n\n1.\n67\n\n\n\n \n1.\n\n\n\n37\n \n\n\n\n0.\n31\n\n\n\n \n1.\n\n\n\n31\n \n\n\n\n0.\n33\n\n\n\n \n  E\n\n\n\nxc\nha\n\n\n\nng\nea\n\n\n\nbl\ne \n\n\n\nN\na \n\n\n\ncm\nol\n\n\n\nc \nkg\n\n\n\n-1\n \n\n\n\n0.\n04\n\n\n\n6 \n0.\n\n\n\n05\n6 \n\n\n\n0.\n04\n\n\n\n8 \n0.\n\n\n\n05\n4 \n\n\n\n0.\n04\n\n\n\n6 \n0.\n\n\n\n05\n2 \n\n\n\n0.\n04\n\n\n\n5 \n0.\n\n\n\n04\n0 \n\n\n\n0.\n05\n\n\n\n5 \n  C\n\n\n\nEC\n (S\n\n\n\noi\nl) \n\n\n\n \ncm\n\n\n\nol\nc \nkg\n\n\n\n-1\n \n\n\n\n2.\n93\n\n\n\n \n2.\n\n\n\n89\n \n\n\n\n3.\n36\n\n\n\n \n5.\n\n\n\n27\n \n\n\n\n4.\n88\n\n\n\n \n4.\n\n\n\n92\n \n\n\n\n3.\n88\n\n\n\n \n4.\n\n\n\n11\n \n\n\n\n3.\n18\n\n\n\n \n  B\n\n\n\nas\ne \n\n\n\nSa\ntu\n\n\n\nra\ntio\n\n\n\nn \n \n\n\n\n%\n \n\n\n\n65\n \n\n\n\n62\n \n\n\n\n68\n \n\n\n\n50\n \n\n\n\n64\n \n\n\n\n60\n \n\n\n\n58\n \n\n\n\n63\n \n\n\n\n62\n \n\n\n\nTo\nxi\n\n\n\nci\nty\n\n\n\n (t\n) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  A\nva\n\n\n\nila\nbl\n\n\n\ne \nFe\n\n\n\n \nm\n\n\n\ng \nkg\n\n\n\n-1\n \n\n\n\n21\n.0\n\n\n\n2 \n25\n\n\n\n.4\n0 \n\n\n\n19\n.9\n\n\n\n3 \n20\n\n\n\n.4\n4 \n\n\n\n24\n.8\n\n\n\n9 \n21\n\n\n\n.7\n9 \n\n\n\n19\n.4\n\n\n\n3 \n37\n\n\n\n.3\n4 \n\n\n\n22\n.2\n\n\n\n3 \n  A\n\n\n\nva\nila\n\n\n\nbl\ne \n\n\n\nM\nn \n\n\n\nm\ng \n\n\n\nkg\n-1\n\n\n\n \n1.\n\n\n\n7 \n1.\n\n\n\n3 \n1.\n\n\n\n29\n \n\n\n\n1.\n85\n\n\n\n \n1.\n\n\n\n76\n \n\n\n\n6.\n61\n\n\n\n \n1.\n\n\n\n43\n \n\n\n\n1.\n42\n\n\n\n \n1.\n\n\n\n39\n \n\n\n\n  A\nva\n\n\n\nila\nbl\n\n\n\ne \nZn\n\n\n\n \nm\n\n\n\ng \nkg\n\n\n\n-1\n \n\n\n\n3.\n1 \n\n\n\n2.\n8 \n\n\n\n2.\n25\n\n\n\n \n2.\n\n\n\n94\n \n\n\n\n2.\n46\n\n\n\n \n2.\n\n\n\n70\n \n\n\n\n3.\n73\n\n\n\n \n2.\n\n\n\n66\n \n\n\n\n1.\n79\n\n\n\n \n  A\n\n\n\nva\nila\n\n\n\nbl\ne \n\n\n\nC\na \n\n\n\nm\ng \n\n\n\nkg\n-1\n\n\n\n \n1.\n\n\n\n7 \n1.\n\n\n\n39\n \n\n\n\n1.\n28\n\n\n\n \n0.\n\n\n\n86\n \n\n\n\n0.\n96\n\n\n\n \n0.\n\n\n\n84\n \n\n\n\n1.\n24\n\n\n\n \n1.\n\n\n\n05\n \n\n\n\n0.\n91\n\n\n\n \nSa\n\n\n\nlin\nity\n\n\n\n / \nal\n\n\n\nka\nlin\n\n\n\nity\n (n\n\n\n\n) \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n  E\n\n\n\nC\n (m\n\n\n\nea\nns\n\n\n\n)  \n  \n\n\n\nm\nS \n\n\n\ncm\n-1\n\n\n\n \n0.\n\n\n\n04\n6 \n\n\n\n0.\n24\n\n\n\n3 \n0.\n\n\n\n04\n9 \n\n\n\n0.\n09\n\n\n\n2 \n0.\n\n\n\n11\n \n\n\n\n0.\n07\n\n\n\n7 \n0.\n\n\n\n03\n5 \n\n\n\n0.\n04\n\n\n\n9 \n0.\n\n\n\n03\n5 \n\n\n\nK\ney\n\n\n\n:  \nSt\n\n\n\nru\nct\n\n\n\nur\ne:\n\n\n\n   \nf, \n\n\n\ngr\n =\n\n\n\n fi\nne\n\n\n\n, g\nra\n\n\n\nnu\nla\n\n\n\nr; \nm\n\n\n\n, s\nbk\n\n\n\n =\n m\n\n\n\ned\niu\n\n\n\nm\n, s\n\n\n\nub\nan\n\n\n\ngu\nla\n\n\n\nr b\nlo\n\n\n\nck\ny;\n\n\n\n\n\n\n\n \nm\n\n\n\n, g\nr =\n\n\n\n m\n, g\n\n\n\nra\nnu\n\n\n\nla\nr; \n\n\n\nf, \ncr\n\n\n\n =\n fi\n\n\n\nne\n, c\n\n\n\nru\nm\n\n\n\nb;\n c\n\n\n\n, s\nbk\n\n\n\n =\n c\n\n\n\noa\nrs\n\n\n\ne,\n su\n\n\n\nba\nng\n\n\n\nul\nar\n\n\n\n b\nlo\n\n\n\nck\ny;\n\n\n\n  c\n, g\n\n\n\nr =\n c\n\n\n\noa\nrs\n\n\n\ne,\n g\n\n\n\nra\nnu\n\n\n\nla\nr. \n\n\n\n \n D\n\n\n\nra\nin\n\n\n\nag\ne/\n\n\n\ngr\nou\n\n\n\nnd\n w\n\n\n\nat\ner\n\n\n\n ta\nbl\n\n\n\ne:\n  \n\n\n\n 2\n =\n\n\n\n w\nel\n\n\n\nl d\nra\n\n\n\nin\ned\n\n\n\n, 3\n =\n\n\n\n fa\nirl\n\n\n\ny \nw\n\n\n\nel\nl d\n\n\n\nra\nin\n\n\n\ned\n; N\n\n\n\nE \n= \n\n\n\nno\nt e\n\n\n\nnc\nou\n\n\n\nnt\ner\n\n\n\ned\n.  \n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201330\n\n\n\nto crop performance). Class designations from the three categorical levels are \ncombined to form a FCC unit. Thus, the soils were classified according to whether \na characteristic was present or not. The FCC units of the nine pedons from the \nbeach ridge sands were determined based on the soil profile characteristics. Each \nFCC unit lists the \u2018type\u2019 and \u2018substrata type\u2019 (which was the same as the type in \nthis study) in capital letters, and the modifiers in lower case letters.\n\n\n\nThe result of FCC in Table 3 shows that all the nine pedons in the study area \nhave the same FCC unit, SehK (except for the variation in topographic positions \nor slope). The results show that the soils are generally characterized by uniformly \nsandy profiles (top and sub-soils), represented by S; they have low cation exchange \ncapacity (CEC), represented by e; they have acidic reaction, represented by h; and \nare deficient in exchangeable potassium, which is represented by K. These results \nare presented in the summary with the kinds of problems presented by the beach \nridge soils for agronomic management of their chemical and physical properties \n(Boul et al. 1975; Sanchez et al. 1982).\n\n\n\nLand Suitability Evaluation \nThe potential (and limitations) of some land qualities/characteristics (climate, soil \nphysical characteristics, topography, wetness or ground water table, fertility and \nsalinity/alkalinity) in determining the suitability of the nine pedons (representing \nthe area of study) for the cultivation of some important economic crops (oil palm, \ncocoa, cashew, coconut, rubber and upland rice) was evaluated. The evaluation \nwas carried out following the conventional method of the FAO (1976) framework \nfor land evaluation.\n\n\n\nThe determination of land suitability involved the matching of the land \nqualities/characteristics (Table 4) with the established requirements (Sys 1985; \nOgunkunle 1993) for each of the crops (oil palm, cocoa, cashew, coconut, rubber \nand upland rice). After matching the land quality/characteristics with the land \nrequirement for the crop, depending on the extent to which the land quality/\ncharacteristic satisfied the requirement, each limiting characteristic was rated \n(Table 5). For the non-parametric evaluation (the method reported here), the \nfinal (aggregate) suitability class in Table 6 is indicated by the most limiting land \nquality/characteristics of the pedon (FAO 1976).\n\n\n\nResults of Matching Land Requirements for Crop Cultivation with Land \nQualities/Characteristics: Oil Palm\n\n\n\nClimate (c)\nThe class score (rating) of the nine pedons in the study area (Table 5), shows that \nthe area is climatically suitable for oil palm, being optimal (100% suitable) in \nterms of annual rainfall and relative humidity and nearly optimal (95% suitable) \nin terms of mean temperature.\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 31\n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 5\n\n\n\n\n\n\n\nLa\nnd\n\n\n\n Q\nua\n\n\n\nlit\nie\n\n\n\ns  \n  IK\n\n\n\nW\n 1\n\n\n\n\n\n\n\n  IK\nW\n\n\n\n 2\n \n\n\n\n  IK\nW\n\n\n\n 3\n \n\n\n\nPe\ndo\n\n\n\nns\n \n\n\n\n \n  ET\n\n\n\nB\n 3\n\n\n\n\n\n\n\n  IB\nK\n\n\n\n 1\n \n\n\n\n  \nIB\n\n\n\nK\n 2\n\n\n\n\n\n\n\n  IB\nK\n\n\n\n 3\n \n\n\n\nET\nB\n\n\n\n 1\n \n\n\n\n E\nTB\n\n\n\n 2\n \n\n\n\nC\nlim\n\n\n\nat\ne \n\n\n\n(c\n) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n  A\n\n\n\nnn\nua\n\n\n\nl r\nai\n\n\n\nnf\nal\n\n\n\nl (\nm\n\n\n\nm\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\n  M\nea\n\n\n\nn \nte\n\n\n\nm\npe\n\n\n\nra\ntu\n\n\n\nre\n(%\n\n\n\n) \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \n  R\n\n\n\nel\nat\n\n\n\niv\ne \n\n\n\nH\num\n\n\n\nid\nity\n\n\n\n(%\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\n  T\nop\n\n\n\nog\nra\n\n\n\nph\ny \n\n\n\n(t)\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n  S\n\n\n\nlo\npe\n\n\n\n %\n \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(8\n\n\n\n0)\n \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nW\net\n\n\n\nne\nss\n\n\n\n (w\n)  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n  D\n\n\n\nra\nin\n\n\n\nag\ne \n\n\n\n \nS1\n\n\n\n(1\n00\n\n\n\n) \nS1\n\n\n\n(1\n00\n\n\n\n) \nS3\n\n\n\n(4\n0)\n\n\n\n \n32\n\n\n\n(1\n00\n\n\n\n) \nS1\n\n\n\n(1\n00\n\n\n\n) \nS3\n\n\n\n(4\n0)\n\n\n\n \nS1\n\n\n\n(1\n00\n\n\n\n) \nS1\n\n\n\n(1\n00\n\n\n\n) \nS3\n\n\n\n(4\n0)\n\n\n\n \nSo\n\n\n\nil \nph\n\n\n\nys\nic\n\n\n\nal\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\ns (\ns)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  T\nex\n\n\n\ntu\nre\n\n\n\n  \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \n  S\n\n\n\ntru\nct\n\n\n\nur\ne \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\n  D\nep\n\n\n\nth\n (c\n\n\n\nm\n)  \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS3\n(6\n\n\n\n0)\n \n\n\n\n F\ner\n\n\n\ntil\nity\n\n\n\n (f\n)  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n  A\n\n\n\npp\nar\n\n\n\nen\nt C\n\n\n\nEC\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\nS1\n(9\n\n\n\n5)\n \n\n\n\n  B\nas\n\n\n\ne \nSa\n\n\n\ntu\nra\n\n\n\ntio\nn \n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \n  p\n\n\n\nH\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\nS3\n(4\n\n\n\n0)\n \n\n\n\n  O\nrg\n\n\n\nan\nic\n\n\n\n c\nat\n\n\n\nio\nn \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\nS2\n(8\n\n\n\n0)\n \n\n\n\n  K\n (m\n\n\n\nol\ne \n\n\n\nfr\nac\n\n\n\ntio\nn)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \nS3\n\n\n\n(4\n0)\n\n\n\n \n  M\n\n\n\ng:\n k\n\n\n\n ra\ntio\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nS1\n\n\n\n(9\n5)\n\n\n\n \nSa\n\n\n\nlin\nity\n\n\n\n /A\nlk\n\n\n\nal\nin\n\n\n\nity\n (n\n\n\n\n) \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  E\nC\n\n\n\n (m\nS/\n\n\n\ncm\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\nS1\n(1\n\n\n\n00\n) \n\n\n\n  A\ngg\n\n\n\nre\nga\n\n\n\nte\ns s\n\n\n\nta\nbi\n\n\n\nlit\ny \n\n\n\nS3\nsf\n\n\n\n \nS3\n\n\n\nsf\n \n\n\n\nS3\nsf\n\n\n\n \nS3\n\n\n\nsf\n \n\n\n\nS3\nsf\n\n\n\n \nS3\n\n\n\nsf\n \n\n\n\nS3\nsf\n\n\n\n \nS3\n\n\n\nsf\n \n\n\n\nS3\nsf\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 5\n\n\n\nSu\nita\n\n\n\nbi\nlit\n\n\n\ny \nC\n\n\n\nla\nss\n\n\n\n sc\nor\n\n\n\nes\n o\n\n\n\nf p\ned\n\n\n\non\ns o\n\n\n\nf b\nea\n\n\n\nch\n ri\n\n\n\ndg\ne \n\n\n\nsa\nnd\n\n\n\ns f\nor\n\n\n\n o\nil \n\n\n\npa\nlm\n\n\n\n c\nul\n\n\n\ntiv\nat\n\n\n\nio\nn\n\n\n\nA\ngg\n\n\n\nre\nga\n\n\n\nte\n su\n\n\n\nita\nbi\n\n\n\nlit\ny \n\n\n\ncl\nas\n\n\n\ns s\nco\n\n\n\nre\n:\n\n\n\n10\n0-\n\n\n\n75\n=S\n\n\n\n1;\n 7\n\n\n\n4-\n50\n\n\n\n=S\n2;\n\n\n\n 4\n9-\n\n\n\n25\n=S\n\n\n\n3;\n 2\n\n\n\n4-\n15\n\n\n\n=N\n1;\n\n\n\n 1\n4-\n\n\n\n0=\nN\n\n\n\n2;\n s \n\n\n\n= \nso\n\n\n\nil \nph\n\n\n\nys\nic\n\n\n\nal\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\n li\nm\n\n\n\nita\ntio\n\n\n\nn;\n f \n\n\n\n= \nso\n\n\n\nil \nfe\n\n\n\nrti\nlit\n\n\n\ny \nlim\n\n\n\nita\ntio\n\n\n\nn \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201332\n\n\n\nTopography (t) and Drainage (w)\nIn terms of topography (slope), six pedons were optimal while three pedons (IKW \n2, ETB 2 and IBK 2) were sub-optimal (80% suitable) for oil palm cultivation \nbecause they had slopes > 4% (Sys, 1985). In terms of drainage, six of the pedons \nwere optimal while three were only marginally suitable for oil palm cultivation \n(Table 6). This shows that topography and drainage are not very serious limitations \nto oil palm cultivation in the area.\n\n\n\nSoil Physical Characteristics (s)\nSoil depth as one of the physical characteristics was optimal in the entire area \nexcept for one pedon (IBK 3) which was sub-optimal (60% suitable). However, \nsoil texture (and structure) was the most limiting of the soil physical characteristics. \nSoil texture for optimum productivity of oil palm should be clay loam, sandy clay \nloam or loam (Sys 1985), but the texture for all the pedons in the area was sand/\nloamy sand (Table 4). This has rendered the entire area only marginally suitable \nfor oil palm cultivation and constitutes a major constraint to oil palm production \n(Table 6). \n\n\n\nFertility (f) and Salinity / Alkalinity (n)\nSoil fertility is another serious constraint limiting oil palm cultivation on the beach \nridge sands soils. Although cation exchange capacity (CEC), base saturation, \norganic carbon and Mg:K ratio were rated sub-optimal (95/80% suitable), soil pH \nand K (mole fraction) were grossly inadequate (40% suitable; Table 5), thereby \nrendering the whole area only marginally suitable for oil palm cultivation. But in \nterms of salinity/alkalinity, the entire area was rated optimal (100% suitable) for \noil palm cultivation.\n\n\n\nAggregate Suitability for Cultivation of Oil Palm, Cocoa, Cashew, Coconut, \nRubber and Upland Rice\n\n\n\nThe individual ratings of the land characteristics for each pedon for oil palm \ncultivation is shown in Table 5, while Table 6 shows the aggregate suitability \nclassification of each pedon for each of the six crops: oil palm and five others \n(cocoa, cashew, coconut, rubber and upland rice) which were also evaluated in a \nsimilar method. \n\n\n\nIn this study, aggregate suitability classes S1 (highly suitable), S2 (moderately \nsuitable), S3 (marginally suitable) N1 (currently not suitable) and N2 (permanently \nnot suitable), are equivalents of suitability class scores (ratings) 100-75, 74-50, \n49-25, 24-15, 14-0, respectively. This study adopted the conventional (FAO, \n1976) method, in which case just one characteristic, that is, least suitable decides \nthe aggregate suitability class of a pedon. Accordingly, all the pedons in the study \narea were classified as marginally suitable (S3) for oil palm cultivation because of \nthe severity of soil physical characteristic (s) and fertility (f) limitations.\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 33\n\n\n\nTA\nB\n\n\n\nLE\n 6\n\n\n\nSu\nita\n\n\n\nbi\nlit\n\n\n\ny \ncl\n\n\n\nas\nsi\n\n\n\nfic\nat\n\n\n\nio\nn \n\n\n\nof\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns p\n\n\n\ned\non\n\n\n\ns f\nor\n\n\n\n si\nx \n\n\n\ncr\nop\n\n\n\ns, \nin\n\n\n\ndi\nca\n\n\n\ntin\ng \n\n\n\nlim\niti\n\n\n\nng\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\ns\n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n I\nSS\n\n\n\nN\n: 1\n\n\n\n39\n4-\n\n\n\n79\n90\n\n\n\n \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n Jo\nur\n\n\n\nna\nl o\n\n\n\nf S\noi\n\n\n\nl S\nci\n\n\n\nen\nce\n\n\n\n V\nol\n\n\n\n. 1\n7:\n\n\n\n x\n \u2013\n\n\n\nx \n( 2\n\n\n\n01\n3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n   \nM\n\n\n\nal\nay\n\n\n\nsi\nan\n\n\n\n S\noc\n\n\n\nie\nty\n\n\n\n o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n \n\n\n\n\n\n\n\nM\nal\n\n\n\nay\nsi\n\n\n\nan\n Jo\n\n\n\nur\nna\n\n\n\nl o\nf S\n\n\n\noi\nl S\n\n\n\nci\nen\n\n\n\nce\n V\n\n\n\nol\n. 1\n\n\n\n7,\n 2\n\n\n\n01\n3 \n\n\n\n    \nTA\n\n\n\nB\nLE\n\n\n\n 6\n \n\n\n\nSu\nita\n\n\n\nbi\nlit\n\n\n\ny \ncl\n\n\n\nas\nsi\n\n\n\nfic\nat\n\n\n\nio\nn \n\n\n\nof\n b\n\n\n\nea\nch\n\n\n\n ri\ndg\n\n\n\ne \nsa\n\n\n\nnd\ns p\n\n\n\ned\non\n\n\n\ns f\nor\n\n\n\n si\nx \n\n\n\ncr\nop\n\n\n\ns, \nin\n\n\n\ndi\nca\n\n\n\ntin\ng \n\n\n\nlim\niti\n\n\n\nng\n c\n\n\n\nha\nra\n\n\n\nct\ner\n\n\n\nis\ntic\n\n\n\ns \n   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n---\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\nC\n\n\n\nro\np \n\n\n\nty\npe\n\n\n\n/s\nui\n\n\n\nta\nbi\n\n\n\nlit\ny \n\n\n\ncl\nas\n\n\n\ns-\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n--\n--\n\n\n\n- \nPe\n\n\n\ndo\nn \n\n\n\n \nO\n\n\n\nil \nPa\n\n\n\nlm\n \n\n\n\nC\noc\n\n\n\noa\n \n\n\n\nR\nub\n\n\n\nbe\nr \n\n\n\nC\nas\n\n\n\nhe\nw\n\n\n\n \nC\n\n\n\noc\non\n\n\n\nut\n \n\n\n\nU\npl\n\n\n\nan\nd \n\n\n\nR\nic\n\n\n\ne \nIK\n\n\n\nW\n 1\n\n\n\n \nS3\n\n\n\n s,\nf \n\n\n\nS2\ns, \n\n\n\nf \nS3\n\n\n\nf \nS2\n\n\n\ns \nS1\n\n\n\n \nS3\n\n\n\nw\n \n\n\n\nIK\nW\n\n\n\n 2\n \n\n\n\nS3\n s,\n\n\n\nf \nS2\n\n\n\ns, \nf \n\n\n\nS3\nf \n\n\n\nS2\nt,s\n\n\n\n \nS2\n\n\n\nt \nS3\n\n\n\nw\n,s \n\n\n\nIK\nW\n\n\n\n 3\n \n\n\n\nS3\n s,\n\n\n\nf \nS3\n\n\n\ns, \nf \n\n\n\nS3\nf \n\n\n\nS2\nw\n\n\n\n,s \nS1\n\n\n\n \nS3\n\n\n\ns \nET\n\n\n\nB\n 1\n\n\n\n \nS3\n\n\n\n s,\nf \n\n\n\nS2\ns, \n\n\n\nf \nS3\n\n\n\nf \nS2\n\n\n\nw\n,s \n\n\n\nS1\n \n\n\n\nS2\n s,\n\n\n\nf \nET\n\n\n\nB\n 2\n\n\n\n \nS3\n\n\n\n s,\nf \n\n\n\nS2\ns, \n\n\n\nf \nS3\n\n\n\nf \nS2\n\n\n\nw\n,s \n\n\n\nS1\n \n\n\n\nS3\ns \n\n\n\nET\nB\n\n\n\n 3\n \n\n\n\nS3\n s,\n\n\n\nf \nS2\n\n\n\nw\n,s \n\n\n\nS3\nf \n\n\n\nS2\nw\n\n\n\n,s \nS1\n\n\n\n \nS3\n\n\n\ns \nIB\n\n\n\nK\n 1\n\n\n\n \nS3\n\n\n\n s,\nf \n\n\n\nS2\ns,f\n\n\n\n \nS2\n\n\n\ns,f\n \n\n\n\nS2\ns \n\n\n\nS1\n \n\n\n\nS3\nw\n\n\n\n,s \nIB\n\n\n\nK\n 2\n\n\n\n \nS3\n\n\n\n s.\nf \n\n\n\nS3\n,s \n\n\n\nS3\nf \n\n\n\nS2\nt,s\n\n\n\n \nS2\n\n\n\nt,s\n \n\n\n\nS3\nw\n\n\n\n.s \nIB\n\n\n\nK\n 3\n\n\n\n \nS3\n\n\n\ns,f\n \n\n\n\nS3\n w\n\n\n\n,s \nS3\n\n\n\nf \nS2\n\n\n\nw\n,s \n\n\n\nS1\n \n\n\n\nS3\ns \n\n\n\n S\nI =\n\n\n\n h\nig\n\n\n\nhl\ny \n\n\n\nsu\nita\n\n\n\nbl\ne;\n\n\n\n S\n2 \n\n\n\n= \nm\n\n\n\nod\ner\n\n\n\nat\nel\n\n\n\ny \nsu\n\n\n\nita\nbl\n\n\n\ne;\n S\n\n\n\n3 \n= \n\n\n\nm\nar\n\n\n\ngi\nna\n\n\n\nlly\n su\n\n\n\nita\nbl\n\n\n\ne \n \n\n\n\n  f\n  =\n\n\n\n fe\nrti\n\n\n\nlit\ny \n\n\n\nlim\nita\n\n\n\ntio\nn;\n\n\n\n s \n= \n\n\n\nso\nil \n\n\n\nph\nys\n\n\n\nic\nal\n\n\n\n c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\n li\n\n\n\nm\nita\n\n\n\ntio\nn,\n\n\n\n t \n= \n\n\n\nto\npo\n\n\n\ngr\nap\n\n\n\nhi\nc \n\n\n\n(s\nlo\n\n\n\npe\n) l\n\n\n\nim\nita\n\n\n\ntio\nn;\n\n\n\n w\n =\n\n\n\n so\nil \n\n\n\nw\net\n\n\n\nne\nss\n\n\n\n  (\ndr\n\n\n\nai\nna\n\n\n\nge\n) l\n\n\n\nim\nita\n\n\n\ntio\nn.\n\n\n\n  \n        SI\n\n\n\n =\n h\n\n\n\nig\nhl\n\n\n\ny \nsu\n\n\n\nita\nbl\n\n\n\ne;\n S\n\n\n\n2 \n= \n\n\n\nm\nod\n\n\n\ner\nat\n\n\n\nel\ny \n\n\n\nsu\nita\n\n\n\nbl\ne;\n\n\n\n S\n3 \n\n\n\n= \nm\n\n\n\nar\ngi\n\n\n\nna\nlly\n\n\n\n su\nita\n\n\n\nbl\ne \n\n\n\n f \n =\n\n\n\n fe\nrti\n\n\n\nlit\ny \n\n\n\nlim\nita\n\n\n\ntio\nn;\n\n\n\n s \n= \n\n\n\nso\nil \n\n\n\nph\nys\n\n\n\nic\nal\n\n\n\n c\nha\n\n\n\nra\nct\n\n\n\ner\nis\n\n\n\ntic\n li\n\n\n\nm\nita\n\n\n\ntio\nn,\n\n\n\n t \n= \n\n\n\nto\npo\n\n\n\ngr\nap\n\n\n\nhi\nc \n\n\n\n(s\nlo\n\n\n\npe\n) l\n\n\n\nim\nita\n\n\n\ntio\nn;\n\n\n\n w\n =\n\n\n\n so\nil \n\n\n\nw\net\n\n\n\nne\nss\n\n\n\n  (\ndr\n\n\n\nai\nna\n\n\n\nge\n) l\n\n\n\nim\nita\n\n\n\ntio\nn.\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201334\n\n\n\nAs shown in Table 6, similar evaluations as in the case of oil palm were done \nfor cocoa, rubber, cashew, coconut and upland rice. The results showed that as in \nthe case of oil palm, the land was also marginally suitable for rubber cultivation, \nwith fertility being the most serious constraint to cultivation. Except for pedon \nETB 1 (which was moderately suitable), the land was also marginally suitable for \nupland rice with soil drainage (w), physical characteristic (s) and fertility (f) being \nserious constraints to upland rice cultivation. For cocoa cultivation, six pedons \n(67% of the area) were moderately suitable (S2) while three pedons, IKW 3, IBK \n2, IBK 3 (33% of the area), were marginally suitable. The most serious constraints \nwere soil physical characteristics, fertility and drainage.\n\n\n\nThe results of the evaluation (Table 6) further showed that soils of the beach \nridge sands were more favourable to the production of cashew. All the pedons were \nclassified as moderately suitable (S2) for cashew cultivation, with soil physical \ncharacteristics, topography and soil drainage being moderate constraints to its \nproduction. However, the crop most favoured by the land qualities/characteristics \nof soils derived from the beach ridge sands was coconut. Seven of the pedons \n(representing 78% of the area) were classified as highly suitable (S1), while two \npedons, IKW 2 and IBK2 (22% of the area), were classified as moderately suitable \n(S2), because of topographic or slope (t) constraints.\n\n\n\nSoil Management for Optimum and Sustainable Crop Production \nThe result of this study and previous works (Tahal Consultants 1982, Petters et \nal. 1989; Udo, 2001) have shown that soils of the beach ridge sands are generally \ncoarse textured. Since they are located in high rainfall areas, they are strongly \nleached and deprived of basic cations (Enwezor et al. 1981). Also, due to the \npresence of pyrites (Ojanuga et al. 2003), they are strongly acidic. Furthermore, \nthe loose nature of the soils makes them very susceptible to water erosion. High \nacidity, low CEC and low buffering capacity results in low fertility status, multiple \nnutrient deficiency and nutrient imbalance, characteristics which are common to \nthese soils.\n\n\n\nThe major management constraints are therefore soil acidity, multiple \nnutrient requirements, nutrient imbalance and soil erosion. To raise the \nproductivity of these soils and also sustain their productive potential, an integrated \nnutrient management system, which adopts an ecological approach, will be most \nappropriate. This approach involves the wise use and management of inorganic \nand organic nutrient sources in an ecologically sound production system (Ajiboye \nand Ogunwale 2010). Judicious lime application may be required to supply \ndeficient basic cations and thus raise the base saturation of these soils.  \n\n\n\nThe role of organic matter/organo-mineral fertilizers is crucial in the \nmanagement of these soils. Not only will it improve the physical properties \nof the soils and reduce erosion, it will also serve as a major reservoir of plant \nnutrients. Therefore, whatever may be the farming system adopted, a reasonable \nlevel of organic matter should be maintained at all times by use of farm yard or \n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 35\n\n\n\ngreen manure. Also, regular soil testing should be carried out for proper fertiliser \nrecommendations to ensure balanced soil nutrient application.\n\n\n\nCONCLUSION\nParent material soils of the beach ridge sands are generally coarse textured, loose, \nhighly leached, strongly acidic and low in native fertility. They are marginally \nsuitable for oil palm, rubber and upland rice cultivation because of serious fertility \nand physical characteristic constraints. The soils are moderately suitable for \ncocoa and cashew cultivation with constraints related to soil drainage, fertility, \nand physical characteristics. However, the soils are highly suitable for coconut \ncultivation.\n\n\n\nTo raise the productivity of these soils to optimum and also maintain it for \nsustainable crop production, integrated nutrient management, involving the use of \norganic/organo-mineral fertilizers, with regular soil testing for a balanced nutrient \napplication is recommended. For effective results, an ecological approach to the \nmanagement of soils derived from the beach ridge sands in the Niger Delta Region \nof Nigeria is most appropriate.\n\n\n\nREFERENCES\nAjiboye, G. A. and J. A. Ogunwale, 2010. Characteristics and classification of soils \n\n\n\ndeveloped over talc at Ejiba, Kogi  State.  Nigeria. Nigerian Journal of Soil \nScience. 20: 1-14.\n\n\n\nBrady, N. C. 1990. The Nature and Properties of Soils (10th ed.). New York: Macmillan \nPublishing Company. \n\n\n\nBuol, S.W., F. D. Hole and R. J. McGracken. 1989. Soil Genesis and Classification \n(3rd ed.). Ames: Iowa State University Press.\n\n\n\nBuol, S.W., P. A. Sanchez, R. B. Cate, Jr., and M. A. Granger. 1975. Soil fertility \ncapability classification: a technical soil classification system for fertility \nmanagement. In: Soil Management in Tropical America, ed. E. Bornemisza \nand A. Alvarado, pp. 126-145. Raleigh, North Carolina: North Carolina State \nUniversity.\n\n\n\nChinene, V.R.N. 1992. Land evaluation using the FAO framework; an example from \nZambia. Soil Use and Manage. 8: 130-139.\n\n\n\nEnwezor, W. O., E.J. Udo and R,A.Sobulo. 1981. Fertility status and productivity of \nthe \u2018acid sands\u2019, pp. 56-73. In: Acid Sands of Southern Nigeria, ed E. J. Udo and \nR. A. Sobulo . SSSN Special Publication, Monograph No.1. \n\n\n\nEsu, I.E. 2005. Characterization, Classifications and Management Problems  of the \nMajor Soil Orders in Nigeria. 26th Inaugural Lecture, University of Calabar, \nCalabar, Nigeria. \n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 201336\n\n\n\nFood and Agriculture Organisation (FAO) 1976. A Framework for Land Evaluation. \nFAO Soils Bull.32, FAO, Rome. \n\n\n\nFAO. 2006. Guidelines for Soil Descriptions (4th ed.). FAO, Rome\n\n\n\nFMANR (Federal Ministry of Agriculture and Natural Resources). 1990. Literature \nReview on Soil Fertility Investigations in Nigeria, ed., W. O. Enwezor, A.C. \nOchiri, E.E. Opuwaribo and E. J. Udo, pp. 49-100. Ibadan: Bobma Publishers,\n\n\n\nIbia, T. O. and E. J. Udo, 2009. Guide to Fertiliser Use for Crops in Akwa Ibom State, \nNigeria. Lagos: Sibon Books Limited.\n\n\n\nIITA (International Institute of Tropical Agriculture). 1979. Selected Methods for \nSoils and Plant Analysis. IITA Manual Series 1. IITA, Ibadan, Nigeria.\n\n\n\nIUSS Working Group WRB. 2007. World Reference Base for Soil Resources 2006, 1st \nUpdate 2007. World Soil Resources Reports No. 103, FAO, Rome.\n\n\n\nJungerius, P.D. 1964. The soils of Eastern Nigeria. Publication Service Geological du \nLuxemburg XIV: 185-198.\n\n\n\nOgunkunle, A.O. 1993. Soil in land suitability evaluation: an example with oil palm \nin Nigeria. Soil Use and Manage. 9:35-40\n\n\n\nOjanuga, A. G., T. A. Okunsami and G. Lekwa (eds). 2003. Wetland Soils of Nigeria: \nStatus of Knowledge and Potentials. Monograph No.2, Soil Science Society of \nNigeria (SSSN).\n\n\n\nOjanuga, A.G. 2006. Management of fadama soils for food security and poverty \nalleviation. In: Management of Fadama Soils for Environmental Quality Food \nSecurity and Poverty Alleviation in Nigeria, ed. S. Idoga, S. A. Ayuba, A Ali, O. \nO. Agbede and S. O. Ojeniyi, pp.10-15. Makurdi, Nigeria: Soil Science Society \nof Nigeria, University of Agriculture. \n\n\n\nPetters, S. W., E. J. Usoro, E. J. Udo, U. W. Obot, and S. N. Okpon. 1989.  Akwa Ibom \nState. Physical Background, Soils and Land Use and Ecological Problems. \nTechnical Report of the Task Force on Soils and Land Use Survey. Nigeria: \nGovt. Printer Uyo. \n\n\n\nSanchez, P.A., W. Couto and S. W. Buol 1982. The Fertility Capability Classification \nSystem. Interpretation, applicability and modification. Geoderma 27: 283-309.\n\n\n\nSCS News. 1984. Soil Survey \u2013 Nature, Purpose and Uses. Soil Taxonomy News 9: \n15-17.\n\n\n\nSoil Survey Staff. 2010. Keys to Soil Taxonomy. Washington, D.C.: USDA \u2013 NRCS.\n\n\n\nUdoh, B.T., I. E. Esu, T. O. Ibia, E. U. Onweremadu and S. E. Unyienyin\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 17, 2013 37\n\n\n\nSys, C. 1985. Land Evaluation. Parts I, II, III. 247p. Publication No.7 of the General \nAdministration of Cooperation Development. Place de Champs de Mars 5, boite \n57, 1050 Bruxelles.\n\n\n\nTahal Consultants. 1982. Qua Iboe River Basin Pre-feasibility Study, Vol. 1. Main \nReport. Cross River Basin Development Authority, Calabar, Nigeria.\n\n\n\nUdo, E.J. 2001. Soil Potentials of the Coastal Zone of Akwa Ibom State. Uniuyo \nConsult Ltd., Uyo, Nigeria.\n\n\n\nUdo, E. J., T. O. Ibia, J. A. Ogunwale, A. O. Ano and I. E. Esu. 2009. Manual of Soil, \nPlant and Water Analysis. Lagos, Nigeria: Sibon Books Ltd.  \n\n\n\nUdo, E.J. and J. A. Ogunwale. 1986. Laboratory Manual for Analysis of Soil, Plant \nand Water Samples. Department of Agronomy, University of Ibadan, Nigeria.\n\n\n\nUdo, E.J. and R. A. Sobulo (eds). 1981. Acid Sands of Southern Nigeria. Special \nPublication, Monograph No. 1, Soil Science Society of Nigeria (SSSN).\n\n\n\nUnyienyin, S. E. 2010. Soil evaluation and agricultural potentials of beach ridge sands \nin three locations in Akwa Ibom State, Nigeria. M.Sc. Dissertation, Department \nof Soil Science, Univ. of Uyo, Nigeria. 156p.\n\n\n\n\n\n\n\nAgricultural Potential of the Beach Ridge Soils\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 24: 121-134  (2020)                         Malaysian Society of Soil Science\n\n\n\nChanges in Microbial Populations and Chemical Properties \nof Undisturbed and Disturbed Secondary Forests Converted \n\n\n\nto Oil Palm Cultivation\n\n\n\nNur-Hanani, M.N.1*, Radziah, O.1,2 and Roslan, I.1 \n\n\n\n1Department of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\n2Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, \n43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nConversion of natural forests into monoculture oil palm plantations may result \nin detrimental effects on soil microbial population and the physical properties. \nIn this study, data on soil microbial populations and soil chemical properties \nwere collected from three different areas namely, undisturbed secondary forest \n(USF), disturbed secondary forest (DSF) and oil palm cultivated area (OP) at three \ndifferent sampling times (June 2012, January 2013, June 2013). Results showed \nthat microbial populations were significantly affected by location and time of \nsampling. The OP had the highest populations of bacteria (6.57 log10cfu g-1 soil) \nand fungi (5.57 log10cfu g-1 soil) in June 2013. Population of phosphate-solubilising \nbacteria was consistently low at the OP compared to that in the secondary forests \n(USF and DSF) at all sampling times. Most of the soil chemical properties were \naffected by time changes. Soil moisture was higher in the secondary forests (USF \nand DSF) in June 2012 and June 2013. Total C (4.09%) and N (0.31%) were higher \nin USF compared to DSF and OP in January 2013.  The findings demonstrate \nthat cultivation of oil palm did not diminish the overall microbial population and \nphysico-chemical properties of the soil. However, differences in soil attributes \nbetween the secondary forests (USF and DSF) and oil palm OP denote that oil \npalm cultivation did have some adverse effects on soil microbial population.\n \nKey words: Soil quality, secondary forest, oil palm cultivated area, soil \n       microbial population, soil chemical properties.\n\n\n\n___________________\n*Corresponding author : E-mail: hanani.hanis@gmail.com\n\n\n\nINTRODUCTION\nThe expansion of oil palm plantations in the past few decades has been a subject \nof much debate due to deforestation, increase in greenhouse gas (GHG) emissions \nand loss of biodiversity  (Khatiwada et al. 2018). Malaysia is the world\u2019s second \nlargest producer and a major exporter of oil palm with a planting area of 5.81 \nmillion hectares or 17.7 % of the total 32.86 million hectares of land area of \nMalaysia (MPOB, 2018).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020122\n\n\n\n However, oil palm is largely grown on highly weathered tropical soils \nwhich are less fertile and have low pH (< 5) with the soil consisting mainly \nof sesquioxides and kaolinite. Under these circumstances, the availability of \nphosphorus is severely limited and the acidity of tropical soils may accelerate the \nloss of basic cations (Ca2+ and Mg2+)  which can reduce biological N fixation and \ncrop yield (Teh 2017). \n External inputs such as fertiliser and pesticides are commonly introduced \nto improve plant productivity and protect the plants against diseases. However, the \napplication of pesticides and fertilisers in agriculture has been known to reduce \nsoil biota activities and diversity. Accumulation of metal contaminants from the \nfertiliser can lead to long-term chronic toxicity. Further, nitrogen-fixing rhizobia \nare more sensitive to metal toxicity (Bitew and Alemayehu, 2017). Hern\u00e1ndez \net al. (2018) state that in the short-term period, glyphosate usage decreases the \nbacterial population.  Paraquat causes disruption in bacteria and actinomycetes \npopulation, whereas fungi are most affected by glyphosate (Masirah et al. 2013).\n Soil chemical and biological properties are considerably critical, since \nthey are closely related to various functional processes of soil (Lu et al.  2013). \nSoil microbial properties serve as  a sensitive indicator providing useful insights \non short-term changes of land use (Moeskops et al. 2010). It has been shown \nthat changes in the composition of soil microbial community can lead to either a \ndecline or an improvement in management practices. Studies have shown that oil \npalm cultivation in Borneo has led to the  extinction of ectomycorrhizal fungal \ncommunities and the loss is attributed to the absence of leaf litter to support organic \nhorizon, interference of heavy vehicles which causes the soil to be compacted and \nchemical inputs from fertilisers and limes which disrupt the pH (McGuire et al. \n2015). However,  under different  circumstances, oil palm cultivation promotes \nmore diversity for certain  bacteria such as actinobacteria when compared to \nforest soil, where there is less availability of carbon and nitrogen ratio (Lee-Cruz \net al. 2013; Tripathi et al. 2012).\n To know more about converting  forests to oil palm plantations,  there \nis a need to evaluate the changes in microbial population and soil physico-\nchemical properties (Allen et al. 2011). It is known that oil palm plantations have \nan adverse impact on the microbial communities as well as physico-chemical \nproperties of the soil. Hence, the study used secondary forests as a reference to \ncompare the changes that occur in an oil palm plantation. The objectives of this \nstudy are: (1) to determine the populations of bacteria, actinomycetes, fungi and \nfunctional microbes (phosphate-solubilising bacteria and N-fixing bacteria) from \nsecondary forests and oil palm cultivated areas in Belaga, Sarawak at different \nsampling periods; and (2) to determine the relationships between the changes \nin microbiological properties and the soil physical and chemical properties in \nsecondary forests and oil palm cultivated areas.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 123\n\n\n\nSite Description\nSoil sampling was carried out at three different areas, namely undisturbed \nsecondary forest [Biodiversity strip 1 (USF)] (longitude: 113\u00ba 956688\u2019\u2013113\u00ba \n962620\u2019E, latitude: 2\u00ba 2991894\u2019- 2\u00ba 2985677\u2019N), disturbed secondary forest \n[Biodiversity strip 2 (DSF)] (longitude: 113\u00ba 973298\u2019-113\u00ba 957065\u2019E, latitude: \n2\u00ba 999828\u2019- 2\u00ba 992196N) and oil palm cultivated area (OP) (longitude: 113\u00ba \n953327\u2019- 113\u00ba956999\u2019E, latitude: 2\u00ba 985197\u2019 - 2\u00ba986037\u2019N) in Sungai Asap, \nBelaga, Sarawak. All of the sampling areas were adjacent to each other, where \nUSF and DSF were 6.0 km and 6.7 km apart, respectively, from OP cultivated \narea.  Sampling was conducted at 6-month intervals (June 2012, January 2013, \nJune 2013). Oil palm was cultivated  on a  total area of 106.4 ha in 2009 by \nsmallholders. The age of the oil palm tree was 5 years  (June 2012), 5\u00bd years,  \n(January 2013) and 6 years (June 2013)  respectively. Sarawak is categorised as \nhaving a tropical rainforest climate, with the wettest period being from November \nto February during North-East Monsoon season and dry season from June to \nAugust. Soil sampling was carried out during both the wet and dry seasons. The \naverage annual temperature ranged from 26.7\u00baC to 27.9\u00baC while the rainfall \nranged from 92 to 327 mm as shown in Table 1. \n\n\n\n\n\n\n\n3 \n \n\n\n\n2\u00ba2991894\u2019-2\u00ba2985677\u2019N), disturbed secondary forest [Biodiversity strip 2 (DSF)] \n(longitude: 113\u00ba973298\u2019-113\u00ba957065\u2019E, latitude: 2\u00ba999828\u2019- 2\u00ba992196N) and oil palm \ncultivated area (OP) (longitude: 113\u00ba953327\u2019- 113\u00ba956999\u2019E, latitude: 2\u00ba985197\u2019 \n2\u00ba986037\u2019N) in Sungai Asap, Belaga, Sarawak. All of the sampling areas were adjacent \nto each other, where USF and DSF were 6.0 km and 6.7 km apart, respectively, from OP \ncultivated area.  Sampling was conducted at 6-month intervals (June 2012, January \n2013, June 2013). Oil palm was cultivated  on a  total area of 106.4 ha in 2009 by \nsmallholders. The age of the oil palm tree was 5 years  (June 2012), 5\u00bd years,  (January \n2013) and 6 years (June 2013)  respectively. Sarawak is categorised as having a tropical \nrainforest climate, with the wettest period being from November to February during \nNorth-East Monsoon season and dry season from June to August. Soil sampling was \ncarried out during both the wet and dry seasons. The average annual temperature ranged \nfrom 26.7oC to 27.9oC while the rainfall ranged from 92 to 327 mm as shown in TABLE \n1.  \n \n\n\n\nTABLE 1 \nTotal rainfall(mm) during sampling period \n\n\n\nSampling \ntime Month/Year Total rainfall(mm) Temperature (\u00baC) Relative humidity \n\n\n\n(%) \n\n\n\n1 June 2012 92.19 27.0 84.5 \n2 January 2013 327.66 26.7 85.8 \n3 June 2013 98.89 27.9 83.4 \n\n\n\n\n\n\n\nSampling Design and Collection \nThis study was arranged in a completely randomised design. Soil sampling was carried \nout at 10 GPS point from each site at depths of 0 \u2013 10 cm. The distance between one \nsampling point to another was 100 m apart. Three replications of soils were taken from \neach sampling point and composited to represent one sampling point. The samples were \nkept in sterile plastic tubes and transported in polystyrene boxes containing ice to the \nlaboratory.  \n\n\n\nSoil Microbial Population Analysis \nA series of tenfold dilutions of the soil suspension were made up to 10-6. Aliquots of 0.1 \nmL from the prepared dilution were spread on respective Nutrient Agar (NA) (Merck \nCat # 1054500500) for bacteria, Actinomycete Agar (AA) for actinomycetes (BD \nDIFCO Cat #212168), Rose-Bengal Streptomycin Agar (RBSA) for fungi, nitrogen-free \nmalate medium for nitrogen-fixing bacteria and National Botanical Research Institute\u2019s \nphosphate growth medium (NBRIP) for phosphate-solubilising bacteria. A spread plate \ntechnique was used and the plates were incubated for 24 h at 28oC.  \n\n\n\nSampling Design and Collection\nThis study was arranged in a completely randomised design. Soil sampling was \ncarried out at 10 GPS point from each site at depths of 0 \u2013 10 cm. The distance \nbetween one sampling point to another was 100 m apart. Three replications of \nsoils were taken from each sampling point and composited to represent one \nsampling point. The samples were kept in sterile plastic tubes and transported in \npolystyrene boxes containing ice to the laboratory. \n\n\n\nSoil Microbial Population Analysis\nA series of tenfold dilutions of the soil suspension were made up to 10-6. Aliquots \nof 0.1 mL from the prepared dilution were spread on respective Nutrient Agar \n(NA) (Merck Cat # 1054500500) for bacteria, Actinomycete Agar (AA) for \nactinomycetes (BD DIFCO Cat #212168), Rose-Bengal Streptomycin Agar \n(RBSA) for fungi, nitrogen-free malate medium for nitrogen-fixing bacteria and \nNational Botanical Research Institute\u2019s phosphate growth medium (NBRIP) for \nphosphate-solubilising bacteria. A spread plate technique was used and the plates \nwere incubated for 24 h at 28\u00baC. \n\n\n\nTABLE 1\nTotal rainfall(mm) during sampling period\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020124\n\n\n\nSoil Chemical Analysis\nSoil pH was measured using a pH meter (Beckman digital pH meter) of 1:2.5 \nsoil/water ratios. Determination of total carbon and total nitrogen contained in \nthe soil samples was carried out by using LECO CNS machine, with 1 g of soil. \nSoil Extractable Phosphorus (P) was determined by using extraction reagent of \nBray 2 (0.03N NH4F + 0.5N HCL) by an auto-analyzer (Lachat instrument, WI, \nUSA). Cation Exchange Capacity (CEC) was measured using ammonium acetate \nmethod (NH4 OAC) at pH 7 followed by displacement of 1N K2 SO4; the value \nwas then determined by an auto-analyzer (Lachat instrument, WI, USA).\n All statistical analyses were carried out using SAS 9.4 Significant \ndifferences of all the measured soil attributes among different land use types were \ntested with two-way ANOVA followed by Tukey\u2019s HSD differences at P<0.05. \n\n\n\nRESULTS\n\n\n\nSoil Microbial Population\nBacterial populations were significantly higher in undisturbed secondary forest \n(USF) and in disturbed secondary forest (DSF) in January 2013, compared to that \nin oil palm cultivated area (OP). However, opposite trends were observed in June \n2013 where populations of bacteria were highest at OP (6.57 log10cfu g-1 soil) \ncompared to that in both the secondary forests (Table 2).\n The actinomycetes population was also significantly affected by location \nand time indicating that actinomycetes are highly sensitive towards environmental \nchanges (Table 3).  Table 2 shows that the populations of actinomycetes decreased \nwith time in both the secondary forests (USF and DSF) and OP. Populations of \nactinomycetes were consistently highest at USF in January 2013 and June 2013 \nalthough, the populations of actinomycetes in DSF  was about the same with OP. \n Table 2 shows that the fungal populations in DSF (3.65 log10cfug-1 soil) \nand OP (3.64 log10cfug-1soil) were the lowest in June 2012 compared to that in \nJanuary 2013 and June 2013 sampling times. On the other hand, the populations \nof fungi were highest (5.57 log10cfug-1 soil) in OP compared to that in secondary \nforests (USF and DSF). Differences in fungi population were observed with \nrespect to time and location (Error! Reference source not found.). \n The population of nitrogen-fixing bacteria showed the significance \nof time (Table 3). It can be seen from Table 2 that a significant difference in \nnitrogen-fixing bacteria populations was only found at OP January 2013, with the \npopulations being the lowest (4.49 log10cfu g-1soil) compared to June 2013; but it \nis to be noted that there was  no difference compared to June 2012. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 125\n\n\n\nTABLE 2\nSoil microbial population of undisturbed and disturbed secondary forests (USF and DSF) \n\n\n\nand oil palm cultivation area (OP) in three sampling periods in Belaga, Sarawak\n\n\n\n\n\n\n\n6 \n \n\n\n\n June 2012 January 2013 June 2013 \n\n\n\n (mean \u00b1 std. err) \n(n=10) \n\n\n\n(mean \u00b1 std. err) \n(n=10) \n\n\n\n(mean \u00b1 std. err) \n(n=10) \n\n\n\nBacterial population (log10cfu g-1 dry soil) \nUSF 5.90 \u00b1 0.03abA 6.18 \u00b1 0.13aA 5.48 \u00b1 0.21bB \nDSF 5.89 \u00b1 0.01abA 6.57 \u00b1 0.37aA 5.74 \u00b1 0.14bB \nOP 5.90 \u00b1 0.01bA 5.35 \u00b1 0.09bB 6.57 \u00b1 0.29aA \nActinomycetes population (log10cfu g-1 dry soil) \nUSF 6.85 \u00b1 0.01aA 4.97 \u00b1 0.09bA 5.02 \u00b1 0.24bA \nDSF 6.83 \u00b1 0.02aA 4.43 \u00b1 0.07bB 3.64 \u00b1 0.13cB \nOP 6.84 \u00b1 0.01aA 4.18 \u00b1 0.04bB 4.33 \u00b1 0.37bAB \nFungal population (log10cfu g-1 dry soil) \nUSF 3.88 \u00b1 0.29aA 4.94 \u00b1 0.52aA 4.90 \u00b1 0.18aB \nDSF 3.65 \u00b1 0.03cA 5.25 \u00b1 0.10aA 4.19 \u00b1 0.17bC \nOP 3.63 \u00b1 0.03cA 4.62 \u00b1 0.29bA 5.57 \u00b1 0.14aA \nNitrogen-fixing bacterial population (log10cfu g-1 dry soil) \nUSF 5.56 \u00b1 0.18aA 5.03 \u00b1 0.29aA 5.61 \u00b1 0.18aA \nDSF 5.51\u00b1 0.21aA 5.47 \u00b1 0.32aA 5.45 \u00b1 0.19aA \nOP 5.68 \u00b1 0.36aA 4.49 \u00b1 0.30bA 5.86 \u00b1 0.39aA \nPhosphate-solubilising bacterial population (log10cfu g-1 dry soil) \nUSF 5.89  \u00b1 0.18aA 5.34  \u00b1 0.15aA 5.45  \u00b1 0.19aA \nDSF 5.23  \u00b1 0.21aA 4.86  \u00b1 0.15aA 5.27  \u00b1 0.13aA \nOP 3.89  \u00b1 0.36aB 3.79  \u00b1 0.21aB 3.75  \u00b1 0.20aB \nNotes: USF= Undisturbed Secondary Forest, DSF= Disturbed Secondary Forest, OP= Oil Palm Cultivated \nArea. Values followed by different uppercase letters within a column indicate difference (p<0.05) among \nlocations. Values followed by different lowercase letters within a row indicate difference (p<0.05) in \ntimes. \n \n\n\n\nThe population of phosphate-solibilising bacteria was significantly affected by \nthe difference in location (Table 3) shows that in June 2012, the population was at the \nlowest (3.89 log10cfu g-1soil) in OP compared to that in secondary forests (USF and \nDSF). Similar differences were recorded consistently in January 2013 (3.79 log10cfu g-\n\n\n\n1soil) and June 2013 (3.75 log10cfu g-1soil), suggesting that phosphate-solubilising \nbacteria were significantly affected by differences in management of soil. \n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \nP values of 2-way ANOVA showing effect of time, location, and their interaction on soil \n\n\n\nmicrobial populations across all locations and times of sampling \n\n\n\nThe population of phosphate-solibilising bacteria was significantly affected by \nthe difference in location (Table 3) shows that in June 2012, the population was \nat the lowest (3.89 log10cfu g-1 soil) in OP compared to that in secondary forests \n(USF and DSF). Similar differences were recorded consistently in January 2013 \n(3.79 log10cfu g-1 soil) and June 2013 (3.75 log10cfu g-1 soil), suggesting that \nphosphate-solubilising bacteria were significantly affected by differences in \nmanagement of soil.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020126\n\n\n\nSoil Chemical Properties\nSoil chemical characteristics varied significantly between land uses (Table 4). \nNotably, pH was significantly lowest in January 2013 compared to June 2012 and \nJune 2013. Significant differences among location were observed in June 2012 \nwhere OP had the lowest pH (5.48). However, in June 2013 pH recorded in DSF \nwas the highest (5.34) compared to USF (4.64) but not significantly different from \nOP area (5.03). Overall, significant differences were observed at different times \nand locations (Table 5).\n A significant interaction of location and time was detected for soil moisture \ncontent (MC) (Table 5). MC was significantly lower in oil palm cultivated area \nat all sampling times. A significant difference with respect to time was detected \nwhere DSF had significantly the highest MC in June 2013 (32.99%) compared to \nJune 2012 and January 2013 (Table 4).\n Total C appears to be affected by time, location and interactive effect \nbetween time and location (Table 5). The significance was more pronounced in \nJanuary 2013 where total C (4.09 %) and total N (0.31%) were higher in USF \ncompared to DSF and OP (Table 4). \n There were significant differences in total S as shown in Error! Reference \nsource not found.. In June 2012, total S in OP was higher (0.008%) compared to \nDSF but there was no difference compared to USF. However, in January 2013, \ntotal S in USF was higher (0.030%) compared to DSF and OP (Table 5).\n Available P was observed to be higher in USF (11.56 mg kg-1 and 12.86mg \nkg-1) compared to DSF and OP in June 2012 and June 2013 (Table 4). Inconsistent \nvalues of available P were observed through all sampling times. Available P in \nUSF was the highest (19.39mg kg-1) in January 2013 compared to June 2012 and \nJune 2013 while available P in DSF and OP were the highest (16.04 mg kg-1 and \n20.90 mg kg-1, respectively) in June 2013 compared to June 2012 and January \n2013. \n Cation Exchange Capacity (CEC) was significantly affected by location. \nAs shown in TABLE 4, significant differences were detected in June 2012 where \nUSF was higher (16.94 cmol(+) kg -1) compared to DSF and OP.\n\n\n\nTABLE 3\nP values of 2-way ANOVA showing effect of time, location, and their interaction on soil \n\n\n\nmicrobial populations across all locations and times of sampling\n\n\n\n\n\n\n\n7 \n \n\n\n\nTABLE 3 \nP values of 2-way ANOVA showing effect of time, location, and their interaction on soil \n\n\n\nmicrobial populations across all locations and times of sampling \n \n\n\n\n  Plocation P time Plocation*time \n\n\n\n \n(df = 2) (df = 2) (df = 4) \n\n\n\nBacterial  NS NS <.0001 \nActinomycetes  <.0001 <.0001 <.0001 \nFungi NS <.0001 0.013 \nNitrogen-fixing bacteria  NS 0.0097 NS \nPhosphate-solubilizing bacteria  <.0001 NS NS \n\n\n\nVariables: Total bacterial population; Total actinomycetes population; Total fungal population; Total \nNitrogen-fixing bacterial population;, Total phosphate-solubilising bacterial population \n\n\n\n\n\n\n\nSoil Chemical Properties \nSoil chemical characteristics varied significantly between land uses (TABLE 4). \nNotably, pH was significantly lowest in January 2013 compared to June 2012 and June \n2013. Significant differences among location were observed in June 2012 where OP had \nthe lowest pH (5.48). However, in June 2013 pH recorded in DSF was the highest (5.34) \ncompared to USF (4.64) but not significantly different from OP area (5.03). Overall, \nsignificant differences were observed at different times and locations (TABLE 5). \n\n\n\nA significant interaction of location and time was detected for soil moisture \ncontent (MC) (TABLE 5). MC was significantly lower in oil palm cultivated area at all \nsampling times. A significant difference with respect to time was detected where DSF \nhad significantly the highest MC in June 2013 (32.99%) compared to June 2012 and \nJanuary 2013 (TABLE 4). \n\n\n\n Total C appears to be affected by time, location and interactive effect between \ntime and location (TABLE 5). The significance was more pronounced in January 2013 \nwhere total C (4.09 %) and total N (0.31%) were higher in USF compared to DSF and \nOP(TABLE 4).  \n\n\n\nThere were significant differences in total S as shown in Error! Reference \nsource not found.. In June 2012, total S in OP was higher (0.008%) compared to DSF \nbut there was no difference compared to USF. However, in January 2013, total S in USF \nwas higher (0.030%) compared to DSF and OP (TABLE 5). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 127\n\n\n\n\n\n\n\n8 \n \n\n\n\n\n\n\n\nTABLE 4 \nChemical properties of undisturbed and disturbed secondary forests (USF and DSF) and \n\n\n\noil palm cultivation area (OP) in three sampling periods in Belaga, Sarawak \n \n\n\n\n June 2012 January 2013 June 2013 \n\n\n\n (mean \u00b1 std. err) \n(n=10) \n\n\n\n(mean \u00b1 std. err) \n(n=10) \n\n\n\n(mean \u00b1 std. err) \n(n=10) \n\n\n\npH    \nUSF 6.36 \u00b1 0.06aA 4.22 \u00b1 0.20bA 4.64 \u00b1 0.09bB \nDSF 6.30 \u00b1 0.04aA 3.83 \u00b1 0.11cA 5.34 \u00b1 0.08bA \nOP 5.48 \u00b1 0.03aB 4.33 \u00b1 0.16bA 5.03 \u00b1 0.19aAB \nMoisture content (%)    \nUSF 25.78  \u00b1 2.47aA 20.55  \u00b1  1.90aA 27.35 \u00b1 1.55aA \nDSF 21.23  \u00b1 2.05bA 19.35 \u00b1 2.37bA 32.99 \u00b1 1.86aA \nOP 14.28  \u00b1 0.97aB 14.22  \u00b1 1.19aA 16.92 \u00b1  3.04aB \nTotal C (%)    \nUSF 1.77 \u00b1 0.02bA 4.09 \u00b1 0.47aA 1.52 \u00b1 0.25bA \nDSF 1.86 \u00b1 0.50aA 1.35 \u00b1 0.03aB 1.31 \u00b1 0.01aA \nOP 1.11 \u00b1 0.16aA 1.16 \u00b1 0.14aB 1.14 \u00b1 0.13aA \nTotal N (%)    USF 0.18  \u00b1 0.01bA 0.31  \u00b1 0.03aA 0.16  \u00b1 0.01bA \nDSF 0.25  \u00b1 0.03aA 0.17  \u00b1 0.02aB 0.16  \u00b1 0.01aA \nOP 0.17  \u00b1 0.03aA 0.12  \u00b1 0.02aB 0.13  \u00b1 0.01aA \nTotal S (%)    \nUSF 0.006 \u00b1 0.0009aAB 0.030 \u00b1 0.004aA 0.024 \u00b1 0.001aA \nDSF 0.005 \u00b1 0.001aB 0.012 \u00b1 0.002aB 0.009 \u00b1 0.001aA \nOP 0.009 \u00b1 0.002aA 0.014 \u00b1 0.002aB 0.036 \u00b1 0.002aA \nAvailable P (mg kg-1)    USF 11.56 \u00b1 0.28bA 19.39 \u00b1 1.00aA 12.86 \u00b1 1.05bA \nDSF 9.49 \u00b1 0.71bB 10.23 \u00b1 1.66aB 16.04 \u00b1 1.86aA \nOP 9.86 \u00b1 0.35bB 10.24 \u00b1 1.73bB 20.90 \u00b1 4.00aA \nCEC (cmol(+)kg-1)    \nUSF 16.94 \u00b1 0.58aA 18.23 \u00b1 0.88aA 14.43 \u00b1 1.66aA \nDSF 9.16 \u00b1 1.30aB 12.82 \u00b1 0.89aA 13.74 \u00b1 2.08aA \nOP 8.30 \u00b1 1.54aB 11.37 \u00b1 3.41aA 9.29 \u00b1 0.90aA \nNotes: USF= Undisturbed secondary forest; DSF= Disturbed secondary forest; OP= Oil palm cultivated \narea. Values followed by different uppercase letters within a column indicate difference (p<0.05) among \nlocations. Values followed by different lowercase letters within a row indicate difference (p<0.05) in time. \nVariables: pH, Moisture Content (MC); Total Carbon (C); Total Nitrogen (N); Total Sulphur (S);Available \nPhosphorus (P); Cation Exchange Capacity (CEC) \n\n\n\nTABLE 4\nChemical properties of undisturbed and disturbed secondary forests (USF and DSF) and \n\n\n\noil palm cultivation area (OP) in three sampling periods in Belaga, Sarawak\n\n\n\nCorrelations Between Microbial Population and Chemical Properties\nSoil properties played an important role in determining the distribution of the \nvarious microbial groups enumerated (Table 6). It can be observed that fungi were \nsensitive to soil chemical properties and changes in pH(r= -0.46). In a similar way, \nactinomycetes abundancy was also controlled by pH (r=0.62). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020128\n\n\n\nTABLE 6 \nCorrelations between microbial populations vs chemical properties across all locations \n\n\n\nand times of sampling\n\n\n\nTABLE 5\nP values of 2-way ANOVA showing effect of time, location, and their interaction on soil \n\n\n\nchemical properties across all locations and times of sampling\n\n\n\n\n\n\n\n9 \n \n\n\n\n\n\n\n\nTABLE 5 \nP values of 2-way ANOVA showing effect of time, location, and their interaction on soil \n\n\n\nchemical properties across all locations and times of sampling \n \n\n\n\n  Plocation Ptime Plocation*time \n\n\n\n \n(df = 2) (df = 2) (df = 4) \n\n\n\npH NS <.0001 <.0001 \nMoisture content <.0001 <.0001 0.02 \nTotal C <.0001 0.001 <.0001 \nTotal N NS NS NS \nTotal S 0.03 0.01 NS \nAvailable P NS 0.0007 0.0007 \nCEC <.0001 NS NS \n\n\n\nVariables: pH; Moisture Content (MC); Total Carbon (C); Total Nitrogen (N); Total Sulphur (S); \nAvailable Phosphorus (P); Cation Exchange Capacity (CEC) \n \n \n\n\n\nAvailable P was observed to be higher in USF (11.56 mg kg-1and 12.86mg kg-1) \ncompared to DSF and OP in June 2012 and June 2013 (TABLE 4). Inconsistent values \nof available P were observed through all sampling times. Available P in USF was the \nhighest (19.39mg kg-1) in January 2013 compared to June 2012 and June 2013 while \navailable P in DSF and OP were the highest (16.04 mg kg-1and 20.90 mg kg-1, \nrespectively) in June 2013 compared to June 2012 and January 2013.  \n\n\n\nCation Exchange Capacity (CEC) was significantly affected by location. As \nshown in TABLE 4, significant differences were detected in June 2012 where USF was \nhigher (16.94 cmol(+) kg -1) compared to DSF and OP. \n\n\n\nCorrelations Between Microbial Population and Chemical Properties \nSoil properties played an important role in determining the distribution of the various \nmicrobial groups enumerated (TABLE 6). It can be observed that fungi were sensitive to \nsoil chemical properties and changes in pH(r= -0.46). In a similar way, actinomycetes \nabundancy was also controlled by pH (r=0.62).  \n\n\n\n\n\n\n\n10 \n \n\n\n\nTABLE 6  \n\n\n\nCorrelations between microbial populations vs chemical properties across all locations \nand times of sampling \n\n\n\n\n\n\n\nCorrelations BAC \npopulation \n\n\n\nACT  \npopulation \n\n\n\nFUN  \npopulation \n\n\n\nNFB  \npopulation \n\n\n\nPSB  \npopulation \n\n\n\npH -0.18 0.62** -0.46** 0.12 0.155 \nMC 0.023 -0.10 -0.044 0.023 0.41** \nTC 0.011 0.055 0.17 -0.07 0.28** \nTN 0.019 0.13 0.023 -0.03 0.16 \nTS 0.27** -0.093 0.30** 0.17 -0.13 \nAP 0.28* -0.29** 0.14 0.10 -0.0016 \nCEC  -0.09 -0.097 0.087 0.04 0.41** \n* and ** indicate the significance of the Pearson correlations at p<0.05 and p<0.01 respectively; n=90 \nVariables: pH; Moisture Content (MC); Total Carbon (C); Total Nitrogen (N); Total Sulphur (S); \nAvailable Phosphorus (P); Exchangeable Potassium (K), Exchangeable Calcium (Ca); Exchangeable \nMagnesium (Mg); Cation Exchange Capacity (CEC); Total bacterial (BAC) population; Total \nActinomycetes (ACT) population; Total fungal (FUN);  Total nitrogen-fixing bacterial (NFB) population; \nTotal phosphate-solubilising bacterial (PSB) population. \n\n\n\n\n\n\n\nDISCUSSION \nPopulation of bacteria was higher in the secondary forests (USF and DSF) compared to \nOP in January 2013. This finding is supported by Miah et al.(2010) where the bacteria \npopulation was lower in shifting cultivation than in forest soil. Response to \nenvironmental features such as sparse vegetation, less litter and leaving the soil exposed \nto full sunlight may contribute to the decline in bacterial population in the oil palm \ncultivated area. Large sized tree species in forests giving full canopy coverage may \nfavour bacterial population in secondary forests areas (USF and DSF) compared to OP. \nHowever, there is evidence that bacteria are also well adapt to a cultivated environment \n(Tripathi et al. 2012). The population of bacteria was also observed to be higher in OP \nsoil compared to secondary forests soils (USF and DSF) in June 2013. Alori et al.(2017) \nreported that adding small amounts of inorganic phosphate to the rhizosphere could \ndrive mineralisation by bacteria. It was also inferred that new individuals and groups \nmight be introduced in moderate disturbance, therefore promoting competition and \ndiversity of the community and establishing a more stable community in cultivated soils \n(Shange et al. 2012). The development of regenerating stands with young trees may \nhave induced a gradual increase in community composition and activity which resulted \n\n\n\nDISCUSSION\nPopulation of bacteria was higher in the secondary forests (USF and DSF) compared \nto OP in January 2013. This finding is supported by Miah et al.(2010) where the \nbacteria population was lower in shifting cultivation than in forest soil. Response \nto environmental features such as sparse vegetation, less litter and leaving the soil \nexposed to full sunlight may contribute to the decline in bacterial population in \nthe oil palm cultivated area. Large sized tree species in forests giving full canopy \ncoverage may favour bacterial population in secondary forests areas (USF and \nDSF) compared to OP. However, there is evidence that bacteria are also well adapt \nto a cultivated environment (Tripathi et al. 2012). The population of bacteria was \nalso observed to be higher in OP soil compared to secondary forests soils (USF \nand DSF) in June 2013. Alori et al.(2017) reported that adding small amounts of \ninorganic phosphate to the rhizosphere could drive mineralisation by bacteria. It \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 129\n\n\n\nwas also inferred that new individuals and groups might be introduced in moderate \ndisturbance, therefore promoting competition and diversity of the community and \nestablishing a more stable community in cultivated soils (Shange et al. 2012). The \ndevelopment of regenerating stands with young trees may have induced a gradual \nincrease in community composition and activity which resulted in an increase in \nthe bacteria population in OP in June 2013;  the change in bacteria population in \nboth secondary forests (USF and DSF) may mostly be due to nutrient availability \n(Mikkelson et al. 2016). Significant differences in chemical properties were also \nobserved in this study. \n Acosta-Mart\u00ednez et al. (2008) suggest that actinomycetes are more \nprevalent in non-disturbed systems compared to soils under agriculture; this is in \nagreement with our  study where populations of actinomycetes were consistently \nthe highest in the  USF, although the populations of actinomycetes in the DSF \nwere about the same as in OP. Research findings of Hill et al.(2011) also suggest \nthat there is no statistically significant evidence that cultivation increases the \nactinomycetes populations.  Furthermore, actinomycetes were more commonly \nfound in  cultivated areas than in forested areas (Lee-Cruz et al. 2013; Shange et \nal. 2012). This may due to its capability of surviving under extreme environments \n(Ghorbani-Nasrabadi et al. 2013). Therefore, it is noted that the difference in soil \nchemical properties with the intervention of natural variables in DSF and OP \nmay be the reason for the differences seen in USF. Positive correlation between \npH was observed in this study. Generally, phosphate-solubilisation  refers to \nthe solubilisation of organic phosphorus and the degradation of the remaining \nportion of the molecule (Tamburini et al. 2014). Since actinomycetes also exhibit \nphosphate solubilisation capacity, the decomposition of phosphorus is expected to \nbe carried out by actinomycetes. The pH of most soils where there are phosphate \nactivities ranges from acidic to neutral values (Alori et al. 2017) whereas most \nsoil actinomycetes show their optimum growth in neutral and slightly alkaline \nconditions (Garcia-Franco et al. 2015). In this study, it was observed that in soils \nwith a  pH of 5.48 and above, the populations of actinomycetes ranged from \n6.83 to 6.85 log10cfu g-1. But in soils with a pH below 5.34, the populations of \nactinomycetes ranged from 3.83 to 5.03 log10cfu g-1. This scenario may explain \nthe abundance of actinomycetes in June 2012 compared to January 2013 and June \n2013. \n Response of soil fungi against perturbation was rapid considering the  \nbrief period of time; fungal populations were affected in DSF and OP, whereas \nin USF, it  was relatively stable throughout the sampling times suggesting that \nfungal populations exhibit a negative response to present and past agriculture \nmanagement such as fertilisation in oil palm plantation and persistence in DSF \n(Guillaume et al. 2016; McGuire et al. 2015). Adversely the populations of fungi \nin this study have been shown to have a preference for human activity. Indeed \nCastaneda et al.(2015) also found that fungal communities flourished more in \nvineyards than in forested areas. Fungal populations were higher in OP compared \nto secondary forests (USF and DSF) although no significant differences were \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020130\n\n\n\nobserved in the first two sampling times. The differences may be attributed to the \nseveral chemical properties such as soil pH and total S, observed in the present \nstudy. The increase in soil acidity was probably brought about by an increase \nin fungal population and the variety of fungal populations. As observed, pH \nvalues were highest in June 2012 compared to January 2013 and June 2013. This \nscenario may explain the disruption in the number of most fungal populations at \nthat particular time. The abundance of fungal populations could be attributed to \ntheir ability to proliferate at low pH (Okonkwo  2010). The increase in fungal \npopulation in the OP area was due to the activities performed for cultivation such \nas crop residue which can either promote or ease competition resulting in a more \ndiverse community (Lee-Cruz et al. 2013; Shange et al. 2012).\n Significance differences of nitrogen-fixing bacteria populations were \nonly found in OP area in January 2013which had the lowest populations compared  \nJune 2012 and June 2013. This is expected, given the occurrence of heavy rainfall \nin January 2013 (327.66 mm) and which may have contributed to the decline \nin the populations of nitrogen-fixing bacteria. According to Allen et al.(2011), \nseasonal distribution of rainfall may affect the health of the soils and this has an \nimpact on soil biological processes.\n The population of phosphate-solubilising bacteria showed a consistent \ndecrease in OP area compared to secondary forests (USF and DSF) suggesting \nthat phosphate-solubilising bacteria have lesser preference for profileration in \ncultivated areas. This is in line with a study by Stanley et al.(2013) where these \nbacteria were found to be susceptible to an environment where there is  paraquat \nuse. Besides, the population of phosphate-solubilising bacteria may have an \naffiliation for the bioavailability of growth-promoting substances (Bhattacharyya \net al. 2013). Thus, it can be  concluded that phosphate-solubilising bacteria in this \nstudy were heterotrophic (Vikram et al. 2007).\n The differences in soil pH among the locations and different sampling \ntimes were presumably due to differences in fertilisation levels and quality and \nquantity of plant litter produced (Hazarika et al. 2014) and natural processes such \nas carbon dioxide evolution from plant roots or soil microbial respiration. These \nprocesses are believed to be responsible for controlling soil pH (Lauber et al. \n2009).\n Moisture content (MC) was significantly lower in the OP area at all \nsampling times. This is in agreement with the study of Firdaus et al.(2010) which \nfound that moisture content in OP area was lower than in forest soils (USF and \nDSF) due to evaporation from the soil surface.\n Significant reductions in concentrations of total C, N and CEC recorded \nin DSF and OP could be due to natural environmental factors such as forest \ncanopy and amount of litter available to protect the soil (Dawoe et al. 2013).  The \ndecline in these factors was expected to speed up the mineralisation process that \nwas observed in DSF and OP area compared to that USF in January 2013. Similar \nfactors were in play in USF where the total C and N saw a decline in June 2012 \nand June 2013. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 24, 2020 131\n\n\n\n As has been observed, inconsistent values of available P were obtained \nfrom USF and DSF through the sampling times. Differences in soil physico-\nchemical properties are associated with vegetation structures due to the differences \nin quantity and quality of leaf or root litter which control the mineralisation \nprocesses (Zhang et al. 2015). The higher value of total S in the OP area may be \nattributed to the cover cropping practices in oil palm cultivation (Ahamadou and \nHuang 2013).\n\n\n\nCONCLUSION\nOur study results suggest that most microbial populations and biochemical \nproperties responded to chemical properties in soils as a consequence of human-\ninduced land use alteration. The detectable changes in biological activities in \nOP area and USF and DSF can be attributed to the decrease in total population \nof bacteria, phosphate-solubilising bacteria, microbial biomass C (MBC), FDA \nhydrolysis, phosphatase, pH, MC, total C and total N. It must also be noted \nthat there were strong effects of location, time and interactions of location and \ntime in actinomycetes populations, MBC, and MC and total C. This finding is \nsupported by significant positive correlations between soil microbial populations \nand chemical properties. Soil degradation which occurred in OP area shows that \nsoils are generally sensitive to disturbance but are resilient, a prerequisite for soil \nto recover from perturbations. Based on this study, further experimentation is \nnecessary since differences in biological and physico-chemical properties across \ndifferent land use sites are likely due to obvious limitations of several factors that \nhave not been measured in this study. \n\n\n\nACKNOWLEDGEMENTS\nThe authors wish to thank the Malaysian Palm Oil Board (MPOB) for a grant and \nUniversiti Putra Malaysia (UPM) for the support extended towards the study.\n\n\n\nREFERENCES\nAcosta-Mart\u00ednez, V., D. Acosta-Mercado, D. 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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 147-162 (2016)                         Malaysian Society of Soil Science\n\n\n\nImpact of Organic Manure and Inorganic Fertiliser on Soil \nEnzymatic Activity and Microbial Diversity in the States of \n\n\n\nTamil Nadu and Madhya Pradesh, India\n\n\n\nSubramanian, S. *, A.Senthil Nagappan, D.N. Kurup\n\n\n\nDepartment of Biotechnology, PSG College of Technology, Coimbatore 641 004.\n\n\n\nABSTRACT\nA laboratory study was conducted to examine the effects of organic and inorganic \ncultivation on soil biological processes and biodiversity. Five soil samples from \neach of the organic manure treated fields and inorganic fertiliser treated fields from \ntwo different states, Tamil Nadu and Madhya Pradesh, in India were examined. \nThe soil types were either black cotton or loamy soil. Two other soil samples from \na fallow area of Indore, Madhya Pradesh, India  were also included for nutrient \nstatus and biodiversity comparison. Soil organic carbon, nitrogen, phosphorus, \nand potassium levels and soil enzymes that reflected soil microbial activity, such \nas dehydrogenase, beta glucosidase, phosphatase and nitrate reductase, were \nestimated. Inorganic fertilizer treated soils had the lowest organic carbon content \n(4.5 g kg-1) compared to the highest (12.2 g kg-1) in organic manure treated soils. \nSimilarly, soil phosphatase, glucosidase and dehydrogenase activities were higher \nby 26%, 28% and 21%, respectively, in organic fertilizer treated soils.  Randomly \nAmplified Polymorphic DNA (RAPD) profiles of soil DNA indicated microbial \nrichness in organic manure treated soil as it had a low Jaccard\u2019s similarity \ncoefficient of 0.577 vs 0.703 in inorganic fertilizer treated soil. Soil microbial \ndiversity and dynamics were found to be greater in the organic system of cropping. \nThese findings suggest that these could be used as potential indicators for soil \nhealth.\n\n\n\nKeywords: Microbial dynamics, RAPD, soil enzymatic assay, farm yard \nmanure, inorganic fertiliser\n\n\n\n___________________\n*Corresponding author : E-mail: selvi@bio.psgtech.ac.in\n\n\n\nINTRODUCTION\nIndia is the third largest consumer of fertilisers, next to China and USA, in the \nworld. Fertiliser consumption of India was 24.48 million tons during the 2013-14 \nperiod (Indian Fertiliser Scenario, 2014). Besides the application of fertilisers, \nconsiderable amounts of agrochemicals such as pesticides and herbicides \nare also used. Use of chemicals at such scale causes environmental pollution, \ndeteriorates soil health and  agro-ecology, and  leads to poor profitability in \nfarming (Robertson and Swinton, 2005). This has basically prompted the demand \nfor organic cultivation for conservation and optimised utilisation of all natural \nresources (Mader et al., 2002).  In addition, at present organic produce generally \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016148\n\n\n\nSubramaniam et al.\n\n\n\ncommand a higher price than conventional produce (Oberholtzer et al., 2005).  \nOrganic farming results in produce  that is higher in dry matter content, higher \nmineral concentrations, lower nitrate (NO3) concentrations, higher vitamin C \nconcentrations, higher phytonutrient content, and better taste (Rosen and Allen, \n2007).\n\n\n\nNutrients are a controlling input to the soil system and the processes within \nit. Carbon content, cycling of nitrogen and phosphorus affect soil dynamics \nand agricultural production (Barber, 1995). Changes in soil organic carbon and \nnitrogen are reflective of crop rotation and fertiliser addition (Omay et al., 1997). \nLong-term inorganic N application decreases organic matter and biological \nactivity, whereas short-term inorganic N application has limited effects on \nsoil enzyme activities and microbial biomass C (Fauci and Dick, 1994).  An \nincrease in soil microbial population viz., bacteria, fungi and actinomycetes, was \nobserved with the application of organic N sources (Krishnakumar et al., 2005) \ncompared to the inorganic form. Microbial community structure and its activity \nin soil are indicators of soil quality and plant productivity (Latour et al., 1996). \nSoil enzyme quantities are suitable indicators of soil quality because they are \nthe measure of soil microbial activity (Dick et al., 1996). Soil enzymes such as \nprotease, glucosidase and alkaline phosphatase were found to directly correlate \nwith the microbial biomass carbon, microbial biomass nitrogen, growth and \nactivity (Melero et al., 2008). An increase in dehydrogenase activity after the \nincorporation of organic carbon input was observed by Parham et al., (2002) and \nMadejon et al., (2007). The present study was carried out to endorse the effects of \norganic manure application on soil quality and microbial diversity. \n\n\n\nMATERIALS AND METHODS\n\n\n\nCollection of Soil Samples\nSoil samples from purely farm yard organic manure applied fields, inorganic \nfertilisers and chemicals treated fields and fallow land were collected from \nCoimbatore District in Tamil Nadu and Indore in Madhya Pradesh State of India. \nSoil samples were collected randomly at five locations at a depth of 15 cm using \na spade. The samples were pooled and composited, processed and sieved through \na 2-mm pore sieve  prior to the analysis. In all the cases, sampling was done at \nthe end of the cropping season. Twelve composite soil samples were collected \nfrom  five organic farming fields (O1, O2, O3, O4, O5) five from inorganic (I1, I2, \nI3, I4, I5) fields and two from fallow land (F1, F2). Organic farming fields were \nfields that strictly used organic manure in the form of farm yard manure with no \napplication of  chemical pesticides and fertilisers. Farm yard manure composition \nis mainly cow dung. Soil samples taken for the study were visually examined and \nsieved for removal of debris. The crops grown and the soil type of the respective \nfields are listed in Table 1. All soil samples were used for soil metagenomic \nanalysis and nutrient content estimation whereas only four among five organic \nand inorganic soil samples were used for soil enzyme activity assays. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 149\n\n\n\nSoil Enzymatic Activity and Microbial Diversity from Fertilizer Application\n\n\n\nTABLE 1\nSoil types used in this study, their location and cultivation practices\n\n\n\nDetermination of Soil Nutrients\nTotal nitrogen and phosphorus in soil samples were estimated by the method \ndescribed by Pape et al., (1982). Soluble phosphorus was extracted by the addition \nof 0.03 N ammonium fluoride and 0.025 N hydrochloric acid. Ammonium \nmolybdate and stannous chloride were added to the extracted solution after \nfiltration with Whatman No 41 filter paper. The spectrophotmetric quantification \nof phosphorus was carried out by measuring the characteristic blue colour \nsolution at 690 nm (UV-1601, Shimadzu). Total nitrogen was determined by \nKjeldahl method. Soil samples were digested with concentrated sulphuric acid in \nthe presence of copper sulphate and potassium sulphate. The ammonia evolved \nduring digestion was collected as distillate with hydrochloric acid as absorbent. \nThe excess acid was titrated with sodium hydroxide solution to determine \nammonia/nitrogen content. The potassium content in the soil was determined by \nflame photometer (Elico Flame photometer CL 378, India). Potassium chloride \nwas used as standard for potassium. The potassium in soil was extracted with \nsolution of 0.5M ammonium acetate and 0.5M acetic acid for 30 min. The organic \ncarbon content of soil was done according to the Walkley and Black procedure \n\n\n\n1 \n\n\n\n\n\n\n\nTable 1 Soil types used in this study, their location and cultivation practices \n\n\n\nS. \nNo. \n\n\n\nSoil \nSample \n\n\n\nFarming System Soil Type Location  Crop  \n\n\n\n1. F1 Fallow soil Black cotton soil, \nheavy \n\n\n\nBadia Kema, \nIndore \n\n\n\nFallow \nsoil \n\n\n\n2. F2 Fallow soil Black cotton soil, \nlight \n\n\n\nBadia Kema, \nIndore \n\n\n\nFallow \nsoil \n\n\n\n3. I1 Inorganic fertilizers and \nchemicals  \n\n\n\nBlack cotton soil, \nheavy \n\n\n\nBadia Kema, \nIndore \n\n\n\nSoya \nbean  \n\n\n\n4. I2 Inorganic fertilizers and \nchemicals \n\n\n\nBlack cotton soil, \nlight \n\n\n\nBadia Kema, \nIndore \n\n\n\nSoya \nbean \n\n\n\n5. I3 Inorganic fertilizers and \nchemicals \n\n\n\nBlack soil  Coimbatore Tomato  \n\n\n\n6. I4 Inorganic fertilizers and \nchemicals \n\n\n\nBlack soil Coimbatore  Chilly  \n\n\n\n7. I5 Inorganic fertilizers and \nchemicals \n\n\n\nLoamy soil Coimbatore  Maize  \n\n\n\n8. O1 Only organic manure used \nin farming \n\n\n\nBlack cotton soil, \nheavy \n\n\n\nBadia Kema, \nIndore \n\n\n\nSoya \nbean  \n\n\n\n9. O2 Only organic manure used \nin farming \n\n\n\nBlack cotton soil, \nlight \n\n\n\nBadia Kema, \nIndore \n\n\n\nSoya \nbean \n\n\n\n10. O3 Only organic manure used \nin  farming \n\n\n\nBlack soil Coimbatore  Tomato  \n\n\n\n11. O4 Only organic manure used \nin  farming \n\n\n\nBlack soil Coimbatore Chilly  \n\n\n\n12. O5 Only organic manure used \nin farming \n\n\n\nLoamy soil Coimbatore  Maize \n\n\n\n\n\n\n\nTable 2   Random primers used for RAPD amplification of the soil DNA in this study \n\n\n\nS.No PRIMER SEQUENCE (5\u2019-3\u2019) REFERENCE \n\n\n\n1. OPG-2 GGCACTGAGG Nimisha et al.,2008 \n\n\n\n2. OPG-3 GAGCCCTCCA Nimisha et al.,2008 \n\n\n\n3. OPG-11 TGCCCGTCGT Nimisha et al.,2008 \n\n\n\n4. OPG-12 CAGCTCACGA Nimisha et al.,2008 \n\n\n\n5. OPG-14 GGATGAGACC Nimisha et al.,2008 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016150\n\n\n\nwith minor modification (Hooda and Kaur, 1999). Briefly this procedure involved \nthe addition of concentrated sulphuric acd and potassium dichromate to the soil \nwith the mixture being boiled at 150\u00b0 C for 30 min. After cooling, the excess \nCr2O7\n\n\n\n2- was titrated with ferrous sulphate to determine the organic carbon content.\n\n\n\nDehydrogenase Assay\nDehydrogenase activity was measured by the procedure described by Pepper et al. \n(1995). Briefly, this procedure utilised triphenyltetrazolium chloride (TTC) as the \nelectron acceptor, which was reduced to red-colored, methanol-soluble, triphenyl \nformazan (TPF). Six g of moist soil from each sample was placed in a test tube \nand incubated statically with 1 mL of 3% TTC (3 g /100 mL deionised water) \nand 3 ml of 0.2 M CaCO3 buffer solution for 24 h at 37\u00b0C. This reaction was \nterminated by 10 ml of methanol, and TPF was extracted with 30 ml of additional \nmethanol. The final extract was filtered with Whatman No.42 filter paper and TPF \nconcentration was determined spectrophotometrically at 485 nm.\n\n\n\nBeta -Glucosidase Assay\nBeta-glucosidase activity was quantified according to procedures described \nby Tabatabai (1994). The method is based on colorimetric measurement of \np-nitrophenol released by \u00e2-glucosidase when soil is incubated with buffered \n(pH 6.0) p-nitrophenyl-\u00df-glucopyranoside solution. Two g of moist soil sample \nwas incubated with 50 mM acetate buffer pH 5, for 1 h at 37\u00b0C. This solution \nwas filtered using Whatman No. 42 filter paper. An aliquot of 750 \u00b5L of sample \nwas mixed with 750 \u00b5L of 5 mM para nitrophenyl-B-glucopyranoside. This was \nincubated for 1 h at room temperature. Para nitrophenol released by the action \nof B-glucosidase on the substrate gave a yellow colour solution. The absorbance \nwas measured at 410 nm. A standard curve was prepared with dilutions of a para \nnitrophenol in buffer solution.\n\n\n\nPhosphatase Assay\nThe method is based on colorimetric measurement of p-nitrophenol released \nby phosphatase when soil is incubated with buffered (pH 6.0) p-nitrophenyl \nphosphate (PNG) solution. Two g of moist soil sample was incubated with 50 \nmM acetate buffer pH 5 for 1 h at 37\u00b0C. This solution was filtered using Whatman \nNo.42 filter paper. An aliquot of 750 \u00b5L of sample was mixed with 750 \u00b5L of \n5 mM para nitrophenyl phosphate. Again the solution was incubated for 1 h at \nroom temperature.  Para nitrophenol released by the action of phosphatase on the \nsubstrate gave a yellow colour solution. The absorbance was measured at 410 nm \nusing UV-visible spectrophotometer (UV-1601, Shimadzu). A standard curve was \nmade with dilutions of a para nitrophenol in buffer solution.\n\n\n\nNitrate Reductase Assay\n The method is based on colorimetric measurement of nitrite colour complex formed \nwhen nitrite is formed as a result of nitrate reductase activity when it combines with \n\n\n\nSubramaniam et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 151\n\n\n\nsulphanilamide and N-(1-Naphthyl) etylenenediamine dihydrochloride. Two g of \nmoist soil sample was incubated with 50 mM, acetate buffer pH 5 for 1 h at 37\u00b0C. \nA volume of 1.9 mL of sample extract was mixed with 100 \u00b5L of 2 mM reduced \nnicotinamide adenine dinucleotide and incubated at 300C for 2 min. Then, 1 ml of \n58 mM sulphanilamide solution and 1 mL of 0.77 mM napthyl- ethylenediamine \ndihydrochloride solutions were added. This mixture was incubated for 10 min at \n250C after thorough mixing. Absorbance was checked for the sample at 540 nm \nusing UV-visible spectrophotometer (UV-1601, Shimadzu). Standard curve was \nprepared using sodium nitrite dilutions.\n\n\n\nIsolation of DNA from Soil \nSoil DNA was isolated using Power Soil DNA isolation kit (MO BIO, Laboratories. \nInc.). The DNA isolation procedure was according to the manufacturer\u2019s protocol.  \nSoil sample taken for isolation of DNA was 0.25g and the DNA was dissolved in \n100\u00b5L TE and stored at -20\u00b0C. \n\n\n\nDNA Quantification\nTo evaluate the purity and concentration of the extracted DNA, absorbance ratios \nat 260 nm/230 nm (DNA / humic acids) and 260 nm/280 nm (DNA / protein) were \ndetermined (Sambrook et al., 1989) using UV-visible spectrophotometer (UV-\n1601, Shimadzu). \n\n\n\nRAPD PCR Amplification\n The random amplification of polymorphic DNA (RAPD) technique is a PCR-\nbased method that uses a short primer (usually 10 bases) to amplify anonymous \nstretches of DNA. The list of random primers used in the experiment is given in \nTable 2. They are custom made from Sigma-Aldrich. \n\n\n\nTABLE 2\nRandom primers used for RAPD amplification of the soil DNA in this study\n\n\n\n2 \n\n\n\n\n\n\n\n\n\n\n\nTable 2   Random primers used for RAPD amplification of the soil DNA in this study \n\n\n\nS.No PRIMER SEQUENCE (5\u2019-3\u2019) REFERENCE \n\n\n\n1. OPG-2 GGCACTGAGG Nimisha et al.,2008 \n\n\n\n2. OPG-3 GAGCCCTCCA Nimisha et al.,2008 \n\n\n\n3. OPG-11 TGCCCGTCGT Nimisha et al.,2008 \n\n\n\n4. OPG-12 CAGCTCACGA Nimisha et al.,2008 \n\n\n\n5. OPG-14 GGATGAGACC Nimisha et al.,2008 \n\n\n\n6. OPG-16 AGCGTCCTCC Nimisha et al.,2008 \n\n\n\n7. PS01 CGTCACAGAG Yang et al.,2000 \n\n\n\n8. PS02 GAGGCCCGTT Yang et al.,2000 \n\n\n\n9. PS03 CAGGCTCTAG Yang et al.,2000 \n\n\n\n10. PS04 GTTGTGCCTG Yang et al.,2000 \n\n\n\n\n\n\n\n\n\n\n\nTable 3 Comparison of soil carbon and major nutrient levels in fallow, organic manure treated \n\n\n\nand inorganic fertilizer applied soil samples. \n\n\n\nS.No Nutrient F1 F2 I1 I2 I3 I4 I5 O1 O2 O3 O4 O5 \n\n\n\n1. Organic \n\n\n\nCarbon  \n\n\n\n(g kg-1) \n\n\n\n4.40 4.00 \n\n\n\n4.80 4.50 8.30 7.50 7.00 9.60 8.10 12.20 10.90 11.29 \n\n\n\n        \u25ca  **  **  **  **   **   **   **    ** \n\n\n\n2. Total \n\n\n\nNitrogen  \n\n\n\n(g kg-1)  \n\n\n\n\n\n\n\n\n\n\n\n0.91 \n\n\n\n\n\n\n\n\n\n\n\n0.77 1.04 0.99 1.23 1.50 1.24 1.42 1.39 1.68 1.53 1.61 \n\n\n\n                                                 \u25ca   \u25ca   **  \u25ca  **   *  **  **  ** \n\n\n\n3. Phosphorus \n\n\n\n(mg kg-1) \n\n\n\n\n\n\n\n3.80 \n\n\n\n\n\n\n\n2.70 12.00 10.00 7.00 14.40 11.20 15.50 14.10 21.20 13.90 17.10 \n\n\n\nSoil Enzymatic Activity and Microbial Diversity from Fertilizer Application\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016152\n\n\n\nRAPD-PCR was performed in a final volume of 25 \u03bcL containing 10X assay \nbuffer, 1.0 unit of Taq DNA polymerase, 200 \u03bcM each of dNTPs, 10 pmol/ \nreaction of random primers and 50 ng template DNA. A thermal cycler  was \nprogrammed for the initial denaturation step (94\u00b0C) of 5 min, followed by 45 \ncycles of 1 min denaturation along with 1 min primer annealing (37\u00b0C) and 2 min \nprimer extension (72\u00b0C), followed by a final 7 min primer extension (72\u00b0C) step. \nAmplified fragments were resolved by electrophoresis on 1.2% agarose gels. \n\n\n\nRAPD Data Analysis\nThe RAPD profiles of the various soil samples for the 10 random primers were \nscored. Presence of amplified fragment of particular size was scored as 1 and 0 \nfor the absence of the same size fragment. The data was used to calculate the \nJaccard\u2019s coefficients. Jaccard\u2019s coefficient is a measure of the similarity between \nsample sets, and is defined as the size of the intersection divided by the size of \nthe union of the sample sets. These values were used to draw a dendrogram using \nUPGMA method. \n\n\n\nStatistical Analysis of Data\nData obtained for soil nutrients and enzymes were statistically analysed, with the \nexception of DNA content. One-way ANOVA was performed using Dunnett\u2019s \nmethod and the level of significance is indicated in the respective tables and \nfigures. Since organic manure and inorganic fertiliser treated soils were compared \nfor differences without control, significance was calculated comparing the first I1  \nsamples,  hence level of significance will not be indicated in I1.\n\n\n\nRESULTS\n\n\n\nComparison of Soil Nutrient Status\nTotal organic carbon content of soils treated with inorganic fertilisers was relatively \nlow, ranging from 4.5-8.3 g kg-1 soil (Table 3). In contrast, soil samples treated \nwith organic manure had a maximum of 12.2 g kg-1 soil of organic carbon (Table \n3) in O5. The least amount of 4 g kg-1 soil of organic carbon content was found \nin fallow soil sample F2 (Table 3). The total nitrogen contents of soil samples \nfollowed the same trend as that of carbon. The nitrogen content of organic manure \ntreated soil samples was the highest (1.39-1.68 g kg-1 soil), followed by inorganic \nfertiliser treated soil samples (0.99-1.5 g kg-1 soil) and fallow soil samples \n(0.77-0.91 g kg-1 soil) (Table 3). Among the organic manure applied soils, the \nphosphorus content was highest in O5  with 21.2 mg kg-1 soil (Table 3) whereas \nit was minimal in I5 (7.0 mg kg-1 soil) and only 3.8 and 2.7 mg kg-1 soil were \nfound in F1 and F2, respectively (Table 3).  Potassium content was observed to be \nmaximum for an organic soil sample O1 (285 mg kg-1 soil), whereas the inorganic \nsoil sample I4 recorded the minimum of 70 mg kg-1 soil which is even lower than \nthe fallow soil sample of  F2 which had 98 mg kg-1 soil (Table 3).\n\n\n\nSubramaniam et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 153\n\n\n\nTABLE 3\nComparison of soil carbon and major nutrient levels in fallow, organic manure applied \n\n\n\nand inorganic fertiliser applied soil samples.\n\n\n\nSoil Enzyme Activities \nDehydrogenase activity was found to be lower in soils treated with chemical \nfertilisers (Figure 1) with a range of 6-7 \u00b5g triphenyl formazon 24 h-1g-1soil. \nThe organic manure treated soils showed a higher activity of  \u22658 \u00b5g triphenyl \nformazon 24 h-1g-1soil (Figure 1). Similarly all organic manure treated soil samples \nhad higher \u03b2-glucosidase activity than the chemical fertiliser treated soil samples \n(Figure. 2). The phosphatase activity was found to be much lower in inorganic \nfertiliser treated soil samples with a minimum of 320 \u00b5g p-nitrophenol h-1 g-1 soil \nin I2 in comparison with organic soil samples which showed the highest activity \nin O4 with  620 \u00b5g p-nitrophenol h-1 g-1 soil (Figure 3). The nitrate reductase \nactivities of all inorganic soil samples were lower than in organic soil samples \nexcept for  I3 (Figure 4).\n\n\n\n2 \n\n\n\n\n\n\n\n6. OPG-16 AGCGTCCTCC Nimisha et al.,2008 \n\n\n\n7. PS01 CGTCACAGAG Yang et al.,2000 \n\n\n\n8. PS02 GAGGCCCGTT Yang et al.,2000 \n\n\n\n9. PS03 CAGGCTCTAG Yang et al.,2000 \n\n\n\n10. PS04 GTTGTGCCTG Yang et al.,2000 \n\n\n\n\n\n\n\n\n\n\n\nTable 3 Comparison of soil carbon and major nutrient levels in fallow, organic manure treated \n\n\n\nand inorganic fertilizer applied soil samples. \nS.No Nutrient F1 F2 I1 I2 I3 I4 I5 O1 O2 O3 O4 O5 \n\n\n\n1. Organic \n\n\n\nCarbon  \n\n\n\n(g kg-1) \n\n\n\n4.40 4.00 \n\n\n\n4.80 4.50 8.30 7.50 7.00 9.60 8.10 12.20 10.90 11.29 \n\n\n\n        \u25ca  **  **  **  **   **   **   **    ** \n\n\n\n2. Total \n\n\n\nNitrogen  \n\n\n\n(g kg-1)  \n\n\n\n\n\n\n\n\n\n\n\n0.91 \n\n\n\n\n\n\n\n\n\n\n\n0.77 1.04 0.99 1.23 1.50 1.24 1.42 1.39 1.68 1.53 1.61 \n\n\n\n                                                 \u25ca   \u25ca   **  \u25ca  **   *  **  **  ** \n\n\n\n3. Phosphorus \n\n\n\n(mg kg-1) \n\n\n\n\n\n\n\n3.80 \n\n\n\n\n\n\n\n2.70 12.00 10.00 7.00 14.40 11.20 15.50 14.10 21.20 13.90 17.10 \n\n\n\n        \u25ca  **    \u25ca    \u25ca   *     \u25ca  **    \u25ca   ** \n\n\n\n4. Potassium \n\n\n\n(mg kg-1) \n\n\n\n\n\n\n\n136 \n\n\n\n\n\n\n\n98 173 149 145 98 70 285 197 235 141 146 \n\n\n\n        \u25ca    \u25ca ** **  **   \u25ca **   \u25ca    \u25ca \n\n\n\n     \u25ca P>0.05         * P<0.05           ** P<0.01 \n\n\n\n3.2 Soil enzyme activities  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFig. 3 Comparison of Phosphatasee enzyme activity observed in organic manure  and inorganic \nfertilizer treated soil samples.  \n \n\n\n\nSoil Enzymatic Activity and Microbial Diversity from Fertilizer Application\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016154\n\n\n\nSubramaniam et al.\n\n\n\nFigure 2: Comparison of betaglucosidase enzyme activity observed in organic manure \nand inorganic fertiliser treated soil samples\n\n\n\nI1,I2,I3 and I4 refer to soil samples from  inorganic fertiliser treated fields and O1,O2, O3 ,O4 \nrefer to soil samples from organic manure treated fields. \u25ca  indicates non significant values, \n* indicates significance at 0.01 level.  Vertical lines on each bar indicate the error observed \nbetween replications. \n\n\n\nFigure 1: Comparison of dehydrogenase enzyme activity observed in organic manure \nand inorganic fertiliser treated soil samples\n\n\n\n12 \n\n\n\n\n\n\n\nFig. 1 Comparison of dehydrogenase enzyme activity observed in organic manure and inorganic \nfertilizer treated soil samples \n \n\n\n\n\n\n\n\nI1,I2,I3,I4 refer to soil samples from  inorganic fertilizer treated farm and O1,O2, O3 ,O4 refer to \nsoil samples from organic manure treated farms . \u25ca Symbol indicates non significant values.  \nVertical lines on each bar indicate the error observed between replications.   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n13 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 2 Comparison Betaglucosidase enzyme activity observed in organic manure and inorganic \nfertilizer treated soil samples \n \n\n\n\n\n\n\n\n\n\n\n\n \nI1,I2,I3,I4 refer to soil samples from  inorganic fertilizer treated farm and O1,O2, O3 ,O4 refer to \nsoil samples from organic manure treated farms . \u25ca Symbol indicates non significant, * indicate \nsignificance at 0.01 level.  Vertical lines on each bar indicate the error observed between \nreplications.  \n\n\n\nI1,I2,I3,I4 refer to soil samples from  inorganic fertilizer treated farm and O1,O2, O3 ,O4 refer \nto soil samples from organic manure treated farms . \u25ca Symbol indicates non significant values.  \nVertical lines on each bar indicate the error observed between replications. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 155\n\n\n\n4 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFig. 3 Comparison of Phosphatasee enzyme activity observed in organic manure  and inorganic \nfertilizer treated soil samples.  \n \n\n\n\n\n\n\n\nI1,I2,I3,I4 refer to soil samples from  inorganic fertilizer treated farm and O1,O2, O3 ,O4 refer to \nsoil samples from organic manure treated farms. \u25ca Symbol indicates non significant, * indicate \nsignificance at 0.01 level.  Vertical lines on each bar indicate the error observed between \nreplications.   \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n5 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFig. 4 Comparison of Nitrate Reductase enzyme activity observed in organic manure and \ninorganic fertilizer treated soil samples.  \n \n\n\n\n \n \n \nI1,I2,I3,I4 refer to soil samples from  inorganic fertilizer treated farm and O1,O2, O3 ,O4 refer to \nsoil samples from organic manure treated farms . \u25ca Symbol indicates non significant, * indicate \nsignificance at 0.05 level and ** indicate significance at 0.01 level. Vertical lines on each bar \nindicate the error observed between replications. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSoil Enzymatic Activity and Microbial Diversity from Fertilizer Application\n\n\n\nFigure 4: Comparison of nitrate reductase enzyme activity observed in organic manure \nand inorganic fertiliser treated soil samples. \n\n\n\nFigure 3: Comparison of phosphatasee enzyme activity observed in organic manure  and \ninorganic fertiliser treated soil samples. \n\n\n\nI1,I2,I3,I4 refer to soil samples from  inorganic fertilizer treated farm and O1,O2, O3 ,O4 refer \nto soil samples from organic manure treated farms. \u25ca Symbol indicates non significant, \n* indicate significance at 0.01 level.  Vertical lines on each bar indicate the error observed \nbetween replications.  \n\n\n\nI1,I2,I3 and I4 refer to soil samples from  inorganic fertiliser treated fields and O1,O2, O3 ,O4 \nrefer to soil samples from organic manure treated fields . \u25ca indicates non significant values, \n* indicates significance at 0.05 level, and ** indicate significance at 0.01 level. Vertical lines on \neach bar indicate the error observed between replications.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016156\n\n\n\nDNA Content \nSoil DNA concentrations were found to be higher in all the organic manure \ntreated soil samples (Table 4) in comparison with inorganic fertelizer treated \nsoil samples. Soil sample O4 recorded a maximum concentration of 5.34 mg of \nDNA per kg of soil. The percentage difference in DNA yield between organic and \ninorganic fertilizer treated soil was found to be highest in the loamy soil sample \nfrom Coimbatore.\n\n\n\nRAPD Profile of Soil DNA\nThe microbial diversity of soil DNA was assessed by performing RAPD analysis \non DNA isolated from soil samples collected from organic and inorganic fertiliser \ntreated soil and fallow soil. The RAPD PCR profile for random primer OPG3 \n(Plate I) indicates a few fragment amplifications in fallow soil samples F1 and F2 \n(Lanes 2 and 3) and inorganic soil samples I1-I5 (Lanes 4,5,6,7 and 8). \n\n\n\nPlate 1.  DNA amplification profile of a RAPD PCR using OPG3 primer on \nDNA isolated from soil samples from fallow land, organic manure and inorganic \nfertiliser treated farms. Lanes 1 and 15 are DNA markers, (lambda double digest), \nLanes 2 and 3 correspond to soil samples F1 and F2, lanes 4,5, 6,7 and 8 are soil \nsamples I1, I2, I3, I4 and I5, lanes 9, 10, 11, 12 and 13 are soil samples O1, O2, \nO3, O4 and O5, lane 14 is positive control for PCR\n\n\n\nTABLE 4\nComparison of soil DNA content isolated from fallow, organic manure treated and \n\n\n\ninorganic fertiliser applied soil samples.\n\n\n\na Difference in DNA yield between organic manure treated  and inorganic fertiliser treated soil samples\n\n\n\n6 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 4 Comparison of soil DNA content isolated from fallow, organic manure treated and \n\n\n\ninorganic fertilizer applied soil samples. \n\n\n\nS.No. Soil type  location and \n\n\n\nCrop grown \n\n\n\nDNA \n\n\n\nconcentration(mg/kg \n\n\n\nsoil ) \n\n\n\nInorganic fertilizer \n\n\n\ntreated soil samples  \n\n\n\n DNA concentration \n\n\n\n(mg/kg soil) \n\n\n\n Organic manure \n\n\n\ntreated soil samples \n\n\n\n % \n\n\n\ndifference a \n\n\n\n\n\n\n\n1. Black cotton soil, Heavy, \n\n\n\nIndore - Soybean \n\n\n\nI1 4.83 O1 5.22 8.00 \n\n\n\n2. Black cotton soil, light \n\n\n\nIndore - Soybean \n\n\n\nI2 4.00 O2 5.16 29.00 \n\n\n\n3. Black cotton soil, \n\n\n\nCoimbatore -Tomato \n\n\n\nI3 4.02 O3 4.80 19.40 \n\n\n\n4. Black cotton soil,  \n\n\n\nCoimbatroe - Chilly \n\n\n\nI4 4.49 O4 5.34 18.93 \n\n\n\n5. Loamy soil Coimbatore - \n\n\n\nMaize \n\n\n\nI5 3.80 O5 5.22 37.36 \n\n\n\n6. Fallow black cotton soil, \n\n\n\nHeavy - No crop \n\n\n\n3.23 mg/kg soil \n\n\n\n7. Fallow black cotton soil, \n\n\n\nlight - No crop \n\n\n\n3.61 mg/kg soil \n\n\n\na Difference in DNA yield between organic manure treated  and inorganic fertilizer \n\n\n\ntreated soil samples \n\n\n\nSubramaniam et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 157\n\n\n\nThe amplified polymorphic fragments in these lanes were not only few \nin number but also very faint compared to organic soil samples O1-O5 (lanes \n9,10,11,12 and 13).  A higher number of bands with more intense DNA bands was \nseen in the organic soils (lanes 9-13). The same trend is seen for all the 10 random \nprimers used in this study. The data was used for similarit y coefficient calculation \n(Table 5).  \n\n\n\nTABLE 5\nPaired group Jaccard\u2019s coefficient matrix generated using RAPD data on soil DNA\n\n\n\nSoil Biodiversity Analysis\n\n\n\n7 \n\n\n\n\n\n\n\n\n\n\n\nLanes 1 and 15 are DNA marker,(lambda double digest), Lanes 2 and 3 correspond to soil \nsamples F1 and F2, lanes 4,5, 6,7 and 8 are soil samplesI1,I2,I3,I4 and I5, lanes 9,10,11,12, and \n13 are soil samples O1,O2,O3, O4 and O5, lane 14 is positive control for PCR \n\n\n\nTable 5 Paired group Jaccard\u2019s coefficient matrix generated using RAPD data on soil DNA  \n\n\n\n F1 F2 I1 I2 I3 I4 I5   O1 O2 O3 O4 O5 \n\n\n\nF1 1.000            \n\n\n\nF2 0.764   1.000           \n\n\n\nI1 0.653  0.694   1.000          \n\n\n\nI2 0.778   0.792   0.781  1.000         \n\n\n\nI3 0.639  0.625   0.597   0.750   1.000        \n\n\n\nI4 0.625 0.667 0.656 0.681 0.655 1.000       \n\n\n\nI5 0.528 0.653 0.781 0.739 0.752 0.653 1.000      \n\n\n\nO1 0.514 0.472 0.611 0.542 0.569 0.583 0.681 1.000     \n\n\n\nO2 0.528 0.542 0.569 0.556 0.472 0.486 0.528 0.486 1.000    \n\n\n\nO3 0.472 0.514 0.569 0.500 0.500 0.542 0.583 0.525 0.556 1.000   \n\n\n\n8 \n\n\n\n\n\n\n\nTable 5 Paired group Jaccard\u2019s coefficient matrix generated using RAPD data on soil DNA  \n\n\n\n F1 F2 I1 I2 I3 I4 I5   O1 O2 O3 O4 O5 \n\n\n\nF1 1.000            \n\n\n\nF2 0.764   1.000           \n\n\n\nI1 0.653  0.694   1.000          \n\n\n\nI2 0.778   0.792   0.781  1.000         \n\n\n\nI3 0.639  0.625   0.597   0.750   1.000        \n\n\n\nI4 0.625 0.667 0.656 0.681 0.655 1.000       \n\n\n\nI5 0.528 0.653 0.781 0.739 0.752 0.653 1.000      \n\n\n\nO1 0.514 0.472 0.611 0.542 0.569 0.583 0.681 1.000     \n\n\n\nO2 0.528 0.542 0.569 0.556 0.472 0.486 0.528 0.486 1.000    \n\n\n\nO3 0.472 0.514 0.569 0.500 0.500 0.542 0.583 0.525 0.556 1.000   \n\n\n\nO4 0.417 0.458 0.542 0.472 0.528 0.486 0.583 0.608 0.511 0.622 1.000  \n\n\n\nO5 0.542 0.528 0.528 0.569 0.514 0.556 0.597 0.607 0.603 0.553 0.541 1.000 \n\n\n\nF1 and F2 refer to soil DNA obtained from fallow land and I1,I2,I3,I4, and I5 refer to soil DNA \ncollected from inorganic fertilizer treated soil and O1,O2,O3,O4 and O5 refer to soil DNA \nobtained from organic manure treated farms respectively \n\n\n\n\n\n\n\nFig. 5 Dendrogram showing the grouping of soil samples based on soil microbial diversity \nobserved from RAPD banding pattern.  \n \n\n\n\n\n\n\n\nSoil Enzymatic Activity and Microbial Diversity from Fertilizer Application\n\n\n\nF1 and F2 refer to soil DNA obtained from fallow land and I1,I2,I3,I4, and I5 refer to soil \nDNA collected from inorganic fertiliser treated soil and O1,O2,O3,O4 and O5 refer to soil \nDNA obtained from organic manure treated farms respectively\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016158\n\n\n\nThe RAPD profile of all 10 random primers was analysed and the fragments were \nscored. The genetic similarity based on RAPD patterns in the form of Jaccard\u2019s \nsimilarity coefficient is presented in Table 5. Lesser values of similarity index were \nobserved for organic soil samples and the average similarity within the organic \nsoil samples was 0.577 (Table 5). In contrast, the inorganic soil samples had higher \nvalues and the average was 0.703 (Table 5).  A dendrogram was constructed from \nJaccard\u2019s average similarity coefficient of soils obtained in UPGMA analysis \nusing RAPD data of 10 random primers (Figure 5). Cluster analysis indicates the \ninorganic fertiliser treated soils have low biodiversity equivalent to fallow soil \nseen as one cluster. The organic soil samples  tend to cluster separately (Figure 5).\n\n\n\nFigure 5: Dendrogram showing the grouping of soil samples based on soil microbial \ndiversity observed from RAPD banding pattern. \n\n\n\nDISCUSSION\nEffect of organic fertilisers on soil quality was evaluated by comparing soil fertility \nstatus, microbial diversity and microbial dynamics in soils treated with organic \nmanure vs inorganic fertilisers and chemicals. Nutrient status of two fallow soil \nsamples was also compared. The organic manure treated soils had higher total \ncarbon, nitrogen, phosphorus and potassium than the chemical fertilisers applied \nsoils and fallow soil.  A similar increase in total N, available P and exchangeable \nK content was observed in soils applied with organic manures (Adeniyan et al., \n2011; Malero et al., 2008). We also found that in the loamy soils of Coimbatore, \norganic fertilisation led to a smaller increase in N content  (7%) compared to  \nthe increase observed in other nutrients such as C, P and K (59%, 52% and 108 \n%,  respectively). Previously, Rosen and Allan (2007) had reported that lack of \navailable nitrogen, synchronous with plant demand in organic cropping system, \nlimits yields. Hence, for an increased yield, compost plus supplemental synthetic \nN applications is suggested (Valenzuela and Crosby, 1998) \n\n\n\n8 \n\n\n\n\n\n\n\nTable \n\n\n\n5 \n\n\n\nPaired \n\n\n\nI3 \n\n\n\n0.639  0.625   0.597   0.750   1.000        \n\n\n\nI4 0.625 0.667 0.656 0.681 0.655 1.000       \n\n\n\nI5 0.528 0.653 0.781 0.739 0.752 0.653 1.000      \n\n\n\nO1 0.514 0.472 0.611 0.542 0.569 0.583 0.681 1.000     \n\n\n\nO2 0.528 0.542 0.569 0.556 0.472 0.486 0.528 0.486 1.000    \n\n\n\nO3 0.472 0.514 0.569 0.500 0.500 0.542 0.583 0.525 0.556 1.000   \n\n\n\nO4 0.417 0.458 0.542 0.472 0.528 0.486 0.583 0.608 0.511 0.622 1.000  \n\n\n\nO5 0.542 0.528 0.528 0.569 0.514 0.556 0.597 0.607 0.603 0.553 0.541 1.000 \n\n\n\nF1 and F2 refer to soil DNA obtained from fallow land and I1,I2,I3,I4, and I5 refer to soil DNA \ncollected from inorganic fertilizer treated soil and O1,O2,O3,O4 and O5 refer to soil DNA \nobtained from organic manure treated farms respectively \n\n\n\n\n\n\n\nFig. 5 Dendrogram showing the grouping of soil samples based on soil microbial diversity \nobserved from RAPD banding pattern.  \n \n\n\n\n\n\n\n\nFallow 1 and 2 refer to soil samples obtained from fallow land and Inorganic 1-5 and Organic 1-\n5 refer to soil samples collected from inorganic fertilizer treated and organic manure treated \nfarms respectively \n\n\n\nSubramaniam et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 159\n\n\n\nSoil enzyme activity is the indicator for microbial dynamics. Activity of soil \nenzymes such as dehydrogenase, \u03b2-glucosidase, phosphatase and nitrate reductase \nwas compared between organically fertilised and inorganic fertiliser treated soils. \nHigher enzyme activity was observed in organic manure applied soil samples than \nin inorganic fertilizer treated soil samples (Figures 1- 4). In the same way compost \napplication in soil was found to increase the microbial biomass, respiration and \ndehydrogenase activity (Carpenter-Boggs et al., 2000). Adding organic manure \ncompost significantly increases the amount of cultivable microorganisms and \nmicrobial biomass, thus enhancing soil respiration and enzyme activities (Zhen et \nal., 2014).  Increased enzyme activities of C and N (\u03b2-glucosidase, \u03b1-galactosidase, \n\u03b2-glucosaminidase,) and similar enzyme activities of P and S (alkaline phosphatase \nand arylsulfatase), were observed in a study comparing different poultry litter \napplications to cultivated Vertisols and pasture (Acosta-Martinez and Daren-\nHarmel, 2006). Among chemical fertiliser treated soil samples, I2 and organic \nmanure treated soil samples, O2 showed minimum activity for all the enzymes \n(Figures 1- 4). This may be due to the soil type (Table 1). Samples treatment \nI2 and O2 are light, black cotton soil from Indore. The fertility status of light \ncotton growing soils is low as they are found to require more N and K application \nthan heavier cotton growing soils of Virginia (Faircloth, 2007). This study also \nsuggests that low fertility status might lead to a lower microbial activity in light \nsoils in comparison with heavy cotton soils. We also observe that the heavy black \ncotton soils from both Indore and Coimbatore (Table 1) showed higher microbial \nactivity (Figures 1- 4). Therefore, soil type seems to be a key determinant for \nmicrobial activity as suggested earlier by Girvan et al., (2003). \n\n\n\nSoil DNA content can be correlated to microbial biomass as all other debris \nwere removed prior to DNA isolation. Extended polar lipid analyses such as \nphospholipids fatty acids (PLFA) and phospholipid ether lipids (PLEL) have also \nbeen performed to assess the microbial biomass (Esperschutz et al., 2007). Light, \nblack cotton soil from Indore showed a DNA content difference of 29% between \nI2 and O2 samples. However, the nutrient status and microbial activity in these \nsamples were observed to be lower than most other soil samples. Hence DNA \ncontent may not be a direct indicator of microbial load and diversity as suggested \nearlier by Yang et al., (2000). They also had reported that chemical fertilisers \ncause an increase in soil biomass but a decrease in soil diversity by performing \na RAPD analyis on soil DNA. They suggest that it could be due to chemical \npollution, mainly ammonium bicarbonates and its intermediates. \n\n\n\nMicrobial diversity using RAPD analysis indicates a higher number of \npolymorphic fragments in organic soil than in inorganic and fallow soil (Figure \n5) suggesting higher diversity. The diversity analysis indicates that microbial \ndiversity in inorganic fertilizer treated soil (I2) is very minimal, equivalent to \nfallow soils as they group together (Figure 6). The microbial community is similar \nin all organic soil samples as they cluster together (Figure 6). Our findings imply \nthat organic system of cropping favours microbial diversity and dynamics. \n\n\n\nSoil Enzymatic Activity and Microbial Diversity from Fertilizer Application\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016160\n\n\n\nCONCLUSION\nThe present comparative analysis was performed on soils collected from organic \nand inorganic fertiliser treated soils. Soil fertility status is found to be correlated \nto soil type. Microbial diversity is found to be similar amongst the various organic \nmanure treated soil samples as their DNA amplification profile is similar and \nthey cluster together. The chemical fertilisers have an impact on the microbial \npopulation in the soil as seen by the reduced number of DNA bands and their \nclustering pattern. 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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n147 \n \n\n\n\nEdaphic Influences on the Nutrient Concentrations and \nAntioxidant Activity of Different Tea Clones (Camellia sinensis \n\n\n\n(O.) Kuntze) Grown at the Lowland Tea Plantation, Bukit \nCheeding, Selangor, Malaysia \n\n\n\n \nAmirah, S.S.1, Khairil, M.1,2*, Murdiono, W.E.1, Halmi, M.I.E.2,3,  \n\n\n\nAmalina, N.R.4, Yong, J.W.H.5, and Burslem, D.F.R.P.6 \n \n\n\n\n1Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, \n43400 Seri Kembangan, Selangor, Malaysia \n\n\n\n2Biodiversity Unit, Institute of Bioscience (IBS), Universiti Putra Malaysia,  \n43400 Seri Kembangan, Selangor, Malaysia \n\n\n\n3Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, \n43400 Seri Kembangan, Selangor, Malaysia \n\n\n\n4Centre of Toxicology (CORE), Faculty of Health Science, Universiti Kebangsaan \nMalaysia (UKM), Jalan Raja Muda Abdul Aziz, 50300 Kuala Lumpur, Malaysia  \n\n\n\n5Department of Biosystems and Technology,  \nSwedish University of Agricultural Sciences (SLU), Alnarp, Sweden \n\n\n\n6School of Biological Sciences, Department of Plant and Soil Sciences, Cruickshank \nBuilding, University of Aberdeen, AB242UU, Aberdeen, United Kingdom \n\n\n\n \n*Correspondence: khairilmahmud@upm.edu.my  \n\n\n\n \nABSTRACT \n\n\n\nTea (Camelia sinensis) is one of the most consumed beverages in the world. Research \non the nutritional characteristics of tea, particularly lowland tea plantations, is limited \nin Malaysia. Thus, we aimed to investigate the nutritional characteristics (N, P, K, Ca, \nMg, Al, Fe) and antioxidant activity of seven clonal teas (663, 2026, 2024, AT53, TV9, \n1294, and 1428) planted at a tropical lowland tea plantation, Bukit Cheeding, Selangor, \nand their association with the soil edaphic factor. All foliar nutrient concentrations \nexcept for Ca and antioxidant activities varied significantly (p<0.05) among tea clones. \nClone AT53 had the highest foliar K (1.84 \u00b1 0.7 mg g-1), Mg (0.80 \u00b1 0.3 mg g-1), Fe \n(12.97 \u00b1 1.4 mg g-1), and Al (16.61\u00b1 1.4 mg g-1). Clone 663 had the highest P (13.76 \u00b1 \n1.06 mg g-1), and clone 2026 had the highest N (4.39 \u00b1 0.2%). Clone 1248 had the \nhighest antioxidant activity at 50.66 \u00b1 3.2 \u00b5g mL-1. Tea foliar N and P concentrations \nwere significantly associated with the N and P of the soil. Besides, several soil nutrients \nwere significantly intercorrelated with foliar nutrient concentrations. Results from this \nstudy may benefit growers in selecting better quality clones and managing lowland tea \nplantation at Bukit Cheeding, Selangor, Malaysia. Good farm management may \nimprove the productivity and sustainability of tea plantations. \n \nKey words: Camellia sinensis; Peninsular Malaysia; antioxidant activities; \nnutrient concentrations; lowland tea \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n148 \n \n\n\n\nINTRODUCTION \nTea (Camellia sinensis) is one of the most popular beverages in the world. Over \nthe past decade, global tea consumption has increased by 3.5%, underlining its \ncontinued popularity (FAO, 2022). Malaysia is a prominent producer and \nconsumer of tea, ranked as the 11th largest tea importer and the 27th largest tea \nexporter worldwide, with a more than 70% increase in local consumption from \n2007 to 2019 (Mansur, 2019).  \n \nTea has become incredibly popular due to its delightful flavour, alluring aroma, \nand myriad of health advantages, which include antioxidants (Bag et al., 2022). \nIt was also reported that an estimated 30,000 phenolic compounds may be \npresent in tea (Bhebhe et al., 2016). Due to its high levels of antioxidants, tea \nconsumption has been linked with potentially preventing various illnesses such \nas cancer, diabetes, arthritis, cardiovascular disease, stroke, genital warts, and \nobesity (Hayat et al., 2015). Tea leaves possess diverse essential minerals, \nincluding macro-elements such as K, Ca, Mg, and P, and trace elements like Fe, \nZn, and Mn (Tseng and Lai, 2022). The mineral composition of tea leaves is \nimpacted by various factors such as geographical location, cultivar type, type of \nsoil, weather conditions, and season change, which ultimately influence the \nquality of tea (Zhao et al., 2017).  \n \nResearch focusing on the nutritional characteristics of tea, particularly those \ngrown in the tropical lowlands of Peninsular Malaysia, remains limited. Past \nstudies in Malaysia have concentrated primarily on the antioxidant activity \n(AOA) and total phenolic content (TPC) of tea leaves from lowland plantations \n(Chan et al., 2007) and the phytochemical and antioxidant properties of highland \nplantations like Sabah (Izzreen and Fadzelly, 2013). Investigations into soil \nproperties effect on nutritional characteristics of tea is limited to highland \nplantations such as Sabah (Chong et al., 2008) and Cameron Highland (Hamzah \net al., 2011).  \n \nThis study has two main objectives, which are (1) to study the nutritional \ncharacteristics (N, P, K, Ca, Al and Fe), TPC, and AOA of the tea leaves from \ndifferent clonal teas, and (2) to investigate the association of foliar nutrient \nconcentrations with the physicochemical of soil at the lowland tea plantations. \nWe hypothesize that the foliar nutrient concentrations and antioxidant activities \nsignificantly vary among tea clones, and these variations are associated with soil \nedaphic factors. To our knowledge, this is one of the first study to compare foliar \nnutrient concentrations, TPC, AOA, and their association with soil \nphysicochemical properties of tea clones grown in Malaysian lowland tea \nplantations. This pioneering effort will offer invaluable insights into tea \ncultivation and plantation management. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n149 \n \n\n\n\nMATERIALS AND METHODS \n \nStudy Site  \nSamples were collected from the BOH Tea Plantation in Bukit Cheeding, \nSelangor (altitude ~20 m asl), 52 km from Kuala Lumpur. According to Kuala \nLangat District Council (2016), the tea plantation located at Bukit Cheeding was \nestablished in 1927 with 210 acres and is operated by BOH Plantations. The \ntemperature ranges from 27 to 31 \u00b0C and has a monthly rainfall range of 53.6 to \n596.3 mm.  \n \nSoil and Leaf Materials \nSeven representatives from Bukit Cheeding clonal tea cultivars were used in this \nstudy. The seven clonal teas are 663, 2026, 2024, TV9, AT53, 1294, and 1248 \n(Figure 1). All the clonal teas were planted for decades and were imported from \nIndia. Nine young and fully expanded leaves were collected from three different \nindividuals of each clonal tea. The leaves samples were kept in an envelope \nbefore analysis. The soil samples were collected at 0 \u2013 15 cm depth using a soil \nauger within a 1-meter radius of each tea plant at each clone location. All the \nsamples were labelled before being transferred to UPM for further analysis. \n \nSoil Analysis \nSoil samples were air-dried at room temperature till the weight was constant. \nThe dried samples were ground with mortar and pestle and sieved through a 2 \nmm sieve to obtain a homogenized powder sample. Another batch of soil \nsamples was sieved with 0.25 mm for total N determination. The soil \nphysicochemical properties that were determined included soil pH, electrical \nconductivity, soil organic matter, cation exchange capacity (CEC) (Sumner and \nMiller, 1996). The total concentrations of P, K, Ca, Mg, Fe, and Al were \ndetermined by digesting the soil sample using aqua regia, and then analyzed by \nmicrowave plasma atomic emission spectrometer (MP-AES Agilent \nTechnologies G8003A) (Mokhtar et al., 2015). While the total N was determined \nusing the Kjeldahl method (Bremner, 1996). \n \nLeaf Analysis \nFresh tea leaves were rinsed with distilled water and dried in an oven at 60\u00b0C for \nfour days. They were then grounded using a grinder until a fine powdered texture \nwas achieved. To determine the nutritional content of foliar (P, K, Ca, Mg, Fe, \nand Al), dry ashing technique was utilized, as described by Musa et al. (2020). \nThe digital ultrasonic bath extracted the tea sample using 80% aqueous methanol \nto determine TPC and antioxidant activity, as described by Bakht et al. (2019), \nwith minor modifications. Total N was analyzed using Kjedahl method \nfollowing Bremner (1996), while other nutrients concentration (P, K, Ca, Mg, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n150 \n \n\n\n\nFe, and Al) were determined by microwave plasma atomic emission \nspectrometer (MP-AES Agilent Technologies G8003A), TPC was analyzed \nfollowing S\u00e1nchez-Rangel et al. (2013) and analyzed using a spectrophotometer \nat the absorbance of 765nm in a microplate well. The absorbances were \nconverted into TPC by comparing against the gallic acid calibration curve (12.5 \n\u2013 400 \u00b5g mL-1) and expressed as mg gallic acid equivalent per millilitre (mg \nGAE mL-1).  \n \nWe analyzed the 1,1-diphenyl-2-picrylhydrazyl (DPPH) free-radical scavenging \nassay of tea leaves following Bobo\u2010Garc\u00eda et al. (2015) and were analyzed using \na spectrophotometer at the absorbance of 517 nm. The AOA was expressed as \nan IC50 value determined by GraphPad Prism 8 program (GraphPad Software, \nSan Diego, CA, USA). Chan et al. (2007) state that lower IC50 values indicate \nhigher antioxidant activity. Other analyses that we used to determine the \nantioxidant activities in tea leaves were using FRAP analysis (P\u00e9rez-Burillo et \nal., 2018). The FRAP reagent was prepared by mixing 25 mL of acetate buffer \n(0.3 M, pH 3.6), 2.5 mL of a solution of 10 mM TPTZ in 40 mM HCl, and 2.5 \nmL of 20 mM FeCl3 with a ratio of 10:1:1 (v/v/v). The extract was then analyzed \nusing a spectrophotometer at the absorbance of 593 nm. A standard curve was \nprepared with 0.1-1.0 mM L-1 concentrations of Iron (II) sulphate, and the results \nwere expressed as mM Fe2+ g-1 dry weight.   \n \nStatistical Analysis \nStatistical analyses were conducted using R version 3.3.1 (R Development Core \nTeam 2021). One-way ANOVA examined the variation of nutrient \nconcentrations, AOA and soil physicochemical content among tea clone \npopulations. Pearson's correlation analysis and principal component analysis \n(PCA) were performed to associate the soil properties and the nutritional content \nof the clonal tea. \n \n\n\n\nRESULTS \n \nNutritional Characteristics, TPC and AOA of Tea \nThe mean (\u00b1 SE) range of foliar N (2.97 \u00b1 0.2 to 4.39 \u00b1 0.2 mg g-1), P (9.19 \u00b1 \n1.2 to 13.76 \u00b1 1.06 mg g-1), K (0.15 \u00b1 0.1 to 1.84 \u00b1 0.7 mg g-1), Mg (0.11 \u00b1 0.0 \nto 0.80 \u00b1 0.3 mg g-1), Fe (2.67 \u00b1 0.4 to 12.97 \u00b1 1.4 mg g-1) and Al (3.66 \u00b1 0.7 to \n16.61 \u00b1 1.4 mg g-1) concentrations in tea vary significantly at p \u2264 0.001 between \nthe seven clones (Table 1). However, there were no significant variations in \nfoliar Ca concentrations among the clonal teas (p > 0.05). Clone AT53 contained \nthe highest amount of nutrients, namely K (1.84 \u00b1 0.7 mg g-1), Mg (0.80 \u00b1 0.3 \nmg g-1), Fe (12.97 \u00b1 1.4 mg g-1) and Al (16.61 \u00b1 1.4 mg g-1). In terms of TPC, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n151 \n \n\n\n\nwe found insignificant variations among the seven clonal teas (p > 0.05), \nindicating all these tea clones have similar TPC (Table 1).  \n \nPCA of foliar element concentrations of tea leaves displayed a first axis \nexplaining 36.77% of the variance, and the first four PC axes cumulatively \nexplained 89.44% of the variance (Figure 2). It was determined that total P and \nFRAP (AOA) variation had a significant positive association with the first PC \naxis (p < 0.05). Variations in foliar N, Ca, Fe, Al, and TPC were all positively \ncorrelated with the second PC axis (Table 2). \n \nPhysicochemical Properties of Soil \nThe soils of the seven tea populations are categorized as acidic soil with pH \nvalues between 3.83 \u00b1 0.2 to 4.80 \u00b1 0.4 (Table 3). The EC, CEC, and soil nutrient \nconcentrations, particularly total N and Ca, varied significantly among the \nsamples (p < 0.05). Based on the PCA, the first axis of a PCA of the soil data \ndescribed 38.14% of the variation, and the first four axes explained 91.06% \nvariation collectively (Figure 3). The first PC axis was shown to have a \nsignificant positive association (p < 0.05) with variation in total N, total P, total \nK, total Ca, Total Mg, total Fe, total Al, pH, CEC and organic matter (OM). The \nsecond PC axis was positively associated with variation in total P, total K, total \nCa, total Mg, and EC concentrations (Table 4). \n \nThe Association of Foliar Nutrient Concentrations with the Soil Edaphic \nWe found that some foliar concentrations had a significant intercorrelation with \nsoil physicochemical. Mean foliar N concentration has a significant correlation \nwith total N (r=0.443, p < 0.05) but was negatively correlated with the total Ca \n(r= -0.626, p < 0.01) in soil (Table 5). Mean foliar P concentration was positively \ncorrelated with the total P (r=0.590, p < 0.01), total K (r=0.666, p < 0.001), total \nCa (r=0.444, p < 0.05), total Mg (r=0.717, p<0.001), and pH (r=0.446, p < 0.05). \nFoliar Mg concentration also had a positive correlation with soil EC (r=0.658, p \n< 0.01). \n \n\n\n\nDISCUSSIONS \n \nNutritional Characteristics and Antioxidant Activity of Tea \nAmong the major nutrients, N played the most crucial role in improving the yield \nand quality of tea (Oh et al., 2006). In our study, the range of foliar N \nconcentration (2.97 to 4.39%, p < 0.01) was consistent with the findings of Tseng \nand Lai (2022), at 2.75 to 4.33%. Nitrogen is deficient in the tea plant when the \nN content of the leaf is less than 3.5% (Owuor and Wanyoka, 1983). As a result, \nthe N levels in the four tea clones (AT53, 1248, 2024, and 2026) are within the \ndesirable range for optimal growth and productivity. A sufficient supply of \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n152 \n \n\n\n\nnitrogen ensures vigorous plant growth and contributes to the production of high-\nquality tea leaves (Xie et al., 2023). Compared to many other crop species, tea \nonly requires a small amount of P and is highly tolerant of P deficiency (Salehi \nand Hajiboland, 2008). The P concentration in our tea samples (ranging from \n9.19 \u00b1 1.2 to 13.76 \u00b1 1.06 mg g-1, p < 0.001) was higher than in the teas planted \nin China at 1.94 to 2.49 mg g-1 (Sun et al., 2019). The elevated P concentrations \nobserved in our results indicated that tea plants have an ample supply of \nphosphorus, which is important for energy transfer, root development, \nphotosynthetic respiration, and overall plant growth (Xia et al., 2021).  \n\n\n\n\n\n\n\n \nFigure 1. The seven clonal tea samples: a)663, b)2026, c)2024, d) AT53, \ne)TV9, f)1294, g)1248. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n153 \n \n\n\n\nTable 1. The mean (\u00b1SE) foliar nutrient concentrations, TPC and AOA of seven lowland tea clones, Banting, Selangor \n \nFoliar 663 2026 2024 AT53 TV9 1294 1248 p value \nN (%) 2.97 \u00b1 0.1 4.39 \u00b1 0.1 3.79 \u00b1 0.1 3.55 \u00b1 0.2 3.10 \u00b1 0.3 3.24 \u00b1 0.4 4.21 \u00b1 0.4 0.018** \n\n\n\nP (mg g-1) 13.76 \u00b1 0.6 9.19 \u00b1 0.7 9.45 \u00b1 0.3 10.22 \u00b1 0.6 11.85 \u00b1 0.5 11.06 \u00b1 0.3 11.11 \u00b1 0.2 0.000**\n* \n\n\n\nK (mg g-1) 0.55 \u00b1 0.1 0.47 \u00b1 0.1 0.31 \u00b1 0.1 1.84 \u00b1 0.4 0.15 \u00b1 0.0 0.24 \u00b1 0.0 0.26 \u00b1 0.1 0.000**\n* \n\n\n\nCa (mg g-1) 0.58 \u00b1 0.1 0.64 \u00b1 0.1 0.38 \u00b1 0.0 0.70 \u00b1 0.1 0.68 \u00b1 0.1 0.38 \u00b1 0.0 0.61 \u00b1 0.1 0.116 \n\n\n\nMg (mg g-1) 0.27 \u00b1 0.1 0.22 \u00b1 0.0 0.14 \u00b1 0.1 0.80 \u00b1 0.2 0.11 \u00b1 0.0 0.23 \u00b1 0.0 0.22 \u00b1 0. 0.000**\n* \n\n\n\nFe (mg g-1) 3.32 \u00b1 0.5 3.43 \u00b1 0.7 6.01 \u00b1 0.7 12.97 \u00b1 0.8 10.26 \u00b1 0.4 2.67 \u00b1 0.2 12.77 \u00b1 0.5 0.000**\n* \n\n\n\nAl (mg g-1) 4.43 \u00b1 0.5 3.72 \u00b1 0.4 16.19 \u00b1 0.1 16.61 \u00b1 0.8 3.66 \u00b1 0.4 3.91 \u00b1 1.0 4.53 \u00b1 0.7 0.000**\n* \n\n\n\nTPC \n(mg GAE g-1) 19.03 \u00b1 0.2 19.23 \u00b1 0.2 19.29 \u00b1 0.3 19.29 \u00b1 0.3 19.30 \u00b1 0.0 19.04\u00b1 0.1 19.64 \u00b1 0.2 0.428 \n\n\n\nIC50 \n(\u03bcg mL-1) 62.90 \u00b1 4.9 72.49 \u00b1 2.0 74.25 \u00b1 2.8 74.36 \u00b1 5.0 73.89 \u00b1 4.3 54.61 \u00b1 3.6 50.66 \u00b1 1.9 0.000**\n\n\n\n* \nFRAP \n(mM Fe2+ g-1) 1.90 \u00b1 0.3 1.65 \u00b1 0.2 1.99 \u00b1 0.1 1.55 \u00b1 0.2 1.86 \u00b1 0.2 1.76 \u00b1 0.3 2.10 \u00b1 0.1 0.551 \n\n\n\nNotes: '* p \u2264 0.05, '**' p \u2264 0.01, '***' p \u2264 0.001. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n154 \n \n\n\n\n \nFigure 2. Biplot of scores for principal component axes (PC) 1 and 2 \nfrom principal component analysis of tea leaves from 7 clones. PC1 \nand PC2 accounted for 36.77% and 24.27% of the total variation, \nrespectively. The arrows show the loadings of each element on the \nfirst two PC axes. \n\n\n\n \nTable 2. Summary statistics of PCA axis related to foliar variables \n\n\n\nImportance of components PC1 PC2 PC3 PC4 \nEigenvalue 3.676 2.427 1.770 1.070 \nPercent of Variance (%) 36.76 24.27 17.70 10.70 \n\n\n\nCumulative Proportion (%) 36.76 61.04 78.74 89.44 \nLoadings of foliar properties     \nTotal N -0.105 0.465 -0.342 0.331 \nTotal P 0.224 -0.242 0.589 -0.133 \nTotal K -0.480 -0.156 0.114 -0.108 \nTotal Ca -0.275 0.100 0.450 0.516 \nTotal Mg -0.456 -0.142 0.164 -0.129 \nTotal Fe -0.290 0.376 0.367 -0.195 \nTotal Al -0.361 0.004 -0.259 -0.591 \nTPC -0.103 0.617 0.132 -0.050 \nDPPH -0.291 -0.157 -0.247 0.138 \nFRAP 0.339 0.352 0.116 -0.411 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n155 \n \n\n\n\nTable 3. Soil properties among seven lowland tea clones, Banting, Selangor \nFoliar 663 2026 2024 AT53 TV9 1294 1248 P value \n\n\n\nN (%) 0.11\u00b1 0.0 0.16 \u00b1 0.0 0.12 \u00b1 0.0 0.08 \u00b1 0.0 0.10 \u00b1 0.0 0.11 \u00b1 0.0 0.11 \u00b1 0.0 0.166 \nP (mg g-1) 0.37 \u00b1 0.0 0.09 \u00b1 0.0 0.12 \u00b1 0.0 0.19 \u00b1 0.0 0.31 \u00b1 0.0 0.37 \u00b1 0.1 0.20 \u00b1 0.0 0.003** \nK (mg g-1) 1.08 \u00b1 0.1 0.39 \u00b1 0.0 0.83 \u00b1 0.1 0.51 \u00b1 0.0 1.16 \u00b1 0.0 0.77 \u00b1 0.0 1.04 \u00b1 0.1 0.000*** \nCa (mg g-1) 1.04 \u00b1 0.4 0.29 \u00b1 0.0 0.24 \u00b1 0.0 0.33 \u00b1 0.0 0.28 \u00b1 0.2 0.53 \u00b1 0.1 0.37 \u00b1 0.0 0.059 \nMg (mg g-1) 0.39 \u00b1 0.0 0.18 \u00b1 0.0 0.20 \u00b1 0.0 0.18 \u00b1 0.0 0.22 \u00b1 0.0 0.19 \u00b1 0.0 0.35 \u00b1 0.0 0.000*** \n\n\n\nFe (mg g-1) 10.22 \u00b1 0.8 9.78 \u00b1 0.9 6.12 \u00b1 0.6 6.51 \u00b1 0.8 8.69 \u00b1 1.0 7.27 \u00b1 0.5 9.45 \u00b1 0.3 0.007** \nAl (mg g-1) 22.15 \u00b1 1.6 23.75 \u00b1 1.4 20.18 \u00b1 1.7 16.17 \u00b1 1.7 20.79 \u00b1 2.0 18.09 \u00b1 1.2 14.16 \u00b1 1.8 0.012* \npH 4.80 \u00b1 0.2 4.18 \u00b1 0.1 4.27 \u00b1 0.0 3.83 \u00b1 0.1 3.86 \u00b1 0.1 4.35 \u00b1 0.2 4.35 \u00b1 0.2 0.022* \nEC (mS cm-1) 35.40 \u00b1 2.0 59.63 \u00b1 3.9 34.70 \u00b1 1.7 80.67 \u00b1 1.9 55.90 \u00b1 0.5 51.47 \u00b1 4.0 37.60 \u00b1 3.0 0.000*** \nCEC (cmolc kg-1) 8.33 \u00b1 0.3 11.83 \u00b1 1.2 9.00 \u00b1 0.6 6.07 \u00b1 0.5 5.47 \u00b1 0.3 6.77 \u00b1 0.2 8.17 \u00b1 0.5 0.000*** \nOM (%) 8.64 \u00b1 0.5 8.58 \u00b1 0.3 8.26 \u00b1 1.2 8.18 \u00b1 0.7 6.44 \u00b1 1.0 7.42 \u00b1 0.7 8.45 \u00b1 0.7 0.443 \n\n\n\nNotes: '*' p \u2264 0.05 , '**' p \u2264 0.01, '***' p \u2264 0.001. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n156 \n \n\n\n\n \nFigure 3. Biplot of scores for principal component axes (PC) 1 and 2 from \nthe principal component analysis showing variation in soil N, P, K, Ca, Mg, \nFe, Al, pH, EC, CEC, and OM from seven tea clone populations. PC1 and \nPC2 accounted for 38.14% and 31.60% of the total variation, respectively. \nThe arrows show the loadings of each variable on the first two PC axes \n \n\n\n\n\n\n\n\nTable 4. Summary statistics of PVA axis related to soil physicochemical properties \nImportance of components PC1 PC2 PC3 PC4 \nEigenvalue 4.195 3.477 1.336 1.009 \nPercent of Variance (%) 38.14 31.60 12.15 9.18 \nCumulative Proportion (%) 38.14 69.74 81.89 91.06 \nLoadings of soil properties     \nTotal N 0.116 -0.469 -0.272 -0.175 \nTotal P 0.206 0.405 -0.271 0.290 \nTotal K 0.261 0.360 -0.120 -0.467 \nTotal Ca 0.398 0.130 -0.020 0.503 \nTotal Mg 0.428 0.137 0.231 -0.058 \nTotal Fe 0.342 -0.101 -0.245 0.070 \nTotal Al 0.123 -0.263 -0.639 0.138 \npH 0.453 -0.057 0.123 0.086 \nEC -0.374 0.008 -0.068 0.562 \nCEC 0.146 -0.510 0.021 -0.067 \nOM 0.193 -0.327 0.544 0.243 \nNote: EC, electrical conductivity; CEC, cation exchange capacity; OM, organic \nmatter \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n157 \n \n\n\n\nTable 5. Pearson correlation coefficients comparing mean foliar N, P, K, Ca, Mg and Al with soil chemical properties for \nseven lowland tea clones, Banting, Selangor \n Pearson correlation \nSoil variable Foliar N Foliar P Foliar K Foliar Ca Foliar Mg Foliar Al \nTotal N 0.443* -0.173 -0.184 -0.202 -0.226 -0.159 \nTotal P -0.378 0.590** -0.175 0.051 -0.099 -0.349 \nTotal K -0.090 0.666*** -0.425* -0.031 -0.392 -0.317 \nTotal Ca -0.626** 0.444* 0.026 0.260 0.062 -0.236 \nTotal Mg -0.129 0.717*** -0.202 0.118 -0.174 -0.369 \nTotal Al -0.200 0.061 0.078 0.067 -0.136 -0.065 \npH -0.424 0.446* -0.277 -0.327 -0.310 -0.302 \nEC 0.096 -0.296 0.658*** 0.272 0.639** 0.259 \nCEC 0.349 -0.332 -0.108 -0.130 -0.192 -0.106 \nOM -0.193 0.107 0.148 -0.083 0.086 0.170 \nSoil PC Axes       \nPC1 0.262 -0.721*** 0.410 0.031 0.395 0.474* \nPC2 0.409 -0.349 -0.033 -0.122 -0.122 -0.056 \n\n\n\nNotes: '*' p \u2264 0.05 , '**' p \u2264 0.01, '***' p \u2264 0.001.  \n  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n158 \n \n\n\n\nWe observed significantly higher K concentrations (ranging from 0.15 \u00b1 0.0 to \n1.84 \u00b1 0.4 mg g-1) in tea leaves across the seven clones compared to the 0.11 mg \ng-1 reported in previous Bangladeshi studies (Rashid et al., 2016). The higher K \nconcentrations observed in our results suggest that the tea plants have an \nabundant supply of K, essential for regulating water uptake, improving drought \ntolerance, and enhancing tea plant health and productivity (Teotia et al., 2016).  \n \nAlthough some research suggests that tea is a calcifuge and acidophilic plant, Ca \nis also a necessary nutrient for tea plants and can increase tea growth after \napplication (Fung and Wong, 2004). In our study, the range of Ca concentrations \n(0.38 \u00b1 0.0 to 0.70 \u00b1 0.1 mg g-1) was slightly lower than other studies reported \nin Thailand, with 5.88 mg g-1 (Chupeerach et al., 2021). Inadequate Ca levels \ncan lead to various physiological disorders in tea, such as leaf tip burn and poor \nroot development. Ca deficiency can also reduce plant tolerance to \nenvironmental stresses, including drought (Benton, 2018).  \n \nWe found that the lowland tea in our study had slightly lower Mg concentrations \nwith a range of 0.14 \u00b1 0.1 to 0.80 \u00b1 0.2 mg g-1 than other research, such as in \nBangladesh with 0.52 mg g-1 (Rashid et al., 2016) and China with 1.66 mg g-1 \n(Li et al., 2018). Mg deficiency reduces chlorophyll production, phloem loading, \nand photoassimilates partitioning between roots and shoots (Uzilday et al., \n2017). The range of foliar Fe concentrations (3.32 \u00b1 0.5 to 12.97 \u00b1 0.8 mg g-1, p \n< 0.001) in our study was higher than a study by Zhang et al. (2018) with the \nrange of Fe concentrations from 0.57 to 0.28 mg g-1. The tropical soil's acidity \nincreased Fe and Al concentrations, which may affect tea's Fe uptake. Therefore, \nadequate iron levels are essential for plant chlorophyll synthesis and enzyme \nactivities (Kumar et al., 2022).  \n \nTea is an Al hyperaccumulator as it accumulates high concentrations of Al (>1 \nmg g-1). Tea has the ability to tolerate high concentrations of Al. This tolerance \nmechanism involves the release of organic acid from the root to prevent direct \ncontact with Al and alkalinization of the rhizosphere by reducing the uptake of \ncations over anions, modified cell wall, redistribution of Al, and internalization \nof Al (Riaz et al., 2018). By generating Al complexes with organic acids or other \nchelators and securing these complexes in the vacuoles to maintain a low level \nof free Al in the plant cytoplasm, Al-accumulating plants can internally detoxify \nAl (Singh et al., 2017). The differences in nutrition concentration between this \nstudy's clonal teas and those of other researchers, as we discussed above, could \nbe influenced by differences in soil properties, nutrient status, and fertilizer \napplication in the fields (Rashid et al., 2016; Sun et al., 2019). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n159 \n \n\n\n\nTPC plays a vital role in determining the antioxidant potential of plant-based \nfoods, including tea leaves (Yang and Liu, 2013). Phenolic compounds are \nknown for their potent antioxidant properties, which can scavenge free radicals \nand protect against oxidative stress-related diseases (Oluwole et al., 2022). The \nTPC of lowland tea leaves (19.03 \u00b1 0.2 to19.64 \u00b1 0.2 mg GAE g-1) in our study \nwas slightly lower compared to Chan et al. (2007) with the value of 76.7 mg \nGAE g-1. Izzreen and Fadzelly (2013) also reported higher TPC values of 80.27 \nmg GAE g-1 (green tea) and 76.93 mg GAE g-1 (black tea) at Sabah Tea \nPlantation, Malaysia. These findings imply that optimizing environmental \nconditions may help enhance the phenolic content of tea leaves, potentially \nleading to increased AOA and associated health benefits. The AOA content of \nthe tea leaves in this study (FRAP range between 1.55 to 2.1 mM Fe2+ g-1) was \nat a medium level when compared to tea plantations in China, where the FRAP \nvalue ranged from 0.61 to 5.38 mM Fe2+ g-1 (Tang et al., 2019). The variation in \nthe FRAP and DPPH values among the tea clones further emphasizes the \ndifferences in AOA and suggests the presence of genetic variations that influence \nthe phenolic compounds responsible for antioxidant properties (Gonbad et al., \n2015; Li et al., 2023). \n\n\n\n \nFoliar nutrient and soil physicochemical association \nFoliar N and P were significantly associated with the soil N and P concentrations. \nThere were also some inter-correlations between soil nutrients and tea's foliar \nnutrient concentrations. However, no direct significant association was found \nbetween foliar Ca, Mg, K, Fe and Al concentrations of clonal tea with soil \nchemistry. As an Al hyperaccumulator, we also found that the differential \nexpression of Al accumulation in tea populations is uncoupled to local variation \nin soil Al concentrations. The insignificant association between most foliar \nnutrient concentrations with soil chemistry is similar to the finding by Tseng and \nLai (2022). The concentration of the nutrient assimilated by plants, particularly \nmetal hyperaccumulators, may be due to their phylogenetic influence, by which \nthe plant can absorb the metal nutrient regardless of the nutrient level in the soil \n(Khairil and Burslem, 2018).  \n \nThe nutrient variation in tea leaves could be associated with the biochemical \nactivities associated soil microbes. Soil microbes can mineralize insoluble \nmineral and organic P compounds, making them available for plants \n(Bergkemper et al., 2016). Yang et al. (2022) reported that rhizosphere microbial \nhad a direct positive effect on several nutrients, including the N and P content of \nthe tea plants. Besides, the changes in soil pH also influence the availability of \nessential plant nutrients, which differ among nutrients, crops, and soil types \n(Holland et al., 2018). At low pH, P is easily fixed by Fe and Al, resulting in the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 147-163 \n \n\n\n\n160 \n \n\n\n\nlack of available P for plants (Redel et al., 2016). Therefore, a higher pH would \nincrease the soluble P and make the P available for plant uptake, especially in \nacid soils (Johan et al., 2021).  \n \n\n\n\nCONCLUSION \nThe variation in the mean foliar nutrients showed that different clones had \nvarying abilities to accumulate nutrients. This variation is, however, not directly \nreflected by all nutrients in the soil except for macronutrients such as N and P. \nSince N and P are the major nutrients for tea, supplying the optimum fertilizer is \nessential for tea growth and productivity. Clones AT53 and 1248 are considered \nbetter clones in accumulating minerals and have a higher AOA. These clones \ncan be strategic choices for growers to produce the best quality of tea with added \nnutritional value and market appeal. Analyzing the effect of nutrient applications \non the growth and physiology of tea clones is required for future research. \n   \n\n\n\nACKNOWLEDGEMENTS \nThe authors are grateful to BOH Plantation Sdn. Bhd. for their invaluable \ncooperation and support in providing the sample subjects for the research study. \nThis study was funded by Geran Putra Universiti Putra Malaysia (grant number \nGP-IPM/2020/9690400). \n \n\n\n\nREFERENCES \nBag, S., Mondal, A., Majumder, A., and Banik, A. 2022. Tea and its phytochemicals: \n\n\n\nHidden health benefits & modulation of signalling cascade by phytochemicals. \nFood Chemistry 371: 131098. \n\n\n\nBakht, M.A., Geesi, M.H., Riadi, Y., Imran, M., Ali, M.I., Ahsan, M.J., and Ajmal, N. \n2019. 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"\n\n___________________\n*Corresponding author : E-mail: denasponti@gmail.com \n\n\n\nINTRODUCTION\nMaize (Zea  mays L.) is not only a staple food for people, but also a major \ncomponent of feeds for the animal industry (Reddy et al., 2013). It is also being \nincreasingly used in biofuels (Ko\u00e7ar and Civas, 2013). Indonesia targeted maize \nproduction of 29,000,000 tons in 2014. Haryono (2012) estimates that the area of \nproduction and productivity had to be increased to approximately to 4,999,000 \nha and 5.82 t ha-1, respectively to meet the production target. With mineral \nsoils for agricultural expansion becoming less available, the role of peatlands is \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 115-129 (2015)                     Malaysian Society of Soil Science\n\n\n\nEffect of Ameliorants on Nutrient Uptake and Maize \nProductivity in Peatlands\n\n\n\nSuswati, D.1*, B.H. Sunarminto2,\tD.\tShiddieq2 and D. Indradewa3\n\n\n\n1Department of Soil Science, Faculty of Agriculture, Tanjungpura University-\nPontianak, Jl. Ahmad Yani, Pontianak. 739630 \n\n\n\n2Department of Soil Science, Faculty of Agriculture,\nGadjah Mada University-Yogyakarta\n\n\n\n3Department of Agronomy, Faculty of Agriculture,\nGadjah Mada University-Yogyakarta\n\n\n\nABSTRACT\nPeat, when used as a growing medium, must be optimally managed to improve \nits soil chemical properties. One alternative to increasing soil pH and available \nnutrient is by applying coastal sediment and salted fish waste which are easily \nobtained and relatively inexpensive. This study examined the effect of different \namounts of coastal sediment and salted fish waste on nutrient uptake and \nproduction of maize on the peatlands of West Borneo (West Kalimantan). One \ncontrol and three dosing regimes were tested on three types of peatlands as follows \n: (1) control case (farmers\u2019 standard practice without additional dosing);(2) 20 Mg \nha-1 of coastal sediment + 0.75 Mg of salted fish waste ha-1; (3) 40 Mg ha-1of coastal \nsediment + 1.5 Mg of salted fish waste ha-1; and (4) 60 Mg ha-1  of coastal sediment \n+ 2.25 Mg of salted fish waste ha-1. The peatlands that were improved were Typic \nHaplohemist, Typic Sulfisaprist and Typic Haplosaprist.  A combination of 40 Mg \nha-1 of coastal sediment and 1.5 Mg ha-1 of salted fish waste was found to be \nthe best dosing regime for each peat type, increasing nutrient uptake (N, P and \nK) and improving the production of maize. Typic Haplosaprists (13.36 Mg ha-1) \nwas found to have the highest maize production, followed by Typic Haplohemists \n(13.06 Mg ha-1) and Typic Sulfisaprists (12.96 Mg ha-1).\n\n\n\nKeywords:\t Coastal\t sediment,\t salted\t fish\t waste,\t nutrient\t uptake,\t\ncorrelation study\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015116\n\n\n\nSuswati, D., B.H. Sunarminto, D. Shiddieq and D. Indradewa\n\n\n\nincreasingly important, especially in a country where there is abundant peatlands \n(Agus et al., 2012). Page et al., (2011) estimate Indonesia\u2019s peatland area as being \n206.95 km2 or 47% of global peatland area, covering almost 10% of the land \nsurface of the country. Indonesia\u2019s peatlands are mostly at low altitudes in the \ncoastal and sub-coastal areas of Sumatra, Papua and Borneo (Kalimantan) islands \n(Jaenicke et al., 2008).\n\n\n\nThe peatland area in Borneo is about 5.77 million ha, of which 1.73 million \nha are in West Kalimantan (Wahyunto et al., 2010) with peat thickness ranging \nfrom 0.5 to 20 m (Page et al., 2002). The bulk of the peatland is covered by \npeat swamp forests (64%), whilst a small portion is being used with smallholder \nfarmers accounting for 4% and 11% of the peatland, respectively (Miettinen \nand Liew, 2010). Peat is mostly acidic to very acidic and naturally accumulates \nunder anaerobic conditions (Sabiham et al., 2012). In addition, peat soils have \nlimitations with regard to the non-availability of potassium (K), sulphur (S), zinc \n(Zn), and copper (Cu) (Masud et al., 2011; Abat et al., 2012).  Peat with low \nfertility may have reduced decomposition rates because of the lack of available \ncations which are usually strongly bound to the negative exchange sites within \npeat (Gogo and Pearce, 2009).\n\n\n\nTwo methods for improving the fertility and productivity of peatlands is \nby adding ameliorants (Nurzakiah et al., 2013) or by reducing the rate of peat \ndecomposition (Husen and Agus, 2011). The alternative ameliorants applied to \nmaize farms in West Borneo peatlands are coastal sediment and salted fish waste \nwhich are easily obtained and relatively cheap. The addition of coastal sediment \nto the peat soil can raise the pH due to the neutralising of hydrogen ions (H+) or \ncations from peat by hydroxide ions (OH-) of base cations contained in the coastal \nsediments (Suyadi, 1995). An increase in the pH of the peat soil to 6 or 7 will \nincrease the activity of its microorganisms (B\u0142o\u0144ska, 2010; Sullivan et al., 2013).\n\n\n\nThe fishing industry produces a large amount of waste which may be of \npotential use for agricultural activities (Mosquera et al., 2011). These large \nquantities of fish waste have not been used efficiently, and the disposal of fish waste \ncan have negative impacts on the local environment (Kim, 2011). Fish  waste has \nalso traditionally been used as a fertiliser in coastal areas, as it is rich in nutrients, \nparticularly nitrogen (N) and phosphorous (P) (Arvanitoyannis and Kassaveti, \n2008). Hudsom (2008) found that corn plants grow faster and look better when \nplanted in soils mixed with germinated fish compost compared to plants grown \nwithout the compost. Fish waste in West Kalimantan is readily available because \nthe region\u2019s marine fisheries produced 89.77 tonnes yr-1 of products and about \n20% (17.96 tons yr-1) of it is considered as waste (Department of Marine and \nFisheries of  West Kalimantan, 2009). This study aimed to determine the effect \nof application of coastal sediment and salted fish waste as ameliorants on nutrient \nuptake productivity  of maize in the peatlands of West Kalimantan.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 117\n\n\n\nAmeliorants for Maize Productivity in Peatlands\n\n\n\nMATERIALS AND METHODS\n\n\n\nStudy Site and Ameliorants\nThe research was conducted at three different sites of Rasau Jaya III subdistrict in \nKubu Raya District of West Kalimantan, Indonesia from April to June 2012, on \npeatlands classified as Typic  Haplohemist  (109o 20\u2019 52.653\u201d E,  0o 14\u2019 59.431\u201d S), \nTypic Sulfisaprist (109o 20\u2019 56.675\u201d E, 0o 15\u2019 40.304\u201d S) and Typic Haplosaprist \n(109o 21\u2019 17.824\u201d E, 0o 14\u2019 11.131\u201d S) (Suswati et al., 2011).\n\n\n\nSalted fish waste used in this study was obtained from local markets. Coastal \nsediments were sourced from Kijing Beach,West Kalimantan. The fertiliser dosing \nwas based on the recommended requirements of  N, P, and K for maize plants. The \nPioner 21 hybrid variety of maize seed was used in this experiment. \n\n\n\nField Sudy\nThe trial was a factorial experiment with five replications conducted using \nrandomised complete block design (RCBD) (Gomez and Gomez, 1984). The \nfirst factor of the experiment consisted of four levels: (1) farmers\u2019practice; (2) \n20 Mg ha-1  of coastal sediment + 0.75 Mg of salted fish waste ha-1; (3) 40 Mg \nha-1 of coastal sediment + 1.5 Mg of salted fish waste ha-1; and (4) 60 Mg ha-1 of \ncoastal sediment + 2.25 Mg of salted fish waste ha-1. The second factor consisted \nof  three types of peat: (1) Typic Haplohemists; (2) Typic Sulfisaprists; and (3) \nTypic Haplosaprists.\n\n\n\nThe study comprised 20 plots (each sized 6 m x 3.8 m) for every peat type \nwith a 1 m protection zone framing the experimental field. The maize was planted \nat a spacing of 75 cm x 20 cm, resulting in 152 plants per plot. Six plant samples \nwere taken randomly for measurement outside of swath for N, P and K uptake, \nwhilst 44 plants (grown in the size 3 m x 2.2 m swath) were used to determine \nmaize productivity per hectare at the end of the study. Measurement of nutrients \nuptake was carried out on samples at the maximum vegetative phase (Gangwar \nand Kalra, 1988). For this study, the maximum vegetative phase was at 54 days \nafter planting which was indicated by the appearance of male flowers for as much \nas 60% of the plant population.\n\n\n\nAmeliorants and Nutrient Uptake Analyses \nSelected physical and chemical properties of ameliorants were determined using \nstandard procedures. The coastal sediment texture analysis was carried out using \nthe pipette method (Sarkar and Haldar, 2005). The ameliorants\u2019 pH values were \ndetermined in a 1:2.5 soil to distilled water suspension using a glass electrode. The \ncontent of organic carbon was determined using the Walkley and Black method.  \nAmeliorant cation exchange capacity (CEC) was determined by leaching with 1M \nammonium acetate buffer adjusted to pH 7.0 followed by steam distillation (Pansu \nand Gautheyrou, 2006).  Available phosphorus in the ameliorants were extracted \nwith NaHCO3 (0.5 M) at pH 8.5 and determined colorimetrically after treatment \nwith ammonium molybdate and stannous chloride at a wave length of 660 nm. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015118\n\n\n\nThe exchangeable base cations were extracted with 1.0 mol L-1 ammonium acetate \n(Pansu and Gautheyrou, 2006). After extraction, the cations were measured using \nan atomic absorption spectrophotometer (AA-6200 Shimadzu). \n\n\n\nPlant macronutrients (N, P and K) content were established after 1 g of plant \nsamples (previously dried at 60\u00b0C for 24 h) were  digested  with  concentrated  \nH2SO4  (for the determination of N) and a mixture of concentrated HNO3 and \nHClO4 (for the determinations of nutrients other than N), after which the extraction \nwas adjusted using up to 50 mL of deionised water. The determination of N, P and \nK was done  using  semi-micro Kjedahl, P with  vanadomolybdate  yellow and K \nwith  the atomic absorption spectrophotometry (AAS) method (Temminghoff and \nHouba,  2004).\n\n\n\nStatistical Analysis\nThe data obtained were subjected to two-way analysis of variance (ANOVA) \nfollowed by a Tukey\u2019s test at 5% level. For some parameters, the correlations \nwere computed. The data were analysed using the Statistical Analysis System \npackage (SAS Institute, 2003).\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nThe Characteristic of Peatlands and Ameliorants\nThe soil characteristics of the three peat soilshave been described by Suswati \net al. (2011). Based on the standards for determining soil chemical properties \ndescribed by Hazelton and Murphy (2007), the three peat soils studied were very \nacidic (3.26 - 3.76), had high total N content (> 0.5%) and organic-C (29.74 - \n43.85%). Available P in Typic Sulfisaprist (8.72 mg P kg-1) was high, where as in \nTypic Haplohemist (3.36 mg P kg-1) and Typic Haplosaprist (1.63 mg P kg-1) the \nlevels were relatively low. The exchangeable K varied between 0.84 - 1.23 mg \nK kg-1. Cation exchange capacity was very high (57.37 - 88.57 cmol(+) kg-1) but \nbase saturation (BS) was low (8.17 - 11.26%), which could inhibit equilibrium of \nnutrients, especially K, Ca and Mg. The content of K, Ca and Mg nutrients was \nlow, which resulted in the plants beign deficient in these macronutrients. This is \nsimilarto the study of Simbolon (2009) which reported low peat soil\u2019s fertility, \ncharacterised by high acidity and low availability of N, P, K, Ca and Mg.\n\n\n\nCoastal sediment and salted fish waste samples were analysed for chemical \nproperties (Table 1). Coastal sediment had a high pH value (8.13),very high \nbase saturation (135.17%) and electrical conductivity (EC) of 9.57 mS/cm. It \ncomprised 10.20% sand, 51.85% silt and 37.95% clay (silty clay loam texture).  \nIt had very low available P and exchangeable K with levels of 1.51 mg P kg-1 \n\n\n\nand 1.42 mg K kg-1, respectively. The addition of coastal sediment on peat soil \nmight raise soil pH due to neutralising of  H+ ions from peat soil by OH- ions from \ncoastal sediment. Coastal sediment also contained high amounts of alkali cations. \nA high content of alkali cations will cause CEC to decrease, increasing BS and \navailability of cations Ca2+, Mg2+, Na+ and K+. The CEC decline occurs because \n\n\n\nSuswati, D., B.H. Sunarminto, D. Shiddieq and D. Indradewa\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 119\n\n\n\nof the peat soil\u2019s organo-cation complex formation between organic acids with \ncations from coastal sediment (such as Fe, Cu, Mn and Zn) (Table 1). Husen et \nal. (2013) explained that these high valence cations might form a ligand complex \nwith organic acids in peat. Tan (2011) explained that organic acids in peat soils are \ncapable of forming complex metal ions, particularly for transition metal such as \nAl, Fe, Cu, Zn and Mn. The coastal sediment was  formed from complex organo-\ncation compounds resulting in cations that are strongly bound and difficult to \nexchange. This is consistent with  the findings of  Wulandari et al. (2014) showing \nthat the addition of sea water can decrease the CEC of peat.\n\n\n\nMeanwhile, salted fish waste hada high plant nutrient content. Content \nof total N, P, K, Ca, Mg and Na were 7.66, 1.26, 0.59, 0.82, 0.30 and 0.29%, \nrespectively. Salted fish waste was  neutral  and  the  organic  matter  content  \nwas  very high  (72.33%)  with a carbon/nitrogen ratio of 5.48. Additionally, the \napplication of salted fish waste may inhibit acidity increases in peat soil, as well \nas enhance BS and availability of cations Ca2+, Mg2+, Na+ and K+ (Suswati, 2006). \nThe EC value of salted fish waste was 4.02 dS m-1 (very high), but due to the \nrelatively small amount applied to the peat soil, it did not impede plant growth. \nThe soil EC after addition of coastal sediment and salted fish waste at the highest \ndose (60 Mg ha-1 of coastal sediment + 2.25 Mg ha-1 of salted fish waste) was only \n1.22 dS/m (Table 2). \n\n\n\nAmeliorants for Maize Productivity in Peatlands\n\n\n\n TABLE 1\nThe chemical characteristics of coastal sediment and salted fish waste\n\n\n\n10 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 1 \nThe chemical characteristics of coastal sediment and salted fish waste \n\n\n\n\n\n\n\nParameter Coastal \nsediment Parameters Salted fish \n\n\n\nwaste \nTexture    \n\n\n\nSand (%) 10.20   - \nSilt (%) 51.85   - \nClay (%) 37.95   - \n\n\n\npH 8.13 pH 6.08 \nOrganic -C (%) 1.96  Organic- C (%) 41.95  \n Total N (%) 7.26   Total N(%) 7.66  \nC/N 0.27 C/N 5.48 \nAvailable P (mg kg-1) 3.45 Total P(%) 1.26  \nExch. K(cmol(+) kg-1) 1.71   Total K(%) 0.59  \n Exch. Ca (cmol(+) kg-1) 14.62   Total Ca(%) 0.82  \n Exch. Mg  (cmol(+) kg-1) 1.73   Total Mg(%) 0.30  \n Exch. Na (cmol(+) kg-1) 2.65   Total Na(%) 0.29  \nCEC  (cmol(+) kg-1) 15.33   - \nBase saturation (%) 135.17   - \nFe (mg kg-1) 5239.64   \nCu (mg kg-1) 29.16   \nMn (mg kg-1) 16.54   \nZn (mg kg-1) 11.49   \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015120\n\n\n\nPlant Nutrient Concentrations andUptake\nThe effect of costal sediment and salted fish waste on N, P, and K concentrations \nand their uptake through each peat soil type is presented in Tables 3 and 4. \nConcentrations of N, P, and K, in the tissue of maize that received treatments \n\n\n\nSuswati, D., B.H. Sunarminto, D. Shiddieq and D. Indradewa\n\n\n\n TABLE 2\nElectrical conductivity of peat soils after ameliorant application\n\n\n\nTABLE 3\nNutrient concentrations in foliar tissue at maximum vegetative phase with ameliorant \n\n\n\napplication and peatland type\n\n\n\n11 \n\n\n\n\n\n\n\n\n\n\n\n \nTABLE 2 \n\n\n\nElectrical conductivity of peat soils after ameliorant application \n \n\n\n\nVariable of \nobservation \n\n\n\nDose of \nameliorant \n(Mg ha-1) \n\n\n\n\n\n\n\nPeatland type \nMean Standard \n\n\n\nerror Typic \nHaplohemists \n\n\n\n\n\n\n\nTypic \nSulfisaprists \n\n\n\n\n\n\n\nTypic \nHaplosaprists \n\n\n\n  0 0.06 0.07 0.07 0.07a 0.003 \nEC 20 + 0.75 0.51 0.41 0.41 0.44b 0.033 \n\n\n\n(dS m-1) 40 + 1.50 1.16 0.74 0.70 0.87c 0.147 \n 60 + 2.25 1.43 1.26 0.96 1.22d 0.137 \n Mean 0.79 0.62 0.53 (-)  \n Standard \n\n\n\nerror 0.311 0.253 0.191 \n  \n\n\n\nNotes: (-) no interaction and (+) interaction.Meanvalues followed by the same letter in the column \nand the same treatment groups did not differ by Tukey's test at 5% probability level. \n\n\n\n\n\n\n\n12 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \nNutrient concentrations in foliar tissue at maximum vegetative phase with ameliorant \n\n\n\napplication and peatland type \n \n\n\n\nVariable of \nobservation \n\n\n\nDose of \nameliorant \n(Mg ha-1) \n\n\n\nPeatland type \n\n\n\nMean Standard \nError \n\n\n\nTypic \nHaplohemists \n\n\n\n\n\n\n\nTypic \nSulfisaprists \n\n\n\n\n\n\n\nTypic \nHaplosaprists \n\n\n\n \n 0 1.61 1.77 1.52 1.63 a 0.072 \n\n\n\nN 20 + 0.75 1.74 1.91 1.75 1.80 b 0.053 \n(%) 40 + 1.50 1.85 1.82 1.83 1.83 b 0.007 \n\n\n\n 60 + 2.25 1.68 1.88 1.81 1.79 b 0.058 \n Mean 1.72 1.84 1.73 (-)  \n Standard \n\n\n\nerror 0.051 0.031 0.071 \n  \n\n\n\n 0 0.22 0.23 0.18 0.21 a 0.016 \nP 20 + 0.75 0.33 0.33 0.29 0.32 b 0.013 \n\n\n\n(%) 40 + 1.50 0.37 0.35 0.31 0.34 b 0.018 \n 60 + 2.25 0.35 0.36 0.31 0.34 b 0.016 \n Mean 0.32 0.32 0.27 (-)  \n Standard \n\n\n\nerror 0.032 0.030 0.031 \n \n\n\n\n\n\n\n\n 0 1.38 1.45 1.42 1.41 a 0.020 \nK 20 + 0.75 1.88 1.82 1.89 1.86 b 0.021 \n\n\n\n(%) 40 + 1.50 2.01 1.89 2.20 2.03 c 0.090 \n 60 + 2.25 1.74 1.96 2.29 2.00 c 0.159 \n Mean 1.75 1.78 1.95 (+)  \n Standard \n\n\n\nerror 0.136 0.114 0.196 \n \n\n\n\n\n\n\n\nNotes: (-) no interaction and (+) interaction.Mean values followed by the same letter in the column \nand the same treatment groups did not differ by Tukey's at 5% probability level. \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 121\n\n\n\nof coastal sediment and salted fish waste were significantly increased compared \nto the control case (Table 3). Nitrogen concentration in plant tissues of maize \ngrown without the addition of coastal sediment and salted fish waste was 1.63%.\nNitrogen concentration in plant tissues of maize grown with coastal sediment and \nsalted fish waste ranged from 1.79 to 1.83%. Both coastal sediment and salted fish \nwaste had high N contents of 7.26 and 7.66%, respectively (Table 1).\n\n\n\nThe concentration of P in maize tissue in the control was 0.21%. The \ntreatment of coastal sediment and salted fish waste increased the concentration \nof P in the plant tissue. Increasing the dose of coastal sediment and salted fish \nwaste did not significantly increase P concentration in plant tissue as the treatment \namounts tested, 20 Mg ha-1 + 0.75 Mg ha-1, 40 Mg ha-1 + 1.5 Mg ha-1 and 60 Mg ha-1 \n\n\n\n+ 2.25 Mg ha-1 produced P concentrations of 0.32, 0.34 and 0.34%, respectively.\nThe use of ameliorant materials significantly increased N uptake whilst the soil \nfactor as well as the interaction between those factors did not significantly impact \non the variables (Table 4). The treatment of 20 Mg ha-1 of coastal sediment + 0.75 \nMg ha-1 of salted fish waste increased N uptake compared to untreated coastal \n\n\n\nAmeliorants for Maize Productivity in Peatlands\n\n\n\nTABLE 4\nNutrients uptake maximum vegetative phase with ameliorant application and\n\n\n\npeatland type\n\n\n\n13 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 4 \nNutrients uptake maximum vegetative phase with ameliorant application and peatland type \n\n\n\n\n\n\n\nVariable of \nobservation \n\n\n\nDose of \nameliorant \n(Mg ha-1) \n\n\n\nPeatland type \n\n\n\nMean Standard \nerror \n\n\n\nTypic \nHaplohemists \n\n\n\n\n\n\n\nTypic \nSulfisaprists \n\n\n\n\n\n\n\nTypic \nHaplosaprists \n\n\n\n \n 0 660.11 768.64 833.42 754.06a 50.559 \n\n\n\nN 20 + 0.75 2238.58 2120.4 2140.82 2166.6b 36.470 \n(mg plant-1) 40 + 1.50 2902.48 3091.56 3735.26 3242.1c 252.061 \n 60 + 2.25 2258.76 2324.86 2129.44 2237.6b 57.388 \n Mean 2014.98 2076.37 2209.74 (-)  \n Standard \n\n\n\nerror 477.210 483.430 593.904 \n  \n\n\n\n 0 91.82 98.12 98.71 96.22 a 2.205 \nP 20 + 0.75 429.72 363.52 353.01 382.08 b 24.011 \n\n\n\n(mg plant-1) 40 + 1.50 577.65 592.76 654.96 608.46 c 23.657 \n 60 + 2.25 472.71 443.32 361.94 425.99 b 33.130 \n Mean 392.98 374.43 367.16 (-)  \n Standard \n\n\n\nerror 105.082 103.634 113.696 \n \n\n\n\n\n\n\n\n 0 569.01  627.13  786.37  660.84 a 64.970 \nK 20 + 0.75 2419.27  2030.51  2289.25  2246.34 b 114.257 \n\n\n\n(mg plant-1) 40 + 1.50 3132.32  3197.42  4787.50  3705.75 c 541.203 \n 60 + 2.25 2339.76  2407.93 \n\n\n\nde \n2701.00 2482.90 b 110.813 \n\n\n\n Mean 2115.09 2065.75 2641.03 (+)  \n Standard \n\n\n\nerror 545.292 537.636 825.351 \n \n\n\n\n\n\n\n\nNotes: (-) no interaction and (+) interaction.Mean values followed by the same letter in the column \nand the same treatment groups did not differ by Tukey's at 5% probability level. \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015122\n\n\n\nsediment + salted fish waste. Nitrogen update further increased with the treatment \nof 40 Mg ha-1 of coastal sediment + 1.5 Mg ha-1 of salted fish waste, but did not \nincrease further with the application of 60 Mg ha-1 of coastal sediment + 2.25 Mg \nha-1 of salted fish waste. Increased availability of N caused maize to absorb more \nN than the control. Jones et al. (1991) state that N uptake by plants is affected by \nsoil pH, temperature and the presence of other ions in the soil solution. Increasing \nsoil pH increases activity of soil microorganisms, which are active at a pH range \nof 6 to 7 (neutral soil). Due to the increased activity of microorganisms in the soil, \nit would lower the ratio of C and N in peat soil, so that N would be available to \nplants. Kakei and Clifford (2000) found that liming can increase the pH in peat \nand thus increase N availability in the peat.\n\n\n\nThere was a declining trend in N uptake with the addition of 60 Mg ha-1 of \ncoastal sediment + 2.25 Mg ha-1 of salted fish waste compared to addition of 40 \nMg ha-1 of coastal sediment + 1.50 Mg ha-1 of salted fish waste on each peatland.\nFrom Table 2, it can be seen that the EC value of 60 Mg ha-1 of coastal sediment \n+ 2.25 Mg ha-1 of salted fish waste (1.22 dS m-1) is significantly higher than the \naddition of 40 Mg ha-1 of coastal sediment + 1.50 Mg ha-1 of salted fish waste \n(0.87 dS m-1). According to Magan et al. (2005), the increase in EC reduces the \nuptake of macronutrients due to the increase in osmotic pressure.\n\n\n\nThe treatment of 20 Mg ha-1 coastal sediment + 0.75 Mg ha-1 of salted fish \nwaste significantly increased P uptake at the maximum vegetative phase compared \nto untreated coastal sediment + salted fish waste (Table 4). The treatment of 40 \nMg ha-1 coastal sediment + 1.5 Mg ha-1 of salted fish waste further increased P \nuptake, but the treatment level of 60 Mg ha-1 coastal sediment + 2.25 Mg ha-1 of \nsalted fish waste significantly decreased the uptake of P compared to the 40 Mg \nha-1 treatment (i.e., from 608.46 to 425.99 mg plant-1).\n\n\n\nA similar trend was seen for P and K uptake. The application of ameliorant \nincreased P uptake on each peatland type up to a certain rate. The P uptake tended \nto decline with the addition of coastal sediment + salted fish waste on each \npeatland due to the decrease in soil pH, thus decreasing the available soil N. The \nanalysis showed a highly significant correlation (r = 0.95*) between N uptake with \nthe uptake of  P (Table 5). According to Jones et al., (1991), P uptake interacts \nwith N uptake and the uptake of other micro-elements such as Cu, Fe, Mn and \nZn. The uptake of N stimulates P uptake through improvement in shoot and root \nplant growth and therefore changing the metabolism of plants, and increasing the \nsolubility and availability of P for plants (Havlin et al., 2005).\n\n\n\nThe treatment of 20 Mg ha-1 coastal sediment + 0.75 Mg ha-1 of salted fish \nwaste increased K uptake compared to untreated coastal sediment + salted fish \nwaste (Table 4). Treatment of 40 Mg ha-1 of coastal sediment + 1.5 Mg ha-1 of \nsalted fish waste further increased K uptake. Potassium uptake was not changed \nwith the application of 60 Mg ha-1  coastal sediment + 2.25 Mg ha-1 of salted fish \nwaste over the 40 Mg ha-1 of coastal sediment + 1.5 Mg ha-1 of salted fish waste \ntreatment. Use of ameliorant materials significantly increased K uptake whilst \nthe soil factor did not show a significant effect on the variables. The effect of soil \n\n\n\nSuswati, D., B.H. Sunarminto, D. Shiddieq and D. Indradewa\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 123\n\n\n\nfactors was not significantly different because the three peat soils were very acidic \n(i.e., pH values ranging from 3.26 to 3.76). According to Diana et al., (2007), \nthese characteristics reduce the availability of nutrients especially K, Ca, and Mg \nthat are bound in such way that it is difficult for them to be utilised by plants. \nPainter (1991) suggests that cations are so strongly bound to negative exchange \nsites within peat that plants in  that soilwill eventually lack nutrients and thus have \nsub-optimal growth.\n\n\n\nPotassium uptake tended to diminish with the addition of ameliorants on all \npeat types because of a significant decrease in soil density; as K might also leach \nout, absorption by maize plant will also decrease. The analysis showed a significant \ncorrelation (r = 0.71*) between the bulk density of the soil with K uptake (Table \n5).  In most soils, loss of K is due to leaching in organic soils (Havlin et al., 2005). \nOn the otherhand Nurani et al. (2007) state that it is generally accepted that a high \nCEC value in peat soils decreases nutrient absorption and especially reduces the \nuptake of K and Ca.\n\n\n\nMaize Yield\nAnalysis of yield per hectare after harvest is shown in Table 6. The harvesting \nwas done 105 days after transplanting (DAT). ANOVA results indicated that \napplication of ameliorant, soils, and their interaction significantly affected plant \nyield. Coastal sediment and salted fish waste of each peat soil increased plant \nyield per hectare (Table 6). In all soils, 60 Mg ha-1 of coastal sediment + 2.25 Mg \nha-1 of salted fish waste were able to increase maize yield per hectare. Treatments \nof 20 Mg ha-1 coastal sediment + 0.75 Mg ha-1 of salted fish waste and 40 Mg ha-1 \n\n\n\nof coastal sediment + 1.5 Mg ha-1 of salted fish waste increased maize yield per \nhectare compared to the untreated,  coastal sediment + salted fish waste. \n\n\n\nThe highest maize yield on each type of peatland was obtained with 40 tons \nha-1 coastal sediment + 1.5 tons ha-1 salted fish waste treatment. The increase in \nmaize yield could also be due to increased N, P, and K uptake in all the treated \n\n\n\nAmeliorants for Maize Productivity in Peatlands\n\n\n\nTABLE 5\nCoefficients of linear correlation between nutrient uptake and some soil properties\n\n\n\n14 \n\n\n\n\n\n\n\n\n\n\n\nTABLE5 \nCoefficients of linear correlation between nutrient uptake and some soil properties \n\n\n\n \nParameters pH EC BD \n\n\n\nN uptake 0.76** 0.62** 0.67** \nP uptake 0.80** 0.57** 0.77** \nK uptake 0.79** 0.61** 0.71** \nCa uptake 0.78** 0.63** 0.75** \nMg uptake 0.71** 0.72** 0.61** \nNa uptake 0.77** 0.71** 0.60** \nS uptake 0.76** 0.62** 0.67** \n\n\n\nNotes : * = significant at \u03b1 = 0.05; **= significant at \u03b1 = 0.01 \n  EC = Electrical conductivity; BD = Bulk density \n \n  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015124\n\n\n\npeatlands. Additionally, the high nutrient uptake in the treatment supposedly was \nin accordance with the plants\u2019 nutrient requirements in a sufficient and balance \nstate which helped to increase crop production. However,  ameliorants at a \nlow dose (20 Mg ha-1 of coastal sediment + 0.75 Mg ha-1 of salted fish waste) \nand a high dose (60 Mg ha-1 of coastal sediment + 2.25 Mg ha-1 of salted fish \nwaste) showed low yield, because uptakes of N, P and K were low on each type \nof peatland. Nitrogen is an essential element in the plant cell (Taiz and Zeiger, \n1991). According to Uhart and Andrade (1995), N deficiency delays development \nof vegetative and reproductive organs of maize plants, reducing the rate of leaf \nappearance and reducing light interception, thus decreasing the growth rate when \n\n\n\nSuswati, D., B.H. Sunarminto, D. Shiddieq and D. Indradewa\n\n\n\nTABLE 6\nEffect of ameliorant and soils on maize yield per hectare\n\n\n\nTABLE 7\nCoefficients of linear correlation between maize plant parameters\n\n\n\n15 \n\n\n\n\n\n\n\n\n\n\n\nTABLE6 \nEffect of ameliorant and soils on maize yield per hectare \n\n\n\n\n\n\n\nDose of \nameliorant \n(Mg ha-1) \n\n\n\nPeatland type \nMean \n\n\n\nStandard \nerror \n\n\n\nTypic \nHaplohemists \n\n\n\n\n\n\n\nTypic \nSulfisaprists \n\n\n\n\n\n\n\nTypic \nHaplosaprists \n\n\n\n \nMaize yield (ton ha-1) \n\n\n\n0 2.12 f 3.75 e 3.82 e 3.23 a 0.555 \n20 + 0.75 11.04 cd 9.96 d 10.68 cd 10.56 b 0.317 \n40 + 1.50 13.06 ab 12.96 ab 13.36 a 13.12 d  0.120 \n60 + 2.25 12.86 ab 11.92 bc 11.49 c 12.09 c 0.405 \nMean 9.77   9.64          9.84 (+)  \nStandard \nError 2.590       2.062          2.083   \n\n\n\n         Notes: (-) no interaction and (+) interaction. Mean values followed by the same letter in \n                    the column and the same treatment groups did not differ by Tukey'sat  \n                    5% probabilitylevel. \n \n \n \n \n \n  \n\n\n\n16 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 7 \nCoefficients of linear correlation between maize plant parameters \n\n\n\n \nParameters N \n\n\n\nuptake \nP \n\n\n\nuptake \nK \n\n\n\nuptake \nCa \n\n\n\nuptake \nMg \n\n\n\nuptake \nNa \n\n\n\nuptake \nS \n\n\n\nuptake \nPlant \nheight \n\n\n\nWeight \ndry plant \n\n\n\nMaize \nyield \n\n\n\nN uptake 1          \n\n\n\nP uptake 0.95** 1         \nK uptake 0.97** 0.91** 1        \nCa uptake 0.98** 0.96** 0.96** 1       \nMg uptake 0.96** 0.94** 0.96** 0.97** 1      \nNa uptake 0.82** 0.86** 0.75** 0.84** 0.81** 1     \nS uptake 0.91** 0.94** 0.87** 0.93** 0.92** 0.88** 1    \nPlant \nheight 0.80** 0.80** 0.75** 0.83** 0.80** 0.88** 0.82** 1   \n\n\n\nWeight dry \nplant 0.98** 0.96** 0.98** 0.99** 0.97** 0.80** 0.91** 0.76** 1  \n\n\n\nMaize \nyield 0.83** 0.85** 0.81** 0.85** 0.85** 0.88** 0.85** 0.91** 0.83** 1 \n\n\n\nNotes:* = significant at \u03b1 = 0.05; **= significant at \u03b1 = 0.01 \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 125\n\n\n\nthe plants is flowering. Phosphorous is an essential nutrient, a component of \ncertain enzymes and proteins, and a component of ATP, RNA, DNA, and phytin \n(Fageria, 2009). Phosphorous deficiency in maize generally reduces dry matter \naccumulation, which slows down the appearance of the leaves, reducing the width \nof the leaf, and reduces the life of the leaf (Colomb et al., 2000). Potassium in \nthe plant acts as an activator of enzymes in the process of photosynthesis, and \nthe metabolism of protein and carbohydrate. Protein synthesis improves crop \nresilience to disease and increases the size of the seed (Jones, 2012). Plants with \nexcess K suffer from Mg and Ca insufficiency (Barker and Pilbeam, 2007). Maize \nproduction per hectare was positively correlated to the uptake of N, P and K; the \nvalue of correlation coefficient (r) for the nutrients considered were N = 0.83**, P \n= 0.85** and K = 0.81** (Table 7).\n\n\n\nCONCLUSIONS\nIt was shown that a combination of coastal sediment and coastal sediment with \nsalted fish waste had a positive effect on the uptake of nutrients and maize \nproductivity in peatlands, which could be potentially exploited using various soil \namelioration strategies without risking agricultural sustainability in a tropical \narea. Results showed that the combination of 40 Mg ha-1 coastal sediment and 1.5 \nMg ha-1 of salted fish waste was the best treatment combination to increase the \nuptake of  N, P, and K and improve the production of maize per hectare. In the \ntropical peatlands, where lime is not affordable for most farmers, the balanced \napplication of coastal sediment and salted fish waste could alleviate problems of \npeat soil infertility, provide a solution for salted fish waste disposal and reduce the \namount of lime needed to increase production of maize per hectare. This research \nneeds to be further extended to other sites and the long-term effects such as salt \naccumulation or increase in EC due to continuous application of these treatments.\n\n\n\nACKNOWLEDGEMENTS\nThe authors thank Prof. Dr. Ir. Saeri Sagiman, Dr. Benito Heru Purwanto, Dr. Sri \nNuryani, and Dr. Sulakhudin for help  rendered during field trials and preparation \nof this paper. \n\n\n\nREFERENCES\nAbat, M.,M.J. McLaughlin, J.K. Kirby,and S.P. Stacey. 2012. Adsorption and \n\n\n\ndesorption of copper and zinc in tropical peat soils of Sarawak, Malaysia.\nGeoderma 176: 58-63.\n\n\n\nAgus, F., Wahyunto, A. Dariah, E. Runtunuwu, E. Susanti, and W. 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"\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n104 \n\n\n\n\n\n\n\nSoil Spatial Variation in a Sloping Mango Orchard  \n\n\n\nof Northern Peninsular Malaysia \n \n\n\n\nShahidin, N.M.1,2, Roslan, I.2, Zaharah, S.S.2, Kang, S.H.2, Elisa, A.A.2, Malisa, M.N.2, \n\n\n\nKamarudin, K.N.1, Murano, H.3 and Abe, S.S.1,4*\n \n\n\n\n \n1Faculty of Plantation and Agrotechnology, Universiti Teknologi MARA Perlis Branch, Perlis, \n\n\n\nMalaysia \n\n\n\n 2Faculty of Agriculture, Universiti Putra Malaysia, Selangor, Malaysia \n3Faculty of Agriculture, Meijo University, Nagoya, Japan \n\n\n\n4Experimental Farm, Kindai University, Yuasa (Arida), Japan \n\n\n\n \n*correspondence author:ishi5ro@hotmail.com  \n\n\n\n\n\n\n\nABSTRACT \nThe present study investigated the spatial variation of the physico-chemical characteristics of lateritic \n\n\n\nsoil profiles (up to 60 cm in depth) in a sloping mango orchard (1 ha; slope gradient = 6 %) in North \n\n\n\nPeninsular Malaysia. The study revealed that horizontal variation for the exchangeable Al and Mg, and \nthe particle size fractions in the topmost soil layer (0\u201315 cm) within the orchard was higher than their \n\n\n\nvertical variation within the soil profile, and that, in contrast, the opposite trend was found for the total \n\n\n\nN, cation exchange capacity, and base saturation. Furthermore, preferential accumulation of soil \n\n\n\norganic matter and nutrients such as total N and available P were found at the lower (LS) and/or middle \n(MS) slope positions than the upper (US) one, while the lower clay content with the higher clay activity \n\n\n\nindex was observed at LS compared to MS and US. These results suggest that these variations occur \n\n\n\nby the scattered accumulation of fertilizer-derived nutrients (i.e. N, P, K, and Mg) in the surface soil \nlayers and the translocation of the surface litter, soil and nutrients towards the downslope in addition to \n\n\n\nenhanced eluviation process with the residual of clays at the downslope in the sloping orchard. \n\n\n\n\n\n\n\nKey words: Lateritic soil, Mangifera indica L., slope position, spatial variation, ultisols \n\n\n\n\n\n\n\nINTRODUCTION \n \n\n\n\nNorthern Peninsular Malaysia is one of the most productive areas of mango (Mangifera indica \n\n\n\nL.) cultivation in this country because the climate in this region, i.e. tropical monsoon climate \n\n\n\nwith a short dry spell (January\u2013March), is highly suitable to induce flowering and fruit \n\n\n\ndevelopment (DOA 2009). Mango is a tree (perennial) crop which has a vigorous rooting \n\n\n\nsystem in the soil up to approximately 0.5 m in depth (Bojappa and Singh 1975) and may have \n\n\n\nthe main root reaching more than 1 m in depth (Lehmann 2003); hence, mango is often planted \n\n\n\nin lateritic soil landscapes because it is efficient in absorption of nutrients and water from deep \n\n\n\nsoil layers and survives well at low-fertility soils and during dry periods (Ganeshamurthy and \n\n\n\nReddy 2015). However, there is a certain gap between the actual and potential yields of mango \n\n\n\nin the lateritic soil areas (de Bie 2004; Zhang et al. 2019), and soil fertility management has \n\n\n\nbeen identified as a key practice in filling this gap (DOA 2009). Hence, further information on \n\n\n\nsoil properties in mango orchards of the study region is essential to formulate a strategy for \n\n\n\nenhancing sustainable mango cultivation. \n\n\n\n\n\n\n\nFrom this viewpoint, our previous study (Shahidin et al. 2018) examined the spatial \n\n\n\nvariation and distribution pattern of the fertility characteristics of the topsoil in a mango \n\n\n\n\nmailto:ishi5ro@hotmail.com\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n105 \n\n\n\n\n\n\n\norchard in this region and found certain variations (coefficient of variation [CV] = 13.7\u201345.1 \n\n\n\n%; n = 50) of most soil properties such as organic C, total N, available P, exchangeable bases, \n\n\n\nexchange acidity and effective cation exchange capacity (ECEC) within a small steep field \n\n\n\n(approximately 1 ha; slope = 6 %). However, soil properties can also vary vertically within a \n\n\n\npedon on sloping lands. In an extreme case, for instance, Kinoshita et al. (2021) reported that \n\n\n\nsoil properties vary to a degree in which soil great group differs within a short distance in a \n\n\n\nsmall sloping crop field (area = 0.2 ha, slope = 7 %) of Sabah, Malaysia. Currently there is a \n\n\n\ndearth of research that takes into account the root structure of the mango tree and the vertical \n\n\n\nvariation of soil profiles in sloping mango orchards. Such information is important to improve \n\n\n\nmango production in lateritic soil landscapes. Therefore, the objective of this study was to \n\n\n\nexamine spatial variation of the physico-chemical properties of lateritic soil profiles in a \n\n\n\nsloping mango orchard in Northern Peninsular Malaysia with an emphasis on the changes in \n\n\n\nsoil characteristics relative to the slope position. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\nStudy Site \n\n\n\nA field survey was conducted at the experimental farm of Universiti Teknologi MARA \n\n\n\n(06\u00b027\u02b915\u02baN; 100\u00b017\u02b901\u02baE), which is located in the middle of Perlis, Malaysia. The study area \n\n\n\nhas a tropical monsoon climate (Am in the K\u00f6ppen-Geiger classification system) with a mean \n\n\n\nair temperature of about 27\u00b0C and average annual precipitation of approximately 1,900 mm. \n\n\n\nThe landform of the study area is predominated by an undulating topography. The soil at the \n\n\n\nexperimental farm is largely classified as Typic Paleudults in the U.S. Soil Taxonomy and is \n\n\n\nlocally referred to as the Terap Series (Satar et al. 2005). \n\n\n\n\n\n\n\nSurvey Site \n\n\n\nA mango orchard (1 ha) situated on sloping land (mean gradient = 6 %) was selected for this \n\n\n\nstudy. Here, the mango trees (cv. Harumanis or MA 128; 5 years old as of September 2014) \n\n\n\nwere planted at a density of 123 trees ha\u22121 in a square (9 m \u00d7 9 m) planting system. The orchard \n\n\n\nhas been fertilized following the recommendation of the Malaysian Department of Agriculture \n\n\n\n(DOA 2009) from the time the orchard was established in September 2009. Briefly, a \n\n\n\ncompound fertilizer (12-12-17-2-8S-TE: Nitrophoska\u00ae Blue TE, Behn Meyer Agricare, \n\n\n\nSelangor, Malaysia), which contains 2 % MgO, 8 % S, 0.02 % B, and 0.01 % Zn (w/w) in \n\n\n\naddition to 12 % N, 12 % P2O5, and 17 % K2O, was applied at 3 kg per tree each year in two \n\n\n\nsplit applications (usually in July and October). In other words, the plot had received fertilizer \n\n\n\nequivalent to 48 kg N ha\u22121, 48 kg P2O5 ha\u22121, and 68 kg K2O ha\u22121 every year until 2013. The \n\n\n\napplication rate was increased up to 3.5 kg tree\u22121 year\u22121 (equivalent to 56 kg N ha\u22121, 56 kg \n\n\n\nP2O5 ha\u22121, and 79 kg K2O ha\u22121) in 2014. The fertilizer was applied on the topmost soil layer \n\n\n\n(0\u201315 cm) by the pocket placement method (i.e. 4 pockets per tree; 2 m away from the tree \n\n\n\nbase). \n\n\n\n\n\n\n\nThe mango trees in the orchard were subjected to structural pruning three times every \n\n\n\nyear. In the case of 2014, the first pruning was done after the harvest of all fruits to reshape the \n\n\n\ntrees (in June), the second was performed to remove the excessive, infected, and dead branches \n\n\n\n(in September), and the third pruning was committed to removing all suckers from the main \n\n\n\nstructural branches (in November) two weeks before the fertilizer application. The control of \n\n\n\nweeds at the study site was optimized by the combined use of chemical and physical methods: \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n106 \n\n\n\n\n\n\n\nthe application of a non-selective herbicide (i.e. glufosinate-ammonium) using a boom sprayer \n\n\n\nin addition to the mechanical clearing by a rotor slasher attached to a tractor once every two or \n\n\n\nthree months. Pruned trees were removed from the orchard, while cleared weeds were left in \n\n\n\nthe orchard. \n\n\n\n\n\n\n\nSoil Sampling \n\n\n\nSoil sampling was conducted at the mango orchard in July 2014. Nine soil pits (75 cm length \n\n\n\n\u00d7 100 cm width \u00d7 90 cm depth) were dug within this plot (Figure 1). These soil pits were \n\n\n\ncategorized into three groups due to the slope positions at the orchard: lower (LS), middle \n\n\n\n(MS), and upper (US) positions. These soil pits were prepared approximately 2 m away from \n\n\n\nthe main trunk in the approximate centre between the mango tree base and the service path. \n\n\n\nSoil samples were taken from four different depths, i.e. 0\u201315, 15\u201330, 30\u201345, and 45\u201360 cm, \n\n\n\nrespectively. The soil samples were dried in an oven at 60\u00b0C for 48 h, gently ground, and \n\n\n\npassed through a mesh sieve (\u03d5 = 2.0 mm) before the laboratory analysis.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 1. (a) Location of the study site in Peninsular Malaysia and (b) schematic layout of the \n\n\n\nsampling locations at the study site \n \n\n\n\n\n\n\n\nLaboratory Analyses \n\n\n\nThe physico-chemical properties of the soil samples were analysed according to the standard \n\n\n\nmethods in the field of soil science. Briefly, soil pH was measured in distilled water in a \n\n\n\nsolid:liquid ratio of 1:2.5 (w/v). Total C and N were simultaneously determined by an \n\n\n\nelemental analyser (TruMac CNS-2000, LECO, St. Joseph). Here, all C in the samples was \n\n\n\nconsidered to exist in organic forms as soil pH in these samples was lower than 6.5 (van \n\n\n\nReeuwijk 2002). Available P was extracted with Bray No. 2 extractant, consisting of 0.03 mol \n\n\n\nL\u22121 ammonium fluoride and 0.1 mol L\u22121 hydrochloric acid (Bray and Kurtz 1945), and its \n\n\n\nconcentration was determined by the flow injection analyser (Lachat QuickChem 8000 series \n\n\n\nFIA+, Zellweger Analytics, Milwaukee). Exchangeable bases (K, Ca, and Mg) and Al were \n\n\n\nextracted with 1 mol L\u22121 ammonium acetate (pH = 7) and 1 mol L\u22121 potassium chloride, \n\n\n\n20 m\n\n\n\nSoil pit\n\n\n\nMango\n\n\n\ntree\n\n\n\nN\n\n\n\na) b)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n107 \n\n\n\n\n\n\n\nrespectively. The concentrations of the bases and Al in the extracts were subsequently \n\n\n\ndetermined by atomic absorption spectrometry (AAnalyst 400, Perkin Elmer, Norwalk) and \n\n\n\ninductively coupled plasma optical emission spectrometry (Optima 8300, Perkin Elmer, \n\n\n\nNorwalk, CT), respectively. Cation exchange capacity (CEC) was measured by the \n\n\n\nSchollenberger method (Schollenberger and Simon 1945). Base and Al saturations were \n\n\n\ncalculated as the occupational percentage of the sum of the exchangeable bases and the Al to \n\n\n\nthe CEC, respectively. Soil particle distribution was measured by the pipette method (Gee and \n\n\n\nBauder 1986). Soil textural class was determined according to the USDA\u2019s soil textural \n\n\n\ntriangle (Murano et al. 2015). The clay activity index was computed through the division of \n\n\n\nCEC by the clay content. \n\n\n\n\n\n\n\nStatistical Analyses \n\n\n\nTo examine soil variability, the horizontal and vertical variations were evaluated by calculating \n\n\n\nthe coefficient of variation (CV) in a horizontal direction within the orchard and in a vertical \n\n\n\nvariation within the soil profile, respectively. For the former, CV was calculated using the soil \n\n\n\ndata (n = 9) at the topmost layer. Meanwhile, for the latter, CV was acquired as the mean of \n\n\n\nnine CVs calculated from soil data of four soil layers comprising the soil profile. One-way \n\n\n\nanalysis of variance (ANOVA) was performed using the slope position as the fixed factor, \n\n\n\nassuming the Gaussian distribution of the populations and the homoscedasticity of their errors. \n\n\n\nThe mean separation was subsequently made at a significance level of p < 0.05 by Tukey\u2019s \n\n\n\ntest. In addition, Pearson\u2019s correlation coefficients were calculated among the examined soil \n\n\n\nvariables. All statistical analyses were done using statistical software (SPSS 23, IBM, Chicago, \n\n\n\nIL). \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nGeneral Fertility Characteristics and Pedogenic Features \n\n\n\nTable 1 shows the physicochemical properties of the soils at the study site. Referring to the \n\n\n\nsoil fertility ratings by Nyi et al. (2017), the soils in the topmost layer (0\u201315 cm) had a strongly \n\n\n\nacidic reaction (pH < 5.0), medium levels of organic C (15\u201329 g kg\u22121) and available P (10\u201315 \n\n\n\nmg kg\u22121), and low levels of total N (< 1.4 g kg\u22121), exchangeable K (< 0.23 cmolc kg\u22121), and \n\n\n\nCEC (< 10 cmolc kg\u22121). These soils also had a relatively high Al saturation (> 11 %) and low \n\n\n\nbase saturation (< 50 %), corresponding to low soil pH and high exchangeable Al. The \n\n\n\nexchangeable Al concentration in the studied soil ranged from moderate (0.5\u20131.0 cmolc kg\u22121) \n\n\n\nto high levels (1.0\u20132.5 cmolc kg\u22121) and it was observed that there would be an Al toxicity risk \n\n\n\nbecause Al saturation (16.2 \u00b1 6.2 %) was higher than 11 % (Nyi et al. 2017). The clay activity \n\n\n\nindex was low, i.e. CEC < 16 cmolc kg\u22121 clay (Kimble et al. 1993) which indicates that clay \n\n\n\nminerals in these soils are probably dominated by low-activity clays such as kaolinite and \n\n\n\ngibbsite (Zhang et al. 2004a). All these soil parameters represent the general characteristics of \n\n\n\nlateritic soils, which have been documented in literature (Sehgal 1998). Moreover, soil pH, \n\n\n\ntotal N, and exchangeable Ca and Mg were found lower than the optimal ranges for mango \n\n\n\ncultivation: pH = 5.5\u20136.5 (DOA 2009); total N = 3\u20136 g kg\u22121; Ca = 3.0\u20135.0 cmolc kg\u22121; Mg = \n\n\n\n0.75\u20131.25 cmolc kg\u22121; while exchangeable K marginally fell into the recommended range: \n\n\n\n0.25\u20130.38 cmolc kg\u22121 (Poffley and Owens 2005). The optimal range of available (Bray 1) P \n\n\n\nfor mango cultivation has not been documented in the literature. Although none of the apparent \n\n\n\nvisible symptoms of these nutrient disorders were observed in the mango trees during the field \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n108 \n\n\n\n\n\n\n\nsurvey (Shahidin et al. 2018), the analysis of the leaf nutrients revealed luxury N but deficient \n\n\n\nCa status of the mango trees at the study site (Shahidin et al. 2022). The particle size \n\n\n\ndistribution of the soils at the study site consisted of more than 50 % clay, less than 20 % of \n\n\n\nsilt, and about 30 % sand and thus soil texture was classified as \u201cclay\u201d. The clay texture has \n\n\n\nbeen observed in many lateritic soils, depending mainly on parent materials (Zhang et al. \n\n\n\n2004b; Ko 2014). \n\n\n\n\n\n\n\nTABLE 1 \nGeneral fertility characteristics of the topmost soil layer (0\u201315 cm) at the study site \n\n\n\n\n\n\n\nProperty Unit Mean (SD) \n\n\n\npH  \u2015 4.4 (0.7) \n\n\n\nExchangeable Al cmolc kg\u22121 1.3 (0.5) \nAl saturation % 16.2 (6.2) \n\n\n\nOrganic C g kg\u22121 16.9 (6.1) \n\n\n\nTotal N g kg\u22121 1.24 (0.44) \nC/N ratio \u2015 13.0 (2.2) \n\n\n\nAvailable P mg kg\u22121 11.9 (10.0) \n\n\n\nExchangeable K cmolc kg\u22121 0.29 (0.15) \n\n\n\nExchangeable Ca cmolc kg\u22121 1.5 (0.3) \nExchangeable Mg cmolc kg\u22121 0.6 (0.3) \n\n\n\nCEC cmolc kg\u22121 7.9 (0.3) \n\n\n\nBase saturation % 31.0 (3.1) \nClay  % 52.5 (7.5) \n\n\n\nSilt % 17.2 (3.6) \n\n\n\nSand % 30.3 (4.3) \n\n\n\nClay activity cmolc kg\u22121 15.4 (2.4) \n\n\n\n\n\n\n\nThe studied soils also had some pedogenic features which are commonly investigated \n\n\n\nin the lateritic soils (Sehgal 1998), including the yellowish red matrix colour (5YR 5/8) and \n\n\n\nthe presence of clay skins (cutans) on the ped surface of the subsoil layers. These are visual \n\n\n\nsigns of the predominant presence of iron hydr(oxy)oxides, e.g. goethite and hematite (Abe \n\n\n\nand Wakatsuki 2010) and the translocation of clay from the upper to the lower horizons within \n\n\n\nthe soil profile (Abe et al. 2009). The latter was in accordance with the increase in the clay \n\n\n\ncontent with increasing soil depth (> 20 %, w/w) within the soil profile which denotes the \n\n\n\nformation of the argillic (clay-illuviated) horizon in these soil profiles (Zhang et al. 2004b).  \n\n\n\n\n\n\n\nSoil Variability: The Horizontal vs. Vertical Variations \n\n\n\nThe horizontal variation of the examined soil variables at the topmost layer within the study \n\n\n\nsite and their vertical variation within the soil profile are comparatively shown in Figure 2. \n\n\n\nAmong the soil layers within the soil profile, the horizontal variation of all soil variables at the \n\n\n\ntopmost layer exhibited the highest CV, except for the exchangeable Ca and Mg. These bases \n\n\n\nshowed the highest CV in the second-top layer within the soil profile (see error bars in Figure \n\n\n\n3). Both horizontal and vertical variations were found to be relatively low (CV < 25 %) for the \n\n\n\npH, C/N ratio, exchangeable Ca, CEC, particle size distribution (i.e. clay, silt, and sand), and \n\n\n\nclay activity index. Most of these soil variables are associated with pedogenesis and their \n\n\n\nrelatively low spatial variations represent relatively monotonous physicochemical \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n109 \n\n\n\n\n\n\n\ncharacteristics at the study site which has formed from homogenous parent material under a \n\n\n\nlong-lasting laterization process in this region. On the other hand, the remaining soil variables \n\n\n\ni.e. exchangeable Al, Al saturation, organic C, total N, available P, exchangeable Mg and K, \n\n\n\nand base saturation showed intermediate (CV = 25\u201375 %) or relatively high (CV > 75%) \n\n\n\nvariation.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 2. Comparison of the coefficients of variation (%) between the horizontal and vertical \n\n\n\nvariations for each of the soil variables at the study site \nNote: Horizontal variation is calculated based on the mean and standard deviation of the topmost soil layers (n = \n\n\n\n9), while vertical variation is presented as the mean of the coefficients of variation (n = 9) calculated from \n\n\n\nthe variation among soil layers (n = 4) within each soil profile. \n  \n\n\n\nMoreover, the horizontal variation of some of these variables such as total N, \n\n\n\nexchangeable K and Ca, CEC, and base saturation was lower than the vertical variation. This \n\n\n\nwas in contrast to pH, exchangeable Al and Mg, Al saturation, clay, silt, and clay activity index \n\n\n\nwhich showed a higher horizontal variation than the vertical variation. The variations of these \n\n\n\nvariables reflect the anthropogenic impacts which are largely derived from agricultural \n\n\n\npractices. In particular, it should be noted that available P, exchangeable K and Mg, and total \n\n\n\nN were regarded as the top four variables which had the highest CV either or both vertical and \n\n\n\nhorizontal variations and that all these variables are related to the nutrients included in \n\n\n\ninorganic fertilizer which has been used at the study site. The most typical case is seen for \n\n\n\navailable P because it exhibited the highest variations among the soil variables investigated in \n\n\n\nthis study (Shahidin et al. 2018) as P is less mobile in soil than the other nutrients due to the \n\n\n\nfixation of fertilizer-derived P with iron and/or manganese in acidic soils (Brady and Weil \n\n\n\n2008). However, this is contradictory to the conclusion of our previous study (Shahidin et al. \n\n\n\n2018) which suggested that spatial characteristics of these four variables in the topmost soil \n\n\n\nlayer may have formed under the stronger influence of intrinsic factors such as soil texture and \n\n\n\nmineralogy rather than extrinsic factors such as agronomic practices (e.g. the application of \n\n\n\nchemical fertilizers), based on the strong spatial dependency judged by the nugget-to-sill (N/S) \n\n\n\nratio < 0.25 in the constructed semi-variograms. This contradiction indicates a possible \n\n\n\nmisinterpretation of geo-statistical results in our previous study (Shahidin et al. 2018) and \n\n\n\nsupports alarming concerns on the N/S ratio by Oliver and Webster (2014) who pointed out \n\n\n\nthat the use of the N/S ratio of the empirical variogram as a measure of spatial dependence has \n\n\n\nflaws because (i) the N/S ratio is affected by measurement error as well as error arising from \n\n\n\nthe uncontrolled fitting over distances shorter than the shortest lag and the choice of model; \n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\nHorizontal Vertical\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n110 \n\n\n\n\n\n\n\n(ii) the fitted sill is an uncertain estimate of the sill of the underlying process; and (iii) the ratio \n\n\n\ntakes no account of the correlation range, which can lead to a sensible inference. \n\n\n\n\n\n\n\nSoil Profile Characteristics Relative to the Slope Position \n\n\n\n\n\n\n\nSoil pH, Exchangeable Al, and Al Saturation \n\n\n\nThe profile distributions of the pH value, exchangeable Al concentration, and Al saturation are \n\n\n\nshown according to the slope position in Figure 3. At the topmost soil layer, a higher pH value \n\n\n\nwas found at LS than those at US and MS without any significant difference. Irrespective of \n\n\n\nthe slope position, the soil pH value decreased as the soil depth increased, except for the \n\n\n\nsecond-top layer (15\u201330 cm) at MS and US which had a slightly higher pH value compared to \n\n\n\nthe overlying topmost layer and the underlying third- (30\u201345 cm) and fourth-top (45\u201360 cm) \n\n\n\nlayers, respectively. The exchangeable Al and Al saturation exhibited an opposite trend to the \n\n\n\nsoil pH: both of them were lower at US than MS and LS throughout the soil profile without \n\n\n\nany significant differences among the slope positions. Furthermore, the exchangeable Al and \n\n\n\nAl saturation were lowest at the topmost soil layer and slightly increased with increasing soil \n\n\n\ndepth up to the third-top layer (30\u201345 cm) within the soil profile. The relationships of pH with \n\n\n\navailable Al and Al saturation adequately reflected the negative correlations between the pH \n\n\n\nand the exchangeable Al as well as between the pH and the Al saturation (Table 2), which is \n\n\n\nseen very commonly in acidic soils as the Al in the soil becomes more soluble with the decline \n\n\n\nin the pH (Brady and Weil 2008). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n111 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 3. Comparison of soil physico-chemical properties in the soil profile among the different slope \n\n\n\npositions at the study site \nNote: Different letters denote significant differences among slope positions at each soil layer. Error bars indicate \n\n\n\nstandard deviation \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n112 \n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \nPearson\u2019s correlation coefficients of soil variables examined in this study (n = 36) \n\n\n\n pH Exch. \n\n\n\nAl \n\n\n\nAl \n\n\n\nsatu. \n\n\n\nOrg. C Tot. N C/N \n\n\n\nratio \n\n\n\nAvail. \n\n\n\nP \n\n\n\nExch. \n\n\n\nK \n\n\n\nExch. \n\n\n\nCa \n\n\n\nExch. \n\n\n\nMg \n\n\n\nCEC Base \n\n\n\nsatu. \n\n\n\nClay Silt Sand \n\n\n\nExch. Al -.319 1              \n\n\n\nAl satu. -.350* .969*** 1             \nOrg. C .364* -.163 -.154 1            \n\n\n\nTot. N .290 -.262 -.220 .958*** 1           \n\n\n\nC/N ratio -.036 .316 .208 -.084 -.338* 1          \n\n\n\nAvail. P .489** -.270 -.237 .567*** .597*** -.278 1         \n\n\n\nExch. K .252 -.402* -.360* .614*** .712*** -.462** .698*** 1        \n\n\n\nExch. Ca .171 .167 .161 .288 .250 -.069 .160 .230 1       \n\n\n\nExch. Mg .109 .186 .177 .237 .175 .009 .022 -.049 .038 1      \n\n\n\nCEC .058 .378* .144 -.135 -.285 .498** -.234 -.318 .031 .059 1     \n\n\n\nBase satu. .204 .024 .080 .517** .518** -.275 .351* .460** .753*** .554*** -.262 1    \n\n\n\nClay -.348* -.236 -.223 -.603*** -.533** -.010 -.554*** -.350* -.363* -.106 -.060 -.384* 1   \n\n\n\nSilt .249 .227 .222 .421* .398* -.058 .417* .226 .289 -.015 .033 .241 -.875*** 1  \n\n\n\nSand .361* .214 .197 .635*** .542** .056 .564*** .380* .360* .180 .067 .430** -.938*** .653*** 1 \n\n\n\nClay act. .373* .343* .232 .484** .362* .210 .420* .171 .323 .085 .468** .202 -.901*** .790*** .842*** \n\n\n\nNote: ***, **, and * denote significant levels at p < 0.001, p < 0.01, and p < 0.05, respectively\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n113 \n\n\n\n\n\n\n\n The higher soil pH values in the topmost soil layers than in the subsoil \n\n\n\nexcept for the second-top layers at US and MS, could reflect the additional buffering \n\n\n\ncapacity accorded by soil organic matter content which was higher in the topmost \n\n\n\nsoil layer than in the subsoil. In fact, organic C showed weak but positive \n\n\n\ncorrelations with the pH value (Table 2). Nevertheless, the lower pH value in some \n\n\n\ntopmost soil layers than in the underlying second-top layers observed at US and \n\n\n\nMS, could be a result of soil acidification from the  application of N fertilizers and \n\n\n\nsubsequent leaching of N at the topmost layers as well as the accumulation of bases \n\n\n\n(Ca and/or Mg) at the second-top layers (Brady and Weil 2008). \n\n\n\n\n\n\n\nOrganic C, Total N, and Available P \n\n\n\nIrrespective of the soil layer, organic C content was found in the order of US < MS \n\n\n\n< LS with some significant differences in particular at the subsoil layers among the \n\n\n\nslope positions (Figure 3). This organic C trend was followed by total N as \n\n\n\nindicated by the very strong positive correlation between these two parameters \n\n\n\n(Table 2). This is because most of them exist as components of soil organic matter \n\n\n\nin the non-calcareous soil (Brady and Weil 2008). Our results might reflect soil \n\n\n\nerosion and run-off which would carry the litter on the land surface from US to MS \n\n\n\nand LS since there was no clear difference in biomass production over the study \n\n\n\nsite which was planted with one specific cultivar and managed uniformly in terms \n\n\n\nof agricultural practices. The translocation of the soil from US to MS and LS was \n\n\n\nalso supported by the lower content of available P in US and MS in comparison to \n\n\n\nLS. The residual (mineral) P, which originates from the inorganic fertilizer, \n\n\n\naccumulates in the upper soil layers through fixation by Al dissolved under acidic \n\n\n\nconditions and can be transported from US and MS to LS by soil erosion and run-\n\n\n\noff. Soil organic matter also plays a role in providing nutrients for crops through \n\n\n\nmicrobial decomposition which subsequently contributes to bearing crop nutrients \n\n\n\nand reduces their loss through leaching. This is supported by the positive \n\n\n\ncorrelations of organic C with total N and available P (Table 2). Therefore, \n\n\n\ngenerally these parameters of soil fertility exhibit similar trends of distribution over \n\n\n\none another within the study site irrespective of the slope position. \n\n\n\n Accumulation of soil organic matter at the topmost soil layer could be \n\n\n\nderived from the organic inputs sourced from the plant residues after the seasonal \n\n\n\npruning and the weeding. The study of Rodrigues et al. (2019) found that mango \n\n\n\ntrees produced almost similar quantities of litter (7.1 Mg ha\u22121 yr\u22121) to the adjacent \n\n\n\nopen ombrophilous forest (9.0 Mg ha\u22121 yr\u22121) on a Brazilian Oxisol and that the \n\n\n\ncontribution of mango trees to the nutrient turnover to the soil via litter is similar to \n\n\n\nthat of the forest. Irrespective of the slope position, the C/N ratio was found at the \n\n\n\nhighest level in the topmost soil layer followed by a gradual decrease towards the \n\n\n\ndeeper soil layers. This vertical trend of soil C/N ratio is often seen in agricultural \n\n\n\nfields and could reflect the organic input with the lower C/N ratio and the \n\n\n\napplication of inorganic N in the surface soils. There was a little difference in the \n\n\n\nC/N ratio among the slope positions as represented by the low CV (Figure 2). \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n114 \n\n\n\n\n\n\n\nExchangeable K, Ca, and Mg, CEC, and Base Saturation \n\n\n\nPreferential accumulation of exchangeable K in the topmost soil layer as compared \n\n\n\nto the subsoil layers (Figure 3) could originate from K fertilizer applied at the \n\n\n\ntopmost soil layer, whereas very low exchangeable K status in the subsoil layers \n\n\n\nwould reflect its loss through the prolonged and intensive leaching (Sehgal, 1998). \n\n\n\nSimilar to the relationship of the organic C with the total N and available P, the \n\n\n\nexchangeable K was positively correlated with organic C (Table 2). This is because \n\n\n\nsoil organic matter, which accumulates in the topmost layer, enhances CEC and \n\n\n\nthus the retention of exchangeable bases (Brady and Weil 2008). Despite its large \n\n\n\nvertical variation, there was little and insignificant difference in the exchangeable \n\n\n\nK among the slope positions. This displays a similar trend as the other bases, i.e. \n\n\n\nCa and Mg. These results suggest that the current scheme of soil fertility \n\n\n\nmanagement using Mg-containing fertilizer is reasonable because of the deficient \n\n\n\nlevel of Mg found in the studied soils. However, an additional application of lime \n\n\n\nis highly recommended due to the deficient level of Ca and strong acidity in these \n\n\n\nsoils. This was also supported by the deficit level of Ca (1.05 \u00b1 0.43 %, n = 15; \n\n\n\noptimum range = 2.00\u20133.50 % (Malik 1989) in mango leaves found at the study site \n\n\n\n(Shahidin et al. 2022). The efficacy of trace elements (Zn and B) included in the \n\n\n\ncompound fertilizer remains unclear and may need further research in future. \n\n\n\nUnlike the other nutrients (N, P, and K) in the soil, which showed a \n\n\n\ndownward decreasing trend in the soil profile in association with the organic C, the \n\n\n\nhighest contents of both exchangeable Ca and Mg were found in the second-top \n\n\n\nlayer (15\u201330 cm) within the soil profile. There were few differences in the \n\n\n\nexchangeable Ca and Mg among the slope positions, except for Ca in the second-\n\n\n\ntop layer of LS and Mg in the topmost and second-top layers of MS which exhibited \n\n\n\na slightly higher concentration than the other layers without significant differences. \n\n\n\nThese distinctive distribution patterns of Ca and Mg within the soil profile could \n\n\n\noccur due to the uplift of the basic cations from the deeper soil layers through the \n\n\n\nuptake of these nutrients by mango trees and their returns to the upper soil layers \n\n\n\nvia litter (Lehmann 2003). The results also suggest that mango roots help the \n\n\n\naccumulation of these bases leached from the overlying soil layer (0\u201315 cm in \n\n\n\ndepth) which had received the compound fertilizer including Mg every year. The \n\n\n\nsecond-top soil layer (15\u201330 cm) is consistent with the active rooting zone of \n\n\n\nmango. Bojappa and Singh (1975) revealed that 75 % of the active mango roots are \n\n\n\npresent in the upper 50 cm soil, and Pinto et al. (1996) reported that 77 % of the \n\n\n\nfine roots of mango trees are found in the subsurface soil layer (20\u201340 cm in depth). \n\n\n\nExchangeable K was positively correlated with pH value, while \n\n\n\nexchangeable Al was negatively correlated with exchangeable K (Table 2). \n\n\n\nCorrespondingly, base saturation exhibited a strong negative correlation with \n\n\n\nexchangeable Al. These results suggest the reduced capacity to retain K due to its \n\n\n\ncompetition with Al for the cation exchange site on the colloid\u2019s surface in acidic \n\n\n\nsoil (Brady and Weil 2008). In contrast, both exchangeable Ca and Mg were \n\n\n\nsignificantly correlated with neither the pH value nor the exchangeable Al, while \n\n\n\nthe base saturation exhibited a strong negative correlation with exchangeable Al. \n\n\n\nThese results suggest less susceptibility of bivalent Ca and Mg to soluble Al \n\n\n\ncompared to monovalent K (Brady and Weil 2008) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n115 \n\n\n\n\n\n\n\nParticle Size Distribution and Clay Activity Index \n\n\n\nParticle size data (Figure 3) indicates that irrespective of the slope position, all soil \n\n\n\nlayers constituting the soil profile had more than 40 % of clay and were categorized \n\n\n\ninto the clay texture. The clay texture of the studied soils suggests their imperfectly \n\n\n\ndrained characteristics after heavy and/or continual rains, although the mango trees \n\n\n\ncan tolerate periodically flooded soils and generally poor soil drainage is not a \n\n\n\nproblem (Crane et al. 2007). The monotonous soil texture found throughout the soil \n\n\n\nprofile and over the slope positions suggest homogeneous parent material and a \n\n\n\nmonotonous soil-forming process spread over the study site. This result is \n\n\n\nconsistent with our field note: there was a great similarity in the soil profile \n\n\n\ndescription irrespective of the slope position (descriptions are not shown here).  \n\n\n\nNevertheless, there were some significant differences in the particle size \n\n\n\ndata among the slope positions: clay content was found in increasing order of US \u2265 \n\n\n\nMS > LS, while the reverse order (i.e. LS > MS \u2265 US) was observed for the contents \n\n\n\nof both sand and silt within the soil profile. In particular, differences in the content \n\n\n\nof clay, silt, and sand between LS and US were statistically significant in most soil \n\n\n\nlayers, whereas those between MS and US were detected in a few soil layers only. \n\n\n\nThe cause for the differences in particle size distribution among the slope positions \n\n\n\nremains unclear but it might be attributed to the different intensities of mineral \n\n\n\nweathering and the different extent of clay eluviation relative to the slope position; \n\n\n\nthe subtle difference in soil hydrologic conditions and landform processes relative \n\n\n\nto the slope position might lead to some difference in the particle size distribution \n\n\n\n(within the same texture category) and clay mineral composition. This speculation \n\n\n\ncan be partially supported by our findings of a larger extent of increase in clay at \n\n\n\nthe subsoils in LS compared to those in US and MS and the higher clay activity \n\n\n\nindex at LS compared to US and MS. The topographic position affects the \n\n\n\nallocation of water and transformation and translocation of materials on the lateritic \n\n\n\nsoil landscapes (Zhang et al. 2004b). At the upper slope, the intensive weathering \n\n\n\nof minerals and leaching of bases would result in the accumulation of low-activity \n\n\n\nclays; on the contrary, at the lower slope, less effective drainage hinders leaching \n\n\n\nof the bases and mineral weathering leading to the soils possibly containing greater \n\n\n\namounts of bases and active clays such as smectite and chlorite (Zhang et al. 2004a; \n\n\n\nAbe et al. 2009).  \n\n\n\n\n\n\n\nCONCLUSION AND RECOMMENDATIONS \n\n\n\nThe findings of this study revealed that the lateritic soils at the study site have \n\n\n\nseveral constraints against agricultural production, including strong acidity along \n\n\n\nwith Al toxicity risk, low availability of crop nutrients, and imperfectly drained \n\n\n\ncharacteristics after heavy and/or continual rains. Soil acidity and deficient level of \n\n\n\nCa are the top-priority issues as indicated by soil pH and exchangeable Ca which \n\n\n\nwere lower than their optimal ranges for mango production (DOA 2009; Poffley \n\n\n\nand Owens 2005). Furthermore, we found higher variations of both vertical and \n\n\n\nhorizontal directions for the soil variables related to fertilizer-derived nutrients, i.e. \n\n\n\ntotal N, available P, and exchangeable K and Mg, than the others and preferential \n\n\n\naccumulation of soil organic matter and nutrients such as total N and available P at \n\n\n\nLS and/or MS positions than in the US layer. Based on the findings of this study, \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2022 Vol. 26: 104-119 \n \n\n\n\n116 \n\n\n\n\n\n\n\nwe recommend the following key management practices for sustainable mango \n\n\n\nproduction in the study region: (1) use of lime, (2) incorporation of organic manure, \n\n\n\nand (3) precision application of fertilizers.  \n\n\n\n\n\n\n\n(1) Liming \n\n\n\nExchangeable Al concentration in the studied soil was found to range from \n\n\n\nmoderate (0.5\u20131.0 cmolc kg\u22121) to high levels (1.0\u20132.5 cmolc kg\u22121). This condition \n\n\n\nis judged as an Al toxicity risk as the Al saturation (16.2 \u00b1 6.2%) is higher than \n\n\n\n11% (Nyi et al. 2017). The application of lime is crucial to efficiently ameliorate \n\n\n\nsoil acidity and strongly acidic soil pH (Correia et al. 2018) to the optimum pH \n\n\n\nrange (5.5\u20136.5) for mango cultivation (DOA 2009). This is also beneficial to \n\n\n\nreplenishing Ca in the soil (Brady and Weil 2008). Our findings suggest that a larger \n\n\n\namount of lime may be required at MS and LS than in US due to the higher Al \n\n\n\nsaturation in those layers.  \n\n\n\n\n\n\n\n(2) Organic manure incorporation \n\n\n\nAs a medium to long-term soil management strategy to improve soil fertility \n\n\n\npotential, the application of organic manures is recommended to increase soil \n\n\n\norganic matter content which was currently found in low (< 14 g kg\u22121) to medium \n\n\n\n(15\u201329 g kg\u22121) levels (Nyi et al. 2017). This practice is also favourable to \n\n\n\nreplenishing soil nutrients and enhancing CEC. Our findings suggest that a larger \n\n\n\namount of organic manure can be applied in US than in the MS and LS along with \n\n\n\nthe control of soil erosion to prevent the translocation of organic matter to the lower \n\n\n\nslopes. \n\n\n\n\n\n\n\n(3) Precision fertilizer application \n\n\n\nThe application of NPK fertilizers can effectively increase the mango yield (Zhang \n\n\n\net al. 2019), considering the low levels of total N, available P, and exchangeable K \n\n\n\nin the studied soils. However, with the substantial spatial (both horizontally and \n\n\n\nvertically) variation of fertilizer-derived nutrients in the studied soil, it is desirable \n\n\n\nto apply fertilizers in a precision approach using straight rather than compound \n\n\n\nfertilizers. In particular, the accumulation and uneven distribution of available P in \n\n\n\nthe topmost soil layer need special care at the study site. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Year Vol. (XX) (Issue) \n \n\n\n\n117 \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe study was funded by the Geran-Putra Inisiatif Putra Siswazah (GP-IPS) from \n\n\n\nUniversiti Putra Malaysia. Nurhaliza M. Shahidin appreciates the scholarship \n\n\n\nprovided by Universiti Teknologi MARA. \n\n\n\n\n\n\n\n\n\n\n\nREFERENCES \n\n\n\nAbe, S.S., G.O. Oyediran, T. Masunaga, S.  Yamamoto, T. Honna and T. \n\n\n\nWakatsuki. 2009. Soil development and fertility characteristics of inland \n\n\n\nvalleys in the rain forest zone of Nigeria: Mineralogical composition and \n\n\n\nparticle-size distribution. Pedosphere 53: 505\u2013514. \n\n\n\nAbe, S.S. and T. Wakatsuki. 2010. 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Balkema, pp. 3\u201310. \n\n\n\nShahidin, M.N., R. Ismail, K.N. Kamarudin and S.S. Abe. 2018. Spatial variability \n\n\n\nof selected chemical properties of a lateritic soil under mango cultivation in \n\n\n\nNorthern Peninsular Malaysia. Tropical Agriculture and Development 62: \n\n\n\n104\u2013108. \n\n\n\nShahidin, M.N., I. Roslan, S.H. Kang, K.N.  Kamarudin, H. Murano and S.S. Abe. \n\n\n\n2022. Effects of chicken manure applied with inorganic fertilizer on mango \n\n\n\ntree nutrition and fruit yield in Northern Peninsular Malaysia. (in review) \n\n\n\nvan Reeuwijk, L.P. 2002. Procedure for Soil Analysis (6th ed.). ISRIC Technical \n\n\n\nPaper No. 9. Wageningen: International Soil Reference and Information \n\n\n\nCentre.  \n\n\n\nZhang, D., C. Wang and X. Li. 2019. Yield gap and production constraints of \n\n\n\nmango (Mangifera indica) cropping systems in Tianyang County, China. \n\n\n\nJournal of Integrative Agriculture 18: 1726\u20131736. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Year Vol. (XX) (Issue) \n \n\n\n\n119 \n\n\n\n\n\n\n\nZhang, M., M.J. Wilson and Z. He. 2004a. Mineralogy of red soils in southern \n\n\n\nChina in relation to their development and charge characteristics. In The Red \n\n\n\nSoils of China: Their Nature, Management and Utilization, ed. M.J. Wilson, \n\n\n\nZ. He and X. Yang, Dordrecht: Kluwer Academic Publishers, pp. 35\u201361. \n\n\n\nZhang, M., Z. He and M.J. Wilson. 2004b. Chemical and physical characteristics \n\n\n\nof red soils from Zhejiang Province, Southern China. In The Red Soils of \n\n\n\nChina: Their Nature, Management and Utilization, ed. M.J. Wilson, Z. He & \n\n\n\nX. Yang. Dordrecht: Kluwer Academic Publishers, pp. 63\u201387. \n\n\n\n \n\n\n\n\n\n"
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"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n138 \n\n\n\n\n\n\n\nDetermining the Phytoremediation Potential of Naturally Growing \n\n\n\nTropical Plant Species at a Sanitary Landfill \n \n\n\n\nRajoo, S. Keeren1,2*, Ismail, Ahmad3, Karam, S. Daljit.4, Arifin, Abdu.5,  \n\n\n\nIzani, Norul1, Gerusu, Geoffery James1,2, Ibrahim, Zahari6,  \n\n\n\nAbdullah, Muhammad Amin1 and Ibrahim, Mohd Hakeem4 \n\n\n\n \n1Department of Forestry Science, Faculty of Agriculture Science & Forestry,  \n\n\n\nUniversiti Putra Malaysia Bintulu Campus, Nyabau Road, 97008 Bintulu, Sarawak, Malaysia \n2Institute of Ecosystem Science Borneo, Universiti Putra Malaysia Bintulu Campus, Nyabau Road, \n\n\n\n97008 Bintulu, Sarawak, Malaysia \n3Academy Science Malaysia, 50480, Kuala Lumpur, Malaysia \n\n\n\n4Department of Land Management, Faculty of Agriculture, UPM, Malaysia \n5Department of Forestry Science & Biodiversity, Faculty of Forestry & Environment, UPM, Malaysia \n\n\n\n 6Forestry Department Peninsular Malaysia, Kuala Lumpur, Malaysia \n\n\n\n \nCorrespondence: *keeren.rajoo@upm.edu.my \n\n\n\n\n\n\n\nABSTRACT \nHeavy metal contamination poses severe threats to ecosystems and human health, necessitating \n\n\n\neffective remediation strategies. Phytoremediation, which leverages plants to remove heavy metals, \n\n\n\noffers a promising solution. However, this approach remains underexplored, particularly in tropical \n\n\n\necosystems like Malaysia. Thus, this study examines the potential of native plant species in addressing \nheavy metal pollution, at Air Hitam Sanitary Landfill (AHSL). This location was selected due to it being \n\n\n\nan urban ecosystem that is susceptible to soil heavy metal contamination from municipal waste disposal \n\n\n\nand atmospheric deposition. Native plant species, namely Pueraria phaseoloides, Dicranopteris \nlinearis, Cyperus rotundus, Acacia spp., and Melastoma malabathricum were found to grow well at \n\n\n\nAHSL, thus were selected for this study. The phytoremediation potential of these plant species were \n\n\n\ndetermined by calculating their translocation (TF) and bioaccumulation factors (BCF). Based on the \nTF and BCF values of all the plants studied, none of the plant species were potential phytoremediators. \n\n\n\nHowever, four plant species were identified as potential bioindicators of Cd. These species were \n\n\n\nPueraria phaseoloides, Cyperus rotundus, Acacia spp. and Melastoma malabathricum. In conclusion, \n\n\n\nthis study underscores the importance of understanding phytoremediation potential within challenging \nenvironments and its contribution to heavy metal mitigation. By investigating native plant species in \n\n\n\nAHSL, the research aids in expanding the application of phytoremediation strategies, ultimately \n\n\n\nfostering ecological restoration, and safeguarding human health. \n\n\n\n\n\n\n\nKeywords: heavy metals; Malaysia; landfill; municipal solid waste; toxic elements \n\n\n\n\n\n\n\nINTRODUCTION \n\n\n\nThe escalating levels of heavy metal soil contamination in our environment pose a grave threat \n\n\n\nto ecological sustainability and human health. Anthropogenic activities, such as industrial \n\n\n\nprocesses, agricultural practices, and waste disposal, have led to the release of toxic heavy \n\n\n\nmetals like cadmium (Cd), iron (Fe), and zinc (Zn), into our soil and water systems. These \n\n\n\nheavy metals, known for their persistence and detrimental effects, can accumulate over time, \n\n\n\nresulting in widespread pollution and ecological disruption (Achary et al. 2017). \n\n\n\n\n\n\n\nThe hazards of heavy metal contamination are multifaceted, encompassing ecological \n\n\n\nimbalances, compromised agricultural productivity, and adverse human health effects (Archary \n\n\n\net al. 2017). Heavy metals can disrupt natural biogeochemical cycles, leading to soil and water \n\n\n\ndegradation. As heavy metals enter the food chain, they pose potential health risks to humans \n\n\n\nthrough the consumption of contaminated crops and water. This underlines the urgency of \n\n\n\n\nmailto:keeren.rajoo@upm.edu.my\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n139 \n\n\n\n\n\n\n\nfinding sustainable approaches to mitigate heavy metal pollution and safeguard both \n\n\n\necosystems and human well-being. \n\n\n\n\n\n\n\nGiven the severity of this issue, the development of effective and sustainable strategies for \n\n\n\nremediating heavy metal contamination has become a pressing concern. Among these \n\n\n\nstrategies, phytoremediation, an eco-friendly and cost-effective approach, has gained \n\n\n\nprominence. Phytoremediation utilizes the natural abilities of certain plant species that are able \n\n\n\nto uptake, accumulate, and detoxify heavy metals from soil and water, thereby offering a \n\n\n\npromising solution to the challenge of heavy metal pollution (Rajoo et al. 2013). \n\n\n\n\n\n\n\nDespite being a cost-effective and environmentally friendly approach to managing heavy \n\n\n\nmental contamination, phytoremediation is still a relatively underutilized strategy (Hernandez \n\n\n\net al. 2022). This is especially true for tropical ecosystems, where the climatic conditions \n\n\n\nsignificantly differ in countries with existing literature in environmental sciences (Lee et al. \n\n\n\n2014; Rajoo et al. 2023). Even worse, phytoremediation studies in tropical countries like \n\n\n\nMalaysia is limited (Rajoo et al. 2013; Hernandez et al. 2022). This knowledge gap has resulted \n\n\n\nin a poor understanding of potential phytoremediators in tropical countries, with limited \n\n\n\napplication of this strategy.  \n\n\n\n\n\n\n\nTherefore, this study was undertaken to address this knowledge gap. The objectives of this \n\n\n\nstudy are: 1) To identify common plant species in tropical sanitary landfills, and 2) To identify \n\n\n\npotential phytoremediators of heavy metals in tropical ecosystems. The study focuses on the \n\n\n\nphytoremediation potential of native plant species thriving in a naturally challenging \n\n\n\nenvironment, specifically a sanitary landfill, the Air Hitam Sanitary Landfill (AHSL). The \n\n\n\nimportance of this research lies in its endeavor to elucidate the capacity of these plants to \n\n\n\ncombat heavy metal contamination and contribute to the restoration of compromised \n\n\n\necosystems. \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nStudy site: Air Hitam Sanitary Landfill (AHSL) \n\n\n\nThe AHSL site is located near the Air Hitam Forest Reserve in Mukim Petaling, Daerah \n\n\n\nPetaling, Puchong (longitude 101\u00b0 39\u2019 55\u2019\u2019 E and latitude 03\u00b0 0\u2019 10\u2019\u2019 N) (Figure 1). The \n\n\n\nSelangor State Government Council approved Worldwide Sita Environmental Management to \n\n\n\ndevelop this sanitary landfill, on 22nd March 1995. ASHL was built in 1995 and was the first \n\n\n\nengineered sanitary landfill site in Malaysia, covering a total of 42 hectares. During the 11 \n\n\n\nyears ASHL operated, it received approximately 6.2 million tons of domestic waste. ASHL is \n\n\n\nsurrounded by residential housing, highways, and manufacturing industries. AHSL was \n\n\n\nofficially closed on 31 December 2006 and the 5-year Landfill Closure and Post Closure \n\n\n\nMaintenance Plan (LCPCMP) was in place (2007-2011). \n\n\n\n\n\n\n\nAHSL was selected for this study since it is a prime location for soil heavy metal contamination \n\n\n\nin an urban ecosystem. As a waste disposal site, there is a high likelihood of heavy metal \n\n\n\naccumulation in the soil. Moreover, since it is surrounded by manufacturing industries and \n\n\n\nheavy vehicle usage, atmospheric deposition of heavy metals like Cd might be prevalent in this \n\n\n\nlocation (Kubier et al. 2019).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n140 \n\n\n\n\n\n\n\n \nFigure 1: Location of Air Hitam Sanitary Landfill \n\n\n\n\n\n\n\nSample collection and analysis \n\n\n\nThree sampling locations were selected throughout AHSL, each at a different phase. Six \n\n\n\nsubplots (20 m x 20 m) were randomly established (completely randomized design) at each \n\n\n\nsampling location. Composite soil samples were obtained using an auger from each subplot at \n\n\n\n0cm-20cm, 20cm-40cm, 40cm-60cm, 60cm-80cm and 80cm-100cm using an auger. All soil \n\n\n\nsamples were kept in a sterilised polyethylene bags and air-dried before being analysed. \n\n\n\nSamples of the soil was air dried for three days, pounded with a mortar and pestle, and then \n\n\n\nsieved through a 2mm mesh. This is done to produce a homogenous mixture for analyses.  \n\n\n\n\n\n\n\nPlants were selected based on abundance. The fresh sample of plants was separated into three \n\n\n\nparts: roots, stem, and leaves. These plant parts were then be dried in an oven at 60\u2070 C for 24 \n\n\n\nhours and shredded into small piece before further analysis.  \n\n\n\n\n\n\n\nAcid digestion was used to determine the concentration of heavy metals in the soil and plant \n\n\n\nsamples collected. After digestion, the total concentrations of heavy metals were determined \n\n\n\nusing Atomic Absorption Spectrometer (AAS) (Gupta 2007). Additionally, basic physico-\n\n\n\nchemical analyses were conducted to characterize the soil characteristics (Gupta 2007). \n\n\n\n\n\n\n\nData and Statistical Analysis  \n\n\n\nIn order to evaluate the phytoremediation potential of sampled plant species, two indicators \n\n\n\nwere used: BCF (metal concentration ratio of plant roots to soil), TF (metal concentration ratio \n\n\n\nof plant shoots to roots). If both the BCF and TF value is above 1, the species has the potential \n\n\n\nto be a hyperaccumulator for the analysed heavy metal, while a BCF value of above 1 and a \n\n\n\nTF value below 1 was indicative of a phytostabilizer species (Rajoo et al. 2013). These \n\n\n\nindicators were calculated as follows: \n\n\n\n\n\n\n\nThe data was statistically analysed using the SPSS program (Version 23). Appropriate \n\n\n\nstatistical analyses were conducted to analyse the research data, such as analysis of variance \n\n\n\n(ANOVA), t-test and regression. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n141 \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nSoil characteristics \n\n\n\nThe upper layer of AHSL exhibited soil that enveloped the buried municipal waste. This soil \n\n\n\ntype was Oxisol and had undergone significant weathering due to prolonged exposure to \n\n\n\nenvironmental conditions. The pH levels of the soil varied between 5.47 and 6.21, with an \n\n\n\naverage pH of 5.94. The pH of the soil plays a role in the availability of heavy metals; typically, \n\n\n\nlower pH values result in higher availability of these metals due to their cationic nature (USDA \n\n\n\nNRCS, 2000). At AHSL, the soil's electrical conductivity (EC) ranged from 115.1 to 293.00 \n\n\n\n\u00b5S cm-1, averaging at 219.4 \u00b5S cm-1. Soils with an EC below 300 \u00b5S cm-1 are generally \n\n\n\nconsidered to have limited microbial activity and are less conducive to plant growth as essential \n\n\n\nenzymatic processes required for nutrient synthesis might be lacking (Brady and Weil 1999). \n\n\n\n\n\n\n\nThis characteristic suggests that the plants thriving at AHSL are primarily early colonizers, \n\n\n\nwhich are species that thrive in disturbed ecosystems. The soil's bulk density spanned from \n\n\n\n1.40 to 1.73 g cm-3, accompanied by a moisture content ranging from 11.56 to 12.50%, and \n\n\n\nporosity ranging from 34.91 to 47.16%. Soil with a bulk density within the range of 1.0 to 2.0 \n\n\n\ng cm-3 is typically indicative of low organic matter content, a trait likely stemming from the \n\n\n\nextensively weathered and exposed soil at AHSL. The diminished porosity can be attributed to \n\n\n\nthe soil's compaction to conceal the buried municipal waste, thereby explaining the reduced \n\n\n\nmoisture content as well. The concentration of extractable phosphorus across all phases of \n\n\n\nAHSL was consistently low. This scarcity of phosphorus is often linked to elevated levels of \n\n\n\niron (Fe) within the soil, a characteristic trait of Oxisol. Additionally, the dearth of organic \n\n\n\nmatter often corresponds to lower levels of nitrogen, phosphorus, and potassium (NPK) in soils, \n\n\n\nwhich is evident in the AHSL context (Cavanagh and O\u2019Halloran 2003). \n\n\n\n\n\n\n\nSoil heavy metal concentrations \n\n\n\nThe heavy metals evaluated in this study are Cd, Fe and Zn. For Cd, average concentrations \n\n\n\nranged from 0.018 ppm to 0.029 ppm across the different AHSL phases, with no significant \n\n\n\ndifferences observed between phases or soil depths. Cd concentration primarily originated from \n\n\n\natmospheric deposition rather than buried municipal waste. Regarding Fe concentrations, there \n\n\n\nwas a significant time-based effect. Phase 7 exhibited the highest Fe concentration (22.18 \u00b1 \n\n\n\n2.58 ppm) compared to earlier phases (Phase 1-5: 20.597 \u00b1 1.27 ppm and Phase 6: 20.097 \u00b1 \n\n\n\n2.952 ppm), but Phase 1-5 had higher mean concentrations than Phase 6, though this difference \n\n\n\nwas not significant. \n\n\n\n\n\n\n\nSimilarly, Zn concentrations were significantly influenced by time. Phase 7 had the highest Zn \n\n\n\nconcentration (55.013 \u00b1 5.613 ppm), differing significantly from earlier phases (Phase 1-5: \n\n\n\n46.510 \u00b1 4.3 ppm and Phase 6: 51.41 \u00b1 5.46 ppm). Phase 6 also had higher Zn concentrations. \n\n\n\nTherefore, Zn concentrations decreased over time, similar to other metals. \n\n\n\n\n\n\n\nLinear regression analyses revealed that soil depth had no significant predictive effect on Cd \n\n\n\nconcentration, while for Fe and Zn, soil depth significantly predicted concentration. Fe \n\n\n\nconcentrations decreased with soil depth, and soil depth accounted for 36.8% of the variability. \n\n\n\nFor Zn, soil depth predicted 53% of the variability, and Zn concentrations also decreased with \n\n\n\ndepth. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n142 \n\n\n\n\n\n\n\nPlant samples \n\n\n\nThe plant samples collected were Pueraria phaseoloides, Dicranopteris linearis, Cyperus \n\n\n\nrotundus, Acacia spp. and Melastoma malabathricum (Figure 2). These species were selected \n\n\n\nas they were in large quantities at all the landfill phases. These species were identified as \n\n\n\npioneer species, indicating that the ecosystem in the landfill was disrupted and damaged (Leslie \n\n\n\n2010). Over time, these pioneer plant species will assist in leading to a more biodiverse \n\n\n\necosystem (Leslie 2010). The Cd concentrations in the plant samples were divided according \n\n\n\nto different plant parts, which are the stem, leaves, and roots. The combined values of Cd \n\n\n\nconcentrations represent the total Cd concentration of the plants. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Pueraria phaseoloides (A), Dicranopteris linearis (B), Cyperus rotundus (C), Acacia \n\n\n\nspp. (D) and Melastoma malabathricum (E) samples. \n\n\n\n\n\n\n\nHeavy metal concentration in plant parts \n\n\n\nThe heavy metal concentrations in the plant samples were divided according to different plant \n\n\n\nparts, which are the stem, leaves and roots. The combined values of these heavy metal \n\n\n\nconcentrations represent the total heavy metal concentration of the plants. \n\n\n\n\n\n\n\nCadmium concentrations in plant parts \n\n\n\nAs shown in Figure 3, Cd concentrations were highest in the leaves for three plant species, \n\n\n\nwhich are Pueraria phaseoloides (0.022 ppm), Cyperus rotundus (0.019 ppm) and Melastoma \n\n\n\nmalabathricum (0.018 ppm). For Dicranopteris linearis, the highest concentration of Cd was \n\n\n\nrecorded in the roots (0.019 ppm) while Acacia spp. exhibited the highest Cd concentration in \n\n\n\nthe stem (0.018 ppm). The highest total Cd concentration was recorded by Pueraria \n\n\n\nphaseoloides (0.056 ppm). For four of the plant species (Pueraria phaseoloides, Cyperus \n\n\n\nrotundus, Acacia spp. and Melastoma malabathricum), the highest Cd concentration was found \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n143 \n\n\n\n\n\n\n\nin the aerial parts of the plant (Leaves and stem). This is because Cadmium is a mobile heavy \n\n\n\nmetal, easily transported from the root of the plant to the stem and leaves (Gregor et al. 2004). \n\n\n\n\n\n\n\nFigure 3: Cd concentrations in different plant parts. \n\n\n\nMean values followed by same letter are not significantly different at P \u22640.05 by Tukey HSD test. \n\n\n\n\n\n\n\nIron concentrations in plant parts \n\n\n\nAs shown in Figure 4, Iron concentrations were highest in the roots for all plant species, which \n\n\n\nare Pueraria phaseoloides (16.34 ppm), Dicranopteris linearis (18.36 ppm),Cyperus rotundus \n\n\n\n(13.31 ppm),  Acacia spp. (9.77 ppm) and Melastoma malabathricum (13.34 ppm). The highest \n\n\n\ntotal Cd concentration was recorded by Cyperus rotundus (26.28 ppm). High metal \n\n\n\nconcentrations are commonly found in the roots of most plants due to its direct contact with \n\n\n\nthe metal in the soil (Rajoo et al. 2013). Previous studies have shown that the presence of Fe \n\n\n\nfor phytoremediation purposes is beneficial as it assists in the uptake of other heavy metals (He \n\n\n\net al. 2015). \n\n\n\n\n\n\n\nFigure 4: Fe concentrations in different plant parts. \n\n\n\nMean values followed by same letter are not significantly different at P \u22640.05 by Tukey HSD test. \n\n\n\n\n\n\n\nZinc concentrations in plant parts \n\n\n\nAs seen in Figure 5, Zinc concentrations were highest in the roots for all plant species, which \n\n\n\nare Pueraria phaseoloides (10.72 ppm), Dicranopteris linearis (17.98 ppm), Cyperus rotundus \n\n\n\n(13.63 ppm),  Acacia spp. (8.73 ppm) and Melastoma malabathricum (12.15 ppm). The highest \n\n\n\ntotal Zn concentration was recorded by Dicranopteris linearis (31.55 ppm). The availability of \n\n\n\nC\nad\n\n\n\nm\niu\n\n\n\nm\n (\n\n\n\np\np\n\n\n\nm\n) \n\n\n\nIr\no\n\n\n\nn\n (\n\n\n\np\np\n\n\n\nm\n) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n144 \n\n\n\n\n\n\n\nZn is very dependent on the soil pH; as pH decreases, the availability of Zn increases (Ripin et \n\n\n\nal., 2014). All the plants studied mostly stored Zn in its roots, which is common in most plants \n\n\n\n(Rajoo et al. 2013). \n\n\n\n\n\n\n\nFigure 5: Zn concentrations in different plant parts. Mean values followed by same letter are \n\n\n\nnot significantly different at P \u22640.05 by Tukey HSD test. \n\n\n\nThe Translocation Factor (TF) and Bioconcentration Factor (BCF) of plant samples \n\n\n\nA plant\u2019s potential as a phytoremediator can be determined by calculating the plant\u2019s BCF \n\n\n\n(metal concentration ratio of plant roots to the soil) and TF (metal concentration ratio of plant \n\n\n\nshoots to roots) values. If both the BCF and TF value is above 1, the species has the potential \n\n\n\nto be a hyperaccumulator while a BCF value of above 1 and a TF value below 1 was a potential \n\n\n\nphytostabilizer species. \n\n\n\n\n\n\n\nTF and BCF of Cadmium \n\n\n\nThe Cadmium BCF values for all plant species was below 1 (Figure 6). The BCF values for all \n\n\n\nrespective species were Pueraria phaseoloides (0.59), Dicranopteris linearis (0.90),Cyperus \n\n\n\nrotundus (0.71), Acacia spp. (0.71) and Melastoma malabathricum (0.63). Four plant species \n\n\n\nhad TF values of above 1, which are Pueraria phaseoloides (1.70), Cyperus rotundus (1.02), \n\n\n\nAcacia spp. (1.20) and Melastoma malabathricum (1.04). The results show that none of these \n\n\n\nspecies were potential phytoremediators of Cd. However, the four plant species that had TF \n\n\n\nvalues of above 1 could be potentially used as bioindicators for Cd. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6: BCF and TF values of Cd in different plant species \n\n\n\n(Note: BCF = Bioconcentration Factor and TF = Translocation Factor) \n\n\n\n\n\n\n\n\n\n\n\nZ\nin\n\n\n\nc\n (\n\n\n\np\np\n\n\n\nm\n) \n\n\n\nC\nad\n\n\n\nm\niu\n\n\n\nm\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n145 \n\n\n\n\n\n\n\nThe TF and BCF of Iron \n\n\n\nSimilar to Cd, the Iron BCF values for all plant species was below 1 (Figure 7). The BCF values \n\n\n\nfor all respective species were Pueraria phaseoloides (0.80), Dicranopteris linearis (0.90), \n\n\n\nCyperus rotundus (0.65), Acacia spp. (0.48) and Melastoma malabathricum (0.65). All the \n\n\n\nplant species also had Fe TF values of lower than 1, the respective values being Pueraria \n\n\n\nphaseoloides (0.23),Dicranopteris linearis (0.29), Cyperus rotundus (0.51) Acacia spp. (0.36) \n\n\n\nand Melastoma malabathricum (0.31). The results show that none of these species were \n\n\n\npotential phytoremediators of Fe. \n\n\n\n\n\n\n\nFigure 7: BCF and TF values of Fe in different plant species \n\n\n\n(Note: BCF = Bioconcentration Factor and TF = Translocation Factor) \n \n\n\n\nThe TF and BCF of Zinc \n\n\n\nThe Zinc TF and BCF values for all plant species was below 1 (Figure 8). The BCF values for \n\n\n\nall respective species were Pueraria phaseoloides (0.21), Dicranopteris linearis (0.36), \n\n\n\nCyperus rotundus (0.34), Acaciaspp. (0.17) and Melastoma malabathricum (0.24). The Zn TF \n\n\n\nvalues of the plant species were Pueraria phaseoloides (0.85), Dicranopteris linearis (0.35), \n\n\n\nCyperus rotundus (0.69) Acacia spp. (0.39) and Melastoma malabathricum (0.38). The results \n\n\n\nshow that none of these species were potential phytoremediators of Zn. This is unlike numerous \n\n\n\nother species which have been found to be suitable phytoremediators of Zn, most notably \n\n\n\nphytoextractors such as running grass and desert broom. \n\n\n\n\n\n\n\nFigure 8: BCF and TF values of Zn in different plant species \n\n\n\n(Note: BCF = Bioconcentration Factor and TF = Translocation Factor) \n\n\n\n\n\n\n\n\n\n\n\nIr\no\n\n\n\nn\n \n\n\n\nZ\nin\n\n\n\nc\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 138 - 146 \n\n\n\n\n\n\n\n146 \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\nBased on the TF and BCF values of all the plants studied, none of the plant species were \n\n\n\npotential phytoremediators. However, four plant species had Cd TF values of above 1, which \n\n\n\nwere Pueraria phaseoloides, Cyperus rotundus, Acacia spp. and Melastoma malabathricum. \n\n\n\nHence, these four plant species could be potentially used as bioindicators for Cd. It is advisable \n\n\n\nthat identified phytoremediator plant species be planted at sites that could cause ecosystem \n\n\n\ncontamination, such as landfills.  \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe authors would like to thank Worldwide Environment for allowing us to conduct this study \n\n\n\nat Air Hitam Sanitary Landfill. The authors would also to thank Universiti Putra Malaysia for \n\n\n\nfunding this research and the APC, under Geran Putra Berimpak (Vot: 9725800). \n\n\n\n\n\n\n\nREFERENCES \nAchary M.S., Satpathy K.K., Panigrahi S., Mohanty A.K., Padhi R.K., Sudeepta B., Prabhu R.K., \n\n\n\nVijayalakshmi S., Panigrahy R.C. 2017. Concentration of heavy metals in the food chain \ncomponents of the nearshore coastal waters of Kalpakkam, southeast coast of India. Food Control \n\n\n\n72:232-243 \n\n\n\nBrady N.C. and Weil R. R. 1999.  The nature and properties of soils. 12th ed. Prentice Hall. Upper \nSaddle River, NJ. \n\n\n\nCavanagh J.A.E. and O\u2019Halloran K. 2003. 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J \nEthnopharmacol. 301: 115780. doi: 10.1016/j.jep.2022.115780 \n\n\n\nRajoo K.S., Arifin A. Hazandy A.H., Karam D.S., Shamshuddin J., Aiza-Shaliha J., Wong W.Z. 2013. \n\n\n\nAssessment of heavy metals uptake and translocation by Aquilaria malaccensis planted in soils \ncontaining sewage sludge. American Journal of Applied Sciences 10(9): 952-964. \n\n\n\nDOI:10.3844/ajassp.2013.952.964 \n\n\n\nRipin S.N.M., Hasan S., Kamal M.L. and Hashim N.M. 2014. Analysis and pollution assessment of \n\n\n\nheavy metal in soil, Perlis. The Malaysian Journal of Analytical Sciences (18): 155-161. \nUnited States Department of Agriculture, Natural Resources Conservation Service, USDA NRCS. \n\n\n\n2000. Heavy metal soil contamination. Soil quality \u2013 Urban technical note No. 3: 1-7. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n56 \n\n\n\n\n\n\n\nChanges in Potassium Sorption and pH Buffering Capacity of Tropical \n\n\n\nAcid Soils Following Application of Charcoal and Sago Bark Ash \n \n\n\n\nPuvan Paramisparam1, Osumanu Haruna Ahmed2, *, Huck Ywih Ch\u2019ng3, Latifah \n\n\n\nOmar1, 4, Prisca Divra Johan1, Nur Hidayah Hamidi1 and Adiza Alhassan Musah5 \n\n\n\n \n1Department of Crop Science, Faculty of Agricultural Science and Forestry, Universiti Putra \n\n\n\nMalaysia, Bintulu Sarawak Campus, Bintulu 97008, Malaysia \n2Faculty of Agriculture, Universiti Islam Sultan Sharif Ali, Kampus Sinaut, KM 33 Jalan Tutong \n\n\n\nKampung Sinaut, Tutong TB1741, Brunei \n3Faculty of Agro-Based Industry, Campus Jeli, Universiti Malaysia Kelantan, Jeli 17600, Malaysia \n\n\n\n1,4Institut Ekosains Borneo (IEB), Faculty of Agriculture and Forestry Sciences, Universiti Putra \nMalaysia, Bintulu Sarawak Campus, Bintulu 97008, Malaysia \n\n\n\n5Graduate School of Management Post Graduate Centre, Management and Science University, \n\n\n\nUniversity Drive, Off Persiaran Olahraga, Section 13, Shah Alam 40100, Selangor, Malaysia \n\n\n\n \n* Correspondence: ahmed.haruna@unissa.edu.bn \n\n\n\n \nABSTRACT \n\n\n\n \nUltisols and Oxisols are the two dominant soils in the tropics. These soils are mostly infertile and have \nlow cation exchange capacity because of their low pH (4 to 5). They are composed of kaolinite and \n\n\n\nsesquioxides which are prone to potassium (K) leaching. To make these soils arable, liming and \n\n\n\nfertilization are required. Nevertheless, this conventional practice alone does not mitigate K availability \nin such soils because of their low pH buffering capacity and low K adsorption capacity. The alkalinity \n\n\n\nof sago (Metroxylon sagu) bark ash and charcoal and the deprotonation of charcoal\u2019s functional groups \n\n\n\nby the carbonates and oxides of sago bark ash have potential benefits. Due to these characteristics of \nsago bark ash and charcoal, they could be utilized to improve soil pH buffering capacity and K \n\n\n\nadsorption capacity to prevent the leaching of K and the pollution of water bodies. Moreover, the use \n\n\n\nof charcoal and sago bark ash to amend soils is a good way of utilizing agro-wastes sustainably. Thus, \n\n\n\nthe objective of this study was to determine the effects of amending tropical acid soils with charcoal \nand sago bark on K sorption and pH buffering capacity. The treatments evaluated were: (i) 300 g soil \n\n\n\nonly, (ii) 250 g charcoal only, (iii) 250 g sago bark ash only, (iv) 300 g soil + 15.42 g charcoal, (v) 300 \n\n\n\ng soil + 7.71 g sago bark ash, and (vi) 300 g soil + 15.42 g charcoal + 7.71 g sago bark ash. Langmuir \nbonding energy constant (KL), Maximum K buffering capacity (MBC), and maximum adsorption \n\n\n\ncapacity (qmax) of the soil with charcoal and sago bark ash were higher than that of soil alone. However, \n\n\n\ndesorption of K was not significantly affected after application of the amendments. On the other hand, \n\n\n\nthe combined use of charcoal and sago bark ash improved the soil\u2019s pH buffering capacity in \ncomparison to the untreated soil because of the inherently high CEC and alkalinity of these \n\n\n\namendments. Therefore, this intervention could contribute to improving K fertilizer use and prevent \n\n\n\nenvironmental pollution and economical loss to farmers. \n \n\n\n\nKey words: Langmuir isotherm, biochar, cation exchange capacity, soil acidity, leaching \n\n\n\n \nINTRODUCTION \n\n\n\n\n\n\n\nPotassium in soils exist in four distinctive forms namely water-soluble potassium (K), \n\n\n\nexchangeable K, non-exchangeable K, and mineral K (Sparks 2000; Jaiswal et al. 2016). The \n\n\n\nkinetic and equilibrium reactions between these four forms of K in soils can determine the fate \n\n\n\nof K fertilizers applied. The K can either be lost through leaching, taken up by plants or remain \n\n\n\nin soil as reserve K. Water-soluble K is the most readily available form of K for plant uptake. \n\n\n\nNevertheless, water-soluble K is mobile and due to the high annual rainfall in the humid tropics \n\n\n\n\nmailto:ahmed.haruna@unissa.edu.bn\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n57 \n\n\n\n\n\n\n\nmost of this form of K is leached out of the soil profile, thus causing K deficiency. Hence, it is \n\n\n\nessential for the applied K to be adsorbed by the soil minerals and organic matter through \n\n\n\nelectrostatic attraction, covalent bonding, or isomorphous replacement when it is not taken up \n\n\n\nby plants.  However, Ultisols and Oxisols are composed of kaolinite clay minerals which have \n\n\n\nminute amounts of nutrient holding sites because the charges are only on the edges of the \n\n\n\ncrystalline structure (Palanivell 2016). In addition, kaolinite minerals are stacked together by \n\n\n\nhydrogen bonding, and this prevents water and nutrients from entering between the layers of \n\n\n\nthese minerals (Miranda-Trevino and Coles 2003). Hence, the low negative charge density of \n\n\n\nkaolinite results in poor K adsorption capacity. \n\n\n\n\n\n\n\nThe adsorption capacity of soils is an important property which determines the extent of \n\n\n\nleaching and redistribution of anions and cations (Wann and Uehara 1978). To temporarily \n\n\n\nhold nutrients before being taken up by plants, the high CEC of soil amendments can be \n\n\n\nexploited (Latifah et al., 2017). For example, surface oxidation of aromatic rings of charcoal \n\n\n\nresults in carboxylation which generates large numbers of negatively charged sites (Qiu et al., \n\n\n\n2008). This could increase the adsorption capacity of soils, enabling K to bind onto the \n\n\n\nnegatively charged sites of soils. Nevertheless, to ensure timely release of the exchangeable K, \n\n\n\nsoil acidification needs to be suppressed. Currently, soil acidification is accelerating because \n\n\n\nof anthropogenic activities. Soil acidification causes aluminium (Al) and iron (Fe) toxicity to \n\n\n\nplants and deficiency of nutrients such as potassium (K), phosphorus (P), calcium (Ca), and \n\n\n\nmagnesium (Mg). Accumulation of Al and Fe ions in low pH soils causes the leaching of base \n\n\n\ncations in soils. In contrast, Al and Fe competition at soil exchange sites can be diminished for \n\n\n\nnutrients such as K to become more reactive in higher pH (Gazey 2018). In this situation, K \n\n\n\ncould constantly move from soil solution to adsorbents or vice versa, depending on the \n\n\n\nrequirements of plants. For this to occur, the pH buffering capacity of the soils must be high. \n\n\n\n\n\n\n\nSoil pH buffering capacity is the ability of a soil to resist change in pH and increase \n\n\n\nproportionally with soil CEC and organic matter content (Moody and Aitken 1997). The \n\n\n\napplication of soil amendments has been shown to increase the buffering capacity of acid soils \n\n\n\n(Shi et al. 2017; Xu et al. 2012; Perumal et al. 2021). Several minerals in soils enable buffering \n\n\n\nagainst changes in pH. For example, Ca, Mg, and K oxides, together with carbonates enhance \n\n\n\nbuffer pH changes during soil acidification. Buffering capacity is important because it enables \n\n\n\nsoil pH stabilization. Changes in pH can affect plants in different ways, especially by limiting \n\n\n\nthe fraction of nutrients in soils that are available to plants but enhance the uptake of \n\n\n\nundesirable minerals such as Al. A high pH buffering capacity influences soil acidification and \n\n\n\neventually increases nutrient availability in soils especially K, which is compromised at low \n\n\n\nsoil pH. \n\n\n\n\n\n\n\nThe direct or indirect effects of charcoal and sago bark ash on K sorption and soil pH buffering \n\n\n\ncapacity has been rarely reported. Therefore, understanding the ability of these amendments to \n\n\n\nresist acidification and cause changes to K sorption in soils is key in ameliorating acid soils. \n\n\n\nTowards this end, it was hypothesized that amending acid soils with charcoal and sago bark \n\n\n\nash will improve K availability by retarding soil acidification. This will also enable timely \n\n\n\nretention and release of K into soil solution. The research questions that this study addressed \n\n\n\nwere as follows: (i) will the use of charcoal and sago bark ash significantly improve pH \n\n\n\nbuffering capacity of acid soils? and (ii) how much of K could be adsorbed and desorbed by \n\n\n\ncharcoal and sago bark ash in response to soil solution equilibrium? This study was hence \n\n\n\nconducted to determine the effects of amending tropical acid soil with charcoal and sago bark \n\n\n\non K sorption and pH buffering capacity. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n58 \n\n\n\n\n\n\n\nMATERIALS AND METHODS \n\n\n\n \nSoil Sampling, Preparation, and Selected Physico-Chemical Analyses \n\n\n\nThe soil used in this study was sampled from an uncultivated secondary forest at Universiti \n\n\n\nPutra Malaysia, Bintulu Sarawak Campus (latitude 3\u00b012\u201911\u201dN and longitude 113\u00b004\u201925\u201dE), \n\n\n\nwhich is a typical representative of Bekenu Series, Typic Paleudults. Despite the high content \n\n\n\nof Al and Fe and abundance of kaolinite clay minerals, it is a commonly cultivated soil in \n\n\n\nSarawak, Malaysia. The area is located at 27.3 m above sea level, and experiences an annual \n\n\n\nrainfall of 2993 mm, a mean temperature of 27\u00b0C, and relative humidity of approximately 80%. \n\n\n\nThe soil samples were collected at a depth of 0\u201320 cm using a shovel. Then, the soil samples \n\n\n\nwere air dried, ground, and sieved to pass a 2-mm sieve, before they were bulked. Prior to the \n\n\n\ndetermination of K sorption and pH buffering capacity, 300 g of soil (Bekenu Series, Typic \n\n\n\nPaleudults) was mixed thoroughly with charcoal and sago bark ash in a container based on the \n\n\n\ntreatments evaluated in this study. The amounts of the charcoal and sago bark ash used were \n\n\n\nderived from the respective literature [charcoal (Free et al. 2010; Ndor et al. 2015) and sago \n\n\n\nbark ash (Mandre et al. 2006; Ozolincius et al. 2007; Perucci et al. 2008)]. The 100% \n\n\n\nrecommended rate of charcoal was 10 t ha\u22121, whereas that of sago bark ash was 5 t ha\u22121. These \n\n\n\nrecommendations were scaled down to the equivalent proportions per 300 g soil. The \n\n\n\ntreatments tested were as follows: \n\n\n\n\n\n\n\nT1: 300 g of soil only  \n\n\n\nT2: 300 g of charcoal only  \n\n\n\nT3: 300 g of sago bark ash only  \n\n\n\nT4: 300 g soil + 15.42 g charcoal  \n\n\n\nT5: 300 g soil + 7.71 g sago bark ash  \n\n\n\nT6: 300 g soil + 15.42 g charcoal + 7.71 g sago bark ash \n\n\n\n \nInitial Characterization of Soil, Charcoal, and Sago Bark Ash \n\n\n\nApart from soil texture, the selected physical and chemical properties of the soil (Bekenu \n\n\n\nSeries, Typic Paleudults) used in this study were within the range reported by Paramananthan \n\n\n\n(2000). However, the soil texture obtained was comparable to that reported in the Soil Survey \n\n\n\nStaff (2014). The sago bark ash used in this study was obtained from Song Ngeng Sago \n\n\n\nIndustries, Dalat, Sarawak, Malaysia whereas the charcoal was obtained from Pertama \n\n\n\nFerroalloys Sdn Bhd, Bintulu, Sarawak, Malaysia. The selected physico-chemical properties \n\n\n\nof the soil, charcoal, and sago bark ash are summarized in Table 1. The soil pH in water and \n\n\n\npotassium chloride (KCl) and electrical conductivity (EC) were measured in a 1:2.5 (soil: \n\n\n\ndistilled water/KCl) using a digital pH meter and an EC meter, respectively (Peech 1965). Soil \n\n\n\ntexture was determined using the hydrometer method (Bouyoucos 1962). Soil total carbon (TC) \n\n\n\nwas calculated as 58% of the organic matter that was determined using loss of weight on \n\n\n\nignition method (Cheftez et al. 1996). The soil samples were analyzed for soil bulk density \n\n\n\nusing the coring method (Dixon and Wisniewski 1995). The soil CEC was determined using \n\n\n\nthe leaching method (Cottenie 1980) followed by steam distillation (Bremner 1965). \n\n\n\nExchangeable cations [K, Ca, Mg, Sodium (Na), and Fe] were extracted with 1 M ammonium \n\n\n\nacetate (NH4OAc), pH 7 using the leaching method (Cottenie 1980). Subsequently the cations \n\n\n\nwere quantified using Atomic Absorption Spectrophotometry (AAnalyst 800, Perkin Elmer \n\n\n\nInstruments, Norwalk, CT, USA). Total K was extracted using Aqua Regia method (Bernas \n\n\n\n1968). The determination of K content in the extracts was conducted using Atomic Absorption \n\n\n\nSpectrophotometry (AAnalyst 800, PerkinElmer, Norwalk, CT, USA). The soil-exchangeable \n\n\n\nacidity, H+, and Al3+ were determined using acid-base titration method (Rowell 1994).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n59 \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nSelected physical and chemical properties of Bekenu Series (Typic Paleudults), charcoal, and \n\n\n\nsago bark ash used in the incubation study \n\n\n\nNote: Values are on dry-weight basis; values obtained: mean \u00b1 standard error; nd: not \n\n\n\ndetermined \n\n\n\n \nPotassium Adsorption and Desorption Determination \n\n\n\nA 2 g sample of soil was weighed into a 250 mL centrifuge bottle. This process was repeated \n\n\n\nfive times per experimental unit. Isonormal K solutions of 0, 100, 200, 300, and 400 mg K L-1 \n\n\n\nwere prepared by dissolving potassium chloride (KCl) in 0.01 M CaCl2 solution in distilled \n\n\n\nwater. A 20 mL of the isonormal K solution was added to the centrifuge bottles to give 0.0, \n\n\n\n2.0, 4.0, 6.0, and 8.0 mg of added K sample-1. The addition of isonormal solution in this \n\n\n\nadsorption study was to sustain a constant ionic strength in the mixtures (adsorbent and \n\n\n\nsolution) in addition to providing competing ions for exchange sites (Kithome et al. 1998). The \n\n\n\nsamples were shaken over night at 180 rpm using an orbital shaker. Thereafter, they were \n\n\n\ncentrifuged for 15 min at 10,000 rpm. The supernatants (equilibrium solution) were collected \n\n\n\nafter the centrifugation followed by analysis for K using atomic absorption spectrophotometry \n\n\n\n(AAnalyst 800, Perkin Elmer Instruments, Norwalk, CT). The K adsorption at equilibrium (qe) \n\n\n\nwas calculated using the formula below described by Peng et al. (2021) \n\n\n\n\n\n\n\nqe =\n(Co \u2212 Ce) \u00d7 V\n\n\n\nm\n \n\n\n\nwhere \n\n\n\nProperty Soil Charcoal Sago bark ash \n\n\n\npH (water) 3.95 7.74 9.99 \n\n\n\npH (KCl) 4.61 7.31 9.66 \n\n\n\nEC (\u00b5S cm-1) 35.10 269.33 5753.00 \n\n\n\nBulk density (g m-3) 1.25 nd nd \n\n\n\n---------------------------------------------------------(%)----------------------------------------------- \n\n\n\nTotal carbon 2.16 nd nd \n\n\n\nTotal N 0.08 nd nd \n\n\n\n-------------------------------------------------------(mg kg-1)------------------------------------------ \n\n\n\nTotal P 22.25 nd nd \n\n\n\nTotal K 101.27 nd nd \n\n\n\n-------------------------------------------------------(cmol kg-1)---------------------------------------- \n\n\n\nCation exchange capacity 4.67 nd 13.13 \n\n\n\nExchangeable acidity 1.15 0.10 nd \n\n\n\nExchangeable Al3+ 0.13 0.047 nd \n\n\n\nExchangeable H+ 1.02 0.05 nd \n\n\n\nExchangeable K+ 0.06 1435.20 9120.00 \n\n\n\nExchangeable Ca2+ 0.02 2346.67 3361.20 \n\n\n\nExchangeable Mg2+ 0.22 409.07 433.73 \n\n\n\nExchangeable Na+ 0.03 99.38 348.00 \n\n\n\nExchangeable Fe2+ 1.09 41.90 8.43 \n\n\n\n------------------------------------------------------------------------------------------------------------- \n\n\n\nSand (%) 71.9 nd nd \n\n\n\nSilt (%) 13.5 nd nd \n\n\n\nClay (%) 14.6 nd nd \n\n\n\nTexture (USDA) Sandy loam nd nd \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n60 \n\n\n\n\n\n\n\nqe  = K adsorption at equilibrium (mg g-1) \n\n\n\nCo  = initial concentration of K (mg L-1) \n\n\n\nCe  = K concentration at adsorption equilibrium (mg L-1) \n\n\n\nV  = volume of K solution used (L) \n\n\n\nm  = mass of sample (g) \n\n\n\n\n\n\n\nPotassium desorption was done using the sediments of the same samples by washing the \n\n\n\nsediments with ethanol using centrifugation at 10,000 rpm for 10 min. Therefrom, the ethanol \n\n\n\nwas discarded. A 20 mL of 0.01 M CaCl2 was added to the samples and shaken overnight at \n\n\n\n180 rpm using an orbital shaker after which they were centrifuged at 10,000 rpm for 15 min. \n\n\n\nTherefrom, the supernatants were collected, and K content was determined using atomic \n\n\n\nabsorption spectrophotometry (AAnalyst 800, Perkin Elmer Instruments, Norwalk, CT). The \n\n\n\ndesorbed K at equilibrium (qde) was calculated using the formula below described by Peng et \n\n\n\nal. (2021)  \n\n\n\n\n\n\n\nqde =\n(Cdo \u2212 Cde) \u00d7 V\n\n\n\nm\n \n\n\n\nwhere \n\n\n\nqde  = K desorption at equilibrium (mg g-1) \n\n\n\nCdo  = K concentration on sample (mg L-1); Cdo = Co \u2212 Ce \n\n\n\nCde  = K concentration at desorption equilibrium (mg L-1) \n\n\n\nV  = volume of 0.01 M CaCl2 solution used (L) \n\n\n\nm  = mass of sample (g) \n\n\n\n \nPotassium Adsorption Isotherm \n\n\n\nPotassium adsorption data for the samples tested in this study were fitted to the Langmuir \n\n\n\nadsorption isotherm (Table 2). This equation was used because it enables the estimation of \n\n\n\nmaximum K sorption (qmax) and a constant related to K binding strength (KL) (Gregory et al. \n\n\n\n2005). The maximum K buffering capacity (MBC) of the sample was calculated from the \n\n\n\nproduct of KL and qm (Wang and Liang 2014). \n\n\n\n \nTABLE 2 \n\n\n\nLangmuir adsorption isotherm model used in this study and its nonlinear and linear forms \n\n\n\n\n\n\n\nIsotherm Nonlinear form Linear form Plot Variables \n\n\n\nLangmuir q\ne\n= \n\n\n\nq\nm\n\n\n\nKLCe\n\n\n\n1+ KLCe\n\n\n\n \nCe\n\n\n\nq\ne\n\n\n\n= \nCe\n\n\n\nq\nm\n\n\n\n+\n1\n\n\n\nKLq\nm\n\n\n\n \nCe\n\n\n\nq\ne\n\n\n\n vs Ce \nKL= \n\n\n\nslope\n\n\n\nintercept\n \n\n\n\nq\nm\n\n\n\n= slope\n-1\n\n\n\n\n\n\n\n \npH Buffering Capacity Determination \n\n\n\nThe pH buffering capacity was determined using the titration method (Costello and Sullivan \n\n\n\n2014). A 5 g sample of each treatment was weighed into 100 mL plastic vials. Afterwards, 0.25 \n\n\n\nM H2SO4 was added to the samples at amounts of 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 mL (1 mL= \n\n\n\n0.1 mol H+ kg\u22121 sample). Each amount of acid (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 mL) was added \n\n\n\ninto a different plastic vial, after which a sufficient amount of distilled water was added to bring \n\n\n\nthe total liquid addition to 50 mL (1:10 sample:distilled water). (For example, for 2 mL of 0.25 \n\n\n\nM H2SO4, 48 mL of distilled water was added). The suspension was stirred thoroughly for 10 \n\n\n\nsec after adding acid and equilibrated for 72 h at room temperature (26 \u25e6C). Before measuring \n\n\n\npH at 72 h, the suspension was stirred for another 10 sec. The pH measurement was done using \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n61 \n\n\n\n\n\n\n\na digital pH meter (SevenEasy pH, Mettler-Toledo GmbH, Greifensee, Switzerland). The pH \n\n\n\nbuffering capacity of the sample or the quantity of acidity (H+) needed to reduce pH by one \n\n\n\nunit was calculated as the negative reciprocal of the slope of the linear regression, sample pH \n\n\n\n(Y-axis) versus the amount of acid added (X-axis):  \n\n\n\n\n\n\n\npH buffering capacity (mol H+ kg\u22121 sample) = -(1/slope) \n\n\n\nwhere slope is the fitted slope of linear regression line for each sample. \n\n\n\n \nStatistical Analysis \n\n\n\nAnalysis of variance (ANOVA) was used to determine treatment effects, whereas the \n\n\n\ntreatments means were compared using Tukey\u2019s Studentized Range (HSD) Test at p \u2264 0.05. \n\n\n\nLinear regression analysis was done to obtain the coefficient of determination (R2) for each \n\n\n\nlinear regression equation. The statistical software used was Statistical Analysis System (SAS) \n\n\n\nversion 9.4. \n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\n \nPotassium Concentration at Adsorption Equilibrium \n\n\n\nPotassium concentrations in the equilibrium solution increased with increasing amounts of K \n\n\n\nadded, regardless of treatment (Table 3). This observation corroborates the findings of \n\n\n\nChoudhury and Khanif (2003), who also reported a linear increase in equilibrium solution K \n\n\n\nconcentration with increasing addition of K. Regardless of the amount of K added, the soil with \n\n\n\ncharcoal and sago bark ash (T6) resulted in significantly lower K in the equilibrium solution \n\n\n\ncompared with the soil alone (T1). The low concentration of K remaining in the equilibrium \n\n\n\nsolution of T6 suggests that the addition of the charcoal and sago bark ash increased the \n\n\n\nadsorption of K. On the other hand, charcoal alone (T2) and the soil with charcoal (T4) resulted \n\n\n\nin lower K concentration at adsorption equilibrium compared with T1 at 200 mg K L-1 and \n\n\n\nhigher. This indicates that charcoal facilitates K adsorption, thus reducing the concentration of \n\n\n\nK in the equilibrium solution. The high concentration of K left in the equilibrium solution for \n\n\n\nT1 is related to the abundance of kaolinite clay minerals. Melo et al. (2001) indicated in their \n\n\n\nfindings that K sorption sites of kaolinite is limited to its external layers, and they are unable \n\n\n\nto fix K to the crystalline units. \n\n\n\n \nTABLE 3 \n\n\n\n\n\n\n\nTreatments effects on potassium concentration at adsorption equilibrium at different \n\n\n\nisonormal potassium solutions. \n\n\n\nTreatment \n\n\n\nPotassium concentration at adsorption equilibrium, Ce (mg L-1) \n\n\n\n0 100 200 300 400 \n\n\n\nAdded K (mg K L-1) \n\n\n\nT1 nd 77.63a \u00b1 3.90 163.65a \u00b1 4.80 250.58a \u00b1 11.26 336.85a \u00b1 2.58 \n\n\n\nT2 nd 60.13ab \u00b1 4.73 137.58bcd \u00b1 3.95 214.58bc \u00b1 4.44 295.02b \u00b1 4.06 \n\n\n\nT3 nd 60.13ab \u00b1 4.73 143.63abc \u00b1 5.53 235.55ab \u00b1 7.91 325.21a \u00b1 5.77 \n\n\n\nT4 nd 52.18b \u00b1 2.87 122.00cd \u00b1 3.45 213.54bc \u00b1 6.76 264.96c \u00b1 5.75 \n\n\n\nT5 nd 66.54ab \u00b1 3.56 155.58ab \u00b1 5.11 238.09ab \u00b1 1.16 332.08a \u00b1 2.79 \n\n\n\nT6 nd 50.23b \u00b1 4.24 116.78d \u00b1 8.11 184.55c \u00b1 4.88 262.62c \u00b1 6.38 \n\n\n\nNote: nd: not detected; different letters within a column indicate significant difference of means \n\n\n\n\u00b1 standard error using Tukey\u2019s test at p \u2264 0.05 \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n62 \n\n\n\n\n\n\n\nPotassium Adsorption \n\n\n\nRegardless of the concentration of the isonormal K solution used, the soil with charcoal and \n\n\n\nsago bark ash (T6) resulted in the highest K adsorption at equilibrium (qe) (Table 4). There \n\n\n\nwere no significant differences in the amount of K adsorbed for sago bark ash alone (T3) and \n\n\n\nthe soil with sago bark ash (T5) compared to the soil alone (T1) at all concentrations of the \n\n\n\nisonormal K solutions. On the other hand, the effect of the soil with charcoal alone (T4) on the \n\n\n\namount of K adsorbed was significantly higher compared with the soil alone (T1), regardless \n\n\n\nof the concentrations of isonormal K solution used. The improvement in the adsorbed K with \n\n\n\nthe inclusion of charcoal is consistent with the surface oxidation of the aromatic rings of the \n\n\n\ncharcoal which creates negative-charged sites (Biedereman and Harpole 2013; Major et al. \n\n\n\n2010). The low K adsorption of the sago bark ash relates to CaCO3, CaO, and MgO of this \n\n\n\namendment. Dissolution of these compounds releases cations such as Ca2+ and Mg2+. Low \n\n\n\nnegative charge density of acid soils and competition with divalent cations released from \n\n\n\ndissolution reactions, hinders K adsorption (Vasconcelos et al. 2010). Nevertheless, the co-\n\n\n\napplication of charcoal and sago ash overcomes this limitation. The dissolution of CaCO3, CaO, \n\n\n\nand MgO also releases anions to deprotonate the oxygen-containing functional groups on the \n\n\n\ncharcoal, thus providing more adsorption sites for the cations including K (Shi et al. 2017). \n\n\n\nThis explains why T6 resulted in the highest K adsorption at equilibrium in spite of the presence \n\n\n\nof the sago bark ash. \n\n\n\n \nTABLE 4 \n\n\n\nTreatments effects on the amounts of potassium adsorbed at equilibrium at different \n\n\n\nconcentrations of added potassium. \n\n\n\nTreatment \n\n\n\nPotassium adsorption at equilibrium, qe (mg g-1) \n\n\n\n100 200 300 400 \n\n\n\nAdded K (mg K L-1) \n\n\n\nT1 0.22b \u00b1 0.04 0.36d \u00b1 0.05 0.49c \u00b1 0.11 0.63c \u00b1 0.03 \n\n\n\nT2 0.40ab \u00b1 0.05 0.62abc \u00b1 0.04 0.85ab \u00b1 0.04 1.05b \u00b1 0.04 \n\n\n\nT3 0.40ab \u00b1 0.05 0.56bcd \u00b1 0.06 0.64bc \u00b1 0.08 0.75c \u00b1 0.06 \n\n\n\nT4 0.49a \u00b1 0.03 0.78ab \u00b1 0.03 0.86ab \u00b1 0.07 1.35a \u00b1 0.06 \n\n\n\nT5 0.33ab \u00b1 0.04 0.44cd \u00b1 0.05 0.62bc \u00b1 0.01 0.68c \u00b1 0.03 \n\n\n\nT6 0.50a \u00b1 0.04 0.82a \u00b1 0.08 1.15a \u00b1 0.05 1.37a \u00b1 0.06 \n\n\n\nNote: Different letters within a column indicate significant difference of means \u00b1 standard error \n\n\n\nusing Tukey\u2019s test at p \u2264 0.05 \n\n\n\n \nLangmuir Adsorption Isotherm \n\n\n\nThe assumptions of Langmuir adsorption model are the occurrence of monolayer adsorption \n\n\n\nand the adsorption sites on the surface have the same force on the adsorbate (Srividya and \n\n\n\nMohanty, 2009; Abdelnaeim et al., 2016). Therefore, once an adsorption site is occupied by \n\n\n\nthe adsorbate, it can no longer adsorb other adsorbates. Based on the significant regression \n\n\n\ncoefficient (R2), the effects of soil alone (T1), charcoal alone (T2), sago bark ash alone (T3), \n\n\n\nsoil with sago bark ash (T5), and soil with charcoal and sago bark ash (T6) were best fitted to \n\n\n\nLangmuir adsorption model (Table 5). Apart from the soil with charcoal (T4), all the treatments \n\n\n\nresulted in strong and positive regression coefficients (R2) \u2265 0.90. Insignificant R2 of the \n\n\n\nLangmuir regression equations for T4 implies that the K adsorption data do not fit Langmuir \n\n\n\nadsorption model and the data could be fitted to other K adsorption models such as Freundlich \n\n\n\nand Temkin (Perumal et al. 2021).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n63 \n\n\n\n\n\n\n\nThe Langmuir bonding energy constant (KL) determines the affinity of adsorbent towards the \n\n\n\nadsorbate. High values of KL suggest strong binding of K (adsorbate) by the treatments \n\n\n\n(adsorbents). Soil alone (T1) resulted in the lowest KL because Al and Fe ions predominate at \n\n\n\nlow pH and displaces K from soil colloids (Gazey 2018). The charcoal and sago bark ash in \n\n\n\nthe treatments improved the KL. The affinity of these amendments for K was higher because \n\n\n\nof their high CEC and alkalinity (Table 1). \n\n\n\n\n\n\n\nThe maximum K buffering capacity (MBC) is the maximum amount of K (adsorbate) loadable \n\n\n\non an adsorbent. Higher MBC enables soils to hold more K in the exchange sites, thus \n\n\n\npreventing leaching in addition to enabling future redistribution. The maximum K buffering \n\n\n\ncapacity was highest in soil with charcoal and sago bark ash (T6). The deprotonation of \n\n\n\ncharcoal\u2019s functional groups by the carbonates and oxides of sago bark ash creates more \n\n\n\nadsorption sites for the K, thereby increasing the MBC of T6 compared to soil alone (T1). Soil \n\n\n\nalone (T1) resulted in the lowest MBC, and this is attributable to the low K sorption capacity \n\n\n\nof 1:1 lattice structure of the kaolinite clay mineral (Schneider et al. 2013). \n\n\n\n\n\n\n\nThe maximum adsorption capacity (qmax) determines the maximum amount of adsorbate per \n\n\n\nunit mass of adsorbent to form a complete monolayer on the surface of the adsorbent. The \n\n\n\nhigher qm of the soil with charcoal and sago bark ash (T6) in comparison with soil alone (T1) \n\n\n\nsuggests that the soil can hold more K on its exchange sites, thus preventing leaching. Because \n\n\n\nleaching depletes water-soluble K, it is essential to maintain the exchangeable K such that this \n\n\n\npool is activated to replenish the K in soil solution.  \n\n\n\n \nTABLE 5 \n\n\n\nTreatments effects on potassium sorption parameters of the isotherm described by Langmuir \n\n\n\nequation \n\n\n\nTreatment  \n\n\n\nEstimated by Langmuir equation \n\n\n\nRegression equation R2 \nqmax  \n\n\n\n(mg g-1) \n\n\n\nKL \n\n\n\n(L mg-1) \n\n\n\nMBC \n\n\n\n(L mg-1) \n\n\n\nT1 y = 0.71x + 311.80  0.93 * 1.41 2.28 \u00d7 10-3 3.21 \u00d7 10-3 \n\n\n\nT2 y = 0.54x + 130.69 0.95 * 1.85 4.13 \u00d7 10-3 7.64 \u00d7 10-3 \n\n\n\nT3 y = 1.08x + 94.29 0.98 * 0.93 1.15 \u00d7 10-2 1.07 \u00d7 10-2 \n\n\n\nT4 y = 0.52x + 93.13 0.69 ns 1.92 5.58 \u00d7 10-3 1.07 \u00d7 10-2 \n\n\n\nT5 y = 1.03x + 151.44 0.95 * 0.97 6.80 \u00d7 10-3 6.60 \u00d7 10-3 \n\n\n\nT6 y = 0.41x + 84.99 0.98 * 2.44 4.82 \u00d7 10-3 1.18 \u00d7 10-2 \n\n\n\nNote: R2: regression coefficient; qmax: maximum adsorption capacity; KL: Langmuir constant \n\n\n\nrelated to the binding energy; MBC: maximum K buffering capacity; ns: not significant at p \u2264 \n\n\n\n0.05; Asterisk (*): significant at p \u2264 0.05 \n\n\n\n \nPotassium Concentration at Desorption Equilibrium \n\n\n\n\n\n\n\nPotassium concentrations in the equilibrium solution increased with increasing amounts of K \n\n\n\nadded, regardless of treatments (Table 6). Among the treatments, the soil with charcoal and \n\n\n\nsago bark ash (T6) resulted in the highest K concentration at desorption equilibrium (Cde), \n\n\n\nregardless of the concentration of isonormal K solution used. The trend of K concentration at \n\n\n\ndesorption equilibrium was opposite to the trend of K concentration at adsorption equilibrium \n\n\n\nfor all the treatments (Table 3). The low K concentration at desorption equilibrium of soil alone \n\n\n\n(T1) indicates that it is prone to desorption of K. In summary, a decrease in K concentration at \n\n\n\ndesorption equilibrium reflects an increase in K desorption. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n64 \n\n\n\n\n\n\n\nTABLE 6 \n\n\n\nTreatments effects on potassium concentration at desorption equilibrium at different \n\n\n\nisonormal potassium solutions \n\n\n\nTreatment \n\n\n\nPotassium concentration at desorption equilibrium, Cde (mg L-1) \n\n\n\n0 100 200 300 400 \n\n\n\nAdded K (mg K L-1) \n\n\n\nT1 nd 14.68c \u00b1 2.43 26.73d \u00b1 4.31 38.71c \u00b1 9.14 52.19c \u00b1 5.16 \n\n\n\nT2 nd 34.88ab \u00b1 4.17 57.14bc \u00b1 3.38 77.15bc \u00b1 5.36 99.03b \u00b1 5.45 \n\n\n\nT3 nd 34.30ab \u00b1 4.28 48.03cd \u00b1 4.39 55.28bcd \u00b1 3.85 65.24c \u00b1 6.19 \n\n\n\nT4 nd 43.01ab \u00b1 3.26 73.36ab \u00b1 3.16 79.16b \u00b1 3.98 128.63a \u00b1 5.13 \n\n\n\nT5 nd 28.53bc \u00b1 2.82 37.20cd \u00b1 3.36 51.99cd \u00b1 1.89 58.38c \u00b1 2.95 \n\n\n\nT6 nd 47.10a \u00b1 4.31 78.75a \u00b1 7.18 108.97a \u00b1 5.58 131.56a \u00b1 7.04 \n\n\n\nNote: nd: not detected; Different letters within a column indicate significant difference of \n\n\n\nmeans \u00b1 standard error using Tukey\u2019s test at p \u2264 0.05 \n\n\n\n \nPotassium Desorption \n\n\n\nPotassium desorption at equilibrium (qde) for the soil with charcoal and sago bark ash (T6) was \n\n\n\nsignificantly lower than that of soil alone (T1) when 100 mg K L-1 was used as an isonormal \n\n\n\nsolution. However, when the K solution concentration was increased to 200\u2009mg\u2009K L-1 and \n\n\n\nbeyond, there were no significant differences in the amounts of K desorbed for all the \n\n\n\ntreatments. This is because desorption of K occurs at a rate that is slower than adsorption. \n\n\n\nHundal and Pasricha (1998) also reported that the equilibrium time for desorption was \n\n\n\napproximately three fold of the values for equilibrium time of adsorption. Such lower rates of \n\n\n\ndesorption than adsorption for K have also been reported by Sparks et al. (1980) for Paleudult \n\n\n\nfrom the Coastal Plain of Virginia. The insignificant effect of T6 on potassium desorption at \n\n\n\nequilibrium compared to T1 can be ascribed to an absence of plants which does not elucidate \n\n\n\nthe effects of plant-soil interaction response. \n\n\n\n \nTABLE 7 \n\n\n\nTreatments effects on the amounts of potassium desorbed at equilibrium at different \n\n\n\nconcentrations of added potassium. \n\n\n\nTreatment \n\n\n\nPotassium desorption at equilibrium, qde (mg g-1) \n\n\n\n100 200 300 400 \n\n\n\nAdded K (mg K L-1) \n\n\n\nT1 0.077a \u00b1 0.015 0.096a \u00b1 0.016 0.107a \u00b1 0.022 0.110a \u00b1 0.003 \n\n\n\nT2 0.050ab \u00b1 0.006 0.053a \u00b1 0.021 0.083a \u00b1 0.012 0.059a \u00b1 0.017 \n\n\n\nT3 0.056ab \u00b1 0.006 0.083a \u00b1 0.020 0.092a \u00b1 0.045 0.096a \u00b1 0.023 \n\n\n\nT4 0.048ab \u00b1 0.004 0.047a \u00b1 0.006 0.073a \u00b1 0.030 0.064a \u00b1 0.013 \n\n\n\nT5 0.049ab \u00b1 0.013 0.072a \u00b1 0.026 0.099a \u00b1 0.014 0.095a \u00b1 0.003 \n\n\n\nT6 0.027b \u00b1 0.002 0.045a \u00b1 0.009 0.065a \u00b1 0.008 0.058a \u00b1 0.008 \n\n\n\nNote: Different letters within a column indicate significant difference of means \u00b1 standard error \n\n\n\nusing Tukey\u2019s test at p \u2264 0.05 \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n65 \n\n\n\n\n\n\n\npH Buffering Capacity of Soil, Charcoal, and Sago Bark Ash \n\n\n\nTable 8 shows the results of the effects of soil alone (T1), charcoal alone (T2), sago bark ash \n\n\n\nalone (T3), soil with charcoal (T4), soil with sago bark ash (T5), and soil with charcoal and \n\n\n\nsago bark ash (T6) on pH buffering capacity. These treatments resulted in negative linear \n\n\n\nrelationships between their pH and the amount of acid added with all the regression coefficients \n\n\n\n(R2) \u2265 0.90 (Figure 1). The fact that pH of the treatments decreased linearly with added mmol \n\n\n\nH+ suggests an occurrence of soil acidification. Treatment 1 resulted in the lowest pH buffering \n\n\n\ncapacity compared with other treatments and this explains why T1 showed the highest decrease \n\n\n\nin pH with addition of mmol H+. This is due to the inherent properties of Bekenu series which \n\n\n\nhas low CEC, BSP, and carbon content but high Al and Fe ions (Table 1). In acid soils, pH is \n\n\n\nbuffered by Al and Fe ions. The addition of H+ into soil solution causes Al and Fe hydroxides \n\n\n\nto solubilize so as to neutralize the change in pH. However, this reaction is reversible because \n\n\n\nAl and Fe ions are unstable and are adsorbed on exchange complexes via hydrolyzation, \n\n\n\ngenerating hydrogen ions as products. For each 1 mole of Al3+ that undergoes complete \n\n\n\nhydrolysis, three H+ are released (Goulding 2016).  \n\n\n\n\n\n\n\nAmong the treatments, sago bark ash alone (T3) resulted in the highest pH buffering capacity \n\n\n\nbecause the ash contains substantial amounts of CaCO3, CaO, and MgO and they serve as pH-\n\n\n\nneutralizing compounds (Saarsalmi et al. 2004). Alternatively, the soil with sago bark ash (T5) \n\n\n\nshowed lower buffering capacity compared with T3 because for T3, the source of acidification \n\n\n\ncomes from H2SO4 alone, whereas for T5, the soil also contributes to acidification. \n\n\n\nNevertheless, incorporation of sago bark ash (T5) improved pH buffering capacity of the soil \n\n\n\nin comparison with T1. The dissolution of pH-neutralizing compounds in the ash releases \n\n\n\norganic anions which consume H+ added into soil solution and slows acidification. \n\n\n\n\n\n\n\nCharcoal alone (T2) and the soil with charcoal (T4) resulted in better pH buffering capacity \n\n\n\ncompared with T1, but lower than those of T3 and T5. The incorporation of charcoal adds to \n\n\n\nthe CEC and organic matter content of the treatments. The surface oxidation of oxygen-\n\n\n\ncontaining functional groups on the charcoal creates negative-charged sites to increase CEC \n\n\n\nwhich could consume the added H+ and retard acidification (Yuan and Xu 2011). The lower \n\n\n\npH buffering capacity of the charcoal in comparison with sago bark ash relates to the charcoal\u2019s \n\n\n\nresistance to decomposition (Paustian et al. 2016) and smaller particle size of the ash which \n\n\n\nfacilitates a rapid reaction. \n\n\n\n\n\n\n\nThe co-application of charcoal and sago bark ash (T6) improved the soil pH buffering capacity \n\n\n\ncompared with the soil with single amendment application (T4 and T5). This is related to the \n\n\n\ninherently high CEC and alkalinity of the amendments (Table 1). The dissolution of carbonates \n\n\n\nand oxides from the ash neutralized the active acidity in the soil in addition to dissociating H+ \n\n\n\nfrom the functional groups of the charcoal. This in turn enables chelation of exchangeable \n\n\n\nacidity pool in the soil by the charcoal. Subsequently, base cations such as K+, Ca2+, Mg2+, and \n\n\n\nNa+ from the amendments could immobilize H+ released from the functional groups and buffer \n\n\n\nacidification. This mechanism entirely relies on pH of the soil wherein functional groups \n\n\n\nadsorb H+ with decreasing pH and dissociate them with increasing pH (Xu et al.2012). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n66 \n\n\n\n\n\n\n\nTABLE 8 \n\n\n\nSummary of pH buffering capacity as affected by soil alone, amendments alone, and soil with \n\n\n\nthe amendments \n\n\n\nTreatment Code Initial pH \npH Buffering Capacity \n\n\n\n(mol H+ kg\u22121 sample) \nRegression Coefficient, R2 \n\n\n\nT1 5.31 \u00b1 0.05 0.25 0.92 * \n\n\n\nT2 7.76 \u00b1 0.06 0.29 0.90 * \n\n\n\nT3 9.78 \u00b1 0.00 0.34 0.92 * \n\n\n\nT4 6.51 \u00b1 0.03 0.26 0.97 * \n\n\n\nT5 6.41 \u00b1 0.02 0.28 0.93 * \n\n\n\nT6 6.65 \u00b1 0.03 0.29 0.92 * \n\n\n\nNote: Asterisk (*) represent significant difference at p \u2264 0.05; the values given are mean \u00b1 \n\n\n\nstandard error. \n\n\n\n \nFigure 1. Linear regression between dilute sulphuric acid added (mol H+ kg\u22121 sample) and pH \n\n\n\nof suspension for the various treatments. Asterisk (*) indicates significant difference at p \u2264 \n\n\n\n0.05. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 56-69 \n\n\n\n\n\n\n\n67 \n\n\n\n\n\n\n\nCONCLUSION \n\n\n\n\n\n\n\nThe co-application of charcoal and sago bark ash to acid soils improves K adsorption. This \n\n\n\nhappens because the deprotonation of the functional groups of charcoal, which is facilitated by \n\n\n\nthe dissolution of carbonates and oxides of sago bark ash, creates more adsorption sites for the \n\n\n\nsoil to hold K. This explains why Langmuir bonding energy constant (KL), Maximum K \n\n\n\nbuffering capacity (MBC), and maximum adsorption capacity (qmax) of T6 are higher than that \n\n\n\nof soil alone (T1). However, desorption of K is not significantly affected by the application of \n\n\n\nthe amendments. On the other hand, co-application of charcoal and sago bark ash to acid soil \n\n\n\nimproves pH buffering capacity because of the inherently high CEC and alkalinity of the \n\n\n\namendments. Knowledge about the ability of the amendments to adsorbed K and buffer \n\n\n\nacidification is essential to provide a basis for mitigating loss of K in tropical acid soils. \n\n\n\nNonetheless, the effect of co-applying charcoal and sago bark ash at different rates on the \n\n\n\nphysicochemical properties of soils needs to be determined to avoid setbacks. Thereafter, a \n\n\n\nfurther study could be embarked on. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nThe authors would like to acknowledge Universiti Putra Malaysia for financial assistance and \n\n\n\nproviding research facilities. Our appreciation is also extended to colleagues and staff of \n\n\n\nUniversiti Putra Malaysia (Malaysia), Universiti Malaysia Kelantan (Malaysia), Management \n\n\n\n& Science University (Malaysia), and Universiti Islam Sultan Sharif Ali (Brunei Darussalam) \n\n\n\nfor their technical support and collaboration. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\n \nAbdelnaeim, M.Y., I.Y. El Sherif, A.A. Attia, N.A. Fathy, M.F. El-Shahat. 2016. Impact of chemical \n\n\n\nactivation on the adsorption performance of common reed towards Cu (II) and Cd \n\n\n\n(II). International Journal of Mineral Processing 157: 80-88. \nBernas, B. 1968. New method for decomposition and comprehensive analysis of silicates by atomic \n\n\n\nabsorption spectrometry. Anal. Chem. 40: 1682\u20131686. \n\n\n\nBiederman, L.A. and W.S. Harpole. 2013.  Biochar and its effects on plant productivity and nutrient \n\n\n\ncycling: A meta-analysis. GCB Bioenergy 5: 202\u2013214. \nBouyoucos, G.J.  1962. 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"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 20: 163-175 (2016)                         Malaysian Society of Soil Science\n\n\n\nAbility of Potassium-Solubilissing Microbes to Solubilise \nFeldspar and Their Effects on Sorghum Growth\n\n\n\nPratama, D.,  I. Anas* , Suwarno\n\n\n\nDepartment of Soil Science and Land Resource, Faculty of Agriculture, \nBogor Agricultural University (IPB), 16680 Bogor, Indonesia\n\n\n\nABSTRACT\nPotassium-solubilising microbes (KSM) are microbes that are able to solubilise \ndifferent kinds of potassium compounds and make their potassium ions available \nto plant growth. This study undertook to: (1) isolate KSM from soil samples, \n(2) evaluate their ability to solubilise not-easily soluble potassium sources, and \n(3) evaluate the effect of selected KSM on the growth of sorghum. KSM were \nisolated from soil samples taken from agricultural land, ex-tin mining land, \nand ex-gold mining land. KSM isolates were further selected according to (1) \npathogenic characteristics, (2) a potassium solubility index on solid medium, \n(3) ability of KSM to solubilise not-easily soluble potassium sources in liquid \nmedium, and (4) ability of KSM to stimulate sorghum growth. The best selected \nKSM were characterised by using molecular analysis. Results showed that KSM \nisolated from ex-mining land have better ability to solubilise feldspar than KSM \nfrom agricultural land. KSB2 and KSB6 isolates have the best ability to stimulate \nsorghum growth. KSB2 has 97.8% similarity with Achromobacter xylosoxidans, \nand KSB6 has 99% similarity with Burkholderia cepacia.\n\n\n\nKeywords: Achromobacter xylosoxidans, Burkholderia cepacia, feldspar, \npotassium-solubilizing microbes, sorghum\n\n\n\n___________________\n*Corresponding author : E-mail: iswandi742@yahoo.com\n\n\n\nINTRODUCTION\nTotal potassium content in soil commonly ranges between 0.5 to 2.5%, depending \non soil type and climatic conditions, but usually 90 to 98% of that total potassium is \nnot in available form (Havlin et al., 2005). In many locations, inorganic potassium \nfertiliser such as KCl is needed to provide enough potassium for plant nutrition. In \nIndonesia, this fertiliser is expensive since it has to be imported, and often what is \navailable in the market is of low quality. Fortuitously, Indonesia has a number of \npotassium sources of low solubility such as feldspar and mica.\n\n\n\nAccording to a report from the Ministry of Energy and Mineral Resources \n(2015), potassium rock potential in Indonesia is around 455 million tons. However, \nsuch a source has low potassium content and is not very soluble because of a slow \nweathering process. Roger et al., (1998) notes that a slow weathering processes \nof potassium rock can be accelerated by using potassium-solubilising microbes \n(KSM). KSM will accelerate the weathering process of minerals containing \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016164\n\n\n\nPratama et al.\n\n\n\npotassium, thereby making potassium more readily available to plants. KSM \ninclude potassium-solubilising bacteria (KSB) such as Bacillus mucilaginosus \n(Han and Lee, 2005), Microbacterium hominis, Flectobacillus sp., Agrobacterium \ntumefasciens, Bacillus cereus, Bacillus coagulans, Bacillus subtilis and Bacillus \nmegaterium (Diep and Hieu, 2013), and also potassium-solubilising fungi (KSF) \nsuch as Aspergillus terreus (Prajapati et al., 2013).\n\n\n\nUsable KSM must be non-pathogenic to plants, animals, and humans and \nalso have a strong ability to solubilise not-easily soluble potassium sources. This \nstudy was done to (i) isolate KSM from soil samples, (ii) evaluate their ability \nto solubilise not-easily soluble potassium sources, and (iii) evaluate the effect of \nselected KSM on the growth of sorghum.\n\n\n\nMATERIALS AND METHODS\nPotassium-solubilising microbes were isolated from soil samples from three \nlocations and from three different vegetations at each location (Table 1). The \nmedium used for isolation was modified Alexandrov medium (Prajapati and Modi, \n2012). Composition of this medium was 0.5% glucose, 0.05% MgSO4.7H2O, \n0.0005% FeCl3, 0.01% CaCO3, 0.2% Ca3PO4, and 0.3% not-easily soluble \npotassium source. Two kinds of potassium feldspar, representing not-easily \nsoluble potassium sources, were used in this study: feldspar from Cirebon, West \nJava with a total K2O content of 1.93% and feldspar from Malang, East Java with \na  total K2O content of 1.74%. Both feldspars were crushed and sieved with a 270 \nmesh sieve before use.\n\n\n\nIsolated KSM were further selected by using (i) test for pathogenicity, (ii) a \npotassium-solubilising index test, (iii) ability to solubilise feldspar, and (iv) ability \nto stimulate growth of sorghum. Beside potassium-solubilising ability, KSM \nisolates were tested for their ability to solubilise not-easily soluble phosphate \nsources. The ability to solubilise phosphate sources was tested by measuring the \nphosphate-solubilising index and soluble phosphate content in liquid medium. \nNot-easily souluble phosphate sources used in this experiment were Ca3(PO4)2 \nwith total P2O5 content of 45.76 % and phosphate rock from Blitar, East Java with \na total P2O5 content of 26.61% total.\n\n\n\nTABLE 1\nKSM isolates sources\n\n\n\n1 \n\n\n\n\n\n\n\nAbility of Potassium-Solubilizing Microbes to Solubilize Feldspar \nand Their Effects on Sorghum Growth \n\n\n\n \nDeni Pratama, Iswandi Anas*, Suwarno \n\n\n\n \nDepartment of Soil Science and Land Resource, Faculty of Agriculture,  \n\n\n\nBogor Agricultural University (IPB), 16680 Bogor, Indonesia. \n \n\n\n\nTABLE 1 \n\n\n\nKSM isolates sources \nType of land Type of vegetation Coordinate \n\n\n\nAgricultural land, \nCikabayan, Indonesia \n\n\n\nTheobroma cacao L. S 06033.144\u2019 ; E 106043.069\u2019 \nElaeis guineensis Jacq. S 06033.106\u2019 ; E 106043.008\u2019 \nCoffea arabica L. S 06033.105\u2019 ; E 106042.977\u2019 \n\n\n\nEx-tin mining land, \nBangka, Indonesia \n\n\n\nMelastoma malabathricum L. S 01059.376' ; E 106008.579\u2019 \nAcacia mangium Willd. S 01059.401\u2019 ; E 106008.589\u2019 \nDillenia suffruticosa (Griff) Martelli S 01059.393\u2019 ; E 106008.389\u2019 \n\n\n\nEx-gold mining, \nPongkor, Indonesia \n\n\n\nDalbergia latifolia Roxb. S 06038.692\u2019 ; E 106034.233\u2019 \nElaeocarpus serratus L. S 06038.627\u2019 ; E 106034.219\u2019 \nSchima wallichii (DC.) Korth. S 06038.745\u2019 ; E 106034.256\u2019 \n\n\n\n\n\n\n\nP/K-solubilizing index = Total diameters (colony + halo zone) (mm) \nColony diameter (mm) \n\n\n\n\n\n\n\nSolubility percentage (%) = Soluble level of K/P (mg/L) x 100 % Total level of K/P in sources (mg/L) \n \n\n\n\n\n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\nTABLE 2 \n\n\n\nTotal colony of KSM that isolated from soil samples \n\n\n\nType of land Type of vegetation \nFeldspar \n\n\n\nfrom Cirebon \n Feldspar \n\n\n\nfrom Malang \nB F  B F \n\n\n\n  ------------- Total colony ------------ \nAgricultural land, \nCikabayan, \nIndonesia \n\n\n\nTheobroma cacao L. 16 0  12 0 \nElaeis guineensis Jacq. 21 4  14 2 \nCoffea arabica L. 13 0  10 3 \n\n\n\nEx-tin mining \nland, Bangka, \nIndonesia \n\n\n\nMelastoma malabathricum L. 8 3  5 2 \nAcacia mangium Willd. 17 2  7 1 \nDillenia suffruticosa (Griff) Martelli 7 0  4 0 \n\n\n\n                                                           \n*Corresponding author : E-mail: iswandi742@yahoo.com \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 165\n\n\n\nPotassium-Solubilizing Microbes on Sorghum\n\n\n\nIsolation of KSM isolates was done by using a modified Alexandrov medium \nas used by Prajapati and Modi (2012). Ten grams of soil sample were put  into 100 \nml Alexandrov broth medium for four days for bacteria and  seven days for fungi. \nTen-fold dilution series of these cultures were prepared by using 0.85% NaCl \nsolution and plated on Alexandrov agar medium by using the spread plate method. \nIsolated colonies with hollow zone on Alexandrov agar medium were classified as \npotassium-solubilising microbes.\n\n\n\nPathogenic selections involved hypersensitivity testing and hemolysis testing. \nThe purpose of hypersensitivity testing is to identify isolates that have pathogenic \ncharacteristics for plants. A modified method of Schaad et al., (2001) was used \nto evaluate the pathogenic characteristics of bacteria isolates. These isolates were \ngrown in nutrient broth (NB) medium for 12 h  and 0.1 ml of isolated bacteria \n(109 CFU) was injected into tobacco leaf (Havana cultivar). Symptoms of necrosis \nwere observed 72 h after injection of the bacteria suspension. A modified method \nof Mahmoud et al., (2013) was used to evaluate the pathogenic characteristics of \nfungus isolates. These isolates were cultured in potato dextrose broth (PDB, 20% \npotato and 1% glucose) for 48 h.  Rice seeds (Ciherang cultivar) were soaked  in a \nfungus isolate culture and subsequently the seeds were placed on sterile wet cotton \nin a petri dish for seven days. As a control treatment, rice seeds were soaked in \nsterile water before placement in a petri dish. The number of  germinating seeds \nwas used as an indicator for the hypersensitivity test. When the percentage of \ngerminating seeds was lower than the control treatment, it means that the fungi \nisolate is testing positive for hypersensitivity.\n\n\n\nHemolysis test was done to identify the pathogenic characteristics of isolates \nto humans and animals.  Isolates were grown on a blood agar medium consisting \nof 1.4% casein enzymic hydrolysate, 0.45% peptic digest of animal tissue, 0.45% \nyeast extract, 0.5% sodium chloride, and 2% agar mixed with 5% sheep blood \nand then incubated for 24 h (Difco, 2009). Formation of a halo zone around the \nmicrobial colony shows that the isolate has pathogen potential for humans and \nanimals.\n\n\n\nThe potassium-solubilising index measurement was done by growing KSM \nthat had passed through a pathogenicity test on Alexandrov agar medium. KSM \nisolates on Alexandrov agar medium were then incubated, three days for bacteria \nand five days for fungi. The best 10 isolates with the highest potassium-solubilising \nindex were taken for phosphate-solubilising index measurement by growing these \nisolates on Pikovskaya agar medium (0.05% yeast extract, 1% dextrose, 0.5% \nphosphate source, 0.05% ammonium sulfate, 0.02%, potassium chloride, 0.01% \nmagnesium sulfate, 0.00001% manganese sulfate, 0.00001% ferrous sulfate, and \n\n\n\n1 \n\n\n\n\n\n\n\nAbility of Potassium-Solubilizing Microbes to Solubilize Feldspar \nand Their Effects on Sorghum Growth \n\n\n\n \nDeni Pratama, Iswandi Anas*, Suwarno \n\n\n\n \nDepartment of Soil Science and Land Resource, Faculty of Agriculture,  \n\n\n\nBogor Agricultural University (IPB), 16680 Bogor, Indonesia. \n \n\n\n\nTABLE 1 \n\n\n\nKSM isolates sources \nType of land Type of vegetation Coordinate \n\n\n\nAgricultural land, \nCikabayan, Indonesia \n\n\n\nTheobroma cacao L. S 06033.144\u2019 ; E 106043.069\u2019 \nElaeis guineensis Jacq. S 06033.106\u2019 ; E 106043.008\u2019 \nCoffea arabica L. S 06033.105\u2019 ; E 106042.977\u2019 \n\n\n\nEx-tin mining land, \nBangka, Indonesia \n\n\n\nMelastoma malabathricum L. S 01059.376' ; E 106008.579\u2019 \nAcacia mangium Willd. S 01059.401\u2019 ; E 106008.589\u2019 \nDillenia suffruticosa (Griff) Martelli S 01059.393\u2019 ; E 106008.389\u2019 \n\n\n\nEx-gold mining, \nPongkor, Indonesia \n\n\n\nDalbergia latifolia Roxb. S 06038.692\u2019 ; E 106034.233\u2019 \nElaeocarpus serratus L. S 06038.627\u2019 ; E 106034.219\u2019 \nSchima wallichii (DC.) Korth. S 06038.745\u2019 ; E 106034.256\u2019 \n\n\n\n\n\n\n\nP/K-solubilizing index = Total diameters (colony + halo zone) (mm) \nColony diameter (mm) \n\n\n\n\n\n\n\nSolubility percentage (%) = Soluble level of K/P (mg/L) x 100 % Total level of K/P in sources (mg/L) \n \n\n\n\n\n\n\n\n \nRESULTS AND DISCUSSION \n\n\n\nTABLE 2 \n\n\n\nTotal colony of KSM that isolated from soil samples \n\n\n\nType of land Type of vegetation \nFeldspar \n\n\n\nfrom Cirebon \n Feldspar \n\n\n\nfrom Malang \nB F  B F \n\n\n\n  ------------- Total colony ------------ \nAgricultural land, \nCikabayan, \nIndonesia \n\n\n\nTheobroma cacao L. 16 0  12 0 \nElaeis guineensis Jacq. 21 4  14 2 \nCoffea arabica L. 13 0  10 3 \n\n\n\nEx-tin mining \nland, Bangka, \nIndonesia \n\n\n\nMelastoma malabathricum L. 8 3  5 2 \nAcacia mangium Willd. 17 2  7 1 \nDillenia suffruticosa (Griff) Martelli 7 0  4 0 \n\n\n\n                                                           \n*Corresponding author : E-mail: iswandi742@yahoo.com \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016166\n\n\n\n2% agar) and then incubated for three days for bacteria and five days for fungi \n(Edi et al., 1996). The potassium and phosphate-solubilising index formula is as \nfollows:\n\n\n\nP/K-solubilising index = Total diameters (colony + halo zone) (mm)\n    Colony diameter (mm)\n\n\n\nThe best 10 isolates from solubilising index measurements were tested for \ntheir ability to solubilise not-easily soluble potassium and phosphate sources in a \nliquid medium. This test was a single factor experiment with 11 treatments and 3 \nreplications. The tests were placed in completely randomised design. Treatments \nthat were used were KSM application consisting of control (without isolates) and \n10 best KSM isolates according to the potassium-solubilising index test. \n\n\n\nKSM ability was tested by growing 10 KSM isolates in 25 ml growth \nmedium (1% glucose, 0.05% yeast extract, and 0.5% not-easily soluble potassium \nor phosphate sources) for 7 days. The suspension  of the isolates was centrifuged \nfor 25 min (4000 rpm) to separate supernatant from residue. The supernatant was \ntaken to measure the soluble potassium or phosphate content.\n\n\n\nSoluble potassium measurement was done by using the modified method \nof Parmar and Sindhu (2013). The supernatant was measured with a flame \nphotometer using K-titrisol as standard solution. A modified method of Susilowati \nand Syekhfani (2014) was used to measure the content of soluble phosphate. \nThe supernatant (1 ml) was mixed with reagent solution (0.5% H3BO3, 0.38% \nammonium molibdate, and 7.5% HCl), to which was added 5 drops of reduction \nsolution. The mixed solution was measured by using a spectrophotometer UV-\nvisible with 660 nm wave length and using KH2PO4 as a standard solution. Based \non potassium and phosphate measurements in liquid medium, potassium and \nphosphate solubility percentage was calculated. The formula for potassium and \nphosphate solubility is\n\n\n\nSolubility percentage (%)  =   Soluble level of K/P (mg/L)            x 100 %\n         Total level of K/P in sources (mg/L)  \n\n\n\nObserved parameters were soluble potassium and phosphate content (mg/L) \nand also percentage of potassium and phosphate solubility (%) in liquid medium. \nThe best five isolates were then taken for the next test, KSM ability to stimulate \nsorghum growth.\n\n\n\nThe five best isolates from the liquid medium test were tested for their ability \nto stimulate sorghum growth. The test was a single factor experiment with six \ntreatments and three replications. Tests were conducted in a completely randomised \ndesign. Treatments were KSM application to growing media consisting of P0 \n(without isolates) and 5 best KSM isolates according to the liquid medium tests \n(P1, P2, P3, P4, and P5).\n\n\n\nPratama et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 167\n\n\n\nA modified method of Basak and Biswas (2010) was used to test the ability \nof KSM to stimulate sorghum growth. Five kg of oven dried soil was taken from \nan agricultural land in Cikabayan, West Java, Indonesia, and  placed into plastic \npolybags with water content of soil being kept at field capacity. Inorganic fertiliser \nconsisting of nitrogen fertiliser (100 mg/L N) and phosphate fertiliser  at a rate \nof 50 mg/L P2O5 was applied into soil medium (1/3 dosage at planting, and 2/3 \ndosage at 3 weeks after planting), then the suspension of the isolates (20 ml KSM \nisolate [109 CFU] + 250 ml aquadest) was applied into the soil medium. Sorghum \nseeds (Numbu cultivar) were planted into the soil medium. To maintain field \ncapacity and keep sufficient water content in soil, watering was done. Parameters \nsuch as plant height (cm), number of leaves, root dry weight (g), and shoot dry \nweight (g), and total potassium in plant (%) were observed for 6 weeks after \nplanting. The best two isolates were taken for characterisation of isolates.\n\n\n\nKSM characterisation consisted of colony morphology (colony form, colour, \nelevation, and ledge form), cell shape, gram test, biochemical test (motility, \ncatalase, indole, urease, glucose fermentation, starch test, and citrate testing) \n(Faddin, 1979) and identity analysis based upon 16s rRNA gene sequences \n(Santosa, 2001). Sequencing results were compared to sequences from the \nEuropean Molecular Biology Laboratory (EMBL) using FASTA in the website \nwww.ebi.ac.uk.   \n\n\n\nObserved data in the liquid medium test and sorghum growth test were \nanalysed using analysis of variance with \u03b1 = 0.05 and continued with Duncan\u2019s \nMultiple Range Test (DMRT), if there were significant responses in the observed \ntreatments.\n\n\n\nRESULTS AND DISCUSSION\nBased on isolation results, 185 isolates consisting of 162 bacteria isolates and \n23 fungi isolates were found from the soil samples (Table 2). Hypersensitivity \ntest results showed that 64 bacteria and 15 fungi isolates did not have pathogenic \npotential to plants. Hemolysis test results showed 36 isolates from the first-round \n64 bacteria isolates and 12 isolates from the initial 15 fungi isolates  did not \nexhibit pathogenic potential to humans and animals. The total number of isolates \nthat did not have pathogenic characteristics for plants, humans and animals were \n48 isolates, i.e., 36 bacterial isolates and 12 fungal isolates.\n\n\n\nPotassium-Solubilizing Microbes on Sorghum\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016168\n\n\n\nTABLE 2\nTotal colony of KSM isolated from soil samples\n\n\n\n Figure 1: KSM isolates on Alexandrov agar medium, a) potassium-solubilising \nbacteria and b) potassium-solubilising fungi\n\n\n\nBased on the potassium-solubilising index, the best 10 isolates with the highest \nscores were selected from 48 isolates that had passed through the pathogenicity \nselection (Table 3). From these KSM isolates, some were found to show ability \nto solubilise not-easily soluble phosphate sources. Among these 10 isolates, 9 \nisolates were also able to solubilise Ca3(PO4)2 and 5 isolates could solubilise \nphosphate from the phosphate rock from Blitar, East Java (Table 4).\n\n\n\n1 \n\n\n\n\n\n\n\nAbility of Potassium-Solubilizing Microbes to Solubilize Feldspar \nand Their Effects on Sorghum Growth \n\n\n\n \nDeni Pratama, Iswandi Anas*, Suwarno \n\n\n\n \nDepartment of Soil Science and Land Resource, Faculty of Agriculture,  \n\n\n\nBogor Agricultural University (IPB), 16680 Bogor, Indonesia. \n \n\n\n\nTABLE 1 \n\n\n\nKSM isolates sources \nType of land Type of vegetation Coordinate \n\n\n\nAgricultural land, \nCikabayan, Indonesia \n\n\n\nTheobroma cacao L. S 06033.144\u2019 ; E 106043.069\u2019 \nElaeis guineensis Jacq. S 06033.106\u2019 ; E 106043.008\u2019 \nCoffea arabica L. S 06033.105\u2019 ; E 106042.977\u2019 \n\n\n\nEx-tin mining land, \nBangka, Indonesia \n\n\n\nMelastoma malabathricum L. S 01059.376' ; E 106008.579\u2019 \nAcacia mangium Willd. S 01059.401\u2019 ; E 106008.589\u2019 \nDillenia suffruticosa (Griff) Martelli S 01059.393\u2019 ; E 106008.389\u2019 \n\n\n\nEx-gold mining, \nPongkor, Indonesia \n\n\n\nDalbergia latifolia Roxb. S 06038.692\u2019 ; E 106034.233\u2019 \nElaeocarpus serratus L. S 06038.627\u2019 ; E 106034.219\u2019 \nSchima wallichii (DC.) Korth. S 06038.745\u2019 ; E 106034.256\u2019 \n\n\n\n\n\n\n\n  Total level of K/P in sources (mg/L)    \nRESULTS AND DISCUSSION \n\n\n\nTABLE 2 \n\n\n\nTotal colony of KSM that isolated from soil samples \n\n\n\nType of land Type of vegetation \nFeldspar \n\n\n\nfrom Cirebon \n Feldspar \n\n\n\nfrom Malang \nB F  B F \n\n\n\n  ------------- Total colony ------------ \nAgricultural land, \nCikabayan, \nIndonesia \n\n\n\nTheobroma cacao L. 16 0  12 0 \nElaeis guineensis Jacq. 21 4  14 2 \nCoffea arabica L. 13 0  10 3 \n\n\n\nEx-tin mining \nland, Bangka, \nIndonesia \n\n\n\nMelastoma malabathricum L. 8 3  5 2 \nAcacia mangium Willd. 17 2  7 1 \nDillenia suffruticosa (Griff) Martelli 7 0  4 0 \n\n\n\nEx-gold mining, \nPongkor, \nIndonesia \n\n\n\nDalbergia latifolia Roxb. 1 4  3 2 \nElaeocarpus serratus L. 5 0  2 0 \nSchima wallichii (DC.) Korth. 9 0  8 0 \n\n\n\nTotal Unit 97 13  65 10 \nNotes: B (Bacteria), F (Fungi) \n\n\n\n\n\n\n\n                                                           \n*Corresponding author : E-mail: iswandi742@yahoo.com \n\n\n\n2 \n\n\n\n\n\n\n\n \n(a)                                      (b) \n\n\n\n   \nFigure 1: KSM isolates on Alexandrov agar medium, a) potassium-solubilizing \n\n\n\nbacteria and b) potassium-solubilizing fungi \n \n\n\n\n \nTABLE 3 \n\n\n\nIsolate code, type of isolate, land and vegetation from 10 best KSM isolate based \non potassium-solubilizing index \n\n\n\n \nIsolate code Type of isolate Type of land Type of vegetation \nKSB1 Bacteria Agricultural land Theobroma cacao L. \nKSB2 Bacteria Ex-gold mining land Dalbergia latifolia Roxb. \nKSB3 Bacteria Agricultural land Elaeis guineensis Jacq. \nKSB4 Bacteria Ex-tin mining land Acacia mangium Willd. \nKSB5 Bacteria Ex-gold mining land Schima wallichii (DC.) Korth. \nKSB6 Bacteria Ex-tin mining land Dillenia suffruticosa (Griff) Martelli \nKSF1 Fungi Agricultural land Elaeis guineensis Jacq. \nKSF2 Fungi Agricultural land Coffea arabica L. \nKSF3 Fungi Ex-tin mining land Acacia mangium Willd. \nKSF4 Fungi Ex-tin mining land Melastoma malabathricum L. \n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nPotassium and phosphate-solubilizing index on growth medium with application \nof the best 10 KSM isolates \n\n\n\n \nIsolate \ncode \n\n\n\nFeldspar  \nfrom Cirebon \n\n\n\nFeldspar  \nfrom Malang \n\n\n\n Ca3(PO4)2 \nPhosphate rock  \n\n\n\nfrom Blitar \n ----- Potassium-solubilizing index ------  ---- Phosphate-solubilizing index ---- \n\n\n\nKSB1 6.60 4.00  1.94 - \nKSB2 6.77 4.73  1.91 - \nKSB3 4.29 4.00  1.50 - \nKSB4 2.59 5.91  3.70 2.32 \nKSB5 2.05 5.46  1.94 1.05 \nKSB6 3.86 3.88  3.56 2.04 \nKSF1 2.23 1.77  1.16 1.67 \n\n\n\nPratama et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 169\n\n\n\nTABLE 3\nIsolate code, type of isolate, land and vegetation from 10 best KSM isolates based on \n\n\n\npotassium-solubilising index\n\n\n\nTen KSM isolates that were tested in liquid medium showed that isolate KSB6 had \nthe best ability to solubilise not-easily soluble potassium and phosphate sources. \nIsolate KSB6 showed ability to solubilise potassium sources from feldspar from \nCirebon and from Malang with soluble potassium content of 248.10 mg/L and \n273.21 mg/L respectively, and with a solubility percentage of 1.29 % and 1.57 \n%. Isolate KSB6 also had the best ability to solubilise phosphate sources from \nCa3(PO4)2 and phosphate rock from Blitar with soluble phosphate content of 893.8 \nmg/L and 172 mg/L, corresponding to a solubility percentage of 1.95 % and 0.07 \n% (Table 5).\n\n\n\nTABLE 4\nPotassium and phosphate-solubilising index on growth medium with application of the \n\n\n\nbest 10 KSM isolates\n\n\n\n2 \n\n\n\n\n\n\n\n \n(a)                                      (b) \n\n\n\n   \nFigure 1: KSM isolates on Alexandrov agar medium, a) potassium-solubilizing \n\n\n\nbacteria and b) potassium-solubilizing fungi \n \n\n\n\n \nTABLE 3 \n\n\n\nIsolate code, type of isolate, land and vegetation from 10 best KSM isolate based \non potassium-solubilizing index \n\n\n\n \nIsolate code Type of isolate Type of land Type of vegetation \nKSB1 Bacteria Agricultural land Theobroma cacao L. \nKSB2 Bacteria Ex-gold mining land Dalbergia latifolia Roxb. \nKSB3 Bacteria Agricultural land Elaeis guineensis Jacq. \nKSB4 Bacteria Ex-tin mining land Acacia mangium Willd. \nKSB5 Bacteria Ex-gold mining land Schima wallichii (DC.) Korth. \nKSB6 Bacteria Ex-tin mining land Dillenia suffruticosa (Griff) Martelli \nKSF1 Fungi Agricultural land Elaeis guineensis Jacq. \nKSF2 Fungi Agricultural land Coffea arabica L. \nKSF3 Fungi Ex-tin mining land Acacia mangium Willd. \nKSF4 Fungi Ex-tin mining land Melastoma malabathricum L. \n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nPotassium and phosphate-solubilizing index on growth medium with application \nof the best 10 KSM isolates \n\n\n\n \nIsolate \ncode \n\n\n\nFeldspar  \nfrom Cirebon \n\n\n\nFeldspar  \nfrom Malang \n\n\n\n Ca3(PO4)2 \nPhosphate rock  \n\n\n\nfrom Blitar \n ----- Potassium-solubilizing index ------  ---- Phosphate-solubilizing index ---- \n\n\n\nKSB1 6.60 4.00  1.94 - \nKSB2 6.77 4.73  1.91 - \nKSB3 4.29 4.00  1.50 - \nKSB4 2.59 5.91  3.70 2.32 \nKSB5 2.05 5.46  1.94 1.05 \nKSB6 3.86 3.88  3.56 2.04 \nKSF1 2.23 1.77  1.16 1.67 \n\n\n\n3 \n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nPotassium and phosphate-solubilizing index on growth medium with application \nof the best 10 KSM isolates \n\n\n\n \nIsolate \ncode \n\n\n\nFeldspar  \nfrom Cirebon \n\n\n\nFeldspar  \nfrom Malang \n\n\n\n Ca3(PO4)2 \nPhosphate rock  \n\n\n\nfrom Blitar \n ----- Potassium-solubilizing index ------  ---- Phosphate-solubilizing index ---- \n\n\n\nKSB1 6.60 4.00  1.94 - \nKSB2 6.77 4.73  1.91 - \nKSB3 4.29 4.00  1.50 - \nKSB4 2.59 5.91  3.70 2.32 \nKSB5 2.05 5.46  1.94 1.05 \nKSB6 3.86 3.88  3.56 2.04 \nKSF1 2.23 1.77  1.16 1.67 \nKSF2 2.22 1.94  1.12 1.68 \nKSF3 2.06 1.07  - - \nKSF4 1.16 1.46  1.50 - \n\n\n\n Notes: Growth medium for test of potassium-solubilizing index is Alexandrov agar and growth \nmedium for test of phosphate-solubilizing index is Pikovskaya agar. The \u201c-\u201d sign means \nthat KSM colonies did not have halo zone on medium. \n \n\n\n\nTABLE 5 \n\n\n\nSoluble content and solubility percentage of potassium and phosphate in growth \nmedium with application of the best 10 KSM isolates \n\n\n\n\n\n\n\nIsolate \ncode \n\n\n\nSoluble K  K solubility  Soluble P  P solubility \n\n\n\nFeldspar \nfrom \n\n\n\nCirebon \n\n\n\nFeldspar \nfrom \n\n\n\nMalang \n\n\n\n Feldspar \nfrom \n\n\n\nCirebon \n\n\n\nFeldspar \nfrom \n\n\n\nMalang \n\n\n\n \nCa3(PO4)2 \n\n\n\nPhosphate \nrock  \n\n\n\nfrom Blitar \n\n\n\n \nCa3(PO4)2 \n\n\n\nPhosphate \nrock  \n\n\n\nfrom Blitar \n ----------- mg/L ----------  ----------- % -----------  ----------- mg/L -----------  ----------- % ----------- \n\n\n\nKSM0   31.65 d   29.54 e  0.16 0.17    62.50 b   25.00 b  0.14 0.01 \n\n\n\nKSB1 238.80 ab 268.99 a  1.24 1.55  718.80 a   71.88 b  1.57 0.03 \n\n\n\nKSB2 242.19 ab 259.40 ab  1.26 1.49  375.00 ab   31.25 b  0.82 0.01 \n\n\n\nKSB3 240.51 ab 253.16 bc  1.25 1.46  753.10 a   62.50 b  1.65 0.02 \n\n\n\nKSB4 222.78 abc 242.62 c  1.15 1.39  793.80 a   90.63 ab  1.73 0.03 \n\n\n\nKSB5 235.02 ab 259.49 ab  1.22 1.49  456.30 ab   75.00 b  1.00 0.03 \n\n\n\nKSB6 248.10 a 273.21 a  1.29 1.57  893.80 a 172.00 a  1.95 0.07 \n\n\n\nKSF1 235.02 ab 248.31 bc  1.22 1.43  553.10 ab 100.00 ab  1.21 0.04 \n\n\n\nKSF2 228.27 abc 248.95 bc  1.18 1.43  700.00 a 106.25 ab  1.53 0.04 \n\n\n\nKSF3 207.59 c 209.92 d  1.08 1.21    96.90 b   28.13 b  0.21 0.01 \n\n\n\nKSF4 218.99 bc 220.40 d  1.14 1.27  515.30 ab   31.25 b  1.13 0.01 \n\n\n\n Notes: KSM0 (without KSM application), numbers in bold showed the biggest result from \nobserved parameter. Means followed by the same letter in the same column were not \nsignificantly different result by DMRT test at 5% level of significance. Percentage of \nsolubility calculated from comparison between soluble K/P content with total K/P content \nin K/P sources. \n\n\n\nPotassium-Solubilizing Microbes on Sorghum\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016170\n\n\n\nTABLE 5\nSoluble content and solubility percentage of potassium and phosphate in growth medium \n\n\n\nwith application of the best 10 KSM isolates\n\n\n\nThe increasing percentage of soluble potassium with feldspar from Cirebon \nas a potassium source is between 556 to 684 %, while the percentage increase of \nsoluble potassium with feldspar from Malang as a potassium source is between \n611 to 825%. The increasing percentage of soluble phosphate with Ca3(PO4)2 as \na phosphate source is between 55 to 1330 %, while the percentage increase of \nsoluble phosphate with phosphate rock from Blitar is between 25 to 588 %.\n\n\n\nBased on Tables 4 and 5, potassium and phosphate-solubilising indexes do not \nalways have a correlation with ability to solubilise not-easily soluble potassium \nand phosphate sources in liquid medium. The solubilising index can serve as an \nindication of the ability of microbes to solubilise not-easily soluble potassium and \nphosphate sources, but it cannot become a single standard by which to determine \nthe best ability to solubilise potassium and phosphate sources. Tests in liquid \nmedium are needed to determine the ability of microbes to solubilise not-easily \npotassium and phosphate sources.\n\n\n\nPotassium-solubilising mechanisms of KSM include organic acids such as \noxalate acid, citrate acid (Liu et al., 2006), tartaric acid (Sheng and He, 2006), citric \nacid, formic acid, and malic acid (Shanware et al., 2014). Organic acids accelerate \nthe weathering process of minerals containing potassium, causing potassium to \nbecome detached and available for plants (Sugumaran and Janarthanam, 2007). \nThe potassium source used in the liquid medium test was potassium feldspar \n(KAlSi3O8). This reacts with organic acids causing ion exchange reactions \nbetween ion H+ from organic acid and ion K+ from potassium minerals. This \n\n\n\n3 \n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nPotassium and phosphate-solubilizing index on growth medium with application \nof the best 10 KSM isolates \n\n\n\n \nIsolate \ncode \n\n\n\nFeldspar  \nfrom Cirebon \n\n\n\nFeldspar  \nfrom Malang \n\n\n\n Ca3(PO4)2 \nPhosphate rock  \n\n\n\nfrom Blitar \n ----- Potassium-solubilizing index ------  ---- Phosphate-solubilizing index ---- \n\n\n\nKSB1 6.60 4.00  1.94 - \nKSB2 6.77 4.73  1.91 - \nKSB3 4.29 4.00  1.50 - \nKSB4 2.59 5.91  3.70 2.32 \nKSB5 2.05 5.46  1.94 1.05 \nKSB6 3.86 3.88  3.56 2.04 \nKSF1 2.23 1.77  1.16 1.67 \nKSF2 2.22 1.94  1.12 1.68 \nKSF3 2.06 1.07  - - \nKSF4 1.16 1.46  1.50 - \n\n\n\n Notes: Growth medium for test of potassium-solubilizing index is Alexandrov agar and growth \nmedium for test of phosphate-solubilizing index is Pikovskaya agar. The \u201c-\u201d sign means \nthat KSM colonies did not have halo zone on medium. \n \n\n\n\nTABLE 5 \n\n\n\nSoluble content and solubility percentage of potassium and phosphate in growth \nmedium with application of the best 10 KSM isolates \n\n\n\n\n\n\n\nIsolate \ncode \n\n\n\nSoluble K  K solubility  Soluble P  P solubility \n\n\n\nFeldspar \nfrom \n\n\n\nCirebon \n\n\n\nFeldspar \nfrom \n\n\n\nMalang \n\n\n\n Feldspar \nfrom \n\n\n\nCirebon \n\n\n\nFeldspar \nfrom \n\n\n\nMalang \n\n\n\n \nCa3(PO4)2 \n\n\n\nPhosphate \nrock  \n\n\n\nfrom Blitar \n\n\n\n \nCa3(PO4)2 \n\n\n\nPhosphate \nrock  \n\n\n\nfrom Blitar \n ----------- mg/L ----------  ----------- % -----------  ----------- mg/L -----------  ----------- % ----------- \n\n\n\nKSM0   31.65 d   29.54 e  0.16 0.17    62.50 b   25.00 b  0.14 0.01 \n\n\n\nKSB1 238.80 ab 268.99 a  1.24 1.55  718.80 a   71.88 b  1.57 0.03 \n\n\n\nKSB2 242.19 ab 259.40 ab  1.26 1.49  375.00 ab   31.25 b  0.82 0.01 \n\n\n\nKSB3 240.51 ab 253.16 bc  1.25 1.46  753.10 a   62.50 b  1.65 0.02 \n\n\n\nKSB4 222.78 abc 242.62 c  1.15 1.39  793.80 a   90.63 ab  1.73 0.03 \n\n\n\nKSB5 235.02 ab 259.49 ab  1.22 1.49  456.30 ab   75.00 b  1.00 0.03 \n\n\n\nKSB6 248.10 a 273.21 a  1.29 1.57  893.80 a 172.00 a  1.95 0.07 \n\n\n\nKSF1 235.02 ab 248.31 bc  1.22 1.43  553.10 ab 100.00 ab  1.21 0.04 \n\n\n\nKSF2 228.27 abc 248.95 bc  1.18 1.43  700.00 a 106.25 ab  1.53 0.04 \n\n\n\nKSF3 207.59 c 209.92 d  1.08 1.21    96.90 b   28.13 b  0.21 0.01 \n\n\n\nKSF4 218.99 bc 220.40 d  1.14 1.27  515.30 ab   31.25 b  1.13 0.01 \n\n\n\n Notes: KSM0 (without KSM application), numbers in bold showed the biggest result from \nobserved parameter. Means followed by the same letter in the same column were not \nsignificantly different result by DMRT test at 5% level of significance. Percentage of \nsolubility calculated from comparison between soluble K/P content with total K/P content \nin K/P sources. \n\n\n\nPratama et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 171\n\n\n\nreaction, besides releasing ion K+ that bind with silicate, also forms a secondary \nmineral, i.e., kaolinite (Al2Si2O5(OH)4) (Fu et al., 2009). \n\n\n\nBased on the test of isolates\u2019 ability in liquid medium, isolates KSB1, KSB2, \nKSB3, KSB5, and KSB6 were found to be the five isolates that have the best ability \nto solubilise feldspar in liquid medium. Test results from these five isolates for \nstimulating sorghum growth showed that KSB2 significantly had the best effect \nfor plant height and shoot dry weight, while KSB6 significantly had the best effect \nfor root dry weight and total potassium in plant (Table 7).\n\n\n\nKSB isolates from ex-mining land (KSB2, KSB5, and KSB6) were found to \nhave better ability to assist sorghum growth than KSB isolates from agricultural \nland (KSB1 and KSB3). There are no studies related to agricultural effects of KSM \nisolates from ex-tin mining or ex-gold mining lands that compare KSM from \nagricultural land as far as we know, although, research has shown that application \nof microbes from ex-coal mining land increased the growth of Acacia crassicarpa \n(Widyati, 2007).\n\n\n\nAdaptation factors are presumably the main factors affecting the different \nabilities of bacteria. The ability of bacteria isolates to obtain food, grow, and \ncompete with indigenous microbes will affect their ability to survive and solubilise \npotassium sources in soil. Roszak and Colwell (1987) have written that bacteria \nliving in critical environments that have lower nutrient content have a higher \nsurvival rate than bacteria from a higher nutrient environments.\n\n\n\nThe higher ability of bacteria from ex-mining land could be caused by \nincreased nutrition content from their environment. Soil from agricultural land \nused for testing of ability of microbes to stimulate sorghum growth had higher \nnutrient content than soil from ex-mining land. When bacteria from ex-mining \nland were applied to that soil medium, the nutrient improvement automatically \nincreased food supply for those bacteria.\n\n\n\nEgamberdiyeva (2007) has reported that total nutrient levels in soils have an \neffect on bacterial growing phase and ability. This finding is supported by Bren \net al., (2013) who have reported that bacteria from a lower nutrient environment \nhave better ability because when nutrition content increases, the growth of these \nmicrobes will increase and cause their ability to increase as well, in this case \nthe ability to solubilise not-easily soluble potassium sources. When growth rate \nof microbes increases, it will increase their ability to produce organic acids. \nIncreased ability to produce organic acids can accelerate weathering processes \nof minerals containing potassium and make available their potassium for plants. \nIncreased content of available potassium in the soil can increase the potassium \ncontent that is absorbed by plants. Increased content of potassium in sorghum is \nreflected in an increase in the total potassium in sorghum itself. Sorghum grown \nin  soils  applied with KSB isolates from ex-mining land have higher potassium \ncontent than the sorghum grown on soils applied with isolates from agricultural \nland (Table 6).\n\n\n\nPotassium-Solubilizing Microbes on Sorghum\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016172\n\n\n\nCharacterisation results from the best two KSB isolates showed that each \nisolate has different characteristics from the other. Molecular analysis showed \nthat KSB2 has 97.8% similarity to Achromobacter xylosoxidans and KSB6 has \n99.0% similarity to Burkholderia cepacia (Table 7). Drancourt et al., (2000) \nreports that molecular analysis results that have a similarity of more than or equal \nto 99% indicates that the isolates are of the same species, and similarity between \n97 to 99% indicates that the isolate are of a different species but belong to the \nsame genus. Based on this statement, KSB2 isolate is possibly a new species from \ngenus Achromobacter, and isolate KSB6 probably belongs to the same species as \nBurkholderia cepacia.\n\n\n\nTABLE 7\nCharacterisation results from the best two KSM isolates\n\n\n\n4 \n\n\n\n\n\n\n\n \nTABLE 6 \n\n\n\nGrowth and total potassium content in sorghum with application of the best 5 \nKSM isolates \n\n\n\n \nTreatments Plant height \n\n\n\n(cm) \nNumber \nof leaves \n\n\n\nShoot dry \nweight (g) \n\n\n\nRoot dry \nweight (g) \n\n\n\nK content \n(%) \n\n\n\nP0   48.33 d 5.00 0.27 e 0.04 d 0.88 d \nP1 (KSB1)   77.13 c 5.33 0.47 d 0.12 d 0.93 c \nP2 (KSB2) 105.33 a 5.67 2.09 a 0.54 b 1.18 a \nP3 (KSB3)   53.17 d 5.33 0.41 d 0.07 d   0.91 cd \nP4 (KSB5)     81.07 bc 5.67 0.83 c 0.27 c 1.02 b \nP5 (KSB6)   92.07 b 6.33 1.62 b 0.67 a 1.20 a \n\n\n\nNotes: P0 (without KSM application). Numbers in bold showed the biggest result from observed \nparameter. Means followed by the same letter in the same column were not significantly \ndifferent result by DMRT test at 5% level of significance. \n\n\n\n\n\n\n\nTABLE 7 \n\n\n\nCharacterization result from the best 2 KSM isolates \nParameters Observation result \n ------------- BPK2 -------------  ------------- BPK6 ------------- \nColony form Round  Irregular \nColony color Whitish  White \nColony elevation Convex  Convex \nColony ledge form Slick  Irregular \nCell shape Coccus  Bacillus/Rod \nGram test Negative  Negative \nMotility test -  + \nCatalase test +  + \nIndole test -  - \nUrease test -  - \nGlucose fermentation test -  - \nStarch test -  - \nCitrate test -  + \nSpecies similarity Achromobacter xylosoxidans  Burkholderia cepacia \nStrain 2001038723  BAB-2826 \nHomology 97.8%  99.0% \n\n\n\nNotes: + (positive reaction), \u2013 (negative reaction). \n \n\n\n\nCONCLUSION \n \n\n\n\nACKNOWLEDGEMENT \nOur gratitude goes to Prof. Dr. Norman Uphoff of Cornell University, \n\n\n\nIthaca, New York, USA for suggestions and improvement of English of this \nmanuscript. \n\n\n\n \nREFERENCES \n\n\n\n4 \n\n\n\n\n\n\n\n \nTABLE 6 \n\n\n\nGrowth and total potassium content in sorghum with application of the best 5 \nKSM isolates \n\n\n\n \nTreatments Plant height \n\n\n\n(cm) \nNumber \nof leaves \n\n\n\nShoot dry \nweight (g) \n\n\n\nRoot dry \nweight (g) \n\n\n\nK content \n(%) \n\n\n\nP0   48.33 d 5.00 0.27 e 0.04 d 0.88 d \nP1 (KSB1)   77.13 c 5.33 0.47 d 0.12 d 0.93 c \nP2 (KSB2) 105.33 a 5.67 2.09 a 0.54 b 1.18 a \nP3 (KSB3)   53.17 d 5.33 0.41 d 0.07 d   0.91 cd \nP4 (KSB5)     81.07 bc 5.67 0.83 c 0.27 c 1.02 b \nP5 (KSB6)   92.07 b 6.33 1.62 b 0.67 a 1.20 a \n\n\n\nNotes: P0 (without KSM application). Numbers in bold showed the biggest result from observed \nparameter. Means followed by the same letter in the same column were not significantly \ndifferent result by DMRT test at 5% level of significance. \n\n\n\n\n\n\n\nTABLE 7 \n\n\n\nCharacterization result from the best 2 KSM isolates \nParameters Observation result \n ------------- BPK2 -------------  ------------- BPK6 ------------- \nColony form Round  Irregular \nColony color Whitish  White \nColony elevation Convex  Convex \nColony ledge form Slick  Irregular \nCell shape Coccus  Bacillus/Rod \nGram test Negative  Negative \nMotility test -  + \nCatalase test +  + \nIndole test -  - \nUrease test -  - \nGlucose fermentation test -  - \nStarch test -  - \nCitrate test -  + \nSpecies similarity Achromobacter xylosoxidans  Burkholderia cepacia \nStrain 2001038723  BAB-2826 \nHomology 97.8%  99.0% \n\n\n\nNotes: + (positive reaction), \u2013 (negative reaction). \n \n\n\n\nCONCLUSION \n \n\n\n\nACKNOWLEDGEMENT \nOur gratitude goes to Prof. Dr. Norman Uphoff of Cornell University, \n\n\n\nIthaca, New York, USA for suggestions and improvement of English of this \nmanuscript. \n\n\n\n \nREFERENCES \n\n\n\nTABLE 6\nGrowth and total potassium content in sorghum with application of the five  best KSM \n\n\n\nisolates\n\n\n\nPratama et al.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 20, 2016 173\n\n\n\nCONCLUSION\nIsolation processes identified 185 KSM isolates from three different sets of soil \nsamples from agricultural land, ex-tin mining land, and ex-gold mining land. \nBased on the pathogenicity test, 48 isolates of 185 isolates were found to have \nnon-pathogenic characteristics. Of these 48 isolates, 10 KSM isolates were found \nto have the highest solubilising indexes. Based on the ability of isolates in liquid \nmedium, five best isolates were found to have the highest ability to solubilise \nfeldspar rock. Of these five isolates, two of the best KSM isolates were found to \nhave the best ability to stimulate sorghum growth. KSM isolates from ex-mining \nland demonstrated better ability to stimulate sorghum growth than isolates from \nagricultural land. The two best isolates were KSB2 which had 97.8% genetic \nsimilarity to Achromobacter xylosoxidans and KSB6 which had 99% similarity \nto Burkholderia cepacia.\n\n\n\nACKNOWLEDGEMENT\nOur gratitude goes to Prof. Dr. Norman Uphoff of Cornell University, Ithaca, New \nYork, USA for suggestions and for editing the language of this manuscript.\n\n\n\nREFERENCES\nBasak, B.B., and Biswas, D.R. 2010. 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"\n\n___________________\n*Corresponding author : E-mail: t.satyanandam@gmail.com \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 25 : 123-138 (2021)                        Malaysian Society of Soil Science\n\n\n\nIsolation and Screening of Indigenous Rhizobia from \nBlackGram Cultivated in Fallow Rice Soils for Plant Growth \n\n\n\nPromoting Traits \n\n\n\nSatyanandam, T.1*, Babu, K.2, Suneeta, D.3, Bhaskararao, C.H.4,\nRosaiah, G.2, and Vijayalakshmi, M.2 \n\n\n\n1Department of Botany, Maris Stella College (Autonomous), Vijayawada-520008, \nAndhra Pradesh, India\n\n\n\n2Department of Botany and Microbiology, Acharya Nagarjuna University,\nNagarjuna Nagar, Guntur-522510, Andhra Pradesh, India\n\n\n\n3Department of Botany, Government Degree College, Thorrur, Mahabubabad, \nTelangana, India.\n\n\n\n4Department of Botany, Government College for Women (Autonomous), \nGuntur-522001, Andhra Pradesh, India\n\n\n\nABSTRACT\nBio fertilisers are relatively safer, environmentally friendly and a cost-effective \napproach to chemical fertiliser usage. The selection of bacterial strains with \nmultiple beneficial characteristics is important to maximise their effectiveness on \nthe host plant. In the present study, four native and indigenous rhizobial strains \n(VM-2, VM-8, VM-9 and VM-15) were isolated from root nodules of blackgram \n(Vignamungo) cultivated in fallow rice soils of Andhra Pradesh, India. All \nthe four strains were screened in vitro for their plant growth-promoting (PGP) \ncharacteristics viz. production of indole acetic acid (IAA), exopolysaccharide \n(EPS), hydrogen cyanide (HCN) and phosphate solubilisation. The results indicated \nthat the rhizobial strains varied in their plant growth promoting activities. All the \nfour strains produced IAA, EPS and also solubilised the insoluble phosphate. \nThe amount of IAA produced varied from strain to strain and relatively high \namounts were recorded in VM-8 (43.4 \u03bcg/ml) followed by VM-15 with 43.1 \u03bcg/\nml. Maximum EPS production was recorded in VM-9 (527 mg/ml) followed by \nVM-8 (483 mg/ml). The phosphate solubilisation efficiency of Rhizobium strains \non solid media ranged between 16% and 17%. In liquid medium, strain VM-2 \nrecorded maximum solubilisation (799\u03bcg/ml) followed by VM-8 (372\u03bcg/ml). All \nthe strains except strain VM-8 were HCN producers. Among these three strains, \nVM-2 and VM-15 showed strong HCN production. These isolates were identified \nas Rhizobium sp. strain VM-2 (KJ 704783), Brady rhizobium sp. strain VM-8 (KJ \n704784), Brady rhizobium sp. strain VM-9 (KJ 704785) and Achromobacter sp. \nstrain VM-15 (KJ501696) after 16S rRNA sequencing. The pot culture experiment \nshowed that VM-8, VM-9 and VM-15 inoculated plants had good results both \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021124\n\n\n\nin inoculated sterilised and inoculated unsterilised soils than the plants grown in \nsterilised uninoculated soils and control soils. The VM-2 strain showed moderate \nresults under plant inoculation test. This study suggests that these four native \nrhizobial strains of PGP can be used as bio fertilisers as well as a bio control agent \nfor enhancing the yield of blackgram in rice fallows. \n\n\n\nKeyword:    Rice fallows, black gram, plant growth promoting characteristics.\n\n\n\nINTRODUCTION\nBlackgram (Vignamungo) is a short duration crop belonging to the Leguminaceae \nfamily. It is also called urad bean. Our (1993) reported that millions of people \nin many countries are consuming it as a part of their diet and is a cheap source \nof protein (17-34% seed protein). Reddy et al. (2011) reported that this legume \nincreases soil fertility by fixing 38 kg N/ha/year in soil from atmosphere. It is \nmainly cultivated in the rice fallows after rice cultivation to conserve soil nutrients \nand utilise the left-over soil moisture present in the rice fallows. Cultivation of \nlegumes in rice fallows can prevent the loss of soil nitrate and additionally capture \natmospheric nitrogen through biological nitrogen fixation process (George et al. \n1992).\n Most of the rhizospheric microorganisms promote plant growth and \ndevelopment either directly (nitrogen fixation, phosphate solubilisation and plant \ngrowth regulators) or indirectly (by controlling the pathogenic microorganisms)\nand are referred to as plant growth promoting rhizobacteria (PGPR). Besides \nsymbiotic nitrogen fixation, Rhizobium can also produce phytohormones like \nIndole acetic acid (Halda-Alija 2003), siderophores and HCN, thereby decreasing \nthe damage due to plant pathogens and ultimately improving plant growth and \nyield (Deshwal et al. 2003; Weller and Cook 1983; Raajjmakers et al. 1999; \nKranthi Kumar and Raghu Ram 2016; Manasa et al. 2017).\n Phosphorous is one of the most important macro nutrients that plays \nan important role in plant metabolism (Sashidhar and Podile 2010). Several \nmicroorganisms in the rhizosphere (rhizobacteria) solubilise inorganic phosphate \nby the production of organic acids (Rodriguez and Fraga1999). Rhizobia is also \na good phosphate solubiliser and tends to increase phosphorous availability to \nplants by solubilising the insoluble phosphates (Halder et al. 1990; Johri et al. \n2003).\n Another important characteristic feature of PGPR is EPS production \nwhich helps in nitrogen fixation by protecting the dinitrogenase enzyme from \nhigh oxygen concentrations (Tank and Saraf 2003). The EPS produced by the \nRhizobium species play a prominent role in the Rhizobium-legume symbiosis (De \nand Basu 1996) particularly in root hair infection and nodule formation (Phillip-\nHollings worth et al.1989; Kranthi Kumar and Raghu Ram 2016). \n Less information is available on these important plant growths promoting \ntraits of native or indigenous Rhizobium strains isolated from blackgram \nparticularly cultivated in rice fallows. The present investigation was undertaken \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 125\n\n\n\nto isolate and screen the indigenous Rhizobium bacteria from root nodules of \nVignamungo plants for their PGPR activities like IAA, EPS, HCN production and \nphosphate solubilisation followed by plant inoculation test.\n\n\n\nMATERIALS  AND METHODS\nIsolation of Rhizobium Strains from Blackgram Root Nodules\nIn the present investigation, the nodulated roots of mature black gram plants \ncultivated in rice fallows of Krishna and Guntur districts of Andhra Pradesh, \nIndia were collected. Rhizobium strains were isolated from freshly collected \nhealthy root nodules on yeast extract mannitol agar (YEMA) medium with 0.1% \nCongo red. The pure cultures of all isolates were maintained on YEMA slants and \npreserved at 4\u00baC (Vincent 1970). The identity of the strains was confirmed by tests \nsuch asGram staining, growth on culture media such as YEMA with Congo red \n(Vincent 1970; 1982), Hofer\u2019s alkaline broth and Glucose Peptone Agar (Vincent \n1970), Ketolactose test (Bernaertz and Deley 1963) and nodulating ability on \nhomologous hosts (Somasegaran and Hoben 1985).\n\n\n\nScreening of Rhizobial Strains for Their Plant Growth Promoting Activities\nIndole Acetic Acid(IAA) Production\nIAA production was determined by the (Gorden and Weber, 1951) method. For \nIAA production, all the four strains were grown separately in 100 ml conical \nflasks containing 30 ml of YEM broth (Skerman 1959) supplemented with \nL-tryptophan (1.5 mg/ml) at pH 7.0 in triplicate on a rotatory shaker for 54 h \nat 30\u00b12\u00b0C. Bacterial growth was determined by taking optical density (OD) \nat 540 nm using a Spectrophotometer (Elico-Cl 157). The broth cultures were \ncentrifuged at 5000 rpm for 20 min and the cell free supernatant was analysed for \nIAA extraction according to Sinha and Basu (1981). To the 10 ml of supernatant, \n2 ml of Salkowsky\u2019s reagent (0.5 M Fecl3 in 35% perchloric acid) was added \nand the mixture was left in the dark for 30 min. The development of pink colour \nindicated IAA production and the optical density was measured at 540 nm using a \nspectrophotometer. The yield of IAA was calculated by using the standard graph \nof authentic IAA (Merck). Data on three replications was maintained.\n\n\n\nExopolysaccharide (EPS) Production\nThe Rhizobium strains were inoculated into Erlenmeyer flasks containing 100 \nml of YEM broth supplemented with 1% Mannitol. The inoculated flasks were \nincubated at 30\u00b12\u00b0C on a rotator shaker at 300 rpm for 72 h. After incubation, \nthe culture broth was centrifuged at 3000 rpm and the supernatant was mixed \nwith two volumes of chilled acetone. The crude polysaccharide developed was \ncollected by centrifugation at 3000 rpm for 30 min. The EPS was washed with \ndistilled water and acetone alternatively, then transferred on to a filter paper and \nweighed after overnight drying at 105\u00b0C (Damery and Alexander 1969). Data on \nthree replications was maintained.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021126\n\n\n\nPhosphate Solubilisation\nThe phosphate solubilising ability of the strains was tested on Pikovskaya\u2019s solid \nagar medium (Pikovskaya 1948) with Tricalcium phosphate (TCP) as insoluble \nphosphate source. The solubilisation efficiency (SE) on solid agar medium was \nexpressed interms of SE (%) (Sri Ram Kumar and Kannapiran 2011; Srivastava et \nal. 2004). The strains which showed a solubilisation zone on solid agar medium \nwere further tested in flasks containing 100ml of Pikovskaya\u2019s broth having an \ninitial pH 7. One ml of the inoculum was inoculated into the broth and the flasks \nwere incubated on a rotary shaker (200rpm) at 28\u00b12\u00baC for 72h. The supernatant \nwas separated from the bacterial cells by centrifugation of flasks at 3000rpm. \nLater the final pH of the supernatant was measured and the liberated P2O5 was \nestimated by adding 2.5 ml of Barton\u2019s reagent to 10ml aliquot of the clear culture \nsupernatant and the volume was made up to 50 ml. After 10 min, the resultant \nyellow colour was read in a calorimeter at 430 nm (Jackson 1973) and the liberated \nP2O5 was estimated by comparing the values with a standard curve prepared with \nK2HPO4.Data on three replications was maintained.\n\n\n\nHydrogen cyanide (HCN)Production\nAll the isolates were screened for their ability to produce HCN. Production of \nHCN was assayed by the method given by Miller and Higgins (1970) with slight \nmodifications. Actively growing bacterial cultures were streaked on YEMA plates \nsupplemented with 4.4 g glycine/L. Filter paper soaked in 0.5% picric acid and 1% \nNa2CO3 was attached to the upper Petri dish lids and the plates sealed with parafilm. \nPlates without inoculum served as control. HCN production was estimated after \nseven days of incubation at room temperature, by observing a colour change in the \nfilter paper from yellow to light brown (low), brown (moderate) or reddish brown \n(strong). Data on three replications was maintained.\n\n\n\nPCR Amplification and Partial Sequencing of 16S rRNA Gene\nThe amplification of PCR and sequencing of 16S rRNA gene of the four isolates, \nVM-2, VM-8, VM-9 and VM-15,was done by using the commercial services of \nMacrogen Inc. Korea.  \n\n\n\nPhylogenetic Analysis of Bacterial Strains\nThe gene sequences of VM-2, VM-8, VM-9 and VM-15 were submitted to BLAST \nfor comparison with Gen Bank sequences employing the Basic Local Alignment \nSearch Tool (http://www.ncbi.nlm.nih.gov/GenBank/). For the phylogenetic \nanalysis, Gene Sequences greater than 600 bp in length were used. \n\n\n\nPlant Inoculation Test\nPot culture experiment and experimental design\nSymbiotic efficiency and persistence of inoculated Rhizobium in soil are necessary \nfor the success of an inoculation program. Screening for these traits is therefore an \nimportant component of inoculation studies. A pot culture experiment was carried \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 127\n\n\n\nout using the most cultivated rice fallow black gram variety, LBG-752, in the \nBotanical garden of Acharya Nagarjuna University, Guntur, Andhra Pradesh, India \nto evaluate the effect of indigenous Rhizobium strains (VM-2, VM-8, VM-9 and \nVM-15) isolated from rice fallows on the growth, nodulation, nitrogen fixation \nand yield of black gram. All the pots used in this experiment were of uniform size \n(27\u00d725 cm) and 5 kg of soil was used in all pots. The experiment was conducted \nin RBD with three replications and three treatments. The treatments were as \nfollows: Treatment-1: Seed inoculation with isolated native strains in sterilised \nsoil; Treatment-2: Seed inoculation with isolated native strains in unsterilized \nsoil; Treatment-3:Growth of seedlings in sterilised soil without inoculation; and \nControl: Growth of seedlings in unsterilized soil without inoculation.\n\n\n\nMaterials used\nThe materials used include rice fallow soils collected from different rice fields, \nblack gram seeds obtained from Regional Agriculture Research Station (RARS), \nLam, Rhizobium cultures, autoclave, earthen pots, plastic tag for labeling, broth \nculture for multiplying the Rhizobium strain and electric orbital shaker.\n \nSoil sterilisation\nRice fallow soils collected from different rice fields were heat sterilised using \n(electric soil steriliser at 65\u00b0C for 90 min) and then autoclaved for 30 min at 130 \nkpa and 121\u00b0C. Then the soil was left to cool and stored in air tight bags.\n\n\n\nSowing and inoculation\nSeeds of black gram LBG-752 which were uniform in size, shape and weight \nwere surface sterilised with 1% mercuric chloride (HgCl2) for 3-4 min and were \nrepeatedly washed with sterilised water. No fertilizer was applied at the time \nof sowing. For seed inoculation, seeds were coated with a paste of Rhizobium \ninoculum containing approximately 108 cells per seed (Somasegaran and Hoben \n1994) and eight such seeds were sown per pot containing 5 kg soil for Treatments \n1&2. The non-coated sterilised seeds were sown in pots of Treatment-3 and \nControl. The experiment was conducted under natural conditions by following all \nagronomic practices which were uniform and normal for all the treatments. \n\n\n\nData Collection\nData was collected from all the treatments at 35 DAS and at 50% flowering stage \non morphological and yield characters such as number of nodules, nodule fresh \nweight (mg), nodule dry weight (mg), leg haemoglobin content (\u00b5g/ml), root \nlength (cm), shoot length (cm), root fresh weight (gm), root dry weight (gm), shoot \nfresh weight (gm), shoot dry weight (gm), number of leaves per plant, number of \nbranches per plant, number of clusters per plant, number of pods per plant, seeds \nper pod, pod length (cm), nodule nitrogen (%), root nitrogen (%), shoot nitrogen \n(%), leaf nitrogen (%), seed nitrogen (%), seed protein and seed yield per plant. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021128\n\n\n\nStatistical Analysis\nStatistical analysis of the PGPR data was performed by using SPSS software \n(version 2.0). Correlation coefficient and ANOVA were calculated for the PGPR \ndata wherever necessary. The data onpot inoculation was statistically analysed \nusing AGRISTAT software. Correlation coefficients between traits regarding pot \nexperiment were calculated by MINITAB 16 software.\n\n\n\nRESULTS AND DISCUSSION\nIsolation of Rhizobial Strains\nFour isolates were obtained from the nodules of blackgram plants grown in \nthe rice fallows of Krishna and Guntur districts of Andhra Pradesh, India. The \nRhizobium colonies on Congo red medium appeared as white, round, transparent, \nand elevated with entire margin. They were Gram-negative rods and did not grow \non Hofer\u2019s medium and glucose peptone agar.  All the strains were negative for the \nproduction of 3-ketolactose from lactose and were finally confirmed as rhizobia \nby the nodulation test (Satyanandam et al. 2014).\n\n\n\nScreening of Rhizobial Strains for Various Plant Growth Promoting Activities\nIn this study all the four strains were screened in vitro for their plant growth \npromoting properties like Indole Acetic Acid production, EPS production, \nPhosphate solubilisation and HCN production. The results revealed that all the \nfour strains were IAA, EPS producers and phosphate solubilisers. Except for \nstrain VM-8, all the other strains showed HCN production (Table 1). \n\n\n\nTABLE 1\nPlant growth promoting activities of different rhizobial isolates\n\n\n\n\n\n\n\nTABLE 1 \nPlant growth promoting activities of different rhizobial isolates \n\n\n\n \n                         IAA           EPS             Phosphate solubilisation               HCN \nStrains  \n \n \n\n\n\nVM-2                      +                    +                               +                                   + \n\n\n\nVM-8                      +                    +                               +                                   \u2212 \n\n\n\nVM-9                      +                    +                               +                                   + \n\n\n\nVM-15                    +                    +                               +                                   + \n\n\n\n' + ' indicates positive                                                 ' \u2212 ' indicates negative \n \n\n\n\nIAA Production \n\n\n\nAll the four strains showed IAA production. The amount of IAA produced varied \n\n\n\nfrom strain to strain and relatively high amounts were recorded in VM-8 (43.4 \n\n\n\n\u03bcg/ml) followed by VM-15 with 43.1 \u03bcg/ml incubated for 54 h when YEM \n\n\n\nmedium was supplemented with 1.5 mg/ml L-tryptophan. A low amount of IAA \n\n\n\nwas produced by VM-9 (35.0 \u03bcg/ml) and VM-2 (19.0 \u03bcg/ml) respectively (Table \n\n\n\n2). In earlier reports, the Rhizobium sp isolated from root nodules of Dalbergia \n\n\n\nlanceolaria produced a high amount of IAA at 2.5 mg/ml L-tryptophan \n\n\n\nconcentration (Ghosh and Basu 2002) while the Rhizobium sp. from root nodules \n\n\n\nof Roystonea regia produced a maximum amount of IAA at 3 mg/ml L-tryptophan \n\n\n\nconcentration (Basu and Ghosh 2001).  Kranthi Kumar and Raghu Ram (2016) \n\n\n\nreported that Ensifer sp. isolated from Vigna trilobata produced a maximum of \n\n\n\n42.5 \u00b5g/ml of IAA in the presence of L-tryptophan 2mg/ml concentration. Manasa \n\n\n\net al. (2017) mentioned that out of 15 Rhizobial strains isolated from different \n\n\n\nIAA Production\nAll the four strains showed IAA production. The amount of IAA produced varied \nfrom strain to strain and relatively high amounts were recorded in VM-8 (43.4 \u03bcg/\nml) followed byVM-15 with 43.1 \u03bcg/ml incubated for 54 h when YEM medium was \nsupplemented with 1.5 mg/ml L-tryptophan. A low amount of IAA was produced \nbyVM-9 (35.0 \u03bcg/ml) and VM-2(19.0 \u03bcg/ml) respectively (Table 2). In earlier \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 129\n\n\n\nreports, the Rhizobium sp. isolated from root nodules of Dalbergial anceolaria \nproduced a high amount of IAA at 2.5 mg/ml L-tryptophan concentration (Ghosh \nand Basu 2002) while the Rhizobium sp. from root nodules of Roystonearegia \nproduced a maximum amount of IAA at 3 mg/ml L-tryptophan concentration \n(Basu and Ghosh 2001). Kranthi Kumar and Raghu Ram (2016) reported that \nEnsifer sp. isolated from Vigna trilobata produced a maximum of 42.5 \u00b5g/ml of \nIAA in the presence of L-tryptophan 2mg/ml concentration. Manasa et al. (2017) \nmentioned that out of 15 Rhizobial strains isolated from different legume crops \nsuch as groundnut, black gram, green gram, soy bean and redgram,11 were able \nto produce IAA. Further, out of 11 isolates, the Rhizobium strain from ground nut \nshowed maximum IAA (24.12 \u03bcg/ml).\n\n\n\nTABLE 2\nProduction of IAA by Rhizobium strains from Vigna mungo\n\n\n\n\n\n\n\nlegume crops such as groundnut, black gram, green gram, soy bean and red gram, \n\n\n\n11 were able to produce IAA. Further, out of 11 isolates, the Rhizobium strain \n\n\n\nfrom ground nut showed maximum IAA (24.12 \u03bcg/ml). \n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \nProduction of IAA by Rhizobium strains from Vigna mungo \n\n\n\n \n                                               Name of                                IAA production \nS. No                                           strain                                             (\u03bcg/ml) \n \n\n\n\n        1                                            VM-2                                              19.0 \n\n\n\n        2                                            VM-8                                              43.4 \n\n\n\n        3                                            VM-9                                              35.0 \n\n\n\n        4                                            VM-15                                            43.1 \n\n\n\nNotes: Each value in the table is a mean of three replicates \nF-calculated (4.256); F-tabulated (1.925); significant at 5% level    \n\n\n\n\n\n\n\n\n\n\n\nEPS production \n\n\n\nMaximum EPS production was recorded in VM-9 (527 mg/ml) followed by VM-8 \n\n\n\n(483 mg/ml). The lowest EPS production was recorded by VM-2 (341 mg/ml) \n\n\n\nfollowed by VM-15 (287 mg/ml) (Table 3). The above results clearly indicate that \n\n\n\nthese isolates are considered as copious EPS producers. The Rhizobium strain \n\n\n\nisolated from the root nodules of Crotalaria saltiana produced 16 \u03bcg /ml \n\n\n\n(Mukhurjee et al. 2011) while that of Rhizobium\u2009 DL\u200910 from Dalbergia \n\n\n\nlanceolaria produced maximum EPS 765\u2009\u03bcg/ml (Ghosh et al. 2005) and \n\n\n\nEPS production\nMaximum EPS production was recorded in VM-9 (527 mg/ml) followed by \nVM-8 (483mg/ml). The lowest EPS production was recorded byVM-2 (341mg/\nml) followed by VM-15 (287 mg/ml) (Table 3). The above results clearly indicate \nthat these isolates are considered as copious EPS producers. The Rhizobium \nstrain isolated from the root nodules of Crotalaria saltiana produced 16 \u03bcg/ml \n(Mukhurjee et al. 2011) while that of Rhizobium  DL 10 from Dalbergia lanceolaria \nproduced maximum EPS 765 \u03bcg/ml (Ghosh et al. 2005) and Rhizobium strain from \nblackgram produced maximum EPS 346 mg/l (Mandal et al. 2007).\n\n\n\nPhosphate Solubilisation\nAll the four strains   of VM-2, VM-8, VM-9 and VM-15 produced a clear zone \naround the colonies after 24 h of incubation on Pikovskaya\u2019s agar medium, which \ngradually increased up to 72h. The solubilisation efficiency (SE) of Rhizobium \nstrains on solid media ranged between 16% and 170%. The Rhizobium strainVM-2 \nshowed maximum solubilisation efficiency (Figure 1) followed by VM-8, VM-\n15 and VM-9. In liquid medium, Rhizobium strain VM-2 recorded maximum \nsolubilisation (799\u03bcg/ml) followed by VM-8 (372\u03bcg/ml), VM-15(353\u03bcg/ml) \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021130\n\n\n\n\n\n\n\nvalue (Sperber 1958; Rodroguez and Fraga 1999; Sridevi et al. 2007; Kranthi \n\n\n\nKumar and Raghu Ram 2016). \n\n\n\n In earlier reports, solubilisation efficiency (SE) of Rhizobium isolates from \n\n\n\nCassiaabsus, Vignatrilobata and three strains from Sesbaniasesban  on solid \n\n\n\nmedia ranged between 33% and 150%.  In liquid medium, maximum solubilisation \n\n\n\nwas recorded with Rhizobium isolate from Cassia absus (620 \u03bcg/ml) (Sridevi and \n\n\n\nMallaiah 2009) while the Rhizobium sp isolated from root nodules of Crotalaria \n\n\n\nretusa recorded maximum solubilisation (840 \u03bcg/ml) in liquid medium \n\n\n\n(Sridevi et al. 2007). In the study of Muhammad Adnan (2016),(Muhammad et \n\n\n\nal. 2016 in ref list????) it was observed that 21% of the tested rhizobia were \n\n\n\nphosphate solubilising bacteria. Among 15 Rhizobial isolates, 7 isolates were able \n\n\n\nto solubilise phosphate on Pikovskaya\u2019s media containing Tricalcium phosphate as \n\n\n\nphosphate source. The solubilisation efficiency of Rhizobium strains on solid \n\n\n\nmedia ranged between 38% and 270% (Manasa et al. 2017). \n\n\n\n\n\n\n\nFigure 1. Phosphate solubilised zone of Rhizobium strain VM-2 \n \n \n \n\n\n\nTABLE 4 \n\n\n\nFigure 1. Phosphate solubilised zone of Rhizobium strain VM-2\n\n\n\nand VM-9 (261\u03bcg/ml) (Table 4). A drop in a pH was accompanied by phosphate \nsolubilisation. Phosphate solubilising microorganisms dissolve insoluble \nphosphates by the production of inorganic or organic acids and/or by a drop in \npH value (Sperber1958; Rodroguez and Fraga 1999; Sridevi et al. 2007; Kranthi \nKumar and Raghu Ram 2016).\n In earlier reports, solubilisation efficiency (SE) of Rhizobium isolates \nfrom Cassiaabsus, Vigna trilobata and three strains from Sesbania sesban \non solid media ranged between 33% and 150%. In liquid medium, maximum \nsolubilisation was recorded with Rhizobium isolate from Cassiaabsus (620 \u03bcg/ml) \n(Sri devi and Mallaiah 2009) while the Rhizobium sp isolated from root nodules of \nCrotalaria retusa recorded maximum solubilisation (840 \u03bcg/ml) in liquid medium \n(Sri devi et al. 2007). In the study of Muhammad Adnan (2016) it was observed \nthat 21% of the tested rhizobia were phosphate solubilising bacteria. Among 15 \nRhizobial isolates, 7 isolates were able to solubilise phosphate on Pikovskaya\u2019s \n\n\n\nTABLE 3\nEPS production by Rhizobium strains from Vigna mungo\n\n\n\n\n\n\n\nRhizobium strain from black gram produced maximum EPS 346 mg/l (Mandal et \n\n\n\nal. 2007). \n\n\n\n\n\n\n\nTABLE 3 \nEPS production by Rhizobium strains from Vigna mungo \n\n\n\n \n                                  Name of                                         EPS production            \nS.No                               strain                                                  (mg/100 ml) \n  \n\n\n\n          1                            VM-2                                                            341  \n\n\n\n          2                            VM-8                                                            483 \n\n\n\n          3                            VM-9                                                            527 \n\n\n\n          4                            VM-15                                                          287 \n\n\n\n \nNotes: Each value in the table is an average of three replicates \nF-calculated (5.637); F-tabulated (2.295); significant at 5% level \n\n\n\n\n\n\n\nPhosphate Solubilisation \n\n\n\nAll the four strains   of VM-2, VM-8, VM-9 andVM-15 produced a clear zone \n\n\n\naround the colonies after 24 h of incubation on Pikovskaya\u2019s agar medium, which \n\n\n\ngradually increased up to 72 h. The solubilisation efficiency (SE) of Rhizobium \n\n\n\nstrains on solid media ranged between 16% and 170%. The Rhizobium strain VM-\n\n\n\n2 showed maximum solubilisation efficiency (Figure 1) followed by VM-8, VM-\n\n\n\n15 and VM-9. In liquid medium, Rhizobium strain VM-2 recorded maximum \n\n\n\nsolubilisation (799\u03bcg/ml) followed by VM-8 (372\u03bcg/ml), VM-15(353\u03bcg/ml) and \n\n\n\nVM-9 (261\u03bcg/ml) (Table 4). A drop in a pH was accompanied by phosphate \n\n\n\nsolubilisation. Phosphate solubilising microorganisms dissolve insoluble \n\n\n\nphosphates by the production of inorganic or organic acids and/or by a  drop in pH \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 131\n\n\n\n\n\n\n\nTABLE 4 \nSolubilisation of tricalcium phosphate by Rhizobium strains from Vigna mungo \n\n\n\n \n                                             P2O5 liberated \nS. No                                          strain                                          (\u03bcg/ml) \n  \n\n\n\n          1                                       VM-2                                                 799 \n\n\n\n          2                                       VM-8                                                  372 \n\n\n\n          3                                       VM-9                                                  261 \n\n\n\n          4                                       VM-15                                                353 \n\n\n\n \nNotes: Each value in the table is an average of three replicates \nSignificant at 1% (p = 0.000)  \n \n\n\n\nHydrogen Cyanide Production \n\n\n\nAmong the four strains screened, except for VM-8 strain, the other three strains \n\n\n\n(VM-2, VM-9 and VM-15) produced HCN. Among these three strains VM-2and \n\n\n\nVM-15 showed strong HCN production by a change in colour of filter paper from \n\n\n\nyellow to reddish brown (Figure 2) and Strain VM-9 showed low HCN production \n\n\n\nby change in colour of filter paper from yellow to light brown (Table 5). Control \n\n\n\nplate did not show any colour (Figure 3). \n\n\n\nTABLE 5 \nProduction of hydrogen cyanide (HCN) by Rhizobium strains \n\n\n\nS. No. Strain Colour \n\n\n\n1. \n\n\n\n2. \n\n\n\n3. \n\n\n\n4. \n\n\n\nVM-2 \n\n\n\nVM-8 \n\n\n\nVM-9 \n\n\n\nVM-15              \n\n\n\nReddish brown \n\n\n\n\u2212 \n\n\n\nLight brown \n\n\n\nReddish brown \n\n\n\nmedia containing Tricalcium phosphate as phosphate source. The solubilisation \nefficiency of Rhizobium strains on solid media ranged between 38% and 270% \n(Manasa et al. 2017).\n\n\n\nHydrogen Cyanide Production\nAmong the four strains screened, except forVM-8 strain, the other three strains \n(VM-2, VM-9 and VM-15) produced HCN. Among these three strains VM-2and \nVM-15 showed strong HCN production by a change in colour of filter paper from \nyellow to reddish brown (Figure 2) and Strain VM-9 showed low HCN production \nby change in colour of filter paper from yellow to light brown (Table 5). Control \nplate did not show any colour (Figure 3).\n\n\n\nTABLE 4\nSolubilisation of tricalcium phosphate by Rhizobium strains from Vigna mungo\n\n\n\n\n\n\n\n\n\n\n\nThe isolates of Rhizobium meliloti from ground nut were able to produce \n\n\n\nHCN (Arora et al. 2001). Thirty three isolates (7.26%) from 454 rhizobial isolates \n\n\n\nhad the ability to produce HCN as reported by Pellock et al. (2002). Yogendra et \n\n\n\nal. (2013) reported that out of the 25 Rhizobium strains tested, only one strain \n\n\n\nproduced hydrogen cyanide (HCN). Muhammad et al. (2016) reported that their \n\n\n\nstudies on PGPR features of the Rhizobium strains obtained from different summer \n\n\n\nlegumes, only 9% of the tested rhizobial strains produced HCN.  Kranthi Kumar \n\n\n\nand Raghu Ram (2016) reported that four Rhizobium strains out of six strains \n\n\n\nisolated from Vignatrilobata showed HCN production. Monika et al. (2017) \n\n\n\nreported that four rhizobial strains from 14 rhizobial isolates had the ability to \n\n\n\nproduce HCN. Out of 15 Rhizobium isolates, eight produced HCN. Further, out of \n\n\n\neight , the Rhizobium strain obtained from red gram exhibited strong HCN \n\n\n\nproduction and the Rhizobium strains obtained from ground nut scored as moderate \n\n\n\nfor HCN production (Manasa et al. 2017).  \n\n\n\n\n\n\n\nFigure  2. HCN production by VM-2                Figure 3. HCN control plate   Figure  2. HCN production by VM-2           Figure 3. HCN control plate\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021132\n\n\n\n The isolates of Rhizobium meliloti from ground nut were able to produce \nHCN (Arora et al. 2001). Thirty-three isolates (7.26%) from 454 rhizobial isolates \nhad the ability to produce HCN as reported by Pellock et al. (2002). Yogendra et \nal. (2013) reported that out of the 25 Rhizobium strains tested, only one strain \nproduced hydrogen cyanide (HCN). Muhammad et al. (2016) reported that their \nstudies on PGPR features of the Rhizobium strains obtained from different summer \nlegumes, only 9% of the tested rhizobial strains produced HCN. Kranthi Kumar and \nRaghu Ram (2016) reported that four Rhizobium strains out of six strains isolated \nfrom Vignatrilobata showed HCN production. Monika et al. (2017) reported that \nfour rhizobial strains from 14 rhizobial isolates had the ability to produce HCN. \nOut of 15 Rhizobium isolates, eight produced HCN. Further, out of eight, the \nRhizobium strain obtained from red gram exhibited strong HCN production and \nthe Rhizobium strains obtained from ground nut scored as moderate for HCN \nproduction (Manasa et al. 2017).\n\n\n\nPhylogenetic Analysis of Four Representative Isolates\nThe phylogenetic analysis of the four gene sequences of 16S r RNA of VM-2, \nVM-8, VM-9 and VM-15 was blasted against the nucleotide database of the NCBI \nand the sequences were aligned with a set of published sequences on the basis of \nthe conserved primary sequence and also by nucleotide BLAST similarity search \nanalysis.  Based on the 16S rRNA gene sequences, the strain VM-2 showed a \nclose relation with Rhiobium sp. strain, VM-8 and VM-9 with Bradyrhizobium \nsp. and VM-15 with Achromobacter sp. The 16S rRNA sequences were deposited \nin NCBI with the accession numbers KJ 704783 (VM-2), KJ 704784 (VM-8),KJ \n704785 (VM-9) and KJ 501696 (VM-15). \n The above results clearly indicate that the strains belong to Rhizobiaceae \n(VM-2), Bradyrhizobiaceae (VM-8, VM-9) and Alcaligenaceae (VM-15) families \nwhich are phylogenetically distinct.\n  \n\n\n\nTABLE 5\nProduction of hydrogen cyanide (HCN) by Rhizobium strains\n\n\n\n\n\n\n\nTABLE 4 \nSolubilisation of tricalcium phosphate by Rhizobium strains from Vigna mungo \n\n\n\n \n                                             P2O5 liberated \nS. No                                          strain                                          (\u03bcg/ml) \n  \n\n\n\n          1                                       VM-2                                                 799 \n\n\n\n          2                                       VM-8                                                  372 \n\n\n\n          3                                       VM-9                                                  261 \n\n\n\n          4                                       VM-15                                                353 \n\n\n\n \nNotes: Each value in the table is an average of three replicates \nSignificant at 1% (p = 0.000)  \n \n\n\n\nHydrogen Cyanide Production \n\n\n\nAmong the four strains screened, except for VM-8 strain, the other three strains \n\n\n\n(VM-2, VM-9 and VM-15) produced HCN. Among these three strains VM-2and \n\n\n\nVM-15 showed strong HCN production by a change in colour of filter paper from \n\n\n\nyellow to reddish brown (Figure 2) and Strain VM-9 showed low HCN production \n\n\n\nby change in colour of filter paper from yellow to light brown (Table 5). Control \n\n\n\nplate did not show any colour (Figure 3). \n\n\n\nTABLE 5 \nProduction of hydrogen cyanide (HCN) by Rhizobium strains \n\n\n\nS. No. Strain Colour \n\n\n\n1. \n\n\n\n2. \n\n\n\n3. \n\n\n\n4. \n\n\n\nVM-2 \n\n\n\nVM-8 \n\n\n\nVM-9 \n\n\n\nVM-15              \n\n\n\nReddish brown \n\n\n\n\u2212 \n\n\n\nLight brown \n\n\n\nReddish brown \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021 133\n\n\n\nPlant Inoculation Test\nPot culture experiment\nAmong the different morphological and yield parameters studied under pot \nculture experiment (Figure 4), indigenous strains such as VM-8, VM-9, and \nVM-15 inoculated plants showed good results both in inoculated sterilised and \nInoculated unsterilised soils than the plants grown in sterilised uninoculated soils \nand control soils both at 35 DAS and at 50% flowering stage. The strain VM-2 \nshowed moderate results among the different parameters studied under pot culture \nexperiment. \n Similar reports of variation among the native or indigenous rhizobial \nstrains inoculation in different crops on different parameters have been reported \nby so many authors. Arroyo et al. (1998) inoculated common bean with native \nBradyrhizobia, Pant and Prasad (2004) treated soybean with native Bradyrhizobia \nwhile Hungria et al. (2015)and Samudin and Kuswantoro (2018) inoculated \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n               \n                              A                                                                     B                                                                \n\n\n\n\n\n\n\n                           C                                                                     D \n \n\n\n\nFigure 4. Experimental view of pot experiment                                                      \nA. Treatment 1: Seed inoculation with isolated native strains in sterilised soil  \n\n\n\nB. Treatment 2: Seed inoculation with isolated native strains in unsterilised soil \n\n\n\nC. Treatment 3: Growth of seedlings in sterilised soil without inoculation \n\n\n\nD.    Control : Growth of seedlings in unsterilised soil without inoculation \n\n\n\n\n\n\n\ninoculated unsterilised soils than the plants grown in sterilised uninoculated soils \n\n\n\nand control soils both at 35 DAS and at 50% flowering stage. The strain VM-2 \n\n\n\nshowed moderate results among the different parameters studied under pot culture \n\n\n\nFigure 4. Experimental view of pot experiment  \n                                                   \n A. Treatment 1: Seed inoculation with isolated native strains in sterilised \n  soil.\n B. Treatment 2: Seed inoculation with isolated native strains in unsterilised \n  soil.\n C. Treatment 3: Growth of seedlings in sterilised soil without inoculation\n D. Control : Growth of seedlings in unsterilised soil without inoculation\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 25, 2021134\n\n\n\nsoybean with native Rhizobium. Other researchers carried out studies on the \nBengal gram (Bhattarai and Maskey1992; Tippannavar and Desai 1992) the \nsoybean (Palaniappan et al.1997), black gram (Neemar et al. 2007), chick pea \n(Yadav et al. 2011), green gram (Bhat et al. 2010) and in dry bean (Karaca and \nUyanoz 2012).\n\n\n\nCONCLUSION\nBased on our study, it is concluded that all the four isolates (VM-2, VM-8,VM-9 \nand VM-15) exhibited plant growth promoting traits like production of IAA, \nEPS, HCN and phosphate solubilisation. Inoculation of most cultivated rice \nfallow black gram variety LBG-752 with these four indigenous rhizobial strains \npromoted plant growth which could be directly attributed to the beneficial effects \nfrom biological N2 fixation and phytohormones, EPS production and indirectly to \nphosphate solubilisation. 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"\n\nINTRODUCTION\nUltisols and Oxisols in Malaysia have the same clay mineralogical composition. \nThis means that these Ultisols and Oxisols are highly weathered, with pH ranging \nfrom 4 to 5 in the B horizons. The areas where the soils are found are subjected to \nextreme weather conditions such as high temperature and high rainfall throughout \n\n\n\nUsing Ground Basalt and/or Organic Fertilizer to Enhance \nProductivity of Acid Soils in Malaysia for Crop Production  \n\n\n\n \nJ. Shamshuddin*, C.I. Fauziah, M. Anda, J. Kapok and \n\n\n\nM.A.R.S. Shazana\n\n\n\nDepartment of Land Management, Faculty of Agriculture, Universiti Putra \nMalaysia, 43400 Serdang, Selangor, Malaysia\n\n\n\nABSTRACT\nThe pH of mineral acid soils in Malaysia ranges from 3 to 5, with the pH of \n\n\n\nhave a pH < 3.5. Under this condition, Al is present at a toxic level for crop \n\n\n\nwhile acid sulfate soils contain jarosite and/or pyrite, mica, vermiculite and \nsmectite beside kaolinite. Studies were conducted in the laboratory, glasshouse \n\n\n\nand/or organic fertilizer on the chemical properties of the three soil types and on \nthe growth of cocoa and rice. The results showed that basalt application improved \nthe chemical fertility of the Ultisols and Oxisols by way of increasing pH, Ca, \nMg, K and P as well as cation exchange capacity. The increase in pH lowered Al \nin the soil solution as it was precipitated as Al-hydroxides. The pH increase is \nattributed to the hydrolysis of silicate released by basalt. However, basalt takes \ntime to disintegrate and dissolve completely. Basalt application decreased pHo. \nThe increase in pH and the concomitant decrease in pHo increased the negative \ncharge of the soils and further improved soil productivity. Results from glasshouse \n\n\n\ncocoa. The critical Al and Mn concentration in soil solution for cocoa growth was \n\n\n\nimprove soil productivity and this was observed when basalt was applied on an \nacid sulfate soil. Basalt dissolves more rapidly under very acidic conditions. The \nincrease in pH and the decrease in Al as well the release of nutrients resulted in an \nincrease in rice yield.\n\n\n\nKeywords: Chemical soil fertility, acid tropical soils, soil acidity,\n                      exchangeable aluminum, manganese\n\n\n\n___________________\n*Corresponding author : Email: samsudin@upm.agri.edu.my\n\n\n\n\n\n\n\n\nresulting in low productivity. Due to low pH and high Al, cocoa does not grow \nwell on these soils. The problems of soil acidity can be overcome by liming \n(Shamshuddin et al. 1991; Ismail et al. 1993; Shamshuddin and Ismail 1995; \nShamshuddin et al. 1998; Shamshuddin et al. et al.\nby applying basalt (Gillman et al. et al.\n\n\n\n-1 \n\n\n\n-1 -1 -1 -1 S \n(Gillman et al\n\n\n\nMost of the Ultisols and Oxisols in the tropics lack organic matter which \ncan supply plant nutrients as well as improve the structure of mineralised soils.  \nCompost is the organic matter usually applied for alleviating the infertility of \nUltisols and Oxisols (Anda et al.  et al.\n\n\n\nAcid sulfate soils in Malaysia with a pH of < 3.5 contain kaolinite, jarosite \n\n\n\nhaving pyrite are drained for development (Shamshuddin and Auxtero 1991; \nShamshuddin et al. 1995; Shamshuddin et al et al.\nbecome extremely acidic due to pyrite oxidation. Acid sulfate soils are distributed \n\n\n\net al\n\n\n\ncocoa (Shamshuddin et al. et al.\nsuccess. Some acid sulfate soils in the country are low in organic matter. The low \nproductivity of the soils can be improved by applying basalt in combination with \norganic fertilizers (Shazana et al.\n\n\n\nThe objective of this paper is to explain the effects of applying basalt and/or \norganic fertiliser on the chemical properties of Ultisols, Oxisols and acid sulfate \nsoils and on the growth of cocoa and rice.\n\n\n\nMineralogical Composition\nThe mineralogy of Ultisols and Oxisols can be determined by XRD analysis. \nFigure 1  shows the XRD diffractogram of the untreated clay fraction of Segamat \nSeries, a common Oxisol in Malaysia. Segamat soil is derived from andesite \n\n\n\nthe country contain gibbsite at high amounts. The mineralogy of Ultisols in the \n\n\n\nsoils. Such soils are infertile due to low pH, low basic cations and low available \nP, but high in Al. The productivity of the soils can be improved by applying \nappropriate amendments such as ground magnesium limestone (locally named \n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\n\n\n\n\n\nThe fertility of acid sulfate soils is controlled by pyrite and this can be \ndetermined by XRD analysis and electron microscopy (Shamshuddin et al. \n1995; Shamshuddin et al.\n(Shamshuddin et al. \nand Auxtero 1991; Enio et al et al\n\n\n\nEffects of Basalt Application on Ultisols and Oxisols\n\n\n\nChanges in Soil pH\nGround basalt reacts with moist soils under glasshouse conditions and changes \nsoil pH and exchangeable aluminum within 6 months of its application \n\n\n\nin month 6 for Bungor soil, an Ultisol (Fig. 2). The soil pH of Munchong soil \n\n\n\n-1, soil pH increased to a value above 5 \n\n\n\ncrop is minimized as most of the Al becomes Al-hydroxides.\n\n\n\nbasalt/ha. At this rate of basalt application, the exchangeable Al was concomitantly \ndecreased to less than 1 cmolc kg-1 soil, a condition suitable for cocoa growth. The \nsame trend of improvement in chemical fertility was observed for the Munchong \nsoil. A suitable rate of ground basalt application to alleviate the infertility of \n\n\n\n-1\n\n\n\nstudy by Anda et al.\nThe initial pH of Bungor soil was lower than that of Munchong soil. This is \n\n\n\nFig. 1: XRD diffractogram of clay fraction of Segamat Series (Anda et al. 2008a)\n\n\n\nProductivity Enhancement of Acid Soil\n\n\n\n\n\n\n\n\npH of Ultisol to be usually lower than that of the Oxisol due to the presence of \nmore exchangeable Al in the former. This was also observed in this study. This \nstudy shows that exchangeable Al in the Bungor soil is 1.66 cmolc kg-1 soil for the \n\n\n\nc \nkg-1 soil.\n\n\n\n-1 did not result \nin the same increase in soil pH. The pH of Bungor was consistently higher than \nthat of Munchong soil (Fig. 2). This is not consistent with the higher exchangeable \nAl in the Bungor soil as compared to that of the Munchong soil. Higher pH in the \n\n\n\nsoils is probably attributed to the differences in chemical reactions because of \ndiffering mineralogy in the clay fraction of the soils. Being an Oxisol, Munchong \n\n\n\n3\n\n\n\nChanges in Exchangeable Al\nThe exchangeable Al in Bungor soil was 1.47 cmolc kg-1 soil. After 6 months of \n\n\n\nc kg-1 soil. \nWithin the same period, exchangeable Al in Munchong soil was reduced from \n\n\n\nc kg-1 soil. A clear reduction of exchangeable Al in both soils \nwith time due to ground basalt application is given in Figure 3.\n\n\n\nImportant minerals in basalt are olivine and pyroxene. When they come into \ncontact with water at low pH under the high temperature of the tropics, these\n\n\n\nFig. 2: Effect of basalt application on soil pH (HSD0.05 = 0.02 for Bungor \n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\nBungor Series\nMunchong Series\n\n\n\n6\n\n\n\n5\n\n\n\n4\n\n\n\n3\n\n\n\n2\n\n\n\n1\n\n\n\n0\n0 5\n\n\n\nRate of basalt application (t/ha)\n\n\n\nS\noi\n\n\n\nl p\nH\n\n\n\n10 20\n\n\n\n\n\n\n\n\n131\n\n\n\nminerals disintegrate and dissolve, resulting in an increase in pH. Olivine dissolves \nfaster than pyroxene.\n\n\n\nBasalt application reduced exchangeable Al in both soils; the decrease \nwas more in Munchong than in Bungor soil. This is consistent with the more \n\n\n\n-1 reduced \nexchangeable Al to less than 1 cmolc kg-1 soil, a level considered good for the \ngrowth of crops sensitive to Al. Thus, the recommended rate of basalt application \n\n\n\n-1.\n\n\n\nChanges in Available P \nAvailable P in the two soils before they were treated with ground basalt was \n\n\n\n-1\n\n\n\n-1\n\n\n\nChanges in Basic Exchangeable Cations \nThe changes in exchangeable Na, K, Mg and Ca in the soils of Bungor and \nMunchong Series 6 months after ground basalt application had also been \ndetermined. In response to basalt application, exchangeable Ca, Mg and K \n\n\n\nFig. 3: Effect of basalt application on exchangeable Al (HSD0.05 = 0.03 for Bungor and \n0.11 for Munchong soils)\n\n\n\nProductivity Enhancement of Acid Soil\n\n\n\nBungor Series\nMunchong Series\n\n\n\n0\n0\n\n\n\n0.2\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n0.8\n\n\n\n1\n\n\n\n1.2\n\n\n\n1.4\n\n\n\n1.6\n\n\n\n1.8\n\n\n\n5\nRate of basalt application (t/ha)\n\n\n\n10 20\n\n\n\nE\nxc\n\n\n\nha\nng\n\n\n\nea\nbl\n\n\n\ne \nA\n\n\n\nl (\ncm\n\n\n\nol\n  /\n\n\n\nkg\n)\n\n\n\nc\n\n\n\n\n\n\n\n\ncations depends on the amount applied. The exchangeable basic cations in the \ntwo soils in order of decreasing amount are Ca >Mg >K >Na. This is consistent \nwith the elemental composition of basalt used in the experiment as determined by \nGillman et al.\n\n\n\nChanges in CEC \n\n\n\nmonths of basalt application (Fig. 4). The CEC was determined in unbuffered 1 \nM NH4Cl which gives the CEC at soil pH. The increase in CEC is related to the \nreaction of the silicate with variable charge minerals, like goethite and hematite. \nWhen silicate reacts with variable charge minerals in the soil, its pHo is lowered \n(Anda et al. Figure 5. Past research in Malaysia shows \nthat the pHo of an Oxisol decreases due to ground basalt application (Shamshuddin \n\n\n\npHo was lowered and pH was concomitantly increased. These phenomena widen \n\n\n\n(Fig. 6).\n\n\n\n0.11 for Munchong soils)\n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\n\n\n\n\n0\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n5\n\n\n\n6\n\n\n\n7\n\n\n\n8\n\n\n\n9\n\n\n\nBungor Series\nMunchong Series\n\n\n\n0 5 10 20\nrate of basalt application (t/ha)\n\n\n\nC\nE\n\n\n\nC\n (m\n\n\n\nol\n   \n\n\n\n/k\ng)\n\n\n\nc\n\n\n\n\n\n\n\n\n133\n\n\n\nEffects of Basalt or Compost Application on Cocoa Growth\n\n\n\nSoil Solution \n-1 increased solution \n\n\n\nThe presence of high amounts of Ca in the soil solution does, to a \ncertain extent, detoxify Al (Alva et al.\n\n\n\nFig. 5: Effect of basalt application on pHo of Segamat soil (M=month)\n\n\n\nFig. 6: Effect pH-pHo on CECB of topsoil of Segamat Series (B = Ca adsorption)\n\n\n\nProductivity Enhancement of Acid Soil\n\n\n\n\n\n\n\n3.6\n\n\n\n3.7\n\n\n\n3.8\n\n\n\n3.9\n\n\n\n4.0\n\n\n\n4.1\n\n\n\n0 10 20 30 40 50 60 70 80\nBasalt rate /t ha -1\n\n\n\npH\n0  v\n\n\n\nal\nue\n\n\n\nM 8\n\n\n\nM12\n\n\n\nM15\n\n\n\n2.0\n\n\n\n4.0\n\n\n\n6.0\n\n\n\n8.0\n\n\n\n10.0\n\n\n\n12.0\n\n\n\n-0.55 0.15 0.85 1.55 2.25 2.95\n\n\n\npH - pH0\n\n\n\nC\nE\n\n\n\nC\nB \n\n\n\n(c\nm\n\n\n\nol\nc\n k\n\n\n\ng-1\n)\n\n\n\n12 months\n\n\n\n24 months\n\n\n\n\n\n\n\n\n134\n\n\n\nEffect of Treatment on Cocoa Growth \nGround basalt was able to ameliorate the Oxisol for cocoa growth even at a low \n\n\n\n-1 needs to be \n\n\n\nAt this rate of basalt application, the cocoa plants grew very well and achieved a \n-1 or higher. \n\n\n\nFigure 7 gives the plot of the relative dry matter weight against soil solution \n\n\n\nconcentration for optimal cocoa growth. Shamshuddin et al\ncritical Al3+ activity for cocoa seedlings grown on acid sulfate soils in Malaysia \n\n\n\nwould have raised the soil pH to 4.5. This means that even at pH below 5, cocoa \nplants can grow healthily when soil solution Al concentration is low. The growth \n\n\n\nTABLE 1\n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\n                     Basalt\n\n\n\n \nRate (t ha-1)                 0                5              10              20                40                   80\n\n\n\n\n\n\n\n \nDMW (g/pot)          709c          742c           846b           873b           899ab              960a\n\n\n\n  \n                                                Compost   \nDMW (g/pot)          587c         654bc       708ab          732ab          803a    763ab   \n\n\n\n\n\n\n\nBasalt rate                                                   Element \n(t ha-1)                        Ca                     Mg                 K                     Al                    Mn \n\n\n\n   M   \n\n\n\n0                              153b                  66c                41c                   16a                  29a \n5                              145b                 139c               36c                   13b                 12b \n10                            171b                 153c               39c                     6c                   9c \n20                            175b                 317b               43c                     3cd                 3d \n40                            222a                 515a               65b                     2d                   3d \n80                            266a                 610a               82a                     2d                   1d \n\n\n\n\n\n\n\nSource: Shamshuddin et al. (2011) \n\n\n\n\u00b5\n\n\n\n\n\n\n\n\n135\n\n\n\nof cocoa could have also been promoted by the presence of high amounts of Ca \nreleased by the dissolving basalt. Alva et al.\ncertain extent, alleviate Al toxicity. Figure 8 gives the plot of relative dry matter \nweight against soil solution Mn concentration. From this diagram, it is estimated \nthat the critical soil solution Mn concentration for optimal cocoa growth is 5.8 \n\n\n\nground basalt application to ameliorate this infertile Oxisols for cocoa production \n-1.\n\n\n\nCompost Application\nCocoa seedlings planted on soils treated with rice husk compost did not grow \n\n\n\ndifference in amount of nutrients released and pH values. Basalt application \nreleased more Ca and Mg and raised pH values higher compared to the rice husk \n\n\n\n-1 \n\n\n\n-1\n\n\n\nhusk compost ha-1 is probably too high a rate of application for the soil. The high \n\n\n\nfor cocoa (Anda et al.\n-1 or below \n\n\n\nwas assumed to be due to the addition of plant nutrients released by the compost \nO5\n\n\n\nFig. 7: Relationship between relative dry matter weight and Al concentration \nin soil solution\n\n\n\nProducttivity Enhancement of Acid Soil\n\n\n\ny = 97.68e-0.02x ;R 2 = 0.90\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n110\n\n\n\n0 2 4 6 8 10 12 14 16 18\nAl concentration (\u00b5M)\n\n\n\nR\nel\n\n\n\nat\niv\n\n\n\ne \ndr\n\n\n\ny \nm\n\n\n\nat\nte\n\n\n\nr  \nw\n\n\n\nei\ngh\n\n\n\nt (\n%\n\n\n\n)\n\n\n\n\n\n\n\n\n136\n\n\n\nthe Al and/or Mn from the soil solution. Organic matter, to some extent, forms \n\n\n\nabsorbed by cocoa. The end result is better cocoa growth as compared to that \nwithout compost treatment.\n\n\n\nEffects of Application of Basalt in Combination with Compost on Cocoa Growth\n\n\n\nEffects of Treatment on Soil Solution Composition \n\n\n\nThroughout the experimental period, the rainfall at the experimental station was \nless than normal resulting in erratic growth of the cocoa seedlings.\n\n\n\nThe application of basalt alone increased the concentration of soil solution \n\n\n\n-1\n\n\n\n-1\n\n\n\nCEC which in turn retained more Ca and Mg on exchange sites of the colloidal \n\n\n\nin Al in soluble form was due in part to the increase in pH, which precipitated \nAl to form inert Al-hydroxides. In the case of soil solution Mn, the decrease in \nconcentration was caused by a pH increase.\n\n\n\nAs the rate of basalt and/or compost increased, soluble Ca and Mg increased, \n\n\n\nt ha-1 -1\n\n\n\n-1 -1\n\n\n\nFig. 8: Relationship between relative dry matter weight and Mn concentration\n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\n\n\n\n\ny = 94.967e-0.01x;R2 = 0.79\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n110\n\n\n\n0 4 8 12 16 20 24 28 32\nMn concentration (\u00b5M)\n\n\n\nR\nel\n\n\n\nat\niv\n\n\n\ne \ndr\n\n\n\ny \nm\n\n\n\nat\nte\n\n\n\nr  \nw\n\n\n\nei\ngh\n\n\n\nt (\n%\n\n\n\n)\n\n\n\n\n\n\n\n\n137\n\n\n\nThe lowest concentration of soil solution Al and Mn was observed for the sample \n-1 -1, with values \n\n\n\nstudy. \n\n\n\nIt was observed that the values of concentration of the various metals \ndetermined in the soil solution were erratic or rather inconsistent with the rate \nof basalt application. This was due to the effect of soil erosion prevailing in \n\n\n\nbasalt had been dispersed and transported to the other plots by rainwater, and \n\n\n\nand has a long residence in Oxisols due to the presence of cellulose (Anda et al. \n\n\n\nTABLE 3\n\n\n\nProductivity Enhancement of Acid Soil\n\n\n\nTreatment \nIon concentration  \n\n\n\nCa Mg K Al Mn \n(\u00b5M)  \n\n\n\nR0B0 72.23 2.99 27.21 18.53 7.03 \nR0B1 110.07 18.04 41.63 15.31 4.87 \nR0B2 142.49 22.81 36.91 11.95 2.86 \nR0B3 55.55 14.90 27.84 8.30 1.90 \nR1B0 118.47 11.05 37.75 11.83 5.18 \nR1B1 131.57 15.59 35.53 11.63 4.06 \nR1B2 148.24 16.73 36.76 9.95 4.32 \nR1B3 40.50 11.56 24.35 5.40 3.24 \nR2B0 128.48 10.59 38.03 10.51 4.76 \nR2B1 133.58 20.07 51.06 11.52 4.37 \nR2B2 148.68 64.42 37.67 8.12 3.81 \nR2B3 104.21 19.65 36.72 6.58 2.73 \nR3B0 176.48 28.70 61.93 6.59 3.35 \nR3B1 151.45 44.69 44.15 4.29 2.49 \nR3B2 74.43 62.10 42.85 4.06 3.22 \nR3B3 67.81 19.76 36.15 1.25 2.53 \nLSD 136.22 22.49 31.87 5.09 2.45 \nP value 0.0044 <0.0001 0.0287 <0.0001 <0.0001 \n\n\n\nNote: 0, 1, 2 and 3 represent 0, 5, 10 and 20 t/ha, respectively \n          R = compost; B = basalt \n\n\n\n(Source: Shamshuddin et al. 2011) \n\n\n\n\n\n\n\n\n138\n\n\n\nEffect of Treatment on Soil pH \n\n\n\nslope. \n\n\n\n-1 resulted in a higher \n\n\n\n-1 -1\n\n\n\n-1\n\n\n\nchange soil pH much. Basalt contains silicate which on dissolution hydrolyses \nto release hydroxyl that increases pH. The soil pH could have been higher had \nthe basalt applied dissolved completely when the soils were sampled. During \nthe sampling, it was observed that clumps of basalt granules were still intact in \n\n\n\nof application, not all ground basalt applied had dissolved. This has important \nimplications on soil management. Basalt is only effective in the long run.\n\n\n\nEffect of Treatment on Exchangeable Al \nc kg-1\n\n\n\nWhen pH increased to above 5, most of the Al in the soil solution precipitated to \n\n\n\nTABLE 4\nChanges in soil pH, exchangeable cations and total organic carbon of Segamat soil as \n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\nTreatment              pH                 Exchangeable cations                  Total organic carbon \n                                                Ca              Mg            Al \n                                                 cmolc/kg                              % \nR0B0                    4.84            1.37          1.07          0.72                           2.00 \nR0B1                    5.26            3.21          2.03          0.36                           2.32 \nR0B2                    4.88            2.22          2.17          0.43                           2.10 \nR0B3                    5.30            2.46          2.15          0.38                           2.51 \nR1B0                    5.31            2.60          1.56          0.33                           2.46 \nR1B1                    5.33            2.96          2.21          0.39                           2.16 \nR1B2                    5.74            3.63          2.72          0.14                           2.76 \nR1B3                    5.38            3.15          2.47          0.14                           2.76 \nR2B0                    5.04            2.82          1.83          0.42                           2.82 \nR2B1                    4.92            2.20          1.94          0.36                           2.45 \nR2B2                    5.21            1.60          1.52          0.27                           2.05 \nR2B3                    5.51            4.16          3.36          0.16                           2.53 \nR3B0                    5.00            4.06          1.87          0.24                           2.66 \nR3B1                    5.39            3.24          2.98          0.19                           2.62 \nR3B2                     5.72            2.60         3.24          0.18                           2.61 \nR3B3                     5.70            4.33         3.76          0.17                           2.40 \nLSD (P<0.05)        0.66            1.64         0.67          0.13                          0.50 \n\n\n\nSource : Shamshuddin et al.  (2011)  \n\n\n\n\n\n\n\n\n139\n\n\n\ncmolc kg-1 -1\n\n\n\nthe value of exchangeable Al was probably due to chelation by organic matter \n(Muhrizal et al\n\n\n\nEffect of Treatment on Exchangeable Ca \nExchangeable Ca in the control treatment was 1.37 cmolc kg-1, a value that \n\n\n\nc kg-1 -1. At the highest \n-1 -1\n\n\n\nwas 4.33 cmolc kg-1 -1\n\n\n\nEffect of Treatment on Exchangeable Mg \n\n\n\ncmolc kg-1 -1\nc kg-1.\n\n\n\nEffect of Treatment on Cocoa Growth \n\n\n\ngrew better than the others, presumably due to the effects of treatments. This was \nexpected as the effects of the treatments, either due to basalt or compost, differed \nbecause they were of different composition. \n\n\n\n-1, cocoa height increased to 144 cm. Application \nof compost alone also increased cocoa height. Cocoa girth increased from 1.8 cm \n\n\n\n-1 -1\n\n\n\nand rice husk compost applications. \n\n\n\nCocoa height increased linearly as N increased. In this experiment, N from \n\n\n\nnitrogen fertilizer has to be applied for better cocoa growth. Cocoa height was not \nrelated in any way to phosphorus in the leaf as shown by the regression analysis. \n\n\n\ncontributed some phosphorus to the soils, which was then taken up by the cocoa \nfor its growth. Likewise, compost had contributed some P. \n\n\n\nProductivity Enhancement of Acid Soil\n\n\n\n\n\n\n\n\nThe K content in the leaf of the control treatment was 1.84 % and this value \n-1\n\n\n\nlinearly with leaf K. As more ground basalt was applied, more K was available in \n\n\n\n-1. Basalt is cheaper than muriate of potash \n\n\n\nCocoa growth was affected by the presence of Al in the soil. Cocoa height \ndecreases linearly as the exchangeable Al increases  The relative cocoa \nheights for the various rates of basalt and/or compost application were estimated \nand these values were plotted against exchangeable Al (Fig. 10). The critical \n\n\n\nc kg-1 soil which means that cocoa is \nvery sensitive to Al.\n\n\n\nthought to be due to the limiting factor of Mn toxicity, which was present in \nexcessive amounts in the soil. Soil pH was below 5 and Mn was measured to be \n\n\n\nthan 5 in order to decrease Mn in the soil solution to an acceptable level for cocoa \ngrowth. The infertility of this acid soil can be overcome by application of ground \n\n\n\n-1\n\n\n\nTABLE 5\nChanges in leaf NPK, cocoa height and girth as affected by basalt and rice husk compost \n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\nTreatment                 Leaf NPK (%)                   Cocoa height (cm)        Cocoa girth \n(cm)  \n                             N            P            K \nR0B0                  1.4          0.11       1.84                        80                               1.8 \nR0B1                  1.7          0.02       1.63                       120                              2.4 \nR0B2                  2.2          0.10       2.70                       165                              3.4 \nR0B3                  2.5          0.10       2.79                       144                              2.8 \nR1B0                  1.9          0.19       3.16                       196                              4.3 \nR1B1                  2.0          0.06       2.38                       201                              5.0 \nR1B2                  2.0          0.13       3.15                       179                              4.9 \nR1B3                  1.9          0.15       2.73                       190                              4.5 \nR2B0                  2.0          0.09       3.65                       202                              5.4 \nR2B1                  2.0          0.22       3.16                       172                              4.1 \nR2B2                  2.0          0.18       2.76                       186                              4.0 \nR2B3                  2.0          0.15       2.62                       204                              5.4 \nR3B0                  2.5          0.17       2.23                       206                              5.2 \nR3B1                  2.5          0.15       2.59                       218                              5.0 \nR3B2                  2.6          0.09       3.10                       211                              5.7 \nR3B3                  2.5          0.15       3.23                       197                              4.7 \nLSD (P<0.05)    1.2           0.13      1.45                          76                              1.8 \n\n\n\nSource: Shamshuddin et al. (2011) \n\n\n\n\n\n\n\n\n141\n\n\n\nEffects of Basalt on an Acid Sulfate Soil \nA study was conducted to determine the effects of basalt application on the \nproperties of an acid sulfate soil under submerged conditions (Shazana et al. \n\n\n\nthe second cycle of submergence was 4.5. The pH of water increased slightly due \nto the reduction process during which hydrogen ions were consumed (Muhrizal \net al. -1 had increased the pH to 5.5, at \nwhich point Al no longer functioned as a threat to rice growth. Water condition \n\n\n\nFig. 10: Relationship between relative cocoa height and exchangeable Al\n\n\n\nProductivity Enhancement of Acid Soil\n\n\n\n\n\n\n\ny = -178.78x + 234.64\nR2 = 0.53\n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\n200\n\n\n\n250\n\n\n\n0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8\nExchangeable Al (cmol c  kg -1 )\n\n\n\nC\noc\n\n\n\noa\n h\n\n\n\nei\ngh\n\n\n\nt (\ncm\n\n\n\n)\n\n\n\n\n\n\n\ny = -82.56x + 107.73\nR2 = 0.53\n\n\n\n0\n\n\n\n20\n\n\n\n40\n\n\n\n60\n\n\n\n80\n\n\n\n100\n\n\n\n120\n\n\n\n0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8\nExchangeable Al (cmol c kg -1 )\n\n\n\nR\nel\n\n\n\nat\niv\n\n\n\ne \nco\n\n\n\nco\na \n\n\n\nhe\nig\n\n\n\nht\n (%\n\n\n\n)\n\n\n\n\n\n\n\n\nIn terms of Al, the value was 9 mg L-1\n\n\n\nbasalt application rate of 4 t ha-1, the Al was 7 mg L-1 and increasing the rate \n-1 -1. At this level of \n\n\n\nconcentration, Al toxicity is minimal.\nSoil pH in the second cycle was also 4.5. The value was increased to 5.5 \n\n\n\nwhen 4 t basalt ha-1\n\n\n\ncmolc kg-1 and at a basalt application rate of 4 t ha-1, the amount was reduced to \nc kg-1. Overall, it is thought that applying basalt at 4 t ha-1\n\n\n\nalleviate the infertility of acid sulfate soils for rice production. Basalt is believed to \ndissolve faster in an acid sulfate soil than either Ultisols or Oxisols in Malaysia.\n\n\n\nEffects of Basalt and/or Organic Fertiliser on Rice\nAnother study was conducted on an acid sulfate soil where rice was used as the \n\n\n\n(Fig. \n11) -1, a \nnegligible value.\n\n\n\n-1\n\n\n\n-1 (Fig. \n12). This indicates that in order to ameliorate the infertility of acid sulfate soils \nfor rice production, basalt can be applied in combination with organic fertilizers. \nOrganic matter helps to increase the rate of reduction, resulting in a more rapid \nincrease in water pH (Muhrizal et al\n\n\n\nFig. 11: Rice growth at harvest in control treatment\n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\n\n\n\n\n\n143\n\n\n\nCONCLUSION\nUltisols and Oxisols in Malaysia are low in productivity due to low pH and \nhigh aluminum. Cocoa growing on such soils produces low yield. Likewise, the \nproductivity of acid sulfate soils is low. In order to overcome the low chemical \nfertility of Ultisols, Oxisols and acid sulfate soils either for cocoa or rice production,  \nbasalt can be applied  in combination with organic fertilizers at an appropriate rate. \nOn dissolution, basalt contributes substantial amounts of Ca, Mg, K, P and S to the \ngrowing crops. At the same time, soil pH increases resulting in the precipitation of \nAl as inert Al-hydroxides. Organic fertilizers, on the other hand, contribute some \nnitrogen. Basalt application is good in the long run for Ultisols and Oxisols as \nit takes time to disintegrate and dissolve completely. However, basalt dissolves \nfaster in acid sulfate soils under submergence. The rate of basalt application to \nameliorate the infertility of acid sulfate soil is 4 t ha-1.\n\n\n\nACKNOWLEDGEMENTS\nThe author wishes to acknowledge Universiti Putra Malaysia and Malaysian \n\n\n\nFig. 12: Rice growth at harvest on soil treated with basalt in combination \nwith organic fertilizer\n\n\n\nProductivity Enhancement of Acid Soil\n\n\n\n\n\n\n\n\n144\n\n\n\nREFERENCES\nAlva, A.K., C.J. Asher and D.G. Edwards. 1986. The role of calcium in alleviating \n\n\n\naluminum toxicity. Australian Journal of Soil Research. 37: 375-383.\n\n\n\nAnda\nand factors controlling charge development of three Oxisols developed from \ndifferent parent materials. Geoderma 143: 153-167.\n\n\n\nAn\nproperties of composting rice husk and their effects on soil and cocoa growth. \nCommunications in Soil Science and Plant Analysis\n\n\n\nground basalt and its effects on Oxisol chemical properties and cocoa growth. \nSoil Science\n\n\n\nA\nthe organic matter content of an Oxisol using rice husk compost: changes in \nthe composition and its chemistry. Soil Science Society of America Journal. \n\n\n\nA\nseedlings on acid sulfate soils as affected by water regime and aluminum. Plant \nSoil\n\n\n\nE\nof the coastal sediments in the Kelantan Plains in the Malay Peninsula during \nthe Holocene. American Journal of Agricultural and Biological Science\n\n\n\nG\ndust to highly weathered soils: effect on soil chemistry. Australian  Journal of \nSoil Science. 39: 799-811.\n\n\n\nGil\nApp.  Geochem\n\n\n\nIsmail, H., J. Shamshuddin and S.R. Syed Omar. 1993. Alleviation of soil acidity in a \nMalaysian Ultisol and Oxisol for corn growth. Plant Soil. 151: 55-65.\n\n\n\nM Composting the Wastes from a Rice Processing Plant. Malaysian \nAgricultural Research and Development Institute, Serdang, Malaysia.\n\n\n\nMills, H.A. and J.B. Jones. 1991. Plant Analysis Handbook II. MicroMacro Publishing \nInc., USA. \n\n\n\nM\naluminum toxicity in an acid sulfate soil using organic materials. Communications \nin Soil Science and Plant Analysis\n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\n\n\n\n\n\n145\n\n\n\nMu\npoor acid sulfate soil upon submergence. Geoderma\n\n\n\nShamshuddin, J. and E.A. Auxtero.  1991. Soil solution composition and mineralogy \nof some active acid sulfate soil as affected by laboratory incubation with lime. \nSoil Science\n\n\n\nShamshuddin, J., I. Che Fauziah and H.A.H. Sharifuddin. 1991. Effects of limestone \nand gypsum application to a Malaysian Ultisol on soil solution composition and \nyields of maize and groundnut. Plant Soil\n\n\n\nShamshuddin, J. and H. Ismail. 1995. Reactions of ground magnesium limestone \nand gypsum in soil soils with variable-charge minerals. Soil Science Society of \nAmerica Journal.\n\n\n\nShamshuddin, J., I. Jamilah and J.A. Ogunwale. 1995. Formation of hydroxyl-\nsulfates from pyrite in coastal acid sulfate soil environments in Malaysia. \nCommunications in Soil Science and Plant Analysis\n\n\n\nShamshuddin, J., H.A.H. Sharifuddin and L.C. Bell.1998. Longevity of magnesium \nlimestone applied to an Ultisol. Communications in Soil Science and Plant \nAnalysis\n\n\n\nSh\nof pyrite oxidation in an acid sulfate soils. Communications in Soil Science and \nPlant Analysis\n\n\n\nSha\norganic materials to an acid sulfate soil on the growth of cocoa (Theobroma \ncacao Science of the Total Environment\n\n\n\nSha Acid Sulfate Soils in Malaysia. Serdang, Malaysia: UPM \nPress.\n\n\n\nS\nby kaolinite, gibbsite, goethite and hematite. Bulletin of the Geological of \nMalaysia\n\n\n\nS\nof corn and groundnut grown on Ultisols as affected by dolomitic limestone and \ngypsum applications. Malaysian Journal of Soil Science.\n\n\n\nS\nChemical Properties of an Ultisol and Oxisols in Malaysia. Pertanika Journal \nof Tropical Agricultural Science. 33: 7-14.\n\n\n\nShamshuddin, J., H.A.H. Shariduddin, I. Che Fauziah, D.G. Edwards and L.C. Bell. \n\n\n\nmagnesium limestone and gypsum. Pertanika Journal of Tropical Agricultural \nScience\n\n\n\nProductivity Enhancement of Acid Soil\n\n\n\n\n\n\n\n\n146\n\n\n\nSh\nplanted on highly weathered soil as effected by application of basalt and/or \nCompost. Communications in Soil Science and Plant Analysis. In press. \n\n\n\nSh\nthe infertility of an acid sulfate soil by using ground basalt with or without \nlime and organic fertilizer under submerged condition. Land Degradation and \nDevelopment. In press.\n\n\n\nS\n\n\n\nmagnesium limestone and organic fertilizer. Journal of Soil Environmental. 9: \n1-9.\n\n\n\nTessens, E. and J. Shamshuddin.1983. Quantitative Relationships between Mineralogy \nand Properties of Tropical Soils.  Serdang, Malaysia: UPM Press.\n\n\n\nJ. Shamshuddin, C.I. Fauziah, M. Anda, J. Kapok and M.A.R.S. Shazana\n\n\n\n\n\n"
"\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 23: 183-198 (2019)                         Malaysian Society of Soil Science\n\n\n\nGreenhouse gas Emissions in Saline and Waterlogged \n\n\n\nR.K. Kaleeswari and R. Bell\n\n\n\nDepartment of Sustainable Land Management, \nMurdoch University, Perth, Australia\n\n\n\nABSTRACT\nAn experiment was conducted to study the impact of water logging and addition \nof organic amendments on green house gas (GHG) emissions in a saline soil. The \ntreatments comprised of water levels maintained at three levels viz., complete \nsaturation, 10 cm and 15 cm below the soil surface. Organic amendment at the \nrate of 7.5 t ha-1 was added. Gas samples were collected at periodic intervals and \nanalysed. The results revealed that CO2, N2O and C2H2 emissions were lower \nunder a water logged condition and were found to be higher at water levels 10 \ncm / 15 cm below the soil surface. There was an increase in emissions from 100 \nto 400 mg kg-1 at 7 days after incubation (DAI). Methane emission was found to \nbe higher in completely saturated soil. Soils at 10 cm water level and amended \nwith the organic material registered a higher value of soil microbial biomass \u2013 C \nof 1320 mg kg-1. Emissions of GHG were enhanced with the addition of organic \nmaterial. This suggests that lack of C substrate is the dominant limitation for GHG \nemissions on saline soil. Lower q CO2 values in water logged soils indicate low \nmicrobial activity.\n\n\n\nKey words: Green House Gas, Organic amendment, Saline soil, Microbial \nbiomass, Water logging\n\n\n\n___________________\n*Corresponding author : kaleeswarisenthur@gmail.com  \n\n\n\nINTRODUCTION\nSoil organic matter levels and forms of soil carbon determine the availability \nof C substrate for microbial mediated reactions of soils. Salinity can affect \nphysiological processes by ion toxicity and ion imbalance (Klados and Tzortzakis \n2014; Kahlaoui et al. 2011; Munns and Tester 2008). As a result, saline soils are \ncharacterised by low organic matter content and reduced organic matter turnover \ndue to poor plant growth and low microbial biomass and activity (Muhammad et \nal. 2006; Tripathi et al. 2006). Salinity has been shown to decrease soil organic \nC (SOC) mineralisation. C mineralisation is higher at low ECe compared to high \nECe and confirms the strong negative effect of salinity on microbial activity (Setia \net al. 2011). In addition, salinity changes the microbial community composition \nbecause microbial genotypes differ in their ability to adapt to salt stress (Nelson \nand Mele 2007; Pankhurst et al. 2001). Water logged soils are regarded as \npotentially large sinks or sources of C (Goreau and De Mello 2007). An anaerobic \nenvironment under water logging facilitates a high C storage value in the soil \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019184\n\n\n\nprofile. There is considerable interest in revegetating saline soils to increase \nbiological productivity and reduce water logging (Bramley et al. 2003; Gardner \n2004). The hypothesis of the research was that greenhouse gas emission would \nbe limited on saline soils by low availability of decomposable C as a substrate for \nmicroorganisms. The objectives of this experiment were to: (i) quantify the rate of \nemission of GHG at various water levels; (ii) to evaluate GHG flux as influenced \nby the organic amendment; and (iii) to study the effect of water levels and organic \nmaterial addition on soil microbial biomass, C and N dynamics (NO3 - N and \nNH4 - N). \n\n\n\nMATERIALS AND METHODS\n\n\n\nColumn Study\nSoil used in the laboratory experiment was collected from 0-15 cm depth of a \nsalinised Grey Sodosol  (Aquertic Natrustalfs) from Walletin creek (Latitude: \n33.315, Longitude: 117.738 33\u00b0) Western Australia about 400 km East of Perth. \nThe initial soil had a pH (saturation paste) of 7.51 and EC of 587 m S cm-1 and \na chloride concentration of 0.45 % (dry wt. basis). Further properties of the soil \nare shown in Table 1. In sealed PVC columns (10 cm diameter) of 0.44 m height, \nsoils were packed at a bulk density of 1.33 Mg m-3 leaving 5 cm headspace. In the \norganic amendment treatment, the chopped Oldman saltbush leaf was mixed with \n0-15 cm surface soil at a rate of 7.5 t ha-1. The Oldman saltbush leaf had 32.8 % \ntotal C, 2.58 % N, C: N ratio of 12.7 and 11.2 % chloride content. Water level was \nmaintained at three levels viz., complete saturation, 10 cm and 15 cm below the \nsoil surface by allowing water by capillary movement from the bottom side of the \ncolumn. At the bottom of the column, tubes were fitted for the flow of deionized \nwater from cans placed at a raised level. Water was allowed inside the sealed \n\n\n\nTABLE 1\nInitial characteristics of experimental soil\n\n\n\nTABLE 1 \n\n\n\nInitial characteristics of experimental soil \n\n\n\n\n\n\n\nProperty Value \n\n\n\npH     (Saturation paste) 7.51 \n\n\n\nElectrical conductivity  \n\n\n\n(Saturation paste) \n\n\n\n587 m S cm-1 \n\n\n\nChloride 0.45% \n\n\n\nOrganic carbon 0.20% \n\n\n\nMicrobial biomass carbon 360 \u00b5g g-1 \n\n\n\nNH4 -N 5.50 \u00b5g g-1 \n\n\n\nNO3 -N 1.65 \u00b5g g-1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 2 \n\n\n\nCumulative CO2 emissions (g kg-1) at different stages \n\n\n\n\n\n\n\nWater level Organic Days after incubation \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 185\n\n\n\nPVC columns, up to the soil surface (complete saturation), 29 cm (10 cm water \nlevel) and 24 cm (15 cm water level) below the soil surface. The treatments were \nreplicated 4 times. The level of water in the column was maintained as per the \ntreatment schedule throughout the study period. The top side of the column was \ntightly closed with the provisions for collection of gas samples and to maintain \npressure inside the column. Soils were maintained at 25\u03bf C and the columns were \nopened to the atmosphere for 30 min every day to allow replenishment of ambient \ngas conditions and then resealed. \n\n\n\nGas Sampling and Analyses\nGas samples were collected from the closed columns in Tedlar air sampling bags \non 1, 3, 5, 7, 9,11, 13 and 15 days after incubation and analysed for CO2 and CH4 \nusing Gas chromatography (Wang and Wang 2003) and for N2O and C2H2 using \nInfra Red Analyzer (Beek et al. 2004). The values were corrected for background \nCO2, CH4 and for N2O and C2H2. Cumulative GHG emissions were calculated by \nadding the emission on day 1 with day 3 and so on.\n\n\n\nSoil analysis\nSoil samples collected at initial and final stages of the experiment were analysed \nfor NH4-N and NO3-N contents using 2 M KCl extraction by Bremner method \n(Black 1965) and determined by auto analyser .\n\n\n\nSoil Microbial Biomass (SMB)\nThe SMB was measured by the chloroform fumigation-extraction procedure \ndescribed by Vance et al. (1987). The amount of SMB-C present in the samples \nwas determined according to Eq. (1) and expressed as mg-C kg-1 oven-dry soil. \nThe SMB values were expressed on oven-dry mass of soil.\n\n\n\n SMB-C (mg-C kg-1) = 2.64 CEx  \u2192      (1)\n\n\n\nwhere CEx = Extractable C in non-fumigated soil - Extractable C in fumigated soil\n\n\n\nMetabolic Quotient\nThe metabolic quotient, q CO2 was determined according to Eq. (2).\n\n\n\nq CO2 (\u03bc g CO2 \u2013C day-1 mg-1 SMB-C) =      r    \u2192 (2)\n                                                                     -------\n                                                                     SMB\nwhere r = respiration rate (\u03bc g CO2 \u2013C day-1) and SMB-C = SMB-C (mg C kg-1)\n\n\n\nStatistical Analysis\nData were statistically analysed using SPSS version 20.0 statistical program. \nThe GHG emission data were square root transformed in order to satisfy the \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019186\n\n\n\nassumptions of ANOVA with back-transformed means presented. The transformed \ndata were analysed as two-way ANOVA and just one-way ANOVA for initial \nor final soil measurements. Where GHG emissions displayed trace amounts \n(negligible release of gases), a dummy value of 0.01 was inserted.\n\n\n\nRESULTS\n\n\n\nGHG Fluxes\nThe CO2 emissions from the completely saturated soil were low throughout the \nperiod of incubation. There was a modest increase from 1000 to 4000 mg kg-1 at \n7 DAI, but thereafter CO2 emission declined to less than 500 mg kg-1 on 15 DAI. \nLowering water level to 10 or 15 cm had little effect on CO2 emissions (Table 2).\nEmission of CO2 was significantly increased even from 1 DAI in organic matter \namended and completely saturated soil. Lowering water level to 10 or 15 cm \nin soil amended with organic matter stimulated CO2 emission threefold at 1-3 \nDAI.  An increase in CO2 emission at water level 15 cm below soil surface was \nrecorded at 7-9 DAI.  However, at water levels of 10 cm / 15 cm, much higher CO2 \nemissions were observed than the completely saturated soil.\n In completely saturated soil, low levels of N2O emissions were measured \nthroughout the incubation period. However, at water levels of 10 or 15 cm, \nstimulated N2O emission was registered  at 1-3 DAI, but thereafter  emission \nlevels dropped close to zero.\n Adding organic matter increased N2O emission substantially in saturated \nsoil at 1 DAI but the level of N2O emission dropped to zero at 5 DAI. In soils at \nwater levels of 10 cm /15 cm, organic matter addition stimulated N2O emission \ninitially and was higher than in water logged soil.\n Over the period of incubation, emission of  N2O was significantly different \ndue to the water levels, organic material addition and stage of incubation. The \ninteractions between the stage of incubation and treatments were also significantly \ndifferent. The soils amended with organic material had significantly higher rates \nof N2O emission compared to that of unamended soils (Table 2).\n In waterlogged soil, C2H2 emissions were < 10 mg kg-1 throughout the \nincubation period and remained consistently low. Lowering water level to 10 cm \nfrom soil surface increased C2H2 emissions to > 10 mg kg-1 up to 3 DAI.\n Adding organic matter to waterlogged soil caused a 5-fold increase in \nC2H2 emissions at 1 DAI. Treatments that received water level at 10 or 15 cm \nfrom soil surface and amended with organic matter maintained C2H2 emissions \nof 20 mg kg-1 at 3 - 15 DAI. Cumulative C2H2 emissions increased at all water \nlevels in soil amended with organic matter (Table 3). Manures stimulate microbial \nrespiration and increase CO2 partial pressure (Chorom and Rengasamy  1997)\n In unamended soil, CH4 emission in water logged soil produced spikes at \n9 to 15 DAI. At water levels of 10 /15 cm, increased levels of CH4 in spikes were \nobserved at 5 and 9 DAI. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 187\n\n\n\nTA\nB\n\n\n\nLE\n 2\n\n\n\nC\num\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nC\nO\n\n\n\n2 e\nm\n\n\n\nis\nsi\n\n\n\non\ns (\n\n\n\ng \nkg\n\n\n\n-1\n) a\n\n\n\nt d\niff\n\n\n\ner\nen\n\n\n\nt s\nta\n\n\n\nge\ns\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 2\n\n\n\n\n\n\n\nC\num\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nC\nO\n\n\n\n2 e\nm\n\n\n\nis\nsi\n\n\n\non\ns (\n\n\n\ng \nkg\n\n\n\n-1\n) a\n\n\n\nt d\niff\n\n\n\ner\nen\n\n\n\nt s\nta\n\n\n\nge\ns \n\n\n\n\n\n\n\nW\nat\n\n\n\ner\n le\n\n\n\nve\nl \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n\n\n\n\nam\nen\n\n\n\ndm\nen\n\n\n\nts \n\n\n\nD\nay\n\n\n\ns a\nfte\n\n\n\nr i\nnc\n\n\n\nub\nat\n\n\n\nio\nn \n\n\n\n1 \n3 \n\n\n\n5 \n7 \n\n\n\n9 \n11\n\n\n\n \n13\n\n\n\n \n15\n\n\n\n\n\n\n\nC\nom\n\n\n\npl\net\n\n\n\ne \n\n\n\nsa\ntu\n\n\n\nra\ntio\n\n\n\nn \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n6.\n\n\n\n36\n \n\n\n\n13\n.3\n\n\n\n3 \n21\n\n\n\n.2\n6 \n\n\n\n44\n.6\n\n\n\n9 \n51\n\n\n\n.8\n6 \n\n\n\n56\n.6\n\n\n\n1 \n59\n\n\n\n.3\n3 \n\n\n\n60\n.9\n\n\n\n8 \n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n1.\n05\n\n\n\n \n2.\n\n\n\n43\n \n\n\n\n4.\n57\n\n\n\n \n8.\n\n\n\n32\n \n\n\n\n10\n.4\n\n\n\n5 \n12\n\n\n\n.2\n1 \n\n\n\n13\n.0\n\n\n\n3 \n13\n\n\n\n.6\n9 \n\n\n\n 1\n0 \n\n\n\ncm\n w\n\n\n\nat\ner\n\n\n\n le\nve\n\n\n\nl \n\n\n\nbe\nlo\n\n\n\nw\n so\n\n\n\nil \nsu\n\n\n\nrf\nac\n\n\n\ne \n \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n17\n\n\n\n.2\n8 \n\n\n\n37\n.8\n\n\n\n7 \n50\n\n\n\n.6\n0 \n\n\n\n65\n.6\n\n\n\n4 \n80\n\n\n\n.8\n9 \n\n\n\n95\n.0\n\n\n\n3 \n11\n\n\n\n0.\n27\n\n\n\n \n11\n\n\n\n9.\n63\n\n\n\n\n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n1.\n45\n\n\n\n \n3.\n\n\n\n20\n \n\n\n\n5.\n11\n\n\n\n \n6.\n\n\n\n73\n \n\n\n\n8.\n63\n\n\n\n \n10\n\n\n\n.4\n3 \n\n\n\n12\n.1\n\n\n\n4 \n12\n\n\n\n.7\n9 \n\n\n\n15\n c\n\n\n\nm\n w\n\n\n\nat\ner\n\n\n\n le\nve\n\n\n\nl \n\n\n\nbe\nlo\n\n\n\nw\n so\n\n\n\nil \nsu\n\n\n\nrf\nac\n\n\n\ne \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n17\n\n\n\n.5\n9 \n\n\n\n38\n.2\n\n\n\n0 \n65\n\n\n\n.9\n3 \n\n\n\n12\n1.\n\n\n\n81\n \n\n\n\n17\n6.\n\n\n\n59\n \n\n\n\n21\n7.\n\n\n\n34\n \n\n\n\n23\n5.\n\n\n\n15\n \n\n\n\n24\n6.\n\n\n\n54\n \n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n1.\n26\n\n\n\n \n3.\n\n\n\n25\n \n\n\n\n5.\n78\n\n\n\n \n10\n\n\n\n.0\n0 \n\n\n\n13\n.5\n\n\n\n5 \n16\n\n\n\n.3\n9 \n\n\n\n17\n.2\n\n\n\n6 \n78\n\n\n\n.6\n2 \n\n\n\n\n\n\n\nS \nT \n\n\n\nS \nx \n\n\n\nT \n \n\n\n\n\n\n\n\n\n\n\n\nC\nD\n\n\n\n (P\n:0\n\n\n\n.0\n1)\n\n\n\n\n\n\n\n1.\n22\n\n\n\n \n1.\n\n\n\n06\n \n\n\n\n3.\n00\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019188\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\nC\num\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\nis\nsi\n\n\n\non\ns (\n\n\n\n\u00b5g\n g\n\n\n\n-1\n) a\n\n\n\nt d\niff\n\n\n\ner\nen\n\n\n\nt s\nta\n\n\n\nge\ns\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 3\n\n\n\n\n\n\n\nC\num\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nN\n2O\n\n\n\n e\nm\n\n\n\nis\nsi\n\n\n\non\ns (\n\n\n\n\u00b5g\n g\n\n\n\n-1\n) a\n\n\n\nt d\niff\n\n\n\ner\nen\n\n\n\nt s\nta\n\n\n\nge\ns \n\n\n\nW\nat\n\n\n\ner\n le\n\n\n\nve\nl \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n\n\n\n\nam\nen\n\n\n\ndm\nen\n\n\n\nts \n\n\n\nD\nay\n\n\n\ns a\nfte\n\n\n\nr i\nnc\n\n\n\nub\nat\n\n\n\nio\nn \n\n\n\n1 \n3 \n\n\n\n5 \n7 \n\n\n\n9 \n11\n\n\n\n \n13\n\n\n\n \n15\n\n\n\n\n\n\n\nC\nom\n\n\n\npl\net\n\n\n\ne \n\n\n\nsa\ntu\n\n\n\nra\ntio\n\n\n\nn \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n8.\n\n\n\n99\n \n\n\n\n13\n.5\n\n\n\n8 \n13\n\n\n\n.7\n1 \n\n\n\n13\n.8\n\n\n\n9 \n13\n\n\n\n.9\n9 \n\n\n\n14\n.0\n\n\n\n9 \n14\n\n\n\n.1\n9 \n\n\n\n14\n.3\n\n\n\n3 \n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n0.\n36\n\n\n\n \n1.\n\n\n\n12\n \n\n\n\n8.\n11\n\n\n\n \n8.\n\n\n\n89\n \n\n\n\n9.\n27\n\n\n\n \n9.\n\n\n\n64\n \n\n\n\n10\n.5\n\n\n\n0 \n10\n\n\n\n.9\n0 \n\n\n\n 1\n0 \n\n\n\ncm\n w\n\n\n\nat\ner\n\n\n\n le\nve\n\n\n\nl \n\n\n\nbe\nlo\n\n\n\nw\n so\n\n\n\nil \nsu\n\n\n\nrf\nac\n\n\n\ne \n \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n11\n\n\n\n.7\n9 \n\n\n\n29\n.7\n\n\n\n9 \n30\n\n\n\n.7\n5 \n\n\n\n31\n.7\n\n\n\n2 \n31\n\n\n\n.9\n6 \n\n\n\n33\n.4\n\n\n\n3 \n33\n\n\n\n.7\n0 \n\n\n\n34\n.1\n\n\n\n5 \n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n0.\n10\n\n\n\n \n3.\n\n\n\n27\n \n\n\n\n10\n.0\n\n\n\n8 \n10\n\n\n\n.1\n8 \n\n\n\n10\n.2\n\n\n\n8 \n10\n\n\n\n.3\n8 \n\n\n\n10\n.4\n\n\n\n0 \n11\n\n\n\n.0\n3 \n\n\n\n15\n c\n\n\n\nm\n w\n\n\n\nat\ner\n\n\n\n le\nve\n\n\n\nl \n\n\n\nbe\nlo\n\n\n\nw\n so\n\n\n\nil \nsu\n\n\n\nrf\nac\n\n\n\ne \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n21\n\n\n\n.2\n4 \n\n\n\n85\n.1\n\n\n\n4 \n10\n\n\n\n0.\n10\n\n\n\n \n10\n\n\n\n0.\n86\n\n\n\n \n12\n\n\n\n3.\n96\n\n\n\n \n12\n\n\n\n4.\n21\n\n\n\n \n12\n\n\n\n4.\n31\n\n\n\n \n12\n\n\n\n4.\n46\n\n\n\n\n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n5.\n07\n\n\n\n \n9.\n\n\n\n76\n \n\n\n\n10\n.0\n\n\n\n4 \n10\n\n\n\n.4\n8 \n\n\n\n11\n.5\n\n\n\n9 \n12\n\n\n\n.5\n4 \n\n\n\n13\n.3\n\n\n\n5 \n19\n\n\n\n.9\n6 \n\n\n\n\n\n\n\nS \nT \n\n\n\nS \nx \n\n\n\nT \n \n\n\n\n\n\n\n\n\n\n\n\nC\nD\n\n\n\n (P\n:0\n\n\n\n.0\n1)\n\n\n\n\n\n\n\n1.\n30\n\n\n\n \n1.\n\n\n\n13\n \n\n\n\n3.\n19\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 189\n\n\n\nSoil Microbial Biomass-C (SMB-C) \nThe SMB\u2013C increased over a period of time. SMB-C was higher at 10 cm water \nlevel below surface soil than in water logged conditions and at 15 cm water level \nbelow soil surface. Soils at 10 cm water level amended with the organic material \nregistered a significantly  higher value of SMB-C (1320 mg kg-1) while soils at 15 \ncm below soil surface amended / unamended with organic material registered a \nlower SMB-C value. \n\n\n\nMetabolic quotient (q CO2)\nIrrespective of water levels and organic material addition, the q CO2 was higher \nin soils at 1 DAI compared to 15 DAI. Treatment that received water level 10 cm \nbelow the soil surface recorded higher q CO2 than 15 cm water level and water \nlogged soil (Table 8). Organic amendments also enhanced the value of q CO2.\n\n\n\nNH4-N and NO3-N.\nSoil N forms viz., NH4-N and NO3-N were lower at 10 cm water level below soil \nsurface indicating a higher rate of mineralisation of N at this water level. This \nfact is further supported by the higher SMB at this water level (Table 7). Addition \nof organic material increased NH4-N levels in the treatments that received water \nlevels at 10 cm/15 cm below soil surface.\n\n\n\nDISCUSSION\nEffect of Water Level and Organic Material on GHG Fluxes\nMaximum emission of GHG was recorded at water level of 15 cm below soil \nsurface. This was also seen in a study by Tiemeyer et al. (2016), where the response \nof CO2 emissions to groundwater level was highly site-specific. Emission of CO2 \nwas the lowest at water logging. Anaerobic habitats have lower CO2 emissions \nthan aerobic soils even though they have much higher C content (Goreau and \nMello 2007).  A lowered water table increases O2 and C substrate available for \nmicrobial activity for releasing CO2 (Jauhiainen et al. 2005). The rate of emission \nof CO2 was higher when soils were well aerated and the water level was at 15 cm \nbelow the soil surface. CO2 emissions are doubled by lowering the groundwater \nlevel from 30 to 80 cm below the ground surface (Renger et al. 2002). Despite high \norganic matter content in organic amended treatment, CO2 fluxes were fairly low \nbecause lack of O2 forced organic matter decomposition into thermodynamically \ninefficient anaerobic pathways. Submerged soils with prolonged anoxic periods \nstore more OC than upland soils due to less efficient decomposition under anoxic \nconditions (Kalbitz et al. 2013). The decomposition or destruction of organic \nmaterials is lessened and incomplete, and the humification of organic matter is \ndecreased under flooded conditions (Sahrawat 2003).\n Production of N2O is primarily due to denitrification. Emission of N2O \nwas higher at 1 DAI in the water logged soil. Denitrification is an obligatively \nanaerobic heterotrophic process in which denitrifiers use NO3 as a terminal e- \nacceptor after O2 is exhausted. Hence in the water logged soil, N2O production was \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019190\n\n\n\nobserved immediately after water logging. However N2O emission was unstable \nand probably proceeded to complete denitrification in the form of N2 emission \nin the waterlogged condition from 3 DAI as lower rates of N2O emission was \nrecorded. Organic material addition increased N2O emission as reported by Zaehle \net al. (2011). Methane is converted to CO2 by methanotrophic microorganisms. \nMore than 80% of CH4 produced in soils is converted to CO2 in the oxidised zone. \nOnly a trace amount of CH4 was produced during the first week of incubation. \nThis could be due to the conversion of C to CO2 and then reduced to C2H2. For \nthe maximum release of C2H2 and CH4, 7 and 9 DAI are required. The rate of \nreduction of C to C2H2 and CH4 at well aerated soils was slower (Tables 3 and 4).\nSlow decomposition rates of organic carbon in anaerobic wetland soils have \nbeen reported by Mitsch et al. (2013). With increasing content of soil organic \nmatter CH4 emission rates were increased (Serrano-Silva et al. 2014). A lower \nC: N ratio of organic matter in the soil may increase organic matter liability by \ndecreasing nitrogen limitation for decomposers (Hodgkins et al. 2014). When the \nwater table approached the soil surface, the CH4 emission rates increased. This is \nin agreement with the findings of Zhu et al. (2014), who reported that seasonal \nCH4 emissions are highly linked to water table fluctuations. The important effect \nof water table on CH4 emission rates is in agreement with observations in other \nstudies (Bridgham et al. 2006; Couwenberg et al. 2011; Le Mer and Roger 2001; \nSerrano-Silva et al. 2014). Common anaerobic conditions are expected to lower \nCO2 emissions but increase those of CH4 (Treat et al. 2015), but emissions from \naerobic soils will likely dominate the permafrost C feedback (Schadel et al. 2016).\n\n\n\nGHG Flux and SMB\nGHG emission and SMB were found to be the lowest in the soils.  Over a \nperiod of time, SMB can adapt to soil conditions and in stabilised conditions, \nthe size of the microbial biomass will reach equilibrium with substrate supply in \nthe soil (Liu et al. 2006); Soil microbial biomass also increased in response to \nincreased C substrate from organic amendments. This study also indicated that the \ndormant population of SMB tolerant to water logging could have adapted to such \nenvironmental conditions over time. \n The qCO2 was higher in soils at 1 DAI as compared to 15 DAI. Irrespective \nof the water levels, qCO2 was enhanced in soils amended with organic material. \nLower qCO2 values in water logged soil indicate low microbial activity in this \nzone. Accumulation of inorganic salts as osmolytes can be toxic; therefore it \nis confined to halophytic microbes which evolve into salt-tolerant enzymes to \nsurvive in highly saline environments. (Wichern et al. 2006).\n\n\n\nNH4-N and NO3-N\nIrshad et al. (2005) reported that nitrification of NH4 to NO3 was reduced by \nsalinity. Salinity level adversely affected even the first step of nitrification thus \nleaving behind greater NH4-N (Muhammad Akhtar et al. 2012). At 10 cm water \nlevel below soil surface, a higher rate of mineralisation of N was registered. A \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 191\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\nC\num\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nC\n2H\n\n\n\n2 e\nm\n\n\n\nis\nsi\n\n\n\non\ns (\n\n\n\n\u00b5g\n g\n\n\n\n-1\n) a\n\n\n\nt d\niff\n\n\n\ner\nen\n\n\n\nt s\nta\n\n\n\nge\ns\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 4\n\n\n\n\n\n\n\nC\num\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nC\n2H\n\n\n\n2 e\nm\n\n\n\nis\nsi\n\n\n\non\ns (\n\n\n\n\u00b5g\n g\n\n\n\n-1\n) a\n\n\n\nt d\niff\n\n\n\ner\nen\n\n\n\nt s\nta\n\n\n\nge\ns \n\n\n\nW\nat\n\n\n\ner\n le\n\n\n\nve\nl \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n\n\n\n\nam\nen\n\n\n\ndm\nen\n\n\n\nts \n\n\n\nD\nay\n\n\n\ns a\nfte\n\n\n\nr i\nnc\n\n\n\nub\nat\n\n\n\nio\nn \n\n\n\n1 \n3 \n\n\n\n5 \n7 \n\n\n\n9 \n11\n\n\n\n \n13\n\n\n\n \n15\n\n\n\n\n\n\n\nC\nom\n\n\n\npl\net\n\n\n\ne \n\n\n\nsa\ntu\n\n\n\nra\ntio\n\n\n\nn \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n48\n\n\n\n.5\n0 \n\n\n\n96\n.0\n\n\n\n0 \n12\n\n\n\n0.\n99\n\n\n\n \n15\n\n\n\n2.\n00\n\n\n\n \n16\n\n\n\n9.\n91\n\n\n\n \n18\n\n\n\n3.\n68\n\n\n\n \n18\n\n\n\n9.\n10\n\n\n\n \n19\n\n\n\n2.\n60\n\n\n\n\n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n6.\n57\n\n\n\n \n8.\n\n\n\n87\n \n\n\n\n11\n.0\n\n\n\n1 \n19\n\n\n\n.3\n2 \n\n\n\n23\n.2\n\n\n\n1 \n28\n\n\n\n.0\n8 \n\n\n\n33\n.2\n\n\n\n4 \n35\n\n\n\n.4\n7 \n\n\n\n 1\n0 \n\n\n\ncm\n w\n\n\n\nat\ner\n\n\n\n\n\n\n\nle\nve\n\n\n\nl b\nel\n\n\n\now\n so\n\n\n\nil \n\n\n\nsu\nrf\n\n\n\nac\ne \n\n\n\n\n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n71\n\n\n\n.2\n1 \n\n\n\n10\n6.\n\n\n\n38\n \n\n\n\n12\n8.\n\n\n\n71\n \n\n\n\n16\n8.\n\n\n\n61\n \n\n\n\n20\n7.\n\n\n\n21\n \n\n\n\n24\n5.\n\n\n\n61\n \n\n\n\n27\n4.\n\n\n\n58\n \n\n\n\n30\n8.\n\n\n\n61\n \n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n10\n.2\n\n\n\n4 \n26\n\n\n\n.2\n1 \n\n\n\n28\n.6\n\n\n\n5 \n32\n\n\n\n.1\n7 \n\n\n\n35\n.5\n\n\n\n4 \n38\n\n\n\n.7\n9 \n\n\n\n43\n.1\n\n\n\n3 \n43\n\n\n\n.7\n7 \n\n\n\n15\n c\n\n\n\nm\n w\n\n\n\nat\ner\n\n\n\n\n\n\n\nle\nve\n\n\n\nl b\nel\n\n\n\now\n so\n\n\n\nil \n\n\n\nsu\nrf\n\n\n\nac\ne \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n97\n\n\n\n.3\n5 \n\n\n\n17\n0.\n\n\n\n85\n \n\n\n\n20\n4.\n\n\n\n45\n \n\n\n\n22\n5.\n\n\n\n25\n \n\n\n\n36\n9.\n\n\n\n45\n \n\n\n\n39\n3.\n\n\n\n35\n \n\n\n\n41\n9.\n\n\n\n75\n \n\n\n\n46\n3.\n\n\n\n65\n \n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n10\n.6\n\n\n\n8 \n23\n\n\n\n.9\n8 \n\n\n\n27\n.8\n\n\n\n5 \n40\n\n\n\n.4\n3 \n\n\n\n46\n.5\n\n\n\n2 \n51\n\n\n\n.9\n3 \n\n\n\n54\n.0\n\n\n\n9 \n55\n\n\n\n.2\n0 \n\n\n\n\n\n\n\nS \nT \n\n\n\nS \nx \n\n\n\nT \n \n\n\n\n\n\n\n\n\n\n\n\nC\nD\n\n\n\n (P\n:0\n\n\n\n.0\n1)\n\n\n\n\n\n\n\n1.\n71\n\n\n\n \n1.\n\n\n\n48\n \n\n\n\n4.\n19\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019192\n\n\n\nhigher level of NH4-N was registered in the treatments that received organic \namendment. The increase in the level of exchangeable NH4-N might be due to \nmineralisation of organic sources by microbial biomass (Niladri Paul et al. 2014)\n\n\n\nCONCLUSIONS\nOur study indicates that GHG emissions can be reduced by restoration of saline \nwater logged soil through tree plantations, agroforestry biodrainage approach, \nirrigation practices with minimum flooding the surface and proper leveling of the \nsurface. Thus multiple approaches are needed to tackle the problem. To enhance \nSOC levels and SMB and to minimise the ill effects of salinity, incorporation of \norganic material is needed. Our results demonstrate that water logging reduces \nemission.\n\n\n\nTABLE  5\nCumulative CH4 emissions (\u00b5g g-1) at different stages\n\n\n\nWater level Organic \namendments\n\n\n\nDays after incubation\n9 11 15\n\n\n\nComplete saturation 7.5 t ha-1 0.31 0.46 0.89\nUn amended 0.10 0.27 2.38\n\n\n\n10 cm water level below \nsoil surface\n\n\n\n7.5 t ha-1 0.10 0.20 0.30\n\n\n\nUn amended 1.88 1.98 2.40\n15 cm water level below \nsoil surface\n\n\n\n7.5 t ha-1 0 0.10 0.20\n\n\n\nUn amended 1.60 3.20 4.80\nS T S x T\n\n\n\nCD (P:0.01) NS NS NS\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 193\n\n\n\nTA\nB\n\n\n\nLE\n 6\n\n\n\nEf\nfe\n\n\n\nct\n o\n\n\n\nf w\nat\n\n\n\ner\n lo\n\n\n\ngg\nin\n\n\n\ng \nan\n\n\n\nd \nor\n\n\n\nga\nni\n\n\n\nc \nam\n\n\n\nen\ndm\n\n\n\nen\nts\n\n\n\n o\nn \n\n\n\nso\nil \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nca\nrb\n\n\n\non\n (g\n\n\n\n k\ng-1\n\n\n\n) a\nnd\n\n\n\n  s\noi\n\n\n\nl m\nic\n\n\n\nro\nbi\n\n\n\nal\n b\n\n\n\nio\nm\n\n\n\nas\ns c\n\n\n\nar\nbo\n\n\n\nn \n(\u00b5\n\n\n\ng \ng-1\n\n\n\n)\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 6\n\n\n\n\n\n\n\nEf\nfe\n\n\n\nct\n o\n\n\n\nf w\nat\n\n\n\ner\n lo\n\n\n\ngg\nin\n\n\n\ng \nan\n\n\n\nd \nor\n\n\n\nga\nni\n\n\n\nc \nam\n\n\n\nen\ndm\n\n\n\nen\nts\n\n\n\n o\nn \n\n\n\nso\nil \n\n\n\nor\nga\n\n\n\nni\nc \n\n\n\nca\nrb\n\n\n\non\n (g\n\n\n\n k\ng-1\n\n\n\n) a\nnd\n\n\n\n  s\noi\n\n\n\nl m\nic\n\n\n\nro\nbi\n\n\n\nal\n b\n\n\n\nio\nm\n\n\n\nas\ns c\n\n\n\nar\nbo\n\n\n\nn \n(\u00b5\n\n\n\ng \ng-\n\n\n\n1)\n \n\n\n\nW\nat\n\n\n\ner\n le\n\n\n\nve\nl \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n a\nm\n\n\n\nen\ndm\n\n\n\nen\nts\n\n\n\n \nSM\n\n\n\nB\n-C\n\n\n\n\n\n\n\nIn\niti\n\n\n\nal\n \n\n\n\n15\n D\n\n\n\nA\nI \n\n\n\nC\nom\n\n\n\npl\net\n\n\n\ne \nsa\n\n\n\ntu\nra\n\n\n\ntio\nn \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n48\n\n\n\n6 c\n  \n\n\n\n97\n7 c\n\n\n\n\n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n11\n5 a\n\n\n\n \n26\n\n\n\n4 \na  \n\n\n\n 1\n0 \n\n\n\ncm\n w\n\n\n\nat\ner\n\n\n\n le\nve\n\n\n\nl b\nel\n\n\n\now\n so\n\n\n\nil \nsu\n\n\n\nrf\nac\n\n\n\ne \n \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n14\n\n\n\n3 a\n \n\n\n\n13\n20\n\n\n\n d\n \n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n22\n5 \n\n\n\nb  \n39\n\n\n\n6 b\n \n\n\n\n15\n c\n\n\n\nm\n w\n\n\n\nat\ner\n\n\n\n le\nve\n\n\n\nl b\nel\n\n\n\now\n so\n\n\n\nil \nsu\n\n\n\nrf\nac\n\n\n\ne \n7.\n\n\n\n5 \nt h\n\n\n\na-1\n \n\n\n\n70\n9 \n\n\n\nd  \n34\n\n\n\n3 a\nb  \n\n\n\nU\nn \n\n\n\nam\nen\n\n\n\nde\nd \n\n\n\n20\n6 b\n\n\n\n \n31\n\n\n\n7 a\nb  \n\n\n\nN\not\n\n\n\ne:\n W\n\n\n\nith\nin\n\n\n\n e\nac\n\n\n\nh \nco\n\n\n\nlu\nm\n\n\n\nn \nm\n\n\n\nea\nns\n\n\n\n fo\nllo\n\n\n\nw\ned\n\n\n\n b\ny \n\n\n\nth\ne \n\n\n\nsa\nm\n\n\n\ne \nle\n\n\n\ntte\nr a\n\n\n\nre\n n\n\n\n\not\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n a\nt P\n\n\n\n<0\n.0\n\n\n\n5 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019194\n\n\n\nTA\nB\n\n\n\nLE\n 7\n\n\n\nEf\nfe\n\n\n\nct\ns o\n\n\n\nf w\nat\n\n\n\ner\n lo\n\n\n\ngg\nin\n\n\n\ng \nan\n\n\n\nd \nor\n\n\n\nga\nni\n\n\n\nc \nam\n\n\n\nen\ndm\n\n\n\nen\nts\n\n\n\n o\nn \n\n\n\nm\net\n\n\n\nab\nol\n\n\n\nic\n q\n\n\n\nuo\ntie\n\n\n\nnt\n (q\n\n\n\n C\nO\n\n\n\n-2\n)\n\n\n\nTA\nB\n\n\n\nLE\n 7\n\n\n\n\n\n\n\nEf\nfe\n\n\n\nct\ns o\n\n\n\nf w\nat\n\n\n\ner\n lo\n\n\n\ngg\nin\n\n\n\ng \nan\n\n\n\nd \nor\n\n\n\nga\nni\n\n\n\nc \nam\n\n\n\nen\ndm\n\n\n\nen\nts\n\n\n\n o\nn \n\n\n\nm\net\n\n\n\nab\nol\n\n\n\nic\n q\n\n\n\nuo\ntie\n\n\n\nnt\n (q\n\n\n\n C\nO\n\n\n\n2)\n \n\n\n\nW\nat\n\n\n\ner\n \n\n\n\nLe\nve\n\n\n\nl \n\n\n\nO\nrg\n\n\n\nan\nic\n\n\n\n\n\n\n\nam\nen\n\n\n\ndm\nen\n\n\n\nts \n\n\n\nN\nH\n\n\n\n4 \n-N\n\n\n\n \nN\n\n\n\nO\n3 \n\n\n\n-N\n \n\n\n\nq \nC\n\n\n\nO\n2 \n\n\n\n(m\ng \n\n\n\nkg\n-1\n\n\n\n d\nay\n\n\n\n-1\n) \n\n\n\nIn\niti\n\n\n\nal\n \n\n\n\n15\n D\n\n\n\nA\nI \n\n\n\nIn\niti\n\n\n\nal\n \n\n\n\n15\n D\n\n\n\nA\nI \n\n\n\nIn\niti\n\n\n\nal\n \n\n\n\n15\n D\n\n\n\nA\nI \n\n\n\nC\nom\n\n\n\npl\net\n\n\n\ne \n\n\n\nsa\ntu\n\n\n\nra\ntio\n\n\n\nn \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n10\n\n\n\n.3\n0 \n\n\n\na  \n6.\n\n\n\n55\n a  \n\n\n\n2.\n36\n\n\n\n a  \n2.\n\n\n\n46\n a  \n\n\n\n13\n.0\n\n\n\n8 \n1.\n\n\n\n66\n \n\n\n\n0 \n7.\n\n\n\n00\n b \n\n\n\nd  \n2.\n\n\n\n10\n b \n\n\n\nc  \n0.\n\n\n\n55\n a  \n\n\n\n0.\n42\n\n\n\n a  \n9.\n\n\n\n13\n \n\n\n\n2.\n13\n\n\n\n\n\n\n\n10\n c\n\n\n\nm\n \n\n\n\nw\nat\n\n\n\ner\n le\n\n\n\nve\nl \n\n\n\nbe\nlo\n\n\n\nw\n so\n\n\n\nil \n\n\n\nsu\nrf\n\n\n\nac\ne \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n2.\n\n\n\n15\n a \n\n\n\nc  \n5.\n\n\n\n35\n a \n\n\n\nb  \n0.\n\n\n\n15\n a  \n\n\n\n0.\n14\n\n\n\n a  \n12\n\n\n\n0.\n85\n\n\n\n \n7.\n\n\n\n58\n \n\n\n\n0 \n5.\n\n\n\n90\n bd\n\n\n\n \n1.\n\n\n\n15\n c  \n\n\n\n0.\n39\n\n\n\n a  \n0.\n\n\n\n13\n a  \n\n\n\n6.\n44\n\n\n\n \n1.\n\n\n\n45\n \n\n\n\n15\n c\n\n\n\nm\n \n\n\n\nw\nat\n\n\n\ner\n \n\n\n\nbe\nlo\n\n\n\nw\n so\n\n\n\nil \n\n\n\nsu\nrf\n\n\n\nac\ne \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n1.\n\n\n\n15\n c \n\n\n\nd  \n7.\n\n\n\n25\n a  \n\n\n\n2.\n70\n\n\n\n a  \n2.\n\n\n\n04\n a  \n\n\n\n24\n.8\n\n\n\n1 \n35\n\n\n\n.4\n2 \n\n\n\n0 \n1.\n\n\n\n90\n b  \n\n\n\n1.\n11\n\n\n\n c  \n3.\n\n\n\n90\n a  \n\n\n\n1.\n71\n\n\n\n a  \n6.\n\n\n\n30\n \n\n\n\n2.\n48\n\n\n\n\n\n\n\nS.\nE \n\n\n\n\n\n\n\n\n\n\n\n \n12\n\n\n\n.9\n9 \n\n\n\n3.\n87\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019 195\n\n\n\nTA\nB\n\n\n\nLE\n 8\n\n\n\nEf\nfe\n\n\n\nct\ns o\n\n\n\nf w\nat\n\n\n\ner\n lo\n\n\n\ngg\nin\n\n\n\ng \nan\n\n\n\nd \nor\n\n\n\nga\nni\n\n\n\nc \nam\n\n\n\nen\ndm\n\n\n\nen\nts\n\n\n\n o\nn \n\n\n\nN\nH\n\n\n\n4 -\nN\n\n\n\n (\u00b5\ng \n\n\n\ng-1\n) a\n\n\n\nnd\n N\n\n\n\nO\n3 -\n\n\n\nN\n (\u00b5\n\n\n\ng \ng-1\n\n\n\n)\n\n\n\n\n\n\n\nTA\nB\n\n\n\nLE\n 8\n\n\n\n\n\n\n\nEf\nfe\n\n\n\nct\ns o\n\n\n\nf w\nat\n\n\n\ner\n lo\n\n\n\ngg\nin\n\n\n\ng \nan\n\n\n\nd \nor\n\n\n\nga\nni\n\n\n\nc \nam\n\n\n\nen\ndm\n\n\n\nen\nts\n\n\n\n o\nn \n\n\n\nN\nH\n\n\n\n4 \n-N\n\n\n\n (\u00b5\ng \n\n\n\ng-1\n) a\n\n\n\nnd\n N\n\n\n\nO\n3 \n\n\n\n-N\n (\u00b5\n\n\n\ng \ng-1\n\n\n\n) \n\n\n\nW\nat\n\n\n\ner\n L\n\n\n\nev\nel\n\n\n\n \nO\n\n\n\nrg\nan\n\n\n\nic\n \n\n\n\nam\nen\n\n\n\ndm\nen\n\n\n\nts \n\n\n\nN\nH\n\n\n\n4 \n-N\n\n\n\n \nN\n\n\n\nO\n3 \n\n\n\n-N\n \n\n\n\nN\nH\n\n\n\n4 \n-N\n\n\n\n +\n N\n\n\n\nO\n3 \n\n\n\n-N\n \n\n\n\nIn\niti\n\n\n\nal\n \n\n\n\n15\n D\n\n\n\nA\nI \n\n\n\nIn\niti\n\n\n\nal\n \n\n\n\n15\n D\n\n\n\nA\nI \n\n\n\nIn\niti\n\n\n\nal\n \n\n\n\n15\n D\n\n\n\nA\nI \n\n\n\nC\nom\n\n\n\npl\net\n\n\n\ne \nsa\n\n\n\ntu\nra\n\n\n\ntio\nn \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n10\n\n\n\n.3\n0 \n\n\n\na  \n6.\n\n\n\n55\n a  \n\n\n\n2.\n36\n\n\n\n a  \n2.\n\n\n\n46\n a  \n\n\n\n12\n.6\n\n\n\n6 \na  \n\n\n\n9.\n01\n\n\n\n a  \n\n\n\n0 \n7.\n\n\n\n00\n b \n\n\n\nd  \n2.\n\n\n\n10\n b \n\n\n\nc  \n0.\n\n\n\n55\n a  \n\n\n\n0.\n42\n\n\n\n a  \n7.\n\n\n\n55\n a  \n\n\n\n2.\n52\n\n\n\n b  \n\n\n\n10\n c\n\n\n\nm\n w\n\n\n\nat\ner\n\n\n\n le\nve\n\n\n\nl b\nel\n\n\n\now\n \n\n\n\nso\nil \n\n\n\nsu\nrf\n\n\n\nac\ne \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n2.\n\n\n\n15\n a \n\n\n\nc  \n5.\n\n\n\n35\n a \n\n\n\nb  \n0.\n\n\n\n15\n a  \n\n\n\n0.\n14\n\n\n\n a  \n2.\n\n\n\n30\n a  \n\n\n\n5.\n46\n\n\n\n a \nb  \n\n\n\n0 \n5.\n\n\n\n90\n bd\n\n\n\n \n1.\n\n\n\n15\n c  \n\n\n\n0.\n39\n\n\n\n a  \n0.\n\n\n\n13\n a  \n\n\n\n6.\n29\n\n\n\n a  \n1.\n\n\n\n28\n b  \n\n\n\n15\n c\n\n\n\nm\n w\n\n\n\nat\ner\n\n\n\n b\nel\n\n\n\now\n so\n\n\n\nil \n\n\n\nsu\nrf\n\n\n\nac\ne \n\n\n\n7.\n5 \n\n\n\nt h\na-1\n\n\n\n \n1.\n\n\n\n15\n c \n\n\n\nd  \n7.\n\n\n\n25\n a  \n\n\n\n2.\n70\n\n\n\n a  \n2.\n\n\n\n04\n a  \n\n\n\n3.\n85\n\n\n\n a  \n11\n\n\n\n.1\n0 \n\n\n\na  \n\n\n\n0 \n1.\n\n\n\n90\n b  \n\n\n\n1.\n11\n\n\n\n c  \n3.\n\n\n\n90\n a  \n\n\n\n1.\n71\n\n\n\n a  \n5.\n\n\n\n80\n a  \n\n\n\n2.\n82\n\n\n\n b  \n\n\n\nN\not\n\n\n\ne:\n W\n\n\n\nith\nin\n\n\n\n e\nac\n\n\n\nh \nco\n\n\n\nlu\nm\n\n\n\nn,\n m\n\n\n\nea\nns\n\n\n\n fo\nllo\n\n\n\nw\ned\n\n\n\n b\ny \n\n\n\nth\ne \n\n\n\nsa\nm\n\n\n\ne \nle\n\n\n\ntte\nr a\n\n\n\nre\n n\n\n\n\not\n si\n\n\n\ngn\nifi\n\n\n\nca\nnt\n\n\n\n a\nt P\n\n\n\n<0\n.0\n\n\n\n5 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 23, 2019196\n\n\n\nREFERENCES\nBeek , C.L., E.W.J. 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"\n\nINTRODUCTION\nBlack pepper (Piper nigrum\n\n\n\nproduction of pepper is urgently needed to meet the increasing population and \n\n\n\nis the high cost of production due to the increasing trend of using inorganic \n\n\n\nyear of planting, respectively. The application of this compound fertilizer will \n\n\n\nImpact of Different Fertilization Methods on the Soil, Yield \nand Growth Performance of Black Pepper (  L.) \n\n\n\n \nYap Chin Ann  \n\n\n\nResearch and Development Division, Malaysian Pepper Board\nKuching, Sarawak, Malaysia\n\n\n\nABSTRACT\nBlack pepper is a high nutrient demanding crop. Fertilizer use and management is \n\n\n\nwas carried out to study the effect of chemical and organic fertilizers on some \n\n\n\nselected integrated fertilizer treatments out-yielded organic and chemical fertilizer \n\n\n\nchanges were observed in physiological processes and plant characteristics, such \n\n\n\ncoupled with lower transpiration rate in integrated fertilizer treatment compared \n\n\n\norganic pepper. In conclusion, to achieve high growth performance and yield in \n\n\n\nKeywords: Black pepper, organic fertilizer, integrated fertilization, growth \nperformance, yield\n\n\n\n___________________\n*Corresponding author : E-mail: \n\n\n\n\n\n\n\n\net al\nas a nitrogen source, the crop suffers greater disease pressure (Fraterrigo et al. \n\n\n\nFurther, the continued use of chemical fertilizers causes health and \nenvironmental hazards such as ground and surface water pollution by nitrate \n\n\n\nsame time alleviate the environmental hazards. As organic farming is becoming \npopular among pepper farmers, one of the options to reduce the use of chemical \nfertilizers could be recycling of organic wastes. Compost derived from organic \n\n\n\nPositive effects of organic waste on soil structure, aggregate stability and water-\net al\n\n\n\net al. et al.\nhas high nutrient contents, especially nitrogen, phosphorus and potassium, while \n\n\n\net al.\nchemical fertilizers further enhances the biomass and grain yield of crops (Cheuk \net al. et al. et al\n\n\n\nTherefore, as an initial step towards this goal, this study was carried out \n\n\n\nin term of soil fertility, growth performance and yield.\n\n\n\nMATERIALS AND METHODS\n\n\n\nland in Melaka on Melaka-Durian-Munchong soil series. The particle size analysis \n\n\n\no\n\n\n\no\n\n\n\nvariety Semongok Aman\n\n\n\nThe site was divided into three blocks or replicates. Each block contained four \nPiper nigrum were planted for each treatment. \n\n\n\ntreatment, the pepper vines were randomly selected to receive the NPK fertilizer \n\n\n\nYap Chin Ann  \n\n\n\n\n\n\n\n\nha-1 -1 -1\n\n\n\nha-1 -1 -1 of Mg in the second year and \n-1 -1 -1 of Mg in the third year of \n\n\n\nmagnesium limestone was applied to neutralize the soil acidity. \n\n\n\nwere compost derived from empty oil palm fruit branches and organic seaweed \n\n\n\n-1 of seaweed \n\n\n\nImpact of Fertilization Methods on Black Pepper\n\n\n\nTABLE 1\nInitial chemical characteristics of Sg Udang soil\n\n\n\nChemical properties of organic based fertilizers\n\n\n\n \nTABLE 1 \n\n\n\nInitial chemical characteristics of Sg Udang soil \n\n\n\nChemical properties  Soil depth  (0  - 25cm)  \npH  3.85  \nCEC (cmol  (+)/  kg)  9.7  \nExchangeable K+ (cmol  (+)/ kg)  0.79  \nExcha ngeable Mg 2+ (cmol  (+)/ kg) 0.72  \nExchangeable Ca 2+ (cmol  (+)/ kg)  1.72  \nExchangeable Na+\n\n\n\n (+)/ kg)  0.09  \nOrganic carbon (%) 1.68  \nC/N ratio  6.61  \nTotal nitrogen (%)  0.25  \nAvailable p hosphorus (mg/kg)  10.32  \nTotal p otassium (mg/kg)  28.52  \n\n\n\n\n\n\n\n (cmol\n\n\n\nTABLE 2 \nChemical properties of organic based fertilizers \n\n\n\nChemical properties  Compost  Seaweed  \nNitrogen (%)  2.2  1.2  \nPhosphorus (%)  1.9  0.26  \nPotassium (%)  1.7  5.20  \nCalcium  (%)  2.72  1.4  \nMagnesium  (%)  1.29  0.94  \nTrace elements  B, Mn, Pb, Zn  Mn, Fe, Zn, Mob, Cu, B , \n\n\n\nCo, S, Cl, Vitamin  \nHormone  - Auxin (150.0 cm 3/Litre)  \n\n\n\nCytokinins (25.0 cm 3/litre)  \n \n\n\n\n\n\n\n\n\n74\n\n\n\nmorning, since stomata are known to be open during this period. The application \nof foliar fertilizer was carried out on the whole pepper crop, including the leaves, \n\n\n\nand chemical fertilizers were utilized. Under this treatment, each pepper crop was \napplied with both organic and compound fertilizers on alternative months. Under \n\n\n\ngrowth parameters. Destructive whole plant dry matter analysis was performed at \n\n\n\nmaturity for yield and yield component, root length and root weight measurements \n\n\n\n C for \n\n\n\nsoil samples were collected and analyzed. Standard methods were adopted for \n\n\n\net al\nleaf nutrients content, mature leaves were collected and oven-dried before P, K, \nMg and Ca contents were analyzed by using dry ashing method as described by \n\n\n\nsoftware.\n\n\n\nRESULTS AND DISCUSSION\n\n\n\nWhite Pepper Yield\nPepper is a perennial crop which starts producing berries after 18 months of \n\n\n\n-1\n\n\n\n-1\n\n\n\nYap Chin Ann  \n\n\n\n\n\n\n\n\n75\n\n\n\nha-1\n\n\n\net al. \n\n\n\ncompared to chemical fertilization whereas under organic fertilization, the pepper \n-1 -1. The differences \n\n\n\nin pepper yield between these three methods of cultivation were due principally \nto difference in nutrients supplied, the average number of fruit spikes per vine \n\n\n\nthe   four different treatments. This observation agreed with the work reported by \n\n\n\nthe main factor attributed to low crop production in plants applied with organic \n\n\n\nmentioned that the production of pepper/vine was dependent on number of spikes \n\n\n\nthe production of pepper berries.\n\n\n\nPlant Dry Matter Weight\nTotal biomass production increased with time irrespective of fertilizer treatment \n\n\n\ntreatment (Fig. 1\nincreased mass of the above and ground organs. The total dry matter accumulation \n\n\n\nannual crops, characterized by better growth rate in the period of leaf production \n\n\n\nImpact of Fertilization Methods on Black Pepper\n\n\n\nTABLE 4\n\n\n\nTABLE 3 \nEffect of fertilizer treatments on yield of pepper (Piper nigrum) \n\n\n\nTreatment Yield (ton/ha-1)  Average no. of fruit \nspikes/vine \n\n\n\nT1- Chemical fertilizer 5.74 \u00b1 0.74 b  801 \u00b1 127b \nT2 - Organic fertilizer 3.98 \u00b1 0.55 c 438 \u00b1 112c\nT3 - Chemical and organic fertilizer 6.98 \u00b1 0.32 a 987 \u00b1 134a \nT4 -  Control plot (no fertilizer) 0.86 \u00b1 0.21 d 134 \u00b1 26d \nMeans in column with different letters are significantly different at 0.05 level using Duncan \nMultiple Range Test. \n\n\n\nTABLE 4 \nEffect of different fertilizer treatments on root after 30 months of planting.  \n\n\n\nFertilizer  treatments  Root dry weight (g)  Root length  \n(cm)  \n\n\n\nT1 - Chemical fertilizer  49.8b  89 .6b  \nT2 - Organic fertilizer  40.6c  75.3c  \nT3 - Chemical and organic fertilizer  59.8a  95.7a  \nT4 -  Control plot (no fertilizer)  32.9d  51.5d  \nMeans in column with different letters are significantly different at 0.05 level using \nDuncan Multiple Range Test . \n\n\n\n\n\n\n\n\n76\n\n\n\na slow decline towards fruit maturation and harvest (Fig. 1\n\n\n\nto harvesting. The analysis also showed that the plots receiving both organic and \n\n\n\net al.\nB. Chinensis\n\n\n\nAllium cepa\n\n\n\nFurthermore, organic fertilizer activates many species of living organisms, \nwhich release phytohormones and may stimulate plant growth and absorption of \nnutrients (Arisha et al.\nhas been reported to sustain crop growth and yield (Makinde et al.\ndependence on organic fertilization resulted in a lower plant biomass compared \nto chemical fertilization. This could be due to limited nutrients that the soil could \n\n\n\norder to sustain high yield and soil nutrient balance.\n\n\n\nRoot Characteristics\n\n\n\nYap Chin Ann  \n\n\n\norganic fertilizer treatments, and T4: control plot (no fertilizer) \n\n\n\nFig. 1. Effect of different fertilizer treatments on plant dry weight of black pepper.\n\n\n\n\n\n\n\n\n77\n\n\n\nweight and root length grown under different fertilizer treatments increased with \n\n\n\nincreased pepper root length, with the highest root length value recorded with \n\n\n\nreported by Atiyeh et al.\nwith that reported by Abdel-Mawgoud et al.\ncontains a growth hormone that can stimulate the growth of pepper roots.  In most \nof the studies, an increased amount of nutrients led to an increase in the rate of \nassimilation and ultimately to higher root growth (Mir et al\nstudy also showed that pepper dry weight growth under integrated fertilization \n\n\n\nwith better root growth and higher physiological activity would synthesize large \namounts of cytokinin that is responsible for promoting cell division. Similar \nresults were also observed by Thakur et al\ngrowth and higher physiological activity in rice was dependent on the production \n\n\n\nLeaf Characteristics\nThe leaf canopy architecture of pepper vine is an important growth characteristic \nfor determining vigour and productivity of black pepper. At the individual plant \nlevel, the pepper vine planted under different fertilizer treatments affect the leaf \ncharacteristics of pepper genotypes. The mean and total leaf area of pepper grown \n\n\n\nchemical fertilizer treatment were higher (19.7 cm  and 195.5 cm\nthan those grown using organic fertilizer treatment (15.7 cm  and 176.9 cm , \n\n\n\n and 159.4 cm\net al.\n\n\n\ncommon response of leaves that absorb an appropriate amount of nutrients.  The \n\n\n\net al. \n\n\n\nImpact of Fertilization Methods on Black Pepper\n\n\n\nTABLE 5\n \n\n\n\nTABLE 5 \nEffect of different fertilizer treatment on leaves after 30 months of planting.  \n\n\n\nFertilizer treatments LAI Mean leaf  \narea (cm 2 )\n\n\n\nTotal leaf \narea (cm2 )\n\n\n\nSpecific leaf \narea (cm 2/g) \n\n\n\nT1- Chemical fertilizer 5.03b 19.7a 195.5a 224.3a \nT2 - Organic fertilizer 3.81c 15.7b 176.9b 164.3b \nT3 - Chemical and organic fertilizer 5.62a 22.6a 230.7a 243.7a \nT4 -  Control plot (no fertilizer) 2.01d 13.57b 159.4b 139.4c  \nMeans in columns with different letters are significantly different at 0.05 level using Duncan \nMultiple Range Test. \n\n\n\n\n\n\n\n\n78\n\n\n\net al.\n\n\n\nin a yield increase (Fig. 2\n\n\n\nto low nutrient availability in organic fertilizers.\n\n\n\nChlorophyll Content and Photosynthesis Rate\n\n\n\n(Shangguan et al et al.\nrate was markedly higher for plant grown under integrated fertilization treatment \n\n\n\nthat net photosynthetic rate was highest in plants that were maintained at the \nhighest nutrients level. The high photosynthetic rates were mainly due to greater \nchlorophyll contents as well as the availability of nutrients to plants. \n\n\n\nYap Chin Ann  \n\n\n\nFig. 2. Relationship between leaf area index and yield of pepper (Piper nigrum).  \n\n\n\n\n\n\n\n\n79\n\n\n\nan indicator of photosynthesis activity (Tranavicience et al.\nstrong linear relationship between nutrient availability and chlorophyll content \naccording to Sabo et al\n\n\n\nleaves, higher chlorophyll content and a higher chlorophyll a/b ratio than those \nunder organic fertilization treatment. This indicates that better nutrients supply \n\n\n\net al.\nConcomitantly, our analysis also showed that organic fertilization treatment \n\n\n\nhas a higher transpiration rate than pepper grown under chemical and integrated \n\n\n\n \nconcentration inside the sub-stomata cavity in integrated fertilization treatment \n\n\n\nis a measurement of carbon gained through photosynthesis with per unit of water \n\n\n\nImpact of Fertilization Methods on Black Pepper\n\n\n\n\n\n\n\n\ntranspired. A higher photosynthetic rate with lower transpiration in integrated \n\n\n\norganic fertilization.  \n\n\n\nEffect of Fertilizer Treatment on Foliar Nutrient Contents\nThe foliar nutrient content of pepper vine under different fertilizer treatments is \n\n\n\nMg and Ca contents of organic, chemical and integrated fertilization treatment \ncompared to unfertilized control treatment. It was also observed that there were \n\n\n\nThis indicated that the nutrient applied might be the optimal fertilizer schedule for \net al.\n\n\n\npepper nutrition planted under integrated and chemical treatments was in the \n\n\n\nYap Chin Ann  \n\n\n\nTABLE 6\nComparison of chlorophyll content, transpiration rate, net photosynthesis and internal \n\n\n\n concentration with different fertilizer treatments.  \n\n\n\nTABLE 7\nFoliar nutrient contents of pepper with different fertilizer treatments. \n\n\n\nTABLE 6 \nComparison of chlorophyll content, transpiration rate, net photosynthesis and internal \n\n\n\nCO2 concentration with different fertilizer treatments.   \n\n\n\nFertilizer treatments Parameters \n T1                 T2 \n\n\n\n \n T3  T4 \n\n\n\nChlorophyll a (mg g-1 FW) 2.95a 1.53b 3.05a 0.75c \nChlorophyll b (mg g-1 FW) 1.18a 0.82b 1.34a 0.43c \nTotal Chlorophyll 4.13b 2.35c 4.39a 1.81d \nNet photosynthesis rate (\u00b5 mol m-2 S-1)  24.86a 12.28b 26.87a 10.56b \nTranspiration (m mol m-2 S-1) 6.35c 7.92b 5.59d 8.21a \nInternal CO2 concentration (ppm) 295.6b 335.9a 267.8b 345.5a \nT1: Chemical fertilizer treatment, T2: organic fertilizer treatment, T3:  Both chemical \nand organic fertilizer treatments, and T4: control plot (no fertilizer)  \nMeans in columns with different letters are significantly different at 0.05 level using \nDuncan Multiple Range Test. \n\n\n\nTABLE 7 \nFoliar nutrient contents of pepper with different fertilizer treatments.  \n\n\n\nNutrient concentration (%) Treatment \nN P K  Mg  Ca  \n\n\n\nT1-  Chemical fertilizer 3.24a 0.44a 1.97a 0.48a 2.32a \nT2 -  Organic fertilizer 0.73b 0.15b 0.97b 0.42b 1.43b \nT3 - Chemical and \n\n\n\norganic fertilizer \n3.42a 0.41a 2.12a 0.52a 2.21a \n\n\n\nT4 -  Control plot (no \nfertilizer) \n\n\n\n0.32c 0.03c 0.29c 0.21c 0.34c \n\n\n\nMeans in column with different letters are significantly different at 0.05 level using Duncan \nMultiple Range Test. \n\n\n\n\n\n\n\n\n81\n\n\n\npepper leaf symptoms grown under limited nutrients content.\n\n\n\nEffect of Fertilizer Treatments on Soil Fertility\n\n\n\nproperties. The plots applied with only organic fertilizers and a combination of both \n\n\n\nas the main factor for reducing soil pH in a cassava-based cropping system.  These \n\n\n\nImpact of Fertilization Methods on Black Pepper\n\n\n\nTABLE 8\nNutrient status in soil after treatments\n\n\n\nPlate 2: Soil acidity symptoms \n\n\n\nTABLE 8 \nNutrient status in soil after treatments \n\n\n\nTreatments pH O.C \n(%)  \n\n\n\nCEC \n(cmol(+) \n\n\n\nkg   )\n \n\n\n\nTot-N \n(%) \n\n\n\nAv. P \n(mg/kg)  \n\n\n\nTotal. K\n(mg kg  ) \n\n\n\nT1- Chemical fertilizer 4.48d 0.95d 7.4d 0.14b 10.58a 38.56a \nT2 - Organic fertilizer 5.28a 1.54a 11.2a 0.20a 9.85b 18.95c \nT3 - Chemical and organic \nfertilizer \n\n\n\n5.03b 1.43b 9.0b 0.22a 11.56a 30.65b \n\n\n\nT4 -  Control plot (no fertilizer) 4.81c 1.04c 8.8c 0.16b 9.21b 16.76d \nMeans in columns with different letters are significantly different at 0.05 level using Duncan \nMultiple Range Test. \n\n\n\n-1\n\n\n\n-1\n\n\n\n\n\n\n\n\nrespectively. This showed that incorporation of organic fertilizers into the soil \n\n\n\n8 indicates the   percent of total nitrogen decrease in the entire plot with a greater \n\n\n\ncompared to that from other treatments. The highest reduction in organic C and \nnitrogen was observed in the plots treated with chemical fertilizers resulting \nfrom stimulated decomposition of soil organic matter and crop residue by the \napplied fertilizer which led to higher mineralization. This could be due to higher \nN uptake by pepper and /or loss through leaching. The availability of phosphorus \n\n\n\nthe utilization of both inorganic and organic fertilizers could supply the plants \nwith good amounts of available phosphorus. From the data obtained, the changes \nin available P were generally low (Adzemi et al.\nbecause P is relatively immobile and strongly adsorbed by soil particles (Ige et al. \n\n\n\n kg-1, \n\n\n\ntreatment. The highest CEC value observed in organic treatment indicated those \nnutrients were highly retained compared to those from other treatments. The \n\n\n\nkg-1\n\n\n\navailability of potassium in the chemical fertilizer treatment indicated that this \n\n\n\n-1 of potassium in order to sustain the \ngrowth and production of pepper berries.\n\n\n\nCONCLUSION\nThe application of organic fertilizer only has led to poor growth performance \n\n\n\nsustainable crop production, integrated use of chemical and organic fertilizers is a \nmore viable option. It is characterized by reduced input of chemical fertilizers and \nthe combined use of chemical fertilizers with organic materials such as animal \nmanure, crop residue, green manure and composts. From the results obtained, it \ncan be concluded that the application of inorganic fertilizers supplemented with \norganic fertilizers could sustain the growth and optimum yield of pepper. Though \n\n\n\nshould be compensated by premium pricing of organic produce. \n\n\n\nYap Chin Ann  \n\n\n\n\n\n\n\n\nImpact of Fertilization Methods on Black Pepper\n\n\n\nACKNOWLEDGMENTS\nThe authors wish to thank the host farmer on whose plots the data was collected. \n\n\n\nREFERENCES\n\n\n\nfertilizers on yield and nutrient uptake by onion. Pakistan Journal of Biological \nSciences.\n\n\n\nhybrids. Research Journal of Agriculture and Biological Sciences\n168.\n\n\n\nphysioco-chemical properties, leaf nutrient contents and yield of yam (Dioscorea \nrotundata Journal of American Science. \n\n\n\nBlack Pepper (Piper nigrum\n\n\n\nof organic waste and inorganic fertilizer on growth, P-uptake and yield of wheat \nSongklanakarin. Journal of Science and Technology\n\n\n\nprocessed organic wastes as components of horticultural potting media for \ngrowing marigold and vegetable seedlings. 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Vagusevicience \n\n\n\nphotosynthetic pigment and carbohydrate contents in the two winter wheat \nvarieties. Agronomy Research\n\n\n\npepper (Piper nigrum erythrina indica live support \nsystem in Johor In: The Pepper Industry- Problems and Prospects, eds. M.Y. \n\n\n\nPertanian Malaysia.  \n\n\n\npepper (Piper nigrum Journal of Plantation Crop.\n\n\n\ncompost for organic farming in Hong Kong. Bioresource Technology. 67: \n\n\n\nvegetable farming systems on the properties of a yellow earth in New South \nAgriculture, Ecosystems and Environment\n\n\n\nand inorganic manures on biometric and yield parameters of chilli (Capsicum \nannuum Crop Research.\n\n\n\nPiper \nnigrum Journal of Agricultural Science and Technology A. 6: 86-89.\n\n\n\nImpact of Fertilization Methods on Black Pepper\n\n\n\n\n\n"
"\n\nINTRODUCTION\nEnvironmental conservation is of vital importance to living beings as it offers \nnumerous benefits. Motivated by this fact, we began to think of developing \nprocedures to obtain organoclays in the Soil Analysis Laboratory, Faculty of \nPhysical Sciences, Universidad Nacional Mayor de San Marcos (UNMSM). \nOrganoclays, better known commercially as organically-modified nanoclays, \nare clays modified with organic cations. These clays have applications in the \nagricultural, industrial and medicinal sectors (Uddin 2008; Perugachi 2006; \nPatel 2014; Aguilar et al. 2006). For example, their use in agriculture allows the \nextraction of contaminants because organoclays are excellent adsorbents and/or \ndegraders of toxic substances like pesticides. \n  Through fieldwork, we obtained two groups of clayey rock \nsamples. One group named La Calera (ARC-CAL), was collected in the Alto Lar\u00e1n \ndistrict, Chincha Province, Ica Region, Peru. The other group of samples, named \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 21: 63- 71 (2017)                         Malaysian Society of Soil Science\n\n\n\nA Preliminary Study on Organoclays from Two Peruvian \nClay Pits \n\n\n\nCer\u00f3n Loayza, M. L.1,*, J. A. AurelioBravo Cabrejos1 and F. A. \nReyes Navarro2\n\n\n\n1 Laboratorio de An\u00e1lisis de Suelos y Arqueometr\u00eda, Facultad de Ciencias F\u00edsicas, \nUniversidad Nacional Mayor de San Marcos (UNMSM),\n\n\n\nCalle Germ\u00e1n Am\u00e9zaga N\u00b0 375, Lima, Per\u00fa.\n2Laboratorio de Cristales  Reales  y  Aleaciones  Met\u00e1licas, Facultad de Ciencias \n\n\n\nF\u00edsicas, Universidad Nacional Mayor de San Marcos (UNMSM), \nCalle Germ\u00e1n Am\u00e9zaga N\u00b0 375, Lima, Per\u00fa.\n\n\n\nABSTRACT\nWe studied two groups of samples called \u201cLa Calera\u201d and \u201cAmotape,\u201d in situ \nfrom the Peruvian districts of Alto Laran and Amotape, respectively. Different \ntechniques were needed to characterise them. By using X-ray diffractometry, we \ndetermined their phases as well as corroborated the cation exchange process in \nsome samples; however, in other samples there was a large interplanar spacing. \nThrough transmission M\u00f6ssbauer spectroscopy, we obtained the valence state \nof iron. Consequently, we determined that the samples contained calcium and \nsodium clays. Additionally, we proceeded to optimise the calcium clays by cation \nexchange to obtain sodium clays, which can be used in wastewater treatment and \nother applications.\n\n\n\nKeywords: Cation exchange, montmorillonite, M\u00f6ssbauer spectroscopy, \ntransmission X-ray diffraction, \n\n\n\n___________________\n*Corresponding author : E-mail: malucelo@hotmail.com\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201764\n\n\n\nAmotape (ARC-AMOT), was collected in the Amotape district, Paita province, \nPiura Region. By using an extraction kit, all these samples were extracted in situ \nfrom several sites and  placed into different containers for coding. \n\n\n\nPreparation tests were carried out in the SAL following a sedimentation \nprocess for the production of clays, followed by another process for optimisation. \nThese processes are based on two scientific works utilising the principle of cation \nexchange (Nigam et al. 2004; Lee and Lee 2004).  Likewise, physico-chemical \nanalyses were carried out to determine the degree of acidity of the samples \n(pH). \n\n\n\nMATERIALS AND METHODS\n\n\n\nSample Extraction and Preparation\nIt is possible to obtain relatively pure montmorillonite from the respective sample. \nCertainly, there are several methods, some of which are better than others. In \nour experimental work, we have followed the procedures of Lee and Lee (2004), \nNigam et al. (2004) and Perugachi (2006). \n\n\n\nThe samples were prepared in the Soil Analysis Laboratory, dried at room \ntemperature, ground in a mortar, and sieved. Then the process of  obtaining the \nnanoclay began. This process is divided into  three stages: (i) obtaining the clay \nfraction; (ii) obtaining montmorillonite followed by the process of ion exchange; \nand (iii) preparation of the nanoclay.\n\n\n\n \nObtaining the Clay Fraction\nUsing the usual sedimentation method, the clay fraction of a size less than 2 \u03bcm \nwas obtained (Lee and  Lee 2004; Nigam et al. 2004). In this method a dispersing \nagent was used for suspension (water/sample/dispersant). Specifically, following \nthe procedure indicated by Perugachi (2006), the organic matter was first removed \nas it  acts as a cementing agent of the clay particles, i.e., it does not allow for easy \ndispersion of the clay particles. Then the  clay fraction was recovered by using the \naforementioned procedure of Perugachi (2006), dried in a 60\u00baC oven, registered \nand finally deposited in a desiccant.\n \nObtaining the Montmorillonite \nOnce the clay material was obtained (<2 \u03bcm), our objective was to increase the \nmontmorillonite probably present in the aforementioned material. Using the \ncation exchange method, calcium cations were exchanged for sodium cations.  \nAgain, following the procedure indicated by Perugachi (2006), the sample  was \nsplit into four equal parts and  placed in the respective tubes of a PLC Series \ncentrifuge of the UNMSM. This was followed by centrifugation and separation of  \nthe liquid from clay. Subsequently, the montmorillonite clay was collected, dried, \nstored in a desiccator, and finally registered. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 65\n\n\n\nPreparation of the Nanoclay\nFor modification of the clay at nano molecular level, the organic modifier \nArquad HTM8-MS was used (Dehydrogenated Tallow, 2-ethylhexyl quaternary \nammonium). Alkyl ammonium ion was used as organic cation to  substitute for \nthe Na+ inorganic cation.\n\n\n\nThe preparation of nanoclays was based on  a procedure suggested by two \nscientific papers ( Lee and Lee 2004; Nigam et al., 2004). It needs to be noted \nthat Perugachi (2006) also adapted the procedures of the above cited authors. \nSpecifically, these were the  steps: (a) In a glass container, 1200 ml of a solution of \nwater/ethanol (4/1, v/v), was prepared, then heated to 60 \u00b0C. Next, 15 g of sodium \nmontmorillonite clay solution was added into the solution and  shaken for 2 h at \n60 \u00b0C. (b) In another container, to 100 ml of distilled water, 5 g of Arquad HTL8-\nMS was added  and then  stirred until homogenisation was achieved. (c) The \nsubstances from these two containers were thoroughly mixed with an ultrasonic \nprocessor for an hour. (d) The mixing was continued  for a further 12 h  at 60 \n\u00b0C with a magnetic stirrer. (e) The supernatant w\u00e1ter was next removed. (f) The \nresidue was washed with a solution of water/ethanol (1/1, v/v). (g) Finally, it was \ndried, stored and registered.\n\n\n\n \nEXPERIMENTAL MEASUREMENTS\n\n\n\nPhysico-chemical Measurements\nIn the Soil Analysis Laboratory, we prepared the samples for the physico-chemical \nanalysis. which was essentially measuring the degree of alkalinity by using a \npHTester BNC/OAKTON(model 35624-10). \n\n\n\nX-ray Diffractometry\nFor  structural analysis of the minerals present in the samples, a BRUKER \ndiffractometer (D8-Focus model) was used, with Cu-K\u03b1 radiation \u03bb = 1.54178 \n\u00c5(40 kV and 40 mA), and a vertical goniometer. The angle scale interval of the \ngoniometer was set at 4\u00b0 <2\u03b8 < 70\u00b0 with the 2\u03b8 advance being 0.02\u00b0 per step at \nintervals of 3s per step.\n\n\n\n57Fe M\u00f6ssbauer Transmission Spectrometry\n Utilising  a conventional spectrometer which had a signal with  sinusoidal velocity \nmodulation, and 1024 channels, detailed information on minerals containing iron \nwas obtained..  The M\u00f6ssbauer spectra were taken at room temperature (~298 K) \nin the SAL. These spectra were obtained in transmission geometry by using a 57Co \nsource in an Rh matrix; subsequently, these same spectra were analysed by using \nthe Normos program by Brand (2002).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201766\n\n\n\nRESULTS AND DISCUSSION \n\n\n\nPhysico-chemical Analysis \nIn relation to  the degree of alkalinity of the samples, Table 1 shows the \nmeasurements of the hydrogen potential of these samples.\n\n\n\nTABLE 1\npH measurements of the samples\n\n\n\nSamples  pH\nARC- CAL      8.36\nARC- AMOT      8.97\n\n\n\nX-ray Diffractometry \nThe results obtained through XRD are shown in Figs. 1 and 2. In Fig. 1,  the \nARC-CAL and ARC-AMOT untreated samples can be observed; in this figure, \nstructural phases can be seen for each of these samples. With regard to ARC-\nAMOT, we observe an overlapping of Mont + Ili (Mont = montmorillonite; Ili \n= illite); likewise, we notice Illi + Non (Non=nontronite) and major peaks of \nnontronite. As opposed to ARC-CAL, where we can see definite and overlapped \npeaks of Mont + Ili, for the ARC-AMOT sample, the results infer that it is calcium \nclay. Therefore, the sample needs to be treated chemically to obtain sodium \nmontmorillonite. \n\n\n\nFig. 1: Superimposed X-ray diffractograms of the ARC-AMOT and  ARC-CAL \nuntreated samples\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 67\n\n\n\nIn Fig. 2, we can see that the ARC-CAL treated simple has two structural \nphases with their main peaks, illite and montmorillonite (typical clay phases). \nWe also note the characteristic peaks of sodium montmorillonite, which has a \nsimple layer of water at approximately d = 12.32 \u00c5. In this regard, Roth (1951) \nand Williams et al. (1953) showed that at 50% relative humidity, sodium \nmontmorillonite has a single layer of water, with d = 12.4 \u00c5; Murray (2007) \nhas updated his book on  these points. Also, the XRD results provides evidence \nthat cation exchange has occurred; therefore, effectively, these are sodium \nmontmorillonite clays. Furthermore, we observe that the peaks were wide, which \ncould be due to the particle size.\n\n\n\n Fig. 3 shows the evolution of the ARC-AMOT XRD diffractogram, where \nwe had earlier observed overlapped peaks for both the treated and untreated \nsamples. The interplanar distance is the same; however, the intensity of its peaks \nhas decreased. In Fig. 4 we observe the final result: superposed peaks of ARC-\nAMOT, modified by the treatment. In this figure, on Amotape samples, specifically, \nwe notice three curves: (a) ARC-AMOT ST (untreated sample, black colour); (b) \nARC-AMOT TQ (with chemical treatment, pink color); (c) final ARC-AMOT \n(after cation exchange, blue color). \n\n\n\nTransmission M\u00f6ssbauer Spectroscopy \nFor a better fit of the hyperfine parameters, the results of TMS were registered at \nroom temperature and at low speed (4 mm/s). In Fig. 5, we have the M\u00f6ssbauer \nspectrum for the ARC-CAL sample. We can observe the presence of three \n\n\n\nFig. 2: X-ray diffractogram of the ARC-CAL treated sample; the observed \ninterplanar distance d = 12.32 \u00c5 corresponds to sodium montmorillonite, \n\n\n\nafter cation exchange treatment.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201768\n\n\n\ndoublets: two of them associated with Fe3+ and Fe2+ sites, located in illite and \nmontmorillonite, respectively (as already seen in the results by XRD). The third \ndoublet is associated with a Fe3+ site. \n\n\n\nIn Fig. 6, we observe the TMS spectrum for the ARC-AMOT sample. We  \nnote the presence of three paramagnetic doublets: two of them associated to Fe3+ \n\n\n\nFig. 3. Superimposed diffractograms of the ARC-AMOT samples. The treated and \nuntreated samples are represented by red and black colours respectively.\n\n\n\nFig. 4. Superimposed X-ray diffractograms of the Amotape samples: (1) untreated (Arc-\nAmot ST, black colour); (2) chemically treated (ARC-AMOT-TQ, pink colour); (3) after \n\n\n\nthe cation exchange (ARC-AMOT-F, blue colour).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 69\n\n\n\nand Fe2+ sites, located in the illite and montmorillonite, respectively; the remaining \ndoublet is associated with a Fe3+ site, which could be assigned to nontronite. These \nresults corroborate the phases previously assigned by XRD.\n\n\n\nFig. 5. ARC-CAL M\u00f6ssbauer spectra registered at room temperature and at \nlow speed (4 mm/s).\n\n\n\nFig. 6. ARC-AMOT M\u00f6ssbauer spectra registered at room temperature and at low speed \n(4 mm/s).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 201770\n\n\n\nCONCLUSIONS\nThrough techniques earlier indicated, we have identified nanoclays in the Soil \nAnalysis Laboratory in UNMSM. For the ARC-CAL sample, which was a sodium \nmontmorillonite clay, we observed a peak with interlayer space d = 12.32 \u00c5, a \nvalue very close to that obtained by other researchers (d = 12.4 \u00c5). For the ARC-\nAMOT IC sample, the interlayer space increased from 14.65 to 18.2 \u00c5. This value \noffers a reason for continuing the investigation.\n\n\n\nFinally, following up on this research will help in the implementation of \nprotocols, optimal procedures and cost reduction. Consequently, all of these will \nlead to  industrial production of nanoclay in the Republic of Peru. \n \n\n\n\nACKNOWLEDGMENTS\nTo the staff of the Soil Analysis Laboratory of UNMSM, we express our most \nprofound gratitude for all their support.\n\n\n\nREFERENCES\nAguilar Jarr\u00edn, E. and A.F. Rigail-Cede\u00f1o. 2006. Propiedades anticorrosivas de un \n\n\n\nrecubrimiento nanocompuesto de \u00e9poxica/amina/nanoarcillas (Anticorrosive \nproperties of a nanocomposite coating epoxy/amine/nanoclay). Revista \nTecnol\u00f3gica ESPOL19(1): 125\u2013132.\n\n\n\nBrand, R. A. 2002. Normos M\u00f6ssbauer fitting Program, University of Dortmund.\n\n\n\nLee, J.Y. and H.K.Lee. 2004. Characterization of organobentonite used for polymer \nnanocomposites. Materials Chemistry and Physics 85(2\u20133): 410\u2013415. \n\n\n\nMurray, H. H. 2007. Applied Clay Mineralogy: Occurrences, Processing and \nApplication of Kaolins, Bentonites, Palygorskite-Sepiolite, and Common Clays. \nElsevier, Amsterdam: Elsevier.\n\n\n\nNigam, V., D.K. Setua, G.N. Mathur and K.K. Kar. 2004. Epoxy-montmorillonite clay \nnanocomposites: Synthesis and characterization. Journal of Applied Polymer \nScience 93(5): 2201\u20132210.\n\n\n\nPatel, H. A. 2014. Nanoclays: Synthesis, Characterization and Applications.  Scholars \nWorld.\n\n\n\nPerugachi, C. R., C. Paredes and M. Cornejo. 2006. Las Nanoarcillas y sus potenciales \naplicaciones en el Ecuador (Nanoclays and their potential applications in \nEcuador).  Revista Tecnol\u00f3gica ESPOL 19(1): 121-124.\n\n\n\nPerugachi, C.R.2006. Modificaci\u00f3n a nivel nanomolecular de las propiedades de las \narcillas pertenecientes al grupo Ancon de la pen\u00ednsula de Santa Elena (PSE).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 21, 2017 71\n\n\n\n(Nanomolecular modification of properties of clays belonging to the Ancon \ngroup in the Santa Elena Peninsula (PSE)).  Graduate thesis, Escuela Superior \nPolit\u00e9cnica del Litoral (ESPOL), Ecuador.\n\n\n\nRoth, R. S. 1951. The structure of montmorillonite  in relation  to the occurrence  and  \nproperties  of certain  bentonites, Ph.D.  Thesis, University of Illinois, USA.\n\n\n\nUddin, F. 2008. Clays, Nanoclays, and Montmorillonite. Minerals Metallurgical and \nMaterials Transactions A39(12): 2804-2814. \n\n\n\nWilliams,F.J., M. Neznayko and D.J. Weintritt. 1953. The effect of  exchangeable \nbases on the colloidal properties of bentonite.  J. Phys.  Chem. 57: :6\u201310.\n\n\n\n\n\n"
"\n\n___________________\n*Corresponding author : E-mail: sunny.goh@gmail.com\n\n\n\nINTRODUCTION\nSensitivity analysis can be broadly categorized into local sensitivity analysis and \nglobal sensitivity analysis. Local sensitivity analysis, that is, normally known as \none-at-a-time (OAT) measure, is carried out by varying single input parameters \nof interest and keeping other parameters at constant value in order to study model \noutput. This is commonly used by various researchers (Vereecken et al. 1990; De \nRoo and Offermans 1995; Jhorar et al. 2002). The obvious problem of this method \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 18: 19-33 (2014)                          Malaysian Society of Soil Science\n\n\n\nSensitivity Analysis Using Sobol \u2018Variance-Based Method \non the Haverkamp Constitutive Functions Implemented in \n\n\n\nRichards\u2019 Water Flow Equation\n\n\n\nGoh Eng Giap 1,2* and Noborio Kosuke1\n\n\n\n1School of Agriculture, Meiji University, 1-1-1 Higashimita, Tama-ku,\nKawasaki 214-8571, Japan\n\n\n\n2School of Ocean Engineering, Universiti Malaysia Terengganu,\n21030 Kuala Terengganu, Terengganu, Malaysia\n\n\n\nABSTRACT\nRichards\u2019 equation was approximated by finite-difference solution and \nimplemented in FORTRAN to simulate water infiltration profile of yolo light \nclay. The simulation was successfully validated by published data of Philip\u2019s \nsemi-analytical solution. Global sensitivity analysis using Sobol\u2019 variance-based \nmethod was also coded in FORTRAN and implemented to study the effect of \nparameter uncertainty on model output variability. Sobol\u2019 sequences were used to \ngenerate quasi-random numbers to study the effect of every possible combination \nof different input parameters\u2019 values, based on each parameter\u2019s uncertainty range \non model outputs. First order sensitivity index (Si ) and total effect index (STi\n\n\n\n) were \nestimated based on quasi-Monte Carlo estimators. Various statistical parameters, \ncoded in FORTRAN, such as kurtosis, skewness, 95% confident intervals, etc. \nwere used to provide a better understanding and description of the model outputs. \nResults found parameter constants (\u03b2, B) and saturated volumetric water content \n(\u03b8s) of Haverkamp constitutive functions to be dominant parameters with a \ncombined 93% of model variability which could be explained by these parameters. \nThe total effect index for every parameter was found to be greater than the first \norder effect index. In addition, global sensitivity analysis tool was able to generate \ninformative sensitivity indicators and a good statistical description compared to \nthe local sensitivity tool.\n\n\n\nKeywords: First order index, global sensitivity analysis, Haverkamp \nconstitutive functions, Richards\u2019 equation, Sobol\u2019 variance-\nbased method, total effect index, uncertainty analysis\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201420\n\n\n\nis that interaction between various input parameters cannot be identified when \ntwo or more input parameters vary simultaneously. \n\n\n\nAn alternative to local sensitivity analysis is the global sensitivity analysis, \nof which there are several varieties ranging from a qualitative screening method to \nquantitative technique (Sobol\u2019 1990; Morris 1991; Campolongo et al. 2007). One \ntype of global sensitivity analysis is the variance-based Sobol\u2019 method (Sobol\u2019 \n1990). Its main purpose is to quantify the variance contribution of input parameters \nto the unconditional variance of model output. This is important in determining \ninput parameters with the greatest influence for parameter prioritization, and input \nparameters with the least influence for parameter fixing (Saltelli et al. 2008). It \nhas been applied on various models, for example, in Soil and Water Assessment \nTool (SWAT) by Nossent et al. (2011), inhalation dose model by Avagliano and \nParrella (2009), CERES-EGC model by Drouet et al. (2011), and Sea Level \nAffecting Marshes Model (SLAMM 5) by Chu-Agor et al. (2011).\n\n\n\nRichards\u2019 equation (Richards 1931) is widely used to predict water \nmovement in variably saturated soils. This equation has important applications \nin hydrology, meteorology, agronomy, environmental protection, and other soil-\nrelated fields (Pachepsky et al. 2003). In the field of environmental protection, an \naccurate prediction of water movement will allow for estimation of the movement \nof pollutants in contaminant transport models (\u0160im\u016fnek and Bradford 2008). It \nhas also been used in geotechnical and geo-environmental engineering to predict \nunsaturated flow of water in unsaturated soils (Barari et al. 2009). The equation \nis a combination of both Darcy\u2019s law and continuity equation, and is known for \nits non-linearity property (Caviedes-Voulli\u00e8me et al. 2013). This is attributed to \nthe relations between soil water content on soil water pressure head and hydraulic \nconductivity (Feddes et al. 1988). Equations which are used to govern the \nrelations are commonly known as a constitutive function, such as the Haverkamp \nconstitutive function (Haverkamp et al. 1977). Namin and Boroomand (2012) state \nthat Richards\u2019 equation numerical solution strategy is still a subject to research.\n\n\n\nAccording to Mishra (2009), there are basically three elements of uncertainty: \n(1) uncertainty of characterization, i.e. distribution types of uncertainty model \ninputs; (2) uncertainty propagation that involves translating the uncertainty in \nmodel inputs to the uncertainty in model outputs; and (3) uncertainty importance to \ndetermine the influential parameters. Uncertainty analysis is superior to sensitivity \nanalysis because it includes both uncertainty characterization and propagation, \nwhereas sensitivity analysis includes only uncertainty importance. It is common \nthat both sensitivity analysis and uncertainty analysis coupled in practice and only \nnamed as sensitivity analysis (Saltelli and Annoni 2010). We reserved the term \nof sensitivity analysis for both analyses. Moreover, the variance-based method \nhas been listed in the US Environmental Protection Agency\u2019s (EPA 2009) list of \nattributes as the preferred sensitivity analysis method. It is claimed to be robust \nand independent, irrespective of model assumptions (Saltelli et al. 2000). \n\n\n\nIn this study, Haverkamp constitutive functions implemented in Richards\u2019 \nequation were subjected to sensitivity analysis by the Sobol\u2019 variance-based \n\n\n\nGoh Eng Giap and Noborio Kosuke\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 21\n\n\n\nmethod to determine the effect of input parameter uncertainty on model outputs. \nThe numerical solution on Haverkamp constitutive functions and Richards\u2019 equation \nwere implemented and tested with a case study from Haverkamp et al. (1977). \nThe uncertainty range of input parameters was established based on significant \ndigits approximation. The variance-based sensitivity analysis was carried out on \nHaverkamp constitutive functions to determine the first order sensitivity index (Si) \nand total effect index (STi\n\n\n\n ). The first order sensitivity index (Si) indicates the main \neffect  output variance of input parameter,  Xi, that could be reduced if it could be \nfixed to a specific value. The STi\n\n\n\n indicates the sum that includes first order index \n(Si), second order index (Sij, Sik, and  Si\u2026q, where q is the total number of input \nparameters in the model), third order index and so on. It is generally acceptable to \ndetermine only Si and STi\n\n\n\n  (Fox et al. 2010). The variance-based method was also \ncompared to the local sensitivity analysis results.\n\n\n\nSobol\u2019 Variance-Based Method as Global Sensitivity Analysis Tool\nModel outputs (Y) is a function of input parameters (X1, X2, X3,...,Xq), and thus, \nthis relation can be written as follows:\n\n\n\n Y = f (X1, X2, X3,..., Xq )     (1)\n\n\n\nIn the variance-based method, total unconditional variance, V(Y), in Eq. (1), can \nbe decomposed into partial variances of increasing dimensionality (Sobol\u2019 1990):\n\n\n\n    (2)\n\n\n\nDividing Eq. (2) by total unconditional variance, it becomes:\n\n\n\n     (3)\n\n\n\n        (4)\n\n\n\n         (5)\n\n\n\nSobol Variance Sensitivity Analysis in Richard\u2019s Equation\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201422\n\n\n\nGoh Eng Giap and Noborio Kosuke\n\n\n\nDue to the ratio of partial variances (e.g. Vi, Vij, etc) to total variance (V(Y)), all the \nsensitivity indices are scaled between 0 and 1 interval. When the summation of all \nfirst order indices gives unity, that is,  \u2211i\n\n\n\nq Si =1, the model is known as additive, \nthat is,  without any interaction effect. Hence, 1- \u2211i\n\n\n\nq Si  indicates interaction effects \nthat could either be one or a combination of second order, or higher order. \n\n\n\nThe total effect index (STi \n) for each input parameter is given by:\n\n\n\n     (6)\n\n\n\nFor instance, if q = 3, total effect index would be given by:\n\n\n\n     (7)\n\n\n\nwhere S1 is first order index of input parameter 1, S12 is second order index of \ninteraction effect between input parameters 1 and 2, S13 is second order index \nof parameters 1 and 3, and S123 is third order index of interaction effect between \ninput parameters 1, 2 and 3. The same method is applied for decomposition of ST2\n\n\n\n  \nand ST3\n\n\n\n total effect index. Since STi\n  includes first to higher order relating to input \n\n\n\nparameter i, STi\n - Si indicates only interaction effect that only account for second \n\n\n\nand higher order indices.\nFirst order sensitivity index and total effect index were estimated by quasi-\n\n\n\nMonte Carlo estimators (Saltelli et al. 2010):  \n\n\n\n  (8)\n\n\n\n    \n     (9)\n\n\n\nBoth yA\n(m) and yB\n\n\n\n(m) are model outputs, as shown in Eqs. (8) and (9). Sobol\u2019 \nquasi-random sequences were used to generate two sets of data, that is, matrix A \nand B corresponding to model outputs of yA\n\n\n\n(m) and yB\n(m), and these dataset were \n\n\n\nconfined between 0 and 1. The advantage of Sobol\u2019 quasi-random sequence is that \nit produced good distribution of n-dimensional unit hypercube; for example, the \nsequence for two dimensions as shown in Figure 1(a)-(b). Haverkamp constitutive \nfunctions have 8 input parameters that must be tested, thus, 8 dimensions were \nrequired for each matrix. The fo\n\n\n\n2 is given by ((1/N) \u2211m\nN\nm\n    \n\n\n\n= 1\n  yA\n\n\n\n(m))2. The yci\n(m) is also \n\n\n\nmodel output; all the dimensions in the matrix were taken from matrix A, except i \ncolumn, that is, dimension, which was taken from matrix B.\n\n\n\nThe number of rows in the matrix indicates the number of simulation runs \nrequired, while the number of columns in the matrix indicates the number of \ninput parameters to be tested. In this study, we have N=15,000 and k= 8 columns. \nTo solve Eqs. (8) and (9), we need two matrix (A and B), i.e. 2N and k input \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 23\n\n\n\nparameters of N for each input parameter, that is, kN. In total, we have to simulate \nN(k+2)=15,000 (8+2)=15,000 runs. A higher N value would result in better \nestimation of sensitivity indices. Nossent et al. (2011) had demonstrated that an N \nvalue of 12,000 for 26 input parameters is sufficient to obtain reliable estimation. \nAll these were coded in Simply FORTRAN software.\n\n\n\nNumerical Solution on Richards\u2019 Equation and Input Parameter Values\nThere are basically three types of Richards\u2019 equation: (1) \u03b8L-based; (2) \u03c8m-based \nand (3) mixed. \n\n\n\nIn this study, we limit to \u03b8L-based, as follows:\n\n\n\n     (10)\n\n\n\nwhere \u03b8L is volumetric water content (m3 m-3); t is time of simulation (s);  z is \nvertical distance of simulation (m); K is hydraulic conductivity of the medium (m \ns-1); \u03c8m is matric pressure head (m); and            is vector unit with a value of positive \none when it is vertically downwards. Its advantages and disadvantages are \ndiscussed in Celia et al. (1990). The limitation of \u03b8L-based governing equation has \nbeen properly addressed, and its preliminary results have been discussed by Goh \nand Noborio (2013).\n\n\n\nEq. (10) was solved numerically using finite difference method with cell-\ncentered and fully implicit for spatial and temporal discretization, respectively. \nThe solution was implemented using Simply FORTRAN software. The algebra \nsolution to Eq. (10), is as follows: \n\n\n\nSobol Variance Sensitivity Analysis in Richard\u2019s Equation\n\n\n\nFigure 1: First (a) 1,000 points and (b) 10,000 points of a two-dimensional Sobol\u2019\nquasi-random sequence are generated for dimension 1 and 2.\n\n\n\n15 \n \n\n\n\n0\n\n\n\n0.2\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n0.8\n\n\n\n1\n\n\n\n0 0.2 0.4 0.6 0.8 1\n\n\n\nD\nim\n\n\n\nen\nsi\n\n\n\non\n 2\n\n\n\n\n\n\n\nDimension 1 \n\n\n\n0\n\n\n\n0.2\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n0.8\n\n\n\n1\n\n\n\n0 0.2 0.4 0.6 0.8 1\n\n\n\nD\nim\n\n\n\nen\nsi\n\n\n\non\n 2\n\n\n\n\n\n\n\nDimension 1 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  (a)      (b) \n\n\n\nFigure 1: First (a) 1,000 points and (b) 10,000 points of a two-dimensional Sobol' quasi-random \nsequence are generated for dimension 1 and 2. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nk \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201424\n\n\n\n  \n  \n  \n     (11)\n\n\n\nwhere k indicates a cell-centered number in z-direction in cartesian coordinate \nsystem; \u2206t (s) is time-step size; \u03b8L(k) \n\n\n\nn (m3 m-3) and \u03b8L(k)\nn+1 (m3 m-3) indicates \n\n\n\nvolumetric water content at old time level (n) and new time level (n+1), \nrespectively; Kk+1\u20442 (m s-1) is hydraulic conductivity at the interface between cell k \nand k + 1; Kk-1\u20442 (m s-1) is hydraulic conductivity at the interface between cell k-1 \nand k; (\u2202\u03c8m  \u2044 \u2202\u03b8L)(k+1\u20442) is partial derivative of \u03c8m with respect to \u03b8L at the interface \nbetween the cell k and k + 1; (\u2202\u03c8m \u2044 \u2202\u03b8L )k-1\u20442 is partial derivative of \u03c8m with respect \nto \u03b8L at the interface between the cell k - 1 and k; \u2206zk+1 (m), \u2206zk (m) and \u2206zk-1 (m) \ncorrespond to the spatial sizes of spacing of cell k + 1, k and k - 1. \u03b8L(k+1)\n\n\n\nn+1 (m3 m-3), \n\u03b8L(k)\n\n\n\nn+1 (m3 m-3) and \u03b8L(k-1)\nn+1 (m3 m-3) are the volumetric water content at new time \n\n\n\nlevel of cell k +1, k and k - 1, respectively. \nAn iterative method was used to solve the mathematical algebra of Eq. (10) \n\n\n\n(Tu et al. 2007). A convergence factor criterion was used to indicate the condition \nfor iteration termination process, that is, absolute maximum difference |\u03b8L(k) \n\n\n\n(n+1) - \n\u03b8L(k)\n\n\n\nn| for every single cell. \n\n\n\nThe constitutive functions implemented were (Haverkamp et al. 1977):\n          \n     (12)\n\n\n\n       \n     (13)\n\n\n\nwhere \u03b1, \u03b2, A and B are fitting parameters; \u03b8r (m3 m-3) is residual volumetric \nwater content; \u03b8s (m\n\n\n\n3 m-3) is saturated volumetric water content; and Ks (m s-1) is \nsaturated hydraulic conductivity. \n\n\n\nBase case values or default values used in the current simulation were from \nHaverkamp et al. (1977) for yolo light clay. Numerical input parameters, that \nis, time-step size and spatial discretization size were from Goh and Noborio \n(2013). These input values are tabulated in Table 1. The time-step size (500 s), \nspatial discretization size (1 cm) and convergent value (10-12 m3 m-3) were set \nafter considering mass balance ratio and iteration number. The upper boundary \ncondition was set at 0.495 m3 m-3. The simulations of water infiltration profile \nat 105, 106 and 3x106 s are shown in Figure 2. The simulations have shown \ncomparable results to the published data of Philip\u2019s semi-analytical solution \nprovided by Haverkamp et al. (1977).\n\n\n\nGoh Eng Giap and Noborio Kosuke\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 25\n\n\n\nSensitivity Analysis\nLocal and global sensitivity analyses were carried out on Haverkamp constitutive \nfunctions implemented in Richards\u2019 equation based on the input parameters shown \nin Table 1. Both analyses were studied at 105 s simulation time. Local sensitivity \nanalysis was carried out to determine normalized sensitivity coefficients by varying \ninput parameters at prescribed percentage, while global sensitivity analysis was \n\n\n\nSobol Variance Sensitivity Analysis in Richard\u2019s Equation\n\n\n\nTABLE 1\nThe default values of input parameters from Haverkamp et al. (1977) for yolo light clay\n\n\n\nNote: \u03b8r is residual volumetric water content, \u03b8s is saturated volumetric water content, \nKs is saturated hydraulic conductivity, \u03b8L (initial) is initial value of volumetric \nwater content, and \u03b1, \u03b2, A and B are fitting coefficients (Eqs. (12) and (13)).\n\n\n\n15 \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Simulated water infiltration profile at 105 s ( ), 106 s ( ) \nand 3x106 s ( ), and in comparison with published data of Philip\u2019s \nsemi-analytical solution at 105 s ( ), 106 s ( ) and 3x106 \n\n\n\n s( ) retrieved from Haverkamp et al. (1977). \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0.0\n\n\n\n50.0\n\n\n\n100.0\n\n\n\n150.0\n\n\n\n200.0\n\n\n\n0.20 0.30 0.40 0.50\n\n\n\nD\nep\n\n\n\nth\n, z\n\n\n\n (c\nm\n\n\n\n)\n\n\n\nVolumetric water content (m3 m-3)\n\n\n\n12 \n \n\n\n\n\n\n\n\nTABLE 1 \nThe default values of input parameters from Haverkamp et al. (1977) for yolo light clay. \n \n\n\n\nParameter Value \n\ud835\udefc\ud835\udefc 739 \n\ud835\udf03\ud835\udf03\ud835\udc5f\ud835\udc5f  0.124 m3 m-3 \n\ud835\udf03\ud835\udf03\ud835\udc60\ud835\udc60 0.495 m3 m-3 \n\ud835\udefd\ud835\udefd 4 \n\ud835\udc34\ud835\udc34 124.6 \n\ud835\udc35\ud835\udc35 1.77 \n\ud835\udc3e\ud835\udc3e\ud835\udc60\ud835\udc60 1.23x10-7 m s-1 \n\ud835\udf03\ud835\udf03\ud835\udc3f\ud835\udc3f \ud835\udc56\ud835\udc56\ud835\udc5b\ud835\udc5b\ud835\udc56\ud835\udc56\ud835\udc61\ud835\udc61\ud835\udc56\ud835\udc56\ud835\udc4e\ud835\udc4e\ud835\udc59\ud835\udc59  0.2376 m3 m-3 \n\n\n\n \nNote: \ud835\udf03\ud835\udf03\ud835\udc5f\ud835\udc5f  is residual volumetric water content, \ud835\udf03\ud835\udf03\ud835\udc60\ud835\udc60  is saturated volumetric water content, \ud835\udc3e\ud835\udc3e\ud835\udc60\ud835\udc60  is saturated hydraulic \n\n\n\nconductivity, \ud835\udf03\ud835\udf03\ud835\udc3f\ud835\udc3f \ud835\udc56\ud835\udc56\ud835\udc5b\ud835\udc5b\ud835\udc56\ud835\udc56\ud835\udc61\ud835\udc61\ud835\udc56\ud835\udc56\ud835\udc4e\ud835\udc4e\ud835\udc59\ud835\udc59  is initial value of volumetric water content, and \ud835\udefc\ud835\udefc , \ud835\udefd\ud835\udefd , \ud835\udc34\ud835\udc34  and \ud835\udc35\ud835\udc35  are fitting \ncoefficients (Eqs. 12 and 13). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nVolumetric water content (m3 m-3)\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201426\n\n\n\ncarried out to determine the fraction of model output variance, that is,  first order \nindex (Si) and total effect index (STi\n\n\n\n), for each parameter, based on uncertainty in \ninput value range. \n\n\n\nLocal Sensitivity Analysis\nInput parameters were varied \u00b1 5 % as recommended by Zheng and Bennett \n(2002), and the results are shown in Figure 3. Apart from positive and negative \nrelations as shown in the respective upper and lower parts of the figure, parameter \n\u03b8L (initial) appeared to have the most sensitivity in positive relations, and this was \nfollowed by parameters Ks, A, \u03b1 and \u03b8r in decreasing order. In negative relations, \nparameters \u03b8s, B and \u03b2 are in decreasing order of importance. \n\n\n\nGlobal Sensitivity Analysis\nThe uncertainty in input parameters could be categorized based on types of \ndistribution. For example in Fox et al. (2010), normal, uniform, triangular and \nlognormal distributions were used. In this study, uniform distribution was used \nfor 8 input parameters, and it was based on significant digit approximation, as in \nTable 2. Similarly, uniform distribution was also used by Saltelli et al. (2004) for \n103 parameters, Yang (2011) for 5 parameters, and also for Campolongo et al. \n(1999) and Morris (1991). This was followed by uncertainty propagation where \nthe uncertainty in input parameters\u2019 values were translated into model output \nvariance. The results are displayed in Figure 4 for each parameter investigated. \n\n\n\nNote:   \u03b8_r is residual volumetric water content, \u03b8_s is saturated volumetric water content, \nK_s is saturated hydraulic conductivity, \u03b8_L(initial) is initial value of volumetric \nwater content, and \u03b1, \u03b2, A and B are fitting coefficients (Eqs. (12) and (13)).\n\n\n\nGoh Eng Giap and Noborio Kosuke\n\n\n\nFigure 3: Normalized sensitivity coefficient (%/%) from local sensitivity analysis.\n\n\n\n16 \n \n\n\n\n\n\n\n\nNote:  \u03b8_r is residual volumetric water content, \u03b8_s is saturated volumetric water content, K_s is saturated hydraulic \nconductivity, \u03b8_L(initial) is initial value of volumetric water content, and \u03b1, \u03b2, A and B are fitting coefficients \n(Eqs. 12 and 13). \n\n\n\n\n\n\n\nFigure 3: Normalized sensitivity coefficient (%/%) from local sensitivity analysis. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n6.44x10-1\n\n\n\n5.08x10-1\n\n\n\n2.08x10-1\n\n\n\n8.25x10-2\n\n\n\n3.85x10-2\n\n\n\n-5.89x10-1\n\n\n\n-1.07\n\n\n\n-4.97\n\n\n\n-8.0 -6.0 -4.0 -2.0 0.0 2.0\n\n\n\n\u03b8_L(initial)\n\n\n\nK_s\n\n\n\nA\n\n\n\n\u03b1\n\n\n\n\u03b8_r\n\n\n\n\u03b2\n\n\n\nB\n\n\n\n\u03b8_s\n\n\n\nNormalized sensitivity coefficient (%/%)\n\n\n\nIn\npu\n\n\n\nt p\nar\n\n\n\nam\net\n\n\n\ner\ns\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 27\n\n\n\nQuantitative contribution for each parameter to model output variance is  given by \nfirst order index (Si) and total effect index (STi\n\n\n\n).\n\n\n\nIn water infiltration front, \u03b2, \u03b8s and B were the most influencing parameters \nfor the model output variance. The S\u03b2 = 0.5177 represents partial variance of \nparameter \u03b2 to total model output variability, while the additive contribution of 8 \ninput parameters gave \u2211ii\n\n\n\n8 \n=1 Si =0.9334. This suggests that the first order sensitivity \n\n\n\nindex of parameter \u03b2 explained 51.77 % of total model output variability, whereas \na 93.34 % of total model variability was captured by 8 parameters. The first \norder index was then followed by parameters \u03b8s and B at 22.98 % and 18.58 %, \nrespectively. The sum of first order index of the three most important parameters \nexplained 93.32 % of the variability in the output. The presence of balance given \nby 1 - \u2211ii\n\n\n\n8\n = 1  Si , suggests  interaction effects. \n\n\n\nThe total effect sensitivity index in Figure 4 shows that the STi\n has a consistent \n\n\n\ntrend as the Si, that is,  the ranking of parameters by Si values agreed well with \nthose from STi\n\n\n\n values. It was noted that all the STi\n  values were greater than Si \n\n\n\nvalues. This again suggests each parameter interacts as a pair, or more with the \nother parameters. STi\n\n\n\n - Si was used to estimate the interaction effect index of second \norder and/or higher order effects. The highest interaction effect was showed by \nST\u03b2\n\n\n\n  - S\u03b2, yielding 6.63 %, followed by parameters \u03b8s and B with corresponding \nvalues of 3.62 % and 2.74 %. The interaction effect shown by ST\u03b2\n\n\n\n - S\u03b2 suggests that \nthis value was the result of interaction between \u03b2 and other parameters, but does \nnot specifically indicate which other parameters are involved and what degree of \ninteraction is present. As stated by Saltelli and Annoni (2010), the purpose of Si \nis to determine the acquirable expected variance reduction if Xi could be fixed, \nwhereas STi\n\n\n\n is the expected variance remaining if all parameters but Xi could be \nfixed. In the current study as we were only interested in identifying the important \nparameters responsible for model output variability, the estimations of Si (Eq. (8)) \nand STi\n\n\n\n (Eq. (9)) were sufficient without the need to identify the interaction effect \nindex. \n\n\n\nThe parameters \u03b1, \u03b8r , A, Ks and \u03b8L (initial) were identified as unimportant \nas both Si and STi \n\n\n\n were low in values. Although there were values given by STi\n  - \n\n\n\nSobol Variance Sensitivity Analysis in Richard\u2019s Equation\n\n\n\nTABLE 2\nUncertainty data range for input parameters\n\n\n\n13 \n \n\n\n\n\n\n\n\nTABLE 2 \nUncertainty data range for input parameters. \n\n\n\n \nParameter Data range \n\ud835\udefc\ud835\udefc 738.5 \u2013 739.499 \n\ud835\udf03\ud835\udf03\ud835\udc5f\ud835\udc5f  0.1235 \u2013 0.124499 \n\ud835\udf03\ud835\udf03\ud835\udc60\ud835\udc60 0.495 \u2013 0.495499 \n\ud835\udefd\ud835\udefd 3.5 \u2013 4.499 \n\ud835\udc34\ud835\udc34 124.55 \u2013 124.6499 \n\ud835\udc35\ud835\udc35 1.765 \u2013 1.77499 \n\ud835\udc3e\ud835\udc3e\ud835\udc60\ud835\udc60 4.4275x10-2 \u2013 4.428499x10-2 \n\ud835\udf03\ud835\udf03\ud835\udc3f\ud835\udc3f \ud835\udc56\ud835\udc56\ud835\udc5b\ud835\udc5b\ud835\udc56\ud835\udc56\ud835\udc61\ud835\udc61\ud835\udc56\ud835\udc56\ud835\udc4e\ud835\udc4e\ud835\udc59\ud835\udc59  0.23755\u20130.2376499 \n\n\n\n \nNote: Refer to Table 1 for parameters\u2019 definitions. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nNote:   Refer to Table 1 for parameters\u2019 definitions.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201428\n\n\n\nSi for these parameters, they were considered negligible when compared to \u03b2, \n\u03b8s and B. Therefore, the model output variability was not significantly affected \nby the parameters\u2019 uncertainty range, and thus, they could be termed as minor \nparameters, based on the uncertainty ranges of parameters tested. Besides, it also \nsuggests that the significant digits of \u03b2, \u03b8s and B should be increased in order to \nreduce the uncertainty range that resulted in high variability in  the model output. \n\n\n\nIn addition to Si and STi , uncertainty propagation also allowed estimation of \nother statistical parameters; for instance, mean, median, 95% confident interval, \nand so on, as tabulated in Table 3. Our results showed a 95% confident interval \nof volumetric water content range between 0.302 and 0.313. A slight increase in \nvalue of median in comparison to mean indicates negative skew of the model \noutput distribution. While von Hippel (2005) has proven that these two parameters \nare inferior indicators for skewness in some cases of distribution, other statistical \nparameters verified the negative skew such as a slight negative value of skewness, \ni.e. -0.034 (a left-skewed), which is also illustrated in Figure 5. An exact normal \ndistribution would have an exact value of 3 for kurtosis, and thus, a value of  2.676 \nfor kurtosis indicates a slight characteristic of platykurtic, that is, lower peak than \nnormal distribution and lighter tails.\n\n\n\nGoh Eng Giap and Noborio Kosuke\n\n\n\nFigure 4: Sensitivity indices versus input parameters. The first order index and total \neffect index are represented by Si and STi \n\n\n\n, respectively.\n\n\n\n17 \n \n\n\n\n\n\n\n\n\n\n\n\nNote: \u03b8_r is residual volumetric water content, \u03b8_s is saturated volumetric water content, K_s is saturated hydraulic \nconductivity, \u03b8_L(init.) is initial value of volumetric water content, and \u03b1, \u03b2, A and B are fitting coefficients \n(Eqs. 12 and 13). \n\n\n\n \nFigure 4: Sensitivity indices versus input parameters. The first order index and total effect index \n\n\n\nare represented by Si and STi, respectively. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0 0\n\n\n\n0.\n23\n\n\n\n0.\n51\n\n\n\n8\n\n\n\n0\n\n\n\n0.\n18\n\n\n\n6\n\n\n\n0 1.\n71\n\n\n\nx1\n0-4\n\n\n\n6.\n12\n\n\n\nx1\n0-6\n\n\n\n2.\n29\n\n\n\nx1\n0-5\n\n\n\n0.\n26\n\n\n\n6\n\n\n\n0.\n58\n\n\n\n4\n\n\n\n9.\n65\n\n\n\nx1\n0-5\n\n\n\n0.\n21\n\n\n\n3\n\n\n\n2.\n26\n\n\n\nx1\n0-5\n\n\n\n1.\n88\n\n\n\nx1\n0-4\n\n\n\n0.00\n\n\n\n0.20\n\n\n\n0.40\n\n\n\n0.60\n\n\n\n0.80\n\n\n\nSe\nns\n\n\n\niti\nvi\n\n\n\nty\n In\n\n\n\ndi\nce\n\n\n\ns\n\n\n\nInput Parameters\n\n\n\nFirst order index\n\n\n\nTotal effect index\n\n\n\nNote:  \u03b8_r is residual volumetric water content, \u03b8_s is saturated volumetric water content, \nK_s is saturated hydraulic conductivity, \u03b8_L(init.) is initial value of volumetric water \ncontent, and \u03b1, \u03b2, A and B are fitting coefficients (Eqs. (12) and (13)).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 29\n\n\n\nThe comparisons between local and global sensitivity analyses have shown \nsome differences. The local tool was able to rank parameters from important to \nunimportant. These parameters were determined by a linear parameter change, \none-at-a-time (OAT) by keeping other parameters constant, which means all \nother input spaces were not fully studied. Uncertainty estimation on model \noutput could still be carried out in local tool by multiplying percentage change \nin input parameter to normalized sensitivity coefficient, as demonstrated by \nGoh and Noborio (2013); however, sensitivity indices for first order, total effect \nindex, interaction effects index, and statistic parameters, as in Table 3, would be \nunavailable.\n\n\n\nIn addition, we observed consistency in parameter ranking for first order \nand total effect index on input parameters, after variation of +10% as shown in \nFigure 6, with local sensitivity tool results in Figure 3. Although both local and \n\n\n\nSobol Variance Sensitivity Analysis in Richard\u2019s Equation\n\n\n\nTABLE 3\nUncertainty analysis on statistical parameters for model output probability distribution \n\n\n\nobtained from the Sobol\u2019 variance-based method.\n\n\n\nFigure 5: Probability (left y-axis) and cumulative (right y-axis) output distribution \nfunctions obtained via Sobol\u2019 variance-based method.\n\n\n\n13 \n \n\n\n\n\n\n\n\nTABLE 2 \nUncertainty data range for input parameters. \n\n\n\n \nParameter Data range \n\ud835\udefc\ud835\udefc 738.5 \u2013 739.499 \n\ud835\udf03\ud835\udf03\ud835\udc5f\ud835\udc5f  0.1235 \u2013 0.124499 \n\ud835\udf03\ud835\udf03\ud835\udc60\ud835\udc60 0.495 \u2013 0.495499 \n\ud835\udefd\ud835\udefd 3.5 \u2013 4.499 \n\ud835\udc34\ud835\udc34 124.55 \u2013 124.6499 \n\ud835\udc35\ud835\udc35 1.765 \u2013 1.77499 \n\ud835\udc3e\ud835\udc3e\ud835\udc60\ud835\udc60 4.4275x10-2 \u2013 4.428499x10-2 \n\ud835\udf03\ud835\udf03\ud835\udc3f\ud835\udc3f \ud835\udc56\ud835\udc56\ud835\udc5b\ud835\udc5b\ud835\udc56\ud835\udc56\ud835\udc61\ud835\udc61\ud835\udc56\ud835\udc56\ud835\udc4e\ud835\udc4e\ud835\udc59\ud835\udc59  0.23755\u20130.2376499 \n\n\n\n \nNote: Refer to Table 1 for parameters\u2019 definitions. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 3 \nUncertainty analysis on statistical parameters for model output probability distribution obtained \nfrom the Sobol\u2019 variance-based method. \n \nStatistical parameters Values \nMean 0.30758 \nMedian 0.30762 \n95 %  Confident Interval 0.302 - 0.313 \nStandard Deviation 3.489x10-3 \nStandard Error of Mean 2.848x10-5 \nMinimum 0.29406 \nMaximum 0.31751 \nSkewness -0.034 \nKurtosis 2.676 \n\n\n\n \nNote: Results were based on 100,000 s simulation time, and 18.5 cm depth from ground surface. \n \n \n \n\n\n\n18 \n \n\n\n\n\n\n\n\nFigure 5: Probability (left y-axis) and cumulative (right y-axis) output distribution functions \nobtained via Sobol\u2019 variance-based method. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0\n\n\n\n3000\n\n\n\n6000\n\n\n\n9000\n\n\n\n12000\n\n\n\n15000\n\n\n\n0\n\n\n\n500\n\n\n\n1000\n\n\n\n1500\n\n\n\n2000\n\n\n\n2500\n\n\n\n0.\n29\n\n\n\n41\n-0\n\n\n\n.2\n95\n\n\n\n4\n0.\n\n\n\n29\n54\n\n\n\n-0\n.2\n\n\n\n96\n8\n\n\n\n0.\n29\n\n\n\n68\n-0\n\n\n\n.2\n98\n\n\n\n2\n0.\n\n\n\n29\n82\n\n\n\n-0\n.2\n\n\n\n99\n6\n\n\n\n0.\n29\n\n\n\n96\n-0\n\n\n\n.3\n01\n\n\n\n0\n0.\n\n\n\n30\n10\n\n\n\n-0\n.3\n\n\n\n02\n3\n\n\n\n0.\n30\n\n\n\n23\n-0\n\n\n\n.3\n03\n\n\n\n7\n0.\n\n\n\n30\n37\n\n\n\n-0\n.3\n\n\n\n05\n1\n\n\n\n0.\n30\n\n\n\n51\n-0\n\n\n\n.3\n06\n\n\n\n5\n0.\n\n\n\n30\n65\n\n\n\n-0\n.3\n\n\n\n07\n9\n\n\n\n0.\n30\n\n\n\n79\n-0\n\n\n\n.3\n09\n\n\n\n2\n0.\n\n\n\n30\n92\n\n\n\n-0\n.3\n\n\n\n10\n6\n\n\n\n0.\n31\n\n\n\n06\n-0\n\n\n\n.3\n12\n\n\n\n0\n0.\n\n\n\n31\n20\n\n\n\n-0\n.3\n\n\n\n13\n4\n\n\n\n0.\n31\n\n\n\n34\n-0\n\n\n\n.3\n14\n\n\n\n8\n0.\n\n\n\n31\n48\n\n\n\n-0\n.3\n\n\n\n16\n1\n\n\n\n0.\n31\n\n\n\n61\n-0\n\n\n\n.3\n17\n\n\n\n5\n0.\n\n\n\n31\n75\n\n\n\n-0\n.3\n\n\n\n18\n9\n\n\n\n0.\n31\n\n\n\n89\n-0\n\n\n\n.3\n20\n\n\n\n3\n\n\n\nC\num\n\n\n\nul\nat\n\n\n\niv\ne\n\n\n\nFr\neq\n\n\n\nue\nnc\n\n\n\ny\n\n\n\nIntervals\n\n\n\nPDF\n\n\n\nCDF\n\n\n\nNote: Results were based on 100,000 s simulation time, and 18.5 cm depth from ground\nsurface.\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 201430\n\n\n\nglobal sensitivity tools illustrated a similar trend of parameter ranking, in reality \nthe range of uncertainty on each input parameter does not have equal percentage \nof variation, for instance in Fox et al. (2010). Under such conditions of different \npercentage variation between input parameters and previously unknown presence \nof interaction effects between input parameters at unexplored input spaces, global \nsensitivity analysis is a very attractive and robust tool.\n\n\n\nCONCLUSION\nThe global sensitivity analysis was shown to have a better coverage of input spaces, \nmore informative indicators than the local sensitivity tool, and was able to provide \nstatistical description on the model outputs. This study also found that parameters \n\u03b2, \u03b8s and B were dominant parameters as they had substantially greater first order \nindex and total effect index than other parameters. As a result, this suggests that \nincreasing significant digits of these parameters with narrower uncertainty range \nwould be able to reduce their influence on model output variability. Total effect \nindex was found to be slightly greater than first order index for every parameter, \nsuggesting that interaction effects between parameters of second order, higher order \nand so forth were not as important as first order index. Although local sensitivity \nanalysis is capable of parameter ranking and uncertainty analysis by varying a \nsingle parameter at a time, global sensitivity analysis would be a better alternative \nas it is based on statistical theory and in addition to those capabilities of local \nsensitivity analysis, it can estimate interaction effects between parameters and also \n\n\n\nGoh Eng Giap and Noborio Kosuke\n\n\n\nFigure 6: Sensitivity indices versus input parameters varied at +10%. The first order \nindex and total effect index are represented by Si and STi \n\n\n\n, respectively.\n\n\n\n19 \n \n\n\n\n\n\n\n\nNote: \u03b8_r is residual volumetric water content, \u03b8_s is saturated volumetric water content, K_s is saturated hydraulic \nconductivity, \u03b8_L(init.) is initial value of volumetric water content, and \u03b1, \u03b2, A and B are fitting coefficients \n(Eqs. 12 and 13). \n\n\n\n\n\n\n\nFigure 6: Sensitivity indices versus input parameters varied at +10%. The first order index and \ntotal effect index are represented by Si and STi, respectively. \n\n\n\n\n\n\n\n8.\n91\n\n\n\nx1\n0-4\n\n\n\n0\n\n\n\n0.\n37\n\n\n\n8\n\n\n\n1.\n63\n\n\n\nx1\n0-2\n\n\n\n3.\n22\n\n\n\nx1\n0-3\n\n\n\n0.\n30\n\n\n\n9\n\n\n\n9.\n78\n\n\n\nx1\n0-3 0.\n\n\n\n19\n1\n\n\n\n1.\n30\n\n\n\nx1\n0-3\n\n\n\n5.\n11\n\n\n\nx1\n0-5\n\n\n\n0.\n46\n\n\n\n2\n\n\n\n2.\n38\n\n\n\nx1\n0-2\n\n\n\n7.\n68\n\n\n\nx1\n0-3\n\n\n\n0.\n38\n\n\n\n9\n\n\n\n1.\n46\n\n\n\nx1\n0-2 0.\n\n\n\n19\n4\n\n\n\n0.00\n\n\n\n0.20\n\n\n\n0.40\n\n\n\n0.60\n\n\n\n0.80\n\n\n\nSe\nns\n\n\n\niti\nvi\n\n\n\nty\n In\n\n\n\ndi\nce\n\n\n\ns\n\n\n\nInput Parameters (varied at +10%)\n\n\n\nFirst order index\n\n\n\nTotal effect index\n\n\n\nNote:  \u03b8_r is residual volumetric water content, \u03b8_s is saturated volumetric water content, \nK_s is saturated hydraulic conductivity, \u03b8_L(init.) is initial value of volumetric \nwater content, and \u03b1, \u03b2, A and B are fitting coefficients (Eqs. (12) and (13)).\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 18, 2014 31\n\n\n\nprovide some informative statistical analysis on model outputs distribution such \nas 95% confidence interval, skewness, kurtosis and other statistical parameters.\n\n\n\nACKNOWLEDGEMENTS\nWe would like to acknowledge the financial support from Meiji University, Japan, \nthe Ministry of Higher Education, Malaysia and Universiti Malaysia Terengganu, \nMalaysia.\n\n\n\nREFERENCES\nAvagliano, S. and L. Parrella. 2009. Managing uncertainty in risk-based corrective \n\n\n\naction design: Global sensitivity analysis of contaminant fate and exposure \nmodels used in the dose assessment. Environmental Modeling and Assessment. \n14(1): 47-57. \n\n\n\nBarari, A., M. Omidvar, A.R. Ghotbiand D.D. Ganji. 2009. Numerical analysis of \nRichards\u2019 problem for water penetration in unsaturated soils. Hydrology and \nEarth System Sciences. 6(5): 6359-6385. \n\n\n\nCampolongo, F., J. Cariboni and A. 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"\n\n___________________\n*Corresponding author : E-mail: karimi_nsrc@yahoo.com\n\n\n\nINTRODUCTION\n \nNitrogen (N) is known as the most limiting factor for crop production and this fact \nis fully understood by farmers and fertiliser producers. The Food and Agriculture \nOrganisation of the United Nations (FAO) (2012)  estimates that more than 115 \nmillion tonnes of N fertilisers will be used worldwide in 2016. Unfortunately, \nthe inevitable fact is that the applied N fertilisers are not fully recovered or taken \nup by plants as 20 to 80% of the applied N fertiliser is lost through leaching and \nvolatilisation (Bruce et al., 1990; Jianga et al., 2010). This situation seems to be \nworse in high pH soils. For instance Karimizarchi, (2011) found that in the wheat \n\n\n\nISSN: 1394-7990\nMalaysian Journal of Soil Science Vol. 19: 83-94 (2015)                       Malaysian Society of Soil Science\n\n\n\nElemental Sulphur Effects on Nitrogen Loss in Malaysian \nHigh pH Bintang Series Soil\n\n\n\nKarimizarchi, M1. , H. Aminuddin2 , M. Y. Khanif2 and O. Radziah2\n\n\n\n1National Salinity Research Centre, Agricultural Research, Education and Extention \nOrganization (AREEO), Tehran, Iran\n\n\n\n2Department of Land Management, Faculty of Agriculture, Universiti\nPutra Malaysia, 43400 Serdang, Selangor, Malaysia.  \n\n\n\nABSTRACT\nAmendment of high pH soils with elemental sulphur (S) can result in a reduction \nof ammonia (NH3) emissions following a local decrease in the soil ammonium \nconcentration and pH. Two laboratory experiments were conducted to determine \nhow the application rates of elemental S impacted urea transformations. Urea was \nsurface applied at a rate of 1652 mg per kg of Bintang Series soil that was incubated \nwith different rates of elemental S (0, 0.5, 1 and 2 g S kg-1 of soil) for three months. \nThe results showed that with the application of elemental S, volatilisation losses \ndecreased quadratically to 30.42% of applied nitrogen (N), for the highest S rate, \nindicating that as more elemental S was added to the soil, a lesser fraction was \nlost as NH3. Cumulative NH3-N emissions were closely related to initial soil pH \n(r=0.62**) and ammonium concentration (r = 0.74**). NH3 volatilisation was the \nmajor pathway for the loss of N from surface applied urea and sulphur coated urea \nin Bintang Series soil. However, acidification of the soil by elemental S reduced \nNH3 volatilisation by 50% as compared to the control. The NH3 volatilisation \npattern in soil treated with different rates of S was the same, but the addition of 2 \ng S kg-1 soil delayed NH3 volatilisation for four days. It should be noted that NH3 \nhydrolysis in Bintang Series soil was fast and only 1.6 days were needed for the \ndisappearance of 50% of of the urea. \n\n\n\nKeywords:\t Ammonia\tloss,\tsoil\tacidification,\tsoil\tincubation\tstudy\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201584\n\n\n\nKarimizarchi, M., H. Aminuddin, M. Y. Khanif and O. Radziah\n\n\n\nfarms of Yazd province in Iran, N use efficiency was low and varied from 0 to \n75%. The results also showed that the application of N fertilisers, according to \nfarmers\u2019 experiences, did not positively and significantly affect plant growth and \nproduction, in half of studied farms in comparison with control treatment. This \nunfavorable situation increases the potential of environmental contamination with \nincreased N use by farmers. \n\n\n\nDepending on the soil and climatic conditions, ammonia (NH3) volatilisation \ncan contribute greatly to N loss. Pacholski et al. (2006) reported that around 48% \nof applied urea (200 kg ha-1) was volatilised as NH3 under the conditions of the \nstudied fields in China. A likelihood of NH3 volatilisation of up to 80% from \nadded urea under field conditions was reported by Gould et al. (1986). Following \nthe application of urea to the soil surface, it may be lost through NH3 volatilisation \n(Jones et al., 2007; Mikkelsen, 2009). Theoretically, a high soil pH increases soil \nconcentrations of NH3 dissolved in soil water, causing higher NH3 volatilisation. \nIt has been documented that the proportion of dissolved NH3 gas is near zero when \nthe pH is below 7.5, but as it rises above 7.5, the dissolved NH3 gas increases \ndramatically (Jones et al., 2007). Additionally, the rate of NH3 volatilisation \ndepends on the rate of urea hydrolysis (conversion of urea to ammonium), weather \nconditions following application, and several soil properties. Multiple, and often \ninterrelated factors make volatilisation difficult to predict; however, soil pH plays \nan over-riding role (Watson et al., 1994).\n\n\n\nPalm oil mill effluent (POME), peat, humic acid and fulvic acid are proposed \nas materials that can decrease NH3 volatilisation from urea fertilisers in the soil \n(Aminuddin, 1994; Reeza et al., 2009; Rosliza et al., 2009). Rosliza et al., (2009) \nfound that applying humic and fulvic acids completely stopped volatilisation of \nNH3, reduced soil pH, and slowed urea hydrolysis. Latifah et al., (2011) and Siva \net al., (1999) found a decrease in NH3 volatilisation in alkaline and acidic soils of \nMalaysia due to other acidifying materials such as sago waste water and humic \nacid, respectively. Soaud et al., (2011) found the positive effect of elemental S, as \nan acidifying soil amendment, on NH3 volatilisation reduction in two soils from \nthe United Arab Emirates. It should be noted that urea is the main source of N \nfertiliser worldwide (Glibert et al., 2006) and an estimated 159 million or more \ntonnes of urea will be used in 2018 (Heffer and Prud\u2019homme, 2014). \n\n\n\nIn the light of the above discussion, the importance of NH3 volatilisation in \nsoils, and the role of soil pH cannot be ignored. However, there is no information \non the role of soil pH on NH3 loss from Bintang Series soil (Karimizarchi et \nal., 2014b). Therefore, this study aimed to investigate the effect of elemental S \napplication rate and soil pH on N transformation and NH3 volatilisation of Bintang \nSeries soil. Understanding the role of elemental S on N volatilisation is important \nfor improving N fertiliser management, minimising environmental impacts, and \nhelping farmers being more economical. \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 85\n\n\n\nEffects of S on N loss in High pH \n\n\n\nMATERIALS AND METHODS\nAlkaline Bintang series soil was used in this study. It was obtained from Taman \nAnggur, Bukit Bintang, Perlis, West Malaysia (6\u00b0 31\u02b9 01.61\u02b9\u02b9 N and 100\u00b0 10\u02b9 \n12.43\u02b9\u02b9 E). The soil was air dried, ground to pass through a 2-mm sieve, and \nstored for analysis and experimentation. To provide a wide range of soil pH, \nthe Bintang Series soil was incubated with different rates of elemental S (0.5, 1 \nand 2 g S kg-1 soil) for three months under laboratory conditions at 25\u00b0C. Each \nincubation unit consisted of 2 kg of soil in a plastic pot, 26 cm in diameter and \n10cm in height. The plastic containers were sealed with perforated plastic sheets \nto minimise water loss whilst maintaining aeration. Water content of the soils \nwas  maintained at 60% field capacity throughout the incubation by weighing and \nadding the required amount of water every week. The added water was mixed \nthoroughly with the soil. The experiment was carried out at the Department of \nLand Management, Universiti Putra Malaysia. \n\n\n\nAmmonia Volatilisation Measurement\nAfter the three batches of soil were incubated with different rates of elemental \nS for three months, they were transferred to a modified closed-dynamic air flow \nsystem (Fenn and Kissel, 1973;1974) and daily amounts of NH3 loss from the \nfollowing five treatments were determined:\nT1 : Soil treated with S coated urea\nT2 : Soil treated with urea\nT3 : Soil treated with 0.5 g S kg-1 soil and urea\nT4 : Soil treated with 1 g S kg-1 soil and urea \nT5 : Soil treated with 2 g S kg-1 soil and urea\n\n\n\nUrea or S coated urea, provided by Petroliam Nasional Berhad (PETRONAS), \nwere both applied at the same rate of 380 mgN per experimental unit. This rate \nof N was equal to 120 kg N per hectare. The system comprised of an aquarium \npump, humidifying unit, soil air chamber and NH3 trapper unit. Compressed air \nfrom a commercial aquarium pump was first humidified by bubbling through 200 \nml of distilled water before entering the gas exchange chamber. An air flow rate \nof 2.5 litres per minute per cylinder was used for NH3 removal. Each soil air \nchamber consisted of a 500 ml Erlenmeyer flask with 230 g of Bintang Series \nsoil that was moistened and maintained, at 60% field water capacity during the \nexperiment. Humidified water prevents the soil from becoming dry. The chambers \nwere weighed daily and any deficits from the original weight was considered \nto represent moisture loss. Distilled water was added on a daily basis, where \nrequired, to maintain the moisture contents of the soils. Volatilised ammonium \nwas collected in 20 ml of 2% boric acid indicator solution and was titrated with \n0.01 N HCl (Page, 1982). The incubation chambers were maintained at laboratory \nconditions (25\u00b0C). \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201586\n\n\n\nQuantification of Urea Transformation \nAnother experiment was conducted with the same treatments and conditions as \nstated in the previous section, and the urea changes as a function of elemental S \napplication rate during the experiment is elucidated in the following paragraphs. \nSoil samples were taken four times (1, 3, 4 and 9 days after urea application) \nand immediately frozen at -200 C. Soil sampling was based on the results of the \nprevious section. The remainingurea and ammonium content in each sample was \ndetermined as stated by Page (1982). Exchangeable ammonium and remaining \nurea were extracted with a 2N KCl-PMA solution. Ammonium content was \ndetermined using the steam distillation method. Remaining urea was determined \nby colorimetric technique (Page, 1982). Soil acidity was determined on soil-water \nsuspension (10 g soil to 25 ml distilled water) using a 24-hour glass electrode after \nshaking it for 30 minutes on a reciprocal shaker (Jones, 2001).\n\n\n\nStatistical Analysis\nData were analysed using SAS (SAS Institute, 2003) commands through either \ncompletely randomised or split plot design with three replications. Tukey\u2019s test \nat \u03b1 = 0.05 was employed to determine the significant differences among the \ntreatments. To model the relationship between soil pH and NH3 volatilisation as \nwell as urea hydrolysis over time, the data were subjected to different regression \nmodels at a probability level of 0.05 with the help of Sigmaplot software. \n\n\n\nRESULTS AND DISCUSSION\n\n\n\nEffect of Elemental S on Daily NH3 Volatilisation\nThe NH3 volatilisation trendsfor the treatments under the conditions of this \nstudy were rather similar as shown in Figure 1. However, the starting day of \n\n\n\nFigure 1: Effect of different treatments (T1: sulphur coated urea, T2: urea, T3: soil treated \nwith 0.5 g S kg-1 soil and urea, T4: soil treated with 1 g S kg-1 soil and urea and T5: soil \n\n\n\ntreated with 2 g S kg-1 soil and urea) on ammonia volatilisation rate from Bintang Series \nsoil over time. Bars show the standard error.\n\n\n\n\n\n\n\n12 \n \n\n\n\n\n\n\n\nTABLE 6 \nPearson correlation coefficients among soil pH, different forms of nitrogen and ammonia \nvolatilisation rates in Bintang Series soil treated with different rates of elemental sulphur. \n\n\n\n\n\n\n\n\n\n\n\npH Ammonium  Urea Volatilisation \nrate \n\n\n\npH  \n1 0.61** -0.38** 0.62** \n\n\n\nAmmonium  \n 1 -0.72** 0.74** \n\n\n\nUrea \n  1 -0.55** \n\n\n\nVolatilisation rate \n   1 \n\n\n\nValues of r followed by ** or * are significant at \u03b1=0.01 and \u03b1=0.05 respectively.ns: non- significant \n \n \n \n \n \n \n \n \n\n\n\n \nFigure 1: Effect of different treatments (T1: sulphur coated urea, T2: urea, T3: soil treated \nwith 0.5 g S kg-1 soil and urea, T4: soil treated with 1 g S kg -1soil and urea and T5: soil \n\n\n\ntreated with 2 g S kg-1 soil and urea) on ammonia volatilisation rate from Bintang Series soil \nover time. Bars show the standard error. \n\n\n\n\n\n\n\n0\n\n\n\n5\n\n\n\n10\n\n\n\n15\n\n\n\n20\n\n\n\n25\n\n\n\n1 2 3 4 5 6 7 8 9 10 11 12\n\n\n\nAm\nm\n\n\n\non\nia\n\n\n\n v\nol\n\n\n\nat\nili\n\n\n\nza\ntio\n\n\n\nn \nra\n\n\n\nte\n \n\n\n\n( %\nN\n\n\n\n /D\nay\n\n\n\n)\n\n\n\nDays after fertilizer application \n\n\n\nT1\nT2\nT3\nT4\nT5\n\n\n\nKarimizarchi, M., H. Aminuddin, M. Y. Khanif and O. Radziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 87\n\n\n\nNH3 loss, the value and the day of volatilisation peak for Bintang Series soil \ntreated with different rates of elemental S were different. In the untreated soil, \nNH3 volatilisation started on the first day of urea application, with 0.8% of total \nN, followed by a steep rise and reached a volatilisation rate peak of 14.55% on \nthe fourth day. However, in soils treated with 2 g S kg-1 soil, NH3 volatilisation \nwas negligible during the first 4 days, followed by a gradual increase that reached \na peak of 4.92% on the day 9 of urea addition. Likewise for untreated soil, NH3 \nvolatilisation for Bintang Series soil which received 0.5 g S kg-1started on day 1 \nof urea addition, whilst the application of 1 g S kg-1 delayed NH3 volatilisation by \njust only 24 hours (Figure 1).\n\n\n\nThe rates of NH3 volatilisation for all treatments, except that of the highest \nS application rate, were at a maximum during the first 3 to 5 days and declined \nthereafter, with a little volatilisation occurring towards the end of day 10. Being \naround one-third of untreated soil, the addition of 2 g S kg-1 soil resulted in a \nvolatilisation peak of 4.92% being shifted by 5 days to day 9 after NH3 application. \nSignificant differences in NH3 volatilisation rates were found between elemental \nS application rates and days after urea application (Table 1), with the highest \nS application rate showing the lowest NH3 volatilisation rate. The highest \nvolatilisation rate for the highest S application rate was 18.76 mg N per day on \nday 9 after the urea application, whilst the volatilisation rate for soils treated with \n0, 0.5, and 1 g S kg-1 soil were 55.44, 39.48, and 41.16 mg N per day, respectively. \n\n\n\nIn other words, it appeared that acidification of Bintang Series soil with \napplications of 1 and 2 g S kg-1 soil stretched the duration of cumulative losses of \nN. This could be attributed to the over-riding influence of the initial soil acidity \non NH3 volatilisation (Jones et al., 2007; Mikkelsen, 2009; Siva et al., 1999). \nThe negligible loss of NH3 during the first four days in soil treated with 2 g S \nkg-1 soil might also be due to the coincidence of this period with that of urea \nhydrolysis: therefore urea was not subjected to NH3 volatilisation until it is was \nfirst transformed into NH3.\n\n\n\nTABLE 1\nEffect of different treatments (T1: sulphur coated urea, T2: urea, T3: soil treated with 0.5 g \nS kg-1 soil and urea, T4: soil treated with 1 g S kg-1 soil and urea and T5: soil treated with \n2 g S kg-1 soil and urea) and days after urea application on daily ammonia volatilisation \n\n\n\nrate (mg nitrogen per day) in Bintang Series soil.\n\n\n\n\n\n\n\n9 \n \n\n\n\nTABLE 1 \nEffect of different treatments (T1: sulphur coated urea, T2: urea, T3: soil treated with 0.5 g S \nkg-1 soil and urea, T4: soil treated with 1 g S kg -1soil and urea and T5: soil treated with 2 g S \n\n\n\nkg-1 soil and urea) and days after urea application on daily ammonia volatilisation rate  \n(mg nitrogen per day) in Bintang Series soil. \n\n\n\na Traces  \nMeans within column followed by the same capital letter and within rows followed bythe  same small letters are \nnot significant at the 0.05 level, according to Tukey\u2019s test \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTreatment Days after urea application  \nMean  1 3 4 9 \n\n\n\nT1 1.12Cc 48.02Ab 70.79Aa 16.56Bc 34.12A \nT2 3.08Ab 52.68Aa 55.44Ba 18.48Bb 32.42A \nT3 2Bd 28.88Ab 39.48C 27.16Ac 24.38B \nT4 Tra 41.16Aa 36.4Ca 19.78Bb 24.33B \nT5 Tr Tr 1.7Db 18.76Ba 5.12C \n\n\n\nEffects of S on N loss in High pH \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201588\n\n\n\nTo determine the role of initial soil acidity on maximum NH3 volatilisation \nrate, the relationship between these two factors was studied. Therewas a linear \nand upward trend between soil pH and the maximum NH3 volatilisation rate, Y = \n-22.84 +11.51 pH, R2 = 0.79**. In other words, with each unit decrease in soil pH, \nthe NH3 volatilisation rate from Bintang Series soil decreased 11.51 mg per day. \n\n\n\nEffect of Elemental S on Cumulative NH3 Volatilisation\nAs can be seen from Figure 2, the cumulative NH3 volatilisation increased \nprogressively for all treatments. Whilst the NH3 volatilisation for the highest S rate \nstarted on day 5 after urea application, it started on day 1 after urea application for \nother treatments. At the end of the experiment, there was no significant difference \nin total NH3 volatilised between untreated soil and those that received 0.5 and 1 \ng S kg-1 soil (Table 2). However, the application of 2 g S kg-1 soil significantly \ndecreased NH3 volatilisation from 82.56% of added urea for untreated soil to \n30.42%. As the only difference between soils treated with elemental S was their \npH, the relationship between soil acidity and total volatilised urea was modelled \nby  Y= -237.03 + 101.32X \u2013 7.81 X2, R2= 0.86**. There was an upward trend in \nNH3 volatilisation from the lowest pH value of 3.77, to a pH value of 6.5, before \nlevelling, indicating the overriding effect of soil acidity. The findings agreed with \nthose of Soaud et al., (2011) who found a reduction in ammonia volatilisation \nfrom 30% to 15% due to the application of elemental S at a rate of 10 tonnes per \nhectare. However, they did not relate this reduction in NH3 volatilisation to soil \npH reduction. Jones et al. (2007) have documented the importance and role of soil \nacidity in NH3 volatilisation.  \n\n\n\nFigure 2: Effect of different treatments (T1: sulphur coated urea, T2: urea, T3: soil treated \nwith 0.5 g S kg-1 soil and urea, T4: soil treated with 1 g S kg-1 soil and urea and T5: soil \n\n\n\ntreated with 2 g S kg-1 soil and urea) on cumulative ammonia volatilisation from Bintang \nSeries soil treated with urea and sulphur coated urea over time. Bars show the standard \n\n\n\nerror.\n\n\n\n\n\n\n\n13 \n \n\n\n\n\n\n\n\n \nFigure 2: Effect of different treatments (T1: sulphur coated urea, T2: urea, T3: soil treated \nwith 0.5 g S kg-1 soil and urea, T4: soil treated with 1 g S kg -1soil and urea and T5: soil \n\n\n\ntreated with 2 g S kg-1 soil and urea) on cumulative ammonia volatilisation from Bintang \nSeries soil treated with urea and sulphur coated urea over time. Bars show the standard \n\n\n\nerror. \n \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n1 2 3 4 5 6 7 8 9 10 11 12\n\n\n\nC\num\n\n\n\nul\nat\n\n\n\niv\ne \n\n\n\nA\nm\n\n\n\nm\non\n\n\n\nia\n V\n\n\n\nol\nat\n\n\n\nili\nza\n\n\n\ntio\nn \n\n\n\n(%\n) \n\n\n\nDays after fertilizer application\n\n\n\nT1\nT2\nT3\nT4\nT5\n\n\n\nKarimizarchi, M., H. Aminuddin, M. Y. Khanif and O. Radziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 89\n\n\n\nEffect of Elemental S on Ammonium Production OverTime\nThe role of elemental S application rate on urea hydrolysis and ammonium \nconcentration is shown in Table 3. Our results showed that application of elemental \nS at rates of 1 and 2 g kg-1 soil significantly decreased ammonium production \nuntil day 4 after urea application. With 50.66 and 41.33 mg kg-1 soil, the lowest \nconcentration of ammonium at day 1 was found for elemental S rates of 1 and 2 g \nkg-1 soil and the highest ammonium concentration of 97.73 mg kg-1 soil was found \nin Bintang Series soil treated with 0.5 g S kg-1 soil,which was not significantly \ndifferent from untreated soil which received urea and sulphur coated urea. \n\n\n\nTABLE 2\nEffect of different treatments on total ammonia volatilised from Bintang Series soil \n\n\n\ntreated with urea or sulphur coated urea.\n\n\n\n\n\n\n\n10 \n \n\n\n\n \nTABLE 2 \n\n\n\nEffect of different treatments on total ammonia volatilised from Bintang Series soil treated \nwith urea or sulphur coated urea. \n\n\n\n\n\n\n\nTreatment \nTotal ammonia volatilised within 12 days \n\n\n\nmg nitrogen Percent \n\n\n\nT1( sulphur coated urea) 313.27A 82.22A \n\n\n\nT2 (urea) 314.58A 82.56A \n\n\n\nT3 (soil treated with 0.5 g S kg-1 \nsoil and urea) 330.35A 86.70A \n\n\n\nT4 (soil treated with 1 g S kg -\n\n\n\n1soil and urea) 262.36A 68.86A \n\n\n\nT5 (soil treated with 2 g S kg-1 \nsoil and urea) 115.92B 30.42B \n\n\n\n         Means within column followed by the same capital letter and within rows followed bythe  same \n         small letters are not significant at the 0.05 level, according to Tukey\u2019s test \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nTABLE 3\nEffectof different treatments (T1: sulphur coated urea, T2: urea, T3: soil treated with 0.5 g \nS kg-1 soil and urea, T4: soil treated with 1 g S kg-1 soil and urea and T5: soil treated with \n2 g S kg-1 soil and urea) and days after urea application on ammonium concentration (mg \n\n\n\nkg-1 soil) in Bintang Series soil.\n\n\n\n\n\n\n\n10 \n \n\n\n\nTABLE 3 \nEffectof different treatments (T1: sulphur coated urea, T2: urea, T3: soil treated with 0.5 g S \n\n\n\nkg-1 soil and urea, T4: soil treated with 1 g S kg -1soil and urea and T5: soil treated \nwith 2 g S kg-1 soil and urea) and days after urea application on ammonium \n\n\n\nconcentration (mg kg-1 soil) in Bintang Series soil. \n \n\n\n\nTreatment \nDays after urea or SCU addition  \n\n\n\nMean 1 3 4 9 \n\n\n\nT1 83.33Ab 506.33Aa 546Aa 492.33Aa 407.00A \n\n\n\nT2 78.66Ac 560Aa 613.66Aa 340.66Bb 398.25A \n\n\n\nT3 97.33Ac 471.33Aab 380.33Bb 492.33Aa 360.33AB \n\n\n\nT4 50.66Bb 375.66ABa 396.66Ba 478.33Aa 325.33B \n\n\n\nT5 41.33Bc 130.66Bb 198.33Cb 494.66Aa 216.25C \n\n\n\nMean 70.27b 408.8a 427a 459.67a  \nMeans within column followed by the same capital letter and within rows followed bythe  same  \nsmall letters are not significant at the 0.05 level, according to Tukey\u2019s test \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nEffects of S on N loss in High pH \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201590\n\n\n\nAmmonium concentration was significantly affected by the lapsed time \nafter urea application. The ammonium concentration for T1, T2, T3, T4 and T5 \ntreatments significantly increased from 83.33, 78.66, 97.33, 50.66 and 41.33 mg \nkg-1 soil, respectively, on day 1 after urea addition to 506.33, 560, 471.33, 375.66 \nand 130.66 mg kg-1 soil, respectively, on day 3 of urea addition (Table 3). In other \nwords, the concentration of ammonium in untreated soils increased six times \nwithin three days whilst that of the soils treated with 2 g S kg-1 soil increased 3.16 \ntimes. This finding agreed with those of  Soaud et al., (2011) who stated that faster \nurea hydrolysis occurs in alkaline soils than in acidic soils. \n\n\n\nEffect of Elemental S on Urea Disappearance\nNitrogen fertilisers such as urea, applied at different rates, undergo transformations \nin Bintang Series soil. In order to derive a method for predicting the time taken \nfor complete hydrolysis of urea in Bintang Series soil, the time courses between \nurea concentrations in Bintang Series soil treated with different rates of elemental \nS and time were modeled. Urea disappearance for all treatments followed a non-\nlinear regression, exponential decay model with two parameters:\n\n\n\n A = A0 e \u2013kt\n\n\n\nThe r square for all treatments was highly significant at levels of less than 0.01 \nwith a range of 0.79 to 0.95 (Table 4). This exponential decay model shows that \nurea disappearance follows a first order reaction, a reaction that proceeds at a rate \n\n\n\nTABLE 4\nUrea (U) or sulphur coated urea (SCU) disappearance equations and coefficients of \n\n\n\ndeterminations for Bintang Series soil treated with different rates of elemental sulphur \nwith half-life.\n\n\n\n\n\n\n\n10 \n \n\n\n\n\n\n\n\nTABLE 4 \nUrea (U) or sulphur coated urea (SCU) disappearance equations and coefficients of \n\n\n\ndeterminations for Bintang Series soil treated with different rates of elemental sulphur with \nhalf-life. \n\n\n\n\n\n\n\nTreatment  Regression equation  R2 \nHalf-life \n(days) \n(\ud835\udc61\ud835\udc61  \n\n\n\nT5 (soil treated with 2 g S kg-1 \nsoil and urea) U = 72.5 e -0.21t 0.79** 1.75 \n\n\n\nT4 (soil treated with 1 g S kg -\n\n\n\n1soil and urea) U= 85.36 e -0.38t 0.86** 1.4 \n\n\n\nT3 (soil treated with 0.5 g S kg-1 \nsoil and urea) U = 170.42 e -0.74t 0.94** 1.6 \n\n\n\nT2 (urea) U = 158.47 e -0.72t 0.82** 1.6 \n\n\n\nT1( sulphur coated urea) SCU = 165.05 e -0.68t 0.95** 1.7 \n\n\n\n             Means within column followed by the same capital letter and within rows followed bythe  same \n             small letters are not significant at the 0.05 level, according to Tukey\u2019s test \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\nKarimizarchi, M., H. Aminuddin, M. Y. Khanif and O. Radziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 91\n\n\n\nthat depends linearly only on one reactant concentration. The integrated forms of \nthe rate law allow the population of reactant to be found at any time after the start \nof the reaction. Plotting Ln A with respect to time for a first-order reaction gives \na straight line with the slope of the line equal to -k. It should be noted that urea \nhydrolysis rate depends on the S application rate as well as time. As \u2018k\u2019 values for \nall treatments were negative, the remaining urea in soil decreased with an increase \nin time. Accordingly, it is expected that 50% of urea applied at Bintang Series soil \nwith different levels of elemental S would disappear very quickly (within 1.4 to \n1.7 days). It should be noted that, based on these models, for all but soils treated \nwith the highest amounts of elemental S (where 10% of the urea remained), after \nday 9 almost all of the of urea applied totally disappeared (Table 4). Warner (1942) \nalso stated that urea hydrolysis follows a first order reaction. The dependency of \nurea hydrolysis rate on soil pH and urea concentration in both experiments were \npreviously studied by Cabrera et al., (1991) and they stated that increasing urea \nconcentration and soil pH cause the urea hydrolysis rate to increase. \n\n\n\nEffect of Urea on Soil pH Over Time\nThis study showed that the addition of urea and S coated urea to Bintang Series \nsoil treated with different rates of elemental S significantly increased soil pH, but \nthe degree and pattern of soil pH change depended on initial soil pH and time \n(Table 5). For instance, the addition of urea to Bintang Series soil treated with 0, \n0.5 and 1 g S kg-1 soil, increased soil pH from the background values of 7.65, 6.33 \nand 4.56 to the maximums of 8.99, 8.31 and 7.79 three days after the application, \nrespectively. This maximum pH coincided with the highest NH3 volatilisation rate \nrecorded from day 2 to 4 (Figure 2). Thereafter, soil acidity values for soil treated \nwith 0, 0.5 and 1 g S kg-1 soil tended to marginally reduce from the maximum of \n8.99, 8.31 and 7.79, respectively, at day 3 to 8.47, 8.24 and 7.8, respectively, after \n9 days of urea addition. However, there was a progressive and upward trend in soil \npH over time for Bintang Series soil treated with 2 g S kg-1 soil. It should be noted \nthat the highest pH value recorded for the highest S application rate, 6.05, was less \n\n\n\n\n\n\n\n11 \n \n\n\n\n\n\n\n\nTABLE 5 \nEffect of sulphur application rate and incubation time on soil pH in Bintang Series soil. \n\n\n\n\n\n\n\nTreatment  Incubation days  \n0a 1 3 4 9 Mean  \n\n\n\nT1 7.65Ac 7.93Ac 8.75Aa 8.61Ba 8.3Bb 8.4B \nT2 7.65Ad 8.16Ac 8.99Aa 8.9Aa 8.47Ab 8.63A \nT3 6.33Bc 7.4Bb 8.31Ba 8.16Ca 8.24Ba 8.03C \nT4 4.56Cc 5.57Cb 7.79Ca 7.72Da 7.8Ca 7.22D \nT5 3.77Dd 3.91Dc 4.44Db 4.37Eb 6.05Da 4.66E \nMean   6.59c 7.63b 7.55b 7.7a  \na soil pH before addition of urea fertiliser.  \nMeans within column followed by the same capital letter and within rows followed bythe  same small letters  \nare not significant at the 0.05 level, according to Tukey\u2019s test \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTABLE 5\nEffect of sulphur application rate and incubation time on soil pH in Bintang Series soil.\n\n\n\nEffects of S on N loss in High pH \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201592\n\n\n\nthan that of other treatments. This shows that the effectiveness of elemental S on \nsoil pH changes over time due to urea application and supports the role of soil pH \non daily NH3 volatilisation rate and cumulative NH3 volatilisation as observed in \nthe conditions of this study (Figures 2 and 3). This fact was also highly supported \nby the significant and positive correlation between soil pH and NH3 volatilisation \nrate as well as ammonium concentration in soil (Table 6). The increase in soil pH \ndue to urea application agreed with the findings of Singh and Beauchamp (1988)\nwho stated that application of urea increases soil pH of Conestogo and Brookston \nsoils from the background levels of 7.65 and 5.7 to 9. They also reported that \ndue to the soil pH of Conestogo soil being close to that of urea, there were few \nchanges over 35 days but the pH of Brookston soil fell below 9, but not less than \n8.5 after 20 days.  \n\n\n\nCONCLUSIONS\nAs outlined above, NH3 volatilisation was the major pathway of N loss of surface \napplied urea and S coated urea in Bintang Series soil. However acidification of the \nsoil by elemental S reduced the NH3 volatilisation from the background of 82% \nin untreated soil to 30% in S amended soil. In addition, as a high rate of N loss \nthrough NH3 volatilisation was recorded in this study, it can be concluded that urea \nis an unsuitable form of N fertiliser for calcareous soils and it is recommended \nthat the efficiency of other N fertiliser sources, such as ammonium sulphate, be \nstudied to develop new directions. \n\n\n\nREFERENCES\nAminuddin, H. 1994. Ammonia volatilization loss from surface placed urea-treated \n\n\n\nPOME pellets. Paper presented at the Malaysian Science and Technology \nCongress. Kuala Lumpur, Malaysia.\n\n\n\nTABLE 6\nPearson correlation coefficients among soil pH, different forms of nitrogen and ammonia \nvolatilisation rates in Bintang Series soil treated with different rates of elemental sulphur.\n\n\n\n\n\n\n\n12 \n \n\n\n\n\n\n\n\nTABLE 6 \nPearson correlation coefficients among soil pH, different forms of nitrogen and ammonia \nvolatilisation rates in Bintang Series soil treated with different rates of elemental sulphur. \n\n\n\n\n\n\n\n\n\n\n\npH Ammonium  Urea Volatilisation \nrate \n\n\n\n \npH  1 0.61** -0.38** 0.62** \n \nAmmonium   1 -0.72** 0.74** \n \nUrea   1 -0.55** \n \nVolatilisation rate    1 \n\n\n\nValues of r followed by ** or * are significant at \u03b1=0.01 and \u03b1=0.05 respectively. \nns: non- significant \n\n\n\n\n\n\n\nKarimizarchi, M., H. Aminuddin, M. Y. Khanif and O. Radziah\n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 2015 93\n\n\n\nBruce, R.R., M.H. Niehaus, E.F. Kanemasu, E.F and J.R. Gilley. 1990. Irrigation of \nAgricultural Crops. Madison, Wisconsin: Crop Science Society of America and \nSoil Science Society of America.\n\n\n\nCabrera, M.L., D.E Kissel and B.R. Bock. 1991. Urea hydrolysis in soil: Effects of \nurea concentration and soil pH. Soil Biology and Biochemistry 23(12): 1121-\n1124. \n\n\n\nF.A.O. 2012. Current World Fertilizer Trends and Outlook to 2016. Rome: Food and \nAgriculture Organization of the United Nations.\n\n\n\nFenn, L.B. and D.E. Kissel. 1973. Ammonia volatilization from surface applications \nof ammonium compounds on calcareous soils: I. General theory. Soil Science \nSociety of America Journal 37(6): 855-859. \n\n\n\nFenn, L.B. and D.E. Kissel. 1974. Ammonia volatilisation from surface applications \nof ammonium compounds on calcareous soils: II. Effects of temperature and \nrate of ammonium nitrogen application. Soil Science Society of America Journal \n38(4): 606-610. \n\n\n\nGlibert, P.M., J. Harrison and C. Heil. 2006. Escalating worldwide use of urea \u2013 A \nglobal change contributing to coastal eutrophication. Biogeochemistry 77: 441\u2013\n463. \n\n\n\nGould, W.D., C Hagedorn and R.G.L. McCready. 1986. Urea transformations and \nfertiliser efficiency in soil. Advances in Agronomy 40: 209-238. \n\n\n\nHeffer, P. &M. Prud\u2019homme. 2014. Fertilizer outlook 2014-2018. 82nd International \nFertilizer Assosication Annual Conference. Sydney, Australia. \n\n\n\nJianga, Z., Q. Zeng, H. Pi, B. Liao, X. Feng and Y. Sun. 2010. Transformation of \nnitrogen and its effects on metal elements by coated urea application in soils \nfrom South China. In Molecular Environmental Soil Science at the Interfaces in \nthe Earth\u2019s Critical Zone (pp. 137-140). Springer.\n\n\n\nJones, C.A., R.T. Koeng, J.W Ellsworth, B.D. Brown and G.D. Jackson. 2007. \nManagement of Urea Fertilizer to Minimize Volatilization. Washington: \nMontana State University Extention.\n\n\n\nJones, J.B. 2001. Laboratory Guide for Conducting Soil Tests and Plant Analysis. \nWashington, D.C.: CRC Press.\n\n\n\nKarimizarchi, M. 2011. Evaluation of nitrogen uptake efficiency in salt affected wheat \nfarms of Iran (pp. 25). Monograph No. 90/609. Yazd. Iran: National Salinity \nResearch Centre.\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and  O. Radziah. 2014a. Maize \nresponse to elemental sulphur application in a high pH soils of Malaysia. Paper \npresented at the The Malaysian Society of Soil ScienceConference, 8-10 April, \nKangar, Perlis. \n\n\n\nEffects of S on N loss in High pH \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science Vol. 19, 201594\n\n\n\nKarimizarchi, M., H. Aminuddin, M.Y. Khanif and O. Radziah. 2014b. Incorporation \nand transformations of elemental sulphur in high pH soils of Malaysia. \nInternational Journal of Soil Science 9(3):133-141. \n\n\n\nLatifah, O., O.H. Ahmed and A.M. Muhamad. 2011. Reducing ammonia loss from \nurea and improving soil exchangeable ammonium and available nitrate in non \nwaterlogged soils through mixing zeolite and sago (Metroxylon sagu) waste \nwater. Int. J. Phys. Sci.6(4): 866-870. \n\n\n\nMikkelsen, R. 2009. Ammonia emissions from agricultural operations: fertilizer. \nBetter Crops 93(4): 9-11. \n\n\n\nPacholski, A., G. Cai, R. Nieder, J. Richter, X. Fan, Z. Zhu and M. Roelcke. 2006. \nCalibration of a simple method for determining ammonia volatilization in the \nfield \u2013 Comparative measurements in Henan Province, China. Nutrient Cycling \nin Agroecosystems 74: 259-273. doi: 10.1007/s10705-006-9003-4. \n\n\n\nPage, A.L. 1982. Methods of Soil Analysis. Part 2. Chemical and Microbiological \nProperties: American Society of Agronomy, Soil Science Society of America.\n\n\n\nReeza, A.A., O.H. Ahmed, N.M.N.A Majid and M.B. Jalloh. 2009. Reducing \nammonia loss from urea by mixing with humic and fulvic acids isolated from \ncoal. American Journal of Environmental Sciences 5(3): 420-426.\n\n\n\nRosliza, S., Ahmed, O.H., and N.M.A. Majid. 2009. Controlling ammonia volatilization \nby mixing urea with humic acid, fulvic acid, triple superphosphate and muriate \nof potash. American Journal of Environmental Sciences 5(5): 605-609. \n\n\n\nSAS Institute. 2003. The SAS System for Windows. \n\n\n\nSingh, Y. and E.G. Beauchamp. 1988. Nitrogen transformations near urea in soil: \nEffects of nitrification inhibition, nitrifier activity and liming. Fertilizer \nResearch 18(3): 201-212. \n\n\n\nSiva, K.B., H. Aminuddin, M.H.A. Husni and A.R. Manas. 1999. Ammonia \nvolatilisation from urea as affected by tropical-based palm oil mill effluent \n(POME) and peat. Communications in Soil Science & Plant Analysis 30(5-6): \n785-804. \n\n\n\nSoaud, A.A., M.E. Saleh, K.A. El-Tarabily, M. Sofian-Azirun &M.M. Rahman. 2011. \nEffect of elemental sulfur application on ammonia volatilisation from surface \napplied urea fertilizer to calcareous sandy soils. Australian Journal of Crop \nScience 5(5): 611-619. \n\n\n\nWarner, R.C. 1942. The kinetics of the hydrolysis of urea and of arginine. Journal of \nBiological Chemistry 142(2): 705-723. \n\n\n\nWatson, C.J., H., Miller, P., Poland, D.J., Kilpatrick, M., Allen, M.K., Garrett, and C.B. \nChristianson, (1994). Soil properties and the ability of the urease inhibitor(n-\nBUTYL) thiophosphoric triamide (nBTPT) to reduce ammonia volatilization \nfrom surface-applied urea. Soil Biology and Biochemistry 26(9): 1165-1171.\n\n\n\nKarimizarchi, M., H. Aminuddin, M. Y. Khanif and O. Radziah\n\n\n\n\n\n"
"\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n70 \n\n\n\nEnhancing Rice Production on Acid Sulfate Soil Using Bio-fertilizer in \n\n\n\nCombination with Ground Magnesium Limestone or Biochar  \n\n\n\n \nPanhwar, Q.A.1*, Shamshuddin, J.2, Naher, U.A.2, Mohd Razi, I.3, Yusoff, M.A.2,  \n\n\n\nAli, A.1 and Depar, N.1 \n\n\n\n \n1Soil and Environmental Science Division, Nuclear Institute of Agriculture, Tandojam 70060, \n\n\n\nSindh, Pakistan \n2Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400 \n\n\n\nUPM, Serdang, Selangor, Malaysia \n3Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, 43400 UPM, \n\n\n\nSerdang, Malaysia \n\n\n\n \n*Correspondence: pawhar107@yahoo.com \n\n\n\n\n\n\n\n\n\n\n\nABSTRACT \n\n\n\n\n\n\n\nLow pH acid sulfate soils are usually nutrient-deficient. They contain toxic metals that affect rice \n\n\n\nplants negatively. A field experiment was carried out to alleviate acidity in an acid sulfate soil for \n\n\n\nrice cultivation. In the study, rice-seedlings (variety MR219) were planted in the experimental \n\n\n\nplots treated with bio-fertilizer, ground magnesium limestone (GML) and biochar, either alone or \n\n\n\nin combination. pH of the untreated topsoil was 3.56, while the exchangeable calcium and \n\n\n\nmagnesium in the topsoil was 4.13 cmolckg-1, below the requirement to sustain growth and/or \n\n\n\nproduction of rice. Addition of bio-fertilizer slightly increased soil pH. The highest soil pH of 5.34 \n\n\n\nwas observed in the plot treated with bio-fertilizer plus GML. Iron in the control plot was believed \n\n\n\nto exist in the form of Fe3+ (pKa 3). Due to treatment with bio-fertilizer plus GML or biochar, soil \n\n\n\npH increased from 3.56 to a level >5. As the soil pH was approaching 4.58 (i.e., the pKa of Fe2+), \n\n\n\nFe3+ was slowly converted to Fe2+.  The form of iron causing toxicity to rice in the treated plots \n\n\n\nwas most likely to be Fe2+, rather than Fe3+. At  soil pH >5, both Fe2+ and Al3+ (the pKa is 5) were \n\n\n\nprecipitated as inert hydroxides, thus, no longer causing toxicity to the rice plants. Beneficial \n\n\n\nmicrobes present in the bio-fertilizer helped produce growth hormones and organic acids that \n\n\n\neventually increased nutrient uptake by rice which in turn enhanced its growth. The organic acids \n\n\n\nfixed some Fe2+ and Al3+ in the soil via chelation process. This phenomenon further reduced their \n\n\n\ntoxicity to the rice plants. Application of bio-fertilizer plus GML or biochar improved soil fertility \n\n\n\nthat resulted in higher rice yield. This notion is supported by the enhancement of the rice yield \n\n\n\nparameters, i.e., plant height (92.21 cm), tiller numbers (6), leaf chlorophyll content (38.14) and \n\n\n\nthe number of filled grains. The use of bio-fertilizer plus GML or biochar is recommended for rice \n\n\n\ncultivation on acid sulfate soils in order to increase rice self-sufficiency level (SSL) and sustain \n\n\n\nfood security in country in the long run. \n\n\n\n\n\n\n\nKey words: Biochar, bio-fertilizer, ground magnesium limestone, organic acids, plant \n\n\n\ngrowth, rice \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nmailto:pawhar107@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n71 \n\n\n\nINTRODUCTION \n\n\n\n\n\n\n\nSustaining food security throughout the world is one of the main concerns of humans living on \n\n\n\nearth. Food should be available to anyone who needs it. Notwithstanding, fertile agricultural lands \n\n\n\nacross the globe are slowly being converted to areas not meant for the purposes of food production. \n\n\n\nIf this goes on unchecked, food production to feed the increasing world population will be \n\n\n\nendangered. We are now seeing farmers starting to utilize available marginal lands in their \n\n\n\ncountries to make a living via agriculture. Attention is currently focused on the vast areas of acid \n\n\n\nsulfate soils, sporadically distributed along the low-lying coastal regions of the tropics. This very \n\n\n\ninfertile soils found abundantly in Thailand, Vietnam, Indonesia and Malaysia are being cropped \n\n\n\nto rice, but the yield is lower than that of their national average (Shamshuddin et al. 2014).  \n\n\n\n\n\n\n\nAcid sulfate soils contain pyrite (FeS2) which on draining releases sulfuric acid due to its oxidation. \n\n\n\nIn the end, Fe and Al are available in the soils at a toxic level, causing problems to plants and other \n\n\n\nliving organisms. Moore et al. (1990) and Shamshuddin et al. (2014) found that the most \n\n\n\nsignificant restraints to crops growing on acid sulfate are: (i) Acidity (comprising the collective \n\n\n\ninfluences of pH, Al-toxicity and nutrient deficiencies), and (ii) Fe-stress (which might be due to \n\n\n\nthe impacts of Fe2+-toxicity and insufficiency of divalent cations)). Application of organic-based \n\n\n\nnutrient sources on acid sulfate soils could boost the reduction process (Muhrizal et al. 2006), \n\n\n\nleading to the release of Fe2+ which is toxic to rice plants (Tran and Vo 2004). \n\n\n\n\n\n\n\nA normal practice to reduce toxicity due to the presence of Al3+ and Fe2+ is to raise soil pH to a \n\n\n\nlevel \u02c3 5 via lime application (Haby 2002; Anda et al. 2009). The capability of lime to upsurge \n\n\n\nsoil pH and reduce Al and Fe solubility to enhance crop yield is widely known (Brown et al, 2008). \n\n\n\nApplication of biochar can increase soil organic carbon, soil pH, and cation exchange capacity \n\n\n\n(CEC). Application of biochar and compost has been shown to improve soil fertility significantly, \n\n\n\nincluding increased soil pH and CEC (Kuzyakov et al. 2017). However, biochar application not \n\n\n\nonly enhances soil fertility, but also results in C sequestration (Cooper et al. 2020). For biochar, \n\n\n\nthe pyrolysis process, feed-stock and time period are the major components of the controlling \n\n\n\nfactors, which directly affect soil physical and chemical properties (Weber and Quicker 2018). \n\n\n\nAccording to Sun et al. (2022), the improvement in soil properties through adding biochar depends \n\n\n\na lot on soil environmental factors, particularly soil pH and soil texture. \n\n\n\n\n\n\n\nOther agronomic practices to ameliorate acidity problems in acid sulfate soils are submergence, \n\n\n\nleaching and applying MnO2 (Sobouti et al., 2020), application of basalt (Jayalath et al. 2016) or \n\n\n\nusing bio-fertilizer (Panhwar et al. 2014). For rice plants, a better option to overcome low pH stress \n\n\n\n(acidic condition) and Al or Fe toxicity is application of ground magnesium limestone or ground \n\n\n\nbasalt in combination with a bio-fertilizer, fortified with beneficial microbes (Panhwar et al. 2014). \n\n\n\nTherefore, a field study was designed and conducted to enhance the fertility of an acid sulfate soil \n\n\n\nin Peninsular Malaysia to increase rice yield through applying bio-fertilizer in combination with \n\n\n\nground magnesium limestone or biochar.   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n72 \n\n\n\nMATERIALS AND METHODS \n\n\n\n\n\n\n\nExperimental Site \n\n\n\nThe field study was conducted on an acid sulfate soil in Semerak-Kelantan, Malaysia, using rice \n\n\n\nvariety MR219. The soil was classified as Typic Sulfaquepts based on Soil Taxonomy (Soil Survey \n\n\n\nStaff 2014). Soil samples were taken from the experimental site at a depth of 0-15 cm using an \n\n\n\nauger for soil characterization. The experimental site, about five meters above sea-level, is located \n\n\n\nat latitude of-5\u00b052\u031b-208\u201dN and longitude of 102\u00b028\u031b 501\u201dE.  \n\n\n\n\n\n\n\nExperimental Details \nBio-fertilizer with or without GML and biochar were applied in the experimental plots 15 days \n\n\n\nbefore transplanting rice seedlings. The experiment comprised: (1) Control; (2) Bio-fertilizer; (3) \n\n\n\nBio-fertilizer+GML; and (4) Bio-fertilizer+biochar, applied with the amendments at 4 ton ha-1 in \n\n\n\neach of the 5\u00d75m size experimental plot. Nitrogen (using urea), phosphorus (using TSP) and \n\n\n\npotash (using MOP) were given at the rate of 120, 30 and 60 kg ha-1,-respectively. The initial pH \n\n\n\nof the untreated soil was 3.56. pH data were recorded monthly till harvest. The experimental design \n\n\n\nwas randomized complete block design (RCBD), with four replications.  \n\n\n\n\n\n\n\nChemical properties of bio-fertilizer, GML and biochar  \n\n\n\nThe bio-fertilizer (with pH 7.30) used in the study contained N (1.19%), P (0.13%) and K (0.63%), \n\n\n\nwhile the Al and Fe contents were low (<0.01) (Table 1). The bio-fertilizer contained phosphate-\n\n\n\nsolubilizing and N2-fixing bacteria (1\u00d710\u22128 CFU g-1). GML was taken from an authorized supplier \n\n\n\nin Malaysia. Table 1 shows that the pH of the GML was high (9.75). Note that the GML used in \n\n\n\nthe study contained some P (1.71 %), but with high amounts of Ca (19.47 %) and Mg (8.67 %). \n\n\n\nThe biochar used was alkaline in nature with low content of N, P and K (0.54, 0.14 and 3.49%, \n\n\n\nrespectively) with some Fe, Al, Ca and Mg (Table 1). \n\n\n\n\n\n\n\nTABLE 1 \n\n\n\nChemical properties of the bio-fertilizer, GML and biochar used in the study \nSource \n\n\n\npH \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEC N P K Fe Al \n\n\n\n\n\n\n\n\n\n\n\nCa \n\n\n\n\n\n\n\n\n\n\n\nMg \n\n\n\nN2 fixing \n\n\n\nbacterial \n\n\n\npopulation  \n\n\n\n(dS m-\n\n\n\n1) \n\n\n\n ----------------------------------- (%)  ----------------------------------\n\n\n\n- \n\n\n\n(CFU g-1) \n\n\n\nBio-fertilizer \n\n\n\n7.30 \n\n\n\n\n\n\n\n\n\n\n\n3.64 1.19 \n\n\n\n\n\n\n\n0.13 \n\n\n\n\n\n\n\n0.63 \n\n\n\n\n\n\n\n< 0.01 \n\n\n\n\n\n\n\n0.01 \n\n\n\n\n\n\n\n\n\n\n\n< 0.01 \n\n\n\n\n\n\n\n\n\n\n\n< 0.01 \n\n\n\n\n\n\n\n1\u00d710\u22128 \n\n\n\nGML \n\n\n\n9.75 \n\n\n\n\n\n\n\n\n\n\n\n- na \n \n\n\n\n1.71 \n\n\n\n\n\n\n\n0.33 \n\n\n\n\n\n\n\n< 0.01 \n\n\n\n\n\n\n\n< \n\n\n\n0.01 \n\n\n\n\n\n\n\n\n\n\n\n19.47 \n\n\n\n\n\n\n\n8.67 \n\n\n\n\n\n\n\nna \n\n\n\n\n\n\n\nBiochar 9.85 \n\n\n\n\n\n\n\n\n\n\n\n1.48 0.54 \n\n\n\n\n\n\n\n0.14 \n\n\n\n\n\n\n\n3.49 \n\n\n\n\n\n\n\n\n\n\n\n< 0.01 \n\n\n\n\n\n\n\n< \n\n\n\n0.01 \n\n\n\n\n\n\n\n\n\n\n\n0.13 \n\n\n\n\n\n\n\n0.078 \n\n\n\n\n\n\n\nna \n\n\n\n*na = not available, GML = Ground magnesium limestone \n\n\n\nRice Seedlings and Transplanting \nMR 219 rice seeds were surface-sterilized following the method of Amin et al. (2004). Twenty-\n\n\n\none-day old rice seedlings were transplanted (4\u00d74 cm) in the experimental plots (4\u00d74 m) at a \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n73 \n\n\n\ndistance of 20\u00d720 cm plant to plant and row to row. The experiment was continued till crop harvest \n\n\n\n- rice agronomic and yield data were taken at harvest.  \n\n\n\n\n\n\n\nSoil Analysis \n\n\n\nSoil pH was analyzed using a pH meter (Benton 2001). Exchangeable-Ca, Mg, and K in the soil \n\n\n\nwere determined by the method of Schollenberger and Simon (1945) using atomic absorption \n\n\n\nspectrometry (Perkin. Elmer, 5100 PC, USA). Exchangeable Al extracted by 1 M KCl (Barnhisel \n\n\n\nand Bertsch 1982) was analyzed by inductively coupled plasma atomic - emission spectroscopy \n\n\n\n(ICPAES). Organic-carbon was determined by the Walkley-Black method, and total N was \n\n\n\nanalysed by Kjeldahl digestion method (Bremner and Mulvaney 1982). Available P was \n\n\n\ndetermined by the Bray and Kurtz II method (Bray and Kurtz 1945). Extractable Fe in the soil was \n\n\n\nanalyzed by the double acid method.  \n\n\n\n\n\n\n\nAgronomical Parameters and Rice Yield  \nRice in the experimental plots was-harvested at crop maturity and plots (one-meter square) were \n\n\n\nselected for the rice grain and straw yield calculations. For the calculation, rice filled grains were \n\n\n\nunglued from the unfilled grains following the method of Seizo (1980). The chlorophyll (SPAD) \n\n\n\nvalues were determined using a MINOLTATM, SPAD-502 meter (Konica-Minolta, Tokyo, \n\n\n\nJapan), while plant-height, root-length, tiller plant-1, number of panicles plant-1 and panicles size \n\n\n\nwere determined by the Dobermann and Fairhurst (2000) method.  \n\n\n\n\n\n\n\nStatistical Analysis \nAll data were statistically examined for analysis of variance (ANOVA) using SAS-Software \n\n\n\nProgram Version-9.3 (SAS 2003). The experimental design was randomized complete block using \n\n\n\nfour replications. The means were separated by Tukey\u2019s-test (5 %) level of confidence.  \n\n\n\n\n\n\n\nRESULTS AND DISCUSSION \n\n\n\n\n\n\n\nChemical Properties of the Untreated Soil \n\n\n\nSoil used in the present study was a real acid sulfate soil based on the data presented in Table 2. It \n\n\n\nwas identified as an acid sulfate soil because of the very low soil pH and the occurrence of the \n\n\n\nyellowish jarosite mottles in the top 50 cm of the soil profile. The initial soil pH in the experimental \n\n\n\nplot was 3.56, while the EC was 0.34 dS m-1. The amount of N, K, Ca and Mg in the topsoil was \n\n\n\ninsuffucient to support or sustain rice production. On the other hand, exchangeable-Al in the \n\n\n\ntopsoil was very high (4.13 cmolckg-1). This could cause toxicity to the rice growing in the field \n\n\n\nplots without undergoing amelioration process through agronomic means. However, the soil \n\n\n\ncontained an adequate quantity of organic matter which to, a certain extent, helped remove Al \n\n\n\nand/or Fe via the chelation process. Addition of the bio-fertilizer with microbes is likely to reduce \n\n\n\nAl3+ and/or Fe2+ toxicity and eventually enhance nutrient uptake by the rice plants. This notion is \n\n\n\nsupported by a study conducted earlier by Johan et al. (2021). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n74 \n\n\n\nTABLE 2 \n\n\n\nChemical characteristics of the acid sulfate soil \n\n\n\nSoil-pH  EC  CEC Soil-OC \n\n\n\ncontent \n\n\n\nTotal-N \n\n\n\ncontent \n\n\n\nAv.P               Exchangeable-cations \n\n\n\nK  Al Ca Mg \n\n\n\n(dS-m-1)  (cmolckg-1)    (%)           (%) (mg kg-1)  --------------- (cmolckg-1) ----------------  \n\n\n\n3.56  0.34 5.92   2.03    0.12  18.73  0.05  4.13  0.49 0.71  \n\n\n\nCEC = Cation-exchange capacity, OC= organic-carbon \n\n\n\n\n\n\n\nEffect of Treatments on Soil pH  \nThe acid sulfate soil under study contained-pyrite, which on oxidation altered to yellowish jarosite \n\n\n\n(Shamshuddin et al, 2014). During the process of pyrite oxidation a high amount of Fe or even Al \n\n\n\nwas released into the soil solution and environment. The hydrolysis of the acid metals intensified \n\n\n\nthe acidification of the soil, reducing its pH to lower than 4 or close to 3. Addition of the bio-\n\n\n\nfertilizer in combination with the amendments increased soil pH to a value of \u02c34.0 (Table 3), and \n\n\n\nit stayed at that level till the rice crop was harvested. It appears that the application of bio-fertilizer \n\n\n\nalone did not increase soil pH much. However, treating the soil with the bio-fertilizer combined \n\n\n\nwith GML or biochar augmented soil pH upto above 5. The maximum soil pH of 5.34 was attained \n\n\n\nby treating the soil with the bio-fertilizer in combination with GML (Table 3).  \n\n\n\n\n\n\n\nThe form of iron and aluminum existing in the soil (or soil solution) hinges on soil pH. The pKa \n\n\n\nof Fe3+ and Fe2+ are respectively 3.00 and 4.58, while the pKa of Al3+ is 5.00. Thus, iron in the \n\n\n\nwater of the untreated acid sulfate soil with pH 3.56 was most likely to be in the form of Fe3+. Due \n\n\n\nto treatment with the bio-fertilizer in combination with the amendments, the pH of water was \n\n\n\ninitially slightly increased to above 4. From then onwards, slowly but surely, Fe2+ started to form \n\n\n\nin high amounts. When the pH of water was close to 4.58, most of the iron was in the form of Fe2+. \n\n\n\nThe form of toxic iron that caused a reduction in rice growth and/or production was most likely to \n\n\n\nbe Fe2+, not Fe3+. When pH of the water  increased up to >5 (Table 3), Fe2+ and Al3+ were \n\n\n\nprecipitated as inert hydroxides; thus, no longer being toxic to the rice growing in the field plots.  \n\n\n\n\n\n\n\nTreating the soil with the bio-fertilizer in combination with either GML or biochar augmented the \n\n\n\nsoil pH significantly. This resulted in a reduction of exchangeable Al (Shamshuddin et al. 2014). \n\n\n\nAt the soil pH >5, Al precipitates as Al-hydroxides. In this study, it was noted that applying GML \n\n\n\nor biochar in combination with the bio-fertilizer increased soil pH to that critical level. Therefore, \n\n\n\nthis agronomic practice helped improve soil fertility. Eventually, the treated acid sulfate soil can \n\n\n\nbe productively used for rice cultivation with yields comparable to that of the normal soils. The \n\n\n\nresults of the current study were in agreement with those obtained by Rahman et al. (2018).  \n\n\n\n\n\n\n\nWe are of the opinion that in the presence of bio-fertilizers, fortified with beneficial microbes, rice \n\n\n\nroots were able to produce growth hormones and/or organic acids. The organic acids so produced \n\n\n\nare known to fix and reduce Al3+ and Fe2+ concentration in the acid sulfate via the process of \n\n\n\nchelation (Panhwar et al. 2014; Panhwar et al. 2020). Thus, bio-fertilizers can play an important \n\n\n\nrole in the alleviation Al3+ and Fe2+ toxicity for rice production on acid sulfate soils, not only in \n\n\n\nMalaysia, but also throughout the ASEAN region.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n75 \n\n\n\nSyazana et al. (2013) and Syazana et al. (2014) stated that GML application increased soil pH \n\n\n\nleading to improved rice yield grown on an acid sulfate soil in Malaysia. Soil pH, total C and the \n\n\n\nCEC of the spodic layer of a Spodosol were increased by biochar treatment (Syuhada et al. 2022). \n\n\n\nAccording to these researchers, the CEC increase due to biochar treatment was positively \n\n\n\ncorrelated with the C-content in the soil. It is plausible that a similar phenomenon can occur in an \n\n\n\nacid sulfate soil treated with biochar. \n\n\n\n\n\n\n\nTABLE 3 \n\n\n\nEffect of treatments on soil pH under field conditions \n\n\n\n   Soil pH \n\n\n\nInitial 30DAS 60DAS 90DAS At harvest \n\n\n\nControl 3.56a 3.81d 4.07c 4.12d 4.03c \n\n\n\nBio-fertilizer. 3.56a 4.17c 4.35b 4.31c 4.25b \n\n\n\nBio-fertilizer.+GML 3.56a 5.34a 5.15a 5.20b 5.17a \n\n\n\nBio-fertilizer.+biochar 3.56a 5.26b 5.29a 5.32a 5.11a \nDAS= days after sowing. Means within the similar columns followed by similar letters are not significantly different \n\n\n\n(P<0.05) \n\n\n\n\n\n\n\nEffects of Treatments on Nutrients in Soil \n\n\n\n\n\n\n\nThe highest macronutrient content was found in the bio-fertilizer treatments combined with GML \n\n\n\nor biochar. The significantly (P<0.05) highest N (0.19 %), P (31.02 mg kg-1), Ca (2.57 cmolc kg-\n\n\n\n1) and Mg (2.39 cmolc.kg-1) were observed in the bio-fertilizer plus GML-treatment. As expected, \n\n\n\nthe highest K (0.29 cmolc-kg-1) and Al (4.04 cmolc kg-1) were observed in the bio-fertilizer plus \n\n\n\nbiochar and the control treatments, respectively (Table 4). Microbes in the bio-fertilizer had the \n\n\n\ncapability to fix N2 and were also able to solubilize unavailable P increasing the macronutrients in \n\n\n\nthe soil (Panhwar et al. 2014). GML addition significantly increased Ca and Mg contents in the \n\n\n\nsoil. Upon dissolution of GML, soil pH readily increased to a higher level. When the pH reaches \n\n\n\nabove .5, Al-in the water would precipitate as inert Al-hydroxides and become inaccessible to the \n\n\n\nrice plants. It is believed that the main concerns and the most noticeable Al toxicity symptoms \n\n\n\nshown by rice plants are root growth inhibition (Frankowski 2016).  \n\n\n\n\n\n\n\nThe effects of acidification have been critically noticed in the soil. The exchange of base cations \n\n\n\n(Ca2+,_Mg2+ and K+) by.H+ and/or.Al3+, and the dissolution of.Al, Fe and Mn-minerals are the most \n\n\n\nimportant mechanisms for soil acidification (Goulding et al, 2016). Toxicity due to Fe2+ and/or \n\n\n\nAl3+ and inequity of nutrients such as P are believed to occur in acid sulfate soils. Toxicity of Al3+-\n\n\n\nis one of the major risk for the survival of plants grown on acidic soils (Boj\u00f3rquez-Quintal et al, \n\n\n\n2017). A significant portion of the nutrients initially present in the biochar in soluble form is \n\n\n\nsusceptible to leaching losses. Panhwar et al. (2020) reported the positive impact of applying bio-\n\n\n\nfertilizer plus GML or biochar on the biochemical properties of acid sulfate in Malaysia in terms \n\n\n\nof increased rice yield. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n76 \n\n\n\n\n\n\n\nTABLE 4 \n\n\n\nEffects of applying bio-fertilizer plus GML or biochar on macronutrients in soil. \n\n\n\n\n\n\n\nTreatments \n\n\n\nTotal=N  Av.-P \n\n\n\n\n\n\n\nExchangeable-cations \n\n\n\nK  Al Ca  Mg \n\n\n\n(%) (mg/kg)  ------------ (cmolc/kg) ------------ \n\n\n\nControl 0.12c 20.73c 0.16d 4.04a 0.64d 0.61d \n\n\n\nBiofertilizer 0.15b 27.27b 0.27c 2.10b 0.97c 0.91c \n\n\n\nBiofertilizer +GML  0.19a 31.02a 0.32b 0.87c 2.57a 2.39a \n\n\n\nBiofertilizer + biochar  0.17a 30.95a 0.39a 0.89c 1.84b 1.65b \nGML = ground magnesium limestone. Means within the similar column followed by the similar letters are not \n\n\n\nsignificantly different (P<0.05) \n\n\n\n\n\n\n\n\n\n\n\nEffect of Treatments on Rice Plant Growth \n\n\n\nThe bio-fertilizer application combined with GML significantly enhanced plant height (92.21 cm), \n\n\n\nleaf chlorophyll SPAD values (38.14) and number of plant tillers (6) (Table 5). This might be the \n\n\n\nsubsequent effects of increasing soil pH that reduced Fe2+-toxicity (Panhwar et al. 2014). Soil pH \n\n\n\nregulates the chemistry of plants nutrient colloidal-solutions. At certain pH levels, various stresses, \n\n\n\ni.e., H+ toxicity and mineral imbalance and their deficiencies can occur in plants (Msimbira and \n\n\n\nSmith 2020).   \n\n\n\n\n\n\n\nThe bio-fertilizer comprising a consortium of N2-fixing and P-solubilizing bacteria produced some \n\n\n\norganic acids and growth promoting phytohormones, whichchelated Al3+ and Fe2+ in the soil and \n\n\n\neventually enhanced rice growth. Phosphate-solubilizing bacteria converted the insoluble P into \n\n\n\nsoluble form and this was made available to the rice plants. Similar findings were reported in an \n\n\n\nearlier study by Panhwar et al. (2014) that the production of phytohormones by beneficial bacterial \n\n\n\npresent in the bio-fertilizer proliferated root growth and augmented nutrient uptake, which \n\n\n\nsubsequently increased rice yield.  \n\n\n\n\n\n\n\nTABLE 5 \n\n\n\nEffects of treatments on rice growth and leaf chlorophyll-content \n\n\n\nTreatments Plant-height  \n\n\n\n(cm) \n\n\n\nChlorophyll \n\n\n\n(SPAD-value) \n\n\n\nTillers plant-1 \n\n\n\nControl 79.34c 32.12c 4c \n\n\n\nBio-fertilizer 88.32b 36.67b 5b \n\n\n\nBio-fertilizer + GML 92.21a 38.14a  6a \n\n\n\nBio-fertilizer + biochar 90.65a 37.34a 6a \nGML = ground magnesium limestone. Means within the similar column followed by the similar letters are not \n\n\n\nsignificantly different (P<0.05) \n\n\n\n\n\n\n\nEffects of treatments on yield and yield contributing characters of rice \n\n\n\nBio-fertilizer, GML or biochar have been found to have a positive impact on crop growth. In this \n\n\n\nstudy, application of bio-fertilizer combined with GML or biochar significantly enhanced the yield \n\n\n\nand yield subsidizing characters of rice. The highest filled grain (83%), number of panicles (17), \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n77 \n\n\n\nsize of panicles (23.27 cm), rice grain (5.67 t ha-1) and straw yield (10.39 t ha-1) were observed in \n\n\n\nthe bio-fertilizer in combination with GML, followed by bio-fertilizer plus biochar treatment \n\n\n\n(Table 6). Similar results were reported by earlier studies that biochar treatment improved soil \n\n\n\nquality. The new environment results in increasing soil microbial activities which enhance the \n\n\n\ngrowth and yield contributing parameters of crops (Yao et al. 2017; Wong et al. 2019). \n\n\n\n\n\n\n\nTABLE 6 \n\n\n\nEffects of treatments on yield and yield contributing characters of rice \n\n\n\n\n\n\n\nGML =-ground magnesium-limestone. Means within the similar column-followed by the similar letters are not \n\n\n\nsignificantly different-(P<0.05) \n\n\n\n\n\n\n\nIt is evident that bio-fertilizer plus GML or biochar had a substantial impact on the growth and \n\n\n\nyield of rice. Amending the acid-sulfate soil with the bio-fertilizer in combination with GML or \n\n\n\nbiochar improved soil fertility, which resulted in an upsurge of plant dry biomass and ultimately \n\n\n\ngrain yield of rice. Syuhada et al. (2022) reported that the use of biochar combined with NPK \n\n\n\nfertilizers on a Spodosol sustained corn growth/production in long run. Similarly, the use of rice \n\n\n\nhusk biochar with inorganic fertilizers improved nutrient availability and enhanced the growth and \n\n\n\nyield of rice in the acidic soils (Oladele et al, 2019). On other hand, GML alone could only supply \n\n\n\nadditional calcium and magnesium to the acid-sulfate soil. This is a good agronomic practice as \n\n\n\nthe soil under this study had insufficient amounts of calcium and magnesium for healthy rice \n\n\n\ngrowth. Hence, the use of bio-fertilizer in combination with GML or biochar is an appropriate \n\n\n\nagronomic practice that not only reduced soil-acidity and Al3+ and/or Fe2+ toxicity, but also \n\n\n\nincreased soil calcium and magnesium to sustain crop production (Jones et al. 2003).  \n\n\n\n\n\n\n\nCONCLUSIONS \n\n\n\n\n\n\n\nApplication of the bio-fertilizer in combination with ground magnesium limestone or biochar \n\n\n\nimproved fertility of the acid sulfate soil under investigation significantly and subsequently \n\n\n\naugmented rice yield.  This notion is indicated or supported by the improvement of rice yield \n\n\n\nparameters such as plant height, tiller numbers, leaf chlorophyll content and the number of filled \n\n\n\ngrains. Therefore, bio-fertilizers fortified with beneficial microbes applied together with GML or \n\n\n\nbiochar is an excellent agronomic practice, which is recommended for rice cultivation on acid \n\n\n\nsulfate soils. The adoption of this agro-tech in Malaysia is likely to result in an increasing level of \n\n\n\nself-sufficiency in rice and lead towards sustaining food security for the country in the long run. \n\n\n\n\n\n\n\nACKNOWLEDGEMENTS \n\n\n\n\n\n\n\nThe authors are thankful to Universiti Putra Malaysia and the Ministry of Higher Education \n\n\n\nMalaysia for providing technical and financial assistance under the LRGS, respectively. \n\n\n\nTreatments  Number of \npanicles plant-1 \n\n\n\nSize of \npanicles -1 \n\n\n\n(cm) \n\n\n\nNumber of \nfilled grains \n\n\n\n(%) \n\n\n\nGrain yield  \n(t ha-1) \n\n\n\nStraw yield  \n(t ha-1) \n\n\n\nControl 10c 19.41d 69c 3.45e 8.56d \n\n\n\nBio-fertilizer  14b 23.08c 78b 4.84c 9.65b \n\n\n\nBio-fertilizer + GML 17a 23.27a 83a 5.67a 10.39a \n\n\n\nBio-ferti.+biochar 16a 22.94b 81a 5.23b 9.83a \n\n\n\n\n\n\n\n\nMalaysian Journal of Soil Science 2023 Vol. 27: 70-79 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n78 \n\n\n\nCONFLICT OF INTEREST \n\n\n\n\n\n\n\nThe authors declare no conflict of interest. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\n \nAmin, M.A., M.A. Uddin and M.A. Hossain. 2004. 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